Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic enhancer partitioning instructs activation of a growth-related gene during exit from naïve pluripotency

During early mammalian development, the chromatin landscape undergoes profound transitions. The Zdbf2 gene—involved in growth control—provides a valuable model to study this window: upon exit from naïve pluripotency and prior to tissue differentiation, it undergoes a switch from a distal to a proximal promoter usage, accompanied by a switch from polycomb to DNA methylation occupancy. Using a mouse embryonic stem cell (ESC) system to mimic this period, we show here that four enhancers contribute to the Zdbf2 promoter switch, concomitantly with dynamic changes in chromatin architecture. In ESCs, the locus is partitioned to facilitate enhancer contacts with the distal Zdbf2 promoter. Relieving the partition enhances proximal Zdbf2 promoter activity, as observed during differentiation or with genetic mutants. Importantly, we show that 3D regulation occurs upstream of the polycomb and DNA methylation pathways. Our study reveals the importance of multi-layered regulatory frameworks to ensure proper spatio-temporal activation of developmentally important genes.
School Quality Assurance Recommendations and Teacher Efficacy in Public Secondary Schools in Ilemela Municipality, Tanzania

Quality education depends on quality teachers. Teachers with high efficacy can easily facilitate the teaching and learning process. This study examined the contribution of school quality assurance recommendations on teacher efficacy in public secondary schools in Ilemela Municipality. The study was guided by two specific objectives: to identify the contribution of school quality assurance recommendations on teacher efficacy and to highlight the challenges facing secondary school teachers in implementing recommendations given by school quality assurance officers. The study employed a mixed approach and a convergent parallel research design. The sample involved 100 respondents, including five heads of public secondary schools, 91 secondary school teachers, the Ward Education Officers, the Municipal Secondary Education officer, the Municipal Director, and the Zonal School Quality Assurance officers. Data was collected through questionnaires and an interview guide. Earlier, a pilot study was conducted to check the validity and reliability of the research instruments. Qualitative data were analyzed thematically, while descriptive statistics were used to analyze quantitative data. A synthesis of the findings indicated that school quality assurance recommendations contribute much to teacher efficacy. This was done through the sessions conducted during the school visit, where teachers were guided, advised, and instructed to improve their efficacy. Moreover, the study found that lack of motivation to teachers with regard to their rights and inadequacies in requisite skills for School Quality Assurance Officers affected the implementation of the recommendation given. The study concludes that school quality assurance recommendations are vital to the growth of teacher efficacy. Thus, educational stakeholders are encouraged to advise teachers to implement the school quality assurance recommendations.
The risk of smartphone addiction in university students and its affecting factors

Aim: In this study, it was aimed to determine the risk of smartphone addiction in university students and to reveal the factors affecting it. Material and Method: This descriptive and cross-sectional study was conducted on first and fourth year students of the Faculty of Health Sciences, Faculty of Economics and Administrative Sciences, and Faculty of Engineering and Architecture of a state university in the Central Anatolia Region. The study was completed with 1181 students who agreed to participate in the research. Data were collected using a sociodemographic information form consisting of 29 questions and a Smartphone Addiction Scale-Short Form (SAS-SF) consisting of 10 questions. Chi-square and logistic regression analysis were used in the comparative analyses, and p
Meningkatkan Kemampuan Bepikir Kreatif melalui Penerapan Pendekatan Saintifik Berbantuan VBA Excel

Masih rendahnya berpikir kreatif siswa SMP dan masih kurangnya penggunaan media belajar berbasis ICT merupakan masalah dalam penelitian ini . Adapun penelitian memiliki tujuan untuk mengetahui efektivitas pembelajaran saintifik dengan bantuan VBA Excel dalam berpikir kreatif siswa. Metode eksperimen merupak metode yang digunakan dalam penelitian ini dengan desain Non Equivalent Control Group Desain, sampel diambil dari dua kelas di satu SMP yang berada di kota Cimahi. Instrumen yang digunakan berbentuk tes kemampuann berpikir kreatif yang berjumlah 5 soal. Hasil dalam penelitian ini menyimpulkan bahwa pembelajaran saintifik dengan bantuan VBA Excel lebih efektif dari pembelajaran biasa dilihat dari kemampuan berpikir kreatif.
BPTM: Blockchain-Based Privacy-Preserving Task Matching in Crowdsourcing

