Search Results (4,755)

This paper introduces an autonomous vision-based tracking system for a quadrotor unmanned aerial vehicle (UAV) equipped with an onboard camera, designed to track a maneuvering target without external localization sensors or GPS. Accurate capture of dynamic aerial targets is essential to ensure real-time tracking and effective management. The system employs a robust and computationally efficient visual tracking method that combines HSV filter detection with a shape detection algorithm. Target states are estimated using an enhanced extended Kalman filter (EKF), providing precise state predictions. Furthermore, a closed-loop Proportional-Integral-Derivative (PID) controller, based on the estimated states, is implemented to enable the UAV to autonomously follow the moving target. Extensive simulation and experimental results validate the system’s ability to efficiently and reliably track a dynamic target, demonstrating robustness against noise, light reflections, or illumination interference, and ensure stable and rapid tracking using low-cost components. Full article

The concept of synergistic water resources, water environment, water ecology remediation, and restoration (3WRR) is essential for addressing the interlinked challenges of water scarcity, pollution, and ecological degradation. An intelligent platform of remediation and restoration project optimization was developed, integrating multi-source data fusion, a coupled air–land–water model, and dynamic decision optimization to support 3WRR in river basins. Applied to the Jinshan Lake Basin (JLB) in China’s Greater Bay Area, the platform assessed 894 scenarios encompassing diverse remediation and restoration plans, including point/non-point source reduction, sediment dredging, recycled water reuse, ecological water replenishment, and sluice gate control, accounting for inter-annual meteorological variability. The results reveal that source control alone (95% reduction in point and non-point loads) leads to limited improvement, achieving less than 2% compliance with Class IV water quality standards in tributaries. Integrated engineering–ecological interventions, combining sediment dredging with high-flow replenishment from the Xizhijiang River (26.1 m³/s), increases compliance days of Class IV water quality standards by 10–51 days. Concerning the lake plans, including sluice regulation and large-volume water exchange, the lake area met the Class IV standard for COD, NH₃-N, and TP by over 90%. The platform’s multi-objective optimization framework highlights that coordinated, multi-scale interventions substantially outperform isolated strategies in both effectiveness and sustainability. These findings provide a replicable and data-driven paradigm for 3WRR implementation in complex river–lake systems. The platform’s application and promotion in other watersheds worldwide will serve to enable the low-cost and high-efficiency management of watershed water environments. Full article

Sustainable Weed Management Practices (SWMPs) are currently underrepresented in European cropping systems despite considerable attention from the research and policymaking communities. In public discourse, their adoption is associated with low yields, high initial investment costs, additional machinery requirements, elevated labor demands and limited or uncertain profitability. Nevertheless, little is known regarding their economic effects when implemented under real-life conditions at the farm level. This study aims to determine the impact of SWMPs against broomrape parasitism on the organization, management and economic performance of industrial tomato farms, considering that broomrapes (Orobanche and Phelipanche species) are a major impediment to the expansion of key crops in the Mediterranean basin due to their resistance toward commonly applied herbicides. For the purpose of economic appraisal, detailed technical and economic data were collected in 2022 from 76 arable farms cultivating industrial tomato in the Region of Thessaly in Central Greece. By combining Principal Component Analysis (PCA) with Two-Step Cluster Analysis (TSCA), a farm typology according to the implementation level of different SWMPs was developed. Based on this typology, a comparative technical and economic analysis revealed important differences in terms of structure, resource utilization and economic performance across the various farm types. “Holistic” farms, which exhibited the highest adoption levels of SWMPs, implemented an effective broomrape management strategy and achieved superior economic outcomes, evidenced by a remarkable net profit of 488.5 €/ha. Conversely, this was either negative or nearly negligible in farm types characterized by low adoption rates, indicating a lack of economic viability in the long run. The findings of this study offer useful recommendations for farm-level decision making, advisory support and policy design toward the promotion of SWMPs. Full article

