Search Results (35,117)

Since its origin in the thermodynamics of the 19th century, the concept of entropy has also permeated other fields of physics and mathematics, such as Classical and Quantum Statistical Mechanics, Information Theory, Probability Theory, Ergodic Theory and the Theory of Dynamical Systems. Specifically, we are referring to the classical entropies: the Boltzmann–Gibbs, von Neumann, Shannon, Kolmogorov–Sinai and topological entropies. In addition to their common name, which is historically justified (as we briefly describe in this review), another commonality of the classical entropies is the important role that they have played and are still playing in the theory and applications of their respective fields and beyond. Therefore, it is not surprising that, in the course of time, many other instances of the overarching concept of entropy have been proposed, most of them tailored to specific purposes. Following the current usage, we will refer to all of them, whether classical or new, simply as entropies. In particular, the subject of this review is their applications in data analysis and machine learning. The reason for these particular applications is that entropies are very well suited to characterize probability mass distributions, typically generated by finite-state processes or symbolized signals. Therefore, we will focus on entropies defined as positive functionals on probability mass distributions and provide an axiomatic characterization that goes back to Shannon and Khinchin. Given the plethora of entropies in the literature, we have selected a representative group, including the classical ones. The applications summarized in this review nicely illustrate the power and versatility of entropy in data analysis and machine learning. Full article

Background: The blood supply chain (BSC) is crucial for providing safe and sufficient blood, but it faces numerous challenges and needs to be robust and resilient. This study provides a comprehensive model for managing and optimizing the BSC in real-world scenarios, including emergency and routine circumstances and with consideration of health equity concepts. Method: Classic time-series models are applied to predict future supply chain circumstances, addressing uncertainty in blood demand and the need for timely supply. A structured framework and medical preferences are prioritized to optimize distribution, minimize blood shortages, minimize wastage due to expiry, and maximize blood freshness. Genetic algorithms (GA) and particle swarm optimization (PSO) are used to solve mathematical models quickly and efficiently, ensuring reliable operation. Result: The model’s outcomes can effectively meet the daily needs of the BSC and assist decision-makers managing blood inventory and distribution, improving robustness and resilience. Conclusions: Utilizing weights allows for the effective management of each objective function to convert the model into a single-objective mixed-integer linear programming (SO-MILP) based on unique conditions, enabling the system to self-adjust for optimal performance, boosting the sustainability of the blood supply chain, and promoting the principle of health equity under diverse real-world settings. Full article

The brushless DC (BLDC) motor is crucial in a variety of industrial and consumer applications due to its efficiency and precise control. This study investigates the heat transfer and cooling mechanisms in liquid-cooled BLDC motors in dishwashers, which are fundamental to maintaining optimal operating temperatures. Elevated temperatures can reduce operational efficiency, emphasizing the importance of effective heat dissipation. Liquid cooling proves to be very effective and offers advantages over air cooling by providing even temperature distribution and more accurate temperature control. Integrating liquid cooling systems into dishwasher designs provides a viable solution for managing motor temperatures while preheating dishwashing water. Using existing water infrastructure, these systems dissipate heat generated during motor operation, increasing energy efficiency and reliability, as analyzed using computational fluid dynamics (CFDs). The aim of this study is to optimize thermal management strategies in BLDC motors, particularly in dishwashers, by filling a critical gap in the existing literature. The goal of this comprehensive analysis is to develop resistant and efficient cooling solutions tailored to dishwasher environments, ultimately extending the life of BLDC motors in home appliances while using heat transfer to preheat water for wash cycles. Full article

Constructing an ecological security pattern in ecologically fragile areas is crucial for maintaining regional ecological stability. This study focuses on the Kunlun Mountain region, identifying ecological sources based on habitat suitability assessments and ecosystem services. An ecological resistance evaluation index system is constructed, considering topography, land use, and habitat quality. The minimum cumulative resistance model is then applied to identify ecological corridors, with areas exhibiting higher ecological currents designated as ecological nodes. By integrating the spatial characteristics of ecosystem services, an ecological security pattern is established. The results are as follows: (1) The ecological source area covers approximately 11.30% of the study area. (2) The cumulative length of ecological corridors is 21,111 km, mainly distributed along valleys, gentle slopes, and oasis areas. (3) The areas of ecological nodes and ecological barriers are 126.75 km² and 46.75 km², respectively. Ecological nodes are mainly distributed on both sides of the Kunlun Mountains, while ecological barriers are primarily located in the central mountainous area of the Kunlun Mountains. (4) The findings recommend establishing an ecological security pattern consisting of “2 horizontal and 4 vertical corridors and 5 zones” to ensure the ecological security of the Kunlun Mountains. The integration of ecological corridors and ecosystem services in constructing a regional ecological security pattern provides valuable decision-making tools for protecting ecosystems and species in fragile areas. Full article

