Search Results (53)

This study examined the impact of organizational capital (OC) on the cost stickiness of Japanese firms and analyzed whether this effect varies with the magnitude of sales changes. Using 12,727 firm-year observations from Tokyo Stock Exchange-listed firms between 2007 and 2024, we estimated the economic value of OC by capitalizing and amortizing selling, general, and administrative (SG&A) expenses, then classified firms into high- and low-OC groups based on the median. Cost stickiness was then compared across groups using the basic, ABJ, and extended models, with robustness checks based on adjusted OC and two-way fixed effects models. The results indicate that high-OC firms exhibit stronger cost stickiness, while low-OC firms display weaker or insignificant stickiness. The effect depends on the magnitude of sales fluctuations: stickiness is pronounced under small changes but diminishes or disappears under larger shocks. Overall, this study contributes by highlighting the role of organizational resources in shaping asymmetric cost behavior, extending explanations beyond adjustment costs or managerial incentives, and providing novel evidence from Japan, where firms generally exhibit cost stickiness regardless of OC level, reflecting institutional and cultural contexts. Full article

Soiling is one of the main performance risks for bifacial photovoltaic (PV) technology, particularly in arid environments such as the Atacama Desert, where dust is deposited asymmetrically on the front and rear surfaces of the modules. This study evaluates one year (July 2022 to June 2023) of soiling behavior in bifacial modules installed in fixed-tilt and horizontal single-axis tracking (HSAT) configurations, enabling a comparison to be made between static and moving structures. The average dust accumulation was found to be 0.33 mg/cm² on the front surface and 0.15 mg/cm² on the rear surface of the fixed modules. In contrast, the respective values for the HSAT systems were found to be lower at 0.25 mg/cm² and 0.035 mg/cm². These differences resulted in performance losses of 5.8% for fixed modules and 3.7% for HSAT systems. Microstructural analysis revealed that wetting and drying cycles had formed dense, cemented layers on the front surface of fixed modules, whereas tracking modules exhibited looser deposits. Natural cleaning events, such as fog, dew and frost, only provided partial and temporary mitigation. These findings demonstrate that bifaciality introduces differentiated soiling dynamics between the front and rear surfaces, emphasizing the importance of tailored cleaning strategies and the integration of monitoring systems that consider bifacial gain as a key operational parameter. These insights are crucial for developing predictive models and cost-effective O&M strategies in large-scale bifacial PV deployments under desert conditions. Full article

Progressive first-failure censoring is a flexible and cost-efficient strategy that captures real-world testing scenarios where only the first failure is observed at each stage while randomly removing remaining units, making it ideal for biomedical and reliability studies. By applying the $\alpha$-power transformation to the exponential baseline, the proposed model introduces an additional flexibility parameter that enriches the family of lifetime distributions, enabling it to better capture varying failure rates and diverse hazard rate behaviors commonly observed in biomedical data, thus extending the classical exponential model. This study develops a novel computational framework for analyzing an $\alpha$-powered exponential model under beta-binomial random removals within the proposed censoring test. To address the inherent complexity of the likelihood function arising from simultaneous random removals and progressive censoring, we derive closed-form expressions for the likelihood, survival, and hazard functions and propose efficient estimation strategies based on both maximum likelihood and Bayesian inference. For the Bayesian approach, gamma and beta priors are adopted, and a tailored Metropolis–Hastings algorithm is implemented to approximate posterior distributions under symmetric and asymmetric loss functions. To evaluate the empirical performance of the proposed estimators, extensive Monte Carlo simulations are conducted, examining bias, mean squared error, and credible interval coverage under varying censoring levels and removal probabilities. Furthermore, the practical utility of the model is illustrated through three oncological datasets, including multiple myeloma, lung cancer, and breast cancer patients, demonstrating superior goodness of fit and predictive reliability compared to traditional models. The results show that the proposed lifespan model, under the beta-binomial probability law and within the examined censoring mechanism, offers a flexible and computationally tractable framework for reliability and biomedical survival analysis, providing new insights into censored data structures with random withdrawals. Full article

