Search Results (4,500)

Objective: Cardiac monitoring in community and home environments requires automated operation, cross-state robustness, and interpretable feedback under resource-constrained and uncontrolled conditions. Unlike accuracy-driven ECG–PCG studies focusing on diagnostic performance, this work emphasizes systematic modeling of cardiac electromechanical coupling for long-term monitoring and engineering feasibility validation. Methods: An automated ECG–PCG coupling analysis and semantic reporting framework is proposed, covering signal preprocessing, event detection and calibration, multimodal coupling feature construction, and rule-constrained LLM-assisted interpretation. Electrical events from ECG are used as global temporal references, while multi-stage consistency correction mechanisms are introduced to enhance the stability of mechanical event localization under noise and motion interference. A structured electromechanical feature set is constructed to support fully automated processing. Results: Experimental results demonstrate that the proposed system maintains coherent event sequences and stable coupling parameter extraction across resting, movement, and emotional stress conditions. The incorporated LLM module integrates precomputed multimodal metrics under strict constraints, improving report readability and consistency without performing autonomous medical interpretation. Conclusions: This study demonstrates the methodological feasibility of an ECG–PCG coupling analysis framework for long-term cardiac state monitoring in low-resource environments. By integrating end-to-end automation, electromechanical coupling features, and constrained semantic reporting, the proposed system provides an engineering-oriented reference for continuous cardiac monitoring in community and home settings rather than a clinical diagnostic solution. Full article

Ship-to-ship (STS) transfer has become a major concern for maritime security and regulatory authorities, as it is frequently exploited for smuggling and other illicit activities. Accurate and timely identification of STS events is therefore essential for effective maritime supervision. Existing monitoring approaches, however, suffer from two inherent limitations: AIS-based surveillance is vulnerable to intentional signal shutdown or manipulation, and remote-sensing-based ship detection alone lacks digital identity information and cannot assess the legitimacy of transfer activities. To address these challenges, we propose a Multi-source Observation framework with SAR and AIS (MOSA), which integrates SAR imagery with AIS data. The framework consists of two key components: STS-YOLO, a high-precision fine-grained ship detection model, in which a dynamic adaptive feature extraction (DAFE) module and a multi-attention mechanism (MAM) are introduced to enhance feature representation and robustness in complex maritime SAR scenes, and the SAR-AIS Consistency Analysis Workflow (SACA-Workflow), designed to identify suspected abnormal STS behaviors by analyzing inconsistencies between physical and digital ship identities. Experimental results on the SDFSD-v1.5 dataset demonstrate the quantitative performance gains and improved fine-grained detection performance of STS-YOLO in terms of standard detection metrics. In addition, generalization experiments conducted on large-scene SAR imagery from the waters near Panama and Singapore, in addition to multi-satellite SAR data (Capella Space and Umbra) from the Gibraltar region, validate the cross-regional and cross-sensor robustness of the proposed framework. The effectiveness of the SACA-Workflow is evaluated qualitatively through representative case studies. In all evaluated scenarios, the SACA-Workflow effectively assists in identifying suspected abnormal STS events and revealing potential AIS inconsistency indicators. Overall, MOSA provides a robust and practical solution for multi-scenario maritime monitoring and supports reliable detection of suspected abnormal STS activities. Full article

In digital-measurement-assisted assembly of large aircraft components, the spatial layout of Enhanced Reference System (ERS) points determines coordinate transformation accuracy and stability. To address manual layout limitations—specifically low efficiency, occlusion susceptibility, and physical deployment limitations—this paper proposes an adaptive planning method under engineering constraints. First, based on the Guide to the Expression of Uncertainty in Measurement (GUM) and weighted least squares, an analytical transformation sensitivity model is constructed. Subsequently, a multi-scale sample library generated via Monte Carlo sampling trains a high-precision BP neural network surrogate model, enabling millisecond-level sensitivity prediction. Combining this with ray-tracing occlusion detection, a weighted genetic algorithm optimizes transformation sensitivity, spatial uniformity, and station distance within feasible ground and tooling regions. Experimental results indicate that the method effectively avoids occlusion. Specifically, the Registration-Induced Error (RIE) is controlled at approximately 0.002 mm, and the Registration-Induced Loss Ratio (RILR) is maintained at about 10%. Crucially, comparative verification reveals an RIE reduction of approximately 40% compared to a feasible uniform baseline, proving that physics-based data-driven optimization yields superior accuracy over intuitive geometric distribution. By ensuring strict adherence to engineering constraints, this method offers a reliable solution that significantly enhances measurement reliability, providing solid theoretical support for automated digital twin construction. Full article

