Search Results (2,091)

Historical inference attacks pose a critical privacy threat in mobile edge computing (MEC), where adversaries exploit long-term task and location patterns to infer users’ sensitive information. To address this challenge, we propose a privacy-preserving task scheduling framework that adaptively balances privacy protection and system performance under dynamic vehicular environments. First, we introduce a dynamic privacy-aware adaptation mechanism that adjusts privacy levels in real time according to vehicle mobility and network dynamics. Second, we design a dual-algorithm framework composed of two complementary solutions: a Markov Approximation-Based Online Algorithm (MAOA) that achieves near-optimal scheduling with provable convergence, and a Privacy-Aware Deep Q-Network (PAT-DQN) algorithm that leverages deep reinforcement learning to enhance adaptability and long-term decision-making. Extensive simulations demonstrate that our proposed methods effectively mitigate privacy leakage while maintaining high task completion rates and low energy consumption. In particular, PAT-DQN achieves up to 14.2% lower privacy loss and 19% fewer handovers than MAOA in high-mobility scenarios, showing superior adaptability and convergence performance. Full article

Timely and accurate fault diagnosis in power transmission systems is critical to ensuring grid stability, operational safety, and minimal service disruption. This study presents a unified deep learning framework that simultaneously performs fault identification, fault type classification, and fault location estimation using a multi-task learning (MTL) approach. Using the IEEE 39–Bus network, a comprehensive data set was generated under various load conditions, fault types, resistances, and location scenarios to reflect real-world variability. The proposed model integrates a shared representation layer and task-specific output heads, enhanced with an attention mechanism to dynamically prioritize salient input features. To further optimize the model architecture, Optuna was employed for hyperparameter tuning, enabling systematic exploration of design parameters such as neuron counts, dropout rates, activation functions, and learning rates. Experimental results demonstrate that the proposed Optimized Multi-Task Learning Attention Network (MTL-AttentionNet) achieves high accuracy across all three tasks, outperforming traditional models such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), which require separate training for each task. The attention mechanism contributes to both interpretability and robustness, while the MTL design reduces computational redundancy. Overall, the proposed framework provides a unified and efficient solution for real-time fault diagnosis on the IEEE 39–bus transmission system, with promising implications for intelligent substation automation and smart grid resilience. Full article

Precision agriculture increasingly recognizes the transformative potential of unmanned aerial systems (UASs) for crop monitoring and field assessment, yet research consistently highlights significant usability barriers as the main constraints to widespread adoption. Complex mission planning processes, including detailed flight plan creation and way point management, pose substantial technical challenges that mainly affect non-expert operators. Farmers and their teams generally prefer user-friendly, straightforward tools, as evidenced by the rapid adoption of GPS guidance systems, which underscores the need for simpler mission planning in UAS operations. To enhance accessibility and safety in UAS control, especially for non-expert operators in agriculture and related fields, we propose a Secure UAS Control Framework (SAUCF): a comprehensive system for natural-language-driven UAS mission management with integrated dual-factor biometric authentication. The framework converts spoken user instructions into executable flight plans by leveraging a language-model-powered mission planner that interprets transcribed voice commands and generates context-aware operational directives, including takeoff, location monitoring, return-to-home, and landing operations. Mission orchestration is performed through a large language model (LLM) agent, coupled with a human-in-the-loop supervision mechanism that enables operators to review, adjust, or confirm mission plans before deployment. Additionally, SAUCF offers a manual override feature, allowing users to assume direct control or interrupt missions at any stage, ensuring safety and adaptability in dynamic environments. Proof-of-concept demonstrations on a UAS plat-form with on-board computing validated reliable speech-to-text transcription, biometric verification via voice matching and face authentication, and effective Sim2Real transfer of natural-language-driven mission plans from simulation environments to physical UAS operations. Initial evaluations showed that SAUCF reduced mission planning time, minimized command errors, and simplified complex multi-objective workflows compared to traditional waypoint-based tools, though comprehensive field validation remains necessary to confirm these preliminary findings. The integration of natural-language-based interaction, real-time identity verification, human-in-the-loop LLM orchestration, and manual override capabilities allows SAUCF to significantly lower the technical barrier to UAS operation while ensuring mission security, operational reliability, and operator agency in real-world conditions. These findings lay the groundwork for systematic field trials and suggest that prioritizing ease of operation in mission planning can drive broader deployment of UAS technologies. Full article

