Search Results (6,834)

This study constitutes the second part of a comprehensive investigation of the persistence of weighted average cost of capital (WACC) rates despite declining risk-free interest rates. While theory suggests that WACC should reflect lower risk-free interest rates and decline with falling government bond yields, empirical evidence reveals minimal adjustment in the reported WACC figures. Disclosed WACC of DAX40 companies remain between 7% and 8% as the yield of a ten-year German government bond fell from 4.1% to −0.2%. After the quantitative risk analysis (part I) systematically lacks market-based and fundamental explanations—demonstrating that neither systematic risk, overall market risk, earnings risk nor leverage increased sufficiently to justify this stability—this article addresses the resulting explanatory gap through qualitative inquiry. Employing a grounded theory methodology, we investigate the causes and consequences of persistent WACC through systematic analysis of 18 problem-centered semi-structured expert interviews (22 respondents comprising corporate finance executives, investment bankers, strategy consultants, auditors). The investigation reveals that behavioral economics (risk aversion, opportunism, subjectivity), organizational constraints (strategic path dependency, implementation complexity, financial criterion rigidity), and model-theoretic discretion (parameter averaging, analyst influence, supplementary risk adjustments) substantially shape practical WACC determination—factors that quantitative risk analysis cannot capture. Practitioners employ disclosed WACC strategically to reconcile investor return requirements with long-term operational stability, avoid audit friction, and hedge geopolitical–monetary risks—consequences that generate capital opportunity costs offsetting traditional value-maximization objectives. Combined quantitative and qualitative evidence yields actionable insights for value-based capital cost methodologies that are aligned with organizational and market realities. Full article

Background/Objectives: Oral squamous cell carcinoma (OSCC) exhibits substantial biological heterogeneity, and current clinicopathological risk stratification incompletely reflects tumor metabolic behavior. Stable isotope ratio mass spectrometry enables the quantitative assessment of carbon and nitrogen isotopic composition, potentially capturing cumulative metabolic reprogramming associated with tumor aggressiveness. This study evaluated whether isotopic signatures of tumor tissue and surgical margins are associated with lymph node metastasis and survival outcomes in OSCC. Methods: In this prospective study, 54 consecutive patients undergoing primary surgical treatment for OSCC were enrolled. Paired samples derived from tumor tissue and surgical margins were analyzed using isotope ratio mass spectrometry to determine the relative abundance of nitrogen-15 and carbon-13 isotopes. The primary endpoint was pathological lymph node metastasis. Secondary endpoints included disease-free survival and overall survival. Paired comparisons were performed using Wilcoxon signed-rank tests with false discovery rate correction. Logistic regression models for nodal metastasis were constructed using Firth penalization with bootstrap internal validation, while survival outcomes were evaluated using Cox proportional hazards models with model complexity restricted according to the number of events. Results: Tumor tissues demonstrated significantly lower δ¹³C and δ¹⁵N values and higher nitrogen-to-carbon ratios compared with surgical margins (all adjusted p < 0.05). In multivariable analysis, tumor δ¹⁵N was independently associated with lymph node metastasis and modestly improved model discrimination. However, it was not independently associated with disease-free or overall survival. Exploratory analyses indicated that higher δ¹³C values in surgical margins were independently associated with shorter disease-free survival. Conclusions: These findings suggest that isotope ratio mass spectrometry-based isotopic profiling identifies reproducible metabolic differences between tumor and margin tissues in OSCC. Tumor δ¹⁵N is associated with lymph node metastasis, whereas margin δ¹³C may reflect recurrence risk and potentially capture metabolic field effects. These findings are hypothesis-generating and warrant validation in larger, independent cohorts. Full article

The durability and serviceability of steel-fiber-reinforced concrete (SFRC) tunnel linings in cold regions are significantly challenged by repeated freeze–thaw actions, making the accurate prediction of frost resistance a critical engineering problem. Although extensive research has been conducted on the freeze–thaw characteristics of concrete, the existing empirical and mechanism-based models remain limited in capturing the complex nonlinear interactions among mixture proportions, steel fiber characteristics, and environmental conditions. Therefore, a data-driven prediction framework based on machine learning was developed in this study. A database containing 277 groups of standardized SFRC freeze–thaw test results was established, incorporating key variables including mixture design parameters, fiber properties, and freeze–thaw cycle conditions. Four machine-learning models, namely, support vector regression, back-propagation neural network, gradient boosting, and extreme gradient boosting (XGB), were constructed and systematically compared. Model accuracy was assessed using MAE, MAPE, MSE, RMSE, and R². The results demonstrate that all models can reflect the nonlinear relationship between the input variables and mass loss rate, while the XGB model exhibits superior predictive performance with a testing R² of 0.91, representing an improvement of approximately 3–28% compared with other models. Meanwhile, the prediction errors are reduced significantly, with RMSE and MAE decreased by about 19–58% and 22–65%, respectively. The proposed approach provides an improved and reliable tool for predicting frost resistance and supports the durability design and optimization of SFRC tunnel linings in severe cold-region environments. Full article

