Miracle-dz/UniFAR-FTUs huggingface dataset:
Dengue Incidence Trends and Its Burden in Major Endemic Regions from 1990 to 2019

Background: Dengue has become one of the major vector-borne diseases, which has been an important public health concern. We aimed to estimate the disease burden of dengue in major endemic regions from 1990 to 2019, and explore the impact pattern of the socioeconomic factors on the burden of dengue based on the global burden of diseases, injuries, and risk factors study 2019 (GBD 2019). Methods: Using the analytical strategies and data from the GBD 2019, we described the incidence and disability-adjusted life years (DALYs) of dengue in major endemic regions from 1990 to 2019. Furthermore, we estimated the correlation between dengue burden and socioeconomic factors, and then established an autoregressive integrated moving average (ARIMA) model to predict the epidemic trends of dengue in endemic regions. All estimates were proposed as numbers and age-standardized rates (ASR) per 100,000 population, with uncertainty intervals (UIs). The ASRs of dengue incidence were compared geographically and five regions were stratified by a sociodemographic index (SDI). Results: A significant rise was observed on a global scale between 1990 and 2019, with the overall age-standardized rate (ASR) increasing from 557.15 (95% UI 243.32–1212.53) per 100,000 in 1990 to 740.4 (95% UI 478.2–1323.1) per 100,000 in 2019. In 2019, the Oceania region had the highest age-standardized incidence rates per 100,000 population (3173.48 (95% UI 762.33–6161.18)), followed by the South Asia region (1740.79 (95% UI 660.93–4287.12)), and then the Southeast Asia region (1153.57 (95% UI 1049.49–1281.59)). In Oceania, South Asia, and Southeast Asia, increase trends were found in the burden of dengue fever measured by ASRs of DALY which were consistent with ASRs of dengue incidence at the national level. Most of the countries with the heaviest burden of dengue fever occurred in areas with low and medium SDI regions. However, the burden in high-middle and high-SDI countries is relatively low, especially the Solomon Islands and Tonga in Oceania, the Maldives in South Asia and Indonesia in Southeast Asia. The age distribution results of the incidence rate and disease burden of dengue fever of major endemic regions showed that the higher risk and disease burden are mainly concentrated in people under 14 or over 70 years old. The prediction by ARIMA showed that the risk of dengue fever in South and Southeast Asia is on the rise, and further prevention and control is warranted. Conclusions: In view of the rapid population growth and urbanization in many dengue-endemic countries, our research results are of great significance for presenting the future trend in dengue fever. It is recommended to policy makers that specific attention needs to be paid to the negative impact of urbanization on dengue incidence and allocate more resources to the low-SDI areas and people under 14 or over 70 years old to reduce the burden of dengue fever.
Ag–Ba0.75Sr0.25TiO3 composites with excellent dielectric properties

The percolative ceramic composites of Ba0.75Sr0. 25TiO3∕Ag (BST/Ag) with dense microstructure were sintered at a low temperature of 960°C. Excellent dielectric properties, such as high dielectric constant (er∼24000), low dielectric loss, and high dielectric tunability, were reported. The dielectric constant is found to be nearly temperature and frequency independent. It is essential to introduce the low-melting-point metal of silver into BST ceramics for significantly enhancing the dielectric properties of the composites. The superproperties make it potential to be used for electronic devices such as high charge-storage capacitors and tunable filters.
Digital tomosynthesis of joints based on a C-arm system

A hand/joint radiograph from systems such as C-arm is a two-dimensional (2D) projection of a three-dimensional (3D) object. In this paper we describe a simple tomosynthesis algorithm that is particularly suited for 3D imaging of joints based on a C-arm system. The algorithm is tested and evaluated using projections from simulated joints and projections generated with a C-arm system from joints formed by chicken bones. The results obtained demonstrate that the algorithm is able to provide accurate 3D reconstruction of joint structures.
A Performance Comparison of Classification Algorithms for Rose Plants

One of the key roles of Botanists is to be able to recognize flowers. This role has become highly challenging given that the number of discovered flower types are nearing half a million. To support Botanists, Information Technology offers promising solutions. Specifically, machine learning techniques are intrinsically appealing due to being precise enough as required. To this aim, two observations on flower leaves are relevant and leverage flower identification: one, flower plants exhibit unique features in their leaves thus allow distinction of their co-located flowers; two, leaves have a much longer life than flowers thus preserve identity properties longer. This paper proposes the use of machine learning-based identification of rose types by leveraging the features from their leaves. For this purpose, the performance of Naive Bayes, Generalized Linear Model, Multilayer Perceptron, Decision Tree, Random Forest, Gradient Boosted Trees, and Support Vector Machine has been analyzed. This study optimizes the RF model by investigating and tuning its various parameters such as the number of trees, the depth of trees, and splitting criteria. The best results are achieved with gain ratio because it takes more distinct values to avoid the problems associated with Information Gain. Optimizing the number of trees and the depth of trees of RF yield better accuracy than other models. Extensive experiments are performed to analyze the results of ensemble algorithms by using the voting method for each instance. Results suggest that the performance of ensemble classifiers is superior to that of individual models.
Confidence Intervals based on Rank Statistics in Linear Models

