Miracle-dz/UniFAR-FTUs huggingface dataset:
THE INFLUENCE OF AUTHENTIC LEADERSHIP ON THE GOVERNANCE OF COUNTY REFERRAL HOSPITALS IN KENYA

Authentic Leadership has received considerable attention because of its potential implications on the governance of an organization. Scholars have outlined authentic leadership as a possible solution for the governance of hospitals today. This study focused on five County referral hospitals. The preparedness of County referral hospitals was a challenge during the implementation of devolution in Kenya. The transfer of national government services to the County governments was occasioned by the promulgation of the Kenyan constitution 2010. Authentic leadership is one of the most effective types of leadership and its influence on the governance of health institutions has rarely been addressed in Kenya. The broad objective of this study was to determine the influence of authentic leadership on the governance of County referral hospitals in Kenya. The specific objectives of this study were to determine the influence of self-awareness, internalized moral perspective, balanced processing, and relational transparency on the governance of County referral hospitals in Kenya. This mixed methods study was anchored on pragmatism philosophy where both qualitative and quantitative data were collected. Questionnaires and interview schedules were used to collect data. The study population included County health leaders such as director of health, County health management team and health program managers. This included doctors, nurses, nutritionists, clinical officers, public health officers, and health workers in the sampled five County referral hospitals. The study employed both purposive and random sampling techniques. Quantitative data was analyzed using SPSS, while qualitative data was analyzed based on emerging themes in narrative form as guided by the study objectives. It was determined that there exists a relationship between authentic leadership, leadership efficacy, and governance of County referral hospitals in Kenya. The government and key stakeholders could apply the results in policy making. Effective policy could ensure that all health institutions engage and develop leaders’ authentic leadership knowledge and skills. The Ministry of Health should utilize the capacity building recommendations to develop those in service in order to improve the leadership and governance of health institutions in Kenya and beyond.
Resilience in Wartime Research: Case of Anticrisis Management at a Ukrainian University

The article presents the experience of Berdyansk State Pedagogical University (Ukraine) in managing a scientific project during a time of war and highlights the problems that arise in this process and ways to solve them. A case study of the implementation of the project under conditions of occupation, relocation of the university, reduced funding, and loss of material and technical base is provided. The importance of effective management of scientific projects in times of war cannot be overestimated, and the experience of Ukraine can be helpful for other countries facing similar challenges.
Subsampled Randomized Hadamard Transformation-based Ensemble Extreme Learning Machine for Human Activity Recognition

Extreme Learning Machine (ELM) is becoming a popular learning algorithm due to its diverse applications, including Human Activity Recognition (HAR). In ELM, the hidden node parameters are generated at random, and the output weights are computed analytically. However, even with a large number of hidden nodes, feature learning using ELM may not be efficient for natural signals due to its shallow architecture. Due to noisy signals of the smartphone sensors and high dimensional data, substantial feature engineering is required to obtain discriminant features and address the “curse-of-dimensionality”. In traditional ML approaches, dimensionality reduction and classification are two separate and independent tasks, increasing the system’s computational complexity. This research proposes a new ELM-based ensemble learning framework for human activity recognition to overcome this problem. The proposed architecture consists of two key parts: (1) Self-taught dimensionality reduction followed by classification. (2) they are bridged by “Subsampled Randomized Hadamard Transformation” (SRHT). Two different HAR datasets are used to establish the feasibility of the proposed framework. The experimental results clearly demonstrate the superiority of our method over the current state-of-the-art methods.
Can Dispersive Pressure Cause Inverse Grading in Grain Flows

Grain flows in the collisional regime are self-regulated in such a way that dispersive pressure at every level tends to balance static pressure, and so cannot promote the upward segregation of large particles in a steady flow. Le Roux adds new insights to this analysis by pointing out that dispersive and static pressures would not balance in unsteady flows. When shear strain rate increases in a grain flow, dispersive pressure, which is positively correlated to shear strain rate, increases too, and the gradient of dispersive pressure becomes higher than the gradient of static pressure. As a consequence, the grain flow expands, thus lowering the dispersive pressure, until the two gradients reequilibrate. During the …
The Association between Levels of Physical Activity and Lifestyle, Life Expectancy, and Quality of Life in Patients with Alzheimer’s Disease

