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High-dimensional prediction problems with complex non-linear feature interactions present significant algorithmic challenges in machine learning, particularly when dealing with imbalanced datasets and multicollinearity issues. This study proposes an innovative Shapley Additive Explanations (SHAP)-enhanced machine learning framework that integrates SHAP with advanced ensemble methods for interpretable financialization prediction. The methodology simultaneously addresses high-dimensional feature selection using 40 independent variables (19 CSR-related and 21 financialization-related), multicollinearity issues, and model interpretability requirements. Using a comprehensive dataset of 25,642 observations from 3776 Chinese A-share companies (2011–2022), we implement nine optimized machine learning algorithms with hyperparameter tuning via the Hippopotamus Optimization algorithm and five-fold cross-validation. XGBoost demonstrates superior performance with 99.34% explained variance, achieving an RMSE of 0.082 and R² of 0.299. SHAP analysis reveals non-linear U-shaped relationships between key predictors and financialization outcomes, with critical thresholds at approximately 10 for CSR_SocR, 1.5 for CSR_S, and 5 for CSR_CV. SOE status, EPU, ownership concentration, firm size, and housing prices emerge as the most influential predictors. Notable shifts in factor importance occur during the COVID-19 pandemic period (2020–2022). This work contributes a scalable, interpretable machine learning architecture for high-dimensional financial prediction problems, with applications in risk assessment, portfolio optimization, and regulatory monitoring systems. Full article

The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of the Beijing–Tianjin–Hebei metropolitan cluster. In recent years, the area has undergone accelerated urbanization and industrial transfer, resulting in drastic land use changes and a pronounced contradiction between arable land protection and the expansion of construction land. The study period is 2016–2023, which covers the key period of the Beijing–Tianjin–Hebei synergistic development strategy and the strengthening of the national arable land protection policy, and is able to comprehensively reflect the dynamic changes of arable land non-agriculturalization under the policy and urbanization process. Multi-temporal Sentinel-2 imagery was utilized to construct a multi-dimensional feature set, and machine learning classifiers were applied to identify arable land non-agriculturalization with optimized performance. GIS-based analysis and the geographic detector model were employed to reveal the spatio-temporal dynamics and driving mechanisms. The results demonstrate that the XGBoost model, optimized using Bayesian parameter tuning, achieved the highest classification accuracy (overall accuracy = 0.94) among the four classifiers, indicating its superior suitability for identifying arable land non-agriculturalization using multi-temporal remote sensing imagery. Spatio-temporal analysis revealed that non-agriculturalization expanded rapidly between 2016 and 2020, followed by a deceleration after 2020, exhibiting a pattern of “rapid growth–slowing down–partial regression”. Further analysis using the geographic detector revealed that socioeconomic factors are the primary drivers of arable land non-agriculturalization in Dachang Hui Autonomous County, while natural factors exerted relatively weaker effects. These findings provide technical support and scientific evidence for dynamic monitoring and policy formulation regarding arable land under urbanization, offering significant theoretical and practical implications. Full article

This paper discusses iconographic features of the deity or “demon” Medjed (Mḏd). The specific and unusual image of this character is only found during the 21st Dynasty and is unknown in the funerary art of the New Kingdom and Late Period. Only oneYe coffin and nine papyri are known in which the image of Medjed is depicted. Eight are in the context of Spell 17 of the Book of the Dead. In the text of Spell 17, Medjed is described in lines 71–72 of Grapow’s Urk. V Abschnitt 24. The “invisibility” of this “demon” is evidently the reason for his unusual iconography: Medjed has a conical shaped body, with human legs. Although he does not have a true head, his eyes are indicated, and he wears a belt. Equally the deity could be depicted as a figure covered entirely in a conical cover except for the eyes and feet, which are visible. This curious treatment can be understood as an attempt by Egyptian artists to depict an invisible being. Full article

