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We formulate a thermodynamic theory applicable to both classical and quantum systems. These systems are depicted as thermodynamic system–bath models capable of handling isothermal, isentropic, thermostatic, and entropic processes. Our approach is based on the use of a dimensionless thermodynamic potential expressed as a function of the intensive and extensive thermodynamic variables. Using the principles of dimensionless minimum work and dimensionless maximum entropy derived from quasi-static changes of external perturbations and temperature, we obtain the Massieu–Planck potentials as entropic potentials and the Helmholtz–Gibbs potentials as free energy. These potentials can be interconverted through time-dependent Legendre transformations. Our results are verified numerically for an anharmonic Brownian system described in phase space using the low-temperature quantum Fokker–Planck equations in the quantum case and the Kramers equation in the classical case, both developed for the thermodynamic system–bath model. Thus, we clarify the conditions for thermodynamics to be valid even for small systems described by Hamiltonians and establish a basis for extending thermodynamics to non-equilibrium conditions. [ABSTRACT FROM AUTHOR]

Nonlinear optical methods, such as vibrational sum frequency generation (vSFG) and second harmonic generation (SHG), are powerful techniques to study elusive structures at charged buried interfaces. However, for the separation and determination of the Stern and diffuse layer spectra at these charged interfaces, complex vSFG spectra and, hence, the absolute phase need to be retrieved. The maximum entropy method is a useful tool for the retrieval of complex spectra from the intensity spectra; however, one caveat is that an understanding of the error phase is required. Here, for the first time, we provide a physically motivated understanding of the error phase. Determining the error phase from simulated spectra of oscillators with a spectral overlap, we show that for broadband vSFG spectra, such as for the silica/water interface, the diffuse and Stern layers' spectral overlap within the O–H stretching window results in a correlation between the error phase and the phase shift between the responses of these layers. This correlation makes the error phase sensitive to changes in Debye length from varying the ionic strength among other variations at the interface. Furthermore, the change in the magnitude of the error phase can be related to the absolute SHG phase, permitting the use of an error phase model that can utilize the SHG phase to predict the error phase and, hence, the complex vSFG spectra. Finally, we highlight limitations of this model for vSFG spectra with a poor overlap between the diffuse and Stern layer spectra (silica/HOD in D2O system). [ABSTRACT FROM AUTHOR]

Empirical force fields employed in molecular dynamics simulations of complex systems are often optimized to reproduce experimentally determined structural and thermodynamic properties. In contrast, experimental knowledge about the interconversion rates between metastable states in such systems is hardly ever incorporated in a force field due to a lack of an efficient approach. Here, we introduce such a framework based on the relationship between dynamical observables, such as rate constants, and the underlying molecular model parameters using the statistical mechanics of trajectories. Given a prior ensemble of molecular dynamics trajectories produced with imperfect force field parameters, the approach allows for the optimal adaption of these parameters such that the imposed constraint of equally predicted and experimental rate constant is obeyed. To do so, the method combines the continuum path ensemble maximum caliber approach with path reweighting methods for stochastic dynamics. When multiple solutions are found, the method selects automatically the combination that corresponds to the smallest perturbation of the entire path ensemble, as required by the maximum entropy principle. To show the validity of the approach, we illustrate the method on simple test systems undergoing rare event dynamics. Next to simple 2D potentials, we explore particle models representing molecular isomerization reactions and protein–ligand unbinding. Besides optimal interaction parameters, the methodology gives physical insights into what parts of the model are most sensitive to the kinetics. We discuss the generality and broad implications of the methodology. [ABSTRACT FROM AUTHOR]

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]

We present here a unit-log-symmetric model based on the bivariate log-symmetric distribution discussed in recent literature. It is a flexible family of distributions over the interval (0, 1). We then discuss its mathematical properties such as stochastic representation, identifiability, symmetry, modality, moments, quantile function, entropy and maximum likelihood estimation, paying particular attention to the special cases of unit-log-normal, unit-log-Student-t and unit-log-Laplace distributions. Finally, some empirical results and a real-life data analysis involving internet acess data are presented for illustrative purpose. [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]

