The excessive application of agrochemicals has resulted in significant workplace exposure for agriculturists and environmental interaction for the general public, particularly in communities adjacent to agricultural zones. Such exposure is associated with detrimental health effects, including mutagenic and cytotoxic impacts. Agrochemical contamination frequently occurs through water, especially in rural villages where conventional water treatment systems are not designed to address these specific contaminants. The efficacy of activated carbon was investigated in this study as an adsorbent for the removal of 2,4-dichlorophenoxyacetic acid (2,4-D) from contaminated water. The concentration of 2,4-D in water samples was quantified using ultraviolet-visible (UV-Vis) spectroscopy at a wavelength of 283 nm. Preliminary adsorption experiments identified pH 2 as the optimal condition for 2,4-D uptake. The adsorption kinetics were best described by the Elovich model, with an equilibrium time of 480 minutes. Equilibrium studies revealed that three isotherm models—Redlich-Peterson, Temkin, and Toth—effectively represented the experimental data, with a maximum adsorption capacity of 252.8 mg/g. The findings underscore the potential of activated carbon as a cost-effective and straightforward treatment method for the removal of 2,4-D from drinking water, particularly in rural areas lacking access to advanced water treatment infrastructure.

Globalization has made the business environment more complex, with many corporations facing increasingly difficult challenges. Furthermore, some corporations place a higher focus on risk management than profit. However, risk management has continued to evolve over the years. Therefore, this study delves into the determinants influencing risk management disclosure (RMD) in energy insurance companies, addressing the complex requirements of risk and transparency. The research presents a new model and examines parameters such as profitability, leverage, liquidity, company size, and ownership structure—including public, institutional, and managerial ownership—within the framework of ISO 31000, moderated by the risk management committee. This study used a quantitative research approach to gather data from 2014 to 2023 for 133 observations through purposive sampling. The findings indicate that company profitability, leverage, liquidity, company size, and ownership structure—including public ownership and managerial ownership—have no positive effect on risk management disclosure (RMD), whereas institutional ownership has a positive impact on RMD. On the other hand, the risk management committee moderates the significant impact of public ownership, institutional ownership, and managerial ownership on RMD. This study underscores the importance of shaping risk management disclosures in the Indonesian insurance sector. This research contributes to a nuanced understanding of the factors driving RMD, offering valuable insights for stakeholders in the energy insurance industry.

Nanofluids, which are suspensions of nanoparticles in base fluids, have demonstrated considerable potential in enhancing thermal conductivity, energy storage, and lubrication properties, as well as improving the cooling efficiency of electronic devices. Despite their promising applications, the industrial utilization of nanofluids remains in the early stages, with further research needed to fully explore their capabilities. This study investigates a generalized nanofluid model, incorporating fractal-fractional derivative (FFD), to better understand the thermophysical behaviors in vertical channel flow. The nanofluid consists of polystyrene nanoparticles uniformly dispersed in kerosene oil. An exact solution to the model is obtained by employing the Laplace transform technique (LTT) in combination with the numerical Zakian’s algorithm. The FFD operator with an exponential kernel is applied to extend the classical nanofluid model. Discretization of the generalized model is achieved using the Crank-Nicolson method, and numerical simulations are performed to solve the resulting equations. The study reveals that, at a nanoparticle volume fraction of 4% (0.04), the heat transfer rate of the nanofluid is significantly higher than that of the base fluid. Furthermore, the enhanced heat transfer leads to improvements in various thermophysical properties, such as viscosity, thermal expansion, and heat capacity, which are crucial for industrial applications. The numerical results are presented graphically to highlight the dependence of the flow and thermal dispersion characteristics on key physical factors. These findings suggest that the use of fractal-fractional models can provide a more accurate representation of nanofluid behavior, particularly for high-precision applications in heat transfer and energy systems.

In the realm of water resource management, optimizing the operation of multiple water pumps plays a pivotal role in ensuring the efficient distribution and conservation of this vital resource. This research paper proposes a novel approach to address this challenge by harnessing the power of Long Range (LoRa) communication technology for controlling multiple water pumps remotely. The study begins by exploring the existing methodologies in water pump control systems and identifies their limitations, particularly in terms of scalability, range, and energy efficiency. Subsequently, it introduces the concept of LoRa technology and its applicability in the domain of multiple water pump control, highlighting its long-range communicating capability, low-power consumption, and suitability for precision agriculture. The system architecture is delineated, encompassing the integration of LoRa transceivers with each water pump, a central control unit, and a user interface for remote monitoring and management. To evaluate the efficacy of the proposed system, a series of experiments are conducted in real-world scenarios, encompassing various operational conditions and geographic locations. Performance metrics including response time and reliability are meticulously measured and analyzed. The findings of this research demonstrate significant improvements in the reliability of water pump control systems using LoRa technology.

