bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of a Computer Technology-Based Teaching System for College Students Mental Health Course
Authors: Xiaowei Dai
Published: May 2024

The hypothetical discussion describes the design of a personal computer innovation-based display structure tailored specifically for subordinates enrolled in an educational well-being course. Using a sophisticated approach, this structure intends to raise understudy devotion, urge dynamic studying, and enhance profitability. The displaying framework promotes reflective thinking, resolute thinking, and the acquisition of practical qualities associated with psychological wellness through the use of intuitive multimedia, competitive elements, and tailored input. This innovative displaying structure allows students to grow a more substantial grasp of mental health concerns while growing their personal psychic flourishing in a consistent and enticing teaching environment through the integration of cutting-edge technology with informative content. Modern instructional tactics stay frequently grounded in scheduled exam-based schooling besides infrequently actually caring about the root cause of pupils' questions, although flipping psychological training methods have emerged in some sectors. The primary goal of this concept is to combine digital intelligence with flipping laboratory brain studies. This essay focuses on the characteristics of fraudulent learning as well as the symbolic significance of flipping their house. What exactly are the advantages of the smart display stage over traditional learning? The following piece chooses crucial 1, the classroom, important 2, the school, main 3, the group, and key 4, from our institution's undergraduates. The A1 and B1 groups guide an academic year of artificial intellect, along with the concept of inverted classrooms for neuroscience. Bundles A2 and B2 depict the under study in accordance with normal training. According to the study data, the size of gatherings A1 and B1 increased respectively. Gatherings A1 and B1 had higher midterm and final normal scores respectively, than assemblages A2 and B2, and their scores have increased by a rate. The influence of artificial intelligence and flipping residence mind study on mental health in education programs may additionally energize subordinates' benefit and forward the advancement of understudy's display throughout autonomous education.
The Role of AI in Financial Markets: Impacts on Trading, Portfolio Management, and Price Prediction
Authors: Sunil Kumar Das
Published: May 2024

Today artificial intelligence (AI) has become an indispensable assistance for human traders. AI systems provide human traders with numerous advantages, such as the capability to conduct genuine high-frequency trading (HFT), which capitalizes on price discrepancies and market anomalies, and to analyze a massive data set from multiple sources in a fraction of a second. The main goal of this research is to examine the role that AI plays in the financial markets, with an emphasis on how it affects trading, portfolio management, and price prediction. In this study, quantitative research methodology was utilised. Primary and secondary sources of data were used in the investigation. An online questionnaire was used to collect the primary data, and finance databases, pertinent industry bulletins, and already published literature were used to collect the secondary data. It was found that there is an increasing incorporation of AI and machine learning technologies into financial institutions. Many of the participants revealed that these technologies are used moderately to significantly in their organizations. The most prominent AI and machine learning applications are “algorithmic trading, risk management, fraud detection, credit scoring, and customer service”.
Multimodal Co-Ranking of Music Sentiment Analysis with the Deep Learning Model
Authors: Tian Ge
Published: May 2024

Deep learning techniques have emerged as powerful tools for analyzing music sentiment, offering a sophisticated understanding of the emotional content embedded within musical compositions. Analyzing music sentiment in piano performance poses a unique challenge due to the multifaceted nature of musical expression and the complexity of piano dynamics. In recent years, deep learning techniques have shown promising results in various music analysis tasks. This paper introduces a novel approach, termed Co-ranking Multimodal Fuzzy Cluster Deep Network (CRMFC-DL), designed specifically for analyzing music sentiment in piano performance. The proposed CRMFC-DL framework leverages the complementary information from both audio and symbolic representations of piano music. By integrating deep neural networks with fuzzy clustering, CRMFC-DL effectively captures the intricate relationships between different musical features and their corresponding sentiment labels. Moreover, the co-ranking mechanism facilitates the joint optimization of multimodal feature representations, leading to enhanced model performance. Through extensive experimentation, CRMFC-DL achieves an average sentiment analysis accuracy of 87.5%, surpassing existing methods by 8.2%. The both audio and symbolic representations of piano music, CRMFC-DL effectively captures the intricate relationships between different musical features and their corresponding sentiment labels.
Design of Training Load Monitoring and Adjustment Algorithm for Athletes: Based on Heart Rate Variability and Body Index Data
Authors: Zhou Ke
Published: May 2024

