Search Results (1,311)

Reliable remaining useful life (RUL) estimation is important for the safe and efficient use of lithium-ion (Li-ion) batteries in electric vehicles (EVs) and energy-storage systems. Most data-driven RUL models are trained under controlled laboratory conditions, but their performance can weaken during field operation, where usage, sensing quality, and degradation paths are less predictable. This study proposes a physics-informed laboratory-to-field (L2F) deep learning framework for battery RUL prediction under limited or unavailable target labels. The framework combines three components: a six-channel laboratory cycle representation comprising voltage, current, temperature, cumulative charge throughput, cumulative energy throughput, and voltage derivative; a gated Transformer–Temporal Convolutional Network (TCN) Fusion backbone for modeling long-range and local degradation patterns; and a staged adaptation policy based on paired-view consistency and covariance alignment. The Fusion backbone achieved the lowest held-out XJTU laboratory root mean square error (RMSE) of 46.67, compared with 48.27 for TCN and 48.85 for the Transformer. In the Tsinghua University (Tsinghua) deployment experiment, measured target RUL labels were unavailable after preprocessing and window construction. Therefore, the direct field-side mean absolute error (MAE), RMSE, and coefficient of determination $R^2$ were not computed. The Tsinghua results are interpreted as an unlabeled deployment-credibility and trajectory-regularity assessment, showing operational continuity, finite vehicle-specific predicted trajectories, and reduced local trajectory volatility after staged adaptation. The S2a + S2b policy reduced Fusion RUL volatility from 8.376 to 0.632. In the XJTU laboratory source representation, Integrated Gradients showed that physics-aware channels contributed 30.98% of the attribution mass, increasing from 19.50% in early-life windows to 31.86% in late-life windows. These attributions explain the laboratory six-channel waveform model and are not used as direct evidence of Tsinghua field-feature importance. Full article

This paper studies platform environments in which participation incentives change discontinuously when valuation crosses salient thresholds. Empirical research on digital and tokenised platforms documents valuation plateaus, abrupt participation shifts, and weak short-run links between price stability and usage dynamics, patterns that are difficult to reconcile with smooth adjustment models. We capture these features structurally by modelling adoption and valuation as a coupled equilibrium system with regime-dependent incentives, formulated as a Filippov differential inclusion. Token prices are determined endogenously through market clearing between usage-driven demand and regime-dependent speculative demand. We establish global well-posedness and identify conditions under which valuation thresholds become attracting manifolds. In these regimes, prices remain anchored at the threshold while adoption continues to evolve, generating persistent valuation plateaus and path dependence. When threshold crossings are regular and the induced dynamics are uniformly dissipative, endogenous boom–bust cycles are ruled out. The framework yields design-relevant insights for digital platforms. Valuation anchoring and volatility depend on governance primitives such as effective circulating supply and regime-dependent speculative depth. A diagnostic numerical analysis links time-at-anchor and sliding intensity to adoption volatility and platform risk, providing empirically interpretable indicators of stability in tokenised platforms. Full article

Task decomposition is a fundamental challenge in multi-agent collaborative maintenance systems, where unstructured natural language instructions must be precisely translated into logically coherent, executable sub-task sequences. This paper formulates task decomposition as a constrained optimal path search problem on a heterogeneous knowledge graph that encodes coal mine equipment topology, fault causality, and maintenance procedures. We construct a composite cost function that systematically integrates semantic similarity from graph neural network embeddings, relation-type weights, and structural path length, transforming instruction parsing into a mathematically tractable combinatorial optimization. The cost function is derived from the principles of shortest-path reasoning in knowledge graphs: the relational weights capture domain-specific association strengths, the semantic similarity term promotes contextually coherent chains, and the path-length penalty prevents unnecessarily long derivations. A multi-hop reasoning algorithm coupling heterogeneous graph convolution with beam search is developed to solve this problem efficiently, achieving high-quality approximate solutions while ensuring computational tractability. The reasoning process is inherently interpretable, as the optimal path directly maps to a traceable atomic task sequence with explicit dependency relations. A formal complexity analysis shows the algorithm scales as $O$($b$·$K$·$d_{max}$), where b is the beam width and d_max is the maximum node degree. Several theoretical properties of the proposed framework are further derived: the cost function is non-negative and strictly monotonic, optimal paths satisfy the optimal substructure property, cycle-free optimal paths always exist, and beam search can yield globally optimal solutions given a sufficiently large beam width. These theoretical conclusions establish mathematical guarantees for the presented decomposition framework. Experiments on 200 composite maintenance instructions with gold-standard annotations (inter-annotator agreement Cohen’s $\kappa =0.88$) demonstrate that the proposed method achieves 94.3% task sequence accuracy (95% CI: 91.2–96.8%) and 96.4% dependency accuracy (95% CI: 93.5–98.1%), substantially outperforming both a rule-augmented baseline (58.6%, 62.1%) and a GPT-4o few-shot chain-of-thought baseline (73.2%, 70.5%); McNemar’s test yields p < 0.001 for both comparisons. The average inference time is 29.7 ms (SD 2.1 ms), meeting stringent industrial real-time constraints. Ablation studies quantify the contribution of each cost function component and confirm the robustness of the chosen beam width and hyperparameters. When integrated into a full multi-agent system, the framework delivers end-to-end response time within 3 s (P50: 1.87 s, P95: 2.83 s) and maintains an 86.7% task success rate even under dual agent failures, validating the robustness of the proposed mathematical formulation. This work establishes a rigorous graph-theoretic foundation for instruction decomposition in multi-agent systems, with direct applicability to safety-critical industrial environments. Full article

