Search Results (3,454)

This paper addresses the machining requirements for large aerospace structural components using robotic systems and proposes a method for rapid workpiece positioning that combines the simplicity of vision-based positioning with the precision of contact-based methods. To enhance the accuracy of robot calibration, a novel approach utilizing a ruby probe for sphere-to-sphere contact calibration of the Tool Center Point (TCP) is introduced. A robot contact calibration model is formulated, transforming the calibration process into a nonlinear least squares (NLS) optimization problem. To tackle the challenges of NLS optimization, a hybrid LM-D algorithm is developed, integrating the Levenberg–Marquardt (L-M) and DIviding RECTangle (DIRECT) algorithms in an iterative process to achieve the global optimum. This algorithm ensures computational efficiency while maximizing the likelihood of finding a globally optimal solution. An iterative convergence termination criterion for TCP calibration is established to determine global convergence, further enhancing the algorithm’s efficiency. Experimental validation was performed on industrial robots, demonstrating the proposed algorithm’s superior performance in global convergence and iteration efficiency compared to traditional methods. This research provides an effective and practical solution for TCP calibration in industrial robotic applications. Full article

This study introduces a comprehensive phygital framework tailored for small local museums, addressing the unique challenges of textile exhibitions. By seamlessly integrating physical artifacts with advanced digital tools through a user-centered design–thinking approach, the platform transforms traditional museum visits into hybrid experiences. The research addresses challenges faced by small museums, such as limited interactivity, static information presentation, and resource constraints. The findings demonstrate that the phygital platform significantly enhances visitor satisfaction, usability, and engagement. Features like mobile applications, chatbots, and gamification foster dynamic interactions, increasing interest in historical textile collections. The evaluation highlights positive impacts on visitor learning and accessibility, with high usability scores and favorable feedback confirming the platform’s success. By bridging physical and digital realms, the platform empowers small local museums to modernize their exhibition experience offerings while preserving their authenticity and cultural significance. This study contributes to the growing literature on phygital strategies in museum contexts, offering practical recommendations for implementing such platforms in resource-constrained settings. The findings underscore the potential of phygital approaches to foster deeper connections with cultural heritage, ensure broader accessibility, and support sustainable visitor engagement. Full article

The city of Valparaíso, Chile, faces significant mobility challenges due to its steep slopes, complex urban infrastructure, and socioeconomic conditions. In this direction, this study explores the potential promotion of E-bike uses by identifying the optimal routes that connect metro stations to strategic hilltop streets in the city. A hybrid methodology combining a multicriteria GIS-based analysis and an experimental study was used to evaluate potential routes and the possibility of increasing the power limitations for non-motorized mobility in Chile. Fifteen routes were assessed based on criteria including the slope, traffic safety, directionality, intersections, and travel distance. The results indicate that routes such as Cumming from Puerto and Bellavista stand out as the most viable for e-bike use given their favorable characteristics. The experimental study revealed that higher-powered E-bikes (500 W and 750 W) would be more able to overcome the steep slopes of Valparaíso, with an average speed of 5.36 km/h and 9.52 km/h on routes with a 10.88% average slope. These findings challenge the current regulatory limit of 250 W for non-motorized vehicles in Chile, highlighting the potential benefits of increasing their power limits to enhance sustainable mobility in the hilly urban contexts of this country. This study highlights the need to adapt urban mobility policies to the unique topographical conditions of each city. Future research should build upon more experimental studies, develop specific street-scale analyses using audit methods, incorporate climate-related variables, and evaluate the economic viability of e-bike infrastructure. Addressing these aspects could position Valparaíso as a leading example of sustainable urban mobility for cities facing comparable challenges. Full article

Hybrid teaching, which integrates traditional in-person learning based on students’ perspectives where online learning offers a flexible approach to education, combines the benefits of technology with face-to-face interactions. Moreover, teaching and learning in a hybrid way met several challenges for both teachers and learners, including technological problems, time management, communication difficulties, and assessment complexities. This systematic review investigates six main research questions: (1) What pedagogical frameworks are used in hybrid teaching and learning? (2) How can we enhance students’ engagement in hybrid teaching and learning? (3) What is the impact of technological integration on hybrid learning scenarios, both for students and teachers? (4) How do training and support measures influence the willingness and ability of university teachers to implement hybrid teaching formats? (5) How do formative assessment and feedback methods in hybrid learning environments enable teachers to effectively monitor student progress and provide tailored support? (6) How does the implementation of hybrid learning affect student learning outcomes? This study identifies the following key themes: technological integration, pedagogical innovation, faculty support, student engagement, assessment practices, and learning outcomes. Our contribution of this literature review is related to teaching and learning by showing teachers the most appropriate way to avoid the challenges encountered when teaching in a hybrid way. These include strong technology integration, innovative pedagogical strategies, strong academic development and support, active student engagement, effective assessment practices, and positive learning outcomes. Full article

