Miracle-dz/UniFAR-FTUs huggingface dataset:
Air Temperature Modeling Based on Land Surface Factors by the Cubist Method (Case Study of Hamoun International Wetland)

The drying up of Hamoun International Wetland (HIW) and the loss of vegetation in this area have led to an increase in ambient temperature. This research examines the changes in the surface of HIW and its role in air temperature (Tair) using data on land surface temperature (LST), vegetation, wind speed, and relative humidity. The Cubist regression model (CRM) is used to simulate the effects of land surface factors (LSFs) on Tair. Four microsites with different plant cover percentages were selected for this purpose. After data collection, 75% of the data were used for modeling and 25% of the data were used for model testing. The results showed that CRM has adequate performance for estimating Tair. The assessment of the relationship between land surface temperature (LST) and Tair at 2 meter height showed that there was a high correlation coefficient between 0.86 and 0.91 in the different microsites. The results of using CRM for estimating Tair showed that this model can estimate air temperature from independent parameters of LST, wind speed, vegetation percentage, and relative humidity with a correlation coefficient of 0.98. In this model, the LST, relative humidity, and vegetation percentage were entered with values of 100%, 93%, and 83% respectively. Wind speed was not included in the model because the measurements were constant and less than 4 m/s throughout the period (no changes).
Learning the stable and metastable phase diagram to accelerate the discovery of metastable phases of boron

Boron, an element of captivating chemical intricacy, has been surrounded by controversies ever since its discovery in 1808. The complexities of boron stem from its unique position between metals and insulators in the Periodic Table. Recent computational studies have shed light on some of the stable boron allotropes. However, the demand for multifunctionality necessitates the need to go beyond the stable phases into the realm of metastability and explore the potentially vast but elusive metastable phases of boron. Traditional search for stable phases of materials has focused on identifying materials with the lowest enthalpy. Here, we introduce a workflow that uses reinforcement learning coupled with decision trees, such as Monte Carlo tree search, to search for stable and metastable boron phases, with enthalpy as the objective. We discover new boron metastable phases and construct a phase diagram that locates their phase space (T, P) at different levels of metastability (ΔG) from the ground state and provides useful information on the domains of relative stability of the various stable and metastable boron phases.
Analyzing Training Exercises with Mathematical Algorithms and Convolutional Neural Network

This paper describes an application, which aims to help people who are doing training exercises at home. The system takes a video as input and provides feedback about the mistakes that were made during the execution of the exercise by using Convolutional Neural Network (CNN) to determine the limb positions and mathematical calculations to find the angles between them. From this information the system can decide with high accuracy, if the range of motion was correct during the following exercises: shoulder press, single arm biceps curl, single arm bent over row, lunge non-alternating version, single arm triceps extension.
Passive and Flexible UHF RFID Tag for Body-Worn Applications

There is a great interest in epidermal devices that provide comfort to users. In this work, an RFID tag for on-body applications on a polyimide substrate was proposed. The tag consists of a low-profile flexible substrate that conforms to the body's curvature, allowing comfortable monitoring of physiological functions. The passive tag has a total dimension of $112.6\times 26.6\ \text{mm}^{2}$. It is composed of a folded dipole antenna, in which two Complementary Split Ring Resonator (CSRR) cells were incorporated to improve its performance in proximity to the body, and the Monza® X-2K Dura chip from Impinj®. According to the simulation results, its maximum read range is 3.21 m. Two experiments were carried out: one without the presence of the human body and another with the tag on the abdomen of a volunteer. The maximum read range achieved in the first configuration was 1.89 m, and in the second configuration, it was 1.10 m.
PneuBot - A Soft Robotic Link

