Search Results (808)

This work addresses the design and implementation of an automated system for the handling and transportation of parts, integrating speed sensors, an optimized PID controller, an HMI interface, and an industrial robotic system. The speed sensors, powered by 5 V DC, enable continuous measurement of the conveyor belt’s speed and direction of rotation, providing the feedback signal required for the control loop. The core element of the system is the implementation of a PID controller applied to a direct current motor responsible for driving the conveyor belt. This controller regulates the motor speed by analyzing the error between the reference speed and the measured speed, using proportional, integral, and derivative actions to improve system stability, reduce steady-state error, and minimize oscillations. The application of PID control makes it possible to achieve an appropriate dynamic response, ensuring accuracy and reliability in the transportation process. System monitoring and operation are carried out through a human–machine interface (HMI) developed in LOGO Web Editor, which communicates with the PLC (LOGO V8) to visualize and control the status of the conveyor belt, sensors, and control elements in real time. This interface facilitates interaction between the operator and the system, allowing both virtual and physical operation. In addition, RAPID programming is used to control the IRB 14000 industrial robot, enabling the reading of PLC signals and the execution of coordinated trajectories between both arms. The operating sequence includes picking up a part with the left arm, placing it on the conveyor belt, and, after detection by sensors and PLC control, subsequent manipulation by the right arm to a specific point. Finally, both arms return to their original position, ensuring synchronized and collision-free operation. Lastly, this work integrates scientific knowledge related to the modeling, analysis, and control of dynamic systems, particularly in the implementation of closed-loop PID control optimized using genetic algorithms. This control is applied directly to an embedded system through the use of an Arduino board as the processing and control platform. Likewise, technological knowledge associated with industrial automation, PLC programming, HMI development, and industrial robotics is incorporated. The convergence of these scientific and technological approaches results in a comprehensive and compelling project that demonstrates the practical application of theoretical concepts in a functional automated system representative of real industrial environments. Full article

The industrial Internet of Things (IoT) allows traditional electromechanical systems to be connected to digital monitoring and control platforms, especially when field devices use industrial protocols that must be integrated into web services without modifying their main operation. This work implements an IoT architecture based on the Open Systems Interconnection (OSI) model to interconnect two Variable Frequency Drives (VFDs) through a LOGO! Programmable Logic Controller (LOGO! PLC), a Human–Machine Interface (HMI), a ZLAN5143D gateway, Node-RED, Message Queuing Telemetry Transport (MQTT), and Adafruit IO. The communication integrates RS485/Modbus RTU at the field level and Modbus TCP/IP over Ethernet at the upper network level using the gateway as the protocol conversion element. The validation was performed through Modbus Poll, variable acquisition, MQTT publication, and web visualization. The results show local communication response, acquisition of frequency, voltage, current, and revolutions per minute (RPM), together with remote control of start, stop, frequency setpoint, and rotation direction. The architecture is presented as a modular solution for electromechanical applications with IoT projection. Full article

This study investigates the use of interpretable machine learning techniques to identify behavioral and demographic patterns associated with diabetes, based on structured population survey data from the Canadian Community Health Survey (CCHS). A decision tree classifier was applied to a dataset comprising $16,824$ respondents and 38 preprocessed features covering lifestyle, well-being, and sociodemographic factors. The model was optimized through grid search with five-fold stratified cross-validation, achieving a test accuracy of $61.3$% (mean $62.6$% ±$0.6$% across a $10\times 5$ repeated stratified cross-validation). Feature importance analysis revealed that age, alcohol consumption patterns, daily energy expenditure, and physical activity were the most influential factors associated with diabetes status, with the top three features exhibiting stable importance across all cross-validation folds. The model produced a set of 32 human-readable decision rules; a sensitivity analysis confirmed that these rules are stable across encoding choices and cross-validation folds. Several model variants were evaluated: a class-weighted decision tree, a logistic regression baseline, an age-only decision tree, and an age and sex logistic regression. The class-weighted model improved minority-class recall (from $0.25$ to $0.53$) at the cost of overall accuracy. A one-hot encoding sensitivity analysis showed that replacing ordinal label encoding of nominal variables with one-hot encoding produces virtually identical results (accuracy: $61.4$% vs. $61.3$%), confirming that the main rules are not artifacts of the encoding choice. Although the classification accuracy is moderate and not significantly better than a majority-class baseline (McNemar’s test, $p=0.455$), the extracted rules confirmed several known associations and revealed interactions between social and lifestyle variables. These rules are intended as hypothesis-generating population-level descriptors rather than validated clinical decision tools, and no causal inference is claimed. This approach demonstrates the value of rule-based models for exploratory public health research. Full article

