Search Results (9,190)

Under the dual challenges of global energy crisis and climate change, the building sector, as a major carbon emitter consuming 33% of global primary energy, has seen its energy efficiency optimization become a critical pathway towards achieving carbon neutrality goals. The Window-to-Wall Ratio (WWR), serving as a core parameter in building envelope design, directly influences building energy consumption, with its optimized design playing a decisive role in balancing natural daylighting, ventilation efficiency, and thermal comfort. This study focuses on the traditional One-Seal dwellings (Yikeyin) in Kunming, China, establishing a dynamic wind field-thermal environment coupled analysis framework to investigate the impact mechanism of window dimensions (WWR and aspect ratio) on indoor thermal comfort under natural wind conditions in transitional climate zones. Utilizing the Grasshopper platform integrated with Ladybug, Honeybee, and Butterfly plugins, we developed parametric models incorporating Kunming’s Energy Plus Weather meteorological data. EnergyPlus and OpenFOAM were employed, respectively, for building heat-moisture balance calculations and Computational Fluid Dynamic (CFD) simulations, with particular emphasis on analyzing the effects of varying WWR (0.05–0.20) on temperature-humidity, air velocity, and ventilation efficiency during typical winter and summer weeks. Key findings include, (1) in summer, the baseline scenario with WWR = 0.1 achieves a dynamic thermal-humidity balance (20.89–24.27 °C, 65.35–74.22%) through a “air-permeable but non-ventilative” strategy, though wing rooms show humidity-heat accumulation risks; increasing WWR to 0.15–0.2 enhances ventilation efficiency (2–3 times higher air changes) but causes a 4.5% humidity surge; (2) winter conditions with WWR ≥ 0.15 reduce wing room temperatures to 17.32 °C, approaching cold thresholds, while WWR = 0.05 mitigates heat loss but exacerbates humidity accumulation; (3) a symmetrical layout structurally constrains central ventilation, maintaining main halls air changes below one Air Change per Hour (ACH). The study proposes an optimized WWR range of 0.1–0.15 combined with asymmetric window opening strategies, providing quantitative guidance for validating the scientific value of vernacular architectural wisdom in low-energy design. Full article

In response to the need for separation and utilization of residual film mixtures after transformation of protaetia brevitarsis larvae, a pneumatic drum-sieve-type separator for transforming mixtures of protaetia brevitarsis larvae was designed. First, the suspension velocity of each component was determined by the suspension speed test. Secondly, the separation process of residual film, larvae, and insect sand was formulated on the basis of biological activities, shape differences, and aerodynamic response characteristics. Eventually, the main structural parameters and working parameters of the machine were determined. In order to optimize the separation effect, a single-factor experiment and a quadratic regression response surface experiment containing three factors and three levels were carried out, and the corresponding regression model was established. The experimental results showed that the effects of the air speed at the inlet, inclination angle of the sieve cylinder, and rotational speed of the sieve cylinder on the impurity rate of the residual film decreased in that order, and that the effects of the rotational speed of the sieve cylinder, inclination angle of the sieve cylinder, and air speed at the inlet on the inactivation rate of the larvae decreased in that order. Through parameter optimization, a better combination of working parameters was obtained: the rotational speed of the sieve cylinder was 24 r/min, the inclination angle of the sieve cylinder was −0.43°, and the air speed at the inlet was 5.32 m/s. The average values of residual film impurity rate and larval inactivation rate obtained from the material sieving test under these parameters were 8.74% and 3.18%, with the relative errors of the theoretically optimized values being less than 5%. The results of the study can provide a reference for the resource utilization of residual film and impurity mixtures and the development of equipment for the living body separation of protaetia brevitarsis. Full article

