Miracle-dz/UniFAR-FTUs huggingface dataset:
Antioxidant Analysis of Kawa Daun (Coffea canephora) Beverage by In Vitro and In Silico Approaches

In Tanah Datar Regency, West Sumatra, Indonesia, the waste of pruning coffee leaves (Coffea canephora) is utilized as a traditional beverage called "Kawa Daun". For a consistent quality of Kawa Daun functional beverage, we evaluated the effect of different smoking times (0, 2, 4, and 6 h) on its in vitro DPPH antioxidant activity. Estimation of antioxidant components from the coffee leaf was conducted in silico using Peroxiredoxin V (PrxV) with 3MNG code as a receptor, and 33 phytochemicals were reported to be present in the coffee leaves as ligands. As a result, Kawa Daun, with a 2-h smoking time, had the highest antioxidant activity. Molecular docking between PrxV and the 33 compounds resulted in the ten most potential compounds based on the affinity energy. They were xanthone (-4.9 kcal/mol), uric acid (-4.8 kcal/mol), xanthosine (-4.8 kcal/mol), caffeine (-4.6 kcal/mol), 3-methylxanthine (-4.6 kcal/mol), 7-methylxanthosine (-4.6 kcal/mol), theobromine (-4.5 kcal/mol), theophylline (-4.5 kcal/mol), caffeic acid (-4.5 kcal/mol), and xanthine (-4.4 kcal/mol). These ten ligands had stronger interactions than the control ligand 1.2-dithiane-4.5-diol (-3.6 kcal/mol). This research showed the potential of Kawa Daun as a functional beverage with antioxidant activity. Further confirmation on the antioxidant potential of this beverage using an in vivo method is recommended.
Deinking paper in a neutral medium, through a treatment with amino acids, in flotation columns

Mixed waste from office paper (MOW) is an excellent source of low-cost, high-quality fiber for the papermaking industry. The limitations in the application of conventional deinking to such paper are related to the negative environmental impacts that chemical substances can cause, which is why deinking in a neutral medium is receiving increasing attention. This study focuses on investigating the effect of the application of the amino acids Aspartate Glutamate and Asparagine, in the deinking of office paper, using a flotation column on a laboratory scale.The methodology involves the sample preparation, a pretreatment, flotation, the manufacture of paper, microscopy analysis, and finally the evaluation of optical properties. Different doses for each of the amino acids and of the mixture of the three were tested, at different conditions of temperature and pH in the pretreatment.The best optical characteristics were obtained when using the mixture of the three amino acids at 0.6% on a dry weight basis of the paper to be deinked, proposed in this study, with 90.8% whiteness. From scanning electron microscopy analysis, no superficial damage to fiber morphology is observed in any of the fiber samples after deinking.
Breaking the Silence - Group Discussions, Social Pressure, and the Adoption of Health Technologies

Social pressure and stigma can hinder the adoption of available technologies, especially in the context of sensitive health issues. We run a field experiment on the take-up of menstrual products in Bangladesh and test a discussion-based intervention. We vary participation in group discussions designed to break the silence around menstruation, where participants share their personal experiences. We find positive effects on the willingness to pay for a known menstrual product (sanitary pads) and on the adoption of a new technology (anti-bacterial menstrual underwear). Our results show changes in restrictive social norms around purchasing the products and lower perceived stigma around menstruation in general.
IMPLEMENTATION OF FIVE CLASSES OF AUTOMATED ECG ARRHYTHMIA CLASSIFICATION USING KNN CLASSIFIER

: Electrocardiography (ECG) signals are classified into five different classes of signals namely, normal, PVC, LBBB, RBBB, and APC to establish the abnormality of the signal. Initially, to find the peaks in the ECG signal Pan-Tompkins algorithm is used. Then, some of the features such as HOS, min-max features, and temporal, are carried out on MIT/BIH arrhythmia database which consists of the huge number of signals including the label of normal and arrhythmia signal. The performance of the classifier is measured with other classifiers; the results showed that KNN provides higher classification accuracy than the other classifiers.
Virtual Studio Technology of Light Cloth

With digital broadcast television continues to evolve,more and more widespread application of virtual studio.Unlike traditional virtual studio lighting,studio of light illumination light the way with new requirements.Virtual studio system design and lighting arrangements will determine the quality of television pictures,That lighting both in the creation of the technical program,Also has artistic,We need to work,Uninterrupted accumulation,study,master,in an effort to make the technology an extension of the art.In this paper,light cloth of virtual studio technology and related notes.
BEHAVIORAL CHARACTERISTICS OF CONSUMERS OF TOURISM PRODUCTS

