Search Results (2,059)

With the increasing integration of regional energy systems, the dynamic coupling characteristics of cooling, heating, and power flows have become significantly pronounced. However, traditional scheduling models often utilize steady-state assumptions that neglect the thermal transmission delay of the pipeline network, leading to spatiotemporal mismatches between energy supply and load demand. To address this issue, this paper proposes an optimal operation strategy for regional Combined Cooling, Heating, and Power (CCHP) systems that explicitly integrates thermal inertia. First, a Pipeline Fluid Micro-element Discretization Method (PFMDM) is developed based on the Lagrangian specification to accurately characterize the dynamic flow and thermal decay processes without the heavy computational burden of partial differential equations. In addition, the accuracy of PFMDM is directly benchmarked against a high-fidelity transient PDE solver (finite-volume TVD–MUSCL scheme) over a wide range of pipe lengths, flow velocities, and thermal loss coefficients, where the outlet-temperature RMSE remains below 0.2 °C. This model quantitatively reveals the “Virtual Energy Storage” (VES) mechanism of the pipeline network. Second, to overcome the “curse of dimensionality” in dynamic scheduling, a Load-Gradient-Based Adaptive Temporal Discretization (LG-ATD) method is proposed. This method maintains a fine-grained baseline for electrical settlement while dynamically aggregating thermal/cooling steps based on load fluctuations. Simulation results demonstrate that the proposed strategy corrects the significant physical deviations of the traditional steady-state model. The analysis reveals that the steady-state model underestimates the required heating and cooling supply capacities by up to 26.66% and 39.15%, respectively, due to the neglect of transmission losses and delays. By leveraging the VES mechanism, the proposed method enables a fuel-shift in the energy-supply structure, substantially decreasing the electricity purchasing cost (by 75.2% in the tested case). This reduction reflects a reallocation from grid purchases to on-site gas-fired cogeneration to maintain physical feasibility under delay and loss effects, and therefore, it is accompanied by an increase in natural gas consumption and a higher total operating cost. Furthermore, the LG-ATD method significantly alleviates the computational burden by substantially compressing the presolved model size and reducing the overall solving time by more than 80%, thereby effectively mitigating the curse of dimensionality for practical engineering applications. Full article

Stimuli-responsive textiles are a rapidly evolving class of functional fiber-based materials that sense and adapt to environmental triggers. Within these enabling technologies, hydrogels and microcapsules are very illustrative, as they offer complementary mechanisms for moisture management, controlled release, and adaptive performance. Hydrogels provide soft, water-rich polymer networks with modifiable swelling, permeability, and mechanics, while microcapsules offer protection and targeted delivery of active agents through engineered shell structures. When integrated into fibrous networks, they impart dynamic detection responses for moisture, temperature, pH, mechanical stress, light, and chemical or biological agents. This review critically examines progress in design, synthesis, and textile integration of hydrogel- and microcapsule-based systems, with emphasis on materials that exhibit stimuli-responsive behavior rather than passive or extended-release functionality. Strategies for incorporating bulk hydrogels, micro- and nanogels, and stimuli-responsive microcapsules into fibers, yarns, and fabrics are discussed in addition to applications such as smart apparel, medical and hygienic textiles, controlled drug delivery, antimicrobial fabrics, and adaptive filtration media. Existing challenges for durability, washability, response kinetics, scalability, and sustainability are highlighted, while future research directions are proposed to advance the development of robust and intelligent textile systems at the nexus of soft matter science and fiber engineering. Full article