The collaborative transformation of digitalization and greening has become a core trend for modern enterprises to build sustainable development systems. As a typical voluntary environmental regulation in China, green factory certification plays an important role in promoting the integration of digital technology and green production. Based on the unbalanced panel data of Chinese A-share listed companies from 2012 to 2022, this paper adopts a multi-period difference-in-differences model to empirically investigate the impact, internal mechanisms and heterogeneous characteristics of green factory certification on corporate digital-green collaborative transformation. The results show that green factory certification can significantly boost the level of corporate digital-green synergy, with an average policy effect of 2.07%. Mechanism tests confirm that this policy realizes the dual transformation through four core paths: stimulating green technological innovation, raising public environmental awareness, improving corporate environmental, social, and governance (ESG) performance and easing financing constraints. Heterogeneity analysis indicates that the promotion effect varies distinctly across regions, enterprise life cycles, pollution types and executive backgrounds. This study reveals the systemic logic of voluntary environmental regulation enabling digital-green collaborative transformation, and provides empirical support and targeted policy suggestions for enterprises and governments to accelerate the integrated development of digitalization and greening. Full article

Driven by the dual-carbon target strategy, the new energy vehicle industry has achieved large-scale and rapid development. As the core protective component of power batteries, the surface quality of battery shells directly determines the operational safety and reliability of batteries. However, defects such as scratches, pits, and cracks easily occur on battery shells during forming processes, including stamping and deep drawing. Traditional manual detection suffers from bottlenecks, such as high labor intensity, low detection efficiency, and high false detection rates, making it difficult to adapt to the large-scale and high-cycle production requirements of modern industry. Firstly, this study systematically elaborates the material system, preparation process, and defect formation mechanism of battery shells, and clarifies the coupling mechanisms of material properties, process parameters, and equipment and environmental conditions for defect evolution. Subsequently, it compares and analyzes the principles, advantages and disadvantages, and applicable scenarios of traditional machine-vision- and deep-learning-based detection technologies, and focuses on analyzing the application performance and optimization paths of single-stage and two-stage object detection algorithms in shell defect recognition. Furthermore, it addresses the core challenges of deep-learning-based battery shell defect detection technologies in data, algorithm deployment, detection dimensions, and other aspects, and proposes targeted optimization strategies. Finally, the development directions, such as system integration and online learning, are forecasted. This study can provide theoretical support and technical references for the intelligent manufacturing of battery shell stamping and forming, as well as for surface defect detection. Full article

Real-time recognition of the transient interface opening between a nickel starting sheet and its titanium starter plate is required to stop high-frequency tamping at the correct instant. This study proposes Yolov11_tapcheck, a deployment-oriented detector based on YOLO11n, and implements a field real-time detection system for nickel starting sheet pre-stripping. To address small target scale, weak texture, reflective background interference, and motion blur, the model introduces efficient multi-scale attention (EMA) and reconstructs the neck as a BiFPN-inspired bidirectional feature-fusion path. A tapcheck dataset was established from production-line images, and the method was evaluated with image-level accuracy, latency, and field control metrics. Compared with the original YOLO11n, Yolov11_tapcheck improved mAP50 from 95.5% to 98.1% and F1 from 93.7% to 97.5%. The average model-pipeline latency was 7.1 ms; when MQTT delivery, OPCSERVER writing, PLC feedback timestamp comparison, and 1 ms polling were included, the average frame-to-PLC acknowledgement latency was 10.24 ms, with a maximum of 11.58 ms, remaining below the 13.333 ms frame interval in 1000 logged samples. Two field stages further showed that the opening success rate for cycles 3–8 increased from about 60–65% to more than 90%, while opening detection success increased from 90% to 100%. These results indicate that the proposed method can support real-time opening recognition and closed-loop control in nickel starting sheet stripping. Full article