Accurate joint estimation of heterogeneous hydraulic conductivity fields and time-varying contaminant source parameters in groundwater systems constitutes a challenging high-dimensional inverse problem, particularly under sparse observational conditions and high computational demands. To alleviate this limitation, this study proposes an autoregressive depthwise convolutional neural network (AR-DWCNN) as a lightweight surrogate model for coupled groundwater flow and contaminant transport simulations. The proposed model employs depthwise separable convolutions and dense connectivity within an encoder–decoder framework to capture nonlinear flow and spatiotemporal transport dynamics while reducing model complexity and computational demand relative to conventional convolutional architectures. The AR-DWCNN is further integrated with an enhanced Iterative Local Updating Ensemble Smoother incorporating Levenberg–Marquardt regularization, enabling efficient joint inversion of high-dimensional hydraulic conductivity fields and multi-period contaminant source strengths. Numerical experiments conducted on a synthetic two-dimensional heterogeneous aquifer demonstrate that the surrogate-assisted inversion framework achieves posterior estimates that closely match those obtained using the numerical forward model, while significantly improving computational efficiency. These results indicate that the AR-DWCNN-based inversion method provides an effective and scalable solution for high-dimensional groundwater contaminant transport inverse problems, offering practical potential for uncertainty quantification and remediation design in complex subsurface systems. Full article

Medical images obtained from advanced imaging devices play a crucial role in supporting disease diagnosis and detection. Nevertheless, acquiring such images is often costly and storage-intensive, and it is time-consuming to diagnose individuals. The use of artificial intelligence (AI)-based automated diagnostic systems provides potential solutions to address the limitations of cost and diagnostic time. In particular, deep learning and explainable AI (XAI) techniques provide a reliable and robust approach to classifying medical images. This paper presents a hybrid model comprising two networks, ResNext101_32x8d and Swin Transformer to differentiate four categories of Alzheimer’s disease: no dementia, very mild dementia, mild dementia, and moderate dementia. The combination of the two networks is applied to imbalanced data, trained on 5120 MRI images, validated on 768 images, and tested on 512 other images. Grad-CAM and LIME techniques with a saliency map are employed to interpret the predictions of the model, providing transparent and clinically interpretable decision support. The proposed combination is realized through a TensorFlow framework, incorporating hyperparameter optimization and various data augmentation methods. The performance evaluation of the proposed model is conducted through several metrics, including the error matrix, precision recall (PR), receiver operating characteristic (ROC), accuracy, and loss curves. Experimental results reveal that the hybrid of ResNext101_32x8d and Swin Transformer achieved a testing accuracy of 98.83% with a corresponding loss rate of 0.1019. Furthermore, for the combination “ResNext101_32x8d + Swin Transformer”, the precision, F1-score, and recall were 99.39%, 99.15%, and 98.91%, respectively, while the area under the ROC curve (AUC) was 1.00, “100%”. The combination of proposed networks with XAI techniques establishes a unique contribution to advance medical AI systems and assist radiologists during Alzheimer’s disease screening of patients. Full article

This work presents the design and experimental validation of a 2 × 2 MIMO communication system assisted by a directly modulated analog radio-over-fiber (A-RoF) fronthaul, targeting low-complexity connectivity solutions for underserved/remote regions. The study details the complete end-to-end architecture, including a wireless access segment to complement the 20-km optical fronthaul link. The system is implemented on an software defined radio (SDR) platform using GNU Radio 3.7.11, running on Ubuntu 18.04 with kernel 4.15.0-213-generic. It also employs adaptive modulation driven by real-time signal-to-noise ratio (SNR) estimation to keep bit error rate (BER) close to zero while maximizing throughput. Performance is characterized over 20 km of single-mode fiber (SMF) using coarse wavelength division multiplexing (WDM) and assessed through root mean square error vector magnitude (${\mathrm{EVM}}_{\mathrm{RMS}}$), throughput, and spectral integrity. The results identify an optimum radio-frequency drive region around $-16$ dBm enabling high-order modulation (e.g., 256-QAM), whereas RF input powers above approximately $-10$ dBm increase ${\mathrm{EVM}}_{\mathrm{RMS}}$ due to nonlinearity in the RF front-end/low-noise amplifier (LNA) and direct modulation stage, forcing the adaptive scheme to reduce modulation order and throughput. Over the optical-power sweep, when the incident optical power exceeds approximately $-8$ dBm, the system reaches ∼130 Mbps (24-MHz channel) with ${\mathrm{EVM}}_{\mathrm{RMS}}$ approaching ∼1%, highlighting the need for careful joint tuning of RF drive, optical launch power, and wavelength allocation across transceivers. Finally, the integrated access link employs diplexers for transmitter/receiver separation in a 2 × 2 configuration with 2.8 m antenna separation and low channel correlation, demonstrating a 10 m proof-of-concept range and enabling end-to-end spectrum/EVM/throughput observations across the full communication chain. Full article

