Miracle-dz/UniFAR-FTUs huggingface dataset:
Black Cultural Strengths and Psychosocial Well-Being: An Empirical Analysis With Black American Adults

Based on historical literature, Black Americans have persisted through centuries of oppression in North America. To survive, they retained Africultural values and adopted group-specific practices. Black cultural values and practices can potentially, if bolstered, increase psychosocial health in this population. In the current study, we examined specific Black values and practices and their collective ability to predict psychosocial health. In a sample 486 Black, middle-class, American adults with a mean age of 31 years, we used structural equation modeling to test a first- and a second-order measurement model, as well as a structural model. We hypothesized that health-promoting aspects of Black racial identity (e.g., racial centrality), racial socialization (e.g., cultural socialization), and racism-related coping (e.g., confrontation), as well as higher levels of communalism and spirituality would indicate one latent factor, Black Cultural Strength. Furthermore, we hypothesized that Black Cultural Strength would be predictive of psychosocial health. After slight model modifications, we found an acceptable fit for the data. Clinical implications and future research directions are discussed.
Informal caregivers and the health of older adult care-recipients

Adults over age 65 frequently experience adverse consequences as the result of unmet care needs. Previous studies have identified factors that predict unmet need, including demographics, family composition, and access to formal caregiving services. Despite the role of gender in predicting unmet care need, these studies have rarely examined whether the association between care configurations and unmet care need is different for men and women. Using data from the National Health and Aging Trends Study, I conducted a sex-stratified analysis to model the risk of unmet care needs, focusing especially on the relationship between care configuration and unmet need. I found that 33% of men, and 37% of women with unmet care needs continue to report those unmet care needs one year later. I also found substantial change in communityresiding individuals’ care configurations from year to year, and high mortality among individuals with multiple caregivers and facility residents. In multiple regression analyses, formal care was negatively associated with unmet care need among women, but not among men. Conversely, men receiving care from one non-spousal caregiver had three times the odds of unmet care than men receiving care from their spouse, but there was no association between spousal care and unmet need in women. These results suggest that different interventions are needed to prevent or resolve unmet care needs for men and women, including enhanced access to formal care for women and monitoring of men who do not receive spousal care.
Optimized Node Clustering in VANETs by Using Meta-Heuristic Algorithms

In a vehicular ad-hoc network (VANET), the vehicles are the nodes, and these nodes communicate with each other. On the road, vehicles are continuously in motion, and it causes a dynamic change in the network topology. It is more challenging when there is a higher node density. These conditions create many difficulties for network scalability and optimal route-finding in VANETs. Clustering protocols are being used frequently to solve such type of problems. In this paper, we proposed the grasshoppers’ optimization-based node clustering algorithm for VANETs (GOA) for optimal cluster head selection. The proposed algorithm reduced network overhead in unpredictable node density scenarios. To do so, different experiments were performed for comparative analysis of GOA with other state-of-the-art techniques like dragonfly algorithm, grey wolf optimizer (GWO), and ant colony optimization (ACO). Plentiful parameters, such as the number of clusters, network area, node density, and transmission range, were used in various experiments. The outcome of these results indicated that GOA outperformed existing methodologies. Lastly, the application of GOA in the flying ad-hoc network (FANET) domain was also proposed for next-generation networks.
Left ventricular hypertrophy and linked cardiovascular risk factors among Congolese licensed civilian aircrew

Objective: To assess the prevalence of left ventricular hypertrophy (LVH) and linked cardiovascular risk factors in civilian aircrew. Methods: Cardiovascular risk factors were assessed among flight and cabin crew undergoing routine clinical and biological evaluation for initial or renewal of aeromedical license. The evaluation also included a standard 12-lead ECG and echocardiography. Echo-based LVH was LVM ≥ 49 g/m 2 . 7 (men) or ≥ 45 g/m 2 . 7 (women). LVH was categorized as mild (men: 49-55 g/m 2 . 7 ; women: 45-51 g/m 2 . 7 ), moderate (men: 56-63 g/m 2 . 7 ; women: 52-58 g/m 2 . 7 ), or severe (men: ≥ 64 g/m 2 . 7 ; women: ≥ 59g/m 2 . 7 ) according to Lang’s report. Results: Among the 379 aircrew members (70.4% men; 23% Caucasians; 62.5% flight crew; mean age 40.6 ± 12.8 years), LVH was present in 36 individuals (9.5%) with mild, moderate and severe pattern observed respectively in 19.4%, 33.3% and 47.2% of the cases. The rate of LVH amounted to 16.7% in normotensive subjects, 25.0% in those with prehypertension, and 58.3% among hypertensive individuals. In addition to age of 40-59y (OR: 8.48; 95% CI: [2.23-12.23]; p = .002) or more (4.22 [1.57-11.35]; p = .004), hypertension (3.55 [1.50 - 8.41]; p = .004), overweight/obesity (5.33 [1.14 - 25.05]; p = .034) and hyperuricemia (5.05 [2.11 - 12.09]; p = .001), all well-known constituents of the metabolic syndrome, were the main factors significantly associated with LVH. Conclusion: The frequency and link of LVH to the components of the metabolic syndrome highlights the need for a comprehensive approach to the management of cardiovascular risk factors in civilian aircrew.
Image Classification using AlexNet with SVM Classifier and Transfer Learning