Frequent rainfall events threaten expressway operations, and toll stations, as critical network nodes, are vulnerable to functional degradation and cascading effects. However, existing traffic resilience studies mainly focus on urban road networks or static assessments, making it difficult to characterize the resilience evolution, recovery process, and predictability of toll-station nodes. This study proposes a resilience quantification and recovery prediction method for expressway toll-station nodes under rainfall disturbances. By integrating multi-source meteorological data, neighborhood propagation relationships, and network topology, a three-level resilience quantification framework is developed across the functional, neighborhood, and network layers. A piecewise exponential function is used to model the damage–valley–recovery process of node resilience and to extract parameters including damage depth and recovery rate. Focusing on the recovery stage, a node recovery prediction model is constructed by combining resilience sequences, meteorological disturbance features, and dual-graph spatial relationships, while dual-graph convolution and long short-term memory (LSTM) are used to capture the spatiotemporal evolution of node recovery. Results show that the proposed method quantifies toll-station node resilience, captures its staged evolution, and effectively predicts recovery. Baseline, cross-scene, and ablation results confirm the value of multi-source feature fusion and dual-graph propagation, supporting the sustainable operation of expressway systems under rainfall disturbances. Full article

The rapid digitalization of higher education has intensified the need for reliable methods to assess higher-order cognitive skills, particularly critical thinking, in adaptive learning environments. However, most existing assessment approaches rely primarily on test outcomes and academic performance indicators, which do not adequately capture the multidimensional and process-based nature of critical thinking. This study proposes a multi-criteria hierarchical model for identifying and quantitatively assessing students’ critical thinking based on individual process indicators of learning activity in an intelligent educational environment. The model integrates cognitive, metacognitive, and behavioral indicators, including knowledge dynamics, task complexity, time characteristics, learning activity intensity, error rate, level of doubt, user interaction patterns, and system operating modes. These indicators are aggregated into a three-component structure representing metacognitive awareness, analytical depth, and strategic learning activity. The proposed model was empirically validated through a quasi-experimental longitudinal study involving 500 university students divided into control and experimental groups. The results demonstrate a statistically significant increase in all latent components of critical thinking and in the integral indicator within the experimental group. The model shows satisfactory internal consistency (Cronbach’s $\alpha \ge 0.77$) and acceptable construct validity confirmed by confirmatory factor analysis. The findings indicate that the proposed model can serve as a practical analytical tool for monitoring critical thinking development and supporting personalized learning trajectories in adaptive digital educational systems. Full article

Economic evaluation plays a pivotal role in investment decision-making for mining projects, especially under volatile market conditions. In this study, a risk-based decision-support framework is developed to assess the economic sustainability of an open-pit gold mining operation by integrating sensitivity analysis with Monte Carlo simulation, where Net Present Value (NPV) is used as the primary performance indicator. The proposed approach provides a flexible and practical computational framework for evaluating investment risk under uncertainty. A case study from an open-pit gold mine in Kyrgyzstan is used to compare two scenarios: continuation of the current operation and an alternative option involving a $30 million investment to improve mill processing performance. The sensitivity analysis shows that gold price, mining cost, and recovery rate are the most influential parameters affecting project outcomes, while Monte Carlo simulation is used to capture uncertainty in these variables and to generate a distribution of possible NPV results. The results indicate that gold price and recovery rate have a dominant influence on project value, and that improving mill performance leads to higher recovery and increased economic returns. The simulation results show a median NPV of approximately 220 million USD with a probability of negative NPV (17.52%), while the enhanced scenario achieves an IRR of approximately 13%, indicating improved financial performance. In addition, the findings suggest that accounting for uncertainty provides more reliable support for investment decisions and contributes to a more efficient use of mineral resources. In this context, the proposed framework contributes to sustainability assessment tools by supporting economically sustainable resource utilization through risk-based evaluation of recovery improvement under uncertainty. While the present study focuses on the economic pillar of sustainability, the framework can provide a basis for future integration of environmental and social indicators. Full article

