Search Results (497)

This article asks a practical question: do current renewable-energy rules encourage end users to become prosumers, or do they mainly serve those already prepared to invest? This analysis maps recent Polish reforms, especially the shift from net metering to value-based net billing and later adjustments to settlements: the return to monthly average pricing for many early adopters, an optional hourly price model, and a strengthened prosumer deposit intended to reduce the sell–buy gap. It also examines the expanding catalogue of prosumer statuses, from the individual prosumer, through collective prosumption in multi-unit buildings, to the virtual prosumer, which may include people without a suitable roof or land. The article then considers civic forms of organisation—energy cooperatives, energy clusters and citizen energy communities—and the role of municipalities as conveners and intermediaries. It argues that regulatory responsiveness, even when market-oriented and technologically aware, is not enough. Uptake depends on whether the rules fit real constraints: finance, administrative effort, grid capacity and fair allocation of risk. Full article

Automation of measurement data acquisition is particularly important when digital instruments do not provide a direct communication interface or when long-term measurements require high repeatability and reduced operator involvement. This paper presents a comparative study of two image-based optical reading methods to acquire indications from a digital multimeter. The reference signal was generated using a Fluke calibrator, while the multimeter display was recorded with an industrial camera. The acquired images were processed independently using two different recognition strategies. The first approach was implemented in the Python environment using a You Only Look Once version 8 small (YOLOv8s) deep learning object detection model trained to classify individual display characters. The second approach was implemented in the Laboratory Virtual Instrument Engineering Workbench (LabVIEW) using a template-based optical character recognition (OCR) method prepared in NI Vision Assistant. Acquisition series were performed for different voltage ranges, with 100 samples acquired for each pipeline and measurement case. All image-acquisition experiments were conducted under controlled laboratory conditions using a fixed camera position, a constant region of interest (ROI), and stable illumination. The detected or recognised characters were reconstructed as numerical values and compared with the reference settings of the calibrator on statistical parameters that describe the dispersion within the series and the deviation of the readings. The study evaluates the practical applicability of both approaches and demonstrates the potential of non-invasive optical reading methods for automated acquisition of indications from measurement instruments. Full article

The European crafts ecosystem faces critical structural threats, declining practitioner numbers, weakening intergenerational transmission, limited digital literacy, and competition from industrial imitation. Existing online craft communities are narrowly material-specific and structurally ill-suited to the cross-disciplinary dialogue required for systemic sector transformation. This paper presents the design, iterative development, and pilot evaluation of the Craeft Community, a multi-stakeholder Virtual Community of Practice (VCoP) developed within the Horizon Europe CRAEFT project. Three research questions guided the study: how a multi-stakeholder VCoP should be structured to overcome disciplinary fragmentation; to what extent a stewarded digital forum can operationalize Situated Learning and Communities of Practice theory; and what factors facilitate or inhibit engagement and post-funding sustainability. Using design-based research, the platform evolved through four iterative phases, culminating in restructuring from a material-based architecture into five transversal thematic pillars, driven by survey evidence from 151 European craft professionals and systematic stakeholder feedback. The pilot phase yielded 86 registered members, 31 posts, and 27 interactions, with Transmission & Training as the most engaged pillar. Qualitative analysis reveals substantive cross-disciplinary discourse alongside a structural Effort-Engagement Gap, a persistent tension between forum participation demands and the gravitational pull of mainstream social media. The study demonstrates that a thematically organized, stewarded VCoP can meaningfully operationalize apprenticeship-based learning in digital settings, advancing craft heritage preservation, economic resilience, and hybrid professional identity formation at the intersection of craft and technology. Full article

