Search Results (21,559)

During the mid-to-late stages of oilfield development, reservoir energy depletion and declining formation pressure coefficients are prevalent challenges. To address the issues of severe fluid loss and extended post-workover fluid recovery periods during conventional operations such as thermal wax removal and pump inspection in low-pressure, waxy wells within a specific block of the Xinjiang Oilfield, a dynamic loss analysis model for workover fluids was developed. Additionally, a wash pressure control valve was engineered to meet the requirements for squeeze killing under abnormal conditions, and an integrated anti-leakage tubing string was designed. This system effectively isolates the workover fluid from the reservoir during interventions, thereby significantly reducing fluid loss and enhancing operational safety. Field applications demonstrate that this technology reduces workover fluid loss by 96% during thermal wax removal and shortens the average post-workover fluid recovery period by 8.7 days after pump inspection. This technology enables rapid restoration of well productivity, lowers operational costs for thermal wax removal and pump inspection, and provides an effective solution for maintaining low-pressure, waxy wells. Full article

Artificial intelligence (AI) has significantly influenced higher education, accelerating the arrival of College 4.0. Given its core mission of cultivating talent through teaching, understanding how AI can empower teaching in higher education is crucial. Utilizing second-hand survey data from the Zhejiang Provincial Department of Education, this study empirically diagnoses the status of AI-empowered teaching in higher education across 81 universities, 4085 faculty members, and 24,095 students, by descriptive statistical analysis. The results reveal critical structural misalignments. At the institutional level, while 94% of universities have formulated AI plans, a severe disciplinary imbalance exists, with science and engineering accounting for 60.1% of specialized courses compared to only 4.5% in agriculture and medicine. At the faculty level, a “high cognition, low practice” gap is evident; although willingness is high, 96% of instructors lack significant industry practice experience. At the student level, a substantial misalignment appears between the demand for AI skills and educational supply. Based on these findings, we propose targeted strategies for optimizing resource allocation and establishing cross-boundary teacher training systems to promote AI-empowered teaching to achieve sustainable higher education. Full article

Modern smart cities face increasing pressure to invest in sustainable and reliable energy systems while navigating uncertainties arising from fluctuating market conditions, evolving technology landscapes, and diverse expert opinions. Traditional multi-criteria decision-making (MCDM) approaches often fail to fully represent these uncertainties as they typically rely on crisp inputs, lack temporal memory, and do not explicitly account for stochastic variability. To address these limitations, this study introduces a novel Stochastic Fractional Fuzzy Tensor (SFFT)-based Group Decision-Making framework. The proposed approach integrates three dimensions of uncertainty within a unified mathematical structure: fuzzy representation of subjective expert assessments, fractional temporal operators (Caputo derivative, $\alpha =0.85$) to model the influence of historical evaluations, and stochastic diffusion terms ($\sigma =0.05$) to capture real-world volatility. A complete decision algorithm is developed and applied to a realistic smart city renewable energy selection problem involving six alternatives and six criteria evaluated by three experts. The SFFT-based evaluation identified Geothermal Energy as the optimal choice with a score of 0.798, followed by Offshore Wind (0.722) and Waste-to-Hydrogen (0.713). Comparative evaluation against benchmark MCDM methods—TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VIšekriterijumsko KOmpromisno Rangiranje), and WSM (Weighted Sum Model)—demonstrates that the SFFT approach yields more robust and stable rankings, particularly under uncertainty and model perturbations. Extensive sensitivity analysis confirms high resilience of the top-ranked alternative, with Geothermal retaining the first position in 82.4% of 5000 Monte Carlo simulations under simultaneous variations in weights, memory parameter ($\alpha \in [0.25,0.95]$), and noise intensity ($\sigma \in [0.01,0.10]$). This research provides a realistic, mathematically grounded, and decision-maker-friendly tool for strategic planning in uncertain, dynamic urban environments, with strong potential for deployment in wider engineering, management, and policy applications. Full article

