[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (1,139)

Search Parameters:
Keywords = TRIM56

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
17 pages, 2295 KiB  
Article
Analysis of Cattle Foot Lesions Recorded at Trimming in the Southwest of England
by Nick Britten, Nicola Blackie, Jon Reader, Richard E. Booth and Sophie Anne Mahendran
Animals 2025, 15(6), 829; https://doi.org/10.3390/ani15060829 - 13 Mar 2025
Abstract
Background: The UK has a high incidence of lameness in cattle, which is costly in terms of economics and welfare. Most causes of bovine lameness originate in the foot but there are several different conditions causing lameness. Quantifying the relative prevalence of different [...] Read more.
Background: The UK has a high incidence of lameness in cattle, which is costly in terms of economics and welfare. Most causes of bovine lameness originate in the foot but there are several different conditions causing lameness. Quantifying the relative prevalence of different lameness causing lesions allows for the focus of preventative measures and research on the most common conditions. Methods: This study analysed trimming records from 23 professional foot trimmers working in the Southwest of England. A total of 97,944 recorded lesions over a 5-year period were analysed for lesion identity, lesion severity, repeat presentation, foot affected, claw affected and whether they were from dairy or beef cattle. Results: The most frequently recorded lesions were digital dermatitis (32%), white line disease (21%), sole ulcer (19%) and sole haemorrhage (13%). White line disease had the highest percentage of lesions requiring veterinary attention and most frequently re-presented. Most lesions were in hind feet and there was a small but significantly greater number recorded in right feet. Beef cattle had a higher percentage of digital dermatitis and lower percentage of sole ulcer compared with dairy cattle. Conclusions: Digital dermatitis was the most common foot lesion of all cattle types. Most feet with lesions only appeared in the data set once, suggesting broadly that foot trimming was largely effective at resolving new foot lesions. More white line lesions were re-presented, along with having more severe grades compared with other lesions. Therapeutic trimming of chronic lesions appeared to be less successful, with re-presentations, on average, every 93 days, compromising welfare for extended periods, and requiring consideration of different veterinary treatment options. Full article
(This article belongs to the Collection Cattle Diseases)
Show Figures

Figure 1

Figure 1
<p>Flow chart of data processing. Red arrows indicate records excluded from analysis. Each record pertains to a single foot inspection event of one foot.</p>
Full article ">Figure 2
<p>Summary of therapeutic trim records.</p>
Full article ">Figure 3
<p>Summary of the percentage of trim records reporting each lesion type. Lesions &lt;10% were amalgamated into ‘other’; details in <a href="#app1-animals-15-00829" class="html-app">Appendix A</a>. Note that, as feet could have had more than one recorded lesion, the percentages total more than 100% (<span class="html-italic">n</span> = 60,334 lesions).</p>
Full article ">Figure 4
<p>Percentage of lesions re-presented on the same foot by trim number from 50,276 feet from 32,557 cows on 346 farms.</p>
Full article ">Figure 5
<p>Percentages of feet with major lesions (50,276) on 96 beef and 250 dairy farms.</p>
Full article ">Figure 6
<p>Percentage of each trim number of all trim records (97,944) on 150 dairy and 96 beef farms.</p>
Full article ">
33 pages, 12739 KiB  
Article
An Equivalent Magnetic-Circuit-Modeling Approach for Analysis of Conical Permanent Magnet Synchronous Motor
by Fengrui Cui, Junquan Chen, Pengfei Hu, Xingyu Wu and Fangxu Sun
Sensors 2025, 25(6), 1788; https://doi.org/10.3390/s25061788 - 13 Mar 2025
Abstract
Shaftless propulsion technology delivers high efficiency and low noise for subsea installations and marine vessels. To enhance thrust performance, the streamlined aft-body contour imposes stringent demands on geometric compatibility between the rim-driven thruster (RDT) motor and hull. This necessitates advanced electromagnetic characterization of [...] Read more.
Shaftless propulsion technology delivers high efficiency and low noise for subsea installations and marine vessels. To enhance thrust performance, the streamlined aft-body contour imposes stringent demands on geometric compatibility between the rim-driven thruster (RDT) motor and hull. This necessitates advanced electromagnetic characterization of conical motors. This paper proposes an equivalent magnetic circuit model (EMCM) that accounts for end effects and magnetic saturation in both the stator and rotor cores for the magnetic field analysis of conical permanent magnet synchronous motor (CPMSM). A 3D EMCM is developed by decomposing the air-gap flux into radial/axial/tangential components. End-field nonlinearities are addressed via lumped-parameter network modeling. Innovatively, a trapezoidal expanded magnet layout and magnet-pole-trimming technology are adopted to ensure sinusoidal flux distribution. Finally, a 10.5 kW prototype with a conical angle of 6.7 degrees is designed using the EMCM and verified through a finite-element analysis (FEA) and experiments. This research provides a theoretical framework for the rapid electromagnetic analysis of the CPMSM. Full article
(This article belongs to the Section Electronic Sensors)
Show Figures

Figure 1

Figure 1
<p>Traditional electric motor scheme in shaftless RDT.</p>
Full article ">Figure 2
<p>CPMSM scheme in shaftless RDT.</p>
Full article ">Figure 3
<p>The topological structure of CPMSM.</p>
Full article ">Figure 4
<p>The distribution of magnetic fields of the CPMSM.</p>
Full article ">Figure 5
<p>Simplified magnetic field model of CPMSM.</p>
Full article ">Figure 6
<p>Flux cancelation mode.</p>
Full article ">Figure 7
<p>Flux partial contribution mode.</p>
Full article ">Figure 8
<p>Flux full contribution mode.</p>
Full article ">Figure 9
<p>Features of PMs within a slot pitch.</p>
Full article ">Figure 10
<p>Modified slot pitch on polar coordinate.</p>
Full article ">Figure 11
<p>Boundaries of the modes on the real axis.</p>
Full article ">Figure 12
<p>Geometry of CPMSM.</p>
Full article ">Figure 13
<p>Modeling of CPMSM.</p>
Full article ">Figure 14
<p>Flux pattern between tooth tips.</p>
Full article ">Figure 15
<p>Definitions for the nodes.</p>
Full article ">Figure 16
<p><span class="html-italic">B</span>–<span class="html-italic">H</span> curve of nonlinear ferromagnetic materials.</p>
Full article ">Figure 17
<p>Iterating process of the EMCM approach of CPMSM.</p>
Full article ">Figure 18
<p>End magnetic circuit of CPMSM.</p>
Full article ">Figure 19
<p>Path of permeances in calculating the Carter coefficient.</p>
Full article ">Figure 20
<p>Magnetic circuit model of half pair of the PM.</p>
Full article ">Figure 21
<p>PM flux leakage path in the CPMSM end.</p>
Full article ">Figure 22
<p>Magnetic field decomposition of CPMSM.</p>
Full article ">Figure 23
<p>The topological structure of the CPMSM rotor.</p>
Full article ">Figure 24
<p>FEA model for CPMSM (no load).</p>
Full article ">Figure 25
<p>FEA model for CPMSM (on load).</p>
Full article ">Figure 26
<p>Air gap flux densities of CPMSM: (<b>a</b>) Radial components of 3D FEA (no load). (<b>b</b>) Radial components of 3D EMCM (no load). (<b>c</b>) Comparison of the radial air gap flux densities between 3D FEA and 3D EMCM (no load). (<b>d</b>) Radial components of 3D FEA (on load). (<b>e</b>) Radial components of 3D EMCM (on load). (<b>f</b>) Comparison of the radial air gap flux densities between 3D FEA and 3D EMCM (on load). (<b>g</b>) Axial components of 3D FEA (no load). (<b>h</b>) Axial components of 3D EMCM (no load). (<b>i</b>) Axial of the radial air gap flux densities between 3D FEA and 3D EMCM (no load). (<b>j</b>) Axial components of 3D FEA (on load). (<b>k</b>) Axial components of 3D EMCM (on load). (<b>l</b>) Comparison of the Axial air gap flux densities between 3D FEA and 3D EMCM (on load).</p>
Full article ">Figure 26 Cont.
<p>Air gap flux densities of CPMSM: (<b>a</b>) Radial components of 3D FEA (no load). (<b>b</b>) Radial components of 3D EMCM (no load). (<b>c</b>) Comparison of the radial air gap flux densities between 3D FEA and 3D EMCM (no load). (<b>d</b>) Radial components of 3D FEA (on load). (<b>e</b>) Radial components of 3D EMCM (on load). (<b>f</b>) Comparison of the radial air gap flux densities between 3D FEA and 3D EMCM (on load). (<b>g</b>) Axial components of 3D FEA (no load). (<b>h</b>) Axial components of 3D EMCM (no load). (<b>i</b>) Axial of the radial air gap flux densities between 3D FEA and 3D EMCM (no load). (<b>j</b>) Axial components of 3D FEA (on load). (<b>k</b>) Axial components of 3D EMCM (on load). (<b>l</b>) Comparison of the Axial air gap flux densities between 3D FEA and 3D EMCM (on load).</p>
Full article ">Figure 26 Cont.
<p>Air gap flux densities of CPMSM: (<b>a</b>) Radial components of 3D FEA (no load). (<b>b</b>) Radial components of 3D EMCM (no load). (<b>c</b>) Comparison of the radial air gap flux densities between 3D FEA and 3D EMCM (no load). (<b>d</b>) Radial components of 3D FEA (on load). (<b>e</b>) Radial components of 3D EMCM (on load). (<b>f</b>) Comparison of the radial air gap flux densities between 3D FEA and 3D EMCM (on load). (<b>g</b>) Axial components of 3D FEA (no load). (<b>h</b>) Axial components of 3D EMCM (no load). (<b>i</b>) Axial of the radial air gap flux densities between 3D FEA and 3D EMCM (no load). (<b>j</b>) Axial components of 3D FEA (on load). (<b>k</b>) Axial components of 3D EMCM (on load). (<b>l</b>) Comparison of the Axial air gap flux densities between 3D FEA and 3D EMCM (on load).</p>
Full article ">Figure 27
<p>CPMSM prototype: (<b>a</b>) stator; (<b>b</b>) rotor.</p>
Full article ">Figure 28
<p>Test bench.</p>
Full article ">Figure 29
<p>Comparison of the CPMSM back-EMF constants between FEA, EMCM, and experiment.</p>
Full article ">
19 pages, 2686 KiB  
Article
Force Expressed by 3D-Printed Aligners with Different Thickness and Design Compared to Thermoformed Aligners: An in Vitro Study
by Francesca Cremonini, Carolina Pancari, Luca Brucculeri, Ariyan Karami Shabankare and Luca Lombardo
Appl. Sci. 2025, 15(6), 2911; https://doi.org/10.3390/app15062911 - 7 Mar 2025
Viewed by 167
Abstract
(1) Background: Clear aligners are favored for their aesthetics in orthodontics, with newer 3D-printed technologies allowing the design of aligners with differential thicknesses and materials, offering advantages in terms of force distribution on the teeth, thereby optimizing treatment biomechanics. This study aimed to [...] Read more.
(1) Background: Clear aligners are favored for their aesthetics in orthodontics, with newer 3D-printed technologies allowing the design of aligners with differential thicknesses and materials, offering advantages in terms of force distribution on the teeth, thereby optimizing treatment biomechanics. This study aimed to compare the initial and final forces of three types of 3D-printed aligners (with different thickness gradients and gingival margins) and traditional thermoformed aligners (with different gingival margins), evaluating stress relaxation and force consistency to determine which material and configuration may be optimal for better force distribution; (2) Methods: Twenty-seven 3D-printed aligners with three design variations and 18 thermoformed aligners were analyzed. Customized models were used to assess force at specific points on the upper incisor (1.1) and molar (2.6). A 3 h stress-relaxation test was conducted at 37 °C, and force data were recorded every second using a motorized compression stand. Statistical analysis was performed using ANOVA, post hoc tests, and Kruskal–Wallis tests for comparisons; (3) Results and Conclusions: Aligners with vertical and horizontal thickness gradients and a gingival margin trimmed 2 mm above the gingival contour exerted the highest forces, particularly at incisal/occlusal points. No significant differences in stress relaxation were observed. The force applied to the molars was consistently higher than the force applied to the incisors. These 3D-printed aligners with both horizontal and vertical gradients may offer a viable alternative to thermoformed aligners. Full article
(This article belongs to the Special Issue Orthodontics: Advanced Techniques, Methods and Materials)
Show Figures

