Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of International Technology Transfer on the Technical Efficiency of High-Tech Manufacturing in China: A RAGA-PP-SFA Analysis

The sustainable development of China’s high-tech manufacturing (HTM) sector is restricted by dependence on technology introduction and foreign direct investment (FDI), low input-output efficiency, and environmental pollution. This study aimed to examine the roles of technology introduction and FDI in improving the technical efficiency of Chinese HTM from an environmental perspective. By integrating stochastic frontier analysis (SFA) and projection pursuit (PP) based on the real-coded accelerated genetic algorithm (RAGA), this study constructed a RAGA-PP-SFA model that considers undesirable outputs. This model includes various outputs, including environmental pollution, in the production function to improve estimation accuracy. Moreover, to verify the robustness of the estimation results, the results were provided when environmental pollutants were taken as input factors. The results showed that technology introduction could significantly promote HTM’s technical efficiency, while FDI had no significant positive effect. By comparing the estimated results with those that did not consider environmental pollution, this study not only reveals different roles of technology introduction and FDI in improving HTM’s technical efficiency but also confirms that ignoring environmental pollution will overestimate their roles (especially the role of FDI) in such improvement.
Selection of an Effective Hand Sanitizer to Reduce COVID-19 Effects and Extension of TOPSIS Technique Based on Correlation Coefficient under Neutrosophic Hypersoft Set

Correlation coefficients are used to tackle many issues that include indistinct as well as blurred information excluding is not able to deal with the general fuzziness along with obscurity of the problems that have various information. The correlation coefficient (CC) between two variables plays an important role in statistics. Likewise, the accuracy of relevance assessment depends on the information in a set of discourses. The data collected for numerous statistical studies is full of exceptions. The concept of the neutrosophic hypersoft set (NHSS) is a parameterized family that deals with the subattributes of the parameters and is a proper extension of the neutrosophic soft set to accurately assess the deficiencies, anxiety, and uncertainty in decision-making. Compared with existing research, NHSS can accommodate more uncertainty, which is the most significant technique for describing fuzzy information in the decision-making process. The core objective of follow-up research is to develop the concept and characteristics of CC and the weighted correlation coefficient (WCC) of NHSS. We also introduced some aggregation operators in the considered environment, which can help us establish a prioritization technique for order preference by similarity to the ideal solution (TOPSIS) based on CC and WCC under NHSS. A decision-making strategy is established to solve multicriteria group decision-making (MCGDM) problems utilizing developed methodology. Moreover, the proposed method is utilized for the selection of an effective hand sanitizer during the COVID-19 pandemic to ensure the validity of the proposed approach. The practicality, effectivity, and flexibility of the current approach are proved through comparative analysis with the assistance of some existing studies.
Verifying an HTTP Key-Value Server with Interaction Trees and VST

We present a networked key-value server, implemented in C and formally verified in Coq. The server interacts with clients using a subset of the HTTP/1.1 protocol and is specified and verified using interaction trees and the Verified Software Toolchain. The codebase includes a reusable and fully verified C string library that provides 17 standard POSIX string functions and 17 general purpose non-POSIX string functions. For the KVServer socket system calls, we establish a refinement relation between specifications at user-space level and at CertiKOS kernel-space level.
Radio Frequency Link and User Selection Algorithm for 5G Mobile Communication System

Based on the millimetre-wave time-domain channel detector, this paper conducts channel measurement and modelling for different scenarios and antenna types in typical frequency bands and thus carries out a study on the propagation characteristics of millimetre-wave channels in typical frequency bands and scenarios for 5G. The time-varying characteristics and modelling methods of millimetre-wave clusters are studied, and the time-varying channel simulation strategy and linear regression fitting method are used to establish the time-varying generation and extinction model of clusters by combining the measured data of 26 GHz large-scale antenna channels. The algorithm is based on the correlation dictionary and achieves the efficient and optimal selection of large-scale antenna arrays in the millimetre-wave band. A low-complexity multiuser hybrid precoding design scheme is proposed based on the wideband millimetre-wave large-scale Multiple-Input Multiple-Output (MIMO) system. The scheme aims to maximize the system spectral efficiency and introduces the net spectral efficiency as a bridge to decouple the analogy precoding design from the digital precoding design, thus significantly reducing the computational complexity. In the analogy precoding stage, a Hungarian algorithm-based beam assignment method is proposed to avoid beam conflicts and maximize the net spectral efficiency of the system; in the digital precoding stage, the equivalent channel matrix of each subcarrier is diagonalized to eliminate multiuser interference. Finally, the effectiveness of the scheme is verified by simulation. In this paper, we focus on the radio frequency (RF) link and user selection algorithm of 5G mobile communication system. Through the study of these two aspects, the RF link of our communication system has high stability and reliability, and through the study of the algorithm used for selection, our results also have a strong practical value.
Predicting Course Grade through Comprehensive Modelling of Students' Learning Behavioral Pattern

