Search Results (16,743)

We introduce the solver ARES: Axial-flow pump Radial Equilibrium through Streamlines. The code implements a meanline method, enforcing the conservation of flow momentum and continuity across a set of discrete streamlines in the axial-flow pump’s meridional channel. Real flow effects are modeled with empirical correlations, including off-design deviation and losses due to profile shape, secondary flows, tip leakage, and the end-wall boundary layer (EWBL). Inspired by aeronautical fan and compressor methods, this implementation is specifically tailored for the analysis of the Outboard Dynamic-inlet Waterjet (ODW), the latest aero-engine-derived innovation in marine engineering. To ensure the reliable application of ARES for the systematic designs of ODW pumps, the present investigation focuses on prediction accuracy. Global and local statistics are compared between numerical estimates and available measurements of three test cases: two single rotors and a rotor–stator waterjet configuration. At mass flow rates near the design point, hydraulic efficiency is predicted within $1\%$ discrepancy to tests. Differently, as the flow coefficient increases, the loss prediction accuracy degrades, incrementing the error for off-design estimates. Spanwise velocity and pressure distributions exhibit good alignment with experiments near midspan, especially at the rotor exit, while end-wall boundary layer complex dynamics are hardly recovered by the present implementation. Full article

The fraction of open Photosystem II (PSII) reaction centers (q_L) is critical for connecting broadband PSII fluorescence (ChlF_PSII) with the actual electron transport from PSII to Photosystem I. Accurately estimating q_L is fundamental for determining ChlF_PSII, which, in turn, is vital for mechanistically estimating the actual electron transport rate and photosynthetic CO₂ assimilation. Chlorophyll fluorescence provides direct physiological insights, offering a robust foundation for q_L estimation. However, uncertainties in the ChlF_PSII–q_L relationship across different plant functional types (PFTs) limit its broader application at large spatial scales. To address this issue, we developed a leaf-level instrument capable of simultaneously measuring actively and passively induced chlorophyll fluorescence. Using this system, we measured light response, CO₂ response, and temperature response curves across 52 species representing seven PFTs. Our findings reveal the following: (1) a strong linear correlation between ChlF_PSII derived from passively induced fluorescence and that from actively induced fluorescence (R² = 0.85), and (2) while the parameters of the ChlF_PSII–q_L relationship varied among PFTs, ChlF_PSII reliably modeled q_L within each PFT, with the R² ranging from 0.85 to 0.96. This study establishes quantitative ChlF_PSII–q_L relationships for various PFTs by utilizing passively induced fluorescence to calculate ChlF_PSII. The results demonstrate the potential for remotely sensed chlorophyll fluorescence data to estimate q_L and strengthen the use of fluorescence-based approaches for mechanistic GPP estimation at large spatial scales. Full article

Piping system failures in process industries pose significant financial, environmental, and social risks, with inadequate design and corrosion being major contributors. This review synthesizes the academic and normative literature on pipeline design and anticorrosive protection strategies, providing a comprehensive examination of pipeline layout determination, material selection, and methods for mitigating corrosion. A particular focus is placed on organic coating as a pivotal strategy for corrosion reduction, with in-depth insights into their selection and evaluation criteria. By highlighting best practices and advancements in design and protection strategies, this review aims to enhance the overall integrity and safety of piping systems. The findings are intended to support industry professionals in implementing more effective measures to prevent pipeline failures and improve system reliability, while also presenting recent advances and current demands. Full article