Figure 1

Figure 1
<p>Master model used to print all aligners.</p>
Full article ">Figure 2
<p>(<b>a</b>) NHVH (NOXI Horizontal Vertical High); (<b>b</b>) NHH (NOXI Horizontal High); (<b>c</b>) NHZ (NOXI Horizontal Zenith).</p>
Full article ">Figure 3
<p>(<b>a</b>) F22 (straight with gingival margin); (<b>b</b>) AS (scalloped gingival margin).</p>
Full article ">Figure 4
<p>The aligner sample immersed in the bath and positioned beneath the load cell.</p>
Full article ">Figure 5
<p>(<b>a</b>) Mean decay curve recorded at the gingival level of tooth 1.1; (<b>b</b>) mean decay curve recorded at the middle level of tooth 1.1; (<b>c</b>) mean decay curve recorded at the incisal level of tooth 1.1 The X-axis represents time in hours, and the Y-axis represents force in Newtons.</p>
Full article ">Figure 5 Cont.
<p>(<b>a</b>) Mean decay curve recorded at the gingival level of tooth 1.1; (<b>b</b>) mean decay curve recorded at the middle level of tooth 1.1; (<b>c</b>) mean decay curve recorded at the incisal level of tooth 1.1 The X-axis represents time in hours, and the Y-axis represents force in Newtons.</p>
Full article ">Figure 6
<p>(<b>a</b>) Mean decay curve recorded at the gingival level of tooth 2.6; (<b>b</b>) mean decay curve recorded at the middle level of tooth 2.6; (<b>c</b>) mean decay curve recorded at the incisal level of tooth 2.6. The X-axis represents time in hours, and the Y-axis represents force in Newtons.</p>
Full article ">Figure 6 Cont.
<p>(<b>a</b>) Mean decay curve recorded at the gingival level of tooth 2.6; (<b>b</b>) mean decay curve recorded at the middle level of tooth 2.6; (<b>c</b>) mean decay curve recorded at the incisal level of tooth 2.6. The X-axis represents time in hours, and the Y-axis represents force in Newtons.</p>
Full article ">
28 pages, 1473 KiB  
Article
Maximum Trimmed Likelihood Estimation for Discrete Multivariate Vasicek Processes
by Thomas M. Fullerton, Michael Pokojovy, Andrews T. Anum and Ebenezer Nkum
Economies 2025, 13(3), 68; https://doi.org/10.3390/economies13030068 - 6 Mar 2025
Viewed by 110
Abstract
The multivariate Vasicek model is commonly used to capture mean-reverting dynamics typical for short rates, asset price stochastic log-volatilities, etc. Reparametrizing the discretized problem as a VAR(1) model, the parameters are oftentimes estimated using the multivariate least squares (MLS) method, which can be [...] Read more.
The multivariate Vasicek model is commonly used to capture mean-reverting dynamics typical for short rates, asset price stochastic log-volatilities, etc. Reparametrizing the discretized problem as a VAR(1) model, the parameters are oftentimes estimated using the multivariate least squares (MLS) method, which can be susceptible to outliers. To account for potential model violations, a maximum trimmed likelihood estimation (MTLE) approach is utilized to derive a system of nonlinear estimating equations, and an iterative procedure is developed to solve the latter. In addition to robustness, our new technique allows for reliable recovery of the long-term mean, unlike existing methodologies. A set of simulation studies across multiple dimensions, sample sizes and robustness configurations are performed. MTLE outcomes are compared to those of multivariate least trimmed squares (MLTS), MLE and MLS. Empirical results suggest that MTLE not only maintains good relative efficiency for uncontaminated data but significantly improves overall estimation quality in the presence of data irregularities. Additionally, real data examples containing daily log-volatilities of six common assets (commodities and currencies) and US/Euro short rates are also analyzed. The results indicate that MTLE provides an attractive instrument for interest rate forecasting, stochastic volatility modeling, risk management and other applications requiring statistical robustness in complex economic and financial environments. Full article
Show Figures

Figure 1

Figure 1
<p>Simulated <math display="inline"><semantics> <mover accent="true"> <mo form="prefix">err</mo> <mo>^</mo> </mover> </semantics></math> values for <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.20</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ncp</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>bdp</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 2
<p>Simulated <math display="inline"><semantics> <mover accent="true"> <mo form="prefix">err</mo> <mo>^</mo> </mover> </semantics></math> values for <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.30</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ncp</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>bdp</mi> <mo>=</mo> <mn>0.35</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 3
<p>Simulated <math display="inline"><semantics> <mover accent="true"> <mo form="prefix">err</mo> <mo>^</mo> </mover> </semantics></math> values for <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.20</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ncp</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>bdp</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 4
<p>Simulated <math display="inline"><semantics> <mover accent="true"> <mo form="prefix">err</mo> <mo>^</mo> </mover> </semantics></math> values for <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.10</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ncp</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>bdp</mi> <mo>=</mo> <mn>0.35</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>Historic US/EU 3-month rates (1 January 2023–31 12 December 2023) as well as forecasted mean and 90% projection bands (1 January 2024–31 March 2024).</p>
Full article ">Figure 6
<p>The contour plots of the probability density function of the forecasted short rate <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">R</mi> <mi>t</mi> </msub> </semantics></math> distribution on 31 March 2024.</p>
Full article ">Figure 7
<p>Sphered empirical residuals for MTLE (<math display="inline"><semantics> <mrow> <mi>bdp</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>), MLTS (<math display="inline"><semantics> <mrow> <mi>bdp</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>), MLE and MLS estimators with respective 95% prediction circles.</p>
Full article ">Figure 8
<p>Empirical backtesting root-MSE and MAPE using MTLE, MLTS, MLE and MLS estimators.</p>
Full article ">Figure 9
<p>Daily logged volatilities: July 2017–June 2020.</p>
Full article ">Figure 10
<p>Estimates of <math display="inline"><semantics> <msup> <mi mathvariant="bold-italic">R</mi> <mo>∗</mo> </msup> </semantics></math> for daily log-volatilities with <math display="inline"><semantics> <mrow> <mi>w</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>.</p>
Full article ">
14 pages, 5549 KiB  
Article
Surface Deformation and Straightness Detection of Electromagnetic Launcher Based on Laser Point Clouds
by Kangwei Yan, Delin Zeng, Long Cheng and Sai Tan
Appl. Sci. 2025, 15(5), 2706; https://doi.org/10.3390/app15052706 - 3 Mar 2025
Viewed by 261
Abstract
Bore deterioration phenomena, such as surface ablation, wear, aluminum deposition, and structural bending, severely restrict the service life and performance of electromagnetic launchers. Efficient bore inspection is necessary to study the deterioration mechanism, guide design, and health management. In this paper, an inspection [...] Read more.
Bore deterioration phenomena, such as surface ablation, wear, aluminum deposition, and structural bending, severely restrict the service life and performance of electromagnetic launchers. Efficient bore inspection is necessary to study the deterioration mechanism, guide design, and health management. In this paper, an inspection system for electromagnetic launchers is presented which utilizes structured light scanning, time-of-flight, and laser alignment methods to acquire bore laser point clouds, and ultimately extracts the surface deformation of rails and insulators, as well as the straightness of the bore, through the registration of point cloud data. First, the system composition and detection principles are introduced. Second, the impacts of the detection device’s attitude deflection are analyzed. Next, focusing on the key registration issue of laser point clouds, a coarse registration method is proposed which utilizes the arc features of the rail by combining circle and parabola equations, thereby maximizing registration efficiency. Finally, the trimmed iterative closest-point (TrICP) algorithm is employed for fine registration to handle non-axisymmetric bore deformations. The experimental results show that the proposed method can detect bore surface deformation and straightness efficiently and precisely. Full article
(This article belongs to the Special Issue Optical Sensors: Applications, Performance and Challenges)
Show Figures

Figure 1

Figure 1
<p>A schematic of the bore surface deformation and straightness detection system.</p>
Full article ">Figure 2
<p>Schematic of laser triangulation for acquiring bore profile point clouds.</p>
Full article ">Figure 3
<p>Schematic of bore straightness extraction.</p>
Full article ">Figure 4
<p>Schematic of detection deviations caused by the borescope trolley attitude pitch.</p>
Full article ">Figure 5
<p>Relationship between detection deviation and front wheel lift height under different wheelbases: (<b>a</b>) Surface deformation detection deviation; (<b>b</b>) Arch straightness detection deviation.</p>
Full article ">Figure 6
<p>Schematic of coarse registration principle.</p>
Full article ">Figure 7
<p>The registration results of the bore profile point clouds.</p>
Full article ">Figure 8
<p>Comparison of calculated and tested values for surface deformation detection deviation.</p>
Full article ">Figure 9
<p>Bore surface deformation detection results: (<b>a</b>) rail surface deformation; (<b>b</b>) insulator surface deformation.</p>
Full article ">Figure 10
<p>Bore straightness detection results: (<b>a</b>) side straightness; (<b>b</b>) arch straightness.</p>
Full article ">Figure 11
<p>Bore profile point cloud registration results under the non-axisymmetric deformation.</p>
Full article ">
36 pages, 848 KiB  
Article
The Role of Parenting Styles in Narcissism Development: A Systematic Review and Meta-Analysis
by Ariana dos Reis, João Paulo Martins and Rui Santos
AppliedMath 2025, 5(1), 23; https://doi.org/10.3390/appliedmath5010023 - 3 Mar 2025
Viewed by 299
Abstract
There has been considerable debate about whether contemporary Western societies are experiencing an increase in narcissistic tendencies, often referred to as a “narcissism epidemic”. This rise highlights the importance of understanding the origins of narcissism, particularly regarding its potential association with parenting styles. [...] Read more.
There has been considerable debate about whether contemporary Western societies are experiencing an increase in narcissistic tendencies, often referred to as a “narcissism epidemic”. This rise highlights the importance of understanding the origins of narcissism, particularly regarding its potential association with parenting styles. Such insights can inform treatment approaches and contribute to paradigm shifts in developmental psychology. This systematic review and meta-analysis examine how different parenting styles are associated with the development of narcissistic traits, using both partial and zero-order correlations as measures of effect. To ensure a consistent conceptualization of parenting styles, the results were evaluated using Baumrind’s parental styles typology. The review follows PRISMA guidelines and is registered in PROSPERO (CRD42024516395). Studies published in English or Portuguese since 2000 were sourced from PubMed (1039 articles) and Scopus (2120 articles), resulting in a final sample of 53 studies across 38 articles. Data synthesis included assessment of statistical heterogeneity (I2 statistic), publication bias (funnel plots, Egger’s test, and the trim and fill method), and methodological quality (adapted Newcastle–Ottawa Scale, NOS). Additionally, sensitivity analyses were conducted to evaluate the effect of excluding studies scoring below eight on the NOS by comparing results from analyses with all studies versus high-quality studies only. Results indicate a significant, albeit weak, association between parenting styles and narcissistic traits, with notable variations between maternal and paternal influences. This analysis provides a comprehensive perspective on the interplay between parenting approaches and the emergence of narcissistic characteristics, underscoring the complexity of factors that contribute to narcissism in contemporary society. Full article
Show Figures

