bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of The Arts in China and Belarus in the Context of State Policy
Authors: Wang Yixuan
Published: March 2024

In the history of international relations, cooperation in the field of art is closely connected with the foreign policy aspect of the development of interstate relations. As Belarusian-Chinese interaction deepens, the cultural factor has become one of the important aspects influencing bilateral relations. This article notes that over the 30 years since the establishment of diplomatic relations between Belarus and China, cultural cooperation has gone through different periods. The history of the development of contacts in the field of art between the two countries is examined, the achievements and current problems faced by both countries in this area are analyzed. An example of cooperation between Belarus and China in the field of art represented by the National Gallery of Belarus and the National Art Museum of China is noted.
Innovative Application of Traditional Patterns in Product Packaging Design
Authors: Wang Yan , Park Jongbin
Published: March 2024

The packaging of goods is aimed at promoting sales, and modern packaging design has gradually transitioned from a focus on practical functionalism to meeting the emotional needs of customers. This means that while ensuring basic functions such as product protection and information display, it is also necessary to further deepen the cultural value of the product. Introducing traditional patterns into product packaging presents a brand new visual expression and ethnic emotional resonance, thereby forming an emotional and cultural sense of identity in the minds of consumers and promoting product sales.
Research on Campus Cultural and Creative Product Design based on Rooting Theory
Authors: Sun Yuxuan
Published: March 2024

To address the problem of homogenization of campus cultural and creative products at this stage, and to build campus cultural and creative products that meet consumer needs by combining campus characteristics. This study conducted semi-structured interviews with teachers, students, and staff on campus and coded and analyzed the interview texts using the rooted theory method. Through the analysis, we obtained a demand value embodiment model containing 12 core concepts and three levels of functional value, experiential value, and innovation value. It enables the design of campus cultural and creative products to better trigger users' emotions, disseminate campus culture, become an important medium for interpreting campus culture and provide a new step-by-step reference and practical path for the application of campus cultural and creative product design.
The Innovative Application of Hubei Shadow Play Art in Literary and Creative Stationery
Authors: Zheng Zexi
Published: March 2024

Hubei shadow play art, as a national intangible cultural heritage, has a long history and unique artistic charm. This study focuses on the innovative application of this art form in the emerging cultural and creative stationery industry, and analyzes its current situation in the contemporary cultural and creative field as well as the problems and challenges it faces. By extracting the classic elements of Hubei shadow play art, combined with modern design and production technology, this paper explores the innovative transformation mode and market feedback of shadow play art in cultural and creative products, and examines the balance between cultural protection and commercial innovation. In addition, in view of the dilemma of market competition and brand building, this study puts forward the strategy of industrial transformation and upgrading, and emphasizes the importance of strengthening the cultivation of cultural and creative talents and international exchanges and cooperation. The results of this research not only provide theoretical guidance for the inheritance and innovation of Hubei shadow play art, but also contribute new experience and perspective to the development of cultural and creative industry.
Football Momentum Analysis based on Logistic Regression
Authors: Wen Zilu , Liu Jinyu , Liu Chenxi
Published: March 2024

In tennis, momentum is pivotal and can be quantified using metrics like Consecutive Win Rate (CWR), Unforced Error Rate (UER), Break Point Save Rate (BPSR), and Fatigue Factor (FF). Each metric provides insight into a player's performance and state during a match. CWR is a clear momentum indicator, reflecting a player's game dominance, while UER highlights potential lapses in concentration or physical condition. BPSR evaluates a player's clutch performance in critical situations, and FF gauges physical exertion. Utilizing logistic regression, we can predict a player's probability to win at any scoring point, incorporating these metrics as variables. The coefficients obtained from MATLAB analysis (e.g., p1_cwr at 22.73 and p2_ff at -3.26) reveal the positive or negative correlation of these factors with a player's winning chances. In the case of the "2023-wimbledon-1301" match, the logistic model's predictions showed a symmetrical distribution of win probabilities between players, suggesting a balance in momentum swings throughout the match. Initial volatility in Player 1's success rate indicated a strong start, which diminished over time, possibly due to fatigue or the opponent's improving performance. Despite the fluctuations and a period of deadlock, Player 1's consistent performance and superior win rate for most of the game secured the victory. This outcome underscores the importance of maintaining momentum and physical resilience in tennis.
Analysis of Health Data for the Elderly based on Medical Website Mining
Authors: Tian Xue , Wang Xia , Li Ying
Published: March 2024