The integration of Internet of Things (IoT) technologies into solar energy systems has transformed them into smart solar energy systems, enabling advanced real-time monitoring, control, and optimization. However, this connectivity also expands the attack surface, exposing critical components to cybersecurity threats that could compromise system reliability and long-term sustainability. This study presents a comprehensive cybersecurity threat modeling analysis for IoT-based smart solar energy systems using the STRIDE threat model to systematically identify, categorize, and assess potential security risks. These risks, if unmitigated, could disrupt operations and hinder large-scale adoption of solar energy. The methodology begins with a system use case outlining the architecture and key components, including sensors, PV modules, IoT nodes, gateways, cloud infrastructure, and remote-access interfaces. A Data Flow Diagram (DFD) was developed to visualize the data flow and identify the critical trust boundaries. The STRIDE model was applied to classify threats, such as spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege across components and their interactions. The DREAD risk assessment model was then used to prioritize threats based on the Damage Potential, Reproducibility, Exploitability, Affected Users, and Disability. The results indicate that most threats fall into the high-risk category, with scores ranging from 2.6 to 2.8, emphasizing the need for targeted mitigation. This study proposes security recommendations to address the identified threats and enhance the resilience of IoT-enabled solar energy systems. By securing these infrastructures, this research supports the transition to sustainable energy by ensuring system integrity and protection against cyber threats. The combined use of STRIDE and DREAD provides a robust framework for identifying, categorizing, and prioritizing risks, enabling effective resource allocation and targeted security measures. These findings offer critical insights into safeguarding renewable energy systems against evolving cyber threats, contributing to global energy sustainability goals in an increasingly interconnected world. Full article

Background/Objectives: The Rapid Geriatric Assessment (RGA) is a tool designed to screen for frailty, sarcopenia, anorexia related to aging, and cognitive impairment. This study aimed to translate and validate the RGA for use among Italian community-dwelling older adults. Methods: This cross-cultural study involved 100 community-dwelling older adults randomly recruited through convenience sampling from general practitioner offices in Genoa (Italy), between January and June 2019. The RGA includes the Simple FRAIL Questionnaire Screening Tool, SARC-F Screening for Sarcopenia, Simplified Nutritional Assessment Questionnaire (SNAQ), and Rapid Cognitive Screening (RCS). These were validated against gold-standard tools: the Abbreviated Comprehensive Geriatric Assessment (aCGA) and Multidimensional Prognostic Index (MPI). Additional assessments included the Timed Up and Go (TUG) and Handgrip test. The validation process included forward–backward translation, synthesis, and consensus by independent reviewers. Psychometric properties, internal consistency (Cronbach alpha), and validity correlations were analyzed. Results: The RGA demonstrated satisfactory psychometric properties, with internal consistency (Cronbach alpha = 0.59) and significant validity correlations (RGA and aCGA, rho = 0.34, p = 0.001; RGA and MPI, rho = 0.49, p < 0.001). Discriminant validity was confirmed by significant correlations between specific subitems and reference measures: FRAIL with TUG (p < 0.05), SARC-F with Handgrip strength (p = 0.013), SNAQ with BMI, and RCS with MMSE (p < 0.001). Conclusions: The Italian version of the RGA is a reliable screening tool for geriatric syndromes in community-dwelling older adults. While it does not replace a CGA, the RGA may identify individuals who may benefit from further evaluation using a complete CGA. Full article

Compressive strength and Young’s modulus are key design parameters in rock engineering, essential for understanding the mechanical behavior of carbonate rocks. Understanding the mechanical behavior of carbonate rocks under varying load conditions is crucial for geotechnical stability analysis. In this paper, empirical relationships are developed to predict the mechanical properties of carbonate rocks. A series of uniaxial and triaxial compression experiments were conducted on carbonate rocks including limestone, dolostone, and granite from Wyoming. In addition, experimental data on different carbonate rocks from the literature are compiled and integrated into this study to evaluate the goodness of fit of our proposed empirical relationships in the prediction of compressive strength and Young’s modulus of carbonate rocks. Regression analysis was used to develop predictive models for the uniaxial compressive strength (UCS), Young’s modulus (E), and triaxial compressive strength (${\sigma }_1$) incorporating parameters such as the porosity (n) and confining pressure (${\sigma }_3$). The results indicated that the UCS and Young’s modulus showed a power relationship with porosity (n), whereas the ${\sigma }_1$ showed a linear relationship with n and ${\sigma }_3$. Furthermore, an analytical model expanded from the wing crack model was applied to predict the ${\sigma }_1$ of limestone based on the coefficient of friction, the initial level of damage, the initial flaw size, and the fracture toughness of the rock. The model showed a good predictability of the ${\sigma }_1$ with a mean bias (i.e., the ratio of the measured to the predicted strength) of 1.07, indicating its reliability in accurately predicting the rock strength. This predictability is crucial for making informed engineering decisions, design optimization, and improving safety protocols in practical applications such as structural analysis and manufacturing processes. Full article