Background: Engaging in physical activity could help improve the quality of life in patients with Alzheimer’s disease. The objective of this study was to determine the association between the levels of physical activity and lifestyle, life expectancy, and quality of life in patients with Alzheimer’s disease in Iran and Italy. Methods: A total of 165 participants from Iran and Italy were enrolled in this cross-sectional study. In Iran, 85 patients participated in the study. In Italy, we enrolled 80 patients at the Sapienza University teaching hospital, Policlinico Umberto 1 in Rome. The inclusion criteria in this study include patients over 60 years old, non-smokers, and non-users of antidepressants and hypnotics. Results: The results of Tukey’s post hoc test of the study conducted in Iran showed that the lifestyle of patients with moderate (p = 0.001) and low (p = 0.009) physical activity levels was significantly better than inactive patients. Life expectancy in patients with moderate physical activity levels was significantly higher than inactive patients (p = 0.011). The quality of life was significantly better in patients with moderate (p = 0.001) and low (p = 0.002) physical activity levels than inactive patients. On the other hand, the findings of Tukey’s post hoc test of the study in Italy showed that the quality of life in patients with low (p = 0.001) and moderate physical activity levels (p = 0.01) was significantly higher than inactive patients. Conclusions: A low to moderate level of physical activity could be associated with an improved lifestyle, life expectancy, and quality of life in patients with Alzheimer’s disease compared to inactivity.
Exploration of the Conformational Scenario for α-, β-, and γ-Cyclodextrins in Dry and Wet Conditions, from Monomers to Crystal Structures: A Quantum-Mechanical Study

Cyclodextrins (CDs) constitute a class of cyclic oligosaccharides that are well recognized and largely applied in the drug delivery field, thanks to their biocompatibility, low cost, and the possibility to be derivatized in order to tune and optimize the complexation/release of the specific drug. The conformational flexibility of these systems is one of their key properties and requires a cost-effective methodology to be studied by combining the accuracy of results with the possibility of exploring a large set of conformations. In the present paper, we have explored the conformational potential energy surface of the monomers and dimers of α-, β-, and γ-cyclodextrins (i.e., 6, 7, and 8 monomeric units, respectively) by means of fast but accurate semiempirical methods, which are then refined by state-of-the-art DFT functionals. Moreover, the crystal structure is considered for a more suitable comparison with the IR spectrum experimentally recorded. Calculations are carried out in the gas phase and in water environments, applying both implicit and explicit treatments. We show that the conformation of the studied molecules changes from the gas phase to the water, even if treated implicitly, thus modifying their complexation capability.
Multi-View Stereo Matching Method Based on Recursive Neural Networks

: This article proposes a recursive layered network reconstruction method with pixel-wise attention cost aggregation to address the problems of textureless areas and poor reconstruction results at scene edges in multi-view stereo matching methods. First, multi-scale features of multiple images are extracted through downsampling and transformed into a cost volume using three-dimensional differentiable homography. Then, a pixel-wise attention aggregation module is added to the cost volume aggregation stage to reweight different pixels and generate a new cost volume. Next, a network with recursive layers is used to regularize the cost volume, replacing the traditional 3D CNN network, and an initial depth map is generated. Finally, the filtered and refined depth maps are merged to generate a three-dimensional dense point cloud. Experimental results show that the proposed network model improves completeness, accuracy, and overall quality by 0.377, 0.363, and 0.370, respectively, compared to other network models, and produces more complete point cloud reconstructions in weak texture areas and scene edges.
Relationships Between Chinese EFL Learners’ IELTS Band Score and Motivated Strategies for Learning

 EFL learners, who study English as a foreign language, often use different learning strategies in the learning process, with positive and negative results in their academic performance. Whether these learning strategies are effective or not are questions to be explored. So, the author examined the relationship between Chinese EFL learners’ test results and the use of motivated strategies for learning in English learning. Participants are students who have taken a high-stake standardized English proficiency test: IELTS (International English Language Testing System) with band score obtained. The results show that students’ performance on high-stakes assessment is significantly associated with the use of motivated learning strategies like internal value in sharp contrast to test anxiety, which bears no relevance. Interviews are then implemented to candidates with different levels of English proficiency to figure out other related factors contributing to the test results.
Functional Classification of Tunisian Children with Cerebral Palsy Based on Mobility and Manual Ability