The training load monitoring and adjustment algorithm for athletes, based on heart rate variability (HRV) and body index data, offers a comprehensive approach to optimizing athletic performance and minimizing the risk of injury. By leveraging HRV data, which reflects the autonomic nervous system's response to training stress, and body index measurements such as body mass index (BMI) or body fat percentage, the algorithm provides insights into athletes' physiological readiness and recovery status. The design of an effective training load monitoring and adjustment algorithm is critical for optimizing athletic performance while minimizing the risk of injury and overtraining. This paper proposes a novel approach that integrates heart rate variability (HRV) and body index data to tailor training programs to individual athlete needs. This paper presents an innovative approach to training load monitoring and adjustment for athletes, utilizing heart rate variability (HRV) and body index data. Through continuous monitoring and analysis of HRV metrics such as RMSSD and LF/HF Ratio, in conjunction with body mass index (BMI) and body fat percentage, personalized load management strategies are developed to optimize athletic performance while mitigating the risk of injury and overtraining. The optimization algorithm outlined in this study allows for real-time adjustments to training loads based on individual physiological responses, ensuring that athletes receive tailored training programs that maximize performance gains and promote long-term health and well-being. By leveraging HRV and body index data, coaches and sports scientists can enhance athletic performance outcomes and support the overall development and longevity of athletes' careers.
Career Interest Assessment: College Students Career Planning Based On Machine Leaning
Authors: Cui Can
Published: May 2024

College students' career planning based on career interest assessment and machine learning integrates advanced technological tools with traditional career counseling methods to provide personalized and data-driven guidance. By utilizing career interest assessment tools, students can explore their strengths, preferences, and aspirations across various career domains. Machine learning algorithms analyze this data to generate tailored recommendations, matching students with career paths that align with their interests and aptitudes. This paper presents a novel approach to career assessment and planning by leveraging the Sugeno Fuzzy Optimized Weighted Machine Learning (SFOwML) framework. The proposed framework integrates fuzzy logic principles with machine learning techniques to provide personalized and accurate career recommendations tailored to individuals' skills, preferences, and values. Through the incorporation of optimization techniques, the framework refines the career assessment model, enhancing its accuracy in predicting suitable career paths for individuals. The practical application of the SFOwML framework offers a valuable tool for career counselors, educators, and individuals seeking guidance in their career choices. The framework incorporates numerical values to quantify individuals' skills, preferences, and values on a scale from 0 to 100, allowing for a more precise evaluation of their career profiles. Through the integration of fuzzy logic principles with machine learning techniques, personalized career recommendations are generated based on these numerical assessments.
Athletes' Action Recognition and Posture Estimation Algorithm Based on Image Processing Technology
Authors: Cheng Lingxiao
Published: May 2024

A posture estimation algorithm based on image processing technology involves analyzing images or video frames to determine the position and orientation of human bodies or specific body parts. This process typically begins with detecting key points or landmarks on the body, such as joints or anatomical features. This paper presents a novel approach for athletes' action recognition and posture estimation using image processing technology, specifically focusing on the Contour Mapping Multivariant Recognition and Estimation (CMMRE) algorithm. The primary objective is to develop a robust system capable of accurately identifying human actions and estimating postures from visual data, with potential applications in sports analysis, surveillance, and human-computer interaction. The CMMRE algorithm employs advanced techniques for contour mapping, feature extraction, and prediction, leveraging deep learning methodologies to analyze and interpret visual information effectively. Through a series of experiments and simulations, the algorithm's performance is evaluated, showcasing its ability to achieve high accuracy in action recognition tasks across various scenarios. The results highlight the algorithm's strengths in accurately predicting actions and estimating postures, while also identifying areas for improvement. The algorithm achieved an average accuracy of 95% in recognizing walking and running actions, and 80% in identifying jumping actions. The results highlight the algorithm's strengths in accurately predicting actions and estimating postures, while also identifying areas for improvement.
Analyzing Players' Tactical Positions and Movement Trajectories in Soccer Matches Using Machine Vision Algorithms
Authors: Wang Juhai
Published: May 2024

Tactical positions and movement trajectories in soccer matches using machine vision algorithms involves leveraging computer vision techniques to track players and the ball throughout the game. By processing video feeds from multiple cameras, machine vision algorithms can identify players, recognize their positions on the field, and map out their movement trajectories over time. This analysis provides valuable insights into team formations, player positioning, and tactical strategies employed during matches. Coaches and analysts can use this information to assess team performance, identify patterns in gameplay, and make data-driven decisions to optimize tactics and training regimes. Machine vision algorithms enhance the understanding of soccer dynamics, facilitating more strategic and effective approaches to coaching and gameplay.. This paper presents a comprehensive analysis of player movement, tactical positions, and detection in soccer matches using machine learning algorithms. Leveraging a simulation environment built upon a Recurrent Neural Network (RNN) model trained on historical match data, we investigate the efficacy of the model in accurately predicting future player positions based on contextual features such as player trajectories and ball movement. Furthermore, we explore the model's ability to estimate tactical movements during specific time intervals, providing valuable insights for coaches and analysts in understanding team strategies and adapting to match dynamics. Additionally, we evaluate player detection systems to assess their capabilities and limitations, laying the groundwork for future improvements in player tracking technologies. Our findings underscore the potential of machine learning in soccer analytics, offering actionable insights for enhancing performance analysis, strategic decision-making, and overall understanding of the game. Our results show a high prediction accuracy, with an average Mean Squared Error (MSE) of 0.05. Furthermore, we explore the model's ability to estimate tactical movements during specific time intervals, achieving an overall prediction accuracy of 85%. Additionally, we evaluate player detection systems to assess their capabilities and limitations, achieving a detection accuracy of 90%.
Using Reinforcement Learning Algorithms to Optimize Practical Skills Development in Higher Vocational and Technical Education
Authors: Zhang Xingli
Published: May 2024

