Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of probioticated banana pith juice

Banana pith has been studied as a lowcost underutilized agricultural waste with abundant therapeutic properties. The objective of this study was to optimize the concentration of prebiotic (inulin), stevia (Stevia rebudiana) and inoculum Lactobacillus acidophilus by varying one variable at a time (OVAT) based on single factorial analysis to develop a probioticated banana pith juice. The banana pith juice with 3% prebiotic had a pH 3.87, acidity 0.45%, total sugars 143.3 ig/mL, reducing sugars 320 ig/mL and microbial viability of 3.88 X 108 CFU/mL, respectively. The juice with 2 % stevia had a pH 3.9,acidity 0.42%,total sugars 250 ig/mL, reducing sugars 247.5 ig/mL and microbial viability of 7.2 X 108 CFU/ mL, respectively. The juice with 3% inoculum size had a pH 3.98, acidity 0.69%, total sugars225 ig/mL, reducing sugars 352.5 ig/mL and microbial viability of 2 X 108 CFU/mL, respectively. Maximum growth of the probiotic was obtained at the end of 48 h of fermentation for 1% prebiotic, 2% stevia and 3% inoculum size, thereby proving its ability and suitability to ferment banana pith juice. There was not much loss in the nutritional value and changes in organoleptic properties of the fresh (control) and probioticated juice.
Language learning experience in school context and metacognitive awareness of multilingual children

Abstract What is the influence of a language learning experience (LLE) in a school context on the metacognitive development of children? To answer that question, we presented 54 multilingual preschoolers with two movie clips and examined their reactions to an exolingual situation of communication. These preschoolers were aged four and a half to six and a half. We observed how they treated the reception of information during situations of communication in which they did not share the same language as their interlocutor. We hypothesised that children with a LLE in a formal context would outperform children without that specific experience (nLLE) with respect to their communicative abilities in this test paradigm. Furthermore, we hypothesised that the differences observed might be due to the fact that LLE children would be more aware of contextual cues than nLLE children. As a result, the LLE children might be more inclined to answer the actor and less inclined to give up the exchange. Our results confirm that a formal learning context (LLE) significantly improves metacognitive awareness and that this is a relevant factor in facilitating the treatment of communicative interactions.
Sentiment Analysis Using Machine Learning Classifiers: Evaluation of Performance

Sentiment analysis is a process that is a very popular concept nowadays because of the high volume of reviews, micro blogs, comments etc., generated in different sites like e-commerce and social networking sites. The main problem in the current system is, for users to know the polarity result of bulk data, which is very tough because users need to study and understand each review in terms of the polarity. Users are expecting a onetime result of the polarity of bulk reviews, comments, micro blogs etc. In social networking sites, users post their status or opinions to share to the world. In this category, Twitter is the most popular one. In twitter, users post many micro blogs related to a topic or crisis etc, and the topic may be linked with a greater number of micro blogs based on the keywords or hash tags used. In twitter, we can search for any topic with keywords or hash tags, and we get a bulk of responses of the users world-wide. If we want to know the exact opinions of the users, we need to analyze the data with sentiment analysis. Sentiment analysis is a concept of defining a statement as positive, neutral, or negative by analyzing words of the statement. Many concepts have been proposed for this requirement, and many sentidatasets have been prepared for this requirement. But by taking the advantages of Machine Learning we are proposing a concept of sentiment analysis in twitter using ML techniques. In this we use multiple ML techniques such as Random Forest, Naïve Bayes and Support Vector Machine for evaluation and comparison of the results.
Differences in the Soil Bacterial Communities Under Organic Farming and Conventional Farming Modes Revealed by 16S rDNA Sequencing

