Search Results (2,457)

In this publication we investigate in depth the design, architecture and implementation of a bio and cultural guiding system with augmented capabilities. Our platform will be able to provide a flexible and user-friendly application for smart mobile devices with Android OS, which will be able to provide to the user the ability to discover nearby places of interest such as museums, archeological sites, religious sites, natural beauty sites, etc. In addition, our application is able to offer users an enhanced, comprehensive and augmented tour experience, based on visual and audio smartphone services, using asynchronous and real-time user–server communication mechanisms, thus eliminating the need for a human tour guide operator in cases of a remote area, guiding service cost or unavailable time slot. The proposed platform integrates visual overlays, audio narration, Geolocation services, and cloud-based data management to enable users to explore cultural, historical, and natural points of interest without the need for a human tour guide. A modular, layered system architecture is developed, combining Firebase Realtime Database, RESTful web services, OpenStreetMap-based navigation, and Android-native components to ensure scalability, flexibility, and real-time responsiveness. UML modeling, including class and sequence diagrams, is employed to formally describe system structure and behavior, while a mathematical data model validates the consistency of the underlying database schema. The implementation demonstrates how AR guiding systems can enhance spatial understanding, accessibility, and user engagement while supporting sustainable tourism practices and efficient destination management. The results indicate that the proposed solution is technically feasible, user-centered, and adaptable to diverse bio-cultural contexts, contributing to the advancement of intelligent, inclusive, and sustainable digital tourism platforms. Full article

Manual visual diagnosis of apple diseases suffers from low efficiency, strong subjectivity and poor scalability for large commercial orchards. Existing research mainly targets diseases on single plant organs, whereas full-growth-cycle detection has to cope with extreme multi-scale differences among lesions. For instance, Valsa canker on tree trunks leads to extensive cortical necrosis, while early-stage anthracnose on fruits appears as tiny spots spanning only a few pixels. These significant scale gaps necessitate robust spatial feature aggregation and anti-noise ability to resist complex background interference. Aiming to achieve rapid and precise detection of diseases on multiple apple organs including leaves, fruits, trunks and branches, this work presents an enhanced YOLOv11 model equipped with the Information Integration Attention (IIA) module. The IIA module is embedded into the key fusion layers of the backbone and neck networks. It strengthens the extraction of fine-grained lesion features, recovers spatial location information via a bidirectional attention mechanism, and suppresses noise induced by uneven lighting and intricate backgrounds. To guarantee stable convergence on low-resource computing devices, a tailored training scheme is designed. Experimental results on a seven-category dataset with 7406 images demonstrate that YOLOv11-IIA reaches a precision of 0.763, a recall of 0.819, mAP@50 of 0.857 and mAP@50-95 of 0.699, which achieves clear performance improvements over the original YOLOv11 (mAP@50 improved from 0.485 to 0.857) and other attention-augmented detectors. The model operates stably on an NVIDIA GTX 1050 4GB GPU with an inference speed of 16 FPS for 640 × 640 input images; comprehensive quantitative computational metrics including parameter count, FLOPs and memory consumption will be fully measured in subsequent UAV deployment experiments. The proposed method provides a reliable technical reference for intelligent apple disease monitoring in smart orchard systems. Full article

Grassland and pasture management increasingly requires timely and reliable decision support to address changing environmental conditions, optimize grazing capacity, and improve herbage production. Although digital technologies such as the Internet of Things (IoT), Cloud Computing, Digital Twins, Artificial Intelligence (AI) and Machine Learning (ML) have been widely adopted, they are often implemented as isolated solutions rather than as an integrated management framework. This paper proposes a cloud-based smart grazing framework that combines field monitoring, biomass forecasting, digital twin monitoring, and stocking optimization within a unified architecture. The framework includes two interconnected algorithms: the first supports the operational grazing management cycle through IoT sensing, biomass forecasting, digital twin monitoring, and stocking optimization, while the second enables secure ML training and model updating for biomass prediction, livestock health assessment, and grazing behavior analysis. To ensure data integrity with low computational overhead, the framework employs SHA-256 hash-based verification rather than a full blockchain implementation. The proposed architecture provides a practical approach for integrating monitoring, prediction, and secure data management to support sustainable grazing management. Full article