Consider the linear models of which the distributions of the errors are non-normal. We propose a method based on rank statistics for constructing confidence intervals for the parameters in the linear models. It is found that the proposed confidence intervals have coverage probabilities which are fairly close to the target value. Furthermore when the skewness of the distributions is large, the expected lengths of the proposed confidence intervals are found to be much shorter than those of the percentile bootstrap confidence intervals, and the classical confidence intervals which are derived by assuming that the errors are normally distributed.Pertimbangkan model linear dengan ralat yang bertaburan tak normal. Kita mencadangkan satu kaedah berasaskan statistik pangkat untuk membina selang keyakinan bagi parameter dalam model linear. Didapati kaedah yang dicadangkan mempunyai kebarangkalian liputan yang menghampiri nilai sasaran. Tambahan pula, apabila nilai kepincangan taburan adalah besar, kaedah yang dicadangkan menghasilkan selang keyakinan yang mempunyai panjang jangkaan yang jauh lebih pendek daripada selang keyakinan yang berdasarkan kaedah bootstrap persentil, dan kaedah klasikal yang mengandaikan bahawa taburan ralatnya adalah normal.
The Different Preferences of Learning among Emergency Medicine Residents in Riyadh, during their Residency Program

Traditional teaching approaches are giving way to new ones in medical education. Residents' interest in readily available educational resources is causing instructors to rethink their teaching strategies. This study aims to find which of these ways of learning is preferred by residents. A descriptive survey study was distributed among emergency residents who doing their program in Riyadh during the period from April 2021 to September 2021. All Saudi board emergency medicine residents R1, R2, R3, R4, and post-training residents within the last two years in Riyadh were included. Data were collected using an electronic survey (SurveyMonkey) using a validated questionnaire. This study analyzed 202 residents and post-graduated and ER residents regarding their learning sources and preferences. Participants were at different levels of training as 23.8% were in their first year of residency, 22.8% were in their second year, 19.3% were in their third year, 22.3% were in their fourth year and 11.9% finished their training during the last two years. The majority of the participants 57.4% claimed that they spend 1-2 hours of their extracurricular time on educational material.
BIM implementation: an empirical validation for a four-wheel model

PurposeBecause BIM adoption is still afflicted by various types of hurdles, a complete BIM implementation model is required to provide the necessary methods for driving BIM adoption. As a result, this study looked into the parts of the BIM implementation model that had the most impact on increasing the percentage of BIM adoption in South Africa.Design/methodology/approachThis study developed a four-wheel model of BIM implementation based on implementation process theory, which includes BIM inspiration, BIM capacity development, BIM use and BIM commitment. To assess BIM capacity development, two sub-constructs (BIM learning process and BIM learning methodologies) were used. Two sub-constructs were used to assess BIM utilisation (efficient BIM application and effective BIM application). The sub-constructs employed to quantify BIM motivation were organisational competitiveness, societal conformity and contractual obligations. Incentives, investments and obligations were used to assess BIM commitment. The model was validated using four assumptions and maximum likelihood estimation – structural equation modelling (MLE-SEM). FindingsThe MLE-SEM results demonstrated unequivocally that all of the constructions are critical components of the BIM deployment paradigm in the South African construction industry. BIM motivation, as characterised by organisational competitiveness and social compliance, has the greatest impact. The findings on BIM motivation also revealed that the desire for technological sophistication, competitiveness and social acceptance by clients are encouraging construction organisations and professionals to embrace BIM adoption.Research limitations/implicationsThis study's findings have contributed to the increasing body of literature on BIM deployment. The study has significant implications for achieving BIM implementation in underdeveloped nations where BIM deployment is either non-existent or in its early stages. The theoretical component of the study serves as the foundation for further analysis of BIM deployment.Practical implicationsThis research is important for identifying BIM goals, developing a BIM implementation framework, allocating resources for BIM implementation and defining key performance indicators for BIM implementation. The BIM implementation aspects outlined in this study will be effective in lowering BIM adoption hurdles.Originality/valueThis study makes a unique contribution to BIM research by providing theoretical and empirical analysis into the elements of the BIM implementation model in a developing country. The study offers an excellent opportunity to further our understanding of BIM application in underdeveloped nations.
Cyber Switching Attacks on Smart Grids

