Search Results (4,205)

During the switch-off process, the contact erosion generated by the AC contactor will seriously affect its performance, thereby directly influencing the normal operation of the power equipment. Therefore, aiming at the problem of contact erosion caused by contact bounce during the switch-on and switch-off period of AC contactors, this paper designed the driving circuits during the switch-on, holding, and switch-off processes. During the switch-on process, DC excitation was used instead of AC excitation to eliminate or reduce the contact bounce. During the holding process, low-voltage DC was used instead of high-voltage AC to save energy and reduce coil losses. During the switch-off process, the contact current was used as the control factor, and the scheme of shunting control was employed to achieve the goal of few or even no arcs. In addition, in order to detect the high voltage and large current signals in the main circuit, the three-phase voltage acquisition circuit and three-phase current acquisition circuit were designed. Therefore, a whole process dynamic control which included the switch-on, holding, and switch-off was formed. Through simulation testing and relevant experimental testing, the results demonstrated the correctness and effectiveness of the designed circuit. Full article

This article proposes a methodology to assess the feasibility of using seaports as testbeds for emerging WEC models, supporting innovation to accelerate sustainable energy transition. The development of wave energy converters (WECs) requires experimental tests at increasing scales, with wave tanks eventually becoming inadequate due to size limitations. The method includes evaluating model requirements, ocean wave conditions at the port entrance, local wind-generated waves, tides, bathymetry, seabed composition, wave propagation within the port, and operational constraints to identify viable test zones. The methodology was applied to the Port of Sines, Portugal, considering a 1:10 REEFS WEC model. Three potential sites were identified. Shelter is adequate but wave conditions matching the model’s requirements (periods from 1.9 to 3.8 s) only occur approximately 100 h per summer. Local wind-generated waves contribute marginally, limited by the short fetch. Upscaling the model (larger than 1:10) may allow testing under longer-period waves, which occur more frequently. A key limitation of port-based testing is the lack of environmental control. Despite statistical planning, suitable conditions during test campaigns cannot be guaranteed. This trade-off offsets the benefits of unrestricted space and no need for a wave-maker. The methodology proved effective, simplifying site assessment and saving resources. Full article

In order to maintain pedestrian safety in vehicle collisions and enhance collision safety, this paper proposes a rapid prediction method of head injuries for pedestrian protection based on deep learning, which could be used to design and optimize pedestrian protection performance during the vehicle design stage. However, traditional finite element simulation methods involve a large computational effort and long calculation time, and multiple computations are required to obtain the corresponding pedestrian head injury results for engine hood structural optimization. Therefore, to accelerate the design process and save time costs, this paper proposes a deep learning-based method for the rapid prediction of pedestrian head injuries. Compared with traditional finite element simulation techniques, this method will greatly improve the efficiency of obtaining head injury results, and its core lies in establishing a prediction model for pedestrian head injury results. The sample data for establishing the prediction model is defined initially, in which the head injury criterion (HIC) and vehicle structure serve as the output and input of the prediction model, respectively. The voxelization method is used to digitally express the car body structure. Convolutional neural networks (CNNs) such as ResNet50, MobileNet, SqueezeNet, and ShuffleNet are used to train the model. After adjusting the dataset and model hyperparameters, the prediction model with the smallest error is obtained. The cross-validation method was used to verify the robustness and generalization ability of the model. The average error rate of the obtained prediction model for predicting head injuries was 14.28%, which proved the accuracy and applicability of the prediction model. Full article