Operational inefficiencies hinder progress in the architecture, engineering, and construction (AEC) industry. Platform-based approaches systematically utilize standardized and variable components and workflows to support customization and reuse across projects, making them viable solutions. This study addresses two research questions: (1) What are the current trends and challenges facing platform-based approaches in the AEC industry? (2) What research opportunities and future directions exist for platform-based approaches in the AEC industry? It conducted a bibliometric review and trend analysis using data collected from Engineering Village, Google Scholar, ScienceDirect, Scopus, SpringerLink, and Web of Science. Research interest increased from 16 publications between 2001 and 2014 to 18 publications in 2024. The UK dominates the field with 193 publications; however, collaboration across author groups remains weak. The trend analysis revealed an imbalanced research distribution, with 70% of publications focusing on product platforms and technological innovation, while governance, knowledge sharing, and stakeholders remain underexplored. Insights from the automotive and consumer goods industries highlight transferable strategies. The novelty and timeliness of this research lie in the multi-layer analyses, which integrated artificial intelligence-assisted bibliometric analysis with qualitative thematic and cross-industry analysis to generate insights on trends and challenges, translating them into a roadmap addressing AEC industry challenges. Full article

The growing volume of decommissioned wind turbine blades, primarily made of glass fibre-reinforced polymers (GFRP), poses major recycling challenges. This study explores a microwave (MW)-assisted thermochemical recycling to recover high-quality fibres from GFRP waste. Two routes were evaluated: (i) a dry route using direct MW heating, and (ii) a semi-wet route involving solvent pre-swelling followed by microwave pyrolysis. The dry route suffered from poor heating due to GFRP’s inherently low dielectric loss, whereas the semi-wet route enabled more effective resin degradation. Five swelling agents were tested: acetic acid (AcOH), hydrogen peroxide (H₂O₂), an AcOH/H₂O₂ mixture, dimethylformamide (DMF), and dimethyl sulfoxide (DMSO). Among these, DMSO achieved 92% resin removal in 9 min at 350 °C. Recycled fibres retained 1.48 ± 0.41 GPa strength (81% of virgin). Gas chromatography–mass spectrometry (GC–MS) analysis of pyrolysis oils revealed predominantly phenolic products with limited bisphenol A (BPA) retention. To demonstrate practical relevance, the semi-wet method was applied to real wind blade waste, where recovered fibres retained 72% of their tensile strength versus virgin fibres. These results indicate that the process remains effective for industrially aged GFRP. This study confirms the feasibility of MW-based semi-wet recycling and offers insights to support future process refinement, which will ultimately contribute to more sustainable end-of-life solutions for GFRP waste. Full article

Cocoa bean shells (CBS) represent a significant by-product of the transformation of cocoa beans, constituting approximately 15% of the total cocoa bean weight. Recently, interest in exploring the potential of these shells as a sustainable source of functional ingredients for use in cosmetics and nutraceuticals has grown. The present study investigates microwave-assisted subcritical water extraction (MASWE) as a green and fast technique to recover bioactive compounds from CBS. A flash extraction (five minutes) at 170 °C yielded a maximum of 45.78 mg of gallic acid equivalents (GAE) per gram of CBS, which was higher than that obtained using conventional conditions (25.73 mg GAE/g CBS with 50% acetone solution). Additionally, the HPLC profile of the extract from MASWE revealed a significant increase in hydroxybenzoic acids and catechin, compared to the conventional extract. Following the optimization of the extraction process, seven distinct resins were examined to isolate a bioactive-enriched fraction: Sepabeads SP700 was found to be the most effective resin for concentrating such compounds, increasing both methylxanthines and TPC selectivity up to 4.2-fold. This valorization approach, integrating MASWE and downstream optimization, offers an innovative strategy to recover added-value products from CBS in line with green extraction and nutraceutical innovation. Full article

