Miracle-dz/UniFAR-FTUs huggingface dataset:
Bank Restructuring and Financial Performance: A Case Study of Commercial Banks in Vietnam

This study examines the impact of bank restructuring on the financial performance of commercial banks in Vietnam. The data for this study was obtained from the audited financial statements of 30 Vietnamese commercial banks from 2007 to 2019. Multiple regression analysis was used for investigation. Financial performance, as evaluated by ROAA, ROEA, and NIM, is the dependent variable. Financial restructuring, ownership restructuring, and operational restructuring are the independent variables. Pooled least squares (Pooled OLS), fixed effects model (FEM), random effects model (REM), and system generalized moment regression model (System GMM) are the estimate methods used to increase the accuracy of the regression coefficient. The research results show that the variables of financial restructuring activities such as government intervention and the ratio of equity to total assets; variables of ownership restructuring such as capital adequacy ratio, privatization of state-owned commercial banks, mergers, and acquisitions; variables of operational restructuring such as employees, branches, the cost to total assets; GDP variables and the second restructuring period have a positive impact on financial performance. Variables such as debt-to-capital ratio, bad debt ratio, state ownership ratio, expense-income ratio, and inflation have a negative effect on financial performance.
PHÂN TÍCH IN SILICO CÁC GENE MÃ HÓA GLUTAMATE DEHYDROGENASE Ở CÂY ĐẬU COVE ( Phaseolus

glutamate tới a-ketoglutarate hoặc 2-oxoglutarate. thực Năm GDH trong gene cây Đậu Các này hai nhóm, nhóm có và và (NADH-GDH) Abstract G lutamate dehydrogenases (GDH, EC 1.4.1.2~4) catalyze reversible deamination reaction of L-glutamate to 2-oxoglutarate or a-ketoglutarate (a-KG). These enzymes play an important role in amino acid metabolism in plants. Five GDH encoded genes were found in common bean genome. These GDHs were classified into two groups, the group I (NADPH-GDH) inclued two genes (PvGDH 4 and PvGDH 5 ) and the group II (NADH-GDH) had three genes (PvGDH 1 , PvGDH 2 and PvGDH 3 ). These genes had different physico-chemical characteristics. All five PvGDH proteins shared the specific substrate (a-ketoglutarate)-binding motif and specific coenzyme (NAD(P)H)-binding motif. In addition, the group II proteins contained the mitochondrial target motif at N-termnal. The expression of common bean GDH genes was divergent in different tissues. Four genes PvGDH 1 , PvGDH 3 , PvGDH 4 and PvGDH 5 were expressed in all tissues at all studied development stages. These results are the basis for further research on the function and application of the GDH of common bean.
Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting

Traffic situation awareness and alerting assisted by adverse weather conditions contributes to improve traffic safety, disaster coping mechanisms, and route planning for government agencies, business sectors, and individual travelers. However, at the city level, the physical sensor-generated data are partly held by different transportation and meteorological departments, which causes problems of “isolated information” for data fusion. Furthermore, it makes traffic situation awareness and estimation challenging and ineffective. In this paper, we leverage the power of crowdsourcing knowledge in social media and propose a novel way to forecast and generate alerts for city-level traffic incidents based on a social approach rather than traditional physical approaches. Specifically, we first collect adverse weather topics and reports of traffic incidents from social media. Then, we extract temporal, spatial, and meteorological features as well as labeled traffic reaction values corresponding to the social media “heat” for each city. Afterwards, the regression and alerting model is proposed to estimate the city-level traffic situation and give the suggestion of warning levels. The experiments show that the proposed model equipped with gcForest achieves the best root mean square error (RMSE) and mean absolute percentage error (MAPE) score on the social traffic incidents test dataset. Moreover, we consider the news report as an objective measurement to flexibly validate the feasibility of proposed model from social cyberspace to physical space. Finally, a prototype system was deployed and applied to government agencies to provide an intuitive visualization solution as well as decision support assistance.
Accounting Conservatism and Feedback Effect of Stock Prices: Evidence from China