Introduction: Cerebral palsy (CP) is a common cause of impairment in children, resulting in unique and individual needs. Services for CP often focus on treating impairments rather than improving activities. To provide a comprehensive description of CP children’s skills, four functional scales were selected: Manual Ability Classification System (MACS), Communication Function Classification System (CFCS), Functional Mobility Scale (FMS), and Functional Independence Measure for Children (WeeFIM). Objective: Our study aims to describe functional profiles of a sample of children with CP in Tunisia by using familiar international functional measures. Methods: A cross-sectional study was conducted on 80 children with CP (43 boys and 37 girls with mean age of 6.55 ± 3.49 years) in Tunisia using a clinical evaluation questionnaire and functional scales: MACS, CFCS, FMS, and WeeFIM. Results: Spastic (75%) was the most common type, followed by dyskinetic (13.8%) and ataxic (11.2%). We found a significant association between CP subtypes, and FMS 500 and CFCS. Most children required assistance in WeeFIM domains: selfcare, mobility, and cognition. Unilateral group members performed higher in both mobility and manual ability scores. The spastic bilateral group had more difficulties with mobility and hand
Numerical investigation into the generation of platicons in microresonators driven by a gain-switched semiconductor laser

Energy-efficient Optical Frequency Combs (OFCs) in the form of the Dissipative Kerr Soliton (DKS) pulses can be generated in the on-chip microresonators with anomalous Group Velocity Dispersion (GVD) pumped by gain switched (GS) Semiconductor Lasers (SLs). However, the achievement of anomalous GVD microresonators needs delicate designs and specific fabrication techniques. In this paper, numerical simulations have been performed for OFCs generation using a GS SL to drive a microresonator with normal GVD which can be readily made in most of the CMOS-compatible platforms. With the aid of rate equations and Lugiato Lefever Equations (LLEs), we numerically demonstrate optical pulses with a flattop square, referred to as platicons, can be generated and their characteristics are influenced by several key system parameters, namely the SL modulation frequency, GVD coefficient and frequency detuning between the SL and microresonator. Moreover, the stability region of the platicon is obtained. Our findings provide a useful guidance and easy way for the generation of energy-efficient chip scale platicons.
Predictors of Subjective Well-Being: Interpersonal Sensitivity, Close Relationship Experiences and Academic Self-Concept

The emphasis on well-being in mental health definitions and the impact of positive psychology studies, it is observed that the interest in the factors explaining the subjective well-being (happiness) of individuals is increasing day by day. Subjective well-being involves cognitive and emotional evaluation of life. These evaluations may vary from one area of life to another and may have positive or negative effects on happiness. For this reason, numerous studies have focused on the relationship between subjective well-being and various aspects of life domains including friendship, romantic relationships, and academic achievement. However, it is noteworthy that the extent and priority of how the dynamics of various life domains collectively contribute to individuals' subjective well-being have not yet to been sufficiently clarified. From this point of view, the present study aimed to investigate the predictive role of the subcomponents of university students' interpersonal sensitivity, academic self-perception and attachment-based experiences in close relationship in their subjective well-being and to determine the priority status of these factors. The participants of the study consisted of 410 (235 female, 175 male) university students between the ages of 18-25. As a result of the correlation analysis, statistically significant relationships were found between all of the sub-dimensions of interpersonal sensitivity, academic self and experiences in close relationship and subjective well-being. The results of the stepwise regression analysis revealed four statistically significant models to explain subjective well-being. At each stage, when the effects of the sub-dimensions included in the previous model were controlled, the explanatory levels of the variables included in the model for subjective well-being were 30% for lack of social self-confidence, 12% for academic effort, 3% for interpersonal anxiety and dependency, and 4% for non-assertive behaviors, respectively. The findings were discussed and supported by the explanations and findings in the literature.
Determination of teleseismic relative phase arrival times using multi-channel cross-correlation and least squares

