Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of the Need for Teacher Training at The Primary School Level

The study's main objective was to determine the need for primary school teacher training. The study's major goals were to (a) investigate the importance of teacher training programs at the primary school level for the effectiveness of the teaching-learning process; (b) determine the need for teacher training at the primary school level; (c) determine the need for professional training at the primary school level, and (c) recommend specific measures to improve primary school teacher training. A survey technique was utilized to conduct the study. Through random sampling, one hundred thirty-eight (138) male-female public primary and Punjab Education Foundation (PEF) affiliated school teachers were selected from the five Tehsils of District Bahawalpur with an equal proportion of urban and rural schools. The data were collected through a questionnaire and interpreted through SPSS V23. The results showed that no one teacher had excellent knowledge of to use learning tools in the classroom, Knowledge of National Profession Standards for teachers, concepts of math, practical application of content knowledge skills and English Language skills. So, there is a need of training for teachers to use the learning tools in the classroom effectively, to know about National professional Standards for teachers, teaching skills of math, science, and English.
On the Malware Detection Problem: Challenges & Novel Approaches

Many solutions to detect malware have been proposed over time, but effective and efficient malware detection still remains an open problem. In this work, I take a look at some malware detection challenges and pitfalls to contribute towards increasing system’s malware detection capabilities. I propose a new approach to tackle malware research in a practical but still scientific manner and leverage this approach to investigate four issues: (i) the need for understanding context to allow proper detection of localized threats; (ii) the need for developing better metrics for AntiVirus (AV) evaluation; (iii) the feasibility of leveraging hardware-software collaboration for efficient AV implementation, and (iv) the need for predicting future threats to allow faster incident responses.
Designing Efficient and Sustainable Predictions of Water Quality Indexes at the Regional Scale Using Machine Learning Algorithms

Water quality and scarcity are key topics considered by the Sustainable Development Goals (SDGs), institutions, policymakers and stakeholders to guarantee human safety, but also vital to protect natural ecosystems. However, conventional approaches to deciding the suitability of water for drinking purposes are often costly because multiple characteristics are required, notably in low-income countries. As a result, building right and trustworthy models is mandatory to correctly manage available groundwater resources. In this research, we propose to check multiple classification techniques such as Decision Trees (DT), K-Nearest Neighbors (KNN), Discriminants Analysis (DA), Support Vector Machine (SVM), and Ensemble Trees (ET) to design the best strategy allowing the forecast a Water Quality Index (WQI). To achieve this goal, an extended dataset characterized by water samples collected in a total of twelve municipalities of the Wilaya of Naâma in Algeria was considered. Among them, 151 samples were examined as training samples, and 18 were used to test and confirm the prediction model. Later, data samples were classified based on the WQI into four states: excellent water quality, good water quality, poor water quality, and very poor or unsafe water. The main results revealed that the SVM classifier obtained the highest forecast accuracy, with 95.4% of prediction accuracy when the data are standardized and 88.9% for the accuracy of the test samples. The results confirmed that the use of machine learning models are powerful tools for forecasting drinking water as larger scales to promote the design of efficient and sustainable water quality control and support decision-plans.
Solving the system of Radiation Magnetohydrodynamics: for solar physical simulations in 3d

In this study we present a finite-volumen scheme for solving the equations of radiation magnetohydrodynamics in two and three space dimensions. Among other applications this system is used to model the plasma in the solar convection zone and in the solar photosphere. It is a non--linear system of balance laws derived from the Euler equations of gas dynamics and the Maxwell equations; the energy transport through radiation is also included in the model. The starting point of our presentation is a standard explicit first and second order finite-volume scheme on both structured and unstructured grids. We first study the convergence of a finite-volume scheme applied to a scalar model problem for the full system of radiation magnetohydrodynamics. We then present modifications of the base scheme. These make it possible to approximate the system of magnetohydrodynamics with an arbitrary equation of state; they reduce errors due to a violation of the divergence constraint on the magnetic field, and they lead to an improved accuracy in the approximation of solution near an equilibrium state. These modifications significantly increase the robustness of the scheme and are essential for an accurate simulation of processes in the solar atmosphere .
The News Sharing Gap: Divergence in Online Political News Publication and Dissemination Patterns across Elections and Countries

