Miracle-dz/UniFAR-FTUs huggingface dataset:
DATA SCIENCE

One of the diseases that is generally characterized by symptoms of an increase in glucose levels in the blood and is one of the body diseases classified as chronic is diabetes. Diabetes suffered by a person from time to time can cause serious damage to other organs such as blood vessels, kidneys, heart and nerves. Machine learning provides various data mining algorithms that can be used to assist medical experts. The accuracy of machine learning algorithms is a measure of the effectiveness of decision support systems. Prediction of diabetes can be seen from the patient's medical record data, therefore the author wants to create a diabetes prediction system independently through a website-based application system. This application system will be combined with data observation, namely the science of data mining using the XGBoost algorithm. The dataset is divided into training data by 80% and testing data by 20%. Before creating the model, various parameter setting scenarios were carried out to evaluate several adjusted parameters, namely colsample_bytree, gamma, learning_rate, max_depth, n_estimators, reg_alpha, reg_lambda, and subsample. After sharing data and tuning parameters, the model results from the XGBoost algorithm have an accuracy of 74.67%, a precision value of 57.40%, a recall value of 65.94% and a specificity value of 78.50%.
Design of a Compact Circular Microstrip Patch Antenna for WLAN Applications

This paper presents the design of a compact circular microstrip patch antenna for WLAN applications which covers the band 5.15 to 5.825 GHz. The antenna is designed using 1.4mm thick FR-4 (lossy)substrate with relative permittivity 4.4 and a microstrip line feed is used. The radius of the circular patch is chosen as 7.62mm. To reduce the size and enhance the performance of the proposed antenna, a circular slot is loaded on circular patch and a square slot is etched on the ground plane of dimension 30mm×30mm. Design of the antenna is carried out using CST Microsoft Studio Sonimulation Software. The proposed antenna resonates at 5.5 GHz with a wider bandwidth of 702 MHz and it provides low return loss of -31.58 dB, good gain of 3.23 dB and directivity of 4.28 dBi and high efficiency of around 79% against the resonance frequency. The geometry of the proposed circular antenna with reduced size and its various performance parameters such as return loss, bandwidth, VSWR, gain, directivity, efficiency and radiation pattern plots are presented and discussed
‘Inductance’ definitions, modeling, and simulation

The nonlinear behavior of the B-H relationship of a ferromagnetic material gives rise to two different types of permeabilities: ‘total permeability’ and ‘derivative permeability’. These are used in this study to define three inductances of a current dependent inductor that is built around a ferromagnetic core: ‘total inductance’, ‘derivative inductance’ and ‘energy related inductance’. The latter is the correct parameter to be used when calculating the energy stored in a current dependent inductor. Based on these inductance definitions, state equations for the various ‘inductances’ were developed and used to implement SPICE compatible models by applying behavioral dependent sources. The theoretical derivations of this work were validated by simulation and experimentally
Lexical Associations of the Word Eden in the Qur’an: A Corpus Linguistics Approach

This study investigates the use of the term “” Eden in the Qur’an and its lexical associations following a corpus-based research approach. There is a controversy regarding the exact meaning and subsequently the translation of the term Eden in the Qur’an. While some researchers consider Eden to be a general descriptive term of all heavens, others argue that term is a name of some group of those heavens with specific characteristics. The study examines whether or not there are, in the Qur’an, specific lexical association patterns that recur exclusively with the mentioning of Eden as a hereafter reward and which can point to a special use of the term. Along with that basic purpose, the study provides the frequency of the occurrence of the word Eden in the Qur’an as well as its collocations. The tool implemented to search through the Qur’an corpus is the concordance program aConCorde. Three semantic categories of lexical associations are found. They are grouped into description of the heavens of Eden, description and deeds of the inhabitants of Eden who deserved this reward, and description and deeds of Allah who grants this reward. The results of the study revealed that most of the lexically associated and the collocated words are also available in other contexts describing heavens in general. They do not signal a special use of the term.
Funktionelle Anatomie des kraniozervikalen Übergangs