Since the last decade, Deep Learning has paved the direction for demanding and wide-ranging applications in almost everyday life and attained quite good performance on a diversity of complex problems such as face recognition, motion detection, health informatics and many more. This paper discusses the performance of state-of-the-art pre-trained Convolutional Neural Networks (CNNs) model. We have analyzed the accuracy and training time of AlexNet (already trained on millions of images) while using it with Support Vector Machines (SVM) classifier and under Transfer Learning (TL). Since training CNN from scratch is time-consuming in case of real problems and is compute-intensive so the image features extraction using already trained AlexNet network reduces effort and computation resources. The learned features are used to train a supervised SVM classifier and transfer learning (fine-tuning) of layers is achieved by retraining in the last few layers of pretrained network for image classification. The classification accuracy while Transfer Learning is obtained greater and more stable than SVM classifier.
Antioxidant and cytotoxic activity of ethyl asetat extracts of cocoa pod husk (Theobroma cacao L)

Isolation of ethyl acetate extract from cocoa pod husks (Theobroma cacao L) and testing of their cytotoxic as well as antioxidant activities has been done. The cytotoxic activities test has been carried out employing Brine Shrimp Lethality Test (BSLT) method, while the antioxidant activities test was employing l,l-diphenyl-2-picrylhydrazyl (DPPH) and vitamin C as positive control. In the cytotoxic test, all fractions showed excellent cytotoxic activity with a range of LC50 = ∞ ppm to 107.15 ppm. The extract also showed antioxidant activity with IC50 = 163.35 ppm and vitamin C = 0.014 ppm. Isolation of the active components of ethyl acetate extract using silica gel as stationary phase and n-hexane:ethyl acetate (9:1) eluent, yielded a combination of 9 fractions (TCE1-TCE9). The separation of TCE 5 produced white needle crystals with a melting point of 128 - 131°C. Based on the stain pattern, the crystal belongs to a phenolic compound.
Controllable Electrochemical Impedance Spectroscopy: From Circuit Design to Control and Data Analysis

This article describes fundamentals of controllable electrochemical impedance spectroscopy (cEIS), from circuit design to control and data analysis. In cEIS, a feedback system controls the process of injecting the excitation signal. We design a two degree-of-freedom robust control system, which guarantees tracking and stability of cEIS in the presence of model uncertainties. This article also addresses the concept of persistently exciting signals. cEIS using current driving mode (CDM) and voltage driving mode, and their differences are highlighted. An online cEIS device is designed and built based on the dc–dc buck converter for batteries online applications, where the excitation signal is superimposed on a dc level. The performance of the fabricated cEIS is evaluated through extensive experiments in CDM. The accuracy of the fabricated cEIS is tested, which results in $\text{0.002}\;\Omega$ root mean square error in the impedance spectra computation of a three-parameter Randles equivalent circuit model (ECM). The performance of the fabricated cEIS is practically verified on a battery cell at different C-rates. First-, second-, and fractional-order Randles ECMs are estimated by using system identification methods, and their impedance spectra are compared with those obtained through the fast Fourier transform.
A continuous dynamical splitting method for solving ‘strongly+weakly’ convex programming problems

ABSTRACT The Douglas–Rachford splitting method is a classical and powerful method for minimizing the sum of two convex functions. In this paper, we introduce two dynamical systems based on this method for solving the minimum value problem of the sum of a strongly convex function and a weakly convex function. Under mild conditions, it is shown that the proposed dynamical systems are globally convergent to fixed point sets of the corresponding Douglas–Rachford operators, respectively, and are globally asymptotically stable if the corresponding fixed point sets are singleton. Furthermore, the globally exponential convergence of the proposed dynamical systems is established under some regularity conditions. A numerical example is reported to illustrate the effectiveness of the dynamical splitting method.
Handover Analysis and Coverage Modelling in Ultra-Dense Heterogeneous Networks