With the advancement in technology, robots are being introduced in most of the production processes to improve the production efficiency. Due to the increasing adoption of robots in industries, the concept of human-robot collaboration has seen an uprising trend where humans and robots work together in a shared workspace such as Industry 5.0 raising concerns about human safety. Collaborative robots known as Cobots are used in industries for this purpose where the robots and the humans work side to side. A higher scale of research has been carried out in the field of optimizing the cobots to provide a safer environment for humans which mainly focuses on accident prevention. Even though the cobots provide a safer environment, the possibility of a robot colliding with humans is still present based on different cobots techniques. This collision would have a lesser importance if the humans feels it as a normal touch. Thus the main idea which is mainly focussed on, in this paper, is to replace the rigid robot links with a soft robot link as the impact during collision is under the tolerable limit for humans. A self-developed soft robot link which uses air as the media for structural support was used. Therefore, a detailed description of the concept of PneuBot and a comparative study on the impact caused by the existing cobots with rigid links along with the force impact of the self-developed robot soft link will be discussed in this paper. A universal robot 5e will be used as a power and force limiting cobot.
Behavioral and Cognitive Comorbidities in Genetic Rat Models of Absence Epilepsy (Focusing on GAERS and WAG/Rij Rats)

Absence epilepsy is a non-convulsive type of epilepsy characterized by the sudden loss of awareness. It is associated with thalamo-cortical impairment, which may cause neuropsychiatric and neurocognitive problems. Rats with spontaneous absence-like seizures are widely used as in vivo genetic models for absence epilepsy; they display behavioral and cognitive problems similar to epilepsy in humans, such as genetic absence epilepsy rats from Strasbourg (GAERS) and Wistar Albino rats from Rijswijk (WAG/Rij). Both GAERS and WAG/Rij rats exhibited depression-like symptoms, but there is uncertainty regarding anxiety-related symptoms. Deficits in executive functions and memory impairment in WAG/Rij rats, i.e., cognitive comorbidities, are linked to the severity of epilepsy. Wistar rats can develop spontaneous seizures in adulthood, so caution is advised when using them as a control epileptic strain. This review discusses challenges in the field, such as putative high emotionality in genetically prone rats, sex differences in the expression of cognitive comorbidities, and predictors of cognitive problems or biomarkers of cognitive comorbidities in absence epilepsy, as well as the concept of “the cognitive thalamus”. The current knowledge of behavioral and cognitive comorbidities in drug-naive rats with spontaneous absence epilepsy is beneficial for understanding the pathophysiology of absence epilepsy, and for finding new treatment strategies.
Observed winter Barents Kara Sea ice variations induce prominent sub-decadal variability and a multi-decadal trend in the warm Arctic cold Eurasia pattern

The observed winter Barents-Kara Sea ice concentration (BKS SIC) has shown a close association with the second empirical orthogonal function (EOF) mode of Eurasian winter surface air temperature (SAT) variability, known as Warm Arctic Cold Eurasia (WACE) pattern. However, the potential role of BKS SIC on this WACE pattern of variability and on its long-term trend remains elusive. Here, we show that from 1979 to 2022, the winter BKS SIC and WACE association is most prominent and statistically significant for the variability at the sub-decadal time scale for 5 to 6 years. We also show the critical role of the multi-decadal trend in the principal component of the WACE mode of variability for explaining the overall Eurasian winter temperature trend over the same period. Furthermore, a large multi-model ensemble of atmosphere-only experiments from 1979 to 2014, with and without the observed Arctic SIC forcing, suggests that the BKS SIC variations induce this observed sub-decadal variability and the multi-decadal trend in the WACE. Additionally, we analyse the model simulated first or the leading EOF mode of Eurasian winter SAT variability, which in observations, closely relates to the Arctic Oscillation (AO). We find a weaker association of this mode to AO and a statistically significant positive trend in our ensemble simulation, opposite to that found in observation. This contrasting nature reflects excessive hemispheric warming in the models, partly contributed by the modelled Arctic Sea ice loss.
A new field approach for the collection of samples for aquatic 14CO2 analysis using headspace equilibration and molecular sieve traps: the super headspace method