Clinical laboratories require accurate forecasting and efficient inventory management to balance service quality and cost under uncertain demand. In this study, we propose a hybrid forecasting–optimization framework tailored to hospital clinical determinations with highly irregular, zero-inflated, and asymmetric consumption patterns. Demand series for 34 items were modeled using Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) structures combined with skew-normal (SN) and zero-inflated skew-normal (ZISN) residuals, with residual centering, truncation, and lambda regularization applied to ensure stable estimation. Model performance was benchmarked against Gaussian SARIMA and non-linear MLP forecasts. The SN/ZISN models achieved improved forecasting accuracy while preserving interpretability and explainability of residual behavior. Forecast outputs were integrated into a Particle Swarm Optimization (PSO) layer to determine cost-minimizing order quantities subject to packaging and budget constraints. The proposed end-to-end framework demonstrated a potential 89% reduction in inventory costs relative to the hospital’s historical policy while maintaining service levels above 85% for high-volume determinations. This hybrid approach provides a transparent, domain-adapted decision support system for supply chain governance in healthcare settings. Beyond the specific case of Chilean hospitals, the framework is adaptable to broader healthcare supply chains, supporting generalizable applications in diverse institutional contexts. Full article

The development of information technology allows firms to access consumer purchase records, enabling them to distinguish between new and old consumers. Firms can then provide these groups with respective product pricing and promised delivery time. This paper develops a two-period dynamic model based on game theory to examine the effects of behavior-based pricing and behavior-based promised delivery time strategies on product price, promised delivery time, firm profits, consumer surplus, and social welfare under conditions where consumers exhibit time-sensitive preferences. We find, first, when firms opt to implement behavior-based pricing and promised delivery time strategies, they should offer lower (higher) prices and longer (shorter) delivery times to new (old) consumers. Second, the implementation of behavior-based pricing and promised delivery time strategies may decrease firm profits while enhancing consumer surplus. Third, when consumers exhibit stronger time-sensitive preferences, behavior-based pricing and promised delivery time strategies can enhance social welfare; conversely, they may have a detrimental impact on social welfare. Finally, we extend the model into three aspects—asymmetric strategy selection, firm logistics service costs, and myopic consumer behavior—to enrich our research and test the robustness of the model. The results of this paper supply managerial implications and theoretical references for firms’ strategic implementation and policy-making by relevant government departments. Full article

This paper presents an empirical investigation into the role of Fintech risk, measured by the Fintech Financial Stress Indicator (FFSI), in shaping the dynamic behavior of bank performance by employing a panel vector autoregressive (PVAR) methodology on a dataset comprising 41 banks across 11 African economies over the semi-annual period from June 2004 to December 2020. The findings reveal that bank performance, measured by return on equity (ROE), exhibits a negative and short-lived response to FFSI shock, while the effects on bank stability, cost efficiency, and return on assets (ROA) are statistically insignificant. In addition, an increase in FFSI significantly enhances both ROA and ROE, with negligible impacts on cost efficiency and stability. In contrast, a decline in FFSI has a significant negative effect on ROE and stability but remains insignificant for ROA and cost efficiency. These results indicate that FFSI shocks have asymmetric effects on ROA, cost efficiency, and bank stability but a symmetric effect on ROE. The findings suggest that engagement in financial innovation initiatives may yield performance benefits for banks, provided such strategies are pursued within a sound regulatory framework to mitigate potential excessive risk-taking. Full article

