Miracle-dz/UniFAR-FTUs huggingface dataset:
Ameliorative effect of micro-algal and medicinal plants on some biochemical properties of bean plants under salinity stress

This study was conducted to examine the ameliorative effects of foliar application of some micro-algal (Chlorella vulgaris and Spirulina platensis) and some medicinal plant leaves (Salix alba, Psidium guajava, and Olea europaea) extracts on Phaseolus vulgaris (Bean) under salinity stress. On a loamy soil, a pots trial was carried out on bean plants grown under salinity stress. Growth characteristics, pigments, osmolytes, total phenol, and antioxidant enzyme contents were determined. S. platensis extract application showed the greatest improvement in shoot length and fresh weight of shoot, which rose 23.5% and 65.1%, respectively compared to the control. The utilized bio-stimulants, particularly S. platensis extracts, remarkably increased the chlorophyll content compared to the control under salinity stress. The photosynthetic pigment, soluble sugars, and soluble protein levels were strengthened by foliar application of bio-stimulant extract. Proline and antioxidant enzyme levels are significantly reduced using algal and plant extracts treatment. These findings support the treatment’s increased contribution to reducing salt stress and their detrimental effects on bean plants.The findings of this study indicate that the use of these biostimulants, especially S. alba, P. guajava, and O. europaea leaf extracts can be considered as an unconventional, ecofriendly, and novel tool in the mitigation of salinity stress.
Indian Currency Recognition System

-Counterfeit notes are one of the biggest problem occurring in cash transactions. For country like India, it is becoming big hurdle. Because of the advances in printing, scanning technologies it is easily possible for a person to print fake notes with use of latest hardware tools. Detecting fake notes manually becomes time-consuming and untidy process hence there is need of automation techniques with which currency recognition process can be efficiently done. This work Extraction of different features in fake and real currency notes and through comparing with each other we can able to differentiate the fake note from the real note. In this approach, feature extracted dataset is used and for detection of the genuineness currency. Security feature of Indian currency note available on front and back side of the notes are used as features. This work uses ORB (Oriented FAST and Rotated BRIEF) and Brute-Force matcher approach to extract the feature of currency. The identification mark, optical variable ink, and extracted currency features decides the currency recognition. The features extraction is performed on the image of the currency and it is compared with the features of genuine currency. The desired result will indicate whether the note is genuine or fake.
AN AUDIO GENERATION MODEL

Speech synthesis is an artificial production of human speech. A text-to-speech (TTS) system, which converts normal language text into speech, has a wide range of uses in industry and academia. Currently, A TTS system mainly consists of two components, text analysis and vocoder. Text analysis is responsible for generating MEL spectrogram from input text, and vocoder is responsible for generating audios from MEL spectrogram. Traditional methods mostly use concatenative TTS to generate speech, that is, a big database of short speech components are recorded from speakers and then combined to construct complete utterances. Thanks to machine learning, recently there is a great leap in NLP field, and many publications discuss tagging emotions and plauses in text. Therefore, to improve the quality of synthesized voice, it would be feasible and valuable to model the raw waveform of an audio signal and generate more naturally-sounded speech. Our aim is to implement a model for generating raw audio waveforms (Figure 1). This model can directly generate speech waveforms close to real human voice from the given language parameters and speech parameters. Frankly speaking, We try to convert digital text into natural speech streams and build a vocoder to implement TTS.
Semantic Purity and Effects Reunited in F (cid:63)

We present (new) F (cid:63) , a dependently typed language for writing general-purpose programs with effects, specifying them within its functional core, and verifying them semi-automatically, by a combination of type inference, SMT solving, and manual proofs. A central difﬁculty is to ensure the consistency of a core language of proofs within a larger language in which programs may exhibit effects such as state, exceptions, non-termination, IO, concurrency, etc. Prior attempts at solving this problem generally resort to a range of ad hoc methods. Instead, the main novelty of F (cid:63) is to safely embrace and extend the familiar style of type-and-effect systems with fully dependent types. F (cid:63) is founded on a λ -calculus with a range of primitive effects and a dependent type system parameterized by a user-deﬁned lattice of Dijkstra monads. For each monad, the user provides a predicate-transformer that captures its effective semantics. At the bottom of the lattice is a distinguished Pure monad, such that computations typeable as Pure are normalizing—our termination argument is based on well-founded relations and is fully semantic. We illustrate our design on a series of challenging programming examples. We outline its metatheory on a core calculus, for which we prove soundness and termination of the Pure fragment. We also discuss selected aspects of its fresh typechecker. The F (cid:63) system is open source; it fully supports our new design; it generates F# and OCaml code; and it bootstraps to several platforms.
Distributed Threshold-Based Offloading for Heterogeneous Mobile Edge Computing

