Search Results (915)

Network connectivity and area coverage are the most important aspects in the applications of wireless sensor networks (WSNs). The resource and energy constraints of sensor nodes, operational conditions, and network size pose challenges to the optimal coverage of targets in the region of interest (ROI). The main idea is to achieve maximum area coverage and connectivity with strategic deployment and the minimal number of sensor nodes. This work addresses the problem of network area coverage in randomly distributed WSNs and provides an efficient deployment strategy using an enhanced version of cuckoo search optimization (ECSO). The “sequential update evaluation” mechanism is used to mitigate the dependency among dimensions and provide highly accurate solutions, particularly during the local search phase. During the preference random walk phase of conventional CSO, particle swarm optimization (PSO) with adaptive inertia weights is defined to accelerate the local search capabilities. The “opposition-based learning (OBL)” strategy is applied to ensure high-quality initial solutions that help to enhance the balance between exploration and exploitation. By considering the opposite of current solutions to expand the search space, we achieve higher convergence speed and population diversity. The performance of ECSO-OBL is evaluated using eight benchmark functions, and the results of three cases are compared with the existing methods. The proposed method enhances network coverage with a non-uniform distribution of sensor nodes and attempts to cover the whole ROI with a minimal number of sensor nodes. In a WSN with a 100 m² area, we achieved a maximum coverage rate of 98.45% and algorithm convergence in 143 iterations, and the execution time was limited to 2.85 s. The simulation results of various cases prove the higher efficiency of the ECSO-OBL method in terms of network coverage and connectivity in WSNs compared with existing state-of-the-art works. Full article

Edge computing (EC) plays a critical role in advancing the next-generation Industrial Internet of Things (IIoT) by enhancing production, maintenance, and operational outcomes across heterogeneous network boundaries. This study builds upon EC intelligence and integrates graph-based learning to propose a Cross-Layer Controller Tasking Scheme (CLCTS). The scheme operates through two primary phases: task grouping assignment and cross-layer control. In the first phase, controller nodes executing similar tasks are grouped based on task timing to achieve monotonic and synchronized completions. The second phase governs controller re-tasking both within and across these groups. Graph structures connect the groups to facilitate concurrent tasking and completion. A learning model is trained on inverse outcomes from the first phase to mitigate task acceptance errors (TAEs), while the second phase focuses on task migration learning to reduce task prolongation. Edge nodes interlink the groups and synchronize tasking, migration, and re-tasking operations across IIoT layers within unified completion periods. Departing from simulation-based approaches, this study presents a fully implemented framework that combines learning-driven scheduling with coordinated cross-layer control. The proposed CLCTS achieves an 8.67% reduction in overhead, a 7.36% decrease in task processing time, and a 17.41% reduction in TAEs while enhancing the completion ratio by 13.19% under maximum edge node deployment. Full article

The Internet of Things (IoT) is transforming how devices communicate, with more than 30 billion connected units today and projections exceeding 40 billion by 2025. Despite this growth, the integration of heterogeneous systems remains a significant challenge, particularly in sensitive domains like healthcare, where proprietary standards and isolated ecosystems hinder interoperability. This paper presents an extended version of the Share design pattern, a lightweight and contract-based mechanism for dynamic service composition, tailored for resource-constrained IoT devices. Share enables decentralized, peer-to-peer integration by exchanging executable code in our examples written in the LUA programming language. This approach avoids reliance on centralized infrastructures and allows services to discover and interact with each other dynamically through pattern-matching and contract validation. To assess its suitability, we developed an emulator that directly implements the system under test in LUA, allowing us to verify both the structural and behavioral constraints of service interactions. Our results demonstrate that Share is scalable and effective, even in constrained environments, and supports formal correctness via design-by-contract principles. This makes it a promising solution for lightweight, interoperable IoT systems that require flexibility, dynamic configuration, and resilience without centralized control. Full article