For ground source heat pump (GSHP) systems, conventional control strategies often suffer from significant hysteresis, leading to energy waste and occupant discomfort. This study proposes and validates a novel Prediction Self-Adaptive Control (PSAC) technology that hybridizes deep learning foresight with robust engineering feedback loops. The architecture integrates a CNN-LSTM model to forecast building thermal loads with high fidelity, and this prediction drives a macro-scale unit commitment module that optimizes chiller sequencing. Simultaneously, a micro-scale self-adaptive feedback mechanism dynamically resets the chilled water supply temperature and modulates pump frequency to eliminate the residual error between the predicted state and the actual building demand, ensuring precise load matching. Field implementation in a 62,500 m² residential complex in Shanghai demonstrated that the CNN-LSTM model achieved a load forecasting accuracy within a ±10% error margin, the PSAC strategy significantly outperformed baseline constant-temperature controls, maintaining indoor temperatures between 23 and 26 °C and relative humidity between 30 and 55% and the system achieved a weekly average System Coefficient of Performance (SCOP) of 3.91 compared to the baseline of 3.30, resulting in an 15.6% reduction in total energy consumption. By decoupling predictive planning from adaptive execution, the system offers a scalable, robust, and highly efficient solution for the decarbonization of HVAC systems in complex climate zones. Full article

Gouty arthritis (GA) is a debilitating autoinflammatory disorder precipitated by the deposition of monosodium urate (MSU) crystals, leading to intense, recurrent joint inflammation and systemic metabolic dysregulation. While hyperuricemia is a prerequisite, the transition to clinical gout involves complex intercellular signaling cascades that are not fully understood. Emerging evidence has identified exosomes,— nanoscale extracellular vesicles, —as critical mediators in this pathological process. Exosomes function as intercellular carriers, transporting a diverse cargo of bioactive molecules, including proteins, lipids, and nucleic acids (e.g., microRNAs), which profoundly influence immune cell activation, inflammasome regulation, and metabolic pathways. This review provides a critical analysis of the dual role of exosomes in both propagating and potentially resolving inflammation in GA. We delve into the intricate mechanisms of exosome-mediated pathogenesis, including the modulation of purine metabolism, lysosomal function, and complement–inflammasome crosstalk. Furthermore, we explore the burgeoning field of exosome-based therapeutics, critically evaluating strategies such as engineered exosomes for targeted drug delivery, mesenchymal stem cell (MSC)-derived exosomes for immunomodulation, and the development of exosomal biomarkers for diagnostics. Additionally, we examine how chemical drugs and herbal compounds may exert therapeutic effects by modulating exosome pathways, offering new insights into integrative treatment approaches. By synthesizing recent findings from proteomic, transcriptomic, and functional studies, we aim to unravel the complexities of exosome signaling in GA and to propose innovative therapeutic avenues that target these pathways to improve patient outcomes. Full article

The endogenous security paradigm has emerged to address the limitations of traditional cybersecurity, which relies on reactive “patching” and struggles against unknown threats, APTs, and supply chain attacks. Centered on the principle that “structure determines security”, it diverges from detection-based approaches by employing systems theory and cybernetics to architect closed-loop systems with “heterogeneous execution, multimodal adjudication, and dynamic scheduling”. This is realized through intrinsic architectural constructs such as dynamism, heterogeneity, and redundancy. Theoretically, it transforms deterministic component-level attacks into probabilistic system-level events, thereby shifting the security foundation from a “cognitive contest” to an “entropy-driven confrontation”. This paper provides a comprehensive review of this paradigm. We begin by elucidating its philosophical foundations and core axioms, focusing on the Dynamic Heterogeneous Redundancy (DHR) model, which converts attacks on specific vulnerabilities into probabilistic events under the core assumption of independent heterogeneous execution entities. Next, we trace the architectural evolution from early mimic defense prototypes to a universal framework, analyzing key developments including expanded heterogeneity dimensions, intelligence-driven dynamic policies, and enhanced adjudication mechanisms. We then explore essential enabling technologies and their integration with cutting-edge trends such as artificial intelligence, 6G, and cloud-native computing. Through case studies of the 5G core network and intelligent connected vehicles, the engineering feasibility of the endogenous security paradigm has been validated, with quantifiable security gains demonstrated. In a live-network pilot of the endogenous security micro-segmentation system for the 5G core, resource consumption (CPU/memory usage) of network function virtual machines remained below 3% under steady-state service loads. The system concurrently maintained microsecond-level forwarding performance and achieved carrier-grade core service availability of 99.999%. These results demonstrate that the endogenous security mechanism delivers high-level structural security with an acceptable performance cost. The paper also critically summarizes current theoretical, engineering, and ecosystem challenges, while outlining future research directions such as “Endogenous Security as a Service” and convergence with quantum-safe technologies. Full article

