Miracle-dz/UniFAR-FTUs huggingface dataset:
Pilot mental health during the COVID-19 pandemic: prevalence rates from semi-structured interviews, and associated vulnerability and protective factors

Introduction Pilots are a unique occupational group who perform a specialised job and face significant stressors. Pilot mental health has received increased attention since Germanwings Flight 9525; however, this research has largely focused on general anxiety, depression, and suicide and relied on a questionnaire-based methodology. This approach is likely to miss various mental health issues that may affect pilot wellbeing, leaving the prevalence of mental health issues in aviation unclear. In addition, the COVID-19 pandemic is likely to have a particular impact on the mental health and wellbeing of pilots, who experienced the devastating effect of COVID-19 on the industry. Method In the present study, we conducted a comprehensive assessment of 73 commercial pilots during the COVID-19 pandemic, using the DIAMOND semi-structured diagnostic interview and explored possible associated vulnerability and protective factors, including life event stressors, personality, passion, lifestyle factors, and coping strategies. Results The COVID-19 pandemic had a significant impact on aviation during the time of this study, affecting 95% of participants. The diagnostic results revealed over one third of pilots had symptoms of a diagnoseable mental health disorder. Anxiety disorders were the most commonly found disorders, followed by Attention Deficit Hyperactivity Disorder (ADHD), Adjustment Disorder, and Depressive Disorders. Pilots’ high life event scores placed them at an increased risk for the development of stress-related illness, though did not explain which pilots had mental health difficulties in this study. Regression analysis supported a diathesis-stress model for pilot mental health, with disagreeableness and obsessive passion contributing to pilots’ development of mental health issues, and nutrition as the most important protective factor. Discussion This study, though limited to the COVID-19 pandemic, provides a valuable precedent for a more thorough assessment of pilot mental health, and contributes to the broader understanding of pilot mental health, including suggestions to target factors associated with the development of mental health issues.
Will nanomedicine become a good solution for the cardiotoxicity of chemotherapy drugs?

Cancer is one of the leading causes of death worldwide, and with the continuous development of life sciences and pharmaceutical technology, more and more antitumor drugs are being used in clinics to benefit cancer patients. However, the incidence of chemotherapy-induced cardiotoxicity has been continuously increasing, threatening patients’ long-term survival. Cardio-oncology has become a research hot spot, and the combination of nanotechnology and biomedicine has brought about an unprecedented technological revolution. Nanomaterials have the potential to maximize the efficacy and reduce the side effects of chemotherapeutic drugs when used as their carriers, and several nano-formulations of frequently used chemotherapeutic drugs have already been approved for marketing. In this review, we summarize chemotherapeutic drugs that are highly associated with cardiotoxicity and evaluate the role of nano-delivery systems in reducing cardiotoxicity based on studies of their marketed or R&D nano-formulations. Some of the marketed chemotherapy drugs are combined with nano-delivery systems that can effectively deliver chemotherapy drugs to tumors and cannot easily penetrate the endothelial barrier of the heart, thus decreasing their distribution in the heart and reducing the cardiotoxicity to some extent. However, many chemotherapy nanomedicines that are marketed or in R&D have not received enough attention in determining their cardiotoxicity. In general, nanomedicine is an effective method to reduce the cardiotoxicity of traditional chemotherapy drugs. However, cardiovascular complications in cancer treatment are very complex diseases, requiring the application of multiple measures to achieve effective management and prevention.
Long-term locked knee ankle foot orthosis use: A perspective overview of iatrogenic biomechanical and physiological perils

A knee ankle foot orthosis (KAFO) may be prescribed to the person with severe neuromusculoskeletal impairment of the lower limb to promote walking stability. The locked knee ankle foot orthosis (L-KAFO) is among the KAFO's routinely prescribed; however, long-term use of the L-KAFO is associated with musculoskeletal (arthrogenic and myogenic) and integumentary changes, and gait asymmetry with increased energy expenditure. Consequently, the risk of developing low back pain, osteoarthritis of the lower limbs and spinal joints, skin dermatitis, and ulceration increases, all of which impact quality of life. This article synthesizes the iatrogenic biomechanical and physiological perils of long-term L-KAFO use. It promotes using recent advances in rehabilitation engineering to improve daily activities and independence for proper patient groups.
Associations between long-term exercise participation and lower limb joint and whole-bone geometry in young and older adults

