Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the cost of eLearning within the field of health professions education: Scoping Review

Background: Existing research on the costs associated for design and deploying eLearning in health professions education is limited. The way in which these learning platforms compare in cost to face-to-face learning is also not well understood. The lack of pre-defined costing models used for eLearning cost data capture has made it difficult to complete cost evaluation. Objective: The key aim of this scoping review is to explore the state of evidence concerning cost capture within eLearning in health professions education. The review explores what data exists to define cost calculations related to eLearning. Methods: Scoping review using a search strategy of MeSH terms and related keywords centered on eLearning and cost calculation with a population scope of health professionals in all countries. The search was limited to English language studies. No restriction was placed on literature publication date. Results:
Wildlife Displacement and Dispersal Area Reduction by Human Activities within Kimana Group Ranch Corridor Near Amboseli, Kenya.

My research focused on Kimana Group Ranch (KGR), an area 251.2 square kilometers in size that forms a major portion of the primary wildlife dispersal corridor between Amboseli National Park and Chyulu Hills/ Tsavo National Parks. Traditionally, humans and wildlife co-existed peacefully on this land, which occupied mainly by the Maasai ethnic group. The Maasai conventionally practiced nomadic pastoralism, a land use compatible with both the migrations of wildlife and the semi-arid environment of the Tsavo/Amboseli ecosystem. British colonialism, however, brought new emphasis on development of the rangelands and subsequent increase of infrastructure and sedentary lifestyles within KGR. Thus, my study sought to determine the current status of Kimana Group Ranch as an effective wildlife dispersal area and corridor by mapping the area occupied by infrastructure as well as wildlife distributions and displacement distances from human activities. In preparation for my research, I spent eight weeks studying Kenyan wildlife ecology and management, as well as environmental policy and local cultures. Actually living in the community I was seeking to help was my motivation for success. Experiencing a land and culture unlike any available to me in the United States, the kind I’d only read about in National Geographic magazines as a little girl, challenged and changed me in a way I could never fully express with mere words. The dramatic landscapes and warm people that comprise Kenya allowed me to experience a side of life that is unavailable to me at home. I was exposed to an outlook which is foundationally different than the rushed success-driven mentality with which I was accustomed. Living side-by-side with the community my research would affect gave me an understanding of the area’s critical needs and concerns that cannot be taught. Thus, my research became valuable not only as an
Revolutionizing Remote Sensing Image Analysis With BESSL-Net: A Boundary-Enhanced Semi-Supervised Learning Network

Deep learning (DL) has become increasingly popular in remote sensing (RS) change detection (CD), leading to the development of massive networks that surpass traditional methods in accuracy and automation. However, the need for enormous amounts of annotated data remains a major concern, and accurate boundary segmentation in RS images is challenging due to their complexity and heterogeneity. Moreover, properly aggregating the bitemporal feature pairs and creating highly discriminative change features are crucial for detection performance. This article proposes a boundary-enhanced semi-supervised network (BESSL-Net) to tackle these issues for CD tasks. The network adopts dual encoders and one decoder architecture for segmentation and incorporates pseudolabeling, contrastive learning (CL), along with a teacher–student scheme to leverage unlabeled data. A boundary extraction module (BEM) is used to conduct boundary segmentation, while a change segmentation feature learning module (SFLM) is used to create discriminative change features in both channel and spatial domains by integrating multilevel features. Three publicly available CD datasets are used to validate the proposed BESSL-Net. Compared with the current state-of-the-art networks, the semi-supervised network demonstrates advanced performance metrics, especially regarding IoU metric of change-class (IoUc), showing improvements ranging from 1% to 9%.
BYD Digital Marketing Strategy in Malaysia

: This article delves into BYD's entry into Malaysia's electric vehicle (EV) market, utilizing the SOSTAC framework. It analyzes the external market situation and proposes digital marketing objectives aligned with the expansion strategy. The suggested digital marketing strategies focus on enhancing brand awareness, promoting products and services, and driving EV adoption in Malaysia. The article recommends five actionable tactics with monitoring metrics to improve BYD's digital communication strategy in Malaysia. As the world shifts towards renewable energy, the automotive industry undergoes a transformative phase, making insights into game-changing market strategies crucial. To address this, the report explores how BYD can deploy effective brand and marketing strategies to succeed in the new market, leveraging local insights for effective tactics.
Enhancing the involvement of decision makers in data mart design

