Miracle-dz/UniFAR-FTUs huggingface dataset:
The incidence of information and communication technologies in early childhood classrooms: A systemic literature review

The importance of information and communication technology (ICT) in education has been increasingly acknowledged due to advancements in technology. Prior research has explored several perspectives on the “use of ICT in early childhood education (ECE) and has highlighted many obstacles to its implementation in the classroom setting”. The synthesis of these research does not lean towards a singular perspective or definitive result, so allowing for further investigation of the issue at a more profound level. This systematic literature review seeks to enhance the current body of knowledge on teachers’ attitudes toward the efficacy of using ICT in ECE classrooms. By using PRISMA procedures, a total of 12 studies were selected to investigate the perspectives of teachers about the use of ICT, the current methods employed, and the efficacy of teaching at ECE level. The study’s findings indicate that, in general, teachers exhibit a significant propensity toward the use of ICT in ECE. However, the implementation of ICT in classrooms is constrained by several factors.
Direct spectrophotometric method to determine cell density of Isochrysis galbana in serial batch cultures from a larger scale fed-batch culture in exponential phase

espanolEn este trabajo, se ha determinado un procedimiento muy util y preciso basado en el metodo espectrofotometrico descrito por la Asociacion Americana Publica de Salud en los Metodos Estandar para el Analisis de Aguas y Aguas Residuales, para calcular la densidad celular de cultivos de Isochrysis galbana realizando una unica medida de absorbancia en fase exponencial de crecimiento que es el modo de trabajo deseable de cualquier planta de produccion de microalgas. De este modo, se realizo una serie de cultivos de Isochrysis galbana en discontinuo a partir de otro cultivo a mayor escala con alimentacion intermitente. Se ha analizado el nivel de crecimiento de esta especie de microalga por espectrofotometria a diferentes longitudes de onda y recuento de celulas en un hemocitometro (camara de Neubauer) mostrando que el tiemplo de duplicacion y la muerte celular aumento con el aumento de la densidad celular inicial. Ademas, se ha demostrado que la absorbancia de estos cultivos sigue una tendencia lineal en funcion del tiempo y densidad celular durante la fase de crecimiento exponencial, resultados en los cuales se basa el metodo directo desarrollado. EnglishIn this work, a very useful and accurate procedure, based on the spectrophotometric method published by the American Public Health Association in the Standards Methods for the Examination of Water and Wastewater, was developed to determine cell density of Isochrysis galbana performing a single direct absorbance measurement in exponential phase of growth, which is the desirable operating mode for any microalgae production plant. Thus, Isochrysis galbana was cultured in serial batch cultures from a larger scale fed-batch culture. The growth performance of this species of microalgae under laboratory conditions was analysed by spectrophotometry at different wavelengths and cell counting in a haemocytometer (Neubauer chamber) showing that doubling times and cell death increased with increasing initial cell density. Besides, it was demonstrated that the absorbance of these cultures followed a linear trend as a function of time and cell density during the exponential phase of growth, results in which the developed direct method is based on.
Warner Bros. and the DCEU: The Interplay of Financial Interaction and Strategic Decision Making

Superhero movies are now an essential component of the worldwide film business, drawing hundreds of millions of spectators to theatres each year to watch them. The DC Extended Universe (DCEU) from Warner Bros. is one such film series that has significantly contributed to the industry. But the film industry's intricacy extends beyond box office success. A company's entire decision-making, marketing approach, and long-term goal planning, may be significantly impacted by the success or failure of its films. This connection has grown even more complicated, especially after Warner Bros. and Discovery, Inc. merged. This essay will examine the financial effects of the DCEU's performances on Warner Bros. and how those decisions have further shaped the DCEU's future course. This essay will also examine how Warner Bros. exploited its success in the movie business to develop its strategic objectives for the future and what this means for the movie business. The analysis shows that the collaboration between Warner Bros. and the DCEU moulded their own trajectories and established a standard for other studios to follow in the ever-evolving film business.
Co-movement of commodity futures and stock markets

