Search Results (659)

The construction sector faces increasing pressure to reduce the embodied energy of building materials while valorizing industrial waste streams. This study evaluates the direct incorporation of post-industrial textile waste (100% cotton and cotton–polyester blends) in its native form to develop high-performance cementitious ceiling sheets. Composites were fabricated under a controlled hydraulic compaction pressure of 2.0 MPa, optimized to achieve matrix densification while preserving the integrity of the fibrous network. Viscoelastic recovery of the compressed fibers induced a hierarchical double-porosity architecture characterized by macro-voids and hollow fiber lumens. This microstructural evolution reduced thermal conductivity to 0.091 W/m·K, approximately 50% lower than commercial cement–fiber benchmarks—without compromising mechanical compliance. Scanning Electron Microscopy (SEM) revealed a mechanistic decoupling between water absorption and dimensional stability. Although the CP15 formulation (15 wt.% cotton–polyester) exhibited high moisture uptake (~21%), thickness swelling remained limited to 1.35%. This dimensional stability is attributed to the hydrophobic polyester framework, which bridges microcracks and constrains hygroscopic expansion within the cellulosic phase. The optimized CP15 composite achieved a Modulus of Rupture (MOR) of 8.75 MPa, exceeding ISO 8336 Category C, Class 2 requirements. Despite increased thickness, the areal density (10.84 kg/m²) remains compatible with standard gypsum-grade suspension systems, eliminating the need for structural modification. These findings establish a scalable, direct-valorization strategy for circular construction materials delivering enhanced thermal insulation and robust performance under tropical climatic conditions. Full article

Accurate wave prediction in coastal waters is essential for marine safety and engineering, yet it is significantly influenced by uncertainties in wind forcing and dissipation parameterization. This study evaluates the sensitivity of the SWAN model around the Korean Peninsula using 2021 data from 138 observation stations. To address structural biases in wind fields, the Drag Coefficient Scaling Factor (CDFAC) was implemented alongside the Komen and ST6 physics packages. While the Komen scheme provided stable performance under normal conditions, the ST6 + CDFAC configuration exhibited superior physical consistency during extreme events. Notably, applying CDFAC to the ST6 package reduced the high-wave (H_s > 3 m) RMSE by approximately 32.7%, decreasing from 0.52 m to 0.35 m. Bathymetric stratified analysis further confirmed that the ST6 scheme maintains robust performance in offshore and deep-water regions (depth > 50 m), achieving a correlation of 0.94 and an RMSE of 0.20 m. This is attributed to ST6’s frequency-dependent saturation approach, which effectively decouples wind-sea and swell components in environments where whitecapping dissipation is the governing energy sink. In contrast, improvements in coastal waters (depth < 50 m) were moderated by topographical dissipation mechanisms such as bottom friction and depth-induced breaking. These findings demonstrate that integrating wind input bias correction with frequency-dependent dissipation physics is vital for reliable wave forecasting and coastal disaster mitigation. Full article

This work introduces Hybrid Membrane–Neural P systems (HMN P systems), a computational model that integrates principles from membrane computing and spiking neural P systems. The resulting framework offers a versatile foundation for the development of bio-inspired arithmetic architectures. Within this setting, we propose a compact family of arithmetic kernels capable of executing signed addition, subtraction, multiplication, and division in both modular and non-modular arithmetic domains. By leveraging intrinsic spike aggregation, spike–anti-spike annihilation, and exhaustive rule application, the proposed designs achieve efficient and reliable arithmetic computation in a constant number of simulation steps under exhaustive semantics and assuming synchronized input, independent of operand values. Addition and subtraction are executed intrinsically upon spike arrival, requiring no internal computation steps, while multiplication and division are completed in a single simulation step by one neuron. Furthermore, we introduce a modular-reduction kernel that operates in two simulation steps with a single neuron, and leverage its modular structure to construct modular multiplication and division through composition with non-modular arithmetic modules. Comparative evaluations against representative SNP and SNQ arithmetic designs demonstrate that HMN kernels achieve operand-independent execution time while requiring fewer neurons. Distinct from most existing approaches, the HMN framework natively supports signed operands through a dual-spike representation, thereby eliminating the need for auxiliary sign-handling mechanisms. Asynchronous spike arrivals can be managed by an optional synchronization membrane; since this mechanism is decoupled from the arithmetic kernels, its overhead is excluded from kernel performance and reported separately. Collectively, these results establish HMN systems as an efficient and modular platform for constant-time arithmetic computation, offering reusable arithmetic kernels that serve as a foundation for higher-level constructions, including those arising in elliptic-curve and modular arithmetic. Full article

