Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Application of Modelling Forest Fire in the Environment: A Bayesian Model Averaging Approach

One important environmental concern that has the potential to inflict serious ecological harm and also affect human life is a forest fire. Several factors can lead to forest fires, but uncertainties on the correct model specification can be a serious issue in the model selection processes. The variable selection approach Bayesian Model Averaging (BMA) accommodates for variability in model by aggregating the quantities by their Posterior Model Probabilities (PMP). This study used a model averaging strategy to model the major factors contributing to forest fires using a model prior that is uniform in choice as recommended by most in literature and uniform information parameter prior. The covariates Duff Moisture Code (DMC), and temperature with a Posterior Inclusion Probability (PIP) of 100% and a fairly large coefficient band appear to be the most relevant of the ten predictors investigated. Other relevant covariates in modeling burned area in the forest are y axis geographical coordinate with PIP of 91.8% and wind with PIP of 84.5%. With a Posterior Model Probability, the best model, 53.4%, found involves the y axis spatial coordinate, DMC, temperature and wind. Having a PIP of more than 50%, the four variables are important in modeling forest fires.
Supersymmetric Grand Unification

At an energy scale around 1016 GeV the Standard Model (SM) is assumed to give way to a theory in which all scalar, spinor and boson fields are in one universal gauge group, a so-called Grand Unified Theory (GUT). In this thesis we review the construction of supersymmetric GUTs and check in Mathematica how well its predictions, gauge coupling unification and Yukawa coupling unification, are met.
Toxic Determination of Cry11 Mutated Proteins Obtained Using Rational Design and Its Computational Analysis

Cry11 proteins are toxic to Aedes aegypti, the vector of dengue, chikungunya, and Zika viruses. Cry11Aa and Cry11Bb are protoxins, which when activated present their active-toxin form in two fragments between 30 and 35 kDa respectively. Previous studies conducted with Cry11Aa and Cry11Bb genes using DNA shuffling generated variant 8, which presented a deletion in the first 73 amino acids and one at position 572 and 9 substitutions including L553F and L556W. In this study, variant 8 mutants were constructed using site-directed mutagenesis, resulting in conversion of phenylalanine (F) and tryptophan (W) to leucine (L) at positions 553 and 556, respectively, producing the mutants 8F553L, 8W556L, and 8F553L/8W556L. Additionally, two mutants, A92D and C157R, derived from Cry11Bb were also generated. The proteins were expressed in the non-crystal strain BMB171 of Bacillus thuringiensis and subjected to median-lethal concentration (LC50) tests on first-instar larvae of A. aegypti. LC50 analysis showed that the 8F553L, 8W556L, 8F553L/8W556L, and C157R variants lost their toxic activity (>500 ng·mL−1), whereas the A92D protein presented a loss of toxicity of 11.4 times that of Cry11Bb. Cytotoxicity assays performed using variant 8, 8W556L and the controls Cry11Aa, Cry11Bb, and Cry-negative BMB171 on the colorectal cancer cell line SW480 reported 30–50% of cellular viability except for BMB171. Molecular dynamic simulations performed to identify whether the mutations at positions 553 and 556 were related to the stability and rigidity of the functional tertiary structure (domain III) of the Cry11Aa protein and variant 8 showed the importance of these mutations in specific regions for the toxic activity of Cry11 against A. aegypti. This generates pertinent knowledge for the design of Cry11 proteins and their biotechnological applications in vector-borne disease control and cancer cell lines.
Tyrosine metabolic reprogramming coordinated with the tricarboxylic acid cycle to drive glioma immune evasion by regulating PD‐L1 expression