Carbon dioxide evasion from inland waters such as lakes, rivers and streams represents a significant component of the global carbon cycle, yet in many parts of the world, relatively little is known about its source. Radiocarbon dating of aquatic CO2 has the potential to provide new insights into C cycling in the terrestrial–aquatic–atmosphere continuum, and whilst a range of methods are available for the collection of samples for 14C analysis, they all have limitations or disadvantages (e.g. slow collection rates and potential non‐equilibrium). These issues are further compounded in remote field sites. Here, we describe a new method for the field collection of CO2 samples from low‐pH waters (pH < 7) for radiocarbon analysis, which involves a scaled‐up version of the widely used headspace equilibration technique coupled with syringe injection of samples into molecular sieve traps for convenient and stable storage. We present the results of laboratory and field tests to verify this ‘super headspace method’ and discuss its advantages compared with existing techniques, particularly for sampling in remote locations. This includes its high portability, speed of use and absence of any special sample preservation requirements. Copyright © 2016 John Wiley & Sons, Ltd.
Design and Development of a Micro Inverter Power Monitoring System to Enhance Photovoltaic System Performance by Integrating LoRaWAN and IoT

This research examines the significance of utilizing Photovoltaic Solar Power Systems (PVSPS) with Micro Inverters and Internet of Things (IoT) technology, particularly LoRaWAN, in addressing challenges related to efficiency and power management. PVSPS has emerged as a leading solution to tackle the increasing global energy consumption and the adverse effects of greenhouse gas emissions. The rapid growth of the PVSPS industry underscores its relevance. This study explores the impact of shading on solar panels connected in series and parallel configurations, emphasizing the importance of proper placement. The results indicate that shading significantly reduces energy production, especially in panels connected in series. Micro Inverters play a crucial role in maintaining stable output voltage, even under shading conditions. However, attention must be paid to their influence on output current. Efficient monitoring and management of PVSPS through IoT technology such as LoRaWAN are key in supporting the transition to sustainable energy. This data underscores the importance of continuous monitoring to identify issues and optimize PVSPS performance, in the face of environmental challenges and climate change.
Differential expression of miRNAs revealed by small RNA sequencing in traumatic tracheal stenosis

Introduction: Traumatic tracheal stenosis (TTS) is a major cause of complex difficult airways, without clinically definitive efficacious drugs available. The aim of this study was to provide a general view of interactions between micro and messenger ribonucleic acids (miRNAs and mRNAs) and many potential mechanisms in TTS via small RNA sequencing. Methods: In this study, the identification of miRNAs was completed using small RNA sequencing and samples from four TTS patients and four normal control cases. By using bioinformatics tools, such as miRanda and RNAhybrid, for identifying the candidate target genes of miRNAs with differential expression in each sample, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were employed for enriching the predicted target genes of miRNAs with differential expression based on the correspondence between miRNAs and their target genes. We detected the expression of the candidate miRNAs using quantitative real-time polymerase chain reaction (qRT-PCR). Results: Twenty-four miRNAs with significant differential expression were identified, including 13 upregulated and 11 downregulated ones. Bioinformation technology was adopted to predict 2,496 target genes. These miRNA-target genes were shown to be primarily enriched in cells and organelles with catalytic activity and binding function, such as binding proteins, small molecules, and nucleotides. Finally, they were observed to process into TTS through the intercellular and signal regulation of related inflammatory signaling and fibrosis signaling pathways. QRT-PCR confirmed the upregulation of miR21-5p and miR214-3p and the downregulation of miR141-3p and miR29b-3p, which was expected to become a high-specific miRNA for TTS. Conclusion: Among all the miRNAs detected, 24 miRNAs demonstrated differential expression between the TTS and normal control groups. A total of 2,496 target genes were predicted by bioinformation technology and enriched in inflammatory and fibrotic signaling pathways. These results provide new ideas for further studies and the selection of targets for TTS in the future.
PNP-Lightweight Model for Predicting Remaining Useful Life of Lithium-Ion Battery to Applying Embedded Systems