Many applications in finance and biology involve multiplicative noise geometric Brownian motion (GBM)-type stochastic differential equations (SDEs) whose drift carries a single dominant periodic component. Such structures arise in seasonal Black–Scholes option pricing, commodity derivatives with annual price cycles, and stochastic biological oscillators driven by a known frequency; the primary contribution of this paper is a high-precision numerical scheme validated on GBM-type test problems with periodic drift. This paper proposes a Strang operator splitting scheme within the Logarithmic Drift-Diffusion Splitting (LDDS) framework, which splits the SDE in y-space into a deterministic drift ODE sub-step (Step A) and an exactly solvable multiplicative diffusion sub-step (Step B). Step A employs the Periodically Fitted Adams–Bashforth–Moulton fourth-order predictor–corrector method (PABM4), which achieves zero local truncation error for trigonometric forcing terms by introducing additional shift terms and simultaneously imposing polynomial exactness conditions and trigonometric fitting conditions. When the Step A forcing belongs to the PABM4 exact function class ${\mathcal{F}}_{\omega }=\mathrm{span}\{1,t,t^2,\sin \omega t,\cos \omega t\}$, the Strang+PABM4 scheme achieves floating-point precision saturation. We investigate three test problems: the cosine-drift GBM (Test Problem 1), the polynomial–trigonometric mixed drift GBM (Test Problem 2), and a dual-frequency drift applicability test (Test Problem 3). Monte Carlo strong error experiments ($M=1000$ paths) validate that Strang+PABM4 achieves saturation at machine precision (≈${10}^{-15}$) on Test Problems 1 and 2, improving precision by ≈${10}^2\times$ over the best algebraically convergent reference. Test Problem 3 identifies the method’s applicability boundary: when the drift contains a second frequency outside ${\mathcal{F}}_{\omega }$, Strang+PABM4 degrades gracefully to order ≈ 4 without catastrophic failure. The floating-point saturation of Strang+PABM4 is contingent on the drift belonging to ${\widehat{\mathcal{F}}}_{\omega }$; when this condition is violated, the method degrades gracefully to algebraic order ≈ 4, as demonstrated in Test Problem 3. Full article

To investigate the feasibility and effectiveness of seismic repair for prefabricated single-beam column joints in modern Chinese traditional-style buildings (MCTBs), low-cycle reversed loading tests were conducted on two original joints with different T-stub web thicknesses and one repaired joint. A repair strategy was proposed in which damaged T-stub connectors were removed and replaced with welded end-plate connections. The seismic behavior of the joints, including failure mode, hysteretic response, skeleton curve, stiffness degradation, ductility, energy dissipation capacity, and seismic performance comparison between the original and repaired joints, was systematically evaluated. In addition, a refined finite element model was established using ABAQUS and validated against the experimental results to investigate the stress distribution, deformation characteristics, and load-transfer mechanism of the joints. The results indicate that damage in the original joints was mainly concentrated in the T-stub connection region, while the beam and column members remained essentially intact, demonstrating an effective damage-control mechanism. Increasing the T-stub web thickness improved the load-carrying capacity, stiffness, and ductility of the joints. After repair, the load-transfer mechanism changed from a blind-bolted T-stub connection to a welded end-plate connection, resulting in a different damage evolution pattern and failure mode. Nevertheless, the repaired joint exhibited stable hysteretic behavior, satisfactory deformation capacity, and favorable energy dissipation performance. Compared with the original joint, the repaired specimen achieved a moment capacity comparison ratio of approximately 116%, stiffness comparison ratios exceeding 120%, and an energy dissipation comparison ratio of approximately 148%. The finite element results agreed well with the experimental observations and further revealed the evolution of stress concentration regions and load-transfer paths before and after repair. The present experimental results demonstrate the feasibility of the proposed repair method for rehabilitating damaged joints while maintaining satisfactory seismic performance without replacing the primary beam and column members. The proposed repair strategy therefore provides a practical and efficient solution for the post-earthquake rehabilitation of prefabricated joints in MCTBs. Full article

