Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinants of capital structure of French SBF 120 listed companies: A comparison between trade-off theory and pecking order theory

In this paper, we attempt to identify the firm-specific determinants of the capital structure of a sample of non-financial firms listed on the SBF 120 French index between 2009 and 2019 and to test whether the determinants offered by the two principal financial theories (e.g., trade-off theory and pecking order theory) are able to provide convincing explanations for their behavior in terms of financing decisions. Capital structure determinants discussed are size, profitability, asset tangibility, growth opportunities, liquidity, effective tax rate, and risk. The empirical analysis is carried out within a panel data estimation framework. Panel estimation techniques of fixed and random effects and ordinary least squares (OLS) estimation have been to test the hypothesized relationships. Empirical results showed that the majority of determinants had been significant. The size of the firm and its previous leverage have been found positively related to present leverage. The growth opportunities and the profitability have been found negatively related and the asset tangibility, the effective tax rate, and the firm risk were not significant. Then two variables follow the trade-off theory predictions, two variables follow those of the pecking order and three others do not follow anyone. No theory alone then can best explain the behavior of the French firms in terms of capital structure. But none of them can be rejected
Systemic risk and real economic activity: A South African insurance stress index of systemic risk

This study investigates the link between systemic risk in the South African insurance sector real economic activity in South Africa. To this end, we use six systemic risk measures, the Conditional Value at Risk (CoVaR), the Marginal Conditional Value at Risk (ΔCoVaR), the Comovement and Interconnectedness of the South African insurance sector (Eigen), the Dynamic Mixture Copula Marginal Expected Shortfall (DMC-MES), the Average Conditional Volatility (Ave-vol), and the South African Volatility Index (SAVI). We first evaluate the significance of each measure by assessing its ability to forecast future economic downturns in South Africa. We find that only two systemic risk measures possess the ability to predict future economic downturns in South Africa. We then use principal component quantile regression analysis to aggregate these measures into a composite stress index of systemic risk for the South African insurance sector and assess the ability of the proposed index to predict future economic downturns in South Africa. Our results reveal that the proposed index is a good predictor of future economic downturns in South Africa. Thus, our results suggest that regulators and risk managers must develop an analysis of systemic risk in the insurance sector with particular attention to its effects on real economic activity. In addition, our index can potentially be used as an instrument to monitor and mitigate systemic risk in the insurance sector in order to ensure the stability of the financial system and the economy in South Africa.
Mechanical thrombectomy for acute ischemic stroke: systematic review and meta-analysis

ABSTRACT Objective To evaluate the safety and efficacy of mechanical thrombectomy associated with standard medical treatment compared with standard medical treatment only to treat patients with acute ischemic stroke. Methods This was a systematic review and metaanalysis of randomized controlled trials. An electronic search was performed in the following databases: MEDLINE ® /PubMed ® , Cochrane Library (Trials), LILACS/IBECS (via Biblioteca Virtual em Saúde (BVS)) and Embase. Complementary searches were also conducted. The selection of studies and data collection were done by two investigators independently. Results The final analysis included 16 publications related to 15 studies. The mechanical thrombectomy was associated to a reduction in the risk of death of all cause (16.81% versus 20.13%; relative risk of 0.85; p=0.04), improvement in the number of patients with functional independence after 90 days (45.65% versus 27.45%; relative risk of 1.65; p<0.01), and improvement in the rate of revascularization (76.2% versus 33.85%; relative risk of 2.20; p<0.01). There was no significant difference in terms of symptomatic intracranial hemorrhage (4.78% versus 3.88%; relative risk of 1.27; p=0.21). Conclusion Mechanical thrombectomy associated with standard medical treatment seem to be safe and effective to treat patients with acute ischemic stroke compared with standard medical treatment only.
Applied monocular reconstruction of parametric faces with domain engineering

Many modern online 3D applications and videogames rely on parametric models of human faces for creating believable avatars. However, manual reproduction of someone's facial likeness with a parametric model is difficult and time-consuming. Machine Learning solution for that task is highly desirable but is also challenging. The paper proposes a novel approach to the so-called Face-to-Parameters problem (F2P for short), aiming to reconstruct a parametric face from a single image. The proposed method utilizes synthetic data, domain decomposition, and domain adaptation for addressing multifaceted challenges in solving the F2P. The opensourced codebase illustrates our key observations and provides means for quantitative evaluation. The presented approach proves practical in an industrial application; it improves accuracy and allows for more efficient models training. The techniques have the potential to extend to other types of parametric models.
Long-Term Outcomes after Vaccine-Induced Thrombotic Thrombocytopenia

