Miracle-dz/UniFAR-FTUs huggingface dataset:
Intramural Sport Participation: An Examination of Participant Benefits, Service Quality, Program Satisfaction, and Student Retention

This study investigates whether the benefits of participation in, and perceived service quality of, an intramural sport program contribute to student retention and overall program satisfaction. Health and wellness outcomes, student learning outcomes, service quality, program satisfaction, and student retention in intramural programming were assessed at one Canadian university. The results reveal that participants experience physical, emotional, social and academic outcomes as a result of participation in intramural programming. The results also reveal that benefits of participation (physical health and wellness, emotional wellness, academic learning outcomes, social learning outcomes) and service quality are associated with greater student retention and program satisfaction. The results highlight the unique association between physical health and wellness benefits and student retention. The results also highlight the association between service quality, social and emotional wellness and program satisfaction among participants. Implications for practice and future research are presented.
Digital Transformation: An Overview of the Current State of the Art of Research

The increasing digitalization of economies has highlighted the importance of digital transformation and how it can help businesses stay competitive in the market. However, disruptive changes not only occur at the company level; they also have environmental, societal, and institutional implications. This is the reason why during the past two decades the research on digital transformation has received growing attention, with a wide range of topics investigated in the literature. The following aims to provide insight regarding the current state of the literature on digital transformation (DT) by conducting a systematic literature review. An analysis of co-occurrence using the software VOSviewer was conducted to graphically visualize the literature’s node network. Approached this way, the systematic literature review displays major research avenues of digital transformation that consider technology as the main driver of these changes. This paper qualitatively classifies the literature on digital business transformation into three different clusters based on technological, business, and societal impacts. Several research gaps identified in the literature on DT are proposed as futures lines of research which could provide useful insights to the government and private sectors in order to adapt to the disruptive changes found in business as a result of this phenomenon, as well as to reduce its negative impacts on society and the environment.
A 360-Degree View of a Hospital by Analysing Patient’s Online Reviews Using Fuzzy Sentiment Analysis

Millions of people use Internet for developing new skills, booking online tickets, socialising, etc. Out of the sundry activities, giving online reviews by customers has become very customary these days and the fastest medium to make one’s voice heard. With the advent of analytics, more specifically, text mining, the online reviews of the customers have made a huge difference in shaping the future strategies of the companies and have also helped them to study the customer responses of their rivals. In an effort to help hospitals analyse the patient’s reviews present online on various social media platforms, this paper analyses the 659 reviews of people across the nation, on one of the best medical college and hospital of India, All India Institute of Medical Sciences, New Delhi. An attempt is made in this article to develop fuzzy sentiment analysis model with integration of naïve base classifier, which helps to analyse reviews of different hospitals and can come up with their own social media competitive analysis strategy. The results reveal the value text mining can bring to the table for any hospital and the immense business value that it holds.
A Dual PI3K/HDAC Inhibitor Downregulates Oncogenic Pathways in Hematologic Tumors In Vitro and In Vivo

Purpose: To investigate the efficacy and mechanism of compound 23, a PI3K/HDAC dual-target inhibitor, on hematologic tumor cells in vitro and in vivo. Methods: The MTS Kit was used to study the antiproliferative effects in vitro. Western blot was used to analyze the involved signaling pathways. Flow cytometry was used to analyze apoptosis and the cell cycle. The antiproliferative effects were evaluated in vivo using EL4 and A20 xenograft models. The CCLE database was used to analyze gene expression. Results: Compound 23 significantly inhibited the proliferation of hematologic tumors; it simultaneously regulated PI3K/HDAC pathways and induced apoptosis and G1-phase arrest in EL4, NB4, and A20 cells in vitro. When tested in vivo, compound 23 significantly inhibited the proliferation of EL4 and A20. The expression levels of ErbB2 and ErbB3 decreased in hematologic tumors compared with it in solid tumors. Conclusion: Compound 23 modulates the PI3K/HDAC pathway, which results in significant inhibition of hematologic tumor proliferation in vivo and in vitro. The differential levels of ERBB2 and ERBB3 might be related to the difference in the effect of compound 23 on hematologic tumors and solid tumors.
Safety and Efficacy of the FRED Jr Flow Re-Direction Endoluminal Device for Intracranial Aneurysms: Retrospective Multicenter Experience With Emphasis on Midterm Results