We have developed a semi-automated method of determining accurate relative phase arrival times and uncertainty estimates for teleseisms recorded on regional networks. Our analysis begins by obtaining preliminary arrival times with a single-trace phase-picking algorithm. For each possible pair of traces we then perform a search for the maximum of their cross-correlation function in order to obtain relative delay times. Depending on event magnitude, the best results are obtained by using correlation windows containing 2 to 4 sec of the initial energy pulse of the phase. The cross-correlation derived delay times are then used to generate an overdetermined system of linear equations whose solution is an optimized set of relative arrival times. We solve for these times using least squares. Cycle skipping is eliminated through the automatic re-evaluation of cross-correlation functions which yield high equation residuals. Quantitative estimates of timing uncertainty are obtained from the variance of equation residuals associated with each trace. Using data from the Washington Regional Seismograph Network, we have found that for reasonably high-quality events, the rms uncertainty in arrival time estimates is on the order of the sample interval (0.01 sec). Reproducibility of delay anomalies is excellent for events from the same geographic locations despite significant differences in waveform character. In connection with a study of the deep structure of the Cascadia Subduction Zone, we include a preliminary examination of the variation of residual patterns with azimuth.
The leading global health challenges in the artificial intelligence era

Millions of people’s health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the “digital revolution,” characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.
Predicting and simulating future land use pattern : a case study of Seremban district

As long as rapid urbanization which is a result of natural population growth and rural urban migration due to push and pull factors of social and economic conditions as well as the moving of urban populations from major city centres to urban fringe areas due to changing lifestyle which emphasized on spacious and more comfortable and environmentally friendly living environment continue to happen; towns and cities will continue to grow and expand to accommodate the growing and complex demand of the people. Experiences have shown that rapid and uncontrolled expansion of towns and cities has led to amongst others the deterioration in the quality of urban environment and sprawling of urban development onto prime agricultural and forest areas as well as cities starting to lose their identity. In order to avoid such phenomena continuing to happen, particularly in the Kuala Lumpur Conurbation Area, towns and cities need to be properly planned and managed so that their growth or expansion can be controlled and managed in a sustainable manner. One of the strategies adopted to curb sprawling development is through the delineation of urban growth or development limits (UGL). This means that the limit of towns and cities need to be studied and identified, so that urban development can be directed to areas that are identified and specified suitable for such development. One of the main tasks in the process of delineating UGL has been included as an important task in the preparation of development plans. With such policy a research study is now being carried out to develop a spatial modelling framework towards delineating UGL through the application and integration of spatial technologies and this will be a basis or framework for land use planners, managers and policy makers to formulate urban land use policies and monitor urban land use development. One of the main analysis involve in the process of performing this task is to understand past urban land development trend and to predict and identify future urban growth areas of the selected study area. This paper highlights the integration of statistical modeling technique via binary logistic regression analysis with GIS technology in understanding and predicting urban growth pattern and area as applied to District of Seremban, Negeri Sembilan. The result shows that urban land use pattern in the study area within the study period are significantly related to more than half of the predictors used in the analysis.
Research on 3D temperature field reconstruction method based on fusion of structured light imaging and infrared thermography

Infrared thermography converts infrared radiation to infrared images and allows the object surface temperature to be measured through radiometric calibration. However, infrared images lack depth information and cannot accurately represent the surface temperature distribution of three-dimensional (3D) objects. At the same time, the temperature measurement results of an infrared thermal imager can be affected by changes in the directional emissivity of the object surface, making it difficult to guarantee the accuracy of temperature measurement. 3D imaging is commonly used to obtain the surface morphology of an object to provide reliable 3D data for subsequent research. The paper uses structured light 3D imaging and infrared thermography to reconstruct the 3D temperature field and investigates the influence of directional emissivity on temperature measurement results. Specifically, the point cloud is obtained using the structured light 3D imaging algorithm based on Gray-code encoding. The reconstructed 3D point cloud is then fused with an infrared image for 3D temperature field reconstruction. Based on the in-depth analysis of the influence of the object surface directional emissivity on the temperature measurement results, the temperature measurement error caused by the change in directional emissivity is corrected by using the geometric shape information of the object surface. The experimental results indicate that the proposed 3D temperature field reconstruction method, which is based on the fusion of structured light imaging and infrared thermography, improves the accuracy of temperature measurement results based on an effective reconstruction of the 3D temperature field. This method has important application value in various fields such as disease assessment, component processing, and instrument testing.