This paper proposes a Positive & Negative Perceptron (PNP)-Lightweight model prediction method for the Remaining Useful Life (RUL) prediction of Lithium-Ion Battery (LIB) for use in low computation environments such as smartphones and embedded systems. The PNP-Lightweight model is based on the PNP model and has a very low number of trainable parameters. We present a PNP-Lightweight model that can predict the RUL of the LIB using the charge and discharge data of the initial LIB. To prove the superiority of the proposed PNP- Lightweight model, we perform the performance evaluation compared with Long short-term memory (LSTM) and Convolution Neural Network (CNN) models. We build Deep-learning Model using the PNP-Lightweight model (DMP), Deep-learning Model using the LSTM model (DML) and Deep-learning Model using the CNN model (DMC) that can predict the RUL of LIB using the LIB's initial charge and discharge data. To evaluate fairly, we organize the same number of layers and the number of nodes in the hidden layer of the three deep-learning models (DMP, DML, and, DMC) As a result of the performance evaluation, we know that the number of trainable parameters of the DMP is 30 times lower than that of DML and 60 times lower than the DMC. We confirm that the DMP has a lower Root Mean Square Error (RMSE) by 16.18 [cycle] and 3.03 [cycle] compared to the DML and DMC, respectively. In addition, the Symmetric Mean Absolute Percentage Error (SMAPE) of the DMP is 3.6% and 3.96% lower than the DML and DMC, respectively. In addition, we also compare the performance between proposed DMP and CNN+LSTM model. We recognize that performance of proposed DMP is superior to CNN+LSTM model.
‘Divergent Work Ageing’ and Older Migrants’ (Un)extended Working Lives

This article theorises older ethnic minority women’s work attitudes and labour market behaviour from an intersectional cumulative perspective within the extended working lives contexts. Empirical evidence has been drawn from interviews with South Asian British Muslim women aged between 50 and 66 living in Greater Manchester, UK. The findings show that the cohort’s ageing process is asynchronous with the British work ageing outlook as their cultural understanding of working age, age roles and successful ageing defies the extended working lives philosophies, and cumulative factors including caring responsibilities, legacy inequalities, and health issues present additional challenges for extending their work lives. Their culture- and context-specific work ageing process remains absent in the UK’s labour market discourse and policy landscape. The article theorises their idiosyncratic work ageing including non-conformist attitudes to extending work lives by proposing a ‘divergent work ageing’ model that can guide policymakers in creating inclusive labour market policies.
Interaction of cold fronts with the Brazilian Plateau: a climatological analysis

Complex orography of elevated terrain and numerous peaks can act as an important physical barrier to the atmospheric flow and can generate a range of meteorological phenomena. The objective of this study is to investigate the effect of the Brazilian Plateau on the spatial distribution of cold fronts and their climatology for the period between 1979 and 2010 using Climate Forecast System Reanalysis data. The cold fronts are detected and divided into three groups, according to their frequency and trajectory over South America: the continental fronts are referred to as the AN group; the fronts that pass over the coastal region of São Paulo are referred to as the SP group; and the fronts that pass over the two areas are referred to as the ANSP group. Composites of means and anomalies of atmospheric variables are constructed for each group. The investigation focuses on the orographic effects of the Brazilian Plateau on the climatology of the cold fronts. The Brazilian Plateau affects the spatial distribution of maximum and minimum frontal frequency. The number of cold fronts over the elevated regions of the Brazilian Plateau is significantly lower than in the surrounding regions. The maximum frontal frequency is found to the east of the Andes and a secondary maximum is found along the Southeast coast of Brazil. The main effect of the Brazilian Plateau on the mean and anomaly fields of meteorological variables is to block the dry and cool post‐frontal air at lower atmospheric levels. The most significant anomalies are found in the ANSP group of cold fronts, which are more intense and move more rapidly eastward. On the other hand, the AN cold front group is the most frequent, but the weakest and easily affected by the Brazilian Plateau orography.
The Lifelong Costs of Urban Smogs

Exposure to smoggy days is a common part of urban life, but can be avoided by vulnerable populations with municipal investment in warnings. This paper provides the first evidence on the long-term effects of early exposure to smog. Variation comes from exposure to the Great London Smog of 1952. Affected cohorts are tracked for up to sixty years using the Office of National Statistics Longitudinal Study. Exposure to the four day smog reduced the size of the surviving cohort by 2% and caused lasting damage to human capital accumulation, employment, hours of work, and propensity to develop cancer.
Targeted Disruption of the Aspartate Pathway: A Promising Approach for Combating Persistent Tuberculosis Infections