The growing reliance of Internet of Energy (IoE)-enabled smart city infrastructures has significantly intensified the complexity of modern urban energy environments due to the integration of renewable resources, electric vehicles, and interconnected IoT devices. These highly dynamic environments introduce critical challenges related to power stability, operational reliability, and intelligent energy management. Therefore, developing accurate, adaptive, and explainable monitoring frameworks has become essential for ensuring resilient urban energy infrastructures. This paper proposes an Explainable Artificial Intelligence (XAI)-driven deep learning framework integrating Multi-Head Attention and SHapley Additive exPlanations (SHAP) for intelligent power stability monitoring in IoE-enabled smart cities. The proposed framework employs an attention-based deep learning architecture to classify stable and unstable operational conditions using multivariate operational power parameters. Furthermore, SHAP-based explainability analysis is incorporated to improve model transparency and identify influential operational factors affecting stability behavior. Using the Electrical Grid Stability Simulated Dataset, the proposed framework achieved 97.35% accuracy and 0.997 ROC-AUC, outperforming several traditional machine learning and deep learning baselines. The explainability results show that temporal response parameters have the strongest impact on stability decisions. This work offers not only high predictive performance but also valuable interpretability, which is essential for practical deployment in real-world smart city energy systems. Full article

The increasing integration of renewable energy sources and smart grid (SG) technologies introduces significant challenges to power system stability due to the inherent variability and uncertainty of electricity generation and demand. While many machine learning and deep learning approaches have been proposed for stability prediction, various studies focus only on predictive performance and offer limited assessment of computational efficiency, statistical significance, and sustainability-related metrics. To address these gaps, this study suggests a hybrid fusion approach that combines Convolutional Neural Networks (CNNs), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF) classifiers through a soft voting strategy for SG stability prediction. The CNN component automatically extracts representative features, while XGBoost and RF contribute complementary classification capabilities, reducing the need for manual feature engineering. In addition to predictive evaluation, a Green AI-oriented benchmarking framework is introduced to evaluate model performance using predictive accuracy, computational runtime, memory consumption, and estimated computational CO₂ emissions associated with model training and inference. The proposed framework is assessed on the UCI SG Stability dataset using stratified cross-validation and statistical significance testing, including Friedman and Nemenyi post hoc analyses. Experimental results demonstrate that the fusion model achieves 97.68% classification accuracy and an AUC of 0.991, exceeding several individual machine learning, deep learning, and ensemble baselines. Statistical analysis shows significant improvements over recurrent deep learning models such as LSTM and GRU. However, the differences from strong tree-based methods, including XGBoost and RF, are not statistically significant. Furthermore, the proposed model reaches a high sustainability score of 0.802, indicating a favorable balance between predictive performance and computational resource requirements. The findings show that the proposed framework is effective and computationally efficient to predict the stability of the smart grid on the UCI benchmark dataset and also serves as a transparent green AI benchmarking methodology for comparative studies in the future. The validation of the approach on real-world smart grid data under noisy, not fully complete, and heterogeneous operating conditions is another interesting research direction for future work. Full article

