Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Media Use for Health Purposes by Chronic Disease Patients in the United States

Background: Social media can be a cost-effective instant tool for exchanging health information among those with chronic diseases. However, few studies have analyzed the nexus between chronic disease and patients' use of the internet for health-related purposes. Objective: The objective of this study is to determine if chronic disease patients in the United States use social media platforms to share health information and/or join groups of similar condition. Materials and Methods: This cross-sectional study conducted a secondary analysis of the Health Information Trends Survey dataset 5 (cycle 1 of 2017 and cycle 2 of 2018) (N = 6650), which is nationally representative of American adults. A series of chi-square tests was carried to examine the association between using social media by chronic disease patients and (a) sharing health information and (b) participating in relevant health groups. Logistic regression analysis was used to determine significant findings. Results: In terms of sharing health information on social media sites, those who were aged 18–49 years (P < 0.0001) and underweight (P = 0.04) were more likely to share health information on social media, while males were less likely to do so (P < 0.0001). In terms of joining relevant health groups on social media, predictors were being aged 35–49 years (P = 0.008), having a Bachelor's or postbaccalaureate degree (P < 0.02) and having depression or anxiety disorder (P = 0.004); males were less likely to join such groups (P = 0.0004). Conclusion: Individuals with chronic conditions, except depression or anxiety disorder, were not likely to participate in social media support groups. Future studies should explore how social media can be used to effectively engage those with chronic diseases, which may assist in disease management.
The Relationship between the Visual-Spatial Perceptions and Face Recognition Skills in Parents of the Children with Autism Spectrum Disorder

The aim of this study is to examine the correlation between the autism symptoms of children with autism spectrum disorder (ASD) and the visual-spatial perceptions and face recognition skills of the parents of these children. In the study, the parents of 30 children (30 mothers, 30 fathers) diagnosed with intellectual developmental disorder (IDD) and the parents of 30 children (30 mothers, 30 fathers) without any psychiatric disorder, totally 180 people, were assessed in order to compare the data of the parents of 30 children (30 mothers, 30 fathers) diagnosed with ASD. Autism behavior checklist was used to assess the symptoms of the children diagnosed with ASD. Benton's Judgment of Line Orientation Test (BJLO), Benton Facial Recognition Test (BFRT), and symptom checklist were used to assess the parents in three groups. It was found that there was no correlation between the autism symptoms of the children diagnosed with ASD and the visual-spatial perceptions and face recognition skills of their parents. The spatial perception skills of the fathers in the ASD group were higher compared to the mothers in this group (P < 0.001), it was found that the 14.4% (P = 0.022) of the BFRT scores of the fathers in the ASD and 11.8% (P = 0.035) of the BFRT scores of the fathers in the IDD group were explained by the BJLO scores. It may be useful to focus on the gender, face recognition skill and the visual-spatial perception performance when researching the genetical aspects of the face processing disorder seen as consistent information in the ASD phenotype.
A Comparative Study of Management of Femoral Shaft Fracture in Children: A Prospective Study

Context: Femoral shaft fracture is the most common major pediatric orthopedic fractures and treatment of them carries a lot of controversies. Aims: The aim of this study was to compare the functional outcome after internal fixation with titanium elastic nail and stainless steel Kirschner wire in unstable femoral shaft fracture in children. Settings and Design: This prospective study was conducted at Sharda University between December 2010 and December 2017 and were followed up for 12 months. Materials and Methods: A total of hundred children between the ages of 2–14 years having closed unstable fracture of the femoral shaft were included in the present study. Fifty children were managed with titanium nails (Group 1) and the same number of children was managed with stainless steel Kirschner wires (K-wires) (Group 2). Functional outcomes were evaluated using the criteria of Flynn et al. Statistical Analysis Used: Statically analysis was limited to calculation of percentage of patients who had unions, malunions, delayed unions, or nonunions and excellent, good, and poor outcomes. Results: Based on the Flynn criteria, 92% of patients had excellent results, 8% had satisfactory results, and no one showed poor results. There was no clinically significant difference between the two groups with respect to time to union, functional outcome, and complication. Conclusion: The clinic-radiological results were not significantly different between titanium nail and Kirschner wire at 1-year follow-up as observed by our study. The cost of Kirschner wire is one-third the cost of titanium nail.
Prevalence of diabetes mellitus and HIV/AIDS among tuberculosis patients in Kerala