Exosomes, nanoscale extracellular vesicles released by virtually all cell types, have emerged as pivotal mediators of intercellular communication and play a crucial role in the pathophysiology of numerous acute and chronic diseases, including a wide spectrum of urological disorders. By acting as sophisticated biological shuttles, exosomes transport a rich and highly specific molecular cargo—comprising proteins, lipids, messenger RNAs, microRNAs, and other nucleic acids—that reflects the physiological or pathological state of their cell of origin. Owing to these unique properties, exosomes are increasingly recognized as promising biomarkers for the diagnosis, prognosis, and monitoring of a broad range of inflammatory, degenerative, and neoplastic diseases. Beyond their diagnostic value, exosomes have attracted considerable attention as therapeutic tools, given their ability to promote tissue regeneration, modulate immune responses, and serve as potential targeted drug-delivery systems for small molecules, biologics, vaccines, and gene-based therapies. Notably, exosomes recapitulate many of the beneficial biological effects traditionally attributed to stem cells, while potentially offering a more practical alternative. As cell-free entities, they may reduce—though not entirely eliminate—several risks associated with cell transplantation, such as uncontrolled proliferation and immune rejection, and they raise fewer ethical concerns, making them attractive candidates for regenerative and precision medicine. In urology, the diagnostic, prognostic, and therapeutic applications of exosomes are rapidly expanding, with particularly promising advances observed in bladder, prostate, and kidney diseases. Growing evidence also supports their relevance in a variety of benign urological conditions, including erectile dysfunction, male infertility, neurogenic bladder, urethral stricture disease, stress urinary incontinence, and bladder pain syndrome. This review synthesizes contemporary knowledge on the biological significance and clinical potential of exosomes in urology, highlighting their emerging role as biomarkers and therapeutics, with a special focus on benign urological disorders. We emphasize that the current evidence base in benign urology is largely preclinical, and that clinical translation, although promising, remains at an early stage. Full article

Background/Objectives: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by fluctuating symptoms, long-term medication use, bodily uncertainty, and complex self-management demands. These features can make patients’ experiences difficult to narrate, recognize, and integrate into everyday life. This study aimed to explore how Chinese women living with SLE use visual narratives to make sense of illness disruption, treatment burden, identity changes, and relational experience. Methods: This qualitative study used Virtual Photovoice, an online visual method in which participants generate and discuss photographs about lived experience, with eight Chinese women living with SLE. Data included participant-generated photographs, brief captions, SHOWeD-based written reflections structured around prompts that move from image description to broader reflection, and transcripts from three online Photovoice workshops. The data were analyzed using reflexive thematic analysis within a participatory-informed Virtual Photovoice design, informed by illness narrative theory. Results: Four themes were developed: Invisible Battlefield, Masks and Boundaries, Anchors of Order, and Longing to Be Seen. Participants used photographs and accompanying accounts to give form to fatigue, pain, and bodily uncertainty; negotiate the boundaries between concealment and disclosure; transform medication routines, dietary practices, and illness-related objects into anchors of order and agency; and contrast embodied relational support with clinical encounters experienced as distant or indicator-centered. Conclusions: The findings show how visual illness narratives can support meaning-making, self-recognition, and reflection on patient-centered communication among women living with SLE. Virtual Photovoice offers a narrative and participatory-informed approach for understanding psychological, embodied, and relational dimensions of chronic illness that are often difficult to express through routine clinical or everyday language. Full article

Background/Objectives: Spinal cord injury (SCI) causes heterogeneous motor, sensory, autonomic, and participation limitations; recovery priorities vary by injury level, completeness, time since injury and residual function. Robotic rehabilitation has expanded from assistive technology to restorative, compensatory and health-promoting interventions, but patient-tailored prescription frameworks remain underdeveloped. Methods: PubMed/MEDLINE was searched from database inception to May 2026 using predefined domain-specific strategies, and findings were synthesized narratively to integrate mechanistic, clinical, safety and implementation evidence. Results: Robotic systems can increase task-specific repetition, sensorimotor feedback, active engagement and quantitative monitoring. Upper-limb robotics are feasible in cervical SCI and may support reach, grasp and activities of daily living, although SCI-specific controlled evidence remains limited. Lower-limb exoskeletons and locomotor robots can support gait practice, upright mobility, exercise exposure and selected secondary health outcomes, but walking speed, energy expenditure, cost, supervision needs and community translation remain important barriers. Sensory and non-motor effects, including proprioceptive input, spasticity, pain, bowel routine, cardiometabolic conditioning, participation and psychological well-being, are clinically relevant but should be interpreted according to evidence strength. Robotics combined with functional electrical stimulation, virtual reality, brain–computer interfaces, non-invasive brain stimulation and artificial intelligence-driven adaptation is promising but not yet routine. Conclusions: Robotic rehabilitation in SCI should be prescribed through a severity-based process that considers lesion level, American Spinal Injury Association Impairment Scale grade, residual voluntary and sensory function, safety, patient priorities and measurable goals. The proposed framework supports transparent selection and prospective validation of individualized robotic rehabilitation and shifts decisions beyond device availability toward clinically meaningful and equitable implementation. Full article