The present article proposes a new Legendre wavelet (LW) filter-based image super-resolution technique. The LW basis vector is used in compact form using the unit step function. It is acquired using LW parameters and is discretized via collocation points. The LW basis is used to design a new filter that transforms the low-resolution image into a high-resolution image. This filter matrix is zoomed into a higher dimension using a discretized wavelet basis. The zero-padded low-resolution original image and the zoomed filter matrix are then multiplied by a constant parameter to enhance the information of the reconstructed image. The value of this constant parameter is optimized using the maximization of the entropy of the reconstructed image to further enhance the quality of the image. After the multiplication operation, the zoomed filter matrix and the zero-padded low-resolution original image are added to reconstruct the high-resolution image. Four existing techniques are compared with the proposed technique visually and through spatial frequency, standard deviation, and entropy. The proposed method shows better visual and quantitative feature performance as compared to other schemes. [ABSTRACT FROM AUTHOR]

Yager (2014) proposed a transformation for negating the happening of an uncertain event which opposes the occurrence of any uncertain event by redistributing its probability equally among the other outcomes. Yager's negation applied repeatedly on any probability distribution converges to the uniform distribution (all events having identical probability of occurrence). In most cases, uniform distribution is the maximum entropy probability distribution (MEPD) as it has maximum uncertainty inherent in it. However, if some information is available regarding the occurrence of events associated with a random experiment, then MEPD may or may not be a uniform distribution. In this work, we have first explored some new properties of Yager's negation and then investigated Yager's negation when we have some additional information about the probability distribution other than the natural constraints. The MEPD in the presence of this additional information is determined using the negation of probabilities. It is shown that the existence of MEPD (and as a result the maximum entropy) largely depends on the parameters of the additional constraints. Some numerical examples have been considered for comparing the MEPD with and without additional constraints. [ABSTRACT FROM AUTHOR]

The main aim of this numerical analysis is to demonstrate the effect of Lorentz force and viscous dissipation on the couple stress-squeezed hybrid-nanofluid flow between two parallel plates under the influence of external squeezing. A novel entropy generation effect is also included to describe the temperature distribution in the couple stress hybrid nanofluid regime. However, the couple stress hybrid nanofluid finds its abundant applications in the various fields of medicine and bio-engineering, particularly, in the removal of obstacles in the arteries, cancer treatment, etc. Inspired by these applications of couple stress hybrid nanofluids, the present problem is devised based on the squeezed parallel plate geometry. The unsteady nonlinear, coupled, two-dimensional, partial differential equations are constructed to disclose the flow and heat transport features. A robust Matlab-based Runge–Kutta fourth-order scheme with shooting technique is used to produce the similarity solutions of the governing equations through the deployment of suitable scaling transformations. Accordingly, it is noted that, enhancing Hartmann and Brinkman numbers increase the entropy generation. Increasing couple stress fluid parameter increases the thermal distribution. Velocity profile shows dual behavior for the raising couple stress fluid parameter. Bejan number increases with increasing Brinkman number. Entropy generation increases with enhancing nanofluid volume fraction. Conclusively, the uniqueness and novelty of the present investigation is the addition of magnetic Ohmic heating and viscous dissipation effects which generalizes the former studies and gives a more redefined numerical simulation of flow and heat transport features of couple stress hybrid nanofluid in the squeezing regime. [ABSTRACT FROM AUTHOR]

