Miracle-dz/UniFAR-FTUs huggingface dataset:
Bronchial Asthma among Medical students in Saudi Arabia

Background: bronchial asthma is a common chronic respiratory disease affecting adolescents and young adults nationality and worldwide and its prevalence has increased in developed and developing countries over the last three decades. Aim of the Work: to establish the prevalence of bronchial asthma among medical students in Saudi Arabia and the relationship between asthma and some associated risk factors. Patients and Methods: a descriptive cross-sectional study. Data were collected from randomly selected undergraduate medical students of 6 universities, during the academic year 2017-2018. A predesigned questionnaire was disseminated to the targeted population to complete it. Results: the prevalence of physician-diagnosed bronchial asthma in our participants was 19.2%. Males reported insignificant higher prevalence of bronchial asthma compared to females (P= 0.195). Bronchial asthma was significantly associated with skin allergy 32.9% (P= 0.001), allergic rhinitis 61.6% (P=0.001), hay fever 12.3 %( P=0.001) and obesity 28.8% (P0.009). While it was insignificantly associated with smoking (P=0.428), passive smoking (P=0.561), diabetes (P=0.254) and performing muscular exercise (P=0.322). Precipitating factors to bronchospasm was dust in 43.8%, certain foods in 1.4%, hot weather in 2.7% and multiple factors in 52.1%. Herbal treatment was tried in 34.2%; desensitization in 6.8% and 68.5% needed emergency treatment. Response to medical treatment was good in 89.1% of cases. Conclusion: the prevalence of physician-diagnosed bronchial asthma in our participants was 19.2%. Males reported insignificant higher prevalence of bronchial asthma than females. Bronchial asthma was significantly associated with skin allergy, allergic rhinitis, hay fever and obesity. While it was insignificantly associated with active smoking or passive smoking.
Thermal Analysis of a Flux-Switching Permanent-Magnet Double-Rotor Machine With a 3-D Thermal Network Model

A fast and precise lumped-parameter thermal network (LPTN) model is presented for a flux-switching permanent-magnet double-rotor machine used for hybrid electric vehicles (HEVs), which has attracted attention due to the compact structure. The three-dimensional (3-D) LPTN model provides the steady-state thermal solution to derive the temperatures at different parts of the machine, in which the power losses of various working conditions are used as heat sources for calculating the temperatures of different operational modes. In the LPTN studies, a transformation of an irregular slot structure into a regular one is applied to simplify the calculation of the winding thermal resistance. A 3-D finite element thermal model of this machine is also established to calculate the temperatures of the main machine components for a comparative study purpose. Tests performed on the designed machine verify that the defined LPTN model has a high ability to predict the nodal temperatures accurately.
A Method to Collect Multi-view Images of High Importance Using Disaster Map and Crowdsourcing

In recent years, research efforts have enabled using the internet in disaster areas, and information technology (IT) is expected to improve our comprehension and evaluation of disasters. In disaster areas, crowdsourcing can be employed to secure human resources and controlled-task distribution. In crowdsourcing, many workers with publicly defined tasks (also called microtasks) are used. A simplified and more efficient microtask framework is required for disaster areas due to the lack of human resources and facilities. In this paper, we describe a method that incorporates information collection from a disaster area into microtasks with machine processing support.
Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP

Metabolic Syndrome (MetS) constitutes of metabolic abnormalities that lead to non-communicable diseases, such as type II diabetes, cardiovascular diseases, and cancer. Early and accurate diagnosis of this abnormality is required to prevent its further progression to these diseases. This paper aims to diagnose the risk of MetS using a new non-clinical approach called “genetically optimized Bayesian adaptive resonance theory mapping” (GOBAM). We evolve the Bayesian adaptive resonance theory mapping (BAM) by using genetic algorithm to optimize the parameters of BAM and its training input sequence. We use the GOBAM algorithm to classify individuals as either being at risk of MetS or not at risk of MetS with a related posterior probability, which ranges between 0 and 1. A data set of 11 237 Malaysians from the CLUSTer study stratified by age and gender into four subcategories was used to evaluate the proposed GOBAM algorithm. The comparative evaluation of our results suggested that the GOBAM performs significantly better than other classical adaptive resonance theory mapping models on the area under the receiver operating characteristic curves (AUC) and others criteria. Our algorithm gives an AUC of 86.42 %, 87.04 %, 91.08 %, and 89.24 % for the young female, middle aged female, young male, and middle-aged male subcategories, respectively. The proposed model can be used to support medical practitioners in accurate and early diagnosis of MetS.
Improving Power, Performance and Area with Test: A Case Study

