Miracle-dz/UniFAR-FTUs huggingface dataset:
Satellite Image Classing Using ELBP & SVM Classifier

-Machine learning-based Support vector machine (SVM) and Extended Local Binary Patterns (ELBP) algorithms were used to classify satellite images into 24 different categories. In addition to satellite photos, this study can classify 24 other classifications. However, identifying the traits of those other classes, such as the human face, football, and rugby, is similarly simple because these other classes contain some distinguishing characteristics that allow for easy classification. The underlying difficulty with satellite photographs is that multiple satellite images may have different properties, making classification difficult. Another issue is that most satellite images are distorted by noise. The noise patterns in the wireless image are calculated using the SVM Classifier, and the predicted noise patterns are subsequently eliminated using the SVM signal classification algorithm. This study finds local binary patterns using the proposed ELBP method. The Extended LBP is required since the patterns of distinct satellite photographs and other class images cannot be determined using only LBP. Based on the extended data obtained, SVM determines the test image's class. In this investigation, the ELBP-SVM technique was used, and the satellite picture correct recognition rate was 95%. The results obtained using MATLAB 2020a are superior to those obtained with other satellite photo classification algorithms.
Becker Digital

Background: Despite a growing body of research from the United States and other industrialized countries on the inverse association between supportive social relationships in the school and youth risk behavior engagement, research on the measurement of supportive school social relationships in Central America is limited. We examined the psychometric properties of the Student Perceptions of School Cohesion (SPSC) scale, a 10-item scale that asks students to rate with a 5-point Likert-type response scale their perceptions of the school social environment, in a sample of public secondary school students (mean age = 15 years) living in central El Salvador. Methods: Students (n = 982) completed a self-administered questionnaire that included the SPSC scale along with measures of youth health risk behaviors based on the Center for Disease Control and Prevention's Youth Risk Behavior Survey. Exploratory factor analysis was used to assess the factor structure of the scale, and two internal consistency estimates of reliability were computed. Construct validity was assessed by examining whether students who reported low school cohesion were significantly more likely to report physical fighting and illicit drug use. Results: Results indicated that the SPSC scale has three latent factors, which explained 61.6% of the variance: supportive school relationships, student-school connectedness, and student-teacher connectedness . The full scale and three subscales had good internal consistency (r s = .87 and α = .84 for the full scale; r s and α between .71 and .75 for the three subscales). Significant associations were found between the full scale and all three subscales with physical fighting (p ≤ .001) and illicit drug use (p < .05). Conclusion: Findings provide evidence of reliability and validity of the SPSC for the measurement of supportive school relationships in Latino adolescents living in El Salvador. These findings provide a foundation for further research on school cohesion and health risk behavior in Latino adolescents living in the U.S. and other Latin American countries.
An Intracranial Tumor Detection using Magnetic Resonance Imaging and Deep Learning

An Intracranial tumor is a malignant CNS cancer, and early prediction can boost patient survival rates. Magnetic Resonance Imagining (MRI) has emerged as an effective non-invasive way to extract 2D or 3D images of human internal organs, eliminating any pain or surgical procedures. Distinguishing normal from abnormal tissue imagery is a challenging task. Yet data-driven models can efficiently classify and detect tumor malignancy. The aim of this study is to efficiently predict Brain tumors by employing sophisticated optimized Deep learning models like CNN and LSTM through limited MRI images dataset of human brain. 253 MRI images dataset acquired from Kaggle covering different angles of the human brain was used. Images of varied sizes and shapes were standardized through data preprocessing, involving resizing and normalizing. One Hot Encoding was used to convert the images to a binary format (0,1) for improved categorization. A 90:10 training-to-testing data ratio was employed. Optimal hyperparameters for the CNN and LSTM models were determined through a trial-and-error approach to enhance model performance on the training data. Model Evaluation by confusion matrix revealed average accuracy, specificity, recall, and misclassification error of 91.51, 92.85, 90.12, and 8.49 for CNN, and 95.54, 92.86, 94.2, and 5.805 for LSTM model.
Jefferson Digital