Urban freight and service operations represent a critical challenge for cities, contributing to greenhouse gas emissions, congestion, and competition for curb space. In addition to parcel deliveries, many service trips combine transport with installation, maintenance, or packaging recovery, generating long vehicle dwell times and inefficient use of public space. This paper investigates alternative operational scenarios for such activities, evaluating technological and organizational options that can reduce their environmental and spatial impacts. The study compares a diesel LCV baseline with four zero-emission configurations: battery electric LCVs; battery electric LCVs integrated with micro-hubs and cargo e-bikes; hydrogen fuel cell LCVs for long-range operations, and hydrogen fuel cell LCVs combined with cargo e-bikes via micro-hubs. The methodological framework is based on a vehicle routing problem (VRP) formulation supported by empirical data from Rome. It integrates indicators of energy use, carbon emissions, and curb-side occupation, and it includes the spatial representation of routes on urban and inter-urban maps to highlight operational differences across the five scenarios. Results indicate that zero-emission vehicles can eliminate tailpipe emissions, while logistics reorganization through decoupling improves the use of public space and enables the recovery of packaging materials. Battery solutions appear best suited to short and medium distances, whereas hydrogen is advantageous for longer routes. Overall, the study shows that combining technological and organizational measures provides a robust pathway toward sustainable logistics and more efficient service operations in metropolitan contexts. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in hazardous and dynamic environments, where path planning requires balancing competing objectives beyond simple distance minimisation. Classical planners such as Dijkstra, A*, and RRT* generate paths efficiently but often overlook mission-specific trade-offs involving energy use, risk avoidance, and reward maximisation. This work proposes a unified evaluation framework that integrates grid-based (Dijkstra, A*, weighted A*) and sampling-based (RRT, CARRT*) planners within parameterised environments embedding a range of functions into penalty and reward zones. A global cost function, $J=\alpha L+\beta E+\gamma P-\delta R,$ is applied post hoc to decouple path generation from mission prioritisation, enabling rapid reassessment under changing objectives such as low-fuel, high-safety, or speed-priority scenarios. Experiments conducted on an Apple M2 CPU, repeated three times per configuration to ensure statistical robustness, demonstrate that CARRT* achieves the lowest mission costs and highest efficiency for fuel- and time-sensitive missions, while deterministic grid-based planners perform better in safety- and reward-oriented contexts in four environments. These findings indicate that optimal UAV path planning depends not only on algorithmic efficiency but also on aligning planner choice with mission priorities. The framework provides a reproducible methodology for benchmarking and deploying mission-aware path planning strategies in research and operational settings. Full article

Digitalisation is reshaping shipyard production, yet its methodological foundations remain fragmented across simulation, optimisation, Artificial Intelligence (AI), and Digital Twin (DT) research streams. This paper presents a domain-specific methodological review of shipyard production modelling from 2010 to 2025, synthesising advances in Discrete-Event Simulation (DES), multi-objective optimisation, hybrid simulation–optimisation architectures, Machine Learning (ML), reinforcement learning (RL), and DT-enabled cyber-physical systems. Using an explicit evaluative framework based on integration depth, validation basis, and decision scope, the review differentiates between analytically mature but execution-decoupled DES/optimisation approaches and integration-rich yet variably validated DT and AI-driven systems. The analysis shows that hybrid DES-optimisation frameworks currently represent the most operationally credible class of methods, delivering measurable production improvements under structured conditions, whereas many DT and AI contributions prioritise architectural integration and data synchronisation over longitudinal yard-wide KPI validation. A comparative assessment of simulation platforms, optimisation engines, and manufacturing execution system/enterprise resource planning/product lifecycle management infrastructures highlights the central role of structured product–process–resource data and execution-layer connectivity, while severe confidentiality constraints and the scarcity of openly available industrial datasets continue to limit reproducibility and benchmarking. Overall, shipyard production research is progressing toward increasingly integrated and cyber-physical systems, but sustained yard-scale validation and shared benchmark development remain critical prerequisites for translating architectural sophistication into demonstrable operational impact. Full article