Sexuality and intimacy are essential aspects of the human experience for all people, contributing significantly to physical and emotional connections, well-being, and quality of life. Despite their importance, these topics are frequently overlooked in stroke rehabilitation, especially for those with cognitive and perceptual impairments. Existing research on post-stroke sexual rehabilitation tends to focus on sexual dysfunction and the secondary physical and psychological stroke symptoms, with little attention to cognitive and perceptual impairments. Cognitive deficits, such as decreased memory, generalized attention, and executive function not only can hinder sexual participation but also raise the complex issue of capacity to consent. This paper argues that it is imperative for researchers and healthcare practitioners to address cognitive and perceptual challenges, understand consent laws in their respective regions, and consider the influence of culture and social norms in order to support the sexual rights and well-being of all stroke survivors. Furthermore, this article provides some practical recommendations, from an occupational therapy perspective, that healthcare practitioners can provide to clients and their partners. Full article

The intensifying global climate warming caused by human activities poses severe challenges to ecosystem stability. Constructing an ecological security pattern can identify ecological land supply and an effective spatial distribution baseline and provide a scientific basis for safeguarding regional ecological security. This study analyzes land-use data from 2000 to 2020 for Golog Tibetan Autonomous Prefecture. The PLUS model was utilized to project land-use potential for the year 2030. The InVEST model was employed to conduct a comprehensive assessment of habitat quality in the study area for both 2020 and 2030, thereby pinpointing ecological sources. Critical ecological restoration zones were delineated by identifying ecological corridors, pinch points, and barrier points through the application of the Minimum Cumulative Resistance model and circuit theory. By comparing ecological security patterns (ESPs) in 2020 and 2030, we proposed a dynamic restoration framework and optimization recommendations based on habitat quality changes and ESPs. The results indicate significant land-use changes in the eastern part of Golog Tibetan Autonomous Prefecture from 2020 to 2030, with large-scale conversion of grasslands into bare land, farmland, and artificial surfaces. The ecological security pattern is threatened by risks like the deterioration of habitat quality, diminished ecological sources as well as pinch points, and growing barrier points. Optimizing the layout of ecological resources, strengthening barrier zone restoration and pinch point protection, and improving habitat connectivity are urgent priorities to ensure regional ecological security. This study offers a scientific foundation for the harmonization of ecological protection and economic development and the policy development and execution of relevant departments. Full article

Achieving global coverage and performance goals for 6G requires seamless integration of satellite and terrestrial networks, yet current operational frameworks lack common standards for managing these heterogeneous infrastructures. This paper addresses the critical need for unified satellite-terrestrial network operations by proposing the CMS framework, a novel task-scheduling-based approach that bridges the operational gap between satellite operations (SatOps) and network operations (NetOps). The framework integrates satellite-specific constraints with network service requirements and QoS metrics through constraint satisfaction programming and multi-objective optimization. Three novel architectures are introduced: integrated operations (embedding NetOps within SatOps), coordinated operations (unified control with separate execution channels), and adaptive operations (mutual adaptation through intelligent interfaces). Each architecture addresses different connectivity scenarios and integration requirements for both sporadic and persistent satellite constellations. The proposed architectures are evaluated against challenges spanning infrastructure and architecture, interoperability and standardization, integrated management, operational dynamics, and technology maturation and deployment. Validation through simulation demonstrates significant performance improvements, with task completion rates improving by 17.87% to 44.02% and data throughput gains of 25.09% to 93.62% compared to traditional approaches. The CMS framework establishes a resilient operational standard for future 6G networks, offering practical solutions to bridge the current divide between satellite and terrestrial network operations. Full article

