Miracle-dz/UniFAR-FTUs huggingface dataset:
Supportive Relationships Mitigate the Effect of Cumulative Exposure to Adverse Childhood Experiences on Depression, Anxiety, Stress, and Suicide Considerations—The Arizona Youth Risk Behavior Survey

Declining adolescent mental health is a significant public health concern during the COVID-19 pandemic. Social distancing and stay-at-home orders have led to missed social connections with peers and adults outside households, and this has increased the risk of mental health problems in children and adolescents, particularly those with adverse childhood experiences (ACEs). Studies have shown that strong interpersonal support improves adolescent mental health. We examined the association between ACEs and poor mental health (including stress, anxiety, and depression) and how the presence of interpersonal support from caring adults and friends and school connectedness can mitigate this relationship among adolescents in Arizona. This study analyzed data from the 2021 Arizona Youth Risk Behavior Survey (YRBS; n = 1181), a population-based survey conducted biennially across the United States. The Arizona sample included high school students in grades 9–12 who were enrolled in public and charter schools. This study revealed that nearly three of four adolescents experienced an ACE, and one of five experienced ≥4 ACEs. Compared with adolescents who experienced zero ACEs, those with ≥4 ACEs experienced less interpersonal support from caring adults, friends, and school and more frequently reported poor mental health and suicidal thoughts. However, adolescents with interpersonal support consistently reported lower rates of mental health issues, even with exposure to multiple ACEs. Post-pandemic programs to improve social relationships with adults, peers, and schools are critical, especially for adolescents with multiple adversities.
Development of Palmyra Palm ( Borassus flabellifer Linn.) Fruit Pulp Based Ready-to-Serve Beverage Using Response Surface Methodology

Palmyrah is a dioecious palm considered to be a native of tropical Africa. It is distributed in Africa, India, Burma and Srilanka. The palm belongs to the family Arecaceae, subfamily Borassoideae and genus Borassus. The palmyrah palms are slow-growing perennials and have no distinguishing features to identify the sex until flowering. The palm commences flowering only after 12–15 years of maturity [1]. The coconut-like fruits are three-sided when young, becoming rounded or more or less oval, 12-15 cm wide and capped at the base with overlapping sepals. The outer covering is smooth, thin, leathery and brown, turning nearly black after harvest. Inside is a juicy mass of long, tough, coarse, white fibers coated with yellow or orange pulp [2]. The soft orange-yellow mesocarp of the ripe fruit is sugary, dense and edible; have excellent flavor and very attractive color which contains gums, albuminoids, fats and is reportedly rich in vitamin A and C [3]. The major reasons for the underutilization of these fruits are separation of the pulp from the fibre and its bitter taste. Borassus also contains bitter compound called flabelliferrins, which are steroidal saponins and this bitter principle was identified as tetraglycoside of the steroid isolated Abstract
Trend Prediction Analysis of Shanghai Composite Index Based on LSTM Neural Network

The prediction of stock indices, particularly the Shanghai Composite Index (SCI), is crucial for understanding the health and direction of China's financial market in the post-pandemic era. Given the challenges of traditional forecasting methods due to the unpredictable nature of stock prices, this study explores the application of the Long Short-Term Memory (LSTM) neural network, complemented by Monte Carlo simulations and regularization with the daily trading data from September 21 2020, to August 13, 2023, to forecast the SCI's price trends for Q4 2023. The findings suggest a trajectory characterized by an initial decline, succeeded by a steady upward trend throughout the entirety of the quarter. Notably, the results have adeptly encapsulated both the lingering effects of the pandemic and the long-term rising trend of the Shanghai Composite Index (SCI). This research accentuates the potential of advanced neural network models in deciphering complex stock market behaviors, offering a groundbreaking perspective for market participants in navigating future investment decisions.
Pedestrianization and Walkability In A Fast Developing UNESCO World Heritage City

A walkability friendly environment is crucial in historical towns and cities built with minimal number of motor vehicles. This research aims to assess physical attributes of pedestrianization in Georgetown, Penang, a UNESCO World Heritage Site regarding walkability. A structured interview was conducted among 170 respondents at six different streets to examine citizens' satisfaction on the pedestrian facilities using six walkability features including a) pathway, b) zebra crossing, c) signage, d) personal safety, e) traffic flow and, f) aesthetics & amenities. Relative Importance Index (RII) analysis show that existing pathways do not meet acceptable standards in promoting a walkable environment. Aesthetic and amenities scores the lowest in satisfactory level and zebra crossing scores the highest in unsatisfactory level. The level of walkability in the World Heritage Site in Georgetown, Penang are found inadequate. Provision of physical walking and built heritage features without providing sufficient activities along the streets do not yield high walkability.
Development of a mixed model using generalized renewal processes and the weibull distribution

