Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediksi Akurasi Kemenangan Pada Permainan Poker Menggunakan Algortima C5.0 Dan WIPSO

Di era teknologi informasi, banyak sekali data yang bisa diambil dari aktivitas manusia yang berbasis sistem komputer. Namun sistem tidak hanya terdapat pada komputer saja, akan tetapi disemua bidang kehidupan manusia, baik dari segi kesehatan, keamanan bahkan dalam game dimana kumpulan data dari kegiatan tersebut menjadi database yang dapat digunakan untuk mencari ilmu baru. Penelitian ini bertujuan untuk memprediksi akurasi permainan poker menggunakan algoritma Weight Improved Particle Swarm Optimization (WIPSO) untuk pemilihan atribut yang kemudian menggunakan algoritma C5.0 untuk memprediksi akurasi. Sebelum diolah, dataset akan diubah dari 11 atribut menjadi 6 atribut. Hasil penelitian ini menunjukkan bahwa akurasi kartu poker akan meningkat, jika menggunakan algoritma C5.0 akurasi yang diperoleh adalah 49.952% sedangkan akurasi yang diperoleh dengan algoritma C5.0 + WIPSO adalah 51.2%.
Quantification of bovine plasma amino acids via liquid chromatography–electrospray ionization-mass spectrometry: Comparison of underivatized and precolumn derivatized methods

Graphical Abstract Summary: We validated and compared 2 methods for quantification of bovine plasma amino acids (AA) via liquid chromatography (LC)-mass spectrometry. The underivatized method may be a cost-effective, high-throughput, and practical alternative for analysis of AA in dairy cattle, particularly if only essential AA values are required. The derivatized method has greater 12C area signal sensitivity and linearity and recovery rates for all AA but requires more sample processing.
The effect of inoculum and glucose addition on polyhydroxyalkanoate production by Brevibacterium sp. B45

Petroleum-based plastics are the major cause of environmental pollution because the plastics need years to be degraded. The difficulties in handling waste of petroleum-based plastic have motivated researchers to produce environmentally friendly plastic materials that are biologically degradable; one of them is polyhydroxyalkanoate (PHA). Polyhydroxyalkanoate is natural biodegradable biopolymers produced by bacteria as an intracellular carbon and energy storage. This polymer is an alternative source of plastics with similar physical properties to petroleum-based plastic.It can be easily biodegraded aerobically and anaerobically. This study examined the potential of one superior isolate as PHA producers, i.e.,Brevibacterium sp. B45. Brevibacterium sp. B45 was cultivated in Ramsay’s minimal medium with inoculum concentrations were 1, 2, and 3% (v/v)and glucose concentrations were 1, 3, and 5% (w/v). The cultivation of Brevibacterium sp. B45 was carried out in a 500 mL Erlenmeyer flask on a shaker incubator with 150 rpm and 30 oC for 72 hours. PHA recovery was carried out by chloroform extraction and characterized by scanning electron microscopy (SEM), Fourier transformed infrared (FTIR), and differential scanning calorimetric (DSC) methods. The highest yield of dried biomass (2.92%) was obtained using 3% inoculum and 3% glucose. The melting temperature (Tm), enthalpy (ΔHf), and crystallinity (Xc) of the PHA product were 172.1 °C, 61.04 J g-1, and 41.08%, respectively. Data of SEM show that a porous surface characterized morphological of purified PHA grains. The functional units of purified PHA grains were C=O, CH3, C-O, C-O-C, C-C, C-H, and -OH. The purified PHA grains show a similar spectrum to the standard Poly-3-hydroxybutyrate (PHB). Therefore, it could be assumed that PHA produced by Brevibacterium sp. B45 was most likely PHB.
Eco-Environment Evaluation of Grassland Based on Remote Sensing Ecological Index: A Case in Hulunbuir Area, China