The shift to teleworking, defined as technology-enabled work arrangements in which employees perform organizational tasks remotely outside traditional office settings, has exposed the limitations of traditional performance management systems, including the lack of direct oversight, micromanagement risks, communication barriers, and employee isolation and well-being. These systems often rely on physical presence or intrusive surveillance rather than outcome-based evaluation. This paper asks how AI-driven performance management can be designed to address the documented challenges of teleworking while safeguarding employee autonomy, fairness, and well-being. The study integrates a comprehensive literature review on AI capabilities with empirical evidence from a sequential mixed-methods study of Canadian public servants, comprising machine learning analysis of over 205,000 tweets, document analysis of federal and provincial teleworking policies, a survey of 176 public servants analyzed using logistic regression, and semi-structured interviews with Government of Canada employees. Grounded in socio-technical theory and the Theory of Planned Behavior, the findings reveal that organizational support, workplace socialization, and attitudes are stronger predictors of teleworking success than digital skills or monitoring, while isolation functions as a measurable risk factor. These empirical patterns are mapped to specific AI capabilities to produce a socio-technical framework organized around three interdependent layers: technological, organizational, and human-centered. The paper contributes an empirically grounded alternative to purely speculative treatments of AI in performance management, offering design requirements derived from what teleworkers actually experience rather than from technological possibilities alone. While the framework is analytically grounded in empirical evidence, behavioral theory, and existing AI capabilities, it has not yet undergone full technical or longitudinal organizational validation. Accordingly, it should be understood as a theoretically and empirically informed design artifact intended to guide future implementation and evaluation efforts. It is worth acknowledging that the study’s key limitations include a Canada-specific public sector sample, modest survey and interview sizes, and the exploratory nature of several proposed AI capabilities; future cross-sectoral, comparative, and longitudinal research is needed to validate and extend the framework. Full article

Large language models (LLMs) offer a flexible interface for human–machine interaction, but their direct use in embedded control remains difficult because low-cost microcontrollers cannot host such models locally and unconstrained language generation is not physically grounded. This paper presents EdgeTalk-MCU, a local host–microcontroller framework for low-latency natural-language control of resource-constrained devices. The system couples a locally deployed LLM on the host side with an ESP32-S3 microcontroller through a lightweight serial protocol and closes the loop with real-time state feedback. The reported end-to-end decision latency of ∼0.15 s refers to the host-side inference pipeline; physical platform latency additionally includes UART round-trip and servo actuation overhead. The design combines two complementary mechanisms: a state-aware prompt constraint that injects task progress and physical state into the host-side policy, and a runtime shield that enforces hard execution consistency before actuation. This decomposition separates raw policy quality from executed safety. Across representative obstacle scenarios in simulation, unshielded controllers remain unreliable—LLM-only and Prompt-only exhibit collision rates of 30.6% and 26.5%, respectively, in the Sudden Obstacle setting—whereas both shielded methods reduce collision to 0%. An ablation study confirms that the runtime shield is the decisive safety mechanism; the state-aware prompt constraint contributes primarily at the raw-proposal level by reducing the fraction of unsafe proposals submitted to the shield, rather than by independently guaranteeing safe execution. Hardware-in-the-loop (HIL) validation on a physical ESP32-S3 platform confirms that the same qualitative pattern holds under real sensing and communication conditions. Full article

