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As the global shift from fossil fuels to the Paris Agreement has accelerated, wind energy has become a key alternative to hydroelectric power. However, existing research often needs to improve in integrating diverse environmental, economic, and climate-related variables when modeling wind energy potential, particularly under future climate change scenarios. Addressing these gaps, this study employs the maximum entropy (MaxEnt) method, a robust and innovative tool for spatial modeling, to identify optimal wind farm sites in Türkiye. This research advances site selection methodologies and enhances predictive accuracy by leveraging a comprehensive dataset and incorporating climate change scenarios. The results indicate that 89% of the current licensed projects will maintain compliance in the future, while 8% will see a decrease in compliance. Furthermore, the wind energy potential in Türkiye is expected to increase because of climate change. These results confirm the suitability of existing project locations and identify new high-potential areas for sustainable wind energy development. This study provides policymakers, investors, and developers actionable insights to optimize wind energy integration into the national energy portfolio, supporting global climate goals by accelerating the adoption of renewable energy sources. [ABSTRACT FROM AUTHOR]

Estimating the probability and consequences of drought disasters is an important task in drought risk assessment, which contributes to the development of mitigation strategies. Based on rainfall data from 2481 stations and the drought-affected arable land of each province from 1961 to 2021, a probabilistic analysis model of drought duration, drought severity and the proportion of affected farmland area (PAFA) was constructed by a three-dimensional copula function. The results show that the distribution functions of drought duration, drought severity and the PAFA are well given based on the principle of maximum entropy and can pass the Kolmogorov–Smirnov (K-S) distribution test with a significance level of 0.05. Among the three Archimedean copulas, Frank's method has a relatively better accuracy, suggesting that it captures the dependencies between drought characteristics better and is more suitable for constructing joint distribution functions. The value of PAFA in Gansu, Inner Mongolia, Shanxi and Liaoning is about 0.3, which is higher than other provinces. Drought duration and drought severity levels of 3 to 4 are the main causes of a PAFA greater than 0.3, which can be used as an early warning line for drought risk. At the same level of PAFA, the drought in the southern region lasted longer and was more intense. [ABSTRACT FROM AUTHOR]

Since the 1990's, it has been recognized that the full explanation of cosmic rays (CR) and their spectrum may require some new physics. The debate on the origin of CR has led to the conclusion that while most CR come from supernova explosions in the Galaxy, CR with very high energies are likely of extragalactic origin. However, a response to several open questions, still unanswered, concerning CR above 1013 eV is required. We herewith study the temporal evolution of the observational CR using data collected by several stations of the ground-based network. The obtained result states that the power spectral density of the CR temporal evolution, especially with a frequency less than 0.1 Hz, exhibits the Kolmogorov-Obukhov 5/3 law that exhibits the energy spectrum of many geophysical quantities. Any small difference found from the 5/3 exponent can be attributed to intermittency corrections and the stations' characteristics. Moreover, natural time analysis applied to the CR time series showed the critical role of the quasi-biennial oscillation to the entropy maximization which occurs following the 5/3 Kolmogorov-Obukhov power law. These findings can be used to more reliably predict extreme CR events that could have an impact even at the molecular level. • The power spectral density of the observed time evolution of CRs obeys the 5/3 law. • The CRs entropy is maximized following the Kolmogorov-Obukhov 5/3-power law. • Small differences from the 5/3 law can be attributed to intermittency corrections. • The Natural Time Analysis of CRs data can lead to the prediction of its extremes. [ABSTRACT FROM AUTHOR]

This work explores the hyperbolic cosine and hyperbolic secant functions within the framework of the maximum entropy principle, deriving these probability distribution functions from first principles. The resulting maximum entropy solutions are applied to various physical systems, including the repulsive oscillator and solitary wave solutions of the advection equation, using the method of moments. Additionally, a different moment analysis using experimental and theoretical inputs is employed to address non-linear systems described by the non-linear Schrödinger equation, non-linear diffusion equation, and Korteweg–de Vries equation, demonstrating the versatility of this approach. These findings demonstrate the broad applicability of maximum entropy methods in solving different differential equations, with potential implications for future research in non-linear dynamics and transport physics. [ABSTRACT FROM AUTHOR]

Federated Learning (FL), as a distributed machine learning framework, can effectively learn symmetric and asymmetric patterns from large-scale participants. However, FL is susceptible to malicious backdoor attacks through attackers injecting triggers into the backdoored model, resulting in backdoor samples being misclassified as target classes. Due to the stealthy nature of backdoor attacks in FL, it is difficult for users to discover the symmetric and asymmetric backdoor properties. Currently, backdoor defense methods in FL cause model performance degradation while reducing backdoors. In addition, some methods will assume the existence of clean samples, which does not match the realistic scenarios. To address such issues, we propose FLSAD, an effective backdoor defense method in FL via self-attention distillation. FLSAD can recover the triggers using an entropy maximization estimator. Based on the recovered triggers, we leverage the self-attention distillation to eliminate the backdoor. Compared with the baseline backdoor defense methods, FLSAD can reduce the success rates of different state-of-the-art backdoor attacks to 2% on four real-world datasets through extensive evaluation. [ABSTRACT FROM AUTHOR]

On-demand meal delivery has become a feature of most cities around the world as a result of platforms and apps that facilitate it as well as the pandemic, which for a period, closed restaurants. Meals are delivered by couriers, typically on bikes, e-bikes, or scooters, who circulate collecting meals from kitchens and delivering them to customers, who usually order online. A Markov model for circulating couriers with n + 1 parameters, where n is the number of kitchens plus customers, is derived by entropy maximization. There is one parameter for each kitchen and customer representing the demand for a courier, and there is one parameter representing the urgency of delivery. It is shown how the mean and variance of delivery time can be calculated once the parameters are known. The Markov model is irreducible. Two procedures are presented for calibrating model parameters on a data set of orders. Both procedures match known order frequencies with fitted visit probabilities; the first inputs an urgency parameter value and outputs mean delivery time, whereas the second inputs mean delivery time and outputs the corresponding urgency parameter value. Model calibration is demonstrated on a publicly available data set of meal orders from Grubhub. Grubhub data are also used to validate the calibrated model using a likelihood ratio. By changing the location of one kitchen, it is shown how the calibrated model can estimate the resulting change in demand for its meals and the corresponding mean delivery time. The Markov model could also be used for the assignment of courier trips to a street network. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT Conference. [ABSTRACT FROM AUTHOR]

Abstract The aim of this paper is to prepare, describe and discuss the models of the current and future distribution of Phthiracarus longulus (Koch, 1841) (Acari: Oribatida: Euptyctima), the oribatid mite species widely distributed within the Palearctic. We used the maximum entropy (MAXENT) method to predict its current and future (until the year 2100) distribution based on macroclimatic bio-variables. To our best knowledge, this is the first-ever prediction of distribution in mite species using environmental niche modelling. The main thermal variables that shape the current distribution of P. longulus are the temperature annual range, mean temperature of the coldest quarter and the annual mean temperature, while for precipitation variables the most important is precipitation of the driest quarter. Regardless of the climatic change scenario (SSP1-2.6, SSP2-4.5, SSP5-8.5) our models show generally the northward shift of species range, and in Southern Europe the loss of most habitats with parallel upslope shift. According to our current model, the most of suitable habitats for P. longulus are located in the European part of Palearctic. In general, the species range is mostly affected in Europe. The most stable areas of P. longulus distribution were the Jutland with surrounding southern coasts of Scandinavia, islands of the Danish Straits and the region of Trondheim Fjord.