Figure 1

Figure 1
<p>PRISMA 2020 flow diagram.</p>
Full article ">Figure 2
<p>Forest graphic of authoritative parenting and overall narcissism [<a href="#B15-appliedmath-05-00023" class="html-bibr">15</a>,<a href="#B19-appliedmath-05-00023" class="html-bibr">19</a>,<a href="#B39-appliedmath-05-00023" class="html-bibr">39</a>,<a href="#B53-appliedmath-05-00023" class="html-bibr">53</a>,<a href="#B54-appliedmath-05-00023" class="html-bibr">54</a>,<a href="#B56-appliedmath-05-00023" class="html-bibr">56</a>,<a href="#B58-appliedmath-05-00023" class="html-bibr">58</a>,<a href="#B59-appliedmath-05-00023" class="html-bibr">59</a>,<a href="#B64-appliedmath-05-00023" class="html-bibr">64</a>,<a href="#B66-appliedmath-05-00023" class="html-bibr">66</a>,<a href="#B68-appliedmath-05-00023" class="html-bibr">68</a>].</p>
Full article ">Figure 3
<p>Forest graphic of authoritarian parenting and overall narcissism [<a href="#B18-appliedmath-05-00023" class="html-bibr">18</a>,<a href="#B19-appliedmath-05-00023" class="html-bibr">19</a>,<a href="#B20-appliedmath-05-00023" class="html-bibr">20</a>,<a href="#B54-appliedmath-05-00023" class="html-bibr">54</a>,<a href="#B56-appliedmath-05-00023" class="html-bibr">56</a>,<a href="#B58-appliedmath-05-00023" class="html-bibr">58</a>,<a href="#B59-appliedmath-05-00023" class="html-bibr">59</a>,<a href="#B60-appliedmath-05-00023" class="html-bibr">60</a>,<a href="#B62-appliedmath-05-00023" class="html-bibr">62</a>,<a href="#B64-appliedmath-05-00023" class="html-bibr">64</a>,<a href="#B72-appliedmath-05-00023" class="html-bibr">72</a>,<a href="#B73-appliedmath-05-00023" class="html-bibr">73</a>,<a href="#B74-appliedmath-05-00023" class="html-bibr">74</a>].</p>
Full article ">Figure 4
<p>Forest graphic of neglectful parenting and overall narcissism [<a href="#B15-appliedmath-05-00023" class="html-bibr">15</a>,<a href="#B21-appliedmath-05-00023" class="html-bibr">21</a>,<a href="#B23-appliedmath-05-00023" class="html-bibr">23</a>,<a href="#B39-appliedmath-05-00023" class="html-bibr">39</a>,<a href="#B53-appliedmath-05-00023" class="html-bibr">53</a>,<a href="#B56-appliedmath-05-00023" class="html-bibr">56</a>,<a href="#B59-appliedmath-05-00023" class="html-bibr">59</a>,<a href="#B60-appliedmath-05-00023" class="html-bibr">60</a>,<a href="#B71-appliedmath-05-00023" class="html-bibr">71</a>].</p>
Full article ">Figure 5
<p>Forest graphic of permissive parenting and overall narcissism [<a href="#B19-appliedmath-05-00023" class="html-bibr">19</a>,<a href="#B20-appliedmath-05-00023" class="html-bibr">20</a>,<a href="#B39-appliedmath-05-00023" class="html-bibr">39</a>,<a href="#B58-appliedmath-05-00023" class="html-bibr">58</a>,<a href="#B70-appliedmath-05-00023" class="html-bibr">70</a>,<a href="#B71-appliedmath-05-00023" class="html-bibr">71</a>].</p>
Full article ">Figure 6
<p>Forest graphic of authoritarian parenting and overall narcissism – with NOS score studies higher or equal to 8 [<a href="#B18-appliedmath-05-00023" class="html-bibr">18</a>,<a href="#B19-appliedmath-05-00023" class="html-bibr">19</a>,<a href="#B54-appliedmath-05-00023" class="html-bibr">54</a>,<a href="#B56-appliedmath-05-00023" class="html-bibr">56</a>,<a href="#B58-appliedmath-05-00023" class="html-bibr">58</a>,<a href="#B59-appliedmath-05-00023" class="html-bibr">59</a>,<a href="#B60-appliedmath-05-00023" class="html-bibr">60</a>,<a href="#B62-appliedmath-05-00023" class="html-bibr">62</a>,<a href="#B64-appliedmath-05-00023" class="html-bibr">64</a>,<a href="#B72-appliedmath-05-00023" class="html-bibr">72</a>,<a href="#B73-appliedmath-05-00023" class="html-bibr">73</a>,<a href="#B74-appliedmath-05-00023" class="html-bibr">74</a>].</p>
Full article ">Figure 7
<p>Forest graphic of permissive parenting and overall narcissism – with NOS score studies higher or equal to 8 [<a href="#B19-appliedmath-05-00023" class="html-bibr">19</a>,<a href="#B39-appliedmath-05-00023" class="html-bibr">39</a>,<a href="#B58-appliedmath-05-00023" class="html-bibr">58</a>,<a href="#B70-appliedmath-05-00023" class="html-bibr">70</a>,<a href="#B71-appliedmath-05-00023" class="html-bibr">71</a>].</p>
Full article ">Figure 8
<p>Forest of graphic authoritative mother and overall narcissism [<a href="#B12-appliedmath-05-00023" class="html-bibr">12</a>,<a href="#B14-appliedmath-05-00023" class="html-bibr">14</a>,<a href="#B16-appliedmath-05-00023" class="html-bibr">16</a>,<a href="#B24-appliedmath-05-00023" class="html-bibr">24</a>,<a href="#B37-appliedmath-05-00023" class="html-bibr">37</a>,<a href="#B38-appliedmath-05-00023" class="html-bibr">38</a>,<a href="#B41-appliedmath-05-00023" class="html-bibr">41</a>,<a href="#B55-appliedmath-05-00023" class="html-bibr">55</a>,<a href="#B63-appliedmath-05-00023" class="html-bibr">63</a>,<a href="#B69-appliedmath-05-00023" class="html-bibr">69</a>].</p>
Full article ">Figure 9
<p>Forest graphic of authoritative father and overall narcissism [<a href="#B12-appliedmath-05-00023" class="html-bibr">12</a>,<a href="#B14-appliedmath-05-00023" class="html-bibr">14</a>,<a href="#B16-appliedmath-05-00023" class="html-bibr">16</a>,<a href="#B24-appliedmath-05-00023" class="html-bibr">24</a>,<a href="#B37-appliedmath-05-00023" class="html-bibr">37</a>,<a href="#B38-appliedmath-05-00023" class="html-bibr">38</a>,<a href="#B41-appliedmath-05-00023" class="html-bibr">41</a>,<a href="#B55-appliedmath-05-00023" class="html-bibr">55</a>,<a href="#B63-appliedmath-05-00023" class="html-bibr">63</a>,<a href="#B69-appliedmath-05-00023" class="html-bibr">69</a>].</p>
Full article ">Figure 10
<p>Forest graphic of authoritative mother and grandiose narcissism [<a href="#B57-appliedmath-05-00023" class="html-bibr">57</a>,<a href="#B65-appliedmath-05-00023" class="html-bibr">65</a>,<a href="#B67-appliedmath-05-00023" class="html-bibr">67</a>].</p>
Full article ">Figure 11
<p>Forest graphic of authoritative father and grandiose narcissism [<a href="#B57-appliedmath-05-00023" class="html-bibr">57</a>,<a href="#B65-appliedmath-05-00023" class="html-bibr">65</a>,<a href="#B67-appliedmath-05-00023" class="html-bibr">67</a>].</p>
Full article ">Figure 12
<p>Forest Graphic Authoritative Parenting Vulnerable Narcissism [<a href="#B53-appliedmath-05-00023" class="html-bibr">53</a>,<a href="#B54-appliedmath-05-00023" class="html-bibr">54</a>,<a href="#B61-appliedmath-05-00023" class="html-bibr">61</a>].</p>
Full article ">Figure 13
<p>Forest graphic of authoritative mother vulnerable narcissism [<a href="#B12-appliedmath-05-00023" class="html-bibr">12</a>,<a href="#B41-appliedmath-05-00023" class="html-bibr">41</a>,<a href="#B57-appliedmath-05-00023" class="html-bibr">57</a>,<a href="#B69-appliedmath-05-00023" class="html-bibr">69</a>].</p>
Full article ">Figure 14
<p>Forest graphic authoritative father vulnerable narcissism [<a href="#B12-appliedmath-05-00023" class="html-bibr">12</a>,<a href="#B41-appliedmath-05-00023" class="html-bibr">41</a>,<a href="#B57-appliedmath-05-00023" class="html-bibr">57</a>,<a href="#B69-appliedmath-05-00023" class="html-bibr">69</a>].</p>
Full article ">Figure 15
<p>Forest graphic of authoritarian mother and overall marcissism [<a href="#B12-appliedmath-05-00023" class="html-bibr">12</a>,<a href="#B14-appliedmath-05-00023" class="html-bibr">14</a>,<a href="#B16-appliedmath-05-00023" class="html-bibr">16</a>,<a href="#B24-appliedmath-05-00023" class="html-bibr">24</a>,<a href="#B29-appliedmath-05-00023" class="html-bibr">29</a>,<a href="#B37-appliedmath-05-00023" class="html-bibr">37</a>,<a href="#B41-appliedmath-05-00023" class="html-bibr">41</a>,<a href="#B55-appliedmath-05-00023" class="html-bibr">55</a>,<a href="#B60-appliedmath-05-00023" class="html-bibr">60</a>,<a href="#B63-appliedmath-05-00023" class="html-bibr">63</a>].</p>
Full article ">Figure 16
<p>Forest graphic of authoritarian father and overall narcissism [<a href="#B12-appliedmath-05-00023" class="html-bibr">12</a>,<a href="#B14-appliedmath-05-00023" class="html-bibr">14</a>,<a href="#B16-appliedmath-05-00023" class="html-bibr">16</a>,<a href="#B24-appliedmath-05-00023" class="html-bibr">24</a>,<a href="#B29-appliedmath-05-00023" class="html-bibr">29</a>,<a href="#B37-appliedmath-05-00023" class="html-bibr">37</a>,<a href="#B41-appliedmath-05-00023" class="html-bibr">41</a>,<a href="#B55-appliedmath-05-00023" class="html-bibr">55</a>,<a href="#B60-appliedmath-05-00023" class="html-bibr">60</a>,<a href="#B63-appliedmath-05-00023" class="html-bibr">63</a>].</p>
Full article ">Figure 17
<p>Forest graphic of authoritarian parenting and vulnerable narcissism [<a href="#B18-appliedmath-05-00023" class="html-bibr">18</a>,<a href="#B54-appliedmath-05-00023" class="html-bibr">54</a>,<a href="#B60-appliedmath-05-00023" class="html-bibr">60</a>,<a href="#B73-appliedmath-05-00023" class="html-bibr">73</a>,<a href="#B74-appliedmath-05-00023" class="html-bibr">74</a>].</p>
Full article ">Figure 18
<p>Forest graphic of authoritarian mother and vulnerable narcissism [<a href="#B12-appliedmath-05-00023" class="html-bibr">12</a>,<a href="#B41-appliedmath-05-00023" class="html-bibr">41</a>,<a href="#B57-appliedmath-05-00023" class="html-bibr">57</a>,<a href="#B60-appliedmath-05-00023" class="html-bibr">60</a>].</p>
Full article ">Figure 19
<p>Forest graphic of authoritarian father and vulnerable narcissism [<a href="#B12-appliedmath-05-00023" class="html-bibr">12</a>,<a href="#B41-appliedmath-05-00023" class="html-bibr">41</a>,<a href="#B57-appliedmath-05-00023" class="html-bibr">57</a>,<a href="#B60-appliedmath-05-00023" class="html-bibr">60</a>].</p>
Full article ">Figure 20
<p>Forest graphic of neglectful mother and overall narcissism [<a href="#B12-appliedmath-05-00023" class="html-bibr">12</a>,<a href="#B24-appliedmath-05-00023" class="html-bibr">24</a>,<a href="#B29-appliedmath-05-00023" class="html-bibr">29</a>,<a href="#B41-appliedmath-05-00023" class="html-bibr">41</a>,<a href="#B55-appliedmath-05-00023" class="html-bibr">55</a>].</p>
