Search Results (1,994)

Bio-based, biodegradable, and renewable polymers offer a promising alternative to traditional synthetic polymers derived from petroleum or other non-renewable resources. However, their use is limited by suboptimal properties and high costs. Incorporating sustainable reinforcements into the polymer matrix significantly improves biopolymer performance while preserving key properties, sustainability, and cost-effectiveness. Bio-based polymeric composites have emerged as a crucial category of biopolymers, playing a key role in advancing a sustainable, circular economy. This review provides an updated overview of bio-based polymer composites and nanocomposites, focusing on reinforcement strategies using natural nanofillers and engineered nanoparticles. We summarize key synthesis and processing methods, discuss structure–property relationships, and highlight recent advances in applications such as food packaging, biomedical devices, energy systems, environmental remediation, 3D printing, and supercapacitors. Polymer nanocomposites are versatile, with their performance depending on the type, size, and interactions between the fillers and the polymer matrix. Progress in metallic, ceramic, carbon-based, natural, and hybrid fillers has improved their properties. Using bio-based polymers and renewable fillers supports sustainability. Natural nanofillers derived from renewable sources and industrial byproducts offer a sustainable approach to developing high-performance, biodegradable nanocomposites. Smart nanocomposites can react to external stimuli by integrating specialized fillers that enhance their mechanical and mobility properties. Shape memory nanocomposites can be remotely activated—using heat, electricity, magnets, or light—enabling advanced applications. Finally, we address major challenges and outline future directions for scalable, circular-material solutions, drawing on perspectives from the circular economy and life cycle assessment (LCA). Full article

This work presents an enhancement to the Dynamic Traffic Management (DTM) framework aimed at reducing signaling overhead between SDN controllers in multi-domain cloud environments. This extension is based on the ability to transmit information regarding the amount of balanced traffic and the optimal transfer pattern. In the baseline periodic mode, the system regularly exchanges the compensation vector ($\overrightarrow{C}$) and the reference pattern ($\overrightarrow{R}$). To minimize communication, we define non-periodic modes that restrict $\overrightarrow{C}$ updates and eliminate $\overrightarrow{R}$ transmission entirely. Within these restricted signaling modes, we further distinguish between reactive and proactive operational schemes. Our experimental results demonstrate that reducing the visibility of optimization goals ($\overrightarrow{R}$ and only sign of $\overrightarrow{C}$) and cutting signaling frequency in this manner maintains a comparable level of cost-efficiency. Specifically, the initial evaluation shows that DTM typically decreases transit costs by 8% to 15%, with maximum savings reaching up to 29% when compared to the worst-case default BGP path scenario. These findings suggest that the DTM mechanism can maintain its economic efficiency even with significantly reduced inter-domain coordination. Full article

Understanding why national decarbonization pathways diverge is essential for designing effective climate and energy policy. Using harmonized data for 1970–2022 from Our World in Data and the Maddison Project Database, this study examines long-run emission trends and electricity-mix transitions in four countries representing distinct energy regimes: Japan, Australia, India, and South Africa. We combine per-capita and total CO₂ trajectories with a Kaya–LMDI decomposition aligned with updated methodological guidelines. Results reveal persistent and deepening transition divergence. Japan experienced partial decoupling before a nuclear vulnerability shock in 2011 reversed progress and temporarily increased fossil dependence. Australia shows a recent erosion of long-standing coal lock-in, driven by policy reform and falling renewable costs. India and South Africa remain highly coal-dependent, with population and income growth overwhelming improvements in energy intensity. Across countries, efficiency gains contributed to emission mitigation, but only structural changes in fuel mix produced sustained reductions in carbon intensity. Taken together, these findings suggest that divergent institutional and infrastructural lock-in conditions—rather than income levels alone—shape the pace, direction, and resilience of decarbonization. The study also speaks to recent international policy debates emphasized by the IPCC and the IEA, as well as to justice-oriented discussions in the energy transition literature. The results highlight major implications for climate policy, energy-system resilience, and just transition strategies. Full article