Intelligent ventilation is positioned as a key axis for reconciling energy efficiency and indoor air quality (IAQ) in residential and non-residential buildings. This review synthesizes 51 recent publications covering control strategies (DCV, MPC, reinforcement learning), IoT architectures and sensor validation, energy recovery (HRV/ERV, anti-frost strategies, low-loss exchangers, PCM-air), active envelope solutions (thermochromic windows) and passive solutions (EAHE), as well as evaluation methodologies (uncertainty, LCA, LCC, digital twin) and smart readiness indicator (SRI) frameworks. Evidence shows ventilation energy savings of up to 60% without degrading IAQ when control is well-designed, but also possible overconsumption when poorly parameterized or contextualized. Performance uncertainty is strongly influenced by occupant emissions and pollutant sources (bioeffluents, formaldehyde, PM2.5). The integration of predictive control, scalable IoT networks, and robust energy recovery, together with life-cycle evaluation and uncertainty analysis, enables more reliable IAQ-energy balances. Gaps are identified in VOC exposure under DCV, robustness to sensor failures, generalization of ML/RL models, and standardization of ventilation effectiveness metrics in natural/mixed modes. Full article

High-quality, interoperable biodiversity information is a prerequisite for effective conservation policy, compliance with European Union (EU) reporting obligations, and efficient environmental decision-making. Greece’s LIFE EL-BIOS (LIFE20 GIE/GR/001317) developed the first National Biodiversity Information System, aiming to aggregate, standardise, and disseminate spatial and non-spatial data for species, habitats, pressures, and trends. This paper provides an economic valuation of this information system as a public, non-market good. We designed a two-stage stated-preference study: (i) a short pre-survey to calibrate initial bids and (ii) the main survey employing double-bounded dichotomous choice (DBDC) contingent valuation with a spike-logit specification. The payment vehicle was a hypothetical monthly subscription in a post-LIFE scenario. The instrument measured time savings (hours), perceived reliability (Likert 1–5), and key demographics/roles. A total of 167 valid responses were collected in September 2025. Participants reported an average of 5.2 h saved per use (median 4; max 14). Among those expressing willingness to pay (WTP), 77% rated EL-BIOS reliability as “High/Very high”. Econometric results indicate time savings as the strongest positive determinant of WTP; perceived reliability is positive and marginally significant; years of experience are negatively associated with acceptance; and cost has a strong negative effect. Mean WTP is estimated at €6.7 per month (median €3.5). Notably, 64% of those unwilling to pay declared protest motives (data should remain public and free). Accordingly, non-payment is decomposed into true zero WTP versus protest-based refusal, i.e., refusal to pay despite acknowledging value. This high protest share reflects principled opposition to paying for public biodiversity data rather than low perceived value of the system. The EL-BIOS database generates measurable productivity gains and social value both through positive WTP and principled protest responses supporting open public data. These findings inform policy on sustainable financing, governance, and long-term operation of national biodiversity information systems. Full article

Efficient documentation and traceability of construction waste are essential for meeting the objectives of the European Green Deal and the Circular Economy. In Poland, the national Database on Products, Packaging, and Waste Management (BDO) serves as the central platform for recording and reporting waste flows, including those generated by the construction sector. However, its current structure imposes substantial administrative burdens, particularly on large-scale projects involving thousands of waste transports. This study examines the documentation workflow within the BDO system as applied to construction activities. Using process mapping, field studies, and interviews, the research identifies key bottlenecks and opportunities for improvement, especially through automation enabled by the integration of external applications connected to BDO via its public Application Programming Interface (API). Among nine identified systems, one was selected due to its comprehensive functionalities tailored to construction-sector needs. A study involving thirty users demonstrated that implementation of this system reduced the time required to issue a Waste Transfer Card (KPO) by 77% and fully automated entries in the Waste Records Register (KEO). As a result, the average administrative workload decreased by 87%. For a representative demolition company generating approximately 46,000 KPOs annually, the total time savings correspond to 8.2 months of full-time administrative work. This reduction translates into annual savings exceeding PLN 47,000 and yields a return on investment of over 100% within the first year. Sensitivity analysis indicates that the system’s effectiveness decreases with lower documentation volumes. The findings confirm that targeted automation and improved interface design can significantly enhance the efficiency, accuracy, and transparency of construction waste documentation. Full article

