Miracle-dz/UniFAR-FTUs huggingface dataset:
Plant Disease Detection and Classification: A Systematic Literature Review

A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.
Karst grassland forage quality and its determinants in Guizhou Province of Southwest China

Forage quality is a key property of grassland ecosystems. In this study, grassland forage qualities were measured at 373 sampling sites throughout Guizhou Province in the karst mountain region of Southwest China, and the factors affecting it were explored. The forage quality level of most plant species was categorized into four levels: (1) preferred forage species; (2) desirable forage species; (3) consumed but undesirable forage species; and (4) non-consumable or toxic forage species. High temperature and precipitation appeared to facilitate the growth of preferred forage species, but limited the growth of other plants. Increasing soil pH had a positive impact on the number and biomass of preferred forage plants, but a negative influence on other plants, especially non-consumable or toxic plants. Both GDP and population density had a positive correlation with the number and biomass of preferred forage species, while such correlations for other levels of forage species tended to be negative. Grazing could lead to a decrease in the preferred forage species. Therefore, it is suggested that by focusing on soil improvement in grassland and maintaining an appropriate grazing intensity, global warming and rapid economic growth in Guizhou Province will likely contribute to increase the forage quality of karst grasslands in Southwest China.
Exploring Overnutrition, Overweight, and Obesity in the Hospital Setting—A Point Prevalence Study

Malnutrition is an international healthcare concern associated with poor patient outcomes, increased length of stay, and healthcare costs. Although malnutrition includes both under and overnutrition, there is a large body of evidence that describes the impacts of undernutrition with limited data on overnutrition in hospitalized patients. Obesity itself is a modifiable risk factor associated with hospital-associated complications. However, there is limited reporting of the prevalence of obesity in hospitals. This one-day cross-sectional study (n = 513) captures the prevalence of both under and overnutrition in a hospitalized population and explores dietetic care provided compared to the Nutrition Care Process Model for hospitalized patients who have obesity. The main findings were: (1) the largest proportion of patients were in the overweight and obese classifications (57.3%, n = 294/513); 5.3% of these patients had severe obesity (class III); (2) patients who were overweight and obese had lower malnutrition risk profiles as well as the prevalence of malnutrition; (3) 24.1% of patients who had obesity (n = 34/141) were receiving dietetic intervention; (4) 70.6% (n = 24/34) did not have a nutrition diagnosis that followed the Nutrition Care Process Model. Study results provide valuable clinical insight into the prevalence of overnutrition and opportunities to improve nutrition care for this vulnerable patient group.
Enterovirus D68 Outbreak in Children, Finland, August–September 2022

We observed an intense enterovirus D68 outbreak in children in southwest Finland in August–September 2022. We confirmed enterovirus D68 infection in 56 children hospitalized for respiratory illnesses and in 1 child with encephalitis but were not able to test all suspected patients. Continuing surveillance for enterovirus D68 is needed.
ON THE INTEGRABILITY OF A MUTHUSWAMY–CHUA SYSTEM

In this paper we study the integrability of the Muthuswamy–Chua system For α = 0 we characterize all its generalized rational first integrals, which contains the Darboux type first integrals. For α ≠ 0 we show that the system has no Darboux type first integrals.
The Evolution of Cellular Structures via Curvature Flow

This dissertation explores cellular structures that evolve over time, primarily through curvature flow. This models coarsening in isotropic polycrystalline materials, an energy-minimizing process in which small cells gradually disappear and the average cell size increases. Chapter 1 is an informal introduction and is accessible to general readers without a particular mathematical or scientific background. In it we introduce cell structures, curvature flow, steady states, and universal steady states. We use many examples to motivate questions we consider in later chapters. Chapter 2 investigates one-dimensional cell structures that evolve through a variety of evolution equations. We show that many cell structures evolve towards universal steady states that depend only on the evolution equations and that are generally independent of initial conditions. Chapter 3 considers two-dimensional systems that evolve via curvature flow. We describe a simulation method, present analysis of its numerical accuracy, and provide a large set of results from simulations. Chapter 4 investigates three-dimensional systems that evolve via mean curvature flow. We describe a simulation method, present analysis of its numerical accuracy, and provide large amounts of data from simulations. We also introduce methods of characterizing the combinatorial structure of individual cells. Chapter 5 compares and contrasts the curvature-flow evolution of twoand three-dimensional cell structures; we also consider properties of two-dimensional cross-sections of three-dimensional structures which have evolved through curvature flow. We conclude in Chapter 6 with a list of directions for further exploration. Appendix A describes a linear measure called the mean width, which plays a crucial role in describing systems which evolve through mean curvature flow. Appendices B and C describe technical details of the twoand three-dimensional simulation methods.
The Application of Permutation Test in the Hypothesis Test