Transfer RNAs (tRNAs) are fundamental adapter components of translational machinery. tRNAs can further serve as a source of tRNA-derived noncoding RNAs that play important roles in various biological processes beyond translation. Among all species of tRNAs, tRNA HisGUG has been known to uniquely contain an additional guanosine residue at the − 1 position (G − 1 ) of its 5 ′ - end. To analyze this − 1 nucleotide in detail, we developed a TaqMan qRT-PCR method that can distinctively quantify human mature cytoplasmic tRNA HisGUG containing G − 1 , U − 1 , A − 1 , or C − 1 or lacking the − 1 nucleotide (starting from G 1 ). Application of this method to the mature tRNA fraction of BT-474 breast cancer cells revealed the presence of tRNA HisGUG containing U − 1 as well as the one containing G − 1 . Moreover, tRNA lacking the − 1 nucleotide was also detected, thus indicating the heterogeneous expression of 5 ′ -tRNA HisGUG variants. A sequence library of sex hormone-induced 5 ′ -tRNA halves (5 ′ -SHOT- RNAs), identified via cP-RNA-seq of a BT-474 small RNA fraction, also demonstrated the expression of 5 ′ -tRNA HisGUG halves containing G − 1 , U − 1 , or G 1 as 5 ′ -terminal nucleotides. Although the detected 5 ′ -nucleotide species were identical, the relative abundances differed widely between mature tRNA and 5 ′ -half from the same BT-474 cells. The majority of mature tRNAs contained the − 1 nucleotide, whereas the majority of 5 ′ -halves lacked this nucleotide, which was biochemically confirmed using a primer extension assay. These results reveal the novel identities of tRNA HisGUG molecules and provide insights into tRNA HisGUG maturation and the regulation of tRNA half production.
The Inﬂuence of Intergenerational Support from Children on Life Satisfaction of the Elderly: The Mediating Effect of Self-efﬁcacy

This paper is intended to investigate the inﬂuence of intergenerational support from children on the life satisfaction of the elderly and explore the mediating effect of self-efﬁcacy. A total of randomly-selected 615 elderly people from Zhejiang and Jiangsu Provinces were measured by a general information questionnaire, life satisfaction scale, children’s intergenerational support scale, and self-efﬁcacy scale. With statistical regression analysis and mediating effect test respectively conducted through SPSS21.0 and Process macro programs, signiﬁ-cantdifferences( P < 0.05)havebeenfoundinage,spouse,householdregistration, and pension concerning the life satisfaction score of the elderly. Intergenerational support from children is positively correlated with life satisfaction ( r = 0.27, P < 0.01) and self-efﬁcacy ( r = 0.13, P < 0.01), and life satisfaction shares a positive correlation with self-efﬁcacy ( r = 0.39, P < 0.01). The results of regression analysis showed that self-efﬁcacy had a signiﬁcant partial mediating effect on the inﬂuenceofintergenerationalsupportfromchildrenonlifesatisfaction. Bothinter-generationalsupportsfromchildrenandself-efﬁcacyconstituteimportantfactors affecting the life satisfaction of the elderly, and self-efﬁcacy can partially mediate the inﬂuence of intergenerational support from children on the life satisfaction of the elderly.
Deepgrid: Empowering Smart Grids with Autonomous Decision-Making for Optimal Utilization of Renewable Energy Resources Through Advanced Deep Learning Systems

: The increasing integration of renewable energy sources in smart grids necessitates intelligent decision-making systems for optimal utilization. This paper presents an autonomous decision-making system leveraging deep learning (DL)techniques to enhance the efficiency of renewable energy resource utilization in smart grids. The proposed DeepGrid system employs advanced neural network models to analyze real-time data, predict energy production patterns, and dynamically optimize grid operations by optimized DL. By autonomously adapting to changing environmental conditions and energy demand, DeepGrid ensures a reliable and sustainable power supply. The model's DL architecture enables it to learn complex relationships within the data, facilitating accurate decision-making for grid management. Through simulation studies, we demonstrate the efficacy of DeepGrid in improving grid stability, minimizing reliance on non-renewable sources, and ultimately contributing to a more sustainable and resilient energy infrastructure.
The Effect of WhatsApp Application on Saudi Electronic University Students' and the Role of Student Engagement on Academic Performance