As we live in smart grid revolution, the conventional power systems turn into a fast pace toward smart grids, this transition creates new and significant challenges on the electrical network security level; In addition to the important features of the smart grids, cyber security transpire to be a serious issue due to connecting all the loads, generation units, renewable resources, substations and switches via communication network. Cyber-physical attacks are classified as the major threatening of smart grids security, this attacks may lead to a many severe repercussions in the smart grid such as large blackout and destruction of infrastructures. Switching attack is one of the most serious cyber-physical attacks on smart grids because it is direct, fast, and effective in destabilizing the grids. We start the thesis by introducing a state-of-the-art on cyber attacks from the power layer point of view i.e. the cyber attacks that affect the smart grid stability and what are the power system based solutions have been done so far to prevent or reduce the cyber attacks severity . As we focus on cyber switching attack and the method of preventing it, firstly a study on the attack principles and effects is introduced, we construct the attack on a single machine connected to an infinite bus through a transmission line. The attack on the target generator implemented by modeling the system using swing equation on Matlab platform, then we verified the result by implementing the same attack on Simulink Platform. Finally we present a novel solution to mitigate such type of attacks by using Thyristor-Controlled Braking Resistor (TCBR). The suggested solution is able to recapture the machine stability directly after the attack.
Interaction of Galaxies with the ICM

Although speculations of an interaction between galaxies and the ICM date back more than thirty years, the impact and importance of a possible intera ction have long remained elusive. In recent years the situation has completely changed. A wealth of data and detailed hydrodynamical simulations have appeared that show the effects of interactions. Single dish observations show that cluster galaxies are deficient in the ir neutral hydrogen content out to two Abell radii. The deficient galaxies tend to be on radial orbits. Detailed imaging of the neutral hydrogen distribution in individual galaxies i n two nearby clusters show a remarkable trend of H I extent with location in the cluster. These trends can be reproduced in simulations of ram pressure stripping by the ICM using SPH and full 3D hydro-codes. Detailed imaging of individual galaxies have found a number of galaxies with undisturbed stellar disks, truncated gas disks that are much smaller tha n the stellar disks, asymmetric extraplanar gas in the center and enhanced central star formation. These phenomena have all been predicted by hydrodynamical simulations. For the fi rst time detailed observations of gas morphology and kinematics are used to constrain simulations. Simple models of ram pressure stripping are consistent with the data for some galaxies, while for other galaxies more than one mechanism must be at work. Optical imaging and spectroscopic surveys show that small H I disks go together with truncated star forming disks, that hydrogen deficiency correlates with suppressed star formation rates and that th e spatial extent of H I deficiency in clusters is matched by or even surpassed by the extent of reduced star formation rates. Recent volume limited imaging surveys of clusters in the local universe show that most gas rich galaxies are located in smaller groups and subclumps, that yet have to fall into the clusters. These groups form an ideal environment for interactions and mergers to occur and we see much evidence for interactions between gas rich galaxies.
Biophysical Effects of Afforestation on Land Surface Temperature in Guangdong Province, Southern China

Developing effective climate mitigation strategies under global warming requires a comprehensive understanding of the biophysical mechanism of how afforestation affects the climate and environment. The planted forests in southern China are an essential carbon sink. However, the impacts of radiative and non‐radiative processes on land surface temperature caused by converting open land (i.e., grassland and cropland) and natural forests to planted forests remain unclear. We used satellite observations and intrinsic biophysical mechanism theory‐based energy balance models to estimate the biophysical impacts of potential afforestation of open land and natural forests on surface temperature from 2000 to 2010 in Guangdong Province, southern China. Results showed that afforestation of open land had a consistent net cooling effect. Due to the afforestation of natural forests, the modeled results revealed that afforestation among all conversion types had a net warming effect of 0.15 ± 0.5 K, which caused by the change in energy redistribution factor although uncertainty remains. While the most significant warming caused by converting natural forest to planted forests was also slightly affected by albedo. The afforestation's non‐radiative and radiative processes led to a slight warming of 0.143 ± 0.43 K and a cooling of −0.096 ± 0.19 K, respectively. The non‐radiative process dominates the effect of afforestation on the surface temperature, with the overall non‐radiative forcing index greater than 73% ± 0.59%. Our study highlights the need of protecting natural forests and provides a practical method for assessing the impacts of afforestation on the local climate and the effectiveness of climate mitigation efforts.
SU4312 Represses Glioma Progression by Inhibiting YAP and Inducing Sensitization to the Effect of Temozolomide

