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Maintaining wheat moisture content within a safe range is of critical importance for ensuring the quality and safety of wheat. High-precision, rapid detection of wheat moisture content is a key factor in enabling effective control processes. A microwave detection system based on metasurface lens antennas was proposed in this study, which facilitates accurate, non-invasive, and contactless measurement of wheat moisture content. The system measures the attenuation characteristics of wheat with varying moisture content from 23.5 GHz to 24.5 GHz in the frequency range. A linear regression equation (coefficient of determination $\mathrm{R}^2$=0.9946) was established by using the measured actual moisture content obtained through the standard drying method, and was used as the prediction model for wheat moisture. Totally, 72 wheat samples were selected for moisture content prediction, yielding a root mean square error (RMSE) of 0.193%, mean absolute error (MAE) of 0.16%, and maximum relative error (MRE) of 5.25%. The results indicate that the proposed microwave detection system, based on metasurface lens antennas, provides an effective method for detecting wheat moisture content.

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The Smoothed Particle Hydrodynamics (SPH) method has been applied to solve the Boussinesq equations in order to simulate hypothetical one-dimensional dam break flows (DBFs) across varying depth ratios. Initial simulations reveal that the influence of Boussinesq terms remains minimal during the early stages of DBF when the depth ratio is less than 0.4. However, these terms become increasingly significant at later stages of the flow. In comparison to simulations based on the Saint-Venant equations, the Boussinesq-SPH model underestimates flow depths in regions of constant elevation while overestimating the propagation speed of the positive surge wave, with this overestimation becoming more pronounced as the depth ratio increases. Notably, the first and third Boussinesq terms exert the greatest influence on the simulation results. The findings also indicate the presence of non-hydrostatic pressure distributions within the DBF, which contribute to the accelerated movement of the positive surge. This study offers valuable insights into the modelling of flows that exhibit non-hydrostatic behaviour, and the results may be instrumental in improving the analysis of similar flow phenomena, especially those involving complex pressure distributions and wave propagation dynamics.

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Accurately predicting whether bank users will opt for time deposit products is critical for optimizing marketing strategies and enhancing user engagement, ultimately improving a bank’s profitability. Traditional predictive models, such as linear regression and Logistic Regression (LR), are often limited in their ability to capture the complex, time-dependent patterns in user behavior. In this study, a hybrid approach that combines Long Short-Term Memory (LSTM) neural networks and a stacked ensemble learning framework is proposed to address these limitations. Initially, LSTM models were employed to extract temporal features from two distinct bank marketing datasets, thereby capturing the sequential nature of user interactions. These extracted features were subsequently input into several base classifiers, including Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbour (KNN), to conduct initial classifications. The outputs of these classifiers were then integrated using a LR model for final decision-making through a stacking ensemble method. The experimental evaluation demonstrates that the proposed LSTM-stacked model outperforms traditional models in predicting user time deposits on both datasets, providing robust predictive performance. The results suggest that leveraging temporal feature extraction with LSTM and combining it with ensemble techniques yields superior prediction accuracy, thereby offering a more sophisticated solution for banks aiming to enhance their marketing efficiency.

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The rise of advanced digital technologies (ADT) within Industry 4.0 has transformed modern industrial operations, with select industry leaders emerging as pioneers in the integration of these technologies. This has positioned them as benchmarks for companies with limited digital capabilities. The vulnerabilities of Industry 4.0 to external disruptions, including natural disasters such as the earthquakes in Japan and Turkey, the COVID-19 pandemic, and especially the ongoing energy crises, exemplified by the war in Ukraine and sanctions on the Russian Federation, have necessitated a shift in business continuity management (BCM) strategies. Traditionally focused on safeguarding information technologies, BCM now places greater emphasis on ensuring energy independence and reducing reliance on state-controlled critical infrastructure. In response to these risks, enterprises are increasingly adopting resilient production models designed to restore functionality after cyberattacks, solar flares, extended power outages, and internet disruptions. The journey toward energy independence spans from initial recognition of the need for action to the implementation of robust solutions, such as Faraday cages for server protection and off-grid energy systems. While rare a decade ago, energy-independent enterprises are becoming more common, as illustrated by the copper smelter in Sevojno, a pioneering example. The acceleration of energy independence among companies has been driven by a series of crises, prompting significant BCM advancements. Early responses to these threats primarily focused on information technology (IT) disaster management methodologies, but Industry 4.0 discussions have evolved toward risk-resilient production systems. This study explores theoretical approaches to enhancing enterprise resilience to modern energy challenges, offering insight into emerging strategies aimed at safeguarding continuity in an increasingly volatile global landscape.