Background: Efficient parking navigation in large and dynamic parking areas requires systems that can adapt to real-time conditions and provide precise vehicle localization. Methods: This paper presents a smart car parking navigation module that integrates camera-based vehicle perception, homography-based ground-plane localization, mobile GNSS positioning, and dynamic route planning into a unified framework. Instance segmentation (YOLOv8n-seg) is used to detect vehicles and extract ground-contact regions, which are associated with parking slots defined in a GeoJSON-based site model. Mobile GNSS data are fused with visual observations via spatio-temporal proximity scoring to enable robust user–vehicle matching without optical identification. An A* routing algorithm dynamically computes and updates navigation paths, adapting to lane obstructions and slot availability in real time. Results: Experimental evaluation on a real six-camera parking facility shows that the proposed segmentation-based localization reduces mean error from 0.732 m to 0.283 m (61.3% improvement), with the 95th-percentile error dropping from 1.892 m to 0.908 m, and outperforming the bounding-box baseline in 85.3% of detections. Conclusions: These results demonstrate that sub-meter vehicle localization and reliable user–vehicle association are achievable using standard surveillance cameras without specialized infrastructure, offering a scalable and cost-effective solution for intelligent parking navigation. Full article

In order to address the challenges of frequency fluctuations and uneven voltage distributions in islanded microgrids, this paper proposes a distributed model predictive control (DMPC) strategy for secondary frequency and voltage regulation, and it adopts the virtual oscillator control (VOC) grid-forming method for the primary control. Firstly, the prediction model is constructed by integrating VOC dynamic equations with virtual inertia terms. Secondly, a cost function incorporating consensus constraints and tracking error terms is designed within the MPC framework, thereby achieving an optimal balance between dynamic consensus speed and steady-state tracking precision. Thirdly, the quadratic programming formulation strategy is used to solve the cost function optimization problem and update the DMPC outputs. Finally, simulation results verify that the proposed strategy ensures rapid frequency restoration and voltage regulation under sudden load variations and communication topology changes, while maintaining a smooth control process. Full article

Traditional VANET authentication schemes often face challenges such as centralization bottlenecks and the updating of vehicle keys or pseudonyms. This paper proposes a layered approach that divides VANET into regions, utilizing dual-blockchain to enable anonymous message authentication between vehicles and RSUs, as well as between vehicles within the VANET. Compared to traditional blockchain authentication methods, this paper introduces an approach that enhances authentication efficiency and ensures information security by establishing secure connections between private and consortium chains through a trusted authority (TA). By leveraging third-party public parameter updates, the automatic updating of private and public keys for VANET nodes is achieved without the need for certificate issuance and updates. This approach facilitates long-term anonymous authentication and communication between VANET nodes, reduces the frequency of authentication interactions, simplifies authentication processes, and lowers computational and communication costs. The proposed scheme is well-suited for practical VANET environments that require low authentication latency and robust large-scale privacy protection. Full article

Sewage sludge management remains a critical challenge in Greece, where increasing regulatory pressure, environmental constraints, and limited stakeholder participation complicate regional decision-making. In particular, the revision of regional Waste Management Plans requires decision-support approaches that are both technically robust and socially legitimate. This study develops and applies a participatory, data-driven multi-criteria decision analysis framework to evaluate sustainable sewage sludge management strategies in the Region of Eastern Macedonia and Thrace. The framework combines structured stakeholder participation with quantitative performance assessment, enabling transparent, reproducible, and systematic comparison of alternative sewage sludge management options. Four realistic sludge management alternatives—composting fr agriculture, forestry use, land restoration, and thermal drying with energy recovery were assessed against fifteen economic, environmental, and social sub-criteria. Data were collected through structured questionnaires administered to forty-four representatives from five stakeholder groups: utilities (water and sewerage service providers), local authorities, scientists/experts, end-users, and citizens. Group preferences were aggregated using equal group weighting to ensure balanced representation. The results show that environmental and economic criteria outweigh social aspects. The highest mean weights were assigned to compliance with environmental requirements for products derived from the disposal method (0.105) and compliance with stricter national environmental legislation (0.104), followed by energy intensity (0.097), installation cost (0.065), and operation and maintenance (O&M) cost (0.061). Overall rankings identified composting and thermal drying as the most preferred options, followed by land restoration and forestry use; sensitivity analysis (±10% variation in sub-criterion weights) confirmed ranking stability. The proposed framework enhances decision transparency by embedding measurable criteria and stakeholder inputs within a structured analytical process. From a policy perspective, it addresses participation gaps in Greek waste planning and offers a transferable decision-support tool for future regional planning. Further extensions may include integration with life cycle assessment and cost–benefit analysis to support adaptive updates under circular economy objectives. Full article

