Miracle-dz/UniFAR-FTUs huggingface dataset:
LOCAL COMMUNITY PERCEPTION ABOUT TOURISM IMPACT AND COMMUNITY SUPPORT FOR FUTURE TOURISM DEVELOPMENT: A STUDY ON SYLHET, BANGLADESH

Tourism is a well-established industry and is known as an income-generating sector and it influences the social aspects for developed as well as countries those are developing. The aim of this paper is to assess the local community perception about tourism’s impact and their support for future tourism development. To conduct this study, a conceptual framework was developed. Additionally, data were collected from 330 respondents, of which the valid response rate is 78.57%. The projected relationships were evaluated by using Partial Least Squares- Structural Equation Modeling technique (PLS-SEM). The findings show a positive relationship among social, economic, and environmental factors and it also influences the quality of life of the local community. This study also specifies the local community’s positive perception towards the development of future’s tourism. This study will make a significant theoretical and practical contribution to the development of community-based tourism in Bangladesh.
Mitophagy Induced by Metal Nanoparticles for Cancer Treatment

Research on nanoparticles, especially metal nanoparticles, in cancer therapy is gaining momentum. The versatility and biocompatibility of metal nanoparticles make them ideal for various applications in cancer therapy. They can bring about apoptotic cell death in cancer cells. In addition to apoptosis, nanoparticles mediate a special type of autophagy facilitated through mitochondria called mitophagy. Interestingly, nanoparticles with antioxidant properties are capable of inducing mitophagy by altering the levels of reactive oxygen species and by influencing signaling pathways like PINK/Parkin pathway and P13K/Akt/mTOR pathway. The current review presents various roles of metal nanoparticles in inducing mitophagy in cancer cells. We envision this review sheds some light on the blind spots in the research related to mitophagy induced by nanoparticles for cancer treatment.
Evaluation of left ventricular systolic function in children with sickle cell anemia: contribution of 2D strain

Background: Children with sickle cell anemia (SCA) are at an increased risk of cardiovascular complications. The aim of this study was to assess the role of speckle tracking echocardiography in detecting subclinical myocardial damage in children with SCA. Methods: A cross-sectional case–control study was conducted at the echocardiography laboratory of the military hospital of Tunis between July and December 2018. Thirty patients with SCA were included. A control(C) group including 30 normally developing children was selected and matched to the SCA group by sex and age. We compared between the two groups: conventional echocardiographic parameters including cardiac output, left ventricular ejection fraction (LVEF), thickness and the global longitudinal strain (GLS). The echocardiographic measurements were indexed according to body surface area. The left ventricular (LV) GLS association with clinical characteristics and echocardiographic parameters were also evaluated. Results: Patients and controls were matched for age and sex: the mean age was (11± 2years) in SCA group versus (12± 1 years) in C group with a sex ratio of (1.31 versus 1.27, respectively). Body surface area was comparable. LV hypertrophy and dilation were revealed in the SCA group, whereas measurements were normal in the C group. No significant differences were observed for cardiac output (p=0.4). LVEF were preserved in both groups. However, two-dimensional (2D) LVGLS was impaired in 46% of SCA group (n=14) with mean value of (-21%±3.07 vs -25%±2.98; p<0.01). In SCA group, impaired LVGLS was significantly associated with LV mass (r = – 0.399, p<0.01), LV tele diastolic diameter(r= -0.419, p<0.01) and left atrial volume (r= - 0.399, p< 0.04). In multivariate analysis, LV mass was the only independent factor. Conclusions: In the present study, LVGLS measurement revealed subclinical LV systolic impairment in patients with SCA. Therefore, 2D strain could be beneficial to detect the natural history of LV dysfunction in SCA.
A kinetic construction of global solutions of first order quasilinear equations

On considere le probleme de Cauchy pour des equations quasilineaires d'ordre 1 de type conservation dans la region t≥0: u t +∑ i=1 u A i (u) x i=0, u(x,o)=u o (x) ou x=(x 1 ,.,x n ), u=u(x,t) et A i , i=1, . , n sont des fonctions continument differentiables a valeurs reelles d'une seule variable reelle
Frequency Entrainment of a Forced van der pol Oscillator.