Permutation test,a statistical inference methodology,was introduced and illustrated in the practical business.It is sample-based distribution-free method,which makes use of "permutation or combination"to get the theoretical sampling distribution of the constructed "statistics",or get the approximate distribution by simulation under the null hypothesis,gets the probability of the sample-at-hand,and draws a conclusion after comparing the probability with the size of the test.It especially supports small samples without distributional assumptions(e.g.normality)and complex designs for which traditional methods are hard to solve.In addition,such questions as the meanings of"permutation",the characteristics of the permutation test.the constructing of the "statistics",the number of the simulation,and the proceedings of the applications were also discussed.
Exact Markov Chain Monte Carlo Algorithms and Their Applications in Probabilistic Data Analysis and Inference

This chapter describes algorithms that use Markov chains for generating exact sample values from complex distributions, and discusses their use in probabilistic data analysis and inference. Its purpose is to disseminate these ideas more widely so that their use will become more widespread, thereby improving Monte Carlo simulation results and stimulating greater research interest in the algorithms themselves. The chapter begins by introducing Markov chain Monte Carlo (MCMC), which stems from the idea that sample values from a desired distribution f can be obtained from the stationary states of an ergodic Markov chain whose stationary distribution is f. To get sample values that have distribution f exactly, it is necessary to detect when the Markov chain has reached its stationary distribution. Under certain conditions, this can be achieved by means of coupled Markov chains—these conditions and the resulting exact MCMC or perfect sampling algorithms and their applications are described.
Impact of Technology Adoption in Forward Logistics: A Quantitative Approach

We are seeing our workplace digitally evolving with more and more processes now being completed using technology; this is all because of Industry 4.0. Industry 4.0 is all about connectivity, and it is an opportunity to revolutionize how the industry responds to societal requirements. The introduction of technology into the industry is evolving towards a sustainable digital supply chain in the face of increasing complexity. The benefits of Industry 4.0 technologies in improving various supply chain performance measures have been well described theoretically and established in the existing literature. However, no integrated model compares available technological options in different supply chain stages and shows the impact in quantitative terms in cost, traceability, flexibility, agility, and other metrics. This study aims to quantify the benefits of implementing these technologies in the supply chain through multi-objective modelling. The objective of the study is to minimize the overall transportation cost function and carbon emissions due to transportation for minimal adverse effects on the environment. We begin by proposing a sustainable logistics model that considers various vehicle types such as autonomous and semi-autonomous vehicles and carbon emissions associated with product transportation. With the robust optimization approach, the methodology seeks to consider technological implementation at every stage of forward supply chain. We deploy our model to a sample toy dataset to analyze the administration of technology. Findings suggest that industry 4.0 technology would be effective for supply chain performance parameters, resulting in sunless carbon discharge with a comparative increase in the operational cost.
Acquired Von Willebrand Factor Deficiency at Patient-Prosthesis Mismatch after AVR Procedure—A Narrative Review

Acquired von Willebrand factor deficiency has been described in patients with aortic valve stenosis due to high shear forces developed during passage through the narrowed valve orifice, which determines structural changes in this molecule. Similar flow conditions are present in patients with an aortic prosthesis that presents a patient-prosthesis mismatch. Patient-prosthesis mismatch is described by the smaller effective orifice area of the prosthesis than the native valve, which would probably determine similar changes in the molecules of the von Willebrand factor, leading to acquiring von Willebrand deficiency.
Continuum perturbation field in quiescent ambience: Common foundation of flows and acoustics

Here, the perturbation equation for a dissipative medium is derived from the first principles for the linearized compressible Navier–Stokes equation without Stokes' hypothesis. Dispersion relations of this generic governing equation are obtained, which exhibits both the dispersive and dissipative nature of perturbations traveling in a dissipative medium, depending upon the length scale. We specifically provide a theoretical cutoff wave number above which the perturbation equation represents diffusive and dissipative nature of the quiescent flow. It is shown that perturbation equations for pressure and velocity retain the same form in one-dimension, but it is not the same for multi-dimensional perturbation fields. Such behavior has not been reported before, as per the knowledge of the authors.
Differentiable-Decision-Tree-Based Neural Turing Machine Model Integrated Into FDTD for Implementing EM Problems