In this paper, we consider a large-scale heterogeneous mobile edge computing system, where each device's mean computing task arrival rate, mean service rate, mean energy consumption, and mean offloading latency are drawn from different bounded continuous probability distributions to reflect the diverse compute-intensive applications, mobile devices with different computing capabilities and battery efficiencies, and different types of wireless access networks (e.g., 4G/SG cellular networks, WiFi). We consider a class of distributed threshold-based randomized offloading policies and develop a threshold update algorithm based on its computational load, average offloading latency, average energy consumption, and edge server processing time, depending on the server utilization. We show that there always exists a unique Mean-Field Nash Equilibrium (MFNE) in the large-system limit when the task processing times of mobile devices follow an exponential distribution. This is achieved by carefully partitioning the space of mean arrival rates to account for the discrete structure of each device's optimal threshold. Moreover, we show that our proposed threshold update algorithm converges to the MFNE. Finally, we perform simulations to corroborate our theoretical results and demonstrate that our proposed algorithm still performs well in more general setups based on the collected real-world data and outperforms the well-known probabilistic offloading policy.
THE IMPACT OF BRAND IMAGE, PRODUCT QUALITY, AND PROMOTION ON THE PURCHASE INTENTIONS OF SCARLETT WHITENING SKIN CARE PRODUCTS AS MEDIATED BY E-WOM

This study aims to determine: (1) the direct influence of brand image towards purchase intention, (2) the direct influence of product quality towards purchase intention, (3) the direct influence of promotion towards purchase intention, (4) the influence of brand image towards purchase intention mediated by E-WOM, (5) the influence of product quality towards purchase intention mediated by E-WOM, (6) the influence of promotion towards purchase intention mediated by E-WOM . The population in this study constituted of Scarlett Whitening skin care product users, whereas the system consists of 96 Scarlett Whitening skin care product users. This research employed a combination of purposive sampling and snowball sampling for its sampling strategy. Distribution of questionnaires to 96 respondents who satisfied the criteria yielded data. E-WOM-related questionnaires on brand image, product quality, promotion, purchasing intent, and E-WOM. SmartPLS 3 was employed during the data analysis procedure. Based on the results of data analysis, the research results obtained that: (1) brand image had directly positive influence towards purchase intention, (2) product quality didn’t have directly positive influence towards purchase intention, (3) promotion had directly positive influence towards purchase intention, (4) brand image didn’t have positive influence towards purchase intention mediated by E-WOM, (5) product quality had positive influence towards purchase intention mediated by E-WOM (full mediation), (6) promotion didn’t have positive influence towards purchase intention mediated by E-WOM.
Conceptual Framework on the Factors Influencing Users’ Intention to Adopt AI-Based Cybersecurity Systems at Workplaces in the UAE

Purpose: Artificial intelligence (AI) technologies are having revolutionizing effects in many industries, including the cybersecurity domains. However, research has consistently shown that the intention to adopt and use AI-based cybersecurity systems at both the organizational and individual levels is relatively low, yet such systems are associated with many potential benefits. In response to this research problem, the present study aims at investigating the factors that influence users' intention to adopt AI cyber-security systems at workplaces in the UAE. Design/methodology/approach: This cross-sectional study will be conducted using the quantitative research methodology, where the correlational research design will be adopted. In this regard, the intended data will be collected through a web-based survey questionnaire from a sample of 178 respondents selected using the purposive sampling method. Upon collection, the data will be cleaned and prepared using the Statistical Package for Social Sciences (SPSS) software. It will then be analyzed using the Partial Least Squares structural equation and Modelling (PLS-SEM) program. Findings: Findings made in this research are expected to help organizations intending to adopt AI-based cybersecurity systems in determining the factors to consider before investing in and implementing AI-based cybersecurity systems. Such knowledge can help in enhancing a successful implementation of such a technology, characterized by minimal resistance from the employees. The insights gained may also be helpful to developers or firms creating AI-based cybersecurity technologies in understanding the considerations they should make when designing such technologies to enhance their adoption rate. Research implications: This study is expected to make significant academic contributions. It is expected to enrich
Comparison of damage to wood pallets in use with damages occurring using the Virginia Tech FasTrack simulation of pallet use

The durability of a pallet affects the amount of use a pallet can withstand before functionality is lost. A reliable prediction of durability can be used to determine the effect of pallet performance on supply chain operating costs. The objective of this research was to correlate damage modes, location, severity, and frequency observed for pallet in the field, to damages observed during Virginia Tech’s FasTrack simulation system. Several 1219 mm x 1016 mm (48 x 40 inch) stringer class wooden pallets used in the field were inspected for damages, and the results were compared to historical pallet damage information from FasTrack. The pallet damage behavior did not change for different levels of damage severity, which indicates that pallets fail as the initial damage worsens due to prolonged use. Inspected pallets from the field showed high damage occurrence on the stringer notches and bottom lead deckboards. Pallets tested via FasTrack exhibited significantly more top deck and end board damage and less stringer damage than observed in the field.
A National Survey of Stress and Burnout in Critical Care Nurses