Die kraniozervikale Transitionszone beinhaltet über den kraniovertebralen Übergang hinaus eine Reihe von Komponenten, die eine hohe Spezialisierung beim Menschen entwickelt haben. Wegen der daraus resultierenden Vielzahl von möglichen Pathologien ist es wichtig, sich dieser besonderen Zusammensetzung bewusst zu sein. So vereinen Atlas, Axis und C3 in sich die meisten Differenzierungen von Grundelementen der Wirbelsäule und wirken dabei mit ebenso ungewöhnlichen Komponenten der Binde- und Stützgewebe zusammen. Die Muskulatur hat über die Bewegung und Sensorik hinaus Leistungen für die Passage zu Pharynx und Larynx zu erbringen. Neben dem Bewegungsapparat erwachsen Krankheitsbilder auch aus Störungen der Leitungsbahnen, also Gefäßen und Nerven.
Effect of Camellia sinensis, Punica granatum Peel, and Quercus persica Extracts on in vitro Fermentation Parameters

This experiment was conducted to study the effects of aqueous and alcoholic extracts of Camellia sinensis leaves (green tea), Punica granatum peel (pomegranate-peel), and Quercus persica (oak) at different concentrations on ruminal fermentation, dry matter and organic matter digestibility, methane production and protozoa population using gas production method. Experimental treatments were: control, 50, 100, and 200 μg/ ml aqueous and methanolic extract of Camellia sinensis, Punica granatum peel, and Quercus persica (19 treatments in total). Cumulative gas production was recorded at 2, 4, 6, 8, 12, 24, 36, 48, 72, and 96 h after incubation. Dry matter digestibility (DMD), organic matter digestibility (OMD), metabolizable energy (ME), pH, and short-chain fatty acids (SCFA) were calculated after 24 h incubation. Gas production at different times, methane production, and protozoa population were also measured. DMD, OMD, and pH were decreased by adding extracts. Microbial mass production (MCP) and microbial mass production efficiency (EMCP) significantly increased at a low level (50 μg/ ml) and significantly decreased at high levels of extracts containing tannins (100 and 200 μg/ ml) (p < 0.01). The treatments also increased short-chain fatty acids (SCFA), reduced methane concentration, and reduced PF and protozoa populations only at the highest levels of extracts (p <0.05).
OL-MEDC: An Online Approach for Cost-Effective Data Caching in Mobile Edge Computing Systems

Mobile Edge Computing (MEC) has emerged to overcome the inability of cloud computing to offer low latency services. It allows popular data to be cached on edge servers deployed within users’ geographic proximity. However, the storage resources on edge servers are constrained due to their limited physical sizes. Existing studies of edge caching have predominantly focused on maximizing caching performance from the mobile network operator's perspective, e.g., maximizing data retrieval success rate, minimizing system energy consumption, balancing the overall caching workload, etc. App vendors, as key stakeholders in MEC systems, need to maximize the caching revenue, considering the cost incurred and the benefit produced. We investigate this novel Mobile Edge Data Caching (MEDC) problem from the app vendor's perspective, and prove its <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math><alternatives><mml:math><mml:mi mathvariant="script">NP</mml:mi></mml:math><inline-graphic xlink:href="xia-ieq1-3107918.gif"/></alternatives></inline-formula>-hardness. We then propose Online MEDC (OL-MEDC), an approach that formulates MEDC strategies for app vendors, without requiring future information about data demands. Its performance is theoretically analyzed and experimentally evaluated. The experimental results demonstrate that OL-MEDC outperforms state-of-the-art approaches by at least 20.41% on average.
Anlotinib Exerts Potent Antileukemic Activities in B-Cell Acute Lymphoblastic Leukemia Via Attenuation of PI3K/AKT/mTOR Signaling Cascade