Introduction: It is estimated that 10 million people fall ill with Tuberculosis (TB) every year worldwide. TB continues to be in the top 10 causes of death globally with India being the home to the world's largest number of TB patients. One of the major factors attributing to this is the presence of comorbidities such as Diabetes Mellitus and HIV/AIDS. Aim: The aim of this study was to determine the prevalence of comorbidities such as Diabetes mellitus & HIV/AIDS among the newly diagnosed TB patients in Kerala in 2019 and also to determine the factors associated with it. Materials and Methods: A cross-sectional study was carried out using the secondary data from NIKSHAY portal. There were a total of 16,527 cases of pulmonary and extrapulmonary TB cases reported from 14 districts of Kerala from January to September 2019. Using a checklist, data regarding the age, gender, type of case, Type of patient, site of disease, drug resistance were collected separately for TB patients suffering from Diabetes mellitus and HIV/AIDS. The data was then entered into Excel sheet and was analyzed using SPSS version 23. Results: Out of the total 16,527 study population, most of the patients were elderly above the age of 60 years (28.6%). The prevalence of Diabetes mellitus (22.6%) was higher among TB patients when compared to HIV/AIDS (1.2%). Males in the age group between 50-59 years were found to be significantly associated with TB- Diabetes Mellitus comorbidities. Diabetes was significantly associated with Pulmonary TB patients, while HIV/AIDS was significantly associated with extrapulmonary TB. Conclusion: Both Diabetes Mellitus and HIV/AIDS are comorbidities that have a strong impact on the diagnosis and management of Tuberculosis patients. Therefore, there is an urgent need to prevent these comorbidities from occurring along with the implementation of early diagnosis and appropriate management strategies. This study is of prime importance especially among Primary care Physicians who are treating TB patients on routine basis. They are particularly important in TB control since they are usually the first to meet a TB suspect, before diagnosis occurs. Both HIV/AIDS and Diabetes mellitus are immunocompromised conditions and these comorbidities can affect the treatment outcomes of TB. Primary care physicians are essential in detecting TB suspects and treating them, thus contribute significantly to reducing the burden of TB.
SDN Enhanced Resource Orchestration of Containerized Edge Applications for Industrial IoT

With the rise of the Industrial Internet of Things (IIoT), there is an intense pressure on resource and performance optimization leveraging on existing technologies, such as Software Defined Networking (SDN), edge computing, and container orchestration. Industry 4.0 emphasizes the importance of lean and efficient operations for sustainable manufacturing. Achieving this goal would require engineers to consider all layers of the system, from hardware to software, and optimizing for resource efficiency at all levels. This emphasizes the need for container-based virtualization tools such as Docker and Kubernetes, offering Platform as a Service (PaaS), while simultaneously leveraging on edge technologies to reduce related latencies. For network management, SDN is poised to offer a cost-effective and dynamic scalability solution by customizing packet handling for various edge applications and services. In this paper, we investigate the energy and latency trade-offs involved in combining these technologies for industrial applications. As a use case, we emulate a 3D-drone-based monitoring system aimed at providing real-time visual monitoring of industrial automation. We compare a native implementation to a containerized implementation where video processing is orchestrated while streaming is handled by an external UE representing the IIoT device. We compare these two scenarios for energy utilization, latency, and responsiveness. Our test results show that only roughly 16 percent of the total power consumption happens on the mobile node when orchestrated. Virtualization adds up about 4.5 percent of the total power consumption while the latency difference between the two approaches becomes negligible after the streaming session is initialized.
Decentralized Control on Coolant Application Rate to Mitigate Fire Propagation in Hydrocarbon Storage Tank Farms

Fire propagation in hydrocarbon storage tank farms is a critical safety issue in oil industries. This is an intriguing topic due to its severe impact on environment, economy, and more importantly on human lives. Contradictions in standards and codes regarding optimum cooling water application rate and spacing between tanks represent an industrial problem, which trigger researchers’ interest. In this paper, thermal radiation from pool fires on adjacent storage tanks surfaces is modelled and calculated. The rate of heat absorbed by the cooling water on these adjacent tanks is discussed, from which the suitable amount of cooling water application rate on each tank segments could be determined. New distribution technique of cooling water application rate according to amount of heat removal required from each tank segment is proposed and applied on a real tanks farm case study. Decentralized application rate control scheme provides promising results in comparison to traditional uniform centralized technique. For controlling application rates on different tank segments, PIC control sliding valve is suggested to be used instead of traditional Deluge ON/OFF valve used for uniform cooling water distribution control.
Aerodynamic Statistics-Based Trajectory Estimation of Hypersonic Maneuvering Target

