Search Results (97,595)

To improve soil clod removal and reduce potato damage in potato combine harvesters, this study investigates the processes involved in soil clod removal and potato collisions within the bar-lift chain separation device of the harvester. It outlines the structure and working principles of the machine, theoretically analyzes the key dimensions of the digging device and potato–soil separation components, and derives specific structural parameters. A dynamic mathematical model of the bar-lift chain is established, from which the dynamic equations are formulated. The analysis identifies factors that influence the dynamic characteristics of the bar-lift chain. This study examines the working principles and separation performance of the potato–soil separation device, with a focus on the collision characteristics between potatoes and both the screen surface and the bars. Key factors such as the separation screen’s line speed, the harvester’s forward speed, and the tilt angle of the separation screen are considered. Simulations are performed using a coupling method based on the Discrete Element Method (DEM) and Multi-Body Dynamics (MBD). Through simulation experiments, the optimal parameter combinations for the potato–soil separation device are determined. The optimal working parameters are identified as a separation screen line speed of 1.25 m/s, a forward speed of 0.83 m/s, and a tilt angle of 25°. Field harvesting experiments indicate a potato loss rate of 1.8%, a damage rate of 1.2%, an impurity rate of 1.9%, a skin breakage rate of 2.1%, and a yield of 0.15–0.21 ha/h. All results meet national and industry standards. The findings of this research provide valuable theoretical references for simulating potato–soil separation in combine harvesters and optimizing the parameters of these devices. Future potential research will consider the automatic regulation of the excavation volume of the potato–soil mixture, aiming to achieve intelligent control of the potato–soil separation operation. Full article

This paper presents a mathematical approach to analyzing carbon abatement costs and the allocation of carbon emission allowances in China’s industrial sectors. We utilize input–output data from 30 Chinese provinces between 2009 and 2018 to estimate carbon abatement costs by applying the slack-based measure (SBM) efficiency model and its dual form. The SBM model captures inefficiencies and offers a rigorous framework for measuring abatement costs. Using these costs, we develop a centralized allocation data envelopment analysis (DEA) model, which maximizes sectoral benefits through optimal reallocation. This DEA model is formalized as a linear programming problem, with the aim of determining the efficient allocations of carbon allowances while maintaining the system’s economic productivity. Furthermore, we construct intertemporal, interregional, and spatiotemporal allocation DEA models to examine the dynamics of carbon emission allowance allocation over time, space, and combined spatiotemporal dimensions. These models offer insights into the efficiency of carbon markets under varying conditions. Our proposed new mathematical formulations reveal optimal allocation strategies that can balance emission reductions with industrial productivity. This study also provides novel mathematical frameworks for analyzing the carbon allowance distribution and contributions to both the theory and application of mathematical optimization in environmental policy design. Our findings reveal that China’s industrial carbon abatement costs exhibit significant interprovincial and regional differences. Developed provinces with higher levels of industrial development have higher carbon abatement costs, while provinces with less-developed industrial sectors have lower costs. Under the interregional allocation scenario of carbon emission allowances that consider abatement costs, developed provinces have smaller industrial carbon emission reductions, whereas less-developed provinces have larger reductions. In the intertemporal allocation scenario, provinces with larger industrial economies face greater emission reduction tasks. Under the combined interregional and intertemporal allocation scenario, industrial sectors in coastal developed provinces have lower carbon emission reductions, while those in inland less-developed provinces have higher reductions, mirroring the spatial allocation results of carbon emission allowances. Full article