Baroreflex is critical to maintain blood pressure homeostasis, and the quantification of baroreflex regulation function (BRF) can provide guidance for disease diagnosis, treatment, and healthcare. Current quantification of BRF such as baroreflex sensitivity cannot represent BRF systematically. From the perspective of complex systems, we regard that BRF is the emergence result of fluctuate states and interactions in physiological mechanisms. Therefore, the three‐layer emergence is studied in this work, which is from physiological mechanisms to physiological indexes and then to BRF. On this basis, since the entropy in statistical physics macroscopically measures the fluctuations of system's states, in this work, the principle of maximum entropy is adopted, and a new index called PhysioEnt is proposed to quantify the fluctuations of four physiological indexes, i.e., baroreflex sensitivity, heart rate, heart rate variability, and systolic blood pressure, which aims to represent BRF in the resting condition. Further, two datasets with different subjects are analyzed, and some new findings can be obtained, such as the contributions of the physiological interactions among organs/tissues. With measurable indexes, the proposed method is expected to support individualized medicine. [ABSTRACT FROM AUTHOR]

Understanding current fire dynamics in the Amazon is vital for designing effective fire management strategies and setting a baseline for climate change projections. This study aimed to analyze recent fire probabilities and project future "fire niches" under global warming scenarios across the Legal Amazon, a scale chosen for its relevance in social and economic planning. Utilizing the maximum entropy method, this study combined a complex set of predictors with fire occurrences detected during 1985–2022. It allowed for the estimation of current fire patterns and projecting changes for the near future (2020–2040) under two contrasting socioeconomic pathways. The results showed strong model performance, with AUC values consistently above 0.85. Key predictors included "Distance to Farming" (53.4%), "Distance to Non-Vegetated Areas" (11.2%), and "Temperature Seasonality" (9.3%), revealing significant influences from human activities alongside climatic predictors. The baseline model indicated that 26.5% of the Amazon has "moderate" to "very high" fire propensity, especially in the southern and southeastern regions, notably the "Arc of Deforestation". Future projections suggest that fire-prone areas may expand, particularly in the southern border regions and near the Amazon riverbanks. The findings underscore the importance of incorporating both ecological and human factors into fire management strategies to effectively address future risks. [ABSTRACT FROM AUTHOR]

The simplest evolutionary model for catching prey by an agent (predator) is considered. The simulation is performed on the basis of a software-emulated Intel i8080 processor. Maximizing the number of catches is chosen as the objective function. This function is associated with energy dissipation and developmental time. It is shown that during Darwinian evolution, agents with an initially a random set of processor commands subsequently acquire a successful catching skill. It is found that in the process of evolution, a logarithmic relationship between astronomical and developmental times arises in agents. This result is important for the ideas available in the literature about the close connection of such concepts as time, Darwinian selection, and the maximization of entropy production. [ABSTRACT FROM AUTHOR]

It is well established that a diverse range of entropic measures, while remarkably adaptable, must inevitably be complemented by innovative approaches to enhance their effectiveness across various domains. These measures play a crucial role in fields like communication and coding theory, driving researchers to develop numerous new information measures that can be applied in a wide array of disciplines. This paper introduces a pioneering isolated entropic measure and its solicitations to queueing theory the study of dissimilarities of uncertainty. By creating the newly developed discrete entropy, we have articulated an optimization principle where the space capacity is predetermined and solitary evidence accessible is around the mean size. Additionally, we have conveyed the solicitations of "maximum entropy principle" to maximize the entropy probability distributions. [ABSTRACT FROM AUTHOR]

This article explores the estimation of Shannon entropy and Rényi entropy based on the generalized inverse exponential distribution under the condition of stepwise Type II truncated samples. Firstly, we analyze the maximum likelihood estimation and interval estimation of Shannon entropy and Rényi entropy for the generalized inverse exponential distribution. In this process, we use the bootstrap method to construct confidence intervals for Shannon entropy and Rényi entropy. Next, we select the gamma distribution as the prior distribution and apply the Lindley approximation algorithm to calculate 'estimates of Shannon entropy and Rényi entropy under different loss functions including Linex loss function, entropy loss function, and DeGroot loss function respectively. Afterwards, simulation is used to calculate estimates and corresponding mean square errors of Shannon entropy and Rényi entropy in GIED model. The research results show that under DeGroot loss function, estimation accuracy of Shannon entropy and Rényi entropy for generalized inverse exponential distribution is relatively high, overall Bayesian estimation performs better than maximum likelihood estimation. Finally, we demonstrate effectiveness of our estimation method in practical applications using a set of real data. [ABSTRACT FROM AUTHOR]

Locusta migratoria (Linnaeus, 1758) is one of the locusts known as important pests of food crops. Outbreaks of this species can cause catastrophic damage to maize, paddy, and many other crops. A species distribution model was used to identify the probability of the locust's current and future potential distribution in the Indonesian archipelago. The study relied on the machine learning method Maximum Entropy (Maxent) Model to forecast the future spread of the species in the Indonesian archipelago and to find the climate variable that influenced the distribution of Locusta migratoria. The results showed an Area Under Curve (AUC) value of 0.956 for the Locusta migratoria model, indicating a highly reliable model. The important variable for the distribution of this species was precipitation, especially during the dry season. A low amount of rainfall increases the possibility of the species existing and being distributed. Maxent prediction models showed the potential distribution in the southern part of the Indonesian archipelago under both middle and worst-case scenarios for 2070. This model can become one of the baselines for early warning systems, targeted monitoring and surveillance, and the use of specific pesticides or biological control agents to prevent or minimize the harm of Locusta migratoria outbreak to agricultural lands in the future. [ABSTRACT FROM AUTHOR]

Türkiye is one of the first regions where olives were domesticated, and olives reflect the country's millennia-old agricultural and cultural heritage. Moreover, Türkiye is one of the leading nations in olive and olive oil production in terms of quality and diversity. This study aims to determine the current and future distribution areas of olives, which is important for Türkiye's socio-economic structure. For this purpose, 19 different bioclimatic variables, such as annual mean temperature (Bio1), temperature seasonality (Bio4), and annual precipitation (Bio12), have been used. The RCP4.5 and RCP8.5 emission scenarios of the CCSM4 model were used for future projections (2050 and 2070). MaxEnt software, which uses the principle of maximum entropy, was employed to determine the current and future habitat areas of the olives. Currently and in the future, it is understood that the Mediterranean, Aegean, Marmara, and Black Sea coastlines have areas with potential suitability for olives. However, the model projections indicate that the species may shift from south to north and to higher elevations in the future. Analyses indicate that the Aegean Region is the most sensitive area and that a significant portion of habitats in the Marmara Region will remain unaffected by climate change. [ABSTRACT FROM AUTHOR]

