Search Results (32,701)

In this paper, we examine aspects of general variational inequalities (GVIs) that are similar to fixed-point problems. Special cases of the proposed unique iterative methods are introduced, including the implicit, explicit, and extra-gradient methods of the proximal point approach. The convergence of the derived expression is analyzed under appropriate assumptions to demonstrate the effectiveness of the proposed method. Numerical results are also presented to evaluate the algorithms’ performance in practice. In these experiments, matrix-scaling tests were conducted, and parameter sensitivity analyses were performed; the convergence speed, computational efficiency, robustness, and stability of the experiments were evaluated. From the results, the Algorithm 3 seems to give very good results, and convergence is achieved after fewer iterations than other algorithms. Additionally, we examine the non-convergent behavior of other algorithms under varying parameters. Overall, our study validates the theoretical findings and highlights the effectiveness of advanced proximal methods for large-scale GVI problems, while also suggesting directions for future research in this area. Full article

With the development of digital technology and artificial intelligence (AI), numerous studies have focused on the applications and impacts of digital technology in the public sector. However, few studies have explored how frontline public service employees, the core subject of public organizations, can improve their proactive service performance. Based on the model of proactive motivation, this paper investigates the influence of digital leadership on employees’ proactive service performance from a micro perspective, as well as the internal mechanisms and boundary conditions underlying this process. Through an analysis of three-wave questionnaire survey data from 234 employees, this study finds that digital leadership has a positive impact on public employees’ proactive service performance through the serial mediation effects of AI service awareness and AI crafting. Furthermore, as an important boundary condition, employees’ public service motivation strengthens the serial indirect effect of digital leadership on proactive service performance. This paper not only extends the literature on digital leadership by adopting a micro-level perspective within the context of public sector digital transformation but also identifies the individual and contextual antecedents of proactive service performance by examining the interactive effect of public service motivation and leadership. Furthermore, this paper offers valuable implications for the practice of digital transformation in public organizations. Full article

This study evaluates the ecological impacts of seawater desalination discharge on coastal marine ecosystems through a sequential analytical framework linking systematic literature synthesis, field-monitoring evidence, spatial analysis, and predictive ecological modeling. The novelty of the study lies in combining multi-regional evidence from Mediterranean coastal zones, Persian Gulf waters, and Pacific coastal environments with threshold-based ecological risk assessment, thereby linking discharge-related environmental stressors with biological responses and ecosystem-function alterations. The systematic review first retained 750 studies published between 2004 and 2024 for qualitative synthesis. On this basis, 59 high-quality references with sufficient numerical information were selected for the main quantitative meta-analysis, while field-monitoring data were used to support the interpretation of distance-based discharge gradients. Spatial interpolation and hierarchical modeling were then applied to evaluate exposure–response patterns and ecological threshold behavior. The results showed that desalination facilities generated measurable ecological impacts mainly within 50–200 m of discharge points, with a critical transition distance of approximately 127 m where hypersaline conditions, typically 1.5–2.0 times ambient seawater levels, were associated with marked changes in marine community structure. Benthic assemblages showed taxon-specific responses, with mollusks and echinoderms exhibiting greater sensitivity than polychaetes and small crustaceans. Marine vegetation declined strongly under combined salinity, thermal, and chemical stress, while phosphonate-based antiscalants accumulated in filter-feeding organisms and produced bioaccumulation factors up to 42.1 times ambient levels. Ecosystem-function indicators, including microbial community composition and sediment organic matter processing, remained altered up to 300 m from discharge points, indicating that functional impacts may extend beyond the primary hypersaline plume. The predictive modeling framework further demonstrated that ecological risk decreased nonlinearly with distance and varied according to discharge intensity, local hydrodynamics, and biological sensitivity. These findings indicate that conventional uniform buffer-based assessment may underestimate the ecological footprint of desalination discharge. Sustainable desalination management should therefore adopt site-specific monitoring, species-sensitive protection thresholds, improved brine-management technologies, and adaptive mitigation strategies based on real-time environmental feedback. Full article