Vaccine-induced thrombotic thrombocytopenia (VITT), or thrombosis with thrombocytopenia syndrome (TTS), is a rare but serious complication of adenovirus-based vaccines against severe respiratory syndrome coronavirus 2 (SARS-CoV-2). Observation of long-term outcomes is important to guide treatment of affected patients. This single-center consecutive cohort study included all patients diagnosed based on (1) vaccination 4 to 21 days before symptom onset, (2) signs or symptoms of venous or arterial thrombosis, (3) thrombocytopenia < 150/nL, (4) positive anti-platelet factor 4 (PF4) antibody, and (5) elevated D-Dimer > 4 times the upper limit of normal. Nine patients were enrolled. Acute management consisted of parenteral anticoagulants, corticosteroids, intravenous immunoglobulin (IVIG), and/or eculizumab. Eculizumab was successfully used in two patients with recurrent thromboembolic events after IVIG. Direct oral anticoagulants were given after hospital discharge. Median follow-up duration was 300 days (range 153 to 380). All patients survived the acute phase of the disease and were discharged from hospital. One patient died from long-term neurological sequelae of cerebral venous sinus thrombosis 335 days after diagnosis. Eight out of nine patients were alive at last follow-up, and seven had fully recovered. Anti-PF4 antibodies remained detectable for at least 12 weeks after diagnosis, and D-Dimer remained elevated in some patients despite oral anticoagulation. No recurrent thromboembolic events, other signs of VITT relapse, or bleeding complications occurred after discharge. In conclusion, VITT appears to be a highly prothrombotic condition. IVIG is not always successful, and eculizumab may be considered a rescue agent. Long-term management with direct oral anticoagulants appears to be safe and effective.
Mapping the relationships between hotel employees’ perceptions of performance appraisal fairness, satisfaction, and psychological contract

Abstract Little is known concerning the extent to which employees’ perception of fairness enhances their satisfaction with the performance appraisal rater, feedback and system. The study tests whether these relationships are associated with the psychological contract that binds employees with their organizations. Drawing on the social exchange theory, this study examines these relationships with data collected from 230 hotel employees in the US. Analysis through structural equation modeling suggests that different types of organizational justices are associated differently with satisfaction with performance appraisal and the level of employees’ mental attachment to the organization. The implications of such findings are discussed.
Combined migration results and time-depth conversions in two layered media

In this contribution, we will expose a strategy for mitigating possible differences in the propagation velocity of the electromagnetic waves within a given Bscan. The idea is based on a combination of different migrated results achieved under different assumed values of the propagation velocity. In cases of practical interest, this can also allow an easy time domain conversion coherent with the detected differences of propagation velocity. In particular, here the case of a two layered soil will be considered.
User Preferences of Privacy-Enhancing Attributes of a Smart Speaker

Abstract Smart speakers become a versatile and convenient device to provide digital contents in our daily life. However, given the challenges of privacy management in smart speakers, it is required to examine how users cope with privacy threats of the devices. In this study, we examine how the privacy-enhancing attributes of smart speakers influence users’ preferences when choosing devices. The preference structure was generated from a conjoint approach, dealing with four privacy-enhancing features (parasite function, data storage method, security notice, and speaker recognition function) and two typical factors affecting consumer decision making (brand and price). Data collected from a survey of 516 consumers identified that, aside from the attribute of price, parasite function was ranked as the most preferred attribute. We also compared the findings across three user clusters. By examining concrete and explicit attributes regarding privacy-enhancing features, the present study provides novel insights into users’ understanding of privacy.
MANIFESTATION OF MUSCULOSKELETAL DISORDERS AMONG DIABETES MELLITUS PATIENTS AT A TERTIARY CENTRE

Objectives: Musculoskeletal diseases, or MSDs, are injuries and illnesses that affect the musculoskeletal system or how the human body moves. Diabetes mellitus and several musculoskeletal disorders (MS) are connected (DM). Examining the connection between musculoskeletal issues and diabetes mellitus in diabetic patients is the goal of this study. Methods: Patients with diabetes mellitus were included in a cross-sectional study. Both the patients' demographic data and the DM features were noted. Clinical tests and research were used to evaluate musculoskeletal and vascular diseases. The factor correlations of MS illnesses were assessed using both univariate and multivariate analysis. Results: A total of 308 individuals were included, of whom 84.5 percent had type 2 diabetes and 15.5 percent had type 1 diabetes. Twenty-five percent of the patients had osteoarthritis. Duputrens contracture was detected in 1.29 percent of cases, Carpel tunnel syndrome in 9.0 percent, and reduced joint mobility in 4.22 percent of cases. In 15.5 percent of cases, shoulder capsulitis was detected. Increased prevalence of hand anomalies was linked to long-term diabetes and dyslipidemia. Conclusions: Numerous elements of diabetes have been connected to musculoskeletal disorders. Haemoglobin A1c levels do not appear to be related to the onset of musculoskeletal disorders.
An Approach to Measure Tool Wear through Image Processing in Incremental Sheet Metal Forming

