Miracle-dz/UniFAR-FTUs huggingface dataset:
Physicochemical and nutritional composition of composite flour enriched with spinach leaves powder for development of value-added baked products

The present study was conducted to see the effect of supplementation of spinach leaves in commonly consumed baked and traditional products. Five types of composite flours were formulated by using wheat flour, bengal gram flour and spinach leaves powder at (4, 6, 8, 10 and 12% levels). Control-I (100% wheat flour), Control-II (100% bengal gram flour), Type-I composite flour (48:48:4), Type-II composite flour (47:47:6), Type-III composite flour (46:46:8), Type-IV composite flour (45:45:10), and Type-V composite flour (44:44:12), respectively. All the formulated composite flour samples were analyzed for their physico-chemical properties. It was observed that water absorption capacity, oil absorption capacity, swelling power, bulk density and least gelation capacity of composite flours increased significantly with increase in the level of incorporation of spinach powder. While solubility and flour dispersability were found to be decreased with increase in the level of spinach powder. Type-I composite flour showed highest values containing 4 per cent level of spinach powder while Type-V composite flour showed lower values containing 12 per cent level of incorporation. Moisture, crude protein, crude fat, crude fibre and ash content of all five types of composite flour samples were found significantly higher than the control sample (wheat flour) while in comparison to bengal gram flour (control), crude protein content in all five types of composite flour samples decreased due to replacement of bengal gram flour with wheat flour. Total iron, calcium, zinc, phosphorus, β-carotene, dietary fibre and anti-oxidant activity of all five types of composite flour found significantly higher than control sample.
Biology and Physics of Magnetotactic Bacteria

Magnetotactic bacteria are able to align their swimming direction to the geomagnetic field lines because they possess a magnetic moment. These bacteria biomineralize magnetic nanoparticles, magnetite or greigite, inside a membrane. The membrane + nanoparticle set is known as magnetosome and intracellular magnetosomes are disposed in a linear chain. Cytoskeleton-like filaments are resposible for the mechanical stability of this chain. The genes responsible for the magnetosome membrane and for the cytoskeleton proteins have been largely studied: the mam genes. The magnetosome chain also confers to the bacterial body a magnetic moment that can be measured through different physical techniques. Because of their response to magnetic field inversions, magnetotactic bacteria are good models to study bacterial motion. Theoretical and experimental studies show that magnetotactic bacteria swim following a trajectory similar to cylindrical helix. Magnetotactic microorganisms have been observed avoiding regions with UV or violet-blue light of high intensity. If the intensity is lower, magnetotactic microorganims show photokinesis, increasing their velocity in the presence of red light and decreasing their velocity in the presence of green light, both relative to the velocity with blue light.
Research on General File Storage Scheme Based on Hadoop

: HDFS is Hadoop's underlying distributed file storage system, designed for large files. When accessing a large number of small files, the problem of low read and write rate and excessive memory load will be encountered. Therefore, HDFS is optimized for the problem of different size files. Firstly, the historical access log is analyzed, the relevance between files is acquired by using Apriori algorithm, the correlation probability model is established, and then a directed graph merging algorithm based on correlation is proposed. In order to solve the problem of low access rate caused by small file merging, prefetching strategy and LRU substitution strategy based on high heat were introduced. Experimental results show that this scheme can effectively reduce the metadata volume of NameNode, improve the memory utilization, and effectively improve the file read and write performance.
Anthropogenic Disturbances Drive Domestic Dog Use of Atlantic Forest Protected Areas

Domestic dog is the most successful invasive mammalian predator species, and reducing its ecological impacts on wildlife is a central conservation goal globally. Free-ranging dogs can negatively interact with wildlife at multiple levels, posing issues for biodiversity conservation in tropical forests, especially in fragmented Atlantic Forest. To optimize future control programs, it is necessary to identify the main factors influencing their habitat use, particularly in natural reserves. We combined camera trapping data and occupancy models to characterize habitat use of dogs in six Atlantic Forest protected areas (134–36,000 ha). Our results show that dogs were more likely to use sites ( Ψ ^ ≥ 0.90) having higher housing density (≥4.00 houses/km2) or higher proportion of croplands and pasture (≥75%) relative to sites with no houses ( Ψ ^ = 0.23 ± 0.10) or lower proportion of croplands and pasture ( Ψ ^ = 0.34 ± 0.08). In addition, dogs had higher detection probability at camera locations on unpaved roads ( p ^ = 0.33 ± 0.05) relative to off-road sites ( p ^ = 0.18 ± 0.04), and in small protected areas with high housing density, that is, more disturbed sites, dogs had higher detection probabilities. Our findings indicate that the probability of dogs using a site within protected area is mainly driven by type and intensity of human activity in the surroundings. Given the urgent need to control free-ranging dogs within protected areas, we strongly recommend that managers target sites/areas within and near protected areas that have a rural housing density ≥ 4.00 houses/km2 or higher proportion of croplands and pasture (≥75%).
Deep Learning at the Mobile Edge: Opportunities for 5G Networks