Navigating through the complex interface of commodities and stock markets, this paper elucidates the pivotal role of commodities in sustaining China's expansive economic and infrastructural panorama. Amidst significant urbanization, industrial advancement, and burgeoning population, China's vast commodities sector plays a vital role in satisfying domestic consumption, facilitating export operations, and underpinning sustained growth. Exploring the intricate linkage between commodities and stock markets, the study reveals a positive correlation between commodity futures and stock market dynamics, enriching the discourse on their co-movements within an emerging economy framework like China. The research, while offering invaluable insights into the complexities of these relationships, also lays the groundwork for future studies, inviting exploration into the causal dynamics and global perspectives. The findings, though illuminative, underscore the necessity for holistic, judicious investment and policy decision-making, reminding stakeholders to meticulously consider broader financial landscapes and market peculiarities alongside analytical insights, thus balancing data-driven decisions with strategic foresight in the vast, interconnected world of financial markets.
The Studies of Infiltration Characteristics on Sandy Saline Alkali Soil by Chemical Amelioration

In the process of one-dimensional infiltration on applied chemical amelioration,experiment designed surface application(PAM dosage was 0.01%,0.04% and 0.08%,respectively Handilong dosage was 75, 150,375g/m2). The results showed that chemical amelioration played a role of reducing infiltration, cumulative infiltration and wetness depth showed a trend which first decreased and then increased with the increase of the application amount of chemical amelioration.And cumulative infiltration and wetness depth were lowest,highest in soil holding water efficiency and biggest in salt leaching amount with 0.04% PAM and 150g/m2 Handilong.The soil moisture content was stable in 5-30cm soil layer,accordingly the largest desalination region in 10-22.5cm soil layer.
Radiation-induced glymphatic dysfunction in patients with nasopharyngeal carcinoma: a study using diffusion tensor image analysis along the perivascular space

Radiation encephalopathy (RE) refers to radiation-induced brain necrosis and is a life-threatening complication in patients with nasopharyngeal carcinoma (NPC) after radiotherapy (RT), and radiation-induced pre-symptomatic glymphatic alterations have not yet been investigated. We used diffusion tensor image analysis along the perivascular space (DTI-ALPS) index to examine the pre-symptomatic glymphatic alterations in NPC patients following RT. A total of 109 patients with NPC consisted of Pre-RT (n = 35) and Post-RT (n = 74) cohorts were included. The post-RT NPC patients, with normal-appearing brain structure at the time of MRI, were further divided into Post-RT-RE- (n = 58) and Post-RT-RE+ (n = 16) subgroups based on the detection of RE in follow-up. We observed lower DTI-ALPS left index, DTI-ALPS right index and DTI-ALPS whole brain index in post-RT patients than that in pre-RT patients (p < 0.05). We further found that post-RT-RE+ patients demonstrated significantly lower DTI-ALPS right (p = 0.013), DTI-ALPS whole brain (p = 0.011) and marginally lower DTI-ALPS left (p = 0.07) than Post-RT non-RE patients. Significant negative correlations were observed between the maximum dosage of radiation-treatment (MDRT) and DTI-ALPS left index (p = 0.003) as well as DTI-ALPS whole brain index (p = 0.004). Receiver operating characteristic (ROC) curve analysis showed that DTI-ALPS whole brain index exhibited good performance (AUC = 0.706) in identifying patients more likely developing RE. We concluded that glympathic function was impaired in NPC patients following RT and DTI-ALPS index may serve as a novel imaging biomarker for diagnosis of RE.
How to Measure Human-Dog Interaction in Dog Assisted Interventions? A Scoping Review

