Miracle-dz/UniFAR-FTUs huggingface dataset:
On the State of Lithospheric Stress in the Absence of Applied Tectonic Forces

Numerous published analyses of the nontectonic state of stress are based on Hooke's law and the boundary condition of zero horizontal deformation. This approach has been used to determine the gravitational stress state as well as the effects of processes such as erosion and temperature changes on the state of lithospheric stress. The major disadvantage of these analyses involves the assumption of lateral constraint which seems unrealistic in view of the observational fact that the crust can deform horizontally in response to applied loads. If the same problems are addressed by assuming that the remote stress state is constant, instead of the condition of zero horizontal deformation, then the resulting stress states are entirely different and in good accord with observations. In the absence of applied tectonic forces the only likely gravitational stress states are those for which all three principal stresses are nearly equal. To the contrary, the gravitational stress states developed on the basis of the lateral constraint assumption can be ruled out. The processes of erosion and sedimentation have slight tendencies to increase and decrease, respectively, the state of deviatoric stress. In particular, for initial stress states in the range of slightly extensional to compressional, erosion has the effect of enhancing the ratio of average horizontal to vertical stress, which may explain, at least in part, the common observation of high near-surface horizontal stresses. Temperature changes have only minor effects on the stress state, as averaged over the thickness of the lithosphere.
Comparison of Body Compositions among Endurance, Strength, and Team Sports Athletes

Regular monitoring and evaluation of body composition determine an athlete’s output, such as their probability of winning competitions. The purpose of this study was to analyze the differences in athletes’ body composition among several sports categories. The data collected were on 251 athletes aged 20 to 30 years during the pre-competition period. The subjects were divided into three groups: endurance, power, and team sports. SECA 515mBCA was used to analyze the athletes’ body composition, which included their body fat percentage (BFP), fat mass (FM), fat-free mass (FFM), skeletal muscle mass (SMM), total body water (TBW), and extracellular water (ECW). The results showed that all body composition parameters were lowest in the endurance category. Fat composition was lowest in the endurance category but did not differ significantly between categories in male athletes. Meanwhile, in female athletes, there was a significant difference in FM between sports categories (p=0.009). FFM, SMM, TBW, and ECW differed significantly between categories (p<0.05). In female athletes, these variables were higher in the team sports category, as opposed to the power category among male athletes. This study concluded that there were differences in lean body mass composition between sport categories, while fat mass did not differ between sport categories in male athletes. This can be attributed to the differences in training programs and needs of each sport category in maximizing performance. These findings can provide advice to sports professionals that each sport category has its own body composition specifications.
Rainfall prediction in the Southeastern region of Bangladesh using the Linear
 Regression Method

Globally, there is a lot of concern about climate change. This study sets out to explain how historical rainfall variations occurred. Pre-monsoon and early monsoon regions will become drier in the future, whereas late monsoon and post-monsoon regions will experience significant variations in rainfall amounts. For meteorologists, predicting rain is a particularly challenging task. In recent years, a variety of models have been applied to assess and accurately forecast rainfall. Climate records can be quite helpful in this regard. Retaining data for a long time can help us predict rainfall more precisely. In this paper, the application of statistical techniques, in particular the linear regression and SVM method is provided for modeling and predicting rainfall across Bangladesh. The rainfall data for the 31 years was provided by the Bangladesh Meteorological Department (BMD), Dhaka. Between 1989 and 2019, this surface-based rain gauge in Bangladesh gathered rainfall data from 8 metrological stations. How much rain falls each month and year has been determined. For each station, the average, median, correlation coefficients, and standard deviation were calculated to evaluate the correctness of the data. It was found that the model's rainfall forecast provided reliable results when the amount of rainfall projected by the model was compared to the amount of rainfall measured by rain gauges at various locations.
Effect of Slow Breathing Exercise on Time Domain Heart Rate Variability in Transfusion Dependent Thalassemic Patients