This paper proposes a coordinated planning method for distribution network lines considering geographical constraints and load distribution, aiming to improve the economy and engineering feasibility of distribution network planning. First, a hierarchical system of geographical constraints based on the Interval Analytic Hierarchy Process (IAHP) is established to systematically quantify the influence weights of spatial factors such as terrain undulation, ecological protection zones, and construction obstacles. Second, the density peak clustering algorithm and load complementarity coefficient are introduced to generate equivalent load nodes, and a spatially continuous load density grid model is constructed to accurately characterize the distribution and complementary characteristics of the load. Third, an improved A-star algorithm is adopted, which integrates a heuristic function guided by geographical weights and load density to dynamically avoid high-cost areas and approach high-load areas. Additionally, Bézier curves are used to optimize the path, reducing crossings and obstacle interference, thus enhancing the implementability of line layout. Verification via a real distribution network case study in a certain area of Guangdong Province shows that the proposed method outperforms traditional planning strategies. It significantly improves the economy, safety, and engineering feasibility of the path, providing effective decision support for distribution network line planning in complex environments. Full article

Digital twin technology is emerging as a critical enabler for the lifecycle management of complex engineering equipment, yet its implementation faces significant hurdles. Generic, one-size-fits-all digital twin platforms often fail to address the unique characteristics of this domain—such as tightly coupled multi-physics, high-fidelity modeling requirements, and the need for real-time model execution under harsh operating conditions. This creates a critical need for a structured, reusable blueprint. However, a dedicated reference architecture that systematically guides the development of such specialized digital twins is notably absent. To bridge this gap, this paper proposes MetaD-DT, a reference architecture designed to enable and streamline the development of digital twins specifically for complex engineering equipment. We detail its comprehensive four-layer architecture, core functional modules, and streamlined graphical development workflow. The MetaD-DT’s efficacy and practical value are validated through two distinct industrial case studies: a health management system for diesel engine Diesel Particulate Filter (DPF) and an intelligent control optimization system for Indirect Air-Cooled (IAC) towers. These applications validate the framework’s ability to support the creation of robust digital twins that can effectively handle complex industrial dynamics and improve O&M (Operation And Maintenance) efficiency. This work provides a systematic architectural blueprint for the future development of specialized and efficient digital twins in the engineering equipment domain. Full article

In practical engineering, large-scale wind power integration typically requires long-distance transmission lines to deliver power to load centers. The resulting weak sending-end systems lack support from synchronous power sources. Under extreme weather conditions, the rapid increase in active power output caused by high wind power generation may lead to voltage instability. In existing projects, a phenomenon of repeated voltage fluctuations has been observed under fault-free system conditions. This phenomenon is induced by the coupling of the characteristics of weak sending-end systems and low-voltage ride-through (LVRT) discrimination mechanisms, posing a serious threat to the safe and stable operation of power grids. However, most existing studies focus on the analysis of voltage instability mechanisms and the optimization of control strategies for single devices, with insufficient consideration given to voltage fluctuation suppression methods under the coordinated operation of wind power and energy storage systems. Based on the actual scenario of energy storage configuration in wind farms, this paper improves the traditional LVRT discrimination mechanism and develops a coordinated voltage ride-through control strategy for permanent magnet synchronous generator (PMSG) wind turbines and energy storage batteries. It can effectively cope with unconventional operating conditions, such as repeated voltage ride-through and deep voltage ride-through that may occur under extreme meteorological conditions, and improve the safe and stable operation capability of wind farms. Using a hardware-in-the-loop (HIL) test platform, the coordinated voltage ride-through control strategy is verified. The test results indicate that it effectively enhances the wind–storage system’s voltage ride-through reliability and suppresses repeated voltage fluctuations. Full article