Introducing deep learning methods into the quality inspection of production lines can reduce labor and improve efficiency, with great potential for the development of manufacturing systems. However, in specific closed production-line environments, robust and high-quality 3D fixed-area quality inspection is a common and challenging problem due to improper assembly, high data resolution, pose perturbation, and other reasons. In this article, we propose a robust 3D fixed-area quality inspection framework for production lines consisting of two steps: recursive segmentation and one-class classification. First, a Focal Segmentation Module (FSM) is proposed to gradually focus on the areas to be inspected by recursively segmenting the downsampled low-resolution point cloud, thereby ensuring efficient high-resolution segmentation. Moreover, Local Reference Frame (LRF)-based rotation-invariant local feature extraction is introduced to improve the robustness of the proposed method to pose variations. Second, a uniquely designed Semi-Nested Point Cloud Autoencoder (SN-PAE) is proposed to improve data imbalance and hard-to-classify samples. Particularly, we first introduce rotation-invariant feature extraction to a point cloud autoencoder to learn descriptive latent variables, then measure the latent variables using a semi-nested Latent Autoencoding Module (LAM). This avoids unreliable chamfer distance measurement and makes SN-PAE a more robust measurement method. In addition, we implement a set of experiments using solder joints as an example. Compared with PointNet++, the memory usage of recursive segmentation is reduced by 92%, and the time cost is reduced by 97.5%. The recall of SN-PAE on unaligned samples exceeds that of competitors by nearly 30% in the classification stage. The results demonstrate the feasibility and effectiveness of the proposed framework. Full article

Oculocutaneous albinism (OCA) increases susceptibility to ultraviolet (UV) skin damage, skin cancer risk, and psychosocial burden. Data from Madagascar are lacking. We conducted a six-month pilot study (July–December 2024) in northern Madagascar (DIANA and SAVA regions). Forty-one individuals with OCA were enrolled. Baseline socio-demographic, clinical, and behavioral data were collected through interviews and dermatological examinations. A structured program provided education, culturally adapted materials, and photoprotective resources, with monthly follow-up visits. The cohort included 22 males and 19 females, with a mean age of 18 years (range: 1 month–35 years). Actinic keratoses were present in 61% of participants, and invasive skin cancer in 4.9%. All patients had photophobia and nystagmus. Social discrimination was reported by 65.9%, with 12.2% describing severe abuse. Baseline photoprotection was inadequate: 43.9% reported no protective practices, 7.3% used sunscreen, and 19.5% avoided midday sun. Follow-up was completed by 20/41 patients (48.8%). Among completers, paired analysis showed a decrease in sunburn prevalence from 95.0% to 10.0% (p < 0.0001), an increase in regular sunscreen use from 0.0% to 100.0% (p < 0.0001), use of protective clothing from 35.0% to 80.0% (p = 0.0039), and adoption of behavioral strategies from 15.0% to 50.0% (p = 0.0156). This first study on OCA in northern Madagascar demonstrates a high burden of UV-related dermatoses and stigma. A low-cost community intervention significantly improved photoprotection. Wider implementation could reduce morbidity and enhance quality of life in resource-limited settings. Full article

Credit card fraud remains a significant concern for financial institutions due to its low prevalence, evolving tactics, and the operational demand for timely, accurate detection. Machine learning (ML) has emerged as a core approach, capable of processing large-scale transactional data and adapting to new fraud patterns. However, much of the literature modifies the natural class distribution through resampling, potentially inflating reported performance and limiting real-world applicability. This systematic literature review examines only studies that preserve the original class imbalance during both training and evaluation. Following PRISMA 2020 guidelines, strict inclusion and exclusion criteria were applied to ensure methodological rigor and relevance. Four research questions guided the analysis, focusing on dataset usage, ML algorithm adoption, evaluation metric selection, and the integration of explainable artificial intelligence (XAI). The synthesis reveals dominant reliance on a small set of benchmark datasets, a preference for tree-based ensemble methods, limited use of AUC-PR despite its suitability for skewed data, and rare implementation of operational explainability, most notably through SHAP. The findings highlight the need for semantics-preserving benchmarks, cost-aware evaluation frameworks, and analyst-oriented interpretability tools, offering a research agenda to improve reproducibility and enable effective, transparent fraud detection under real-world imbalance conditions. Full article