Background and Purpose: Flow diversion is increasingly used as an endovascular treatment for intracranial aneurysms. In this retrospective multicenter study, we analyzed the safety and efficacy of the treatment of intracranial, unruptured, or previously treated but recanalized aneurysms using Flow Re-Direction Endoluminal Device (FRED) Jr with emphasis on midterm results. Materials and Methods: Clinical and radiological records of 150 patients harboring 159 aneurysms treated with FRED Jr at six centers between October 2014 and February 2020 were reviewed and consecutively included. Clinical outcome was measured by using the modified Rankin Scale (mRS). Anatomical results were assessed according to the O'Kelly-Marotta (OKM) scale and the Cekirge-Saatci Classification (CSC) scale. Results: The overall complication rate was 24/159 (16%). Thrombotic-ischemic events occurred in 18/159 treatments (11%). These resulted in long-term neurological sequelae in two patients (1%) with worsening from pre-treatment mRS 0–2 and mRS 4 after treatment. Complete or near-complete occlusion of the treated aneurysm according to the OKM scale was reached in 54% (85/158) at 6-month, in 68% (90/133) at 1-year, and in 83% (77/93) at 2-year follow-up, respectively. The rates of narrowing or occlusion of a vessel branch originating from the treated aneurysm according to the CSC scale were 11% (12/108) at 6-month, 20% (17/87) at 1-year, and 23% (13/57) at 2-year follow-up, respectively, with all cases being asymptomatic. Conclusions: In this retrospective multicenter study, FRED Jr was safe and effective in the midterm occlusion of cerebral aneurysms. Most importantly, it was associated with a high rate of good clinical outcome.
Review on Emotion Recognition Based on Electroencephalography

Emotions are closely related to human behavior, family, and society. Changes in emotions can cause differences in electroencephalography (EEG) signals, which show different emotional states and are not easy to disguise. EEG-based emotion recognition has been widely used in human-computer interaction, medical diagnosis, military, and other fields. In this paper, we describe the common steps of an emotion recognition algorithm based on EEG from data acquisition, preprocessing, feature extraction, feature selection to classifier. Then, we review the existing EEG-based emotional recognition methods, as well as assess their classification effect. This paper will help researchers quickly understand the basic theory of emotion recognition and provide references for the future development of EEG. Moreover, emotion is an important representation of safety psychology.
Saikosaponin D Inhibits Peritoneal Fibrosis in Rats With Renal Failure by Regulation of TGFβ1/ BMP7 / Gremlin1/ Smad Pathway

Peritoneal dialysis (PD) can improve the quality of life of patients with kidney disease and prolong survival. However, peritoneal fibrosis can often occur and lead to PD withdrawal. Therefore, it is imperative to better understand how to inhibit and slow down progression of peritoneal fibrosis. This study aimed to investigate the regulatory effect of Saikosaponin d (SSD), a monomer extracted from the plant Bupleurum, on peritoneal fibrosis and the contribution of TGFβ1/BMP7/Gremlin1 pathway cross-talk in this process. To this aim, we used a model 5/6 nephrectomy and peritoneal fibrosis in rats. Rats were divided into four groups, namely a control group (saline administration); a model group (dialysate administration; group M); a SSD group (dialysate and SSD administration); and a positive drug group (dialysate and Benazepril Hydrochloride administration; group M + A). Histological analysis indicated that peritoneal fibrosis occurred in all groups. WB, ELISA, and PCR essays suggested that TGFβ1 and Gremlin1 levels in group M were significantly higher than those in group C, whereas BMP7 expression was significantly lower. TGFβ1, Gremlin1 and BMP7 levels were significantly lower in the group where SSD was administered than in the other groups. The expression of BMP7 in SSD group was significantly increased. In addition, levels of Smad1/5/8 as assessed by PCR, and levels of p-Smad1/5/8 expression as assessed by WB were also significantly higher in the SSD group than in the M group. Expression of vimentin and α-SMA, two important markers of fibrosis, was also significantly decreased. Our study suggests a role for the TGFβ1/BMP7/Gremlin1/Smad pathway in peritoneal fibrosis with potential therapeutic implications. Finally, our results also suggest that the monomer SSD may be able to reverse peritoneal fibrosis via regulation of the TGFβ1/BMP7/Gremlin1/Smad pathway.
Research on Workers Integration in Smart Factories With Multi-Agent Control System

In today’s modern manufacturing environments, Multi-Agent Manufacturing System (MAMS) has been a fundamental approach for developing industrial applications that can cope with complexity, uncertainty, and dynamicity. It has become a significant resource for improving smart factories. However, little attention has been paid to integrate human workers into these overall control systems efficiently. This article is intended to take a step forward and propose a human worker integration scheme under MAMS. The multi-skilled feature of workers and the collaboration between workers are considered in this research. First, a Worker Agent (WoA) model with wearable devices is proposed as a critical design building block. After that, based on the conventional hybrid manufacturing control architecture (HyMCA), a WoA integration interface is investigated to implement the MAMS model. Then, a working mechanism of WoA in the task allocation process is studied. After that, a matching algorithm that considers different scenarios between workers and tasks is proposed based on the Hungarian Algorithm and Genetic Algorithm. Finally, program simulations and actual experiments are carried out to verify the effectiveness of the worker integration mechanism proposed in this paper.
Automatic Speech Recognition: Systematic Literature Review