MXenes, a class of two-dimensional transition metal carbides and nitrides, emerged as a promising material for next-generation energy storage and corresponding applications due to their unique combination of high electrical conductivity, tunable surface chemistry, and lamellar structure. This review highlights recent advances in MXene-based composites, focusing on their integration into electrode architectures for the development of supercapacitors, batteries, and multifunctional devices, including triboelectric nanogenerators. It serves as a comprehensive overview of the multifunctional capabilities of MXene-based composites and their role in advancing efficient, flexible, and sustainable energy and sensing technologies, outlining how MXene-based systems are poised to redefine multifunctional energy platforms. Electrochemical performance optimization strategies are discussed by considering surface functionalization, interlayer engineering, scalable synthesis techniques, and integration with advanced electrolytes, with particular attention paid to the development of hybrid supercapacitors, triboelectric nanogenerators (TENGs), and wearable sensors. These applications are favored due to improved charge storage capability, mechanical properties, and the multifunctionality of MXenes. Despite these aspects, challenges related to long-term stability, sustainable large-scale production, and environmental degradation must still be addressed. Emerging approaches such as three-dimensional self-assembly and artificial intelligence-assisted design are identified as key challenges for overcoming these issues. Full article

This study presents the first investigation into the application of ultrasonic vibration-assisted electrical discharge machining (UV-EDM) using graphite electrodes for external cylindrical surface machining—an essential surface in the production of tablet punches and sheet metal-forming dies. A custom ultrasonic horn was designed and fabricated using 90CrSi material to operate effectively at a resonant frequency of 20 kHz, ensuring stable vibration transmission throughout the machining process. A Box–Behnken experimental design was employed to explore the effects of five process parameters—vibration amplitude (A), pulse-on time (Ton), pulse-off time (Toff), discharge current (Ip), and servo voltage (SV)—on two key performance indicators: material removal rate (MRR) and surface roughness (Ra). The optimization process was conducted in two stages: single-objective analysis to maximize MRR while ensuring Ra < 4 µm, followed by a hybrid multi-objective approach combining NSGA-II and the Analytic Hierarchy Process (AHP). The optimal solution achieved a high MRR of 9.28 g/h while maintaining Ra below the critical surface finish threshold, thus meeting the practical requirements for punch surface quality. The findings confirm the effectiveness of the proposed horn design and hybrid optimization strategy, offering a new direction for enhancing productivity and surface integrity in cylindrical EDM applications using graphite electrodes. Full article

Soybean (Glycine max [L.] Merr.) cultivation is expanding in Central Europe due to the development of early-maturing cultivars and growing demand for plant-based protein produced without the use of genetically modified organisms. However, nitrogen (N) management remains a major challenge in temperate climates, where variable weather conditions can significantly affect nodulation and yield. This study evaluated the effects of three nitrogen fertilisation doses (0, 30, and 60 kg N·ha⁻¹), applied in the form of ammonium nitrate (34% N) and two commercial rhizobial inoculants—HiStick Soy (containing Bradyrhizobium japonicum strain 532C) and Nitragina (including a Polish strain of B. japonicum)—on nodulation, nitrogen uptake, and seed yield. A three-year field experiment (2017–2019) was conducted in southwestern Poland using a two-factor randomized complete block design. Nodulation varied significantly across years, with the highest values recorded under favourable early-season moisture and reduced during drought. In the first year, inoculation with HiStick Soy significantly increased nodule number and seed yield compared to Nitragina and the uninoculated control. Nitrogen fertilisation consistently improved seed yield, although it had no significant effect on nodulation. The highest nitrogen use efficiency was observed with moderate nitrogen input (30 kg N·ha⁻¹) combined with inoculation. These findings highlight the importance of integrating effective rhizobial inoculants with optimized nitrogen fertilisation to improve soybean productivity and nitrogen efficiency under variable temperate climate conditions. Full article

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide. Advances in screening, diagnosis, and management have transformed clinical practice, particularly with the integration of immunotherapy and target therapies. Methods: A systematic literature search was carried out for the period between October 2016 to September 2024. Phase II and III randomized trials evaluating ICI monotherapy, ICI–chemotherapy combinations, and dual ICI regimens in patients with advanced NSCLC were included. Outcomes of interest included overall survival (OS), progression-free survival (PFS), and treatment-related adverse events (AEs). Results: PD-1-targeted therapies demonstrated superior OS compared to PD-L1-based regimens, with cemiplimab monotherapyranking highest for OS benefit (posterior probability: 90%), followed by sintilimab plus platinum-based chemotherapy (PBC) and pemetrexed—PBC. PFS atezolizumab plus bevacizumab and PBC, and camrelizumab plus PBC were the most effective regimens. ICI–chemotherapy combinations achieved higher ORRs but were associated with greater toxicity. The most favorable safety profiles were observed with cemiplimab, nivolumab, and avelumab monotherapy, while atezolizumab plus PBC and sugemalimab plus PBC carried the highest toxicity burdens. Conclusions: In PD-L1-unselected advanced NSCLC, PD-1 blockade—particularly cemiplimab monotherapy—and rationally designed ICI–chemotherapy combinations represent the most efficacious treatment strategies. Balancing efficacy with safety remains critical, especially in the absence of predictive biomarkers. These findings support a patient-tailored approach to immunotherapy and highlight the need for further biomarker-driven and real-world investigations to optimize treatment selection. Full article