Background: Situational awareness is an essential component of wildland firefighter safety. In the US, crew lookouts provide situational awareness by proxy from ground-level locations with visibility of both fire and crew members. Aims: To use machine learning to predict potential lookout locations based on incident data, mapped visibility, topography, vegetation, and roads. Methods: Lidar-derived topographic and fuel structural variables were used to generate maps of visibility across 30 study areas that possessed lookout location data. Visibility at multiple viewing distances, distance to roads, topographic position index, canopy height, and canopy cover served as predictors in presence-only maximum entropy modelling to predict lookout suitability based on 66 known lookout locations from recent fires. Key results and conclusions: The model yielded a receiver-operating characteristic area under the curve of 0.929 with 67% of lookouts correctly identified by the model using a 0.5 probability threshold. Spatially explicit model prediction resulted in a map of the probability a location would be suitable for a lookout; when combined with a map of dominant view direction these tools could provide meaningful support to fire crews. Implications: This approach could be applied to produce maps summarising potential lookout suitability and dominant view direction across wildland environments for use in pre-fire planning. We use machine learning to predict the probability an area is suitable for a wildland firefighter lookout based on incident data, roads, and lidar-derived visibility, terrain, and vegetation information. This approach may aid pre-fire planning and enhance situational awareness by providing maps of potential lookout locations. [ABSTRACT FROM AUTHOR]

In this research work, BaZr0.85Ho0.10Y0.025Nd0.025O3-δ (BZHYN) electrolyte ceramic was synthesized through a cost-effective flash pyrolysis route followed by conventional sintering for intermediate-temperature solid oxide fuel cells. The calcined powder and sintered pellet were characterized through various techniques like high-resolution X-ray diffraction (HRXRD), high-resolution transmission electron microscopy (HRTEM), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectra (EDS), and Raman spectroscopy. The HRXRD pattern of calcined and sintered pellet shows the pure cubic phase with P m 3 ¯ m space group symmetry through the Rietveld refinement. The study of the electron-density distribution of calcined powder and sintered pellet calculated by the maximum entropy method reveals the presence of oxygen vacancies at the octahedral site in the sintered sample. The microstructure of the fracture surface of the sintered sample indicates two types of grain with a relative density of 93.7% through FESEM. The Raman analysis confirms the distortion along the c-axis and oxygen vacancies in the octahedral site of BZHYN ceramic. Impedance spectroscopy measurement was conducted in the temperature range of 50 to 700 °C and frequency range of 1 Hz to 10 MHz. The Nyquist plots obtained in the temperature range of 350–700 °C reveal three distinct relaxation processes attributed to grain, grain boundary, and electrode effect. The temperature-dependent exponent (n) associated with grain and grain boundary decreases with the increase in temperature, indicating that large polaron hopping is involved in the electrical conduction mechanism. [ABSTRACT FROM AUTHOR]

In this study, PROP adsorption was investigated using activated carbon derived from Bactris Guineensis residues and physical statistical modeling. The characterization results indicate high specific surface areas (624.72 and 1125.43 m2 g−1) and pore diameters (2.703 and 2.321 nm) for the peel and stone-activated carbon, respectively. Adsorption equilibrium was investigated at different temperatures (298 to 328 K), and it was found that the adsorption capacity increased with temperature, reaching maximum values of 168.7 and 112.94 mg g−1 for the peel and stone-activated carbon, respectively. The application of physical statistical modeling indicates that a monolayer model with one energy site is adequate for describing both systems, with an R2 above 0.986 and a low BIC of 20.021. According to the steric parameters, the density of molecules per site tends to increase by 116.9% for the stone and 61.6% for the peel. In addition, the model indicates that the number of molecules decreases with increasing temperature from 1.36 to 0.81 and from 1.03 to 0.82. These results indicate that temperature controls the number of receptor sites and the orientation in which propranolol is adsorbed at the surface. The adsorption energies were similar for both systems (approximately 10 kJ mol−1), which indicates that the adsorption occurred due to physical interactions. Finally, the application of thermodynamic potential functions indicates that the maximum entropy is reached at concentrations of half-saturation (Ce 3.85 and 4.6 mg L−1), which corresponds to 1.60 × 10–18 and 1.86 × 10–18 kJ mol−1 K−1 for the stone and peel, respectively. After this point, the number of available sites tends to decrease, which indicates the stabilization of the system. The Gibbs energy tended to decrease with increasing concentration at equilibrium, reaching minimum values of − 1.73 × 10–19 and − 1.99 × 10–19 kJ mol−1, respectively. Overall, the results obtained here further elucidate how the adsorption of propranolol occurs for different activated carbons from the same source. [ABSTRACT FROM AUTHOR]

The acquisition of vegetation coverage information is crucial for crop field management, and utilizing visible light spectrum vegetation indices to extract vegetation coverage information is a commonly used method. However, most visible light spectrum vegetation indices do not fully consider the relationships between the red, green, and blue bands during their construction, making it difficult to ensure the accurate extraction of coverage information throughout the crop's entire growth cycle. To rapidly and accurately obtain potato vegetation coverage information, drones were used in this study to obtain high-resolution digital orthoimages of potato growth stages. Based on the differences in the grayscale values of potato plants, soil, shadows, and drip irrigation belts, this study presents a combination index of blue and green bands (BGCI) and a combination index of red and green bands (RGCI). The vegetation index intersection method was used with 10 vegetation information indices to extract vegetation coverage, and the differences in extraction accuracy were compared with those of the maximum entropy method and bimodal histogram method. Based on the high-precision fraction vegetation coverage (FVC) extraction results, the Pearson correlation coefficient method and random forest feature selection were used to screen 10 vegetation and 24 texture features, and the top six vegetation indices most strongly correlated with the FVC were selected for potato growth stage FVC estimation and accuracy verification. A high-precision potato vegetation coverage estimation model was successfully established. This study revealed that during the potato tuber formation and expansion stages, the BGCI combined with the vegetation index intersection method achieved the highest vegetation coverage extraction accuracy, with overall accuracies of 99.61% and 98.84%, respectively. The RGCI combined with the vegetation index intersection method achieved the highest accuracy, 98.63%, during the maturation stage. For the potato vegetation coverage estimation models, the model based on the BGCI achieved the highest estimation accuracy (R2 = 0.9116, RMSE = 5.7903), and the RGCI also achieved good accuracy in terms of vegetation coverage estimation (R2 = 0.8987, RMSE = 5.8633). In the generality verification of the models, the R2 values of the FVC estimation models based on the BGCI and RGCI were both greater than 0.94. A potato vegetation coverage estimation model was constructed based on two new vegetation information indices, demonstrating good accuracy and universality. [ABSTRACT FROM AUTHOR]