University CubeSat programs are often conducted under severe constraints in budget, personnel, schedule, and knowledge continuity, while still requiring sufficient verification to support mission success. This paper presents the verification, testing, on-orbit operation, and anomaly analysis of COSMIC, a 3U university CubeSat developed by Yonsei University in less than eleven months from project kickoff to launch. COSMIC adopted a protoflight model approach, prioritized commercial off-the-shelf components with flight heritage and prior laboratory experience, and concentrated limited resources on subsystem, FlatSat, system-level, environmental, communication, and scenario-based testing. The test program is reviewed against the subsequent on-orbit results to identify which verification activities contributed to anomaly prevention, fault isolation, and recovery. The results show that system-level integration testing, thermal vacuum cycle testing, and scenario testing were particularly effective in revealing interface, software, and deployment-related issues before launch and in supporting early orbit recovery. However, the mission also exposed limitations in recovery-logic verification, long-duration software testing, communication robustness, and independent reset paths. Based on these findings, practical recommendations are derived for resource-constrained university CubeSat teams seeking to tailor verification activities without simply reducing test rigor. Full article

Business analytics increasingly relies on analytical performance models to support real-time, data-driven decision-making in multi-stage service processes. Discrete-event simulation and process mining offer valuable insights but face scalability and explainability limits when closed-form performance indicators are needed for optimization. This paper develops a systematic procedure for constructing BCMP queueing-network models directly from the structural properties of a business process formalized in BPMN. The framework introduces a six-level state-space hierarchy (V0–V5) that ranges from a full path encoding to a single system-level counter, and three allocation strategies (D1–D3) that govern how the actions of an organizational unit are distributed among its workers. A product-form stationary distribution is derived for the resulting open network with FCFS and infinite-server nodes, yielding closed-form expressions for the mean sojourn time at a node and for the mean process flow time. We validate the framework on the university admissions campaign at RUDN University (2023 cycle, more than 85,000 applications). Numerical experiments show that the baseline mean flow time of about 174 min can be reduced to 111 min—a 33% improvement—by only a 3% increase in total staffing, allocated by greedy coordinate descent. The closed-form model agrees with discrete-event simulation under exponential service times to within 1%, and its analytical tractability enables real-time staffing recommendations that simulation alone cannot deliver. Full article

Aluminum/magnesium (Al/Mg) hybrid structures are promising candidates for lightweight engineering, but reliable joining is still limited by brittle intermetallic compounds (IMCs), oxide films, pores, cracks, and corrosion-related degradation. This review summarizes recent advances in Al/Mg dissimilar-metal joining, including solid-state welding, fusion welding, brazing, resistance-based joining, and mechanical joining. Emphasis is placed on process characteristics, interfacial reactions, defect formation, mechanical properties, service reliability, and simulation-assisted process understanding. The reviewed studies indicate that joint reliability cannot be interpreted solely from IMC thickness; phase type, continuity, spatial distribution, interfacial morphology, and involvement in the fracture path are also critical. Solid-state and high-speed impact processes can restrict continuous Al–Mg reaction layers by reducing thermal exposure and promoting plastic contact, whereas fusion-based processes provide greater manufacturing flexibility but require stricter control of molten-pool behavior, Mg evaporation, porosity, and interlayer stability. Recent numerical simulations and data-driven studies are further discussed as tools for mechanism-guided parameter design. This review provides an integrated comparison of joining routes and highlights future needs for standardized testing, fatigue and corrosion evaluation, thermal-cycling assessment, coupled service-performance analysis, and process selection for engineering applications. Full article

A generalized $(a,b)$-knight’smove is an outcome when the knight moves on a rectangular chessboard a cells in either the horizontal or vertical direction, followed by a perpendicular move of b cells. An $(a,b)$-knight’s tour on a chessboard is a sequence of $(a,b)$-knight’s moves such that the knight visits every cell of the chessboard exactly once. Such a tour is said to be closed if the final position of the knight coincides with its initial position; otherwise, the tour is said to be open. In this paper, we investigate the existence of open $(2,3)$-knight’s tours on an $m\times n$ rectangular chessboard. In particular, we establish sufficient and necessary conditions under which a $5p\times n$ rectangular chessboard admits an open $(2,3)$-knight’s tour. The results presented here provide a complete characterization for this class of rectangular chessboards. Full article