Quantum memory is essential for the prolonged storage and retrieval of quantum information. Nevertheless, no current studies have focused on the creation of effective quantum memory for continuous variables while accounting for the decoherence rate. This work presents an effective continuous-variable quantum key distribution method with parameter optimization utilizing the Elitist Elk Herd Random Immigrants Optimizer (2E-HRIO) technique. At the outset of transmission, the quantum device undergoes initialization and authentication via Compressed Hash-based Message Authentication Code with Encoded Post-Quantum Hash (CHMAC-EPQH). The settings are subsequently optimized from the authenticated device via 2E-HRIO, which mitigates the effects of decoherence by adaptively tuning system parameters. Subsequently, quantum bits are produced from the verified device, and pilot insertion is executed within the quantum bits. The pilot-inserted signal is thereafter subjected to pulse shaping using a Gaussian filter. The pulse-shaped signal undergoes modulation. Authenticated post-modulation, the prediction of link failure is conducted through an authenticated channel using Radial Density-Based Spatial Clustering of Applications with Noise. Subsequently, transmission occurs via a non-failure connection. The receiver performs channel equalization on the received signal with Recursive Regularized Least Mean Squares. Subsequently, a dataset for side-channel attack authentication is gathered and preprocessed, followed by feature extraction and classification using Adaptive Depthwise Separable Convolutional Neural Networks (ADS-CNNs), which enhances security against side-channel attacks. The quantum state is evaluated based on the signal received, and raw data are collected. Thereafter, a connection is established between the transmitter and receiver. Both the transmitter and receiver perform the scanning process. Thereafter, the calculation and correction of the error rate are performed based on the sifting results. Ultimately, privacy amplification and key authentication are performed using the repaired key via B-CHMAC-EPQH. The proposed system demonstrated improved resistance to decoherence and side-channel attacks, while achieving a reconciliation efficiency above 90% and increased key generation rate. Full article

This article describes a pilot project studying the potential benefits of using virtual reality (VR) in design reviews of cruise ship interiors. The research was conducted as part of a 2020–2022 research project targeting at sustainable shipbuilding methods. It was directly connected to an ongoing cruise ship building project, executed in cooperation with four companies constructing interiors. The goal was to use VR reviews instead of, or in addition to, constructing physical mock-up sections of the ship interiors, with expected improvements in sustainability and stakeholder communication. A number of virtual 3D models were created, imported into a virtual reality environment, and presented to customers. Experiences were collected through interviews and surveys from both the construction companies and customers. The results indicate that VR can be an efficient tool for design reviews. The designs can often be evaluated better in VR than using traditional methods. Material savings are possible by using virtual mock-ups instead of physical ones. However, it was also discovered that the visual rendering capabilities of the used software environment do not provide the realism that would be desired in some reviews. To overcome this limitation, more resources would be needed in preparing the models for VR reviews. Full article

Introduction: Substance use disorders (SUDs) pose a significant public health challenge, with current treatments often exhibiting limited effectiveness and high relapse rates. Transcranial direct current stimulation (tDCS), a noninvasive neuromodulation technique that delivers low-intensity direct current via scalp electrodes, has shown promise in various psychiatric and neurological conditions. In SUDs, tDCS may help to modulate key neurocircuits involved in craving, executive control, and reward processing, potentially mitigating compulsive drug use. However, the precise neurobiological mechanisms by which tDCS exerts its therapeutic effects in SUDs remain only partly understood. This review addresses that gap by synthesizing evidence from clinical studies that used neuroimaging (fMRI, fNIRS, EEG) and blood-based biomarkers to elucidate tDCS’s mechanisms in treating SUDs. Methods: A targeted literature search identified articles published between 2008 and 2024 investigating tDCS interventions in alcohol, nicotine, opioid, and stimulant use disorders, focusing specifically on physiological and neurobiological assessments rather than purely behavioral outcomes. Studies were included if they employed either neuroimaging (fMRI, fNIRS, EEG) or blood tests (neurotrophic and neuroinflammatory markers) to investigate changes induced by single- or multi-session tDCS. Two reviewers screened titles/abstracts, conducted full-text assessments, and extracted key data on participant characteristics, tDCS protocols, neurobiological measures, and clinical outcomes. Results: Twenty-seven studies met the inclusion criteria. Across fMRI studies, tDCS—especially targeting the dorsolateral prefrontal cortex—consistently modulated large-scale network activity and connectivity in the default mode, salience, and executive control networks. Many of these changes correlated with subjective craving, attentional bias, or extended time to relapse. EEG-based investigations found that tDCS can alter event-related potentials (e.g., P3, N2, LPP) linked to inhibitory control and salience processing, often preceding or accompanying changes in craving. One fNIRS study revealed enhanced connectivity in prefrontal regions under active tDCS. At the same time, two blood-based investigations reported the partial normalization of neurotrophic (BDNF) and proinflammatory markers (TNF-α, IL-6) in participants receiving tDCS. Multi-session protocols were more apt to drive clinically meaningful neuroplastic changes than single-session interventions. Conclusions: Although significant questions remain regarding optimal stimulation parameters, sample heterogeneity, and the translation of acute neural shifts into lasting behavioral benefits, this research confirms that tDCS can induce detectable neurobiological effects in SUD populations. By reshaping activity across prefrontal and reward-related circuits, modulating electrophysiological indices, and altering relevant biomarkers, tDCS holds promise as a viable, mechanism-based adjunctive therapy for SUDs. Rigorous, large-scale studies with longer follow-up durations and attention to individual differences will be essential to establish how best to harness these neuromodulatory effects for durable clinical outcomes. Full article

