Field Of Science Research Articles

“Recovery is Complicated”: A Qualitative Exploration of Canadian University Students’ Diverse Recovery Experiences

ABSTRACT While the field of recovery science has burgeoned over the past two decades, little research has considered the experiences of university students in recovery from addiction, particularly in Canada. Addressing this gap, using a recovery capital framework, 16 qualitative interviews were conducted to explore students’ meanings, experiences, and barriers/facilitators to recovery from problematic substance use and/or behaviors. Reflexive thematic analysis revealed three main themes: 1. multiple recovery pathways and identities; 2. stigma and disclosure; and 3. proximity to campus community and resources. Recommendations to create more recovery-friendly campuses are provided.

RamanSPy: An Open-Source Python Package for Integrative Raman Spectroscopy Data Analysis.

Raman spectroscopy is a nondestructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardization, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of tools for spectroscopic analysis that supports day-to-day tasks, integrative analyses, the development of methods and protocols, and the integration of advanced data analytics. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis, and machine learning in Python. RamanSPy is hosted at https://github.com/barahona-research-group/RamanSPy, supplemented with extended online documentation, available at https://ramanspy.readthedocs.io, that includes tutorials, example applications, and details about the real-world research applications presented in this paper.

Development of competencies in the Medical Informatics Initiative (MII)-educational offers for acompetent and secure handling of medical data

In order to achieve the goals of the Medical Informatics Initiative (MII), staff with skills in the field of medical informatics and data science are required. Each consortium has established training activities. Further, cross-consortium activities have emerged. This article describes the concepts, implemented programs, and experiences in the consortia. Fifty-onenew professorships have been established and 10new study programs have been created: 1bachelor's degree and 6consecutive and 3part-time master's degree programs. Further, learning and training opportunities can be used by all MII partners. Certification and recognition opportunities have been created.The educational offers are aimed at target groups with abackground in computer science, medicine, nursing, bioinformatics, biology, natural science, and data science. Additional qualifications for physicians in computer science and computer scientists in medicine seem to be particularly important. They can lead to higher quality in software development and better support for treatment processes by application systems.Digital learning methods were important in all consortia. They offer flexibility for cross-location and interprofessional training. This enables learning at an individual pace and an exchange between professional groups.The success of the MII depends largely on society's acceptance of the multiple use of medical data in both healthcare and research. The information required for this is provided by the MII's public relations work. There is also an enormous need in society for medical and digital literacy.

Deep Learning-Based Computer-Aided Diagnosis of Osteochondritis Dissecans of the Humeral Capitellum Using Ultrasound Images.

Ultrasonography is used to diagnose osteochondritis dissecans (OCD) of the humerus; however, its reliability depends on the technical proficiency of the examiner. Recently, computer-aided diagnosis (CAD) using deep learning has been applied in the field of medical science, and high diagnostic accuracy has been reported. We aimed to develop a deep learning-based CAD system for OCD detection on ultrasound images and to evaluate the accuracy of OCD detection using the CAD system. The CAD process comprises 2 steps: humeral capitellum detection using an object-detection algorithm and OCD classification using an image classification network. Four-directional ultrasound images of the elbow of the throwing arm of 196 baseball players (mean age, 11.2 years), including 104 players with normal findings and 92 with OCD, were used for training and validation. An external dataset of 20 baseball players (10 with normal findings and 10 with OCD) was used to evaluate the accuracy of the CAD system. A confusion matrix and the area under the receiver operating characteristic curve (AUC) were used to evaluate the system. Clinical evaluation using the external dataset resulted in high AUCs in all 4 directions: 0.969 for the anterior long axis, 0.966 for the anterior short axis, 0.996 for the posterior long axis, and 0.993 for the posterior short axis. The accuracy of OCD detection thus exceeded 0.9 in all 4 directions. We propose a deep learning-based CAD system to detect OCD lesions on ultrasound images. The CAD system achieved high accuracy in all 4 directions of the elbow. This CAD system with a deep learning model may be useful for OCD screening during medical checkups to reduce the probability of missing an OCD lesion. Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.