Time domain measure of heart rate variability (HRV) is a newersystem to seek out any alteration in cardiacautonomic nerve function (CANF) in transfusion dependent thalassemic (TDT) patients. Slow breathing exercise(SBE) plays important role in improvement of HRV in health and various diseases.This research aims to observe theeffect of slow breathing exercise (SBE) on time domain measures of HRV in TDT patients. This prospectiveinterventional study was done in the Department of Physiology, Bangabandhu Sheikh Mujib Medical University(BSMMU), Dhaka in 2018 on 60 male TDT patients aged 15-30 years. Thirty patients received conventionaltreatment and 30 patients performed slow breathing exercise along with the conventional treatment for 3 months. Ageand sex matched healthy control were taken to compare. All subjects were followed up at baseline and after 3 months.Time domain HRV parameters were recorded by Power Lab 8/35. For statistical analysis paired sample 't' test andindependent sample 't' test were done. Mean RR interval, Mean SD, RMSSD and pRR50% were found significantlylower in TDT patients compared to healthy control at baseline and on the other hand mean heart rate was significantlyhigher in this group of patients. After 3 months of slow breathing exercise, significant decrement of mean heart rateand significant increment of others parameters occurred with trend of improvement in cardiac autonomic nervefunction in TDT patients. In conclusion slow breathing exercise may improve cardiac autonomic nerve function andsympathovagal balance in transfusion dependent thalassemic patients. Bangabandhu Sheikh Mujib Med. Coll. J. 2022;1(2):65-69
Hybrid Model for Efficient Assamese Text Classification using CNN-LSTM

: In modern times, there has been an exorbitant rise in unstructured digital text, owing to the ever-increasing use of internet. Therefore, to be able to extract knowledge out of it, a perceived need was felt to organize the enormous amount of digital text into di ff erent categories. This is why Text Classification is considered a critical task in NLP (Natural Language Processing). This research suggests a hybrid model(C-LSTM-ATC) that combines the benefits of two deep learning models, namely, the Convolutional Neural Network (CNN) and Long and Short Term Memory (LSTM), to categorize Assamese text, a topic that largely remains unexplored till now. Another hybrid model was also tried by combining LSTM with the Support Vector Machine (LSTM-SVM). The C-LSTM-ATC model performs splendidly with an accuracy of 97.2% while the LSTM-SVM model outputs an accuracy of 92.2% when tested on the dataset prepared. The model was trained using as many as 768 Assamese text documents and the test results showed that the proposed C-LSTM-ATC model produces more accurate classification and higher F1 scores, than the LSTM-SVM and also the CNN and LSTM models when used separately.
Prognostic estimation for acute ischemic stroke patients undergoing mechanical thrombectomy within an extended therapeutic window using an interpretable machine learning model

Background Mechanical thrombectomy (MT) is effective for acute ischemic stroke with large vessel occlusion (AIS-LVO) within an extended therapeutic window. However, successful reperfusion does not guarantee positive prognosis, with around 40–50% of cases yielding favorable outcomes. Preoperative prediction of patient outcomes is essential to identify those who may benefit from MT. Although machine learning (ML) has shown promise in handling variables with non-linear relationships in prediction models, its “black box” nature and the absence of ML models for extended-window MT prognosis remain limitations. Objective This study aimed to establish and select the optimal model for predicting extended-window MT outcomes, with the Shapley additive explanation (SHAP) approach used to enhance the interpretability of the selected model. Methods A retrospective analysis was conducted on 260 AIS-LVO patients undergoing extended-window MT. Selected patients were allocated into training and test sets at a 3:1 ratio following inclusion and exclusion criteria. Four ML classifiers and one logistic regression (Logit) model were constructed using pre-treatment variables from the training set. The optimal model was selected through comparative validation, with key features interpreted using the SHAP approach. The effectiveness of the chosen model was further evaluated using the test set. Results Of the 212 selected patients, 159 comprised the training and 53 the test sets. Extreme gradient boosting (XGBoost) showed the highest discrimination with an area under the curve (AUC) of 0.93 during validation, and maintained an AUC of 0.77 during testing. SHAP analysis identified ischemic core volume, baseline NHISS score, ischemic penumbra volume, ASPECTS, and patient age as the top five determinants of outcome prediction. Conclusion XGBoost emerged as the most effective for predicting the prognosis of AIS-LVO patients undergoing MT within the extended therapeutic window. SHAP interpretation improved its clinical confidence, paving the way for ML in clinical decision-making.
Levered beta: Influence of debt and corporate income tax on company systematic risk: Discussion on the correct algebraic equation