The design phase of a data warehousing project remains difficult for both decision makers and requirements analysts. In this paper, we tackle this difficulty through two contributions. First, we propose a natural language based and goal-oriented template for requirements specification that includes all concepts of the decision-making process. The use of familiar concepts and natural language makes our template more accessible and helps decision makers in validating the specified requirements, which avoids producing data mart that does not meet their needs. Secondly, we propose a decision-making ontology that provides for a systematic decomposition of decision-making goals, which allows new requirements to emerge. This automatic requirements elicitation helps analysts to overcome their lack of domain knowledge, which avoids producing erroneous requirements.
An Adaptive ROI Detection System for Spatiotemporal Features

In this paper, an adaptive ROI(region of interest) detection system for spatialtemporal features is proposed. It utilizes spatiotemporal features for the purpose of detecting ROI. It is assumed that motion representing temporal visual conspicuity between adjacent frames takes higher priority over spatial visual conspicuity. Because objects or regions in motion usually draw stronger attention than others in motion pictures. In case of still images visual features that constitute topographic feature maps are used as spatial features. Comparative experiments with a human subjective evaluation show that correct detection rate of visual attention region is improved by exploiting both spatial and temporal features compared to the case of exploiting either feature.
Um Algoritmo de Posicionamento de Pontos de Coleta para uma Rede de Sensores Baseada em Ônibus Urbanos

Cidades Inteligentes podem utilizar dados obtidos por uma rede de sensores para oferecer melhores servicos. Entretanto, uma rede de sensores estaticos cobrindo toda aarea de uma cidade pode apresentar um custo proibitivo. Assim, uma alternativa e explorar a estrategia de instalar sensores em veiculos, que oferecem mobilidade ao sensoriamento, reduzindo o numero de sensores necessarios. Este trabalho analisa o desempenho de uma rede formada pelos onibus de transporte publico da cidade, como plataforma de mobilidade para sensores. O trabalho tambem propoe um algoritmo para a escolha de pontos de coleta e o avalia utilizando informacoes reais dos onibus no Rio de Janeiro, analisando o compromisso entre a latencia na entrega dos dados dos sensores e o numero de pontos de coleta utilizados.
Does a good fit matter? Exploring teaching styles, emotion regulation, and child anxiety in the classroom

The central goal of the present study was to examine how a child's emotion regulation ability may moderate the relations between teaching styles and anxiety in childhood. Participants were 33 children (21 males, 12 females; mean age 7.5 years, standard deviation = 0.42), their mothers and teachers. Children completed the Early Adolescent Temperament Questionnaire – Revised to assess their emotion regulation, mothers completed the Child Behavior Checklist to assess their child's anxiety and teachers completed the Teaching Styles Inventory to assess various teaching styles. Results indicated different patterns of associations between teaching styles and anxiety for well‐regulated versus dysregulated children. For example, it was found that children who are better able to regulate their emotions are better at coping with the potentially stressful context brought on by the expert teaching style than those children lower in regulation abilities. Preliminary evidence suggested that different teaching styles might be associated with different outcomes among children with differing regulatory characteristics. Results are discussed using the goodness‐of‐fit model.
The Projective Vision Toolkit

Projective vision research has recently received a lot of attention and has claimed some important results in current literature. In this paper, we present a compilation of tools that we have created to allow further research into the field. Not only can experienced projective vision researchers use these tools, but they also have use as a visual learning aid for those just undertaking the task of learning projective vision. We will discuss tools for computing interest points, correspondence matching, computing the fundamental matrix, computing the trilinear tensor, and our intent for future releases of the Projective Vision Toolkit(PVT).
Fouling in offshore areas southeast of the Zhujiang（Pearl） River Delta,the northern South China Sea