AI-based predictive approach via FFB propagation in a driven-cavity of Ostwald de-Waele fluid using CFD-ANN and Levenberg–Marquardt

The integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques into computational science has ushered in a new era of innovation and efficiency in various fields, with particular significance in computational fluid dynamics (CFD). Several methods based on AI and Machine Learning (ML) have been standardized in many fields of computational science, including computational fluid dynamics (CFD). This study aims to couple CFD with artificial neural networks (ANNs) to predict the fluid forces that arise when a flowing fluid interacts with obstacles installed in the flow domain. The momentum equation elucidating the flow has been simulated by adopting the finite element method (FEM) for a range of rheological and kinematic conditions. Hydrodynamic forces, including pressure drop between the back and front of the obstacle, surface drag, and lift variations, are measured on the outer surface of the cylinder via CFD simulations. This data has subsequently been fed into a Feed-Forward Back (FFB) propagation neural network for the prediction of such forces with completely unknown data. For all cases, higher predictivity is achieved for the drag coefficient (CD) and lift coefficient (CL) since the mean square error (MSE) is within ± 2% and the coefficient of determination (R) is approximately 99% for all the cases. The influence of pertinent parameters like the power law index (n) and Reynolds number (Re) on velocity, pressure, and drag and lift coefficients is also presented for limited cases. Moreover, a significant reduction in computing time has been noticed while applying hybrid CFD-ANN approach as compared with CFD simulations only.

The effect of heterogeneous distributions of social norms on the spread of infectious diseases

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, suddenly erupted in China at the beginning of 2020 and soon spread worldwide. This has resulted in an outstanding increase on research about the virus itself and, more in general, epidemics in many scientific fields. In this work we focus on the dynamics of the epidemic spreading and how it can be affected by the individual variability in compliance with social norms, i.e. in the adoption of preventive social norms by population’s members, which influences the infectivity rate throughout the population and through time. By means of theoretical considerations, we show how such heterogeneities of the infection rate make the population more resistant against the epidemic spreading. Finally, we depict possible empirical tests aimed to confirm our results.

CONTINUITY IN POLITICAL AUTHORITY OF THE PRC: FEATURES OF THE FIRST GENERATION FOREIGN POLICY

The issue of succession to political power in the People's Republic of China is a frequently discussed topic in academic research. The fifth generation of Chinese power, the Xi Jinping administration, without opposing the political views of previous generations, continues its political course adhering to the basic foreign policy principles since the time of Mao Zedong. Therefore, the issue of continuity of China's foreign policy concepts is of particular interest. The main goal of the work is to consider the conceptual foundations of the foreign policy of the first generation of Chinese power and its evolution of development associated with changes in international relations of that time. The novelty of the study lies in the fact that foreign policy concepts formed during the time of Mao Zedong create optimal conditions for rationalizing the foreign policy of today's China. Objectives include assessing certain historical events in Chinese foreign policy and diplomacy and determining how they influenced the subsequent development of Chinese foreign policy. The research topic was considered based on the theory of political realism using methods of historical-comparative, structural, content analysis, and systematization methods. These methods helped to consider the problem and systematize the research results comprehensively. The results of the study provide an opportunity to deeply understand the issues of succession of Chinese political power and China's current foreign policy positions in a historical context. In addition, the results of the study can be widely used in research in the field of international relations, political science, Chinese studies, and history.

Advancing 100m sprint performance prediction: A machine learning approach to velocity curve modeling and performance correlation.

This study presents a novel approach to modeling the velocity-time curve in 100m sprinting by integrating machine learning algorithms. It critically addresses the limitations of traditional speed models, which often require extensive and intricate data collection, by proposing a more accessible and accurate method using fewer variables. The research utilized data from various international track events from 1987 to 2019. Two machine learning models, Random Forest (RF) and Neural Network (NN), were employed to predict the velocity-time curve, focusing on the acceleration phase of the sprint. The models were evaluated against the traditional exponential speed model using Mean Squared Error (MSE), with the NN model demonstrating superior performance. Additionally, the study explored the correlation between maximum velocity, the time of maximum velocity occurrence, the duration of the maximum speed phase, and the overall 100m sprint time. The findings indicate a strong negative correlation between maximum velocity and final time, offering new insights into the dynamics of sprinting performance. This research contributes significantly to the field of sports science, particularly in optimizing training and performance analysis in sprinting.

Effect of FYM and Different Levels of Panchgavya on Growth and Establishment of Papaya (Carica papaya L.) cv. Red Lady

Today, with the advancement of environment pollution and health that is obtained from the improper use of inorganic fertilizer, production and use of organic fertilizer is considered as an important approach in the field of soil science in the world’s interest to investors. Research was conducted during the period 2023 on central research farm of department of horticulture, (NAI) SHUATS, Prayagraj. Field trial was designed on Randomized Block Design with three replications and ten treatments. Seedling were raised in polybag and 45 days old seedling were transplanted to the main field. Manure and fertilizer were applied as per treatment in ten equal split doses at 30,60 and 90days intervals. The treatment T9 (RDF) FYM+ Panchgavya (90ml/l) registered significantly maximum vegetative growth, chlorophyll content and survivality %. On the basis of our experimental findings, it can be concluded that the best result was found in treatment T9 (RDF) FYM+ Panchgavya (90ml/l) in term of vegetative growth parameters viz., plant height (cm), Number of leaves per plant, stem diameter (mm), leaf area (cm2) and root to shoot ratio, leaf area index (LAI), chlorophyll content and survivality % of papaya (Carica papaya L.) cv. Red lady.