A practical fixed-time robust containment control method for multiple autonomous surface vehicles (multi-ASVs) is proposed in this study. This method addresses the containment control problem of multi-ASVs, considering both collision risks and external disturbances. This control scheme improves the cooperative performance of the formation and guarantees safe collision avoidance behavior. First, to enable the online estimation of unknown time-varying disturbances from the external environment, a fixed-time disturbance observer (FNDO) is designed based on fixed-time control theory. Second, the distributed kinematic controller is modified to include the partial derivatives of the artificial potential energy function (APEF), thereby preventing collisions among multi-ASVs. Third, by applying fixed-time theory, graph theory, and fixed-time dynamic surface control techniques, a practical fixed-time robust containment controller for multi-ASVs is proposed. Additionally, the entire closed-loop control system is guaranteed to be practical and fixed-time stable through stability analysis. Finally, the proposed control strategy has been validated by simulation results. Full article

This paper presents research on the security performance of a multi-user interference-based mixed RF/FSO system based on SWIPT untrusted relay. In this work, the RF and FSO channels experience Nakagami-m fading distribution and Málaga (M) turbulence, respectively. Multiple users transmit messages to the destination with the help of multiple cooperating relays, one of which may become an untrusted relay as an insider attacker. In a multi-user network, SWIPT acts as a charging device for each user node. In order to prevent the untrusted relays from eavesdropping on the information, some users are randomly assigned to transmit artificial noise in order to interfere with untrusted relays, and the remaining users send information to relay nodes. Based on the above system model, the closed-form expressions of secrecy outage probability (SOP) and average secrecy capacity (ASC) for the mixed RF/FSO system are derived. The correctness of these expressions is verified by the Monte Carlo method. The influences of various key factors on the safety performance of the system are analyzed by simulations. The results show that the security performance of the system is considerably improved by increasing the signal–interference noise ratio, the number of interfering users, the time distribution factor and the energy conversion efficiency when the instantaneous signal-to-noise ratio (SNR) of the RF link instantaneous SNR is low. Full article

In industrial applications, hydraulic presses maintain workloads by controlling the hydraulic cylinder to extend and retract, ensuring optimum tracking performance in terms of position and force. Dealing with nonlinear and multinode systems, such as hydraulic systems, often requires an advanced approach that frequently includes machine learning and artificial intelligence methods. Introducing an adaptive control system to significantly improve the response of hydraulic presses is a challenge. Therefore, a polynomial regression model predictive control (PR-MPC) mechanism is proposed in this paper to compensate for external disturbances such as the forming processes and friction dynamics. Using polynomial regression modeling and least squares optimization, the approach produces highly accurate data-driven models with an R² value of 0.948 to 0.999. The simplicity of polynomial regression facilitates the integration of smart algorithms into an expert system with additional decision-making rules. Remote adaptive control integrated within a 5G network is based on I 4.0 distributed system guidelines that provide insights into the behavior of the hydraulic press. The results of real-time experiments have shown that the PR-MPC mechanism integrated into the expert system reduces the absolute response error of the hydraulic press by up to 98.7% compared to the initial control system with a PID regulation. Full article

Effective control of animal infectious diseases is crucial for maintaining robust livestock production systems worldwide. Porcine meat constitutes approximately 35–40% of global meat production with the largest producers being China and the European Union (EU). Emerging viral pathogens in swine, like porcine bocavirus (PBoV), have not garnered significant attention, leaving their pathogenic characteristics largely unexplored. This review aims to bridge this knowledge gap by conducting a comprehensive analysis of the existing literature on PBoV. We explore the virus’s genome structure, discovery, classification, detection methods, pathogenesis, and its potential public health implications. Additionally, we discuss the distribution and economic impact of PBoV, which includes potential losses due to decreased productivity, increased veterinary costs, and trade restrictions. By highlighting the current state of knowledge, this review seeks to enhance the understanding of PBoV, thereby aiding in its prevention and control, and mitigating its economic impact on the swine industry. Full article