Digital Twins (DTs) have become essential tools for the design, diagnostics, and prognostics of mechanical systems. In gearbox applications, DTs are often built using physics-based simulations guided by ISO standards. However, standards-based approaches may suffer from complexity, licensing limitations, and computational costs. The concept of symmetry is inherent in gear mechanisms, both in geometry and in operational conditions, yet practical applications often face asymmetric load distributions, misalignments, and asymmetric and symmetric nonlinear behaviors. In this study, we propose a hybrid method that integrates data-driven modeling with standard-based simulation to develop efficient and accurate digital twins for gear transmission systems. A digital twin of a spur gear transmission is generated using KISSsoft^®, employing ISO standards to compute safety factors across varied geometries and load conditions. An automated MATLAB-KISSsoft^® (COM-interface) enables large-scale data generation by systematically varying key input parameters such as torque, pinion speed, and center distance. This dataset is then used to train a neural network (NN) capable of predicting safety factors, with hyperparameter optimization improving the model’s predictive accuracy. Among the tested NN architectures, the model with a single hidden layer yielded the best performance, achieving maximum prediction errors below 0.01 for root and flank safety factors. More complex failure modes such as scuffing and micropitting exhibited higher maximum errors of 0.0833 and 0.0596, respectively, indicating areas for potential model refinement. Comparative analysis shows strong agreement between the NN outputs and KISSsoft^® results, especially for root and flank safety factors. Performance is further validated through sensitivity analyses across seven cases, confirming the NN’s reliability as a surrogate model. This approach reduces simulation time while preserving accuracy, demonstrating the potential of neural networks to support real-time condition monitoring and predictive maintenance in gearbox systems. Full article

With the accelerated development of urban underground spaces, prefabricated tunnel linings have become a research focus due to their advantages in construction efficiency and cost effectiveness. However, issues such as stress concentration at joints and insufficient overall stability hinder their broader application. This study investigates a cut-and-cover prefabricated tunnel project in the Chongqing High-Tech Zone through scale model tests and numerical simulations to systematically compare the mechanical behaviors of cast-in situ linings and three-segment prefabricated linings under surrounding rock loads. The experimental results show that the ultimate bearing capacity of the prefabricated lining is 15.3% lower than that of the cast-in situ lining, with asymmetric failure modes and cracks concentrated near joint regions. Numerical simulations further reveal the influence of joint stiffness on structural performance: when the joint stiffness is 30 MN·m/rad, the bending moment of the segmented lining decreases by 37.7% compared to the cast-in situ lining, while displacement increments remain controllable. By optimising joint pre-tightening forces and stiffness parameters, prefabricated linings can achieve stability comparable to cast-in situ structures while retaining construction efficiency. This research provides theoretical and technical references for the design and construction of open-cut prefabricated tunnel linings. Full article

As semiconductor technologies continue to scale aggressively, electromigration (EM) has become critical in modern VLSI design. Since traditional EM assessment methods fail to accurately capture the complex behavior of multi-segment interconnects, recent physics-based models have been developed to provide a more accurate representation of EM-induced stress evolution. However, numerical methods for these models result in large-scale systems, which are computationally expensive and impractical for complex interconnect structures. Model order reduction (MOR) has emerged as a key enabler for scalable EM analysis, with moment-matching (MM) techniques offering a favorable balance between efficiency and accuracy. However, conventional Krylov-based approaches often suffer from limited frequency resolution or high computational cost. Although the extended Krylov subspace (EKS) improves frequency coverage, its symmetric structure introduces significant overhead in large-scale scenarios. This work introduces a novel MOR technique based on the asymmetric extended Krylov subspace (AEKS), which improves upon the conventional EKS by incorporating a sparsity-aware and computationally efficient projection strategy. The proposed AEKS-based moment-matching framework dynamically adapts the Krylov subspace construction according to matrix sparsity, significantly reducing runtime without sacrificing accuracy. Experimental evaluation on IBM power grid benchmarks demonstrates the high accuracy of our method in both frequency-domain and transient EM simulations. The proposed approach delivers substantial runtime improvements of up to 15× over full-order simulations and 100× over COMSOL, while maintaining relative errors below 0.5%, even under time-varying current inputs. Full article