A deep neural network model for Chinese toponym matching with geographic pre-training model

ABSTRACT Multiple tasks within the field of geographical information retrieval and geographical information sciences necessitate toponym matching, which involves the challenge of aligning toponyms that share a common referent. The multiple string similarity approaches struggle when confronted with the complexities associated with unofficial and/or historical variants of identical toponyms. Also, current state-of-the-art approaches/tools to supervised machine learning rely on labeled samples, and they do not adequately address the intricacies of character replacements either from transliterations or historical shifts in linguistic and cultural norms. To address these issues, this paper proposes a novel matching approach that leverages a deep neural network model empowered by geographic language representation model, known as GeoBERT, which stands for geographic Bidirectional Encoder Representations from Transformers (BERT). This model harnesses the groundbreaking capabilities of the GeoBERT framework by extending a generalized Enhanced Sequential Inference Model architecture and integrating multiple features to enhance the accuracy and robustness of the toponym matching. We present a comprehensive evaluation of the proposed method’s performance using three extensive datasets. The findings clearly illustrate that our approach outperforms the individual similarity metrics used in previous studies.

Raman and Mid-Infrared Spectroscopy Coupled With Machine–Deep Learning for Adulterant Detection in Ground Turmeric

The intricate nature of the global food supply chain and the presence of regulations spanning multiple jurisdictions contribute to an increased likelihood of food adulteration. This underscores the need for effective monitoring methods to guarantee the safety and nutritional quality of our food. In this context, the application of infrared spectroscopy-based techniques emerges as an environmentally friendly, non-invasive, and waste-minimizing solution for authenticating food products. Infrared spectra serve as unique molecular fingerprints, offering a multidimensional representation of how chemical bonds in the material interact with infrared light. Chemometrics, which are primarily linear-based models, play a crucial role in extracting essential information from spectral data, enabling dimensionality reduction, classification, and predictive analysis. Recent progress in the field of big data science and artificial intelligence has brought forth machine learning and deep learning algorithms explicitly designed to uncover features from complex multidimensional data, encompassing both linear and nonlinear relationships. These advancements have the potential to enhance the detection of adulterants in food products. This study assesses the accuracy of various shallow machine learning models and a deep learning model based on a one-dimensional convolutional neural network (1D CNN). The evaluation is conducted using Raman and infrared spectral data obtained from ground turmeric samples that were deliberately adulterated with five distinct substances. The study highlights the improved classification accuracy achieved through the implementation of the 1D CNN model.

Photocatalytic Degradation of Quinolones by Magnetic MOFs Materials and Mechanism Study

With the rising incidence of various diseases in China and the constant development of the pharmaceutical industry, there is a growing demand for floxacin-type antibiotics. Due to the large-scale production and high cost of waste treatment, the parent drug and its metabolites constantly enter the water environment through domestic sewage, production wastewater, and other pathways. In recent years, the pollution of the aquatic environment by floxacin has become increasingly serious, making the technology to degrade floxacin in the aquatic environment a research hotspot in the field of environmental science. Metal–organic frameworks (MOFs), as a new type of porous material, have attracted much attention in recent years. In this paper, four photocatalytic materials, MIL-53(Fe), NH2-MIL-53(Fe), MIL-100(Fe), and g-C3N4, were synthesised and applied to the study of the removal of ofloxacin and enrofloxacin. Among them, the MIL-100(Fe) material exhibited the best photocatalytic effect. The degradation efficiency of ofloxacin reached 95.1% after 3 h under visible light, while enrofloxacin was basically completely degraded. The effects of different materials on the visible photocatalytic degradation of the floxacin were investigated. Furthermore, the photocatalytic mechanism of enrofloxacin and ofloxacin was revealed by the use of three trappers (®O2−, h+, and ®OH), demonstrating that the role of ®O2− promoted the degradation effect of the materials under photocatalysis.