Laparoscopic surgery provides minimally invasive access to the abdominal cavity but poses control challenges for robotic systems due to the fulcrum constraint at the abdominal wall and the simultaneous interaction of the instrument with both the abdominal wall and internal soft tissue. While current clinical platforms (e.g., da Vinci) primarily rely on visual feedback and do not possess force sensors at the instrument tip, the transition to autonomous robotic surgery requires precise force feedback to ensure safety and effective tissue manipulation. Therefore, developing methods to decouple interaction forces using a single force sensor configuration is a critical enabling technology for future instrumented surgical robots. This paper presents a force-decoupling method that estimates, using only one force sensor, the individual forces applied to the abdominal wall and to internal soft tissue through a viscoelastic modeling approach based on Maxwell elements. Two configurations were evaluated, showing that a single-element Maxwell model provides the best trade-off between accuracy and computational complexity, achieving estimation errors of 9% and 13% for abdominal wall forces, with a root mean square error (RMSE) below 0.36 N. The method was implemented and experimentally validated in a force-controlled robotic system, demonstrating its effectiveness in improving force regulation and interaction safety without requiring additional sensors. Full article

Background/Objectives: Multimodal neuroimaging, particularly the integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), has emerged as a key methodology for investigating brain function and classifying neural activity. However, the efficient fusion of these two signals remains a formidable challenge due to their significant spatio-temporal heterogeneity. This paper presents the BiSTF-Net, which integrates decoupled and bi-directional spatio-temporal fusion mechanisms to enhance the performance of cognitive task recognition. Methods: In BiSTF-Net, the spatial features of EEG and fNIRS are mutually guided and enhanced through an efficient bi-directional cross modal guidance (Bi-CMG). Then, the temporal latencies of fNIRS signals are aligned in a data-driven manner using adaptive temporal alignment (ATA). Subsequently, the aligned features are deeply fused into a modality-invariant, discriminative representation via a symmetric cross-attention fusion (SCAF) module. Results: Evaluated on the mental arithmetic (MA), motor imagery (MI), and word generation (WG) tasks, the BiSTF-Net achieves average accuracies of 83.33%, 82.09%, and 84.99% respectively. Conclusions: The BiSTF-Net exhibits superior performance compared to the existing methods, offers a robust and interpretable solution for multimodal EEG-fNIRS cognitive task classification, and provides a methodological foundation for future extensions to other multimodal data and broader real-world clinical applications. Full article

Circular economy (CE) decouples value creation from virgin resource use and waste in the medical device sector, which faces stringent patient-safety, quality, and regulatory obligations. Green Additive Manufacturing (AM) offers a precise, digitally driven route to implement CE through dematerialization, on-demand localized production, topology optimization, and material circularity. In this study, a comprehensive CE framework is tailored to medical device manufacturing that integrates eco-design, material circularity, remanufacturing, and regulatory compliance across the product life cycle. Methods include an International Organization for Standardization (ISO) 14040/44-aligned life cycle assessment, process energy metering, sterilization-compatibility studies, mechanical/biocompatibility verification to relevant standards, and a techno-economic/circularity analysis with Monte Carlo uncertainty quantification. Three case studies are explored using bio-based PA11 (selective laser sintering), recycled polyethylene terephthalate glycol (fused deposition modeling), and low-volatile organic carbon biocompatible photopolymer (stereolithography): (1) a patient-specific wrist orthosis, (2) a dental surgical guide, and (3) a single-use catheter Y-connector. Results indicate 38–68% reductions in embodied greenhouse-gas emissions, 22–54% energy savings per functional unit, and up to 80% mass recapture through in-process powder/runner reuse while maintaining clinical performance and regulatory conformity. Design-for-circularity patterns (DfC) were created for DfDisassembly, DfSter, DfTraceability, DfUpgrade, and DfPowder-Loop and provide a governance architecture combining ISO 13485 QMS, ISO 10993 biological evaluation, the European Union’s Medical Device Regulation (Regulation (EU) 2017/745), and the United States Food and Drug Administration’s guidance on Additive Manufactured (3D-printed) medical devices, guidance with unique device identification for closed-loop returns. The paper concludes with an Industry 5.0 roadmap for hospital-proximate micro-factories, materials passports, and digital product passports enabling verified circular flows at scale. Full article