Task matching in crowdsourcing is designed to provide convenient task information retrieval and has been extensively explored. In general, the task matching process is required to be reliable and to meet privacy requirements. However, most existing privacy-preserving task matching solutions for crowdsourcing focus on privacy issues but ignore the reliability of the process. In this paper, we propose a blockchain-based task matching scheme for crowdsourcing with a secure and reliable matching. Instead of utilizing a centralized cloud server, we employ smart contracts, an emerging blockchain technology, to provide reliable and transparent matching. In this way, data confidentiality and identity anonymity are achieved effectively and efficiently. The extensive privacy analysis and performance evaluation show that our solution is secure and feasible.
Evaluation of Industry 4.0 on Sustainable Supply Chain of manufacturing companies: A Thematic Literature Review.

Purpose of this paper: As past industrial revolutions gave way to the technological era known as Industry 4.0, increased desire for horizontal, vertical, and end-to-end digital integration became prevalent. Adopting Industry 4.0 has significant impact on supply chain network sustainability, according to previous research. However, very little study has been done on how to use Industry 4.0 technology to successfully manage supply chain network sustainability. The goal of this paper is to provide a thematic analysis of existing research on Industry 4.0's impact on manufacturing supply chain sustainability.
Text Mining Approaches for Semantic Similarity Exploration and Metadata Enrichment of Scientific Digital Libraries. (Approches en fouille de texte pour une exploration par similarité sémantique et un enrichissement en métadonnées des bibliothèques numériques)

Pour les scientifiques et chercheurs, s’assurer que la connaissance est accessible pour pouvoir etre reutilisee et developpee est un point crucial. De plus, la facon dont nous stockons et gerons les articles scientifiques et leurs metadonnees dans les bibliotheques numeriques determine la quantite d’articles pertinents que nous pouvons decouvrir et auxquels nous pouvons acceder en fonction de la signification reelle d’une requete de recherche. Cependant, sommes-nous en mesure d’explorer tous les documents scientifiques semantiquement pertinents avec les systemes existants de recherche d’information au moyen de mots-cles ? Il s’agit la de la question essentielle abordee dans cette these. L’objectif principal de nos travaux est d’elargir ou developper le spectre des connaissances des chercheurs travaillant dans un domaine interdisciplinaire lorsqu’ils utilisent les systemes de recherche d’information des bibliotheques numeriques multidisciplinaires. Le probleme se pose cependant lorsque de tels chercheurs utilisent des mots-cles de recherche dependant de la communaute dont ils sont issus alors que d’autres termes scientifiques sont attribues a des concepts pertinents lorsqu’ils sont utilises dans des communautes de recherche differentes. Afin de proposer une solution a cette tâche d’exploration semantique dans des bibliotheques numeriques multidisciplinaires, nous avons applique plusieurs approches de fouille de texte. Tout d’abord, nous avons etudie la representation semantique des mots, des phrases, des paragraphes et des documents pour une meilleure estimation de la similarite semantique. Ensuite, nous avons utilise les informations semantiques des mots dans des bases de donnees lexicales et des graphes de connaissance afin d’ameliorer notre approche semantique. En outre, la these presente quelques implementations de cas d’utilisation du modele que nous avons propose
A Cost-Effective and Low-Complexity Predictive Control for Matrix Converters Under Unbalanced Grid Voltage Conditions