Abstract : A van der Pol relaxation oscillator that is subjected to external sinusoidal forcing can exhibit stable and unstable periodic and almost periodic responses. For some forcing amplitudes it even happens that two stable subharmonics having different periods may coexist. The stable responses of such forced oscillators are investigated. By numerically computing the rotation number of stable oscillations for various values of the forcing amplitude and oscillator tuning, descriptions are obtained of regions of phase locking, successive bifurcation of stable subharmonic and almost periodic oscillations, and overlap regions where two distinct stable oscillations can coexist.
NESTED SHELLS REVEAL THE REJUVENATION OF THE ORION–ERIDANUS SUPERBUBBLE

The Orion–Eridanus superbubble is the prototypical superbubble owing to its proximity and evolutionary state. Here we provide a synthesis of recent observational data from WISE and Planck with archival data, allowing us to draw a new and more complete picture on the history and evolution of the Orion–Eridanus region. We discuss the general morphological structures and observational characteristics of the superbubble and derive quantitative properties of the gas and dust inside Barnard’s Loop. We reveal that Barnard’s Loop is a complete bubble structure that, together with the λ Ori region and other smaller-scale bubbles, expands within the Orion–Eridanus superbubble. We argue that the Orion–Eridanus superbubble is larger and more complex than previously thought, and that it can be viewed as a series of nested shells, superimposed along the line of sight. During the lifetime of the superbubble, Hii region champagne flows and thermal evaporation of embedded clouds continuously mass-load the superbubble interior, while winds or supernovae from the Orion OB association rejuvenate the superbubble by sweeping up the material from the interior cavities in an episodic fashion, possibly triggering the formation of new stars that form shells of their own. The steady supply of material into the superbubble cavity implies that dust processing from interior supernova remnants is more efficient than previously thought. The cycle of mass loading, interior cleansing, and star formation repeats until the molecular reservoir is depleted or the clouds have been disrupted. While the nested shells come and go, the superbubble remains for tens of millions of years.
Calibration as a Standard Method for Treatment of Nonresponse

Both producers and users of statistics are aware that nonresponse can impair the quality of the estimates, and therefore considerable resources are spent on data collection procedures aiming at preventing nonresponse from occurring. Nevertheless, we have to accept some missing values. Since we can be practically certain that the nonresponse is not the result of a simple random selection mechanism, we try to adjust for the selection bias at the estimation stage. Furthermore, we know that the nonresponse creates an additional component of variance. The literature provides two standard methods for treating nonresponse: imputation and weighting. In the ®rst method, missing values are replaced by proxies; in the second, the design weights are multiplied by adjustment weights aiming at nonresponse bias reduction. This article is concerned with weighting. To ®x ideas we introduce some notation. Consider the ®nite population of N elements U  f1;1⁄4; k;1⁄4;Ng. We wish to estimate the total Y  SUyk, where yk is the value of a typical study variable, y, for the kth element. For short, SA will be used for Sk[A, where A Í U is an arbitrary set. Let s be a sample of size n drawn from U with the probability p s. The inclusion probabilities are then pk  Ss3kp s and pkl  Ss3fk;lgp s. Let dk  1=pk denote This article suggests a simple and uni®ed approach to the use of auxiliary information for reducing both the sampling error and the nonresponse bias in a survey. We propose a point estimator based on calibration and a corresponding variance estimator. They are general in regard to both the sampling design and the form of the auxiliary information. The calibration procedure generates ®nal weights which are as close as possible to speci®ed initial weights (the design weights), while respecting known auxiliary population totals or unbiased estimates of these totals. When population totals are used, the resulting point estimators are consistent in the sense that the ®nal weights give perfect estimates when applied to each auxiliary variable. A clear tendency in our empirical ®ndings is that an increased auxiliary information content will reduce both variance and nonresponse bias of the point estimator. Despite some residual bias, the coverage rate of our con®dence intervals comes close to a nominal 95%.
Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches

