Miracle-dz/UniFAR-FTUs huggingface dataset:
Remote sensing-based evapotranspiration determination: A review of single-source energy balance models

Remote Sensing evapotranspiration models are critical in order to understand the cycling of water in the environment. Initially, an outline of the concepts related to evapotranspiration, as well as the shortcomings of land-based methods, is presented. The aim of the study was based on reviewing remote sensing evapotranspiration models which provide an alternative data source. These models have proved to be a cheaper alternative to mapping and estimating spatiotemporal evapotranspiration measurements across local and regional scales. This paper reviews the single-source energy balance model, which differs from the two-source model, for estimating spatiotemporal measurements of evapotranspiration. The single-source energy balance model is underpinned by mathematical equations which differentiate the various single-source evapotranspiration models (Surface Energy Balance Systems, Simplified Surface Energy Systems, Surface Energy Balance Algorithm, and Mapping Evapotranspiration at high Resolution and with Internalised Calibration). The soil surface and forest canopy components were observed to be the major difference between the single and dual-source models. Further advice was discussed on the implementation of the OpenET tool, which provides an open and accessible satellite-based estimation of evapotranspiration for improved water management.
Genome-Wide Identiﬁcation and Analysis of the R2R3-MYB Gene Family in Theobroma cacao

: The MYB gene family is involved in the regulation of plant growth, development and stress responses. In this paper, to identify Theobroma cacao R2R3-MYB ( TcMYB ) genes involved in environmental stress and phytohormones, we conducted a genome-wide analysis of the R2R3-MYB gene family in Theobroma cacao (cacao). A total of 116 TcMYB genes were identiﬁed, and they were divided into 23 subgroups according to the phylogenetic analysis. Meanwhile, the conserved motifs, gene structures and cis -acting elements of promoters were analyzed. Moreover, these TcMYB genes were distributed on 10 chromosomes. We conducted a synteny analysis to understand the evolution of the cacao R2R3-MYB gene family. A total of 37 gene pairs of TcMYB genes were identiﬁed through tandem or segmental duplication events. Additionally, we also predicted the subcellular localization and physicochemical properties. All the studies showed that TcMYB genes have multiple functions, including responding to environmental stresses. The results provide an understanding of R2R3-MYB in Theobroma cacao and lay the foundation for a further functional analysis of TcMYB genes in the growth of cacao.
Introduction to Statistics: A Calculus-Based Approach

1. Methods for Describing Data. 2. Introduction to Probability. 3. Random Variables, Probability, Functions, And Distribution Functions. 4. Discrete and Continuous Probability and Models. 5. The Distribution of a Function of Random Variables. 6. Sampling Distributions. 7. Estimating Population Parameters. 8. Testing Hypothesis. 9. Tests of Hypothesis II. 10. Other Applications. Appendix. Index.
Metre and Semantics in the Poetry of Czech Post-Symbolists Accessed via LDA Topic Modelling

The article deals with the relationship between semantics and poetic meter in the works of Czech post-symbolist poets and their predecessors. We access the phenomena by means of a machine-driven meter recognition on one hand and LDA topic modelling on the other. We first show how the poetic groups differ in their general preferences for particular topics. Next we analyze the topic distributions in two dominant metres (i.e. iamb and trochee) across the poetic groups.
Server‐aided multiparty private set intersection protocols for lightweight clients and the application in intelligent logistics

In numerous data application scenarios, various data can be represented in the form of data sets, and the intersection is often the common concern of multiple users. Using private set intersection (PSI) protocol, users can securely compute the intersection of their sets without disclosing their private input and other additional information. At the same time, there is also a strong practical demand for statistical analysis of intersection data. However, when multiple parties are involved, the efficiency of the multiparty PSI protocol decreases dramatically as the number of users increases. In this paper, we construct a novel server‐aided multiparty PSI protocol, which can transform the complex multiparty computation problem into an efficient two‐party computation problem. In addition, we design a series of server‐aided party PSI statistical protocols to compute the statistics of the intersection elements, such as the sum, average, variance, range (maximum, minimum), and the cardinality of intersection (the size of intersection). In our protocol, the clients only need to upload their private data to the servers in blinded form and do not need to keep online during server computing. Experiments show that our protocol has high computation and communication efficiency and is suitable for lightweight clients. In addition, we also introduce an application of our protocols in the field of intelligent logistics.
Design and Performance Test of Specific Primers to Detect Bovine DNA Fragments using Multiplex PCR Technique for Halal Authentification