The main purpose of the study. In the context of the development of tourism enterprises in Azerbaijan, it is necessary to identify rational directions for the use of marketing opportunities both at the company and state levels. To achieve this goal, the following tasks have been set: •Assess the role of marketing in marketing an innovative product; •Determining the behavioral characteristics of consumers of tourism products; •Study of experience in the application of marketing technologies in the tourism industry. Research methodology. Organizes research on the competitiveness of national tourism enterprises in domestic and international markets and generalizations from world experience in this field. Importance of research application. Adoption of the concept on development of national cluster of state tourism marketing policy, creation of normative-legal base in accordance with the concept of tourism development in each field included in tourism cluster on the basis of world experience and international tourism standards, development and adoption of "Tourism development in Azerbaijan" program, makes it necessary to carry out activities such as the regulation of tourism activities. The results of the study. The state's tourism policy must be developed and implemented in terms of time and space. In terms of time, we mean short-term and long-term tourism policies. As a result, the state's tourism policy serves to increase the efficiency of the national tourism cluster and increase the competitiveness of this sector of the national economy. Originality and scientific novelty of the research. In order to develop the national tourism cluster in Azerbaijan, a set of practical measures for tourism marketing activities for local tourism companies and the Government of Azerbaijan is being developed.
IMPACT OF AWARENESS PROGRAMME ON KNOWLEDGE RELATED TO PREVENTION OF BREAST CANCER AMONG MOTHERS IN SELECTED RURAL AREA

The current study has been undertaken to assess knowledge score regarding Prevention of Breast cancer among mothers by awareness programme in Selected rural area, Gwalior. The research design used for study was pre- experimental in nature. The tool for study was self-structured knowledge questionnaire which consists of 2 parts-PART- I consisted questions related to Socio-demographic data; PART-II consisted of self -structured knowledge questionnaire to assess knowledge score regarding Prevention of Breast cancer among mothers. The data was analyzed by using descriptive & inferential statistical methods. The most significant finding was that 20.5% of mothers were having average knowledge regarding Prevention of Breast cancer whereas 79.5% had fair knowledge after post-test. It was suggested that nurses must educate mothers regarding Prevention of Breast cancer.
To app or not to app? Understanding public resistance in using COVID-19 digital contact tracing and its criminological relevance

In the context of the COVID-19 pandemic, digital contact tracing has been developed and promoted in many countries as a valuable tool to help the fight against the virus, allowing health authorities to react quickly and limit contagion. Very often, however, these tracing apps have been facing public resistance, making their use relatively sparse and ineffective. Our study, which relies on an interdisciplinary approach bringing together criminological and computational expertise, unpacks this issue by looking at key social dynamics at the basis of people resistance in using the NHS contact tracing app in England and Wales. It analyses a large Twitter dataset to investigate interactions between relevant user accounts, and to identify the main narrative frames (lack of trust; negative liberties) and mechanisms (polluted information; conspiratorial thinking; reactance) explaining and enabling people’s resistance in using the NHS contact tracing app. Our study builds on concepts of User eXperience (UX) and algorithm aversion and demonstrates their relevance to a key criminological problem which is resistance to official technologies.
E XPERIENCE OF A DULTS WITH U PPER - LIMB D IFFERENCE AND THEIR V IEWS ON S ENSORY F EEDBACK FOR P ROSTHESES : A M IXED M ETHODS S TUDY

Background: Upper-limb prostheses are regularly abandoned, in part due to the mismatch between user needs and prostheses performance. Sensory feedback is among several technological advances that have been proposed to reduce device abandonment rates. While it has already been introduced in some high-end commercial prostheses, limited data is available about user expectations in relation to sensory feedback. The aim of this study is thus to use a mixed methods approach to provide a detailed insight of users’ perceptions and expectations of sensory feedback technology, to ensure the addition of sensory feedback is as acceptable, engaging and ultimately as useful as possible for users and, in turn, reduce the reliance on compensatory movements that lead to overuse syndrome. Methods: The study involved an online survey (N=37) and video call interviews (N=15) where adults with upper-limb differences were asked about their experience with limb difference and prosthesis use (if applicable) and their expectations about sensory feedback to prostheses. The survey data were analysed quantitatively and descriptively to establish the range of sensory feedback needs and their variations across the different demographics. Reflective thematic analysis was performed on the interview data, and data triangulation was used to understand key behavioural Guiding principles were developed based on the results of a survey and interviews to inform the development and assessment of sensory feedback for upper-limb prostheses.
KNOWLEDGE TRANSFER TO AND AMONG END-USERS IN PRE-PACKAGED ENTERPRISE APPLICATION SOFTWARE IMPLEMENTATION: An Exploratory Study of the Roles of Communities of Practice

