Search Results (1,000)

We present a CFD-driven surrogate modeling framework that integrates a Convolutional Autoencoder (CAE) with a Deep Neural Network (DNN) for the rapid prediction of urban wind environments and their subsequent use in UAV trajectory planning. A Reynolds-Averaged Navier–Stokes (RANS) CFD database is generated, parameterized by boundary-condition descriptors, to train the surrogate for velocity magnitude and turbulent kinetic energy (TKE). The CAE compresses horizontal flow fields into a low-dimensional latent space, providing an efficient representation of complex flow structures. The DNN establishes a mapping from input descriptors to the latent space, and flow reconstructions are obtained through the frozen decoder. Validation against CFD demonstrates that the surrogate captures velocity gradients and TKE distributions with mean absolute errors below 1% in most of the domain, while residual discrepancies remain confined to near-wall regions. The approach yields a computational speed-up of approximately 4000× relative to CFD, enabling deployment on embedded or edge hardware. For path planning, the domain is discretized as a k-Non-Aligned Nearest Neighbors (k-NANN) graph, and an A* search algorithm incorporates heading constraints and surrogate-based TKE thresholds. The integrated pipeline produces turbulence-aware, dynamically feasible trajectories, advancing the integration of high-fidelity flow predictions into urban air mobility decision frameworks. Full article

Population aging and rising rehabilitation demands highlight the need for advanced assistive devices to improve mobility in individuals with motor impairments. Existing back-support lifting nursing beds often lack adequate human–machine adaptability, safety, and emotional consideration. This study presents a human-centered, data-driven optimization pipeline that integrates behavior–emotion dual recognition, simulation verification, and parameter optimization with user demand mining, biomechanical simulation, and sustainable practices. The design utilizes GreenAI, focusing on low-power algorithms and eco-friendly materials, ensuring energy-efficient AI models and reducing the environmental footprint. A dual-channel data fusion method was developed, combining movement parameters from sit-to-lie transitions with emotional needs extracted from e-commerce reviews using the Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) models. The fuzzy Kano model prioritized design objectives, identifying multi-position adjustment, joint protection, armrest optimization, and interaction comfort as key targets. An AnyBody-based human–device model quantified muscle (erector spinae, rectus abdominis, trapezius) and hip joint loads during posture changes. Simulations verified the design’s ability to improve load distribution, reduce joint stress, and enhance comfort. The optimized nursing bed demonstrated improved adaptability across user profiles while maintaining functional reliability. This framework offers a scalable paradigm for intelligent rehabilitation equipment design, with potential extension toward AI-driven adaptive control and clinical validation. This sustainable methodology ensures that the device not only meets rehabilitation goals but also contributes to a more environmentally responsible healthcare solution, aligning with global sustainability efforts. Full article

Pharmaceuticals and personal care products (PPCPs) are among the most frequently detected emerging pollutants in aquatic sediments, raising increasing concerns due to their persistence, bioaccumulation potential, and ecological impact. As sediments act both as reservoirs and secondary sources of contamination, effective and environmentally responsible analytical methodologies are essential for accurate environmental monitoring and risk assessment. This review presents a critical evaluation of extraction-based workflows for PPCP determination in sediment matrices, covering literature published from 2015 to 2025. We systematically analyze each step of the analytical pipeline, including sample pre-treatment, extraction, clean-up, and instrumental analysis, while emphasizing how method selection and optimization affect recovery rates, sensitivity, and detection limits. Special attention is paid to the physicochemical characteristics of PPCPs that govern extraction behavior, as well as to the trade-offs between analytical efficiency and environmental sustainability, such as solvent type, energy demand, and method greenness. By consolidating current knowledge, this work aims to lay a theoretical foundation for researchers and practitioners in selecting suitable, robust, and sustainable analytical strategies for effective environmental protection. Full article