Adulterating meat products with several species, including non-halal species, is often found in commercial products. Therefore, non-halal ingredients are a major source of concern for Muslims. Multiplex polymerase chain reaction (PCR) with multiple primers can detect contamination of meat components from non-halal species in a single reaction process, making it more effective and efficient. Multiplex PCR is a PCR technique that uses multiple primers and DNA samples in one reaction to amplify multiple target regions. In this study, a pair of species-specific primers encoding the Cytochrome c oxidase subunit I (CO1) gene were designed to amplify bovine DNA, tested for specificity, and applied in multiplex PCR technique together with D-loop primers for pigs, Cyt-b for rats, and 12S rRNA for dogs. The CO1 primers, along with D-loop primers for porcine, Cyt-b primers for rats, and 12S rRNA primers for dogs, can be used to detect specific bovine DNA with a size of 279 bp and sequence similarity of 96%.
The Effects of Nano-ZrO₂ on the Mechanical and Electrical Properties of Silicone Rubber and a Corresponding Mechanism Analysis

Improving the performance of silicone rubber (SiR) for cable accessories is the key to the safety and reliability of high-voltage direct-current (HVDC) transmission. In this article, the effects of zirconia nanoparticles (nano-ZrO2) on the electrical and mechanical properties of SiR were investigated. Nano-ZrO2 at different mass fractions was doped into SiR matrices. Then, a series of experiments was carried out to evaluate the performance of the modified SiR in terms of tensile strength, breakdown strength, and space charge characteristics. The results showed that both the mechanical and electrical properties of the modified SiR improved. The tensile strength and the breakdown strength increased significantly with the mass fraction of nano-ZrO2, maximally increasing by 21.3% and 15.4%, respectively. In addition, the incorporation of nano-ZrO2 into SiR inhibited space charge accumulation and accelerated the dissipation of the charges. Nano-ZrO2 also improved the degree of cross-linking of the modified SiR, resulting in a more compact interior structure. The synthetic effect of the improved structure and interfaces facilitated by the nanoparticles may be the origin of the improved properties of the SiR nanocomposites.
k-Means Clusterization and Machine Learning Prediction of European Most Cited Scientific Publications

In this article we investigate the determinants of the European “ Most Cited Publications ”. We use data from the European Innovation Scoreboard-EIS of the European Commission for the period 2010-2019. Data are analyzed with Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, and Pooled OLS. Results show that the level of “ Most Cited Publications ” is positively associated, among others, to “ Innovation Index ” and “ Enterprise Birth ” and negatively associated, among others, to “ Government Procurement of Advanced Technology Products ” and “ Human Resources ”. Furthermore, we perform a cluster analysis with the k-Means algorithm either with the Silhouette Coefficient and the Elbow Method. We find that the Elbow Method shows better results than the Silhouette Coefficient with a number of clusters equal to 3. In adjunct we perform a network analysis with the Manhattan distance, and we find the presence of 4 complex and 2 simplified network structures. Finally, we present a confrontation among 10 machine learning algorithms to predict the level of “ Most Cited Publication ” either with Original Data-OD either with Augmented Data-AD. Results show that the best machine learning algorithm to predict the level of “ Most Cited Publication ” with Original Data-OD is SGD, while Linear Regression is the best machine learning algorithm for the prediction of “ Most Cited Publications ” with Augmented Data-AD.
On Least Squares Estimation when the Dependent Variable is Grouped

This paper examines the problem of estimating the parameters of an underlying linear model using data in which the dependent variable is only observed to fall in a certain interval on a continuous scale, its actual value remaining unobserved. A Least Squares algorithm for attaining the Maximum Likelihood estimator is described, the asymptotic bias of the OLS estimator derived for the normal regressors case and a "moment" estimator presented. A "two-step estimator" based on combining the two approaches is proposed and found to perform well in both an economic illustration and simulation experiments.
Robust subtractive stability measures for fast and exhaustive feature importance ranking and selection in generalised linear models

We introduce the relatively new concept of subtractive lack‐of‐fit measures in the context of robust regression, in particular in generalised linear models. We devise a fast and robust feature selection framework for regression that empirically enjoys better performance than other selection methods while remaining computationally feasible when fully exhaustive methods are not. Our method builds on the concepts of model stability, subtractive lack‐of‐fit measures and repeated model identification. We demonstrate how the multiple implementations add value in a robust regression type context, in particular through utilizing a combination of robust regression coefficient and scale estimates. Through resampling, we construct a robust stability matrix, which contains multiple measures of feature importance for each variable. By constructing this stability matrix and using it to rank features based on importance, we are able to reduce the candidate model space and then perform an exhaustive search on the remaining models. We also introduce two different visualisations to better convey information held within the stability matrix; a subtractive Mosaic Probability Plot and a subtractive Variable Inclusion Plot. We demonstrate how these graphics allow for a better understanding of how variable importance changes under small alterations to the underlying data. Our framework is made available in R through the RobStabR package.
Machine-Learning-Based Compact Modeling for Sub-3-nm-Node Emerging Transistors