Introduction: Features of lower limb bone geometry are associated with movement kinematics and clinical outcomes including fractures and osteoarthritis. Therefore, it is important to identify their determinants. Lower limb geometry changes dramatically during development, partly due to adaptation to the forces experienced during physical activity. However, the effects of adulthood physical activity on lower limb geometry, and subsequent associations with muscle function are relatively unexplored. Methods: 43 adult males were recruited; 10 young (20–35 years) trained i.e., regional to world-class athletes, 12 young sedentary, 10 older (60–75 years) trained and 11 older sedentary. Skeletal hip and lower limb geometry including acetabular coverage and version angle, total and regional femoral torsion, femoral and tibial lateral and frontal bowing, and frontal plane lower limb alignment were assessed using magnetic resonance imaging. Muscle function was assessed recording peak power and force of jumping and hopping using mechanography. Associations between age, training status and geometry were assessed using multiple linear regression, whilst associations between geometry and muscle function were assessed by linear mixed effects models with adjustment for age and training. Results: Trained individuals had 2° (95% CI:0.6°–3.8°; p = 0.009) higher femoral frontal bowing and older individuals had 2.2° (95% CI:0.8°–3.7°; p = 0.005) greater lateral bowing. An age-by-training interaction indicated 4° (95% CI:1.4°–7.1°; p = 0.005) greater acetabular version angle in younger trained individuals only. Lower limb geometry was not associated with muscle function (p > 0.05). Discussion: The ability to alter skeletal geometry via exercise in adulthood appears limited, especially in epiphyseal regions. Furthermore, lower limb geometry does not appear to be associated with muscle function.
Retrospective observational study of chloral hydrate use in mechanically-ventilated pediatric intensive care unit (PICU) patients 2012–2017

Introduction: Chloral hydrate (CH) has long been utilized as a pediatric procedural sedation agent. However, very little is published describing CH use in a pediatric intensive care unit (PICU) setting. The aim of this retrospective observational cohort study was to investigate and describe the use of CH in mechanically-ventilated, critically ill children at a large pediatric tertiary referral hospital. Methods: Data were extracted from the hospital electronic medical record and a locally maintained registry of all children admitted to the PICU between 2012 and 2017. Patients admitted to the cardiovascular ICU were not included in this review. The clinical and pharmacy data for 3806 consecutive PICU admissions of mechanically-ventilated, critically ill children were examined. Results: 283 admissions received CH during their first ICU stay. CH-exposed children were younger (16 months vs. 35 months, p < 0.001), the median total dose of CH (indexed to duration of ventilation) was 11 mg/kg/day, the median time to first CH dose was 3 days and more CH doses were administered at night (1112 vs. 958, p < 0.001). We constructed a propensity score to adjust for the differences in patients with and without CH exposure using logistic regression including variables of age, sex, diagnosis, and PRISM3 score. After adjustment, the median length of mechanical ventilation was 5 days longer in the CH-exposed group (95% Confidence Interval [CI] 4–6) compared to unexposed CH patients. Similarly, the median length of ICU duration was 9.4 days longer (95% CI 7.1–11.6) and median length of hospital admission duration was 13.2 days longer (95% CI 7.8–18.6) in CH-exposed patients compared to CH-non-exposed. After adjustment, CH-exposed patients had a 9% higher median exposure to HFOV (95% CI 3.9–14.6), but did not have higher median exposures to new tracheostomy (95% CI −0.4–2.2) or ECMO (95% CI −0.2–5.0). Discussion: As part of an extended sedation regimen in mechanically-ventilated and critically ill children, CH is associated with somewhat higher complexity of illness and longer ICU durations.
Universality Conjectures for Curvature Flow on Graphs, and Limits of Embedded Cell Complexes

Curvature flow on embedded graphs and cell complexes embedded in $\mathbb{R}^n$ is of great mathematical interest, and has important physical applications in materials science. In order to formalize universality conjectures about the long term behavior of these systems, we define notions of local topological convergence and local geometric convergence for embedded graphs and regular cell complexes in $\mathbb{R}^n. $ At the end of the paper, we present computational evidence for these conjectures.
L(2,1)-Circular Labelings of Cartesian Products of Complete Graphs

For positive integers j and k with j ≥ k,an L(j,k)-labeling of a graph G is an assignment of nonnegative integers to V(G) such that the difference between labels of adjacent vertices is at least j,and the difference between labels of vertices that are distance two apart is at least k.The span of an L(j,k)-labeling of a graph G is the difference between the maximum and minimum integers it uses.The λj,k-number of G is the minimum span taken over all L(j,k)-labelings of G.An m-(j,k)-circular labeling of a graph G is a function f :V(G) → {0,1,2,. ,m-1} such that |f(u)-f(v)|m ≥ j if u and v are adjacent;and |f(u)-f(v)|m ≥ k if u and v are at distance two,where |x|m = min{|x|,m-|x|}. The minimum integer m such that there exists an m-(j,k)-circular labeling of G is called the σj,k-number of G and is denoted by σj,k(G). This paper determines the σ2,1-number of the Cartesian product of any three complete graphs.
Expiry Date Character Recognition on Canned Goods Using Convolutional Neural Network VGG16 Architecture