Partial blockages in pressurised pipe systems present significant challenges for precise detection, characterisation, and ongoing monitoring. Transient test-based techniques, which utilise sharp but small pressure waves, have shown considerable potential due to their safety and diagnostic capabilities. This paper investigates the transient response of an extended partial blockage—an evolution of a discrete partial blockage that protrudes longitudinally—an increasingly complex condition which has a greater impact on the behavior of pipe systems. Through Computational Fluid Dynamics simulations, the interaction of pressure waves with extended partial blockages of different severity and lengths is examined to assess the resulting pressure response. The results confirm that the pressure signature, generated by extended partial blockages, differs markedly from those of discrete partial blockages. In particular, the magnitudes of the first and second pressure peaks enable accurate characterisation of the severity and extent of the extended partial blockage. These results demonstrate that transient test-based techniques can play a significant role in managing water pipe systems, facilitating more targeted maintenance interventions. Broader implementation of these techniques could enable water utilities to reduce energy consumption, maintain water quality with lower chlorine dosing, and prevent the progression of partial blockages to total pipeline blockage. Full article

To enable real-time monitoring of particle mass flow rate in petrochemical pneumatic conveying systems, thereby facilitating process control optimization and improving energy efficiency, an online measurement system for petrochemical particle mass flow based on a non-intrusive electrostatic coupled capacitance sensor is developed. The measurement system determines particle flow velocity by analyzing electrostatic signals using a cross-correlation method, and calculates particle concentration by applying a pre-calibration that correlates capacitance signals with concentration values. These two parameters are then combined to calculate the real-time particle mass flow rate. The performance of the developed system is evaluated under different pipe diameters and particle concentration ranges, in both lab-scale and pilot-scale pneumatic conveying rigs. The obtained results show that the measurement system achieved a maximum relative error of 5.5% for mass flow measurements in the lab-scale 50 mm pneumatic conveying pipeline when the particle concentration range was between 2.04 kg/m³ and 6.43 kg/m³. As for the pilot-scale 100 mm pneumatic conveying, the maximum relative error of the particle concentration measurement was 3.6% when the particle concentration range was 30.98~68.87 kg/m³. These results demonstrate that the developed system has strong adaptability and reliability, highlighting its broad potential for industrial applications. Full article

The growing complexity of modern building systems requires advanced monitoring frameworks to improve fault detection, energy efficiency, and operational resilience. Digital Twin (DT) technology, which integrates real-time data with virtual models of physical systems, has emerged as a promising enabler for predictive diagnostics. Despite growing interest, key challenges remain, including the neglect of short- and long-term forecasting across different scenarios, insufficiently robust data preparation, and the rare validation of models on multi-zone buildings over extended test periods. To address these gaps, this study presents a comprehensive DT-enabled framework for predictive monitoring and anomaly detection, validated in a multi-zone educational building in Rhode Island, USA, using a full year of operational data for validation. The proposed framework integrates a robust data processing pipeline and a comparative analysis of machine learning models, including LSTM, RNN, GRU, ANN, XGBoost, and RF, to forecast short-term (1 h) and long-term (24 h) indoor temperature variations. The LSTM model consistently outperformed other methods, achieving R² > 0.98 and RMSE < 0.55 °C for all tested rooms. For real-time anomaly detection, we applied the hybrid LSTM–Interquartile Range (IQR) method on one-step-ahead residuals, which successfully identified anomalous deviations from expected patterns. The model’s predictions remained within a ±1 °C error margin for over 90% of the test data, providing reliable forecasting up to 16 h ahead. This study contributes a validated, generalizable DT methodology that addresses key research gaps, offering practical tools for predictive maintenance and operational optimization in complex building environments. Full article