The formation of congestion on an urban road network is a key issue for the development of sustainable mobility in future smart cities. In this work, we propose a reductionist approach by studying the stationary states of a simple transport model using a random process on a graph, where each node represents a location and the link weights give the transition rates to move from one node to another, representing the mobility demand. Each node has a maximum flow rate and a maximum load capacity, and we assume that the average incoming flow equals the outgoing flow. In the approximation of the single-step process, we are able to analytically characterize the traffic load distribution on the single nodes using a local maximum entropy principle. Our results explain how congested nodes emerge as the total traffic load increases, analogous to a percolation transition where the appearance of a congested node is an independent random event. However, using numerical simulations, we show that in the more realistic case of synchronous dynamics for the nodes, entropic forces introduce correlations among the node states and favor the clustering of empty and congested nodes. Our aim is to highlight the universal properties of congestion formation and, in particular, to understand the role of traffic load fluctuations as a possible precursor of congestion in a transport network. [ABSTRACT FROM AUTHOR]

Equilibrium density fluctuations at the molecular level produce cavities in a liquid and can be analyzed to shed light on the statistics of the number of molecules occupying observation volumes of increasing radius. An information theory approach led to the conclusion that these probabilities should follow a Gaussian distribution. Computer simulations confirmed this prediction across various liquid models if the size of the observation volume is not large. The reversible work required to create a cavity and the chance of finding no molecules in a fixed observation volume are directly correlated. The Gaussian formula for the latter probability is scrutinized to derive the changes in enthalpy and entropy, which arise from the cavity creation. The reversible work of cavity creation has a purely entropic origin as a consequence of the solvent-excluded volume effect produced by the inaccessibility of a region of the configurational space. The consequent structural reorganization leads to a perfect compensation of enthalpy and entropy changes. Such results are coherent with those obtained from Lee in his direct statistical mechanical study. [ABSTRACT FROM AUTHOR]

Robust geometric fitting is one of the crucial and fundamental problems in computer vision and pattern recognition. While random sampling and consensus maximization have been popular strategies for robust fitting, finding a balance between optimization quality and computational efficiency remains a persistent obstacle. In this paper, we adopt an optimization perspective and introduce a novel maximum consensus robust fitting algorithm that incorporates the maximum entropy framework into the consensus maximization problem. Specifically, we incorporate the probability distribution of inliers calculated using maximum entropy with consensus constraints. Furthermore, we introduce an improved relaxed and accelerated alternating direction method of multipliers (R-A-ADMMs) strategy tailored to our framework, facilitating an efficient solution to the optimization problem. Our proposed algorithm demonstrates superior performance compared to state-of-the-art methods on both synthetic and contaminated real datasets, particularly when dealing with contaminated datasets containing a high proportion of outliers. [ABSTRACT FROM AUTHOR]

The high-pressure structures of K-Ag alloys were examples of pressure-induced electron transfer from the electropositive potassium to the electronegative silver. We re-examined the crystal and electronic structures of KAg2, K2Ag, and K3Ag using powder X-ray diffraction and theoretical calculations. Our findings establish a connection between the morphologies of these three phases and the precursor face-centered cubic Ag. For K2Ag, we discovered a disordered structure that better matches the X-ray pattern. Valence electron density distributions obtained from the maximum entropy method, along with charge density calculations, provide a comprehensive understanding of the evolution of chemical bonding in these systems. It was found that K atoms share their valence electrons during alloy formation, contributing to K-Ag and Ag-Ag bonds in K2Ag and KAg2, while no Ag-Ag bonds are present in K3Ag. These results indicate the Zintl-Klemm model may be too simplistic to describe the structure and bonding in high-pressure binary intermetallic compounds. The Zintl-Klemm concept explains the structure and chemical bonding of intermetallic compounds at high pressures — such as high-temperature superconducting metal superhydrides. Here, the authors elucidate the electronic structures of three high-pressure potassium silver alloys, providing an example of where the Zint-Klemm concept needs to be expanded. [ABSTRACT FROM AUTHOR]

The security of vehicle communication becomes increasingly important due to the transmission requirement of large-scale security and privacy data in the Internet of Vehicles (IoV). This paper investigates the physical layer secure transmission with reconfigurable intelligent surface (RIS) assistance in the coexistence of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V). It establishes a communication model for physical layer secure transmission where the eavesdropping user exists. Based on this model, we propose a V2I physical layer secure transmission rate (PLSTR) maximization problem and solve it by employing an alternating optimization algorithm based on generalized Rayleigh entropy and semi-definite relaxation programming so as to obtain optimal V2I base station precoding and RIS reflection coefficient matrix. Simulation results further validate the superiority of the proposed algorithm and analyze the impact of relevant parameters on system performance. [ABSTRACT FROM AUTHOR]

Actinidia latifolia, with the highest vitamin C content in its genus, is a unique wild relative of kiwifruit that could be important for genetic breeding research. Climate change significantly influences the distribution range of wild plants. Accurately assessing the potential distribution of wild kiwifruit and its response to climate change is crucial for the effective protection and sustainable utilization of its germplasm resources. In this study, we utilized the optimized MaxEnt model to predict the potential habitats of A. latifolia in China, employing the jackknife test to assess the importance of environmental variables in our modeling process. The results showed that annual precipitation (Bio12) and temperature annual range (Bio7) emerged as the most influential environmental variables affecting the distribution of this kiwifruit wild relative. As radiative forcing and time increase, the potential habitats of A. latifolia in China are projected to shrink southward, thereby exacerbating habitat fragmentation. This research offers significant scientific references for the investigation, protection, cultivation, and application of wild relatives of the kiwifruit. [ABSTRACT FROM AUTHOR]

The spectral and luminescent properties of the styryl derivative of thioflavin T (ThT) 2-{(1E,3E)-4-[4-(dimethylamino)-2,6-dimethylphenyl]buta-1,3-dien-1-yl}-3-ethyl-1,3-benzothiazolium-3 tosylate (Th-C23) were studied. The fluorescence decay kinetics of Th-C23 incorporated into amyloid fibrils (AF) were not exponential and could be represented as a unimodal distribution of fluorophores over duration α(t). ThT and Th-C23 were shown to form an effective donor–acceptor (D–A) pair when incorporated into the AF structure. The degree of polarization of the acceptor fluorescence was high (~0.4) within the entire spectrum, despite the energy transfer, and was explained by the special orientation of D–A pairs, parallel and antiparallel. The measured fluorescence spectrum of the donor (ThT) and absorption spectrum of the acceptor (Th-C23) were used to calculate the critical energy-transfer radius R0, which could take values from 54 to 68 Å depending on the mutual orientation and quantum yield of the donor and acceptor. [ABSTRACT FROM AUTHOR]