Soil bacterial communities are different in various agricultural ecosystems. To assess the differences in soil bacterial communities between organic and conventional farming modes, high-throughput Illumina MiSeq sequencing was conducted. A total of 3, 919 operational taxonomic units were identified and classified as 26 phyla, 42 classes, 78 orders, 120 families, 281 genera, and 340 known species. The histogram of the microbial species distribution at genera levels showed that Massiliaand Lysobacter increased quickly in soil under organic farming mode. Analysis of soil bacterial diversity showed that the soil under the organic farming had a greater bacterial diversity. Linear discriminant analysis effect size analysis showed that the major bacterial groups identified in the soil sample CK1 (2015.4) and CK6 (2017.10) under conventional farming mode were largely different from those in the soil sample 06 (2017.10) under organic farming mode. Redundancy analysis demonstrated that available nitrogen was the most important factor regulating bacterial composition under the organic farming mode. Massilia, Pseudomonas, Lysobacter and Pseudarthrobacterabundances showed a strong positive correlation with the content of available nitrogen. The results provided an important reference for the utilization of soil microorganisms under the organic farming mode.
Improving Grafting Efficiency of Dicarboxylic Anhydride Monomer on Polylactic Acid by Manipulating Monomer Structure and Using Comonomer and Reducing Agent

Maleic anhydride (MA) grafted polylactic acid (PLA) acting as reactive compatibilizer for PLA blends and composites has been reported. However, melt free-radical grafting of MA on PLA is often subject to steric and electron effects of the substituents in the monomer and low initiation efficiency, yielding low grafting efficiency (Eg). In this work, five dicarboxylic anhydride monomers, including MA, itaconic anhydride (IA), cis-1,2,3,6-tetrahydrophthalic anhydride (TA), 4-allyltrimellitate anhydride (ATA), and 4-methacryloxyethyl trimellitate anhydride (META), were grafted onto PLA, and the effect of steric hindrance on Eg was assessed. It is noted that Eg values followed the order of ATA > META ≫ MA ≥ TA > IA. The introduction of styrene as a comonomer selectively increased the Eg values of three electron-deficient monomers, MA, IA, and META, while Sn(Oct)2 as a reducing agent increased Eg for all monomers. Both styrene and Sn(Oct)2 exhibited a synergistic effect when used in grafting MA, IA, and META.
The effects of hallux valgus and hallux rigidus 
surgery on gait biomechanics: a systematic review

Hallux valgus (HV) and hallux rigidus (HR) are common foot conditions that lead to deterioration of health status. This review aims to summarize and update information on the currently published research explicitly related to the influence of HV and HR surgeries on lower extremity gait biomechanics. A review of papers published from 1999 to 2020 has been conducted. The databases searched included Science Direct, PubMed Central, PubMed and EBSCO. After HV surgeries, most of patients’ spatio-temporal parameters remain the same as they were before them. Changes in plantar pressure parameters of the hallux region are inconclusive and depend on a study. In patients after HR surgeries, such as cheilectomy and Ist metatarsophalangeal joint (MTP I) arthrodesis, they do not affect knee or hip kinematics. The influence of MTP I arthrodesis on ankle joint kinematics is unclear. Cheilectomy in most cases increases MTP I motion in the sagittal plane. The HV angle and the intermetatarsal angle have significantly decreased after surgeries in all papers. American Orthopedic Foot and Ankle Society MTP–IP score results are also much better than their pre-operative values in patients after HV and HR surgeries.
Augmented Multiple Vehicles’ Trajectories Extraction Under Occlusions With Roadside LiDAR Data

Object occlusion is a common issue in Light Detection and Ranging (LiDAR)-based vehicle tracking technology. The occlusions can cause variance in vehicle location and speed calculation. How to link the vehicle trajectories caused by occlusion issues is a challenge for traffic engineers and researchers. This paper developed an augmented vehicle tracking method under occlusions with the roadside LiDAR data. The proposed method can be divided into two parts. The first part based on the corner point is used to choose a representative vehicle tracking point. And the second part based on the GNN algorithm is employed to link the vehicles’ trajectories under two occlusion situations. The performance of the proposed method has been evaluated using roadside lidar data collected from four different scenarios. The test results showed that more than 89% of disconnected trajectories can be fixed with the proposed method, which is superior compared to the state-of-the-art method. The proposed method can benefit a lot of transportation areas, such as traffic volume count, vehicle speed tracking, and traffic safety analysis.
Dialogue Management in Conversational Systems: A Review of Approaches, Challenges, and Opportunities