Abstract Are journalists and Facebook users equally interested in political news? Introducing the conceptualization and measurement of the “news sharing gap”, this study compares the sharing of political news by Facebook users to the production of political news by news media organizations. To paint a broad picture of these differences, we compare the news sharing gap (a) across election and routine periods and (b) across eight countries: Australia, Austria, Brazil, Germany, the Netherlands, Romania, Spain, and the United Kingdom. Analyzing 265,714 articles shared over 12 million times on Facebook, findings show that elections are broadly linked to increases in political news publication, but even larger increases in political news sharing. The study reveals how, overall, political news is shared more often than news publication patterns would suggest, proposing higher political interest by Facebook users than previously thought. In most cases, political news sharing far outpaces political news production in the form of a “negative” news sharing gap, with the relative demand for political news (in the form of news sharing) being higher than the supply. Lastly, building upon previous work, we propose and validate a distant supervised machine learning method for multilingual, large-scale identification of political news across distinct languages, contexts and time periods.
A Review: Consensus Algorithms on Blockchain

Blockchain is a distributed public ledger that keeps track of all transactions that have ever taken place in the system. As a distributed ledger, a consensus mechanism is required to ensure all the transaction functions properly. In order to reach a consensus, it is critical to emphasize the importance of performance and efficiency. The use of the right consensus algorithm will signif-icantly improve the efficiency of a blockchain application. This paper reviewed several types of consensus algorithms used in blockchain and discusses the idea of a new consensus algorithm that can improve the performance of consortium blockchain.
Regional informal institutions, local governance and internal migration in Vietnam

ABSTRACT Internal migration is an institutionalized process; it is affected not only by economic factors but also by institutional forces. This study thus employs institutional theory to investigate the importance of regional informal institutions and local governance quality on types of migration, as well as their impacts on migrants’ earnings. Examining a set of national internal migration data in Vietnam, we find that pure socialist regions (the north) attract in-migration for job-seeking purposes, while pre-capitalist regions (the south) attract in-migration for business opportunity-seeking purposes. More importantly, migration across regions with dissimilar informal institutions yields adverse effects on migrants’ earnings. Finally, migration from regions with a lower governance quality to regions with a higher governance quality improves migrants’ earnings.
Fused Deposition Modeling PEEK Implants for Personalized Surgical Application: From Clinical Need to Biofabrication

Three-dimensional printing (3DP) technology is suitable for manufacturing personalized orthopedic implants for reconstruction surgery. Compared with traditional titanium, polyether-ether-ketone (PEEK) is the ideal material for 3DP orthopedic implants due to its various advantages, including thermoplasticity, thermal stability, high chemical stability, and radiolucency suitable elastic modulus. However, it is challenging to develop a well-designed method and manufacturing technique to meet the clinical needs because it requires elaborate details and interplays with clinical work. Furthermore, establishing surgical standards for new implants requires many clinical cases and an accumulation of surgical experience. Thus, there are few case reports on using 3DP PEEK implants in clinical practice. Herein, we formed a team with a lot of engineers, scientists, and doctors and conducted a series of studies on the 3DP PEEK implants for chest wall reconstruction. First, the thoracic surgeons sort out the specific types of chest wall defects. Then, the engineers designed the shape of the implant and performed finite element analysis for every implant. To meet the clinical needs and mechanical requirements of implants, we developed a new fused deposition modeling technology to make personalized PEEK implants. Overall, the thoracic surgeons have used 114 personalized 3DP PEEK implants to reconstruct the chest wall defect and further established the surgical standards of the implants as part of the Chinese clinical guidelines. The surface modification technique and composite process are developed to overcome the new clinical problems of implant-related complications after surgery. Finally, the major challenges and possible solutions to translating 3DP PEEK implants into a mature and prevalent clinical product are discussed in the paper.
Generation of Optical Harmonics by Intense Pulses of Laser Radiation

Abstract We discuss effects of optical depth, beam focusing and non-perturbative atomic response on the spectrum of optical harmonics generated by an intense beam of laser radiation (up to 1014W cm−2) in a small volume of atomic vapour. We find remarkable enhancement of high harmonics at intensities beyond the perturbative limit.
Human Cytomegalovirus Induces Vitamin-D Resistance In Vitro by Dysregulating the Transcriptional Repressor Snail

Vitamin-D supplementation is considered to play a beneficial role against multiple viruses due to its immune-regulating and direct antimicrobial effects. In contrast, the human cytomegalovirus (HCMV) has shown to be resistant to treatment with vitamin D in vitro by downregulation of the vitamin-D receptor. In this study, we aimed to elucidate the mechanism and possible biological consequences of vitamin-D resistance during HCMV infection. Mechanistically, HCMV induced vitamin-D resistance by downregulating the vitamin-D receptor (VDR) within hours of lytic infection. We found that the VDR was inhibited at the promoter level, and treatment with histone deacetylase inhibitors could restore VDR expression. VDR downregulation highly correlated with the upregulation of the transcriptional repressor Snail1, a mechanism likely contributing to the epigenetic inactivation of the VDR promoter, since siRNA-mediated knockdown of Snail partly restored levels of VDR expression. Finally, we found that direct addition of the vitamin-D-inducible antimicrobial peptide LL-37 strongly and significantly reduced viral titers in infected fibroblasts, highlighting VDR biological relevance and the potential of vitamin-D-inducible peptides for the antiviral treatment of vitamin-D deficient patients.
Deciphering the lncRNA and mRNA profiles of Min pig backfat after acute cold stress