Simple Summary A particular type of human-dog interaction is established in dog-assisted interventions (DAIs). This interaction is based on strong human-animal cooperation and the possibility of mutual benefit during the intervention and involves complex dynamics and multisensory channel communication. The issue of measuring human-dog interaction is not new to the field of anthrozoology, but it becomes even more exciting in DAIs, given the central role that the human-animal relationship plays. In this scoping review, we look at the methods and tools that have been used to date to analyse dog-human interaction, relationships and bonding in this context. The results highlight the need for further development and refinement of the tools in terms of validity and reliability. Other emerging trends in research are the need to take into account the perspective of the dog involved and the influence of the dog-handler relationship on the outcomes of interventions in terms of the well-being and functionality of the dyad. The identified methods and tools can be used by both researchers and practitioners to further investigate aspects of human-dog interaction in the field of DAIs. Abstract Human-dog interaction is the working tool through which the therapeutic, educational and recreational goals of dog-assisted interventions (DAIs) are achieved. A better understanding of the characteristics of this interaction could improve the effectiveness of DAIs. This scoping review addresses the question: how has the human-dog connection been measured in the context of DAIs? After searching the Web of Science and Scopus platforms, only peer-reviewed, primary research studies reporting measures of therapy dog-human interaction, relationship and bond were included. A total of 70 included articles provided information on what was measured (interaction, relationship or bond) and how, as well as the general context (DAIs or experimental situations with therapy dogs). While the majority of the articles identified use behavioural analysis methods to analyse the interaction between the participant/recipient and the therapy dog during DAIs, it was possible to identify some more structured tools that assess the participant/recipient’s interaction, relationship or bond with the therapy dog, as well as tools that consider the animal’s perspective or focus on the dog-handler dyad, indicating growing areas of research. The tools and methods identified can be used by both practitioners and researchers to further explore aspects of human-dog interaction in the field of DAIs.
The Investigation on Anime-Themed Recommendation Systems

In today's digital era, the way we enjoy entertainment has experienced a significant revolution, and this transformation also encompasses the world of anime. With a seemingly endless array of anime series accessible through online streaming services, users frequently grapple with a formidable dilemma: the paradox of abundance. This paper introduces a sophisticated anime recommendation system, carefully crafted to tackle this quandary and enhance the overall anime-watching journey. Drawing upon the capabilities of machine learning and data analysis, the system's primary goal is to provide personalized anime recommendations based on user preferences and behavior. This paper not only outlines the development of an intelligent anime recommendation system but also takes a critical look at the existing body of research in the fields of recommendation systems and anime-related studies. Emphasizing the significance of personalized recommendations, it highlights the crucial role they play in enhancing user engagement and satisfaction within the world of anime streaming. The system itself is a culmination of various techniques and methodologies, employing a hybrid approach that combines collaborative filtering, content-based filtering, and advanced machine learning techniques. Linear models, random forests, and boosting algorithms are skillfully harnessed for prediction purposes, showcasing the system's versatility and adaptability. Preliminary results presented in this paper offer a tantalizing glimpse into the system's potential to deliver tailored recommendations, ultimately enriching the user experience and fostering greater engagement with the captivating universe of anime.
Nonthermal Plasma Synthesis of Metallic Ti Nanocrystals

Nanoscale metallic titanium (Ti) offers unique energetic and biocompatible characteristics for the aerospace and biomedical industries. A rapid and sustainable method to form purified Ti nanocrystals is still in demand due to their high oxygen affinity. Herein, we report the production of highly purified Ti nanoparticles with a nonequilibrium face center cubic (FCC) structure from titanium tetrachloride (TiCl4) via a capacitively coupled plasma (CCP) route. Furthermore, we demonstrate a secondary H2 treatment plasma as an effective strategy to improve the air stability of a thin layer of nanoparticles by further removal of chlorine from the particle surface. Hexagonal and cubic-shaped Ti nanocrystals of high purity were maintained in the air after the secondary H2 plasma treatment. The FCC phase potentially originates from small-sized grains in the initial stage of nucleation inside the plasma environment, which is revealed by a size evolution study with variations of plasma power input.
Recent Advances in Characterization Techniques for the Interface in Carbon Nanotube-Reinforced Polymer Nanocomposites