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The mining sector plays a pivotal role in the economies of South Africa and Zimbabwe, yet limited attention has been given to the determinants of human capital disclosure within this industry. This study aims to address this gap by investigating the key factors influencing human capital reporting practices among the largest mining companies in these two countries. A quantitative approach was employed, utilising self-administered questionnaires to gather data from six major mining companies operating in both South Africa and Zimbabwe. Factor analysis was conducted to identify the primary determinants shaping human capital disclosure. The findings reveal that company structure, including audit committee characteristics, board size and composition, and assets, significantly influence disclosure practices. Performance-related factors, such as cost-effectiveness, return on training investments, liquidity, employee return on investments, and return on equity, also play a crucial role. Furthermore, market-related factors, including lobby pressure groups, media exposure, levels of debt, creditor pressure, and government regulations, were found to impact disclosure decisions. The results indicate that human capital disclosure mitigates information asymmetry, thereby strengthening relationships between company management and key stakeholders. It is also suggested that improved disclosure enhances corporate transparency, boosts investor confidence, and can positively influence a company’s perceived value. Given these findings, it is recommended that mining companies in South Africa and Zimbabwe adopt comprehensive reporting frameworks that incorporate human capital metrics. The adoption of such frameworks may align corporate practices with global reporting standards and enhance the sustainability and accountability of companies in the sector.

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Rolling bearings play a critical role in various industrial applications. However, the complexity and diversity of data, along with the challenge of selecting the most representative features from a large set and reducing dimensionality to lower computational costs, pose significant challenges for accurately predicting the remaining useful life (RUL) of rolling bearings. To address this, a hybrid model combining the broad learning system (BLS) and multi-scale temporal convolutional network (MsTCN) is proposed for RUL prediction of rolling bearings. The BLS is employed to capture a broad range of features from the full-life signals of rolling bearings, while the MsTCN adaptively extracts multi-scale temporal features, effectively capturing both short-term and long-term dependencies in the bearing’s operational process. Additionally, the fusion and optimization of features extracted by BLS and MsTCN enhance the representational power of the prediction model. Experiments conducted on the PHM2012 bearing dataset demonstrate that the proposed method significantly improves model performance and prediction accuracy.

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This paper investigates the search for an exact analytic solution to a temporal first-order differential equation that represents the number of customers in a non-stationary or time-varying $M / D / 1$ queueing system. Currently, the only known solution to this problem is through simulation. However, a study proposes a constant ratio, $\beta$ (Ismail's ratio), that relates the time-dependent mean arrival and mean service rates, offering an exact analytical solution. The stability dynamics of the time-varying $M / D / 1$ queueing system are then examined numerically in relation to time, $\beta$, and the queueing parameters. On another note, many potential queueing-theoretic applications to traffic management optimization are provided. The paper concludes with a summary, combined with open problems and future research pathways.

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Electricity remains one of the most vital resources for industrial, domestic, and agricultural applications. However, electricity theft has emerged as a significant challenge, contributing to substantial power losses and severe economic repercussions for utility companies. This study examines the role of smart meters (SMs) in minimizing electricity theft and reducing energy losses by transitioning from traditional analogue meters to advanced SMs equipped with automated billing and metering systems. Data collected from the SM system in the Akre energy distribution network reveal that, following the implementation of SMs, overall electrical power losses were reduced by 17.1%, while theft incidents decreased by 96.4%. These results demonstrate that the deployment of SMs significantly contributes to lowering total power losses and yields considerable financial benefits for both utility providers (UPs) and consumers. Moreover, the system enhances the ability to remotely monitor and control customer meters, allowing continuous oversight of meter readings without requiring physical visits. This remote functionality strengthens theft prevention measures, improves grid reliability, and reduces operational costs. The findings highlight the potential of the SM system in advancing power efficiency and promoting a more secure and cost-effective energy distribution network.

