Search Results (166)

Labor shortages in seedling nurseries, particularly in manual inspection and replanting, hinder operational efficiency despite advancements in automation. This study aims to develop a cost-effective, GPU-free machine vision system to automate the detection of deficient seedlings in plug trays, specifically for small-scale nursery operations. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. Under controlled laboratory conditions, the DDRP-Machine achieved high detection accuracy (96.0–98.7%) and precision rates (82.14–83.78%). Benchmarking against deep-learning models such as YOLOv5x and Mask R-CNN showed comparable performance, while requiring only one-third to one-fifth of the cost and avoiding complex infrastructure. The Batch Detection (BD) mode significantly reduced processing time compared to Continuous Detection (CD), enhancing real-time applicability. The DDRP-Machine demonstrates strong potential to improve seedling inspection efficiency and reduce labor dependency in nursery operations. Its modular design and minimal hardware requirements make it a practical and scalable solution for resource-limited environments. This study offers a viable pathway for small-scale farms to adopt intelligent automation without the financial burden of high-end AI systems. Future enhancements, adaptive lighting and self-learning capabilities, will further improve field robustness and including broaden its applicability across diverse nursery conditions. Full article

Transformer-based models have accelerated EEG motor imagery (MI) decoding by using self-attention to capture long-range temporal structures while complementing spatial inductive biases. This systematic survey of Scopus-indexed works from 2020 to 2025 indicates that reported advances are concentrated in offline, protocol-heterogeneous settings; inconsistent preprocessing, non-standard data splits, and sparse efficiency frequently reporting cloud claims of generalization and real-time suitability. Under session- and subject-aware evaluation on the BCIC IV 2a/2b dataset, typical performance clusters are in the high-80% range for binary MI and the mid-70% range for multi-class tasks with gains of roughly 5–10 percentage points achieved by strong hybrids (CNN/TCN–Transformer; hierarchical attention) rather than by extreme figures often driven by leakage-prone protocols. In parallel, transformer-driven denoising—particularly diffusion–transformer hybrids—yields strong signal-level metrics but remains weakly linked to task benefit; denoise → decode validation is rarely standardized despite being the most relevant proxy when artifact-free ground truth is unavailable. Three priorities emerge for translation: protocol discipline (fixed train/test partitions, transparent preprocessing, mandatory reporting of parameters, FLOPs, per-trial latency, and acquisition-to-feedback delay); task relevance (shared denoise → decode benchmarks for MI and related paradigms); and adaptivity at scale (self-supervised pretraining on heterogeneous EEG corpora and resource-aware co-optimization of preprocessing and hybrid transformer topologies). Evidence from subject-adjusting evolutionary pipelines that jointly tune preprocessing, attention depth, and CNN–Transformer fusion demonstrates reproducible inter-subject gains over established baselines under controlled protocols. Implementing these practices positions transformer-driven BCIs to move beyond inflated offline estimates toward reliable, real-time neurointerfaces with concrete clinical and assistive relevance. Full article

This study focuses on the cooperative formation control problem of six-degree-of-freedom (6-DOF) fixed-wing unmanned aerial vehicles (UAVs) under constraints of limited communication resources and strict time requirements. The core innovation of the proposed framework lies in the deep integration of a dynamic self-triggered communication mechanism (DSTCM) with a prescribed-time control strategy. Furthermore, a fuzzy control strategy is designed to effectively suppress system disturbances, enhancing the robustness of the formation. The designed DSTCM not only retains the adaptive triggering threshold characteristic of dynamic event-triggered communication, significantly reducing communication frequency, but also completely eliminates the need for continuous state monitoring required by traditional event-triggered mechanisms. As a result, both communication and onboard computational resources are effectively conserved. In parallel, a novel time-varying unilateral constrained performance function is introduced to construct a prescribed-time controller, which guarantees that the formation tracking error converges to a predefined residual set within a user-specified time. The convergence process is independent of initial conditions and strictly adheres to full-state constraints. A rigorous Lyapunov-based stability analysis demonstrates that all signals in the closed-loop UAV velocity and attitude system are semi-globally uniformly ultimately bounded (SGUUB). Furthermore, the proposed DSTCM ensures the existence of a strictly positive lower bound on the inter-event triggering intervals of the UAVs, thereby avoiding the occurrence of Zeno behavior. Numerical simulation results are provided to verify the effectiveness and superiority of the proposed control scheme. Full article