The Incremental sheet metal forming (ISF) is come into the light due to its unique forming technique. In ISF, the metal sheets are transformed into the final product without using dedicated died. The plastic deformation of metal sheets is conducted through a simple forming tool. Its processing time resembles that the ISF is suitable for the formation of customized products, prototypes, and low volume production. Tool life in manufacturing processes is an important consideration for productivity. The present study is an approach to use image processing techniques to measure the exact location and amount of tool wear in the ISF tool which is made of 440C steel. Presently the complex histogram plot is under study. Therefore to predict the tool wear, feed-forward backpropagation (FFB) algorithm is utilized. It is reported that the maximum predicted tool wear of 0.0663mm is found in the trail run 05 with an error of 0.0104mm. The best-fitted value of the FFB model is observed at the epoch 05 with the value of 5.922e-005. The overall coefficient of performance i.e. R2 of FFB modeling is reported as 90.52 % with the mean absolute error (MAE) of 0.0042 which shows a good agreement of the prediction model.
Smartphone apps as a motivating tool in English language learning

Since smartphones are getting cheaper, sophisticated, and multifunctional, there are opportunities for learners to engage in more meaningful English language learning. This study employs a mixed-method research design, with the purpose of identifying the use of English language apps outside the classroom and depicting students’ English language apps use behaviour i.e. motivation from the Self-Determination Theory (SDT). The findings show that students use the apps related to grammar the most and followed by English apps related to speaking, reading and, vocabulary. Findings also reveal students in this study display three types of motivation from the perspective of SDT i.e. autonomy, competence, and relatedness. Based on the findings, a model of Pedagogical Considerations of Using Smartphone for English Language Learning (PCUSELL) is suggested. As a conclusion, the authors suggests that educators in higher educational institution educators should consider the potential of smartphone English apps in their teaching and learning activities, particularly in this new normal of teaching and learning i.e. epoch of the COVID-19 pandemic.
Thévenin Equivalent Impedance Estimation for Power Electronic Devices in Smart Grids

Distributed generation (DG) based on renewable energy sources (RESs) as well as electric mobility and battery storage are currently penetrating electrical energy networks, especially at medium voltage (MV) and low voltage (LV) levels. The energy conversion is mostly based on power electronic devices. The control stability of these power converters is becoming increasingly important as more and more converters are being established in the grids, transforming the grid dynamic response. The Thévenin equivalent (TE) impedance at the point where a converter is connected can be used to represent the grid response seen from the converter. Therefore, an accurate TE parameter estimation is a key enabling feature for high control stability and performance enhancement in smart grids. An analysis of the main sources of TE estimation errors is given in this paper. Focusing on its core mathematical calculation, a new TE estimation algorithm based on least squares fitting for non-linear systems is presented. Simulation results show higher accuracy than a classical linear calculation method, especially at a high degree of measurement errors and reference frame shifts. More accurate TE impedance results can be beneficial for controller optimization of power converters, which will subsequently contribute to the stability of the future power supply systems with high integration of RESs as a whole.
Predicting Entrepreneurship Learning Factors on Entrepreneurial Intent in Student Universities

This study aims to examine the determinants of entrepreneurial learning that depend on entrepreneurial intentions in college students in Lampung, Indonesia. The survey was born with an online questionnaire from June until July 2021. Data were processed using SmartPLS 3.2. 7. The data analysis found significant direct and indirect effects. The constructing indicators can predict latent variables with a recommended value of more than 0.7. We also found that this research contributes additional conceptual framework theories for lecturers teaching entrepreneurship learning and decision-makers in universities to focus more on developing entrepreneurship education curricula.
Local Clustering over Labeled Graphs: An Index-Free Approach