1-Introduction Soil erosion is one of the most important environmental issues in the world; so that, soils that are affected by degradation processes such as compaction, loss of nutrients and water retention capacity lead to soil erosion and loss in fertile agricultural lands. Gully erosion is the most severe type of water erosion, which is one of the main factors of land degradation. Spatial forecasting of gully erosion using models that are based on land sensitivity to gully erosion and their output leads to the preparation of gully erosion risk maps in the form of gully erosion is the most suitable strategy for land management planning in watersheds to prevent gully erosion. If there is a zoning map of gully erosion in watersheds, economic and social damages could be controlled through appropriate measures and strategies in addition to preventing heavy costs to control gully erosion. According to the available models and relationships, it can be concluded that the most efforts in gully erosion modeling have been focused on the location of gullies as well as predicting the possibility or non-possibility of gully occurrence; So that, only a part of the effective factors have been investigated due to variability of factors affecting the gully. While the present research aims to determine the most important factors affecting the gully erosion and prioritize the factors and finally zoning the gully points and sensitivity to the gully in Maroon watershed. Maroon watershed is located in the southern and southwestern slopes of the Middle Zagros. The area of the watershed is 3837 km2, the maximum height from the sea level is 3420.3 m and the minimum is 372.8 m. The average annual rainfall is 815 mm, the long-term average annual temperature in te representative stations of the plains is and the stations located in the highlands are 19 and 16 ºC respectively. According to the Dumarten system, the climate type is Mediterranean to semi-arid. In summary, the present research was carried out as follows: 1) Selecting the area, preparation of the distribution map of gully occurrence (dependent variable) and its random division into two training or calibration (70 percent) and experimental or forecasting (30 percent) categories, 2) preparation of the maps of 23 effective factors (independent variables), 3) selecting the effective factors using alignment test between the effective factors and the occurrence of gullies, 4) running the model, 5) validation and evaluation of the model, and 6) preparation of a zoning map of susceptibility to gully erosion. In the present study, the location of gullies created in the Takht Daraz watershed was first recorded by field visit, and based on the recorded points, they were divided into two groups of training data (70%) and experimental data (30%) respectively in order to calibrate and validate the models. 23 factors affecting the occurrence of gully erosion were identified based on scientific references and watershed conditions, and their maps were prepared in ARCGIS environment. The weight of these factors was determined based on the frequency ratio, and then the correlation of the effective factors was investigated using the collinearity test. In the next step, the maximum entropy model was calibrated and validated using the weighted data of the effective factors and the spatial training and experimental data of the gully distribution. The Jackknife test and the receiver operating characteristic index were respectively used to determine the thresholds of effective factors in the occurrence of gully erosion as well as evaluating the efficiency of the studied models. 3-Results According to the collinearity analysis, the variance inflation rate of the investigated variables was less than 0.5, which indicates the non-collinearity between the investigated variables and all the variables were included in the modeling process. Based on the results of MaxEnt model (maximum entropy), a gully erosion sensitivity map was obtained, based on which the very high sensitivity class with 52 percent of gully occurrences has the highest density of gullies; So that the ratio of the frequency of gullies in this class was 6.73 and the Seed Cell Index area was 0.15. The very low sensitivity class has the lowest amount and frequency ratio of gully by 0.02 and nuclear cell surface index at the rate of 46.8. According to the information obtained from the map, the highest percentage of the area is related to very low sensitivity class (46%), which has eight pixels of gullies, and the lowest area of the watershed is related to the very high sensitivity class (8%) which has 420 pixels of gully. In general, based on the maximum entropy method, 34% of the watershed is in the medium to very high sensitivity class. Also, the validation results of the maximum entropy model based on the index of the area under the curve (AUC) showed that the area under the curve in this method was 0.85, which indicates the high accuracy of the maximum entropy model. 4-Discussion & Conclusions In the present study, the spatial distribution of gullies and its relationship with the effective environmental parameters of the Takht Daraz basin have been used in order to evaluate the sensitivity of gully erosion. For this purpose, different methods and models including witness weight, belief model and entropy method have been used to prepare a gully erosion sensitivity map. Google Earth image interpretation, past research and extensive field visits were used to prepare the gully distribution map, and 70% of the gullies were used for modeling and 30% for validation. In the field of effective parameters in gully erosion, 23 effective parameters were identified and prepared using research literature, access to data and environmental conditions of the study area. The results showed that the entropy model with the area under the curve (AUC) of 0.86 had high prediction accuracy. The results of the effective factors on gully erosion showed that according to the Jackknife test, the lithological factor had the greatest effect on the areas under the curve. The results of determining the parameters weight also showed that different parameters have different participation rates in the gully erosion occurrence; so that, the parameters of land-use and geology had the largest share of participation in the occurrence of gully erosion, with a contribution of 24% and 18% respectively. [ABSTRACT FROM AUTHOR]

A multimodal distribution based uncertainty analysis method for cross-domain aircraft morphing wing mechanisms is proposed to address the engineering issue of the reliability of morphing mechanisms. This method is based on Gaussian mixture model, isotropic sparse mesh method combined with maximum entropy method analysis. In the working environment of the morphing wings, the external load exhibits a multimodal distribution with changes in flight altitude and geographical location. Traditional uncertainty methods are difficult to accurately determine the reliability of aircraft under the influence of multiple variable influencing factors. Therefore, the proposed method is proposed to evaluate the reliability of morphing wing mechanisms. Firstly, a Gaussian mixture model is used to establish the mixture density function of the pressure and the leading edge size of the variant aircraft. Secondly, the integral points and weights of the multimodal random variables are calculated by the sparse grid method. Finally, an adaptive convergence mechanism is used to improve the uncertainty propagation accuracy. After a mathematical example and two engineering examples, it can be considered that the proposed method has a certain reference value in analyzing the uncertainty propagation under the multimodal distribution state of multiple factors. [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]

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]

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]