This paper presents the design and development of a new modular wall-climbing robot—Modular Wall Climbing-1 (MC-1)—for solving the problem of autonomous wall switching observed in wall-climbing robots. Each modular robot is capable of independently adhering to vertical surfaces and maneuvering, making it a fully autonomous robotic system. Multiple modules of MC-1 are connected by an electromagnet-based magnetic attachment method, and wall transitions are achieved using a servo motor mechanism. Moreover, an ultrasonic sensor is employed to measure the unknown wall-inclination angle. Mechanical analysis is conducted for MC-1 at rest individually and in combination to determine the required suction force. Experimental investigations are performed to assess the robot’s crawling ability, loading capacity, and wall-transition performance. The results demonstrate that the MC-1 robot is capable of multi-angle wall transitions for executing multiple tasks. It provides a new approach for wall-climbing robots to collaborate during wall transitions through a quick attachment-and-disassembly device and an efficient wall detection method. Full article

Grounded in the theory of metacognitive prediction error minimization, this study is the first to propose and empirically validate the mechanism of implicit metacognitive predictive processing by which bodily interaction influences the Aha! experience. Three experimental groups were designed to manipulate the level of temporal synchrony in bodily interaction: Immediate Mirror Group, Delayed Mirror Group, and No-Interaction Control Group. A three-stage experimental paradigm—Prediction, Execution, and Feedback—was constructed to decompose the traditional holistic insight task into three sequential components: solution time prediction (prediction phase), riddle solving (execution phase), and self-evaluation of Aha! experience (feedback phase). Behavioral results indicated that bodily interaction significantly influenced the intensity of the Aha! experience, likely mediated by metacognitive predictive processing. Significant or marginally significant differences emerged across key measures among the three groups. Furthermore, fNIRS results revealed that low-frequency amplitude during the “solution time prediction” task was associated with the Somato-Cognitive Action Network (SCAN), suggesting its involvement in the early predictive stage. Functional connectivity analysis also identified Channel 16 within the reward network as potentially critical to the Aha! experience, warranting further investigation. Additionally, the high similarity in functional connectivity patterns between the Mirror Game and the three insight tasks implies that shared neural mechanisms of metacognitive predictive processing are engaged during both bodily interaction and insight. Brain network analyses further indicated that the Reward Network (RN), Dorsal Attention Network (DAN), and Ventral Attention Network (VAN) are key neural substrates supporting this mechanism, while the SCAN network was not consistently involved during the insight formation stage. In sum, this study makes three key contributions: (1) it proposes a novel theoretical mechanism—implicit metacognitive predictive processing; (2) it establishes a quantifiable, three-stage paradigm for insight research; and (3) it outlines a dynamic neural pathway from bodily interaction to insight experience. Most importantly, the findings offer an integrative model that bridges embodied cognition, enactive cognition, and metacognitive predictive processing, providing a unified account of the Aha! experience. Full article

Sophisticated voluntary movements are essential for everyday functioning, making the study of how the brain controls muscle activity a central challenge in neuroscience. Investigating corticomuscular control through non-invasive electrophysiological recordings is particularly complex due to the intricate nature of neuronal signals. To address this challenge, we present a novel experimental methodology designed to study corticomuscular control using electroencephalography (EEG) and electromyography (EMG). Our approach integrates a serious gaming biofeedback system with a specialized experimental protocol for simultaneous EEG-EMG data acquisition, optimized for corticomuscular studies. This work introduces, for the first time, a method for assessing brain–muscle functional connectivity during the execution of a demanding motor task. By identifying neuronal sources linked to muscular activity, this methodology has the potential to advance our understanding of motor control mechanisms. These insights could contribute to improving clinical practices and fostering the development of novel brain–computer interface technologies. Full article

