Miracle-dz/UniFAR-FTUs huggingface dataset:
How Entrepreneurship Education at Universities Influences Entrepreneurial Intention: Mediating Effect Based on Entrepreneurial Competence

Research shows that entrepreneurial activities significantly promote economic development, which enhances the importance of the innovative entrepreneurial potential of college students. This study analyzes the effect of entrepreneurship education on entrepreneurial intention from the perspective of planned behavior theory. By examining the significant role of entrepreneurship education at colleges and universities on economic and social development, we established a conceptual model. To understand the relationship between entrepreneurship education and entrepreneurial intention, the hypotheses propose the intermediary role of entrepreneurial ability, and the study provides evidence from China the relationship between entrepreneurship education and entrepreneurial intention. Improving entrepreneurial intention and encouraging college students to establish businesses through entrepreneurship education in universities is crucial. This study proposes a hypothetical model of the relationship between entrepreneurial competence and entrepreneurial intention in entrepreneurship education at universities. Using a questionnaire survey of college students with practical experience in the Yangtze River Delta of China, the bootstrap method in the SPSS macro program process software verifies the hypotheses. The results show that entrepreneurial teaching, business plan competition, and entrepreneurial practice support positively affect entrepreneurial competence. In addition, entrepreneurial competence plays an intermediary role in the relationship between entrepreneurial teaching, business plan competition, entrepreneurship practice support, and entrepreneurial intention. Entrepreneurship education improves the ability to establish a business in the present and in entrepreneurial activities in the future. Entrepreneurial competence obtained through entrepreneurship education continuously affects entrepreneurial intention.
Diet Data Collected Using 48-h Dietary Recall: Within—and Between-Person Variation

Background and Aims: Forty-eight-hour dietary recall is a valuable source of information regarding food consumption in a population-based sample. This method covers 2 consecutive days in a single interview. Nevertheless, the number of assessments and the sample size necessary to estimate usual intake are unknown. We aimed to assess sources of variation, sample sizes, and numbers of days necessary to estimate usual nutrient intake using the 48-h dietary recall. Methods: This was a population-based cross-sectional study including 237 participants, 11–90 years old, selected using multistage probabilistic sampling to obtain data using 48-h dietary recall. Analysis of variance was used to calculate within- and between-person variation and determine the statistical parameters necessary to calculate sample size and the number of days required to calculate the usual energy and nutrient intake. Results: Within-person variation was generally lower than between-person variation, except for calcium (CVw2 = 40.8; CVb2 = 38.4%), magnesium (CVw2 = 27.4; CVb2 = 18.7%), and monounsaturated fat (CVw2 = 20.0; CVb2 = 17.3%) for the entire group and magnesium for women (CVw2 = 28.3; CVb2 = 91.8%). The number of days and sample size required to determine usual energy and nutrient intake varied substantially with gender and age (e.g., vitamin C in women N = 9, in men N = 1,641). Conclusions: Energy and nutrient intake assessment using the 48-h dietary recall misrepresents within-person variation but can generate acceptable results for between-person variation. The calculation of sample size and number of days required to determine usual energy and nutrient intake might have been affected by inadequate assessment of the within-person variation.
DHEA as a Biomarker of Stress: A Systematic Review and Meta-Analysis