Freemium, a popular business model in the digital economy, offers a basic product for free while charging for advanced features or value-added services. This pricing strategy enables platforms to attract a broad user base and then monetize through premium offerings. Customer characteristics and service price are important factors affecting customer choice behavior in such a model. Based on consumption stickiness, we consider a monopoly that provides value-added services by incorporating a multinomial logit model into a two-stage dynamic pricing model. First, we analyze the optimal pricing of value-added services under a normal sales scenario. We then consider optimal pricing during the marketing period under two strategies—level improvement for value-added services and quality reduction for a basic product—and analyze the applicability of each. The results show that increasing the value-added service level has a positive effect on the optimal price of value-added services, whereas reducing the basic product quality has no effect on the optimal price. Furthermore, the numerical simulation shows that when the depth of consumer stickiness is low, the optimal marketing strategy reduces the quality of the basic product, the price of value-added services should be higher than that in the normal sales period but lower than the price under the level-improvement strategy for value-added services; otherwise, improving the level of the value-added services becomes the optimal approach. This study provides a theoretical basis and decision support for product quality design and service pricing that applies to freemium platforms. Full article

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. Full article

Despite advances in Convolutional Neural Networks (CNNs) for intelligent fault diagnosis in CNC machine tools, bearing fault diagnosis in CNC feed systems remains challenging, particularly in multi-scale feature extraction and generalization across operating conditions. This study introduces an enhanced multi-scale feature network (MSFN) that addresses these limitations through three integrated modules designed to extract critical fault features from vibration signals. First, a Soft-Scale Denoising (S2D) module forms the backbone of the MSFN, capturing multi-scale fault features from input signals. Second, a Multi-Scale Adaptive Feature Enhancement (MS-AFE) module based on long-range weighting mechanisms is developed to enhance the extraction of periodic fault features. Third, a Dynamic Sequence–Channel Attention (DSCA) module is incorporated to improve feature representation across channel and sequence dimensions. Experimental results on two datasets demonstrate that the proposed MSFN achieves high diagnostic accuracy and exhibits robust generalization across diverse operating conditions. Moreover, ablation studies validate the effectiveness and contributions of each module. Full article

In lace textile manufacturing, the dyeing process in parallel machine environments faces challenges from sequence-dependent setup times due to color family transitions, machine eligibility constraints based on weight capacities, and probabilistic re-dyeing operations arising from quality inspection failures, which often lead to increased tardiness. To tackle this multi-constrained problem, a stochastic integer programming model is formulated to minimize total estimated tardiness. A novel symmetry-driven two-population collaborative differential evolution (TCDE) algorithm is then proposed. It features two symmetrically complementary subpopulations that achieve a balance between global exploration and local exploitation. One subpopulation employs chaotic parameter adaptation through a logistic map for symmetrically enhanced exploration, while the other adjusts parameters based on population diversity and convergence speed to facilitate symmetry-aware exploitation. Moreover, it also incorporates a symmetrical collaborative mechanism that includes the periodic migration of top individuals between subpopulations, along with elite-set guidance, to enhance both population diversity and convergence efficiency. Extensive computational experiments were conducted on 21 small-scale (optimally validated via CVX) and 15 large-scale synthetic datasets, as well as 21 small-scale (similarly validated) and 20 large-scale industrial datasets. These experiments demonstrate that TCDE significantly outperforms state-of-the-art comparative methods. Ablation studies also further verify the critical role of its symmetry-based components, with computational results confirming its superiority in solving the considered problem. Full article

Accurate estimation of groundwater levels in river basins is essential for effective water resource planning. Innovations in deep learning and artificial intelligence (AI) have been introduced into this field to enhance the accuracy of long-term groundwater level estimation. This study employs the Transformer deep learning model to estimate groundwater levels, with a benchmark comparison against the long short-term memory (LSTM) model. These models were applied to estimate groundwater levels in the Yellow River Basin, where approximately 1100 monitoring wells are located. Monthly average groundwater level data from the period 2018–2023 were collected from these wells. The two models were used to estimate groundwater levels for the period 2003–2017 by incorporating remote sensing information. The Transformer model was enhanced to simultaneously capture features from both historical temporal data and surrounding spatial data, while automatically enhancing key features, effectively improving estimation accuracy and robustness. At the basin-averaged scale, the enhanced Transformer model outperformed the LSTM model: R² increased by approximately 17.5%, while RMSE and MAE decreased by approximately 12.4% and 10.9%, respectively. The proportion of poorly predicted samples decreased by an average of approximately 12.1%. The estimation model established in this study contributes to improving the quantitative analysis capability of long-term groundwater level variations in the Yellow River Basin. This could be helpful for water resource development planning in this densely populated region and likely has broad applicability in other river basins. Full article

