Search Results (19,340)

The aim of this study was to evaluate the treatment efficiency and applicability of using textile-based mats as roof biofilters on urban buildings for purifying preliminary treated wastewater (PTW) collected from a three-chamber septic tank. Therefore, a pilot plant with a 15° pitched wooden roof and two tracks for laying two mats made of different materials—polypropylene (PP), designated as Mat 1, and polyethylene terephthalate (PET), designated as Mat 2—was constructed at ground level under outdoor conditions. The plant was operated in parallel for a period of 455 days. Significant differences (p < 0.05) were observed in the results of the mass removal efficiencies between the two mats, with Mat 1 achieving mean removals of five-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), ammonium-nitrogen (NH₄-N), and total nitrogen (TN) of 85%, 73%, 75%, and 38%, respectively, and Mat 2 achieving comparatively higher removals of 97%, 84%, 90%, and 57%, respectively. The mean concentrations of BOD₅ and COD at the outflow of both mats met the minimum water quality requirements for discharge and successfully met the minimum water quality class B for agricultural reuse. However, the comparatively low mean E. coli removal efficiencies of 2.0 and 2.4 log-units in Mat 1 and Mat 2, respectively, demonstrate the need for an effluent disinfection system. Highly efficient mass removal efficiencies were observed in the presence of dense vegetation on the mats, which may lead to a potential improvement in the urban climate through high daily evapotranspiration. Overall, this study demonstrates the potential for using lightweight, textile-based mats on rooftops to efficiently treat PTW from urban buildings, offering a promising decentralized wastewater management approach for climate-resilient cities. Full article

The growing demand for sustainable and multifunctional construction materials, particularly those capable of addressing durability and energy challenges, has motivated the development of conductive and photothermally active geopolymers. This study investigated the use of an Fe-rich spinel aggregate (FSA) as a high-density filler in geopolymers composed of ground granulated blast furnace slag and bauxite residue, with a fixed addition of 1 wt% graphite (binder-based) to enhance electrical conductivity. The effects of different FSA replacement percentages (0–100%) on compressive strength, electrical conductivity, photothermal efficiency, and chemical resistance were evaluated. An increase in the FSA content translated to an increase in the final compressive strength, with 100% FSA replacement achieving the highest value of 45.5 ± 2.5 MPa at 28 days. As the FSA content increased, the electrical resistivity decreased to as low as 42 Ω·m at 100% replacement. Under simulated solar flux conditions (1 kW/m²), photothermal analysis revealed that the 100% FSA mixtures exhibited the highest surface temperature increase of 9.8 °C after 300 s, indicating their superior thermal responsiveness. Furthermore, acid immersion in 10% HCl for 28 days showed mass gain in all geopolymers, with the highest gain observed at 50% FSA (+11.51%). Similarly, the strength increased after acid exposure up to a 75% FSA content. These findings highlight the multifunctional potential of FSA-enhanced geopolymers for high-mechanical-performance, electrically conductive, photothermally active, and chemically durable materials as multifunctional construction materials. Full article

The global construction industry is facing pressure to reduce environmental impact by improving energy efficiency amid rising energy demands and growing concerns about climate change. Consequently, sustainable building practices, like the Passive House (PH) design, prioritize minimizing building energy demand. In Saudi Arabia, where cooling loads dominate electricity use, implementing PH could significantly lower energy demand. However, research on PH challenges in the Saudi climate is limited, which highlights the importance of investigating the barriers and potential solutions for PH adoption in this context. This study investigates barriers to PH adoption and proposes context-specific solutions for Saudi Arabia. A mixed-methods approach, including a literature review and structured questionnaires among construction professionals, was used. Thematic analysis and importance–performance analysis (IPA) helped prioritize barriers and identify strategies. By combining evidence from the literature and practitioner surveys, this study uniquely prioritizes PH adoption barriers and proposes tailored solutions for Saudi Arabia’s hot climate. The results showed that the most critical barriers include a lack of supportive building codes, the absence of government incentives, low awareness, contractor resistance, and limited material availability. At the same time, key strategies were identified, such as revising building regulations, offering tax incentives, and adapting PH design with improved shading and HVAC systems. Overall, these findings indicate that removing the identified barriers and implementing the suggested strategies can reduce energy demand and demonstrate the feasibility of PH in Saudi Arabia’s hot climate, thereby supporting the Kingdom’s broader sustainability goals. Full article