The maximum information entropy production model (MaxEnt), the relative humidity at equilibrium approach (ETRHEQ), and the Surface Flux Equilibrium model (SFE) are three recently developed models to estimate evapotranspiration. Although the connection between ETRHEQ and SFE is evident, no attempts have been made to investigate the congruence, distinctions, or potential complementarity between the two models and MaxEnt. Our mathematical analysis demonstrates that minimizing the vertical variance of RH in ETRHEQ is equivalent to minimizing the dissipation function of energy fluxes in MaxEnt, under the assumption of the same eddy diffusivity of heat and water vapor and with a specific expression for the ratio between the thermal inertia terms for H and LE. The connection between ETRHEQ, SFE, and MaxEnt is independent of Monin‐Obukhov similarity theory (MOST)'s extremum solution, and MOST's extreme solution can be viewed as equivalent to introducing a constant correction factor to account for atmospheric stability. While ETRHEQ and MaxEnt can be united within a single hydrometeorological framework, they diverge in their approaches to modeling evapotranspiration, particularly in how they address the roles of vegetation and land surface heterogeneity. More importantly, the unified framework suggests that turbulence fluxes within the atmospheric boundary layer adhere to the principles of maximum information entropy production. The way in which dissipation, along with its associated entropy production, is established using information entropy theory deviates from traditional thermodynamic entropy formulations. Exploring the connection between thermodynamic and information entropy and developing proper formulations of dissipation for energy fluxes presents an appealing avenue for prospective research. Plain Language Summary: This paper seeks to establish a common theory for explaining the effectiveness of the maximum information entropy production model (MaxEnt), the relative humidity at equilibrium approach (ETRHEQ), and the Surface Flux Equilibrium model (SFE) in estimating evapotranspiration over a wide range of conditions. It uncovers that under reasonable assumptions, ETRHEQ's method in terms of minimizing the vertical variance in relative humidity is equivalent to MaxEnt's approach to minimizing dissipation. The united theory suggests that the movement of air and energy in the lower atmosphere follows the principles of maximum information entropy production, a concept that is a bit different from traditional ideas of entropy in thermodynamics. Looking into how thermodynamic entropy and information entropy are connected and figuring out the right way to calculate dissipation and entropy production for energy flux could lead to new insights in atmospheric science and land‐atmosphere interactions. Key Points: The study presented a pure theorical analysis to unifying MaxEnt, ETRHEQ, and SFE within the same hydrometeorological frameworkMinimizing the vertical variance of RH in ETRHEQ is equivalent to minimizing the dissipation function of energy fluxes in MaxEntThe connection between MaxEnt, ETRHEQ, and SFE is independent of Monin‐Obukhov similarity theory (MOST)'s extremum solution [ABSTRACT FROM AUTHOR]

The coherent recombination of a direct and seabed reflected path is sensitive to the geophysical properties of the seabed. The concept of feature-based inversion is used in the analysis of acoustic data collected on a vertical line array (VLA) on the New England continental shelf break in about 200 m of water. The analysis approach for the measurements is based on a ray approach in which a direct and bottom reflected path is recombined, resulting in constructive and destructive interference of the acoustic amplitudes with frequency. The acoustic features have the form of prominent nulls of the measured received levels as a function of frequency as a broadband (500–4500 Hz) source passes the closest point of approach to the VLA. The viscous grain shearing (VGS) model is employed to parameterize a two-layer seabed model. The most likely seabed is a sand sediment with a porosity of about 0.42. There is a possibility of a thin (less than 0.5 m) surface layer having a slightly higher porosity between 0.45 and 0.50. Using the estimates for the VGS parameters inferred from the short-range frequency features, a normal mode model is used to predict the received acoustic levels over larger range scales. [ABSTRACT FROM AUTHOR]

Modular supramolecular complexes, where different proteins are assembled to gather targeting capability and photofunctional properties within the same structures, are of special interest for bacterial photodynamic inactivation, given their inherent biocompatibility and flexibility. We have recently proposed one such structure, exploiting the tetrameric bacterial protein streptavidin as the main building block, to target S. aureus protein A. To expand the palette of targets, we have linked biotinylated Concanavalin A, a sugar-binding protein, to a methylene blue-labelled streptavidin. By applying a combination of spectroscopy and microscopy, we demonstrate the binding of Concanavalin A to the walls of Gram-positive S. aureus and Gram-negative E. coli. Photoinactivation is observed for both bacterial strains in the low micromolar range, although the moderate affinity for the molecular targets and the low singlet oxygen yields limit the overall efficiency. Finally, we apply a maximum entropy method to the analysis of autocorrelation traces, which proves particularly useful when interpreting signals measured for diffusing systems heterogeneous in size, such as fluorescent species bound to bacteria. [ABSTRACT FROM AUTHOR]

[Display omitted] The adsorption of heavy metal ions such as Pb(II) onto negatively charged minerals such as silica is expected to alter the structure and the interactions at the silica/aqueous interfaces. Besides the solution pH, the inner-sphere sorption of Pb(II) is expected to regulate the surface charge/potential, hypothesized to control the actions of monovalent anions in the aqueous environment. These complex pictures can be probed directly using surface-sensitive sum-frequency generation (SFG) spectroscopy. The pH-dependent water structure within the double layer at silica/aqueous interfaces under the influence of different ions was examined using SFG. The recorded SFG spectra were deconvoluted into the Stern layer (SL) and diffuse layer (DL) using the maximum entropy method in conjunction with the electrical double-layer theory. Standalone monovalent sodium salts do not exhibit ion-specific effects on the silica/aqueous interfaces. However, the mixture of Pb(II) species and each of these salts display profound ion-specific effects on the structure of silica/aqueous interfaces, indicating the role of Pb(II) as an enabler of the ion-specificity of the investigated monovalent anions. The interesting effect arises from a complex interplay between the physical processes (i.e., electrostatic interactions, screening effects, etc.) and chemical processes such as the hydrolysis of Pb(II) ions, ion complexation, protonation and deprotonation of the surface silanol group. [ABSTRACT FROM AUTHOR]

A matter of fact is that extremal principles have been introduced in mechanics in more (Euler, Lagrange) or less (Hamilton) than 200 years ago. One may also observe an impact of thermodynamic extremal principles based on maximum dissipation due to all the entropy production expressed in several disciplines. According fields are theory of communication, statistical mechanics and later physics of earth since already 70 years. The current paper offers some (historical) overview on several applications. "Ziegler's principle" is an implementation of the maximum entropy production going out to the dissipation and yielding a maximum dissipation. The goal of this paper is now the implementation of this extremal principle performed along an algebraic concept. Such a concept can be extended to a system with several internal variables as outlined by Coleman and Gurtin in context with the Gibbs (free) energy. [ABSTRACT FROM AUTHOR]