Using a sample of Chinese listed firms between 2001 and 2019, we examine the impact of accounting conservatism on feedback effect of stock prices. After controlling for endogeneity, we find that conservatism enhances the sensitivity of investment to Tobin’s Q. We also find an information channel drives the relation between accounting conservatism and feedback effect of stock prices. Cross-sectional analyses show that the effect of accounting conservatism on feedback effect of stock prices is more pronounced for firms with worse corporate governance, higher sales volatility, and lower industry competition. Our paper highlights the role of accounting conservatism on stock price informativeness. JEL classification: G14; M40; M41
Influencer Marketing: Concept and Approaches

With the rapid growth of usage of social media sites and marketing expenditures of digital and social media, the brands have been compelled to adopt innovative strategies of advertising. One such significant strategy is influencer advertising, which connects online personas and bloggers with brands or services which in result target social media users to impact their buying behaviour as their opin ions have significant impact on audience specifically youth audience. Over the last few years, this marketing strategy has chiefly centered on social media networks, which in turn is embraced by majority of brands to market through social media influencers. Although influencer advertising is a modern phenomenon and a contemporary strategy, it justifies being a viable option for marketers to build and maintain relationship with target audience. Companies engage influencers and bloggers to promote products and services to influence their fan-base. The purpose of the paper is to discuss how brands utilize influencer marketing as a marketing tool.
MODELLING AND TESTING OF SATURATED CORE FAULT CURRENT LIMITER

A fault current limiter (FCL) is a nonlinear device with negligible impedance under normal conditions which is able to switch to a high impedance state as soon as the current passing through it exceeds a given threshold. Among the various FCL topologies, saturated core fault current limiters have recently reached the demonstration phase in medium to high-voltage networks. ASG Power Systems has manufactured and tested a 36kV rated saturated core type fault current limiter (FCL). The Paper presents a sophisticated coupled-circuit model of the limiter and compares the model results with the results of full-scale testing of the limiter.
An Empirical Analysis of the Effect of Supply Chain Disruptions on Operating Performance

This paper documents the impact of supply chain disruptions on operating performance. Based on a sample of 885 disruptions announced by publicly traded firms, we estimate that in the year leading to the announcement, firms on average experience a 107% drop in their operating income, 114% drop in return on sales and 93% drop in return on assets. During this time period the level of return on sales drops by 13.78% and return on assets by 2.32%. Firms that experience disruptions face on average 6.92% lower sales growth, 10.66% growth in cost, and 13.88% growth in inventories. More importantly, firms do not quickly recover from the negative economic consequences of disruptions. During the two-year time period after the disruption announcement, the changes in operating income, sales, total costs, and inventories are insignificantly different from zero. We also find that it does not matter who caused the disruption, what the reason was for the disruption, or what industry a firm belongs to disruptions have a negative impact across the board.
Symbiotic Cyanobacteria in Lichens

Cyanolichens are obligate symbioses between fungi and cyanobacteria. They occur in many types of environments ranging from Arctic tundra and semi-deserts to tropical rainforests. Possibly even a majority of their global species diversity has not yet been described. Symbiotic cyanobacteria provide both photosynthate and fixed nitrogen to the fungal host and the relative importance of these functions differs in different cyanolichens. The cyanobiont can either be the sole photosynthetic partner or a secondary symbiont in addition to a primary green algal photobiont. In addition, the cyanolichen thallus may incorporate a plethora of other microorganisms. The fungal symbionts in cyanolichens are almost exclusively ascomycetes. Nostoc is by far the most commonly encountered cyanobacterial genus. While the cyanobacterial symbionts are presently not readily identifiable to species, molecular methods work well on the generic level and offer practical means for identifying symbiotic cyanobacterial genotypes. The present diversity of lichen cyanobionts may partly reflect the evolutionary effects of their lichen-symbiotic way of life and dispersal.
Validating the Bot Usability Scale for Satisfaction with Chatbots: Factor Structure, Convergent Validity and Relation with Workload