Background/Objectives: Delayed graft function (DGF) remains a common early complication after deceased donor kidney transplantation and is challenging to anticipate using routine pre-implant clinical variables alone. We investigated whether high-throughput Fourier transform infrared (FTIR) spectroscopy of static cold storage preservation fluid (not machine perfusion perfusate) captures biochemical information associated with DGF and warrants further evaluation alongside routine pre-implant clinical predictors. Methods: In this single-center retrospective cohort, we analyzed preservation fluid samples from 56 kidney transplants originating from 49 deceased donors (7 donors contributed two kidneys); DGF occurred in 14/56 (25.0%). Dried-film FTIR spectra were acquired using a plate-based high-throughput accessory, and analyses focused on the fingerprint region (900–1800 cm⁻¹) with prespecified preprocessing and quality control. We developed and compared clinical-only, FTIR-only, and combined predictive models and estimated performance using donor-blinded 5-fold StratifiedGroupKFold cross-validation (grouped by donor code) to prevent leakage across paired kidneys. Results: Donor-blinded discrimination (pooled out-of-fold ROC-AUC) was 0.775 for the clinical-only model, 0.814 for the FTIR-only model, and 0.796 for the combined model; probabilistic accuracy (Brier score; lower is better) was 0.162, 0.194, and 0.177, respectively. Calibration intercepts were negative and slopes were <1, indicating overly extreme risk estimates under strict donor-blinded validation and supporting recalibration prior to deployment. Decision curve analysis suggested a positive net benefit for clinically plausible thresholds. Conclusions: These findings support the feasibility of rapid, low-cost FTIR profiling of routinely available preservation fluid as a proof-of-concept approach for exploratory DGF risk stratification, rather than as a clinically deployable prediction tool. Given the small sample size and the instability of subgroup estimates, the main next steps are external validation in larger multicenter cohorts, prospective workflow studies, and model updating/recalibration. Full article

The paper introduces a computationally efficient semantic-aware route planning framework for indoor mobile robots, designed for real-time execution on resource-constrained edge hardware (Raspberry Pi 5, CPU-only). The proposed architecture fuses monocular object detection with 2D LiDAR-based range estimation and integrates the resulting semantic annotations into the Nav2 Route Server for penalty-weighted route selection. Object localization in the map frame is achieved through the Angular Sector Fusion (ASF) pipeline, a deterministic geometric method requiring no parameter tuning. The ASF projects YOLO bounding boxes onto LiDAR angular sectors and estimates the object range using a 25th-percentile distance statistic, providing robustness to sparse returns and partial occlusions. All intrinsic and extrinsic sensor parameters are resolved at runtime via ROS 2 topic introspection and the URDF transform tree, enabling platform-agnostic deployment. Detected entities are classified according to mobility semantics (dynamic, static, and minor) and persistently encoded in a GeoJSON-based semantic map, with these annotations subsequently propagated to navigation graph edges as additive penalties and velocity constraints. Route computation is performed by the Nav2 Route Server through the minimization of a composite cost functional combining geometric path length with semantic penalties. A reactive replanning module monitors semantic cost updates during execution and triggers route invalidation and re-computation when threshold violations occur. Experimental evaluation over 115 navigation segments (legs) on three heterogeneous robotic platforms (two single-board RPi5 configurations and one dual-board setup with inference offloading) yielded an overall success rate of 97% (baseline: 100%, adaptive: 94%), with 42 replanning events observed in 57% of adaptive trials. Navigation time distributions exhibited statistically significant departures from normality (Shapiro–Wilk, p < 0.005). While central tendency differences between the baseline and adaptive modes were not significant (Mann–Whitney U, p = 0.157), the adaptive planner reduced temporal variance substantially (σ = 11.0 s vs. 31.1 s; Levene’s test W = 3.14, p = 0.082), primarily by mitigating AMCL recovery-induced outliers. On-device YOLO26n inference, executed via the NCNN backend, achieved 5.5 ± 0.7 FPS (167 ± 21 ms latency), and distributed inference reduced the average system CPU load from 85% to 48%. The study further reports deployment-level observations relevant to the Nav2 ecosystem, including GeoJSON metadata persistence constraints, graph discontinuity (“path-gap”) artifacts, and practical Route Server configuration patterns for semantic cost integration. Full article