SU4312, initially designed as a multi-target tyrosine kinase inhibitor, is consequently reported to inhibit tumor angiogenesis by blocking VEGFR. However, although SU4312 can penetrate the brain–blood barrier, its potential to inhibit glioma growth is unknown. In this study, we report that SU4312 inhibited glioma cell proliferation and down-regulated yes-associated protein (YAP), the key effector of the hippo pathway. The exogenous over-expression of YAP partially restored the inhibitory effect of SU4312 on glioma progression. Interestingly, SU4312 sensitized the antitumor effect of temozolomide, both in vitro and in vivo. Moreover, SU4312 decreased the M2tumor-associated macrophages and enhanced anti-tumor immunity by down-regulating the YAP-CCL2 axis. In conclusion, our results suggest that SU4312 represses glioma progression by down-regulating YAP transcription and consequently CCL2 secretion. SU4312 may be synergistic with temozolomide for glioma treatment.
Parametric Assessment of Concrete Constituent Materials Using Machine Learning Techniques

Nowadays, technology has advanced, particularly in machine learning which is vital for minimizing the amount of human work required. Using machine learning approaches to estimate concrete properties has unquestionably triggered the interest of many researchers across the globe. Currently, an assessment method is widely adopted to calculate the impact of each input parameter on the output of a machine learning model. This paper evaluates the capability of various machine learning methodologies in conducting parametric assessments to understand the influence of each concrete constituent material on its compressive strength. It is accomplished by conducting a partial dependence analysis to quantify the effect of
Learning to Tag Environmental Sounds in Nightlong Audio

Environmental sound events are defined as sounds occurring naturally or produced due to human activity. Devices need to identify the environmental sounds for better perception of the environment. The emergence of environmental sound classification using machine learning has led to the development of more context-aware technologies like smart homes, multimedia search, etc. In this thesis, different sound events and their starting and ending times are determined using classification models. The audio data is provided by the German Aerospace Center and is composed of night-long audio clips that are recorded near an airport and contain different environmental sounds. Many challenges like noise in data, sounds of different lengths, and their effects on the classifier are also discussed. We investigated if different models prefer to identify sounds of different lengths in the same audio. An overlapping window approach is devised to improve the identification of starting and ending times of the predicted events. The results of the thesis are encouraging as the best model is able to identify various sound events with an accuracy of 0.94 on a balanced dataset of 4 different classes. Finally, a system is also conceptualized where the environmental sounds are identified, and their spans are visualized against time.
Drivers’ Perceptions of Smartphone Applications for Real-Time Route Planning and Distracted Driving Prevention

Given the increasing importance and availability of traffic-related smartphone applications, understanding their potential use is vital, especially in developing countries. This research explores motorist perceptions of the installation and use of two smartphone applications—a distraction-prevention application and a real-time traffic information and navigation application—in Qatar, a rapidly developing country in the Arabian Gulf region. This study represents the first attempt to investigate the potential market for these types of applications in a region with a unique social and cultural environment. A questionnaire-based survey was conducted to examine the drivers’ interest in using both applications, their willingness to buy the applications, and their data privacy concerns. The results indicated that the potential market for these types of smartphone applications in Qatar is high. The potential for the real-time route planning application was found to be much higher than that of the antidistraction application, especially among female drivers. A high percentage of the drivers, especially younger and local drivers, were less enthusiastic about installing and using the distracted driving prevention application. Most of the participants willing to use both smartphone applications did not have data privacy concerns, but in return for allowing the applications to access their data, they expected some reduction in travel time and a safer trip. These findings provide a direction for the development of future policies and smart solutions in this region.
The Effects of Presession Conditions on Behavior During a Brief Functional Analysis

Title: The Effects of Presession Conditions on Behavior During a Brief Functional Analysis Previous research has demonstrated that patterns of responding during a no-interaction condition of a functional analysis (FA) may predict FA outcomes. Thus, behavior analysts may use an initial no-interaction condition to identify problem behavior maintained by social reinforcement when time and resources are limited. However, in a clinic setting, most functional analyses are conducted in between or immediately after instructional sessions or structured play. Therefore, the proposed study was designed to examine the differential effects of presession conditions of naturally occurring activities (work versus play) at a clinic setting during a modified brief functional analysis. Results showed that presession conditions did not affect rates of responding for behaviors maintained by automatic reinforcement. Implications and future directions are discussed.
When crisis strikes - Changes in work and professional identity among social workers in Norwegian Child Welfare Protection services during COVID-19

When the COVID-19 pandemic reached Norway in March 2020, comprehensive action was taken by the government, leading to the lockdown of welfare institutions, schools and kindergartens, and strict restrictions on physical meetings. This had severe consequences for Child Welfare Protection services (CWP). The restrictions stopped child welfare protection home visits and professionals in other welfare institutions, which usually observe children and notify child welfare protection services, were no longer able to identify children at risk. This article, drawing on interviews with 10 social workers, explores their experience during COVID-19. Applying Lipsky’s concept of street-level bureaucrats and theories of professional identity, the article documents how COVID-19 not only restricted, but also modified social workers jobs, and led to self-reflection on their professional identity. In turn, the findings suggest the potential for beneficial changes in practices in the aftermath of COVID-19 © This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License