Triboelectric nanogenerators (TENGs) have emerged as efficient mechanical-energy harvesters with advantages—simple architectures, broad material compatibility, low cost, and strong environmental tolerance—positioning them as key enablers of self-powered systems. This review synthesizes recent progress in energy-storage interfaces, power management, and system-level integration for TENGs. We analyze how intrinsic source characteristics—high output voltage, low current, large internal impedance, and pulsed waveforms—complicate efficient charge extraction and utilization. Accordingly, this work highlights a variety of power-conditioning approaches, including advanced rectification, multistage buffering, impedance transformation/matching, and voltage regulation. Moreover, recent developments in the integration of TENGs with storage elements, cover hybrid topologies and flexible architectures. Application case studies in wearable electronics, environmental monitoring, smart-home security, and human–machine interfaces illustrate the dual roles of TENGs as power sources and self-driven sensors. Finally, we outline research priorities: miniaturized and integrated power-management circuits, AI-assisted adaptive control, multimodal hybrid storage platforms, load-adaptive power delivery, and flexible, biocompatible encapsulation. Overall, this review provides a consolidated view of state-of-the-art TENG-based self-powered systems and practical guidance toward real-world deployment. Full article

The mental and emotional health of an athlete is crucial for their performance and well-being. Sports-related stress can significantly impair their mental health. Further, there were minimal tools available to measure Sports resilience, specifically during COVID-19 restrictions or earlier. This study reports the preliminary validation of the Sports Mind Inventory (SMI) in athletes from different geographical areas (n = 66), with the majority of participants from Mauritius, and tests the SMI in elite athletes practicing the Yoga of Immortals (YOI). YOI is a unique combination of specific yogic postures, breathing exercises, sound therapy & meditation, which has demonstrated benefit in improving measures of mental health. The exploratory factor analysis of the 24-item SMI resulted in a six-factor inventory. The confirmatory factor analysis of these six-factor SMI showed goodness-of-fit index (0.935), and Cronbach’s alpha coefficient (α) of 0.949, showing good fit and reliability. The correlation between overall scale and individual factors showed diverse degree of positive correlations. This validated SMI was then tested to investigate whether YOI can enhance athletes’ resilience to sports-related stress. Participants were a diverse set of athletes based in Mauritius who routinely engage in a wide range of athletic activities. Participants were randomly assigned to receive four weeks of YOI or no intervention. Both groups completed the SMI questionnaire at baseline and again after four weeks. The YOI intervention significantly increased (p = 0.002) the total mean SMI scores, and underlying factors, i.e., Factor 1: Positive and Competitive sports mindset (p = 0.014), Factor 2: Social relatedness and adaptability (p = 0.008), Factor 3: Resilient mindset and self-confidence (p = 0.036), Factor 4: Sports Resilience and Emotional Responses (p = 0.001). This indicated improved sports resilience and psychological health. No improvement was observed in the control group. The correlation analysis in YOI group at week-4 showed positive correlation between overall scales and underlying construct. In conclusion, SMI showed acceptable fitness to measure sport resilience. This YOI intervention helped in improving sports-related stress and improved athletes’ resilience. Full article

This study introduces an innovative self-regulating intelligent optimal balancing control framework for inverted pendulum-type mechatronic platforms, designed to enhance reference tracking accuracy and improve disturbance rejection capability. The control procedure is synthesized by synergistically integrating a baseline Linear Quadratic Regulator (LQR) with a fuzzy controller via a customized linear decomposition function (LDF). The LDF dissociates and transforms the LQR control law into compounded state tracking error and tracking error derivative variables that are eventually used to drive the fuzzy controller. The principal contribution of this study lies in the adaptive modulation of these compounded variables using reconfigurable tangent hyperbolic functions driven by the cubic power of the error signals. This nonlinear preprocessing of the input variables selectively amplifies large errors while attenuating small ones, thereby improving robustness and reducing oscillations. Moreover, a model-free online self-tuning law dynamically adjusts the variation rates of the hyperbolic functions through dissipative and anti-dissipative terms of the state errors, enabling autonomous reconfiguration of the nonlinear preprocessing layer. This dual-level adaptation enhances the flexibility and resilience of the controller under perturbations. The robustness of the designed controller is substantiated via tailored experimental trials conducted on the Quanser rotary pendulum platform. Comparative results show that the prescribed scheme reduces pendulum angle variance by 41.8%, arm position variance by 34.6%, and average control energy by 28.3% relative to the baseline LQR, while outperforming conventional fuzzy-LQR by similar margins. These results show that the prescribed controller significantly enhances disturbance rejection and tracking accuracy, thereby offering a numerically superior control of inverted pendulum systems. Full article