Carbon-based polymer composites are widely used in wearable devices due to their exceptional electrical conductivity and flexibility. However, their temperature-dependent resistance variations pose significant challenges to device safety and performance. A negative temperature coefficient (NTC) can lead to overcurrent risks, while a positive temperature coefficient (PTC) compromises accuracy. In this study, we present a novel hybrid composite combining carbon nanotubes (CNTs) with NTC properties and carbon black (CB) with PTC properties to achieve a near-zero temperature coefficient of resistance (TCR) at an optimal ratio. This innovation enhances the safety and reliability of carbon-based polymer composites for wearable heating applications. Furthermore, a thermochromic pigment layer is integrated into the hybrid composite, enabling visual temperature indication across three distinct zones. This bilayer structure not only addresses the TCR challenge but also provides real-time, user-friendly temperature monitoring. The resulting composite demonstrates consistent performance and high precision under diverse heating conditions, making it ideal for wearable thermotherapy pads. This study highlights a significant advancement in developing multifunctional, temperature-responsive materials, offering a promising solution for safer and more controllable wearable devices. Full article

Multi-cable cooperative control is essential for cable-driven space manipulators to achieve in-orbit services such as fault spacecraft maintenance, fuel injection, on-orbit assembly, and orbital garbage removal. To prevent the cables from becoming slack or excessively tight, the force in each cable must be distributed appropriately. The force distribution among different cables requires real-time adjustments; otherwise, the system may become unstable. This paper proposes a compliance control method based on the force–position hybrid drive mode to address the challenges of multi-cable cooperative control. Firstly, the mapping relationship between the cable space and the joint space of the cable-driven space manipulator is established. Then, the force mapping relationship for this structure is derived. The control scheme categorizes the cables into two types: active-side cables and antagonistic-side cables. Position control and force control are implemented separately, significantly reducing the computational requirements and enhancing the overall performance of the control system. Finally, the feasibility of the proposed algorithm is demonstrated through simulations and compared with the PID control method. When tracking the same trajectory, the proposed method reduces the tracking error by 49.14% and the maximum force by 58.58% compared to the PID control method, effectively addressing the problem of force distribution in multi-rope coordinated control. Full article

Ground penetrating radar (GPR) image inversion is of great significance for interpreting GPR data. In practical applications, the complexity and nonuniformity of underground structures bring noise and clutter interference, making GPR inversion problems more challenging. To address these issues, this study proposes a two-stage GPR image inversion network called MHInvNet based on multi-scale dilated convolution (MSDC) and hybrid attention gate (HAG). This method first denoises the B-scan through the first network MHInvNet1, then combines the denoised B-scan from MHInvNet1 with the undenoised B-scan as input to the second network MHInvNet2 for inversion to reconstruct the distribution of the permittivity of underground targets. To further enhance network performance, the MSDC and HAG modules are simultaneously introduced to both networks. Experimental results from simulated and actual measurement data show that MHInvNet can accurately invert the position, shape, size, and permittivity of underground targets. A comparison with existing methods demonstrates the superior inversion performance of MHInvNet. Full article

In this article, I argue that Eurafricans’ invisibility in Zambia’s national census, history, and social framework is an echo of colonial whiteness stemming from the destructive legacy of illegitimacy perpetuated by British officials in Northern Rhodesia (present-day Zambia) during the colonial era (1924–64), which continues to the present day. This is evidenced by the absence of Eurafricans in the Zambia national censuses. This contribution calls for the British government to apologise to the Eurafrican community for the legacy of illegitimacy and intergenerational racial trauma it bestowed on the community. Zambia’s tribal ‘ethnic’ and ‘linguistics’ census classification options prevent a comprehensive understanding of Zambia’s multi-racial history and the development of a hybrid space that embraces a ‘mixed-race’ Eurafrican (of European and African heritage) Zambian identity. Through an autoethnographic account of my Eurafrican uncle Aaron Milner, I reflect on Zambian Eurafricans’ historical racial positioning as ‘inferior interlopers’, which has contributed to their obscurity in Zambia’s national history and census. However, my reflection goes beyond Milner’s story in Zambia. It is my entryway to highlight how race and colonial whiteness interconnected and underpinned racial ideology in the wider British Empire, and to draw attention to its echoes in various contemporary sociopolitical contexts, including census terminology in Australia and Zambia and Western nations’ anti-Black immigration policies. Full article