: This paper is concerned with the roles of Communities of Practice (CoPs) in knowledge transfer during the implementation of a particular IT artefact, i.e. the Pre-packaged Enterprise Application Software (PEAS i ) or also known as Enterprise Resource Planning (ERP) software. Using an in-depth longitudinal case-study across different stages of a Financial PEAS implementation in a large Australian university, we assess the effectiveness and applicability of the practices of CoPs for transferring the PEAS knowledge to and among end-users. The key finding of this paper is that CoPs can be utilized to enhance knowledge transfer for a better PEAS implementation result. Our findings also indicate that CoPs can be assigned to steward ii this dynamic PEAS knowledge in its most updated version among the very people who are its owners.
New statistical approach to interpret power transformer frequency response analysis: non-parametric statistical methods

The frequency response analysis (FRA) test has been recognised as one of the sensitive tools for detecting electrical and mechanical faults inside power transformers. However, there is still no universally systematic interpretation technique for these tests. Many research efforts have employed different statistical criteria in order to aid the interpretative capability of the FRA, but it is shown that the methods used so far are based on parametric statistics, which need a set of assumptions about the normality, randomness and statistical independence of FRA data. Therefore, this study aims to propose some non-parametric statistical methods which are based on explicitly weaker assumptions than such classical parametric methods. The proposed statistical methods are applied to the experimental FRA measurements obtained from two test objects: a three phase distribution transformer (35/0.4 kV, 100 kVA) to study the winding interturn fault and a two winding transformer (1.2 MVA, 10 kV) for the study of radial deformation. The non-parametric statistical methods, namely Spearman correlation coefficient, Wilcoxon signed rank test and Friedman test are used to compare FRA traces. It was found through this research work that the applied non-parametric methods can effectively reflect the differences between compared FRA data and diagnose the fault.
Application of Improved Algorithm Based on Four-Dimensional ResNet in Rural Tourism Passenger Flow Prediction

In rural tourism, precise visitor flow forecasts may aid management in making better decisions. It aids in the reduction of visitor crowds and trash. It also has the potential to improve visitor security. As a result, it is critical to continue to encourage tourism’s long-term growth. However, regional tourist flow of rural tourism has the characteristics of high volatility, complex nonlinearity, and susceptibility to seasonal influences. Moreover, a single neural network model cannot learn both temporal and spatial correlation. Therefore, by examining the variables impacting regional tourist flow and integrating residual networks with fully linked networks, this research offers an enhanced Quad-ResNet model for forecasting regional tourist flow of rural tourism. To be specific, this model learns spatial correlation through deep convolution; combines four residual networks to learn temporal proximity, similarity, periodicity and tendency; and uses one fully connected network to learn seasonal effects. Furthermore, this study compares the Quad-ResNet model with LSTM, CNN, and ST-ResNet models on the same dataset for regional tourist flow prediction experiments. The findings reveal that the Quad-ResNet model has less error and is substantially simpler to train and predict than the LSTM model, making it more suited for predicting regional visitor flows in rural tourism. For relevant stakeholders, the generated model may serve as a useful decision-making tool.
DATA-DRIVEN ROBUST MODEL FOR CONTAINER SLOT ALLOCATION WITH UNCERTAIN DEMAND

The global container transportation market is complex and changeable, resulting in high uncertainty of container cargo demand. Effective container slot allocation (CSA) decisions are difficult for shipping companies to make. A data-driven robust model was developed for optimizing the CSA under highly uncertain cargo demands to maximize the revenues of shipping companies. The features of the empty container transportation were integrated into the model due to the imbalance of container import and export. The Copula method was employed to construct the uncertain set to deal with the limited historical demand information. Moreover, the model can be transformed into a simple and manageable robust optimization problem by introducing the protection level. Finally, the effectiveness of the optimal robust CSA policy was verified by numerical examples. Results demonstrated that the robust optimization model effectively balances the relationship between the revenue and the risk preference of shipping companies and maximizes the revenue by using limited cargo demand information. The optimal robust slot allocation policy is more stable under the heavy tail cargo demand.
Routing for Symmetric FPGA's and FPIC's

A new class of routing structures with fixed orthogonal wire segments and field programmable switches at the intersections of the wire segments is proposed. In comparison with the conventional two-dimensional field-programmable gate array (FPGA) routing structure, this class of routing structures has the advantage of using a smaller number of active programmable switches. An existing field-programmable interconnect chip (EPIC) routing structure can be included as a special case in our class of routing structures. Using a probabilistic model, we prove that complete routing can be achieved with a high degree of probability in a routing structure of this class in which the number of tracks in each channel approaches the lower bound asymptotically. We present a sequential routing algorithm based on the solution of the single net routing problem. We take into account the delay introduced by the active programmable switches on a routing path and formulate the single net routing problem as a node-weighted Steiner minimum tree (NWSMT) problem in a bipartite graph G. Since our single net routing problem is NP-complete, a polynomial time approximate algorithm is proposed. We prove that our single net routing algorithm produces an optimal solution for some special classes of bipartite graphs. In general, the solution obtained by our algorithm has a performance bound of min{Δ(V\Z,|Z|-1}. In addition, we also prove that it is NP-complete to determine a solution that approximates the optimal solution within any constant bound. Experimental results for several industrial circuits show a reduction of up to 41% in the number of active programmable switches when compared with corresponding results for the conventional FPGA routing structure.
Decoding the Mechanism behind the Pathogenesis of the Focal Segmental Glomerulosclerosis