Background Critical care nurses (CCNs) experience a higher level of stress and burnout than nurses in other specialties. Approximately 50% of CCNs are mildly stressed, and almost 20% are moderately stressed. Prolonged periods of stress can lead to burnout, which has been shown to have deleterious effects on quality and patient safety. Objectives The purpose of this study is to determine the prevalence of burnout among a national sample of CCNs and the association with environmental factors. Methods A national survey of CCNs working in the United States was implemented using an exploratory descriptive design. The anonymous survey was developed iteratively according to best practices of survey design. The survey included the Perceived Stress Scale and the Copenhagen Burnout Inventory tool. Pretesting and pilot testing were conducted with CCN specialists, and the survey was revised based on their feedback. An anonymous link was distributed to respondents using convenience sampling through social media and further disseminated via snowball sampling. Results Two hundred seventy nurses responded to the survey. The mean (SD) Perceived Stress Scale score in the study population was 18.5 (6.4), indicating moderate stress. The mean (SD) Copenhagen Burnout Inventory score was 61.9 (16.5), indicating moderate burnout. Our study found that the overall health of the work environment was one of the most important factors associated with both stress and burnout. Conclusions This study has demonstrated the relationship between the health of the work environment and burnout among CCNs. It is imperative that health care organizations evaluate and implement strategies to optimize the health of the work environment to mitigate burnout and its negative sequelae on the nurse, patient, and system.
When guanxi Fills Structural Holes: Promoting Tacit Knowledge Conversion in Scientific and Technological Achievements Transformation Team

: Tacit knowledge conversion is the key to the transformation of scientific and technological achievements. However, tacit knowledge is highly personal, difficult to articulate, and subconscious, making it particularly hard to convert. Social network has been viewed as the main channel of tacit knowledge conversion, but its disperse structural holes can cause poor communication among sci-tech achievements transformation team members, leading to limited information mobilization. Guanxi can alleviate such disadvantages, because it pursues resource mobilization in the form of favor exchange, renqing accumulation and mianzi preservation. Therefore, this project puts forward a scientific hypothesis that strategically leveraging guanxi to buffer the negative impacts of structural holes can promote the effective conversion of tacit knowledge, so as to advance the sci-tech achievements transformation. The project attempts to adopt a mixed-methods research approach which involves quantitative surveys and social network analysis, as well as qualitative interviews 、 observation and case studies. This project aims to explore the dynamics of social networks for tacit knowledge conversion in sci-tech achievements transformation team, investigating the impacts of structural holes and guanxi on the transfer, acquisition, application and integration of tacit knowledge respectively. In addition, it intends to study the moderating effect of guanxi on structural holes, exploring how guanxi and structural holes coordinate beneficially to promote the effective conversion of tacit knowledge among sci-tech achievements transformation team members. It can hence be seen that this project has significant theoretical and practical implications for the transformation of scientific and technological achievements.
A fast algorithm for multifield representation and multiscale simulation of high‐quality 3D stochastic aggregate microstructures by concurrent coupling of stationary Gaussian and fractional Brownian random fields

This study presents a multiscale computational framework for the representation and generation of concrete aggregate microstructures on the basis of the multifield theory, which couples the stationary Gaussian random field with the fractional Brownian random field. Specifically, the stationary Gaussian field is utilized to simulate the morphological shape of an aggregate on the coarse scale, whereas the surface topography of the aggregate on the fine scale is represented by the fractional Brownian field. To bridge the 2 scales, a concurrent coupling formula is proposed. This coupled technique allows for smooth transition between the coarse and fine scales and permits the rapid generation of highly realistic concrete aggregates that can be tailored to the desired quality and requirements, making the algorithm computationally appealing. In the generation of the random fields on the 2 scales, the Fourier representation of block circulant covariance matrices with circulant blocks is exploited, which yields substantial efficiency advantages over the conventional Cholesky decomposition approach in factorizing covariance matrices as well as simulating random fields. Meanwhile, a microsurface postprocessing and reconstruction procedure is also developed to convert the generated random fields into realistic 3D shapes. The numerical methodology proposed in this study offers tremendous potential for a plethora of applications in cement‐based materials.
Causal Effect Estimation under Interference on Hypergraphs