Abstract Due to the existence of the blood–brain barrier in glioma, traditional drug therapy has a poor therapeutic outcome. Emerging immunotherapy has been shown to have satisfactory therapeutic effects in solid tumors, and it is clinically instructive to explore the possibility of immunotherapy in glioma. We performed a retrospective analysis of RNA‐seq data and clinical information in 1027 glioma patients, utilizing machine learning to explore the relationship between tyrosine metabolizing enzymes and clinical characteristics. In addition, we also assessed the role of tyrosine metabolizing enzymes in the immune microenvironment including immune infiltration and immune evasion. Highly expressed tyrosine metabolizing enzymes 4‐hydroxyphenylpyruvate dioxygenase, homogentisate 1,2‐dioxygenase, and fumarylacetoacetate hydrolase not only promote the malignant phenotype of glioma but are also closely related to poor prognosis. The expression of tyrosine metabolizing enzymes could distinguish the malignancy degree of glioma. More importantly, tyrosine metabolizing enzymes regulate the adaptive immune process in glioma. Mechanistically, multiple metabolic enzymes remodel fumarate metabolism, promote α‐ketoglutarate production, induce programmed death‐ligand 1 expression, and help glioma evade immune surveillance. Our data suggest that the metabolic subclass driven by tyrosine metabolism provides promising targets for the immunotherapy of glioma.
Demand for Weather-Index Insurance among Selected Arable Crop Farmers in Guinea Savannah, Nigeria

This study examines the demand for weather-index insurance among selected arable crop farmers in guinea-savannah, Nigeria. Cross-sectional data were used for this study. A multistage sampling procedure was employed to select 320 respondents (Maize and Sorghum Farmers) for the study. The analytical tools used in the study are Descriptive statistics and a discrete choice analysis (Mixed Logit) Model. The choice experiment revealed a negative coefficient for the Alternative Specific contract (ASC), suggesting that the crop farmers in the study area had a positive attitude toward weather index insurance. The coefficient of premium rate also had a negative value which means farmers prefer low-price insurance contracts to higher-price insurance contracts when other insurance attributes are held constant. On average, farmers were willing to pay about 5.7 percent of their expected harvest for an insurance contract. This revealed that a moderate reduction in the premium paid by farmers could probably increase their choice of Weather Index Insurance (WII) significantly. Therefore, the premium rate should be made flexible and affordable to smallholder farmers.
Study of piezoelectric effect in GaN thin films using a modified Michelson interferometer

A single-beam interferometer has been set up to study the piezoelectric response of III-V semiconductors. Small displacements, induced by a driving voltage at a fixed frequency, can be detected and give a direct measurement of the piezoelectric coefficient. The system calibration is checked using a quartz specimen, and the measured d/sub 11/ value is 2.34 pm/V. Measurements on GaN films grown by chemical vapour deposition are also reported. Errors arising from non-uniform film deposition and from substrate bending are discussed.
Classification of Normal and Abnormal Heart Sounds Using Empirical Mode Decomposition and First Order Statistic

Analysis of heart sound signals for automatic segmentation and classification has revealed in recent decades that it has the potential to detect pathology accurately in clinical applications. Various audio signal processing techniques have been used to reduce the subjectivity of heart sound analysis. This study aims to classify normal and abnormal heart sound signals. The feature extraction process was optimized by EMD and calculated using five first-order statistical parameters: mean, variance, kurtosis, skewness, and entropy. The classification system is optimized with a mutual information algorithm to select traits that can significantly improve system performance. In addition, the selection of the optimal system configuration also includes the k-fold cross-validation and kNN methods with k values ​​and the proper distance type. Based on the test results, the highest accuracy of 98.2% was obtained when the value of k = 1 and the type of cosine distance on kNN with a five-fold cross-validation system evaluation model.
Initial Approach for Construction of a Public Dataset for Emotional Analysis Through Brain-Computer Interfaces and Second Language Platforms

In recent years, the deepening of the role of emotions in education has increased, since it has been shown that emotions influence what we learn and retain, in different areas such as learning a second language. Likewise, the analysis of emotional data in learning environments has been implemented through various methods, such as the collection of vital signs, where brain-computer interfaces provide a means to capture emotional metrics from brain activity. Furthermore, public data sets in this domain are scarce. Consequently, this work presents the initial approach to generate an Emotional Dataset, for emotional analysis based on the collection of emotions, through an Emotiv Insight as a Brain Computer Interface. Also, through the Babbel application for learning second languages. As a result of this study, it will be possible to obtain the general and projected structure for the resulting data set, based on the generated approach.
A LIGHTWEIGHT WHATSAPP END-TO-END ENCRYPTION