Accurate dynamic model and response prediction of industrial robots (IRs) are prerequisites for production optimization before actual operation. In this study, a hybrid-driven dynamic position prediction (HDPP) approach integrating a parametric dynamic model (PDM) and learning-based residual error compensators (RECs) is developed to estimate the actual position of a robot end-effector based on the reference input trajectory. Firstly, a PDM consisting of a flexible dynamic model of the mechanical system and a servo system model is constructed as the primary predictor in HDPP. Meanwhile, a reinforcement learning (RL)-based parameter identification method is presented to obtain independent dynamic parameters, which integrates a CAD model, least squares estimation, and RL. Then, an REC based on the temporal convolutional network long short-term memory (TCN-LSTM) is proposed for each joint to compensate for the residual error after PDM prediction. A TCN is employed as the input of LSTM to extract and compress the discontinuous features, which can enhance the compensator’s accuracy and stability. Additionally, a dynamics-integrated (DI) dataset construction scheme is developed for network training to boost the prediction accuracy. Finally, a series of experiments and comparative analysis are preformed to validate the performance of HDPP in terms of prediction accuracy and stability. Full article

Background/Objectives: The synthesis of (E)-1-(1,3-diphenylallyl)-1H-1,2,4-triazoles and related compounds as anti-mitotic agents with activity in breast cancer was investigated. These compounds were designed as hybrids of the microtubule-targeting chalcones, indanones, and the aromatase inhibitor letrozole. Methods: A panel of 29 compounds was synthesized and examined by a preliminary screening in estrogen receptor (ER) and progesterone receptor (PR)-positive MCF-7 breast cancer cells together with cell cycle analysis and tubulin polymerization inhibition. Results: (E)-5-(3-(1H-1,2,4-triazol-1-yl)-3-(3,4,5-trimethoxyphenyl)prop-1-en-1-yl)-2-methoxyphenol 22b was identified as a potent antiproliferative compound with an IC₅₀ value of 0.39 mM in MCF-7 breast cancer cells, 0.77 mM in triple-negative MDA-MB-231 breast cancer cells, and 0.37 mM in leukemia HL-60 cells. In addition, compound 22b demonstrated potent activity in the sub-micromolar range against the NCI 60 cancer cell line panel including prostate, melanoma, colon, leukemia, and non-small cell lung cancers. G₂/M phase cell cycle arrest and the induction of apoptosis in MCF-7 cells together with inhibition of tubulin polymerization were demonstrated. Immunofluorescence studies confirmed that compound 22b targeted tubulin in MCF-7 cells, while computational docking studies predicted binding conformations for 22b in the colchicine binding site of tubulin. Compound 22b also selectively inhibited aromatase. Conclusions: Based on the results obtained, these novel compounds are suitable candidates for further investigation as antiproliferative microtubule-targeting agents for breast cancer. Full article

Chinese herbal medicines have become a new green feed additive in the aquaculture industry. The aim of this study is to investigate the effects of traditional Chinese herbal medicines (Isatidis radix, Forsythia suspensa, and Schisandra chinensis) on the growth performance, antioxidant capacity, and intestinal microbiota of hybrid snakehead (Channa maculata ♀ × Channa argus ♂). A total of 600 fish (mean weight: 15.85 ± 0.15 g) were randomly assigned to five groups, including the control group (CG), I. radix extract group (IRE), F. suspensa extract group (FSE), S. chinensis extract group (SCE), and the Chinese herbal medicine mixture group (CHMM; a mixture of extracts of I. radix, F. suspensa, and S. chinensis at the ratio of 1:1:1) for 6 weeks. The results show that the IRE-supplemented diet improved the survival rate (SR), feed efficiency ratio (FE), and condition factor (CF) compared to others. Compared to the control group, the activity of superoxide dismutase (SOD) in plasma and intestine was significantly increased in the FSE and CHMM groups, whereas the content of malondialdehyde (MDA) in plasma and liver was significantly reduced in the SCE group. A 16s rRNA analysis indicates that dietary supplementation with FSE significantly promoted the proliferation of Fusobacteriota, while IRE supplementation increased the alpha diversity of intestinal bacteria. In conclusion, the addition of I. radix to the diet of hybrid snakehead improves growth, antioxidant capacity, and liver and intestine health, and modulates the intestinal microbiota of snakehead positively. Full article