Para analisar intervencoes em sistemas reparaveis, a literatura apresenta diversas metodologias visando modelar o comportamento de tempos entre intervencoes. Tais intervencoes podem ser modeladas por Processos Estocasticos Pontuais visando analisar o comportamento probabilistico dos tempos entre eventos. Especificamente, os Processos de Renovacao Generalizados permitem o estudo de tempos entre intervencoes medindo a qualidade de impacto de cada intervencao e a resposta do sistema a tais intervencoes - isto e feito utilizando o conceito de idade virtual. Em tal conceito e possivel se aplicar dois tipos de modelos Kijima (tipo I e II). Sendo assim, esse trabalho apresenta um modelo capaz de estudar a qualidade de intervencoes utilizando-se de uma mistura entre os dois modelos Kijima onde e possivel capturar a atuacao de cada um desses sobre as intervencoes proporcionalmente. Especificamente, uma nova abordagem sobre a idade virtual dos modelos Kijima e apresentada, bem como propriedades matematicas dos Processos de Renovacao Generalizados utilizando a distribuicao de probabilidadeWeibull. Por fim, a aplicabilidade do modelo e verificada em dados reais de alguns problemas presentes na literatura.
The Effect of the Digital Book-Assisted Randai Learning Model on Students' Problem-Solving Skills and Information Literacy

Purpose:The research aims to see the effect of using digital books based on the RANDAI learning model on students' problem-solving skills and information literacy in biology learning. Theoritical Framework: The integration of local wisdom will make the material more contextual so that learning becomes more meaningful. Digital books that are integrated with local wisdom can be concretized through digital books based on the RANDAI learning model. Methodology:The study was conducted at a secondary school in the province of West Sumatra, Indonesia. The learning applied in this study uses the theoretical framework of the RANDAI model (Reciting, Analyzing the problem, Narrating the solution, Doing the solution, Assessing the solution, and Implementing the solution) which is problem-based learning integrated with local wisdom. Data collection was carried out through a test referring to Polya's problem-solving skills while for information literacy using a questionnaire referring to the Association of College and Research Library indicators. Data were analyzed using a parametric t-test and n-gain analysis. Result and discussion: This study found that problem-solving skills and information literacy through learning with the RANDAI learning model assisted by digital books can promote students' problem-solving skills in science learning. Conclusion: the RANDAI learning model with the help of digital books has an effect on the problem-solving abilities and information literacy of high school students.
Research on Factors Affecting China's Birth Rate and The Relation Between Economic Development and The Birth Rate

Many scholars have previously studied the birth rate and its development, but there are still gaps in the research on which factors play a dominant role in the birth rate. Therefore, this paper will provide a clear study of this issue, using China as an example to help major countries solve the problem of low birth rates and economic development. The population issues caused by low birth rate were researched and the main factors for birth rate were found from research. The decisive factors that determine the birth rate in China were analyzed. The link between birth rate and economic development is examined. This study figures out that the two main factors that influence the birth rate are the divorce rate and the female employment rate. There is a relatively strong correlation between the birth rate and economic development. The formula and the principle of Vector Autoregressive (VAR), Bayesian Information Criterion (BIC) and Times Series Graphical lasso (TSGlasso), were researched and used.
Analysis of Autonomous Vehicle Control Algorithm Based on Different Models

As a matter of fact, the rise of autonomous vehicles (AVs) has necessitated the development of advanced control algorithms to ensure safe and efficient operation in recent years. With this in mind, the aim of this paper is to provide a comprehensive analysis of control algorithms for self-driving vehicles and to identify research opportunities for innovation by comparing the strengths and weaknesses of existing algorithms. On this basis, through a careful dissection of existing literature as well as case studies, this study provides an in-depth understanding of different control algorithms and a comparative analysis of performance metrics from real-world tests and simulations. To be specific, three models will be applied. According to the analysis, the results of this study emphasize the critical role of accurate vehicle dynamics in the development of complex automated driving control algorithms at the same time, paving the way for safer, more efficient as well as sustainable transportation solutions.
Prediction of the risk of developing heart disease using logistic regression

Heart disease (HD) accounts for more deaths every year than other illnesses. World Health Organization (WHO) assessed 17.9 million life losses caused by heart disease in 2016, demonstrating 31% of all international life losses. Three-quarters of these life losses occur in low and middle-income nations. Machine learning (ML), due to advanced precision in pattern recognition and classification, demonstrates to be in effect in complementing decision-making and threat prediction from the huge number of HD data created by the healthcare sector. Thus, this study aims to develop a logistic regression model (LRM) for predicting the risk of getting HD in ten years. The study explores the different methodologies for improving the performance of base LRM for predicting whether a person gets HD after ten years or not. The result demonstrates the capability of LRM in predicting the risks of getting HD after ten years. The LRM achieves 97.35% accuracy with the recursive feature elimination and random under-sampling. This implies that the LRM can play an important role in precautionary methods to avoid the risk of HD.
Troubles psychiques révélant une hypothyroïdie profonde