This study presents an intelligent framework for the analysis of multidimensional geomechanical states in underground mining systems based on numerical simulation and machine learning methods. A three-dimensional geomechanical model of the Zholymbet deposit was developed in the RS3 environment using the generalized Hoek–Brown failure criterion. Numerical simulations were performed for representative mining scenarios characterized by complex excavation interaction and stress redistribution. The modelling results were transformed into a multidimensional geomechanical dataset containing stress, deformation, displacement, and yielding parameters. Principal component analysis (PCA) was applied to investigate the internal structure of the geomechanical state space and identify dominant patterns controlling the rock mass behavior. Clustering analysis revealed several geomechanical regimes corresponding to stable, transitional, and instability-prone conditions. Isolation Forest anomaly detection demonstrated that atypical geomechanical states are not randomly distributed but spatially localized near excavation systems and mining horizons. The obtained results indicate that hazardous geomechanical conditions are governed by complex interactions between stress concentration, deformation intensity, yielding processes, and excavation geometry. The proposed approach provides a basis for intelligent interpretation of large-scale numerical modelling results and may support geomechanical risk assessment in underground mining operations. Full article

In this article, we construct a framework for analyzing the equilibrium and stability of networked multi-sector economic systems via fixed-point analysis. We represent directional intersectoral dependencies, nonlinear feedback effects, and heterogeneous adjustment dynamics in the model by the coupled and tripled fixed-point theory in the graphically extended suprametric spaces. The graphical structure encodes supply-chain and influence networks, whereas asymmetric and nonuniform interaction strengths are encoded in the suprametric setting. Furthermore, we prove the existence, uniqueness, and convergence of equilibrium solutions under new generalized contraction conditions. We apply the theoretical findings in nonlinear state systems in which prices in interdependent markets are adjusted using integral equations. The results of numerical simulations show consistent convergence, and the sensitivity parameter of the network structure significantly influences the determination of economic stability and speed of adjustment. Full article

Cast iron components are indispensable in aerospace and automotive systems, yet their milling operations are severely affected by regenerative chatter, which degrades machining quality and damages equipment. Although various chatter prediction methods have been reported, the optimal interpolation strategy of full-discretization methods (FDMs) for multi-factor coupled dynamic systems remains unclear. This study proposes an enhanced FDM to fill this research gap. A dynamic milling model accounting for regenerative effects, modal coupling and process damping is established, and an improved FDM based on Lagrange interpolation is further developed. A systematic single-factor analysis is carried out to examine the performance of 1st–4th-order interpolation for state, delay and periodic terms. Counter-intuitively, convergence analysis and stability lobe diagram (SLD) verification reveal that higher-order interpolation does not guarantee better performance. The optimal orders are identified as 2nd/3rd for state terms, 3rd for delay terms and 1st for periodic terms. Accordingly, the proposed 321-FDM (3rd-order state, 2nd-order delay, 1st-order periodic) exhibits higher accuracy and computational efficiency compared with benchmark methods, namely the semi-discretization method and Hermite-based 3rd-order FDM. Milling experiments on cast iron workpieces validate the established model and the 321-FDM, and the experimental stability thresholds agree well with numerical predictions. This work presents a validated, high-performance stability prediction tool for chatter avoidance in cast iron machining. Full article

Existing studies on energy tunnels mainly focus on short-term heat transfer and neglect long-term thermal accumulation. This paper establishes a one-dimensional unsteady heat transfer model using Robin boundary conditions, considering air–lining coupled heat transfer and seasonal tunnel air temperature variations. The model is verified with experimental and numerical results, and the relative error is less than 1%. Simulations of 20-year continuous operation show that the host rock temperature presents obvious periodic fluctuations. The thermal influence zone expands rapidly at the initial operation stage and gradually stabilizes. Sensitivity analysis indicates that thermal conductivity, air flow velocity and circulating fluid velocity significantly affect the long-term thermal performance. Higher thermal conductivity speeds up heat diffusion, higher air velocity strengthens convective heat transfer, and higher fluid velocity improves heat exchange efficiency but increases pumping consumption. The model can accurately predict long-term temperature evolution, providing theoretical support for the design and operation optimization of energy tunnels. Full article