Full article ">Figure 21
<p>Forest graphic of neglectful father and overall narcissism [<a href="#B12-appliedmath-05-00023" class="html-bibr">12</a>,<a href="#B24-appliedmath-05-00023" class="html-bibr">24</a>,<a href="#B27-appliedmath-05-00023" class="html-bibr">27</a>,<a href="#B29-appliedmath-05-00023" class="html-bibr">29</a>,<a href="#B41-appliedmath-05-00023" class="html-bibr">41</a>,<a href="#B55-appliedmath-05-00023" class="html-bibr">55</a>].</p>
Full article ">Figure 22
<p>Forest graphic of neglectful parenting and vulnerable narcissism [<a href="#B53-appliedmath-05-00023" class="html-bibr">53</a>,<a href="#B60-appliedmath-05-00023" class="html-bibr">60</a>,<a href="#B71-appliedmath-05-00023" class="html-bibr">71</a>].</p>
Full article ">Figure 23
<p>Forest Graphic Permissive Mother Overall Narcissism [<a href="#B12-appliedmath-05-00023" class="html-bibr">12</a>,<a href="#B38-appliedmath-05-00023" class="html-bibr">38</a>,<a href="#B55-appliedmath-05-00023" class="html-bibr">55</a>].</p>
Full article ">Figure 24
<p>Forest graphic of permissive father and overall narcissism [<a href="#B12-appliedmath-05-00023" class="html-bibr">12</a>,<a href="#B38-appliedmath-05-00023" class="html-bibr">38</a>,<a href="#B55-appliedmath-05-00023" class="html-bibr">55</a>].</p>
Full article ">Figure A1
<p>Authoritative parenting overall. Egger’s test <span class="html-italic">p</span>-value = 0.5533, trim and fill method: two missing studies.</p>
Full article ">Figure A2
<p>Authoritarian parenting overall. Egger’s test <span class="html-italic">p</span>-value = 0.7727, trim and fill method: no missing studies.</p>
Full article ">Figure A3
<p>Neglectful parenting overall. Egger’s test <span class="html-italic">p</span>-value = 0.2736, trim and fill method: four missing studies.</p>
Full article ">Figure A4
<p>Permissive parenting overall. Egger’s test <span class="html-italic">p</span>-value = 0.9582, trim and fill method: no missing studies.</p>
Full article ">Figure A5
<p>Authoritative mother overall. Egger’s test <span class="html-italic">p</span>-value = 0.3003, trim and fill method: two missing studies.</p>
Full article ">Figure A6
<p>Authoritative father overall. Egger’s test <span class="html-italic">p</span>-value = 0.7393, trim and fill method: two missing studies.</p>
Full article ">Figure A7
<p>Authoritative mother grandiose. Egger’s test <span class="html-italic">p</span>-value = 0.7240, trim and fill method: no missing studies.</p>
Full article ">Figure A8
<p>Authoritative father grandiose. Egger’s test <span class="html-italic">p</span>-value = 0.9242, trim and fill method: no missing studies.</p>
Full article ">Figure A9
<p>Authoritative parenting vulnerable. Egger’s test <span class="html-italic">p</span>-value = 0.4534, trim and fill method: no missing studies.</p>
Full article ">Figure A10
<p>Authoritative mother vulnerable. Egger’s test <span class="html-italic">p</span>-value = 0.4423, trim and fill method: two missing studies.</p>
Full article ">Figure A11
<p>Authoritative father vulnerable. Egger’s test <span class="html-italic">p</span>-value = 0.7393, trim and fill method: two missing studies.</p>
Full article ">Figure A12
<p>Authoritarian mother overall. Egger’s test <span class="html-italic">p</span>-value = 0.8892, trim and fill method: three missing studies.</p>
Full article ">Figure A13
<p>Authoritarian father overall. Egger’s test <span class="html-italic">p</span>-value = 0.6535, trim and fill method: three missing studies.</p>
Full article ">Figure A14
<p>Authoritarian parenting vulnerable. Egger’s test <span class="html-italic">p</span>-value = 0.5602, trim and fill method: two missing studies.</p>
Full article ">Figure A15
<p>Authoritarian mother vulnerable. Egger’s test <span class="html-italic">p</span>-value = 0.8658, trim and fill method: no missing studies.</p>
Full article ">Figure A16
<p>Authoritarian father vulnerable. Egger’s test <span class="html-italic">p</span>-value = 0.8546, trim and fill method: no missing studies.</p>
Full article ">Figure A17
<p>Neglectful mother overall. Egger’s test <span class="html-italic">p</span>-value = 0.4241, trim and fill method: no missing studies.</p>
Full article ">Figure A18
<p>Neglectful father overall. Egger’s test <span class="html-italic">p</span>-value = 0.9341, trim and fill method: no missing studies.</p>
Full article ">Figure A19
<p>Neglectful parenting vulnerable. Egger’s test <span class="html-italic">p</span>-value = 0.8289, trim and fill method: two missing studies.</p>
Full article ">Figure A20
<p>Permissive mother overall. Egger’s test <span class="html-italic">p</span>-value = 0.5700, trim and fill method: two missing studies.</p>
Full article ">Figure A21
<p>Permissive father overall. Egger’s test <span class="html-italic">p</span>-value = 0.2734, trim and fill method: two missing studies.</p>
Full article ">Figure A22
<p>Authoritative parenting overall. Estimated slope <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>^</mo> </mover> <mo>=</mo> <mo>−</mo> <mn>0.008</mn> </mrow> </semantics></math>, CI 95% [−0.025, 0.010], and <span class="html-italic">p</span>-value = 0.3919 for <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">H</mi> <mn>0</mn> </msub> <mo>:</mo> <mi>β</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure A23
<p>Authoritarian parenting overall. Estimated slope <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>^</mo> </mover> <mo>=</mo> </mrow> </semantics></math> 0.020, CI 95% [0.008, 0.031], and <span class="html-italic">p</span>-value = 0.0008 for <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">H</mi> <mn>0</mn> </msub> <mo>:</mo> <mi>β</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure A24
<p>Authoritative mother overall. Estimated slope <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>^</mo> </mover> <mo>=</mo> <mo>−</mo> <mn>0.011</mn> </mrow> </semantics></math>, CI 95% [−0.028, 0.006], and <span class="html-italic">p</span>-value = 0.2107 for <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">H</mi> <mn>0</mn> </msub> <mo>:</mo> <mi>β</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure A25
<p>Authoritative father overall. Estimated slope <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>^</mo> </mover> <mo>=</mo> <mo>−</mo> <mn>0.023</mn> </mrow> </semantics></math>, CI 95% [−0.059, 0.013], and <span class="html-italic">p</span>-value = 0.2039 for <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">H</mi> <mn>0</mn> </msub> <mo>:</mo> <mi>β</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure A26
<p>Authoritarian mother overall. Estimated slope <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>^</mo> </mover> <mo>=</mo> </mrow> </semantics></math> 0.008, CI 95% [0.0018, 0.0138], and <span class="html-italic">p</span>-value = 0.0109 for <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">H</mi> <mn>0</mn> </msub> <mo>:</mo> <mi>β</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure A27
<p>Authoritarian father overall. Estimated slope <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>^</mo> </mover> <mo>=</mo> </mrow> </semantics></math> 0.004, CI 95% [−0.004, 0.013], and <span class="html-italic">p</span>-value = 0.3130 for <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">H</mi> <mn>0</mn> </msub> <mo>:</mo> <mi>β</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
Full article ">
17 pages, 6050 KiB  
Article
Coal Mining Machine Localization Method Based on Non-Gaussian Summation Parallel Kalman Filter Group
by Chenrong Xi, Fan Zhang, Yang Yu and Hui Song
Processes 2025, 13(3), 694; https://doi.org/10.3390/pr13030694 - 28 Feb 2025
Viewed by 237
Abstract
Coal mining machine positioning technology is the key to realizing unmanned and intelligent mining of the comprehensive mining zone. Based on the traditional Strapdown Inertial Navigation System combined with Kalman-filtering coal mining machine positioning technology, non-integrity constraints are introduced, and the error of [...] Read more.
Coal mining machine positioning technology is the key to realizing unmanned and intelligent mining of the comprehensive mining zone. Based on the traditional Strapdown Inertial Navigation System combined with Kalman-filtering coal mining machine positioning technology, non-integrity constraints are introduced, and the error of the output of the above system is filtered by an optimized Kalman filtering method proposed in this paper: non-Gaussian summation and a parallel Kalman filter bank. This method decomposes the non-Gaussian system into a linear combination of multiple Gaussian systems through the parallel Kalman filter group, then fuses the states occupying different weight coefficients and designs a method of Gaussian-term number trimming to solve the problem of parameter explosion in the filtering process, and ultimately obtains the optimal estimation of the positioning information of the coal mining machine. Experiments show that, for the coal mining machine positioning issue in the complex noise interference environment of intelligent mines, the non-Gaussian summation and parallel Kalman filter group method in this paper, compared with the traditional particle filtering method, greatly reduces the three-dimensional attitude error, three-dimensional velocity error, three-dimensional position error in the nine dimensional parameters of the estimation error, and the average estimation error. The average estimation error is reduced by 49%, 52%, 50%, 53%, 51%, 48.8%, 50.1%, 54%, and 51.3%, respectively, which significantly improves the positioning accuracy of coal mining machines, and has stronger real-time performance, stability, and accuracy in the coal mining machine positioning system. Full article
(This article belongs to the Section Advanced Digital and Other Processes)
Show Figures