This study presents a comprehensive review of the viability of hydrogen as an energy carrier for offshore wind energy compared to existing electricity carrier systems. To enable a state-of-the-art system comparison, a review of wind-to-hydrogen energy conversion and transmission systems is conducted alongside wind-to-electricity systems. The review reveals that the wind-to-hydrogen energy conversion and transmission system becomes more cost-effective than the wind-to-electricity conversion and transmission system for offshore wind farms located far from the shore. Electrical transmission systems face increasing technical and economic challenges relative to the hydrogen transmission system when the systems move farther offshore. This study also explores the feasibility of using seawater for hydrogen production to conserve freshwater resources. It was found that while this approach conserves freshwater and can reduce transportation costs, it increases overall system costs due to challenges such as membrane fouling in desalination units. Findings indicated that for this approach to be sustainable, proper management of these challenges and responsible handling of saline waste are essential. For hydrogen energy transmission, this paper further explores the potential of repurposing existing oil and gas pipeline infrastructure instead of constructing new pipelines. Findings indicated that, with proper retrofitting, the existing natural gas pipelines could provide a cost-effective and environmentally sustainable solution for hydrogen transport in the near future. Full article

With growing global demand for sustainable decarbonization, hydrogen energy systems have emerged as a key pillar in achieving carbon neutrality. This study assesses the greenhouse gas (GHG) reduction efficiency of Republic of Korea’s hydrogen ecosystem from a life-cycle perspective, focusing on production and utilization stages. Using empirical data—including the national hydrogen supply structure, fuel cell electric vehicle (FCEV) deployment, and hydrogen power generation records, the analysis compares hydrogen-based systems with conventional fossil fuel systems. Results show that current hydrogen production methods, mainly by-product and reforming-based hydrogen, emit an average of 6.31 kg CO₂-eq per kg H₂, providing modest GHG benefits over low-carbon fossil fuels but enabling up to a 77% reduction when replacing high-emission sources like anthracite. In the utilization phase, grey hydrogen-fueled stationary fuel cells emit more GHGs than the national grid. By contrast, FCEVs demonstrate a 58.2% GHG reduction compared to internal combustion vehicles, with regional variability. Importantly, this study omits the distribution phase (storage and transport) due to data heterogeneity and a lack of reliable datasets, which limits the comprehensiveness of the LCA. Future research should incorporate sensitivity or scenario-based analyses such as comparisons between pipeline transport and liquefied hydrogen transport to better capture distribution-phase impacts. The study concludes that the environmental benefit of hydrogen systems is highly dependent on production pathways, end-use sectors, and regional conditions. Strategic deployment of green hydrogen, regional optimization, and the explicit integration of distribution and storage in future assessments are essential to enhancing hydrogen’s contribution to national carbon neutrality goals. Full article

Green synthesis methods of silver nanoparticles have gained great attention because they offer sustainable, eco-friendly, and less-toxic alternatives to traditional methods. This study sheds light on the green synthesis of chitosan silver nanoparticle composites, providing a comparative evaluation of microwave-assisted (M1) and a one-pot (M2) reduction methods. The morphological, crystallinity, and structural uniformity characteristics were evaluated by UV-Visible, Raman spectroscopy, X-ray diffraction (XRD) and scanning electron microscopy (SEM) with employing image processing pipeline based on deep learning model for segmentation and particles size estimation. The UV-visible spectrum exhibited independent SPR peaks ranging from 400 to 450 nm for all samples; however, microwave assisted-synthesis possessed narrower and more intense peaks indicative of better crystallinity and mono-dispersity. SEM depicted smaller, more uniformly dispersed particles for microwave-assisted (M1), while deep learning segmentation showed lower particle size variability (σ ≈ 24–43 nm), compared to polydisperse (σ ≈ 16–59 nm) in M2 samples. XRD showed crystalline face-centered cubic (FCC) silver with dominant peaks in M1 samples, whereas M2 had broader, less intense peaks with amorphous features. Raman vibrations revealed more structural order and homogenous capping in M1 than M2. Therefore, microwave-assisted (M1) showed better control on nucleation, particle size, crystallinity, and homogeneity due to a faster and uniform energy distribution. The future research would focus on the antimicrobial evaluation of such nanoparticles in agronomy. Full article