In order to understand the type and extent of marine fouling in offshore areas southeast of the Zhujiang (Pearl) River delta, within the period form May 1986 to June 1987, two biological buoys were deployed at water depths of 95 m and 113 m located in 114 km and 160 km off the coast of Hong Kong, respectively. Moreover, the fouling community of a Marex hydrological buoy located in 115 m depth water 172 km off Hong Kong was also surveyed. The results show that a total of 78 species were collected and identified. The panels exposed for 3 months were mainly dominated by stalked barnacles Conchoderma hunteri and Lepas anatifera and hydroids Orthopyxis sp. As for the buoys, including the subsurface buoy, and their mooring systems exposed for 6 and 12 months, respectively, some hard foulers such as common oysters, pearl oysters, acorn barnacles and bryozoans were also found. The compositions of fouling communities also varied greatly with depth.
Towards a systematic revision of the superfamily Cyrenoidea (Bivalvia: Imparidentia): species delimitation, multi-locus phylogeny and mitochondrial phylogenomics

Cyrenoidea is a superfamily of bivalves (Bivalvia: Imparidentia) currently comprising three families (Cyrenidae, Cyrenoididae and Glauconomidae). The superfamily is widely distributed in marine, brackish and freshwater environments, with an estimated 60 or more living species. Recent phylogenetic results have confirmed the monophyly of Cyrenoidea and placement in Venerida. Nevertheless, a comprehensive phylogenetic analysis of Cyrenoidea remains elusive and the phylogeny is unresolved due to inadequate sampling in previous studies. Moreover, the taxonomy and delimitation of most species, originally based on shell morphology, have not yet been tested with molecular data. Here, we constructed three molecular datasets by sequencing three markers ( COI + 16S rRNA + 28S rRNA) and complete mitogenomes for Geloina coaxans (Gmelin, 1791) and Glauconome virens (Linnaeus, 1767). COI barcoding clarifies the validity of Geloina coaxans and Geloina erosa that have been subject to controversy regarding synonymy. Additionally, the barcoding supports the existence of multiple cryptic species within the Geloina expansa complex. A multi-locus dataset ( COI + 16S rRNA + 28S rRNA) provides the most comprehensive phylogeny of all eight recognised genera of Cyrenoidea to date. Phylogenetic results indicate that the currently recognised family Cyrenidae is polyphyletic. The type species Geloina coaxans , Cyanocyclas limosa (Maton, 1811) and Polymesoda caroliniana (Bosc, 1801) that have long been classified within the family Cyrenidae based on shell morphology, have a closer relationship with Cyrenoida floridana Dall, 1896 than with other Cyrenidae. Therefore we transfer the genera Geloina , Cyanocyclas and Polymesoda from the family Cyrenidae to the family Cyrenoididae. The mitochondrial phylogenomics further support the family-level relationships in Cyrenoidea obtained from the three-gene analyses, confirming that the newly defined Cyrenoididae is closely related to Glauconomidae as the sister group. We observed a novel gene arrangement in Glauconome virens , the first report on the mitogenome of the family Glauconomidae, by comparing gene arrangements. Three patterns of gene rearrangement identified in Cyrenoidea are shared by the families Glauconomidae, Cyrenoididae and Cyrenidae, suggesting that gene arrangements can be a valuable tool for phylogenetic studies.
Benefits of Network Coding in Disruption Tolerant Networks

In this report, we investigate the benefits of applying a form of network coding known as Random Linear Coding (RLC) to unicast communications in mobile Disruption Tolerant Networks (DTNs). Under RLC, DTN nodes store and forward random linear combinations of packets as they encounter other DTN nodes. We first consider RLC applied to a single block of K packets where (a) all K packets have the same source and destination, (b) the K packets have different sources but a common destination and (c) the K packets each have a different source/destination pair; we also consider the case where blocks of K packets arrive according to a Poisson bulk arrival process. The performance metric of interest is the delay until the last packet in a block is delivered. We show that for the single block case, when bandwidth is constrained, applying RLC over packets destined to the same node achieves (with high probability) the minimum delay to deliver the block of data. We find through simulation that the benefit over non-network-coded packet forwarding increases further when buffer space within DTN nodes is limited. For the case of multiple blocks, our simulations show that RLC offers only slight improvement over the non-coded scenario when only bandwidth is constrained, but more significant benefits when both bandwidth and buffers are constrained. We remark that when the network is relatively loaded, the RLC scheme achieves improvements over non-coded schemes only if the spreading of the information is appropriately controlled.
Personalized Breast Cancer Screening: A Risk Prediction Model Based on Women Attending BreastScreen Norway