Figure 1

Figure 1
<p>Flow chart of coal mining machine positioning method based on non-Gaussian summation Kalman filter group.</p>
Full article ">Figure 2
<p>Principle of coal mining machine positioning method.</p>
Full article ">Figure 3
<p>Schematic diagram of digital twin of coal mining machine working scene.</p>
Full article ">Figure 4
<p>Coal mining machine attitude error.</p>
Full article ">Figure 5
<p>Coal mining machine velocity error.</p>
Full article ">Figure 6
<p>Coal mining machine position error.</p>
Full article ">Figure 7
<p>Estimation of attitude error of coal mining machine.</p>
Full article ">Figure 8
<p>Estimation of velocity error of coal mining machine.</p>
Full article ">Figure 9
<p>Estimation of position error of coal mining machine.</p>
Full article ">
23 pages, 9402 KiB  
Article
Cooperative Path Planning for Multiple UAVs Based on APF B-RRT* Algorithm
by Cailong Wu, Zhengyu Guo, Jian Zhang, Kai Mao and Delin Luo
Drones 2025, 9(3), 177; https://doi.org/10.3390/drones9030177 - 27 Feb 2025
Viewed by 291
Abstract
Aiming at the path planning problem of an unmanned aerial vehicle (UAV) in a complex unknown environment, this paper proposes a cooperative path planning algorithm for multiple UAVs. Using the local environment information, several rolling path plannings are carried out by the Artificial [...] Read more.
Aiming at the path planning problem of an unmanned aerial vehicle (UAV) in a complex unknown environment, this paper proposes a cooperative path planning algorithm for multiple UAVs. Using the local environment information, several rolling path plannings are carried out by the Artificial Potential Field Bidirectional-Rapidly exploring Random Trees (APF B-RRT*) algorithm. The APF B-RRT* algorithm optimizes the search space by pre-sampling and adapts with an adaptive step while fusing with the APF algorithm for guiding sampling. Then, the generated path is trimmed and smoothed to obtain the optimized path. Then, through the sampling constraint, several paths can be planned at the same time, which are guaranteed not to collide. The model predictive control (MPC) is used to realize the cooperative control of the UAVs, that is, the UAVs reached the destination simultaneously along the planned path. This algorithm achieves some progress in solving the problems of slow convergence speed, an unstable result and an unsmooth path in UAV path planning. Simulation and comparison show that the APF B-RRT* algorithm has certain advantages in algorithm performance. Full article
(This article belongs to the Special Issue Swarm Intelligence in Multi-UAVs)
Show Figures

Figure 1

Figure 1
<p>Overall framework of multiple UAVs path planning.</p>
Full article ">Figure 2
<p>RRT* algorithm planning process. (<b>a</b>) RRT* node expansion process. (<b>b</b>) A simple path planned by the RRT* algorithm. The black area is the obstacle, the red line is the planned path.</p>
Full article ">Figure 3
<p>B-RRT* algorithm planning process. The green line is the random tree <span class="html-italic">T1</span> , the yellow line is another random tree <span class="html-italic">T2.</span></p>
Full article ">Figure 4
<p>Schematic diagram of comprehensive force.</p>
Full article ">Figure 5
<p>Pre-sampling process.</p>
Full article ">Figure 6
<p>Force analysis diagram of APF.</p>
Full article ">Figure 7
<p>Path rolling planning process. The red dash line circles are the rolling windows, the yellow point are sub-target point.</p>
Full article ">Figure 8
<p>Path trimming process. The green line is the initial path, and the red line is the trimmed path.</p>
Full article ">Figure 9
<p>The spatial cooperation constraints detection process.</p>
Full article ">Figure 10
<p>Planning process of two algorithms. The dashed circle is the rolling windows, the blue line is the random tree generated when searching, and the red line is the planned path. (<b>a</b>) Planning result of the B-RRT* algorithm. (<b>b</b>) Planning process of the APF B-RRT* algorithm.</p>
Full article ">Figure 11
<p>Planning results of path trimming and smoothing. The green line is the initial track. The blue track is the trimmed track. The red track is the smoothed track.</p>
Full article ">Figure 12
<p>Planning results of three algorithms. (<b>a</b>) 3D view. (<b>b</b>) Front view. (<b>c</b>) Top view.</p>
Full article ">Figure 13
<p>Cooperative path planning of 4 UAVs in 3D-map1. (<b>a</b>) 3D view of the result. (<b>b</b>) Front view of the result. (<b>c</b>) Top view of the result. (<b>d</b>) The minimum distance between 4 UAVs.</p>
Full article ">Figure 13 Cont.
<p>Cooperative path planning of 4 UAVs in 3D-map1. (<b>a</b>) 3D view of the result. (<b>b</b>) Front view of the result. (<b>c</b>) Top view of the result. (<b>d</b>) The minimum distance between 4 UAVs.</p>
Full article ">Figure 14
<p>Cooperative path planning of 6 UAVs in 3D-map2. (<b>a</b>) 3D view of the result. (<b>b</b>) Front view of the result. (<b>c</b>) Top view of the result. (<b>d</b>) The minimum distance between 6 UAVs.</p>
Full article ">Figure 14 Cont.
<p>Cooperative path planning of 6 UAVs in 3D-map2. (<b>a</b>) 3D view of the result. (<b>b</b>) Front view of the result. (<b>c</b>) Top view of the result. (<b>d</b>) The minimum distance between 6 UAVs.</p>
Full article ">
22 pages, 2410 KiB  
Article
DAHD-YOLO: A New High Robustness and Real-Time Method for Smoking Detection
by Jianfei Zhang and Chengwei Jiang
Sensors 2025, 25(5), 1433; https://doi.org/10.3390/s25051433 - 26 Feb 2025
Viewed by 183
Abstract
Recent advancements in AI technologies have driven the extensive adoption of deep learning architectures for recognizing human behavioral patterns. However, the existing smoking behavior detection models based on object detection still have problems, including poor accuracy and insufficient real-time performance. Especially in complex [...] Read more.
Recent advancements in AI technologies have driven the extensive adoption of deep learning architectures for recognizing human behavioral patterns. However, the existing smoking behavior detection models based on object detection still have problems, including poor accuracy and insufficient real-time performance. Especially in complex environments, the existing models often struggle with erroneous detections and missed detections. In this paper, we introduce DAHD-YOLO, a model built upon the foundation of YOLOv8. We first designed the DBCA module to replace the bottleneck component in the backbone. The architecture integrates a diverse branch block and a contextual anchor mechanism, effectively improving the backbone network’s ability to extract features. Subsequently, at the end of the backbone, we introduce adaptive fine-grained channel attention (AFGCA) to effectively facilitate the fusion of both overarching patterns and localized details. We introduce the ECA-FPN, an improved version of the feature pyramid network, designed to refine the extraction of hierarchical information and enhance cross-scale feature interactions. The decoupled detection head is also updated via the reparameterization approach. The wise–powerful intersection over union (Wise-PIoU) is adopted as the new bounding box regression loss function, resulting in quicker convergence speed and improved detection outcomes. Our system achieves superior results compared to existing models using a self-constructed smoking detection dataset, reducing computational complexity by 23.20% while trimming the model parameters by 33.95%. Moreover, the mAP50 of our model has increased by 5.1% compared to the benchmark model, reaching 86.0%. Finally, we deploy the improved model on the RK3588. After optimizations such as quantization and multi-threading, the system achieves a detection rate of 50.2 fps, addressing practical application demands and facilitating the precise and instantaneous identification of smoking activities. Full article
Show Figures

Figure 1

Figure 1
<p>The architecture of DAHD-YOLO.</p>
Full article ">Figure 2
<p>The component diagram of the DBCA module, including two sub-diagrams, namely ConvDBB and CAA.</p>
Full article ">Figure 3
<p>Diagram of AFGCA.</p>
Full article ">Figure 4
<p>Diagram of ECA-FPN.</p>
Full article ">Figure 5
<p>Regression results guided by different BBR losses.</p>
Full article ">Figure 6
<p>Comparison of feature visualization after adding different attention mechanisms in FPN.</p>
Full article ">Figure 7
<p>Comparison of convergence speeds between Wise-PIoU and traditional loss functions.</p>
Full article ">Figure 8
<p>Comparison of heatmap effects: (<b>a</b>) YOLOv8 base model; (<b>b</b>) improved model with AFGCA.</p>
Full article ">Figure 9
<p>Comparison of the detection effects of the original model and the improved model in complex scenarios.</p>
Full article ">
18 pages, 268 KiB  
Article
The Cost of Downstream Adverse Outcomes Associated with Allogeneic Blood Transfusion: A Retrospective Observational Cohort Study
by Michelle Roets, David John Sturgess, Kerstin Hildegard Wyssusek, Sung Min Lee, Melinda Margaret Dean and Andre van Zundert
Healthcare 2025, 13(5), 503; https://doi.org/10.3390/healthcare13050503 - 26 Feb 2025
Viewed by 219
Abstract
Background: ‘Downstream’ adverse outcomes associated with transfusion-related immune modulation (TRIM) occur postoperatively. The potential associations between these outcomes (and costs) and perioperative transfusion are often not considered by clinicians and therefore underestimated. When considering TRIM, many advantages of intraoperative cell salvage (ICS) were [...] Read more.
Background: ‘Downstream’ adverse outcomes associated with transfusion-related immune modulation (TRIM) occur postoperatively. The potential associations between these outcomes (and costs) and perioperative transfusion are often not considered by clinicians and therefore underestimated. When considering TRIM, many advantages of intraoperative cell salvage (ICS) were previously confirmed. Methods: The main aim of this retrospective observational study was to evaluate the cost implications associated with perioperative adverse outcomes following allogeneic blood transfusion (ABT). Secondly, further analysis considered downstream costs following ICS. This manuscript does not aim to provide evidence of improved outcomes following ICS compared to ABT. These outcomes were previously demonstrated. Instead, it is important to consider downstream cost implications if patients receive ABT, despite previously proven benefits related to ICS. Surgical patients (n = 2129) receiving blood transfusion at the Royal Brisbane and Women’s Hospital (Queensland, Australia) (2016–2018) were included: receiving ICS only (n = 115), allogeneic red blood cells (RBCs) only (n = 1944), or RBCs and ICS (n = 70). Data retrieved from eight hospital databases were exported, and a novel Structured Query Language (SQL) database was developed to link data points. Adverse outcomes previously associated with TRIM were assessed using International Classification of Diseases-10 (ICD-10) coded data. Generalised linear models were used to model costs and adjust for confounding factors. Results: Most adverse outcomes (≥3) occurred following RBCs and ICS (37.1%), followed by RBCs (23.7%) and ICS (16.5%). As potentially important determinants of overall expenditure, the lowest marginal mean intensive care stay (days, cost) was after ICS (2.1 days, AUD 10,027), followed by RBCs and ICS (3.8 days, AUD 18,089), and then RBCs (5.5 days, AUD 26,071). When considering blood products (other than packed red blood cells), the average cost per patient was lowest for ICS (AUD 48), followed by RBCs (AUD 533) and RBCs and ICS (AUD 819). Conclusions: We confirmed that the cost associated with allogeneic blood transfusion was significant; patients receiving packed red blood cells (pRBCs) experienced more adverse outcomes and higher hospital costs than those receiving ICS. These results are limited to retrospective data and require further prospective validation. Full article
(This article belongs to the Section Critical Care)
15 pages, 4260 KiB  
Article
Investigation of Ultra-Thin Glass Scribing Mechanism
by Dawei Li, Jiahao Li, Huaye Kong, Jinzhu Guo, Liyong Huang and Yao Liu
Coatings 2025, 15(3), 275; https://doi.org/10.3390/coatings15030275 - 26 Feb 2025
Viewed by 261
Abstract
To reveal the scribing mechanism of ultra-thin glass, single-factor scribing tests were carried out. The effects of the scribing wheel angle θ, scribing force F, and scribing speed v on the lateral cracks width w, scribing depth d, median [...] Read more.
To reveal the scribing mechanism of ultra-thin glass, single-factor scribing tests were carried out. The effects of the scribing wheel angle θ, scribing force F, and scribing speed v on the lateral cracks width w, scribing depth d, median cracks size l, and cross-section deflection angle α were analyzed to present the scribing quality. The results show that w increases with an increase in θ and F. Further, l and d increase with an increase in F. However, d shows an increasing trend with the increase in θ, and l shows a decreasing trend. In the range of 120–140°, α shows a trend of increasing first and then decreasing with an increase in F. The 120° scribing wheel angle, 20 N scribing force, and 100–400 mm/s scribing speed show the best scribing quality, which limits micro-cracks at the initiation stage without any damage or chipping. Under this condition, the breaking surface edges were free of debris and cracks. A smooth and trim Wallner ripple was obtained from the median cracks with a minimum deflection angle. Full article
Show Figures