Fluorinated gases, e.g., CF3I, C3F8, C4F8, C4F7N, and C5F10O, show potential to replace SF6 in power industry due to their high dielectric strength and low global warming potential. However, particle condensation from arc plasmas of these compounds may reduce dielectric performance. We perform a systematic investigation of particle condensation in two-temperature (2T) arc plasmas of various SF6 replacements mixed with CO2, N2, and O2, by the Gibbs free energy minimization and entropy maximization methods. The influences of buffer gases, non-equilibrium degree, and gas pressure on particle condensation are discussed in various cases. The results indicate that O2 is necessary to prevent graphite formation in carbon–fluorine gaseous arcs, and specific mixing ratios of CO2 and N2 are required to avoid graphite and iodine crystals in CF3I arc plasmas. The relationship between condensation temperature and non-equilibrium degree is complex, with peaks and valleys observed for graphite and iodine crystal condensation temperatures. Moreover, different calculation methods (Gibbs free energy minimization versus entropy maximization) show varying sensitivity of condensation temperatures to pressure changes. All the above findings highlight the importance of considering non-equilibrium effects and multiple condensed species in evaluating arc plasma compositions of SF6 replacements. [ABSTRACT FROM AUTHOR]

A novel structural reliability analysis method that combines the improved beluga whale optimization (IBWO) and the arctangent function‐based maximum entropy method (AMEM) is proposed in this paper. It aims to augment the accuracy of failure probability prediction in structural reliability analysis based on the traditional maximum entropy method (MEM). First, the arctangent function is introduced to avoid the effects of truncation error and numerical overflow in the traditional MEM. The arctangent function can nonlinearly transform the structural performance function defined on the infinite interval into a transformed performance function defined on the bounded interval. Subsequently, the undetermined Lagrange multipliers in the maximum entropy probability density function (MEPDF) of the transformed performance function are obtained using IBWO at a swifter convergence speed with heightened convergence accuracy. Finally, the MEPDF of the transformed performance function can be obtained by combining the IBWO and AMEM, and the structural failure probability can be predicted. The analysis of the metro bogie frame as an engineering example reveals that compared with the traditional MEM using the genetic algorithm to solve the Lagrange multipliers, the proposed method diminishes the relative error in failure probability prediction from 20.51% to only 0.09%. This method significantly enhances the prediction accuracy of failure probability. [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]

This study proposes the foremost application of the entropy concept to investigate the streamwise S-type mean velocity distribution with infection-phenomenon in turbulent open-channel flows and turbulent Couette flows. Probability based fractional entropy is used for this study. Using the concept of entropy maximization the velocity distribution models are derived. Proposed models are fully analytical and applicable to the whole flow depth in both for open-channel flows and the turbulent Couette flows. All the proposed models are validated with different experimental data sets, field data and DNS data available in the literature. The validation results show good performance for both of the velocity distribution models. Apart form validation, the present velocity distribution models are compared with deterministic (non-entropy based) models since there exists no entropy based velocity distributions to describe the infection-phenomenon in turbulent open-channel flows and turbulent Couette flows. To understand the performance of these models, the relative error and the coefficient of determination ( R 2 ) are computed. Results of the error analysis show the good efficiency of the proposed models for several experimental and field data sets. [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]

Aim s : Haemorrhagic fever with renal syndrome (HFRS) is a significant zoonotic disease transmitted by rodents. The distribution of HFRS in the European part of Russia has been studied quite well; however, much less is known about the endemic area in the Russian Far East. The mutual influence of the epidemic situation in the border regions and the possibility of cross‐border transmission of infection remain poorly understood. This study aims to identify the spatiotemporal hot spots of the incidence and the impact of environmental drivers on the HFRS distribution in the Russian Far East. Methods and Results: A two‐scale study design was performed. Kulldorf's spatial scan statistic was used to conduct spatiotemporal analysis at a regional scale from 2000 to 2020. In addition, an ecological niche model based on maximum entropy was applied to analyse the contribution of various factors and identify spatial favourability at the local scale. One spatiotemporal cluster that existed from 2002 to 2011 and located in the border area and one pure temporal cluster from 2004 to 2007 were revealed. The best suitability for orthohantavirus persistence was found along rivers, including those at the Chinese–Russian border, and was mainly explained by land cover, NDVI (as an indicator of vegetation density and greenness) and elevation. Conclusions: Despite the stable incidence in recent years in, targeted prevention strategies are still needed due to the high potential for HRFS distribution in the southeast of the Russian Far East. [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]

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.