A huge amount of research has been done in the field of speech signal processing in recent years. In particular, there has been increasing interest in the automatic speech recognition (ASR) technology field. ASR began with simple systems that responded to a limited number of sounds and has evolved into sophisticated systems that respond fluently to natural language. This systematic review of automatic speech recognition is provided to help other researchers with the most significant topics published in the last six years. This research will also help in identifying recent major ASR challenges in real-world environments. In addition, it discusses current research gaps in ASR. This review covers articles available in five research databases that were completed according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. The search strategy yielded 82 conferences and articles related to the study’s scope for the period 2015–2020. The results presented in this review shed light on research trends in the area of ASR and also suggest new research directions.
Water Vapor Treatment of Low-Temperature Deposited SnO2 Electron Selective Layers for Efficient Flexible Perovskite Solar Cells

Tin oxide (SnO2) electron selective layers (ESLs) processed by low-temperature plasma-enhanced atomic layer deposition (PEALD) hold promise for fabricating lightweight and efficient flexible lead halide perovskite solar cells (PVSCs). However, the as-synthesized SnO2 ESLs typically lead to flexible PVSCs with lower open-circuit voltage (VOC) and fill factor (FF) as well as a higher degree of current density–voltage (J–V) hysteresis, compared to PVSCs fabricated on rigid substrates. Here, we report that facile water vapor treatment of PEALD-synthesized SnO2 ESLs can effectively improve the VOC and FF while reducing the degree of J–V hysteresis. The improvement in device performance is mainly attributed to the improved conductivity and electrical mobility of SnO2 ESLs enabled by water vapor treatment. With such treatment, our best flexible PVSC fabricated on a commercial substrate shows a power conversion efficiency of 18.36 (17.12)% when measured under a reverse (forward) voltage scan and a stabilized effi.
Breaking the Limits in Ternary Logic: An Ultra-Efficient Auto-Backup/Restore Nonvolatile Ternary Flip-Flop Using Negative Capacitance CNTFET Technology

Despite the advantages of ternary logic, it has suffered from excessive transistor count and limited noise margin. This work proposes an ultra-efficient nonvolatile ternary flip-flop (FF) based on negative capacitance carbon nanotube field-effect transistors (NC-CNTFETs). By harnessing the negative differential resistance effect in NC-CNTFETs, the proposed design is similar to a conventional volatile binary FF regarding the number of transistors and control signals. During a scheduled power gating or a sudden power outage, the proposed ternary FF benefits from an auto-backup/auto-restore capability without employing any additional transistors, nonvolatile devices, or control signals. This leads to zero device overhead, which is a breakthrough in designing nonvolatile memory circuits. On the other hand, the back-to-back slave latch’s hysteretic behavior provides an extraordinary static noise margin that transcends the noise margin of both conventional ternary and binary latches. The simulation results indicate that eliminating additional backup and restore circuitries provides 43% improvements in transistor count, 59% improvements in power saving and 98% improvements in energy-saving than state-of-the-art binary and ternary FFs. Moreover, the proposed design presents a 1.5 times higher static noise margin than the conventional binary and ternary FFs. Our proposed approach opens new doors in realizing ultra-efficient nonvolatile ternary circuits and systems in neuromorphic applications using ferroelectric-based transistors.
Impact of Power Smoothing Techniques on the Long-Term Performance of Battery Energy Storage Systems

Power smoothing (PS) techniques are employed to mitigate the network problems caused by the variable output power of non-dispatchable renewable energy sources (RESs). Keystone of these techniques is the use of a battery energy storage system (BESS) acting as an intermediate energy buffer to smooth the RES output power. Scope of this paper is to investigate the impact of the most well-established PS techniques on the long-term performance of BESSs focusing on capacity degradation. To this end, an accurate BESS aging model based on the rainflow cycle-counting algorithm is integrated to the examined PS techniques to estimate the cycle aging of the BESS. Furthermore, a parametric analysis with high-resolution RES measurements is performed to identify the most critical parameters that affect the PS capability and the BESS capacity degradation.
Analysis and Detection of Android Ransomware for Custom Encryption

At present, the detection of encrypted ransomware under the Android platform mainly relies on analyzing the API call of the encryption function. But for ransomware that uses a custom encryption algorithm, the method will be invalid. This article analyzed the files before and after encryption by the ransomware, and found that there were obvious changes in the information entropy and file name of the files. Based on this, this article proposed a detection method for encrypted ransomware under the Android platform. Through pre-setting decoy files and the characteristic judgment before and after the execution of the sample to be tested, completed the detection and judgment of the ransomware. Having tested 214 samples, this method can be porved to detect encrypted ransomware accurately under the Android platform, with an accuracy rate of 98.24%.
Force from Shape—Estimating the Location and Magnitude of the External Force on Flexible Instruments