Accurate representation of the pressure drop–flow rate (Δp–q) relationship of nozzle-type inflow control devices (ICDs) is critical for reliable reservoir–wellbore coupled simulation. Conventional ICD models in reservoir simulators rely primarily on empirical correlations or tabulated data, but commonly used formulations cannot consistently capture the linear behavior in the low-flow regime or the transition between flow regimes, which may reduce physical fidelity and numerical robustness. To overcome this limitation, this study proposes a unified characteristic-curve representation that integrates linear, transitional, and quadratic flow regimes into a single continuous and differentiable function through a physically constrained least-squares formulation, and further develops a physics-informed neural network (PINN) to learn the ICD pressure–flow relationship while enforcing physical consistency. The trained PINN model is embedded into a multi-segment well model within a reservoir–wellbore coupled simulation framework and evaluated using a mechanistic reservoir model containing permeability streaks with varying permeabilities. The results show that the proposed method improves numerical convergence and accurately reproduces ICD pressure–flow behavior across multiple flow regimes, providing a more physically consistent and robust representation of ICD performance for inflow control analysis and reservoir simulation. Full article

The influence of debonding in concrete-filled steel tube (CFST) columns on the dynamic characteristics of super high-rise buildings is a common concern that remains insufficiently understood. The abnormal vibration incident of the SEG Plaza on 18 May 2021, also known as the 5·18 incident, serves as a typical case highlighting this issue. After two decades of service, the first-order bending frequency of the building decreased by approximately 6.1%, and extensive CFST column debonding was observed, with the maximum debonding rate reaching up to 97% on certain middle floors. To investigate the influence of CFST column debonding on structural dynamic characteristics, this study first derives a theoretical relationship between debonding parameters, namely angle and distance, and the equivalent bending stiffness of CFST columns. This analytical formulation is then implemented and validated through finite element simulations at multiple scales, including planar frame analysis in ABAQUS, a thin-interlayer simulation method in ANSYS, and full-building modeling in ETABS. Results show that for a planar frame, when a CFST column debonds at 270°, the structural natural frequency decreases by 0.984%; when the debonding angle is 180° with a 2 mm gap, the first-order frequency decreases by 0.141%. Numerical simulation of the SEG Plaza structural model predicts a reduction in the first-order frequency of 0.987% under the observed debonding conditions, confirming that debonding impairs force transmission, reduces structural stiffness, and alters natural frequencies. This study provides a mechanistic basis for evaluating stiffness degradation in long-service super high-rise buildings. Full article

Background: The triglyceride–glucose (TyG) index has emerged as a simple surrogate marker of insulin resistance and metabolic disruption. In the context of ST-elevation myocardial infarction (STEMI), such disturbances have been associated with adverse cardiovascular outcomes, more complex angiographic profiles, and microvascular complications. However, data on the association between TyG and intracoronary thrombus burden (TB) in STEMI remain limited. Methods: In this prospective observational study, we included consecutive STEMI patients treated with primary percutaneous coronary intervention (pPCI). The TyG index was calculated using the following formula: ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. TB was graded according to the modified thrombolysis in myocardial infarction (mTIMI) thrombus classification score after restoration of antegrade flow with a wire or small balloon when the culprit vessel was initially totally occluded. Patients were categorized as low-TB (LTB; mTIMI grades 1–3) and high-TB (HTB; mTIMI grade 4). The primary outcome was HTB; secondary outcomes were distal embolization and no-reflow. Associations between TyG and outcomes were assessed using univariable and multivariable logistic regression, restricted cubic spline analysis, and receiver operating characteristic (ROC) curves to evaluate incremental predictive value. Results: A total of 309 patients were analyzed. The TyG index was significantly higher in the HTB group compared with the LTB group (9.12 ± 0.62 vs. 8.92 ± 0.64, p = 0.004). In a stepwise multivariable model, TyG remained independently associated with HTB (adjusted odds ratio = 1.61; 95% confidence interval: 1.11–2.37; p = 0.014). Adding TyG to a baseline clinical model only numerically improved discrimination for HTB, as reflected by a small increase in ROC area under the curve. Restricted cubic spline analysis demonstrated a monotonic rise in the probability of HTB with higher TyG values. Higher TyG also showed non-significant trends toward increased odds of distal embolization and no-reflow. Conclusions: The TyG index was independently associated with HTB in STEMI patients undergoing pPCI and may serve as an accessible adjunctive marker for incremental risk stratification beyond conventional clinical and angiographic factors. Full article