This study systematically identifies and prioritizes barriers to Industry 4.0 adoption in manufacturing within a developing economy. We used a mixed-methods approach—combining a systematic literature review and PLS-SEM. The research synthesizes 45 critical factors across nine I4.0 pillars, mapped to five sustainability dimensions. Data from 160 professionals show the technological dimension (β = 0.218) to be the most significant broad barrier. Analysis of high outer loadings (≥0.80) highlights key specific barriers: IT infrastructure gaps and poor technological leverage; a lack of organizational and digital readiness; cultural fragmentation and weak knowledge systems; high implementation and cyber threat costs; and low customization demands with absent data standards. The study proposes a maturity model and strategic framework to help policymakers address these barriers and promote sustainable digital transformation. Full article

You Only Look Once (YOLO) is a convolutional neural network-based object detection algorithm widely used in real-time vision applications. However, its high computational demand leads to significant power consumption and cost when deployed in graphics processing units. Field-programmable gate arrays offer a low-power alternative. However, their efficient implementation requires architecture-level optimization tailored to limited device resources. This study presents an optimized YOLOv2 accelerator for the Zynq-7000 system-on-chip (SoC). The design employs 16-bit integer quantization, a filter reuse structure, an input feature map reuse scheme using a line buffer, and tiling parameter optimization for the convolution and max pooling layers to maximize resource efficiency. In addition, a stall-based control mechanism is introduced to prevent structural hazards in the pipeline. The proposed accelerator was implemented on the Zynq-7000 SoC board, and a system-level evaluation confirmed a negligible accuracy drop of only 0.2% compared with the 32-bit floating-point baseline. Compared with previous YOLO accelerators on the same SoC, the design achieved up to 26% and 15% reductions in flip-flop and digital signal processor usage, respectively. This result demonstrates feasible deployment on XC7Z020 with DSP 57.27% and FF 16.55% utilization. Full article

This paper introduces an open-source software framework for implementing Field-Oriented Control (FOC) on a Brushless DC Motor (BLDC) across its entire speed range. The control strategy employs a Direct FOC method with a single Hall sensor combined with Space Vector Pulse Width Modulation (SVPWM) and complementary sensorless techniques. The BLDC motor and supporting circuits are modeled and validated through both simulation and hardware implementation. A modular software architecture enables deployment via distinct system components, promoting hardware abstraction and reducing platform-specific dependencies. The entire setup is conceptualized and executed in MATLAB/Simulink R2024b and the framework supports remote experimentation through a web-based interface, requiring only a single MATLAB license. This scalable solution is designed for academic researchers and industry practitioners alike, offering an accessible low-cost platform for motor control development, validation, and early-stage prototyping. Full article

This paper proposes a battery emulator based on a bidirectional non-inverting buck-boost power electronics converter. With the capability of bidirectional operation, it can emulate both charging and discharging processes. The proposed emulator is controlled with the advanced I² dual current-mode control (I²DCMC) algorithm, combined with a feedforward control, which ensures fast and accurate tracking of the voltage and current characteristics of the batteries. The emulator is universal in terms of the various mathematical models of the batteries, which can be implemented in real time. It has no limitations regarding different battery types. Detailed analysis and the design procedure of the proposed battery emulator are presented. The performances of the emulator are validated with simulation and experimental results for three battery types: polymer Li-ion, conventional Li-ion, and lead–acid battery. Both steady and transient states are analyzed, especially transitions between charging and discharging phases. The possibility of simple time scaling of charging/discharging processes is successfully achieved and demonstrated, which is very important in making tests faster, with preserved battery characteristics. Considering its low-cost and user-friendly operation, the proposed emulator can be a good alternative to the real batteries in experimental tests of different power electronics systems. The prototype, which is developed for the experimental verification of the emulator, is designed for and limited to the research of lower power ratings systems of up to 100 W. It is suitable in education to easily demonstrate the behavior of the batteries in multiple scenarios in controlled laboratory conditions. Full article