Background/Objectives: Modeling analyses for colorectal cancer (CRC) screening focusing solely on the costs of screening do not fully capture the value of screening programs. We evaluated the clinical and economic effects of CRC stool-based screening tests, including impacts on cancer-related outcomes. Methods: A cohort-based decision-analytic cost-estimator model estimated outcomes for a single round of screening with next-generation multi-target stool DNA (ng mt-sDNA) test or fecal immunochemical test (FIT) from a US payer perspective. Undiagnosed cancers were assumed to become symptomatic (and detected) within 10 years. Clinical assumptions, advanced precancerous lesion and CRC prevalence, and test performance inputs were from clinical trial data. Adherence rates for initial screening and follow-up colonoscopy after a positive result were from real-world data. Input costs included the screening tests, follow-up colonoscopy (with and without polypectomy), and CRC treatment. Results: Compared with FIT, more individuals completed ng mt-sDNA (321,000 vs. 713,000, respectively), leading to the detection of more CRC cases (436 with FIT vs. 2235 with ng mt-sDNA), more advanced precancerous lesions, and more CRC at earlier stages. The cost of screening per patient screened was USD 801 for ng mt-sDNA and USD 124 for FIT. Follow-up colonoscopy cost was USD 149 million with ng mt-sDNA versus USD 22 million with FIT, whereas CRC treatment costs were lower for ng mt-sDNA (USD 1423 million versus USD 1474 million, respectively). When accounting for both direct and CRC averted costs, the total cost of screening and treatment was USD 1383 million with ng mt-sDNA versus USD 1427 million with FIT. Conclusions: Higher screening costs with ng mt-sDNA versus FIT are counterbalanced by savings realized from enhanced CRC prevention and earlier detection due to the superior test performance and better adherence with ng mt-sDNA. Full article

The continuous tightening of European regulatory requirements, particularly under the General Safety Regulation (GSR), has considerably increased the scope and cost of proving ground testing required for the validation of Advanced Driver Assistance Systems (ADASs) and Automated Driving Systems (ADSs). This study presents a methodology for constructing complex proving ground test scenarios aimed at supporting early-stage functional validation and cost-efficient preparation for type approval. The method is based on the systematic analysis of proving ground–relevant ADAS regulations and the classification of test case variations according to sensing, actuation, and execution complexity. By filtering and combining representative test cases, minimum and maximum complexity scenarios were developed and evaluated on the ZalaZONE proving ground in Hungary. The results demonstrate that the proposed approach can substantially reduce test duration, facility occupancy, and overall validation costs, while maintaining the representativeness and credibility of results. Beyond cost savings, the methodology offers a scalable and practical framework for physical validation, supporting manufacturers in achieving regulatory compliance with reduced time and expenditure. Full article

This study investigates the materials and fabrication selection criteria for 3D-printed aluminosilicate components aimed for passive cooling and CO₂ adsorption in indoor conditions, considering their manufacturing environmental impact. The dual-function components were fabricated using Liquid Deposition Modelling (LDM), an Additive Manufacturing (AM) technique utilising customised slurry-based feedstock materials. To assess the environmental implications of the production process, the study employs the Life Cycle Assessment (LCA) methodology, a standardised framework used to quantify potential environmental impacts across the product’s life cycle. The study outlines a systematic approach to materials and fabrication processes selection, focusing on the functional properties required, the importance of locally sourced materials, and the constraints imposed by the fabrication techniques. The fabrication methodology was analysed for material/energy efficiency and waste generation. Post-processing stages were evaluated to identify opportunities for energy savings, particularly by exploring Low-Temperature Firing (LTF). The selected criteria proved efficient in enhancing shaping control and minimising shrinkage variability, with a recorded weight loss of 3.04% via LTF. The LCA results indicated that the 23% reduction in climate change impact was primarily driven by the lower electricity demand of the LTF Protocol, demonstrating that energy-efficient post-processing is a critical lever for sustainable ceramic fabrication. Full article