Expiration dates have a significant role in the health and safety of consumers. It declares when a processed product like canned goods should be consumed. Once it has passed the expiration date, the product turns into food waste. This has been a major concern globally wherein one-third of the food manufactured is wasted. Using the researchers' dataset, the study focused on creating a system that uses image processing methods to recognize the characters, and the dot-matrix print expiration dates, in canned goods using the prescribed format: day, month, and year via Convolutional Neural Network Architecture. The process of image preprocessing, recognition, extraction, and classification of expiration date characters from the input image will take place in the raspberry pi microcontroller. An LCD screen was utilized to display the translated expiration dates from sample canned goods. The research used 30 sample canned goods, 27 of them were recognized correctly while the 3 others are recognized incorrectly. With this, the system was able to achieve 90% accuracy and proved that the system can recognize the expiration date on canned goods.
An RFID-Assisted Smart Livestock and Poultry Farming System on the Cloud

Green and healthy food is being favored by consumers. In this paper, we design a radiofrequency identification (RFID)-based smart farming Internet of Things (IoT) system on the cloud, which provides an integrated automated information management platform for livestock and poultry farms. The system realizes the functions of video surveillance, environmental monitoring, management, environmental control and automatic feeding, manure treatment, disease diagnosis, and traceability. Utilization of this system ensures healthy animal farming and food safety, improves the quality and yield of animal farms, and thus bring considerable economic benefits to the farmers.
Multi-variate Factors Assessment of Harmful Algal Blooms (HABs)

Harmful Algal Blooms (HABs) are becoming a serious concern in many water bodies. Predicting the formation of these blooms help waterbody stakeholders take appropriate actions and treatment plans for algae management. Many factors such as Alkalinity, Surface Temperature (ST), Precipitation, the effect of nutrients, etc., contribute to bloom formation and can vary based on geographical regions. This paper is focused on a region-based analysis and identifying factors contributing to the large concentration of cyanobacteria. The study includes several lakes from the states of Ohio, Indiana, and Kentucky. We investigate a multi-variate time series analysis using Vector Autoregressive (VAR) / Vector Error Correction Model (VECM) and ARIMAX models and evaluated their Root Mean Square Error(RMSE) and Mean Arctangent Absolute Percentage Error (MAAPE) scores. The preliminary results indicate that the Northern region dataset has a forecasting accuracy of 98%. VAR/VECM models outperform ARIMAX models. The Granger-causality test indicates that Surface Temperature (ST) played an important role in bloom formation in the Northern region lakes, while other regional results in conducive.
Visualization System for Traditional Crafts based on Text Mining with Sentiment Analysis

The establishment of traditional crafts is not only the pursuit of craftsmanship, the inheritance and innovation of traditional crafts, but also a microcosm of the history of an era. In recent years, users have also been using online forms to browse regionally specific traditional crafts. However, there are numerous and varied traditional crafts, and users do not have an intuitive and comprehensive overview of the traditional crafts in that region. Therefore, in this work, we aim to develop a visualization system for traditional crafts to help users better understand local traditional crafts and to promote the enhancement of traditional crafts and regional revitalization. For this, we utilize text mining with sentiment analysis to visualize traditional crafts designated by the Minister of Economy, Trade, and Industry (METI). Text mining of traditional crafts enables users to learn about the characteristics of local traditional crafts, and sentiment analysis of traditional crafts allows users to understand the emotional characteristics of the crafts that reflect their history. Finally, we developed a visualization system for traditional crafts and validated its usefulness, and we confirmed that users could learn about local traditional crafts comprehensively and intuitively.
An Improved Method of Reliable Content Extraction from Partially Unreliable Publications in Plural Number of Sources over the Network

This paper discusses a method of simplifying the model of information dissemination and mainly eliminating errors statistically without going into semantics for the same content collected from many information sources scattered over the network. In the methods we proposed in the past, it is assumed that the same content collected from the sources can be divided into parts that have independent values. By taking a majority vote about the value of each part, the content consisting of only the part with major values is reconstructed. For the improvement of the extraction accuracy of the reliable part of the target content, more efficient weighting ways of majority voting and their effects are discussed.
Sliding Mode Controller with Disturbance Estimator for Fuzzy Logic Controller fed PMSM Drives

