Miracle-dz/UniFAR-FTUs huggingface dataset:
Alcohol consumption and the risk of heart failure: the Suita Study and meta-analysis of prospective cohort studies

Background Alcohol consumption is a modifiable lifestyle, but its role in heart failure (HF) development is controversial. Herein, we investigated the prospective association between alcohol consumption and HF risk. Methods A total of 2,712 participants (1,149 men and 1,563 women) from the Suita Study were followed up every two years. Cox regression was applied to calculate the hazard ratios (HRs) and 95% confidence intervals (95% CIs) of HF risk for heavy drinking (≥46 g/day in men or ≥23 g/day in women) and never drinking compared to light drinking (<23 g/day in men or <11.5 g/day in women). Then, we combined the results of the Suita Study with those from other eligible prospective cohort studies in a meta-analysis using the random-effects model. Results In the Suita Study, within a median follow-up period of 8 years, 319 HF cases (162 in men and 157 in women) were detected. In men, but not women, never and heavy drinking carried a higher risk of HF than light drinking: HRs (95% CIs) = 1.65 (1.00, 2.73) and 2.14 (1.26, 3.66), respectively. Alike, the meta-analysis showed a higher risk of HF among heavy drinkers: HR (95% CI) = 1.37 (1.15, 1.62) and abstainers: HR (95% CI) = 1.18 (1.02, 1.37). Conclusion We indicated a J-shaped association between alcohol consumption and HF risk among Japanese men. The results of the meta-analysis came in line with the Suita Study. Heavy-drinking men should be targeted for lifestyle modification interventions. Supplementary information The online version contains supplementary material available at https://doi.org/10.1265/ehpm.22-00231.
How Does Person-Environment Fit Relate to Career Calling? The Role of Psychological Contracts and Organizational Career Management

Purpose The formation of one’s career calling involves endowing work with meaning and realizing oneself in work, and it has become a focus of organizational behavior research in the past decade. Although there are many studies on the outcome variables of career calling, research on the antecedents of career calling formation is relatively scarce, and its mechanisms are unclear. Based on fit theory and social exchange theory, we analyzed the data of 373 employees and explored the relationship between person-environment fit (focusing on person-organization fit and person-job fit), psychological contract, career calling and organizational career management. Methods We adopted a multi-timepoint data collection method to analyze data from 373 employees from an internet technology company. A mediated moderation model and hypotheses were tested using Mplus 8.3 software. Results The results showed that person-organization fit and person-job fit were positively related to career calling, and the psychological contract played a partial mediating role. The moderating effect of organizational career management on person-organization fit, person-job fit and the psychological contract was also confirmed. Moreover, the mediating effect of the psychological contract was stronger when organizational career management was higher. Conclusion We examined the important influence of individual-level and organizational-level factors on the formation of career calling. The findings highlight the important role and mechanism of person-environment fit in the formation of career calling through psychological factors, which has managerial implications for how to develop employees’ career calling.
Sleep Disturbances in At-Risk Mental States and First Episode of Psychosis: A Narrative Review on Interventions

Sleep disturbances are a common yet often overlooked symptom of psychosis that can drastically affect the quality of life and well-being of those living with the condition. Sleep disorders are common in people diagnosed with schizophrenia and have significant negative effects on the clinical course of the illness and the functional outcomes and quality of life of patients. There is a limited number of studies addressing this question in first-episode psychosis (FEP). In this narrative review, we aimed to provide an overview of sleep disorders in populations with FEP and at-risk mental states (ARMS). The review was focused on the various treatments currently used for sleep disorders, including both non-pharmacological and pharmacological treatments. A total of 48 studies were included. We found that sleep disturbances are associated with attenuated psychotic symptoms and other psychopathological symptoms in ARMSs. The association of sleep disturbances with the transition to psychosis has been poorly investigated. Sleep disturbances have an impact on the quality of life and the psychopathological symptoms of people suffering from FEP. The non-pharmacological treatments include cognitive behavioral therapy for insomnia, bright light therapy, cognitive restructuring techniques, sleep restriction therapy, basic sleep hygiene education, and the provision of portable sleep trackers. Other treatments include antipsychotics in acute phases and melatonin. The early intervention in sleep disturbances may improve overall prognosis in emerging psychosis populations.
Sparsity optimization and RRSP-based theory far l-bit compressive sensing