Background: The mortality and morbidity rates of cardiovascular disease (CVD) are rising sharply in many developed and developing countries. CVD is a fatal disease that requires early and timely diagnosis to prevent further damage and ultimately save patients’ lives. In recent years, numerous studies have explored the automated identification of different categories of CVDs using various deep learning classifiers. However, they often rely on a substantial amount of data. The lack of representative training samples in real-world scenarios, especially in developing countries, poses a significant challenge that hinders the successful training of accurate predictive models. In this study, we introduce a framework to address this gap. Methods: The core novelty of our framework is the combination of Multi-Resolution Wavelet Features with Scale-Invariant Feature Transform (SIFT) keypoint density maps and a lightweight residual attention neural network (ResAttNet). Our hybrid approach transforms one-dimensional ECG signals into a three-channel image representation. Specifically, the CWT is used to extract hidden features in the time-frequency domain to create the first two image channels. Subsequently, the SIFT algorithm is implemented to capture additional significant features to generate the third channel. These three-channel images are then fed to our custom residual attention neural network to enhance classification performance. To tackle the challenge of class imbalance present in our dataset, we employed a hybrid strategy combining the Synthetic Minority Over-sampling Technique (SMOTE) with Edited Nearest Neighbors (ENN) to balance class samples and integrated Focal Loss into the training process to help the model focus on hard-to-classify instances. Results: The performance metrics achieved using five-fold cross-validation are 99.60% accuracy, 97.38% precision, 98.53% recall, and 97.37% F1-score. Conclusions: The experimental results showed that our proposed method outperforms current state-of-the-art methods. The primary practical implication of this work is that by combining a novel, information-rich feature representation with a lightweight classifier, our framework offers a highly accurate and computationally efficient solution, making it a significant step towards developing accessible and scalable computer-aided screening tools. Full article

Background: Electronic health records (EHRs) remain a vital source of clinical information, yet processing these heterogeneous data is extremely labor-intensive. Summarization of these data using Large Language Models (LLMs) is considered a promising tool to support practicing physicians. Unbiased, automated quality control is crucial for integrating the tools into routine practice, saving time and labor. This pilot study aimed to assess the potential and constraints of self-contained evaluation of summarization quality (without expert involvement) based on automatic evaluation metrics and LLM-as-a-judge. Methods: The summaries of text data from 30 EHRs were generated by six open-source low-parameter LLMs. The medical summaries were evaluated using standard automatic metrics (BLEU, ROUGE, METEOR, BERTScore) as well as the LLM-as-a-judge approach using the following criteria: relevance, completeness, redundancy, coherence and structure, grammar and terminology, and hallucinations. Expert evaluation was conducted using the same criteria. Results: The results showed that LLMs hold great promise for summarizing medical data. Nevertheless, neither the evaluation metrics nor LLM judges are reliable in detecting factual errors and semantic distortions (hallucinations). In terms of relevance, the Pearson correlation between the summary quality score and the expert opinions was 0.688. Conclusions: Completely automating the evaluation of medical summaries remains challenging. Further research should focus on dedicated methods for detecting hallucinations, along with investigating larger or specialized models trained on medical texts. Additionally, the potential integration of retrieval-augmented generation (RAG) within the LLM-as-a-judge architecture deserves attention. Nevertheless, even now, the combination of LLMs and the automatic evaluation metrics can underpin medical decision support systems by performing initial evaluations and highlighting potential shortcomings for expert review. Full article

In this article, we present a tool for matching resumes to job posts and vice versa (job post to resumes). With minor modifications, it may also be adapted to other domains where text matching is necessary. This tool may help organizations save time during the hiring process, as well as assist applicants by allowing them to match their resumes to job posts they have selected. To achieve text matching without any model training (zero-shot matching), we constructed dynamic structured prompts that consisted of unstructured and semi-structured job posts and resumes based on specific criteria, and we utilized the Chain of Thought (CoT) technique on the Mistral model (open-mistral-7b). In response, the model generated structured (segmented) job posts and resumes. Then, the job posts and resumes were cleaned and preprocessed. We utilized state-of-the-art sentence similarity models hosted on Hugging face (nomic-embed-text-v1-5 and google-embedding-gemma-300m) through inference endpoints to create sentence embeddings for each resume and job post segment. We used the cosine similarity metric to determine the optimal matching, and the matching operation was applied to eleven different occupations. The results we achieved reached up to 87% accuracy for some of the occupations and underscore the potential of zero-shot techniques in text matching utilizing LLMs. The dataset we used was from indeed.com, and the Spring AI framework was used for the implementation of the tool. Full article