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The incorporation of fractional calculus into nanofluid models has proven effective in capturing the complex dynamics of nanofluid flow and heat transfer, thereby enhancing the precision of predictions in this intricate field. In this study, the dynamics of a viscoelastic second-grade nanofluid model are examined through the application of the Laplace transform technique on a vertical plate. Initially, the model is formulated as coupled partial differential equations to describe the second-grade nanofluid system. The governing equations are then rendered dimensionless using appropriate dimensionless parameters. The non-dimensional model is subsequently generalized by introducing a modified Caputo fractional derivative operator. To model a homogenous nanofluid, nanoparticles of $\mathrm{Al}_2 \mathrm{O}_3$ in nanometer-sized form are suspended in mineral transformer oil. The Laplace transform is employed to solve the momentum, energy, and mass diffusion equations, providing analytical solutions. Graphical and tabular analyses are conducted to assess the influence of various physical parameters—including the fractional order, nanoparticle volume fraction, and time parameter—on the velocity, thermal, and concentration profiles. The results indicate that increasing the nanoparticle volume fraction, fractional order, and time parameter significantly enhances the rate of heat transfer. Additionally, it is observed that the velocity, temperature, and concentration profiles are notably affected by increasing the volume fraction of nanoparticles. The accuracy and reliability of the obtained solutions are validated through comparisons with existing literature. This work advances the understanding of nanofluid dynamics and presents valuable insights for industrial applications, particularly in enhancing heat transfer performance.

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This study investigates the structural performance and mass optimization of traditional walkers by comparing aluminum alloy and polymer matrix composites (PMCs) through advanced finite element analysis (FEA) using the ANSYS simulation platform. The FEA results reveal that peak stress, reaching 251.9 MPa, is concentrated at the front wheel support region, highlighting a critical area prone to structural vulnerability. Special attention is required to address potential mechanical limitations in key zones, such as the rear suspension, to prevent premature failure. Comparative analysis demonstrates that walkers fabricated from carbon-epoxy PMCs offer superior stiffness, reduced weight, and enhanced resistance to deformation compared to aluminum alloy counterparts. Notably, under descent conditions, the maximum elastic strain in the carbon-epoxy walker reaches 0.00399 mm/mm, localized in the front wheel support area, as indicated by the simulation results. These findings underscore the significant role of material selection in improving structural integrity and performance across varying operational conditions. The equivalence of stress and strain energy distributions further substantiates the advantages of composite materials over conventional alloys, suggesting that PMCs enable enhanced durability without compromising weight efficiency. The research emphasizes a human-centred approach, aligning material performance with user needs to develop mobility aids that offer long-term structural reliability. Beyond addressing immediate structural concerns, the findings lay the groundwork for future studies involving optimization algorithms and the exploration of alternative composites for assistive devices. The study provides valuable insights into stress distribution, deformation behaviour, and mechanical response, promoting continuous innovation in the design and development of mobility aids.

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Laser additive manufacturing, a pivotal technology in advanced manufacturing, is extensively applied in the restoration industry. However, its development has been hindered by challenges such as residual stress and excessive grain size during the manufacturing process. The integration of ultrasonic enhancement technology with laser cladding has emerged as a prominent research direction, offering significant improvements in the quality and performance of the cladding layer. This review focuses on two primary approaches: ultrasonic-enhanced synchronous laser cladding and ultrasonic strengthening as a post-processing method. The ultrasonic processes discussed include ultrasonic vibration, ultrasonic rolling, ultrasonic impact, and their composite variants. Each method is evaluated for its ability to modify the microstructure, alleviate defects, and enhance the mechanical properties of the cladding layer. While ultrasonic enhancement during synchronous laser cladding primarily facilitates greater molten pool agitation, post-processing techniques induce severe plastic deformation on the surface of the cladding layer. Both approaches have been shown to reduce residual stress, refine grain structure, and improve surface hardness. The underlying mechanisms governing these improvements, particularly microstructural evolution and grain refinement, are examined in detail. Additionally, the potential advantages and limitations of each ultrasonic introduction method are discussed. Finally, the application prospects and future development trends of ultrasonic-enhanced laser cladding are explored, with particular attention to the role of ultrasonic technology in enhancing the durability, wear resistance, and corrosion resistance of cladding layers. The synergy between ultrasonic techniques and laser cladding promises to expand the potential of additive manufacturing in both industrial and repair applications.

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In order to better understand the competitive dynamics between e-commerce platforms and traditional retail outlets, a Stackelberg game model was developed. Subsequently, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed to determine the Pareto solution set for this multi-objective optimization problem. The findings reveal that: a) The effect of consumer reference quality can lead enterprises to adjust their strategy levels downwards, potentially resulting in profit loss under certain conditions. b) When the influence of competitive intensity on market demand is minimal, a reduction in enterprise profits occurs in both centralized and cost-sharing decision-making frameworks, with more significant detriment observed in the cost-sharing mode; conversely, when the influence is substantial, enhancements in competitive intensity can significantly increase overall system profits. c) The model's validity was confirmed through the application of the NSGA-II.
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