Undesirable water-in-crude oil emulsions in the oil and gas industry can lead to several issues, including equipment corrosion, high-pressure drops in pipelines, high pumping costs, and increased total production costs. These emulsions are commonly treated with surface-active chemicals called demulsifiers, which can break an oil–water interface and enhance phase separation. This study introduces a novel approach based on neural networks to estimate demulsification efficiency and to aid in the selection of demulsifiers under field conditions. The influence of various types of demulsifiers, demulsifier concentration, time required for demulsification, temperature and asphaltene content on the demulsification efficiency is analyzed. To improve model accuracy, a modified full-scale factorial design of experiments and the comparison of response surface method with multilayer perception neural networks were conducted. The results demonstrated the advantages of using neural networks over the response surface methodology such as a reduced settling time in separators, an improved crude oil dehydration and processing capacity, and a lower consumption of energy and utilities. The findings may enhance processing conditions and identify regions of higher demulsification efficiency. The neural network approach provided a more accurate prediction of maximum of demulsification efficiency compared to the response surface methodology. The automated multilayer perceptron neural network, with an architecture consisting of 3 input layers, 14 hidden layers, and 1 output layer, demonstrated the highest validation performance R² of 0.991932 by utilizing a logistic output activation function and a hyperbolic tangent activation function for the hidden layers. The identification of shifted optimal values of time required from demulsification, demulsifier concentration, and asphaltene content along with sensitivity analysis confirmed advantages of automated neural networks over conventional methods. Full article

This study presents a novel energy harvesting device that combines piezoelectric and electromagnetic transduction to extract energy from fluid flow within pipelines to supply power to wireless sensor nodes for the digital transformation of pipeline networks. The proposed harvester consisted of a permanent magnet, an unimorph circular piezoelectric plate, an adjustable housing, two wound coils, and a coil holder. In laboratory tests, the harvester demonstrated an ability to produce 831.7 µW of AC power and 680 µW of DC power at a flow pressure of 2.90 kPa and a flow rate of 11.083 L/s. The energy harvester charged a power backup from 1.01 V to 4.49 V in a time duration of 120 min. Additionally, a low-power wireless system for monitoring pipeline pressure was developed and integrated with this energy harvesting system. By incorporating this technology into the digitization of pipeline systems, continuous power generation is possible, ensuring the reliable and autonomous operation of sensors for real-time data collection and monitoring of the pipeline network. The hybrid flow energy harvester surpasses both earlier standalone electromagnetic and piezoelectric flow energy harvesters. Full article

Atopic dermatitis (AD) is a multifactorial skin disorder characterized by immune and barrier dysfunction. The gut–skin axis is a bidirectional pathway through which gut and skin influence each other via microbial metabolites. Bioactive metabolites produced by microbial transformation of phytochemicals show potential for AD prevention. This study developed a computational systems biology pipeline that prioritized gut-derived metabolites from Philippine medicinal plants by integrating metabolite prediction, pharmacokinetics, network analysis, and molecular simulations. From 2231 predicted metabolites, 31 satisfied pharmacological criteria and were mapped to 199 AD-associated targets, with ALB, CASP3, and PPARG identified as hub genes. Two metabolites, THPOC and PM38, exhibited complementary target affinities and strong binding stability. THPOC stabilized ALB and CASP3, supporting barrier integrity and apoptosis regulation, while PM38 strongly engaged PPARG, modulating lipid metabolism and anti-inflammatory transcription. They exhibited comparable or superior docking scores, stable MD interactions, and favorable binding free energies, compared to abrocitinib, an approved AD treatment. DFT analysis confirmed electronic stability and donor–acceptor properties linked to target selectivity. These findings highlight THPOC and PM38 as promising immunometabolic modulators acting on key AD-related pathways. Collectively, this study introduces a reproducible systems-based computational discovery framework, offering a novel preventive strategy for AD. Full article