This research is based on Landsat5 TM, Landsat8 OLI/TIRS remote sensing data using RSEI model to analyze and monitor the ecological environment and its temporal and spatial changes in the forest-grass transition zone in Northeast China from 2004 to 2019. The change characteristics of the ecological environment of different types of land cover types are monitored by RSEI method, and the response of different land cover types to natural factors such as precipitation and temperature is analyzed at the same time. The distribution of RSEI in the study area presents the characteristics of high in the east and low in the west. The eastern mountainous area is densely covered with woodland, which is the area with the best ecological environment quality in the study area. The grassland in the western plain and the saline-alkali land around the river are the areas with poor ecological environment in the study area. Climate, precipitation, topography and other natural elements work together to form the quality of the ecological environment in the study area roughly bounded by 120? E. In years with poor natural conditions, this dividing line will have a clear eastward shifting trend, especially in the northern part of the study area. The spatial distribution of RSEI in the study area has a high degree of spatial autocorrelation, and Global Moran’s I has been above 0.8 over the years. In terms of temporal changes in ecological conditions, the ecological environment in the study area was basically stable from 2004 to 2008, with a slight deterioration; it improved significantly from 2008 to 2011; however, it deteriorated significantly from 2011 to 2019. According to the results of partial correlation analysis, the ecological environment of the former is highly correlated with natural elements such as climate and precipitation, while the latter is mainly affected by human factors.
An estimation framework to quantify railway disruption parameters

Railway network operations form complex systems. Any disruption adversely impacts the operations, causing long delays. Many studies investigate the effect of a railway incident; however, a holistic quantification is lacking. This study aims to present an estimation framework for flexible traffic management systems, which can help reduce network delays and enable dispatchers to make better-informed decisions. An incident's impact on the network is estimated by creating a sequence of models, which predict two key variables. Firstly, the incident duration is predicted, which is used to predict the second variable: total delay caused by the incident. Various influencing attributes are examined, such as weather, network and railway-related attributes. Their relationship with the response variables is studied in order to understand the incident's impact. Using incident data from the Danish Railways, machine learning models are estimated. The results show that neural networks outperform other competing models for total delay modelling, resulting in improved prediction by the estimation framework, thus giving higher accuracy than the stand-alone models in the study.
Flipped Classroom in Higher Education: Evaluation of the Process in the Framework of Community of Inquiry

The purpose of this research was to determine the effectiveness of the instruction process using the flipped classroom (FC) model within the scope of Computer Networks and Communication course. Theoretically, the community of inquiry model was taken as a reference and the effectiveness of the instruction process was evaluated accordingly. Students were preservice teachers studying Computer Education and Information Technology in the third grade in the Van region in Turkey. There were a total of 19 students, of which 12 are male and seven are female. FC process was carried out in the spring semester of 2018–2019 and lasted 11 weeks in total. A mixed-method design was used to gathering the data. Therefore, the research design was an explanatory design from mixed-methods designs. The quantitative data collection tool was the Turkish version of the Community of Inquiry Survey. The quantitative data were analyzed by dividing the students into very low, low, high, and very high levels. The qualitative data collection tool was the interview form created concerning the community of inquiry model. The qualitative data were analyzed by the use of the community of inquiry coding template's themes. At the end of the instruction process, students had a very high level of cognitive, social, and teaching presence perception. This situation was detailed and supported with the qualitative data obtained. Concerning the cognitive presence, the students firstly talked about that they started the learning process with videos. Regarding the social presence, they mentioned the motivating effect of the questions. About the teaching presence, participants firstly mentioned the direct instruction role of the videos.
Assessment of Microalgae Oil as a Carbon-Neutral Transport Fuel: Engine Performance, Energy Balance Changes, and Exhaust Gas Emissions