This work presents the development and evaluation of a low-cost and low-power IoT system for monitoring slope instabilities, rockfalls, and landslides using LoRa communication. The prototype integrates commercial ESP32-based hardware with an SX1276 transceiver, a triaxial MEMS accelerometer, and a GPS module for real-time tilt and location measurements. A tilt-estimation expression was derived from accelerometer data, enabling adaptation to different terrain inclinations. Laboratory tests were performed to validate the stability and accuracy of the inclination measurement, followed by outdoor LoRa range tests under mixed line-of-sight conditions. A lightweight dashboard was implemented for real-time visualization of GPS position, signal quality, and tilt data. The results show reliable tilt detection, consistent long-range communication, and low power consumption, highlighting the potential of the proposed prototype as a scalable and energy-efficient tool for geotechnical monitoring. Full article

The global elderly population continues to increase, and the demand for leisure programs that support active aging is growing. In particular, group-based outdoor activities for seniors are often conducted in large public areas such as parks, ecological gardens, and cultural sites. As many of these spaces are now being integrated into smart city infrastructures equipped with IoT-based sensing and location-aware services, opportunities for data-driven safety support are expanding. However, in these wide and crowded environments, a small number of social workers are responsible for supervising many elderly participants, which creates monitoring blind spots. In addition, age-related cognitive and physical decline increases the risk of wandering and sudden health deterioration, making timely detection and response difficult. To address this problem, we propose a safety monitoring system for seniors. The system is based on a cloud platform that collects location data from GPS modules and motion information from embedded sensors on mobile devices. It provides real-time tracking of each participant and periodically evaluates their safety state. When abnormal conditions are detected, alerts are delivered to both social workers and the corresponding senior. A prototype implementation, consisting of a cloud server and mobile applications for social workers and elderly users, has been developed. The system is evaluated through a field test conducted on a university campus. Full article

Industry 4.0 advanced technologies are increasingly used to monitor workers and reduce accident risks to ensure workplace safety. In this paper, we present an on-premise, rule-based safety management system that exploits the fusion of data from an Ultra-Wideband (UWB) Real-Time Locating System (RTLS) and AI-based video analytics to enforce context-aware safety policies. Data fusion from heterogeneous sources is exploited to broaden the set of safety rules that can be enforced and to improve resiliency. Unlike prior work that addresses PPE detection or indoor localization in isolation, the proposed system integrates an UWB-based RTLS with AI-based PPE detection through a rule-based aggregation engine, enabling context-aware safety policies that neither technology can enforce alone. In order to demonstrate the feasibility of the proposed approach and showcase its potential, a proof-of-concept implementation is developed. The implementation is exploited to validate the system, showing sufficient capabilities to process video streams on edge devices and track workers’ positions with sufficient accuracy using a commercial solution. The efficacy of the system is assessed through a set of seven safety rules implemented in a controlled laboratory scenario, showing that the proposed approach enhances situational awareness and robustness, compared with a single-source approach. An extended validation is further employed to confirm practical reliability under more challenging operational conditions, including varying camera perspectives, diverse worker clothing, and real-world outdoor conditions. Full article