Value-added services (VASs) are widely used to incentivize user adoption in the platform economy. While considering the symmetry of cross-network externalities of a platform, i.e., suppliers and manufacturers exert balanced and mutually reinforcing influences on each other’s participation, this study develops a stylized game model to investigate platforms’ optimal bilateral user pricing decisions and VAS provision strategies, such as outsourcing to a third-party service provider (Model OS) or in-house provision (Model PS). Then, the platform’s and the third-party service provider’s optimal pricing decisions are derived, and the equilibrium results are compared. The findings demonstrate that a platform should implement Model PS when the outsourced VAS cost coefficient is sufficiently high or the outsourced VAS quality and cost coefficient are low concurrently. Only when the outsourced VAS quality is relatively high and cost coefficient is in a low range should a platform choose Model OS. Additionally, to address the problem of declines in supply chain members’ profits caused by investment in low-quality outsourced VASs (VAS utility provided by a third party exceeds the specific value 1.38), this study also proposes a feasible VAS cost-sharing contract (Model CS) to incentivize the third-party provider to provide investment in high-quality VASs. The contract design can achieve a “win-win” outcome when the sharing ratio is at a moderate rate (especially a range from 0.291 to 0.5) and the outsourced VAS cost coefficient meets suitable thresholds. Full article

Background/Objectives: Postnatal depression imposes a substantial burden on wellbeing as well as costs estimated to exceed $7 billion for every one-year cohort of births in Australia. Despite this, most cases go untreated, a major barrier being the poor rate of treatment uptake. We developed and pilot tested an integrated screening and clinical decision support system (PIRIMID) to assist maternal and child health nurses (MCHNs) to create individualised management plans, with specific referral pathways, for women depressed postnatally. We assessed the feasibility of PIRIMID by examining acceptability for both nurses and women, ease of implementation, and referral rates, and we monitored treatment uptake and depression. Methods: An extensive co-design and consultation process was used to develop PIRIMID. A pilot cluster randomised controlled trial (RCT) was conducted comparing PIRIMID to Routine care, with partial crossover (PIRIMID followed by crossover to Routine care and Routine care followed by continued Routine care). A state-wide survey of MCHNs in Victoria, Australia, explored perceived benefits and barriers of PIRIMID from a consumer perspective. Results: Twelve MCHNs (PIRIMID: n = 6; Routine care: n = 6) and 229 women (conditions: PIRIMID, n = 52; Crossover Routine care, n = 42; Routine care, n = 57; Continued Routine care, n = 78) were recruited to the RCT. Median scores for depression, anxiety and stress symptoms were low and similar at all timepoints and conditions. PIRIMID was acceptable and helpful to MCHNs and women, and most MCHNs rated integration into their existing clinical systems as easy. There were trends in favour of higher referral rates by PIRIMID MCHNs (18%, 95% CI: 5–40) compared with other conditions (10–15%, 95% CIs: 6–29, 2–27, 6–26), but treatment uptake appeared similar across conditions. The statewide survey (n = 292) revealed that 84% of MCHNs would use PIRIMID, and the main potential barriers to use would be time constraints and technical issues. Conclusions: This pilot work indicates that PIRIMID shows promise as a feasible and acceptable tool to assist MCHNs to develop management plans for women depressed postnatally. Further research with adequate statistical power is needed to explore effects on treatment uptake with larger samples of postnatally depressed women. Full article

Neuromorphic circuits emulate the brain’s massively parallel, energy-efficient, and robust information processing by reproducing the behavior of neurons and synapses in dense networks. Memristive technologies have emerged as key enablers of such systems, offering compact and low-power implementations. In particular, locally active memristors (LAMs), with their ability to amplify small perturbations within a locally active domain to generate action potential-like responses, provide powerful building blocks for neuromorphic circuits and offer new perspectives on the mechanisms underlying neuronal firing dynamics. This paper introduces a novel second-order locally active memristor (LAM) governed by two coupled state variables, enabling richer nonlinear dynamics compared to conventional first-order devices. Even when the capacitances controlling the states are equal, the device retains two independent memory states, which broaden the design space for hysteresis tuning and allow flexible modulation of the current–voltage response. The second-order LAM is then integrated into a FitzHugh–Nagumo neuron circuit. The proposed circuit exhibits oscillatory firing behavior under specific parameter regimes and is further investigated under both DC and AC external stimulation. A comprehensive analysis of its equilibrium points is provided, followed by bifurcation diagrams and Lyapunov exponent spectra for key system parameters, revealing distinct regions of periodic, chaotic, and quasi-periodic dynamics. Representative time-domain patterns corresponding to these regimes are also presented, highlighting the circuit’s ability to reproduce a rich variety of neuronal firing behaviors. Finally, two unknown system parameters are estimated using the Aquila Optimization algorithm, with a cost function based on the system’s return map. Simulation results confirm the algorithm’s efficiency in parameter estimation. Full article