Background/Objectives: Cardiotoxicity remains a leading cause of drug withdrawal. Conventional preclinical models, such as two-dimensional (2D) cell cultures and animal studies, often fail to accurately predict human cardiac responses. While 2D cultures lack the complex architecture and dynamic functionality of native myocardium, interspecies differences limit the translational relevance of animal models. The objective of this study was to develop a human-relevant, in vitro platform that enables predictive and functional assessment of drug-induced cardiotoxicity. Methods: Here, we present a high-throughput in vitro platform for cardiotoxicity screening using three-dimensional (3D) cardiac tissues derived from human induced pluripotent stem cells (hiPSCs) within a thermoresponsive extracellular matrix-derived hydrogel. The hydrogel enables homogeneous encapsulation, differentiation in 3D, and long-term assembly into a functional cardiac tissue. Maturation was validated by immunostaining for cardiac-specific markers, and calcium imaging was employed to monitor electrical signal propagation. Contractile performance, defined by beat rate and contraction amplitude, was quantified using video-based motion analysis. The platform was applied to evaluate the dose-dependent effects of various cardioactive compounds, including β-adrenergic agonists ((-) epinephrine and dopamine), a cardiotoxic chemotherapeutic (doxorubicin), a sinus node inhibitor (ivabradine), a calcium channel blocker (verapamil), and a β-adrenergic antagonist (metoprolol). Results: The engineered cardiac tissues exhibited functional maturation and stable contractile behavior. Drug testing demonstrated compound-specific, dose-dependent functional responses. For each compound, the system faithfully reproduced the expected physiological responses. Conclusions: This human-relevant, scalable platform enables sensitive, multiparametric functional assessment of cardiac tissues, offering a cost-effective and predictive tool for preclinical drug safety testing. By bridging the gap between in vitro assays and human physiology, it holds promise to enhance translational accuracy while reducing reliance on animal models. Full article

Background/Objectives: New vaccine platforms that rapidly yield low-cost, easily manufactured vaccines are highly desired, yet current approaches lack key features. We developed the Killed Whole-Cell/Genome-Reduced Bacteria (KWC/GRB) platform, which uses a genome-reduced Gram-negative chassis to enhance antigen exposure and modularity via an autotransporter (AT) system. Integrated within a Design–Build–Test–Learn (DBTL) framework, KWC/GRB enables rapid iteration of engineered antigens and immunomodulatory elements. Here, we applied this platform to the HIV-1 fusion peptide (FP) and tested multiple antigen engineering strategies to enhance its immunogenicity. Methods: For a new vaccine, we synthesized DNA encoding the antigen together with selected immunomodulators and cloned the constructs into a plasmid. The plasmids were transformed into genome-reduced bacteria (GRB), which were grown, induced for antigen expression, and then inactivated to produce the vaccines. We tested multiple strategies to enhance antigen immunogenicity, including multimeric HIV-1 fusion peptide (FP) designs separated by different linkers and constructs incorporating immunomodulators such as TLR agonists, mucosal-immunity-promoting peptides, and a non-cognate T-cell agonist. Vaccines were selected based on structure prediction and confirmed surface expression by flow cytometry. Mice were vaccinated, and anti-FP antibody responses were measured by ELISA. Results: ELISA responses increased nearly one order of magnitude across design rounds, with the top-performing construct showing an ~8-fold improvement over the initial 1mer vaccine. Multimeric antigens separated by an α-helical linker were the most immunogenic. The non-cognate T-cell agonist increased responses context-dependently. Flow cytometry showed that increased anti-FP-mAb binding to GRB was associated with greater induction of antibody responses. Although anti-FP immune responses were greatly increased, the sera did not neutralize HIV. Conclusions: Although none of the constructs elicited detectable neutralizing activity, the combination of uniformly low AlphaFold pLDDT scores and the functional data suggests that the FP region may not adopt a stable native-like structure in this display context. Importantly, the results demonstrate that the KWC/GRB platform can generate highly immunogenic vaccines, and when applied to antigens with well-defined native tertiary structures, the approach should enable rapidly produced, high-response, very low-cost vaccines. Full article