Mobile edge computing (MEC) within 5G networks brings the power of cloud computing, storage, and analysis closer to the end user. The increased speeds and reduced delay enable novel applications such as connected vehicles, large-scale IoT, video streaming, and industry robotics. Machine Learning (ML) is leveraged within mobile edge computing to predict changes in demand based on cultural events, natural disasters, or daily commute patterns, and it prepares the network by automatically scaling up network resources as needed. Together, mobile edge computing and ML enable seamless automation of network management to reduce operational costs and enhance user experience. In this paper, we discuss the state of the art for ML within mobile edge computing and the advances needed in automating adaptive resource allocation, mobility modeling, security, and energy efficiency for 5G networks.
Is there a common threshold to subfossil chironomid assemblages at 16 m water depth? Evidence from the Tibetan Plateau

Fluctuating lake levels are an important driver of ecosystem change, and changes in the precipitation/evaporation balance of a region can lead to undesirable changes in ecosystem functioning. Large-scale changes in hydrology will become increasingly more likely as a result of ongoing climate change in the coming century. This is especially true for the Tibetan Plateau, which plays a crucial role as the “Asian water tower” for the surrounding densely populated regions. Chironomids (Diptera: Chironomidae) have proven to be one of the most valuable bioindicators for monitoring and reconstructing the development of aquatic ecosystems. Besides temperature, water depth and salinity are two of the most important environmental factors affecting chironomids. To study the relationship between chironomids and water depth, we analyzed surface sediment samples of two large Tibetan lakes, Selin Co and Taro Co. These lakes have similar environmental conditions (e.g. elevation, temperature and oxygenation) but show strong differences in salinity (7–10 and 0.5 ppt, respectively). Our results show that the chironomid assemblages in both lakes have similar water depths at which the fauna abruptly changes in composition, despite different faunal assemblages. The most important boundaries were identified at 0.8 and 16 m water depth. While the uppermost meter, the “splash zone”, is characterized by distinctly different conditions, resulting from waves and changing water levels, the cause of the lower zone boundary remains enigmatic. Even though none of the measured water depth-related factors, such as water temperature, oxygen content, sediment properties, light intensity or macrophyte vegetation, show a distinct change at 16 m water depth, comparison to other records show that a similar change in the chironomid fauna occurs at 16 m water depth in large, deep lakes around the world. We propose that this boundary might be connected to water pressure influencing the living conditions of the larvae or the absolute distance to the surface that has to be covered for the chironomid larvae to hatch. We conclude that water depth either directly or indirectly exerts a strong control on the chironomid assemblages even under different salinities, resulting in distribution patterns that can be used to reconstruct past fluctuations in water depths.
PENGARUH PEMBERIAN PUPUK UREA DAN PUPUK KANDANG KAMBING TERHADAP BEBERAPA SIFAT KIMIA TANAH DAN HASIL TANAMAN SAWI HIJAU

This study was aimed to elucidate the effect of the application of urea fertilizer and goat manure on some soil chemical properties and yield of green mustard plant ( Bressicca j uncea L). This study was conducted with a randomized block design consisting of two factors. The first factor was the application of goat manure (K) consisting of three levels, i.e. (K1) 1 t goat manure ha -1 , (K2) 1.5 t goat manure ha -1 , and (K3) 2 t goat manure ha -1 . The second factor was the application of urea fertilizer consisting of two dose levels, i.e. (U1 75 kg) urea fertilizer ha -1 , and (U2) 100 kg urea fertilizer ha -1 . The measured parameters were soil organic-C content, soil total N content, and fresh weight of the green mustard plant. Results of the study showed that the combination of 2 t goat manure ha -1 and 100 kg urea fertilizer ha -1 was the best treatment that significantly affected soil organic-C content, soil total N, soil C/N ratio, and yield of fresh weight of the mustard green plant.
Measuring the Impact of Using Different Tools on Classification System Results

A huge amount of textual data is available on the web. These data need to be classified under labels or classes to make the search more efficient and easier. Achieved by using automatic classification is used for this task. Many factors impact on the performance of the classifier system, such as the amount of using dataset, the data dispersion degree, preprocessing tools, feature extraction methods, terms weighting, and data reduction. So, researchers constantly compete to build a robust classifier with good performance. This study focuses on the effect of using different tools in preprocessing and term weighting stages. The experimental results applied on two different languages (Arabic and English languages). Also, the experimental results were compared with the recent related works.
Artificial Neural Network Based DGA Botnet Detection