Ultra-Dense Networks (UDNs) are one of the most important trends towards next generation cellular systems. It is expected that small cell densification will offload traffic from traditional macro base stations, and thus significantly boost network capacity. Despite its promising capacity gains, UDNs can lead to frequent handovers (HOs), which in turn can cause significant network overheads and a decline in user experience. With the aim of modelling handovers in the context of ultra-dense heterogeneous networks, we first propose a low-complexity analytical framework for multi-target small cell handovers. Our proposed HO framework accurately models important context-aware parameters of user velocity, small cell density and the effect of received power filtering and HO failure. To avoid load imbalance, we derive a simple HO threshold condition that leverages multiple cell load conditions while also guaranteeing the expected throughput of small cell users. Furthermore, we also propose a novel approach to model coverage regions of overlapping small cells. Based on this model, we derive the cumulative distribution function of the sojourn time in small cells using boundary length and chord length distributions of small cell coverage regions. Our model is comprehensive enough to capture both inter-tier and intra-tier HOs in small cell networks. The derived analytical results provide guidance for optimizing handover parameters based on user velocity and small cell density to reduce network overhead and improve user experience. Finally, a downlink coverage
Application of Artificial Intelligence to Automate Construction Materials Data Classification

The applications of Big Data analytics and Artificial Intelligence (AI) have gained a widespread attention in the construction industry in recent years following the promulgation of Industry 4.0. In the realm of construction research, AI has been utilised widely in areas such as structural design optimization, resource and equipment planning, and project scheduling. The research presented in this paper is aimed to utilise AI to assist with the automatic classification of the large volume of construction material orders created by users through an online marketplace website. Such big data of material orders contained numerous errors (e.g. typographical errors and incorrect units) that were extremely time consuming to correct before the datasets can be used to for further business intelligence analysis. In this research, the dataset was obtained from a business-to-business e-commerce company in Thailand, namely BUILK. The data from BUILK was the construction materials purchase orders created by BUILK’s customers through its website, which contained hundreds of thousand unorganized records. In this study, Artificial Neural Networks (ANNs ) was applied to automate the categorization of approximately 220,000 records of reinforcement steels orders. The ANNs model was developed and trained using over 32,000 records, with approximately 92 percent of prediction accuracy. The model automatically categorized the steel reinforcement data into 11 groups; Deformed Bars, Round Bars, Wire mesh (Deformed Bars), Wire mesh (Round Bars), Stirrup, Anchor, Material and Others. The outcome of this research helped the company to easily analyze the data to generate insights for its business management and development.
Social Media in Ecuador: Impact on Journalism Practice and Citizens’ Understanding of Public Politics

ABSTRACT The omnipresence and ubiquitous nature of online news on social media has challenged the traditional news production process of most news organizations worldwide. However, most research on this topic has dealt with the impact of social networks on global North societies. In this article, drawing upon a world-systemic approach, we focus on how social media transforms journalism practice in Ecuador and explore its main influences on how journalists make sense of and evaluate their professional roles. Based on interviews with 40 Ecuadorian journalists having a variety of backgrounds and levels of experience, we show how social media affects journalists’ professional practices, conceptualizing these influences as systemic and antisystemic phenomena. The most important systemic consequences of social media are related to the immediate nature of news, the changing identity of journalists and the growing wave of sensationalism, while the most important antisystemic feature is the nourishing of an indigenous orientation whereby Ecuadorian journalists favor their own rhetorical and narrative traditions against global (central) mores.
Control of Spiral Waves in Reaction-Diffusion Systems Using Response Function

This thesis is motivated by the desire to understand spiral wave dynamics in reactiondiffusion systems with particular focus on the FitzHugh-Nagumo model. We attempt to control the behaviour of spiral waves using controller dynamics. Response functions characterise the behaviour of spiral waves under perturbations, and so it is natural to use these for control purposes. In this project, we consider perturbations of the FitzHugh-Nagumo equation using control functions with different support. We calculate the response functions using the adjoint linear system of the FitzHugh-Nagumo equation with 1D controller dynamics and also characterise the control functions with the smallest support function which can be used to control the system in periodic and meander regimes. We find the minimum size of the support function that the radius is comparable to the region of the non zero response function.
Logical-linguistic model for multilingual Open Information Extraction