The optimal performance of direct current (DC) motors is intrinsically linked to their mathematical models’ precision and their controllers’ effectiveness. However, the limited availability of motor characteristic information poses significant challenges to achieving accurate modeling and robust control. This study introduces an approach employing artificial neural networks (ANNs) to estimate critical DC motor parameters by defining practical constraints that simplify the estimation process. A mathematical model was introduced for optimal parameter estimation, and two advanced learning algorithms were proposed to efficiently train the ANN. The performance of the algorithms was thoroughly analyzed using metrics such as the mean squared error, epoch count, and execution time to ensure the reliability of dynamic priority arbitration and data integrity. Dynamic priority arbitration involves automatically assigning tasks in real-time depending on their relevance for smooth operations, whereas data integrity ensures that information remains accurate, consistent, and reliable throughout the entire process. The ANN-based estimator successfully predicts electromechanical and electrical characteristics, such as back-EMF, moment of inertia, viscous friction coefficient, armature inductance, and armature resistance. Compared to conventional methods, which are often resource-intensive and time-consuming, the proposed solution offers superior accuracy, significantly reduced estimation time, and lower computational costs. The simulation results validated the effectiveness of the proposed ANN under diverse real-world operating conditions, making it a powerful tool for enhancing DC motor performance with practical applications in industrial automation and control systems. Full article

Several industries are increasingly focused on enhancing the delivery of active ingredients through the skin to optimize therapeutic outcomes. By facilitating the penetration of active ingredients through the skin barrier, these enhancers can significantly improve the efficacy of various formulations, ranging from skincare products to therapeutic agents targeting systemic circulation. As the understanding of skin physiology and the mechanisms of drug absorption deepen, these industries are adopting permeation enhancers more widely, ultimately leading to better patient outcomes and expanded treatment options. However, the structure and physiological function of the skin can vary according to different factors, such as the area of the body and between individuals. These variations, along with external environmental exposures, aging and pathological conditions, introduce complexities that must be carefully considered when designing effective delivery systems. Considering the intricacies of skin structure and physiology, tailoring systems to account for regional differences, individual variability, and changes induced by environmental factors or disease is critical to optimizing therapeutic outcomes. This review discusses the features of skin structure, physiology, and pathologies, as well as the application of permeation enhancers in these contexts. Furthermore, it addresses the use of animal skin models in transdermal delivery and dermatological studies, along with the latest developments in this field. Full article

Minerals constitute only 5% of the typical human diet but are vital for health and functionality. Copper, a trace element, is absorbed by the human gut at 30–40% from diets typical of industrialized countries. The liver produces metallothioneins, which store copper. Copper is crucial for mitochondrial respiration, pigmentation, iron transport, antioxidant defense, hormone production, and extracellular matrix biosynthesis. Copper deficiency, often caused by mutations in the ATP7A gene, results in Menkes disease, an X-linked recessive disorder. On the contrary, Wilson disease is characterized by toxic copper accumulation. Cuproptosis, a unique form of cell death regulated by copper, is a subtype of necrosis induced by enhanced mitochondrial metabolism and intracellular copper accumulation. This process can reduce the malignant potential of tumor cells by inhibiting glucose metabolism. Therapeutically, copper and its complexes have shown efficacy in malignancy treatments. The disruption of copper homeostasis and excessive cuproplasia are significant in colorectal cancer development and metastasis. Therefore, manipulating copper status presents a potential therapeutic target for colorectal cancer, using copper chelators to inhibit copper formation or copper ion carriers to promote cuproptosis. This review highlights the role of copper in human physiology and pathology, emphasizing its impact on colorectal cancer and potential therapeutic strategies. Future AI-based approaches are anticipated to accelerate the development of new compounds targeting cuproptosis and copper disruption in colorectal cancer. Full article