Due to the fact that only a few significant components can capture the key information of the signal, acquiring a sparse representation of the signal can be interpreted as finding a sparsest solution to an underdetermined system of linear equations. Theoretical results obtained from studying the sparsest solution to a system of linear equations provide the foundation for many practical problems in signal and image processing, sample theory, statistical and machine learning, and error correction. The first contribution of this thesis is the development of sufficient conditions for the uniqueness of solutions of the partial l\(_0\)-minimization, where only a part of the solution is sparse. In particular, l\(_0\)-minimization is a special case of the partial l\(_0\)-minimization. To study and develop uniqueness conditions for the partial sparsest solution, some concepts, such as l\(_p\)-induced quasi-norm, maximal scaled spark and maximal scaled mutual coherence, are introduced. The main contribution of this thesis is the development of a framework for l-bit compressive sensing and the restricted range space property based support recovery theories. The l-bit compressive sensing is an extreme case of compressive sensing. We show that such a l-bit framework can be reformulated equivalently as an l\(_0\)-minimization with linear equality and inequality constraints. We establish a decoding method, so-called l-bit basis pursuit, to possibly attack this l-bit l\(_0\)-minimization problem. The support recovery theories via l-bit basis pursuit have been developed through the restricted range space property of transposed sensing matrices. In the last part of this thesis, we study the numerical performance of l-bit basis pursuit. We present simulation results to demonstrate that l-bit basis pursuit achieves support recovery, approximate sparse recovery and cardinality recovery with Gaussian matrices and Bernoulli matrices. It is not necessary to require that the sensing matrix be underdetermined due to the single-bit per measurement assumption. Furthermore, we introduce the truncated l-bit measurements method and the reweighted l-bit l\(_1\)-minimization method to further enhance the numerical performance of l-bit basis pursuit.
Dermal Fibroblasts as the Main Target for Skin Anti-Age Correction Using a Combination of Regenerative Medicine Methods

This article includes the data from current studies regarding the pathophysiological mechanisms of skin aging and the regenerative processes occurring in the epidermis and dermis at the molecular and cellular level, mainly, the key role of dermal fibroblasts in skin regeneration. Analyzing these data, the authors proposed the concept of skin anti-age therapy that is based on the correction of age-related skin changes by stimulating regenerative processes at the molecular and cellular level. The main target of the skin anti-age therapy is dermal fibroblasts (DFs). A variant of the cosmetological anti-age program using the combination of laser and cellular methods of regenerative medicine is presented in the paper. The program includes three stages of implementation and defines the tasks and methods of each stage. Thus, laser technologies allow one to remodel the collagen matrix and create favorable conditions for DFs functions, whereas the cultivated autologous dermal fibroblasts replenish the pool of mature DFs decreasing with age and are responsible for the synthesis of components of the dermal extracellular matrix. Finally, the use of autological platelet-rich plasma (PRP) enables to maintenance of the achieved results by stimulating DF function. It has been shown that growth factors/cytokines contained in α-granules of platelets injected into the skin bind to the corresponding transmembrane receptors on the surface of DFs and stimulate their synthetic activity. Thus, the consecutive, step-by-step application of the described methods of regenerative medicine amplifies the effect on the molecular and cellular aging processes and thereby allows one to optimize and prolong the clinical results of skin rejuvenation.
Microbial functioning in Mediterranean forest soils: Does land use legacy matter?