Indoor positioning remains a recurrent and significant challenge in research. Unlike outdoor environments, where the Global Positioning System (GPS) provides reliable location information, indoor scenarios lack direct line-of-sight to satellites or cellular towers, rendering GPS inoperative and requiring alternative positioning techniques. Despite numerous approaches, indoor contexts with resource limitations, energy constraints, or physical restrictions continue to suffer from unreliable localization. Many existing methods employ a fixed number of reference anchors, which sets a hard balance between localization accuracy and energy consumption, forcing designers to choose between precise location data and battery life. As a response to this challenge, this paper proposes an energy-aware indoor positioning strategy based on Mobile Ad Hoc Networks (MANETs). The core principle is a self-adaptive control loop that continuously monitors the network’s positioning accuracy. Based on this real-time feedback, the system dynamically adjusts the number of active anchors, increasing them only when accuracy degrades and reducing them to save energy once stability is achieved. The method dynamically estimates relative coordinates by analyzing node encounters and contact durations, from which relative distances are inferred. Generalized Multidimensional Scaling (GMDS) is applied to construct a relative spatial map of the network, which is then transformed into absolute coordinates using reference nodes, known as anchors. The proposal is evaluated in a realistic simulated indoor MANET, assessing positioning accuracy, adaptation dynamics, anchor sensitivity, and energy usage. Results show that the adaptive mechanism achieves higher accuracy than fixed-anchor configurations in most cases, while significantly reducing the average number of required anchors and their associated energy footprint. This makes it suitable for infrastructure-poor, resource-constrained indoor environments where both accuracy and energy efficiency are critical. Full article

Powdered foods are matrices transformed into fine, loose solid particles through dehydration and/or milling, which enhances stability, storage, and transport. Due to their high commercial value and susceptibility to fraudulent practices, detecting adulterants in powdered foods is essential for ensuring food safety and protecting consumer health and the economy. Food fraud in powdered products, such as spices, cereals, dairy-based powders, and dietary supplements, poses an increasing risk to public health and consumer trust. These products were selected as representative matrices due to their high nutritional and economic relevance, which also makes them more susceptible to adulteration and hidden potential health risks from hidden contaminants. Recent studies highlight the potential of spectroscopic techniques combined with chemometrics as rapid, non-destructive, and cost-effective tools for authentication. This narrative review synthesizes recent literature (2020–2025) on the application of near-infrared (NIR) spectroscopy combined with chemometric techniques for adulterant detection in powdered foods. Advances in spectral preprocessing, variable selection, classification, and regression models are discussed alongside the most common adulterants and their nutritional and toxicological implications. Furthermore, the applicability of portable versus benchtop NIR devices is compared. The main contribution of this review lies in critically analyzing methodological frameworks, mapping current gaps, and identifying emerging trends, such as digital integration, self-adaptive chemometric models, and real-time on-site authentication, positioning NIR spectroscopy as a promising tool for food authentication and quality control. Full article

Diabetic foot ulcers are among the most severe complications of diabetes mellitus, disproportionately affecting populations in low- and middle-income countries. Digital health technologies have emerged as promising tools for prevention, diagnosis, and management; however, their effectiveness, usability, and applicability within public health systems remain insufficiently defined. This systematic review aimed to critically synthesize the clinical effectiveness, perceived usability, and methodological quality of digital interventions for the care of individuals with diabetes-related foot ulcers. A comprehensive search was performed in PubMed, Scopus, Web of Science, Embase, and Google Scholar for studies published between 2012 and 2024. Eighteen studies met the inclusion criteria, encompassing mobile health applications, wearable sensor devices, artificial intelligence-based tools, and telehealth platforms. Methodological quality was assessed using the Mixed Methods Appraisal Tool. Artificial intelligence-driven approaches demonstrated high diagnostic accuracy, with sensitivity and specificity above 90% for ulcer detection and classification. Mobile applications showed positive effects on self-efficacy, glycemic control, and adherence to preventive foot care, while usability scores were consistently high. Wearable sensor devices demonstrated potential for reducing ulcer recurrence, though supporting evidence remains limited. Across studies, recurrent methodological limitations included small sample sizes, absence of control groups, lack of economic evaluations, and barriers related to digital literacy and interoperability between systems. Most investigations were conducted in high-income countries, with limited consideration of public health contexts such as the Brazilian Unified Health System. In conclusion, digital health technologies show promise in improving the care of individuals with diabetes-related foot complications but face significant challenges regarding scalability, equity of access, and integration into public healthcare systems. Future research should prioritize context-adapted designs, robust clinical trials, and economic evaluations to inform health policies and support the rational adoption of these tools within universal health coverage frameworks. PROSPERO registration number: CRD420251023152. Full article