The axiomatic structure of the κ -statistcal theory is proven. In addition to the first three standard Khinchin–Shannon axioms of continuity, maximality, and expansibility, two further axioms are identified, namely the self-duality axiom and the scaling axiom. It is shown that both the κ -entropy and its special limiting case, the classical Boltzmann–Gibbs–Shannon entropy, follow unambiguously from the above new set of five axioms. It has been emphasized that the statistical theory that can be built from κ -entropy has a validity that goes beyond physics and can be used to treat physical, natural, or artificial complex systems. The physical origin of the self-duality and scaling axioms has been investigated and traced back to the first principles of relativistic physics, i.e., the Galileo relativity principle and the Einstein principle of the constancy of the speed of light. It has been shown that the κ -formalism, which emerges from the κ -entropy, can treat both simple (few-body) and complex (statistical) systems in a unified way. Relativistic statistical mechanics based on κ -entropy is shown that preserves the main features of classical statistical mechanics (kinetic theory, molecular chaos hypothesis, maximum entropy principle, thermodynamic stability, H-theorem, and Lesche stability). The answers that the κ -statistical theory gives to the more-than-a-century-old open problems of relativistic physics, such as how thermodynamic quantities like temperature and entropy vary with the speed of the reference frame, have been emphasized. [ABSTRACT FROM AUTHOR]

In this work, satellite data from the Clouds and Earth's Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are analyzed to determine how the global absorbed sunlight and global entropy production rates have changed from 2002 to 2023. The data is used to test hypotheses derived from the Maximum Power Principle (MPP) and Maximum Entropy Production Principle (MEP) about the evolution of Earth's surface and atmosphere. The results indicate that both the rate of absorbed sunlight and global entropy production have increased over the last 20 years, which is consistent with the predictions of both hypotheses. Given the acceptance of the MPP or MEP, some peripheral extensions and nuances are discussed. [ABSTRACT FROM AUTHOR]

Deformation, as the most intuitive index, can reflect the operation status of hydraulic structures comprehensively, and reasonable analysis of deformation behavior has important guiding significance for structural long-term service. Currently, the health evaluation of dam deformation behavior has attracted widespread attention and extensive research from scholars due to its great importance. However, given that the sluice is a low-head hydraulic structure, the consequences of its failure are easily overlooked without sufficient attention. While the influencing factors of the sluice's deformation are almost identical to those of a concrete dam, nonuniform deformation is the key issue in the sluice's case because of the uneven property of the external load and soil foundation, and referencing the traditional deformation statistical model of a concrete dam cannot directly represent the nonuniform deformation behavior of a sluice. In this paper, we assume that the deformation at various positions of the sluice consist of both overall and individual effects, where overall effect values describe the deformation response trend of the sluice structure under external loads, and individual effect values represent the degree to which the deformation of a single point deviates from the overall deformation. Then, the random coefficient model of panel data is introduced into the analysis of sluice deformation to handle the unobservable overall and individual effects. Furthermore, the maximum entropy principle is applied, both to approximate the probability distribution function of individual effect extreme values and to determine the early warning indicators, completing the assessment and analysis of the nonuniform deformation state. Finally, taking a project as an example, we show that the method proposed can effectively identify the overall deformation trend of the sluice and the deviation degree of each measuring point from the overall deformation, which provides a novel approach for sluice deformation behavior research. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of short pulse time and high instantaneous dose rate of pulsed X-ray, a TLD-based few-channel spectrometer is studied. In this paper, the basic structure and principle of the spectrometer are introduced, the absorption plates and detectors of the spectrometer are selected. The energy response matrix of the few-channel spectrometer for single energy photons of different energies is simulated by Monte Carlo method and its influence on the energy resolution of the spectrometer is analyzed. The maximum entropy method is used to verify the pulsed X-ray spectrum resolution. The results show that the TLD-based few-channel spectrometer can be used to measure short pulse and high dose rate pulse X-ray energy spectrum, and it is also suitable for other high dose rate radiation fields.

We develop a thermodynamic model describing the binding of RNA binding proteins (RBP) to oligomers in vitro. We apply expectation-maximization to infer the specificity of RBPs, represented as position-specific weight matrices (PWMs), by maximizing the likelihood of RNA Bind’n Seq data from the ENCODE project. Analyzing these public data we find sequence motifs that can partly explain the data for more than half of the studied 111 RBPs, and for 48 of the proteins these motifs are consistent with the known specificity. Our code is publicly available, facilitating analysis of RBP binding data.

Full understanding of proteostasis and energy utilization in cells will require knowledge of the fraction of cell proteins being degraded with different half- lives and their rates of synthesis. We therefore developed a method to determine such information that combines mathematical analysis of protein degradation kinetics obtained in pulse--chase experiments with Bayesian data fitting using the maximum entropy principle. This approach will enable rapid analyses of whole- cell protein dynamics in different cell types, physiological states, and neurodegenerative disease. Using it, we obtained surprising insights about protein stabilities in cultured cells normally and upon activation of proteolysis by mTOR inhibition and increasing cAMP or cGMP. It revealed that >90% of protein content in dividing mammalian cell lines is long- lived, with half- lives of 24 to 200 h, and therefore comprises much of the proteins in daughter cells. The well- studied short- lived proteins (half- lives < 10 h) together comprise <2% of cell protein mass, but surprisingly account for 10 to 20% of measurable newly synthesized protein mass. Evolution thus appears to have minimized intracellular proteolysis except to rapidly eliminate misfolded and regulatory proteins. [ABSTRACT FROM AUTHOR]

Humans learn concepts both from labeled supervision and by unsupervised observation of patterns, a process machines are being taught to mimic by training on large annotated datasets--a method quite different from the human pathway, wherein few examples with no supervision suffice to induce an unfamiliar relational concept. We introduce a computational model designed to emulate human inductive reasoning on abstract reasoning tasks, such as those in IQ tests, using a minimax entropy approach. This method combines identifying the most effective constraints on data via minimum entropy with determining the best combination of them via maximum entropy. Our model, which applies this unsupervised technique, induces concepts from just one instance, reaching human-level performance on tasks of Raven's Progressive Matrices (RPM), Machine Number Sense (MNS), and Odd-One-Out (O3). These results demonstrate the potential of minimax entropy learning for enabling machines to learn relational concepts efficiently with minimal input. [ABSTRACT FROM AUTHOR]