With the development of subway transportation, how to excavate large-section tunnels and find more convenient and reliable support methods has become an issue that cannot be ignored. This paper addresses issues such as low construction efficiency of core rock columns during the construction of large-section subway tunnels in sandstone–mudstone interbedded geological conditions. It proposes an optimized support scheme that replaces traditional core rock columns with temporary steel supports (steel columns). Finite element analysis was used to compare the deformation of the surrounding rock when retaining the core rock columns, using temporary steel columns to replace the core rock columns, and not providing additional support. Five interlayer positions and four interlayer angles were analyzed to identify the most dangerous geological conditions. Based on this analysis, the reasonable spacing of the temporary steel columns was investigated. The results indicate that temporary steel columns and core rock columns can effectively reduce vertical deformation of the surrounding rock, with steel columns showing slightly better results. Replacing core rock columns with steel columns is feasible. To control tunnel rock mass deformation, this project should ensure that the spacing between temporary steel columns is maintained between 21.88 m and 56.80 m. However, in construction sections with good rock mass conditions, the spacing can be extended as long as safety is ensured. Full article

Three-dimensional bioprinting holds great promise for tissue engineering, but struggles with fabricating complex curved geometries such as vascular networks. Though precise, traditional Cartesian bioprinters are constrained by linear layer-by-layer deposition along fixed axes, resulting in limitations such as the stair-step effect. Multi-axis robotic bioprinting addresses these challenges by allowing dynamic nozzle orientation and motion along curvilinear paths, enabling conformal printing on anatomically relevant surfaces. Although robotic arms offer lower mechanical precision than CNC stages, accuracy can be enhanced through methods such as vision-based toolpath correction. This study presents a modular multi-axis robotic embedded bioprinting platform that integrates a six-degrees-of-freedom robotic arm, a pneumatic extrusion system, and a viscoplastic support bath. A streamlined workflow combines CAD modeling, CAM slicing, robotic simulation, and automated execution for efficient fabrication. Two case studies validate the system’s ability to print freeform surfaces and vascular-inspired tubular constructs with high fidelity. The results highlight the platform’s versatility and potential for complex tissue fabrication and future in situ bioprinting applications. Full article

Mental disorders (MDs) constitute significant risk factors for self-harm and suicide. The incidence of MDs has been increasing annually, primarily due to inadequate diagnosis and intervention. Early identification and timely intervention can effectively slow the progression of MDs and enhance the quality of life. However, the high cost and complexity of in-hospital screening exacerbate the psychological burden on patients. Moreover, existing studies primarily focus on the identification of individual subcategories and lack attention to model explainability. These approaches fail to adequately address the complexity of clinical demands. Early screening of MDs using EEG signals and deep learning techniques has demonstrated simplicity and effectiveness. To this end, we constructed a Dual-Branch Network (DBN) leveraging resting-state Quantitative Electroencephalogram (QEEG) features. The DBN is designed to enable the detection of multiple categories of MDs. Firstly, a dual-branch feature extraction strategy was designed to capture multi-dimensional latent features. Further, we propose a Multi-Head Attention Mechanism (MHAM) that integrates dynamic routing. This architecture assigns greater weights to key elements and enhances information transmission efficiency. Finally, the diagnosis is derived from a fully connected layer. In addition, we incorporate SHAP analysis to facilitate feature attribution. This technique elucidates the contribution of significant features to MD detection and improves the transparency of model predictions. Experimental results demonstrate the effectiveness of DBN in detecting various MD categories. The performance of DBN surpasses that of traditional machine learning models. Ablation studies further validate the architectural soundness of DBN. The DBN effectively reduces screening complexity and demonstrates significant potential for clinical applications. Full article