Background: AI-driven mental health solutions offer transformative potential for improving mental healthcare outcomes, but identifying the most effective approaches remains a challenge. This study addresses this gap by evaluating and prioritizing AI-driven mental health alternatives based on key criteria, including feasibility of implementation, cost-effectiveness, scalability, ethical compliance, user satisfaction, and impact on clinical outcomes. Methods: A fuzzy multi-criteria decision-making (MCDM) model, consisting of fuzzy TOPSIS and fuzzy ARAS, was employed to rank the alternatives, while a hybridization of the two methods was used to address discrepancies between the methods, each emphasizing distinct evaluative aspect. Results: Fuzzy TOPSIS, focusing on closeness to the ideal solution, ranked personalization of care (A5) as the top alternative with a closeness coefficient of 0.50, followed by user engagement (A2) at 0.45. Fuzzy ARAS, which evaluates cumulative performance, also ranked A5 the highest, with an overall performance rating of Si = 0.90 and utility degree Qi = 0.92. Combining both methods provided a balanced assessment, with A5 retaining its top position due to high scores in user satisfaction and clinical outcomes. Conclusions: This result underscores the importance of personalization and engagement in optimizing AI-driven mental health solutions, suggesting that tailored, user-focused approaches are pivotal for maximizing treatment success and user adherence. Full article

Two species of wild sheep inhabit Iran: Asiatic mouflon (Ovis gmelini) and urial (O. vignei). Phylogenetic relationships between populations distributed in this country are complex and still remain unclear. This study aimed to clarify, by genetic assessment, the phylogenetic status of Kerman wild sheep, considered to be a hybrid of the two species. For this purpose, we created a dataset that included specimens of O. gmelini, O. vignei, and Kerman sheep. We applied genome-wide SNP genotyping technology to analyze population structure and genetic diversity of these groups. Using Neighbor-Net and PCA plots, it was demonstrated that Kerman sheep were differentiated from other groups and occupy an intermediate position between O. gmelini and O. vignei. Using Admixture analysis, two ancestral components were identified in this population; however, admixed ancestry was not confirmed by f3 statistics. Genetic diversity in Kerman wild sheep was significantly higher than in any group of O. vignei, but lower than in O. gmelini. Additionally, we examined complete mitochondrial genomes and it was demonstrated that the matrilineal ancestor of Kerman sheep belonged to O. vignei. Our results lead to the conclusion that Kerman wild sheep can be recognized as a separate subspecies of O. vignei. Full article

In recent years, metal surface plasmon resonance sensors and dielectric guided-mode resonance sensors have attracted the attention of researchers. Metal sensors are sensitive to environmental disturbances but have high optical losses, while dielectric sensors have low losses but limited sensitivity. To overcome these limitations, hybrid resonance sensors that combine the advantages of metal and dielectric were proposed to achieve a high sensitivity and a high Q factor at the same time. In this paper, a hybrid hollow cylindrical tetramer array was designed, and the effects of the hole radius, external radius, height, period, incidence angle, and polarization angle of the hollow cylindrical tetramer array on the refractive index sensing properties were quantitatively analyzed using the finite difference time domain method. It is found that the position of the resonance peaks can be freely tuned in the visible and near-infrared regions, and a sensitivity of up to 542.8 nm/RIU can be achieved, with a Q factor of 1495.1 and a figure of merit of 1103.3 RIU⁻¹. The hybrid metal–dielectric nanostructured array provides a possibility for the realization of high-performance sensing devices. Full article

This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, coupled with a bibliometric study of the broader literature, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency. Key approaches analyzed involve image classification, image segmentation, regression, and object detection methods that use diverse data types ranging from RGB and multispectral images to radar and thermal data. By processing UAV and satellite data with CNNs, real-time and large-scale crop monitoring can be achieved, supporting advanced farm management. A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. Future directions point toward integrating IoT and cloud platforms for real-time data processing and leveraging large language models for regulatory insights. Potential research advancements emphasize improving increased data accessibility and hybrid modeling to meet the agricultural demands of climate variability and food security, positioning CNNs as pivotal tools in sustainable agricultural practices. A related repository that contains the reviewed articles along with their publication links is made available. Full article