Due to no dc-link energy storage element in the matrix converter (MC), its input and output performances are highly sensitive to the unbalanced grid conditions. In order to mitigate the adverse effects of the unbalanced grid voltages, this paper proposes a simple and effective control strategy based on the finite-control set model predictive control (FCS-MPC). In this case, an extended instantaneous power theory is adapted to generate the source current references. As a result, both sinusoidal source currents and balanced output currents are ensured. Besides, complicated positive and negative sequence decomposition is avoided. Furthermore, an extended state observer (ESO) is designed to eliminate the grid voltages sensors, which not only estimates the grid voltages but also provides the delayed grid voltage information required for the source current reference calculation. The effectiveness of the proposed method is verified by the simulation and experimental results.
Efficient Charging Pad for Unmanned Aerial Vehicle Based on Direct Contact

Recently there has been significant interest in the development of autonomously Unmanned Aerial Vehicles (UAVs), especially the rotor-based robots, which are highly maneuverable and can vertically take-off and land. However, the discharge characteristics and charge storage capacity limitations of their lithium-ion battery can restrict their flight time endurance. The utilization of an automatic drone charging station is therefore desirable for these robots. This paper proposes a fully automatic contact-based charging station for UAVs to recharge UAV's and thereby solve the UAV's flight endurance limitations. The ground station comprises square-shaped copper plates of consecutively polarized positively and negatively in the form of a chess board with specific dimensions to ensure electrical contact when landing. The design methodology employed with the charging station accounts for the variations of the orientation of the UAV after landing on the platform. Furthermore, this innovation employs an autonomous recharging process after touchdown. Subsequently, this technology relaxes usual flight time constraints and improves overall mission times. The UAV equipped with a suitable hardware circuit the onboard circuit consists of six bridge diode rectifiers to modulate the polarity of the four UAV's contact terminals that allows autonomous recharge regardless of the yaw angle between UAVs and the charging pad platform, this, in turn, simplifies landing protocols. The result shows that the charging circuit successfully charged the UAV battery until 12.5v.
Biclustering of Gene Expression Data Based on Binary Artificial Fish Swarm Algorithm

Many existing biclustering algorithms has been used to determine co-expressed genes in gene expression data under subsets of experimental conditions. The Mean Squared Residue (MSR) or the Average Correlation Value (ACV) often be employed as fitness functions. But this measure may not find some relevant genes with shifting and scaling patterns. Here we introduce a new approach - Binary Artificial Fish Swarm Algorithm (BAFSA), which possesses an improved Meta-heuristic search algorithm that combines traditional artificial fish swarm algorithm (AFSA) with binary forms. To find genes with shifting and scaling patterns, we used a fitness function based on the linear correlation. The biclustering algorithm based on BAFSA has been applied to Mice Protein Expression dataset and many biologically significant biclusters are found, which exhibited the superb performance. Then the performance of the proposed method is compared to CC, QUBIC and FLOC.
Analysis of Optimization Algorithms via Integral Quadratic Constraints: Nonstrongly Convex Problems

In this paper, we develop a unified framework able to certify both exponential and subexponential convergence rates for a wide range of iterative first-order optimization algorithms. To this end, we construct a family of parameter-dependent nonquadratic Lyapunov functions that can generate convergence rates in addition to proving asymptotic convergence. Using Integral Quadratic Constraints (IQCs) from robust control theory, we propose a Linear Matrix Inequality (LMI) to guide the search for the parameters of the Lyapunov function in order to establish a rate bound. Based on this result, we formulate a Semidefinite Programming (SDP) whose solution yields the best convergence rate that can be certified by the class of Lyapunov functions under consideration. We illustrate the utility of our results by analyzing the gradient method, proximal algorithms and their accelerated variants for (strongly) convex problems. We also develop the continuous-time counterpart, whereby we analyze the gradient flow and the continuous-time limit of Nesterov's accelerated method.
Health Extension Workers Involvement in the Utilization of Focused Antenatal Care Service in Rural Sidama Zone, Southern Ethiopia: A Cross-Sectional Study