Hypergraphs offer a powerful abstraction for representing multi-way group interactions, allowing hyperedges to connect any number of nodes. In contrast to prevailing approaches that focus on capturing statistical dependencies, our research explores hypergraphs from a causal perspective. Specifically, we tackle the problem of estimating individual treatment effects (ITE) on hypergraphs, aiming to determine the causal impact of interventions (e.g., wearing face covering) on outcomes (e.g., COVID-19 infection) for each individual node. Existing ITE estimation methods either assume no interference between individuals or consider interference only among connected individuals in regular graphs. However, such assumptions may not hold in real-world hypergraphs. Recognizing this, we propose a novel causality learning framework HyperSCI by modeling high-order interference on hyper-graphs. Through extensive experiments on real-world hypergraphs, we validate the effectiveness of HyperSCI and highlight the potential of causal inference in hypergraphs with complex group interactions.1
Authorship Disputes in Scholarly Biomedical Publications and Trust in the Research Institution

Introduction When authorship disputes arise in academic publishing, research institutions may be asked to investigate the circumstances. We evaluated the association between the prevalence of misattributed authorship and trust in the institution involved. Methods We measured trust using a newly validated Opinion on the Institution’s Research and Publication Values (OIRPV) scale (range 1–4). Mayer and Davies’ Organizational Trust for Management Instrument served as control. Association between publication misconduct, gender, institution type, policies, and OIRPV-derived Trust Scores were evaluated. Results A total of 197 responses were analyzed. Increased reporting of authorship misconduct, such as gift authorship, author displacement within the authors’ order on the byline, and ghost authorship, were associated with low Trust Scores (P<0.001). Respondents from institutions whose administration had made known (declared or published) their policy on authorship in academic publications awarded the highest Trust Scores (median 3.06, interquartile range 2.25 to 3.56). Only 17.8% favored their administration as the best authority to investigate authorship dispute honestly. Of those who did not list the administration as their preferred option for resolving disputes, 58.6% (95/162) provided a Trust Score <2.5, which conveys mistrust in the institution. Conclusions Increased reporting of publication misconducts such as gift authorship, author displacement within the order of the authors’ byline, and ghost authorship was associated with lower Trust Scores in the research institutions. Institutions that made their policies known were awarded the highest Trust Scores. Our results question whether the research institutions’ administrations are the appropriate authority for clarifying author disputes in all cases.
Comparison of PID and Fuzzy PID Controller for a Linearised Magnetic Bearing

This paper is a presentation of a linear model of a magnetic bearing and a comparison of two controllers concerning their control performance. Initially, the benefits obtained by the use of magnetic bearings in the electromechanical systems are presented. Afterwards, the necessity for a suitable controller design is discussed in terms of the instability of the open loop system. Looking for a suitable control manner, a controller that behaves non-linearly, such as a fuzzy PID controller has been chosen in this paper. The use of such a non-linear controller improves both the results and the range of its use of the above system. The linear and the Fuzzy PID controller are compared using extensively simulation tests with the existence of white noise signal. The results of these comparisons, concerning the stability of these controllers are discussed and presented. © 2017 Published by Faculty of Engineering Corresponding author: Pantelis G. Nikolakopoulos Department of Mechanical Engineering and Aeronautics in the University of Patras Rio, Patras, Greece. E-mail: pnikolakop@upatras.gr
Continuance Intention on Gamifikasi in E-Learning Using Extended Expectation-Confirmation Model

In an effort to examine user continuance intentions after using gamification in e-learning, this research was conducted to identify the factors that influence gamification in e-learning using the theoretical model of ECM, then analyze the relationship between the influencing construct variables, and explain the practical and theoretical implications of the effect of gamification in e-learning on user's continued intention to reuse. The theoretical model was developed from the Extended Expectation-Confirmation model (ECM) by integrating the influencing determinants. Descriptive analysis and hypothesis testing using the SEM technique used the Amos 24 application to analyze and interpret data from 496 respondents who participated in an online questionnaire distributed via chat applications and social media. Significant test results have been found for users after using gamification in e-learning, the confirmation variable has a high positive correlation in influencing satisfaction according to the expectations felt by users. The results of the Pearson correlation test conducted show that if the value of gamification user satisfaction in e-learning increases, the intention to continue using e-learning will also increase as found in the five hypothetical determinants that have a direct effect on continuous intention, namely perceived usefulness, satisfaction, engagement, flow, perceived enjoyment. The practical meaning of the findings has a significant effect on continuous intention for educational institutions to research and develop innovative online learning models with gamification to increase the level of user participation, for teachers to ensure that the implementation of the gamification feature has a positive direct effect on sustainability intentions, for users to be able to provide confirmation of benefits obtained with a sense of satisfaction to continue using gamification in e-learning. This study predicts the continuance intention of gamification users in e-learning by analyzing theoretical models influenced by 4 basic ECM constructs plus 3 exogenous variables of engagement, perceived enjoyment and flow. Testing the construct hypothesis, presenting a comprehensive SEM analysis, producing direct and indirect positive significance tests on continuous intention.