The development of Internet medicine provides a convenient platform for doctor-patient communication, and provides an important data source for paying attention to the health of the elderly. In this study, a large number of consultation records obtained from the medical website was screened and cleaned, and the medical thesaurus was generated by training of its own data to solve the problem of inaccurate professional terminology segmentation caused by using default jieba segmentation. At the same time, we use the trained medical thesaurus to conduct topic mining of the consultation data, and it is found that the most concerned problems in the field of elderly health are cerebro-cardiovascular, pulmonary and stomach diseases, so as to provide further medical advice and targeted services.
A New Approach to Enhance Smart City Applications Through Integrating GIS BIM Technology
Authors:   Building Information Modeling and Management Master Programme, Syrian Virtual University, Bushra Bushra ,   Professor in the Master's Program in Building Information Modeling and Management at the Syrian Virtual University, Maya Rana
Published: March 2024

The research deals with the study and analysis of smart cities, the requirements for their establishment, and the role of Building Information Modeling (BIM) in building sustainable, energy-efficient smart cities. The primary objective is to put a smart city transformation methodology and apply it on Lattakia city as a case study, using Building Information Modeling (BIM). We proposed new tools in an integrated methodology to transform from ordinary to smart city, and implemented basics of its objects, including GISBIM technologies integration, insuring both sustainable and energy saving vision at general planning designs. Implementing the chosen methodology steps at an organized area in Syria – Lattakia City, starting with building an accurate geodatabase, then, obtaining Sun Shadow Frequency analysis, which shows that December contains the highest shade values, with cumulative coverage rate 79.84%, while June instead contain the lowest shade value, reaching only 33.84%. But in general, most of the area has shade coverage that does not exceed 15% most of the day, therefore no need to make an adjustment to the distribution of building blocks. After that, in order to secure the energy needed for the study Area, analyzing the Land Surface Temperature (LST) done using the ArcGIS Pro® software, and locating a renewable energy station site (solar plant). On the other hand, analyzing a residential building energy through annual PV analysis has been done, by using Revit® software through Insight™ extension, with two cases: first one, building without changing building material, while second one done after changing building material to save solar energy inside and rising roof slope up to 10%, and we achieve valuable energy savings by applying this modification to building material. And finally, introducing the infrastructure underground network, that serving transformation to smart city.
Alzheimer's disease intelligent detection combining XGBOOST and NARX
Authors: Wei Peng
Published: March 2024

Due to the current situation of mental health illness, which causing a huge impact on the society. In this paper, an attempt has been made to analyses and predict the data from ANDI using single and composite algorithms. This paper used chi-square test, Spearmans correlation coefficient and maximum mutual information number, cost-sensitive learning, SMOTE, ADASYN, SMOTE+ENN, SMOTE+TOMEK to investigations. Specifically, this paper adopted the random forest to fill the data, and besides, given the fact that the data shows the characteristics of imbalance, this paper identifies the method of SMOTE TOMEK integrated sampling, XGBOOST and Bayesian optimization scheme to give the best performance and the best classification was obtained by XGBOOST combined with SMOTE-TOMEK. Furthermore, this paper used the NARX network to track the changes generated by time-based indicators, providing another insight to refine the study of intelligent diagnosis of Alzheimer.
Comparison of K-Means, K-Medoids and K-Means++ algorithms based on the Calinski-Harabasz index for COVID-19 epidemic in China
Authors: Hu Zhengcao
Published: March 2024

The novel coronavirus spreads from person to person through close contact and respiratory droplets such as coughing or sneezing. Various studies have been conducted globally to deal with COVID-19. However, no cure for the virus has been found , and efficient data processing methods for sudden outbreaks have not yet been identified. This study compares three algorithms for data sets to analyze clustering patterns to determine the best data processing method. The data of this study comes from the Chinese Center for Disease Control and Prevention, including two attributes of confirmed cases and death cases. We selected the data from the initial stage of the outbreak until October 31, 2021. We compared the data analysis and processing results of the clustering of the spread of the new coronavirus in China by the K-Means, K-Medoids and K-Means++ algorithms. By comparing the Calinski-Harabasz index values from K=2 to K=10, the results show that the K-Means, K-Medoids and K-Means++ algorithms have almost the same clustering effect when K does not exceed 6, but when the K value is greater than 6. When the K-Medoids clustering effect is significantly better, therefore, from the three clustering algorithms used, it can be concluded that the best method for clustering the spread of the novel coronavirus outbreak in China is the K-Medoids method. The results of this study provides ideas for future researchers to choose an appropriate cluster analysis method to effectively process the data in the early stages of the epidemic.
Signal detection algorithms for massive MIMO system
Authors: Yang Yanbo
Published: March 2024