In recent years, deep learning methods have made breakthroughs in the field of rotating equipment fault diagnosis, thanks to their powerful data analysis capabilities. However, the vibration signals usually incorporate fault features and background noise, and these features may be scattered over multiple frequency levels, which increases the complexity of extracting important information from them. To address this problem, this paper proposes a Masked and Cascaded Multi-Branch Attention Network (MCMBAN), which combines the Noise Mask Filter Block (NMFB) with the Multi-Branch Cascade Attention Block (MBCAB), and significantly improves the noise immunity of the fault diagnostic model and the efficiency of fault feature extraction. NMFB novelly combines a wide convolutional layer and a top k neighbor self-attention masking mechanism, so as to efficiently filter unnecessary high-frequency noise in the vibration signal. On the other hand, MBCAB strengthens the interaction between different layers by cascading the convolutional layers of different scales, thus improving the recognition of periodic fault signals and greatly enhancing the diagnosis accuracy of the model when processing complex signals. Finally, the time–frequency analysis technique is employed to explore the internal mechanisms of the model in depth, aiming to validate the effectiveness of NMFB and MBCAB in fault feature recognition and to improve the feature interpretability of the proposed modes in fault diagnosis applications. We validate the superior performance of the network model in dealing with high-noise backgrounds by testing it on a standard bearing dataset from Case Western Reserve University and a self-constructed composite bearing fault dataset, and the experimental results show that its performance exceeded six of the top current fault diagnosis techniques. Full article

Achieving reliable navigation is critical for GNSS receivers subject to spoofing attacks. Utilizing the inherent sparsity and inconsistency of spoofing signals, this paper proposes an anti-spoofing framework for GNSS receivers to detect, classify, and recover positions from spoofing attacks without additional devices. A sparse decomposition algorithm with non-negative constraints limited by signal power magnitudes is proposed to achieve accurate spoofing detections while extracting key features of the received signals. In the classification stage, these features continuously refine each channel of the receiver’s code tracking loop, ensuring that it tracks either the authentic or counterfeit signal components. Moreover, by leveraging the inherent inconsistency of spoofing properties, we incorporate the Hausdorff distance to determine the most overlapped position sets, distinguishing genuine trajectories and mitigating spoofing effects. Experiments on the TEXBAT dataset show that the proposed algorithm detects 98% of spoofing attacks, ensuring stable position recovery with an average RMSE of 6.32 m across various time periods. Full article

In comparative analyses, specific features of the Spanish welfare and housing systems have often been emphasized. The case of Barcelona illustrates the extent to which these features are the result of a long-standing historical trajectory and the decisive impact of the challenges and policy responses adopted during Franco’s lengthy, dark, and gloomy regime. This period marked a significant shift, not only due to the persistent shortage of social rental housing, but also because of the early consolidation of a homeownership culture and its dominance in working-class suburban areas—a legacy that is completely different from that of the welfare states of Western Europe. Through a review of the literature and the analysis of primary sources, ongoing research on Barcelona seeks to clarify the factors and processes that led to this transformation, as well as its evolution during the democratic period, within an international context of economic liberalization and the dismantling of the welfare state. Full article

Fuel price increases have long been a contentious issue in Indonesia, sparking intense public and political debates. This study examines how digital media, particularly Kompas.com and Tempo.co, shape public discourse on fuel price hikes through mediatization. Using discourse network analysis, this study compares the political narratives surrounding fuel price increases during the administrations of Susilo Bambang Yudhoyono (2013) and Joko Widodo (2022). The findings reveal a shift in dominant discourse—opposition to price hikes was prominent in both periods, with government authority and economic justification emphasized in 2013, whereas concerns over rising living costs and social unrest dominated in 2022. This study highlights how mediatization has transformed policymaking from deliberative discussions into fragmented media battles, where digital platforms amplify competing narratives rather than facilitating consensus. Kompas.com predominantly featured counter-discourses, while Tempo.co exhibited stronger pro-government narratives in 2013. This study suggests that while digital media plays a crucial role in shaping policy perceptions, it does not necessarily translate into policy influence. It contributes to the broader understanding of the media’s role in policy debates. It underscores the need for more strategic government communication to manage public expectations and mitigate political unrest surrounding fuel price adjustments. Full article