Background: Recent technological advances, such as 3D laser scanning, digital photogrammetry and virtual autopsy, have significantly reshaped the acquisition and interpretation of scientific evidence in criminal proceedings. These tools enable highly detailed visualizations that can enhance factual understanding while also introducing potential cognitive biases for judges. This study aims to assess the legal, epistemological and forensic implications of 3D virtual reconstruction as evidence, examining its admissibility, scientific robustness, and associated risks within contemporary criminal trials. Methods: An interdisciplinary methodology was applied, integrating criminal procedural law, forensic medicine, digital forensics and legal epistemology. The study includes doctrinal analysis, a review of the literature from the last decade, Italian and comparative case law, and evaluation of European forensic guidelines (particularly ENFSI). Direct observation of laboratory practices and technical assessment of reconstruction tools were conducted to evaluate scientific validity, repeatability, traceability and transparency. Results: The findings reveal that 3D reconstruction provides high metric accuracy, durable digital preservation of the crime scene and improved communication of complex dynamics to the court. It allows experts to test alternative hypotheses, reducing ambiguity in technical explanations. Nevertheless, several critical issues emerged: the persuasive power of highly realistic imagery, dependence on non-transparent software processes, potential vulnerabilities in the chain of custody for digital data, and the absence of a unified Italian regulatory framework governing digital scientific evidence. Conclusions: 3D virtual reconstruction constitutes a powerful but epistemologically complex form of scientific evidence. Its probative value depends on transparent methodology, verifiability and strict compliance with adversarial safeguards. The study underscores the need for national technical standards, clear admissibility criteria and specialized training for legal professionals. A coherent regulatory framework is essential to ensure that digital technologies enhance, rather than distort, the pursuit of truth in criminal justice. Full article

Reliable state estimation is essential for the secure and efficient operation of sustainable energy systems, especially under the increasing integration of renewable energy, distributed resources, and heterogeneous sensing devices. However, in practical power systems, SCADA, PMU, and AMI measurements often have different sampling rates, accuracies, communication delays, and availability levels, which makes reliable data completion and multi-source fusion difficult. This paper focuses on the state estimation problem of renewable-integrated distribution networks under multi-source heterogeneous measurement conditions. In such distribution networks, the increasing penetration of distributed renewable energy resources and the joint deployment of multiple measurement devices, including SCADA, PMU, and AMI, may lead to incomplete measurements, asynchronous sampling, differences in measurement accuracy, and reduced system observability. To address these issues, this paper proposes a model-based digital twin reference-guided physics-constrained DDPM framework to improve the quality of missing-measurement completion and the reliability of state estimation in distribution-network scenarios. A four-layer simulation-oriented cyber–physical framework is first constructed to integrate physical sensing, model-based digital twin reference mapping, AI-based measurement completion, and state estimation feedback. Within this framework, a physics-constrained self-supervised denoising diffusion probabilistic model is developed to recover missing measurements by combining observed data, digital twin reference measurements, real-time topology information, and power system operational constraints. The completed pseudo-measurements and physical measurements are then fused through a credibility-aware weighting strategy that considers timeliness, data integrity, measurement accuracy, and virtual–real consistency verification under simulation settings. Simulation results on the IEEE 14-bus system show that the proposed method improves pseudo-measurement completion and supports more reliable voltage magnitude and phase angle estimation under different measurement configurations. Under the tested simulation settings and multi-source measurement configurations, the results indicate that the proposed method can improve pseudo-measurement completion and support more reliable voltage magnitude and phase angle estimation. However, its performance under frequent topology switching, high missing-data ratios, and complex abnormal data conditions remains to be further evaluated. Full article