As 5G communication networks are maturing, we have higher and higher requirements for the detection of communication signals. In this paper, for the Massive MIMO system signal detection problem, we mainly summarize the detection algorithms that can be used to replace the traditional ZF and MMSE, so as to avoid large-scale matrix inverse and reduce the computational complexity. It mainly includes the general iterative method, typically represented by SSOR, which makes the transmit signal matrix constantly close to the ideal value by iterating; the other is the level expansion class solution method, which takes the order expansion of the level as the initial value of the iteration to accelerate the convergence rate of the algorithm, typically represented by the MLI algorithm. However, today where the demand for communication is gradually increasing and the number of users is constantly getting larger, the performance of the above algorithms may degrade seriously, so the AI signal detection algorithm is a good alternative, which learns autonomously through deep neural networks, including model-driven and data-driven schemes.
House price prediction based on different models of machine learning
Authors: Chuhan Ni
Published: March 2024

Housing price prediction is a typical regression problem in machine learning. Common algorithms include linear regression, support vector regression, random forest, and extreme gradient boosting models based on integrated learning methods. Among the specific problems, different models in the specific problem will get different results. This research will compare these three models to show which model is more accurate and robust. Given the practical problem of housing price prediction, various characteristics of houses are carried out. The research will analyze and study, apply a variety of regression models, and compare the performance of the above three models on this problem, make the horizontal comparison of the advantages and disadvantages of different models, and analyze the difference in effect Line analysis and summary.
The advantages and short circuit characteristics of SiC MOSFETs
Authors: Li Ruizhe
Published: March 2024

SiC MOSFETs have exhibited considerable benefits in high-frequency, high-voltage, and high-temperature power electronics applications with outstanding material attributes as a result of the rapid advancement of power electronics technology. SiC MOSFETs slower short-circuit tolerance and faster switching rates provide new issues for the short-circuit prevention technology. In the opening section of the study, Si and SiC MOSFETs are compared and evaluated using various models and parametric factors. It has been demonstrated that SiC MOSFETs outperform Si MOSFETs in a variety of conditions and applications. The many SiC MOSFET short-circuit failure types as well as their underlying theories are initially explained in the papers main body. In addition, it examines the fundamentals of short-circuit test procedures and SiC MOSFET test circuits. The issues and limitations of the currently available SiC MOSFET short-circuit protection technology are then explored, along with factors impacting the short-circuit of SiC MOSFETs that are thoroughly examined. Lastly, the SiC MOSFET short-circuit protection technology development trend is forecasted, and potential future areas for improvement and innovation are considered. SiC MOSFET short-circuit protection technology will be enhanced and optimized to satisfy the needs of efficient and dependable power electronic systems as technology advances and application requirements expand.
Assessing and neutralizing multi-tiered security threats in blockchain systems
Authors: Cai Yize
Published: March 2024

Blockchain technology, the backbone of digital cryptocurrencies, has rapidly ascended as a pivotal tool in modern commerce due to its decentralized, immutable nature. It offers fresh, innovative avenues for overcoming trust issues inherent in traditional trading systems. Yet, the unique traits that make blockchain advantageous also render it vulnerable. Cybercriminals are ceaselessly innovating, devising new tactics to exploit these vulnerabilities and resulting in a surge of security incidents that have led to substantial economic losses. The increasing frequency and sophistication of these attacks jeopardize the integrity and stability of blockchain networks. This paper offers a comprehensive study of blockchain system architecture, the principles underlying various attack methods, and viable defense strategies, all organized within a hierarchical framework. Initially, the paper categorizes blockchain attacks according to the hierarchy of blockchain systems, providing a detailed exploration of the characteristics and principles behind these attacks at each level. Next, the paper summarizes existing countermeasures and proposes effective new strategies for bolstering blockchain security. The paper concludes with a recap of its key findings and outlines the landscape for future research in blockchain security.
Distributed U-net model-based image segmentation for lung cancer detection
Authors: Hu Tianzuo
Published: March 2024