Accurate monitoring of respiratory rates is critical for early detection of a range of clinical conditions. However, standard manual counting or inadequate clinical monitoring often fails to provide reliable measurements. This study evaluated and validated the PneumoWave biosensor for respiratory rate measurement across a broad physiological range and different body postures (45°, 90°, and 180°) in both in vitro and in vivo settings. In vitro validation was performed using a SimMan ALS manikin operated at respiratory settings of 6–30 breaths per minute, with 10 s periods of simulated apnea. In vivo validation involved 20 healthy volunteers performing metronome-guided breathing while wearing bilateral PneumoWave biosensors. In vitro results demonstrated an excellent correlation between biosensors and manikin respiratory settings and captured all apnea events (r = 0.99, ICC = 0.99). In vivo findings showed good agreement with direct observational count (r = 0.99, R² = 0.99, ICC = 0.99), with 97% of apnea events captured by both devices in all positions. Body postures had no significant impact on biosensor accuracy. These findings demonstrate that the PneumoWave biosensor provides accurate and reliable respiratory monitoring and supports its potential as a robust, non-invasive tool for continuous clinical and remote patient monitoring. Full article

One-dimensional mass transfer resistance-in-series framework was developed theoretically and validated experimentally using a flat-plate polytetrafluoroethylene/polypropylene (PTFE/PP) membrane module to predict CO₂ absorption fluxes and concentration distributions. The decline in CO₂ absorption efficiency along the membrane module is primarily attributed to increased concentration polarization resistance and a reduced driving force concentration gradient. To alleviate these limitations, carbon fiber promoters were strategically embedded to suppress concentration polarization, reduce the mass transfer resistances, and enhance turbulence intensity. In the present study, device performance was further improved by implementing properly ascending or descending hydraulic equivalent widths along the absorbent feed channel. Under the descending configuration, an absorption flux enhancement of up to 44.94% was achieved relative to an empty-channel module (i.e., without S-ribbed carbon fiber inserts). Theoretical formulations were established to predict absorption fluxes under varying monoethanolamine (MEA) volumetric flow rates, CO₂/N₂ mixture flow rates, and inlet CO₂ feed concentrations. The model predictions showed good agreement with experimental results obtained using MEA solutions under both ascending and descending hydraulic width operations, demonstrating effective mitigation of polarization effects and enhanced absorption flux along the absorbent feed channel. An economic assessment of the S-ribbed carbon fiber module was conducted by evaluating the trade-off between absorption flux enhancement and incremental power consumption. The results indicate that the proposed design provides a practical and economically viable approach for improving the performance of membrane-based CO₂ capture technologies. In addition, an enhanced Sherwood number correlation, expressed in a simplified form, was developed and employed to estimate the mass transfer coefficients of CO₂ membrane absorption modules incorporating S-ribbed carbon fiber promoters. Full article

Facial expression recognition (FER) is crucial for endowing service robots with emotional perception capabilities. Achieving high-performance facial expression recognition hinges on effectively balancing the capture of subtle local textures with the understanding of overall facial configurations. However, coordinating local feature variations with global semantic dependencies in unconstrained environments while maintaining semantic alignment remains a challenge. To address this issue, we propose FER-SDAM, a network architecture based on hierarchical attention collaboration. Through a dual-attention hierarchical collaboration mechanism, this architecture introduces an Attention Consistency Loss (ACL) to explicitly align shallow structural awareness with deep global dependencies. It simultaneously captures structural sensitivity and cross-regional correlations, facilitating the effective fusion of local structural information with global semantics, thereby balancing accuracy, robustness, and computational efficiency. We conducted extensive experiments on AffectNet, RAF-DB, and their subsets containing occlusion and pose variations, achieving accuracy rates of 68.12%, 66.68%, and 88.87% on the AffectNet-7, AffectNet-8, and RAF-DB datasets, respectively. The experimental results demonstrate that FER-SDAM achieves a critical balance between accuracy and efficiency, delivering highly competitive recognition performance while maintaining low computational overhead, making it an ideal solution for real-time deployment in service robots. Full article