The current state of characterization techniques for the interface in carbon nanotube-reinforced polymer nanocomposites is reviewed. Different types of interfaces that exist within the nanocomposites are summarized, and current efforts focused on understanding the interfacial properties and interactions are reviewed. The emerging trends in characterization techniques and methodologies for the interface are presented, and their strengths and limitations are summarized. The intrinsic mechanism of the interactions at the interface between the carbon nanotubes and the polymer matrix is discussed. Special attention is given to research efforts focused on chemical functionalization of carbon nanotubes. The benefits and disadvantages associated with covalent and noncovalent functionalization methods are evaluated, respectively. Various techniques used to characterize the properties of the interface are extensively reviewed. How the mechanical and thermal properties of the nanocomposites depend on the physical and chemical nature of the interface is also discussed. Better understanding and design of the interface at the atomic level could become the forefront of research in the polymer community. Potential problems going to be solved are finally highlighted.
Object Detection Algorithms Based on Convolutional Neural Networks

Object detection is a fundamental and challenging task in computer vision, and it has attracted much attention from researchers worldwide. In recent years, deep learning technology has made remarkable progress and enabled new possibilities for object detection. Convolutional neural networks (CNNs), which are powerful tools for feature extraction and representation learning, have become the dominant approach for object detection, surpassing the traditional methods. This article reviews the development history of CNNs and their applications to object detection. It also introduces and compares two main branches of CNN-based object detection algorithms: region-based methods, which use a two-stage pipeline to first generate candidate regions and then classify them, and regression-based methods, which directly predict the bounding boxes and labels of objects in a single stage. Finally, it summarizes the current state-of-the-art and discusses the future directions of object detection research.
Fuzzy logic method-based stress detector with blood pressure and body temperature parameters

In this study, using the fuzzy logic method, a stress detection tool was created with body temperature and blood pressure parameters as indicators to determine a person's stress level. This tool uses the LM35DZ sensor to detect body temperature, the MPX5100GP sensor to read blood pressure values, and Arduino Uno as a data processor from sensor readings which are then calculated using the fuzzy logic method as a stress level decision-maker. The resulting output measures blood pressure, body temperature, and the stress level experienced by a person, which will be displayed on the liquid crystal display. Based on the results of testing the body temperature parameter, the highest error generated was 1.17%, and for the blood pressure parameter, the highest error was 2.5% for systole and 0.93% for diastole. Furthermore, testing the stress level displayed on the tool is compared to the depression, anxiety, and stress scales 42 (DASS 42), a psychological stress measuring instrument. From the results of testing the tool with the questionnaire, the average conformity level is 74%.
CCNB1 may as a biomarker for the adipogenic differentiation of adipose-derived stem cells in the postoperative fat transplantation of breast cancer

Background Adipose-derived stem cells (ADSCs) are closely associated with the survival rate of transplanted fat in breast reconstruction after breast cancer surgery. Nevertheless, the intrinsic mechanisms regulating ADSCs adipogenic differentiation remain ambiguous. The aim of our study was to explore the relevant genes and pathways to elucidate the potential mechanisms of adipogenic differentiation in ADSCs. Methods The Gene Expression Omnibus (GEO) dataset GSE61302 was downloaded and analyzed to identify differentially expressed genes (DEGs). Key genes and signaling pathways were obtained through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional and enrichment analysis. Protein-protein interaction (PPI) network and hub gene analyses were performed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape software. Finally, the transcription levels of hub genes in the adipogenic differentiated group and undifferentiated group of ADSCs were compared via real-time quantitative polymerase chain reaction (RT-qPCR). Results In total, 1,091 DEGs were identified through bioinformatics analysis of the adipogenic differentiated group and undifferentiated group. If was then found that the 10 downregulated key genes, CCNB1, NUSAP1, DLGAP5, TTK, CCNB2, KIF23, BUB1B, CDC20, CDCA8, and KIF11 may play important roles in the adipogenic differentiation of ADSCs. Subsequent in vitro experimental verification also revealed that the messenger RNA (mRNA) expression levels of cyclin B1 in adipogenic differentiated cells and undifferentiated cells were significantly different at the early stage (P<0.05), but there was no significant difference at the late stage (P>0.05). Conclusions As a key gene, CCNB1 might be a potential biomarker in the adipogenic differentiation of ADSCs at the early stage.
The Impact of P-Type Layer on Performance and Stability of Thin Film Silicon Solar Cells in Relation to Third Generation: A review Analysis