Until now, in the wake of the COVID-19 pandemic in 2019, lung diseases, especially diseases such as lung cancer and Chronic Obstructive Pulmonary Disease (COPD), have become an urgent global health issue. In order to mitigate the goal problem, early detection and accurate diagnosis of these conditions are critical for effective treatment and improved patient outcomes. To further research and reduce the error rate of hospital diagnoses, this comprehensive study explored the potential of Computer-Aided Design (CAD) systems, especially utilizing advanced deep learning models such as U-Net. And compared with the literature content of other authors, this study explores the capabilities of U-Net in detail and enhances the ability to simulate CAD systems through the VGG16 algorithm. An extensive dataset consisting of lung CT images and corresponding segmentation masks, curated collaboratively by multiple academic institutions, serves as the basis for empirical validation. In this paper, the efficiency of U-Net model is evaluated rigorously and precisely under multiple hardware configurations, such as single CPU, single GPU, distributed GPU and federated learning, and the effectiveness and development of the method in the segmentation task of lung disease are demonstrated. Empirical results clearly affirm the robust performance of the U-Net model, most effectively utilizing four GPUs for distributed learning, and these results highlight the potential of U-Net-based CAD systems for accurate and timely lung disease detection and diagnosis huge potential.
Empirical validation of federated learning with YOLO v7-tiny for road sign detection: A simulation-based comparative study
Authors: Bi Yizhen
Published: March 2024

Detecting road signs is a critical component in the development of intelligent driving systems. While centralized machine learning approaches have demonstrated potential in this field, the untapped potential of Federated Learning warrants exploration. This research aims to bridge this gap by examining the feasibility of applying Federated Learning within edge Artificial Intelligence (AI) computing environments for the purpose of road sign detection. Utilizing the You Only Look Once (YOLO) v7-tiny model and a range of experimental parameters demonstrates that Federated Learning is viable and outperforms centralized approaches under specific conditions. The study's empirical analysis highlights the sensitivity of detection accuracy to varying experimental parameters. The study contributes to the existing literature by establishing the efficacy of Federated Learning in road sign detection, particularly in edge AI settings constrained by hardware limitations and privacy concerns. However, the study acknowledges limitations, including the lack of deployment on actual edge AI devices and a restricted range of experimental parameters. Future research should aim for more exhaustive experiments with broader datasets, diverse parameters, and real-world edge AI environments. These findings offer valuable insights for future implementations in intelligent automotive systems.
An overview of the application of data mining in the campus
Authors: Yao Chengxiang
Published: March 2024

With the advent of campus informatization, it has been observed that the utilization of big data has the potential to facilitate the integration of education and enhance students' learning efficiency. The utilization of big data technology has been increasingly prevalent in contemporary society, leading to a growing reliance on its applications by the general population. Consequently, there is a rising need for educational platforms that can cater to this evolving demand. In the present scenario, China is persistently augmenting the development of campus infrastructure with the aim of attaining modern education objectives and ushering in a new era of educational facilities. The utilization of big data technology, its current status in contemporary usage, and the strategies for achieving widespread adoption have emerged as crucial concerns within the present wave of information technology throughout educational institutions. Simultaneously, the utilization of data mining technology enhances the learning environment for students, mitigates the constraints of campus intelligence, and offers substantial assistance for many educational endeavours inside the campus setting. The application of data mining technology for the advancement of campus management has emerged as a pivotal focal point and a significant breakthrough. This research examines the concept of the convenience zone in data mining and explores its application policy by conducting a comprehensive assessment of relevant literature. The study reveals that there is a lack of widespread awareness and adoption of big data in various contexts where it is necessary. Additionally, the potential utilization of big data at educational institutions, namely on campus, is explored.
Investigating and mitigating blockchain poisoning attacks
Authors: Hu Haoyu
Published: March 2024