In this paper, we study local clustering over labeled graphs, which extracts a subgraph with nodes having high label density matched to the query labels as well as high structure density around a seed node. Despite the progress made in the last few years, we observe two major limitations of existing methods: (I) The candidate subgraphs have to comply with strict topology-driven models and better candidates can be pruned by these topological constraints; (II) The topological constraints give rise to substantial computational overheads and existing works have to construct prohibitively large indexes for online processing. To mitigate these limitations, we explore the idea of using conductance in local clustering that ensures structure density through minimizing conductance. Conductance is a well-understood metric primarily for detecting unlabeled clusters but for labeled graphs, applying conductance directly is insufficient because the label information is not taken into consideration. To this end, we propose a novel Label-Aware Motif weighted framework (LAM) to transform the labeled graph to a weighted graph so that both the label and the structure proximity of nodes are captured. We define label-aware motifs as small high-order structures of nodes with query labels. Nodes within a label-aware motif are both closely connected and relevant to query labels, which ease the process of identifying labeled clusters. Our theoretical study shows that LAM is able to better distinguish the desired candidates under the personalized pagerank distribution from the seed node on random graphs generated by the stochastic block model. Based on such nice properties of LAM, we propose an index-free peeling algorithm to efficiently search local clusters on labeled graphs. Extensive experiments on both real-world and synthetic networks show that our proposed algorithm can achieve up to 90% relative effectiveness improvements (F1 scores), while using 10 times less memory than the SOTA algorithm.
Contrastive Learning for Sequential Recommendation

Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical inter-actions. Despite their success, we argue that these approaches usually rely on the sequential prediction task to optimize the huge amounts of parameters. They usually suffer from the data sparsity problem, which makes it difficult for them to learn high-quality user representations. To tackle that, inspired by recent advances of contrastive learning techniques in the computer vision, we propose a novel multi-task framework called Contrastive Learning for Sequential Recommendation (CL4SRec). CL4SRec not only takes advantage of the traditional next item prediction task but also utilizes the contrastive learning framework to derive self-supervision signals from the original user behavior sequences. Therefore, it can extract more meaningful user patterns and further encode the user representations effectively. In addition, we propose three data augmentation approaches to construct self-supervision signals. Extensive experiments on four public datasets demonstrate that CL4SRec achieves state-of-the-art performance over existing baselines by inferring better user representations.
Alpine Wetland Evolution and Their Response to Climate Change in the Yellow-River-Source National Park from 2000 to 2020

Clarifying the response of wetland changes to climate change can improve the scientific conservation and utilization capabilities of wetland ecosystems, which is vital for their sustainable development. In this study, the spatial distribution and area changes of the different types of wetlands in the Yellow-River-Source National Park (YRSNP) were obtained using the object-based classification method for the years 2000, 2010, and 2020. The relationship between wetland change and climate factors was investigated by combining grey relation analysis and correlation analysis. The response of wetland change to different climatic factors was consequently clarified. The results showed that the river wetlands and lake wetlands increased significantly from the year 2000 to 2010 (4.04% and 4.21%, respectively). However, the total wetland area demonstrated a decreasing trend (7.08%), primarily due to the significant decrease in the marsh wetlands (6.81%). The total wetland area demonstrated a slightly increasing trend from the year 2010 to 2020 (0.14%), in which river wetlands and lake wetlands increased by 3.25% and 2.09%, respectively, while the marsh wetlands demonstrated a tendency to be stable. From the year 2000 to 2010, 75.53% of precipitation and 27.68% of temperature demonstrated a significant increase and an obvious warm–humid climate trend. However, from the year 2010 to 2020, the trend of increasing precipitation weakened, the temperature decreased slightly, and the warm–humid climate trend was not significant. From the year 2000 to 2020, the YRSNP river wetlands and lake wetlands were significantly and positively correlated with temperature and precipitation, while the marsh wetlands were most affected by climate warming, especially the warm-season temperatures. The spatial–temporal difference was not obvious in the correlation coefficient between marsh wetlands area change and the precipitation and temperature. The results of the study can provide a theoretical basis and technical support for the conservation of wetland ecosystems in the Three-River-Source National Park.
The emergence of digital ecosystem governance: An investigation of responses to disrupted resource control in the Swedish public transport sector

Digital ecosystem governance entails the management of complex, dynamic power relationships. As entrant platform providers seek to cultivate an ecosystem, they must carefully navigate these power relationships when dealing with governance tensions. Providers generally seek to leverage the ecosystem's generative potential by facilitating a variety of interactions and distributing design rights. Simultaneously, they need to ensure stability and order by imposing rules that resolve contentious matters and restrict ecosystem participants' degrees of freedom. This study explores how and why providers can induce ecosystem actors to engage in collaborative negotiation regarding such governance tensions through a case study of the introduction of an open data platform in the Swedish public transport sector. Our analysis offers three main contributions. First, it provides an empirical demonstration that entrepreneurial threats, as well as opportunities, can trigger platform launches and drive collaborative negotiation of digital ecosystem governance. Second, it extends conceptualizations of boundary resources beyond the current focus on transactional elements by demonstrating the role of interactive boundary resources in the negotiation of governance grounded in both social and systemic power relationships. Third, it shows how positive reinforcement can complement punitive measures to increase acceptance of design rules.