Notwithstanding the substantial progress acheved since 2010 in the attempts to realize the potential of microalgae biofuels in the transportation sector, the prospects for commercial production of CO2-neutral biofuels are more challenging today than they were in 2010. Pure P. moriformis microalgae oil was subjected to unmodified engine performance testing as a less investigated type of fuel. Conventional diesel was used as a reference fuel to compare and to contrast the energy balances of an engine as well as to juxtapose performance and emission indicators for both unary fuels. According to the methodology applied, the variation of BSFC rates, BTE, smoke opacity, NOx, HC, CO2, O2, and exhaust gas temperature on three different loads were established during compression ignition (CI) engine operation at EGR Off, 25% EGR, 18% EGR and 9% EGR modes, respectively. Simulation model (AVL Boost/BURN) was employed to assess the in-cylinder process parameters (pressure, pressure rise, temperature, temperature rise, ROHR, and MFB). Furthermore, the first law energy balances for an engine running on each of the test fuels were built up to provide useful insights about the peculiarities of energy conversion. Not depending on EGR mode applied, the CI engine running on microalgae oil was responsible for slightly higher BTE values, drastically reduced smoke opacity, higher CO2 values, and smaller O2 concentration, marginally increased NOx levels and lower total energy losses (in %) if compared to the performance with diesel fuel.
Management Recommendations for Prostate Cancer during the COVID-19 pandemic: A Systematic Review

Introduction: The COVID-19 pandemic has delayed screening, diagnostic workup, and treatment in prostate cancer (PCa) patients. Our purpose was to review PCa screening, diagnostic workup, active surveillance (AS), radical prostatectomy (RP), radiotherapy (RT), androgen deprivation therapy (ADT) and systemic therapy during the COVID-19 pandemic. Materials and Methods: We performed a systematic literature search of MEDLINE, EMBASE, Scopus, LILACS, and Web of Science, according to Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols (PRISMA-P) statement for relevant material published from December 2019 to February 2021. Results: Prostate biopsy can be delayed, except when high-risk PCa is suspected or the patient is symptomatic. Active surveillance is appropriate for patients with very low risk, low risk (LR) and favorable intermediate risk (FIR). RP and RT for high risk and very high risk can be safely postponed up to 3 months. Hypofractionated external beam RT (EBRT) is recommended when RT is employed. ADT should be used according to standard PCa-based indications. Chemotherapy should be postponed until the pandemic is contained. Conclusions: The international urological community was not prepared for such an acute and severe pandemic. PCa patients can be adequately managed according to risk stratification. During the COVID-19 pandemic, LR and FIR patients can be followed with active surveillance. Delaying RP and RT in high risk and locally advanced disease is justified.
Treatment Effect of Exercise Intervention for Female College Students with Depression: Analysis of Electroencephalogram Microstates and Power Spectrum

This paper aims to assess the effect of exercise intervention on the improvement of college students with depression and to explore the change characteristics of microstates and the power spectrum in their resting-state electroencephalogram (EEG). Forty female college students with moderate depression were screened according to the Beck Depression Inventory-II (BDI-II) and Depression Self-Rating Scale (SDS) scores, and half of them received an exercise intervention for 18 weeks. The study utilized an EEG to define the resting-state networks, and the scores of all the participants were tracked during the intervention. Compared with those in the depression group, the power spectrum values in the θ and α bands were significantly decreased (p < 0.05), and the duration of microstate C increased significantly (p < 0.05), while the frequency of microstate B decreased significantly (p < 0.05) in the exercise intervention group. The transition probabilities showed that the exercise intervention group had a higher probability from B to D than those in the depression group (p < 0.01). In addition, the power of the δ and α bands were negatively correlated with the occurrence of microstate C (r = −0.842, p < 0.05 and r = −0.885, p < 0.01, respectively), and the power of the β band was positively correlated with the duration of microstate C (r = 0.900, p < 0.01) after exercise intervention. Our results suggest that the decreased duration of microstate C and the increased α power in depressed students are associated with reduced cognitive ability, emotional stability, and brain activity. Depression symptoms were notably improved after exercise intervention, thus providing a more scientific index for the research, rehabilitation mechanisms, and treatment of depression.
SOIL PROPERTIES PREDICTION FOR REAL-TIME SOIL SENSOR BASED ON NEURAL NETWORK