Analyzing the survival and development environment, internal dynamics, and development direction of specialized farmers’ cooperatives in underdeveloped areas to enhance the vitality of the development of the agricultural industry is a key strategy for the work of the “Three Rural Issues” in China. Based on the data of 3194 specialized farmers’ cooperatives in Yunnan Province from 2000 to 2023, this paper utilizes the spatial measurement method and survival analysis method to study the spatial distribution of their survival and related influencing factors. The study found the following: (1) Cooperatives show a spatial aggregation trend from “high to high” to “low to high”, and the formation of new sub-core areas is accelerating. (2) The establishment stage of cooperatives shows an obvious annual cycle effect, and cooperatives established in the early stage show stronger survival resilience. (3) The factor of “organizational characteristics and technological innovation” significantly prolongs the survival time of cooperatives, while the factor of “establishment stage” has a negative effect. (4) The influence of a cooperative’s asset size and trademark on its operational durability tends to decrease over time, but the influence of relatedness remains relatively stable. (5) Over time, the survival and development patterns of cooperatives at the provincial level show obvious differentiation, and the clustering phenomenon of “low-high” development gradually appears in minority autonomous counties. The results of this study provide a scientific basis for deepening and strengthening the study of the basic rural business system. Full article

Karst rocky desertification (KRD) is a significant issue that affects the ecological and economic sustainability of southwest China. Obtaining the accurate distribution of different levels of KRD can provide decision-making support for the effective management of KRD. The Sustainable Development Goals Science Satellite 1 (SDGSAT-1) is the world’s first scientific satellite serving the 2030 Agenda for Sustainable Development of the United Nations, and is dedicated to developing high-resolution, multi-scale, global public datasets to support policy and decision-making support systems for sustainable development. SDGSAT-1 multispectral data provide detailed ground information with a spatial resolution of 10 m and a rich spectral resolution. In this study, we combined the red-modified carbonate rock index (RCRI, an index that characterizes the degree of carbonate rock exposure) and the normalized difference red edge index (NDRE, an index that characterizes the degree of vegetation coverage) to propose a novel feature space method based on SDGSAT-1 multispectral data to classify the different levels of KRD in the Jinsha County of Guizhou Province, a representative region with significant KRD in southwest China. This method effectively identified different levels of KRD with an overall classification accuracy of 87%. This was 20% higher than that of the grading index method, indicating that SDGSAT-1 multispectral data have promising potential for KRD classification. In this study, we offer a new insight into the classification of KRD and a greater quantity of remote-sensing data to monitor KRD over a wider area and for a longer period of time, contributing to the economic development and environmental protection of KRD areas. Full article

This review will discuss heart failure, introduce a new drug finerenone, and discuss clinical studies with a focus on its effects on heart failure. Heart failure is a condition or syndrome characterized by an impairment of the pumping ability of the heart, thus no longer keeping up with the demands of the body. There are several types of heart failure; among them are heart failure with reduced ejection fraction, with mildly reduced ejection fraction and with preserved ejection fraction. Heart failure can be caused by several factors including lifestyle factors and diseases such as hypertension, type 2 diabetes mellitus and other cardiovascular diseases. Chronic kidney disease is also a risk factor of heart failure, as it leads to a state of inflammation that can impair the cardiovascular system over time. The novel nonsteroidal mineralocorticoid receptor antagonist finerenone antagonizes the mineralocorticoid receptor and thereby decreases the amount of fibrosis and inflammation that is observed in many heart failure patients. It shows an equal tissue distribution among heart and kidney, a high affinity and selectivity for the mineralocorticoid receptor and little risk of hyperkalemia and feminization. It also exhibits a reduction in the incidence of cardiovascular outcomes among patients with chronic kidney disease and type 2 diabetes mellitus. Therefore, finerenone has been proposed as a beneficial medication for reducing heart failure, especially in patients with diabetes and chronic kidney disease. Further studies are to be conducted to clarify the effects of finerenone alone and in combination with other drugs. Full article