Reliable anomaly early warning for hydropower station sump wells remains challenging due to the strong nonlinearity of water level dynamics and the limited adaptability of conventional fixed-threshold alarms. Here, we present a hybrid deep learning framework—termed CNN–Attention–LSTM–BO—that fuses multi-scale local feature extraction, adaptive temporal weighting, and sequential dependency modeling within a unified architecture, with all critical hyperparameters tuned via Bayesian optimization. A four-dimensional input representation is first constructed from the raw water level signal and its first- and second-order differences together with the drainage pump operating state, capturing both trend and transient information. One-dimensional convolutions at multiple kernel scales encode short-range fluctuation patterns, a Bahdanau-style temporal attention layer selectively amplifies informative time steps, and a stacked LSTM propagates long-horizon risk dependencies. At the decision stage, a dual dynamic thresholding scheme couples an improved $3\sigma$ criterion with kernel density estimation (KDE) to partition the smoothed risk score into three graded alert levels (normal/warning/critical), replacing the binary alarm paradigm. Experiments on the SWaT benchmark yield an average area under the ROC curve (AUC) of 0.9246, an average Accuracy of 0.8812, and a best single-well false alarm rate (FAR) of 3.21% (Well-4), with an average FAR of 8.97% across three wells, outperforming both traditional limit-value alarms and ablated variants lacking CNN or attention modules. Full article

SARS-CoV-2, the causative agent of COVID-19, emerged in late 2019 and rapidly developed into a global health crisis. In this study, we analysed 173,194 SARS-CoV-2 genomes from the GISAID database to explore the intra-continental dynamics and distribution of variants across Africa between 2020 and 2024. We have identified 1377 distinct lineages, which were classified by clade to assess associations with infection and mortality rate. So, we conducted a Shannon entropy analysis to confirm the diversity and we applied a Correspondence Analysis (CA). Our findings revealed that one of the deadliest in Africa during the Delta wave, lineage AY.45 predominated in the South Africa cluster, whereas AY.34.1 drove transmission in the Atlantic West Africa cluster, underscoring regional heterogeneity. Furthermore, early in the pandemic, men exhibited a 39% higher risk of infection compared to women (aOR: 1.39, 95% CI [1.34–1.45]), particularly in association with clade G. By contrast, later stages were dominated by clade GRA, which disproportionately affected the elderly (≥70 years; aOR: 1.39, 95% CI [1.33–1.45]) and children (0–9 years; aOR: 1.26, 95% CI [1.20–1.33]). Our analysis highlighted that the pandemic on the African continent unfolded as a mosaic of epidemics shaped by diverse variants and regional epidemiological contexts. These findings emphasize the importance of genomic surveillance to capture local epidemic signatures and inform region-specific public health strategies. Full article

This paper presents a theoretically grounded integration of deterministic Q-learning with relational game theory (QLRG) for efficiently identifying minimal winning coalitions in Online Social Networks (OSNs). We address the fundamental challenge that coalition formation is NP-hard under traditional approaches by leveraging structural properties of relational dependencies and Armstrong’s axioms to transform the problem into one solvable in polynomial time. Our framework reduces the state space from exponential O(2ⁿ) to O(n²) through a sufficient statistic representation based on coalition size, follower reach, and terminal status, while achieving O(n⁴) time complexity under deterministic, static, and sufficiently symmetric influence structures. The QLRG framework introduces three critical innovations: (1) a principled agent selection mechanism derived directly from the Q-function that eliminates heuristic weight tuning; (2) a formal Boost action defined through temporal closure operators that captures influence spread dynamics; and (3) a constrained MDP formulation that enforces relational consistency through action elimination rather than penalty terms. We prove that the Bellman optimality operator forms a contraction mapping, guaranteeing deterministic convergence to optimal policies with established rates of O(1/√k) for decreasing learning rates or linear convergence up to bias for constant rates. To bridge the gap between this idealized model and the asymmetry inherent in real OSNs, we further develop a cluster-based sufficient statistics approach. By partitioning the network into communities with bounded internal variation, we relax the global symmetry requirement while preserving polynomial state space complexity, and obtaining a single within-community swap changes the optimal Q-value by at most ε_i/(1−γ), which is a local Lipschitz continuity result. The implications of this are both theoretical and practical, and they form the bedrock for relaxing the global symmetry assumption in the QLRG framework. Empirical validation on synthetic networks satisfying the symmetry assumption demonstrates that QLRG consistently identifies minimal winning coalitions matching the optimal solutions found by exhaustive search, while operating with polynomial-time complexity. Unlike conventional approaches, our framework simultaneously satisfies four critical properties: deterministic convergence, policy optimality, minimal coalition identification, and computational tractability. The work bridges computational social science and operations research, providing a mathematically rigorous foundation for strategic decision-making in influencer marketing and coalition formation. While the framework requires symmetry assumptions that may only hold approximately in real-world OSNs, it establishes an idealized baseline for future extensions addressing stochasticity, dynamics, and partial observability. This research represents a paradigm shift from empirical improvements to theoretically grounded convergence guarantees for coalition formation problems, demonstrating how structural mathematical insights can transform intractable problems into efficiently solvable ones without sacrificing solution quality. Full article