Background: Psychosocial stress is a significant public health problem inducing consequences for quality of life. Results about the use of dehydroepiandrosterone (DHEA) as a biomarker of acute stress are conflicting. We conducted a systematic review and meta-analysis to demonstrate that DHEA levels could be a biomarker of stress. Methods: PubMed, Cochrane Library, Embase, and ScienceDirect databases were searched on March 19, 2021 using the keywords “acute stress” AND “DHEA” OR “Dehydroepiandrosterone. ” Articles needed to describe our primary outcome, i.e., induction of acute stress and at least two measures of DHEA. Results: We included 14 studies, with a total of 631 participants, in our meta-analysis. The DHEA levels increased overtime after acute stress [standardized mean difference (SMD) = 1.56, 95%CI = 1.13–1.99]. Stratification by time showed a main peak at the end of stress (SMD = 2.43, 95%CI = 1.59–3.27), followed by a progressive decrease (coefficient = −0.11, 95%CI = −0.19 to −0.17, p = 0.020). There was no significant change 1 h after the end of acute stress. Metaregressions showed an impact of mental stress (SMD = 2.04, 95%CI = 1.43–2.65), sex (SMD = 0.02, 95%CI = 0.00–0.04), age (SMD = −0.12, 95%CI = −0.2 to −0.05), and obesity (SMD = 0.31, 95%CI = −0.00 to 0.63). There was no difference whatever the type of fluid (blood or saliva) and the measurement technique used. Conclusions: DHEA is a biomarker of acute stress, with a short-term increase (1 h). DHEA increases following acute mental stress, whatever the type and duration of mental stress. Women, young people, and obese individuals had a higher response. Blood and saliva measures were comparable.
Corrigendum: In situ Pumping Rate of 20 Marine Demosponges Is a Function of Osculum Area

Max Planck Institute for Marine Microbiology, Bremen, Germany, 2 Institut de Ciències del Mar, Consejo Superior de Investigaciones Cientifícas (ICM-CSIC), Barcelona, Spain, Centre d’Estudis Avançats de Blanes, Consejo Superior de Investigaciones Cientifícas (CEAB-CSIC), Girona, Spain, 4 School of Zoology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel, 5 Faculty of Marine Sciences, Ruppin Academic Center, Michmoret, Israel, Department of
The Effect of Mutations in the TPR and Ankyrin Families of Alpha Solenoid Repeat Proteins

Protein repeats are short, highly similar peptide motifs that occur several times within a single protein, for example the TPR and Ankyrin repeats. Understanding the role of mutation in these proteins is complicated by the competing facts that 1) the repeats are much more restricted to a set sequence than non-repeat proteins, so mutations should be harmful much more often because there are more residues that are heavily restricted due to the need of the sequence to repeat and 2) the symmetry of the repeats in allows the distribution of functional contributions over a number of residues so that sometimes no specific site is singularly responsible for function (unlike enzymatic active site catalytic residues). To address this issue, we review the effects of mutations in a number of natural repeat proteins from the tetratricopeptide and Ankyrin repeat families. We find that mutations are context dependent. Some mutations are indeed highly disruptive to the function of the protein repeats while mutations in identical positions in other repeats in the same protein have little to no effect on structure or function.
Trust-Based Distributed State Estimation in the Presence of Cyber-Attacks Tested With Hardware-in-the-Loop

This letter tackles the problem of estimating the state of a plant when communications are corrupted by cyber-attacks or sensor faults occur. An observer structure based on multi-hop subspace decomposition is proposed, which allows each agent to identify its observable and unobservable subspaces and to reconstruct them based on its own measurements and on the information exchanged with the rest of agents. To deal with cyber-attacks, this letter proposes a method based on weighting the information provided by agents according to an assessment of its trustworthiness. An algorithm to dynamically adjust the weighting parameters is provided and the performance of the proposed technique is assessed using a hardware-in-the-loop platform.
Frontières des communautés polarisées : application à l'étude des théories complotistes autour des vaccins