Designing advanced electrode architectures with tailored morphology and redox synergy is essential for achieving high-performance supercapacitive energy storage. In this study, a PVP-assisted hydrothermal approach was employed to synthesize binder-free NiCo₂O₄ nanostructured electrodes directly on nickel foam substrates. By modulating the PVP concentration (0.5–2 wt%), hierarchical flower-like nanosheets were engineered, with the NiCo-P₁ sample (1 wt% PVP) exhibiting an optimized structure, superior electroactive surface area, and enhanced ion accessibility. Comprehensive electrochemical analysis revealed that NiCo-P₁ delivered an outstanding areal capacitance of 36.5 F/cm² at 10 mA/cm², along with excellent cycling stability over 15,000 cycles with 80.97% retention. Kinetic studies confirmed dominant diffusion-controlled redox behavior with high OH⁻ diffusion coefficients and minimal polarization. An asymmetric pouch-type supercapacitor device (NiCo-P₁//AC) exhibited a wide operating window of 1.5 V, achieving a remarkable areal capacitance of 187 mF/cm², energy density of 0.058 mWh/cm², and capacitive retention of 78.78% after 5000 cycles. The superior performance is attributed to the synergistic integration of mixed-valence Ni and Co species, engineered nanosheet morphology, and low interfacial resistance. This work underscores the significance of surfactant-directed design in advancing cost-effective, high-performance electrodes for next-generation flexible energy storage technologies. Full article

In integrated permanent magnet synchronous motors (IPMSMs) coupled with mechanical devices such as ball screws and reducers, complex nonlinear friction characteristics often arise, leading to asymmetrical distortions such as position “flat-top” and speed “ramp-up”. These phenomena significantly degrade the system’s positioning accuracy. To address this issue, this paper introduces a symmetry-inspired nonlinear predictive speed control approach based on the Stribeck piecewise linearized friction compensation and a generalized proportional integral (GPI) observer. The proposed method leverages the inherent symmetry in the Stribeck friction model to describe the nonlinear behavior, employing online piecewise linearization via the least squares method. A GPI observer was designed to estimate the lumped disturbance, including time-varying components in the speed dynamics, friction model deviations, and external loads. By incorporating these estimates, a nonlinear predictive controller was developed, employing a quadratic cost function to derive the optimal control law. The experimental results demonstrate that, compared to traditional integral NPC and PI controllers, the proposed method effectively restores system symmetry by eliminating the “flat-top” and “ramp-up” distortions while maintaining computational efficiency. Full article

The challenges and increasing number of elderly individuals requiring remote monitoring at home highlight the need for technological innovations. This study devised an eHealth mobile application designed to detect abnormal movement behavior and alert caregivers when a lack of movement is detected for an abnormal period. By utilizing the built-in accelerometer of a conventional mobile phone, an application was developed to accurately record movement patterns and identify active and idle states. Fuzzy logic, an artificial intelligence (AI)-inspired paradigm particularly effective for real-time reasoning under uncertainty, was integrated to analyze activity data and generate timely alerts, ensuring rapid response in emergencies. The approach reduced development costs while leveraging the widespread familiarity with mobile phones, facilitating easy adoption. The approach involved collecting real-time accelerometry data, analyzing movement patterns using fuzzy logic-based inferencing, and implementing a rule-based decision system to classify user activity and detect inactivity. This pilot study primarily validated the devised fuzzy logic method and the functional prototype of the mobile application, demonstrating its potential to leverage universal smartphone accelerometers for accessible remote monitoring. Using fuzzy logic, temporal and behavioral symmetry in movement patterns were adapted to detect asymmetric anomalies, e.g., abnormal inactivity or falls. The study is particularly relevant considering lonely individuals found deceased in their homes long after dying. By providing real-time monitoring and proactive alerts, this eHealth solution offers a scalable, cost-effective approach to improving elderly care, enhancing safety, and reducing the risk of unnoticed deaths through fuzzy logic. Full article