The increase in the share of debt in the capital structure is accompanied by an increase in the required return on equity because companies are exposed to higher financial risk. The beta coefficient of debt-financed companies differs from the beta coefficient of companies that are financed exclusively with equity. Namely, the beta coefficient, under the influence of financial leverage, tends to increase with the growth of indebtedness, that is the systematic risk measured by the coefficient beta of debt-financed companies is higher than the systematic risk of non-leveraged companies, due to financial risk. Because interest payments on the debt of leveraged companies are excluded as an expense from the tax base, corporate income tax reduces the beta coefficient of a company with debt, compared to the beta coefficient of the same company when income tax is abstracted. The higher the corporate income tax, that will be the lower the beta coefficient of companies that have debt in the capital structure. There are several algebraic equations, by different authors, for the beta coefficient of leveraged companies. The algebraic equation for the beta coefficient of leveraged companies, which is derived in this paper, was obtained using the net operating income approach.
Automatic Colorization of Digital Movies Using Decolorization Models and SSIM Index

Re-colorization of images or movies is a challenging problem due to the infinite RGB solutions for a monochrome object. In general, the process is assisted by humans, either by providing colorization hints or relevant training data for ML/AI algorithms. Our intention is to develop a mechanism for fully unguided (and with no training data used) colorization of movies. In other words, we aim to create acceptable colored counterparts of movies in domains where only monochrome visualizations physically exist (e.g. IR, UV, MRI, etc. data). Following our past approach to image colorization, the method assumes arbitrary rgb2gray models and utilizes a few probabilistic heuristics. Additionally, we maintain the temporal stability of colorization by locally using structural similarity (SSIM) between adjacent frames. The paper explains the details of the method, presents exemplary results and compares them to the state-of-the art solutions. NOTE: All figures are best viewed in color and high resolution.
Absorption and frequency shift of GPR signals in sandy and silty soils: empirical relations between quality factor Q, complex permittivity and clay and water contents

The arrival time, shape and amplitude of a reflected GPR wavelet depend mainly on the real and imaginary parts of dielectrical permittivity and electrical conductivity. All these parameters are strongly affected by the water and clay content of soils. From a comparison of the amplitude and shape of the direct and reflected wavelets the quality factor Q can be determined independently as an additional soil parameter. To investigate the relationships between the influencing factors, we conducted high-frequency GPR reflection measurements on soil samples with varying clay and water contents. The Q factor, derived from the spectral ratio method, is found to be between 5–15 for our samples that range from about 5–90% in sand content, 5–65% in silt content, 3–63% in clay content and 0–37% in pore water content. A multivariate non-linear empirical function linking water and clay content to Q agrees well with the observations. We found that the ratio of the real and imaginary parts of the dielectric permittivity depends basically on the clay content. Therefore, it may become possible to determine the clay content of soils in situ from a combination of GPR and conductivity measurements. The Q values determined from spectral ratios are confirmed by synthetic radargrams and agree with the observed decrease of the central frequency of GPR reflections caused by absorption.
Multimodal Neural Networks in the Problem of Captioning Images in Newspapers