Background: Health extension workers (HEWs) are primarily been assigned in rural areas of Ethiopia to provide maternal and child health services. Few studies have been done to investigate HEWs’ contributions towards maternal health services. This study describes HEWs involvement in the utilization of focused antenatal care (FANC). Methods: A population-based cross-sectional survey was conducted between January 21 and February 4, 2017. Mothers (2300) who gave birth in the last 6 months (0-6 months) in randomly selected 30 kebeles in the rural Sidama zone, participated in the study. A face-to-face interview was done using a structured questionnaire adapted from the Saving Newborn Lives Program. The main outcome variable was FANC utilization. Descriptive statistics and multivariate logistic regression analysis were used using SPSS statistical software. Results: The FANC was used by 525 (24.36%; 95% confidence interval [CI]: 22.5%-26.2%) women. Health extension workers accounted for 244 (46.47%; 95% CI: 43.5-47.7%) of mothers. The FANC utilization was less likely among those who were illiterate (adjusted odds ratio [AOR]: .32; 95% CI: .18-.57) and those who attended first cycle (AOR: .41; 95% CI: .23-.74), those who attended secondary cycle (AOR: .47; 95% CI: .27-.82), primipara (AOR: 0.53; 95% CI: .35-.83), and those who gave birth at home (AOR: .66; 95% CI: .51-.84). Mothers who had knowledge of pregnancy danger signs (AOR: 1.42; 95% CI: 1.2-1.7) and exposure to mass media (AOR: 1.35; 95% CI: 1.1-1.66) were more likely to utilize FANC. Conclusions: FANC utilization in this study was low compared to other studies. The HEWs had a major contribution to the services. However, it is low when compared to the plan set by the state ministry of health. The existing health extension program could be strengthened by increasing the number of HEWs. Empowering rural mothers through continuous education program to enhance the utilization of maternal health services.
Manifold Learning for Cross-project Software Defect Prediction

Traditional software defect prediction studies usually built models using within-project data. However, there are not enough local data repositories for us to build the software defect prediction model in practice. Recently, cross-project software defect prediction (CSDP) has been proposed. Due to distributions of source and target domains are different, the existing CSDP models do not investigate how to use mixed project data to predict target. In this paper, we propose a method termed geodesic flow kernel software defect prediction (GFKSDP) to solve the problem of different distributions between source domain and target domain. Our GFKSDP method shrinks the differences of source and target domains by integrating an infinite number of subspaces that characterize the changes of geometric and statistical properties from source domain to target domain. Our method can adaptively determine significant parameters to reduce computational complexity. Besides, this method does better than traditional studies in unsupervised learning. Experimental results in AEEEM and Relink datasets show that the proposed method can effectively improve the performance of cross-project software defect prediction. And the proposed method outperforms state-of-the-art methods in unsupervised learning.
The Effect of Beta-Carotene Intake on Lung Cancer Development

Lung cancer remains the leading cause of cancer death in the United States, comprising nearly 30% of all cancer-related mortality. Investigation into nutritional supplementation with beta-carotene as a means of reducing lung cancer incidence remains an active and intriguing area of research. The present review summarizes the salient findings in the literature to date. Although a number of observational studies have suggested an association between carotenoid consumption and reduced lung cancer incidence, two major double-blind, randomized, placebo-controlled trials have not yielded similar results. Not only did these studies fail to demonstrate a protective effect of beta-carotene in lung carcinogenesis, they indicated that beta-carotene supplementation may increase one’s risk of developing lung cancer. Potential reasons for such discrepancies between observational studies and these randomized trials are explored, along with hypotheses concerning the potential carcinogenic risk of beta-carotene. However, such theories remain unsubstantiated at present and therefore no recommendations may currently be made regarding beta-carotene intake for the purpose of reducing lung cancer incidence. Until such contradictions within the literature are resolved from basic science and epidemiologic perspectives, the optimal lung cancer prevention strategy shall remain the traditional triad of smoking cessation, smoking prevention, and avoidance of carcinogenic environmental insults such as asbestos.
Micro-force sensing techniques and traceable reference forces: a review