Background: The surge in COVID-19 cases during the pandemic created a disease burden. An epidemiological study on COVID-19 is required as not much is known about the impact of containment and mitigation on health. We aimed to compare the epidemiological features during the years of the COVID-19 pandemic in the Vidarbha region in Maharashtra, India, to understand the epidemiology changes throughout the pandemic’s progression. Method: All of the cases reported at our testing centers in Nagpur and its periphery during the three years of the pandemic (i.e., from February 2020 to December 2022) were included. Descriptive analyses of variables of interest and statistical measures were compared. Results: There were 537,320 tests recorded during the study period. Of these, 13,035 (13.29%), 42,909 (13.70%), and 19,936 (15.91%) tested positive in 2020, 2021, and 2022, respectively. Hospitalization decreased from 2020 to 2022. An age group shift from 45 to 16–30 years over the pandemic was noticed. Seasonally, positivity peaked in September (27.04%) in 2020, in April (43.4%) in 2021, and in January in 2022 (35.30%). The estimated case fatality ratio was highest in 2021 (36.68%) over the three years in the hospital setting. Conclusion: Understanding the changing epidemiology of SARS-CoV-2 strengthens our perceptive of this disease, which will aid in improving the healthcare system in terms of both controlling and preventing the spread of COVID-19. Full article

This study investigates the effects of key atmospheric features on mineral dust emissions and transport in the Sahara–Sahel region, focusing on the Bodélé Depression, during June 2006 and 2011. We use a combination of high-resolution atmospheric simulations (AROME model), satellite observations (MODIS), and reanalysis data (ERA5, ECMWF) to examine the roles of the low-level jet (LLJ), Saharan heat low (SHL), Intertropical Discontinuity (ITD), and African Easterly Jet (AEJ) in modulating dust activity. Our results reveal significant interannual variability in aerosol optical depth (AOD) between the two periods, with a marked decrease in June 2011 compared to June 2006. The LLJ emerges as a dominant factor in dust uplift over Bodélé, with its intensity strongly influenced by local topography, particularly the Tibesti Massif. The position and intensity of the SHL also play crucial roles, affecting the configuration of monsoon flow and Harmattan winds. Analysis of wind patterns shows a strong negative correlation between AOD and meridional wind in the Bodélé region, while zonal wind analysis emphasizes the importance of the AEJ and Tropical Easterly Jet (TEJ) in dust transport. Surprisingly, we observe no significant correlation between ITD position and AOD measurements, highlighting the complexity of dust emission processes. This study is the first to combine climatological context and case studies to demonstrate the effects of African monsoon variability on dust uplift at intra-seasonal timescales, associated with the modulation of ITD latitude position, SHL, LLJ, and AEJ. Our findings contribute to understanding the complex relationships between large-scale atmospheric features and dust dynamics in this key source region, with implications for improving dust forecasting and climate modeling efforts. Full article

The periodic vehicle routing problem (PVRP) is a well-known challenge in real-life logistics, requiring the planning of vehicle routes over multiple days while enforcing visitation frequency constraints. Although numerous metaheuristic and exact methods have tackled various PVRP extensions, real-world settings call for additional features such as depot configurations, tight visitation frequency constraints, and heterogeneous fleets. In this paper, we present a two-phase biased–randomized algorithm that addresses these complexities. In the first phase, a round-robin assignment quickly generates feasible and promising solutions, ensuring each customer’s frequency requirement is met across the multi-day horizon. The second phase refines these assignments via an iterative search procedure, improving route efficiency and reducing total operational costs. Extensive experimentation on standard PVRP benchmarks shows that our approach is able to generate solutions of comparable quality to established state-of-the-art algorithms in relatively low computational times and stands out in many instances, making it a practical choice for real life multi-day vehicle routing applications. Full article