Land use is known to be one of the major factors driving soil microbial and physico‐chemical properties. Understanding its long‐term effect remains a major challenge in assessing current soil functioning. Here, soil microbial and physico‐chemical properties of recent and very recent forests (forests developed in 1958 vs. after 1958) were compared with those of ancient forests (present in 1860 and 1958) to assess the effect of Land Use Legacy (LUL). LUL effects were further analysed depending on contrasting (i) climate conditions (sub‐humid vs. humid Mediterranean climates) and (ii) seasons (winter and summer), to examine whether LUL modified microbial responses to different spatio‐temporal climate conditions. Microbial indicators (lignocellulolytic activities, basal respiration, and microbial biomass) and physico‐chemical properties (C and N contents, mineralogical analyses, pH and conductivity) were assessed. A strong effect of past agricultural practice (terrace cultivation) was observed in soils from very recent forests: reduced microbial biomass and activities as well as number of Quercus pubescens stems together with increased phosphorous content and pH. Interestingly, LUL effect did not affect microbial and physico‐chemical responses to seasonal contrasts (winter vs. summer). Microbial response to LUL was not influenced by climate while climate modified LUL effects on some physico‐chemical properties (CaCO3, Corg, and K content). Moreover, soil recovered “pristine” physico‐chemical and microbial functional properties after at least 60 years of reforestation.
Removing False Contour Artifact for Bit-depth Expansion

Bit-depth expansion is a process of enhancing the image quality by increasing the number of intensity levels. To solve this problem, a hybrid method is proposed, where the pixels are categorized into smooth and complex regions, and are processed using different methods. The pixels in the smooth region are reconstructed with a smooth prior, and a Bayesian estimator is used for the pixels in the complex region. The proposed method effectively removes the false contour artifacts while requiring less computation than conventional methods. In addition, the method shows good quantitative performance, and the PSNR gains over the best existing method are 1.45 dB and 0.26 dB for 4 bits and 3 bits expansion cases, respectively.
Roadheader horizontal swing movement based on VP technology and SVR algorithm

In tunnel construction, over-excavation and under-excavation by the roadheader often occur, which can lead to rough excavation cross-sections that require subsequent treatment resulting in additional time and cost. With a view to improving the quality of excavated tunnels, increasing the speed of the roadheader in tunneling applications, and accurately controlling the excavation process, a virtual prototype (VP) of the roadheader is built using Pro/Engineer, ADAMS, ANSYS, and EASY5. A mechanical model of the system is established and relationships between hydraulic piston expansion length, speed, and boom swing angle are determined. Then, a support vector regression (SVR) internal model controller is introduced into the roadheader swing system. An electromechanical fluid coupling automatic control system model is also established. Combining VP technology with the SVR algorithm, an internal model and inverse model of the roadheader horizontal swing system are obtained. Multi-domain co-simulations are performed. When the SVR internal model controller is incorporated, the gyration platform is more reliable, and the target position can be quickly tracked by the system. The cutting test bench of roadheader was built, and the cutting experiments were carried out by using SVR and PID control methods. Although the PID control system has a faster response, it is only 1.61 s faster than SVR, and the PID control method has a much higher over-excavation amount than SVR control method, and the maximum over-excavation amount is 3.22 times of SVR controller. Therefore, the SVR controller has better control over the over-excavation phenomenon and higher control accuracy. So the proposed method can be used to effectively solve the over-excavation and under-excavation problem.
Can Bike-Sharing Reduce Car Use in Alexandroupolis? An Exploration through the Comparison of Discrete Choice and Machine Learning Models