In the context of the coordinated operation of microgrids and community energy storage systems, achieving optimal resource allocation under complex and uncertain conditions has emerged as a prominent research focus. This study proposes a robust collaborative optimization model for microgrids and community energy storage systems under a game-theoretic environment where potential fraudulent behavior is considered. A multi-energy collaborative system model is first constructed, integrating multiple uncertainties in source-load pricing, and a max-min robust optimization strategy is employed to improve scheduling resilience. Secondly, a game-theoretic model is introduced to identify and suppress manipulative behaviors by dishonest microgrids in energy transactions, based on a Nash bargaining mechanism. Finally, a distributed collaborative solution framework is developed using the Alternating Direction Method of Multipliers and Column-and-Constraint Generation to enable efficient parallel computation. Simulation results indicate that the framework reduces the alliance’s total cost from CNY 66,319.37 to CNY 57,924.89, saving CNY 8394.48. Specifically, the operational costs of MG1, MG2, and MG3 were reduced by CNY 742.60, CNY 1069.92, and CNY 1451.40, respectively, while CES achieved an additional revenue of CNY 5130.56 through peak shaving and valley filling operations. Furthermore, this distributed algorithm converges within 6–15 iterations and demonstrates high computational efficiency and robustness across various uncertain scenarios. Full article

Objectives: Patients with rare diseases in China face extremely high medical expenses. The current coverage framework remains inadequate in terms of coverage depth and proactive risk control, underscoring an urgent need for institutional reform. Methods: This study employs a policy content analysis approach to review the current landscape of rare disease protection in China. Drawing on risk management theory and the health capital model, it constructs an analytical framework to examine potential institutional reforms through the lens of risk response pathways and the efficiency of health investment. Results: The findings reveal that basic medical insurance (BMI) provides limited financial protection for patients with rare diseases. Among China’s 31 provincial-level administrative centers, 24 have set general outpatient reimbursement ceilings under the urban and rural resident basic medical insurance (URRBMI) at 1000 RMB or less. In comparison, 24 cities have set outpatient reimbursement limits under the urban employee basic medical insurance (UEBMI) at 6000 RMB or less. The security system relies predominantly on the BMI, while supplementary mechanisms have failed to provide effective support or continuity in coverage. Current policies are generally reactive, with coverage typically triggered only after a confirmed diagnosis and often lacking early intervention or preventive strategies. Conclusions: China’s rare disease security system urgently requires structural improvements in coverage depth and proactive risk management. The proposed Dedicated Insurance Scheme for Rare Diseases (DISRD) presents a feasible and sustainable model for China’s multi-tiered system of securing rare diseases. It provides valuable institutional insights for other countries and regions seeking to build public health systems with proactive risk control capabilities. Full article

Background: Zebrafish-based drug discovery systems provide significant advantages over mammalian models for high-throughput in vivo screening. Among these, the NF-κB eGFP reporter system significantly enhances drug discovery in zebrafish by enabling real-time, high-resolution monitoring of pathway activity in live organisms, thereby streamlining mechanistic studies and high-throughput screening. Methods: We developed a novel AI (Quantifish and Orange software)-based zebrafish precision individualized 96-well ZF plates (0–7 dpf) and individualized MT tanks (8 dpf–4 mpf) protocol for the transposon-mediated transgenesis of the NFκB eGFP reporter system. Results: One-cell stage embryos were administered NFκB reporter construct and Tol2 transposase mRNA via microinjection and transferred to separate wells of a 96-well ZF plate. Bright-field and fluorescence images of each well were captured at 5 dpf in the F0, F1, and F2 generations using the automated confocal high-content imager CQ1. The Quantifish software was used for the automated detection and segmentation of zebrafish larval fluorescence intensity in specific regions of interest. Quantitative data on the fluorescence intensity and distribution patterns were measured in Quantifish, and advanced statistical and machine learning methods were applied using Orange. Imaging data with eGFP expression results were assessed to evaluate the efficiency of the transgenic protocol. Discussion: This AI-enhanced precision protocol allows for high-throughput screening and quantitative analysis of NFκB reporter transgenesis in zebrafish, enabling the efficient identification and characterization of stable transgenic lines that exhibit tissue-specific expression of the NF-κB reporter, such as lines with induced expression restricted to the retina following LPS stimulation. This approach streamlines the evaluation of regulatory elements, enhances data consistency, and reduces animal use, making it a valuable tool for zebrafish drug discovery. Full article