As known to all, problems consume more and more computational resources, for example, memory and time, with the gradual enhancements of both the size of the physical domain and complexity in numerical methods. Computational efficiencies have to be obviously influenced, especially for the finite-difference time-domain (FDTD) solver due to its inflexibility in discretization principle. To further overcome this problem, the neural turing machine (NTM) model, based on the differentiable decision tree (DDT), is chosen and incorporated to improve the efficiencies during the FDTD solver in our work. The DDT-based NTM model has the remarkable advantages of both trees and neural networks, which can better explain for the numerical data. Meanwhile, it has full differentiability through neural networks and can be trained by the powerful deep-learning processes. The proposed DDT-based NTM model could not only enhance both accuracy and efficiency but also successfully integrate into the FDTD solver to implement the microstrip transmission line.
Biased Anisotropic Diffusion--A Unified Regularization and Diffusion

We present and analyze a global edge detection algorithm based on variational regularization. The algorithm can also be viewed as an anisotropic diffusion method. We thereby unify these two, from the original outlook, quite different method. Th is puts anisotropic diffusion, as a method in early vision, on more solid grounds; it is just as well-founded as the well-accepted standard regularization techniques. The unification also brings the anisotropic diffusion method an appealing sense of optimality, thereby intuitively explaining its extraordinary performance. The algorithm to be presented moreover has the following attractive properties. 1. It only requires the solution of a single boundary value problem over the entire image domain--almost always a very simple (rectangular) region. 2. It converges to the solution of interest. The first of these properties implies very significant advantages over other existing regularization methods; the computation cost is typically cut by an order of magnitude or more. The second property represents considerable advantages over the existing diffusion methods; it removes the problem of deciding when to stop, as well as that of actually stopping the diffusion process.
User Dynamics-Aware Edge Caching and Computing for Mobile Virtual Reality

In this article, we present a novel content caching and delivery approach for mobile virtual reality (VR) video streaming. The proposed approach aims to maximize VR video streaming performance, i.e., minimizing video frame missing rate, by proactively caching popular VR video chunks and adaptively scheduling computing resources at an edge server based on user and network dynamics. First, we design a scalable content placement scheme for deciding which video chunks to cache at the edge server based on tradeoffs between computing and caching resource consumption. Second, we propose a machine learning-assisted VR video delivery scheme, which allocates computing resources at the edge server to satisfy video delivery requests from multiple VR headsets. A Whittle index-based method is adopted to reduce the video frame missing rate by identifying network and user dynamics with low signaling overhead. Simulation results demonstrate that the proposed approach can significantly improve VR video streaming performance over conventional caching and computing resource scheduling strategies.
Identification of immune-related genes in acute myocardial infarction based on integrated bioinformatical methods and experimental verification

Background Acute myocardial infarction (AMI) is one of the leading causes of death worldwide. The etiology of AMI is complex and has not been fully defined. In recent years, the role of immune response in the development, progression and prognosis of AMI has received increasing attention. The aim of this study was to identify key genes associated with the immune response in AMI and to analyze their immune infiltration. Methods The study included a total of two GEO databases, containing 83 patients with AMI and 54 healthy individuals. We used the linear model of microarray data (limma) package to find the differentially expressed genes associated with AMI, performing weighted gene co-expression analysis (WGCNA) to further identify the genes associated with inflammatory response to AMI. We found the final hub genes through the protein-protein interaction (PPI) network and least absolute shrinkage and selection operator (LASSO) regression model. To verify the above conclusions, we constructed mice AMI model, extracting myocardial tissue to perform qRT-PCR. Furthermore, the CIBERSORT tool for immune cells infiltration analysis was also carried out. Results A total of 5,425 significant up-regulated and 2,126 down-regulated genes were found in GSE66360 and GSE24519. A total of 116 immune-related genes in close association with AMI were screened by WGCNA analysis. These genes were mostly clustered in the immune response on the basis of GO and KEGG enrichment. With construction of PPI network and LASSO regression analysis, this research found three hub genes (SOCS2, FFAR2, MYO10) among these differentially expressed genes. The immune cell infiltration results revealed that significant differences could be found on T cells CD4 memory activated, Tregs (regulatory T cells), macrophages M2, neutrophils, T cells CD8, T cells CD4 naive, eosinophils between controls and AMI patients.
Neuroimaging Correlates of Treatment Response to Transcranial Magnetic Stimulation in Bipolar Depression: A Systematic Review