Les manifestations psychiatriques font partie du tableau clinique de l’hypothyroïdie et sont classiquement caractérisées par un syndrome anxieux et/ou dépressif. Elles sont associées à des troubles somatiques qui dominent la scène clinique et conduisent à suspecter le diagnostic. Les troubles psychotiques au cours de l’hypothyroïdie sont connus depuis de nombreuses années [1,2] mais rarement décrits. Ils apparaissent parfois au premier plan et rendent alors le diagnostic difficile. Le cas clinique rapporté est celui d’une personne prise en charge aux urgences pour un état délirant aigu révélant une dysthyroïdie profonde.
The Relationship Between Physical Exercise and Aggressive Behavior Among College Students: The Mediating Roles of Life Satisfaction, Meaning in Life, and Depression

Purpose The deleterious impact of aggressive behavior on college students necessitates urgent mitigation. To explore the influencing factors and underlying mechanisms of aggressive behaviors among college students, this study aims to validate the mediating roles of life satisfaction, meaning in life, and depression by examining the relationship between physical exercise and aggressive behaviors among college students. Methods The Physical Activity Rating Scale-3 (PARS-3), Satisfaction with Life Scale, Meaning in Life Questionnaire, Chinese Version DASS-21 and 12-item Perception of Aggression Scale (POAS) were tested on 1596 college students from three universities in western China, and SPSS 26.0 and Mplus 8.3 were used for analysis. Results Physical exercise exhibited a significant negative correlation with both aggressive behaviors and depression among college students (r = –0.57, P < 0.001; r = –0.36, P < 0.001), as well as a significant positive correlation with life satisfaction and meaning in life (r = 0.45, P < 0.001; r = 0.27, P < 0.001). Regarding the impact of physical exercise on aggressive behaviors among college students, the mediating effects of life satisfaction, meaning in life, and depression were significant. The respective effect sizes were −0.11, −0.08, and −0.03. The chain mediation effect of life satisfaction through depression was also found to be significant, with an effect size of −0.02. Conclusion This study elucidates the mechanistic pathways through which physical exercise influences aggressive behavior among college students. The relationship between physical exercise and aggressive behavior is influenced by the individual mediating effects of life satisfaction, meaning in life, and depression, as well as the chain mediation effect of life satisfaction through depression. These findings provide a novel perspective on the prevention and intervention of aggressive behaviors among college students in China and potentially worldwide. This suggests that more attention should be paid to the organic combination of students’ physical activity and mental health education.
Effects of Different Feeding Frequencies on Broiler Chickens’ Growth Performance and Intestinal Villus Development

This experiment was conducted to determine the effects of feeding frequency on growth performance and development of small intestine in broiler chickens from 1 to 14 days of age. Forty Aber acres broiler chicks at 1 day old were randomly assigned to 4 treatments as follows - T1: feeding once daily (100 % diet at 6:30), T2: feeding twice daily (50 % diet at 6:30 and 18:30), T3: feeding 4 times per day (25 % diet at 6:30, 11:30, 16:30 and 21:30) and T4: feeding 6 times per day (16.67 % diet at 6:30, 9:30, 12:30, 15:30, 18:30 and 21:30). Chickens were fed with commercial broiler rations for 14 days. Growth performance parameters including BW, BW gain, FI and FCR were determined. Changes in intestinal villus morphology were examined by hematoxylin and eosin (H&E) dye staining. Results showed that chickens fed 4 and 6 times per day had lower (p < 0.05) BW, BW gain and FI when compared with chickens fed once daily, from 1 to 7 days of age. However, at 14 days of age, there were no significant differences in all parameters of growth performance. There were no changes in gross anatomy of the small intestine. Fixed feeding 4 and 6 times per day resulted in the villus height of the jejunum and ileum being higher (p < 0.05) than when feeding once a day. Feeding 6 times per day resulted in the highest villus density in jejunum among feed regimes. The results suggested that both once and multiple daily feedings were suitable approaches to achieve optimum growth performance for broiler chickens from 1 to 14 days of age. Fixed feeding 6 times per day being the most beneficial for improving intestinal villus development. HIGHLIGHTS Feeding frequency is one of feed restriction method in broilers management From 1 to 7 days of age feeding fed 4 and 6 times per day show lower growth performance than feeding once daily, but not for 14 days of age Fixed feeding 4 and 6 times per day present the villus height at jejunum and ileum higher than feeding once daily Fixed feeding 6 times per day is the most effective solution for enhancing intestinal villus development Here, the experiment demonstrates for the first time that the compensatory growth effects by frequently feeding is at least mediated through adaptation of villus morphology in the small intestine along with improved feed consumption GRAPHICAL ABSTRACT
Overexpression of Insulin Receptor Substrate 1 (IRS1) Promotes Radioresistance in A172 Glioblastoma Cell Line