: With an estimated 10 million new cases being reported each year, tuberculosis (TB) continues to pose a threat to global health. The determination of Mycobacterium tuberculosis (Mtb) in the host, notwithstanding standard anti-infection medicines, represents a critical test. This study investigates the capability of designated interruption of the aspartate pathway as an imaginative way to deal with battle industrious TB contaminations. Due to its crucial role in Mtb's metabolic processes, the aspartate pathway has gained attention in recent years. This pathway is pivotal for the biosynthesis of amino acids, nucleotides
A modification of logistic regression with imbalanced data: F-measure-oriented Lasso-logistic regression

: Logistic regression (LR) is one of the most popular classifiers. However, LR cannot perform effectively on imbalanced data. There are two approaches to imbalanced data for LR, including resampling techniques and modifications to the log-likelihood function. These approaches improve performance measures of LR in some cases, but their effectiveness is not robust in general. In this paper, we propose a classifier called F-measure-oriented Lasso-Logistic Regression (F-LLR) to deal with imbalanced data. The base learner of F-LLR is Lasso-Logistic regression (LLR) which imposes the prior on the magnitude of parameters by a hyper-parameter λ . The optimal λ is determined by an adjustment of the cross-validation procedure which aims for the highest F-measure instead of the highest accuracy. F-LLR addresses imbalanced data by the combination of Under-sampling and Synthetic Minority Oversampling Technique (SMOTE) selectively based on the scores of the training data. The empirical study shows that F-LLR increases F-measure and KS as compared with LLR and the traditional balanced methods, such as the resampling techniques (Random Under-sampling, Random Over-sampling, and SMOTE) and the modifications to log-likelihood function (Ridge and Weighted likelihood estimation).
Machine Learning Algorithm: Texture Analysis in CNO and Application in Distinguishing CNO and Bone Marrow Growth-Related Changes on Whole-Body MRI

Objective: The purpose of this study is to analyze the texture characteristics of chronic non-bacterial osteomyelitis (CNO) bone lesions, identified as areas of altered signal intensity on short tau inversion recovery (STIR) sequences, and to distinguish them from bone marrow growth-related changes through Machine Learning (ML) and Deep Learning (DL) analysis. Materials and methods: We included a group of 66 patients with confirmed diagnosis of CNO and a group of 28 patients with suspected extra-skeletal systemic disease. All examinations were performed on a 1.5 T MRI scanner. Using the opensource 3D Slicer software version 4.10. 2, the ROIs on CNO lesions and on the red bone marrow were sampled. Texture analysis (TA) was carried out using Pyradiomics. We applied an optimization search grid algorithm on nine classic ML classifiers and a Deep Learning (DL) Neural Network (NN). The model’s performance was evaluated using Accuracy (ACC), AUC-ROC curves, F1-score, Positive Predictive Value (PPV), Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE). Furthermore, we used Shapley additive explanations to gain insight into the behavior of the prediction model. Results: Most predictive characteristics were selected by Boruta algorithm for each combination of ROI sequences for the characterization and classification of the two types of signal hyperintensity. The overall best classification result was obtained by the NN with ACC = 0.91, AUC = 0.93 with 95% CI 0.91–0.94, F1-score = 0.94 and PPV = 93.8%. Between classic ML methods, ensemble learners showed high model performance; specifically, the best-performing classifier was the Stack (ST) with ACC = 0.85, AUC = 0.81 with 95% CI 0.8–0.84, F1-score = 0.9, PPV = 90%. Conclusions: Our results show the potential of ML methods in discerning edema-like lesions, in particular by distinguishing CNO lesions from hematopoietic bone marrow changes in a pediatric population. The Neural Network showed the overall best results, while a Stacking classifier, based on Gradient Boosting and Random Forest as principal estimators and Logistic Regressor as final estimator, achieved the best results between the other ML methods.