Transcranial magnetic stimulation (TMS) has become a promising strategy for bipolar disorder (BD). This study reviews neuroimaging findings, indicating functional, structural, and metabolic brain changes associated with TMS in BD. Web of Science, Embase, Medline, and Google Scholar were searched without any restrictions for studies investigating neuroimaging biomarkers, through structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), and single photon emission computed tomography (SPECT), in association with response to TMS in patients with BD. Eleven studies were included (fMRI = 4, MRI = 1, PET = 3, SPECT = 2, and MRS = 1). Important fMRI predictors of response to repetitive TMS (rTMS) included higher connectivity of emotion regulation and executive control regions. Prominent MRI predictors included lower ventromedial prefrontal cortex connectivity and lower superior frontal and caudal middle frontal volumes. SPECT studies found hypoconnectivity of the uncus/parahippocampal cortex and right thalamus in non-responders. The post-rTMS changes using fMRI mostly showed increased connectivity among the areas neighboring the coil. Increased blood perfusion was reported post-rTMS in PET and SPECT studies. Treatment response comparison between unipolar depression and BD revealed almost equal responses. Neuroimaging evidence suggests various correlates of response to rTMS in BD, which needs to be further replicated in future studies.
TROPICAL AGRICULTURAL SCIENCE

Due to their nutritional value, alternative vegetable materials such as soybean are needed to promote sustainable aquaculture development. However, phytate in soybean could interfere with the protein digestion of the fish body. This study examines the effectiveness of phytate enzymes in enhancing protein digestibility, growth performance, mineral digestion, and body chemical composition of Sangkuriang catfish ( Clarias gariepinus var. Sangkuriang) juveniles. The study was completely randomized design with 4 treatments and 3 repetitions. One hundred thirteen Sangkuriang catfish juveniles (7.65 ± 0.14 g) were used for each repetition. The fish were fed with an experimental diet supplemented with various doses of phytase enzyme: 0 (A), 500 (B), 1,000 (C), and 1,500 (D) FTU/kg of feed. The mineral digestibility, protein digestibility, protein efficiency ratio (PER), feed conversion ratio (FCR), the efficiency of feed utilization (EFU), apparent digestibility coefficients (ADCp), relative growth rate (RGR), and survival rate (SR) were observed. Data were analyzed using analysis of variance followed by Duncan’s multiple range test. The results showed that adding 1,000 FTU/ kg of phytase enzyme (C) exhibited the highest PER, FCR, EFU, ADCp, and RGR
Water Quality Index Estimations Using Machine Learning Algorithms: A Case Study of Yazd-Ardakan Plain, Iran

Excessive population growth and high water demands have significantly increased water extractions from deep and semi-deep wells in the arid regions of Iran. This has negatively affected water quality in different areas. The Water Quality Index (WQI) is a suitable tool to assess such impacts. This study used WQI and the fuzzy hierarchical analysis process of the water quality index (FAHP-WQI) to investigate the water quality status of 96 deep agricultural wells in the Yazd-Ardakan Plain, Iran. Calculating the WQI is time-consuming, but estimating WQI is inevitable for water resources management. For this purpose, three Machine Learning (ML) algorithms, namely, Gene Expression Programming (GEP), M5P Model tree, and Multivariate Adaptive Regression Splines (MARS), were employed to predict WQI. Using Wilcox and Schoeller charts, water quality was also investigated for agricultural and drinking purposes. The results demonstrated that 75% and 33% of the study area have good quality, based on the WQI and FAHP-WQI methods, respectively. According to the results of the Wilcox chart, around 37.25% of the wells are in the C3S2 and C3S1 classes, which indicate poor water quality. Schoeller’s diagram placed the drinking water quality of the Yazd-Ardakan plain in acceptable, inadequate, and inappropriate categories. Afterwards, WQI, predicted by means of ML models, were compared on several statistical criteria. Finally, the comparative analysis revealed that MARS is slightly more accurate than the M5P model for estimating WQI.
Anisotropic Correlation Functions for Objective Analysis

Abstract Covariance models used in the data assimilation step of operational forecasting generally assume isotropy of height field correlations on constant pressure levels. Because of the evidence that this assumption is a significant source of forecast error, especially In regions of low density data, a two-dimensional anisotropic correlation model has been derived. Using a simple autoregressive scheme, cumbersome extension of the modeling problem has been avoided and much of the direction-dependent variability of observed statistics is resolved. Compared to deviations of observed correlation values around the best fitting isotropic model, the residual variance has been reduced by 56&%.