Capacitive-coupling non-contact voltage sensors face a key challenge: their probe-conductor coupling capacitance varies, making it hard to accurately determine the division ratio. This capacitance is influenced by factors like the conductor’s insulation material, radius, and relative position. To address this challenge, this paper proposes a sensor gain self-calibration method based on switching capacitors. This method obtains multiple sets of real-time measurement outputs by connecting and switching different standard capacitors in parallel with the sensor’s structural capacitance, and then simultaneously solves for the coupling capacitance and the voltage under test, thereby achieving on-site autonomous calibration of the sensor gain. To effectively suppress interference from stray electric fields in the surrounding space, a shielded coaxial probe structure and corresponding back-end processing circuitry were designed, significantly enhancing the system’s anti-interference capability. Finally, an experimental platform incorporating insulated conductors of various diameters was built to validate the method’s effectiveness. Within the 100–300 V power-frequency range, the reconstructed voltage amplitude shows a maximum relative error of 1.06% and a maximum phase error of 0.76°, and harmonics are measurable up to the 50th order. Under inter-phase electric field interference, the maximum relative error of the reconstructed voltage amplitude is 1.34%, demonstrating significant shielding effectiveness. For conductors with diameters ranging from 6 mm² to 35 mm², the measurement error is controlled within 1.57%. These results confirm the method’s strong environmental adaptability and broad applicability across different conductor diameters. Full article

Liquid crystalline block copolymers (LCBCPs) have emerged as an adaptable hybrid class at the intersection of self-assembling block copolymers and liquid crystalline ordering, producing multi-tiered architectures that can be finely programmed for multifunctional performance. This review surveys recent advances in their structure–property relationships and highlights applications spanning nanotechnology, biomedical systems, flexible photonics, stimuli-responsive, energy storage, and soft robotics. Particular emphasis is placed on how molecular design enables precise tuning of structural, optical, mechanical, and stimuli-responsive functions, positioning LCBCPs as strong candidates for next-generation functional materials. We also discuss current challenges, including scalability, phase control, and advanced characterization, and outline promising research directions to accelerate their translation from laboratory concepts to real-world technologies. Full article

In industrial scenarios, the control of permanent magnet synchronous servo motors is mostly achieved with proportional–integral controllers, which require manual adjustment of control parameters. At the same time, the performance of the servo system is usually disturbed by internal characteristic changes, load changes, and external factors. Therefore, preset control parameters may not achieve the desired optimal performance. Many scholars use intelligent algorithms, such as neural networks, to adaptively tune control parameters. However, the offline pre-training of neural networks is often time- and resource-consuming. Due to the lack of a model pre-training process in the neural network online self-tuning process, randomly setting the initial network weight seriously affects the position tracking performance of the servo control system in the start-up phase. In this paper, the physical model and the traditional frequency domain-tuning method of the three-closed-loop permanent magnet synchronous servo system are analyzed. Combined with the neural network PI control parameter self-tuning method and physical symmetry, a physical information-driven optimization framework is proposed. To demonstrate its superiority, the neural network PI controller and the proposed optimization framework are used to control the single-axis sine wave trajectory. The results show that the optimization framework proposed can effectively improve the position tracking control performance of the servo control system in the start-up phase by setting the threshold of the servo control parameters, reduce the position tracking control error to 0.75 rads in the start-up phase, and reduce the position tracking drop caused by a sudden load by 25%. This method achieves the independent optimization adjustment of control parameters under position tracking control, providing a reference for the intelligent control of permanent magnet synchronous servo motors. Full article