In the recent years, botnet has become a serious threat to network security. As the result, the botnet detection solution is becoming an important topic for network security. DNS request is usually the first step to contact the C&C server of the bots controlled by the bot master and the detection of the DNS request domains is an effective way in detecting the bots. However, most botnets based on DNS protocol adopt Domain Generation Algorithm (DGA), which can change the domain randomly to hide themselves. Therefore, the traditional signature-based approach is rendered ineffective. Compared with the conventional ways of detection, the detection based on machine learning can obtain better detection result. In this work, we propose a botnet detection architecture based on Artificial Neural Network. We implement and evaluate the practicability of this solution with real datasets.
DESIGN AND ANALYSIS OF T-STUB U-SLOT NOTCH BAND FREQUENCY RECONFIGURABLE ANTENNA USING PARAMETRIC ANALYSIS FOR WIRELESS COMMUNICATION APPLICATIONS

© 2020 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 15 DESIGN AND ANALYSIS OF T-STUB U-SLOT NOTCH BAND FREQUENCY RECONFIGURABLE ANTENNA USING PARAMETRIC ANALYSIS FOR WIRELESS COMMUNICATION APPLICATIONS
First draft genome sequencing of the Salicola genus

Salicola sp. strain SBJ9 is an extremely halophilic bacterium newly isolated from a hypersaline lake in Sfax (Tunisia). It is an aerobic Gram negative bacterium, bacilli shaped and motile. The strain SBJ9 grows optimally at 150 g/l (2.57 M) NaCl, 35 °C and pH 7 and it is able to hydrolyze proteins and lipids at high salinities. Actually, Salicola genus contains only two species (S. marasensis and S. salis). This research reported the first draft genome of a bacterium belonging to Salicola and the different obtained and annotated sequences and genes. Salicola sp. strain SBJ9 genomic sequence contains 4.643. 441 bp with a G+C content of 60.52%. The annotation monitored by the RAST server reveals 65 RNA sequences and 3995 coding genes including 255 and 182 sub-systems of protein metabolism and lipids, respectively. The further analysis of SBJ9 genomic sequence will clarify the ideas regarding the adaptation of the Salicola strains and their proteins to the extremely high salt concentrations and will permit the development of new biocatalyzers for industrial applications.
The complete chloroplast genome of Tetradoxa omeiensis (Adoxaceae) provides insights into the phylogenetic relationship of Adoxaceae

Abstract The complete chloroplast genome of Tetradoxa omeiensis, which is endemic and endangered to the Hengduan Mountains, was sequenced and characterized in this study. The chloroplast genome is 157,502 bp in length, and it is consisted of four distinct regions: a large single-copy region (LSC, 86,526 bp), a small single-copy region (SSC, 18,682 bp) and a pair of inverted repeat regions (IRs, 26,147 bp). A total of 134 genes were annotated, including 86 protein-coding genes (80 PCG species), 40 tRNA genes (33 tRNA species) and 8 ribosomal RNA genes (4 rRNA species). We also reconstructed a comprehensive phylogenetic relationship of the family Adoxaceae, which demonstrated that the three Adoxaceae species clustered into two well-supported clades, and T. omeiensis is most closely related to the Adoxa moschatellina.
Phased array ultrasonic inspection of near-surface cracks in a railhead and its verification with rail slicing

In this study, near-surface cracks in a railhead are inspected thoroughly using phased array ultrasonic testing (PAUT). This research finds an alternative technique to inspect for near-surface cracks because the conventional non-destructive testing method for rail inspection lacks the capacity to inspect the near-surface crack profile. This study shows that PAUT can determine not only the crack depth but also the near-surface crack profile, so that the inspector can estimate the stage of crack growth and how the crack propagates. This information is valuable to the rail maintainer as one of the considerations for deciding the thickness of metal to remove when grinding the rail. In this study, after the measurement, the inspected region of the cracked railhead is sliced into thin pieces so that crack network information can be extracted. A 3D image reconstruction of the surface cracks based on the crack marks from all of the sliced rail pieces is performed. This image is then used as a reference to confirm the PAUT results. The results show that PAUT can clearly deliver crack profile estimation and provide an accurate estimation of a 3.51 mm crack-tip depth with an absolute error range of 8%-18%. The results also suggest that PAUT is a potential method for installation in a measurement train for near-surface crack inspection.
Research on Key Technologies of Knowledge Graph Construction Based on Natural Language Processing

As we all know, building a domain knowledge graph from a large amount of text requires a very large amount of work, including entity recognition, entity disambiguation, relationship extraction, and event extraction, etc. It is difficult to build a very comprehensive domain knowledge graph from scratch. Fortunately, with the rapid progress of natural language processing technology, we can use a large number of natural language processing tools to help us build a domain knowledge graph. This article mainly studies the extraction of domain terms in the process of constructing the knowledge graph. The natural language processing techniques used are mainly new word discovery, word segmentation, and keyword extraction. This paper improves the existing imperfect natural language processing technologies and applies them to the process of constructing the domain knowledge graph in order to construct the domain knowledge graph accurately and efficiently.
The Mediator Role of Thriving in the Relationship between Self-Efficacy and Mindfulness in Middle-Adolescence Sample.