Multimodal multiobjective optimization problems (MMOPs) are widely encountered in real-world applications. While numerous evolutionary algorithms have been developed to locate equivalent Pareto-optimal solutions, existing Multimodal Multiobjective Evolutionary Algorithms (MMOEAs) often struggle to handle large-scale decision variables and sparse Pareto sets due to the curse of dimensionality and unknown sparsity. To address these challenges, this paper proposes a novel approach named MASR-MMEA, which stands for Large-scale Sparse Multimodal Multiobjective Optimization via Multi-stage Search and Reinforcement Learning (RL)-assisted Environmental Selection. Specifically, to enhance search efficiency, a multi-stage framework is established incorporating three key innovations. First, a dual-strategy genetic operator based on improved hybrid encoding is designed, employing sparse-sensing dynamic redistribution for binary vectors and a sparse fuzzy decision framework for real vectors. Second, an affinity-based elite strategy utilizing Mahalanobis distance is introduced to pair real vectors with compatible binary vectors, increasing the probability of generating superior offspring. Finally, an adaptive sparse environmental selection strategy assisted by Multilayer Perceptron (MLP) reinforcement learning is developed. By utilizing the MLP-generated Guiding Vector (GDV) to direct the evolutionary search toward efficient regions and employing an iteration-based adaptive mechanism to regulate genetic operators, this strategy accelerates convergence. Furthermore, it dynamically quantifies population-level sparsity and adjusts selection pressure through a modified crowding distance mechanism to filter structural redundancy, thereby effectively balancing convergence and multimodal diversity. Comparative studies against six state-of-the-art methods demonstrate that MASR-MMEA significantly outperforms existing approaches in terms of both solution quality and convergence speed on large-scale sparse MMOPs. Full article

The growing interest in sustainable and structurally diverse sorbent materials has intensified the search for effective biosorbents that can complement or replace conventional adsorbents. This work presents the potential use of Chlorella vulgaris dried biomass and its modifications (ultrasound-treated, lipid-extracted, and combined forms) for diclofenac (DCF) sorption from aqueous solutions. It was demonstrated that sorption efficiency significantly depends on the solution’s pH. Lowering the pH from 6 to 2 increases the sorption from 5% to 68%, while 99% desorption occurred at pH 9. The adsorption isotherms for intact biomass and after lipid extraction (CV-E2) are best described by the Langmuir and Freundlich models; for ultrasonically treated biomass (CV-E1) by the Temkin model; and for ultrasound-assisted solvent extraction (CV-E3) by the Dubinin–Radushkevich model. These findings demonstrate that cellular lipids and particle characteristics critically govern sorption mechanisms, highlighting dried Chlorella biomass as a structurally and chemically tunable biosorbent. Importantly, the key sorption experiments were performed under strongly acidic conditions (pH 2), which differ from typical wastewater or surface water matrices. Therefore, the presented results should be regarded as a proof of concept illustrating the mechanistic potential of dried Chlorella biomass as a tunable sorptive material, with prospective relevance for separation science and laboratory-scale analytical applications rather than direct environmental remediation. Full article

Accurate genotyping of small insertions and deletions (InDels; <5 bp) remains technically challenging in routine molecular breeding, largely due to the limited resolution of agarose gel electrophoresis and the labor-intensive nature of polyacrylamide-based assays. Here, we present the Tri-Primer Amplification Refractory Mutation System (TP-ARMS), a simple and cost-effective PCR-based strategy that enables high-resolution genotyping of small InDels using standard agarose gels. The TP-ARMS employs a universal reverse primer in combination with two allele-specific forward primers targeting insertion and deletion alleles, respectively. This design allows clear discrimination of homozygous and heterozygous genotypes using a two-tube PCR workflow. The method showed complete concordance with Sanger sequencing in detecting 1–5 bp InDels across multiple crop species, including rice (Oryza sativa) and quinoa (Chenopodium quinoa). In addition, using a TP-ARMS reduced experimental time by approximately 90% compared with PAGE-based approaches and avoided the high equipment and DNA quality requirements of fluorescence-based assays. The practical applicability of the TP-ARMS was demonstrated in breeding populations, including efficient genotyping of a 3-bp InDel in OsNRAMP5 associated with cadmium accumulation and a 6-bp promoter InDel in OsSPL10 underlying natural variation in rice trichome density across 370 accessions. Collectively, the TP-ARMS provides a robust, scalable, and low-cost solution for precise small InDel genotyping, with broad applicability in marker-assisted breeding and functional genetic studies. Full article