In this work, we report a facile and tunable electrodeposition approach for engineering polyacrylic acid (PAA)-modified Co₃O₄ electrodes on nickel foam for high-performance asymmetric pouch-type supercapacitors. By systematically varying the PAA concentration (0.5 wt %, 1 wt %, and 1.5 wt %), we demonstrate that the CO-1 sample (1 wt % PAA) exhibited the most optimized structure and electrochemical behavior. The CO-1 electrode delivered a remarkable areal capacitance of 3467 mF/cm² at 30 mA/cm², attributed to its interconnected nanosheet morphology, enhanced ion diffusion, and reversible Co²⁺/Co³⁺/Co⁴⁺ redox transitions. Electrochemical impedance spectroscopy confirmed low internal resistance (0.4267 Ω), while kinetic analysis revealed a dominant diffusion-controlled charge storage contribution of 91.7%. To evaluate practical applicability, an asymmetric pouch-type supercapacitor device was assembled using CO-1 as the positive electrode and activated carbon as the negative electrode. The device operated efficiently within a 1.6 V window, achieving an impressive areal capacitance of 157 mF/cm², an energy density of 0.056 mWh/cm², a power density of 1.9 mW/cm², and excellent cycling stability. This study underscores the critical role of polymer-assisted growth in tailoring electrode architecture and provides a promising route for integrating cost-effective and scalable supercapacitor devices into next-generation energy storage technologies. Full article

In the modern corporation, understanding sustainable cost management practices is essential for promoting economic resilience and resource efficiency. This study investigates how ownership structures influence the behavior of selling, and general and administrative (SG&A) costs during periods of sales fluctuations in South Korean firms, with particular attention to Chaebols. Drawing upon agency theory and corporate governance perspectives, we examine whether proxies for agency costs, namely, free cash flow, asset utilization ratios, and operating expense ratios, explain variations in SG&A cost responses to changes in revenue. Utilizing a panel dataset of 4279 firm-year observations from KOSPI-listed companies over the period 2011–2021, we employ Pooled Ordinary Least Squares (OLS), Fixed Effects, Random Effects, and Generalized Method of Moments (GMM) estimations to model SG&A cost behavior. The analysis incorporates regression-based interaction terms that capture asymmetric cost adjustments during sales declines, commonly referred to as cost stickiness. Our findings indicate that firms with concentrated ownership, such as Chaebols, exhibit significantly lower SG&A cost stickiness, reflecting stronger financial discipline and more efficient resource allocation. In contrast, firms with dispersed ownership demonstrate more pronounced cost stickiness, consistent with governance frictions and managerial discretion. These results emphasize the moderating role of ownership structure in cost behavior and highlight its implications for sustainable corporate governance. Our study contributes to the literature on cost management and financial sustainability by offering empirical insights from a distinctive institutional setting. Policy recommendations include enhancing internal controls, promoting transparent cost practices, and encouraging shareholder oversight to reinforce long-term efficiency. Full article

The dissemination of rumors can lead to significant economic damage and pose a grave threat to social harmony and the stability of people’s livelihoods. Consequently, curbing the dissemination of rumors is of paramount importance. The model in the text assumes that the population is homogeneous in terms of transmission behavior. This homogeneity is essentially a manifestation of translational symmetry. This paper undertakes a thorough examination of the impact of time delay on the dissemination of rumors within social networking services. We have developed a model for rumor dissemination, establishing the positivity and boundedness of its solutions, and identified the existence of an equilibrium point. The study further involved determining the critical threshold of the proposed model, accompanied by a comprehensive examination of its Hopf bifurcation characteristics. In the expression of the threshold $R_0$, the parameters appear in a symmetric form, reflecting the balance between dissemination and suppression mechanisms. Furthermore, detailed investigations were carried out to assess both the localized and global stability properties of the system’s equilibrium states. In stability analysis, the symmetry in the distribution of characteristic equation roots determines the system’s dynamic behavior. Through numerical simulations, we analyzed the potential impacts and theoretically examined the factors influencing rumor dissemination, thereby validating our theoretical analysis. An optimal control strategy was formulated, and three control variables were incorporated to describe the strategy. The optimization framework incorporates a specifically designed cost function that simultaneously accounts for infection reduction and resource allocation efficiency in control strategy implementation. The optimal control strategy proposed in the study involves a comparison between symmetric and asymmetric interventions. Symmetric control measures may prove inefficient, whereas asymmetric control demonstrates higher efficacy—highlighting a trade-off in symmetry considerations for optimization problems. Full article