Energy-efficient Train Control (EETC) strategy needs to meet safety, punctuality, and energy-saving requirements during train operation, and puts forward higher requirements for online use and adaptive ability. In order to meet the above requirements and reduce the dependence on an accurate mathematical model of train operation, this paper proposes a train-speed trajectory-optimization method combining data-driven energy consumption estimation and deep reinforcement learning. First of all, using real subway operation data, the key unit basic resistance coefficient in train operation is analyzed by regression. Then, based on the identified model, the energy consumption experiment data of train operation is generated, into which Gaussian noise is introduced to simulate real-world sensor measurement errors and environmental uncertainties. The energy consumption estimation model based on a Backpropagation (BP) neural network is constructed and trained. Finally, the energy consumption estimation model serves as a component within the Deep Deterministic Policy Gradient (DDPG) algorithm environment, and the action adjustment mechanism and reward are designed by integrating the expert experience to complete the optimization training of the strategy network. Experimental results demonstrate that the proposed method reduces energy consumption by approximately 4.4% compared to actual manual operation data. Furthermore, it achieves a solution deviation of less than 0.3% compared to the theoretical optimal baseline (Dynamic Programming), proving its ability to approximate global optimality. In addition, the proposed algorithm can adapt to the changes in train mass, initial set running time, and halfway running time while ensuring convergence performance and trajectory energy saving during online use. Full article

Probe-based capture represents a highly sensitive and cost-effective approach for overcoming host background and enriching viruses in metagenomic NGS (mNGS) libraries. Using clinical specimens collected globally from patients with fever or respiratory illness, we generated mNGS libraries by random priming and Nextera XT tagmentation, followed by target enrichment (teNGS) with Comprehensive Viral Research Panel (CVRP) probes. Capture pool sizes and total reads were optimized, and libraries were initially sequenced separately. Using only 3–4% of reads required for standard mNGS, teNGS achieved increased sensitivity, 100–10,000× increases in depth, and >50% genome coverage for pathogens with titers ≥ 1000 cp/mL. Application to >2000 clinical specimens from various matrices and to contrived samples containing viruses absent from the CVRP probe set enabled detection of diverse viral families and established a minimum 65% nucleotide identity for hybridization, respectively. To save time and resources, teNGS and mNGS libraries were then combined into one sequencing run: teamNGS. In addition to streamlining the workflow, teamNGS also improved genome recovery. Coupling methods maintain the sensitivity and coverage for viruses achieved by enrichment alone while also ensuring comprehensive recovery of non-viral microbes. teamNGS has the potential to improve patient management and lower the rates of unnecessary testing and antibiotic use. Full article

This paper presents an energy-aware Artificial Intelligence of Things framework designed for intelligent remote device control in residential settings. The system architecture is grounded in the Power Administration Device (PAD), a cost-effective and non-intrusive smart plug prototype that measures real-time electricity consumption and actuates appliance power states. The PAD transmits data to a scalable, cross-platform cloud infrastructure, which powers a web-based interface for monitoring, configuration, and multi-device control. Central to this framework is Cross-Feature Time-MoE, a novel neural forecasting model that processes the ingested data to predict consumption patterns. Integrating a Transformer Decoder with a Top-K Mixture-of-Experts (MoE) layer for temporal reasoning and a Bilinear Interaction Layer for capturing complex cross-time and cross-feature dependencies, the model generates accurate multi-horizon energy forecasts. These predictions drive actionable recommendations for device shut-off times, facilitating automated energy efficiency. Simulation results indicate that this system yields substantial reductions in energy consumption, particularly for high-wattage appliances, providing a user-friendly, scalable solution for household cost savings and environmental sustainability. Full article