Groundwater resources in the Kingdom of Saudi Arabia (KSA) are important for meeting the needs of human communities, agriculture, and industry. In Western KSA, groundwater from coastal aquifers is an essential resource that complements desalinated seawater. Therefore, ensuring the quality and contamination of groundwater has emerged as a critical priority for preserving water security. The aim of this research is to evaluate the groundwater quality and its vulnerability to contamination within the Wadi Marawani Basin. To achieve this aim, water quality indices (WQIs), the DRASTIC model, and machine learning (ML) algorithms were employed alongside a Geographic Information System (GIS). The results of the chemical analysis of 64 water samples were used in these assessments. Furthermore, several input parameters were evaluated using the DRASTIC model to estimate the DRASTIC index (DI) and generate a groundwater vulnerability map. Three ML algorithms—specifically, a Multilayer Perceptron (MLP), a Random Forest (RF), and a Decision Tree (DT)—were utilized to forecast WQIs such as the total dissolved solids (TDS) and sodium adsorption ratio (SAR), in addition to the DRASTIC index (DI). The results revealed that around 36% of the samples were classified as fresh water (<1000 mg/L). The SAR ranged from 1.10 to 32.50, indicating that most samples were suitable for irrigation. Approximately 22% of the basin was classified as demonstrating high vulnerability, whereas about 78% demonstrated low-to-moderate vulnerability. Assessment of the ML models showed high predictive accuracy for the TDS, SAR, and DI. The MLP-Vul. model attained an R² value of 1.00 and RMSE value of 0.01, the RF-Vul. model achieved an R² of 0.94 and RMSE of 3.17, and the DT-Vul. model attained an R² of 0.92 and RMSE of 3.57. Although there was a minor increase in RMSE across all models during the testing phase, their predictive performance remained clear. Full article

Sea anemones detect external stimuli through the deformation of their soft tentacles, which exhibit multi-directional force sensitivity. Inspired by this mechanism, we designed a capacitive three-dimensional force flexible tactile sensor composed of a hollow hemisphere and a hollow cylinder. The device was fabricated using 3D printing combined with a Layer-By-Layer assembly process. For normal forces, the sensor achieved sensitivities of approximately 0.66 N⁻¹ in the 0–1 N range and 0.15 N⁻¹ in the 2–10 N range. For tangential forces, the four symmetrically distributed electrodes exhibited opposite monotonic capacitance variation trends. The sensor exhibited a force resolution of 0.02 N, a lower detection limit of 0.04 N, a hysteresis error as low as 3.5%, and a response/recovery time of up to 50 ms under a 0–10 N load. Moreover, the device demonstrated good stability under 1000 load–unload cycles and over a temperature range from 20 °C to 100 °C. Its utility was further validated through multi-scenario applications, including game controller manipulation, gripper-based object recognition, Morse code and Huffman coding transmission, as well as multi-joint human motion detection. These results demonstrate that the proposed bioinspired sensor offers a promising solution for flexible force sensing, human–machine interaction, and wearable health monitoring. Full article