: Digital applications have enhanced the educational experience and students performing. This paper discussed the effect of WhatsApp application on students engagement and its role on academic performance. The research addressed five hypotheses to determine the role of WhatsApp usage on student engagement. The paper also considered some influential factors that are affected using WhatsApp, including major. The intended research method the study would take was the quantitative approach using the survey to collect the data from the students. It aimed to understand the effect of major on the use of WhatsApp on student engagement and then students’ academic performance. The findings of this study would specifically participate in enhancing students engagement and their educational experiences. It would also provide educators with the importance use of technology to implement it in their teaching styles and learning process.
A casting procedure for reproducing coarse‐grained sedimentary surfaces

In the ﬁeld, the measurement of near‐bed hydraulics remains problematic. Greater precision is possible in the laboratory, but, in the case of gravels, it is difﬁcult to create a water‐worked channel‐bed that is realistic enough to replicate faithfully the conditions found in nature. In this paper, a technique to reproduce coarse‐grained sedimentary fabrics of large areal extent is described. It involves moulding natural river‐bed surfaces from which facsimiles are cast. Remarkably realistic casts with dimensions of 1 m by 2 m have been produced and their quality assessed using spatial data derived using automated digital photogrammetry. The casts reproduce the prototype surfaces with errors at millimetre scale (0·5 per cent of the microrelief). The technique has facilitated the introduction of sedimentary surfaces that incorporate natural, complex structures of grains up to cobble size into experimental channels where detailed studies of near‐bed hydraulics can be carried out. Copyright © 2003 John Wiley & Sons, Ltd.
Population cycles in rock ptarmigan Lagopus muta: modelling and parameter estimation

We have modelled population change of rock ptarmigan using data from spring censuses and age ratios (spring and late summer) from a study area in north-east Iceland 1981 – 2004. Modelling of mortality rates has shown a significant time trend (increasing) for mortality of adult birds (Z 2), but not for excess autumn and winter mortality of juvenile birds (Z X,W ). The juvenile mortality rate Z X,W has, however, changed in a cyclic way peaking 2 – 4 years after the peak in ptarmigan numbers. The model fits the abundance data well and projections give rise to cyclic behaviour with an 11 – 12 year period provided the adult mortality rate is not too high. Higher adults mortality values result in damped oscillations. Sensitivity test, excluding two outliers, improved the model fit. Two delayed density dependent factors emerge from the model to explain the cyclic behaviour in the ptarmigan population: (a) Excess juvenile autumn and winter mortality rates, lagging ptarmigan population by 2 – 4 years. (b) Negative impact of population size on chick production, lag 2 years. Gyrfalcon, being a resident specialized predator, fits predictions for the ptarmigan cycle to be generated by predation as the falcons show a phase shift longer than one-quarter of the ptarmigan cycle, also there is significant relation between excess juvenile autumn and winter mortality rates and falcon numbers. Our thesis is that the primary mechanism for the ptarmigan cycle is winter predation by gyrfalcons on first-year birds giving rise to the delayed density response (a). In addition the second delayed density response (b), the late summer gyrfalcon predation on hens and subsequent total loss of their broods, is implicitly accounted for by the model.
GSK-3 post-transcriptionally regulates TNF-α biosynthesis in THP-1 macrophages