As more low power devices are needed for applications such as IOT, reducing power and area is becoming more critical. Reducing power consumption and area caused by using full scan should be considered as a method to help achieve these stricter requirements. This is especially important in designs using near-threshold technology. In this work, we use partial scan to attempt to improve power, performance and area on a CPU core and a GPU shader core. We present our non-scan DFF selection algorithm that maximizes non-scan DFF count while achieving ATPG results close to those of the full scan design on both a CPU and a GPU shader core. In addition, we present the PPA (power, performance and area) results of these designs for both the full scan and partial scan.
Anthropomorphic Soft Pneumatic Fingers Towards Full Dexterity of Human Hand

Human fingers are highly dexterous due to the combination of multiple joints and degrees of freedom (DoFs). With the rise of soft material robots, many gripper prototypes have utilized soft robot technology. Nonetheless, it is still challenging to build a soft finger with a similar dexterity to human hand. Owing to the soft pneumatic actuator (SPA) fabrication flexibility, we have proposed a multiple DoFs soft pneumatic fingers design scheme that can mimic the motion patterns of human hand. Unlike the most SPA fingers where the motion patterns are fixed and limited, here our soft fingers can function like the human finger movements to a greater extent. The design is composed of three parts: (i) a SPA with two independent chambers as the main part of finger; (ii) a constraint layer made of unstretchable fabric which simulates tendon; (iii) a fiber-reinforced three-channel fluidic elastomer actuator (FEA). The abundance in DoFs expands the range of motions of the fingers and enables them to reach where human fingers can go. By controlling the internal pressure of the actuator, a variety of human finger motion patterns are achieved. Since each chamber's pressure can be individually controlled, the dexterous position control of the finger has been made possible. Fabrication process of the soft pneumatic fingers is presented, followed by the characterizations to provide a better understanding of their behaviors. In the last part of the article, a robotic hand composed of five fingers is fabricated for demonstration.
A Hybrid Connectivity Oriented Telemedician System For Indian Landscape Using Raspberry Pi SBC & IOT

The proposed system is based on Raspberry Pi Single board computer system. This system is designed to connect to any type of network such as GPRS, LAN, Wi-Fi for telemedicine. This system will serve as communication backbone for telemedicine implementation in rural India. Recently, remote healthcare systems have received increasing attention in the last decade, explaining why intelligent systems with physiology signal monitoring for e-health care are an emerging area of development. Therefore, this work adopts a system which includes continuous collection and evaluation of multiple vital signs, long-term healthcare, and a cellular connection to a medical center in emergency case and it transfers all acquired raw data by the internet in normal case. The proposed system can continuously acquire four different physiological signs, for example, ECG, SpO2, temperature, and blood pressure and further relayed them to an intelligent data analysis. The proposed system also has a friendly web-based interface for medical staff to observe immediate pulse signals for remote treatment. Once abnormal event happened or the request to real-time display vital signs is confirmed, all physiological signs will be immediately transmitted to remote medical server through both cellular networks and internet. Also data can be transmitted to a family member's mobile phone or doctor's phone through GPRS. A prototype of such system has been successfully developed and implemented, which will offer high standard of healthcare with a major reduction in cost for our society. By using this system doctors are easily communicate with high experience and forensic people.
Control of a Two-Dimensional Magnetic Positioning System with Deep Reinforcement Learning and Feedback Linearization

This paper presents a neuro-controller based on deep reinforcement learning to control the nonlinear dynamics of a two-dimensional magnetic positioning system. The feedback-linearized model of the magnetic positioning system is used to generate training data for the neuro-controller. The neuro-controller is trained using the Deep Deterministic Policy Gradient (DDPG) algorithm. The effectiveness of the proposed control strategy is verified with different desired set-points and trajectories, and diverse working conditions.
Investigation of Effective Intensity of Static Stretching Exercise Based on Spring-Pot Viscoelastic Model: Intensity Based on Maximum Tolerance Joint Torque Affects Viscoelastic Ratio of Muscle During Static Stretching

Static stretching is typically performed as an easy method for reducing muscle tone in several fields of sports and rehabilitation. Stretching intensity is important for realizing effective static stretching. However, its instruction is ambiguous; therefore, people sometimes get injured owing to excessive intensity. To perform static stretching with appropriate intensity that results in positive effects, the intensity and effect of static stretching should be quantified. In this study, we investigated the relation between the stretching intensity based on the maximum tolerance joint torque and the change in the muscle viscoelasticity of forearm muscles based on the spring-pot viscoelastic model for evaluating the change in muscle condition during static stretching. We set the stretching intensity using a wrist joint torque adjusting device and measured the forearm muscle hardness using a palpation robot. The results showed that the muscle viscoelasticity decreased significantly during 1-min constant-angle stretching, and the amount of decrease was the largest for 60% of the maximum tolerance joint torque at the maximum joint angle. This suggests that the optimized stretching intensity can be derived from the joint torque measurement at the maximum joint angle.
1-bit Compressive Diffusion in Networks Based on Bayesian Inference