Micro-force measurement with high resolution, accuracy, and reliability is of interest in a broad range of applications including gravitational-wave detection, intelligent healthcare, bionic robotics, and micromanipulation. Herein, the researchand development in recent years of micro-force sensors based on various principles is reviewed thoroughly, presenting their characteristics and applications, as well as summarizing their advantages and limitations. The most indispensable component of force sensors, elastic sensitive elements, is underlined. Next, four kinds of not widely used but promising sensors are also introduced briefly. Finally, the traceable reference forces are analyzed, concluding with a future perspective into the corresponding challenges and opportunities of micro-force sensors for future research. This review aims at providing references for developing micro-force sensors and improving their performance.
Putting the Probability Heuristics Model to the Test

In the last decades there was a shift from more logically inspired theories describing human reasoning towards the new paradigm of probabilistic approaches. One of the most prominent models for syllogistic reasoning is the Probability Heuristics Model (PHM) which has been formulated based on ﬁ ve heuristics. The contribution of this article is: (i) to provide an analysis of different formalizations of the PHM, (ii) to examine the impact of each heuristic, and (iii) to identify possible violations of underlying assumptions in present implementations. A systematic analysis of the model parameters shows a surprising variation in parameter values across experiments. A Bayesian modeling approach explains this variance of parameters. Implications for probabilistic approaches are discussed.
A Pilot Study: Favorable Effects of Clostridium butyricum on Intestinal Microbiota for Adjuvant Therapy of Lung Cancer

Simple Summary A previous study has reported that the intestinal microbiota plays important roles in drug efficacy and toxicity in response to anticancer treatment. Clostridium butyricum could increase intestinal beneficial bacteria and has been clinically used in many diseases. Therefore, we tried to assess the roles of Clostridium butyricum by examining the composition, structure, diversity, marked differences, and interactional network of intestinal microbiota, as well as the progression-free survival, overall survival, and adverse events. The results showed that Clostridium butyricum supplement made some favorable changes in intestinal microbiota, such as the higher total richness of the genus Clostridium, Bifidobacterium, and Lactobacillus, and no distinguishing opportunistic pathogenetic markers, as well as the reduction in adverse events. This makes probiotics be promising adjunctive therapeutic avenues for lung cancer. Abstract Probiotics as medications have previously been shown to change intestinal microbial characteristics, potentially influencing cancer therapy efficacy. Patients with non-squamous non-small cell lung cancer (NS-NSCLC) treated by bevacizumab plus platinum-based chemotherapy were randomized to obtain Clostridium butyricum supplement (CBS) or receive a placebo as adjuvant therapy. Clinical efficacy and safety were assessed using progression-free survival (PFS), overall survival (OS), and adverse events (AE). Intestinal microbiota was longitudinally explored between CBS and placebo groups over time. Patients who took CBS had significantly decreased bacterial richness and abundance, as well as increased the total richness of the genus Clostridium, Bifidobacterium, and Lactobacillus compared to the placebo group (p < 0.05). Beta diversity and the interactional network of intestinal microbiota were distinctly different between CBS and placebo group. However, there were no significant variations between them in terms of microbial taxonomical taxa and alpha diversity. The potential opportunistic pathogen Shewanella was still detectable after treatment in the placebo group, while no distinguishing microbial markers were found in the CBS group. In terms of clinical efficacy, the CBS group had a significantly reduced AE compare to the placebo group (p < 0.05), although no significantly longer PFS and OS. Therefore, favorable modifications in intestinal microbiota and significant improvements in drug safety make probiotics be promising adjunctive therapeutic avenues for lung cancer treatment.