Optimizing practical skills development in higher vocational and technical education involves a multifaceted approach. Firstly, curriculum design should integrate hands-on learning experiences, industry-relevant projects, and internships to bridge the gap between theoretical knowledge and real-world application. Secondly, institutions should invest in state-of-the-art facilities, equipment, and technology to provide students with a simulated work environment conducive to skill mastery. This paper, explored the integration of reinforcement learning algorithms within the Seahorse Optimization Probability Education (SHOPE) framework to optimize practical skills development in higher vocational and technical education. Through extensive experimentation and analysis, we investigate the effectiveness of various reinforcement learning algorithms in enhancing vocational teaching strategies, skill assessment processes, and classification tasks within educational contexts. Our findings highlight the capability of SHOPE to iteratively refine teaching methodologies, leading to significant improvements in student skill acquisition, success rates, and classification accuracy over multiple epochs. With the adaptive teaching strategies, and optimized vocational education programs tailored to individual student needs. For instance, our results demonstrate an average skill improvement ranging from 35% to 65% across different reinforcement learning algorithms. Moreover, success rates for mastering targeted skills reach levels between 75% and 92%.
Medical image Classification using Transfer Learning: Convolutional Neural Network Models approach
Authors: Zobeda Hatif Naji Al-Azzwi
Published: May 2024

A relatively low life expectancy in their greatest grade is caused by more debilitating diseases in the medical sector. Any kind of misdiagnosis may lead to improper medical intervention and lower the likelihood that a patient will survive. Making an appropriate treatment plan to cure and enhance the quality of life for patients suffering from any kind of illness starts with an accurate diagnosis. Convolutional neural networks and computer-aided disease detection systems have produced success stories and advanced the science of machine learning significantly. Compared to more conventional neural network layers, the deep convolutional layers automatically extract significant and reliable characteristics from the input space. Within the suggested structure, we use two convolutional neural network architectures (VGG16 and VGG19) to perform two types of medical imaging (x-ray and MRI) investigations in order to categorize brain tumors as normal and up normal as well as classify x-ray pneumonia or normal. The transfer learning strategies by VGG16 and vgg19 models that is, using MRI and x-ray to fine-tune and freeze are then examined in each investigation. In order to increase dataset samples and decrease the likelihood of over-fitting, data augmentation techniques are performed to MRI slices and x-rays for results that can be more broadly utilized. In the proposed studies, the fine-tune VGG19, VGG16 architecture attained highest accuracy up to 0.95with x-ray and 0.80 respectively. The accuracy0.98 used MRI in terms of classification and detection with VGG19.
The Specific Application and Meaning Expression of Folk Art in Urban Landscape Design Under the Optimized Interactive Genetic Algorithm
Authors: Haifeng Na
Published: May 2024

With the rapid and stable growth of China's economy and the rapid development of urban construction, urban complexes have sprung up in major cities. The lag in urban ecological environmental protection, maintenance and construction has brought great pressure to the needs of today's urban residents in terms of life, safety, and sense of belonging. In the major cities with many high-rise buildings in contemporary China, land resources are becoming more and more scarce, and the urban ecological environment is in urgent need of recycling, and due to the blind imitation of Western culture and design mode and the neglect of my country's traditional regional culture, the urban landscape lacks interaction, resonance, and sense of belonging with citizens, and the phenomenon of landscape similarity emerges in various cities, focusing on the landscape space of urban complexes. There are also these problems. Urban residents urgently need a third space that can adjust their physical, mental, and spiritual needs. How to design an urban complex landscape that meets the aesthetic needs and humanistic needs of contemporary cities and has regional characteristics has become the first important task of my research. Folk art is an artistic treasure created by the working people in their production and life. Folk art is the embodiment of cultural regionality and the foundation of national culture. It awakens people's awareness of the importance of local culture, awakens people's sense of belonging, and is closer to the local public life. Today, the living soil and social and humanistic environment of folk art are in the process of urbanization in our country, and there is a trend of gradual disappearance of lifestyle changes. How to make the contemporary urban complex landscape an organic soil for the survival, expression, application, and development of folk art is an important task in contemporary urban landscape design. Based on optimization, related concepts such as symbols, folk art symbols, urban complexes, urban complex landscape design, etc. have been sorted out. The relevant experimental results show that the construction land accuracy of the logistic regression model based on genetic algorithm has increased from 78.0% to 85.3%. kappa increased from 74.5% to 81.2%. Research shows that the logistic regression parameter optimization method based on genetic algorithm has better simulation effect than the conventional logistic regression method and is more suitable for the situation of unbalanced data distribution and many data features in the simulation of large-scale urban land dynamic changes.
Analysis of the influencing factors of students' vocational ability based on joint improvement of convolutional neural network and multimodal data
Authors: Yuzi Hu
Published: May 2024