Abstract WhatsApp is one of the world’s most widely used messaging applications, with more than 2.44 billion monthly active users worldwide as of 2022. To ensure the security and privacy of its users’ communications, WhatsApp uses end-to-end encryption (E2EE) to protect messages and calls from unauthorized access, including WhatsApp itself. However, the E2EE can be computationally intensive and pose challenges for low-end devices or slow network connections. This paper proposes a lightweight E2EE protocol optimized for mobile devices that reduces energy consumption and computational overhead while maintaining the same security level.
PARP3 Affects Nucleosome Compaction Regulation

Genome compaction is one of the important subject areas for understanding the mechanisms regulating genes’ expression and DNA replication and repair. The basic unit of DNA compaction in the eukaryotic cell is the nucleosome. The main chromatin proteins responsible for DNA compaction have already been identified, but the regulation of chromatin architecture is still extensively studied. Several authors have shown an interaction of ARTD proteins with nucleosomes and proposed that there are changes in the nucleosomes’ structure as a result. In the ARTD family, only PARP1, PARP2, and PARP3 participate in the DNA damage response. Damaged DNA stimulates activation of these PARPs, which use NAD+ as a substrate. DNA repair and chromatin compaction need precise regulation with close coordination between them. In this work, we studied the interactions of these three PARPs with nucleosomes by atomic force microscopy, which is a powerful method allowing for direct measurements of geometric characteristics of single molecules. Using this method, we evaluated perturbations in the structure of single nucleosomes after the binding of a PARP. We demonstrated here that PARP3 significantly alters the geometry of nucleosomes, possibly indicating a new function of PARP3 in chromatin compaction regulation.
Genetic variability, heritability, genetic advance, association between fruit yield and yield components of okra (Abelmoschus esculentus L. Moench) accessions grown in two different years

Genetic improvement of okra for yield is significant because of the nutritional, economic and health benefits inherent in it and to overcome the low genetic potential of the existing varieties and environmental factors which are the major constraints to okra yield. Therefore, this study was carried out to understand the genetic nature of yield, its association with yield components on which selection can be made and to determine the effect of different years on genetic gain. The experiment involved the evaluation of twenty okra accessions in the rainy seasons of year 2019 and 2020 in a randomised complete block design. Data were collected on fourteen characters and analysed. Genotype × Year was highly significant for all the analysed characters. High heritability and high genetic advance as percent of mean were recorded for most of the characters in both years indicating that these traits were less influenced by the environment and presence of additive gene action. Genotypic coefficient of variation, heritability and genetic advance were observed to be higher for some traits in 2019 but lower for those traits in 2020. Pod yield had positive correlation with only plant height at 50% flowering, pod length, number of seeds per pods and peduncle length in both years 2019 and 2020 while the remaining were negatively correlated with pod yield in both years except petiole length and number of pods per plant. Therefore, selection based on these traits could be highly rewarding and may not be considered based on different years.
Rebuilding of New Experimental Tests on a Double Cone at Mach 9

This paper deals with the numerical rebuilding of an experimental test performed at Alta Spa in the frame of the italian project CAST on a double cone geometry, obtained scaling down the one already tested in the LENS facility at Calspan [1]. Also the nozzle flow was simulated, in order to get the correct non-equilibrium freestream conditions for the test article. The results are compared with the experimental data provided by Alta Spa [2]. A sensitivity analysis has been also performed in order to verify the effect of a number of parameters for which some uncertainties are still present.
Using the Y-balance Test as a Predictor Tool for Evaluating Non-contact Injuries in University League Football Players: A Prospective Longitudinal Study