RÉSUMÉ. Les données des réseaux sociaux sont de plus en plus utilisées pour en extraire de la valeur, dans des domaines tels que le marketing, la politique ou la sociologie. Celles-ci peuvent être représentées sous forme de graphes, en modélisant précisément les interactions à travers des liens dirigés et pondérés. Dans l’analyse des données des réseaux sociaux, l’étude des com-munautés est une étape essentielle. Toutefois, pour une interprétation ﬁne des phénomènes, il est également nécessaire d’étudier leurs interactions et de pouvoir détecter des traces de pola-risation. Nous proposons une méthode qui permet d’évaluer l’antagonisme des communautés et d’identiﬁer leurs frontières dans des réseaux pondérés et dirigés. Notre méthode s’accom-pagne d’une implémentation disponible en accès libre. Nous validons expérimentalement notre proposition par l’étude des théories complotistes autour de tweets liés à la vaccination contre la COVID-19. ABSTRACT. Social network data are increasingly used to extract value from them, in different domains such as marketing, politics or sociology. These data can be represented by graphs which model the interactions between individuals through directed and weighted links. The detection and study of communities in online social networks are important tasks to understand the behavior of users. However, for a detailed interpretation of a phenomena, it is also necessary to study their interactions and to be able to detect and evaluate polarization of communities. We propose a method which allows to evaluate the antagonism of the communities and to identify their boundaries in weighted and directed networks. An implementation is available in open access. We experimentally validate our proposal by studying conspiracy theories around tweets related to COVID-19 vaccines
The Effectiveness of Using Bell Inequality Test for Information Retrieval in Arabic Texts

In information retrieval, getting the most relevant documents to the target topic on the top places of the search results and evaluating a query’s effectiveness are essential tasks. This article suggests a new method for searching texts in the Arabic language by using a quantum-like semantic model. Also, it shows the analogy between natural language texts and quantum-like systems by using Bell’s test. The HAL (Hypertext analog to language) matrix is applied for building the quantum-space. The effect of the window size of the HAL matrix is also a subject of research. In this work, three experiments are discussed. The first one is applying semantic-quantum to many Arabic texts and the applicability to use this model in natural language processing. In the second experiment, the model will be checked for its ability to find the correlation between titles and texts through a dataset consisting of five-hundred Arabic texts. Our model will be applied in the third experiment to retrieve data from the Arabic dataset consisting of a hundred texts with ten queries and evaluate the retrieved data by three criteria (precision, recall, and F-measure). This approach provides a promising way for information retrieval in Arabic texts, depending on the relation between words of texts. That will help to order retrieved files to get the relevant files to a query in the top places, and that will increase the efficiency of the search engine.
Analyse et conception par patrons de l'équité de systèmes d'information durables

Designing sustainable system involves complex interactions between environmental resources, social impact/adoption, and financial costs/benefits. In a constrained world, this means achieving a balanced design across those dimensions but also inside specific dimensions, e.g. social, to cope with the diversity of users and stakeholders. This paper explores the concept of fairness using a modelling approach based on an reference sustainability meta-model. Starting from a set of existing cases, different fairness patterns were identified to cope with adoption, anticipation,distributive justice, transparency. They were generalised and documented using an existing sustainability template. In the analysis process, a small extension to the meta-model is also proposed to identify and reason on assumptions and barriers to achieve the desired values. Finally, a partial application case study on sustainability aspects of the COVID-19 crisis management is presented. MOTS-CLÉS : système durable, équité, patron de conception, méta-modèle de durabilité, étude de cas, COVID-19
Robust Nonlinear Control for a Piezoelectric Actuator in a Robotic Hand Using Only Position Measurements

A robust nonlinear control based only on position measurements to compensate for the strong hysteresis nonlinearity in a piezoelectrically actuated robotic hand is proposed. Based on a high-gain observer to estimate the hysteresis response and nonlinear control law, locally exponential stable results are obtained. The observer and controller are arranged to conform to an output-feedback scheme, which is simple to implement and does not require additional computation as soon as the model parameters are identified and known. The proposed control technique is valuable for hysteresis that is modeled with the classical Bouc-Wen model. The results provide a closed-loop system that is robust under external disturbances in all the system states. Simulations show the effectiveness of the approach.
Synthesis of Some Novel Bis 1,3,4-Oxadiazole Fused Azo Dye Derivatives as Potent Antimicrobial Agents

bis(5-(furan-2-yl)-1,3,4-oxadiazol-2-yl)azo dyes were synthesized by multistep reaction sequences, which is diazotized and coupled with different napthols and quinoline. Structure of newly synthesized compounds were characterized and confirmed by IR, NMR and Mass spectral studies. The newly synthesized azo dye fused with (5-(furan-2-yl)-1,3,4- oxadiazole) were screened for their acute toxicity and gross behavioral studies and in-vitro anti-microbial activity. Synthesized compounds exhibit significant biological activity and can certainly hold greater promise in discovering safer biologically active molecules.
Haptic Rendering of Arbitrary Serial Manipulators for Robot Programming