Attracted by their easy-to-use interfaces and captivating benefits, conversational systems have been widely embraced by many individuals and organizations as side-by-side digital co-workers. They enable the understanding of user needs, expressed in natural language, and on fulfilling such needs by invoking the appropriate backend services (e.g., APIs). Controlling the conversation flow, known as Dialogue Management, is one of the essential tasks in conversational systems and the key to its success and adoption as well. Nevertheless, designing scalable and robust dialogue management techniques to effectively support intelligent conversations remains a deeply challenging problem. This article studies dialogue management from an in-depth design perspective. We discuss the state-of-the-art approaches, identify their recent advances and challenges, and provide an outlook on future research directions. Thus, we contribute to guiding researchers and practitioners in selecting the appropriate dialogue management approach aligned with their objectives, among the variety of approaches proposed so far.
RESEARCH ON REAL-TIME DETECTION METHOD OF FACE WEARING MASK WITH LARGE TRAFFIC BASED ON DEEP LEARNING

Aiming at the problem of low accuracy of traditional face detection methods for large-volume mask-wearing people during the prevention and control of the new crown pneumonia epidemic, this paper proposes a real-time detection method for large-volume mask-wearing faces based on deep learning. The method uses the overall design of the backbone network, the FPN feature fusion network, the detection network and the parameter optimization method of the algorithm, and completes the model training on the mask-wearing face training set. In the detection process, the NMS algorithm is used to post-process the prediction results to realize multi-scale perception of the input face and improve the detection accuracy of the face wearing a mask. Experimentally verified, the detection accuracy of this method on the mask-wearing face test set is 0.919, and the average of Easy-0.841, Medium-0.802 and Hard-0.600 is obtained on the three subsets of the WIDER FACE test set. Detection accuracy (mAP). Compared with traditional face detection methods, it has universal advantages, and the video inference speed of the method in this paper reaches 55fps, which can meet the task requirements of real-time face detection with large traffic. In addition, the project team has successfully deployed this method to a fully automatic infrared thermal imaging temperature measurement warning system and put it into use in many places in Beijing, which is of great significance to preventing the spread of the epidemic.
A direct test of Kuznets in a developing economy: a cross-district analysis of structural transformation and inequality in Indonesia

ABSTRACT This paper empirically tests the Kuznets hypothesis that structural transformation – the movement of labour from low productivity to higher productivity sectors – is associated with rising inequality in a developing economy. We assemble a unique data set of inequality and sectoral employment shares of almost 300 districts of Indonesia and sector-specific measures of inequality for the period 1992–2017. Unlike studies that use value added shares to test the Kuznets hypothesis, this paper is among a few that directly test it with sectoral employment shares. We find that structural change and rising inequality coevolve in different ways depending on the modality of structural transformation. Specifically, we find in the Indonesian case that structural transformation has a tendency to be associated with falling inequality during a period of industrialization and increasing inequality during a period of tertiarization.
Improving Reading TOEFL Score through Note Taking Strategy

There are still a number of less skilled readers in higher degree education where academic texts are staple needs (Hellekjaer, 2009). This study investigated a technique to help students increase their reading ability in TOEFL reading section. A technique named Notetaking technique was implemented during two cycles of classroom action research. There were 3 meetings for each cycle. The participants were 17 Economics students who are taking TOEFL Preparation course at Universitas Syiah Kuala, Aceh, Indonesia. The result from cycle one showed no increase since the average only incline to 38.58. Then in cycle 2, there was a significant increase reaching 45.11 (which is the minimum TOEFL standard for the university). It can be deduced that this technique might not be appropriate for large classes as it demands individual assistance. Besides, a good skill of organizing information is fundamental in this strategy to maximize the students’ effort in answering the TOEFL questions during the test.
Classification and Categorization of COVID-19 Outbreak in Pakistan