Grid-forming (GFM) battery energy storage system (BESS) inverters are becoming a cornerstone of resilient microgrids, where severe voltage sags and abrupt operating shifts can challenge both voltage regulation and controller stability. Finite-set model predictive control (FS-MPC) offers fast transient response and multi-objective coordination, yet conventional designs rely on static cost-function weights that are typically tuned offline and may become suboptimal under disturbance-driven regime changes. This paper proposes a forecast-guided KAN-adaptive FS-MPC framework that (i) formulates the inner-loop predictive control in the stationary αβ frame, thereby avoiding PLL dependency and mitigating loss-of-lock risk under extreme sags, and (ii) introduces an Operating Stress Index (OSI) that fuses load forecasts with reserve-margin or percent-operating-reserve signals to quantify grid vulnerability and trigger resilience-oriented control adaptation. A lightweight Kolmogorov–Arnold Network (KAN), parameterized by learnable B-spline edge functions, is embedded as an online weight governor to update key FS-MPC weighting factors in real time, dynamically balancing voltage tracking and switching effort. Experimental validation under high-frequency microgrid scenarios shows that, under a 50% symmetrical voltage sag, the proposed controller reduces the worst-case voltage deviation from 0.45 p.u. to 0.16 p.u. (64.4%) and shortens the recovery time from 35 ms to 8 ms (77.1%) compared with static-weight FS-MPC. In the islanding-like transition case, the proposed method restores the PCC voltage within 18 ms, whereas the static baseline fails to recover within 100 ms. Moreover, the deployed KAN governor requires only 6.2 μs per inference on a 200 MHz DSP, supporting real-time embedded implementation. These results demonstrate that forecast-guided adaptive weighting improves transient resilience and power quality while maintaining DSP-feasible computational complexity. Full article

Risk management is critical to maintain consistency between estimated and actual costs in construction investment projects, especially those that incorporate tourism and heritage components. This study aims to quantify the impact of risk factors on construction investment costs and to estimate an updated maximum project budget at a defined confidence level using an integrated expert-based and probabilistic approach. The approach combines a Frequency–Impact matrix, weighted scaling, and PERT/Monte Carlo simulation, thereby transforming expert judgments into comparable numerical parameters suitable for predictive modeling. The methodology is applied to the rehabilitation of the Esmeralda Hotel project in Cuba, a heritage asset characterized by high cultural value and technical complexity. The results quantify the effects of prioritized risk factors, compute their impact coefficients, and re-estimate the project’s upper budget limit at a 95% confidence level. The findings show that risk drivers associated with higher-complexity construction processes concentrate the main vulnerabilities and explain most of the increase in total cost. In addition, the analysis indicates that contingency margins established by regulation are insufficient to absorb the project’s observed variability. The proposed model supports proactive budget control by anticipating cost deviations, improving resource allocation, and strengthening decision-making under high uncertainty. Its flexible structure enables adaptation to different project types and serves as a practical decision-support tool for investors, designers, and project managers seeking greater financial accuracy and reduced risk of cost overruns. Full article

Accurate soil thermal resistivity is crucial for real-time cable ampacity determination to maximize cable utilization. However, the determination of soil thermal resistivity involves solving a computationally expensive multi-physical field inverse problem where a high-fidelity model (HFM) is used for performance evaluations. Surrogate-assisted evolutionary algorithms (SAEAs) are computationally efficient for such problems; model management strategies (MMSs) are key to SAEAs. Nevertheless, most MMSs struggle to balance the computational cost and the search accuracy due to their reliance on fitness value errors. In fact, maintaining a similar function landscape between the surrogate and the HFM is more essential than achieving precise fitness values on the surrogate. Consequently, a rank-based hybrid MMS-driven two-stage SAEA is proposed. Stage 1 focuses on identifying promising regions. To ensure the similarity between the surrogate and HFM function landscape and thus guide the evolution accurately, a global MMS is proposed. Specifically, a new function landscape similarity metric is proposed to adaptively adjust the surrogate update frequency. A new rank-error-based individual selection strategy selects key individuals for exact evaluations to refine the surrogate similarity. Stage 2 performs a refined local search within the identified promising region, utilizing a local MMS to re-evaluate the optimum of a local surrogate built around the best solution searched in Stage 1. Optimization results confirm the proposed method’s superiority on test functions and a prototype cable. Full article