Focal segmental glomerulosclerosis (FSGS) is a chronic glomerular disease associated with podocyte injury which is named after the pathologic features of the kidney. The aim of this study is to decode the key changes in gene expression and regulatory network involved in the formation of FSGS. Integrated network analysis included Gene Expression Omnibus (GEO) datasets to identify differentially expressed genes (DEGs) between FSGS patients and healthy donors. Bioinformatics analysis was used to identify the roles of the DEGs and included the development of protein-protein interaction (PPI) networks, Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and the key modules were assured. The expression levels of DEGs were validated using the additional dataset. Eventually, transcription factors and ceRNA networks were established to illuminate the regulatory relationships in the formation of FSGS. 1130 DEGs including 475 upregulated genes and 655 downregulated genes with functional enrichment analysis were determined. Further analysis uncovered that the validated hub genes were defined as candidate genes, including Complement C3a Receptor 1 (C3AR1), C-C Motif Chemokine Receptor 1(CCR1), C-X3-C Motif Chemokine Ligand 1 (CX3CL1), Melatonin Receptor 1A (MTNR1A), and Purinergic Receptor P2Y13 (P2RY13). More importantly, we identified transcription factors and mRNA-miRNA-lncRNA regulatory networks associated with the candidate genes. The candidate genes and regulatory networks discovered in this study can help to comprehend the molecular mechanism of FSGS and supply potential targets for the diagnosis and therapy of FSGS.
Metarepresentation Choice of Context in Virtual Environment: Negotiating Identity on Youtube Video Blogging

: This study focuses on the the analysis focuses more specifically on the multimodality channel used by the blogger as the source of metarepresentation choice of context identity by the addressee. The previous research was done previously focuses on the sender user on this case was done to observe the prediction of relevance and intentions prediction of context identity. By observing similar object, the Daniel Howell channel in YouTube platform, the analysis was done by using the adaptation of Shifman’s meme analysis framework, the analysis focuses on three dimensions of the video; content, form, and stance. The result shows that The stance of the internet message is built based on two important features; the language features and the structural features. The most dominant language features used in the video by Danniel Howell is sarcasm and insult. This linguistic strategy is used to create sense of relatable and to form his identity as a failure who is never sure of what he is saying. Meanwhile structural features plays very important role in creating the humorous effect as the context of the internet message.
CRYPTO CURRENCY PREDICTION USING DEEP LEARNING

— the economic system has visible the charge of bitcoin hastily grow due to its promising outlook on the future for crypto currencies. Traders have taken observe of numerous blessings crypto provides over the traditional banking system. One such trait is that crypto forex lets in for decentralized banking, meaning bitcoin or any alternate coins(normally referred as alt coins) can't be regulated by way of powerful banks. There may be also a marketplace cap of 21 million bitcoins that can be in movement, therefore a surplus of bitcoins cannot be "revealed" which would bring about inflation. Bitcoin resolves the difficulty with transaction protection by way of using a block chain, or a ledger, which statistics the records of each transaction ever made into one long hexadecimal "chain" of nameless transactions, which continues transaction records obvious, but also exclusive. Recurrent neural networks (RNN) with long term memory cells(LSTM) cells can be used . We show that the price of crypto coins can be expected with machine learning.
Distributed formation for large swarm of agents.

Recent advances in sensor technology have led to the development of complex networked control systems consisting of large number of interacting agents, thus opening up new opportunities and challenges in analysis and control of such complex systems. Recently, formation control of a large swarm of networked multi-agent system has received considerable attention. Due to the large size of the system and the communication limitations of the complex networks, traditional formation control methods fail to demonstrate satisfactory performance. This thesis presents several methodologies to solve distributed formation control problem for a large swarm of networked multi-agent system. Formation control of network of agents with single-integrator dynamic can be simplified as a consensus problem. This thesis firstly addresses the distributed consensusability problem in complex networked agent systems based on the structural observability of network systems. A notion of unobservable node is introduced which leads to structural unobservability, and the relationship between the existence of unobservable nodes and the distributed consensusability of network systems is explored. Subsequently, a simple tool is presented to identify the unobservable nodes of a network system which is equivalent to examining the distributed consensusability of the network system. Moreover, a simple mathematical method is proposed to modify a distributed consensusable network into a weight-balanced one.