LiDAR-based 3D object detection is critical for autonomous driving perception. Ensuring robust sensing under adverse weather is essential for safe deployment. Current physics-based simulation methods focus on atmospheric effects but offer limited ground-level modeling, leading to domain gaps between simulated and real-world snowy data. Ground-level effects are challenging to model due to diverse physical interactions: wet surface reflectivity changes, vehicle-induced spray, and multi-layer snow scattering. This paper proposes a simulation method with more comprehensive ground-effect modeling for snowfall scenarios. Our approach introduces two modules: (i) an extended spray model with precipitation-controlled parameters that jointly models spray noise and wet ground attenuation, and (ii) a multi-layer dual-mode backscattering model that captures both diffuse and specular reflections on snow-covered ground. Both modules share a unified precipitation-driven parameterization. Higher snowfall rates simultaneously control spray generation, wet surface reflectivity, and snow accumulation depth. This design ensures physical consistency and makes the approach applicable across diverse LiDAR systems without sensor-specific tuning. Experiments on the STF dataset demonstrate consistent improvements over four state-of-the-art methods under both heavy and light snowfall. Clear-weather performance is preserved. Evaluations on roadside LiDAR further confirm generalizability to infrastructure-based scenarios. Full article

Most established street-vitality assessment tools were developed in temperate, predominantly Western urban settings and therefore do not adequately capture the climatic and socio-spatial conditions of hot–arid cities. This study develops and validates the Resident-Centered Street Vitality Framework (RCSVF) using Riyadh as a case study representative of the Arabian Desert urban context. Drawing on a cross-sectional quantitative design, the research integrates a resident survey across nineteen neighborhoods (N = 1102), physical observations of 133 street segments, a visual preference survey (N = 418), and GIS-based spatial analysis. The results reveal marked intra-urban inequality in perceived environmental quality and demonstrate that service proximity is a substantially stronger predictor of residential satisfaction than street physical quality alone. Residents consistently rated shading, green space, and pedestrian infrastructure as the weakest dimensions of their neighborhoods. These findings indicate that street vitality in hot–arid settings cannot be validly assessed through imported observer-based metrics. A resident-centered, climate-responsive framework is required to capture how thermal exposure, functional accessibility, and everyday social use interact in shaping street experience. Full article

This study evaluates the techno-economic performance of solar photovoltaic (PV) systems for powering a 7 t/day shea butter processing plant to address electricity constraints limiting rural processing and local value capture. Annual electricity demand is modeled under three operational scenarios: (i) a typical processing season from November to February; (ii) an extended season until mid-May; and (iii) near year-round operation with eleven months of processing. Using detailed load modeling and techno-economic simulations in HOMER Pro, off-grid PV/battery systems and grid-connected PV hybrids are compared using the levelized cost of electricity (LCOE). In scenario 1, the national grid remains the most cost-effective solution. Scenario 2 reveals that integrating 35% solar PV into the grid becomes economically attractive, offering a recoverable value of 263.33 thousand USD within 7.73 years. In scenario 3, the grid/PV/battery configuration emerges as the optimal solution, providing the lowest cost of electricity at 0.246 USD/kWh compared to 0.319 USD/kWh for a grid-only supply and delivering an internal rate of return (IRR) of 20.7%. Under the same scenario, the standalone PV/battery system also demonstrates strong economic viability, with a cost of 0.292 USD/kWh and an IRR of 9.2%, lower than average tariffs from PV mini-grid developers in sub-Saharan Africa. These results demonstrate the profitability and viability of PV-based systems in powering food processing facilities in off-grid regions. Full article