Addressing the problems of the difficulty in reconstructing high-resolution hyperspectral images caused by dynamic degradation characteristics, the poor adaptability of traditional static degradation models, and the oversimplified noise modeling, this paper proposes a degradation-aware dynamic Fourier network (DADFN) for hyperspectral super-resolution. This method employs a dual-channel split module to decouple and encode spectral and spatial degradation information, realizes the independent mapping of spectral and spatial features via a multi-layer perceptron module, and integrates a spectral–spatial dynamic cross-attention fusion module to generate 3D dynamic blur kernels tailored to different bands and spatial positions. The proposed method designs a multi-scale spectral–spatial collaborative constraint (MSSCC) loss function to ensure the coordinated optimization of modeling rationality, spectral continuity, and spatial detail fidelity. Experiments on the CAVE and Harvard benchmark datasets demonstrate that the DADFN algorithm outperforms the baseline methods in all evaluation metrics, which proves the proposed method’s strong robustness in real-world complex degradation scenarios. This method provides a novel solution balancing physical interpretability and performance superiority for hyperspectral image super-resolution tasks and holds significant value for advancing its applications in remote sensing monitoring, precision agriculture, and other related fields. Full article

Wildfire events are increasing in frequency and severity, creating an urgent need for early, accurate, and energy-efficient forest fire prediction systems that can operate at a large scale. A fundamental challenge in edge-level forest fire prediction lies in jointly achieving high detection accuracy while minimizing wireless transmissions and communication-related energy consumption. This paper proposes a communication-aware hierarchical wireless sensor network (WSN) framework that performs fire versus normal environmental state classification directly at the network edge. Multi-modal physical and constrained virtual sensor readings are fused into short-term temporal supervectors and processed locally using lightweight random forest classifiers deployed on sensor nodes and cluster heads. A temporal 2-of-3 voting mechanism is applied at the edge to suppress transient noise and improve prediction reliability before triggering communication. The proposed design enables selective, event-driven transmission, where only temporally validated abnormal states are forwarded through the hierarchy, thereby decoupling detection accuracy from continuous data reporting. Extensive experiments using real multi-modal environmental sensor data and statistically rigorous 5-fold GroupKFold cross-validation—ensuring strict node-level separation between training and testing—demonstrate the effectiveness of the approach. The proposed framework achieves a node-level accuracy of 98.82 ± 1.75% and a scenario-level detection accuracy of 96.52 ± 0.89%. Compared to periodic reporting and the LEACH protocol, the system reduces wireless transmissions by over 66% and communication-related energy consumption by more than 66% across network sizes ranging from 100 to 1000 nodes. The main contributions of this work are summarized as follows: (1) a communication-aware hierarchical Edge-AI framework for early forest fire prediction that performs local inference and temporal validation directly at sensor nodes; (2) a constrained virtual sensing strategy integrated with temporal supervector modeling to enhance spatial coverage while preserving reliability; and (3) a statistically rigorous large-scale evaluation demonstrating joint optimization of prediction accuracy, transmission reduction, and communication energy efficiency across network sizes ranging from 100 to 1000 nodes. These results show that accurate early forest fire prediction can be achieved through edge-level inference and selective communication, substantially extending network lifetime while maintaining statistically reliable detection performance. Full article

Synthetic aperture radar (SAR) serves as a prominent remote sensing (RS) technology, permitting continuous maritime surveillance regardless of weather or time. Although deep learning-based detectors have achieved promising results in SAR imagery, the majority of current algorithms rely on axis-aligned bounding boxes, which are insufficient for accurately representing arbitrarily oriented ships, especially under speckle noise, complex coastal clutter, and real-time deployment constraints. To address this limitation, we propose a YOLO-based oriented ship detector (YOSDet). Specifically, a dynamic aggregation module (DAM) is incorporated into the backbone to enhance feature representation against non-stationary backscattering. An objective-guided detection head (OGDH) is developed to decouple classification and localization, complemented by a localization quality estimator (LQE) to calibrate classification confidence by mitigating the impact of scattering center shifts. Comparative evaluations conducted on three public SAR ship detection benchmarks validate the effectiveness of YOSDet. The proposed model outperforms existing detectors, achieving $mAP$ scores of 96.8%, 88.5%, and 67.3% on the SSDD+, HRSID, and SRSDD-v1.0 datasets, respectively. Furthermore, the consistency of our approach in both nearshore and offshore environments is confirmed through rigorous quantitative and qualitative assessments. Full article