Diabetes is a chronic metabolic disorder that affects millions of people worldwide, making early detection crucial for effective management. This study assesses the effectiveness of three machine learning (ML) models, Logistic Regression (LR), Naïve Bayes (NB), and Decision Tree (DT), in predicting diabetes based on data from 392 individuals, including their demographic and clinical characteristics. The dataset underwent preprocessing to maintain data integrity, was standardized for model compatibility, and analyzed through feature correlation heatmaps, feature importance assessments, and statistical significance tests. The findings revealed that LR surpassed the other models, with the highest accuracy (78%), precision (73%), and F1-score (65%) for diabetic cases. NB showed moderate performance with 75% accuracy, while DT demonstrated the lowest accuracy (71%) due to overfitting. Receiver Operating Characteristic (ROC) analysis revealed strong discriminative power across all models, although perfect Area Under the Curve (AUC) scores indicate potential overfitting needing further validation. The study emphasizes the significance of key features like Glucose, Body Mass Index (BMI), and Age, which showed notable differences between diabetic and non-diabetic individuals. By enabling early detection and proactive management, these models can contribute to reducing diabetes-related complications, enhancing patient outcomes, and lessening the burden on healthcare systems. Future research should investigate ensemble learning, deep learning, and real-time data integration from Internet of Things (IoT) devices to improve predictive accuracy and scalability.

A novel composite mechanical bulging process suitable for the manufacture of medium-duty commercial vehicle drive axle housings is proposed. The analytical expression for the limit bulging forming coefficient of tube blanks under conditions below the metal recrystallization temperature is derived, and the influence of the matching of various force parameters on the limit bulging forming coefficient is analyzed. The appropriate range for the axial auxiliary load during radial bulging is also presented. Based on the derived theory, a 5-ton commercial vehicle drive axle housing is selected as the research object. The key processes in the forming process are numerically simulated to obtain the metal flow state, stress-strain distribution, and wall thickness variation. The types and locations of defects that may occur during the bulging process are also predicted. To address the phenomenon of local wall thinning in the composite mechanical bulging process of the drive axle housing, a set of orthogonal simulation experiments is designed, focusing on the wall thickness thinning rate in the bridge arch bulging area and the crack-prone region, with respect to the process parameters. Based on the numerical simulation results, response surface equations are established for the expansion core's movement speed and axial auxiliary thrust in relation to the wall thickness thinning rate. Through parameter estimation of the response surface equation and regression analysis of significant influencing factors, the effects of process parameters on wall thickness thinning are obtained: the thinning rate in the bridge arch bulging area decreases with increasing expansion core movement speed and axial auxiliary thrust, while the thinning rate in the crack-prone region increases. The optimization of the response surface model and the determination of the optimal process parameter combination, based on field production conditions, show that the numerical simulation results and the wall thickness measurements from process experiments are in close agreement. No cracks occur in the axle housing, and the thinning is effectively alleviated. In contrast, mechanical bulging without axial auxiliary thrust leads to cracks, thus validating the feasibility of the proposed process scheme and the effectiveness of the parameter optimization. This research provides valuable technical reference for upgrading the manufacturing technology of large-span axle-tube products.

The forecasting of wheat commodity prices plays a crucial role in mitigating financial risks for stakeholders across the agricultural supply chain. In this study, the predictive performance of three models—Simple Moving Average (SMA), Extreme Gradient Boosting (XGBoost), and a hybrid SMA-XGBoost model—was evaluated to determine their efficacy in capturing both linear trends and complex nonlinear patterns inherent in wheat price data. A 10-lag structure was employed to integrate historical dependencies and seasonal fluctuations, thereby enhancing the accuracy of trend identification. The dataset was partitioned into training (75%) and testing (25%) subsets to facilitate an objective performance assessment. The XGBoost model, known for its capability in modelling nonlinear dependencies, demonstrated the highest forecasting precision, achieving a Mean Absolute Percentage Error (MAPE) of 1.64%. The hybrid SMA-XGBoost model, which leveraged the complementary strengths of both SMA and XGBoost, yielded a MAPE of 1.75%, outperforming the standalone SMA model, which exhibited a MAPE of 2.60%. While the hybrid model displayed slightly lower accuracy than XGBoost, it offered greater stability and robustness by effectively balancing trend extraction and nonlinear adaptability. These findings highlight the hybrid approach as a viable alternative to purely machine learning-based forecasting methods, particularly in scenarios requiring resilience to diverse market fluctuations. The proposed methodology provides a valuable tool for policymakers, agricultural producers, and market analysts seeking to enhance decision-making strategies and optimize risk management within the agricultural sector.