The integration of bacterial biotechnology into construction and geotechnical practices is redefining approaches to material sustainability, infrastructure longevity, and environmental resilience. Over the past two decades, research activity in construction biotechnology has expanded rapidly, with more than 350 publications between 2000 and 2024 and a five-fold increase in annual output since 2020. Beyond bibliometric growth, technical studies have demonstrated the remarkable performance of bacterial systems: for example, microbial-induced calcium carbonate precipitation (MICP) can increase the compressive strength of treated soils by 60–70% and reduce permeability by more than 90% in field-scale trials. In concrete applications, bacterial self-healing has been shown to seal cracks up to 0.8 mm wide and improve water tightness by 70–90%. Similarly, biofilm-mediated corrosion barriers can extend the durability of reinforced steel by significantly reducing chloride ingress, while bacterial biopolymers such as xanthan gum and curdlan enhance soil cohesion and water retention in eco-grouting and erosion control. The novelty of this review lies in its interdisciplinary scope, integrating microbiological mechanisms, materials science, and engineering practice to highlight how bacterial processes can transition from laboratory models to real-world applications. By combining quantitative evidence with critical assessment of scalability, biosafety, and regulatory challenges, this paper provides a comprehensive framework that positions construction biotechnology as a transformative pathway towards low-carbon, adaptive, and resilient infrastructure systems. Full article

Background/Objectives: The transition from theoretical knowledge to clinical practice poses significant challenges for nursing students globally. This critical period requires comprehensive educational support to build confidence and competence. While short-term preparatory courses have shown promise internationally, their effectiveness within the Saudi Arabian context remains understudied. This study aimed to evaluate nursing students’ satisfaction and self-confidence following participation in short-term preparatory courses conducted before clinical placements at Taibah University, Saudi Arabia. Methods: A descriptive cross-sectional study was conducted from February to April 2025. Data were collected from 117 undergraduate nursing students (response rate: 80.7%) using a validated questionnaire adapted from the National League for Nursing’s Student Satisfaction and Self-Confidence in Learning instrument. The preparatory courses included nursing care plan development, hospital orientation, and infection control procedures delivered over two weeks. Statistical analysis included descriptive statistics and Pearson correlation analysis. Results: Students reported high levels of satisfaction (mean = 4.29 ± 0.92) and self-confidence (mean = 4.31 ± 0.81) scores. The highest satisfaction was with instructor effectiveness (mean = 4.31 ± 1.05) and teaching methods (mean = 4.32 ± 1.01). Students demonstrated strong confidence in personal learning responsibility (mean = 4.44 ± 0.88) and skill development (mean = 4.32 ± 0.95). A strong positive correlation existed between satisfaction and self-confidence (r = 0.79, p < 0.001). Conclusions: Short-term preparatory courses effectively enhanced nursing students’ satisfaction and self-confidence in the Saudi Arabian context. The strong correlation between these constructions suggests that educational interventions improving one dimension is likely to benefit the other. These findings support integrating structured preparatory programs into nursing curricula to facilitate successful clinical transitions. Full article

Introduction: The mental health of nursing professionals is an escalating global concern, particularly due to the inherently challenging work conditions they frequently encounter. This study aimed to investigate the prevalence of Minor Mental Disorders (MMD) and resilience levels among nursing professionals, analyzing the relationship between these constructs and identifying resilience’s potential protective role. Methods: This was a quantitative, descriptive, correlational, and cross-sectional study. The sample consisted of 203 nursing professionals (including nursing assistants, technicians, and nurses) from two healthcare institutions in the interior of São Paulo, Brazil. Data were collected between August and October 2019. Instruments utilized included a sociodemographic and professional questionnaire, the Self-Report Questionnaire (SRQ-20) for MMD screening, and the Wagnild & Young Resilience Scale. Results: The overall prevalence of MMD in the studied sample was 31.0%. Mean scores for the SRQ-20 domains were observed as follows: Depressive/Anxious Mood (1.33), Somatic Symptoms (1.63), Reduced Vital Energy (1.77), and Depressive Thoughts (0.39). A key finding indicated that resilience did not demonstrate a significant direct predictive role on MMDs when the effect of quality of life was controlled. However, resilience showed a significant positive correlation with Quality of Life (QoL) (coef. = 0.515; p < 0.001). Furthermore, QoL emerged as a robust and statistically significant negative association with all dimensions of MMD. Discussion: These findings suggest that resilience may function as an indirect moderator or precursor to QoL, with QoL, in turn, exerting a more direct and substantial influence on the reduction of MMDs. This integrated perspective aligns with the understanding that resilience contributes to a more adaptive assessment of stressors and, consequently, to better QoL, thereby minimizing the detrimental effects of stress on mental health. Conclusion: This study reaffirms the high prevalence of Minor Mental Disorders among nursing professionals, highlighting Quality of Life as a primary target for interventions aimed at promoting mental well-being. It also emphasizes resilience as a valuable individual resource that indirectly supports mental health by enhancing QoL. A holistic understanding of occupational stressors, psychosocial, and biological mechanisms is crucial for developing effective and targeted support strategies for these essential professionals. Full article