In this paper, we present an artificial neural network (ANN)-based compact model to evaluate the characteristics of a nanosheet field-effect transistor (NSFET), which has been highlighted as a next-generation nano-device. To extract data reflecting the accurate physical characteristics of NSFETs, the Sentaurus TCAD (technology computer-aided design) simulator was used. The proposed ANN model accurately and efficiently predicts currents and capacitances of devices using the five proposed key geometric parameters and two voltage biases. A variety of experiments were carried out in order to create a powerful ANN-based compact model using a large amount of data up to the sub-3-nm node. In addition, the activation function, physics-augmented loss function, ANN structure, and preprocessing methods were used for effective and efficient ANN learning. The proposed model was implemented in Verilog-A. Both a global device model and a single-device model were developed, and their accuracy and speed were compared to those of the existing compact model. The proposed ANN-based compact model simulates device characteristics and circuit performances with high accuracy and speed. This is the first time that a machine learning (ML)-based compact model has been demonstrated to be several times faster than the existing compact model.
Burn injury diagnostic imaging device's accuracy improved by outlier detection and removal

Multispectral imaging (MSI) was implemented to develop a burn diagnostic device that will assist burn surgeons in planning and performing burn debridement surgery by classifying burn tissue. In order to build a burn classification model, training data that accurately represents the burn tissue is needed. Acquiring accurate training data is difficult, in part because the labeling of raw MSI data to the appropriate tissue classes is prone to errors. We hypothesized that these difficulties could be surmounted by removing outliers from the training dataset, leading to an improvement in the classification accuracy. A swine burn model was developed to build an initial MSI training database and study an algorithm’s ability to classify clinically important tissues present in a burn injury. Once the ground-truth database was generated from the swine images, we then developed a multi-stage method based on Z-test and univariate analysis to detect and remove outliers from the training dataset. Using 10-fold cross validation, we compared the algorithm’s accuracy when trained with and without the presence of outliers. The outlier detection and removal method reduced the variance of the training data from wavelength space, and test accuracy was improved from 63% to 76%. Establishing this simple method of conditioning for the training data improved the accuracy of the algorithm to match the current standard of care in burn injury assessment. Given that there are few burn surgeons and burn care facilities in the United States, this technology is expected to improve the standard of burn care for burn patients with less access to specialized facilities.
The Potential of Antibody Technology and Silver Nanoparticles for Enhancing Photodynamic Therapy for Melanoma

Melanoma is highly aggressive and is known to be efficient at resisting drug-induced apoptotic signals. Resection is currently the gold standard for melanoma management, but it only offers local control of the early stage of the disease. Metastatic melanoma is prone to recurrence, and has a poor prognosis and treatment response. Thus, the need for advanced theranostic alternatives is evident. Photodynamic therapy has been increasingly studied for melanoma treatment; however, it relies on passive drug accumulation, leading to off-target effects. Nanoparticles enhance drug biodistribution, uptake and intra-tumoural concentration and can be functionalised with monoclonal antibodies that offer selective biorecognition. Antibody–drug conjugates reduce passive drug accumulation and off-target effects. Nonetheless, one limitation of monoclonal antibodies and antibody–drug conjugates is their lack of versatility, given cancer’s heterogeneity. Monoclonal antibodies suffer several additional limitations that make recombinant antibody fragments more desirable. SNAP-tag is a modified version of the human DNA-repair enzyme, O6-alkylguanine-DNA alkyltransferase. It reacts in an autocatalytic and covalent manner with benzylguanine-modified substrates, providing a simple protein labelling system. SNAP-tag can be genetically fused with antibody fragments, creating fusion proteins that can be easily labelled with benzylguanine-modified payloads for site-directed delivery. This review aims to highlight the benefits and limitations of the abovementioned approaches and to outline how their combination could enhance photodynamic therapy for melanoma.
Is Estimation Of Blood Glucose By Glucometer More Reliable Than Laboratory Analysis? A Comparative Study Done At Rural Hospital Of Maharashtra