Background: Gastrointestinal (GI) cancers remain a major global health concern, where timely and accurate interpretation of endoscopic findings plays a decisive role in patient outcomes. In recent years, deep learning–based decision support systems have shown considerable potential in assisting GI diagnosis; however, their broader adoption is often limited by patient privacy regulations, uneven data availability, and the fragmented nature of clinical data across institutions. Federated learning (FL) offers a practical solution by enabling collaborative model training while keeping patient data local to each hospital. Methods: Vision Transformers (ViTs) are particularly well suited for endoscopic image analysis due to their ability to capture long-range contextual information. Nevertheless, their high computational and communication costs pose a significant challenge in federated settings, especially when data distributions vary across clients. To address this issue, we propose a privacy-preserving federated framework that combines ViTs with a Clinical-Entropy Guided Quantum Evolutionary Algorithm (CEQEA) for adaptive token pruning. The CEQEA leverages the diagnostic diversity of each client’s local dataset to guide population initialization, evolutionary updates, and mutation strength, allowing the pruning strategy to adapt naturally to different clinical profiles. Results: The proposed framework was evaluated on curated upper- and lower-GI tract subsets of the HyperKVASIR dataset under realistic non-IID federated conditions. On the final test sets, the model achieved a mean micro-averaged accuracy of 92.33% for lower-GI classification and 90.19% for upper-GI classification, while maintaining high specificity across all diagnostic classes. At the same time, the adaptive pruning strategy reduced the number of tokens processed by approximately 40% and decreased the number of required federated communication rounds by 33% compared to ViT-based federated baselines. Conclusions: Overall, these results indicate that entropy-aware, quantum-inspired evolutionary optimization can effectively balance diagnostic performance and efficiency, making transformer-based models more practical for privacy-preserving, multi-institutional gastrointestinal endoscopy. Full article

Lightweight day-ahead photovoltaic (PV) forecasting models encounter a significant technical challenge: under resource-constrained deployment conditions, it is difficult to simultaneously address weather-regime heterogeneity, maintain model interpretability, and preserve adaptability as operating conditions evolve. To address this issue, we propose a multimodal short-term photovoltaic (PV) forecasting method that integrates weather-mode partitioning using the Clustering Using Representatives (CURE) algorithm with a self-updating Batch-Lasso model. First, the meteorological-PV dataset is partitioned along two dimensions by combining seasonal grouping with CURE clustering within each season, producing representative weather modes and enhancing the fidelity of weather pattern classification. Second, to extract informative predictors from high-dimensional meteorological inputs while maintaining interpretability, we formulate per-mode Lasso regression and adopt the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) to efficiently solve for the sparse regression coefficients. Third, we introduce a batch-based self-update and correction mechanism with rollback verification, enabling the mode-specific models to be refreshed as new historical data become available while preventing performance degradation. Compared with representative machine learning baselines, the proposed method maintains competitive accuracy with substantially lower computational and storage overhead, enabling high-frequency and energy-efficient inference on resource-constrained terminals, thereby reducing operational burdens and computational energy costs and better meeting the deployment needs of sustainable energy systems under heterogeneous weather conditions. Full article

Unmanned Aerial Vehicle (UAV)-assisted hierarchical federated learning (HFL) has emerged as a promising architecture for Internet of Things (IoT)-based smart agriculture, which enables scalable model training over large and sparse farmlands. In this setting, UAVs act as mobile edge servers, aggregating local updates from distributed agricultural IoT devices and relaying them to the cloud server. While HFL improves scalability and reduces communication overhead, it still faces critical security threats due to its reliance on public wireless channels and the vulnerability of model aggregation to malicious updates. In this paper, we propose a secure authentication scheme that integrates anomaly detection with elliptic curve cryptography (ECC)-based mutual authentication to protect both the communication and training phases. In the proposed scheme, UAVs authenticate participating clients before receiving their local models, then perform anomaly detection to identify and exclude malicious participants. If a client is found to be malicious, its identity credentials are revoked and broadcast by the cloud server to prevent future participation. The security of the proposed scheme is formally verified using Burrows–Abadi–Needham (BAN) logic, the Real-or-Random (RoR) model, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, along with informal security analysis. The performance evaluation includes comparisons of security features, computation cost, and communication cost with other related schemes, and an experimental assessment of anomaly detection performance. The results demonstrate that our scheme provides strong security guarantees, low overhead, and effective malicious client detection, making it well suited for UAV-assisted HFL in smart agriculture. Full article