This second part extends the system-level framework established in Part I by examining advanced adhesive and sealant technologies through a performance-, circularity-, and application-oriented perspective relevant to contemporary packaging systems. While Part I focused on material classification, bonding and sealing mechanisms, regulatory aspects, and interfacial design principles, the present review analyses how advanced adhesive and sealant systems behave under realistic converting, sealing, service, recycling, and end-of-life conditions. Particular attention is devoted to bio-based and compostable adhesives, recyclable mono-material architectures, advanced multilayer sealants, debond-on-demand systems, and smart or reversible interfaces designed to support circular packaging strategies. The review critically discusses the principal adhesive and sealant performance metrics—including bond strength, seal strength, seal initiation temperature (SIT), hot-tack behaviour, cohesive durability, processing robustness, and hydrothermal resistance—in relation to packaging reliability, barrier preservation, processability, and compatibility with industrial converting operations. The analysis additionally addresses interfacial failure mechanisms, recyclability constraints associated with multilayer structures, food-contact compliance, migration and non-intentionally added substances (NIAS), and the growing role of design-for-disassembly and circularity-oriented interfacial engineering. Emerging transition strategies involving waterborne systems, low-migration formulations, recyclable sealants, dynamic covalent networks, and controlled debonding technologies are evaluated in terms of their potential to reconcile packaging performance with sustainable material management. By integrating material-specific developments with system-level packaging considerations, this review highlights how adhesive and sealant interfaces increasingly represent critical design variables governing the balance between mechanical performance, sealing reliability, processability, recyclability, compostability, and circularity in next-generation packaging systems. Full article

Durable medical devices (DMD) are essential healthcare assets, yet their management in public hospitals is constrained by fragmentation, limited traceability, reactive maintenance, and weak lifecycle integration. This study proposes a framework for digitally enabled, sustainable, and circular DMD management. A mixed-methods design integrated a literature review, expert consultation using the Best–Worst Method, weighted technology nominations, and case-based process mapping in Brazilian hospitals. Eleven experts assessed the criteria guiding Industry 4.0 technology selection for DMD management and the technologies best responding to these priorities; nine consistent judgments were aggregated. Patient-Centered Care, Operational Efficiency, and Resource Efficiency and Cost Reduction emerged as the leading influences on technology selection. Big Data and Analytics, Artificial Intelligence, the Internet of Things, Cloud Computing, Cyber-Physical Systems, Smart Sensors, and Machine Learning formed the priority portfolio, accounting for 84% of the weighted score. The cases contextualized these priorities by revealing discontinuous information flows, limited asset visibility, corrective maintenance, fragmented governance, and weak end-of-life practices. By connecting decision priorities and technological capabilities with observed gaps, the TO-BE framework organizes sustainable procurement, traceable use, predictive maintenance, redeployment, refurbishment, and responsible disposal through material and information flows, providing a pathway for digital and circular transformation in resource-constrained healthcare systems. Full article

Non-OCPP electric vehicle (EV) chargers lack site-integrated monitoring and supply-control, limiting their use in real-time energy management systems. This paper presents a Smart Distribution Panel that closes this gap via panel-side observability and an operator-commanded single-phase/three-phase (1$\mathsf{\Phi }$/3$\mathsf{\Phi }$) supply reconfiguration. In a single-site feasibility study, the panel is characterised with one consumer Tesla Wall Connector Gen 3 (Non-OCPP) and one Tesla Model Y at a Taiwan 3$\mathsf{\Phi }$3W 220 V site, using a CPM-80 meter (IEC 62053-22 class 0.2S), interlocked contactors, an e-stop relay, and a Raspberry Pi 4 data path. Phase-mode transitions interrupt charging for ∼5 s, resolved by the IEC 61851-1 handshake. A 74.5-min stepwise session—driven by the OCPP DC fast charger with the Non-OCPP Wall Connector idle—yields a site-specific ensemble PCC-bus sensitivity $V=224.85-0.147P$ ($R^2=0.991$), characterising the site + DC-charger + base-load ensemble observed through the panel’s metering rather than the panel’s Non-OCPP path; an OpenDSS bounded sanity check gives an upper-bound slope of 0.26–0.28 V/kW. The one-second data stream supports a non-autoregressive (Non-AR) LSTM residual-detection layer (8-seed RMSE $0.310\pm 0.084$ V). A synthetic voltage-drop sensitivity sweep (ROC-AUC 0.81–0.97) characterises sensitivity to injected perturbations, not real-world fault detection. Feasibility is demonstrated only for the tested single-site configuration; multi-site, multi-EVSE, multi-EV generalisation, autonomous demand response, and OCPP session-level features are not demonstrated and are stated as future work. Full article