This research develops a dual physics-based machine learning system to forecast fuel consumption and CO₂ emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship. Full article

The interaction between a shock wave and a boundary layer promotes corner separation and prevents performance enhancement in a supersonic compressor cascade. Different vortex generator (VG) designs are presented to control corner separation in a supersonic compressor cascade, including endwall VGs (EVG), suction surface VGs (SVG), and combined endwall and suction surface VGs (E-SVGs). It is demonstrated that EVG and coupled E-SVGs reduce losses in the supersonic compressor cascade. For an optimal EVG, the total loss is reduced by 24.6% and the endwall loss is reduced by 33.6%. The coupled E-SVG better controls corner separation and reduces endwall losses by 56.9%. The suppression mechanism is that vortices alter the direction of the separated flow, allowing it to overcome the chordwise pressure gradient. Moreover, the VGs change the shock structure near the endwall. For the EVG, clockwise vortices are effective in controlling corner separation due to their minor effect on the shock structure near the endwall. However, anticlockwise vortices are not suitable for controlling corner separation in the supersonic compressor because they increase the shock strength induced by the VG. The control mechanism of the coupled E-SVG on corner separation is also discussed. Full article

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: The deployment of the OCC system resulted in a 35.0% reduction in the false discovery rate, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs, improving contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation and reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVM hallucinations with ablation study; and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges. Full article

A multi-objective genetic algorithm is tailored to optimize the design of a wavelength interleaver/deinterleaver device. An interleaver combines data streams from two physical channels into one. The deinterleaver does the opposite job. The WDM requirements for this device include channel spacing of 50 GHz, channel bandwidth of 20 GHz, free spectral range of 100 GHz, maximum channel dispersion of 30 ps/nm, and maximum crosstalk of −23 dB. The challenges for the optimization process include the lack of a closed-form expression for the device performance and the trade-off between the conflicting performance parameters. So, for this multi-objective problem, the proposed approach maneuvers to find a compromise between the performance parameters within a few minutes, saving the designer the laborious design process previously proposed in the literature, which relies on visually inspecting the Z-plane for the dynamics of the transmission poles and zeros. Designs of better performance are achieved, with fewer ring resonators, a channel dispersion as low as 1.6 ps/nm, and crosstalk as low as −30 dB. Full article

The novel enhanced dynamic game method (EDGM) is proposed to advance game-based design approaches, with a focus on enhancing solution distribution, precision, and the ability to reveal the dynamic influence sensitivity of design variables on objective functions. An integrated mathematical model is developed by combining EDGM with PARSEC and CST parameterization methods, forming a systematic framework for offshore wind turbine airfoil optimization. Targeting airfoils with approximately 30% and 35% thickness, the study aims to improve annual energy production (AEP) and optimize the polar moment of inertia. Redesigned airfoils using the EDGM-integrated model exhibit significant enhancements in aerodynamic performance and anti-flutter capability compared to baseline airfoils DU97W300 and DU99W350. The methodology’s superiority is validated through analyses of pressure distributions, lift-to-drag ratios, and streamline patterns, as well as comparative evaluations using HV and Spacing metrics, demonstrating EDGM’s potential for broader engineering applications in complex multi-objective optimization scenarios. Full article