Real-time soil sensor (RTSS) was built and tested, making use of a near-infrared spectrophotometer, which offered a convenient and quick method for in-situ soil organic matter (SOM), total nitrogen (TN), pH and moisture content (MC) measurement. The sensor could collect a spectrum absorbance of soil (i.e. 500-1650 nm with 7 nm interval). Neural network was used for making prediction model for each soil component. The training and testing of neural network was based on 1300 dataset was taken by the RTSS from 7 location of Japan where included paddy and upland crop field. Input variables represent spectrum absorbance of the points of interest, while the output variables represent SOM, TN, pH and MC data of the points of interest, which was analyzed in the laboratory. After the neural network has been successfully trained, its performance was tested on a separate testing set. The result of MC, pH, SOM and TN prediction indicated that the NN model validated coefficient of determination of R2=0.91, 0.75, 0.95 and 0.96, respectively.
The thermal state of the North Atlantic and macro‐circulation conditions in the Atlantic‐European sector, and changes in sunshine duration in Central Europe

This article explains the reasons for the long‐term variability in sunshine duration (SD) in Central Europe. It presents a recently discovered mechanism regulating changes in sunshine duration, not described so far in the literature. The periods of global dimming and global brightening were linked with changes in the annual frequency of the macrotypes of the middle‐tropospheric circulation and the variability in the surface component of the thermohaline circulation in the North Atlantic (NA THC). On the basis of the totals of annual sunshine duration from the years 1951–2015 from 21 stations located in the Netherlands, Germany, Denmark, Poland, Switzerland, Austria, the Czech Republic and Slovakia, it was shown that changes in NA THC account for onethird of the variability in sunshine duration, while changes in the frequency of macrotypes W and E, forced by the changes in NA THC, each separately account for about 26 and 22% of the variability in the totals of annual sunshine duration in Central Europe.
Circuit Parameter Range of Photovoltaic System to Correctly Use the MPP Linear Model of Photovoltaic Cell

The real-time linearization of a photovoltaic (PV) cell has been implemented well by the proposition of two maximum power point (MPP) linear models (MPP Thevenin cell model and MPP Norton cell model). However, there is no work to specially analyze the circuit parameter range (CPR) to correctly use them, which seriously impedes the development of the linear control theory involving them. To deal with this problem, in this paper, PV systems with three usual outputs are analyzed and the expressions of their CPR are proposed under ideal conditions. Meanwhile, these expressions are improved to match the practical application. They disclose the relationships between load (or bus voltage) and model parameters of the MPP Thevenin cell model (MPP-TCM) when the MPP of PV system always exists. They also reveal the constraints of load (or bus voltage) when the MPP-TCM is always available. Finally, by some simulation experiments, the accuracy of the expressions of the CPR is verified, the regular patterns of the CPR changing with weather are disclosed, and the comparison of the CPR for different PV systems are made. In this work, the relationships between MPP-TCM and circuit parameters are successfully found, disclosing the constraints among parameters when the MPP-TCM is used to implement the overall linearization of a PV system.
The effect of plasma treated carbon felt on the performance of aqueous quinone‐based redox flow batteries

4,5‐dihydroxybenzene‐1,3‐disulfonic acid (Tiron) and anthraquinone‐2,7‐disulfonic acid (AQDS) are interesting redox couples for aqueous quinone‐based redox flow batteries (QRFBs) because of their high solubility and good reaction reversibility in acidic condition. Tiron is rate‐determining material due to its slow reaction kinetics. To improve that, Tiron is transformed into 2,4,5,6‐tetrahydroxybenzene‐1,3‐disulfonic acid (TironA), which is effectively formed during the first cycle of QRFB. Once TironA is formed, a desirable two‐electron redox reaction with stable and reproducible charge and discharge step of QRFB occurs, although the electron transfer of TironA is still lower than that of AQDS. To further facilitate that of TironA, oxygen (O2) and nitrogen (N2) plasma‐treated carbon felt (CF) electrodes are suggested. Oxygen functional groups formed onto CF by O2 plasma become the active sites for redox reaction of TironA. As the amount of oxygen functional groups formed increases, the redox reactivity of TironA is enhanced. In contrast, when N2 plasma is used, pyrrolic N and quaternary N are mainly formed. With the formation, (i) hydrogen atoms within pyrrolic N act as proton donor and interact with oxygen groups of TironA and (ii) nitrogen cations within quaternary N interact with the negatively charged atoms of TironA by electrostatic interaction. Thus, both reaction kinetic of TironA and performance of QRFB increase. Regarding the performance of QRFB using N2 plasma‐treated CF, its energy efficiency (EE), discharging capacity, and state of charge are 62%, 17.3 Ahr·L−1 and 64.5% for 50 cycle, which correspond to excellent achievements.
Exploring the Space-Calorie Association: Preliminary Evidence from Reaction Time Performance