To analyze the effects of remanufacturing subsidies and emission reduction investments on pricing and quality decisions under cap-and-trade regulation, four profit-maximization Stackelberg game models for a remanufacturing supply chain (RSC), i.e., without remanufacturing subsidies and emission reduction investments, with remanufacturing subsidies only, with emission reduction investments only, and with both remanufacturing subsidies and emission reduction investments, are constructed, derived, compared, and analyzed. Results show that government subsidies and emission reduction investments can improve profits for the RSC members, while possibly leading to more total carbon emissions. Furthermore, it is worth noting that profit growth and emission reduction can be achieved even though reducing remanufacturing subsidies in some scenarios. Moreover, increasing emission reduction targets will reduce profits of the RSC members but does not necessarily contribute to emission reduction. Therefore, to help the RSC improve profits and reduce emission, the policymaker should formulate differentiated policies based on the types of manufacturers. For the non-abating manufacturer, the government should set higher emission reduction targets and cut down subsidies; for the low-efficiency abating manufacturer, higher emission reduction targets and subsidies are more suitable. However, for the high-efficiency abating manufacturer, lower emission reduction targets and subsidies are more effective. Full article

Racetrack NbTi superconducting coil is a key component in Maglev train systems due to its excellent mechanical processing performance and lower construction cost. However, dynamic pressures during high-speed operations can influence contact pressures and cause internal filament damage, leading to critical current degradation and quench, which threaten the stable operation of the superconducting magnet. Considering that the NbTi coil has a typical hierarchical structure and comprises thousands of filaments, this study constructs an efficient multiscale framework combining the finite element method (FEM) and self-consistent clustering analysis (SCA) to study the multiscale responses of the NbTi coil. The mechanical responses of the two-scale racetrack coil under monotonic and periodic pressures are investigated, and the effects of the friction contacts between strands are also discussed. The study reveals that internal contacts significantly influence local contact pressures and microscopic stresses, and periodic loading leads to stress accumulation with cycle times. The proposed framework efficiently captures critical microscale responses and can be applied to other multiscale materials and structures. Full article

Optimizing metadata indexing remains critical for enhancing distributed file system performance. The Traditional Log-Structured Merge-Trees (LSM-Trees) architecture, while effective for write-intensive operations, exhibits significant limitations when handling massive metadata workloads, particularly manifesting as suboptimal read performance and substantial indexing overhead. Although existing learned indexes perform well on read-only workloads, they struggle to support modifications such as inserts and updates effectively. This paper proposes SwiftKV, a novel metadata indexing scheme that combines LSM-Tree and learned indexes to address these issues. Firstly, SwiftKV employs a dynamic partition strategy to narrow the metadata search range. Secondly, a two-level learned index block, consisting of Greedy Piecewise Linear Regression (Greedy-PLR) and Linear Regression (LR) models, is leveraged to replace the typical Sorted String Table (SSTable) index block for faster location prediction than binary search. Thirdly, SwiftKV incorporates a load-aware construction mechanism and parallel optimization to minimize training overhead and enhance efficiency. This work bridges the gap between LSM-Trees’ write efficiency and learned indexes’ query performance, offering a scalable and high-performance solution for modern distributed file systems. This paper implements the prototype of SwiftKV based on RocksDB. The experimental results show that it narrows the memory usage of index blocks by 30.06% and reduces read latency by 1.19×~1.60× without affecting write performance. Furthermore, SwiftKV’s two-level learned index achieves a 15.13% reduction in query latency and a 44.03% reduction in memory overhead compared to a single-level model. For all YCSB workloads, SwiftKV outperforms other schemes. Full article