In this study, the insert length, location within the coat protein-encoding gene, and sequence orientation of the target fragment were optimized to construct an efficient virus-induced gene silencing (VIGS) system in melon using a Begomovirus solanumdelhiense vector. Existing systems are mostly RNA viruses, requiring in vitro synthesis of viral strands that are prone to degradation, although they exhibit high infectivity and stability in cucurbit hosts and ease of manipulation. This vector was selected for its more stable genome structure and these advantages. The melon phytoene desaturase (CmPDS), a key gene of carotenoid biosynthesis, was selected as a reporter gene to evaluate the effects of the VIGS system. Our results revealed that the melon leaves in all the VIGS treatments exhibited a typical photobleaching phenotype at 21 days post-inoculation. Moreover, reverse transcription quantitative real-time PCR revealed a significant reduction in the mRNA levels of PDS in melon. The highest silencing efficiency (lowest PDS mRNA levels) was achieved by the VIGS vector harboring a 165 bp CmPDS fragment at the 3′ end of the AV1. These findings not only establish a more efficient VIGS protocol for melon but also provide a foundation for developing novel virus-based silencing tools applicable to functional genomics and cucurbit crop improvement, particularly for traits requiring precise gene expression modulation such as disease resistance and fruit quality. Full article

Accurate short- and medium-term forecasting of photovoltaic (PV) power generation is vital for grid stability and renewable energy integration. This study presents a comparative scenario-based approach using Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU) models trained with one year of real-time meteorological and production data from a 250 kWp grid-connected PV system located at Dicle University in Diyarbakır, Southeastern Anatolia, Turkey. The dataset includes hourly measurements of solar irradiance (average annual GHI 5.4 kWh/m²/day), ambient temperature, humidity, and wind speed, with missing data below 2% after preprocessing. Six forecasting scenarios were designed for different horizons (6 h to 1 month). Results indicate that the LSTM model achieved the best performance in short-term scenarios, reaching R² values above 0.90 and lower MAE and RMSE compared to CNN and GRU. The GRU model showed similar accuracy with faster training time, while CNN produced higher errors due to the dominant temporal nature of PV output. These results align with recent studies that emphasize selecting suitable deep learning architectures for time-series energy forecasting. This work highlights the benefit of integrating real local meteorological data with deep learning models in a scenario-based design and provides practical insights for regional grid operators and energy planners to reduce production uncertainty. Future studies can improve forecast reliability by testing hybrid models and implementing real-time adaptive training strategies to better handle extreme weather fluctuations. Full article

With the widespread adoption of smartphones and wearable devices, localization systems have become increasingly important in modern society. While Global Positioning System (GPS) technology is widely accepted as a standard outdoors, accurately determining user location indoors remains a significant challenge despite extensive research efforts. Indoor positioning systems (IPSs) play a critical role in various sectors, including retail, tourism, transportation, healthcare, and emergency services. However, existing solutions require costly infrastructure deployments, complex area mapping, or offer suboptimal user experiences without achieving satisfactory accuracy. This paper introduces FloorTag, a scalable, low-cost, and minimally invasive hybrid IPS designed specifically for smartphone platforms. FloorTag leverages a combination of 2D visual markers placed on floor surfaces at key locations, and inertial sensor data from mobile devices. Each marker is associated with a unique identifier and precise spatial coordinates, enabling an immediate reset of accumulated localization errors upon detection. Between markers, a pedometer-based dead reckoning module maintains continuous location tracking. The localization process is designed to be seamless and unobtrusive to the user. When activated by the app during navigation, the phone’s rear camera, naturally angled toward the floor during walking, captures markers. This solution avoids explicit user scans while preserving the performance benefits of visual positioning. To model the indoor environment, FloorTag introduces the concept of Path-Points, which discretize the walkable space, and Informative Layers, which add semantic context to the navigation experience. This paper details the proposed methodology and the client–server system architecture and presents experimental results obtained from a prototype deployed in an academic building at the University of Catania, Italy. The findings demonstrate reliable localization at approximately 2 m spatial granularity and near-real-time performance across varying lighting conditions, confirming the feasibility of the approach and the effectiveness of the system. Full article