Effect of Particle Size on Optical and Electrical Properties of Thin Films – A Simulation Approach

Abstract This work presents a simulated investigation of the relationship between surface roughness and particle size as well as the resulting impact on the thin film's physical, optical, and electrical properties. Non-porous thin films are created through computer simulation for a range of particle sizes. Random deposition with a surface relaxation technique is used for the generation of non-porous thin films. The fundamental data of two well-known materials, ZnO and CdSe, were used for the theoretical calculation of different parameters like bandgap, absorption coefficient, refractive index, dielectric constant conductivity, or others. It is also shown that the pre-assumed values of all these parameters are well matched with the experimental results reported by different groups. This is probably the first effort to develop a direct relation between the particle size with different fundamental optical or electrical properties of different materials. This would definitely help the experimental researchers in the field of material science to design their experiments as per the desired requirements.


STAGES OF DEVELOPMENT OF THE EDUCATION AND SCIENCE SYSTEM OF KAZAKHSTAN AND INTERNATIONAL INTEGRATION (ON THE EXAMPLE OF CHINA AND JAPAN

The article aims to study the relationship of Kazakhstan with China and Japan on the basis of a description of the structure and features of the educational and scientific sphere of Kazakhstan. For this purpose, the following tasks are set: The article describes the Republic of Kazakhstan's achievements based on the reforms carried out in education and science in the post-independence period; as well as the specific features and structural conditions of domestic education and science; The latest developments in the field of education and science in Kazakhstan are also included. The article discusses legal and institutional prerequisites, contracts signed at the government level, and multilateral agreements that formed the basis for close relations between Kazakhstan and the People's Republic of China. These include economic, cultural educational, and scientific cooperation. These contents are a kind of novelty of the article. The following conclusions can be considered as the main results of the article. First, China's influence on Kazakhstan's education and science system is growing. This can be characterized by the fact that there is an increase in Chinese language learners in Kazakhstan, an increase in Chinese language teaching centers, an annual increase in the number of students visiting China, and the sale of Chinese-language literature in bookstores and libraries in Kazakhstan. Secondly, relations between Japan and Kazakhstan in education and science are only developing, but the development shows excellent prospects. This is because Japan is essential to Kazakhstan's foreign policy and economic cooperation. Therefore, it is concluded that relations between Kazakhstan and Japan in the field of education and culture at the current level have a solid basis.

Impact of Artificial Intelligence on Indian Banking Sector- A Study of Banks

Artificial intelligence, or AI, is a cross-disciplinary approach to understanding, modeling, and creating intelligence of various forms. It is a critical branch of cognitive science, and its influence is increasingly being felt in other areas, including the humanities. Intelligence might be defined as the ability to learn and perform suitable techniques to solve problems and achieve goals, appropriate to the context in an uncertain, ever-varying world. A fully pre-programmed factory robot is flexible, accurate, and consistent but not intelligent. Artificial Intelligence (AI), a term coined by emeritus Stanford Professor John McCarthy in 1955, was define as “the science and engineering of making intelligent machines”. Much research has humans program machines to behave in a clever way, like playing chess, but, today, people emphasize machines that can learn, at least somewhat like human beings do. Autonomous systems can independently plan and decide sequences of steps to achieve a specified goal without micro-management. A hospital delivery robot must autonomously navigate busy corridors to succeed in its task. In AI, autonomy doesn’t have the sense of being self-governing common in politics or biology.

Artificial intelligence applied to MRI data to tackle key challenges in multiple sclerosis.

Artificial intelligence (AI) is the branch of science aiming at creating algorithms able to carry out tasks that typically require human intelligence. In medicine, there has been a tremendous increase in AI applications thanks to increasingly powerful computers and the emergence of big data repositories. Multiple sclerosis (MS) is a chronic autoimmune condition affecting the central nervous system with a complex pathogenesis, a challenging diagnostic process strongly relying on magnetic resonance imaging (MRI) and a high and largely unexplained variability across patients. Therefore, AI applications in MS have the great potential of helping us better support the diagnosis, find markers for prognosis to eventually design more powerful randomised clinical trials and improve patient management in clinical practice and eventually understand the mechanisms of the disease. This topical review aims to summarise the recent advances in AI applied to MRI data in MS to illustrate its achievements, limitations and future directions.

Exercise interventions following bariatric surgery are poorly reported: A systematic review and a call for action.