While modelling students’ learning behavior or preferences has been found as a crucial indicator for their course achievement, very few studies have considered it in predicting achievement of students in online courses. This study aims to model students’ online learning behavior and accordingly predict their course achievement. First, feature vectors are developed using their aggregated action logs during a course. Second, some of these feature vectors are quantified into three numeric values that are used to model students’ learning behavior, namely, accessing learning resources (content access), engaging with peers (engagement), and taking assessment tests (assessment). Both students’ feature vectors and behavior model constitute a comprehensive students’ learning behavioral pattern which is later used for prediction of their course achievement. Lastly, using a multiple criteria decision-making method (i.e., TOPSIS), the best classification methods were identified for courses with different sizes. Our findings revealed that the proposed generalizable approach could successfully predict students’ achievement in courses with different numbers of students and features, showing the stability of the approach. Decision Tree and AdaBoost classification methods appeared to outperform other existing methods on different datasets. Moreover, our results provide evidence that it is feasible to predict students’ course achievement with a high accuracy through modelling their learning behavior during online courses.
A Mechanised Proof of the Time Invariance Thesis for the Weak Call-By-Value λ-Calculus

The weak call-by-value λ -calculus L and Turing machines can simulate each other with a polynomial overhead in time. This time invariance thesis for L , where the number of β -reductions of a computation is taken as its time complexity, is the culmination of a 25-years line of research, combining work by Blelloch, Greiner, Dal Lago, Martini, Accattoli, Forster, Kunze, Roth, and Smolka. The present paper presents a mechanised proof of the time invariance thesis for L , constituting the first mechanised equivalence proof between two standard models of computation covering time complexity. The mechanisation builds on an existing framework for the extraction of Coq functions to L and contributes a novel Hoare logic framework for the verification of Turing machines. The mechanised proof of the time invariance thesis establishes L as model for future developments of mechanised computational complexity theory regarding time. It can also be seen as a non-trivial but elementary case study of time-complexity-preserving translations between a functional language and a sequential machine model. As a by-product, we obtain a mechanised many-one equivalence proof of the halting problems for L and Turing machines, which we contribute to the Coq Library of Undecidability Proofs.
An Efficient Decision Support System for the Selection of Appropriate Crowd in Crowdsourcing

Crowdsourcing is a complex task-solving model that utilizes humans for solving organizational specific problems. For assigning a crowdsourced task to an online crowd, crowd selection is carried out to select appropriate crowd for achieving the task. The efficiency and effectiveness of crowdsourcing may fail if irrelevant crowd is selected for performing a task. Early decisions regarding selection of a crowd can ultimately lead to successful completion of tasks. To select most appropriate crowd from crowdsourcing, this paper presents a decision support system (DSS) for appropriate selection of crowd. The system has been implemented in the Superdecision tool by plotting hierarchy of goals, criteria, and alternatives. Various calculations have been done for performing the proposed research. Results of the study reveal that the proposed system is effective and efficient for selection of crowd in crowdsourcing by performing various pairwise computation of the study.
A New Fractional Model for Cancer Therapy with M1 Oncolytic Virus

)e aim of this work is to propose and analyze a new mathematical model formulated by fractional differential equations (FDEs) that describes the dynamics of oncolytic M1 virotherapy. )e well-posedness of the proposed model is proved through existence, uniqueness, nonnegativity, and boundedness of solutions. Furthermore, we study all equilibrium points and conditions needed for their existence. We also analyze the global stability of these equilibrium points and investigate their instability conditions. Finally, we state some numerical simulations in order to exemplify our theoretical results.
Balanced Joint Progressively Hybrid Type-I Censoring Samples in Estimating the Lifetime Lomax Distributions

(e comparative life testing for products from different production lines under joint censoring schemes has received some attention over the past few years. Mondal and Kundu recently used the balanced joint progressive type-II censoring scheme to discuss the comparative exponential and Weibull populations. (is paper implements the balanced censoring scheme with a hybrid progressive type-I censoring scheme known as a balanced joint progressive hybrid type-I censoring scheme (BJPHCS). (e life Lomax products’ model formulation from two different lines of production with BJPHCS is discussed. (e model parameters are estimated under maximum likelihood estimation for point and the corresponding asymptotic confidence intervals. Under independent gamma priors, the Bayes estimators and associated credible intervals are obtained with the help of MCMC technique. (e validity of the theoretical results developed in this paper for estimation problems is discussed through numerical example and Monte Carlo simulation study, which report the estimators’ quality. Finally, we give a brief comment describing the numerical results.
Improved Genetic Algorithm to Solve the Scheduling Problem of College English Courses