The splashback radius, coinciding with the minimum in the dark matter radial density gradient, is thought to be a universal definition of the edge of a dark matter halo. Observational methods to detect it have traced the dark matter using weak gravitational lensing or galaxy number counts. Recent attempts have also claimed the detection of a similar feature in Sunyaev–Zel'dovich (SZ) observations of the hot intracluster gas. Here, we use the FLAMINGO simulations to investigate whether an extremum gradient in a similar position to the splashback radius is predicted to occur in the cluster gas profiles. We find that the minimum in the gradient of the stacked 3D gas density and pressure profiles, and the maximum in the gradient of the entropy profile, broadly align with the splashback feature though there are significant differences. While the dark matter splashback radius varies with specific mass accretion rate, in agreement with previous work, the radial position of the deepest minimum in the log-slope of the gas density is more sensitive to halo mass. In addition, we show that a similar minimum is also present in projected 2D pseudo-observable profiles: emission measure (X-ray), Compton- y (SZ), and surface mass density (weak lensing). We find that the latter traces the dark matter results reasonably well albeit the minimum occurs at a slightly smaller radius. While results for the gas profiles are largely insensitive to accretion rate and various observable proxies for dynamical state, they do depend on the strength of the feedback processes. [ABSTRACT FROM AUTHOR]

Considering that the choice of loss function plays a significant role in the derivation of Bayesian estimators, we propose a novel asymmetric loss function named the weighted Q-symmetric entropy loss for computing the estimates of the parameter and reliability function of the Burr XII distribution. This paper covers the classical maximum-likelihood, uniformly minimum-variance unbiased, and Bayesian estimation methods under the squared error loss, general entropy loss, Q-symmetric entropy loss, and new loss functions. Through Monte Carlo simulation, the respective performances of the considered estimators for the reliability function are evaluated, indicating that the Bayesian estimator under the new loss function is more efficient than those under other loss functions. Finally, a real data set is used to demonstrate the practicality of the presented estimators. [ABSTRACT FROM AUTHOR]

To achieve frequency stability and economic efficiency in isolated microgrids, grid operators face a trade-off between multiple performance indicators. This paper introduces a data-driven adaptive load frequency control (DD-ALFC) approach, where the load frequency controller is modeled as an agent that can balance different objectives autonomously. The paper also proposes a priority replay soft actor critic (PR-SAC) algorithm to implement the DD-ALFC method. The PR-SAC algorithm enhances the policy randomness by using entropy regularization and maximization, and improves the learning adaptability and generalization by using priority experience replay. The proposed DD-ALFC method based on the PR-SAC algorithm can achieve higher adaptability and robustness in complex microgrid environments with multiple performance indicators, and improve both the frequency control and the economic efficiency. The paper validates the effectiveness of the proposed method in the Zhuzhou Island microgrid. [ABSTRACT FROM AUTHOR]

Background: This study investigates the relationship between triglyceride-glucose (TyG) index trajectories and the results of ablation in patients with stage 3D atrial fibrillation (AF). Methods: A retrospective cohort study was carried out on patients who underwent AF Radiofrequency Catheter Ablation (RFCA) at the Cardiology Department of the Fourth Affiliated Hospital of Zhejiang University and Taizhou Hospital of Zhejiang Province from January 2016 to December 2022. The main clinical endpoint was determined as the occurrence of atrial arrhythmia for at least 30 s following a 3-month period after ablation. Using a latent class trajectory model, different trajectory groups were identified based on TyG levels. The relationship between TyG trajectory and the outcome of AF recurrence in patients was assessed through Kaplan-Meier survival curve analysis and multivariable Cox proportional hazards regression model. Results: The study included 997 participants, with an average age of 63.21 ± 9.84 years, of whom 630 were males (63.19%). The mean follow-up period for the participants was 30.43 ± 17.75 months, during which 200 individuals experienced AF recurrence. Utilizing the minimum Bayesian Information Criterion (BIC) and the maximum Entropy principle, TyG levels post-AF RFCA were divided into three groups: Locus 1 low-low group (n = 791), Locus 2 low-high-low group (n = 14), and Locus 3 high-high group (n = 192). Significant differences in survival rates among the different trajectories were observed through the Kaplan-Meier curve (P < 0.001). Multivariate Cox regression analysis showed a significant association between baseline TyG level and AF recurrence outcomes (HR = 1.255, 95% CI: 1.087–1.448). Patients with TyG levels above 9.37 had a higher risk of adverse outcomes compared to those with levels below 8.67 (HR = 2.056, 95% CI: 1.335–3.166). Furthermore, individuals in Locus 3 had a higher incidence of outcomes compared to those in Locus 1 (HR = 1.580, 95% CI: 1.146-2). Conclusion: The TyG trajectories in patients with stage 3D AF are significantly linked to the outcomes of AF recurrence. Continuous monitoring of TyG levels during follow-up may help in identifying patients at high risk of AF recurrence, enabling the early application of effective interventions. [ABSTRACT FROM AUTHOR]

Two-phase flow in channels of small dimensions is often a non-stationary process, the nature of such flow is oscillatory. Due to small channel dimensions, high heat flux, parallel channels interactions, pressure and temperature oscillations, the character of the phenomena occurring during boiling is complex. The changes of the measured signals are observed in different time scales. In order to examine in detail two-phase flow parameters changes, many acquisition devices are often installed. This solution becomes challenging concerning mini and microchannel heat-exchangers due to space limitation and modifications of an experimental setup. This paper presents a novel application of multiscale entropies for spatial and temporal analysis of two-phase flow based on only one registered parameter. This analysis is performed based on pixel brightness changes in photo frames registered by a high speed camera during two-phase flow. The spatial changes of pixel brightness are observed on single frames and temporal changes are examined using a set of frames (in time). The Composite Multiscale Sample Entropy is applied to identify two-phase flow patterns and to analyze the complexity of phase distribution. Using Multivariate Multiscale Sample Entropy the most rapid changes of phase distribution in a multichannel heat exchanger are determined. [ABSTRACT FROM AUTHOR]

In our exploration, we aimed at identifying seismic anomalies using limited ionospheric data for earthquake forecasting and we meticulously compiled datasets under conditions of minimal geomagnetic disturbance. Our systematic evaluation affirmed the ITransformer as a potent tool for the feature extraction of ionospheric data, standing out within the domain of transformer-based time series prediction models. We integrated the maximum entropy principle to fully leverage the available information, while minimizing the influence of presuppositions on our predictions. This led to the creation of the MaxEnt SeismoSense Model, a novel composite model that combines the strengths of the transformer architecture with the maximum entropy principle to improve prediction accuracy. The application of this model demonstrated a proficient capability to detect seismic disturbances in the ionosphere, showcasing an improvement in both recall rate and accuracy to 71% and 69%, respectively, when compared to conventional baseline models. This indicates that the combined use of transformer technology and the maximum entropy principle could allow pre-seismic anomalies in the ionosphere to be sensed more efficiently and could offer a more reliable and precise approach to earthquake prediction. [ABSTRACT FROM AUTHOR]