Coronavirus is a potentially fatal disease that normally occurs in mammals and birds. Generally, in humans, the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person. Coronavirus is a family of viruses that is more lethal than other unpremeditated viruses. In December 2019, a new variant, i.e., a novel coronavirus (COVID-19) developed in Wuhan province, China. Since January 23, 2020, the number of infected individuals has increased rapidly, affecting the health and economies of many countries, including Pakistan. The objective of this research is to provide a system to classify and categorize the COVID-19 outbreak in Pakistan based on the data collected every day from different regions of Pakistan. This research also compares the performance of machine learning classifiers (i.e., Decision Tree (DT), Naive Bayes (NB), Support Vector Machine, and Logistic Regression) on the COVID-19 dataset collected in Pakistan. According to the experimental results, DT and NB classifiers outperformed the other classifiers. In addition, the classified data is categorized by implementing a Bayesian Regularization Artificial Neural Network (BRANN) classifier. The results demonstrate that the BRANN classifier outperforms state-of-the-art classifiers. © 2021 Tech Science Press. All rights reserved.
Modeling the mind: Assessment of if…then… profiles as a window to shared and idiosyncratic psychological processes

People’s behavior is characterized by stable if…then… profiles, or if in x situation then behavior a, but if in y situation then behavior b. But how do researchers conceptualize and measure if…then… profiles? Drawing from Cognitive-Affective Processing System (CAPS) theory, we discuss recent developments in assessing if…then… profiles, and how such profiles can provide a window for elucidating key aspects of the underlying personality system. Specifically, the Highly-Repeated Within-Person (HRWP) approach assesses how a behavior varies as a function of key features in a situation, and operationalizes if…then… profiles as regression betas. We illustrate how the HRWP approach can be applied to data from often-used social cognitive tasks, wherein an individual is exposed to a large number of situations that differ on a dimension that has been experimentally-manipulated by the researcher, and their behaviors to the situations are tracked. The HRWP approach allows researchers to more precisely assess a given individual’s if…then… pattern, make stronger causal inferences about a given individual’s personality system, and empirically investigate, rather than simply assume, if there are meaningful differences between individuals in the causal processes.
A Carbon Nanotube (CNT)-based SiGe Thin Film Solar Cell Structure

در این مقاله ، ساختاری از سلول خورشیدی فیلم نازک SiGe بر اساس نانولوله کربنی (CNT) ارائه شده است. ما طراحی و شبیه سازی دستگاه را با استفاده از Silvaco TCAD ارائه می دهیم. مدل سازی ساختار سلول خورشیدی فیلم نازک SiGe مبتنی بر CNT انجام شده و سپس با ساختار سلول خورشیدی فیلم نازک SiGe مقایسه می شود. نتایج حاصل از شبیه سازی عددی نشان می دهد که سلول خورشیدی فیلم نازک SiGe مبتنی بر CNT دارای پارامترهای بهتری مانند جریان اتصال کوتاه ، ولتاژ مدار باز ، ضریب پر کردن ، حداکثر توان و کارایی در مقایسه با ساختارهای قبلی است. نتایج شبیه سازی همچنین ثابت می کند که بازده 40.36٪ افزایش می یابد.
Circularly Polarized Leaky-Wave Antenna Based on a Dual-Mode Hollow Waveguide

This communication presents a compact leaky-wave antenna providing circular polarization from a dual-mode hollow waveguide (WG). The monolithic structure is based on a rectangular metallic guide that is perforated with crossed slots. The antenna is fed by a standard WR42 port, which is connected to the leaky WG by a transition that allows for dual-mode excitation. The axial ratio (AR) is determined by the size of the WG section and the dimensions of the crosses. In order to preserve the polarization purity, the geometry of the first radiating elements is modulated. Designed at $K$ -band, the present proposal is suitable for space applications since it relies on a full-metal structure. A design methodology is proposed based on the dual-mode dispersion analysis. Selective laser melting is used to monolithically 3-D print the antenna. Measurements are in good agreement with simulations and the AR is 0.92 dB at 20.5 GHz.
Establishing a Graduate Agropreneur Business Model for Food Security: A Case Study of the Melon Manis Terengganu (MMT) Fertigation Project