To address the pressing challenges of low new energy utilization, high power system operating costs, and compromised power supply reliability in regional grids, we propose a multi-time-scale source–storage–load coordinated scheduling strategy that explicitly accounts for the characteristic distribution of grid-connected energy storage stations, including their state-of-charge constraints, round-trip efficiency profiles, and location-specific operational dynamics. A day-ahead scheduling framework is developed by integrating the multi-time-scale behavioral patterns of diverse load-side demand response resources with the dynamic operational characteristics of energy storage stations. By embedding intra-day rolling optimization and real-time corrective adjustments, we mitigate prediction errors and adapt to unforeseen system disturbances, ensuring enhanced operational accuracy. The objective function minimizes a weighted sum of system operation costs encompassing generation, transmission, and auxiliary services; wind power curtailment penalties for unused renewables; and load shedding penalties from unmet demand, balancing economic efficiency with supply quality. A mixed-integer programming model formalizes these tradeoffs, solved via MATLAB 2020b coupled CPLEX to guarantee optimality. Simulation results demonstrate that the strategy significantly cuts wind power curtailment, reduces system costs, and elevates new energy consumption—outperforming conventional single-time-scale methods in harmonizing renewable integration with grid reliability. This work offers a practical solution for enhancing grid flexibility in high-renewable penetration scenarios. Full article

Background/Objectives: Pediatric orbital tumors are rare and complex, requiring multidisciplinary care at specialized centers. Contemporary treatment paradigms emphasize centralized care delivery through experienced multidisciplinary teams to optimize patient outcomes. Recent advances in surgical planning technologies and intraoperative navigation systems have substantially enhanced surgical safety through improvement in tumor resection and reconstruction and reduction in complications, including recurrence of the lesion. Computed-aided surgical technologies enable precise virtual planning, minimally invasive approaches and more precise reconstruction methods when necessary by mean of patient-specific cutting guides, premolded orbital plates or individual patient solutions (IPS) prosthesis. Three-dimensional biomodelling visualizes tumor architecture and aids localization while preserving neurovascular structures, and real-time neuronavigation improves safety and efficacy. Methods: We conducted a retrospective analysis of 98 pediatric patients with orbital tumors treated between 2014 and 2025 at a tertiary center to evaluate the use of computed-assisted surgical technologies and the indications for treatment. Inclusion criteria comprised all cases where computer-assisted techniques were employed. Patients were classified into two groups: Group 1—intraconal or extensive periorbital lesions with eye-sparing intent treated via craniofacial approaches; Group 2—periorbital tumors with orbital wall involvement, to analyze the use of the different technologies. Data collected included tumor age, type, location, technology used, adjunctive treatments, and postoperative outcomes. Results: Twelve patients underwent computer-assisted surgery. Technologies employed over the last six years included intraoperative navigation, 3D planning with/without tumor segmentation, orbital-wall reconstruction by mirroring, IPS or titanium mesh bending, and preoperative biomodelling. Patients were grouped by tumor location and treatment goals: Group 1—intraorbital lesions (primarily intraconal or 270–360° involvement), including one case of orbital encephalocele treated transcranially; Group 2—periorbital tumors with orbital-wall destruction, treated mainly via midfacial approaches. Intraoperative navigation was used in 10/12 cases (8/11 with tumor segmentation); in 3 cases with ill-defined margins, navigation localized residual tumor. Virtual surgery predominated in Group 2 (4 patients) and one in Group 1, combined with cutting guides for margins and Individual Prosthetic Solutions (IPS) prosthesis fitting (two patients: titanium and PEEK). In two cases, virtual plans were performed, STL models printed, and premolded titanium meshes used. No complications related to tumor persistence or orbital disturbance were observed. Conclusions: Advanced surgical technologies substantially enhance safety, efficiency, and outcomes in pediatric orbital tumors. Technology-assisted approaches represent a paradigm shift in this complex field. Additional studies are needed to establish evidence-based protocols for systematic integration of technology in pediatric orbital tumor management. Full article