In middle adolescence, young people being aware of self have more self-efficient in developmental issues due to their self-regulation. In this sense, thriving based on the self-regulation approach is thought to be a mediator for adolescents to experience self-efficacy and mindfulness. Thus, this study was aimed to investigate thriving as a mediator on the relationship between mindfulness and self-efficacy among high school students. A total of 309 high school students (245 females, 64 males), with the average age 16.97 (SD = 0.69), attending Uskudar Municipality Youth Academy in Istanbul between 2018-2019 for assistance towards their university exams participated in this study. The 44.1% of participants (n = 136) were in Grade 11, and 55.9% (n = 173) were in Grade 12. Mindfulness Attention Awareness Scale, Thriving at School Scale, and General Self Efficacy Scale were applied on participants to collect data. Pearson correlation coefficients were calculated to detect the relationship between variables, and the mediating role of thriving on the relationship between mindfulness and self-efficacy was tested using a simple mediation model. Results showed that variables were significantly correlated to each other, and thriving increases with increased mindfulness, while selfefficacy increases with increased thriving. The mediation model in its entirety proved statistically significant, accounting for 23% of the total variance. The findings have been discussed in line with relevant literature on the issue and recommendations on their possible application have been given.
A robust framework for cloud‐based software development outsourcing factors using analytical hierarchy process

Managing the cloud‐based software development outsourcing (CSDO) activities across the geographically distributed development sites are much challenging. This study aims to identify the success factors (SFs) for CSDO and prioritize them based on their significance. To achieve this objective, we conducted a systematic literature review (SLR) and survey study with industrial and academic experts. Finally, we applied the analytical hierarchy process (AHP) to develop the framework based on the prioritization of the identified SFs. We believe that the findings of this study will assist the industry practitioners and researchers in developing effective strategies for the successful implementation of CSDO activities.
Exact and Heuristic Algorithms for Some Spatial-aware Interest Group Query Problems

Location-based social networks are important issues in the recent decade. In modern social networks, such as Twitter, Facebook, and Plurk, attempt to get the accurate address positions from their users, and try to reduce the gap between virtuality and reality. This paper mainly aims at both the interests of Internet users and their real positions. This issue is called the spatial-aware interest group query problem (SIGQP). Given a user set U with n users, a keyword set W with m words, and a spatial object set S with s items, each of which contains one or multiple keywords. If a user checks in a certain spatial object, it means the user could be interested in that part of keywords, which is countable to clarify the interests of the user. The SIGQP then tries to find a k -user set U_k, k ≤ n , such that the union of keywords of these k users will equal to W , and additionally, the diameter (longest Euclidean distance of two arbitrary users in U_k) should be as small as possible. The SIGQP has been proved as NP-Complete, and two heuristic algorithms have been proposed. Extended from SIGQP, another problem is in finding the smallest k for U_k to cover all the keywords, with the users’ distance as the secondary criterion, called as “minimum user spatial-aware interest group query problem” (MUSIGQP). This paper designs a branch & bound method and a measure & conquer method to solve SIGQP and MUSIGQP respectively, and a performance analysis is given.
Phylogeny of dependencies and dependencies of phylogenies in genes and genomes

Molecular evolution, based on the study of sequencing data, established itself as a fundamental approach in the study of the history of living organisms (noticeably through the inference of phylogenetic trees). Classical molecular evolution methods rely on the decomposition of genomes into entities that are supposed independent: genes. However we know that genes do not evolve independently: their potential biological function lead them to be influenced by (and influence) the evolution of other genes. Moreover, their position along chromosomes imply that they share events of structural mutations (duplication, loss of a chromosome fragment) with neighbouring genes. Similarly, a gene individual history inscribes itself in the history of the species that bears it. I show that not taking into account this inter-dependency relationships (co- evolutionary relationships) during the inference of gene trees results in an overesti- mation of the differences between gene trees as well as between gene tree and species tree. Modelling efforts these last year have allowed the integration of gene and species co-evolution information to the reconstruction of gene trees. Besides, researchers have proposed models describing the evolution of the relationships linking genes, but without integration of this information in the tree building process. My works aim to combine these advances in a method that modify gene trees according to a criterion that integrates sequence information and information coming from co-evolution relationships. This method, applied to mammals and fungi, leads to gene histories that are more congruent (simpler adjacency histories, longer events of loss or transfer, .)