This paper evaluates the performance of the permanent magnet synchronous motor (PMSM) using a fuzzy logic controller (FLC) based integrated method. This integrated method consists of non-singular fast terminal sliding mode controller (NFTSMC) and sliding mode disturbance estimator (SMDE). To maintain the balance between fast convergence and chattering effect, a sliding mode reaching law (SMRL) is proposed. This reaching law (SMRL), not only reduces chattering effect but also improves controller's performance in tracking. Furthermore, to reduce load disturbances, sliding mode disturbance estimator (SMDE) is proposed. The outputs of NFTSMC and SMDE are given to Fuzzy Logic Controller (FLC) which uses inference mechanism to generate final output. These results are evaluated by using MATLAB/ SIMULINK software. Simulation results states that FLC based integrated scheme has fast dynamic response, reduced chattering effect and preferred disturbance estimation.
A Physics Learning System Using Gamification for High-School Students

With rapid proliferation of digitalization and compulsion by COVID-19 pandemic, learning formats have been changing from face-to-face to online. Online education enables learners to take courses from anywhere, anytime, but it can also cause some problems for learners who struggle to maintain motivation. In addition, for STEAM education, it is important to engage in hands-on activities, but the ongoing pandemic has made it difficult for students to gather in one place to perform such activities. Incorporating gamification into online education can potentially motivate students and make STEAM education more interactive. On this premise, we have developed PhyGame as a learning system to help high-school students learn Physics. The system includes common game elements such as badges and leaderboards, and interactive simulation of Physics concepts embodying game-like charm. It also includes three modes of learning that allow students to adjust the difficulty according to their own learning levels, and a function that automatically saves learning log. For evaluation, PhyGame was used by students (N=23) at a high school in central Tokyo. The students rated the system on a scale of 1 to 10, and the main results are as follows: (1) Using PhyGame made learning enjoyable (mean score: 7.74); (2) PhyGame provided a good UI/UX (mean score: 7.83); (3) The overall experience with PhyGame was satisfactory (mean: 7.00). Our evaluation results show that interactive and gamified learning systems like PhyGame have a positive impact on user engagement and motivation.
Effectiveness of Relative Evaluation Feedback Using Motivation Tests and Learning Data in University Mathematics

This study seeks to improve the performance of students with low motivation and grades in university mathematics. Objective and subjective data were collected simultaneously from e-learning and using motivation tests, respectively, and analyzed by employing machine learning. Then, the results were provided as feedback to learners, which enabled them to undertake self-regulated learning. The results show that post-intervention, the average final exam score is significantly different for the experimental and control groups at 71.9 and 66.2 points, respectively (two-sided t-test; alpha = 0.05; t (89) = 2.35, p = 0.0105). This demonstrates that the feedback provided was helpful for students' learning outcomes. This made it possible to reflect on one's own learning activities by using relative evaluation feedback. This outcome is important in that it can promote self-regulated learning and enable better learning activities.
An Efficient Influence Maximization Technique Based on Betweenness Centrality Measure and Clustering Coefficient (Bet-Clus)

Graph theory has found its rigorous applicability in defining the social network phenomenas. Influence maximization in real world graphical networks have been an area of keen interest to researcher. Determining the initial seed set population for an influence spread model is found to be an N-P complete problem. Many optimization and heuristic algorithms have been proposed to solve the problem in the past. However, all the proposed solutions are constrained in terms of the scalability, their correctness etc. In this article, a graph theoretical approach based metric has been defined to effectively identify the initial seed set population for the network. The seed nodes are selected based on their clustering coefficient and betweenness centrality scores on a ranking basis. The top ’k’ ranked nodes are selected as seed nodes (information carriers) in the network. Using Independent Cascade (IC) diffusion model, the network coverage is calculated. The proposed method attains higher network coverage in comparison to the base IC model. Also, the method’s performance is compared and found to be superior when the seed nodes are selected based on other centrality measures and graph measures. The results have been evaluated over four different benchmark networks- CORA, Citeseer, PubMed (Citation Networks) and Amazon Computers Network (Product Network).
An Implementation of Answer Code Validation Program for Code Writing Problem in Java Programming Learning Assistant System

To assist Java programming learning of novice students, we have developed the web-based Java Programming Learning Assistant System (JPLAS). It provides various exercise problems to cultivate code reading and code writing skills to support studies at various levels. The code writing problem (CWP) asks a student to write a source code to pass the given test code where the correctness is verified by running them on JUnit. Previously, we implemented the answer platform for students to solve CWP assignments with the automatic runs. However, the teacher needs to manually run them one by one to verify a lot of source codes from students. In this paper, we implement the answer code validation program that automatically verifies all the source codes for one test code and reports the number of tests that each code passed with the CSV file. For evaluations, we applied the program to a total of 999 source codes from 12-43 students for a total of 45 test codes that were assigned in the Java programming course in Okayama university, including for final examinations. The application results confirm the validity and effectiveness of the proposal.