This paper presents the effectiveness of different multimodal neural networks in captioning newspaper scan images. These methods were evaluated on a dataset created for the Temporal Image Caption Retrieval Competition, which is a part of the FedCSIS 2023 conference. The task was to predict a relevant caption for a picture taken from a newspaper, chosen from a given list of captions. The results we obtained show the promising potential of image captioning using CLIP architectures and emphasize the importance of developing new multimodal methods for problems that combine multiple disciplines, such as computer vision with natural language processing.
Electrophysiological Evidence of Stroboscopic Training in Elite Handball Players: Visual Evoked Potentials Study

Stroboscopic training enhances perceptual cognition and motor skills; however, neurophysiological mechanisms underlying this adaptation are not fully understood. This study aimed to investigate the effects of a six-week stroboscopic training program on the conductivity of the visual pathway in elite handball players, specifically related to their visual processing of retinal location and viewing conditions. The study included 22 handball players who were randomly assigned to an experimental or a control group. Both groups performed handball-specific in-situ tasks, but only the experimental group underwent stroboscopic training. Participants were assessed three times using visually evoked potential recordings measured by P100 implicit time and amplitude under three viewing conditions (dominant eye, non-dominant eye, and binocular) and two retinal locations (extra-foveal and foveal vision) before and after the six-week training period, and again four weeks later. The results showed a significant TIME vs. GROUP effect of P100 implicit time for the dominant eye in extra-foveal vision (F2,40 = 5.20, p = 0.010, ηp2 = 0.206), extra-foveal binocular viewing (F2,40 = 3.32, p = 0.046, ηp2 = 0.142), and dominant eye foveal vision (F2,40 = 4.07, p = 0.025, ηp2 = 0.169). Stroboscopic training significantly improved early visual processing by reducing the P100 implicit time for the dominant eye and binocular vision, particularly in extra-foveal vision. The improvements were more noticeable in the short compared to the long term.
On the Fructification of Zeilleria (Sphenopteris) delicatula, Sternb., sp.; with Remarks on Urnatopteris (Sphenopteris) tenella, Brongt., and Hymenophyllites (Sphenopteris) quadridactylites, Gutbier, sp.

So much confusion has arisen between Sphenopteris delicatula, Sternb. , Sphenopteris tenella, Brongt. , and Sphenopteris quadridactylites, Gutbier, that in dealing with Sphenopteris delicatula, Sternb. , it is also necessary to give a list of the synonyms of the two other species, which, in the barren condition, approach it somewhat closely. The small-pinnuled members of the genus Sphenopteris, Brongt. , are, under the most favourable circumstances, from the delicate nature of their fronds, extremely difficult to determine. As they usually occur in a very fragmentary condition, and the segmentation of their pinnules varies considerably according to the position held by the pinnæ on the frond (the pinnules of the upper pinnæ being generally more simple than those on the lower pinnæ), the difficulty of discriminating the species is considerably increased. When the specimens have suffered any lengthened maceration, the limb of the pinnules usually disappears entirely, and there is nothing but the veins left. In this state, the specific identification of these delicate Sphenopteroids is very unsatisfactory, if not quite useless. Fortunately the fruit of Sphenopteris delicatula, Sternb. , Sphenopteris tenella, Brongt. , and Sphenopteris quadridactylites, Gutbier, is known; and though their barren fronds may possess a considerable likeness, their fructification is very distinct; but even the barren fronds, when well preserved, are sufficiently marked to enable one to determine the species with absolute certainty. Zeilleria, nov. gen. Involucres borne at the extremities of the pinnule-segments, which are more or less produced to form a pedicel;in the earlier condition the involucres are globular, but at maturity
Keypoints Method for Recognition of Ship Wake Components in Sentinel-2 Images by Deep Learning