IGF binds and activates to insulin like growth factor receptor (IGF-1R). IGF-1R activation triggers the signaling pathways that mediates proliferation, cell survival and metastasis (7). Insulin receptor substrate 1 (IRS1) is the main adaptor molecule that interacts with IGF-1R after ligand binding. IGF-1R phosphorylates tyrosine residues of IRS1 that leads to activation of IRS1 proteins. Activated IRS1 induces the activation of AKT and MAPK proteins that regulates metabolic and proliferative functions of the cells (8,9). Therefore, IRS1 overexpression is an important factor in tumorigenesis.
Merging Virtual World with Real-Life Behavior: A Concept for a Smartphone App to Influence Young People’s Travel Behavior

European trends in children’s travel patterns show decreasing rates of trips walked or cycled. Against this background, a concept for a smartphone app was developed to promote active travel modes for children and adolescents. The app collects players’ travel data as input for a game: a high level of real-life environmentally friendly and active travel modes leads to a higher score. Competing players redeem the points they have collected to reach specific virtual locations on a map and win real-life rewards (e.g., shopping vouchers). The game was developed based on the user-centered design approach—an iterative process between design, prototyping, and evaluation. This paper presents the game concept alongside the results of a prototype field test at three schools in the province of Lower Austria, comprising 57 students aged 12 to 18. Results suggest that the game was easy to use and entertaining. However, younger players lost interest faster than older ones. Players emphasized improvements in relation to mode detection and tracking of individual trips, since fair playing conditions were requested by all age groups. Although the level of knowledge about sustainable mobility is high among young people, the game was rated as being a good tool for raising awareness regarding the environmental and health effects of mode choice. The promising results of this research project need to be transferred into a business model that can provide ongoing game updates, keep the players interested, and achieve long-term effects.
Analysis of connection between dust storm and climate factors in northern China

Based on the meteorological observation data,the article analyses the connection between the annual frequency change of dust storm and climate factors in Northern China.The results show that(1) the frequency of dust storm is not complete consistent with the distribution of wind days in northern China and it is most affected by the regional characteristic conditions;(2) the frequency change of dust storm reflects the decreasing occurrence trend of dust storm,which is fairly consistent with the continual temperature rising and summer precipitation increase in northern China;(3) according to the annual and quarterly frequency change character,precipitation and temperature jointly affected the surface vegetation and soil conditions,which consequently affect the occurrence risk ranks of dust storm.Finally,the article make a suggestion on dust storm risk assessment and establishing dust storm early warning system.
Civil Society and the Platform Society in the Age of Post-Representative Politics

The article explores the concept of civil society in connection with Web 2.0 and its transformative impact on society and politics, contrasting it with the earlier era of Web 1.0. Web 2.0, characterized by enhanced user interaction and content production, has given rise to a new media ecosystem that has reshaped the way people communicate, obtain information, and participate in the polity. The article highlights the emergence of popular social media platforms and how they have become integral to the platform society. These platforms have not only revolutionized online interactions but have also intersected with offline realms, including politics, commerce, and social relations. The article further explores the implications of Web 2.0 for civil society, contentious politics, and citizen engagement, examining the rise of protests, transnational movements, and the impact of digital resources on democracy and (digital) citizenship. Finally, the work discusses the potential of digital counter-democracy in the platform society and the need for democratic control in the digital age.
A deep learning framework for accurate diagnosis of colorectal cancer using histological images

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, with high mortality and incidence rates. Early detection of the disease may increase the probability of survival, making it critical to develop effective procedures for precise treatment. In the past few years, there has been an increased use of deep learning techniques in image classification that aid in the detection of various types of cancer. In this study, convolutional neural network (CNN) models were used to classify colorectal cancer into benign and malignant. After applying various data preprocessing techniques to the image dataset, we evaluated our prototypes using three distinct subsets of testing data, representing 20%, 30%, and 40% of the total dataset. Additionally, four pre-trained CNN models (ResNet-18, ResNet-50, GoogLeNet, and MobileNetV2) were trained, and the network architectural techniques were compared by applying the Adam optimizer. Finally, we assessed the performance of algorithms in terms of accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). In this research, deep learning approaches demonstrated high efficacy in accurately diagnosing colorectal cancer. This indicates that these techniques have an important and significant value for advancing medical research.