With increasing penetration of distributed energy resources, peer-to-peer (P2P) electricity trading has attracted attention for locally utilizing surplus renewable energy. This paper proposes a distributed consensus-based P2P electricity trading method that explicitly considers prosumer equipment operation constraints. Each prosumer autonomously solves a daily scheduling problem considering electricity demand, PV generation, battery operation, grid purchase and sale, and P2P trades with neighboring prosumers. P2P prices and desired trading quantities are iteratively adjusted through local information exchange. After convergence, bidirectional trades are converted into net one-way trades, and the final feasible daily schedule is obtained by re-optimizing with fixed trading quantities. Numerical simulations were conducted for six low-voltage prosumers using annual residential demand data and a representative daily PV generation profile. In the base case, the proposed method reduced annual electricity cost by 13.7% compared with the no-P2P case, while its total cost was only 2.3% higher than that of the centralized benchmark. Unlike the centralized benchmark, which increased costs for some prosumers, the proposed method reduced costs for all prosumers. Wheeling-charge sensitivity analysis showed that the charge affects P2P trading volume and benefit allocation. Future work will address tariff design, PV uncertainty, scalability, and distribution-network constraints. Full article

Tight oil and gas reservoirs have become an important alternative to conventional hydrocarbon resources worldwide. They are characterized by dense formations, strong heterogeneity, and the low natural productivity of individual wells, making well pattern deployment and injection–production parameter optimization highly challenging. In real development, tight oil and gas fields usually involve hundreds or even thousands of wells. If each well is analyzed and optimized individually, a large amount of computation is required. Meanwhile, uncertainty in geological models further increases the complexity of development scheme design. Traditional manual adjustment methods based on engineering experience are inefficient and make it difficult to obtain an optimal well pattern suitable for the efficient development of tight oil and gas reservoirs under complex constraints, thus showing obvious limitations. To address these problems, this study first analyzes the strengths, weaknesses, and applicability of existing well placement optimization methods. Based on this analysis, we propose an optimization design method that integrates numerical simulation software for tight oil and gas reservoirs with modern intelligent optimization algorithms, enabling rapid and effective integrated optimization of horizontal well placement and fracturing in tight reservoirs. After being applied to Block X of a tight oil field, this optimization method achieved favorable field results, with an average cumulative oil and gas equivalent production of 31,400 metric tons per well, providing a new approach for the effective development of similar tight oil and gas reservoirs. Full article

This paper examines the role of Multimodal Journey Planners (MJPs) as a link between user-oriented personalization and the broader societal goals of sustainable urban mobility. In smart cities, MJPs may serve as digital decision-support tools that connect individual mobility choices with broader sustainability objectives. Although contemporary journey planners increasingly display multiple criteria, such as travel time, cost, CO₂ emissions, and number of transfers, they still generally rely on predefined and non-personalized criterion weights and rarely infer travellers’ actual preferences from observed choices. The paper therefore proposes a transparent methodological proof-of-concept that combines multicriteria decision-making and inverse optimization to discover individual preference weights and enable personalized, preference-aware planning of multimodal routes. The Weighted Sum Method (WSM) is adopted as the basic ranking framework, and the proposed approach is evaluated within a controlled methodological testbed based on multimodal journey scenarios in Vienna. The results indicate that, within the available methodological testbed, the preference-discovery-based model achieved closer in-sample agreement with user-provided route evaluations than the model based on explicitly rated criteria. This was observed in the ranking-agreement analysis, where a more favourable penalty-point ratio was obtained in 19/21 cases (90.5%) and in the numerical error comparison, where lower in-sample reconstruction errors were obtained for 18/21 users (85.71%) across all scenarios. The paper further considers the tension between individual and system-level goals, as well as a conceptual extension toward system-aware re-ranking of alternatives. Within the broader framework of smart mobility, the importance of interoperability and open data is also recognized, with National Access Points (NAPs) for multimodal travel information potentially representing an important precondition for the development of advanced and transparent MJP solutions. Full article