So far, no standardised scale to measure chatbot satisfaction is in widespread use, hindering research in this field. For this reason, the Bot Usability Scale (BUS) was developed. The aim of this study was to replicate and extend the validation of the BUS. Therefore, the main objectives were to confirm the BUS’ previously established five-factor structure, high internal consistency, and strong positive correlation with the UMUX-Lite. A further objective was to test whether the BUS correlates negatively with workload, since workload and satisfaction typically have an inverse relationship. For this, 58 participants solved tasks for five chatbots and rated each chatbot on the BUS, UMUX-Lite and NASA-TLX in an online study. In total, 388 BUS-questionnaires were collected and analysed using confirmatory factor analysis and Cronbach’s alpha. For 297 out of all 388 BUS-questionnaires, corresponding UMUX-Lite and NASA-TLX questionnaires were also collected. Their relationship with the BUS was analysed using Kendall’s Tau and linear mixed models. As expected, the five-factor structure was confirmed, the BUS had a high internal consistency, a strong correlation with the UMUX-Lite, and a negative relationship with workload. Overall, these findings support the use of the BUS as a standardized measure for chatbot satisfaction, enabling replicable research on this topic, comparison of chatbots, and the establishment of benchmarks for desirable satisfaction levels. However, the factor analysis revealed a variance of 0 [-0.18, 019] for item 1, suggesting a potential Heywood case which may require modification or removal. Future research should aim to replicate these findings with larger, simple random samples, with chatbots with overall low satisfaction rates and high workload scores and using multiple tasks per chatbot. Moreover, future studies should research whether item 1 presents a Heywood case, if so, why it occurs and whether modification or removal of item 1 is appropriate. Finally, researchers should investigate the relationships between the BUS and variables other than workload.
Deep Learning for Movie Review Sentiment Analysis

: Sentiment analysis [6] has long been an important problem of study in NLP and machine learning, for finding public sentiments on products, brands or services. Previous approaches to sentiment analysis have included unsupervised learning, Naive Bayes classifiers and SVM. In this paper, we focus on sentiment analysis of movies using text reviews. Sentiment analysis can be a challenging problem to solve because our language is rather complex and a single word can have either positive or negative connotations based on the context. We will be using the Large Movie Review Dataset [4] given by Stanford AI lab, which is a binary sentiment classification dataset based on IMDB reviews of movies, and contains 50,000 reviews with a 50:50 train: test split. The objective is to classify a movie as good or bad, based on its text review. We will approach this problem using the Tf-Idf vector of the corpus and applying a deep learning model on top of it. This model achieved an accuracy of 90.7%, which is a significant improvement over the current approaches. Future extensions to this approach could include more powerful deep learning models like LSTM or GRU, which can extract even more contextual information.
Antithrombotic and antihemolytic effects of Lagerstroemia speciosa (Lythraceae) aqueous extract

Introduction: Aqueous extract of Lagerstroemia speciosa (EALS) (Lythraceae) is widely used to treat diabetes. This plant has been shown an in vitro thrombolytic activity that indicates its potential to prevent the formation of blood clots in vivo. Thus, this study was undertaken to evaluate the antithrombotic and antihemolytic effects of EALS. Methods: Rats of both sexes (200 ± 5 g) were divided into five groups of six animals. Each group received orally distilled water, EALS (250, 500, 1000 mg/kg), and acetylsalicylic acid (100 mg/kg) for five days. After treatment, the FeCl3-induced arterial thrombus formation method was used to determine occlusion time. A coagulometer was used to detect activated partial thromboplastin time (aPTT) and prothrombin time (PT). Rabbit blood was used to determine clot lysis activity in vitro and antihemolytic activity using the 2,2-azobis hydrochloride (2-methylpropionamidine) (AAPH) method. Results: EALS increased the occlusion time in a dose-dependent manner. At the dose of 1000 mg/kg, EALS increased the occlusion time significantly, from 4.59 ± 2.45 minutes to 15.52 ± 2.38 minutes (P<0.01). At high concentrations (1-4 mg/mL), EALS showed a significant increase in aPPT and PT (P<0.05). Streptokinase and EALS (4 mg/mL) induced significant clot lysis with percentage values of 78.48 ± 2.2 % and 49.5 ± 1.53 %, respectively (P<0.001). EALS inhibited AAPH-induced hemolysis. Conclusion: EALS exhibited antithrombotic and antihemolytic activities. The antithrombotic property of the plant could be attributed to its anticoagulant and thrombolytic activities. Regular consumption of L. speciosa leaves may prevent or treat thrombotic diseases.
Interactive Explainable Artiﬁcial Intelligence in Movie Recommender Systems