With the burgeoning adoption of smart contracts and the intrinsic anonymity that blockchain technology provides, the past few years have witnessed an exponential surge in the deployment of blockchain platforms for managing crypto assets. While Ether stands as a paragon of blockchains equipped with smart contract capabilities, its noteworthy to mention that such blockchains present a blank canvas in the form of a data list, dedicated for unbridled program storage. This vacant space, though pivotal for various operations, can become a potential Achilles heel. Specifically, if this repository gets tainted with malicious data, the repercussions can reverberate across the entire blockchain, disrupting its core functions. The research in focus shines a spotlight on a particularly insidious threat, termed blockchain poisoning attack. Through this stratagem, malevolent actors can, with minimal expense, embed detrimental files within the data space, thereby extensively polluting the blockchain. This isnt just a theoretical hazard; its practical implications are stark and can jeopardize the integrity of countless transactions. In a commendable feat of investigative rigor, the research meticulously decoded the modus operandi behind these blockchain poisoning attacks. By employing a multi-pronged research approach, encompassing in-depth studies and iterative inquiries, it successfully deconstructed the exact techniques that malefactors employ.
Federated Learning-based neural networks for autonomous driving
Authors: Ma Tingyu
Published: March 2024

An emerging technology with the capacity to revolutionize the transportation sector is autonomous driving, offering the promise of heightened safety, efficiency, and convenience. However, the widescale deployment of autonomous vehicles presents a multitude of challenges, notably the necessity for robust and adaptable machine learning (ML) models capable of handling a wide array of dynamic real-world scenarios. Enter Federated Learning (FL), a decentralized ML approach that has gained recognition as a potential solution to these challenges. This paper delves into the primary advantages of FL within the context of autonomous driving. It highlights FL's capacity to seamlessly adapt to edge devices, respond to localized changes, and continually enhance safety and performance. The document substantiates these advantages through numerous case studies and empirical evidence, demonstrating how FL can potentially elevate the vision, decision-making, control systems, data transmission, and learning model capabilities of autonomous vehicles. By harnessing the collective intelligence of autonomous vehicles while preserving data privacy and security, FL holds the potential to propel us closer to a future where safe, efficient, and autonomous transportation becomes an attainable reality.
Federated Learning-based neural networks for airline passenger satisfaction prediction
Authors: Lang Xiyu , Liu Yuxiao , Zhang Yang
Published: March 2024

Preserving user data privacy is paramount for airlines. However, achieving highly accurate predictions of passenger satisfaction while safeguarding the privacy of individual companys data remains a considerable challenge. To achieve this objective, we utilized a privacy-preserving machine learning technique known as Federated Learning (FL). FL allows model training on decentralized devices while ensuring data security and privacy. The FL process comprises client training processes, communication rounds, and weight aggregation. We simulate FL principles using multiple processes to enable distributed learning. Clients preprocess data and train local models, ensuring data privacy. Communication rounds involve clients downloading the global model, local training, and transmitting updates to a central server. Weight aggregation methods like Federated Averaging merge these updates, preserving data privacy. Additionally, we leverage Artificial Neural Networks (ANNs) as foundational techniques. ANNs consist of input, hidden, and output layers, with weight adjustments based on real value differences to achieve accuracy. Our approach combines FL with ANNs to demonstrate FLs potential in privacy-preserving predictive analytics. We use the Airline Passenger Satisfaction dataset for modeling and evaluate the impact of neural network depth and submodel quantity on prediction accuracy. Our experimental results reveal that neither the depth of neural networks nor the number of submodels significantly affects prediction accuracy. FL emerges as a promising approach to balance data privacy and prediction accuracy effectively.
A comparative analysis of VGG19 and Magenta models for Neural Style Transfer
Authors: Xu Wenhe
Published: March 2024

In recent years, artificial intelligence (AI) has witnessed remarkable progress, with deep learning techniques playing a pivotal role in this transformative journey. One prominent facet of this progress is Neural Style Transfer (NST), a domain within computer vision and image processing. NST entails merging the content of one image with the stylistic attributes of another, resulting in a seamless fusion of content and style. This paper presents a comprehensive comparative analysis of two influential style transfer models, VGG19 and Magenta, shedding light on their strengths and limitations. VGG19, a well-established convolutional neural network, excels in feature extraction, dimensionality reduction, and image classification, making it ideal for computer vision tasks. In contrast, Magenta, a creative content generation framework, specializes in producing innovative content, such as music and art, while promoting human-AI collaboration. Evaluation reveals that Magenta's generated images tend to exhibit softer colors but higher clarity, while VGG19 produces vibrant and faithful color reproductions with finer details. The choice between these models should be driven by specific task requirements. VGG19 is suitable for tasks emphasizing color fidelity, while Magenta excels in image clarity. Future research directions may involve enhancing algorithmic capabilities to harness the strengths of both models and improving efficiency in style transfer techniques. This analysis empowers researchers, practitioners, and enthusiasts to make informed decisions when selecting a model aligned with their objectives, contributing to the evolving landscape of AI-assisted creative content generation.
Research on improving accuracy and efficiency of animal data collection and classification using machine learning
Authors: Yang Qinlong
Published: March 2024