This study addresses the major engineering challenges of leaving roadways along the goaf in shallow-buried coal seam tunnels through water-bearing soft rock. It focuses on three core issues: the mechanism of rock mass softening upon water exposure, large-deformation control, and directional pressure relief technology. By integrating laboratory testing, theoretical analysis, numerical simulation, and field testing methods, the evolution of macro- and micro-mechanical properties of rock under water–rock interaction can be studied. The research developed constant-resistance large-deformation rock bolts with “yielding within resistance and resisting within yielding” characteristics, revealed the mechanism of directional fracturing through shaped charge blasting, and proposed a synergistic control technology for along-the-goal rib retention: “shaped charge blasting for roof fracturing and pressure relief + reinforced rib support + debris retention devices.” Research findings indicate: increased sandstone water content triggers dissolution of calcareous cement and expansion of clay minerals, leading to rock strength degradation and accelerated deformation, yet the failure mode remains uniaxial shear failure. The developed constant-resistance large-deformation anchor core device maintains a stable working resistance of approximately 350 kN within a 396–405 mm tensile deformation range, significantly enhancing the support system’s crack-resistant capacity under pressure. The focused jet directs cracks to penetrate along predetermined paths, forming planar damage zones and effectively suppressing vertical damage to the surrounding rock. Based on field monitoring, the tunnel was divided into advance support zones, temporary support zones, and stable tunnel sections, enabling a differentiated support scheme. The engineering application achieved stable tunnel retention and safe reuse. This study provides key theoretical foundations and technical approaches for controlling rock mass stability in similar tunnel conditions. Full article

In mechanical engineering, interest in reliable and practicable technologies for nano- and microstructuring of tool surfaces is increasing. Femtosecond laser structuring offers a promising approach that combines high processing speeds with high precision. However, a knowledge gap remains regarding the optimal process parameters for achieving specific surface patterns on hot-work tool steel substrates. The current study aims to investigate the effects of laser scanning parameters on the formation of self-organized surface structures and the resulting topography and morphology. Therefore, samples were irradiated using a 300 fs laser with linearly polarized light (λ = 1030 nm). Scanning electron microscopy revealed four structure types: laser-induced periodic surface structures (LIPSSs), micrometric ripples, micro-crater structures, and pillared microstructures. The results for surface area and line roughness indicate that high laser pulse overlaps lower the strong ablation threshold more effectively than high scanning line overlaps, promoting the formation of pillared microstructures. For efficient ablation and increased surface roughness, higher pulse overlaps are therefore advantageous. In contrast, at low fluences, higher scanning line overlaps support a more homogeneous formation of nanostructures and reduce waviness. Full article