Simple Summary The study aimed to develop and validate a prediction model that can be used to classify women for tailored breast cancer screening based on their individual risk. The model included data on age, mammographic density, family history of breast cancer, body mass index, age at menarche, alcohol consumption, exercise, pregnancy, hormone replacement therapy, and benign breast disease for 57,411 women screened in BreastScreen Norway 2007–2019. The 4-year predicted risk of breast cancer ranged between 0.2% and 7.3%, with 95% of the population having a risk of 0.6–2.3%. The differences in the predicted risk favor personalized screening for breast cancer. Abstract Background: We aimed to develop and validate a model predicting breast cancer risk for women targeted by breast cancer screening. Method: This retrospective cohort study included 57,411 women screened at least once in BreastScreen Norway during the period from 2007 to 2019. The prediction model included information about age, mammographic density, family history of breast cancer, body mass index, age at menarche, alcohol consumption, exercise, pregnancy, hormone replacement therapy, and benign breast disease. We calculated a 4-year absolute breast cancer risk estimates for women and in risk groups by quartiles. The Bootstrap resampling method was used for internal validation of the model (E/O ratio). The area under the curve (AUC) was estimated with a 95% confidence interval (CI). Results: The 4-year predicted risk of breast cancer ranged from 0.22–7.33%, while 95% of the population had a risk of 0.55–2.31%. The thresholds for the quartiles of the risk groups, with 25% of the population in each group, were 0.82%, 1.10%, and 1.47%. Overall, the model slightly overestimated the risk with an E/O ratio of 1.10 (95% CI: 1.09–1.11) and the AUC was 62.6% (95% CI: 60.5–65.0%). Conclusions: This 4-year risk prediction model showed differences in the risk of breast cancer, supporting personalized screening for breast cancer in women aged 50–69 years.
ICT-Enabled Responses to Climate Change in Rural Agricultural Communities

Rural agricultural communities (RAC) are placed at the forefront of climate change impacts, and are the most prone to suffer from its effects. The unpredictability of weather patterns and the increased frequency and intensity of severe events such as floods and cyclones, are posing unprecedented challenges on vulnerable sectors such as agriculture (crop and livestock production, fisheries and forestry), affecting the livelihood of nearly half of the economically active population in developing countries.
Microbial production of CO2 in red soil in Stone Forest National Park

Lunan stone forest is a kind of typical karst in China,Which is mainly developed under red soil.In the winter of 1999,three study sites were chosen in stone forest national park according to vegetation cover,geomporphologic location and soil types,CO2 concentration was measured with Gastec punp at different dephts of soil (20,40,60cm) and at the same time soil samples were gatered and soil properties such as soil moisure,pH,soil organic content were analyzed and the total nmuber of viable microbes were counted in laboratory,In the study,dependent variable was chosen as the mean soil log(PCO2),and soil properties were chosen as the independent variables.Multiple stepwise regression analysis showed that the totla amount of microbes and soil moisture are the best indicators of the CO2 production,With the equation LOG(PCO2)=-0.039(TNM)-0.056 (Mo)+1.215 accounting for 86% of the variation of the soil CO2 concentration,where TNM is the total number of microbes in teh soil and Mo is the moisture of soil sample.
Scaling up local diagnostic studies to understand development issues in a heterogeneous mountain environment: an introduction to the SAM program.