Students' professional ability includes innovation and entrepreneurship ability, independent ability, quality ability, professional ability and adaptability. The school's practical training teaching and professional setting, as well as the unique talent training model have a profound impact on training and development of students' professional ability. The continuous progress and development of society indicates that the competitiveness of occupational positions in the future will continue to increase. The talent market will also put forward newer and more difficult requirements for the quality of students. In study and life, cultivating students' good professional ability is the fundamental purpose of colleges and universities. Employers tend to pay more attention to overall quality of candidates when selecting employees. Whether students can enter the society smoothly after graduation has become an important task. It can be seen that it is very important to analyze the cultivation of students' professional ability and the influencing factors of professional ability. This work combines convolutional neural network (CNN) with the analysis of factors influencing students' vocational ability. First, this work proposes an improved residual block (IRB). It increases the width of the residual network by adding a residual connection channel to learn richer features. Second, this work proposes an improved attention module (IAM). It combines channel attention with spatial attention to improve model performance. Third, this work combines IRB and IAM to propose an SVAIFANet. The model can be applied to the analysis of factors influencing students' vocational ability. Fourth, this work conducts various experiments for proposed method, experimental data verify correctness for this method.
STATISTICAL STUDY OF SOME INDICATORS IN THE LIVESTOCK SECTOR
Authors:   University of Craiova, Craiova, Romania, Ramona-Maria DIMITROV
Published: May 2024

In this paper, some indicators covering milk production, weight gain and feeding efficiency are studied, such as the duration of lactation, total amount of milk, daily amount of milk, percentage of fat, protein, lactose in milk, for a representative sample taken from S.C.D.A Simnic. In the first part, a descriptive statistical analysis is made of the indicators in the zootechnical field presented, followed by econometric methods regarding confidence intervals for the average of the entire population, at a threshold of 5%. Also, the correlation matrix between the indicators is analyzed and three validated linear regression models are presented with the interpretation of the results. These results help to optimize management in the zootechnical field by making decisions based on quantitative analyses.
RISK MANAGEMENT FOR COOPERATIVES OF SAVINGS AND LOANS AND SHARIA FINANCING: LESSON FROM INDONESIA
Authors:   UIN K.H. Abdurrahman Wahid Pekalongan, Indonesia, Rizky ANDREAN , Muhammad Khoirul FIKRI ,   UIN K.H. Abdurrahman Wahid Pekalongan, Indonesia, Santi Nailul IZATY ,   UIN K.H. Abdurrahman Wahid Pekalongan, Indonesia
Published: May 2024

This research aims to find out and describe the risks that exist in sharia financial institutions in the form of Cooperatives of Savings and Loans and Sharia Financing (KSPPS). This research uses a qualitative descriptive approach in the form of library research, which uses secondary data from journal articles and regulations relevant to the research topic. The research results show that there are various risks that KSPPS can experience. According to Minister of Labour Decree No. 193 of 2017, risk is the potential for financial losses and worsening social performance of Cooperatives of Savings and Loans and Sharia Financing/Sharia Business Units of Savings and Loans and Sharia Financing (KSPPS/USPPS). Based on these regulations, risk consists of strategic risk, financing risk, financial risk, and operational risk. Risk management in the management of KSPPS and similar institutions is an absolute priority to pay attention to. The risk often experienced by KSPPS is financing risk. Risk management in dealing with financing consists of risk analysis steps, which can be in the form of SWOT analysis, prudential analysis, 5C analysis, and 7P analysis, as well as risk handling mechanisms, which can be in the form of persuasive efforts, rescheduling, reconditioning, restructuring, combinations, and confiscation of collateral.
THE VALUE OF THE CORRELATION BETWEEN MILITARY LEADER CHARACTER AND ORGANIZATIONAL CULTURE AS VECTORS FOR INCREASING ORGANIZATIONAL PERFORMANCE
Authors:   “Nicolae Bălcescu” Land Forces Academy, Sibiu, Romania, Alexandru-Marius RIZESCU
Published: May 2024

In this article, I wanted to conduct an exploratory study to demonstrate the importance of the military leader's character and how performance growth is achieved in the military organization. The study seeks to identify whether there is a significant relationship between the interconnections, implications and valences of military leader character related to increasing organizational effectiveness and organizational efficiency, organizational performance, and the significance by which character enhances the valences of the military leader's skills and the military of each military structure. The issue of character is becoming a major requirement and is considered essential to the professional performance of a military leader. Thus, the formation and development of those professionally valuable character traits in association with organisational culture, performance and professional competence is a complex, timely and comprehensive area of organisational concern.
TYPES OF RISKS WITHIN PROJECTS
Authors:   University of Craiova, Craiova, Romania, Irina Andreea PEGULESCU
Published: May 2024