In this paper, an improved genetic algorithm is designed to solve the above multiobjective optimization problem for the scheduling problem of college English courses. Firstly, a variable-length decimal coding scheme satisfying the same course that can be scheduled at different times, different classrooms, and different teaching weeks per week is proposed, which fully considers the flexibility of classrooms and time arrangements of the course and makes the scheduling problem more reasonable. Secondly, a problem-specific local search operator is designed to accelerate the convergence speed of the algorithm. Finally, under the framework of optimal individual retention, the selection operator, crossover operator, and variation operator are improved. It is experimentally demonstrated that the designed algorithm not only has a faster convergence speed but also improves the diversity of individuals to a certain extent to enhance the search space and jump out of the local optimum. Research shows that the improved genetic algorithm has improved average fitness value and time compared with traditional genetic algorithm. At the same time, the use of the largest fuzzy pattern algorithm effectively solves the conflict problem of college English lesson scheduling, thereby improving the solution of college English lesson scheduling. Through the research of this article, the management system of college English course scheduling has been made more intelligent, and the rational allocation of teaching resources and the completion of education and teaching plans have been improved.
Topic Detection and Tracking Techniques on Twitter: A Systematic Review

Social networks are real-time platforms formed by users involving conversations and interactions. This phenomenon of the new information era results in a very huge amount of data in different forms and modalities such as text, images, videos, and voice. The data with such characteristics are also known as big data with 5-V properties and in some cases are also referred to as social big data. To find useful information from such valuable data, many researchers tried to address different aspects of it for different modalities. In the case of text, NLP researchers conducted many research studies and scientific works to extract valuable information such as topics. Many enlightening works on different platforms of social media, like Twitter, tried to address the problem of finding important topics from different aspects and utilized it to propose solutions for diverse use cases. The importance of Twitter in this scope lies in its content and the behavior of its users. For example, it is also known as first-hand news reporting social media which has been a news reporting and informing platform even for political influencers or catastrophic news reporting. In this review article, we cover more than 50 research articles in the scope of topic detection from Twitter. We also address deep learning-based methods.
Swarm Robot Exploration Strategy for Path Formation Tasks Inspired by Physarum polycephalum

Physarum polycephalum, a unicellular and multiheaded slime mould, can form highly efficient networks connecting separated food sources during the process of foraging. These adaptive networks exhibit a unique characteristic in that they are optimized without the control of a central consciousness. Inspired by this phenomenon, we present an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to overcome the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. For the proposed algorithm (EAIPP), we experimentally present robustness tests and obstacle tests conducted to analyse the performance of our algorithm and compare the proposed algorithm with other swarm robot foraging algorithms that also focus on the path formation task. This work has certain significance for the research of swarm robots and Physarum polycephalum. For the research of swarm robotics, our algorithm not only can lead multirobot as a whole to overcome the limitations of very simple individual agents but also can offer better performance in terms of search efficiency and success rate. For the research of Physarum polycephalum, this work is the first one combining swarm robots and Physarum polycephalum. It also reveals the potential of the Physarum polycephalum foraging principle in multirobot systems.
A New Circumscribed Self-Excited Spherical Strange Attractor

Studying new chaotic flows with specific characteristics has been an open-ended field of exploring nonlinear dynamics. Investigation of chaotic flows is an area of research that has been taken into consideration for many years; thus, it helps in a better understanding of the chaotic systems. In this paper, an original chaotic 3D system, which has not been investigated yet, is presented in spherical coordinates. A unique feature of the proposed system is that its velocity becomes zero for a specific value of the radius variable. Hence, the system’s attractor is expected to be stuck on one side of a plane in spherical coordinates and inside or outside a sphere in the corresponding Cartesian coordinates. It means that the attractor cannot pass through the sphere or even touch it. The introduced system owns two unstable equilibria and a self-excited strange attractor. The 1D and 2D system’s bifurcation diagrams concerning the alteration of two bifurcation parameters are plotted to investigate the system’s dynamical properties. Moreover, the system’s Lyapunov exponents in the corresponding period of bifurcation parameters are calculated. Then, two 2D basins of attraction for two different third dimension values are explored. Based on the basin of attraction, it can be found that the sphere has attraction itself, partially, and some initial conditions are led to the sphere, not to the strange attractor. Ultimately, the connecting curves of the proposed system are explored to find an informative 1D set in addition to the system’s equilibria.
Making Circles: Recent Advance in Chemical and Enzymatic Approaches in Peptide Macrocyclization