Background: Adult patients with B-cell acute lymphoblastic leukemia (B-ALL) have unfavorable prognosis. Recurrent disease is the major challenge for treatment success and has limited effective therapeutic strategies, suggesting that alternative therapeutics are unmet medical needs. Anlotinib, a novel multi-target inhibitor, is capable of blunting the proangiogenic activity of VEGFR, PDGFR and FGFR, and has shown strong antitumor effects across distinct solid tumors. No study to date has investigated the antileukemic efficacy of Anlotinib in B-ALL, thus prompting us to initiate this study.Methods: A published B-ALL microarray data was analyzed to identify the novel potential treatment choice using Expression2Kinases program. Cell viability was determined by Cell Counting Kit-8 (CCK-8). Annexin V/PI staining kit and staining kit were used to assess cell apoptosis and cell cycle distribution, respectively. The mechanism of action of Anlotinib was investigated with Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting. A B-ALL Patient Derived Xenograft (PDX) animal model was generated and then use to analyze the in vivo antileukemia activity of Anlotinib.Results: In this study, we observed that recurrent B-ALL patients exhibited high proangiogenic activity relative to newly-diagnosed patients, as evidenced by upregulation of VEGF/VEGFR signaling and its downstream targets, including PIK3/AKT/mTOR pathway. In vitro studies demonstrated that Anlotinib was potent to reduce cell viability, induce cell apoptosis and block cell cycle at G2/M phase in B-ALL cells. In a patient-derived-xenograft (PDX) model, Anlotinib dramatically attenuated leukemia burden and improved prognosis as compared to the control group. Mechanistically, blockade of three important proangiogenic mediators, comprising VEGFR2, PDGFR-beta and FGFR3, played a critical role in the cytotoxicity of Anlotinib against B-ALL. Moreover, Anlotinib significantly dampened the activity of PI3K/AKT/mTOR pathway which resides in the convergence of these proangiogenic signals, manifested by dephosphorylation of PI3K, AKT and mTOR.Conclusions: In summary, our results indicate that Anlotinib exerts potent anti-leukemic efficacy in B-ALL preclinical models via perturbation of proangiogenic signalings and inactivation of the common downstream PI3K/AKT/mTOR signaling. This work provides evidence and a rationale for future evaluation of Anlotinib in B-ALL management in clinical settings.
How to Gain the Most from Structural Conditionality of IMF-Supported Programs

Structural conditionality of IMF-supported programs is designed to support structural reforms by countries borrowing from the IMF. Taking stock of program conditions and their implementation, this paper finds that conditionality focuses on fiscal, monetary and financial issues—areas where IMF expertise is strong—and shies away from structural areas such as labor or product market reforms. Hence, tackling deep-rooted structural issues during IMF-supported programs often remained elusive. To ensure countries gain most from IMF conditionality, the paper outlines an evaluation matrix for prioritizing and designing structural reforms, and applies it to case studies.
Integrating Discharge‐Concentration Dynamics Across Carbon Forms in a Boreal Landscape

The flux of terrestrial carbon across land‐water boundaries influences the overall carbon balance of landscapes and the ecology and biogeochemistry of aquatic ecosystems. The local consequences and broader fate of carbon delivered to streams is determined by the overall composition of carbon inputs, including the balance of organic and inorganic forms. Yet, our understanding of how hydrologic fluxes across different land‐water interfaces regulate carbon supply remains poor. We used 7 years of data from three boreal catchments to test how different land‐water interfaces (i.e., forest, wetland, and lake) modulate concentration‐discharge (C‐Q) relationships for dissolved organic carbon (DOC), carbon dioxide (CO2), and methane, as well as the balance among forms (e.g., DOC:CO2). Seasonal patterns in concentrations and C‐Q relationships for individual carbon forms differed across catchments. DOC varied between chemostasis and transport limitation in the forest catchment, between supply limitation and chemostasis in the wetland catchment, and was persistently chemostatic in the lake outlet stream. Carbon gases were supply limited overall, but exhibited chemostasis or transport limitation in the forest and wetland catchments linked to elevated flow in summer and autumn. Unique C‐Q relationships for individual forms reflected the properties of different interfaces and underpinned changes in the composition of lateral carbon supply. Accordingly, DOC dominated the carbon flux during snowmelt, whereas gas evasion increased in relative importance during other times of the year. Integrating the C‐Q dynamics of individual carbon forms provides insight into the shifting composition of lateral export, and thus helps to predict how hydrologic changes may alter the fate of carbon supplied to streams.
Adoption of Soil Conservation Measures