Few things are more fascinating than finding new interactions between previously unrelated pathways. Glycogen synthase kinase-3 (GSK-3), a ubiquitous kinase initially known for its role in regulating glycogen metabolism, has recently been found to be an indispensable regulator of the TLR4-mediated inflammatory response. GSK-3 inhibition exhibits potent anti-inflammatory effects by acting on both arms of the inflammatory response, reducing the secretion of pro-inflammatory cytokines, and promoting the production of anti-inflammatory cytokines. Tumor Necrosis Factor-α (TNF-α) is among the most important inflammatory cytokines. Aberrant TNF-α expression is associated with various inflammatory conditions, including sepsis and cancer. Thus, understanding the mechanisms regulating TNF-α production could reveal potential therapeutic strategies for TNF-α-associated diseases. Consequently, this study aimed to examine the effect of GSK-3 inhibition on TLR4-induced TNF-a production by THP-1 macrophages. THP-1 macrophages were stimulated with LPS and nigericin in the presence and absence of GSK-3 inhibitor, and TNF-α protein and mRNA levels were evaluated by ELISA and Real-time PCR, respectively. GSK-3 inhibition significantly attenuated TNF-α protein levels in a dose-dependent manner, whereas TNF-α mRNA levels remained unaffected, reflecting a possible post-transcriptional modulation of TNF-α biosynthesis by GSK-3. However, more comprehensive research is needed to elucidate the precise contribution of GSK-3 to TNF-α biosynthesis and to identify novel therapeutic mechanisms to alleviate inflammatory diseases associated with abnormal TNF-α production.
Nonsymmetrical Benzene − Pyridine-Based Nickel Pincer Complexes Featuring Borohydride, Formate, Ethyl, and Nitrosyl Ligands

: Nonsymmetrical nickel PCN pincer complexes [Ni( i Pr PCN X )Cl] (X = NMe, CH 2 ) are obtained by metalation of the benzene − pyridine-based pincer ligand i Pr PCN X (X = NMe, CH 2 ) with [Ni(dme)Cl 2 ] (dme = 1,2-dimethoxyethane). These nickel species a ﬀ orded the respective borohydride and ethyl complexes [Ni( i Pr PCN X )- L] (L = BH 4 , Et) upon treatment with NaBH 4 and EtMgBr (or Et 2 Mg), respectively. Reacting [Ni( i Pr PCN X )( κ 2 -BH 4 )] with CO 2 gave the formate complexes [Ni-( i Pr PCN X )(OCHO)]. Treatment of [Ni( i Pr PCN X )Cl] with the nitrosyl salt NOBF 4 led to the formation of the unusual cationic square-pyramidal nickel nitrosyl pincer complexes [Ni( i Pr PCN X )(NO)Cl] + featuring a strongly bent NO ligand ( ∠ Ni − N − O 128.5 ° ). These systems can be described as {NiNO} 10 according to the Enemark − Feltham convention. Despite of the bent nature of the NO ligand the ν NO is observed at 1849 cm − 1 which is more typical for a linear Ni − N − O arrangement. The structure of these complexes is rationalized by means of DFT calculations. The molecular structures of representative complexes are presented.
Big Data Analysis for Unstructured Data in Nigeria Court System

: Big Data Analysis for unstructured data involves analyzing and processing large amounts of unstructured data, such as text, images, and audio, to extract meaningful insights and knowledge. Techniques used in big data analysis for unstructured data include Natural Language Processing (NLP), Computer Vision, and Speech Recognition. Big data analysis can be useful in the Nigerian court system for analyzing unstructured data, such as legal documents, witness statements, and court transcripts. The goal would be to identify patterns and relationships within the data that can help make more informed decisions, improve processes, and increase the efficiency of the court system. This paper presents an improved Hybrid model for legal case document classification. The system starts by collecting legal case documents from an online domain. The collected documents are in pdf format. The collected pdf files were converted to texts using a pdf miner library in python. The converted texts were used in creating tables using the pandas library. After the creation of the dataset table, the dataset was pre-processed by removing Nan values, and non-alphanumeric values, and also performing tokenization. The tokenized data was then passed into principal component analysis for the selection of important features. The selected features were then used in training an LSTM model for the classification of the legal case documents. The result of the LSTM is outstanding, having an accuracy of 98% for training. The model was deployed to the web, for easy execution, testing, and assessment.
Learning Optimizers for Local SGD

Communication-efficient variants of SGD, specifically local SGD, have received a great deal of interest in recent years. These approaches compute multiple gradient steps locally, that is on each worker, before averaging model parameters, helping relieve the critical communication bottleneck in distributed deep learning training. Although many variants of these approaches have been proposed, they can sometimes lag behind state-of-the-art optimizers for deep learning. In this work, we incorporate local optimizers that compute multiple updates into a learned optimization framework, allowing to meta-learn potentially more efficient local SGD algorithms. Our results demonstrate that local learned optimizers can substantially outperform local SGD and its sophisticated variants while maintaining their communication efficiency. We show that the learned optimizers can generalize to new datasets and architectures, demonstrating the potential of learned optimizers for improving communication-efficient distributed learning.
Remote Sensing Image Scene Classification Based on Multidimensional Attention and Feature Enhancement