Monitoring the curing process is crucial for guiding and optimizing the curing procedures of composite material repair patches. Traditional embedded online monitoring methods are limited in their ability to track the curing process of these patches. This paper presents a composite material curing monitoring platform designed using dielectric methods. It integrates temperature control, pressure control, dielectric signal acquisition, control and display modules, and is specifically tailored for bag molding curing of repair patches. The platform measures the ionic viscosity of T300 2019B composites, analyzes the curing index, and correlates it with DSC-cured degree tests. The results indicate that the multiple ionic viscosity curves obtained from monitoring exhibit consistent trends, with correlation coefficients between curves exceeding 0.96. The changes in curing index align with the changes in curing degree, demonstrating that the platform can reliably and accurately monitor the ionic viscosity of repair patches. This platform enables effective monitoring of the ionic viscosity during the curing process of composite material repair patches. Full article

To enhance the maintenance efficiency and operational stability of rolling bearings, this work establishes a methodology for bearing life prediction, employing digital twin systems to evaluate the remaining useful life of rolling bearings. A comprehensive digital twin-integrated model for the entire lifecycle of rolling bearings is constructed using the Modelica language. This model generates sufficient and reliable lifecycle twin data for the bearings. Due to the symmetrical physical structure of the bearings, the generated twin data also have symmetry. Based on this characteristic of bearings, a remaining useful life (RUL) prediction algorithm is developed using a recurrent neural network (RNN), specifically an improved gated recurrent unit (GRU) model. An optimization algorithm is employed to adjust the hyperparameters and determine the initial fault point of the bearing. A multi-feature dataset is constructed, effectively enhancing the precision and reliability of lifespan estimation. Based on existing measured data of the bearing’s entire lifecycle, the rolling bearing’s digital twin-integrated model parameters are updated. Through the parameter degradation component of the twin, the lifecycle twin data of the rolling bearing are generated. By combining twin data with actual measurement data, this method addresses the limitations of traditional approaches in situations where complete lifecycle data of bearings are scarce, providing reliable technical support for the intelligent maintenance and optimization of rolling bearings. Full article

Background/Objectives: Adverse childhood experiences (ACEs) impact social, emotional, psychological, and physical development, often leading to health risk behaviors in adulthood. Instruments like the Adverse Childhood Experiences International Questionnaire (ACE-IQ) are essential for assessing ACEs globally and confirming their association with health outcomes in adulthood. Methods: This study evaluates the ACE-IQ’s validity in a Chilean cohort by analyzing the prevalence of ACEs and the instrument’s psychometric properties. Structural validity, internal consistency, and concurrent validity were assessed using the Marshall Scale as a comparative measure. Additionally, binary and frequency scoring methods were compared. Results: Structural validity analyses showed the best fit for three- and four-dimensional models using frequency scoring. The overall internal consistency of the scale was adequate (α > 0.7), although dimensions such as childhood neglect and violence outside the home demonstrated lower internal consistency. Concurrent validity showed significant positive correlations between ACE-IQ scores (both binary and frequency methods) and the Marshall Scale. Conclusions: The ACE-IQ demonstrates adequate reliability for the full scale, with strong evidence of construct validity using the frequency scoring method and concurrent validity for both scoring methods. These findings support the ACE-IQ’s use for measuring childhood adversities in Chile and assessing their association with adult health outcomes. Full article