The implementation of bike-sharing systems (BSSs) is expected to lead to modifications in the travel habits of transport users, one of which is the choice of travel mode. Therefore, this research focuses on the identification of factors influencing the shift of private car users to BSSs based on stated preference survey data from the city of Alexandroupolis, Greece. A binary logit model is employed for this purpose. The estimation results indicate the impacts of gender, income, travel time, travel cost and safety-related aspects on the mode shift, through which behavioural insights are derived. For example, car users are found to be twice as sensitive to the cost of BSSs than to that of car. Similarly, they are highly sensitive to BSS travel time. Based on the behavioural findings, policy measures are suggested under the following categories: (i) finance, (ii) regulation, (iii) infrastructure, (iv) campaigns and (v) customer targeting. In addition, a secondary objective of this research is to obtain insights from the comparison of the specified logit model with a machine learning approach, as the latter is slowly gaining prominence in the field of transport. For the comparison, a random forest classifier is also developed. This comparison shows a coherence between the two approaches, although a discrepancy in the feature importance for gender and travel time is observed. A deeper exploration of this discrepancy highlights the hurdles that often occur when using mathematically more powerful models, such as the random forest classifier.
CO-OCCURRENCE OF MOSQUITO COMMUNITIES IN DISTURBED ENVIRONMENTS USING MARKOV RANDOM FIELDS (MRFs) IN ST. JOHNS COUNTY, FLORIDA

The distribution of mosquito communities is predicted by complex micro- and macrohabitat systems. While macrohabitat variables are significant in modeling the distribution of individual mosquito species, the distribution of mosquito communities in disturbed urban and semi-urban environmental gradients was overlooked in most of the previous models. In our study, we used conditional Markov Random Fields (CRF) to evaluate spatial co-occurrence patterns between mosquito vectors of eastern equine encephalitis (EEEV) and west Nile virus (WNV) in a disturbed urban environment in Saint John’s County, Florida. We aimed to 1) quantify the strength and direction of spatial unconditional and conditional correlations between mosquito assemblages in disturbed environments, and 2) evaluate whether the strength of correlations between mosquito assemblages is conditional on landscape or climate variables. We leveraged the longitudinal surveillance effort using Biogents sentinel traps (BGS) conducted by Anastasia Mosquito Control Districts in disturbed urban environments during 2017-2020. The distribution of high mosquito abundance, especially Aedes albopictus, Ae. aegypti, Ae. vexans, Ae. taeniorhynchus, Culex nigripalpus, Cx. salinarius, and Cx. quinquefasciatus, were conditionally correlated with other EEEV and WNV vector species in reduced woody and herbaceous wetlands and evergreen forests (-54.44%), and in urban developed landscapes (3.44%) during 2019 and 2020. Moreover, conditional correlations between mosquito species pairs were positively associated with increased total precipitation and in areas with high average minimum and maximum temperatures. Our results show that the micro- and macrohabitat characteristics demonstrated spatial effects on distribution and correlations between species pairs of EEEV and WNV mosquito vectors across disturbed environments. Our findings could be used to better understand the joint effects of drivers on mosquito diversity at a specific locality, interspecific interactions among mosquito assemblages, and how this diversity changes across environmental gradients.
A fault diagnosis method of aircraft hydraulic system based on CNN-LSTM

The hydraulic system is an important part of the aircraft and is critical to flight safety. Therefore, the realization of fault diagnosis of the aircraft hydraulic system is of great significance to improve the safety and reliability of the aircraft. Aiming at the problem of insufficient fault data of the newly developed equipment, a virtual sample is formed through modeling, simulation and fault injection, which is combined with the real sample of the test bench to train the model. Aiming at the characteristics of uncertainty, nonlinearity and time-varying of hydraulic system, a fault diagnosis method of aircraft hydraulic system based on the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is proposed. The results show that the proposed hybrid algorithm improves the accuracy of fault diagnosis by 5%~10% compared with SVM and single LSTM, which proves the effectiveness of the algorithm.
Photoactivatable surfaces resolve the impact of gravity vector on collective cell migratory characteristics