Compressive diffusion strategy, especially the 1-bit compressive diffusion, has the potential to greatly reduce the communication load between nodes in a network for distributed parameter estimation. However, how to deal with the hybrid data, i.e., the 1-bit quantized external information from other nodes and the high-precision local data, has heavy impact on the overall estimation performance. Most existing estimation algorithms based on Least Mean Square (LMS) cannot work very well in such a context, i.e., the combination of the above information is not guaranteed beneficial in general unless certain combination rule is properly chosen. In this paper, from a perspective of Bayesian inference framework instead, a parameter estimation algorithm is proposed for 1-bit compressive diffusion. Based on the vertex-message inference, the inherent combination policy assigns different weights to measurements depending on their data types and degrees of uncertainty. In this way, nodes in the network can always take advantage of the extra information in 1-bit exchanged data to enhance the accuracy of local estimation, and at the same time avoid any possible performance loss caused by the high uncertainty of compressive data.
A Review on RNA Secondary Structure Prediction Algorithms

Undoubtedly, RNA has vital functions on organisms. As a single stranded nucleic acid, it tends to bend and twirl and it forms a stable structure of itself. This is what comes to be known as the RNA secondary structure. RNA secondary structure is of use in determining the functionalities of RNA sequences as well as in pharmaceutical developments. Furthermore, predicting the secondary structure of an RNA is a crucial step in predicting its tertiary structure, which is its three dimensional form. For the problem of RNA secondary structure, researchers have developed many algorithms over the years in an attempt to make accurate predictions. In this paper, we review some of the recent works that made use of artificial intelligence in RNA secondary structure prediction. The reviewed articles show that by the power of novel artificial intelligence methods and ensembles of the old techniques, there are promising outcomes for future research.
Improving the Pressure Bearing Capability of Soft Pneumatic Actuators for Hand Rehabilitation Based on a Novel Sealing Method

Enough air pressure bearing capability is extremely important for realizing the functions of soft pneumatic actuators. However, there is few theoretical analysis or experimental analysis in literature, to the best knowledge of the authors about the structure design of the sealing junction of which the pressure bearing capability of actuator mainly depends on. In this paper, affecting factors of the pressure bearing capability of the soft pneumatic actuators for hand rehabilitation were explored through the design of the structures and the conduction of the contrast experiments. According to the test results, a novel integrated structure of the sealing junction was presented and applied to the soft pneumatic actuators. Finally, the effectiveness and reliability of the soft pneumatic actuators based on the proposed sealing method were verified in a prototyped hand rehabilitation system.
Correct Proof of Shafer's Belief Conditioning Formulas

In his Mathematical Theory of Evidence published in 1976, Shafer did propose belief and plausibility conditioning formulas based on Dempster's rule of combination. It turns out that the proof given by Shafer for belief conditioning is incorrect and in this paper we present the correct proof of Shafer's belief conditioning formula.
Detecting Edges with Bi-Directional Tracing Across Multiple Scales

For our human vision system, natural images are understood via a progressive and hierarchical process; that is, a multi-scale analysis. Motivated by this fact, a multi-scale algorithm for edge detection is proposed in this paper. The novelty of the proposed algorithm lies in that a bi-directional tracing strategy is used, which first conducts a forward tracing (FT) operation in coarse-to-fine direction and then a backward confirm (BC) operation in fine-to-coarse direction across multiple scales. With the FT operation, the location accuracy of detected edges is improved; with the BC operation, spurious edges are removed. The edge map generated with this bi-directional tracing strategy can therefore have a better quality than that generated by the existing multi-scale edge detectors. Extensive simulation results have shown that our new algorithm is superior to a number of state-of-the-art edge detection algorithms in both subjective visual assessment and objective measurement.
Additional Kernel Observer to Prevent Privilege Escalation Attacks by Focusing on System Call Privilege Changes

In recent years, there has been an increase in attacks that exploit operating system vulnerabilities. In particular, if an administrator's privilege is acquired by an attacker through a privilege escalation attack, the attacker can operate the entire system and the system can suffer serious damage. In this paper, an additional kernel observer (AKO) method is proposed. It prevents privilege escalation attacks that exploit operating system vulnerabilities. We focus on the fact that a process privilege can be changed only by specific system calls. AKO monitors privilege information changes during system call processing. If AKO detects a privilege change after system call processing, whereby the invoked system call does not originally change the process privilege, AKO regards the change as a privilege escalation attack and applies countermeasures against it. In this paper, we describe the design and implementation of AKO for Linux x86, 64 bit. Moreover, AKO can be expanded to prevent the falsification of various data in the kernel space. We present an expansion example that prevents the invalidation of Security-Enhanced Linux. Evaluation results show that AKO is effective against privilege escalation attacks, while maintaining low overhead.
Resource Scheduling for Energy-Efficient in Cloud-Computing Data Centers