Thin-walled structures are widely used in aeronautical and aerospace engineering due to their light weight and high structural performance. Ensuring their integrity is crucial for safety and reliability, which is why structural health monitoring (SHM) methods, such as guided wave-based techniques, have been developed to detect and characterize damage in such components. This study presents a novel damage identification procedure for guided wave-based SHM using deep neural networks (DNNs) trained with experimental data. This technique employs the so-called wave damage interaction coefficients (WDICs) as highly sensitive damage features that describe the unique scattering pattern around possible damage. The DNNs learn intricate relationships between damage characteristics, e.g., size or orientation, and corresponding WDIC patterns from only a limited number of damage cases. An experimental training data set is used, where the WDICs of a selected damage type are extracted from measurements using a scanning laser Doppler vibrometer. Surface-bonded artificial damages are selected herein for demonstration purposes. It is demonstrated that smart DNN interpolations can replicate WDIC patterns even when trained on noisy measurement data, and their generalization capabilities allow for precise predictions for damages with arbitrary properties within the range of trained damage characteristics. These WDIC predictions are readily available, i.e., ad hoc, and can be compared to measurement data from an unknown damage for damage characterization. Furthermore, the fully trained DNN allows for predicting WDICs specifically for the sensing angles requested during inspection. Additionally, an anglewise principal component analysis is proposed to efficiently reduce the feature dimensionality on average by more than $90\%$ while accounting for the angular dependencies of the WDICs. The proposed damage identification methodology is investigated under challenging conditions using experimental data from only three sensors of a damage case not contained in the training data sets. Detailed statistical analyses indicate excellent performance and high recognition accuracy for this experimental data-based approach. This study also analyzes differences between simulated and experimental WDIC patterns. Therefore, an existing DNN trained on simulated data is also employed. The differences between the simulations and experiments affect the identification performance, and the resulting limitations of the simulation-based approach are clearly explained. This highlights the potential of the proposed experimental data-based DNN methodology for practical applications of guided wave-based SHM. Full article

Background/Objectives: Suicidal behaviors (SBs) and non-suicidal self-injury (NSSI) are significant mental health concerns in children and adolescents. The hypothalamic–pituitary–adrenal (HPA) axis, of which cortisol is a key hormone, has been implicated in these behaviors. This narrative review aims to explore whether cortisol levels play a role in SBs and NSSI in youth and to synthesize current evidence on this topic. Methods: A comprehensive literature search was conducted on studies published through November 2024, using PubMed, Web of Science, and Google Scholar databases. Studies were screened for eligibility, including only human studies published in English, with no animal models or studies excluding cortisol levels. A narrative synthesis approach was used due to the methodological diversity across studies. Due to limited adolescent-focused research, studies involving adults were also considered. Results: Findings indicate inconsistent cortisol patterns in relation to SBs and NSSI. Elevated cortisol levels are linked to SBs, with some studies suggesting they may predict future suicide attempts, though no definitive cause-and-effect relationship is established. Conversely, cortisol levels in relation to NSSI show mixed results, with some studies reporting no differences. Cortisol responses to stress, measured by saliva, blood, and hair, reveal complex interactions with psychological factors such as depression and impulsivity, influencing cortisol secretion. Discussion: Despite some evidence pointing to a role of cortisol dysregulation in SBs and NSSI, the relationship remains unclear due to study heterogeneity, including small sample sizes and methodological variations. Gender and the type of stressor used in studies also complicate the findings. Future research should prioritize longitudinal studies, better control for confounding factors, and utilize more diverse cortisol assessment methods to clarify these links. Conclusions: While cortisol may play a role in the pathophysiology of SBs and NSSI, further research is needed to establish clearer, more reliable patterns. Identifying alterations in cortisol levels may aid in early detection and targeted interventions for at-risk adolescents. Full article

A new radio frequency (RF) probe using pogo pin tips for integrated chip (IC) measurement up to 50 GHz is proposed. It offers high durability due to the pogo pins and meets three key design criteria for general IC measurement: (1) a 45° tilted shape with a 70 μm tip protrusion for easy microscope inspection, (2) linear pogo pin alignment for commercial chip pad contact, and (3) a 250 μm pitch compatible with standard IC pad pitches. This design is distinct from traditional pogo pin probe cards which place pogo pins in vertical form, in a diagonal arrangement, and at wide intervals. The probe exhibits a low insertion loss of 1.6 dB at 45 GHz. A printed circuit board (PCB)-based calibration standard for the calibration of the designed probe is constructed, which is adjusted to inductance and capacitance values using a simulation to form the Vector Network Analyzer (VNA) calibration set. The measurements of a commercial amplifier IC using this probe show a nearly identical performance to commercial RF probes, confirming its accuracy and reliability. Full article