Revitalizing Malaysia’s agriculture, to cater to food security issues, has become a priority, given the current economic conditions faced by the government, due to the worldwide COVID19 pandemic. There are, however, a set of complex issues, which involve human capital development, trade agreements, domestic structural problems, and the appropriateness of techniques that must be tackled. Graduate agropreneurs, though, seem to be the future of the nation; while they, continue to struggle with profitability in these difficult economic times. The agricultural industry, however, supports the view that through business model innovation, farms can increase their competitive advantage. This paper identifies and describes some of the elements needed for these graduate agropreneurs when they consider business model innovation. A qualitative approach was used in this study to interview successful graduate agropreneurs involved in Melon Manis Terengganu (MMT) fertigation farming. The paper concludes that the relevance of a business model, in agriculture, relies on the fact that global competition and technological advances urge agropreneurs to look for new business structures and new ways to interact within the business environment. On the other hand, the agropreneur projects should take into consideration the viability of the agriculture projects to encourage a new generation of farmers; the young, energetic, and knowledgeable agropreneurs, to venture into high technology farming which also has substantial income potential. All factors, however, merit attention when graduate agropreneurs develop new business models for their farms.
TLR3 and TLR7 Cross-talk Induces Synergistic Response in the Chicken Peripheral Blood Mononuclear Cells

First line of defense against invasion of microbial pathogen is mediated by innate or natural immunity. Innate immune cells recognize a set of conserved molecular structures on microbes through germ line encoded receptors called pattern recognition receptors (PRRs). Toll-like receptors (TLRs) are type of PRRs and engagement of TLRs with their cognate ligands activate downstream signaling events that result in the induction of innate immune response by secreting inflammatory cytokines and type I interferons (IFNs). The secretion of inflammatory mediators not only activate innate immunity but also prime adaptive immune response which is responsible for the International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 10 Number 02 (2021) Journal homepage: http://www.ijcmas. com
Diurnal fluctuations in musical preference

The rhythm of human life is governed by diurnal cycles, as a result of endogenous circadian processes evolved to maximize biological fitness. Even complex aspects of daily life, such as affective states, exhibit systematic diurnal patterns which in turn influence behaviour. As a result, previous research has identified population-level diurnal patterns in affective preference for music. By analysing audio features from over two billion music streaming events on Spotify, we find that the music people listen to divides into five distinct time blocks corresponding to morning, afternoon, evening, night and late night/early morning. By integrating an artificial neural network with Spotify's API, we show a general awareness of diurnal preference in playlists, which is not present to the same extent for individual tracks. Our results demonstrate how music intertwines with our daily lives and highlight how even something as individual as musical preference is influenced by underlying diurnal patterns.
Determination of heavy metals in soil used for potato cultivation by atomic absorption spectroscopy in awi Zone, Amhara Region, Ethiopia

The study evaluates heavy metals in soil used for Potato cultivation by Atomic Absorption Spectroscopy in awi Zone, Amhara Region, Ethiopia. In this study, 75 soil samples from three agricultural areas, namely: Guaguasa Shikudad, Fagita Lecoma and Banja districts were collected and analyzed for Lead (Pb), Cadmium (Cd), Nickel (Ni), Copper (Cu) and Zinc (Zn) using Atomic Absorption Spectroscopy. The results obtained revealed that the mean heavy metal concentrations for Pb, Cd, Ni, Cu, and Zn were 75.33–77.00mg/kg, 17.11–18.76mg/kg, 60.26-101.78mg/kg, 125.17-383.39mg/kg and 244.20—287.87mg/kg respectively. In general, the level of metals in soil collected from the sampling sites are found to be decreased in the order of: Cu>Zn>Ni>Pb>Cd. Soil analysis showed that the concentration of Cu, Cd and Ni higher than permissible levels there WHO/FAO thresholds for agricultural soils the soil is polluted by toxic heavy metals (Cu, Cd and Ni). Therefore, the soils studied were harmful for the cultivation of potatoes and other agricultural purposes.