The convergence of Industrial Internet of Things (IIoT) and digital twin technology offers new paradigms for process automation and control. This paper presents an integrated IIoT and digital twin framework for intelligent control of a gas–liquid separation unit with interacting flow, pressure, and differential pressure loops. A comprehensive dynamic model of the three-loop separator process is developed, linearized, and validated. Classical stability analyses using the Routh–Hurwitz criterion and Nyquist plots are employed to ensure stability of the control system. Decentralized multi-loop proportional–integral–derivative (PID) controllers are designed and optimized using the Integral Absolute Error (IAE) performance index. A digital twin of the separator is implemented to run in parallel with the physical process, synchronized via a Kalman filter to real-time sensor data for state estimation and anomaly detection. The digital twin also incorporates structured singular value ($\mu$) analysis to assess robust stability under model uncertainties. The system architecture is realized with low-cost hardware (Arduino Mega 2560, MicroMotion Coriolis flowmeter, pneumatic control valves, DAC104S085 digital-to-analog converter, and ENC28J60 Ethernet module) and software tools (Proteus VSM 8.4 for simulation, VB.Net 2022 version based human–machine interface, and ML.Net 2022 version for predictive analytics). Experimental results demonstrate improved control performance with reduced overshoot and faster settling times, confirming the effectiveness of the IIoT–digital twin integration in handling loop interactions and disturbances. The discussion includes a comparative analysis with conventional control and outlines how advanced strategies such as model predictive control (MPC) can further augment the proposed approach. This work provides a practical pathway for applying IIoT and digital twins to industrial process control, with implications for enhanced autonomy, reliability, and efficiency in oil and gas operations. Full article

Introduction: Multiple sclerosis (MS) is a chronic neurodegenerative disease that entails high costs, progressive disability, and reduced quality of life (QoL). Telerehabilitation (TR), supported by new technologies, is emerging as an alternative or complement to in-person rehabilitation, potentially lowering socioeconomic impact and improving QoL. Aim: The objective of this study was to evaluate the effect of TR on the QoL of people with MS compared with in-person rehabilitation or no intervention. Materials and methods: A systematic review of randomized clinical trials was conducted (March–May 2025) following PRISMA guidelines. Searches were run in the PubMed-Medline, EMBASE, PEDro, Web of Science, and Dialnet databases. Methodological quality was assessed with the CASP scale, risk of bias with the Risk of Bias 2 tool, and evidence level and grade of recommendation with the Oxford Classification. The protocol was registered in PROSPERO (CRD420251110353). Results: Of the 151 articles initially found, 12 RCTs (598 total patients) met the inclusion criteria. Interventions included (a) four studies employing video-controlled exercise (one involving Pilates to improve fitness, another involving exercise to improve fatigue and general health, and two using exercises focused on the pelvic floor muscles); (b) three studies using a monitoring app to improve manual dexterity, symptom control, and increased physical activity; (c) two studies implementing an augmented reality system to treat cognitive deficits and sexual disorders, respectively; (d) one platform with a virtual reality headset for motor and cognitive training; (e) one study focusing on video-controlled motor imagery, along with the use of a pain management app; (f) a final study addressing cognitive training and pain reduction. Studies used eight different scales to assess QoL, finding similar improvements between groups in eight of the trials and statistically significant improvements in favor of TR in four. The included trials were of good methodological quality, with a moderate-to-low risk of bias and good levels of evidence and grades of recommendation. Conclusions: TR was more effective in improving the QoL of people with MS than no intervention, was as effective as in-person treatment in patients with EDSS ≤ 6, and appeared to be more effective than in-person intervention in patients with EDSS between 5.5 and 7.5 in terms of QoL. It may also eliminate some common barriers to accessing such treatments. Full article