Video on Demand platforms employ artiﬁcially intelligent movie recommender systems to support users in ﬁnding suitable content. However, prior work suggests that users still face several problems. Users reportedly feel overwhelmed by the high number of recommendations and ﬁnd it hard to make sense of these suggestions and how these systems work. At the same time, current systems only employ few or no possibilities to control and make sense of the recommendation algorithms. This study investigates interactive explanations as an approach to improve sense-making and user-system trust. An initial study (based on 12 semi-structured inter-views) examined the acceptability of expressive feedback on watched movies to derive highly personalised explanations. While this study found extensive feedback to be only moderately accepted because of high interaction effort, explanations of the eventual recommendations were found to be in high favour. Therefore, a follow-up within-subject online user study (N=30) compared three prototypes of interactive explanations (feature-weighting, timeline, and genre-swapping) with a non-interactive/non-explainable baseline. These prototypes used the participant’s Netﬂix viewing activity to make personalised recommendations. In particular, each prototype’s effect on different levels of sense-making, user-system trust, and other variables was measured. The results show that interactive explanations are an effective way to enable users to make sense of recommender systems and their output. Moreover, all interactive systems were found to improve user-system trust signiﬁcantly. An overreaching demand for more control and increased transparency in movie recommender systems furthermore suggests that Video on Demand platforms should start to employ interactive explanations.
Entropy-driven Mg 2+ -induced folding of TPP riboswitch RNA

Mg 2+ is well-known to facilitate the structural folding of RNA. However, the thermodynamic and dynamic roles of Mg 2+ in RNA folding remain elusive. Here, we exploit smFRET (single-molecule fluorescence resonance energy transfer) and ITC (isothermal titration calorimetry) to study the mechanism of Mg 2+ in facilitating the folding of TPP (thiamine pyrophosphate) riboswitch RNA. The results of smFRET identify that the presence of Mg 2+ compacts the RNA, but also enlarge the dynamical dispersity among individual RNA molecules, resulting in a large gain of entropy. The compact yet flexible conformations triggered by Mg 2+ may help the riboswitch to recognize its specific ligand and further fold. This is supported by the ITC experiments, in which the Mg 2+ induced RNA folding is driven by entropy ( ΔS ) instead of enthalpy ( ΔH ). Our results complement the understanding of Mg 2+ -induced RNA folding. The strategy developed in this work can be used to model other RNAs’ folding at different conditions.
Optimization of territories and transport routes for hazardous products in a distribution network

In a system of distribution of products are involved in different factors that determine their efficiency, profitability or optimal state, among these factors is the type of goods to be collected or delivered, it must also be considered the physical-chemical composition, hazard index to transport etc., in this sense, exist different standards for collection, delivery and transportation of such material causing with it an increase in operating costs associated with territory design and planning of distribution routes, the current paper present a general model based in mixed integer programming which integrates both problems, seeking to minimize the total distance traveled by the vehicle in each territory. It should be noted that one of the main features of this model is that it only considers the collection of goods in the distribution network which gives us the opportunity to offer low-cost solutions in terms of time and quality, addressing the specific and general characteristics of the emerging markets in Mexico
Detection of hemagglutinin using Affinité's P4SPR TM as a potential platform to monitor production of influenza vaccine