Intensive Care Units (ICUs) face unprecedented challenges in resource allocation, particularly during health crises in which algorithmic systems may be exposed to adversarial manipulation. A transformer-based expert system, ICU-Transformer, is presented to optimize resource allocation across 200 ICUs in Physionet while maintaining robustness against data poisoning attacks. The framework incorporates a Robust Multi-Head Attention mechanism that achieves an AUC-ROC of 0.891 in mortality prediction under 20% data contamination, outperforming conventional baselines. The system is trained and evaluated using data from the MIMIC-IV and eICU Collaborative Research Database and is deployed to manage more than 50,000 ICU admissions annually. A Resource Optimization Engine (ROE) is introduced to dynamically allocate ventilators, Extracorporeal Membrane Oxygenation (ECMO) machines, and specialized clinical staff based on predicted deterioration risk, resulting in an 18% reduction in preventable deaths. A Surge Capacity Planner (SCP) is further employed to simulate disaster scenarios and optimize cross-hospital resource distribution. Deployment across the Physionet ICU Network demonstrates improvements, including a 2.1-day reduction in average ICU bed turnover time, a 31% decrease in unnecessary admissions, and an estimated USD 142 million in annual operational savings. During the observation period, 234 algorithmic manipulation attempts were detected, with targeted disparities identified and mitigated through enhanced auditing protocols. Full article

Tsunamis and submarine earthquakes pose severe risks to coastal regions, demanding rapid and reliable monitoring systems. While the Deep-ocean Assessment and Reporting of Tsunamis (DART) system has been globally deployed, its dependence on pressure sensors and one-to-one communication limits its applicability to the Korean East Sea. This paper introduces the Korean Tsunami and Earthquake Monitoring System, which integrates seafloor seismometers and proposes a dedicated Medium Access Control (MAC) protocol optimized for multi-node underwater acoustic communication. The study performs a comprehensive analytical derivation of closed-form expressions for channel utilization and energy consumption under diverse node configurations and acoustic conditions. The analytical results confirm that the proposed MAC protocol maintains stable performance, supports multi-node operation, and enables long-term monitoring within the limited energy budget of underwater devices. The derived results also provide practical design implications for underwater network planning, including guidelines on node placement, frame duration, and control packet timing for efficient data delivery. Although empirical validation remains as future work, the findings establish theoretical benchmarks and engineering insights for the design of next-generation underwater monitoring systems tailored to Korean coastal environments. Full article

Microbial fermentation stands as the foundational technology in modern biorefineries, yet its industrial scalability is critically constrained by product inhibition, prohibitive downstream separation costs, and substrate inhibition. Metal–organic frameworks (MOFs) offer a tunable material platform to address these challenges through rational design of pore size, shape, and chemical functionality. This review systematically chronicles the evolution of MOF applications in biomass fermentation across four generations, demonstrating a synergistic mapping where the core fermentation challenges—product toxicity, substrate toxicity, and separation energy intensity—align with the inherent MOF advantages of high adsorption capacity, programmable selectivity, and tunable functionality. The applications progress from first-generation passive adsorbents for in situ product removal, to second-generation protective agents for mitigating inhibitors, and third-generation immobilization scaffolds enabling continuous processing. The fourth-generation systems transcend passive scaffolding to position MOFs as active metabolic partners in microbe-MOF hybrids, driving cofactor regeneration and tandem biocatalysis. By synthesizing diverse research streams, ranging from defect engineering to artificial symbiosis, including defect engineering strategies, this review establishes critical design principles for the rational integration of programmable materials in next-generation biorefineries. Full article

This study analyzes the injection behavior of fossil and sustainable aviation fuel blends, in comparison with conventional diesel fuel, using a common-rail injection system applied to reciprocating engines. Neat commercial diesel and Jet A1 were tested as fossil fuels. A neat Fischer–Tropsch Synthetic Paraffinic Kerosene was tested and blended with Jet A1. Another alternative fuel, Hydrotreated Vegetable Oil, was also blended with Jet A1. The blending proportion was established to meet 51 as the derived cetane number, as required for fuels used in diesel reciprocating engines. Experimental tests were carried out under an energizing time of 2 ms at injection pressures between 50 and 110 MPa, with a fuel temperature ranging from 293 to 313 K, and a constant back pressure of 5 MPa, using a 130 µm single-hole injector. The results show that kerosene fuel exhibits slightly lower injection rates and total injected mass than diesel fuel, mainly due to their lower density. Under low-pressure conditions, an increase in hydraulic injection delay with diesel fuel is observed, mainly at the highest tested temperature. Mass flow rate, hydraulic injection delay, injection duration, total mass injected, and nozzle discharge coefficient do not show significant variations within the tested temperatures. Fossil kerosene fuel and its blend with Synthetic Paraffinic Kerosene show slightly higher injection rates. Overall, the results indicate that both neat kerosene and the studied blends may achieve injection characteristics comparable to diesel fuel, supporting their technical feasibility in reciprocating engines within the framework of the Single Fuel Concept. Full article