Outdoor industrial sites, including logistics terminals, construction yards, and civil infrastructure worksites, increasingly require supervisory systems for monitoring autonomous mobile units under variable wireless and operational conditions. This study presents an edge-cloud telemetry platform that connects V2X on-board and roadside units to a normalized data pipeline and an operator dashboard. The architecture assigns frame reception and data validation to the edge layer, while cloud services perform stream ingestion, storage, querying, and visualization using a Kafka-Elasticsearch-Grafana stack. A fixed supervisory schema was defined for position, heading, speed, mission state, battery level, and error flags so that virtual fields used in early validation can later be replaced by measured signals without changing downstream interfaces. Physical field validation was conducted using a single test vehicle in a construction-site emulation environment to evaluate communication continuity and dashboard refresh behavior. Multi-unit applicability was examined at the architecture and schema levels, and a preliminary payload-level capacity estimate was derived using the telemetry frequency and payload-length assumptions. Under the tested site conditions, the system maintained continuous reception and visualization over an approximately 700 m distance from the RSU-side reference location. The measured end-to-end display delay averaged 0.78 s, with a standard deviation of 0.059 s and a maximum of 0.96 s. Under a 10 Hz status-message condition, the estimated pure-payload traffic was approximately 23 kbps per mobile unit. These results indicate that V2X-based edge-cloud telemetry can provide a practical baseline for supervisory monitoring in outdoor industrial sites, while simultaneous multi-vehicle validation, detailed network-load evaluation, and long-term field testing remain necessary future work. Full article

This paper proposes a hybrid SMC + ESO + RBF control architecture designed to evaluate trajectory tracking and liquid sloshing suppression in tanker robots navigating complex terrains within a simulated environment. A multi-variable dynamic model integrates the differential drive mobile platform with an equivalent mass-spring-damper sloshing system under terrain disturbances. To achieve robust stability, an Extended State Observer (ESO) neutralizes baseline generalized disturbances, while a Radial Basis Function (RBF) neural network adaptively compensates for residual nonlinear coupled sloshing errors. Practical stability and uniform ultimate boundedness (UUB) of the closed-loop system are proven via Lyapunov theory under bounded network approximation errors and observer uncertainties. Numerical simulations in MATLAB/Simulink demonstrate that the proposed controller achieves a baseline Root Mean Square Error (RMSE) of 0.0109 m, representing an 84.1% improvement over traditional Sliding Mode Control (SMC). Parametric sensitivity analysis under variable liquid filling ratios (30%, 50%, and 70%) and a circular steering topology indicates notable adaptability, with the tracking RMSE bounded between 0.0085 m and 0.0129 m under the considered virtual scenarios. Within the simulated environment, the system successfully smooths control profiles and dampens liquid oscillations, demonstrating a promising potential to support transport safety and mitigate actuator chattering under virtual constraints. However, these qualitative observations serve as preliminary hypotheses and must be formally verified through future hardware-in-the-loop (HIL) experiments to evaluate the impact of physical non-idealities, including sensor noise, actuator saturation, communication delays, and wheel slip. These findings confirm the competitive analytical robustness of the SMC + ESO + RBF framework in stabilizing tanker robots within highly uncertain simulated operational environments. Full article

Background: Admission to an intensive care unit (ICU) exposes family members of adult patients to substantial informational, emotional, and decisional burden. In recent years, digital tools have increasingly been used to support communication, information delivery, virtual visiting, psychological support, diary writing, and surrogate decision making in ICU settings, although the available literature remains heterogeneous in terms of intervention type, purpose, timing, and outcomes assessed. Methods: A scoping review was conducted according to Joanna Briggs Institute methodology and reported following PRISMA-ScR. The literature search was performed between January and March 2026 in PubMed/MEDLINE, Scopus, and CINAHL. After duplicate removal, title/abstract screening, and full-text assessment, 32 studies were included in the qualitative synthesis. Results: The included studies were published between 2016 and 2026, used heterogeneous methodological designs, and originated from different international contexts. Six main categories of digital tools were identified: educational websites and online information resources; decision aids and tablet-based tools; virtual visiting and video communication systems; digital diaries and writing practices; psychological support or self-management applications; and digital assessment or family-engagement platforms. Overall, informational and communication-oriented tools appeared to provide the clearest signals of usefulness for family orientation, information access, communication, and relational continuity, whereas evidence regarding psychological and decisional outcomes remained more variable and largely preliminary. Conclusions: Digital tools for family members of adult ICU patients represent a relevant and evolving component of family-centered critical care. Their value appears to depend on the family need addressed, the timing of implementation, and their integration into clinical workflows. Overall, the available literature suggests that digital tools may be particularly useful for family orientation, information access, and communication, whereas their impact on psychological and decisional outcomes remains less certain and requires further investigation. Full article