The realization of a project requires the application of stages, which are often prone to the appearance of risks. They can affect, negatively or positively, the evolution of a project, from all points of view. A good identification and framing of risks can lead to their effective management, throughout the life of a project, in order to implement an effective project. Through the present research, we consider the global framing of risks, to support their effective management, for the benefit of projects.
THE POTENTIAL FOR THE DEVELOPMENT OF CARBON MARKETS IN ASIA AND AFRICA IN ACHIEVING GLOBAL CARBON NEUTRALITY
Authors:   Institute for Regional Economics Studies Russian Academy of Sciences, Dregulo A. M. , Khodachek A. M. ,   Higher School of Economics
Published: May 2024

Although carbon regulation is situational and immature on the global agenda, it is clearly be-coming the dominant factor of competitiveness in foreign markets. As a result, the role of the introduction of carbon units for economic growth in the coming years will increase rapidly, espe-cially for countries in which the domestic market of carbon units is not sufficiently deficient. The article examines the current problems and prospects for the development of carbon markets in Asia, Africa and the Middle East, such as the lack of an institutional regulatory system, lack of infrastructure and control over carbon markets, but nevertheless which have a fairly high potential for development in the global economy.
SOCIAL INVOLVEMENT IN THE IMPLEMENTATION OF THE CONCEPT SUSTAINABLE DEVELOPMENT AS A FACTOR OF REGIONAL GOVERNANCE
Authors:   Institute for Regional Economics Studies Russian Academy of Sciences, Zamyatina M. F.
Published: May 2024

The article discusses the modern concept of sustainable development based on the principles of ESG as a response to global challenges, substantiates the need for social involvement of the population, business, authorities in order to speed up the process of implementing this concept in regional management, briefly analyzes the barriers, the main components of social involvement and methodological approaches.
ANALYSIS OF THE IMPACT OF URBANIZATION ON HEALTH INDICATORS ENVIRONMENT OF THE REGIONS OF THE RUSSIAN FEDERATION
Authors:   Higher School of Economics, Kutepova P. S. , Kolchinskaya E. E. ,   Higher School of Economics; ICSEI Leontief Center, Vaikhonsky A. E. ,   St. Petersburg State University of Economics
Published: May 2024

Urbanization, as a process of increasing the role of cities and urban infrastructure, is intricately connected with the sustainable development of territories. It is important to understand the effects of the increasing rate of urbanization on the ecological state of Russian Federation’s regions. A dataset was formed, and a regression analysis was conducted, which indicated the presence of both positive and negative effects of urbanization. On the one hand, with an increase in the share of the urban population, the number of MSW increases, and the condition of the air worsens, because emissions of pollutants into the atmospheric air from motor transport increase. On the other hand, the condition of the water basin is improving, as well as the number of exported MSW from the territories of the regions of the Russian Federation is increasing. It was concluded that it is necessary to monitor and control the state of the environment, as well as the use of environ-mental innovations to minimize the harm caused to the environment.
LABOR POTENTIAL IN THE DIGITAL PARADIGM ECONOMICS AND PUBLIC ADMINISTRATION
Authors:   Institute for Regional Economics Studies Russian Academy of Sciences, Sviridenko M. V. , Leontieva A. N. ,   Institute for Regional Economic Studies Russian Academy of Sciences, Bublis O. M. ,   St. Petersburg State University
Published: May 2024

The digital transformation of the economy and society is an objective process that determines the contours of the future socio–economic development of the regions of Russia. The article examines the theoretical interpretations of the concept of "labor potential", using the example of the subjects of the North-Western Federal District of the Russian Federation using content analysis and SWOT analysis methods, strategic planning documents are considered: state programs to promote employment and strategies for digital transformation of the regions of the North-West in order to determine the degree of consistency of the regulatory framework for the development of the labor market and the sphere employment and the digital economy. Recommendations are given on the need to adapt state pro-grams in the field of employment to the requirements of the digital economy, ensuring synchronization of target blocks and tools for the implementation of strategic planning documents in the field of em-ployment and digital development of the subjects of the Russian Federation.
Medical Students’ General Beliefs and Specific Perceptions about Patient Feedback Before and after Training in a Clinical Context
Authors: Eijkelboom M. Charlotte L. , De Kleijn Renske A. M. , Frenkel Joost , Van der Schaaf Marieke F.
Published: May 2024