This study assessed the transparency and replicability of exercise-based interventions following bariatric surgery by evaluating the content reporting of exercise-based clinical trials. The study design of the present article is a systematic review. PubMed, Scopus, Web of Sciences, PsycINFO, and Cochrane were searched from their inception to May 2023. Eligible studies were clinical trials including exercise interventions in participants following bariatric surgery. There were 28 unique exercise interventions. Two independent reviewers applied the exercise prescription components of Frequency, Intensity, Time, and Type (FITT; four items) and the Consensus on Exercise Reporting Template (CERT; 19 items). Exercise interventions were organized into four major exercise components: aerobic training, resistance training, concurrent training, and "others." The FITT assessment revealed that 53% of the trials did not report the training intensity, whereas 25% did not indicate the duration of the major exercise component within the training session. The mean CERT score was 5 out of a possible score of 19. No studies reached CERT score >10, while 13 out of the total 19 CERT items were not adequately reported by ≥75% of the studies. This study highlights that the exercise interventions following bariatric surgery are poorly reported, non-transparent, and generally not replicable. This precludes understanding the dose-response association of exercise and health-related effects and requires action to improve this scientific field.

Mapping science through editorial board interlocking: connections and distance between fields of knowledge and institutional affiliations

AbstractResearch is a global enterprise underpinned by the general belief that findings need to be true to be considered scientific. In the complex system of scientific validation, editorial boards (EBs) play a fundamental role in guiding journals’ review process, which has led many stakeholders of sciences to metaphorically picture them as the “gatekeepers of knowledge.” In an attempt to address the academic structure that governs sciences through editorial board interlocking (EBI, the cross-presence of EB members in different journals) and social network analysis, the aim of this study is threefold: first, to map the connection between fields of knowledge through EBI; second, to visualize and empirically test the distance between social and general sciences; and third, to uncover the institutional structure (i.e., universities) that governs these connections. Our findings, based on the dataset collected through the Open Editors initiative for the journals indexed in the JCR, revealed a substantial level of collaboration between all fields, as suggested by the connections between EBs. However, there is a statistically significant difference between the weight of the edges and the path lengths connecting the fields of natural sciences to the fields of social sciences (compared to the connections within), indicating the development of different research cultures and invisible colleges in these two research areas. The results also show that a central group of US institutions dominates most journal EBs, indirectly suggesting that US scientific norms and values still prevail in all fields of knowledge. Overall, our study suggests that scientific endeavor is highly networked through EBs.

Absolutism of The Caliphate During the Abbasid Dynasty

The Abbasid Dynasty was the second Caliphate to rule Baghdad for more than five centuries (132H/750 AD to 656H/1258 AD) after conquering the Umayyad Caliphate against the background of the Umayyad Dynasty's oppression of the Abbasid Dynasty. During the five centuries of rule the Abbasid Caliphate reached its heyday in the field of science, both general science and religion. The Abbasid system of government refers to four aspects. In the first and third periods of Abbasid rule, Persian culture was greatly influenced and in this period more emphasis was placed on the cultivation of Islamic civilization and culture than territorial expansion. In the second and fourth periods influenced by Turkish culture which was very dominant in politics and government. While the fifth period was marked by the entry of the Seljuks into Baghdad until the fall of Baghdad to the Mongols. Each dynasty had its own style of leadership as well as the Abbasid Dynasty which applied absolutism in its leadership. Where the Caliph has authority over matters related to the state and religion. The caliph has unlimited authority and can only be replaced when the caliph dies.

Bibliometric Analysis of Scientific Publications on Communicative Behavior Problems

Purpose of research. The purpose of the study is to analyze the dynamics of the publication activity of scientific research in the field of studying communicative behavior. Communicative behavior is an object with an interdisciplinary research status. It falls into the area of research interest of various scientific fields. A bibliometric analysis of publication activity was carried out in connection with solving several tasks: to find out areas of knowledge in which the greatest publication activity is concentrated, to study the methods that are used in various fields of communicative behavior analysis, to identify organizations that are included in this research search.Methods. The study was conducted in two scientific electronic databases: the Russian Scientific Electronic Library Elibrary.ru and the international open platform for researchers and scientific organizations Lens. The bibliometric analysis was used as the research method. It involves the quantification of documentary information flows and it is based on quantitative research indicators reflecting the state of science as a whole or its individual branches.Results. As a result of the bibliometric analysis of publication activity on the problems of scientific research in the field of communicative behavior, communicative behavior in the digital environment and communicative behavior in social networks, a significant increase in publication activity was revealed in both international and Russian information databases over the past 10 years (2013 - 2023). It is also noted that the predominant number of publications on the issues studied is concentrated in the field of psychology. However, we can trace a shift in the focus of foreign research on communicative behavior towards digitalization in information technology, and Russian research towards the field of digital technologies in the educational environment.Conclusion. The results obtained indicate the high relevance, as well as the scientific and practical significance of the research topic on the problems of communicative behavior in general, as well as communicative behavior in the digital environment and social networks, in particular.