The control of ventilation systems is often performed by automatic algorithms which often do not consider the future evolution of the system in its control politics. Digital twins allow system forecasting for a more sophisticated control. This paper explores a novel methodology to create a Machine Learning (ML) model for the predictive control of a ventilation system combining Computational Fluid Dynamics (CFD) with Artificial Intelligence (AI). This predictive model was created to forecast the temperature and humidity evolution of a ventilated room to be implemented in a digital twin for better unsupervised control strategies. To replicate the full range of annual conditions, a series of CFD simulations were configured and executed based on seasonal data collected by sensors positioned inside and outside the room. These simulations generated a dataset used to develop the predictive model, which was based on a Deep Neural Network (DNN) with fully connected layers. The model’s performance was evaluated, yielding final average absolute errors of 0.34 degrees Kelvin for temperature and 2.2 percentage points for relative humidity. The presented results highlight the potential of this methodology to create AI-driven digital twins for the control of room ventilation. Full article

This research evaluates the performance of six metaheuristic algorithms in the active and reactive power management of wind turbines (WTs) integrated into an AC microgrid (MG). The population-based genetic algorithm (PGA) is proposed as the primary optimization strategy and is rigorously compared against five benchmark techniques: Monte Carlo (MC), particle swarm optimization (PSO), the JAYA algorithm, the generalized normal distribution optimizer (GNDO), and the multiverse optimizer (MVO). This study aims to minimize, through independent optimization scenarios, the operating costs, power losses, or CO₂ emissions of the microgrid during both grid-connected and islanded modes. To achieve this, a coordinated control strategy for distributed generators is proposed, offering flexible adaptation to economic, technical, or environmental priorities while accounting for the variability of power generation and demand. The proposed optimization model includes active and reactive power constraints for both conventional generators and WTs, along with technical and regulatory limits imposed on the MG, such as current thresholds and nodal voltage boundaries. To validate the proposed strategy, two scenarios are considered: one involving 33 nodes and another one featuring 69. These configurations allow evaluation of the aforementioned optimization strategies under different energy conditions while incorporating the power generation and demand variability corresponding to a specific region of Colombia. The analysis covers two-time horizons (a representative day of operation and a full week) in order to capture both short-term and weekly fluctuations. The variability is modeled via an artificial neural network to forecast renewable generation and demand. Each optimization method undergoes a statistical evaluation based on multiple independent executions, allowing for a comprehensive assessment of its effectiveness in terms of solution quality, average performance, repeatability, and computation time. The proposed methodology exhibits the best performance for the three objectives, with excellent repeatability and computational efficiency across varying microgrid sizes and energy behavior scenarios. Full article

The need for electrical energy is dramatically increasing, pushing researchers and industrial communities towards the development and improvement of microgrids (MGs). It also encourages the use of renewable energies to benefit from available sources. Thereby, the implementation of a photovoltaic (PV) system with a hybrid energy storage system (HESS) can create a standalone MG. This paper presents an MG that uses photovoltaic energy as a principal source. An HESS is required, combining batteries and supercapacitors. This MG responds “insure” both alternating current (AC) and direct current (DC) loads. The batteries and supercapacitors have separate parallel connections to the DC bus through bidirectional converters. The DC loads are directly connected to the DC bus where the AC loads use a DC-AC inverter. A control strategy is implemented to manage the fluctuation of solar irradiation and the load variation. This strategy was implemented with a new logic control based on Boolean analysis. The logic analysis was implemented for analyzing binary data by using Boolean functions (‘0’ or ‘1’). The methodology presented in this paper reduces the stress and the faults of analyzing a flowchart and does not require a large concentration. It is used in this paper in order to simplify the control of the EMS. It permits the flowchart to be translated to a real application. This analysis is based on logic functions: “Or” corresponds to the addition and “And” corresponds to the multiplication. The simulation tests were executed at T_au  =  6 s of the low-pass filter and conducted in 60 s. The DC bus voltage was 400 V. It demonstrates that the proposed management strategy can respond to the AC and DC loads. Full article