ABSTRACT Cold stress can promote fat metabolism and reduce fat deposition. The present study compared differentially expressed (DE) lncRNAs and mRNAs in the subcutaneous back fat of Min pigs subjected to 24 h acute cold stress. Transcriptome sequencing was used to identify DE lncRNAs and mRNAs. A lncRNA mRNA co expression network was constructed using the confirmed lncRNAs. GO and KEGG analyses were performed to investigate the biological processes involved in the response to acute cold stress. A total of 166 DE lncRNAs and 269 DE mRNAs were found. Several genes such as ANGPTL4, KLF11 and NUPR1 were related to lipid metabolism. Interferon stimulating genes (ISGS) such as RSAD2, ISG20, ISG15, DDX60, MX2, MX1 and IFI6 were all significantly up-regulated. In addition, genes related to blood flow, respiration, and oxidative stress were also up-regulated. Many terms related to defense response to a virus were found in the GO annotation. A lncRNA mRNA co expression network comprising 46 specific mRNAs and the top 10 up-regulated DE lncRNAs was constructed. According to the results, the interaction between lncRNAs and mRNAs was complex. This study provides genome-wide gene and lncRNA expression profiles in adipose tissue of pigs after acute cold stress.
Stock Price Trend Analysis Method Based on Change Cycle Clustering

: With the continuous development of financial informatization and the continuous improvement of China's economic system, analyzing and mining financial data has become an important means to study financial problems. Compared with other industries in the financial field, stock data is easier to collect and store, and its application is more convenient. Analyzing and predicting the changing trend of the future stock market through the historical data of stocks is helpful to reduce the risk of investors and increase income. It has become a research hotspot in the financial field. Using the historical price data of domestic stocks, a method based on a clustering algorithm is proposed to analyze the periodic characteristics of stock price changes. This method can provide a basis for the prediction of stock price and the detection of trading behaviour in the stock market.
Grain Hermetic Storage Adoption in Northern Uganda: Awareness, Use, and the Constraints to Technology Adoption

Post-harvest storage losses (PHLs) remain significant in Sub-Saharan Africa (SSA) due to several factors mainly insect pests and molds. Hermetic storage technologies (HSTs) are being promoted to address these storage losses. In Uganda, HSTs were first introduced in 2012. However, its use among farming households remains low today. Data were collected from 306 smallholder farmers from four districts of Northern Uganda using a pre-tested semi-structured questionnaire to understand their knowledge, use, and constraints to the adoption of hermetic storage. A multivariate Logit regression model was used to find the significance of the factors affecting adoption. Results showed low awareness and use of hermetic storage among smallholder farmers. Only 53.3% of the interviewed farmers were aware of the use of hermetic storage for grain storage. The SuperGrain bag was the most known form of hermetic storage (35.3%), followed by the Purdue Improved Crop Storage (PICS) bag (34.9%), metallic silo (15.5%), and plastic silo (14.4%). Hermetic storage use was even lower as only 17.6% storage in Northern Uganda. The logit regression models showed that only training in hermetic storage significantly (p = 0.002) affected farmers’ decision to adopt hermetic storage. Understanding the factors that constrain the adoption of HSTs could provide policymakers with important information to initiate and design policies and programs aimed at reducing crop storage losses.
Webpage Intelligent Parsing Algorithm Based on Text and Symbol Density

: Web page intelligent parsing is an inevitable part of data collection. News web pages contain a lot of information with little relevance to the topic, which makes it difficult to locate the text content directly and quickly during the data collection process. This paper proposes a web page intelligent parsing algorithm based on text and symbol density. Through empirical research on the pages of mainstream news websites in China, the algorithm can quickly and accurately extract the text of news web pages.
Long‐term Camellia oleifera cultivation influences the assembly process of soil bacteria in different soil aggregate particles