Introduction: Despite its high potential, patient feedback does not always result in learning. For feedback to be effective students must engage with it, which partly depends on their perceptions of feedback. To better understand student engagement with patient feedback in a clinical context, this study explored the following research questions: 1) What are medical students’ general beliefs about patient feedback and what are their specific perceptions of feedback messages? 2) What is the difference between these general beliefs and feedback message perceptions before and after patient feedback training?
Methods: The study context was a 12-week clerkship combining Pediatrics and Gynecology, which included feedback training for students and asking for patient feedback. Ninety 4th-year medical students completed pre- and post-clerkship questionnaires. The questionnaires (Beliefs about Patient Feedback Questionnaire, Feedback Perception Questionnaire) were adapted from validated peer-feedback questionnaires. Questionnaires were quantitatively analyzed.
Results: Both pre- and post-clerkship, students had positive general beliefs about patient feedback and positive perceptions of the feedback messages they received. However, paired t-tests showed that students’ general beliefs and feedback message perceptions became less positive after feedback training and experience.
Discussion: Patient feedback is not an easy means to learn and students do not become feedback literate in terms of patient feedback overnight. We suggest that future researchers further explore reasons for the decline in positive perceptions of patient feedback. We suggest implementing longitudinal feedback training in medical curricula, where students are guided and supported in the complex task of learning from patients through feedback.
Phenotypic and Genotypic Characterization of Sheep Breeds in Diverse Habitats of Baluchistan Province through the Analysis of the vertnin Gene
Authors: Rameez Raja Kaleri  , Hubdar Ali Kaleri  , Ghulfam Ali Mughal  , Ahmed Nawaz Khosa
Published: May 2024

This study was conducted to identify the vertnin gene in four different sheep breeds of Balochistan by studying their genetic characteristics and analyzing molecular markers. For this purpose, four different sheep breeds, Balochi, Rakhshani, Harnai, and Bibrik, were selected. A total of 10 animals from each breed were chosen to study the impact of breed and the vertnin gene on carcass features. The selection of animals was based on their phenotypic characteristics such as age and teeth. Blood samples of 10 ml were collected and transported to the Laboratory of Molecular Genetics at the Faculty of Veterinary and Animal Sciences, LUAWMS Uthal, Balochistan. Genomic DNA (gDNA) extraction was carried out using a modified inorganic method. The results showed that Balochi had a change in amino acid cytosine (C) > into thymine (T) mutation, Rakhshani had a change in guanine (G) > into adenine (A) mutation, Bibrik had a change in adenine (A) > into cytosine (C) mutation, and Harnai had a change in thymine (T) > into cytosine (C) mutation. The maximum divergence was 0.15715 bp in relation to the comparison in all studied sheep breeds. The phylogenetic results showed that the Balochi breed has 95–97% similarities with the other breeds when sequences were compared. The results revealed that the gel-electrophoresis bands of the Balochi breed were slightly higher, respectively. It was concluded that the vertnin gene exists in different breeds of sheep and might show a broad range of differences, even with similar DNA sequences. However, the purity based on the vertnin gene is at high risk, as a notable difference was found between the Balochi and Harnai sheep.
PCR-based Detection and Prevalence of Theileria Species in Sheep from Quetta District, Balochistan
Authors: Tariq Nabeela , Khan Maria , Shaikh Tahreem , Zil e Huma  , Shakeela daud
Published: May 2024

Background: Theileriosis is a serious hemoparasitic illness that severely limits the production of small ruminants. The current study aims for early and accurate detection of theileriosis using polymerase chain reaction for treatment as conventional techniques are not specific. Blood samples were collected from sheep between July and October of the year 2020. A total of 100 samples were taken, 69 samples were from tick-infested sheep exhibiting illness signs, while 31 samples were from tick-free sheep.  To detect Theileria species in sheep, DNA was extracted, and the PCR method was used. The prevalence of T. lestoquardi and T. ovis was 22.47% and 64.04%, respectively, with a 13.48% frequency of mixed infection with both species. T. ovis was detected in around 80% of tick-infested sheep, indicating a significant association (P<0.01) with ticks. T. lestoquardi was found in 28.99% of tick-infested sheep, indicating a significant connection (P<0.001) with ticks. The incidence of T. ovis was significantly related to the summer season but T. lestoquardi was non-significantly related to the summer season (P>0.05). A high prevalence of Theileriosis is found in Quetta. The parameters studied were strongly correlated to the infection except for T. lestoquardi with the summer season. Early detection with the help of polymerase chain reaction can accelerate treatment and reduce transmission to increase livestock production in Pakistan.
Caffeine-Containing Local Products and Their Effects on Liver and Kidney Histopathology: A Comparative Study
Authors: Javed Anam , Rabia Manzoor  , Muhammad Adnan  , Ghulam Hayder  , Muhammad Aqil  , Saman Gul e , Ashfaq Gulshan
Published: May 2024