The wakes generated by moving vessels represent relevant patterns in remotely sensed images. They are a marker of ship presence and can be processed to infer route, speed, size, and type of ships. Automatic wake detection can be exploited by law enforcement agencies and local authorities for ensuring a wide range of applications, including maritime traffic surveillance, border control, and protection of marine protected areas. The topic is thus attracting increasing interest from the remote sensing community. This letter contributes in this context presenting a novel approach based on the detection of the keypoints of wake components by convolutional neural networks (CNNs) in electro-optical satellite imagery. The selected approach to deep learning (DL) relies on a transfer-learning procedure fine-tuning the ImageNet weights. This is performed through an ad hoc developed dataset realized from Sentinel-2 multispectral images and automatic identification system (AIS) data in northern Europe. The experimental results confirm the robustness of the proposed method, which is tested against different spectral bands from visible to near-infrared and also by a domain shifting on lower resolution Landsat-9 images. Fractional errors in the positioning of the wake vertex are lower than 10% and the achieved heading accuracy is below 10°. The proposed method is faster than the traditional approaches based on Radon transform (RT), and due to its lightweight nature, our model can be executed efficiently on edge-AI devices, enabling real-time processing onboard.
Time-series Anomaly Detection and Classification with Long Short-Term Memory Network on Industrial Manufacturing Systems

Modern manufacturing systems collect a huge amount of data which gives an opportunity to apply various Machine Learning (ML) techniques. The focus of this paper is on the detection of anomalous behavior in industrial manufacturing systems by considering the temporal nature of the manufacturing process. Long Short-Term Memory (LSTM) networks are applied on a publicly available dataset called Modular Ice-cream factory Dataset on Anomalies in Sensors (MIDAS), which is created using a simulation of a modular manufacturing system for ice cream production. Two different problems are addressed: anomaly detection and anomaly classification. LSTM performance is analysed in terms of accuracy, execution time, and memory consumption and compared with non-time-series ML algorithms including Logistic Regression, Decision Tree, Random Forest, and Multi-Layer Perceptron. The experiments demonstrate the importance of considering the temporal nature of the manufacturing process in detecting anomalous behavior and the superiority in accuracy of LSTM over non-time-series ML algorithms. Additionally, runtime adaptation of the predictions produced by LSTM is proposed to enhance its applicability in a real system.
Response behaviour of seals and penguins to helicopter surveys over the pack ice off East Antarctica

Data on the response behaviour of seals (crabeater, leopard and Ross) and penguins (Adélie and emperor) to helicopter surveys over the pack ice off East Antarctica are presented. The surveys involved Sikorsky S76 helicopters flying at altitude 130 m and speed 90 knots along straight-line transects. The relative frequency of alert and movement behaviours by seal and penguin groups decreased with distance out to 800 m from the flight path. Penguin groups were more likely to show a movement response than seals at all distances. The perpendicular distances moved relative to the flight path were small (maximum 20 m, mean ≤ 3 m, for both seal and penguin groups) relative to the width of the area searched (800 m), and there was no evidence that response movement resulted in a spiked detection histogram. Observers were more likely to feel confident in identifying seal and penguin species if the animals responded to the helicopter by changing the body posture or moving. In this application of aerial survey, the response behaviour elicited by the passing helicopter was considered to be beneficial from a technical perspective, and the disturbance caused to the surveyed populations negligible from an ethical perspective.
L1-Norm Principal Component Analysis Using Quaternion Rotations

Principal component analysis (PCA) based on L1-norm has drawn growing interest in recent years. It is especially popular in the machine learning and pattern recognition communities for its robustness to outliers. Although optimal algorithms for L1-norm maximization exist, they have very high computational complexity and can be used for evaluation purposes only. In practice, only approximate techniques have been considered so far. Currently, the most popular method is the bit-flipping technique, where the L1-norm maximization is viewed as a combinatorial problem over the binary field. Recently, we proposed exhaustive, but faster algorithm [1] based on two-dimensional Jacobi rotations that also offer high accuracy. In this paper, we develop a novel variant of this method that uses three-dimensional rotations and quaternion algebra. Our experiments show that the proposed approach offers higher accuracy than other approximate algorithms, but at the expense of the additional computational cost. However, for large datasets, the cost is still lower than that of the bit-flipping technique.