The bovine tongue is a complex and very important muscular and gustatory organ, yet a comprehensive understanding of its gustatory apparatus across diverse genetic resources remains elusive. In this study, we conducted a multidimensional analysis of the lingual morphology and taste bud (TB) distribution in 40 specimens from 12 representative bovine breeds and species across China, encompassing Bos taurus taurus (Taurine cattle), Bos taurus indicus (Zebu cattle), Bubalus bubalis (water buffalo), and Bos grunniens (domestic yak). Morphometric measurements and histological quantifications were integrated to evaluate the influence of species, sex, age, and geographical factors. Given the relatively limited sample size per breed, these findings are presented as exploratory research. Our results revealed that yak and water buffalo showed the most distinct morphological patterns of mechanical papillae compared to the other populations. Taurine and Zebu cattle displayed more similar lingual morphology traits. Although high phenotypic correlations were observed between lingual morphometric parameters and quantitative papillae indicators, factors such as age, altitude, and feeding methods showed minimal influence on lingual phenotypic variation within this cohort (p > 0.05). Furthermore, we constructed a topological atlas of TB distribution, revealing that TB distribution patterns are decoupled from macro-anatomical dimensions, highlighting the complexity of the bovine gustatory system. These findings provide a quantitative baseline for ruminant comparative anatomy and offer structural insights into the evolutionary adaptation and nutrient regulation mechanisms of diverse bovine species in varying environments. Full article

Handling class imbalance remains a central challenge in machine learning, particularly in pattern recognition tasks where identifying rare but critical anomalies is of paramount importance. Traditional generative models often decouple data synthesis from classification, leading to a distribution mismatch that limits their practical benefit. To address these shortcomings, we introduce Causal Cooperative Networks (CCNETS), a modular framework that establishes a functional causal link between generation, inference, and reconstruction. CCNETS is composed of three specialized cooperative modules: an Explainer for latent feature abstraction, a Reasoner for probabilistic label prediction, and a Producer for context-aware data synthesis. These components interact through a dynamic causal feedback loop, where classification outcomes directly guide targeted sample synthesis to adaptively reinforce vulnerable decision boundaries. A key innovation, our proposed Zoint mechanism, enables the adaptive fusion of latent and observable features, enhancing semantic richness and decision-making robustness under uncertainty. We evaluated CCNETS on two distinct real-world datasets: Credit Card Fraud Detection dataset, characterized by extreme imbalance (fraud rate < 0.2%), and the AI4I 2020 Predictive Maintenance dataset (failure rate < 4%). Across comprehensive experimental setups, CCNETS consistently outperformed baseline methods, achieving superior F1-scores, and AUPRC. Furthermore, data synthesized by CCNETS demonstrated enhanced generalization and learning stability under limited data conditions. These results establish CCNETS as a scalable, interpretable, and hybrid soft computing framework that effectively aligns synthetic data with classifier objectives, advancing robust imbalanced learning. Full article

Task-oriented dialogue systems face a tension between comprehensive constraint elicitation (task adequacy) and conversational efficiency (minimizing turns). Current preference learning frameworks treat preferences as static, unable to capture the dynamic evolution of interaction states that evolve across dialogue progression. We present Dual-DPO, a framework that embeds multi-objective preferences into data construction via turn-aware scoring. Our approach decouples objective balancing from policy updates through offline preference scalarization, addressing the optimization instability challenges in online multi-objective reinforcement learning. Experiments on MultiWOZ 2.4 demonstrate 28–35% dialogue turn reduction while maintaining Joint Goal Accuracy > 89% ($p<0.001$). Pareto frontier analysis shows 94% coverage with hypervolume $HV=0.847$. Independent expert evaluation by 10 PhD-level researchers ($n=300$ assessments, inter-rater agreement $\alpha =0.78$) confirms 32% user satisfaction improvement ($p<0.001$). Theoretical analysis demonstrates that offline scalarization, which correlates with improved optimization stability, achieves $3.2\times$ lower gradient variance than online multi-reward optimization by eliminating sampling stochasticity. Our approach enables balanced treatment of competing objectives through Pareto-optimal trade-offs. These results highlight a symmetric and balanced treatment of competing objectives within a Pareto-optimal optimization framework. Full article