Background Football is a highly competitive sport, and participants can experience various contact and non-contact sports injuries in the sporting process. In any elite sport, screening players using different scientific tools is an important injury prevention strategy. The Y- Balance test (YBT) was found to be a predictive tool for non-contact injury. However, the use of criteria from these tests to predict injuries has not been substantiated and should be further investigated. Purpose The aim of this study was to determine the predictors for injury among athletes using baseline YBT, number of matches, and minutes of physical activity; the cutoff scores for predictors of injury, including baseline YBT, number of matches, and minutes of physical activity; and the clinical prediction rules for predicting injury in this population. Methods A total of 39 young student football players were included in this study. The mean age was 20.28 years, and the mean body mass index (BMI) was 23.83 kg/m2. A baseline assessment of the participant’s characteristics was taken and each participant performed the YBT once before starting the league. After the university league football players had finished their tournament, we asked them questions related to non-contact injuries. Results The results showed that the prevalence of injury was 17.95% among this population. An increase in the YBT score was significantly associated with a decrease in the odds of having an injury [odds ratio (OR) 95% confidence interval (CI): 0.94 (0.88, 0.99), p = 0.047). In addition, the number of matches was significantly associated with an increase in the odds of having an injury p = 0.012. However, the minutes of physical activity were not statistically significant p = 0.065. The highest Youden index was ≤97.89, with a sensitivity of 87.50% and specificity of 71.43%, for the posterior medial reach and ≤92.88, with a sensitivity of 90.62% and specificity of 57.14%, for the posterior lateral reach. The clinical prediction rule was an area under the curve (AUC) of 0.88. Conclusions The results of the study provide evidence for the potential utility of the YBT as a predictor tool for evaluating non-contact injuries in university league football players. By identifying players with lower YBT scores who were at higher risk for injury, targeted interventions could be implemented to address functional movement deficits and potentially reduce injury risk.
3D kinematics of cylindrical nanoparticle manipulation by an atomic force microscope based nanorobot

In recent years, the positioning of cylindrical nanoparticles, like CNTs and nanowires, by means of Atomic Force Microscope (AFM) nanorobots has been investigated widely. Despite this growing scienti c interest, no one has studied 3D model simulations of di erent modes (sliding, rolling and rotation). Previous work has only focused on twodimensional simulations, which often cannot be extended to the manipulation of cylindrical particles. The aim of this study is to present a 3D model for the positioning of cylindrical nanoparticles. In order to validate the results, 2D and 3D simulations have been compared. Moreover, the ndings show that both simulations act similarly with a gradual decrease in the e ects of 3D parameters. We have developed a 3D kinematic model, which makes it possible to predict the positioning process from the moment the tip of the cantilever touches the particle to when the particle reaches the desired point. These simulations also determine the displacements of the particle and cantilever during the time when the particle is stuck into the substrate. In response to real-time monitoring limitations, the introduced kinematic simulation is able to predict the motion behavior of a cylindrical nanoparticle during the manipulation process.
A metasurface-backed planar low-profile dual-band monopole antenna

This paper discusses a low-profile, planar, efficient, and directed CPW-fed monopole antenna with a metasurface (MS) for WLAN as well as modern civil and military applications. A stacked 7 × 7 order metasurface (MS) and CPW-fed slotted patch antenna are sandwiched together to create the metasurface antenna (MSA). The overall size of the proposed prototype is 0. 25λ0 × 0. 25λ0 × 0. 06λ0 only, where λ0 is the free-space wavelength at 2.34 GHz. This antenna has fractional bandwidths of 6.8% and 53.8% at two different frequencies of 2.34 and 8.94 GHz, respectively. With a peak gain of 8.7 dBi and radiation efficiency of 91.1% at 11.51 GHz, the antenna offers directional radiation characteristics in both the E- and H-planes. A comparable circuit model for the proposed prototype has also been described in order to analyze the outcomes of the electromagnetic simulation. The intended prototype has been fabricated, and the experimentally measured results closely resemble the simulated ones.
Sampling based learning