Modern railway maintenance is transitioning toward a condition-based maintenance system to stably operate the core infrastructure of sustainable smart cities. However, technical limitations remain in manually converting and analyzing massive amounts of inspection data into Building Information Modeling (BIM) objects. This causes information delays and technical severance in data-driven smart-city infrastructure. To address these challenges, this study proposes an Agentic BIM framework that integrates Large Language Model (LLM), Model Context Protocol (MCP), and Retrieval Augmented Generation (RAG) technologies. The proposed methodology standardizes the control channel between the LLM and BIM software through a central MCP server, while securing the accuracy of engineering judgments by utilizing the RAG pipeline to reference national railway-track-maintenance guidelines. System validation results demonstrated that geometric inspection data, including gauge and alignment, were automatically generated as BIM objects without human intervention. Furthermore, the maintenance grades and deadlines for sections exceeding thresholds were immediately highlighted within the model as visual attributes. Consequently, this framework proves an autonomous decision-making system that organically links inspection data with maintenance regulations. By transforming static, manual-labor-centered maintenance workflows into intelligent automated models, it increases the efficiency of railway infrastructure management while providing a scalable technical foundation for overall asset management of future smart-city infrastructure. Full article

Energy system optimization seeks to utilize multiple energy sources efficiently under technical, economic, and environmental constraints. The increasing integration of renewable energy and the need for sustainable operation have made the optimal planning and management of hybrid energy systems crucial. Classical optimization methods, including Linear Programming, Nonlinear Programming, and simulation-based models, often face limitations when addressing high-dimensional and nonlinear problems. This study introduces a deep learning–based surrogate modeling framework for sizing the components of hybrid renewable energy systems. Initially, Particle Swarm Optimization (PSO) is employed to determine the optimal component sizes for a large number of synthetically generated hourly solar irradiance and load profiles. These optimal solutions are then used as target labels. The associated annual time-series data are transformed into multi-channel Data Map (DMAP) images, which serve as inputs for convolutional neural networks (CNNs). After training, the CNN models are capable of directly estimating the required number of photovoltaic (PV) panels, inverter capacity, and battery units from the DMAP images, eliminating the need to perform the iterative PSO optimization during the prediction stage. Various convolutional neural network architectures, including ResNet, DenseNet121, RegNet, ConvNeXt, EfficientNet, SqueezeNet, MobileNet, and InceptionV3, were evaluated for this multi-output regression task. The results indicate that ResNet and DenseNet121 achieve the best performance, while ConvNeXt provides strong results with a modern architectural design. Among the evaluated models, DenseNet121 achieved coefficients of determination (R²) of 0.934, 0.988, and 0.947 for predicting the sizes of the PV array, inverter, and battery bank, respectively. These results correspond to an average prediction accuracy of approximately 90.6%. ResNet produced similar performance, with its highest R² value reaching 0.983 for inverter sizing. Lightweight networks such as SqueezeNet and MobileNet demonstrate notable effectiveness for resource-constrained systems, whereas InceptionV3 underperforms in leveraging its multi-scale architecture. These results demonstrate that, once the models have been trained, deep learning–based surrogate models can generate sizing decisions comparable to those obtained using PSO with only a fraction of the computational effort. As a result, they provide a fast and practical alternative to conventional iterative optimization methods for component sizing in smart grid and sustainable energy planning applications. Full article