The use of superabsorbent polymers, sometimes known as SAP, is a tremendously efficacious method for reducing the amount of autogenous shrinkage (AS) that occurs in high-performance concrete. This study utilizes support vector regression (SVR) as a standalone machine-learning algorithm (MLA) which is then ensemble with boosting and bagging approaches to reduce the bias and overfitting issues. In addition, these ensemble methods are optimized with twenty sub-models with varying the nth estimators to achieve a robust R2. Moreover, modified bagging as random forest regression (RFR) is also employed to predict the AS of concrete containing supplementary cementitious materials (SCMs) and SAP. The data for modeling of AS includes water to cement ratio (W/C), water to binder ratio (W/B), cement, silica fume, fly ash, slag, the filer, metakaolin, super absorbent polymer, superplasticizer, super absorbent polymer size, curing time, and super absorbent polymer water intake. Statistical and k-fold validation is used to verify the validation of the data using MAE and RMSE. Furthermore, SHAPLEY analysis is performed on the variables to show the influential parameters. The SVM with AdaBoost and modified bagging (RF) illustrates strong models by delivering R2 of approximately 0.95 and 0.98, respectively, as compared to individual SVR models. An enhancement of 67% and 63% in the RF model, while in the case of SVR with AdaBoost, it was 47% and 36%, in RMSE and MAE of both models, respectively, when compared with the standalone SVR model. Thus, the impact of a strong learner can upsurge the efficiency of the model.
Genetic Markers for Thrombophilia and Cardiovascular Disease Associated with Multiple Sclerosis

Multiple sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system (CNS) with an unknown etiology, although genetic, epigenetic, and environmental factors are thought to play a role. Recently, coagulation components have been shown to provide immunomodulatory and pro-inflammatory effects in the CNS, leading to neuroinflammation and neurodegeneration. The current study aimed to determine whether patients with MS exhibited an overrepresentation of polymorphisms implicated in the coagulation and whether such polymorphisms are associated with advanced disability and disease progression. The cardiovascular disease (CVD) strip assay was applied to 48 MS patients and 25 controls to analyze 11 genetic polymorphisms associated with thrombosis and CVD. According to our results, FXIIIVal34Leu heterozygosity was less frequent (OR: 0.35 (95% CI: 0.12–0.99); p = 0.04), whereas PAI-1 5G/5G homozygosity was more frequent in MS (OR: 6.33 (95% CI: 1.32–30.24); p = 0.016). In addition, carriers of the HPA-1a/1b were likely to have advanced disability (OR: 1.47 (95% CI: 1.03–2.18); p = 0.03) and disease worsening (OR: 1.42 (95% CI: 1.05–2.01); p = 0.02). The results of a sex-based analysis revealed that male HPA-1a/1b carriers were associated with advanced disability (OR: 3.04 (95% CI: 1.22–19.54); p = 0.01), whereas female carriers had an increased likelihood of disease worsening (OR: 1.56 (95% CI: 1.04–2.61); p = 0.03). Our findings suggest that MS may be linked to thrombophilia-related polymorphisms, which warrants further investigation.
Genome-Wide Identification of Auxin-Responsive GH3 Gene Family in Saccharum and the Expression of ScGH3-1 in Stress Response