In all of the learning problems, after the parametric modelling we need to devise a way to learn the optimal parameters, using some training samplings, or some indirect rules, with respect to some criterion, e.g. a defined loss-function. Usually this can be cast as maximizing (or minimizing, with an additional negative sign) a function of parameters, training data, and the prior knowledge, commonly known as MAP or maximum a posteriori.
Performance Analysis of Different Hardware Platforms in Wireless Sensor Networks

Wireless Sensor Networks (WSN) encompasses rapid development in research area through large scale applications. The main objective is to compare four different hardware platforms such as Rene, Mica, Spec and Blue in order to find efficient hardware platform that can provide best performance within certain constraints while working on TinyOS as operating system. The four different hardware platforms are compared on the basis of certain constraints such as data communication, rendezvous(mJ/day), alarm checking, sensing, sleep (units in mJ/day), expected lifetime (units in years). According to this investigation, mica outperforms the four different platforms in terms of decoupling between RF and data path speed, bit rate, and timing accuracy. However, future specifications will be found to be more efficient based on CMOS processes and RF radio technology, which results in lower costs, smaller sizes, lower power requirements, and a decrease in complexity.
The IP6K Inhibitor LI-2242 Ameliorates Diet-Induced Obesity, Hyperglycemia, and Hepatic Steatosis in Mice by Improving Cell Metabolism and Insulin Signaling

Obesity and nonalcoholic fatty liver disease (NAFLD) are global health concerns, and thus, drugs for the long-term treatment of these diseases are urgently needed. We previously discovered that the inositol pyrophosphate biosynthetic enzyme IP6K1 is a target in diet-induced obesity (DIO), insulin resistance, and NAFLD. Moreover, high-throughput screening (HTS) assays and structure−activity relationship (SAR) studies identified LI-2242 as a potent IP6K inhibitor compound. Here, we tested the efficacy of LI-2242 in DIO WT C57/BL6J mice. LI-2242 (20 mg/kg/BW daily, i.p.) reduced body weight in DIO mice by specifically reducing the accumulation of body fat. It also improved glycemic parameters and reduced hyperinsulinemia. LI-2242-treated mice displayed reduced the weight of various adipose tissue depots and an increased expression of metabolism- and mitochondrial-energy-oxidation-inducing genes in these tissues. LI-2242 also ameliorated hepatic steatosis by reducing the expression of genes that enhance lipid uptake, lipid stabilization, and lipogenesis. Furthermore, LI-2242 enhances the mitochondrial oxygen consumption rate (OCR) and insulin signaling in adipocytes and hepatocytes in vitro. In conclusion, the pharmacologic inhibition of the inositol pyrophosphate pathway by LI-2242 has therapeutic potential in obesity and NAFLD.
Noise-robust registration of microscopic height data using convolutional neural networks

In this work, a deep convolutional neural network is proposed to improve the registration of microtopographic data. For this purpose, different mechanical surfaces were optically measured using a confocal laser scanning microscope. A wide range of surfaces with different materials, processing methods, and topographic properties, such as isotropy and anisotropy or stochastic and deterministic features, are included. Training and testing datasets with known homographies are generated from these measurements by cropping a fixed and moving image patch from each topography and then randomly perturbing the latter. A pseudo-siamese network architecture based on the VGG Net is then used to predict these homographies. The network is trained with a supervised learning approach where the Euclidean distance between the predicted and the ground truth gives the loss function. The 4-point homography parameterization is used to improve the loss convergence. Furthermore, different amounts of image noise are added to enhance the prediction’s robustness and prevent overfitting. The effectiveness of the proposed method is evaluated through different experiments. First, the network performance is compared to intensity-based and feature-based conventional registration algorithms regarding the resulting error, the noise-robustness, and the processing speed. In addition, images from the Microsoft Common Objects in Context (COCO) dataset are used to verify the network’s generalization capability to new image types and contents. The results show that the learning-based approach offers much higher robustness regarding image noise and a much lower processing time. In contrast, conventional algorithms have a smaller registration error without image noise.