Trajectory tracking and estimation of hypersonic glide vehicles (HGVs) is a very challenging issue in the defense systems. Insufficient knowledge about the HGV and inaccurate dynamic models for the accelerating HGV are the main challenges in this regard. In the present study, an integrated nonlinear Markov acceleration model is established to formulate the nonlinear dynamic characteristics of HGVs. Since the aerodynamic accelerations of the HGV are dominant and the corresponding aerodynamic coefficients are unknown, a statistics-based aerodynamic model is proposed. The proposed aerodynamic model is capable of providing primary information of the aerodynamic characteristics even without knowing the configuration of the HGV. Then, considering the maneuver mode of the vehicle, the iterative extended Kalman filter (IEKF) is applied to track the trajectory of the HGV by using the proposed model. Obtained results from the numerical simulation for the equilibrium glide mode and skip maneuver mode indicate that the proposed model can effectively improve the velocity estimation accuracy by about 40%-50% and acceleration estimation accuracy by about 20%-50% in the given examples.
Geometric-visual descriptor for improved image based localization

This paper addresses the problem of image based localization. The goal is to find quickly and accurately the relative pose from a query taken from a stereo camera and a map obtained using visual SLAM which contains poses and 3D points associated to descriptors. In this paper we introduce a new method that leverages the stereo vision by adding geometric information to visual descriptors. This method can be used when the vertical direction of the camera is known (for example on a wheeled robot). This new geometric visual descriptor can be used with several image based localization algorithms based on visual words. We test the approach with different datasets (indoor, outdoor) and we show experimentally that the new geometric-visual descriptor improves standard image based localization approaches.
Standard Cell Optimization Techniques for Near Threshold Computing – A Review

With advances in technology, the number of transistors in the chip increases resulting in large increase in the power budget. The increasing power budgets prohibit these devices being used in the design. Voltage scaling has been proven to be very effective to decrease power consumption. Near threshold design is a powerful knob for improving energy efficiency and to reduce the power density in the integrated chips. In near threshold region, the supply voltage is approximately equal to the threshold voltage of the transistors. Near threshold computing promises improved energy efficiency, but it is more sensitive to parametric variations compared to conventional super threshold operations. Therefore, it is very critical to develop optimized standard cells for near threshold operation to improve the energy efficiency and variability characteristics. This paper explores the importance of optimized standard cell design to reduce the delay, power consumption, and variability issues of digital circuits and compares various standard cell optimization techniques for ultra-low power applications from recent literatures. Near threshold design is best suited to energy and performance constrained intelligent edge devices for Internet of things applications.
An Adaptive Signal Conditioning System for Launch Vehicle Telemetry Applications

This paper deals with the design and development of an embedded microcontroller based Adaptive Signal Conditioning System (ASCS). The system is especially relevant to applications such as launch vehicle telemetry where the number of parameters to be acquired is large as well as their types and dynamic ranges vary over a wide band. We bring out the salient aspects of the system design by way of both the hardware and software to show how it supports adaptability of all the major signal conditioning parameters such as gain, offset and bandwidth. The test results are also brought out to establish the system functionality. Its application potential is then discussed to show the advancement that it offers for telemetry in the context of advanced Space Transportation Systems and next generation missions such as Reusable Launch Vehicles and Human Space Flight.
Deep Learning-Based Belief Propagation Algorithm over Non-Binary Finite Fields

In this paper, we present a deep learning method for improving the belief propagation algorithm over non-binary Galois fields. The method is based on the deep unfolding structure of the factor graph representing LDPC matrix where the inference iterations according to Hadamard transform belief propagation algorithm are translated into layers of the neural networks. Afterwards, we introduce genetic algorithm to optimize the weights of the neural networks. The trained neural networks render much better performances than conventional belief propagation algorithms for both decoding of linear block codes and the compressed sensing reconstructions. The loss in terms of training process are also depicted, which show that the algorithm can converge after acceptable times of training.
A Broadband Flexible Strip-Slot Coupled Radiating Structure for Millimeter Applications

A group of broadband strip-slot radiating structures based on flexible liquid crystal polymer (LCP) are presented and analyzed. One of the designs realized a passband from 26.6GHz to over 40GHz. The proposed structures also have an omnidirectional radiation over +z hemisphere, and a complete ground plane to isolate from components and structures on the other side of the ground. The performance of the antenna when curved is also verified.
Joint Optimization of Target Assignment and Resource Allocation for Multi-Target Tracking in Phased Array Radar Network