MagnetoRheological Brakes (MRBs) can provide wear-free, electrically controllable braking torque, but repeated high-load braking can cause rapid heat accumulation in the narrow rotor–stator gap and degrade MRF performance. This study evaluates rotor-only, stator-only and combined rotor–stator liquid-cooling configurations using transient 3-D conjugate heat-transfer CFD in ANSYS Fluent 2024 R1 for a UN Regulation No. 13-H-based 10-cycle duty profile (8.5 s acceleration, 20 s constant speed and 2.5 s braking per cycle). The activated MRF is modeled as an incompressible laminar Herschel–Bulkley fluid during braking, while the field-OFF phases use a Newtonian viscosity of 0.114 Pa·s; viscous dissipation and coil volumetric heating are included as internal heat sources. Cooling simulations apply water with a 130 kPa (absolute) inlet pressure and a conservative +20% heat-load margin with adiabatic external boundaries. Baseline uncooled dynamometer data (no integrated cooling) verify the thermal implementation, with a 7.06% underprediction of the measured temperature rise. In the uncooled case, the MRF reaches a temperature of 501 K after ten cycles; rotor-only and stator-only cooling reduce temperatures but do not fully suppress cumulative heating, whereas the combined configuration maintains the MRF below 400 K after ten cycles. These results indicate that cooling both dominant heat paths is required for stable MRB thermal operation under severe repeated braking. Full article

Achieving sustainable urban water management is increasingly critical amid climate change, water scarcity, population growth, and intensifying pressure on freshwater resources. Life Cycle Sustainability Assessment (LCSA) has emerged as a promising integrated framework to evaluate the holistic sustainability of municipal wastewater reuse systems by combining environmental Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (S-LCA). This paper presents a critical narrative review synthesizing current evidence on LCSA applications specifically in urban municipal wastewater reuse contexts—encompassing non-potable urban, agricultural, and potable reuse—and identifying which configurations deliver genuine sustainability benefits, where major methodological shortcomings persist, and how the field can advance toward more robust, policy-relevant practice. The evidence base was assembled through structured searches in Scopus, Web of Science, and Google Scholar, screened from over 300 references to 25 peer-reviewed studies retained for in-depth analysis. Among the retained studies, positive outcomes were most often reported under low-carbon energy grids, short transport distances, and appropriate reuse-type matching: hybrid decentralized systems were associated with overall sustainability scores up to 4.8 times higher than conventional supply, while cluster-scale systems were associated with reductions in global warming potential of 15–40%. Persistent pitfalls include weak pillar integration, an underdeveloped social dimension, inconsistent system boundaries, and insufficient consideration of absolute sustainability. In response, this paper proposes the RENEW-LCSA Framework (Renewable Water-Oriented Life Cycle Sustainability Assessment Framework)—a practical six-step operational approach incorporating reuse-type-specific Social LCA indicators, explicit absolute sustainability assessment against the Water Scarcity Index (WSI) and local carbon budgets, and evidence-based integration of Nature-Based Solutions (NbSs). Three paradigmatic cases—Windhoek (Namibia), Singapore (NEWater), and Los Angeles County (USA)—illustrate the framework’s systematic application. By moving beyond optimistic narratives, this review advances the credibility of LCSA as a decision-support tool for safe, equitable, and circular urban water management, contributing to SDG 6 and the European Water Reuse Regulation (2020/741). Full article

Global warming is profoundly altering the structure and function of permafrost peatland ecosystems, but how soil microorganisms as core regulators of biogeochemical cycles respond to the process remains unclear. We investigated peatlands of the continuous, the discontinuous, and the isolated permafrost zones in the climatically sensitive high-latitude Greater Khingan Mountains using 16S rRNA gene high-throughput sequencing and soil physicochemical analysis to systematically reveal the spatial differentiation patterns, community assembly processes, and primary environmental factors of bacterial communities. The results indicated that bacterial alpha diversity was highest in the discontinuous permafrost zone, and both permafrost type and soil depth exerted significant effects on bacterial community composition. From the continuous to the isolated permafrost zones, the relative abundance of the dominant phylum Proteobacteria decreased, while phylum Chloroflexota showed a gradual increasing trend. Co-occurrence network analysis suggested that bacterial network complexity was highest in the continuous permafrost zone, and network stability decreased along the permafrost gradient. From the continuous to the isolated permafrost zone, the relative contribution of stochastic processes declined, whereas that of deterministic processes increased. Partial least squares path modeling (PLS-PM) further demonstrated that soil pH, total organic carbon (TOC), total nitrogen (TN), and soil water content (SWC) were major drivers of bacterial communities, with their effects differing among permafrost zones. Our study elucidated the synergistic evolutionary patterns of bacterial community composition, assembly mechanisms, and environmental drivers under permafrost degradation, providing key scientific evidence for predicting the feedback of high-latitude peatlands to climate warming. Full article