Figure 1

Figure 1
<p>Schematic of scribing force.</p>
Full article ">Figure 2
<p>Fracture modes.</p>
Full article ">Figure 3
<p>Experimental setup.</p>
Full article ">Figure 4
<p>Schematic diagram of scribing and breaking process. (<b>a</b>) Glass scribing and breaking process, (<b>b</b>) scribing line, and (<b>c</b>) cross-section view.</p>
Full article ">Figure 5
<p>Effect of different scribing wheel angles and scribing forces on the surface topography of glass substrate. (<b>a</b>) <span class="html-italic">θ</span> = 90° and <span class="html-italic">F</span> = 20 N, (<b>b</b>) <span class="html-italic">θ</span> = 120° and <span class="html-italic">F</span> = 20 N, (<b>c</b>) <span class="html-italic">θ</span> = 140° and <span class="html-italic">F</span> = 30 N.</p>
Full article ">Figure 6
<p>Cutting cross-section morphology of glass by using (<b>a</b>) <span class="html-italic">θ</span> = 90° and <span class="html-italic">F</span> = 3 N, (<b>b</b>) <span class="html-italic">θ</span> = 90° and <span class="html-italic">F</span> = 10 N, (<b>c</b>) <span class="html-italic">θ</span> = 110° and <span class="html-italic">F</span> = 10 N, (<b>d</b>) <span class="html-italic">θ</span> = 120° and <span class="html-italic">F</span> = 20 N, (<b>e</b>) <span class="html-italic">θ</span> = 140° and <span class="html-italic">F</span> = 20 N, (<b>f</b>) <span class="html-italic">θ</span> = 140° and <span class="html-italic">F</span> = 30 N.</p>
Full article ">Figure 7
<p>Effect of scribing force and scribing wheel angle on lateral crack size.</p>
Full article ">Figure 8
<p>Effect of scribing force on the propagation rate of lateral cracks.</p>
Full article ">Figure 9
<p>Effect of scribing force and scribing wheel angle on cutting micro-crack depth and median crack length (line graph—median crack; bar graph—micro-crack depth).</p>
Full article ">Figure 10
<p>Stress variation at the crack tip with different (<b>a</b>) scribing wheel angle and (<b>b</b>) scrining force.</p>
Full article ">Figure 11
<p>Height cloud map and 3D views of glass cross-sections at different cutting parameters. (<b>a</b>) surface profile for deflection angle measurement, (<b>b</b>) deflection angle at <span class="html-italic">θ</span> = 130° and <span class="html-italic">F</span> = 16 N, and (<b>c</b>) deflection angle at <span class="html-italic">θ</span> = 90° and <span class="html-italic">F</span> = 10 N.</p>
Full article ">Figure 12
<p>(<b>a</b>) Effect of scribing force and scribing wheel angle on cross-section deflection angle at 90–110°; (<b>b</b>) 120°, (<b>c</b>) 130°, (<b>d</b>) 140°; effect of scribing force on median crack and cross-section deflection angle.</p>
Full article ">Figure 13
<p>Effect of scribing speed on lateral cracks, median cracks, micro-crack depths, and cross-section deflection angle. (<b>a</b>) lateral crack and deflection angle, (<b>b</b>) microcrack depth and median crack.</p>
Full article ">
51 pages, 13898 KiB  
Article
Turkey B Cell Transcriptome Profile During Turkey Hemorrhagic Enteritis Virus (THEV) Infection Highlights Upregulated Apoptosis and Breakdown Pathways That May Mediate Immunosuppression
by Abraham Quaye, Brett E. Pickett, Joel S. Griffitts, Bradford K. Berges and Brian D. Poole
Viruses 2025, 17(3), 299; https://doi.org/10.3390/v17030299 - 21 Feb 2025
Viewed by 295
Abstract
Infection with the turkey hemorrhagic enteritis virus (THEV) can cause hemorrhagic enteritis, which affects young turkeys. This disease is characterized by bloody diarrhea and immunosuppression (IMS), which is attributed to apoptosis of infected B cells. Secondary infections due to IMS exacerbate economic losses. [...] Read more.
Infection with the turkey hemorrhagic enteritis virus (THEV) can cause hemorrhagic enteritis, which affects young turkeys. This disease is characterized by bloody diarrhea and immunosuppression (IMS), which is attributed to apoptosis of infected B cells. Secondary infections due to IMS exacerbate economic losses. We performed the first transcriptomic analysis of a THEV infection to elucidate the mechanisms mediating THEV-induced IMS. After infecting and sequencing mRNAs of a turkey B-cell line, trimmed reads were mapped to the host turkey genome, and gene expression was quantified with StringTie. Differential gene expression analysis was followed by functional enrichment analyses using gprofiler2 and DAVID from NCBI. RT-qPCR of select genes was performed to validate the RNA-seq data. A total of 2343 and 3295 differentially expressed genes (DEGs) were identified at 12 hpi and 24 hpi, respectively. The DEGs correlated with multiple biological processes including apoptosis, ER unfolded protein response, and cell maintenance. Multiple pro-apoptotic genes, including APAF1, BMF, BAK1, and FAS were upregulated. Genes that play a role in ER stress-induced unfolded protein response including VCP, UFD1, EDEM1, and ATF4 were also upregulated and may contribute to apoptosis. Our data suggest that several biological processes and pathways including apoptosis and ER response to stress are important aspects of the host cell response to THEV infection. It is possible that interplay between multiple processes may mediate apoptosis of infected B-cells, leading to IMS. Full article
(This article belongs to the Special Issue Advances in Endemic and Emerging Viral Diseases in Livestock)
Show Figures