This paper presents a Threat Analysis and Risk Assessment (TARA) of the takeover request (TOR) component in Advanced Driver Assistance Systems (ADAS) for SAE Level 2–3 automation. A TOR prompts the human driver to retake control when the system approaches its Operational Design Domain limits or when risk increases; late, false, or muted requests directly impact safety. The study models the TOR pipeline (perception, driver monitoring, decision logic, in-vehicle networks, and Human–Machine Interface) as assets and data flows, applies STRIDE-based threat identification using Microsoft Threat Modeling Tool and Ansys Medini Analyze, and rates risks under ISO/SAE 21434 with traceability to ISO 26262, ISO 21448, and UNECE R155/R157. The assessment produces 165 threat rows, with an initial risk distribution of 1 Critical, 113 High, 34 Medium, and 17 Low. Results show that tampering, denial of service, and spoofing dominate the TOR threat landscape, with the central processing unit, sensor-to-CPU links, and HMI channels as primary trust anchors. After applying mitigation measures including secure boot, message authentication, intrusion detection, redundancy checks, and encrypted communication, the residual post-mitigation security levels were reduced to 0 Critical, 0 High, 13 Medium, 101 Low, and 51 Negligible. Unlike other ADAS TARA studies, this TOR-focused analysis shows that cybersecurity risk is shaped by the interaction between cyber compromise, driver-readiness estimation, HMI delivery, fallback execution, and the limited handover time budget. The results support a defence-in-depth mitigation strategy for secure TOR operation in SAE Level 2–3 vehicles. Full article

Motor imagery brain–computer interfaces (MI-BCIs) have important applications in neurorehabilitation, assistive communication, and non-muscular human–machine interaction. From a bionic neural-interfacing perspective, MI-BCI decoding provides a computational bridge between biological motor intention and external machine control. However, reliable motor imagery electroencephalography (MI-EEG) classification remains challenging due to the highly non-stationary features of MI-EEG and limited interpretability. In this work, we propose PG-MCTFormer, a prior-guided multi-scale convolutional Transformer for MI-EEG classification that integrates rhythm-aware temporal filtering, dual-scale spatial modeling, and contextual decoding within a unified architecture. We evaluated the model on the publicly available BCI Competition IV 2a dataset, achieving 85.08% average accuracy and a Cohen’s kappa of 0.80, with significant performance improvement over the traditional methods. Comprehensive multi-view interpretability analyses in the frequency, temporal, and spatial domains further show that the learned filters remain aligned with canonical MI-related bands, discriminative evidence concentrates in the middle-to-late imagery interval, and the spatial prior is refined into subject-adaptive sensorimotor topographic patterns. These results indicate that explicit neurophysiological priors can improve both the robustness and the interpretability of MI-EEG decoders for biomimetic neural-interface applications. Full article

Critical infrastructure restoration (CIR) is a disaster-management and sustainability challenge because prolonged disruption of energy, water, transport, communications, healthcare, and public-administration services can amplify social, economic, and environmental losses. This PRISMA 2020-reported systematic review synthesizes post-2016 scientific literature and official policy, legal, standards, and technical documents on CIR and AI decision support. The review identified 55 records, removed 1 duplicate, excluded 1 ineligible record, and retained 53 core sources for qualitative synthesis, including 31 scholarly publications and 22 official documents. Manual screening was used; no automated screening or AI-assisted exclusion tools were applied. The results are organized around four research questions covering regulatory frameworks, recovery practices, supporting systems, and AI model families. The synthesis shows that CIR is shaped by layered governance through NIS2, the CER Directive, the AI Act, and national measures; by operational recovery practices such as continuity planning, cyber crisis coordination, interdependency mapping, and model-supported restoration; by digital platforms including SCADA/ICS, IoT sensing, GIS/common operating pictures, decision-support systems, simulation environments, and digital twins; and by AI methods ranging from classical machine learning and computer vision to reinforcement learning and generative assistants. However, evidence maturity remains uneven, with many AI applications still simulation-based, sector-specific, or weakly validated in real restoration settings. The review contributes an integrated CIR-oriented framework showing that AI creates practical value when embedded in interoperable, human-supervised, regulation-aware, and empirically validated restoration architectures that support sustainable service continuity rather than isolated automation. Full article