This study presents a comprehensive analysis of CO₂ trapping mechanisms in subsurface reservoirs by integrating numerical reservoir simulations, geochemical modeling, and machine learning techniques to enhance the design and evaluation of carbon capture and storage (CCS) strategies. A two-dimensional reservoir model was developed to simulate CO₂ injection dynamics under realistic geomechanical and geochemical conditions, incorporating four primary trapping mechanisms: residual, solubility, mineralization, and structural trapping. To improve computational efficiency without compromising accuracy, advanced machine learning models, including random forest, gradient boosting, and decision trees, were deployed as smart proxy models for rapid prediction of trapping behavior across multiple scenarios. Simulation outcomes highlight the critical role of hysteresis, aquifer dynamics, and producer well placement in enhancing CO₂ trapping efficiency and maintaining long-term storage stability. To support the credibility of the model, a qualitative validation framework was implemented by comparing simulation results with benchmarked field studies and peer-reviewed numerical models. These comparisons confirm that the modeled mechanisms and trends align with established CCS behavior in real-world systems. Overall, the study demonstrates the value of combining traditional reservoir engineering with data-driven approaches to optimize CCS performance, offering scalable, reliable, and secure solutions for long-term carbon sequestration. Full article

The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly from the working separator under 81 different operational scenarios. The intelligent models were used to recommend optimal settings for drum speed, belt speed, vibration intensity, and drum angle, thereby maximizing separation quality and minimizing energy consumption. the smart separation module utilizes YOLOv11n-seg and achieves a mean average precision (mAP) of 0.838 across 7163 industrial instances from aluminum, copper, and plastic materials. For shape classification (sharp vs. smooth), the model reached 91.8% accuracy across 1105 annotated samples. Furthermore, the thermal monitoring unit can detect iron contamination by analyzing temperature anomalies. Scenarios with iron showed a maximum temperature increase of over 20 °C compared to clean materials, with a detection response time of under 2.5 s. The architecture integrates a Digital Twin using Azure Digital Twins to virtually mirror the system, enabling real-time tracking, behavior simulation, and remote updates. A full connection with the PLC has been implemented, allowing the AI-driven system to adjust physical parameters autonomously. This combination of AI, IoT, and digital twin technologies delivers a reliable and scalable solution for enhanced separation quality, improved operational safety, and predictive maintenance in industrial recycling environments. Full article

The escalating environmental noise pollution along urban rail transit corridors, exacerbated by rapid urbanization, necessitates innovative and efficient noise control measures. A comprehensive investigation was conducted that utilized field measurements of train passing-by noise to establish a statistical energy analysis model for evaluating the acoustic performance of both vertical (VB) and fully enclosed (FB) barrier configurations. The study incorporated Maa’s theory of micro-perforated plate (MPP) parameter optimization and developed a neural network surrogate model focused on insertion loss maximization for barrier geometric design. Key findings revealed significant barrier-induced near-track noise amplification, with peak effects observed at the point located 1 m from the barrier and 2 m above the rail. Frequency-dependent analysis demonstrated a characteristic rise-and-fall reflection pattern, showing maximum amplifications of 1.47 dB for VB and 4.13 dB for FB within the 400–2000 Hz range. The implementation of optimized MPPs was found to effectively eliminate the near-field noise amplification effects, achieving sound pressure level reductions of 4–8 dB at acoustically sensitive locations. Furthermore, the high-precision surrogate model (R² = 0.9094, MSE = 0.8711) facilitated optimal geometric design solutions. The synergistic combination of MPP absorption characteristics and geometric optimization resulted in substantially enhanced barrier performance, offering practical solutions for urban rail noise mitigation strategies. Full article

The operational and indoor environmental quality of buildings has a significant impact on global energy consumption and human quality of life. One of the key directions for improving building performance is the optimization of building control systems. In modern buildings, the presence of numerous actuators and monitoring points makes manually designed control algorithms potentially suboptimal due to the complexity and human factors. To address this challenge, model predictive control based on artificial neural networks can be employed. The advantage of this approach lies in the model’s ability to learn and understand the dynamic behavior of the building from monitoring datasets. It should be noted that the effectiveness of such control models is directly dependent on the forecasting accuracy of the neural networks. In this study, we adapt neural network architectures such as GRU and TCN for use in the context of building model predictive control. Furthermore, we propose a novel hybrid architecture that combines the strengths of recurrent and convolutional neural networks. These architectures were compared using real monitoring data collected with a custom-developed device introduced in this work. The results indicate that, under the given experimental conditions, the proposed hybrid architecture outperforms both GRU and TCN models, particularly when processing large sequential input vectors. Full article