Figure 1

Figure 1
<p>Model of THEV-induced immunosuppression in turkeys. THEV infection of target cells is indicated with black dotted arrows. Black unbroken arrows indicate cell activation. Red arrows indicated signals leading to cell death (apoptosis/necrosis). Blue arrows indicate all cytokines released by the cell. Blue arrows with square heads indicated an event leading to IMS. Adapted from Rautenschlein et al. [<a href="#B8-viruses-17-00299" class="html-bibr">8</a>].</p>
Full article ">Figure 2
<p>Principal component analysis (PCA) of turkey B-cells during THEV infection. At 12 hpi (<b>A1</b>), the results indicate that the first (PC1) and second (PC2) principal components account for 96% and 3% of the variation in the samples, respectively. Whereas PC1 and PC2 account for 96% and 2% of the variation, respectively at 24 hpi (<b>A2</b>). Poisson distance matrices illustrating the RNA-seq library integrity within treatment (infected versus mock) groups, with color scale representing the distances between biological replicates for both 12 hpi samples (<b>B1</b>) and 24 hpi samples (<b>B2</b>). Dark colors represent high correlation (similarity) between the samples involved. Volcano plots of DEGs between THEV-infected versus mock-infected cells at 12 hpi (<b>C1</b>) and 24 hpi (<b>C2</b>). Red, blue, and grey dots represent upregulated, downregulated, and non-significant genes, respectively, for both 12 hpi samples (<b>C1</b>) and 24 hpi samples (<b>C2</b>).</p>
Full article ">Figure 3
<p>DEGs of THEV-infected versus mock-infected samples at different time points. (<b>A</b>) Bar plot of number DEGs identified. Red represents upregulated genes and blue represents downregulated genes. Heatmaps of scaled expression data (Z-scores) of DEGs. DEGs identified at 12 hpi are shown in (<b>B1</b>) and DEGs at 24 hpi in (<b>B2</b>). Venn diagrams showing the number of DEGs identified at different time points. For the upregulated genes (<b>C1</b>), the blue circle represents genes at 4 hpi, the yellow circle, 12 hpi, and the green circle, 24 hpi. For the downregulated genes (<b>C2</b>), the red circle represents genes at 72 hpi, while all the other time points retain the colors from (<b>C1</b>).</p>
Full article ">Figure 4
<p>Dotplot of enriched gene ontology biological processes (BP). Significant BP GO terms identified for upregulated DEGs at 12 hpi and 24 hpi are shown in (<b>A</b>,<b>B</b>), respectively. Significant BP GO terms for downregulated DEGs at 12 hpi and 24 hpi are shown in (<b>C</b>,<b>D</b>), respectively. The <span class="html-italic">y</span>-axis indicates GO terms and the <span class="html-italic">x</span>-axis represents the rich factor, which indicates the ratio of the number of DEGs annotated to the term to the total number of genes annotated to the term. The diameter indicates the number of genes overlapping the gene ontology term, and the color indicates the enrichment <span class="html-italic">p</span>-value.</p>
Full article ">Figure 5
<p>Upregulation of ER unfolded protein response (UPR). KEGG pathway analysis shows multiple key genes involved in the ER UPR were upregulated. All genes from our DEG list are annotated with the red star. Known turkey-specific pathways are colored green, while reference pathways are left uncolored. Notably, <span class="html-italic">ATF4</span>, <span class="html-italic">PERK</span>, <span class="html-italic">VCP</span> (<span class="html-italic">p97</span>), <span class="html-italic">TRAF2</span>, <span class="html-italic">UFD1</span>, and several BCL2 and heat shock proteins are upregulated. We see that the PERK branch of the UPR pathway linked to apoptosis is upregulated. Another pathway linked to apoptosis via <span class="html-italic">BAX</span> is shown, as well as the ERAD protein degradation pathway. Note that due to limited annotation of the host genome, a significant proportion of the DEGs were not recognized by the database; hence, not shown here. Figure generated from KEGG pathway analysis in DAVID [<a href="#B29-viruses-17-00299" class="html-bibr">29</a>].</p>
Full article ">Figure 6
<p>Validation of representative DEGs involved in apoptosis, protein synthesis, and ER-stress responses by RT-qPCR. MDTC-RP19 cells infected with THEV- or mock-infected were subjected to RT-qPCR analysis for the relative expression of the indicated DEGs at 24 hpi. <span class="html-italic">GAPDH</span> was used as the internal control. Data are expressed as the mean ± SD. All genes (THEV-infected) are statistically differentially expressed relative to their time-matched mock-infected counterparts based on Student’s t-test. The <span class="html-italic">p</span>-values are indicated on top of each bar, and the fold changes for each gene are indicated inside its corresponding bar.</p>
Full article ">
17 pages, 2442 KiB  
Article
On the Aerodynamic Performance of a Blended-Wing-Body, Low-Mach Number Unmanned Aerial Vehicle
by Nikolaos Lampropoulos, Alexandros Vouros, Ioannis Templalexis and Theodoros Lekas
Fluids 2025, 10(3), 54; https://doi.org/10.3390/fluids10030054 - 20 Feb 2025
Viewed by 264
Abstract
A study on aerodynamic design studies of a blended wing–body (BWB) unmanned aerial vehicle (UAV) operating at low Mach numbers is presented. First, a parametric investigation based on analytical equations is carried out to identify the range of the necessary wetted area for [...] Read more.
A study on aerodynamic design studies of a blended wing–body (BWB) unmanned aerial vehicle (UAV) operating at low Mach numbers is presented. First, a parametric investigation based on analytical equations is carried out to identify the range of the necessary wetted area for the UAV to maximize endurance at a Mach number close to 0.1. A base-of-reference configuration is designed, and its aerodynamic performance is evaluated by utilizing a panel method in Xflr5. An optimization algorithm is then incorporated to trim the UAV and produce the ‘clean’ configuration. Computational fluid dynamics (CFD) simulations are performed within the OpenFoam environment to produce first the updated drag polars, and then, to analyze the integration of the nacelle and the pair of electric ducted fans (EDFs) used for the propulsion system. In particular, when examining the integration of the nacelle with a spinning electric ducted fan (EDF) standing as the propulsion system of the vehicle, a rotating, sliding mesh computational approach is adopted. Results indicate that the clean configuration is characterized by strong longitudinal stability so that the UAV has the potential to fly trimmed at very low speeds. Mounting EDFs on the back of the fuselage is conducive to higher loading with minimal drag penalty. An increased lift-to-drag ratio is achieved. Reduced wake mixing due to the EDF’s jet flow is observed. The spanwise flow that is conducive to pitch brake and loss of stability is also weak, as the suction produced by the EDF diverts the flow inboard. Full article
Show Figures

Figure 1

Figure 1
<p>Tasks and analytical/computational tools used in the present work.</p>
Full article ">Figure 2
<p>(<b>a</b>) C<sub>L</sub>/C<sub>D</sub> as a function of Mach number for S<sub>wet</sub> = 8, 12.5 and 18 m<sup>2</sup>; (<b>b</b>) C<sub>L</sub><sup>1/2</sup>/C<sub>D</sub> as a function of Mach number for S<sub>wet</sub> = 8, 12.5 and 18 m<sup>2</sup>.</p>
Full article ">Figure 3
<p>Initial (base-of-reference) UAV configuration: (<b>a</b>) planform and (<b>b</b>) frontal area of the BWB UAV model (scaled dimensions).</p>
Full article ">Figure 4
<p>Aerodynamic coefficients and drag polars of the base-of-reference and the clean, optimized (trimmed) configuration: (<b>a</b>) C<sub>L</sub> as a function of angle of attack; (<b>b</b>) C<sub>L</sub> as a function of C<sub>D</sub>; (<b>c</b>) C<sub>m</sub> as a function of angle of attack; (<b>d</b>) CFD results of C<sub>m</sub> as a function of angle of attack for the trimmed configuration only. In this latter case, momentum is measured from the center of gravity of the UAV.</p>
Full article ">Figure 5
<p>Standard and reflexed NACA2412 airfoils used for the aft region (airfoil 3 is used for a very small central region close to the axis of symmetry: (<b>a</b>) airfoils geometry; (<b>b</b>) reflexed region).</p>
Full article ">Figure 6
<p>UAV configurations: (<b>a</b>) ‘clean’ UAV configuration (C1); (<b>b</b>) UAV with nacelle integration (C2); (<b>c</b>) UAV with integrated EDF, i.e., nacelle and rotor (C3); (<b>d</b>) ducted fan.</p>
Full article ">Figure 7
<p>Forces calculations with the number of revolutions: (<b>a</b>) Lift force; (<b>b</b>) Drag force.</p>
Full article ">Figure 8
<p>Contours of velocity magnitude at the vicinity of the nacelle and the ducted fan: (<b>a</b>) nacelle integration (C2); (<b>b</b>) ducted-fan integration C32 (Flight conditions IAS = 77.75 knots, α = 4°).</p>
Full article ">Figure 9
<p>Streamlines around the UAV: (<b>a</b>) At the mid-semi-span of C2 (y = −1.2 m, maximum lateral velocity U<sub>y</sub> = 6.4 m/s); (<b>b</b>) At the mid-semi-span of C3 (y = −1.2 m, maximum lateral velocity U<sub>y</sub> = 6.7 m/s); (<b>c</b>) Close to the wingtip of C2 (y = −2.3 m, maximum lateral velocity U<sub>y</sub> = 12 m/s); (<b>d</b>) Close to the wingtip of C3 (y = −2.3 m, maximum lateral velocity U<sub>y</sub> = 12 m/s).</p>
Full article ">Figure 10
<p>Pressure coefficient distribution at several spanwise positions: (<b>a</b>) symmetry plane, (<b>b</b>) symmetry plane (focused), (<b>c</b>) 40% of the span, (<b>d</b>) 50% of the span and (<b>e</b>) 95% of the span.</p>
Full article ">
24 pages, 10428 KiB  
Article
Lycorine hydrochloride Suppresses the Proliferation and Invasion of Esophageal Cancer by Targeting TRIM22 and Inhibiting the JAK2/STAT3 and Erk Pathways
by Jingyan Liu, Liangxian Qiu, Jialing Chen and Tao Zeng
Cancers 2025, 17(5), 718; https://doi.org/10.3390/cancers17050718 - 20 Feb 2025
Viewed by 264
Abstract
Background: Tumor metastasis and poor drug efficacy are two of the most common causes of therapeutic failure in cancer patients. The underlying molecular mechanism requires further exploration, and novel effective curative strategies are urgently needed. Nature is a rich source of novel drugs, [...] Read more.
Background: Tumor metastasis and poor drug efficacy are two of the most common causes of therapeutic failure in cancer patients. The underlying molecular mechanism requires further exploration, and novel effective curative strategies are urgently needed. Nature is a rich source of novel drugs, and Lycorine hydrochloride (Lyc.HCL) is a natural alkaloid with tremendous therapeutic potential. However, the molecular mechanisms of its antitumor activity are still unknown. In the current study, we investigated the effects and mechanisms of Lyc.HCL against esophageal squamous cell carcinomas (ESCCs), which pose serious threats to human life. Methods: An MTS assay and a clone formation assay were used to assess the viability of ESCC cell lines after Lyc.HCL treatment in vitro. Apoptosis and cell cycle regulation were analyzed using flow cytometry. Wound healing and Transwell assays were used to analyze cell migration, while invasion was analyzed using the Matrigel Transwell assay. We detected the expression of tripartite motif-containing 22 (TRIM22) through immunohistochemistry and Western blotting. A docking experiment was performed to explore the targets of Lyc.HCL. The expression levels of Janus kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) and phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/extracellular signal-regulated kinase (Erk) pathway components were detected through Western blotting. A rescue experiment was performed to determine the potential role of TRIM22. In addition, we explored the in vivo anti-ESCC effects and mechanism of Lyc.HCL by using it to treat tumor-bearing mice. Results: The Lyc.HCL treatment was found to inhibit esophageal squamous cell carcinoma cell proliferation both in vitro and in vivo by blocking the cell cycle at the G2 phase, inhibiting cell migration and invasion. We found that the TRIM22 protein was highly expressed in ESCCs but not in normal esophageal tissue. Lyc.HCL directly targeted TRIM22, decreasing the expression of TRIM22 and the JAK2/STAT3 and Erk signaling pathways, both in vitro and in vivo. Using animal experiments, we observed that the depletion of TRIM22 delayed tumor growth, but this effect was significantly reversed upon TRIM22 overexpression. Conclusions: Taken together, these findings demonstrate that Lyc.HCL can effectively suppress ESCC both in vitro and in vivo by targeting TRIM22 and regulating the JAK2/STAT3 and Erk pathways. These results suggest that Lyc.HCL may serve as a potential novel therapeutic for ESCC, with TRIM22 emerging as a promising target for treatment. Full article
(This article belongs to the Section Molecular Cancer Biology)
Show Figures