In recent years, the widespread availability of wearable devices and smartphones has enabled the large-scale collection of human activity data, fostering new opportunities for automatic workout recognition and personalized fitness monitoring. However, the centralized storage of video recordings raises critical privacy concerns, particularly when raw data contain identifiable individuals. Federated Machine Learning provides a paradigm designed with the aim of reducing privacy risks; here, models are collaboratively trained across distributed clients without sharing their sensitive data. In this paper, we propose an approach for workout video classification with Federated Machine Learning, enhanced by explainability through Gradient-weighted Class-Activation Mapping. The proposed method is evaluated on a real-world multi-class exercise video dataset, organized into eight biomechanically coherent macro-classes. In the experimental analysis, we consider several federated configurations in terms of the number of clients, the chosen aggregation strategy, and global communication rounds. The obtained results demonstrate that different aggregation strategies achieve comparable overall accuracy, while explainability effectively highlights the discriminative regions associated with exercise execution, revealing meaningful differences in model behavior between aggregation strategies and uncovering misclassifications driven by contextual biases, demonstrating the trustworthiness of the proposed approach for explainable workout video classification. Full article

The deployment of Deep Learning (DL) for anomaly detection in Industrial IoT (IIoT) is critically hampered by the non-stationary nature of industrial data streams and the lack of forensic-grade explainability. This systematic review synthesizes 48 peer-reviewed studies (2021–2025) to quantify the performance collapse of static models under concept drift and to establish operational criteria distinguishing post hoc feature attribution (Type A XAI) from forensic root-cause diagnosis (Type B XAI). Our analysis reveals three critical findings: (1) static DL models suffer a 15–22% F1-score degradation across wastewater, manufacturing, and energy sectors when deployed in non-stationary environments, rendering them operationally non-viable without continuous adaptation; (2) the current literature remains saturated with Type A explainability (80% of corpus through 2023), creating a Forensic Gap where operators receive statistical correlations but lack actionable maintenance directives; and (3) emerging 2024–2025 research marks a paradigm shift toward Type B methodologies, yet no unified framework bridges real-time detection with deep causal reasoning. To address these gaps, we contribute the following: (1) a validated operational taxonomy (Cohen’s $\kappa =0.84$) with reproducible five-criterion rubric enabling forensic XAI classification; (2) the first quantitative synthesis of drift penalties in industrial deployments; and (3) a three-tier Edge-Cloud Forensic XAI architecture that achieves 70% communication payload reduction via compressed latent vectors while integrating tnGAN-based data imputation (handling 20–30% missing data) and physics-guided causal reasoning engines. Our framework decouples millisecond-level edge detection from 1–3 s cloud-based forensic diagnosis, ensuring both operational responsiveness and actionable industrial insight. We conclude that the future of safety-critical IIoT demands “Forensic-by-Design” architectures leveraging machine unlearning for drift adaptation and LLM-based natural language interfaces for operator-facing explanations, positioning Industry 5.0 to bridge the gap between algorithmic detection and human-centered decision support. Full article

Geospatial Artificial Intelligence (GeoAI) enables the automated generation of built environment map features, such as building outlines/footprints, on a global scale. However, the integration of these AI-generated datasets into Volunteered Geographic Information (VGI) platforms like OpenStreetMap (OSM) risks incorporating ‘AI slop’, consisting of geometrically inconsistent/unreliable data, into the online map. While the OSM “Code of Conduct for Automated Edits” provides a policy framework for data ingestion, it lacks a machine-enforceable mechanism for real-time quality gating. This paper proposes a GeoAI-Gatekeeper to perform this task—an automated process that applies empirical Acceptable Quality Thresholds (AQT) to address the GeoAI data governance problem. Because the Gatekeeper utilizes an intrinsic, no-reference evaluation of geometric fidelity, it can assess incoming AI-generated data streams in real-time without requiring ground-truth benchmarks. Importantly, it focuses exclusively on the geometric validation of building footprints, acknowledging for now that semantic enrichment, such as tagging, remains a human-centric task. The presented GeoAI-Gatekeeper is a working prototype developed for a specific urban area, systematically triaging incoming AI-generated data into three tiers; Auto-Accept, Manual Review, and Reject. It provides a Web-GIS interface for Human-in-the-Loop (HITL) functionality to ensure the OSM community remains the final arbiter of acceptable data quality. Testing the Gatekeeper in Dublin (Ireland) demonstrates that our solution can auto-ingest 93.6% of features with a 14x reduction in human review effort while still adhering to OSM’s cartographic integrity standards. By implementing qualitative community guidelines into machine-enforceable thresholds, our approach introduces a viable methodology for next-generation hybrid VGI systems. Importantly, it ensures that the transition towards automated data ingestion reinforces, rather than undermines, the reliability of global crowd-source mapping datasets. Full article