—Remote sensing image scene classification is a challenging task that involves automatically assigning labels to remote sensing images based on predefined categories. The inherent intra-class diversity and inter-class similarity of remote sensing images make it difficult for classification models to capture the discriminative key information necessary for accurate labeling, resulting in classification confusion. This paper proposes a novel method called Multidimensional Attention and Feature Enhancement (MA-FE) to address this issue. The proposed MA-FE method comprehensively captures essential features in different dimensions of channel and position through the Multidimensional Attention (MA) module, which integrates and combines the captured features. The Feature Enhancement (FE) module then amplifies the discriminative features to suppress the interference of useless information, thus improving the representation ability of the model. We conducted detailed experiments on three public remote sensing datasets and performed a comparative evaluation with multiple remote sensing scene classification methods proposed in recent years. The overall accuracies of the proposed MA-FE method on these datasets were 99.66%, 95.68%, and 93.21%, respectively. Our experimental results demonstrate that the proposed MA-FE method is more effective in extracting complex features in remote sensing images than other methods, thereby proving its effectiveness.
Light Simulation with Participating Media

In this project we address the problem of light scattering in participating materials. We create a complete simulation of this phenomenon in a more general case than previous work. We analyse the directional part of light, in order to install a clear basis for future work. We derive two models from this analysis: the spherical Gaussians approximation and the double exponential approximation. These models are placed in the scope of the planned development of an improved method for scattering. We also code a custom ray tracer to have a complete pipeline of rendering and to understand the underneath concepts. The validation of our simulation is done by comparing the results of d’Eon [2]
Naïve Huntington’s disease microglia mount a normal response to inflammatory stimuli but display impaired development of innate immune tolerance that can be counteracted by ganglioside GM1

Chronic activation and dysfunction of microglia have been implicated in the pathogenesis and progression of many neurodegenerative disorders, including Huntington’s Disease (HD). HD is a genetic condition caused by a mutation that affects the folding and function of huntingtin (HTT). Signs of microglia activation have been observed in HD patients even before the onset of symptoms. It is unclear, however, whether pro-inflammatory microglia activation in HD results from cell-autonomous expression of mutant HTT or is the response of microglia to a diseased brain environment, or both. In this study, we used primary microglia isolated from HD knock-in mice (Q140) and wild-type (Q7) mice to investigate their response to inflammatory conditions in vitro in the absence of confounding effects arising from brain pathology. We show that naïve Q140 microglia do not undergo spontaneous pro-inflammatory activation and respond to inflammatory triggers, including stimulation of TLR4 and TLR2 and exposure to necrotic cells, with similar kinetics of pro-inflammatory gene expression as wild-type microglia. Upon termination of the inflammatory insult, the transcription of pro-inflammatory cytokines is tapered off in Q140 and wild-type microglia with similar kinetics. However, the ability of Q140 microglia to develop tolerance in response to repeated inflammatory stimulations is partially impaired, potentially contributing to the establishment of chronic neuroinflammation in HD. We further show that ganglioside GM1, a glycosphingolipid with anti-inflammatory effects in wild-type microglia, not only decreases the production of pro-inflammatory cytokines and nitric oxide in activated Q140 microglia, but also dramatically dampen microglia response to re-stimulation with LPS in an experimental model of tolerance. These effects are independent from the expression of interleukin 1 receptor associated kinase 3 (Irak-3), a strong modulator of LPS signaling involved in the development of innate immune tolerance and previously shown to be upregulated by immune cell treatment with gangliosides. Altogether, our data suggest that external triggers are required for HD microglia activation, but a cell-autonomous dysfunction that affects the ability of HD microglia to acquire tolerance might contribute to the establishment of neuroinflammation in HD. Administration of GM1 might be beneficial to attenuate chronic microglia activation and neuroinflammation.