The programming of manipulators is a common task in robotics, for which numerous solutions exist. In this work, a new programming method related to the common master-slave approach is introduced, in which the master is replaced by a digital twin created through haptic and visual rendering. To achieve this, we present an algorithm that enables the haptic rendering of any programmed robot with a serial manipulator on a general-purpose haptic interface. The results show that the proposed haptic rendering reproduces the kinematic properties of the programmed robot and directly provides the desired joint space trajectories. In addition to a stand-alone usage, we demonstrate that the proposed algorithm can be easily paired with existing visual technology for virtual and augmented reality to facilitate a highly immersive programming experience.
An Integrated Group Decision-Making Approach Considering Uncertainty Conditions

The management of business information processes needs effective decision-making models. That means to involve different methods, techniques, and principles to improve competitiveness and to achieve the planned business results. In this context, the article deals with the problem of group decision-making under uncertain conditions. To cope with such problems some well-known optimization strategies of Wald, Laplace, Hurwitz, and Savage are modified to take into account the experts’ opinions with different importance when forming the final group decision. Numerical testing is based on a case study for CRM software selection. The results are discussed based on the proposed models under two different cases derived from the case study. The conducted numerical testing of the proposed models demonstrates their applicability to cope simultaneously with multiple experts’ evaluations and uncertainty conditions.
Evaluating the New AI and Data Driven Insurance Business Models for Incumbents and Disruptors: Is there Convergence?

AI and data technologies are a catalyst for fundamental changes to insurance business models. The current upheaval is seeing some incumbent insurers trying to do the same more effectively, while others evolve to fully utilize the new capabilities and users these new technologies bring. At the same time, technologically advanced organizations from outside the sector are entering and disrupting it. Within this upheaval however, there are signs of a convergence towards an ideal and prevailing business model. This research identifies one exemplar incumbent and one disruptor and evaluates whether their models are converging and will become similar eventually. The findings support a high degree of convergence, but some differences are likely to remain even after this transitionary period. The differences identified are firstly in the evaluation of risk and secondly that traditional insurers prioritize revenue generation from what is their primary activity, while new entrants prioritize expanding their user base.
Non-Mergeable Sketching for Cardinality Estimation

Cardinality estimation is perhaps the simplest non-trivial statistical problem that can be solved via sketching. Industrially-deployed sketches like HyperLogLog, MinHash, and PCSA are mergeable, which means that large data sets can be sketched in a distributed environment, and then merged into a single sketch of the whole data set. In the last decade a variety of sketches have been developed that are non-mergeable, but attractive for other reasons. They are simpler, their cardinality estimates are strictly unbiased, and they have substantially lower variance. We evaluate sketching schemes on a reasonably level playing field, in terms of their memory-variance product (MVP). E.g., a sketch that occupies $5m$ bits and whose relative variance is $2/m$ (standard error $\sqrt{2/m}$) has an MVP of $10$. Our contributions are as follows. Cohen and Ting independently discovered what we call the Martingale transform for converting a mergeable sketch into a non-mergeable sketch. We present a simpler way to analyze the limiting MVP of Martingale-type sketches. We prove that the \Martingale{} transform is optimal in the non-mergeable world, and that \Martingale{} \fishmonger{} in particular is optimal among linearizable sketches, with an MVP of $H_0/2 \approx 1.63$. E.g., this is circumstantial evidence that to achieve 1\% standard error, we cannot do better than a 2 kilobyte sketch. \Martingale{} \fishmonger{} is neither simple nor practical. We develop a new mergeable sketch called \Curtain{} that strikes a nice balance between simplicity and efficiency, and prove that \Martingale{} \Curtain{} has limiting $\MVP\approx 2.31$. It can be updated with $O(1)$ memory accesses and it has lower empirical variance than \Martingale{} \LogLog, a practical non-mergeable version of HyperLogLog.
Faster Algorithms for Bounded Liveness in Graphs and Game Graphs