Gretchen Hagen3 (GH3), one of the three major auxin-responsive gene families, is involved in hormone homeostasis in vivo by amino acid splicing with the free forms of salicylic acid (SA), jasmonic acid (JA) or indole-3-acetic acid (IAA). Until now, the functions of sugarcane GH3 (SsGH3) family genes in response to biotic stresses have been largely unknown. In this study, we performed a systematic identification of the SsGH3 gene family at the genome level and identified 41 members on 19 chromosomes in the wild sugarcane species, Saccharum spontaneum. Many of these genes were segmentally duplicated and polyploidization was the main contributor to the increased number of SsGH3 members. SsGH3 proteins can be divided into three major categories (SsGH3-I, SsGH3-II, and SsGH3-III) and most SsGH3 genes have relatively conserved exon-intron arrangements and motif compositions. Diverse cis-elements in the promoters of SsGH3 genes were predicted to be essential players in regulating SsGH3 expression patterns. Multiple transcriptome datasets demonstrated that many SsGH3 genes were responsive to biotic and abiotic stresses and possibly had important functions in the stress response. RNA sequencing and RT-qPCR analysis revealed that SsGH3 genes were differentially expressed in sugarcane tissues and under Sporisorium scitamineum stress. In addition, the SsGH3 homolog ScGH3-1 gene (GenBank accession number: OP429459) was cloned from the sugarcane cultivar (Saccharum hybrid) ROC22 and verified to encode a nuclear- and membrane-localization protein. ScGH3-1 was constitutively expressed in all tissues of sugarcane and the highest amount was observed in the stem pith. Interestingly, it was down-regulated after smut pathogen infection but up-regulated after MeJA and SA treatments. Furthermore, transiently overexpressed Nicotiana benthamiana, transduced with the ScGH3-1 gene, showed negative regulation in response to the infection of Ralstonia solanacearum and Fusarium solani var. coeruleum. Finally, a potential model for ScGH3-1-mediated regulation of resistance to pathogen infection in transgenic N. benthamiana plants was proposed. This study lays the foundation for a comprehensive understanding of the sequence characteristics, structural properties, evolutionary relationships, and expression of the GH3 gene family and thus provides a potential genetic resource for sugarcane disease-resistance breeding.
Health Status Assessment of Diesel Engine Valve Clearance Based on BFA-BOA-VMD Adaptive Noise Reduction and Multi-Channel Information Fusion

Regarding the problem of the valve gap health status being difficult to assess due to the complex composition of the condition monitoring signal during the operation of the diesel engine, this paper proposes an adaptive noise reduction and multi-channel information fusion method for the health status assessment of diesel engine valve clearance. For the problem of missing fault information of single-channel sensors in condition monitoring, we built a diesel engine valve clearance preset simulation test bench and constructed a multi-sensor acquisition system to realize the acquisition of diesel engine multi-dimensional cylinder head signals. At the same time, for the problem of poor adaptability of most signal analysis methods, the improved butterfly optimization algorithm by the bacterial foraging algorithm was adopted to adaptively optimize the key parameter for variational mode decomposition, with discrete entropy as the fitness value. Then, to reduce the uncertainty of artificially selecting fault characteristics, the characteristic parameters with a higher recognition degree of diesel engine signal were selected through characteristic sensitivity analysis. To achieve an effective dimensionality reduction integration of multi-channel features, a stacked sparse autoencoder was used to achieve deep fusion of the multi-dimensional feature values. Finally, the feature samples were entered into the constructed one-dimensional convolutional neural network with a four-layer parameter space for training to realize the health status assessment of the diesel engine. In addition, we verified the effectiveness of the method by carrying out valve degradation simulation experiments on the diesel engine test bench. Experimental results show that, compared with other common evaluation methods, the method used in this paper has a better health state evaluation effect.
The Impact of Using Picture Narrating Strategy on Primary School Pupils̕ Speaking Skills and Anxiety

The aim of the study is to determine the impact of picture narrating strategy on pupils speaking skills and anxiety. Four instruments -prepared by the researchers- that involved: a speaking skills checklist, a speaking skills rubric, a speaking test and an anxiety scale were used. Results of the study indicated that pupils speaking skills performance was considerably higher and their anxiety was lower than their counterparts of the control group in speaking test and anxiety scale. Based on the results, it was concluded that picture narrating strategy could be used for improving speaking skills and decreasing pupils’ anxiety.
Parenting Programme Structure, Potential Barriers, and Facilitators: A Scoping Review