Understanding the mechanisms that govern microbial community assembly across a soil–plant continuum is crucial for predicting the responses of ecosystems to environmental changes. However, the impact of the health status of plants on microbial assemblies across these continuums remains poorly understood. In this study, we investigated the stochastic and deterministic processes associated with different aggregate particles and Camellia oleifera growth stages (sapling, maturity, and degeneration periods). The composition of bacterial communities in C. oleifera soil was significantly affected by cultivation length and aggregate size, and cultivation length plays a more important role in the regulation of bacterial composition. The assembly processes of the bacterial community were dominated by deterministic processes in soil aggregates at different growth stages. Soil structurality determined soil α‐diversity and β‐diversity to a greater extent than soil nutrient content. This study provides a comprehensive understanding of the evolutionary mechanisms of soil bacterial community in different soil aggregates and C. oleifera plantation lengths and highlights the significance of soil physical properties to mediate soil microbial community composition and abundance.
Hydroprocessing and Blending of a Biomass-Based DTG-Gasoline

The number of annually registered internal-combustion vehicles still exceeds electric-driven ones in most regions, e.g., Germany. Ambitious goals are disclosed with the European Green Deal, which calls for new technical approaches and greenhouse gas neutral transition technologies. Such bridging technologies are synthetic fuels for the transportation sector, e.g., using the bioliq ® process for a CO 2 -neutral gasoline supply. Fuels must meet the applicable national standards to be used in existing engines. Petrochemical parameters can be variably adapted to their requirements by hydroprocessing. In this work, we considered the upgrading of aromatic-rich DTG gasoline from the bioliq ® process. The heavy gasoline was therefore separated from the light one by rectification. We investigated how to selectively modify the petrochemical parameters of the heavy gasoline, especially the boiling characteristics, to make the product suitable as a high-quality blending component. Three commercially available Pt/zeolite catalysts were tested over a wide range of temperature and space velocity. We achieved high gasoline yields, while the content of light end compounds up to a boiling temperature of 150°C could be increased significantly. In contrast to the high naphthenic content of the gasoline, the obtained octane numbers were satisfactory. Especially the Motor Octane Number turned out unexpectedly high and showed a dependency on the isomerization of the naphthenic rings. By blending the upgraded heavy gasoline with the previously separated light gasoline, we could finally show that hydroprocessing is suitable for adjusting petrochemical parameters. The aromatic concentration was 37.5% lower than that in the original raw gasoline, while the boiling characteristics improved significantly. Additionally, the final boiling point was 82°C lower, which is beneficial for the emission behavior.
Comparative Genome Analysis Reveals the Genomic Basis of Semi-Aquatic Adaptation in American Mink (Neovison vison)

Simple Summary In nature, every animal lives in its suitable environment to support life and reproduction. Due to survival pressure, the ancient ancestors of land animals changed from living in water to living on land through a long time of evolution. But as more and more animals live on land, the pressure to survive increases, and some animals continue to evolve and re-enter the water. Species evolved from water to land, fins became limbs, and re-entering organisms evolved webbing, all in an effort to better adapt to their environment. The American mink’s life is extremely dependent on the water environment, but its external changes are less pronounced than those of other water-dependent animals. Since the limited external changes are not sufficient to indicate that the American mink is a semi-aquatic mammal, we can explore the evidence of aquatic adaptation at the molecular level. Through comparative genomic analysis, we obtained that American mink has certain adaptive evolution to aquatic environment in olfactory, coagulation, immunity and other aspects. The results of this study have important reference significance for exploring biological evolution and species conservation. Abstract Although the American mink is extremely dependent on water and has evolved a range of aquatic characteristics, its structural adaptation to water is still less obvious than that of other typical semi-aquatic mammals, such as otters. Therefore, many scholars consider it not to be a semi-aquatic mammal. In order to make the point that minks are semi-aquatic mammals more convincing, we provide evidence at the micro (genome)-level. In particular, we used the genomes of the American mink and 13 mammalian species to reconstruct their evolutionary history, identified genes that affect aquatic adaptation, and examined the evolution of aquatic adaptation. By analyzing unique gene families, the expansion and contraction of gene families, and positive selection genes, we found that the American mink genome has evolved specifically for aquatic adaptation. In particular, we found that the main adaptive characteristics of the American mink include the external structural characteristics of bone and hair development, as well as the internal physiological characteristics of immunity, olfaction, coagulation, lipid metabolism, energy metabolism, and nitrogen metabolism. We also observed that the genomic characteristics of the American mink are similar to those of other aquatic and semi-aquatic mammals. This not only provides solid genomic evidence for the idea that minks are semi-aquatic mammals, but also leads to a clearer understanding of semi-aquatic species. At the same time, this study also provides a reference for the protection and utilization of the American mink.