Macrocyclic peptides have emerged as an important class of molecules for drug discovery due to their enhanced stability and bioavailability. Since the 1990s, ligation chemistries have been extensively studied for preparing peptide macrocycles. The ligation chemistries usually go through an energetically favored capture coupled with an acyl transfer reaction to overcome the disfavored entropy ring formation and facilitate the formation of the cyclic amide bond. Concurrently with chemical methods, several enzymatic approaches utilizing peptide ligases have also been explored. These peptide ligases are enzymes that involve in the production of naturally-occurring cyclic peptides in diverse organisms. Many enzymes have been isolated and proved to be highly efficient in the production of cyclic peptides. Here, we review the recent advance in chemical ligation and enzymatic approaches in making cycles.
A Proposed Research Framework for Semantic Social Network Analysis

: In the present scenario, social networks and social networking platforms are growing very rapidly. Therefore, analysis of these networks has become quite significant. In this regard, social network analysis may be performed in a better way by embedding some semantics to it. Semantic social network analysis is prominent for node visualization and analysis to yield better results. In this context, the need is to provide some research frameworks for semantic social networks analysis and to explore various research issues regarding the role of concerned key technologies like ontology and SPARQL. In this paper, first, origin and growth of social networks and social network analysis have been presented. Second, a few significant tools for social network analysis have been presented along with a comparative study of their different features. Third, a research framework is proposed for semantic social network analysis along with steps related to it. Fourth, various significant research concerns of semantic social network analysis in regard to ontology and SPARQL have been explored. Finally future scope and conclusion is being made.
Positive Organizational Scholarship: What does it achieve? By: Arran Caza and Kim S. Cameron

Positive Organizational Scholarship (POS) is a relatively new development in organization studies, having formally begun with a 2003 edited collection of the same name (Cameron, et al., 2003b). Since that time, it has attracted considerable attention (e.g., George, 2004; Caza and Roberts, 2006; Fineman, 2006; Caza, 2008). The theoretical basis and scope of POS have been addressed quite recently (Dutton and Glynn, 2007; Dutton and Sonenshein, 2007), so this chapter only summarizes these issues, in favor of concentrating on the research and practice of POS. After discussing the domain and precursors of POS, primary attention is given to what POS has accomplished to date. These accomplishments have two facets, as POS involves a research perspective and an approach to managing organizations. This chapter considers the accomplishments of POS in both areas.
Design of Finfet Based Low Power Dynamic Comparator

At current scenario, over power consumption becomes one of the major backlogs. In the design of cmos technology the short channel effect and leakage current may tends to become a vital drawback. In order to overcome this situation FINFET based dynamic comparator have been introduced, as it reduces the leakage and short channel effect. The main advantage of FINFET based dynamic comparator is that it provides 30% faster efficiency than CMOS.Dual input and dual output dynamic comparator is needed in high speed ADC application. In this project, a FINFET based low power dynamic comparator is designed with the help of tanner 13v tool. Its significant parameter is power consumption, delay, power delay product, which is being measured in a simultaneous manner.
OpenCV face detection and web based Record method and technique

: Face Detection is a complex and complicated process because it includes facial Recognition features of the eyes, nose and mouth. Face Detection COVID 19 is becoming necessary for social distillation. Due to the use of different algorithms, face detection has become more powerful, fast,perfect and is being further improved, so it can be used in robots, to catch thieves, to find missing people.
Assessment of Attitude, Subjective Norm and Perceived Behavioral Control on Physical Activity of Alcoholics Using Structural Equation Modeling

Background: Structural equation modeling (SEM) is a series of statistical methods that allow complex relationships between one or more independent variables and one or more dependent variables. Objective: The study aimed at determining the practice of physical activity using structural equation modeling as statistical approach. Methods: This cross-sectional study was conducted from August to November 2018 amongst recuperating alcoholics receiving rehabilitation in Asumbi treatment center of Homabay County, Kenya. Structural equation modeling determined the evidence of physical activity amongst recuperating alcoholics. Results: Structural model parameter estimation indicated that attitude was a better predictor (β=0.62, p<0.001, n=207), followed by subjective norm (β=0.60, p<0.001, n=207) then perceived behavioral control (β=0.55, p<0.001, n=207) was indirectly and directly predicted. Conclusion: Structural Equation Modeling is a powerful tool for causal inference among the observed and latent variables.