Land degradation resulting from soil erosion is a major problem in rain-fed agricultural areas in India. This study analyses the key determinants of farmers’ decisions to adopt on-farm soil and water conservation (SWC) measures in the rain-fed watershed areas of Siddipet district in Telangana. Here, SWC measures have been undertaken by the government and NGOs at the sub-watershed/community level and by individual farmers at the farm level. The study is based on a primary survey of over 400 farmers conducted in January–March 2018. In addition to estimating the influence of biophysical and market access variables on farmers’ decisions to undertake SWC practices, the study includes a logistic model that found a complementarity between community and individual plot-level interventions to improve soil health. The findings also highlight the influence of conservation measures practised in the neighbourhood on farmers’ decisions to implement SWC measures.
Author response: TMEM120A is a coenzyme A-binding membrane protein with structural similarities to ELOVL fatty acid elongase

TMEM120A, also named as TACAN, is a novel membrane protein highly conserved in vertebrates and was recently proposed to be a mechanosensitive channel involved in sensing mechanical pain. Here we present the single-particle cryogenic electron microscopy (cryo-EM) structure of human TMEM120A, which forms a tightly packed dimer with extensive interactions mediated by the N-terminal coiled coil domain (CCD), the C-terminal transmembrane domain (TMD), and the re-entrant loop between the two domains. The TMD of each TMEM120A subunit contains six transmembrane helices (TMs) and has no clear structural feature of a channel protein. Instead, the six TMs form an a-barrel with a deep pocket where a coenzyme A (CoA) molecule is bound. Intriguingly, some structural features of TMEM120A resemble those of elongase for very long-chain fatty acids (ELOVL) despite the low sequence homology between them, pointing to the possibility that TMEM120A may function as an enzyme for fatty acid metabolism, rather than a mechanosensitive channel.
Orta Anadolu’da Yağmura Dayalı Koşullar Altında Bazı Yeni Tescilli Arpa Çeşitlerinin Tane Verimi ve Verim Stabilitelerinin Belirlenmesi

In this study, it was aimed to determine the stability of 10 barley cultivars (Tarm 92, Ince 04, Karatay 94, Yalin, Cetin 2000, Osvit, Mizgin, Yuksel, Keykubad and Misket) under rainfed conditions in the Central Anatolia Region in terms of grain yield by using GGEbiplot analysis method. The trials were carried out according to the randomized block trial design in 4 locations (Eskisehir, Konya, Yenikent and Haymana of Ankara) during the 2018-2019 growing season. Two-rowed barley (Hordeum vulgare conv. distichon), sixrowed barley (Hordeum vulgare conv. hexastichon) and hulless barley (Hordeum vulgare var. nudum Hook. f.) genotypes were used in the experiments. According to the results of variance analysis related to grain yield, genotype (G), environment (E) and genotype × environment interaction (GEI) were found to be statistically significant (p<0.01). Cultivars showed average grain yield values between 441.0-587.2 kg da. In the GGE-biplot analysis applied to the grain yield data, 66.25% of the variation was distributed on PC1 and 25.05% on PC2. Trial environments created 2 mega-environments. In the evaluation according to the average environment coordinate (AEC); Keykubad, Mizgin, Yuksel and Misket, registered in 2020, were placed beyond the AEC ordinate. Two-row barley cultivar Keykubad has the highest PC1 value and has been determined as the most stable variety besides showing the highest performance. Although Yalin and Osvit cultivars of hulless barley are located behind the AEC ordinate, Yalin showed a more stable structure with its position closer to the AEC apsis. Mizgin, one of the six-row cultivars, has a more stable appearance by being well above the AEC ordinate compared to Cetin 2000. It was determined that Keykubad, Mizgin, Yuksel, Misket and Osvit, which are registered barley cultivars in 2020, have equivalent and superior characteristics to standard cultivars in terms of grain yield.
Madograms help to quantify mountain frontal zones—An approach towards comparative spatial analysis of complex landforms