While renewable energy deployment has accelerated in recent years, fossil fuels continue to play a dominant role in electricity generation worldwide. This necessitates the development of transitional strategies to mitigate greenhouse gas emissions from this sector while gradually reducing reliance on fossil fuels. This study investigates the potential of blending green hydrogen with natural gas as a transitional solution to decarbonize Jordan’s electricity sector. The research presents a comprehensive techno-economic and environmental assessment evaluating the compatibility of the Arab Gas Pipeline and major power plants with hydrogen–natural gas mixtures, considering blending limits, energy needs, environmental impacts, and economic feasibility under Jordan’s 2030 energy scenario. The findings reveal that hydrogen blending between 5 and 20 percent can be technically achieved without major infrastructure modifications. The total hydrogen demand is estimated at 24.75 million kilograms per year, with a reduction of 152.7 thousand tons of carbon dioxide per annum. This requires 296,980 cubic meters of water per year, equivalent to only 0.1 percent of the National Water Carrier’s capacity, indicating a negligible impact on national water resources. Although technically and environmentally feasible, the project remains economically constrained, requiring a carbon price of $1835.8 per ton of carbon dioxide for economic neutrality. Full article

The transformation of transport towards solutions based on renewable energy sources (RES) and energy storage systems represents a response to global climate and regulatory challenges. The integration of electric vehicles with charging infrastructure and the power grid reduces emissions and enhances system flexibility; however, it simultaneously introduces new areas of risk and should therefore be subject to significance assessment. This study applies an integrated methodology for assessing the significance of changes, combining FMEA-based analysis with risk registers and sustainability indicators (six criteria). The transport system and associated storage infrastructure were compared before and after the implementation of RES, considering criteria such as the effects of system failure, complexity, innovation, monitoring, reversibility, and additionality. The results indicate that traditional risks associated with fossil fuels (e.g., exhaust emissions, pipeline failures) are eliminated, but new risks emerge. The highest increases in Risk Priority Numbers (RPN) were observed for cyber threats, charging infrastructure overloads, and the cyclic degradation of energy storage systems. Environmental and organizational risks also intensified, including those related to battery recycling as well as the lack of regulatory frameworks and procedures. The integration of transport with RES and energy storage should be regarded as a significant change. In addition to environmental and energy benefits, it introduces new, complex risk areas that require in-depth risk analysis, the implementation of monitoring systems, and adequate regulatory and preventive measures. At the same time, the proposed methodology enables the identification of changes critical to power system stability, the improvement of energy efficiency, and the advancement of the transition towards climate neutrality. Full article

The rapid adoption of hydrogen fuel cell vehicles (HFCVs) in the Beijing–Tianjin–Hebei (BTH) hub accentuates the mismatch between renewable-based hydrogen supply in Hebei and concentrated demand in Beijing and Tianjin. We develop a mixed-integer linear model that co-configures a hydrogen pipeline network and optimizes hourly flow schedules to minimize annualized cost and CO₂ emissions simultaneously. For 15,000 HFCVs expected in 2025 (137 t d⁻¹ demand), the Pareto-optimal design consists of 13 production plants, 43 pipelines and 38 refueling stations, delivering 50 767 t yr⁻¹ at 68% pipeline utilization. Hebei provides 88% of the hydrogen, 70% of which is consumed in the two megacities. Hourly profiles reveal that 65% of electrolytic output coincides with local wind–solar peaks, whereas refueling surges arise during morning and evening rush hours; the proposed schedule offsets the 4–6 h mismatch without additional storage. Transport distances are 40% < 50 km, 35% 50–200 km, and 25% > 200 km. Raising the green hydrogen share from 10% to 70% increases total system cost from USD 1.56 bn to USD 2.73 bn but cuts annual CO₂ emissions from 142 kt to 51 kt, demonstrating the trade-off between cost and decarbonization. The model quantifies the value of sub-day pipeline scheduling in resolving spatial–temporal imbalances for large-scale low-carbon hydrogen supply. Full article