Abstract Introduction: Diabetes is a major public health problem that is approaching epidemic proportions globally. Self-monitoring of blood glucose (SMBG) by the American Diabetes Association (ADA) is promoted in diabetic patients so that they achieve and maintain specific glycemic goals. Objectives : To compare the blood glucose estimation methods; capillary blood and venous blood by glucometer, venous plasma glucose estimation by auto analyzer and to find variation in results. Material & Methods : 60 patients attending Outpatient department of JIIUs IIMSR and Noor hospital, Warudi, Badnapur, Maharashtra who were advised blood glucose estimation were selected. Finger prick (capillary) blood glucose & glucose estimation of venous blood was done by glucometer; and venous plasma glucose estimation was done by auto analyzer in laboratory. Result & Conclusion : Mean values of capillary glucose estimation by glucometer were higher than plasma glucose estimation in laboratory on auto analyzer and the difference was found to be statistically significant. Venous glucose estimation on glucometer gave high erroneous results as compared to plasma glucose estimation in laboratory.
Dominant Currency and Value-Added Exports

This study investigates the real effects of exchange rate changes on exports with three key features; pricing in a dominant currency like the US dollar, imported intermediary use in production, and exports dependent on major demanded countries. We test the longitudinal effects of exchange rates using a data set of both gross and value-added bilateral exports. The key finding distinguishes the positive panel effect of US dollar depreciation due to the dollar liquidity effect from the negative panel effects of other currencies depreciations due to the intermediary import effect. The two detailed results stand out mostly due to the impacts of intermediate goods imports. First, the panel effect of currency depreciation on value-added exports is smaller than gross exports. Second, the panel effect of depreciation on intermediary goods exports is bigger than final goods exports. Also, the panel effects of income and exports-FDI feedback are significant, enriching for the relationship between trade flows and foreign investment. Received: 29 April 2022 / Accepted: 9 August 2022 / Published: 2 September 2022
Novel Ensemble Model Recommendation Approach for the Detection of Dyslexia

There are a large number of neurological disorders being explored regarding possible management and treatment, with dyslexia being one of the disorders that affect children at the onset of their learning process. Dyslexia is a developmental neurological disorder that prevents children from learning. The disorder has a prevalence of around 10% across the globe, as reported by most of the literature on dyslexia. The early detection and management of dyslexia is one of the primary pursuits among different research. One such domain that leads this pursuit of the early detection and management of dyslexia is artificial intelligence. With so much effort being expended to explore the applicability of artificial intelligence to address the problem of dyslexia detection, in this work, an ensemble model for the early detection of dyslexia is proposed and recommend. The work experimentally considers a pool of ensembles with rigorous validation on a large sized dataset. The final ensemble model recommendation for detection is expressed after evaluating all of the ensemble frameworks based on a number of evaluation parameters. Our experiments reveal that the subspace discriminant ensemble showed superiority for the detection of dyslexia with an accuracy of 90% on five-fold cross validation with the least training time. An accuracy of 90.90% was achieved using boosted trees with a holdout validation of 30%, while with no validation the subspace K-Nearest Neighbor (KNN) outperformed the other ensembles with an accuracy of 99.9%.
Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning

Background: Dental caries is a prevalent, complex, chronic illness that is avoidable. Better dental health outcomes are achieved as a result of accurate and early caries risk prediction in children, which also helps to avoid additional expenses and repercussions. In recent years, artificial intelligence (AI) has been employed in the medical field to aid in the diagnosis and treatment of medical diseases. This technology is a critical tool for the early prediction of the risk of developing caries. Aim: Through the development of computational models and the use of machine learning classification techniques, we investigated the potential for dental caries factors and lifestyle among children under the age of five. Design: A total of 780 parents and their children under the age of five made up the sample. To build a classification model with high accuracy to predict caries risk in 0–5-year-old children, ten different machine learning modelling techniques (DT, XGBoost, KNN, LR, MLP, RF, SVM (linear, rbf, poly, sigmoid)) and two assessment methods (Leave-One-Out and K-fold) were utilised. The best classification model for caries risk prediction was chosen by analysing each classification model’s accuracy, specificity, and sensitivity. Results: Machine learning helped with the creation of computer algorithms that could take a variety of parameters into account, as well as the identification of risk factors for childhood caries. The performance of the classifier is almost unbiased, making it generalizable. Among all applied machine learning algorithms, Multilayer Perceptron and Random Forest had the best accuracy, with 97.4%. Support Vector Machine with RBF Kernel (with an accuracy of 97.4%) was better than Extreme Gradient Boosting (with 94.9% accuracy). Conclusion: The outcomes of this study show the potential of regular screening of children for caries risk by experts and finding the risk scores of dental caries for any individual. Therefore, in order to avoid dental caries, it is possible to concentrate on each individual by utilizing machine learning modelling.