The mountain rural communities in northern Viet Nam are among the poorest in the nation, and have benefited the least from the recent economic growth. The Mountain Agrarian Systems Program (SAM) has been working in Bac Kan Province since 1998, studying the complexity of the mountains in an interdisciplinary fashion to understand the needs and possibilities of farmers in the area. A research methodology was developed to deal with the extreme diversity, rapid institutional changes, and substantial external influences at play in the studied region, and consisted of multiple monographic studies and a regional geographic approach. A scaling-up process allowed us to generalize our site-specific results to broader areas. The studies indicate that the pessimistic outlooks of certain authors are exaggerated, although much work remains in order to raise the living standards of the mountainous regions to those of the rest of the country. Future development activities in the mountainous areas should focus on making efficient use of scarce resources, building on social capital toward community-based resource management, and diversifying farmer income sources. The SAM Program results also offer indicators to identify those individuals in greatest need of development assistance.
CONCEPT OF OPERATIONS FOR COMMERCIAL AND BUSINESS AIRCRAFT SYNTHETIC VISION SYSTEMS

A concept of operations (CONOPS) for the commercial and business (CaB) aircraft synthetic vision systems (SVS) is described. The CaB SVS is expected to provide increased safety and operational benefits in normal and low visibility conditions. Providing operational benefits will promote SVS implementation in the Net, improve aviation safety, and assist in meeting the national aviation safety goal. SVS will enhance safety and low visibility conditions. The goal for developing SVS is to support operational minima as low as Category 3b in a variety of environments. For departure and ground operations, the SVS goal is to enable operations with a runway visual range of 300 feet. The system is an integrated display concept that provides a virtual visual environment. The SVS virtual visual environment is composed of three components: an enhanced intuitive view of the flight environment, hazard and obstacle defection and display, and precision navigation guidance. The system will support enhanced operations procedures during all phases of flight - ground operations, departure, en route, and arrival. The applications selected for emphasis in this document include low visibility departures and arrivals including parallel runway operations, and low visibility airport surface operations.
Investigating the 'latent' deficit hypothesis : age at time of head injury, executive and implicit functions and behavioral insight

This study investigated the ‘latent deficit’ hypothesis in two groups of head-injured patients with predominantly frontal lesions, those injured prior to steep morphological and corresponding functional maturational periods for frontal networks (≤ age 25), and those injured >28 years. The latent deficit hypothesis proposes that early injuries produce enduring cognitive deficits manifest later in the lifespan with graver consequences for behavior than adult injuries, particularly after frontal pathology (Eslinger, Grattan, Damasio & Damasio, 1992). Implicit and executive deficits both contribute to behavioral insight after frontal head injury (Barker, Andrade, Romanowski, Morton & Wasti, 2006). On the basis of morphological and behavioral data, we hypothesised that early injury would confer greater vulnerability to impairment on tasks associated with frontal regions than later injury. Patients completed experimental tasks of implicit cognition, executive function measures and the DEX measure of behavioural insight (Behavioral Assessment of the Dysexecutive Syndrome: Wilson, Alderman, Burgess, Emslie, & Evans, 1996). The Early Injury group were more impaired on implicit cognition tasks compared to controls that Late Injury patients. There were no marked group differences on most executive function measures. Executive ability only contributed to behavioral awareness in the Early Injury Group. Findings showed that age at injury moderates the relationship between executive and implicit cognition and behavioral insight and that early injuries result in long-standing deficits to functions associated with frontal regions partially supporting the latent deficit hypothesis.
Lymphoma Obinutuzumab plus Lenalidomide ( GALEN ) for the treatment of relapse / refractory aggressive lymphoma : a phase II LYSA study

Roch Houot ● Guillaume Cartron ● Fontanet Bijou ● Sophie de Guibert ● Gilles A. Salles 4 ● Christophe Fruchart ● Krimo Bouabdallah ● Marie Maerevoet ● Pierre Feugier ● Steven Le Gouill ● Hervé Tilly ● Rene-Olivier Casasnovas ● Cécile Moluçon-Chabrot ● Eric Van Den Neste ● Pierre Zachee ● Marc Andre ● Christophe Bonnet ● Corinne Haioun ● Achiel Van Hoof ● Koen Van Eygen ● Lysiane Molina ● Emmanuelle Nicolas-Virelizier ● Philippe Ruminy ● Franck Morschhauser