Thin-film Silicon (Si) Solar Cells (SCs) have emerged as a promising candidate for next-generation photovoltaic devices due to their potential for cost-effectiveness and versatility. However, achieving high efficiency and long-term stability remains a significant challenge in their development. The primary challenges with thin-film Si SCs have been the poor infrared light absorption of Hydrogenated Microcrystalline Silicon (c-Si: H) and the light-induced metastability of Hydrogenated Amorphous Silicon (a-Si: H). This study reviewed the influence of the p-type layer, a key component in thin-film Si SCs, on both performance and stability. Third generation SCs are optimized for high Power Conversion Efficiency (PCE) at low manufacturing costs. According to the review of the comparison analysis, Patel K. et al., (2021) obtained the highest PCE of 30.17% with the a-Si/MoTe2 material (Photocurrent Density Jsc = 32.41mA/cm2, An Open-Circuit Voltage Voc = 1.093V, and Fill Factor (FF) = 85.12%) of the state-of-the-art previous works. The findings presented here contribute valuable insights toward the development of efficient and durable SC technology, crucial for advancing the adoption of renewable energy sources in our ever-growing energy demand.
The Impact of Green Finance on Low-Carbon Transformation of Manufacturing Enterprises: Evidence from Hebei Province

This study analyzes the effect of green finance on the low-carbon transition of manufacturing firms in Hebei Province. The study utilizes data from listed manufacturers in Hebei Province between 2010 and 2022. The findings demonstrate that green finance in Hebei Province efficiently enhances resource utilization efficiency, reduces carbon dioxide emissions, and elevates firms’ green total factor productivity. Besides, the study uncovered obstacles confronting firms who aim to switch from customary polluting industries to ecologically sustainable production techniques, including limited access to development capital for transition and difficulties in attracting high-caliber personnel. The study data indicate that green finance has considerably expanded in Hebei Province, particularly in the domain of green securities. The latter exceeds the scale of the majority of regions in China; nevertheless, development across programs reveals some unevenness. The study advises the Hebei provincial government and financial regulators to enhance their encouragement for green finance development and refine existing policies to mitigate shortcomings.
Optimizing Cybersecurity Attack Detection in Computer Networks: A Comparative Analysis of Bio-Inspired Optimization Algorithms Using the CSE-CIC-IDS 2018 Dataset

In computer network security, the escalating use of computer networks and the corresponding increase in cyberattacks have propelled Intrusion Detection Systems (IDSs) to the forefront of research in computer science. IDSs are a crucial security technology that diligently monitor network traffic and host activities to identify unauthorized or malicious behavior. This study develops highly accurate models for detecting a diverse range of cyberattacks using the fewest possible features, achieved via a meticulous selection of features. We chose 5, 9, and 10 features, respectively, using the Artificial Bee Colony (ABC), Flower Pollination Algorithm (FPA), and Ant Colony Optimization (ACO) feature-selection techniques. We successfully constructed different models with a remarkable detection accuracy of over 98.8% (approximately 99.0%) with Ant Colony Optimization (ACO), an accuracy of 98.7% with the Flower Pollination Algorithm (FPA), and an accuracy of 98.6% with the Artificial Bee Colony (ABC). Another achievement of this study is the minimum model building time achieved in intrusion detection, which was equal to 1 s using the Flower Pollination Algorithm (FPA), 2 s using the Artificial Bee Colony (ABC), and 3 s using Ant Colony Optimization (ACO). Our research leverages the comprehensive and up-to-date CSE-CIC-IDS2018 dataset and uses the preprocessing Discretize technique to discretize data. Furthermore, our research provides valuable recommendations to network administrators, aiding them in selecting appropriate machine learning algorithms tailored to specific requirements.