A novel method combining the maximum entropy principle, the Bayesian-inference of ensembles approach, and the optimization of empirical forward models is presented. Here, we focus on the Karplus parameters for RNA systems, which relate the dihedral angles of γ, β, and the dihedrals in the sugar ring to the corresponding 3J-coupling signal between coupling protons. Extensive molecular simulations are performed on a set of RNA tetramers and hexamers and combined with available nucleic-magnetic-resonance data. Within the new framework, the sampled structural dynamics can be reweighted to match experimental data while the error arising from inaccuracies in the forward models can be corrected simultaneously and consequently does not leak into the reweighted ensemble. Carefully crafted cross-validation procedure and regularization terms enable obtaining transferable Karplus parameters. Our approach identifies the optimal regularization strength and new sets of Karplus parameters balancing good agreement between simulations and experiments with minimal changes to the original ensemble. [ABSTRACT FROM AUTHOR]

Hydrologic systems are characterized by processes which may occur on, below, and above the land surface. These processes are irreversible, and the irreversibility produces entropy. In thermodynamics, entropy is a measure of the loss of heat energy which is a reflection of disorder. The entropy of a system achieves its maximum at steady state, meaning that the maximum entropy production corresponds to the most probable state. In information theory, entropy is a measure of uncertainty or disorder or information imbued in the random variable describing the hydrologic system. The maximum uncertainty corresponds to maximum entropy or the most probable distribution of the variable which leads to the principle of maximum entropy or the principle of minimum cross entropy, subject to the given constraints. The most probable or maximum entropy-based distribution is confirmed by the theorem of concentration. The form of entropy (Shannon, Tsallis, Renyi, or Kapur), principle of maximum entropy, principle of minimum cross-entropy, and the concentration theorem constitute the theory of entropy. This paper presents a general framework based on the entropy theory, and demonstrates its application for modeling a number of surface and subsurface hydrologic and water quality processes, including hydrometric network evaluation, eco-index, surface runoff, infiltration, soil moisture, velocity distribution, sediment concentration, sediment discharge, sediment yield, channel cross section, rating curve, and debris flow. [ABSTRACT FROM AUTHOR]

Seismic fragility analysis and resilience assessment of large‐span cable‐stayed bridge structures are critical for evaluating their seismic performance. However, there is a scarcity of research on the effects of multi‐support ground motions and their non‐Gaussian characteristics on seismic fragility and resilience. This paper aims to addresses this issue. Initially, random ground motions with spatial variability and non‐Gaussian characteristics are simulated using the Spectral Representation Method (SRM) and the Unified Hermite Polynomial Model (UHPM). Subsequently, the Fractional Exponential Moments‐based Maximum Entropy Method (FEM‐MEM) and the Adaptive Gaussian Mixture Model (AGMM) are employed for seismic reliability‐based fragility analysis, overcoming the shortcomings of conventional lognormal assumption. Component‐ and system‐level fragility analyses are conducted sequentially, followed by seismic resilience assessment of bridge structures based on the results of system‐level fragility analysis. A numerical example is presented to validate the proposed method. Computational results indicate that: (1) The proposed method offers higher accuracy and broader applicability for seismic fragility analysis of large‐span cable‐stayed bridge structures compared to traditional assumptions. (2) The non‐Gaussian characteristics of ground motions may significantly impact the seismic fragility analysis and resilience assessment of large‐span bridge structures. [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]

AbstractThe classification of inter-patient ECG data for arrhythmia detection using electrocardiogram (ECG) signals presents a significant challenge. Despite the recent surge in deep learning approaches, there remains a noticeable gap in the performance of inter-patient ECG classification. In this study, we introduce an innovative approach for ECG classification in arrhythmia detection by employing a 1D convolutional neural network (CNN) to leverage both morphological and temporal characteristics of cardiac cycles. Through the utilization of 1D-CNN layers, we automatically capture the morphological attributes of ECG data, allowing us to represent the shape of the ECG waveform around the R peaks. Additionally, we incorporate four RR interval features to provide temporal context, and we explore the potential application of entropy rate as a feature extraction technique for ECG signal classification. Consequently, the classification layers benefit from the combination of both temporal and learned features, leading to the achievement of the final arrhythmia classification. We validate our approach using the MIT-BIH arrhythmia dataset, employing both intra-patient and inter-patient paradigms for model training and testing. The model’s generalization ability is assessed by evaluating it on the INCART dataset. The model attains average accuracy rates of 99.13% and 99.17% for 2-fold and 5-fold cross-validation, respectively, in intra-patient classification with five classes. In inter-patient classification with three and five classes, the model achieves average accuracies of 98.73% and 97.91%, respectively. For the INCART dataset, the model achieves an average accuracy of 98.20% for three classes. The experimental outcomes demonstrate the superiority of the proposed model compared to state-of-the-art models in recognizing arrhythmias. Thus, the proposed model exhibits enhanced generalization and the potential to serve as an effective solution for recognizing arrhythmias in real-world datasets characterized by class imbalances in practical applications. [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 concept of attention has proven to be very relevant in artificial intelligence. Relative entropy (RE, aka Kullback‐Leibler divergence) plays a central role in communication theory. Here, these concepts, attention, and RE are combined. RE guides optimal encoding of messages in bandwidth‐limited communication as well as optimal message decoding via the maximum entropy principle. In the coding scenario, RE can be derived from four requirements, namely being analytical, local, proper, and calibrated. Weighted RE, used for attention steering in communications, turns out to be improper. To see how proper attention communication can emerge, a scenario of a message sender who wants to ensure that the receiver of the message can perform well‐informed actions is analyzed. In case only the curvature of the utility function maxima are known, it becomes desirable to accurately communicate an attention function, in this case a by this curvature weighted and re‐normalized probability function. Entropic attention communication is here proposed as the desired generalization of entropic communication that permits weighting while being proper, thereby aiding the design of optimal communication protocols in technical applications and helping to understand human communication. It provides the level of cooperation expected under misaligned interests of otherwise honest communication partners. [ABSTRACT FROM AUTHOR]