ABSTRACT Despite considerable interest in the impact of space travel on human health, the influence of the gravity vector on collective cell migration remains unclear. This is primarily because of the difficulty in inducing collective migration, where cell clusters appear in an inverted position against gravity, without cellular damage. In this study, photoactivatable surfaces were used to overcome this challenge. Photoactivatable surfaces enable the formation of geometry-controlled cellular clusters and the remote induction of cellular migration via photoirradiation, thereby maintaining the cells in the inverted position. Substrate inversion preserved the circularity of cellular clusters compared to cells in the normal upright position, with less leader cell appearance. Furthermore, the inversion of cells against the gravity vector resulted in the remodeling of the cytoskeletal system via the strengthening of external actin bundles. Within the 3D cluster architecture, enhanced accumulation of active myosin was observed in the upper cell-cell junction, with a flattened apical surface. Depending on the gravity vector, attenuating actomyosin activity correlates with an increase in the number of leader cells, indicating the importance of cell contractility in collective migration phenotypes and cytoskeletal remodeling. GRAPHICAL ABSTRACT
Artificial Neural Networks for predicting taxpaying behaviour of Indonesian firms

Big data and sophisticated analytics might help tax authorities extract actionable data insights. In response, this paper employs an Artificial Neural Networks (ANN) model to predict and discover the determinants of firms’ taxpaying behaviour. Examining 538,254 firm-level administrative data across fiscal years 2014 and 2019, this study is the first to apply ANN to exploit the taxpaying behaviour of Indonesian firms. Multi-Layer Perceptron Neural Network-based models were trained to predict three categories of taxpaying measurement—i.e., Corporate Tax Turnover Ratio (CTTOR)—across varying magnitudes of annual turnover. The models predicted the firms’ taxpaying behaviour with an average accuracy rate above 92%. This study also reveals heterogeneous channels responsible for firms’ taxpaying behaviour across groups. The findings demonstrate other business income and positive fiscal adjustment to be significant predictors of taxpaying behaviour for small and medium firms. In contrast, operating profit margin, other business expenses, and negative fiscal adjustment are prominent predictors for large corporations. The findings of this study can provide valuable assistance to decision-makers and relevant stakeholders in tax administrations by identifying potential areas of misreporting in annual tax returns. This evidence-based approach could enable tax administrations to develop more effective policies while potentially reducing the need for extensive monitoring and associated costs.
Blending in Etulo compound words

In most languages of the world, compounding is a word formation process that is frequently employed in forming new lexemes. This study attempts to investigate this word formation process in Etulo. It is basically concerned with finding out the noun-noun compounds in the language as well as ascertaining the applicability of the conceptual blending theory to the analysis of the compound words. The data for the study were elicited from Etulo native speakers resident in Adi, Buruku Local Government Area of Benue State. A number of the Etulo noun-noun compounds are analyzed based on the tenets of the conceptual blending theory to show how two different concepts are integrated into one in the language. Our findings show that blending theory can account for the Etulo noun-noun compound words. Furthermore, there is the existence of vital relations such as property, analogy, similarity and uniqueness between the mental spaces and in the blended space. In addition, the principle of integration and the relevance principle are satisfied by the blend. Finally the noun-noun compounds examined trigger the double scope integration network.
Socioemotional competencies and behaviour management at school: An exploratory socio-educational intervention

Socioemotional competencies are based on self-knowledge, self-regulation, social awareness, relational skills and responsible decision-making, presenting a close relationship with the efficient management of behaviours in the classroom. In collaboration with 3 schools in the interior of Portugal, an exploratory socio-educational intervention project was developed over a period of 3 months, with the purpose of promoting the socioemotional competencies of 41 primary school children, as a basis for building classroom behaviour management strategies that would facilitate the teachers’ educational action. The intervention involved a process based on collaborative work between teachers, psychologists and educologists, having been able to produce 8 activities that promote socioemotional competencies. Ethnographic procedures, namely, the use of participant observation, field notes and informal interviews, were used to produce scientifically valid and socially useful knowledge. The results show that each activity works on more than one socioemotional skill. The data allowed to outline 6 efficient strategies to manage classroom behaviour. This exploratory project, besides contributing to enriching the educational project of these schools, proved to be a production of knowledge capable of contributing to filling the gap in terms of guidelines for socioemotional education in Portugal based on field evidence.