Precision agriculture and sustainable farming practices require autonomous environmental monitoring systems capable of operating in remote areas with limited energy and connectivity. However, the high cost of existing professional technology remains a significant barrier to widespread adoption. This study presents the development of a solar-powered smart sensing node designed for autonomous operation that integrates TinyML and dual-mode wireless connectivity via LoRaWAN and Wi-Fi for intelligent monitoring. The system features a custom-designed cup anemometer and multispectral sensing capabilities integrated into a compact single-tower architecture. All structural components, including radiation shields and a modular PVC frame, were designed for low-cost manufacturing and mass production. A single hermetic housing protects the core control electronics and is designed to improve durability in harsh outdoor environments. A Multi-Layer Perceptron model was implemented on the edge to enable intelligent data fusion and compensation, while a dynamic sampling strategy optimized power consumption. Experimental results demonstrate the feasibility of the proposed architecture through adaptive spectral acquisition over a daily illumination cycle, embedded MLP-based sensor fusion, and telemetry-oriented data compression that substantially reduces the number of transmitted samples. The main contribution of this work is a system-level architecture that integrates sensing, embedded intelligence, solar-energy harvesting, hybrid wireless communication, and telemetry optimization into a compact, low-cost, and field-deployable prototype IoT platform for sustainable precision agriculture. Full article

The increasing penetration of distributed energy resources, variable renewable generation, battery energy storage systems, electric vehicles, and power-electronic interfaces is changing the way modern smart grids and microgrids are operated, protected, and controlled. This systematic literature review follows the PRISMA 2020 framework and examines 87 original research papers, complemented by a supplementary synthesis of 18 contextual studies that provide bibliometric, historical, and conceptual perspectives on the evolution of AI in smart grids. The primary studies are organized into six thematic clusters: energy management and forecasting; cybersecurity and intrusion detection; renewable energy integration and microgrid management; fault detection, diagnosis, and grid stability; explainable and trustworthy artificial intelligence; and emerging technologies, including digital twins, blockchain, the Internet of Things, edge computing, and federated learning. The review shows that deep learning, reinforcement learning, and ensemble machine learning are increasingly used for load forecasting, demand response, converter-interfaced renewable integration, intrusion detection, and operational optimization. However, the literature remains uneven. Fault detection, converter-aware protection, and real-time stability assessment receive considerably less attention than energy management and cybersecurity, despite their importance for inverter-based resources, grid-forming converters, electric-vehicle charging systems, and battery interfacing. Four critical gaps are identified: limited cross-grid generalizability, weak validation under realistic converter and protection constraints, insufficient adversarial robustness of AI-enabled defense systems, and limited explainability in real-time safety-critical applications. The paper provides a structured taxonomy, identifies deployment barriers, and proposes research directions for trustworthy AI in power-electronic-rich smart grids and microgrids. Full article

Smart mobility strategies often emphasize digital information, applications, and data-driven transport management, while giving less attention to the stop-level conditions that shape passengers’ everyday experience. This study develops and applies a Smart Bus Stop Readiness Index (SBSRI) to assess seven interchange bus stops in Jeddah, Saudi Arabia. The index integrates five weighted dimensions: Passenger Information and Digital Readiness; Physical and Thermal Comfort Provision; Pedestrian Accessibility and Universal Design; Safety and Security; and Land-Use and Activity Integration. Data were collected through field audits, spatial mapping, passenger observations, and a short survey of 71 users. The results indicate that the selected stops have operational interchange importance but generally limited readiness. The mean SBSRI score was 39.07/100; under the adopted planning-oriented classification scheme, only Al-Balad Main Station A achieved moderate readiness, while the remaining stops were classified as showing low or very low readiness. Physical and Thermal Comfort Provision was the weakest dimension, particularly in relation to shade, seating, shelter, and shaded waiting areas. Passenger information and pedestrian accessibility also showed substantial deficiencies. Sensitivity analysis indicated that the principal stop rankings remained stable under alternative weighting scenarios, although category labels were more responsive to threshold selection. This study concludes that smart bus stop readiness should be assessed as a socio-technical condition integrating digital systems with climate-responsive waiting provision, pedestrian accessibility, safety, and the surrounding urban context. Full article