In this study, we have investigated magnetohydrodynamic Couette flow across a deformable porous regime with entropy generation having equal suction and injection velocities. The aim of this work is to analyze the novel impact of thermophoresis deposition, activation energy, and magnetic effect by considering viscosity and thermal conductivity as dependent on temperature in a deformable porous regime. The dimensional equations are turned into nonlinear ordinary differential equations (ODEs) through proper similarity variables. To solve these ODEs, we utilized the MATLAB bvp4c approach. Graphs are used to study the behavior of many physical parameters such as skin friction, Bejan number, velocity, displacement, entropy generation, concentration, and temperature. It is found that the viscosity parameter reduces the solid displacement, whereas it enhances the fluid concentration. Due to the impact of suction/injection and drag parameters, fluid velocity becomes reduced. The thermal conductivity parameter raises entropy generation and temperature, but it decays the Bejan number. The volume fraction parameter plays an interesting behavior in skin friction. Moreover, the current work is compared with prior research work while neglecting the newly introduced effects, and the results remain consistent. [ABSTRACT FROM AUTHOR]

We consider a population of finite size M, where the current number N of entities, N ∈ { 0 , 1 , 2 , ... , M } , determines its states. We geometrically analyze the amounts of information iN and iM−N, carried by the random variables N and (M−N), respectively, and population entropy S = E (i N) = E (i (M − N)). The population is modeled by a family of Birth-Death Processes of size M+1, indexed by the population utilization parameter ρ (i.e., birth/death ratio), which determines its macrostates. Considering the quantities: x = (N − E (N)) & y = (i N (ρ) − S (ρ)) and M x = ((M − N) − E (M − N)) & M y = (i M − N (ρ) − S (ρ)) as vectors, it is shown that the angles: φ = ∠ (x ; y) and ψ = ∠ ( M x ; M y) are supplementary ones, that is φ (ρ) + ψ (ρ) = π , ρ > 0. Expressions for their inner products < x , y > and < M x , M y > , being the covariances of N&iN(ρ) and (M − N) & i M − N (ρ) , respectively, which sum equals zero, with respect to the parameter ρ are also obtained. Further, what is especially important, it is revealed that φ = ψ = π / 2 , that is both inner products equal zero, at the point ρMmax, which is the value of parameter ρ at which the entropy has maximum value. Finally, for an information linear population only, it is shown that N obeys uniform distribution at the point ρM, max, that is that S (ρ M , max ) = ln ⁡ (M + 1). These results are further illustrated on populations described by truncated geometrical, binomial, and truncated Poisson distribution. [ABSTRACT FROM AUTHOR]