This paper analyzes the behavior of the entropy of a nuclear fuel rod under deep burnup conditions, beyond standard operational ranges, reaching up to 60 years. The evolution of the neutron source distribution in a pressurized water reactor (PWR) fuel pin was analyzed using the Monte Carlo method and Shannon information entropy. To maintain proper statistics, a novel scaling method was developed, adjusting the neutron population based on the fission rate. By integrating reactor physics with information theory, this work aimed at the deeper understanding of nuclear fuel behavior under extreme burnup conditions. The results show a “U-shaped” entropy evolution: an initial decrease due to self-organization, followed by stabilization and eventual increase due to degradation. A minimum entropy state is reached after approximately 45 years of pin operation, showing a steady-state condition with no entropy change. This point may indicate a physical limit for fuel utilization. Beyond this point, entropy rises, reflecting system degradation and lower energy efficiency. The results show that entropy analysis can provide valuable insights into fuel behavior and operational limits. The proposed scaling method may also serve to control a Monte Carlo simulation, especially for the analysis of long-life reactors. Full article

The retrieval of global significant wave height (SWH) data is crucial for maritime navigation, aquaculture safety, and oceanographic research. Leveraging the high temporal resolution and spatial coverage of Cyclone Global Navigation Satellite System (CYGNSS) data, machine learning models have shown promise in SWH retrieval. However, existing models struggle with accuracy under high-SWH conditions and discard a significant number of such observations due to low quality, which limits their effectiveness in global SWH retrieval, particularly for monitoring tropical cyclone (TC) events. To address this, this study proposes a daily global SWH retrieval framework through the enhanced eXtreme Gradient Boosting model (XGBoost-SC), which incorporates Cumulative Distribution Function (CDF) matching to introduce prior distribution information and reduce errors for SWH values exceeding 3 m. An enhanced loss function is employed to improve accuracy and mitigate the distribution bias in low-SWH retrieval induced by CDF matching. The results were tested over one million sample points and validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) SWH product. With the help of CDF matching, XGBoost-SC outperformed all models, significantly reducing RMSE and bias while improving the retrieval capability for high SWHs. For SWH values between 3–6 m, the RMSE and bias were 0.94 m and −0.44 m, and for values above 6 m, they were 2.79 m and −2.0 m. The enhanced performance of XGBoost-SC for large SWHs was further confirmed in TC conditions over the Western North Pacific and in the Western Atlantic Ocean. This study provides a reference for large-scale SWH retrieval, particularly under TC conditions. Full article

CloudSim is a versatile simulation framework for modeling cloud infrastructure components that supports customizable and extensible application provisioning strategies, allowing for the simulation of cloud services. On the other hand, Distributed Acoustic Sensing (DAS) is a ubiquitous technique used for measuring vibrations over an extended region. Data handling in DAS remains an open issue, as many applications need continuous monitoring of a volume of samples whose storage and processing in real time require high-capacity memory and computing resources. We employ the CloudSim tool to design and evaluate a cloud computing scheme for long-range, polarization-independent DAS using coherent detection of Rayleigh backscattering signals and uncover valuable insights on the evolution of the processing times for a diverse range of Virtual Machine (VM) capacities as well as sizes of blocks of processed data. Our analysis demonstrates that the choice of VM significantly impacts computational times in real-time measurements in long-range DAS and that achieving polarization independence introduces minimal processing overheads in the system. Additionally, the increase in the block size of processed samples per cycle results in diminishing increments in overall processing times per batch of new samples added, demonstrating the scalability of cloud computing schemes in long-range DAS and its capability to manage larger datasets efficiently. Full article

The use of supercritical carbon dioxide (ScCO₂) as a replacement for volatile organic solvents in coatings has the potential to reduce air pollution. This paper presents the findings of a molecular dynamics simulation study investigating the dissolution behavior of polyvinylidene fluoride (PVDF) in ScCO₂ assisted by five co-solvents. On the basis of solubility parameters, interaction binding energy, and radial distribution functions, the impacts of temperature, pressure, and co-solvents on the compatibility of ScCO₂ and PVDF were investigated at the microscopic level. The simulation results demonstrated that low-temperature and high-pressure conditions facilitate the dissolution of PVDF in ScCO₂, where the optimal conditions are 308.15 K and 16 MPa. The enhancement of the solubility performance of ScCO₂ slowed down with increasing pressure, but was more sensitive to changes in temperature. The weak attraction between PVDF and ScCO₂ was synergized by van der Waals and electrostatic forces, making it challenging to achieve complete and homogeneous mixing. The use of co-solvents with strong polarity can enhance the solvent system’s solubility. Ethanol and 2-butoxy-1-ethanol have obvious solubilizing abilities due to the hydrogen bond donors, which can generate hydrogen bonding interactions with ScCO₂, increase the polarity of the solvent system, and promote the compatibility of ScCO₂ with PVDF. Full article