Caffeine has several modified natural and synthetic forms and it is the most abundantly consumed drug around the globe. It is present in fruits, seeds and leaves of many plants but coffee contains highest content of caffeine and is considered its richest source. It is also found in much high ratio in non-alcoholic beverages, i.e., tea and carbonated drinks like coke. The aim of current mammalian model based investigation was to evaluate the possible histopathological variations influencing the function of kidney and liver due to the intake of selective caffeinated products: Arabica and Robusta beans’ blend based coffee and coke. The 30 albino mice of male gender and 1.5 month age were acclimatized for  ten days and later on, categorized as group I (control group which was treated with standard mice feed), experimental group II (treated with coffee blend) and experimental group III (treated with coke). The renal and hepatic histological sections were cut and stained for weekly observations. Hematoxyline and eosin staining was used and microscopic observations were recorded at 40X. The ANOVA based statistical analyses showed that significant variations occurred in body weight, diameter of renal blood vessels, glomeruli and of necrotic areas of both experimental groups (p ≤ 5%). Moreover in hepatic tissues, vein wall thickness, diameter of bile ducts and liver lobules also indicated significant variations. The conclusion is excessive and regular intake of above mentioned composition having coffee and coke may result in diverse physiological disturbances and a programmed general public awareness is required regarding their limited intake.
Prevalence of Microplastic Pollution in Freshwater Ecosystem: A Case Study of Thal Canal
Authors: Syed Daniyal Kazim Naqvi  , Batool Aniqa , Muhammad Asad Ghufran  , Rauf Zeeshan , Syeda Umme Kulsoom  , Iqra Parveen  , Asif Ali Shah
Published: May 2024

Microplastic pollution in the aquatic ecosystems is a hot global debate due to wide spread effects on human life and environment. This study aims to identify microplastic pollution presence in the Main Line (ML) Thal canal, its distributaries and provide an overall estimation of microplastics concentration in surface water of Thal Canal. Six major sampling points at ML canal and distributaries were assessed for microplastic prevalence out of which five locations showed microplastic contamination. Size, structure and type of microplastic were assessed using light microscopy and FTIR. The study provided baseline information about the prevalence of microplastics in Thal Canal and evaluated their categories according to their size, color and type of polymers. Microplastics concentration in the canal ranged from 6.4 ± 0.5 to 8.8 ± 0.5 particles/m³. Films, mostly transparent in coloration, were the most prominent microplastic type appeared in this study. Polyethylene with 55% presence was the most prevalent type of microplastics found in the canal and the distributaries. This study provides a better understanding of the extent of microplastic pollution assessment in Thal canal with equal emphasis on Microplastic presence in distributaries which may be beneficial in identifying the introduction of microplastics at sources.
Enfrentamento do estresse ocupacional na percepção da enfermagem paliativa durante a pandemia pela COVID-19
Authors: Santiago Fernanda Barcellos , Abrahão Ana Lúcia , Cunha Daianny Arrais de Oliveira da , Siqueira Alex Sandro de Azeredo , Kirby Endi Evelin Ferraz , Beserra Vanessa dos Santos , Neves Luciene Miguel Lima
Published: May 2024

Objetivo: Compreender as principais estratégias utilizadas para reduzir o estresse psicológico, na percepção da equipe de enfermagem na atenção paliativa oncológica, durante a pandemia da COVID-19. Método: Estudo exploratório descritivo com abordagem qualitativa ancorado na psicodinâmica do trabalho de Christophe Dejours como referencial teórico. A pesquisa foi realizada no setor de internação e emergência de uma unidade hospitalar pertencente a um centro de referência no tratamento de câncer em cuidados paliativos no Rio de Janeiro/Brasil. A coleta de dados ocorreu entre os meses de abril e julho de 2020. A amostra foi composta por 20 membros da equipe de enfermagem da assistência aos pacientes em cuidados paliativos oncológicos com suspeitas e confirmados para coronavírus. Coleta de dados através de entrevista semiestruturada, com dados organizados e analisados com base no referencial metodológico do Discurso do Sujeito Coletivo (DSC), através do Qualiquantisof®. Resultados: (DSC): DSC1- “Espiritualidade como estratégia na redução do estresse psicológico”; DSC2- “Apoio terapêutico como estratégia na redução do estresse psicológico”; DSC3- “Valorização profissional como estratégias na redução do estresse psicológico”; DSC4- “Educação/Cursos de capacitação como estratégias na redução do estresse psicológico”. Conclusão: o estudo propiciou um canal de comunicação entre os profissionais, e indicou alternativas para minimizar o estresse, como: rodas de conversas, assistência psicológica e nutricional, educação continuada para estabelecer alinhamento de diretrizes e fornecer conhecimento no enfrentamento ao COVID-19, apoio e melhor comunicação com a instituição.
Wind speed prediction
Authors: Li Alvin Xianghan
Published: May 2024