This study's primary objective is to investigate the Jeffery–Hamel model and entropy generation on the Magnetohydrodynamic (MHD) flow of second-grade hybrid nanofluid across stretchable converging and diverging channels. Silver (Ag) and ferroferric oxide (Fe3O4) are nanoparticles, using blood as the base fluid. The controlling nonlinear coupled Partial Differential Equations (PDEs) are transformed into Ordinary Differential Equations (ODEs) with similarity transformations and then solved using the Homotopy Perturbation Method (HPM) and shooting technique (Runge–Kutta fourth order) in the MAPLE software. The Homotopy Perturbation Method (HPM) is compared to the Numerical Method (NM), and the results are more accurate and reliable. The effects of velocity, temperature, entropy production, and the Bejan number on physical parameters like a magnetic field, Reynolds number, magnetic field, porosity, and the Brinkman number are discussed through graphs and tables. The heat transfer and skin friction coefficients are also studied and portrayed as graphs. The velocity profile increases for second-grade hybrid nanofluid across stretchable converging and diverging channels as the magnetic field parameter increase. The velocity profile decreases as Deborah's number increases for the converging channel. As Deborah's number increases, the velocity profile increases for the diverging channels. The magnetic field and volume fraction increase as the skin friction and Nusselt number increase for second-grade hybrid nanofluid across stretchable converging and diverging channels. This theoretical model, which incorporates MHD with blood flow, is essential for biomedical applications, magnetic resonance imaging (MRI), particularly radiofrequency ablation (RFA), tumour treatment, and cancer therapy. [ABSTRACT FROM AUTHOR]

We study the optimal performance of an endoreversible quantum dot heat engine, in which the heat transfer between the system and baths is mediated by qubits, operating under the conditions of a trade-off objective function known as the maximum efficient power function defined by the product of power and efficiency of the engine. First, we numerically study the optimization of the efficient power function for the engine under consideration. Then, we obtain some analytic results by applying a high-temperature limit and compare the performance of the engine at maximum efficient power function to the engine operating in the maximum power regime. We find that the engine operating at maximum efficient power function produces at least 88.89 % of the maximum power output while at the same time reducing the power loss due to entropy production by a considerable amount. We conclude by studying the stochastic simulations of the efficiency of the engine in maximum power and maximum efficient power regime. We find that the engine operating at maximum power is subjected to fewer power fluctuations as compared to the one operating at maximum efficient power function. [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]

This study highlights the significance of entropy generation in the Falkner–Skan flow of Casson fluid past a wedge. To investigate the energy analysis, the governing equations include the heat transport equation in the presence of internal heat source, and the energy transport accounts for heat dissipation using viscous dissipation and Joule heating effect. The mathematical formulation of the problem leads to a set of nonlinear coupled partial differential equations. To obtain a similarity solution, similarity variables are introduced. The resulting differential equations are solved numerically using the shooting technique in conjunction with the Runge–Kutta–Fehlberg 45 (RKF‐45) method. Graphical representations are utilized to demonstrate the physical significance of the relevant parameters. The study analyzes the impact of various parameters on the velocity, temperature, and entropy distributions for three wedge positions: stationary, forward‐moving, and backward‐moving. The results show that an increase in the wedge angle parameter and Casson parameter leads to an increase in fluid velocity, while fluid entropy increases rapidly with an increase in the Brinkmann number, power law Falkner–Skan parameter, and Reynolds number. Moreover, with an increment in the Prandtl and Eckert number, the Nusselt number coefficient decelerates for both static and moving wedge. [ABSTRACT FROM AUTHOR]

In this article, firstly, definitions and examples of soft sets and fuzzy soft sets are given. Then, multi-soft sets and multi-fuzzy soft sets are described and exampl es are examined. The concept of entropy, one of the instruments measuring uncertainty, is given. A new method is proposed by creating an algorithm. The entropies of multi-fuzzy soft sets are found and converted to matrices and some analysis are done by finding the maximums of their sums.Thus, by following the algorithm stages, the most effective parameter value and amount are found, and data that supports the decision process is obtained. Then, the effects of two different drug groups are evaluated by following the algorithm steps. An algorithm to be used in the decision-making process is created and used on the sample. When the analysis and algorithm results are observed, it is understood that it is the closest result to the best choice. As a result, it isdetected that the most accurate selection would be approached by using similarity measure, andentropy algorithm in multi-fuzzy soft sets. [ABSTRACT FROM AUTHOR]

Several different industries and disciplines can benefit from heat transfer in peristaltic flow in curved ducts, including biomedical engineering, industrial processes, microfluidics etc. In this study, the thermal aspects of peristaltic flow are thoroughly examined, with a particular focus on the impact of entropy generation in a symmetric curved duct. Notably, the curvature of the duct introduces additional complexity, enhancing the significance of this research. The resulting system of equations is then analytically solved using homotopy perturbation method (HPM). It is observed that fluid’s temperature is increasing directly with aspect ratio but amplitude ratio and curvature exhibit inverse impact on it. It is also evaluated that the fluid’s peristaltic pressure is enhanced by large curvature. The obtained results offer valuable guidance for designing and optimizing peristaltic systems in a wide range of applications, including biomedical devices, industrial processes, and microfluidic systems. [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]