Agriculture accounts for a significant economic share in less-developed countries, especially Kosovo, where there is a lignite-dominated energy supply. Lignite’s partial replacement with locally produced renewable energy sources could play an essential role in reducing farmers’ costs and preparing the country for EU accession. Using a sample of 120 farmers, the Best–Worst Scaling (BWS) technique was used to assess farmers’ preferences for renewable energy applications and to measure the importance of seven key characteristics associated with the willingness to become energy self-sufficient. The results show a significant preference for “lower energy costs” and “environmental friendliness”. Using cluster analysis, it is shown in a statistically reliable way that while the decisions of smaller farms are influenced by economic factors and the role of energy self-sufficiency is negligible, a non-negligible share of larger farms already have already adopted self-sufficiency in energy production (solar panels, byproducts) and also consider the environment and convenience aspects necessary in their decisions. Farmers play an important role in local economic development. Therefore, regulatory schemes with differentiation by farm size may play an important role in promoting local energy management in Kosovo and similar less developed countries. Full article

Introduction: In orthodontics, determining the parameters of tooth size and dental arch and conducting Bolton analysis is crucial for diagnosis, treatment planning, and patient outcomes. This study evaluates the accuracy and reliability of measuring dental-arch dimensions on digital models created using the CEREC Primescan intraoral scanner, compared to measurements taken from plaster models. Methods: The study included two types of dental models (plaster and intraoral scan) from sixty-three subjects. Impressions were taken to create plaster models, and the subjects’ mouths were scanned with the CEREC Primescan system (Dentsply Sirona, Charlotte, NC) to create digital models. Intra-arch measurements included tooth heights and widths, overjet, and overbite. The arch width and depth were examined at the first permanent upper or lower molar. The paired t-test and Bland–Altman plot were used to determine the accuracy, while intra-rater and inter-rater correlation coefficient values were calculated to assess the reliability of measurements from the intraoral scan compared to those from the plaster model. Results: For tooth heights, there was a statistically significant difference in only one measurement (tooth 34) between the plaster and digital models, with an average difference of 0.01 mm. For tooth widths, there was a statistically significant difference in only one measurement (tooth 15) with an average difference of 0.03 mm. The Bland–Altman plots of almost all of measurements of tooth heights and widths showed that differences between the two models were within the limits of agreement. The inter- and intra-rater correlation coefficient values for measurements on the digital model were found to be statistically insignificant. Conclusion: Measuring dental dimensions on digital models obtained through the Primescan intraoral digital system yielded similar results to those obtained from plaster models and showed excellent reliability, indicating its potential application in clinical practice. Full article

This systematic review explores the most validated methodologies for measuring managerial skills that contribute to sustainable organizational development, with a focus on confirmatory factor analysis (CFA). Using PRISMA guidelines and the PICOS framework, a systematic search in the Web of Science (6810 articles) and Scopus (11,267 articles) identified 27 relevant studies. Our findings emphasize the significance of valid, reliable measurement scales for key managerial competencies, such as leadership, decision-making, communication, and teamwork, which enhance employability and foster sustainable management aligned with the Sustainable Development Goals (SDGs). CFA emerges as a robust technique for ensuring methodological rigor in competency assessment, confirming theoretical models with empirical data. This study identifies gaps in current measurement frameworks, advocating for expanding models to incorporate digital transformation, sustainability leadership, and crisis management skills. Additionally, it underscores the importance of developing context-specific instruments that reflect sectoral and cultural variations. This review contributes to management education and workforce development by providing a validated framework for assessing managerial skills, supporting organizations in aligning leadership training with sustainability-driven business goals. Our findings offer practical implications for designing competency-based curricula and corporate training programs to enhance organizational resilience in an evolving global landscape. Full article