Many industrial processes, from manufacturing to food processing, incorporate rotating elements as principal components in their production chain. Failure of these components often leads to costly downtime and potential safety risks, further emphasizing the importance of monitoring their health state. Vibration signal analysis is now a common approach for this purpose, as it provides useful information related to the dynamic behavior of machines. This research aimed to conduct a comprehensive examination of the current methodologies employed in the stages of vibration signal analysis, which encompass preprocessing, processing, and post-processing phases, ultimately leading to the application of Artificial Intelligence-based diagnostics and prognostics. An extensive search was conducted in various databases, including ScienceDirect, IEEE, MDPI, Springer, and Google Scholar, from 2020 to early 2024 following the PRISMA guidelines. Articles that aligned with at least one of the targeted topics cited above and provided unique methods and explicit results qualified for retention, while those that were redundant or did not meet the established inclusion criteria were excluded. Subsequently, 270 articles were selected from an initial pool of 338. The review results highlighted several deficiencies in the preprocessing step and the experimental validation, with implementation rates of 15.41% and 10.15%, respectively, in the selected prototype studies. Examination of the processing phase revealed that time scale decomposition methods have become essential for accurate analysis of vibration signals, as they facilitate the extraction of complex information that remains obscured in the original, undecomposed signals. Combining such methods with time–frequency analysis methods was shown to be an ideal combination for information extraction. In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. Meanwhile, transformer-based models are emerging as a promising venue for the prediction of RUL values, along with data transformation. Given the conclusions drawn, future researchers are urged to investigate the interpretability and integration of the diagnosis and prognosis models developed with the aim of applying them in real-time industrial contexts. Furthermore, there is a need for experimental studies to disclose the preprocessing details for datasets and the operational conditions of the machinery, thereby improving the data reproducibility. Another area that warrants further investigation is differentiation of the various types of fault information present in vibration signals obtained from bearings, as the defect information from the overall system is embedded within these signals. Full article

Water is the source of life on Earth. Therefore, water pollution is one of the greatest problems in the world. On this basis, the current study focuses on accelerating industrial pollutant removal from water using light by designing effective photocatalysts. This target was achieved through a triple-action effect. This effect depends on the integration of the doping process with nanotube formation in addition to the surface plasmon resonance of gold for titanium oxides. In this way, titanium oxide nanoparticles were prepared and converted to nanotubes during the doping process. These nanoparticles and nanotubes were supported by gold nanoparticles to use this triple-action effect for increasing charge carriers and active sites of the photocatalysts and preventing recombination reactions. High-resolution transmission electron microscopy (HRTEM) and selected area electron diffraction (SAED), Raman spectra, energy-dispersive X-ray spectrometer (EDX), and X-ray diffraction were used to clarify the triple-action effect on the structure of the photocatalysts. The optical properties and activity of the prepared photocatalysts were studied in terms of the photocatalytic degradation of the green dyes (acid green 1). The experimental results indicated that the triple-action effect has a strong positive role in increasing industrial pollutant removal with or without light. Here, the percentage of photocatalytic decomposition reached 100% after 17 min of light radiation. In addition, 27% of the pollutants were removed without light radiation. In conclusion, the current study indicated that the triple-action effect could solve the drawbacks of titanium oxide by creating new photo-active sites and novel tracks for charge carriers in addition to preventing recombination reactions. Full article

Diosgenin (DSG) is a phytosterol saponin mainly found in Dioscorea zingiberensis C.H. Wright. It has shown promising results in treating various diseases such as cancer, diabetes, arthritis, asthma, and cardiovascular diseases. Diosgenin is also an important medicinal chemical for synthesizing various steroid medicines. The production of diosgenin by acid hydrolysis generates a large amount of wastewater, leading to severe environmental pollution. However, producing diosgenin through microbial fermentation can effectively reduce environmental pollution. Numerous studies have demonstrated that various microorganisms can produce diosgenin via solid-state fermentation. Nevertheless, due to the complexity, high maintenance costs, uneven heat production, and other characteristics of solid-state fermentation, it is not commonly used in the industrial production of diosgenin. In contrast, liquid fermentation offers advantages such as simple operation, easy maintenance, and stable fermentation, making it more suitable for the industrial production of diosgenin. However, few studies have focused on producing diosgenin using liquid fermentation. In this study, endophytic Bacillus licheniformis SYt1 was used to produce diosgenin via liquid fermentation, with Dioscorea tuber powder as a substrate. Soxhlet extraction and silica gel column chromatography were employed to identify the diosgenin from the liquid fermentation products. Suitable fermentation conditions were screened and identified. The environmental variables that significantly affect the diosgenin yield were determined by the Plackett–Burman design (P-BD) with eight factors. The three factors (peptone, yeast extract powder and inorganic salt) with the greatest influence on the diosgenin yield were selected and further optimized using a response surface methodology (RSM). The final culture conditions were determined to be 35.79 g/L of peptone, 14.56 g/L of yeast extract powder, and 1.44 g/L of inorganic salt. The yield of diosgenin under these conditions was 132.57 mg/L, which was 1.8 times greater than the yield under pre-optimization conditions. This effective, clean, and promising liquid fermentation method possesses the potential to replace the traditional acid hydrolysis method for the industrial production of diosgenin. Full article