With the help of wind farms, wind energy is a vital renewable energy source that contributes significantly to the worlds energy balance. The lifespan and maintenance costs of wind turbines will be reduced with an accurate wind speed prediction. On the other hand, wind speed is highly volatile and unpredictable. Thus, it is essential to do research into creating complex models and algorithms for precise wind speed prediction. So far, some of the most promising models include Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Autoregressive Moving Average (ARMA). Python, as an advanced and versatile programming language, is exceptionally suited for scripting the algorithms of these sophisticated models. This paper will use the data from Austin Texas and apply a Support Vector Machine (SVM) for wind speed prediction involves several stages, including data collection, data preprocessing, model selection, model training, parameter optimization, model validation, and prediction. Wind energy resource optimisation, maintenance cost reduction, and total wind farm efficiency can all be significantly improved by incorporating these models into predictive analytics and continuously improving them against changing data.
Historical and Future global warming under different RCP emission scenarios
Authors: Xue Liminhao
Published: May 2024

Understanding the relationship between carbon emissions and climate change has become increasingly urgent amidst the universal endorsement of sustainable development and the establishment of net-zero carbon emission target (The Paris Agreement). In this study, model projections based on Coupled Model Intercomparison Project Phase 5 (CMIP5) are used to examine future climate change under different representative concentration pathway (RCP) emission scenarios till 2300. Besides analysis of future global average temperature change under the emission scenarios of RCP2.6, RCP4.5, RCP6.0, and RCP 8.5, historical data is first collected during the past three decades (1990-2021). The results show that the simulated global warming ranges from 0.9 to 1.3 between 1990 and 2021, consistent with the observations (0.93). Carbon emissions in China arose mostly from manufacturing and construction, electricity and heat and transport. Emissions from electricity, heat and transport increase by a high ratio of 7.7 and 3.5 times due to industry development. Under different RCP scenarios, the global surface temperature anomaly will increase by 0.2-1.0, 1.2-2.0, 3.5-4.3 and 3.0-8.5 , respectively. The surface temperature anomaly values under RCP8.5 are much higher than those under other emission scenarios. Only in the RCP2.6 scenario (sustainability), the surface warming in all simulations stays below a 2 threshold at the end of the 21st century. To attain net zero emissions, it is highly demanded to develop strict emission policies, chart a course toward decarbonization, and institute effective measures aimed at realizing net zero emissions within the short to medium term. Concurrently striving for economic objectives, managing the equilibrium between economic growth and environmental sustainability emerges as a critical imperative.
Plane and vertical design research on end-around taxiways at high plateau airports
Authors: Wu Shitao
Published: May 2024

The end-around taxiways have been proven to effectively reduce the risk of runway incursions caused by frequent aircraft crossings on closely spaced parallel runways, thus enhancing airport capacity. This practice has gained popularity in recent years, especially in large airports. However, there is currently limited experience in designing and operating end-around taxiways, particularly in high plateau airports facing challenging conditions such as a high water table and low obstacle clearance gradient. In this paper, we present a case study of the second runway project at a specific airport and propose various operational schemes for end-around taxiway construction, including straight, oblique, and controlled designs. We calculate aircraft payload under different obstacle clearance gradients using flight performance analysis. Taking into account both operational and groundwater levels, we determine the appropriate plane and vertical design of end-around taxiways. The findings of this research provide valuable references for the design of end-around taxiways.
Altered biotoxicity of cadmium to freshwater green algae by different concentrations of polystyrene
Authors: Han Mu Chen
Published: May 2024

Microplastic pollution has become a global problem, threatening water bodies and aquatic organisms around the world. Heavy metal pollution in the aquatic environment is an environmental issue of long-term concern, and relatively few studies have been conducted on the compound toxic pollution of freshwater algae by microplastics and heavy metals. Therefore, in this study, Cd and polystyrene (PS) were selected as representative heavy metals and microplastics in the environment, and the toxic effects of the two pollutants on freshwater algae were investigated by determining the biomass of algal growth, chlorophyll a content, and the bioconcentration of heavy metals. It was found that the two pollutants alone could inhibit the growth and photosynthesis of the algae, and the toxicity increased with the increase of concentration. When the two pollutants were combined, the toxicity of Cd depended on the concentration of PS, and both low (1 mg/L) and high (100 mg/L) concentrations of PS increased the toxicity of Cd in a synergistic manner, whereas the medium concentration (10 mg/L) of PS showed an antagonistic effect, which was able to inhibit the toxicity of Cd.
Performance analysis and application fields of reactive powder concrete
Authors: Zhao Yuchen
Published: May 2024

Reactive Powder Concrete (RPC) is a novel concrete material prepared from highly active composite admixtures, cement, fine sand, and micro steel fibers through appropriate curing and processing techniques. RPC not only exhibits ultra-high strength and durability but also demonstrates outstanding toughness, good volume stability, and excellent environmental performance. Its use can reduce the demand for natural resources, extend the service life of structures, and reduce waste generation, aligning with the direction of sustainable development and green building. This makes it an ideal choice for constructing large-span lightweight structures and structures operating in harsh environmental conditions, attracting extensive research attention in the international engineering materials field. In summary, RPC, as a new type of concrete material, has broad application prospects in the construction field. With continuous research and technological development, RPC will undoubtedly bring more innovations and new solutions to the construction industry, promoting its continuous progress and development.