Becoming a parent is a challenging transition, and stress factors may arise. This scoping review aims to map, from the literature, the structure, potential barriers, and facilitators to be considered when conducting a parenting programme for parents of children up to 3 years old. It followed the JBI methodology and included studies with parents of children up to 3 years old (Participants), studies about parenting programme structure, its potential barriers, and facilitators (Concept) in the healthcare or community setting (Context). Qualitative and/or quantitative study designs and grey literature publications between 2016 and 2021 were eligible. The search was performed in three stages in CINAHL Plus with Full Text, MEDLINE with Full Text, and PubMed databases. It was also conducted in OpenGrey, ProQuest, Portuguese Open Access Scientific Repository, and Google Scholar. Fourteen articles were selected. The following aspects were identified regarding parenting programmes: benefits; structuring elements to be considered; facilitating factors and possible barriers to its development, and measurement instruments to assess the programme. Parenting programmes are important ways to contribute to a healthy, sustainable, and resilient society. It should be adapted to individuals, groups, and communities. They add value to parents, children, and society and should be carefully adapted to the group’s needs.
A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving

Multi-target tracking, a high-level vision job in computer vision, is crucial to understanding autonomous driving surroundings. Numerous top-notch multi-object tracking algorithms have evolved in recent years as a result of deep learning’s outstanding performance in the field of visual object tracking. There have been a number of evaluations on individual sub-problems, but none that cover the challenges, datasets, and algorithms associated with visual multi-object tracking in autonomous driving scenarios. In this research, we present an exhaustive study of algorithms in the field of visual multi-object tracking over the last ten years, based on a systematic review approach. The algorithm is broken down into three groups based on its structure: methods for tracking by detection (TBD), joint detection and tracking (JDT), and Transformer-based tracking. The research reveals that the TBD algorithm has a straightforward structure, however the correlation between its individual sub-modules is not very strong. To track multiple objects, the JDT technique combines multi-module joint learning with a deep network framework. Transformer-based algorithms have been explored over the past two years, and they have benefits in numerous assessment indicators, as well as tremendous research potential in the area of multi-object tracking. Theoretical support for algorithmic research in adjacent disciplines is provided by this paper. Additionally, the approach we discuss, which uses merely monocular cameras rather than sophisticated sensor fusion, is anticipated to pave the way for the quick creation of safe and affordable autonomous driving systems.
Enhanced photon production from quark - gluon plasma: Finite lifetime effect

Photon production from a thermalized quark-gluon plasma of finite lifetime is studied directly in real time with a nonequilibrium formulation that includes off-shell (energy nonconserving) effects. To lowest order we find that production of direct photons form a quark-gluon plasma of temperature T ~ 200 MeV and lifetime t ~ 10-20 fm/c is strongly enhanced by off-shell (anti)quark bremsstrahlung q(\bar{q})->q(\bar{q})\gamma. The yield from this nonequilibrium finite-lifetime effect dominates over those obtained from higher order equilibrium rate calculations in the range of energy E > 2 GeV and falls off with a power law for E >> T.
Rave: A Modular and Extensible Framework for Program State Re-Randomization

Dynamic software diversification is an effective way to boost software security. Existing diversification-based approaches often target a single node environment and leverage in-process agents to diversify code and data, resulting in an unnecessary attack surface on a fixed software/hardware stack. This paper presents Rave, a practical system designed to enable out-of-bound program state shuffling on a moving target environment, avoiding any sensitive agent code invoked within the running target. Rave relies on a user-space page fault handling mechanism introduced in the latest Linux kernel and seamlessly integrates with CRIU, the battle-tested process migration tool for Linux. Rave consists of two components: librave, a library for static binary analysis and instrumentation, and CRIU-Rave, a runtime that dynamically updates program execution states (e.g., internal stack data layout and the machine node the program runs on). We built a prototype of Rave and evaluated it with four real-world server applications and 13 applications from the SPEC CPU 2017 and the SNU C version of NAS Parallel Benchmarks (NPB) benchmark suites. We demonstrated that Rave can continuously re-randomize the program state (e.g., internal stack layout, instruction sequences, and machine node to run on). The evaluation shows that Rave increases the internal program state entropy with an additional ≈200 ms time overhead for each re-randomization epoch on average.