In the rapidly evolving landscape of logistics and last-mile delivery, optimizing efficiency and minimizing costs are paramount. This paper introduces a novel heuristic approach designed to enhance the efficiency of a truck-and-drone delivery system. Our method addresses the complex challenge of coordinating the movements of a truck, which serves as a mobile depot, and an unmanned aerial vehicle (UAV or drone), which performs rapid, short-distance deliveries. Our system proposes a two-step heuristic. For truck routes, we utilized the Concorde Solver to determine the optimal path, based on real-world road distances between locations in Bacău County, Romania. This data was meticulously collected and processed as a Traveling Salesman Problem (TSP) instance with precise geographical information. Concurrently, a drone is deployed for specific deliveries, with routes calculated using the Haversine formula to determine accurate distances based on geographical coordinates. A crucial aspect of our model is the integration of the drone’s limited autonomy, ensuring that each mission adheres to its operational capacity. Computational experiments conducted on a real-world dataset including 93 localities from Bacău County, Romania, demonstrate the effectiveness of the proposed two-stage heuristic. Compared to the optimal truck-only route, the hybrid truck-and-drone system achieved up to 15.59% cost reduction and 38.69% delivery time savings, depending on the drone’s speed and autonomy parameters. These results confirm that the proposed approach can substantially enhance delivery efficiency in realistic distribution scenarios. Full article

Unmanned Aerial Vehicles (UAVs) can deliver rapid, spatially resolved measurements of volcanic gases that often precede eruptions, yet most deployments remain manual or preplanned and are slow to react to seismic unrest. In the present work, we present a simulation-validated design of an earthquake-triggered, autonomous workflow for early detection of CO₂ anomalies, demonstrated through a conceptual case study focused on the Santorini caldera. The system ingests real-time seismic alerts, generates missions automatically, and executes a two-stage sensing strategy: a fast scan to build a coarse CO₂ heatmap followed by targeted high-precision sampling at emerging hotspots. Mission planning includes wind-and terrain-aware flight profiles, geofenced safety envelopes and a facility-location approach to landing-site placement; in a Santorini case study, we provide a ring of candidate launch/landing zones with wind-contingent usage, illustrate adaptive replanning driven by heatmap uncertainty and outline calibration and quality-control steps for robust CO₂ mapping. The proposed methodology offers an operational blueprint that links seismic triggers to actionable, georeferenced gas information and can be transferred to other island or caldera volcanoes. Full article

In recent years, the rapid increase in installed photovoltaic (PV) capacity in Poland has created significant challenges for low-voltage distribution networks. Excess generation during peak solar hours frequently leads to local overvoltage conditions that exceed regulatory limits, causing PV inverters to disconnect from the grid. This phenomenon reduces the efficiency of distributed renewable energy integration and results in direct financial losses for prosumers. The present study quantifies these losses on an annual basis for a single-family household located in southeastern Poland, where overvoltage incidents occurred 614 times over 78 days in 2024. Real operational data from the residential PV installation were analyzed and complemented with detailed PVsyst simulations to determine the amount of energy curtailed due to inverter disconnections. The analysis revealed that daily energy losses can reach up to 22% of potential production, depending on the duration and frequency of overvoltage events. Furthermore, several technical and organizational measures are proposed to mitigate the issue, including grid reinforcement strategies and demand-side management. The findings highlight the necessity of addressing overvoltage in low-voltage distribution networks to ensure system reliability, enhance renewable energy integration, and maintain the economic viability of residential PV investments. Full article

The rapid uptake of electric vehicles (EVs) across Europe offers opportunities for reducing transport-related emissions but also presents new challenges for electricity grids, particularly in relation to congestion and peak demand. Although most lifecycle assessment (LCA) studies show that EVs emit fewer greenhouse gases (GHG) over their lifetimes than internal combustion engine (ICE) vehicles with comparable power output, particularly under the European electricity mix, the environmental impacts of mitigating grid congestion through smart charging (by time and location) are rarely quantified using real-world data. Using a high-granularity LCA tool that utilizes hourly data, this study assesses EV deployment strategies across Europe and provides case-specific insights. The presented LCA tool provides a transferable methodology to support data-driven decision-making for urban planners, distribution system operators, and policymakers committed to accelerating sustainable mobility transitions utilizing GHG payback time thresholds as a practical metric for evaluating infrastructure sustainability. Results demonstrate that EVs reduce GHG emissions by 55–99% per kilometer compared to ICE vehicles, especially when benefiting from renewable-powered charging and smart bidirectional infrastructure. Furthermore, the analysis highlights that counter-congestion strategies deliver additional savings, though outcomes depend strongly on grid carbon intensity and charger utilization patterns. Full article