High-entropy alloys (HEAs) represent one of the most promising emerging material families, particularly for advanced surface engineering applications. In this work, a near-high-entropy alloy (near-HEA) coating was produced on a 316L stainless steel substrate using laser metal deposition (LMD) from a powder mixture of Inconel 625, Cr and Mo, without the intentional addition of Fe. Due to dilution from the substrate, the resulting alloy contained elevated Fe content while maintaining Cr, Ni and Mo concentrations within the generally accepted compositional range of HEAs. The deposited layer exhibited a dual-phase microstructure consisting of a face-centered cubic (FCC) phase and a highly distorted tetragonal phase forming a periodic network with a characteristic length scale of several hundred nanometers. The hardness of the coating increased to approximately three times that of the substrate, reaching values of 600–700 HV. To further modify the surface properties, laser-induced periodic surface structures (LIPSS) were generated on the polished coating using femtosecond pulsed laser irradiation at different energy densities. The morphology and subsurface structure of the resulting periodic patterns were investigated by scanning electron microscopy. LIPSS with characteristic dimensions ranging from the micrometer to nanometer scale were successfully produced. Cross-sectional analyses revealed that the underlying dual-phase microstructure remained continuous within the laser-structured regions, indicating that LIPSS formation occurred predominantly via metallic ablation without significant phase transformation or amorphization. These results demonstrate the combined applicability of LMD and femtosecond laser structuring for producing mechanically enhanced, micro- and nanostructured near-HEA coatings with potential for advanced surface-related functionalities. Full article

Lunar dust exhibits exceptionally strong adhesion, abrasiveness, and electrostatic charging due to long-term exposure to extreme temperature cycling (−183 °C to 127 °C), high vacuum, and intense radiation. With the rapid advancement of global lunar exploration programs and the planned construction of lunar bases, lunar dust has become a critical threat to exploration equipment, spacesuits, and spacecraft sealing systems. This paper systematically reviews recent progress in lunar dust mitigation technologies from the perspective of engineering application requirements. Key micro-mechanism factors governing dust adhesion and removal efficiency are analyzed, and the protection mechanisms and application scenarios of traditional lunar dust mitigation technologies are comprehensively discussed, including both active and passive approaches. Active protection technologies generally provide effective dust removal but suffer from high energy consumption, whereas passive strategies can reduce dust adhesion but face challenges in mitigating dynamic dust accumulation. To overcome these limitations, recent studies have increasingly focused on active–passive synergistic strategies that integrate surface modification with dynamic dust removal. Such approaches enable improved efficiency and adaptability by combining long-term dust resistance with real-time removal capability. Based on the latest research advances, this paper further proposes an integrated technical framework for the engineering design of efficient lunar dust protection. Full article

We developed an AI-assisted zero-trust control system at low capital expenditure to retrofit brownfield Ethernet environments without disruptive hardware upgrades or costly software-defined networking migration. Legacy network infrastructures in small and medium-sized enterprises (SMEs) lack the flexibility and programmability required by modern zero-trust architectures, creating a persistent security gap between static Layer-1 deployments and dynamic cyber threats. The developed system addresses this gap through a modular architecture that integrates genetic-algorithm-based virtual local area network (VLAN) optimization, large language model-guided firewall rule synthesis, threat-intelligence-driven policy automation, and telemetry-triggered adaptive isolation. Network assets are enumerated and evaluated through a risk-aware clustering model to enable micro-segmentation that aligns with the principle of least privilege. Optimized segmentation outputs are translated into pfSense firewall policies through structured prompt engineering and dual-stage validation, ensuring syntactic correctness and semantic consistency. A retrieval-augmented generation pipeline connects live telemetry with historical vulnerability intelligence, enabling rapid policy adjustments and automated containment responses. The system operates as an overlay on existing managed switches, orchestrating configuration changes through standards-compliant interfaces such as simple network management protocol and network configuration protocol. Experimental evaluation in a representative SME testbed demonstrates substantial improvements in segmentation granularity, refining seven flat subnets into thirty-four purpose-specific VLANs. Compliance scores improved significantly, with the International Organization for Standardization/International Electrotechnical Commission 27001 rising from 62.3 to 94.7% and the National Institute of Standards and Technology Cybersecurity Framework alignment increasing from 58.9 to 91.2%. All 851 automatically generated firewall rules passed dual-agent validation, ensuring reliable enforcement and enhanced auditability. The results indicate that the system developed provides an operationally feasible pathway for legacy networks to achieve zero-trust segmentation with minimal cost and disruption. Future extensions will explore adaptive learning mechanisms and hybrid cloud support to further enhance scalability and contextual responsiveness. Full article