Figure 1

Figure 1
<p>High expression of TRIM22 in esophageal squamous cell carcinoma patient specimens. Immunohistochemistry assay shows the expression of TRIM22 in normal esophageal tissue, peritumoral tissue, and esophageal cancer tissue.</p>
Full article ">Figure 2
<p>Effect of <span class="html-italic">Lyc.HCL</span> on proliferation of human esophageal squamous cell carcinoma (ESCC) cell lines. YES2 and KYSE150 cells were treated with the indicated concentrations of <span class="html-italic">Lyc.HCL</span> for 24 h, 48 h, and 72 h. Cell viability was assessed using the MTS assay. IC50 values were calculated using GraphPad Prism 5.0 software. Data are presented as mean ± SD.</p>
Full article ">Figure 3
<p>Effect of <span class="html-italic">Lyc.HCL</span> on cell colony formation and cell cycle of ESCC cells. (<b>A</b>) Colony formation assay results for YES2 and KYSE150 cells. ESCC cells were treated with the indicated concentrations of <span class="html-italic">Lyc.HCL</span> for 14 days. (<b>B</b>) YES2 and KYSE150 cells were treated with vehicle, 2, 4, and 6 µmol/mL of <span class="html-italic">Lyc.HCL</span> for 48 h, and then stained with PI and subjected to FACS; the black triangle indicates the specific phases of cell cycle.</p>
Full article ">Figure 4
<p><span class="html-italic">Lyc.HCL</span> inhibited migration and invasion of YES2 and KYSE150 cells. (<b>A</b>,<b>B</b>) YES2 and KYSE150 cells were treated with vehicle, 2, 4, and 6 µmol/mL <span class="html-italic">Lyc.HCL</span> for 48 h. Cell migration was evaluated using the wound healing assay. Scale bar: 200 µm. (<b>C</b>) YES2 and KYSE150 cells, pretreated with vehicle, 2, 4, and 6 µmol/mL of <span class="html-italic">Lyc.HCL</span> for 12 h, were plated onto the apical side of the filters in serum-free medium containing either vehicle or <span class="html-italic">Lyc.HCL</span>. Medium containing 20% FBS was placed in the basolateral chamber for 24 h to act as a chemoattractant. Cells on the bottom of the filter were stained using 0.5% crystal violet and then counted. (<b>D</b>) Before the experiment, the Transwell chamber was covered with matrix glue. YES2 and KYSE150 cells, pretreated with vehicle, 2, 4, and 6 µmol/mL <span class="html-italic">Lyc.HCL</span> for 12 h, were plated onto the apical side of the filters in serum-free medium containing either vehicle or <span class="html-italic">Lyc.HCL</span>. Medium containing 20% FBS was placed in the basolateral chamber for 24 h to act as a chemoattractant. Cells on the bottom of the filter were stained using 0.5% crystal violet and then counted.</p>
Full article ">Figure 5
<p>TRIM22 expression in YES2 and KYSE150 cells can be regulated by <span class="html-italic">Lyc.HCL</span>. (<b>A</b>–<b>D</b>) Docking model of <span class="html-italic">Lyc.HCL</span> with TRIM22. (<b>A</b>) Docking affinity and binding poses of <span class="html-italic">Lyc.HCL</span> with TRIM22. (<b>B</b>) Interaction pattern of <span class="html-italic">Lyc.HCL</span> with the residues of TRIM22. (<b>C</b>) <span class="html-italic">Lyc.HCL</span> binding with the pocket through hydrogen bonds, where pink represents hydrogen bond donors and green represents hydrogen bond acceptors. (<b>D</b>) Two-dimensional diagram of receptor and ligand. (<b>E</b>) Western blotting was performed to assess TRIM22 expression in YES2 and KYSE150 cells treated with vehicle or <span class="html-italic">Lyc.HCL</span> at concentrations of 2, 4, and 6 µmol/mL for 48 h. (<b>F</b>) YES2 and KYSE150 cells were transfected with either a TRIM22 plasmid or siRNA to overexpress or knockdown TRIM22, respectively. (<b>G</b>) <span class="html-italic">Lyc.HCL</span> treatment reduced the overexpression of TRIM22 in YES2<sup>TRIM22</sup> and KYSE150<sup>TRIM22</sup> cells.</p>
Full article ">Figure 6
<p><span class="html-italic">Lyc.HCL</span> exerted an antiproliferative effect on ESCC cells by targeting TRIM22 through regulating the cell cycle. (<b>A</b>,<b>B</b>) Representative images showing that TRIM22 overexpression alleviated <span class="html-italic">Lyc.HCL</span>-induced inhibition of colony formation, whereas shTRIM22 promoted <span class="html-italic">Lyc.HCL</span>-induced colony formation inhibition. (<b>C</b>,<b>D</b>) Cell cycle assays revealed that TRIM22 overexpression reduced <span class="html-italic">Lyc.HCL</span>-induced cell cycle arrest, while shTRIM22 increased <span class="html-italic">Lyc.HCL</span>-induced cell cycle arrest (green for G1, blue for G2, and yellow for S phase).</p>
Full article ">Figure 7
<p>TRIM22 overexpression promotes cell migration and invasion, while TRIM22 knockdown reduces these abilities. (<b>A</b>,<b>B</b>) Wound healing assay showing the migration of ESCC<sup>TRIM22</sup> cells, ESCC<sup>shTRIM22</sup> cells, and <span class="html-italic">Lyc.HCL</span>-treated cells. (<b>C</b>,<b>D</b>) Transwell migration assay assessing the migration ability of ESCC<sup>TRIM22</sup>, ESCC <sup>shTRIM22</sup>, and <span class="html-italic">Lyc.HCL</span>-treated cells. (<b>E</b>,<b>F</b>) Transwell invasion assay evaluating the invasive potential of ESCC<sup>TRIM22</sup>, ESCC<sup>shTRIM22</sup>, and <span class="html-italic">Lyc.HCL</span>-treated cells. Before the experiment, the Transwell chamber was coated with Matrigel. Cells that invaded through the membrane were stained with 0.5% crystal violet and counted.</p>
Full article ">Figure 8
<p><span class="html-italic">Lyc.HCL</span> suppresses the JAK2/STAT3 and ERK signaling pathways by targeting TRIM22 in ESCC. The expression levels of THIM22, JAK2, STAT3/p-STAT3, PI3K, mTOR, AKT/p-AKT, and Erk/p-Erk were evaluated using the corresponding antibodies and Western blotting. All the experiments were independently repeated at least three times. (<b>A</b>) YES2 and KYSE150 cells were treated with the indicated concentrations of <span class="html-italic">Lyc.HCL</span> for 48 h, and the expression levels of the JAK2/STAT3 and ERK pathway components were assessed by Western blotting. (<b>B</b>,<b>C</b>) The Western blot results showed changes in the expression of TRIM22, JAK2, p-STAT3, PI3K, mTOR, p-AKT, and p-Erk in YES2, KYSE150, YES2<sup>TRIM22</sup>, YES2<sup>shTRIM2</sup>2, KYSE150<sup>TRIM22</sup>, KYSE150<sup>shTRIM22</sup>, and <span class="html-italic">Lyc.HCL</span>-treated cells.</p>
Full article ">Figure 9
<p>Schematic diagram of the TRIM22-JAK2/STAT3 and ERK signaling pathway axis.</p>
Full article ">Figure 10
<p><span class="html-italic">Lyc.HCL</span> alleviates tumor growth by targeting TRIM22 and regulating the JAK2/STAT3 and ERK pathways in vivo. KYSE150 control, KYSE150<sup>shTRIM22</sup>, and KYSE150<sup>TRIM22</sup> cells were subcutaneously injected into nude mice. Seven days later, the mice were intraperitoneally injected with either DMSO or <span class="html-italic">Lyc.HCL</span> (5 or 10 mg/kg, twice a day) until day 23. (<b>A</b>) Tumor tissues were harvested after the mice were euthanized on day 23. (<b>B</b>) The body weights of the nude mice were recorded throughout the treatment period. (<b>C</b>) The in vivo tumor sizes were measured using a vernier caliper during the treatment. (<b>D</b>) A Western blot analysis was performed to assess the expression of JAK2/STAT3 and ERK pathway components in tumor tissues dissected from the different treatment groups.</p>
Full article ">Figure 11
<p>HE and IHC analysis of tissues from the different groups. (<b>A</b>) Representative HE images of dissected tumor tissues from the different groups. Scale bar: 500 μm. IHC analysis of (<b>B</b>) TRIM22, (<b>C</b>) JAK2, and (<b>D</b>) p-AKT expression in tumor tissues from the different groups. Scale bar: 500 μm. Representative HE images of dissected (<b>E</b>) liver and (<b>F</b>) lung tissues from the different groups. Scale bar: 500 μm.</p>
Full article ">
12 pages, 4111 KiB  
Article
Transcriptomic Responses of Litchi to the Application of Exogenous Melanin Under Cold Stress
by Fachao Shi, Yonghua Jiang, Hailun Liu, Yingjie Wen and Qian Yan
Agronomy 2025, 15(2), 505; https://doi.org/10.3390/agronomy15020505 - 19 Feb 2025
Viewed by 262
Abstract
The late spring cold spell severely affects the growth of litchi flower buds. Melatonin, as a signaling molecule, can enhance the plant’s ability to resist abiotic stress by regulating multiple physiological processes. However, there are few studies on the function of melatonin in [...] Read more.
The late spring cold spell severely affects the growth of litchi flower buds. Melatonin, as a signaling molecule, can enhance the plant’s ability to resist abiotic stress by regulating multiple physiological processes. However, there are few studies on the function of melatonin in litchi under cold stress. In the present study, 100 μM of melatonin was selected based on the ABA content in litchi seedlings. To identify genes potentially involved in melatonin and cold stress conditions in litchi, four RNA-seq libraries of litchi leaves under melatonin and cold conditions were constructed. In total, 6.4–8.5 Gb of trimmed bases were generated in each library. Thirty-five genes were randomly selected for qRT-PCR analysis. The results showed a strong positive correlation between the data from qRT-PCR and RNA-seq. A total of 4590 differentially expressed genes (DEGs) were identified in the treatment of melatonin (1845) and melatonin in cold condition (2745). The expression of several genes belonging to starch and sucrose metabolism, plant hormones (auxin, ABA), MAPK, and alpha-linolenic acid metabolism pathways were differentially expressed. The enhanced carbohydrate metabolism might lead to litchi seedlings treated with melatonin to produce more metabolic energy. Abscisic acid can improve cold tolerance. Collectively, our results reveal that pretreatment with melatonin (100 μM) protects litchi seedlings from cold stress through plant hormones and carbohydrate metabolism and provides potential genes for future research. Full article
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
Show Figures

Figure 1

Figure 1
<p>The ABA content in litchi leaves detected by LC-MS under different content of melatonin; **** represents the significant difference at a <span class="html-italic">p</span> &lt; 0.001 level.</p>
Full article ">Figure 2
<p>Analysis of RNA data: (<b>A</b>) the density distribution of FPKM genes in different groups; (<b>B</b>) PCA plot analysis of different groups; (<b>C</b>) Pearson’s correlation analysis of groups; (<b>D</b>) 35 genes were selected to verify the accuracy of the data; the horizontal coordinate represents the RNA-seq data, and the vertical coordinate represents the qRT-PCR results.</p>
Full article ">Figure 3
<p>GO enrichment of DEGs in different groups.</p>
Full article ">Figure 4
<p>Statistics of differentially expressed genes: (<b>A</b>) different expressed genes in different groups; (<b>B</b>) petal map of different expressed genes; (<b>C</b>) volcano plot of different expressed genes in CK vs. MT group; (<b>D</b>) volcano plot of different expressed genes in MT vs. MTcold group.</p>
Full article ">Figure 5
<p>KEGG analysis of different expressed genes in group CK vs. MT and cold vs. MTcold.</p>
Full article ">Figure 6
<p>Gene expression in different pathways: different expression genes in plant hormones pathway (<b>A</b>) and MAPK pathway (<b>B</b>).</p>
Full article ">Figure 7
<p>Gene expression in different pathways: different expression genes in starch pathway (<b>A</b>) and alpha-linolenic acid pathway (<b>B</b>).</p>
Full article ">
Back to TopTop