Textile materials made from staple fibers typically have protruding fibers on their surface, commonly referred to as surface hairiness. During fraying, the surface of the textile material is susceptible to damage, which affects its appearance and leads to fuzzing by roughening or the emergence of new fibers. The propensity for fuzzing is assessed using the standard visual method (EN ISO 12945-4:2020), which is intuitive and cost-effective but better suited for evaluating more pronounced surface phenomena, such as pilling. This is mainly because fuzzing is usually accompanied by pilling, and their simultaneous occurrence makes separate analysis difficult. As a result, instrumental methods for assessing fuzzing that provide a more objective evaluation are rarely reported. In this research, an advanced digital imaging assessment method was introduced, using an innovative apparatus that, with simultaneous assessment of pilling, enabled separate digital imaging of the same textile fabric specimen’s surface fuzzing through a refined viewing angle. Additionally, newly developed software enabled digital analysis and acquisition of quantitative numerical values related to surface fuzzing. The research was conducted on six single-component woven fabrics made from cotton, wool, viscose, polyamide 6.6, polyester, and acrylic. Fuzzing was induced using an ICI tester (EN ISO 12945-1:2020) and a Martindale tester (EN ISO 12945-2:2020) through predefined box revolutions and fuzzing rubs ranging from 125 to 30,000. Fuzzing was assessed using both the standard visual method and the advanced digital imaging assessment method, with grading according to established classes based on the percentage change in fuzzing layer height. The results highlight the applicability of the advanced digital assessment method, as it separately captures the occurrence of fuzzing and distinguishes it from pilling. Full article

Background/objectives: Catheter-related bloodstream infection (CRBSI) is a frequent and morbid complication in catheter-dependent maintenance hemodialysis, and rapid risk stratification is needed while awaiting cultures. This study aimed to evaluate admission complete blood count-derived indices—neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and pan-immune-inflammation value (PIV)—for identifying CRBSI. Methods: This single-center retrospective study (1 January 2011–31 October 2024) included adult catheter-dependent hemodialysis patients classified as CRBSI or controls. CRBSI required compatible clinical findings and concordant growth of the same microorganism(s) in paired simultaneous catheter and peripheral blood cultures. Controls were hospitalized for non-infectious reasons without infection during the index admission. Indices were calculated from admission blood counts. Discrimination was assessed using ROC analysis, and adjusted associations were evaluated using multivariable logistic regression. Results: Among 286 patients (147 CRBSI, 139 controls), CRBSI cases had higher NLR, SII, and PIV and lower LMR; PLR did not differ. NLR showed the numerically highest discriminatory performance among the evaluated indices (AUC 0.737; cut-off 5.96; sensitivity 68.7%, specificity 68.3%; p < 0.001). SII (cut-off 1189.21; AUC 0.693) and PIV (cut-off 821.62; AUC 0.686) had moderate discrimination, and LMR was modest (cut-off 1.65; AUC 0.642); PLR was not discriminatory (AUC 0.559; p = 0.086). In models adjusted for age, sex, hypertension, and cardiovascular disease, NLR remained associated with CRBSI (OR 1.159; p < 0.001), together with hypertension (OR 2.441; p = 0.017) and cardiovascular disease (OR 2.626; p < 0.001). Conclusions: Admission hematologic inflammation indices, particularly NLR, showed moderate ability to discriminate CRBSI from non-infectious admissions in catheter-dependent hemodialysis patients and may provide rapid adjunctive information while awaiting microbiological confirmation. Full article