As an effective and efficient way to consolidate computing resources and computing services, cloud computing has been more and more popular. However, radically increasing of requests exert tremendous pressure on the cloud computing center and generate adverse impact on quality of service. In this case, more servers are deployed to provide quality service. One challenge is how to minimize energy consumption as long-running bring enormous energy consumption to infrastructure service providers. From a different perspective, this paper transforms the conflict between quality of service and energy consumptions into one between profits and costs in this paper. Appropriate loss of QoS is allowed as long as the benefits of cloud service providers can be maximized. To this end, this paper proposes a novel scheduling scheme for data center, in which a contest model has been developed. The performance of the proposed scheme is evaluated in terms of scheduling strategies under different system configurations and user traffic. The results indicate the feasibility of the proposed scheme.
Application of Machine Learning Model on Streaming Health Data Event in Real-Time to Predict Health Status Using Spark

In healthcare field, a huge amount of data collected in real-time by IoT systems, remote sensing device and other data collection tools brings new challenges that focus primarily on data size and the fast growth rate of such large data. Applying machine learning model on this voluminous data having varying velocity becomes extremely complex for traditional methods of data mining. To deal with this challenge, Apache Spark, a powerful big data processing tool can be used successfully for streaming data event against machine learning through in-memory and distributed computations. This work aims at developing a real-time health status prediction system with breast cancer use case using spark streaming framework with machine learning especially Decision Tree. The system focus on applying machine learning model on streaming data coming with rapid rate to predict health status based on several input variables. Based on this, the system first preprocesses the dataset and analyzes it to create an offline model for learning system, the model then deployed on system and use it in real-time to predict health status.
Will Sentiments in Comments Influence Online Video Popularity?

Online videos become popular but most users ignored the functions such as comment and like or dislike and merely focused on number of views. This research deploys sentiment analysis to investigate the influence of comments and construct a video popularity model. We also use the concept of appraisal theory as the foundation in the proposed model that indicates the viewers may have different decisions of watching behavior when revealing the sentiment behind the existed comments. We selected 8 channels on YouTube for experiments. There were 306 videos and 59,103 comments and we collected 200 samples. The results showed high number of views may not result in high positive comments. The influence of own factors of videos such as title of videos, video thumbnail, and content was higher than objective factors such as number of views, like or dislike, and sentiment analysis. Finally, we discovered sentiment indirectly influences the decision of watching behavior.
Inter-kingdom Comparative Analysis of Translationally Controlled Tumor Protein (TCTP) Provides Clues for Their Lineage-specific Evolution

Translationally controlled tumor protein (TCTP) is a conserved, multifunctional protein that is involved in numerous cellular processes in eukaryotes. Although the functions of TCTP have been investigated in animals, invertebrates, and plants, few lineage-specific activities of this molecule, have been reported. Here, we report the interkingdom comparative analysis of 625 TCTP genes from 377 eukaryotic species, including protozoa, fungi, invertebrates, vertebrates, and plants. Phylogenetic and secondary structure analyses reveal lineage-specific evolution of TCTP, and interkingdom comparisons highlight the lineage-specific emergence of, or changes in, secondary structure elements in TCTP proteins from different kingdoms. Furthermore, secondary structure comparisons between TCTP proteins within each kingdom, combined with measurements of the degree of sequence conservation, suggest that TCTP genes have evolved to conserve protein secondary structures in a lineage-specific manner. Additional tertiary structure analysis of TCTPbinding proteins and their interacting partners and docking simulations between these proteins further imply that TCTP gene variation may influence the tertiary structures of TCTPbinding proteins in a lineage-specific manner. Overall, our analysis suggests that TCTP has undergone lineage-specific evolution and that structural changes in TCTP proteins may affect the tertiary structure of TCTP-binding proteins and their binding partners in a lineage-specific manner.
A Method of Calibration and Measuring Foeal Length for Pan-Tilt-Zoom Camera*

In view of the installed Pan-Tilt-Zoom (PTZ) camera, a calibration method is proposed in this paper. Unlike other calibration methods, we consider that simple distortion models cannot describe the distortion of non-axisymmetric lenses. To solve this problem, we use a distortion matrix to describe the distortions of different parts of the lens under non-axisymmetric conditions. The camera intrinsic parameters and distortion matrix are then determined at discrete steps in a monotonically increasing zoom sequence that spans the full zoom range of the camera. Finally, we briefly discuss how this can be used to estimate intrinsic parameters and distortion matrix of the image captured within the camera's full range of pan tilt and zoom configurations.