This paper explores the transformative impact of agent-based modeling (ABM) on the architecture, engineering, and construction (AEC) industry, highlighting its indispensable role in revolutionizing project management, construction processes, safety protocols, and sustainability initiatives including energy optimization and occupants’ comfort. Through an in-depth review of 178 documents published between 1970 and 2024 on current practices and the integration of ABM with emerging digital technologies, this study underscores the critical importance of ABM in facilitating enhanced decision-making, resource optimization, and complex system simulations. For instance, ABM is shown to reduce project delays by up to 15% through enhanced resource allocation and improve safety outcomes by simulating worker behavior and identifying potential hazards in dynamic construction environments. The results reveal ABM’s potential to significantly improve construction methodologies, integrate technological advancements seamlessly, and contribute to the development of sustainable and resilient building practices. Furthermore, this paper identifies key areas for future research, including the exploration of ABM’s capabilities in conjunction with other digital innovations to unlock new avenues for efficiency and sustainability in construction. This study sets out a forward-looking agenda for providing this modeling approach to address contemporary challenges and harness opportunities for innovation and growth in the AEC sector. Full article

Torrefaction was applied to enhance the fuel properties of sawdust (SD) and bark wood (BW), biomass wastes from the rubberwood processing industry. Design Expert (DE) software was used in an experimental design to study the effects of affecting factors including torrefaction temperature and time as well as the biomass size towards the desirable properties such as HHV, mass yield, fixed carbon content, and eco-efficiency values. Promising results showed that the HHVs of the torrefied SD (25 MJ/kg) and BW (26 MJ/kg) were significantly increased when compared to preheated SD (17 MJ/kg) and preheated BW (17 MJ/kg) and in a range similar to that of coal (25–35 MJ/kg). The TGA, FTIR, biomass compositions, and O/C ratios suggested that thermochemical reactions played a significant role in the torrefaction at which thermal degradation coupled with possible in situ chemical reactions took place, to some extent. The optimal conditions of the torrefaction were identified at 320 °C and 30 min for SD, and 325 °C and 30 min for BW. The maximum HHVs at the optimal condition were 22, 23, and 20 MJ/kg while the eco-efficiency values were 29.18, 27.89, and 13.72 kJ/kg CO_{2_eq}*THB for torrefied SD, torrefied BW, and coal, respectively. The findings of this study indicate that torrefied rubberwood residues enhanced HHV, eco-efficiency, and less contribution to CO₂ emissions compared to fossil fuels. Full article

Refineries are industrial complexes of great economic importance which are located close to major cities. A pool fire accident that can occur from an oil leak combined with wind can result in disastrous consequences for such an industry. This study investigates the characteristics of an isolated n-heptane square pool fire of 36 m² under the influence of a cross wind. The pool fire characteristics are numerically studied using open-source Computational Fluid Dynamics (CFD) software, such as FireFoam (v4.1) and Fire Dynamic Simulator (FDS) (version 6.9.0). The turbulent flow field and the fire characteristics were simulated with the LES Method. The crucial parameters of the pool fire, such as (a) the temperature and velocity fields, (b) the flame length and height, (c) the surface emissive power, and (d) the flame tilt angles, were computed. Comparisons against experimental data for both small and large-area pool fires from the literature were made successfully. The flame tilt angle is shown to correlate very well with the reciprocal of the Richardson number, which was approximated within a multiplication constant to the Froude number. Thus, both the reciprocal Richardson number and Froude number can be used for correlating the flame tilt angle. It is shown that both of these numbers are used to correlate the tilt angle of experimental pool fires with effective diameters from a fraction of a meter to approximately 16 m, and wind speeds up to 7 m/s. The goodness of a linear fit based on the sum of the residual squares is 0.91. Full article