This paper describes the electronic structure, bonding nature and magnetic properties of Mg0.5Ni0.5−xZnxFe2O4 (x = 0.1, 0.2, 0.3, 0.4) nano-spinel ferrite samples synthesized by the co-precipitation method. Spinel structure with Fd 3 ̄ m space group is confirmed by XRD analysis with trace amounts of hematite. The results of XRD and FTIR confirm the formation of spinel structure. The estimated average crystallite size ranges from 35 to 59 nm by different methods. The FESEM analysis revealed that the samples have a generally porous aspect. Particle size analysis indicates that the average particle size is approximately 150 nm. Covalent bond exists between the tetrahedral A site – oxygen atom (A–O) and ionic nature exists between the octahedral B site – oxygen atom (B–O) in the two sub lattices of the ferrite unit cell, as determined by the maximum entropy method. Mg0.5Ni0.3Zn0.2Fe2O4 demonstrates high A–O covalency and B–O covalency/ionic boundary based on MEM electron density analysis. [ABSTRACT FROM AUTHOR]

Within extended thermodynamics, a mixture of non-reactive Eulerian fluids is described with the multi-velocity and multi-temperature system of Euler-like equations, consisting in conservation laws for mass densities and balance laws for momentum and energy densities of each mixture component. The source terms are of phenomenological nature and appear in the system due to the mutual interaction between species. The aim of this paper is to derive such a macroscopic model starting from the system of Boltzmann-like equations describing a mixture of both monatomic and polyatomic gases. The method relies on an exact maximum entropy principle and evaluation of the Boltzmann multi-species collision operators for the collision kernel used in a recent rigorous mathematical analysis. Based on the obtained highly non-linear momentum and energy production terms, we propose a non-linear closure for the phenomenological model in the case of multi-velocity/single-temperature and single-velocity/multi-temperature. For the multi-velocity and multi-temperature case, we propose a closure via linearization in the neighborhood of the mixture mean velocity and temperature. [ABSTRACT FROM AUTHOR]

Graph theory plays a significant role in the applications of chemistry, pharmacy, communication, maps, and aeronautical fields. A benzenoid is a class of chemical compounds with at least one benzene ring(hexagon as a graph) and resonance bonds in the benzene ring give increased stability in benzenoids. The molecules of chemical compounds are modeled as a graph to study the properties of the compounds. The geometric structure of the compound relates to a few physical properties such as boiling point, enthalpy, π-electron energy, and molecular weight. Entropy is a thermodynamic function in physics that measures the randomness and disorder of molecules in a particular system or process based on the diversity of configurations that molecules might take. Degree-based entropy is used to address a wide range of problems in the domains of mathematics, biology, chemical graph theory, organic and inorganic chemistry, and other disciplines. This paper focusses on computing analytical expressions of degree-based entropy measures for benzenoid systems. [ABSTRACT FROM AUTHOR]

The moment method is commonly used in reliability analysis, in which the maximum entropy method (MEM) and polynomial fitting (PF) have been widely used due to their advantages in accuracy and efficiency, respectively. In this paper, we propose a novel reliability analysis method by combining MEM and PF. The probability density function is preliminarily estimated using the fractional moment maximum entropy method (FM-MEM), based on which PF is then used to further improve the accuracy. The proposed method can avoid the phenomenon of the negative probability density and function oscillations in PF effectively. Moreover, the order of the exponential polynomial in the FM-MEM is adaptively selected in the preliminary solution calculation process. An iterative process for the number of exponential polynomial terms is also proposed, using the integral of the moment error function and the integrals of the local and global negative probability density as the convergence criteria. Four numerical examples and one engineering example are tested, and the results are compared with those of the Monte Carlo simulation and the classical FM-MEM results, respectively, demonstrating the good performance of the proposed method. [ABSTRACT FROM AUTHOR]

We introduce the info‐metrics approach to empirical asset pricing under ambiguity. We apply relative entropy as a pseudo‐metric of model discrepancy, and generalized maximum entropy as a principle of statistical inference, to cross‐sectional asset pricing tests. We show that a single‐factor market representation of the CAPM under ambiguity can explain the cross‐section of U.S. stock returns without the aid of additional risk factors. The additional factors can be interpreted as compensations for idiosyncratic ambiguity. The approach can also recover the market price of ambiguity that sets a lower (entropy‐based) bound on stock prices, which can be understood as investors' "margin of safety" against extreme market events. [ABSTRACT FROM AUTHOR]