The construction of high-fidelity virtual factories relies heavily on the accurate reconstruction of historical production timelines. While traditional Manufacturing Execution Systems (MESs) provide idealized, static schedules, they inherently struggle to capture the “stochastic variability” and “undefined events” caused by machine-specific behaviors on the industrial shop floor. To bridge the gap between top-down scheduling and bottom-up physical reality, this study proposes the Power-Aware State Segmentation for Solder Paste Printers (PAS-SPP) algorithm. Utilizing non-intrusive, high-frequency continuous power features captured via an Industrial Internet of Things (IIoT) architecture with PA310 meters, the algorithm employs a synergistic combination of amplitude thresholding (${\theta }_{high}$) and temporal constraints (${\tau }_{blank}$, ${\tau }_{min}$) to actively filter transient electrical noise and accurately bound macroscopic operational blocks. This robust filtering thereby avoids the accuracy degradation commonly caused by noise interference in the analysis processes of traditional machine learning models. Consequently, the mechanism effectively decouples operational states into a virtual Solder Paste Printer (vSPP) behavioral meta-model integrated with a Finite State Machine (FSM). Empirical validation across distinct production cases demonstrates that the proposed model not only accurately extracts standard 33–35 s cycle times but also reveals critical hidden characteristics, such as 68 s automated cleanings and dynamically adjusted “3-to-1” print-to-clean ratios. Furthermore, a comprehensive comparative analysis was conducted against static MES logs, Naive Power Thresholding (NPT), and a Gaussian Hidden Markov Model (GMM-HMM). Evaluated under identical manufacturing-process conditions, the results reveal that PAS-SPP effectively mitigates the cascading misalignments in static schedules and avoids the severe over-segmentation limitations inherent in point-by-point probabilistic decoding, thereby achieving highly accurate state decoupling. Finally, this study systematically defines the method’s applicability boundaries across diverse DT domains, confirming its indispensable role as a non-intrusive, broadly applicable event-triggering foundation for the broader smart manufacturing ecosystem. Full article

This study develops a scenario-based analytical framework for assessing smart microgrids in industrial zones as infrastructure investments and for analyzing multi-actor risk allocation and governance. No empirically validated return is reported for a specific Bulgarian industrial zone. Public institutional, market, financial, technology and environmental sources are instead used to benchmark the scenario assumptions. Three scenarios are evaluated for a reference zone with annual consumption of 12,000 MWh. The model incorporates photovoltaic (PV) degradation, battery round-trip efficiency of 85–90%, annual usable-capacity degradation, one modeled battery replacement within a 12–15-year service interval, component-based capital and operating expenditures, and an author-defined semi-quantitative likelihood–impact risk matrix. Net present value (NPV) is EUR −1,114,726 in the conservative scenario, EUR 697,836 in the baseline scenario and EUR 3,424,835 in the favorable scenario. Discounted payback is not achieved within 20 years in the conservative scenario and is approximately 14.1 and 7.0 years in the baseline and favorable scenarios, respectively. Deterministic one-at-a-time sensitivity analysis identifies electricity price, capital expenditures, and the direct PV self-consumption ratio as the dominant financial drivers.The indicative reduction in location-based emissions associated with grid electricity purchases is 802.9–1385.5 tonnes of carbon dioxide equivalent (tCO₂e) per year under the selected grid-average electricity emission factor. The contribution lies in integrating public-data availability assessment, external parameter benchmarking, battery service life, degradation and replacement economics, investment appraisal, threshold analysis, semi-quantitative risk prioritization and contractual risk allocation within one reproducible framework. The outputs remain illustrative and require project-level validation using measured hourly loads, binding prices, financing terms and enforceable contracts. Full article