The present study was designed to investigate the representation of calorie levels in space. Previously an association between numerical magnitude and space has been established, namely, the Spatial Numerical Association of Response Codes (SNARC) effect. The spatial-numerical association reveals representation of smaller and larger numbers by the left and right sides, respectively. In addition, previous studies showed that spatial arrangement of foods in space affects the food selection behavior. In three experiments, the presence of an association between calorie magnitude and space was tested to understand how it could potentially affect food selection behavior. Reaction times were recorded to investigate the speed of information processing. In Experiment 1, locations of low and high calorie food stimuli were (in)congruent in terms of the space-calorie association. In Experiment 2, endogenous spatial cues were used to bias attention to investigate if this bias would lead to formation of the space-calorie association. Finally, Experiment 3 investigated whether green and red colors evoke approach or avoidance behavior and prevent formation of the space-calorie association. In all experiments, results revealed lack of an association between space and calorie, that is, presenting low/high calorie items on the left/right hemispace, respectively, did not modulate the processing speed.
Keberlanjutan Dana Simpan Pinjam Perempuan Eks Program Nasional Pemberdayaan Masyarakat Mandiri Perdesaan dalam Peningkatan Ekonomi Masyarakat Desa

This study aims to examine the sustainability of the ex-PNPM Independent Rural Women's Savings and Loan (SPP) in improving the economy of the Sei Rampah sub-district community. Methodologically, this research uses qualitative methods with a descriptive approach. Data collection techniques are interviews, focused group discussions (FGD), and observation of the processes and results of SPP activities in Sei Rampah sub-district and collect secondary data in the form of literature studies on literature, documents, scientific writings and similar research studies related to the problem research. Data analysis was performed by reducing data, presenting data and drawing conclusions. The result of this research is that the sustainability of SPP Ex PNPM MPd funds is still continuing and developing very well with significant benefits for improving the economy of the poor. The institution that manages the SPP ex PNPM MPd fund is the BKAD which is reorganized through inter-village deliberations which are determined as joint decisions of the village head. The BKAD institutional status is still ad hoc and has not been transformed into a legal entity. The Ex-PNPM MPD Women's Savings and Loans Program (SPP) has not joined into a business unit within the Joint BUMDesa so that business development has not been maximized and utilization and loans have not been evenly distributed to all poor households.
Sentiment Analysis on Social Media for Albanian Language

The recent advances in technology and particularly, the rising prominence of social media platforms have made it possible to express our emotions through electronic means, which have led to the creation of large collections of unstructured textual documents. These collections can be saved and potentially studied with many modern technologies like Text Mining, Machine Learning and Natural Language Processing to obtain new knowledge from them. Sentiment Analysis is a field of Natural Language Processing that focuses on extracting sentiment from text. Moreover, as a Text Mining technique expresses the ability to track the subjective opinion of a text produced by an entity. The purpose of this paper is to test and review different approaches in Sentiment Analysis for messages in the Albanian language found on Twitter. Additionally, we compare the results among different methods and note the challenges that arise while finally we suggest future directions for further research. This paper’s research was conducted as follows: the data was pre-processed, before being converted from text to vector representation using a range of feature extraction techniques such as Bag-of-Words, TF-IDF, Word2Vec, and Glove. We study the performance of sentiment classification techniques from three main approaches: traditional machine learning, lexicon-based and deep learning approach. For model evaluation, since they were trained in unbalanced data, we used not only classical evaluation criteria such as Accuracy, Specificity, Precision, and Recall but more appropriate criteria such as F-measure, Balanced Accuracy, and Matthews Correlation Coefficient (MCC). According to all these criteria, our experiments revealed that LSTM based RNN with Glove as a feature extraction technique provides the best results with F-score = 87.8%, followed by Logistic Regression.