This article explores the value of madograms to characterize the geomorphology of frontal belts of mountain ranges. Madograms are a geostatistical, variogram‐related concept, allowing for the analysis of altitude variability as a function of distance. After simple idealized digital elevation models were tested to reveal characteristic madogram shapes for various landform configurations, madograms were calculated for three frontal zones of different origin and morphology, located in the Sudetes range (Central Europe). Isopleth madograms and Ward's clustering method were applied to investigate similarities and differences within and between frontal zones. The results proved consistent with the accumulated, largely qualitative knowledge about geomorphology of the frontal zones and confirm the usefulness of the method, which allows for more insightful quantification of the morphology of mountain frontal zones, recognition of their internal spatial structure, and discrimination between fronts of various morphology and origin. Various recommendations for future users are also offered.
Extracting Knowledge Entities from Sci-Tech Intelligence Resources Based on BiLSTM and Conditional Random Field

SUMMARY There are many knowledge entities in sci-tech intelligence resources. Extracting these knowledge entities is of great importance for building knowledge networks, exploring the relationship between knowledge, and optimizing search engines. Many existing methods, which are mainly based on rules and traditional machine learning, require signiﬁcant human involvement, but still su ﬀ er from unsatisfactory extraction accuracy. This paper proposes a novel approach for knowledge entity extraction based on BiLSTM and conditional random ﬁeld (CRF). A BiLSTM neural network to obtain the context information of sentences, and CRF is then employed to integrate global label information to achieve optimal labels. This approach does not require the manual construction of features, and outperforms conventional methods. In the experiments presented in this paper, the titles and abstracts of 20,000 items in the existing sci-tech literature are processed, of which 50,243 items are used to build benchmark datasets. Based on these datasets, comparative experiments are conducted to evaluate the e ﬀ ectiveness of the proposed approach. Knowledge entities are extracted and corresponding knowledge networks are established with a further elaboration on the correlation of two di ﬀ erent types of knowledge entities. The proposed research has the potential to improve the quality of sci-tech information services.
Effect of Self-talk on Pentanque Shooting Accuracy

The background of this research is the petanque precision shooting athletes who are inconsistent in obtaining scores. Accuracy is an important part of petanque, especially in the precision shooting numbers, so that it is necessary to have a mental training process that can contribute to the athlete's ability to improve their accuracy. The purpose of this research is to see the effect of a mental training in the form of self-talk on improving shooting accuracy in petanque. Looking at the objectives of this study, the pre-experimental method with the one group pre-test and post-test design has been used. The instrumental test in this study is a shooting test of 20 athletes from FIPJP (Federation Internationale de Petanque et Jeu Provencal). The results of this research indicate that the test results are t = 2.690, with db = 19 and the number Sig. (2-tailed) or p-value = 0.014 <0.05. From this result, it can be concluded that self-talk practice can significantly improve the accuracy of the shooting petanque. The implication of self-talk is self-control. Athletes appear to be better able to control arm movements, control emotions, and regulate breathing when shooting. This can be seen from the increase in the scores of the athletes.
Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method

Spare parts management is a critical issue in the industrial field, alongside planning maintenance and logistics activities. For accurate classification in particular, the decision-makers can determine the optimal inventory management strategy. However, problems such as criteria selection, rules explanatory, and learning ability arise when managing thousands of spare parts for modern industry. This paper presents a deep convolutional neural network based on graph (G-DCNN) which will realize multi-criteria classification through image identification based on an explainable hierarchical structure. In the first phase, a hierarchical classification structure is established according to the causal relationship of multiple criteria; in the second phase, nodes are colored according to their criteria level status so that the traditional numerical information can be visible through graph style; in the third phase, the colored structures are transferred into images and processed by structure-modified convolutional neural network, to complete the classification. Finally, the proposed method is applied in a real-world case study to validate its effectiveness, feasibility, and generality. This classification study supplies a good decision support to improve the monitor-focus on critical component and control inventory which will benefit the collaborative maintenance.