The influenza vaccine is manufactured each year to protect people against serious illness that require hospitalization and reduce the number of mortalities associated with influenza. Therefore, the timely production and validation of influenza vaccine lots are of utmost importance. Each vaccine lot must be evaluated for potency, which is based on the amount of the hemagglutinin (HA) content found on influenza viruses in the vaccine. Using HA, these viruses are known to infect host cells by binding to sialic acid at the end of complex glycans [1].
A Brief Survey of Textual Dialogue Corpora

Several dialogue corpora are currently available for research purposes, but they still fall short for the growing interest in the development of dialogue systems with their own specific requirements. In order to help those requiring such a corpus, this paper surveys a range of available options, in terms of aspects like speakers, size, languages, collection, annotations, and domains. Some trends are identified and possible approaches for the creation of new corpora are also discussed.
Compressor Discharge Pipe Failure Investigation With A Review Of Surge, Rotating Stall, And Piping Resonance.

Rotating stall, even with multiple cells, can contribute to piping excitation due to acoustic energy traveling down the pipe as a plane wave. The main objective of this paper is to describe causes and solution of fatigue cracks in discharge piping due to two forms of acoustic energy, with occasional direct surge loading of a 50,000 hp centrifugal compressor. Described are transverse modes of the gas inside pipes, as well as structural mode interaction of pipe shell 49 COMPRESSOR DISCHARGE PIPE FAILURE INVESTIGATION WITH A REVIEW OF SURGE, ROTATING STALL, AND PIPING RESONANCE
Discovery of gamma-ray emission from the extragalactic pulsar wind nebula N157B with the High Energy Stereoscopic System

We present the significant detection of the first extragalactic pulsar wind nebula (PWN) detected in gamma rays, N157B, located in the large Magellanic Cloud (LMC). Pulsars with high spin-down luminosity are found to power energised nebulae that emit gamma rays up to energies of several tens of TeV. N157B is associated with PSRJ0537-6910, which is the pulsar with the highest known spin-down luminosity. The High Energy Stereoscopic System telescope array observed this nebula on a yearly basis from 2004 to 2009 with a dead-time corrected exposure of 46 h. The gamma-ray spectrum between 600 GeV and 12 TeV is well-described by a pure power-law with a photon index of 2.8 \pm 0.2(stat) \pm 0.3(syst) and a normalisation at 1 TeV of (8.2 \pm 0.8(stat) \pm 2.5(syst)) \times 10^-13 cm^-2s^-1TeV^-1. A leptonic multi-wavelength model shows that an energy of about 4 \times 10^49erg is stored in electrons and positrons. The apparent efficiency, which is the ratio of the TeV gamma-ray luminosity to the pulsar's spindown luminosity, 0.08% \pm 0.01%, is comparable to those of PWNe found in the Milky Way. The detection of a PWN at such a large distance is possible due to the pulsar's favourable spin-down luminosity and a bright infrared photon-field serving as an inverse-Compton-scattering target for accelerated leptons. By applying a calorimetric technique to these observations, the pulsar's birth period is estimated to be shorter than 10 ms.
Signalling by protein and drug development: a systems-centred view

Protein-modification cycles catalysed by opposing enzymes, such as kinases and phosphatases form the backbone of signalling networks. Whereas historically, kinases have been at the research forefront, a systems-centred approach reveals predominant roles of phosphatases in controlling the network response times and the spatiotemporal profiles of signalling activities. Emerging evidence suggests that phosphatase kinetics are critical for the network function and cell-fate decisions. Protein phosphatases can operate as both immediate and delayed regulators of signal transduction, capable of attenuating or amplifying signalling. This versatility of phosphatase action emphasises the need for systems biology approaches to comprehend cellular signalling networks and predict the cellular outcomes of combinatorial drug interventions.