Refuse-derived fuel (RDF) presents a viable strategy to concurrently address the challenges of municipal solid waste management and the need for alternative energy. In this context, the present review systematically synthesizes recent advances in RDF preparation, combustion behavior, and efficient utilization technologies. The study examines the full chain of RDF production—including waste selection, mechanical/optical/magnetic sorting, granulation, briquetting, and chemical modification—highlighting how pretreatment technologies influence fuel homogeneity, calorific value, and emissions. The thermochemical conversion characteristics of RDF are systematically analyzed, covering the mechanism differences among slow pyrolysis, fast pyrolysis, flash pyrolysis, pyrolysis mechanisms, catalytic pyrolysis, fragmentation behavior, volatile release patterns, and kinetic modeling using Arrhenius and model-free isoconversional methods (e.g., FWO). Special attention is given to co-firing and high-efficiency combustion technologies, including ultra-supercritical boilers, circulating fluidized beds, and rotary kilns, where fuel quality, ash fusion behavior, slagging, bed agglomeration, and particulate emissions determine operational compatibility. Integrating recent findings, this review identifies the key technical bottlenecks—feedstock variability, chlorine/sulfur release, heavy-metal contaminants, ash-related issues, and the need for standardized RDF quality control. Emerging solutions such as AI-assisted sorting, catalytic upgrading, optimized co-firing strategies, and advanced thermal conversion systems (oxy-fuel, chemical looping, supercritical steam cycles) are discussed within the broader context of carbon reduction and circular economy transitions. Overall, RDF represents a scalable, flexible, and high-value waste-to-energy pathway, and the review provides insights into future research directions, system optimization, and policy frameworks required to support its industrial deployment. Full article

The development of highly dynamic bipedal robots demands sensing capable of capturing key contact-related signals in real time, particularly the Center of Pressure (CoP). CoP is fundamental for locomotion control and state estimation and is also of interest in biomedical applications such as gait analysis and lower-limb assistive devices. To enable reliable CoP acquisition under dynamic walking, this paper presents a foot-mounted measurement system and an online self-calibration method that adapts sensor scale and bias parameters during locomotion using both external foot sensors and the robot’s proprioceptive measurements. We demonstrate an online self-calibration pipeline that updates foot-sensor scale and bias parameters during a walking experiment on a NAO-V5 platform using a sliding window optimization. The reported results indicate improved within-trial consistency relative to an offline-calibrated reference baseline under the tested walking conditions. In addition, the framework reconstructs a digitized estimate of the vertical ground reaction force (vGRF) from load-cell readings; due to ADC quantization and the discrete offline calibration dataset, the vGRF signal may exhibit stepwise behavior and should be interpreted as a reconstructed (digitized) quantity rather than laboratory-grade continuous force metrology. Overall, the proposed sensing-and-calibration pipeline offers a practical solution for dynamic CoP acquisition with low-cost hardware. Full article

Photovoltaic thermal systems are capable of simultaneously generating electricity and recovering thermal energy from the rear surface of photovoltaic modules. When integrated with an air-source heat pump, the thermal energy recovered from an air-based photovoltaic thermal system can be utilized as an auxiliary heat source, thereby improving heating performance and reducing electricity consumption. In this study, a demonstration-scale performance assessment of an air-based photovoltaic thermal-assisted air-source heat pump system was conducted in a real building located in Asan, South Korea. Performance analysis was based on measured operational data collected over a one-month period in March 2024, corresponding to late-winter to early-spring conditions when heating demand was still present. During the measurement period, the average plane-of-array solar irradiance was approximately 600 W/m², with peak values reaching up to 1000 W/m². Under these conditions, the air-based photovoltaic thermal collector provided average electrical and thermal power outputs of 1.96 kW and 2.2 kW, respectively, while peak outputs reached 3.3 kW for electricity generation and 3.8 kW for thermal energy recovery. The daily thermal energy production remained relatively stable, ranging from 17.8 to 21.7 kWh. Furthermore, approximately 45–60% of the recovered thermal energy was effectively transferred to a buffer tank through an air-to-water heat exchanger, indicating stable solar heat recovery and storage performance. When the recovered thermal energy was supplied to the air-source heat pump during daytime heating operation, a preheating effect was observed, resulting in reduced electricity consumption and improved heating performance. The coefficient of performance increased from 2.24 during nighttime operation to 2.81 under solar-assisted daytime conditions, corresponding to a notable reduction in electricity consumption under solar-assisted daytime operation, compared with nighttime operation without PVT preheating. Overall, the results indicate that, under the tested late-winter to early-spring heating conditions, the integrated air-based photovoltaic thermal and air-source heat pump system can enhance heating performance and reduce electricity consumption, demonstrating its practical feasibility as a solar-assisted heating solution rather than representing generalized annual performance. Full article