This paper presents a Digital Twin (DT)-based framework for the control, monitoring, and intelligent optimization of an Assembly/Disassembly/Repair Mechatronic Production Line (A/D/R MPL), developed as a laboratory platform aligned with Industry/Education 4.0/5.0 paradigms. The A/D/R MPL is assisted by two complementary cyber–physical robotic systems: an Assembly/Disassembly/Replacement Cyber–Physical Robotic System (A/D/R CPRS), and a Mobile Cyber–Physical Robotic System (MCPRS), enabling both fixed and mobile intelligent operations. The CPRS is equipped with an industrial robotic manipulator (IRM) responsible for A/D/R tasks, while the A/D Mechatronic Line (A/D ML) consists of seven interconnected workstations (WS1–WS7) dedicated to storage, transport, quality control, and final product handling. MCPRS includes a wheeled mobile robot (WMR), carrying a robotic manipulator (RM) and Mobile Visual Servoing System (MVSS). Each workstation is connected to a local slave programmable logic controller (PLC), which communicates via PROFIBUS with a master PLC located at the CPRS level. Additional communication infrastructures include LAN PROFINET and LAN Ethernet for local integration, and WAN Ethernet connectivity enabled through open platform Communication-Unified Architecture (OPC-UA), ensuring interoperability, scalability, and remote accessibility. Also, MODBUS TCP as serial industrial communication is used between the master PLC and the MCPRS. Virtual environment supports task planning through Augmented Reality (AR) and real-time monitoring through Virtual Reality (VR). The system behaviour is modelled with synchronized hybrid Petri Nets (SHPNs) which describe the discrete and hybrid dynamics of A/D/R processes. Artificial intelligence (AI) techniques are integrated into the DT framework for optimal task scheduling and adaptive decision-making. As a laboratory-scale implementation, the proposed system provides a comprehensive platform for experimentation, validation, and education. It supports Education 4.0/5.0 objectives by facilitating hands-on learning, human–machine interaction, and the integration of emerging technologies such as AI, Digital Twins, AR/VR, and cyber–physical systems. At the same time, it embodies Industry 4.0/5.0 principles, including interoperability, decentralization, sustainability, robustness, and human-centric design. Full article

Background/Objectives: Autism Spectrum Disorder (ASD) is a set of neurological and neurodevelopmental disorders characterized by difficulties in social communication and interaction, repetitive behaviors, and sensory processing differences. Recent studies have shown that circRNAs play a crucial role in the pathophysiology of ASD. In this study, we present an exploratory machine learning framework integrating circRNA sequence features, miRNA interactions, gene targets, and pathway enrichment analysis to investigate ASD-associated molecular signatures. Methods: Differential circRNAs were identified from human peripheral blood datasets, and informative features were selected using attribute-based filtering and Information Gain ranking. Machine learning models were developed using the WEKA platform. Results: The HyperPipes classifier achieved the highest performance (92.5% accuracy under cross-validation). Analysis using an independent ASD gene expression dataset showed consistent discriminative patterns of the derived gene-level signatures across multiple machine learning classifiers. The competitive endogenous RNA network and enriched gene pathways were also analyzed. Conclusions: Overall, this study provides a computational, preliminary framework for analyzing circRNA-associated molecular patterns in ASD. Findings should be interpreted in the context of limited sample size and dataset availability. Full article