With the progressive depletion of conventional oil and gas resources and the increasing demand for alternative energy, organic-rich sedimentary rock—oil shale—has attracted widespread attention as a key unconventional hydrocarbon resource. Pyrolysis is the essential process for converting the organic matter in oil shale into recoverable hydrocarbons, and a detailed understanding of its behavior is crucial for improving development efficiency. This review systematically summarizes the research progress on the pyrolysis characteristics of oil shale under laboratory conditions. It focuses on the applications of thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) in identifying pyrolysis stages, extracting kinetic parameters, and analyzing thermal effects; the role of coupled spectroscopic techniques (e.g., TG-FTIR, TG-MS) in elucidating the evolution of gaseous products; and the effects of key parameters such as pyrolysis temperature, heating rate, particle size, and reaction atmosphere on product distribution and yield. Furthermore, the mechanisms and effects of three distinct heating strategies—conventional heating, microwave heating, and autothermic pyrolysis—are compared, and the influence of inherent minerals and external catalysts on reaction pathways is discussed. Despite significant advances, challenges remain in quantitatively describing reaction mechanisms, accurately predicting product yields, and generalizing kinetic models. Future research should integrate multiscale experiments, in situ characterization, and molecular simulations to construct pyrolysis mechanism models tailored to various oil shale types, thereby providing theoretical support for the development of efficient and environmentally friendly oil shale conversion technologies. Full article

The stroke control of a hydrostatic transmission (HST) is crucial for improving the energy efficiency and power variability of heavy-duty vehicles, including agricultural, construction, mining, and forestry equipment. This study introduces a new control strategy: an Artificial Neural Network (ANN) controller that imitates a Multiple Model Predictive Controller (MPC). The goal is to compare their performance in controlling the HST’s stroke. The proposed controller is designed to track complex stroke reference trajectories for both primary and secondary regulations under realistic disturbances, such as engine and load torques, which are influenced by soil and road conditions for an HST system in line with a nonlinear and time-varying mathematical model. Processor-in-the-Loop simulations suggest that the ANN controller holds several advantages over the Multiple MPC and classical control strategies. These benefits include its suitability for multi-input–multi-output systems, its insensitivity to external stochastic disturbances (like white noise), and its robustness against modeling errors and uncertainties, making it a promising option for real-time HST implementation and better than the Multiple MPC scheme in terms of simplicity and computational cost-effectiveness. Full article

Inverting seawater sound speed profiles (SSPs) using Bayesian methods enables optimal parameter estimation and provides a quantitative assessment of uncertainty by analyzing the posterior distribution of target parameters. However, in nonlinear geophysical inversion problems like acoustic tomography, calculating the posterior distribution remains challenging. In this study, a Bayesian framework is used to construct the posterior distribution of target parameters based on acoustic travel-time data and prior information. A Hamiltonian Monte Carlo (HMC) approach is developed for SSP inversion, offering an effective solution to the computational issues associated with complex posterior distributions. The HMC algorithm has a strong physical basis in exploring distributions, allowing for accurate characterization of physical correlations among target parameters. It also achieves sufficient sampling of heavy-tailed probabilities, enabling a thorough analysis of the target distribution characteristics and overcoming the low efficiency often seen in traditional methods. The SSP dataset was created using temperature–salinity profile data from the Hybrid Coordinate Ocean Model (HYCOM) and empirical formulas for SSP. Experiments with acoustic propagation time data from the Kuroshio Extension System Study (KESS) confirmed the feasibility of the HMC method in SSP inversion. Full article