The Jurassic lacustrine oil shale in southwest China has become a primary production layer due to its high yield and substantial reserves. However, influenced by the lacustrine environment, the vertical profile of the lacustrine shale reservoir shows alternating deposits of shale and carbonate rock. This complex lithological combination results in significant heterogeneity in reservoir types, reservoir distribution, and internal structure. Currently, research on micro-pore structure and hydrocarbon storage mechanisms in lacustrine shales is insufficient, necessitating the elucidation of their micro-characteristics to support future exploration and development. This research focuses on the Da’anzhai Member of Jurassic Ziliujing Formation. Various techniques—including organic geochemical analysis, X-ray diffraction, physical property testing, gradient centrifugation, and gradient drying NMR monitoring—were employed to investigate the micro-pore structure and fluid storage mechanisms of the lacustrine shale reservoir. The following insights were gained from this research. The organic matter pores (OMP) and inorganic pores (IP) developed within the Da’anzhai lacustrine shale reservoir together create the storage space for shale oil, while micro-fractures further enhance the reservoir’s storage capacity and flow performance. Lacustrine shale oil exists in three storage states: mobile oil, bound oil, and adsorbed oil. Mobile oil is primarily located within the micro-fractures and large pores (greater than 350 nm) of the shale reservoir and is the main target for industrial extraction. Bound oil is mainly found in the meso-pores, micropores, and narrow pore structures between rock grains (30 nm to 350 nm), and, theoretically, could potentially be developed through engineering methods such as hydraulic fracturing. Adsorbed oil, due to its close binding with organic matter and clay mineral surfaces, is difficult to release effectively using conventional techniques. The OM abundance, the mineral composition of lacustrine shale, and the pore structure all influence the storage states of shale oil. While a high TOC value increases the amount of mobile oil, the strong adsorption properties of kerogen and organic matter lead to the accumulation of adsorbed oil, which inhibits oil flow. Clay minerals further restrict oil flow by enhancing adsorption, while brittle minerals facilitate the movement of mobile oil by expanding pore space. Based on fractal geometry theory and multi-scale testing results, the large pores in the Da’anzhai lacustrine shale have a high fractal dimension and exhibit complex shapes. However, as pore complexity increases, the amount of adsorbed oil rises significantly, which in turn reduces the proportion of movable oil. Full article

Against the backdrop of high-proportion renewable energy grid integration, modeling accuracy for substations incorporating wind and solar power is critical. Traditional modeling methods rely on theoretical parameters and lack sufficient accuracy. This study uses the 154 kV/23 kV Yeonggwang Substation in Jeollanam-do, South Korea (connected to three wind farms and three solar power plants, with 35 Micro-Phasor Measurement Unit (μPMU) measurement points deployed) as a case study. It investigates three-phase detailed modeling using Power System Computer Aided Design (PSCAD) and μPMU data-driven calibration. Based on substation topology and equipment parameters, a simulation model encompassing main transformers, transmission lines, renewable energy units, and loads was established. A hierarchical calibration system of “data preprocessing—parameter identification—iterative correction” was constructed, employing an iterative optimization strategy of “main grid layer—renewable energy layer—load layer.” A multi-objective optimization function centered on voltage, current, and power was developed. Verification results show that after calibration, the mean relative error rates (MRE) for voltage, current, active power and reactive power are 2.46%, 2.57%, 2.52% and 3.96% respectively, with mean error reduction rates (MERRs) of 80%, 82.75%, 81.33%, and 74.94% compared to pre-calibration values. The uniqueness of the calibration method proposed in this study lies in its use of actual μPMU measurement data to drive PSCAD model parameter calibration, achieving precise matching with the actual characteristics of the substation. This provides a reference method for modeling and digital twin construction of similar substations, demonstrating significant engineering application value. Full article