Power wireless sensor networks (PWSNs) provide essential field-level sensing and communication support for smart grids, where topology authenticity directly affects communication reliability and network operation. However, wormhole attacks can forge false adjacency relationships through low-latency tunnels, thereby disrupting topology consistency and misleading routing decisions. In practical power environments, metallic obstruction, multipath reflection, and non-line-of-sight (NLOS) propagation may further cause normal-ranging anomalies to resemble attack-induced topology distortion, making reliable wormhole attack detection challenging. To address this issue, this paper proposes a topology-stress-based wormhole attack defense method with ultra-wideband (UWB) physical-layer awareness. The first-path power ratio and root-mean-square delay spread extracted from UWB channel impulse responses are used to evaluate link-ranging reliability and construct adaptive stiffness coefficients. Local backbone links are modeled as virtual springs, and a topology stress indicator is derived from the residual deformation after potential-energy minimization to quantify the geometric inconsistency caused by forged adjacency relationships. Furthermore, a Beta-based temporal evidence fusion mechanism is introduced to support graded node access decisions and improve decision stability. Simulation and hardware validation results demonstrate that the proposed method effectively suppresses NLOS-induced false alarms while maintaining high sensitivity to wormhole attacks. Compared with representative baseline methods, it achieves more stable detection performance under increasing ranging errors and different attack intensities. Hardware experiments further show that topology stress can clearly distinguish normal links, NLOS-affected links, and forged wormhole links, confirming its effectiveness for topology-authenticity verification in power wireless sensor networks. Full article

Background/Objectives: Angiographic stenosis severity and functional significance are discordant in up to 65% of intermediate coronary lesions. Fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) has shown better clinical outcomes than standard angiography-guided PCI, therefore functional significance defines revascularization. This review evaluates the contemporary evidence for intracoronary physiology assessment tools, such as FFR, non-hyperemic pressure ratios (NHPRs), angiography-derived wire-free indices, and microvascular function testing, and proposes a framework for their implementation into clinical practice. Methods: We conducted a narrative review, synthesizing data from landmark randomized controlled trials (DEFER, FAME I–III, DANAMI-3-PRIMULTI, COMPARE-ACUTE, DEFINE-FLAIR, iFR-SWEDEHEART, iMODERN, FAVOR III China and Europe, FAST III, ALL-RISE, CorMicA), along with pooled analyses, meta-analyses, position papers, and relevant guidelines. Results: FFR-guided revascularization resulted in a 28% reduction in cardiac death or myocardial infarction in pooled analyses (HR 0.72, 95% CI 0.54–0.96). leading to a Class I, Level A indication. NHPRs, including iFR, achieved non-inferiority to the FFR at 1 year; however, a 5-year pooled meta-analysis raised concerns of increased all-cause mortality with iFR guidance compared to the FFR (HR 1.34, 95% CI 1.08–1.67). Approximately 20% of lesions show FFR–iFR discordance, driven by vessel-specific physiology and microvascular factors. Wire-free technologies yielded conflicting results: the FAVOR III China trial favored the QFR over angiography, yet FAVOR III Europe failed non-inferiority versus the FFR, while the recent FAST III and ALL-RISE trials demonstrated the non-inferiority of angiography-derived physiology at 1 year. Up to 40% of patients with angina have non-obstructed coronary arteries, and coronary vasomotor function testing can identify treatable microvascular endotypes improving symptoms and quality of life. Conclusions: Functional invasive coronary angiography is advocated to decipher vessel hemodynamics and to guide treatment. The FFR remains the gold standard for invasive physiological assessments, while NHPRs and wire-free technologies are valuable adjuncts with specific indications and limitations. A thorough microvascular evaluation is essential for differentiating between various INOCA endotypes and is gradually being adopted by the interventional community. While NHPRs and virtual technologies struggle to dethrone the king FFR, a comprehensive intracoronary physiology assessment is essential to guide treatment. Full article