Graphs and games on graphs are fundamental models for the analysis of reactive systems, in particular, for model-checking and the synthesis of reactive systems. The class of ω -regular languages provides a robust specification formalism for the desired properties of reactive systems. In the classical infinitary formulation of the liveness part of an ω -regular specification, a “good” event must happen eventually without any bound between the good events. A stronger notion of liveness is bounded liveness, which requires that good events happen within d transitions. Given a graph or a game graph with n vertices, m edges, and a bounded liveness objective, the previous best-known algorithmic bounds are as follows: (i) O ( dm ) for graphs, which in the worst-case is O ( n 3 ); and (ii) O ( n 2 d 2 ) for games on graphs. Our main contributions improve these long-standing algorithmic bounds. For graphs we present: (i) a randomized algorithm with one-sided error with running time O ( n 2 . 5 log n ) for the bounded liveness objectives; and (ii) a deterministic linear-time algorithm for the complement of bounded liveness objectives. For games on graphs, we present an O ( n 2 d ) time algorithm for the bounded liveness objectives.
Finite-Dimensional Controllers for Consensus in a Leader-Follower Network of Marginally Unstable Infinite-Dimensional Agents

We consider the output-feedback state consensus problem for a homogeneous multi-agent system consisting of one leader agent and <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> follower agents. The dynamics of each of these agents is governed by a single-input single-output regular linear system (RLS), with the input to the leader agent being zero at all times. The transfer function of this RLS has all its poles in the closed left-half of the complex-plane, with only a finite number of them lying on the imaginary axis. Each follower agent can access the relative output of all its neighboring agents and some of the follower agents can access the relative output of the leader. The communication graph associated with the exchange of relative outputs is directed. Under this setting, we first establish that a controller solves the above leader-follower consensus problem if and only if it can simultaneously stabilize <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> regular linear systems. We then adapt a recently developed frequency-domain technique to construct a stable finite-dimensional output-feedback controller which solves the simultaneous stabilization problem, and hence the consensus problem. We demonstrate the efficacy of our controller design technique using a numerical example.
Evaluating Age Bias In E-commerce

Software should be designed for diverse end users particularly because people of different ages, genders and/or levels of digital literacy use software differently. Understanding the needs of diverse end-users is essential when developing successful software. This article explores how people from different age groups behaved when they used e-commerce applications to inform the development of an age bias evaluation tool. We focused on age because the software industry is dominated by a younger workforce, and currently there are no systematic methods to evaluate age bias in software. We chose the e-commerce domain as the use of e-commerce is increasing rapidly, particularly because of the COVID-19 pandemic lockdown. Following the InclusiveMag methodology, this study aimed to determine if there are specific requirements for different generations. We explored the views of people from different age groups about e-commerce applications using semi-structured interviews and field notes when observing people as they used an e-commerce application. The interviewees were purposively sampled from well recognised generational classifications based on age, including Generation Z (Gen Z) and Generation Y (Gen Y) (combined), Generation X (Gen X), Baby Boomers (BB), and the Silent Generation (SG). Based on four different facets, we built personas to represent the different age groups and performed a cognitive walkthrough with the personas for two e-commerce applications. The results found potential age bias against older people when using e-commerce applications. This will directly inform the development of an age-inclusiveness Magnifier (AgeMag) to help identify age bias within e-commerce applications.