While significant efforts have been made to recycle metals like lithium or cobalt for sustainable development in the battery industry, the reuse of other high-value materials, such as fluorinated carbon nanotubes (FCNTs), remains underexplored. This study introduces a novel, eco-friendly method to repurpose quality-downgraded FCNTs (QD-FCNTs) as effective nano-additives in polyacrylamide (PAAm) microgel composites for water retention applications. Because QD-FCNTs are rich in surface defects, by employing a mild surface treatment with a low dosage of organic electron donor N,N,N${}^{\prime }$,N${}^{\prime }$-tetramethyl-p-phenylenediamine, we enhanced the hydrophilicity of QD-FCNTs without additional energy input or a large quantity of harmful chemicals. This treatment significantly improved the interaction between QD-FCNTs and PAAm microgels, leading to a 55% increase in water retention time compared to the composites made of untreated QD-FCNTs. Our findings present a sustainable approach to extending the lifecycle of FCNTs, contributing to the circular economy and offering practical solutions for potential water management in agriculture and environmental technologies. Full article

This study presents an advanced framework integrating LLAMA_V2, a large language model, into Open Radio Access Network (O-RAN) systems. The focus is on efficient network slicing for various services. Sensors in IoT devices generate continuous data streams, enabling resource allocation through O-RAN’s dynamic slicing and LLAMA_V2’s optimization. LLAMA_V2 was selected for its superior ability to capture complex network dynamics, surpassing traditional AI/ML models. The proposed method combines sophisticated mathematical models with optimization and interfacing techniques to address challenges in resource allocation and slicing. LLAMA_V2 enhances decision making by offering explanations for policy decisions within the O-RAN framework and forecasting future network conditions using a lightweight LSTM model. It outperforms baseline models in key metrics such as latency reduction, throughput improvement, and packet loss mitigation, making it a significant solution for 5G network applications in advanced industries. Full article

The sustainable management of domestic water use is essential for China’s ongoing development. Despite its significance, public service water use, especially within the hotel industry, is frequently neglected. This industry not only accounts for substantial water usage, but it has also witnessed exponential growth over the past decade. In light of this, this study developed a survey on the water demand and behavior of hotel consumers and built a water consumption model to analyze the different characteristics of the total volume of water used by various water appliances. Furthermore, by dividing the hierarchical intervals of each water-use behavior, it identified the main water-saving target of each water appliance. The results showed that the daily water demand of each consumer in hotels ranged from 45 to 141 L/(person·d). Among the various water-use appliances, faucets had the highest proportion of excessive water use, reaching nearly 40%, which implies a relatively large potential for saving water. Additionally, showers were the next major contributors to water consumption, whereas the proportion of excessive water use for water closets was relatively small. Owing to different habits, there is also variability in the main groups of people targeted for water conservation across water appliances, with females having the largest potential for water consumption in hotels. The results of this study can provide greatvaluable insights for enhancing water-use efficiency in hotels and standardizing water management practices within the industry. Full article

Evaluating the behavior of materials and their response under low-velocity dynamic impact (less than 30 m/s) is a challenging task in various industries. It requires customized test methods to replicate real-world impact scenarios and capture important material responses accurately. This study introduces a novel spring-actuated testing setup for low-velocity impact (LVI) scenarios, addressing the limitations of existing methods. The setup provides tunable parameters, including adjustable impactor mass (1 to 250 kg), velocity (0.1 to 32 m/s), and spring stiffness (100 N/m to 100 kN/m), allowing for flexible simulation of dynamic impact conditions. Validation experiments on steel plates with a support span of 800 mm and thickness of 5 mm demonstrated the system’s satisfactory accuracy in measuring impact forces (up to 714.2 N), displacements (up to 40.5 mm), and velocities. A calibration procedure is also explored to estimate energy loss using numerical modeling, further enhancing the test setup’s precision and utility. The results underline the effectiveness of the proposed experimental setup in capturing material responses during low-velocity impact events. Full article