This study proposes a joint optimization scheme of target assignment and resource allocation for multi-target tracking in phased array radar network. The key idea of the proposed scheme is to simultaneously improve the multi-target tracking accuracy and minimize the total dwell time of the phased array radar network by optimizing the target-to-radar assignment, revisit time control, bandwidth and dwell time allocation, such that both the target tracking performance and the low probability of intercept (LPI) performance of the network system can be enhanced. The closed-form expression for the predicted Bayesian Cramér-Rao lower bound (BCRLB) with target-to-radar assignment, revisit time, bandwidth and dwell time is derived, which is used to represent the tracking accuracies of multiple targets. The resulting mixed-integer, non-linear and non-convex optimization problem is subsequently solved by a developed efficient three-stage solution technique. Finally, simulation results demonstrate the advantages of the proposed scheme, in terms of the multi-target tracking accuracy and the achieved LPI performance of the underlying system.
Orthogonal Features Fusion Network for Anomaly Detection

Generative models have been successfully used for anomaly detection, which however need a large number of parameters and computation overheads, especially when training spatial and temporal networks in the same framework. In this paper, we introduce a novel network architecture, Orthogonal Features Fusion Network (OFF-Net), to solve the anomaly detection problem. We show that the convolutional feature maps used for generating future frames are orthogonal with each other, which can improve representation capacity of generative models and strengthen temporal connections between adjacent images. We lead a simple but effective module easily mounted on convolutional neural networks (CNNs) with negligible additional parameters added, which can replace the widely-used optical flow n etwork a nd s ignificantly im prove th e pe rformance for anomaly detection. Extensive experiment results demonstrate the effectiveness of OFF-Net that we outperform the state-of-the-art model 1.7% in terms of AUC. We save around 85M-space parameters compared with the prevailing prior arts using optical flow n etwork w ithout c omprising t he performance.
RD-CNN: A Compact and Efficient Convolutional Neural Net for Sound Classification

Classification of signals (sounds, biomedical, etc.) occurs in various circumstances, often requiring low-complexity implementations for various resources-constrained platforms such as mobile or embedded systems within the IoT framework. Herein we describe preliminary results for a novel recognition system where a compact and fast transform, the reaction-diffusion transform (RDT), is used to generate spectral images. Such images are then processed into a novel type of compact convolution neural network (called NL-CNN) where nonlinear convolution is emulated. The ESC-50 environmental sound database with 50 sound categories was considered for parameter tuning and performance evaluation. Despite its low complexity, a reasonable well accuracy is demonstrated (up to 76.5%) close to the human accuracy reported on the same dataset and better in both accuracy and complexity when compared to similar approaches reported in the literature and based on mel-cepstrum. These results provide a basis for a novel and effective method for time-series recognition in general.
Capturing Public Concerns About Coronavirus Using Arabic Tweets: An NLP-Driven Approach

This In order to analyze the people reactions and opinions about Coronavirus (COVID-19), there is a need for computational framework, which leverages machine learning (ML) and natural language processing (NLP) techniques to identify COVID tweets and further categorize these in to disease specific feelings to address societal concerns related to Safety, Worriedness, and Irony of COVID. This is an ongoing study, and the purpose of this paper is to demonstrate the initial results of determining the relevancy of the tweets and what Arabic speaking people were tweeting about the three disease related feelings/emotions about COVID: Safety, Worry, and Irony. A combination of ML and NLP techniques are used for determining what Arabic speaking people are tweeting about COVID. A two-stage classifier system was built to find relevant tweets about COVID, and then the tweets were categorized into three categories. Results indicated that the number of tweets by males and females were similar. The classification performance was high for relevancy (F=0.85), categorization (F=0.79). Our study has demonstrated how categories of discussion on Twitter about an epidemic can be discovered so that officials can understand specific societal concerns related to the emotions and feelings related to the epidemic.
A Novel Continuous Finite-Time Extended State Observer Design for a Class of Uncertain Nonlinear Systems

In this paper, an innovative technique to design a finite-time extended state observer (FT-ESO) for a class of nonlinear systems is proposed. This FT-ESO aims to estimate the full state of a nonlinear system as well as the uncertainties and/or disturbances in a finite time. First, the nonlinear model of the system is transformed into the normal form using a diffeomorphism map. Then, as the main innovation of this paper, the nonlinear system is transformed into a new time-varying form to achieve the finite-time boundedness criteria of the estimation error variables using the asymptotic stability methods. Next, without any prior knowledge about the upper bounds of the uncertainties and/or disturbances, and only based on the output measurements, the FT-ESO is designed. In this approach, the time-varying gains of the extended state observer are designed to converge the observation error to a neighborhood of zero while remaining uniformly bounded in finite-time. Finally, the efficiency of the proposed FT-ESO for classes of uncertain nonlinear systems with unknown measurement noise is illustrated by numerical simulations.