Network functions virtualization (NFV) is an emerging technology that enables flexible service deployment for supporting the Beyond 5G/6G network. NFV transforms physical network devices into virtual network functions (VNF) over Edge Computing capabilities, thereby facilitating the agility of network services and reducing management costs. To effectively monitor Internet of Things (IoT) network resources, service function chaining (SFC) is used for its virtualizations to ensure the multi-service requirements are sufficiently in capability, scalability, and flexibility for computation workloads alignments. However, to satisfy the resource availability requirements and efficiency under several conditions, SFC reconfiguration methods face the challenges in meeting significant latency requirement of delay-sensitive applications while reaching the importance of energy saving on orchestration timespan. In this paper, we propose task management-aware SFC and orchestrating schemes, namely GNN-PPO. In this framework, we utilize the Graph Neural Network (GNN), which relies on the message-passing neural network (MPNN), to capture all the abstraction of physical resource nodes and link capabilities over MEC node states. In particularly, GNN is divided construction into two phrases: (1) GNN represents nodes for all the Mobile edge computing (MEC) nodes, which have a global view on resources of computation and communicational capabilities that could serve as carriers; (2) VNFs are transferred into graph networks by using feature-extraction MPNN to manage each VIM that seeks an optimal and reliable analysis of traffic fluctuations. Lastly, Deep Reinforcement Learning (DRL) is used to embrace the network determination in policy strategy, which utilizes a Proximal Policy Gradient (PPO). On the other hand, we propose a novel network architecture based on PPO to perform the design for the optimization of resource utilization and facilitate energy consumption on MEC servers under diverse setting scenarios, which enables continuous policy enforcement for our system. With the experimental results, we compare our proposed solution with reference schemes in terms of rewards with learning rate and batch size, average request acceptance, SFC success, packet delivery, throughput, and resource utilization ratio that confirm the scheme’s scalability and practical suitability for IoT network deployment. Full article

Sensor-based environmental monitoring systems are often affected by missing, noisy, and unreliable measurements caused by sensor faults, sparse deployment, calibration drift, and communication interruptions. To address these challenges, this study proposes an intelligent virtual sensor generation framework that integrates physical-constraint-based preprocessing, statistical virtual sensor modeling, KL-divergence-based fusion, deep generative augmentation, and temporal prediction. The raw weather-station data are first refined using threshold-based filtering, physical validity constraints, and Isolation Forest-based outlier detection. To handle the circular nature of wind direction, the angle is encoded using sine and cosine components during modeling and reconstructed using the atan2 function for evaluation. Multiple statistical methods, including Inverse Distance Weighting, Kernel Density Estimation, Ridge Regression, and Copula-based modeling, are employed to generate complementary virtual sensor data. These outputs are adaptively fused using KL divergence according to their distributional similarity with real sensor data. The fused datasets are further augmented using Variational Autoencoders and Conditional Tabular Generative Adversarial Networks, and then evaluated using BiLSTM and BiGRU models with MAE, MSE, and RMSE metrics. The experimental results demonstrate that the proposed framework generates physically valid and distributionally consistent virtual sensor data. Fusion-based methods outperform standalone approaches, while VAE-based augmentation generally provides better statistical fidelity and lower prediction errors than CTGAN. Additional validation using a public NOAA weather-station dataset further supports the transferability of the proposed fusion-based virtual sensing workflow. Comparisons with TimeGAN and diffusion-based temporal generative baselines, supported by Wilcoxon signed-rank testing, confirm the statistical significance and competitive performance of the proposed framework. A quantitative computational analysis also demonstrates the practical feasibility of the framework in terms of training time, inference time, memory consumption, and scalability. Overall, the proposed framework offers a reliable and scalable solution for virtual sensing in sensor-sparse and fault-prone environmental monitoring systems. Full article