This paper shows that the empirical distribution of cross-sectional analyst coverage in China's stock markets follows an exponential law in a given month from 2011 to 2020. The findings hold in both the emerging (Shanghai) and the developed market (Hong Kong). Moreover, the unique distribution parameter (i.e., mean) is directly related to the amount of market-wide information. Average analyst coverage exhibits a significant negative predictive power for stock-market uncertainty, highlighting the role of security analysts in diminishing the total uncertainty. The exponential law can be derived from the maximum entropy principle (MEP). When analysts, who are constrained by average ability in generating information (i.e., the first-order moment), strive to maximize the amount of market-wide information, this objective yields the exponential distribution. Contrary to the conventional wisdom that security analysts specialize in the generation of firm-specific information, empirical findings suggest that analysts primarily produce market-wide information for 25 countries. Nevertheless, it remains unclear why cross-sectional analyst coverage reflects market-wide information, this paper provides an entropy-based explanation. [ABSTRACT FROM AUTHOR]

This research focuses on the slopes of an active volcano, seeing its importance in studying disaster mitigation, especially evacuation routes, to minimize fatality during a volcanic eruption. Determining evacuation routes using local wisdom is used as a guide for communities around the mountain slopes to safeguard themselves in the event of a mountain eruption. This research aims to model an eruption disaster evaluation pathway using a local wisdom approach. This research employs a mixed-method approach, combining quantitative and qualitative methods to process and analyze relevant variables. The ultimate output is a map illustrating volcanic disaster evacuation routes. For vulnerability analysis related to volcanic disasters, this research uses quantitative methods with the Maximum Entropy algorithm. The research identifies slope as the environmental variable with the highest gain when used in isolation, suggesting its significant informational value. The modeling used was in the 12th iteration of 25 model test replications, with AUC training data reaching a value of 0.877 and AUC test data of 0.932, indicating highly effective modeling results. In addition, the local wisdom from the community plays a crucial role in recognizing impending eruption signs, with an emphasis on observing natural symptoms in the surrounding environment. The people of Nagari Batu Palano do not have specific social traditions related to volcanic eruptions. Instead, their primary response involves surrendering to God and seeking a safe place if an eruption occurs. In relation to saltwater pools, there is a need for further studies to establish the reliability of the information produced. [ABSTRACT FROM AUTHOR]

Aiming to address the problems of uneven brightness and small defects of low contrast on the surface of lithium-ion battery electrode (LIBE) coatings, this study proposes a defect detection method that combines background reconstruction with an enhanced Canny algorithm. Firstly, we acquire and pre-process the electrode coating image, considering the characteristics of the electrode coating process and defects. Secondly, background reconstruction and the difference method are introduced to achieve the rough localization of coating defects. Furthermore, the image with potential defects undergoes enhancement through improved Gamma correction, and the PSO-OTSU algorithm with adaptive searching is applied to determine the optimal segmentation. Finally, precise defect detection is accomplished using the improved Canny algorithm and morphological processing. The experimental results show that, compared with the maximum entropy method, the region growth method, and the traditional Canny algorithm, the algorithm in this paper has a higher segmentation accuracy for defects. It better retains defect edge features and provides a more accurate detection effect for defects like scratches, dark spots, bright spots, metal leakage, and decarburization, which are difficult to recognize on the background of coating areas of electrodes. The proposed method is suitable for the online real-time defect detection of LIBE coating defects in actual lithium-ion battery industrial production. [ABSTRACT FROM AUTHOR]

The nonequilibrium statistical thermodynamics framework for thermally activated dislocation ensembles introduced in Parts 1 and 2 is applied to interpret and explain the fraction of plastic work done by the external applied stress that is dissipated as heat, the so-called Taylor–Quinney coefficient, for both pure metals and complex alloys. Important roles played by internal stresses and anelastic deformation are discussed for certain alloys, leading to their very low fraction of plastic work converted to heat. Conditions for the formation and development of new configurational subsystems are outlined and discussed, including the key role played by transitional or accommodational subsystems associated with interface regions between adjacent configurational subsystems with mismatch of reaction enthalpy. Size effects are attributed to decreased likelihood of access to energetically favorable accommodational configurations of dislocations in these transitional subsystems as a function of increased surface area-to-volume ratio, rather than the more common attribution to geometrically necessary dislocations or pileups. Generalized normality is discussed in terms of the requirement that post reaction glide should have the same thermodynamic driving force as that driving the dislocation through the saddle point of the reaction pathway. Noteworthy exceptions are considered based on physical mechanisms. It is argued that although the maximal intrinsic entropy production (MEP) rate heuristic cannot be rigorously proven, the Jaynesian approach of maximum information entropy change for each increment of nonequilibrium evolution is physically consistent with that of maximal intrinsic entropy production for a statistically representative set of dislocation barriers and subsystems. This consistency emerges from the increasing number density of reactions as the degree of correlation of enthalpy barriers increases among subsystems (with associated constrained local equilibrium states), which manifests corresponding increase of the configurational entropy change of pending reactions; the latter serves as a proxy for thermal dissipation associated with post reaction saddle point dynamic reconfiguration and extended glide of dislocations. Implications are discussed for both the bottom-up construction of ensemble intrinsic entropy production and the top-down construction which imposes MEP with constraints. Applicability of the Meyer–Neldel Rule for enthalpy–entropy compensation for dislocation reactions is considered at the ensemble level; it is argued to apply only under scenarios with strongly stress-assisted thermally activated reactions and for high degree of correlation of enthalpy barriers among subsystems. Certain contrasts are drawn between the present statistical thermodynamics framework and contemporary theories that emphasize the role of configurational entropy of dislocations. We close with discussion of utility of atomistics and discrete dislocation modeling methods in addressing some key outstanding issues to advance understanding, along with potentially fruitful roles for data science and machine learning. [ABSTRACT FROM AUTHOR]

Background: Anopheles stephensi is native to Southeast Asia and the Arabian Peninsula and has emerged as an effective and invasive malaria vector. Since invasion was reported in Djibouti in 2012, the global invasion range of An. stephensi has been expanding, and its high adaptability to the environment and the ongoing development of drug resistance have created new challenges for malaria control. Climate change is an important factor affecting the distribution and transfer of species, and understanding the distribution of An. stephensi is an important part of malaria control measures, including vector control. Methods: In this study, we collected existing distribution data for An. stephensi, and based on the SSP1-2.6 future climate data, we used the Biomod2 package in R Studio through the use of multiple different model methods such as maximum entropy models (MAXENT) and random forest (RF) in this study to map the predicted global An. stephensi climatically suitable areas. Results: According to the predictions of this study, some areas where there are no current records of An. stephensi, showed significant areas of climatically suitable for An. stephensi. In addition, the global climatically suitability areas for An. stephensi are expanding with global climate change, with some areas changing from unsuitable to suitable, suggesting a greater risk of invasion of An. stephensi in these areas, with the attendant possibility of a resurgence of malaria, as has been the case in Djibouti. Conclusions: This study provides evidence for the possible invasion and expansion of An. stephensi and serves as a reference for the optimization of targeted monitoring and control strategies for this malaria vector in potential invasion risk areas. [ABSTRACT FROM AUTHOR]