Micro-foundational HRM has advanced our understanding of how employees perceive and respond to HR practices, yet explanations of how HR systems can generate and sustain coordinated action in day-to-day work remain underspecified. This article presents a theory-building integrative review that specifies a constrained, generative mechanism grounded in observable interaction episodes. We propose a functional architecture that assigns constructs to distinct explanatory roles: enabling states (Role A), interaction episodes as the behavioral engine (Role B), and emergent coordination products (Role C). Psychological safety is positioned as an enabling condition that shifts the likelihood and quality of enactment, whereas collective leadership behavior (CLB) is defined as response-inclusive influence episodes (an influence attempt plus an observable response such as uptake, contestation, neglect, or sanction). We formalize a recursive safety–CLB cycle in which response patterns update subsequent safety and influence dispersion over time, which can yield divergent coordination trajectories even when HR conditions are broadly similar. The framework generates discriminant predictions about response profiles, dispersion versus centralization of influence, and temporal signatures, and it clarifies minimal design requirements for testing recursion with episode-level and intensive longitudinal evidence. We discuss implications for micro-foundational HRM, measurement alignment, and testable design-relevant implications for HR system design as an interaction-relevant cue environment. Full article

Inclusive AI-mediated mathematics education for students with learning difficulties refers to a human-centered approach to mathematics teaching and learning that uses artificial intelligence (AI), adaptive technologies, and data-rich environments to support learners who experience persistent challenges in mathematics. These challenges may take the form of a formally identified developmental learning disorder with impairment in mathematics, broader learning difficulties, low and unstable achievement, irregular engagement, or heightened mathematics anxiety that places students at risk of disengagement and poor long-term outcomes. This approach integrates early screening, personalized instruction, and affect-aware support to address both cognitive difficulties and the emotional burden associated with mathematics anxiety. Situated within digitally augmented schools, homes, and community spaces typical of smart cities, it seeks to reduce stress and anxiety, prevent the reproduction of educational inequalities, and promote equitable participation in science, technology, engineering, and mathematics (STEM) pathways. It emphasizes Universal Design for Learning (UDL), ethical and transparent use of learner data, and sustained collaboration among teachers, families, technologists, urban planners, and policy-makers across micro (individual), meso (school and community), and macro (urban and policy) levels. Crucially, AI functions as decision support rather than replacement of pedagogical judgment, with teachers maintaining human-in-the-loop oversight and responsibility for inclusive instructional decisions. Where learner data include fine-grained logs or affect-related indicators, data minimization, clear purpose limitation, and child- and family-friendly transparency are essential. Implementation should also consider feasibility and sustainability, including staff capacity and resource constraints, so that inclusive benefits do not depend on high-cost infrastructures. Full article

Coal mine drill pipes are subjected to periodic impacts and high-intensity loads in complex underground environments, making them prone to developing micro-cracks that gradually expand, leading to equipment failure and major safety accidents. To address this issue, this paper proposes a framework for ultrasonic crack detection in drill pipes, which leverages a sliding-window root mean square (SWRMS) index for feature representation and a convolutional neural network for accurate classification in noisy environments. The influence mechanism of cracks on ultrasonic echoes was studied, and the SWRMS index was introduced to characterize the ultrasonic signal features. This index reflects the spatial position of the crack through the peak position and reveals the crack size through the amplitude, achieving a unified representation of both crack position and size. Furthermore, to address challenges such as spurious echoes and noise interference caused by the drill pipe’s threaded structure in practical engineering applications, convolutional neural network (CNN) was constructed to achieve intelligent identification of drill pipe cracks in high-noise environments. A data augmentation method using alternating noise levels was designed to simulate the scattering effect caused by the drill pipe’s threads and actual noise interference. The results show that CNN exhibits superior recognition performance under different noise levels, maintaining a classification accuracy of 94.4% even at a 75% noise level. The research results verify that the proposed method has significant advantages in crack detection accuracy and noise robustness, providing effective support for real-time monitoring and intelligent diagnosis of key components such as coal mine drill pipes. Full article