Force sensing is highly desirable in minimally invasive medical applications, since this feature shows great potential for reducing tissue damage and enhancing manipulation safety. However, embedding force sensors in medical devices is challenging and costly. This article explores the possibility to use shape sensing as a measure to extract force information. In this work, a model-based approach that allows simultaneous shape and force sensing is proposed. Shape information is reconstructed employing a multicore fiber with fiber Bragg grating sensors spaced over the fiber length. This fiber is capable of distributed 3D shape sensing. It is shown how by making use of extended Kalman filter and a mechanics model of the flexible instrument, it becomes possible to estimate both the magnitudes and locations of externally applied forces. Experiments were carried out to validate the proposed method for both one and two external forces applied at arbitrary locations in different directions on a flexible instrument. Results show that one-directional force magnitude and location can be estimated with an average error of 23.08 mN (15.39%) and 11.06 mm (6.51%), respectively. For two-directional forces, results of the load near the base show an average error of 52.01 mN (30.59%) for the magnitude and 29.24 mm (17.20%) for the location. For the load applied simultaneously near the tip, the mean magnitude error is 16.79 mN (11.19%) and the average location error is 10.18 mm (5.99%). The force sensing algorithm can run in real time with an approximate frequency of 59 Hz. In these experiments, it can be observed that the force-sensing accuracy, which depends on the sensitivity of the shape of flexible instruments with respect to the external force, can vary drastically in function of the force application point and force direction.
Inferring Loop Invariants for Multi-Path Loops

Loop invariant plays an important role in program analysis and verification. Equipping each loop with a sound and useful invariant is a crucial step for full program verification and program understanding. However, inferring sound and useful loop invariants remains a challenge due to the complex control structure of loops, especially for loops that contain multiple paths. In this paper, we first analyze the main challenges in loop invariant inference, then introduce a new approach to generate sound and useful loop invariants using a divide-and-conquer strategy. Specifically, we use Path Dependency Automaton (PDA) to model loops by which we boil down the problem of loop invariant inference to state invariant inference of the PDA. We propose an algorithm to infer state invariants of the PDA and construct loop invariants from state invariants. We implement our approach in a tool named InvInfer. We evaluate InvInfer on various benchmarks. The results show that our approach is remarkably more effective and efficient than several state-of-the-art approaches, especially on loops with multiple paths.
Research on D2D Communication Technology in the 5G Smart Grid Scenario

As a new era of communication support technology, 5G has been widely used in smart grids. With the continuous development, maturity and improvement of D2D communication technology, it has been incorporated into the framework of the fifth-generation mobile communication system and has become one of the key technologies of 5G. This article starts with the power consumption problem of D2D relay cooperative communication, so as to select the best relay. Using the Lagrangian multiplier method as a theoretical application, the node power distribution optimization in the D2D relay cooperative communication system is performed, and the power expression under the optimal power distribution scheme is obtained, and finally select the relay node that can minimize the power consumption of the communication system. Through simulation, the performance of the relay selection algorithm proposed in this article is verified, and the conclusion is drawn.
Techniques for Suicidal Ideation Prediction: a Qualitative Systematic Review

Suicide is an increasingly present issue in our society whose eradication could be greatly aided by decision support technologies that can objectively identify early markers of suicidal ideation. We present our paper which reviews various existing techniques for suicidal ideation prediction. These techniques are broadly divided into two major categories – 1) Text-based indicators (which employ NLP) and 2) Behavioural indicators (like eye gaze and smile which are classified using ML algorithms). The existing techniques aim at classifying individuals as suicidal or non-suicidal using classifiers such as Random Forest, Logistic Regression, Naïve Bayes, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron and XGBoost. Each technique has its advantages and disadvantages. We aim to bring all these diverse techniques to one place and try to show how each one of them is individually contributing to prediction. We also shed light on how combining these techniques and eliminating the disadvantages could lead to better suicide prediction.
Experimental Testing of High-Capacity Bandwidth Efficient Visible Light Communication with Silicon-based RGBY-LED

The shortage of spectrum resource is the most urgent challenge in future wireless communication networks. Moreover, the high-speed transmission and massive connection raises stringent requirements for the communication technology. While visible light communication (VLC) can effectively deal with the above problems. In this paper, a high-speed VLC system with low-complexity pre-equalization is proposed. We use the silicon-based RGBY-LED which is high-efficient and invented by Nanchang university as the light source to provide the communication and illumination, simultaneously. A novelly low-complexity pre-equalization is proposed to compensate the frequency response of the LED. The experiment demonstrate a 2.76 Gb/s data rate by multiplexing wavelengths of four colors beyond the distance of 4 meters.