Abstract Open Information Extraction (OIE) is a modern strategy to extract the triplet of facts from Web-document collections. However, most part of the current OIE approaches is based on NLP techniques such as POS tagging and dependency parsing, which tools are accessible not to all languages. In this paper, we suggest the logical-linguistic model, which basic mathematical means are logical-algebraic equations of finite predicates algebra. These equations allow expressing a semantic role of the participant of a triplet of the fact (Subject-Predicate-Object) due to the relations of grammatical characteristics of words in the sentence. We propose the model that extracts the unlimited domain-independent number of facts from sentences of different languages. The use of our model allows extracting the facts from unstructured texts without requiring a pre-specified vocabulary, by identifying relations in phrases and associated arguments in arbitrary sentences of English, Kazakh, and Russian languages. We evaluate our approach on corpora of three languages based on English and Kazakh bilingual news websites. We achieve the precision of facts extraction over 87% for English corpus, over 82% for Russian corpus and 71% for Kazakh corpus.
Evidence-informed teaching : an evaluation of progress in England. Research report July 2017 (DFE- RR696).

This is the final report of the Evidence-informed teaching: evaluation of progress study. Drawing on an evidence review, content analysis of websites, documents and social media and nearly 100 interviews with teachers and leaders it examines teachers' and leaders awareness of, engagement with and use of research evidence. For teachers, the key finding was that evidence-informed teaching meant drawing on research evidence to integrate and trial in their own practice, rather than directly applying research findings,prompted by a need to solve a practical problem. For schools, the key finding was that the most strongly research-engaged schools were highly effective, well-led organisations within which 'research use' meant integrating research evidence into all aspects of their work as part of an ethos of continual improvement and reflection. In the most highly research-engaged schools, senior leaders played a key role. A set of recommendations are made to improve research engagement in English schools.
An exploration of the teaching beliefs and practices of adult basic education and training teachers of reading.

iv Table of contents . v Acronyms and abbreviations.ix CHAPTER 1: INTRODUCTION 1.1 Background and purpose . 1 Context of the research project . 4 History of the site . 4 1.2 Rationale . 6 1.3 Methodology . 8 1.4 Outline of chapters . 9 CHAPTER 2: LITERATURE REVIEW 2.
Vortex flows of moist air with non-equilibrium and homogeneous condensation

A small-disturbance model is presented for the complex dynamics of vortex flows of moist air in a straight, circular pipe with non-equilibrium and homogeneous condensation. The model explores the nonlinear interactions among the vortex near-critical swirl ratio and the small amount of water vapour in the air. The condensation rate is calculated according to classical nucleation and droplet growth models. The asymptotic analysis gives the similarity parameters that govern the flow problem. These are the flow inlet swirl ratio $\unicode[STIX]{x1D714}$, the inlet Mach number $Ma_{0}$, the initial humidity $\tilde{\unicode[STIX]{x1D714}}_{0}$, the number of water molecules in a characteristic fluid element $n_{C}$, the inlet centreline super-saturation ratio $S_{0}$ and the ratio of characteristic condensation and flow time scales $K$. Also, the flow field may be described by an ordinary first-order nonlinear differential equation for the flow evolution coupled with a set of four first-order ordinary differential equations along the pipe for the calculation of the condensate mass fraction. An iterative numerical scheme which combines the Runge–Kutta integration technique for the flow dynamics with Simpson’s integration rule for the calculation of the condensation variables is developed. Specifically, equilibrium states are determined, including the possibility of the appearance of multiple states under the same boundary conditions, and the stability characteristics of these states are described. The model is used to study the effects of humidity and of energy supply from nanoscale condensation processes on the large-scale dynamics of vortex flows as well as the effect of flow swirl on condensation processes in swirling flows.
Privacy-preserving constrained spectral clustering algorithm for large-scale data sets

With the increasing concern on the preservation of personal privacy, privacy-preserving data mining has become a hot topic in recent years. Spectral clustering is one of the most widely used clustering algorithm for exploratory data analysis and usually has to deal with sensitive data sets. How to conduct privacy-preserving spectral clustering is an urgent problem to be solved. In this study, the authors focus on introducing the notion of differential privacy, which is considered as the de facto standard of privacy-preserving data analysis, into spectral clustering. Specifically, by combining the well-studied constrained spectral clustering with the Wishart mechanism in a novel way, the authors propose a differentially private constrained spectral clustering (DP-CSC) algorithm. The DP-CSC algorithm is proved to capture asymptotic property and achieves ϵ -differential privacy. To illustrate the effectiveness and efficiency of DP-CSC, the authors conduct experiments on five real-word data sets. The results indicate that the DP-CSC algorithm can provide acceptable clustering accuracy with short running time while preserving individual privacy.