Rational allocation and coordinated operation of water resources in arid inland river basins are crucial for sustaining irrigated agriculture, maintaining ecological baseflow and ensuring reservoir safety. To address this need, this study develops and evaluates joint-operation schemes for the Jingou River-Hongshan Reservoir irrigation system in Xinjiang, northwestern China, to improve coordination among irrigation water supply, ecological baseflow maintenance and reservoir safety. A monthly reservoir-canal-irrigation operation model is formulated with irrigation demands, ecological flow constraints and key engineering limits. Using this model, operating schemes are generated to explore trade-offs among three objectives: shortages, reliability and non-beneficial reservoir releases. The non-dominated schemes obtained from multi-objective optimization are then ranked using an entropy-weighted TOPSIS framework, from which representative solutions are selected for further interpretation. The results indicate that the top-ranked schemes deliver comparable and relatively well-balanced performance across the objectives. Under the preferred compromise scheme, annual irrigation shortages amount to about 39% of total demand, the mean satisfaction level of irrigation and ecological requirements reaches roughly 57%, and the combined index of spill losses and end-of-year storage deviation remains low. Schemes that push shortage reduction or reliability enhancement to extremes tend to increase spill losses, compromise storage security or both, thereby degrading overall performance. The proposed optimization-ranking framework offers a transparent basis for identifying robust operating strategies that reflect local management priorities and is transferable to other reservoir-supported irrigation systems in arid regions. Full article

Urban resilience has emerged as a crucial objective for achieving sustainable urban development. However, its practical integration into planning remains limited. This study evaluates the extent to which resilience thinking is integrated into Chengdu’s urban planning system by combining policy-theoretical analysis with empirical evidence. Drawing on a framework of nine resilience attributes, we conduct content analysis of Chengdu’s three types of statutory plan documents (Socioeconomic Development Plan, Urban and Rural Plan, and Land Use Plan) and a questionnaire survey of 70 expert planners. The results reveal that resilience is reflected implicitly in the plans through engineering-oriented attributes such as robustness, efficiency, and connectivity. In contrast, social and ecological attributes like inclusion, redundancy, and innovation are largely absent. Planners demonstrate moderate awareness of resilience, yet associate it predominantly with rapid response and infrastructure robustness rather than long-term adaptation or community capacity-building. These findings indicate the dominant top-down, growth-centric planning logic that constrains the adoption of broader socio-ecological resilience concepts. This paper concludes with policy recommendations for institutionalizing resilience in Chinese urban planning through legal mandates; multi-sectoral coordination; and participatory, adaptive planning frameworks. Full article

The rapid evolution of autonomous vehicles necessitates increasingly sophisticated cognitive capabilities to handle complex, unstructured environments. This study explores the cognitive potential of Large Language Models (LLMs) in autonomous navigation and swarm control systems, addressing the limitations of traditional rule-based approaches. The research investigates whether multimodal LLMs, specifically a customized version of LLaVA 7B (Large Language and Vision Assistant), can serve as a central decision-making unit for autonomous vehicles equipped with cameras and distance sensors. The developed prototype integrates a Raspberry Pi module for data acquisition and motor control with a main computational unit running the LLM via the Ollama platform. Communication between modules combines REST API for sensory data transfer and TCP sockets for real-time command exchange. Without fine-tuning, the system relies on advanced prompt engineering and context management to ensure consistent reasoning and structured JSON-based control outputs. Experimental results demonstrate that the model can interpret real-time visual and distance data to generate reliable driving commands and descriptive situational reasoning. These findings suggest that LLMs possess emerging cognitive abilities applicable to real-world robotic navigation and lay the groundwork for future swarm systems capable of cooperative exploration and decision-making in dynamic environments. These insights are particularly valuable for researchers in swarm robotics and developers of edge-AI systems seeking efficient, multimodal navigation solutions. Full article