With the continuous expansion of machine learning algorithms in various application domains, the application value of new algorithms, such as Support Vector Machines and Convolutional Neural Networks in data classification, has garnered increasing attention. This paper takes machine learning algorithms as the research entry point, explores the concept of machine learning, and delves into its application value in data classification. This paper, starting with an overview of machine learning algorithms, analyzes the supervised and unsupervised learning problems in machine learning, focusing on the applications of Convolutional Neural Networks, Support Vector Machine models, and logistic regression algorithms in data classification. This study emphasizes designing and implementing a machine learning-based image classification system. Through an in-depth exploration of the application of machine learning algorithms in data classification, a fully functional system is constructed, encompassing multiple modules, including machine vision and software development. This system accurately classifies and recognizes images, providing practical tools and technical support for image processing and analysis. In this study, the goal of achieving good image classification is realized through research and the application of machine learning algorithms. By designing and implementing a machine learning-based image classification system, the accuracy and efficiency of classification in handling massive data are improved. This system also demonstrates wide-ranging prospects in software development and machine vision, among other fields.
Pilot facial feature recognition and fatigue classification method under high exposure environment
Authors: Pei Huining , Sun Jiali
Published: March 2024

To improve the recognition efficiency of pilot fatigue detection in high exposure environment, a facial feature recognition and fatigue classification method for pilots is proposed. Firstly, the high exposure images are pre-processed with frequency domain light normalization; secondly, a human face detector is trained based on YOLOv5s network model and detects the face, eyes and mouth; again, the fatigue feature values of eyes and mouth are obtained by fusing PERCLOS fatigue detection algorithm; finally, a fatigue classifier is built by RBF-ELM neural network and the fatigue parameters of eyes, mouth and yawning The fatigue parameters of the eye, mouth and yawn features were input to the classifier for fatigue classification. The experimental results show that the fatigue detection speed of the proposed method is up to 310 FPS when computed with GPU, which has higher recognition rate and better robustness compared with the classical PCA, CNN and DCT methods. The method can effectively improve the accuracy of pilot facial feature recognition and eye localization in high exposure environment, and has high practicality for non-contact pilot fatigue detection.
Falling Body Motion in Time Scale Calculus
Authors: Pelen Neslihan Nesliye , Kayar Zeynep
Published: March 2024

The falling body problem for different time scales, such as ℝ, ℤ, hℤ, qℕ0, ℙc,d is the subject of this study. To deal with this problem, we use time-scale calculus. Time scale dynamic equations are used to define the falling body problem. The exponential time scale function is used for the solutions of these problems. The solutions of the falling body problem in each of these time scales are found. Moreover, we also test our mathematical results with numerical simulations.
Can Clinical Presentations or Hematological Indices Predict Meningitis in Febrile Children?
Authors: Akram Nabeeha Najatee , AL-Baldawi Shahad Ali Ahmed
Published: March 2024

The aim of this study is to assess the role of clinical presentations and hematological indices in the diagnosis of meningitis in febrile children during a meningitis outbreak. A cross-sectional study, recruiting (236) febrile children underwent lumbar puncture for suspected meningitis. Participants were divided into two groups based on the results of cerebrospinal fluid (CSF) analysis: the case group included all those who carried a diagnosis of meningitis while the rest of the patients whose CSF analysis result was negative for meningitis was taken as a control group. The clinical characteristics, hematological indices were compared between both groups. Children with proved meningitis were significantly of older age and resides more in rural areas than control P value= (0.001), (0.014) respectively. The Neutrophil to lymphocyte ratio and platelet counts were the only significant hematological indices that elevated in children with meningitis P value= (0.001), (0.018) respectively. Headache and neck stiffness were documented more in children with meningitis P value = (<0.001) whilst seizure was significantly less common in children with meningitis P value = (<0.001). During a meningitis outbreak, pediatrician in low resource country can use a constellation of clinical and hematological characteristics to identify febrile children who in need of lumbar puncture to exclude meningitis.
Occupational safety and health and the future of work in retail in the post-COVID-19 context
Authors: Szücs Luca ,   International Labour Organization. Sectoral Policies Department,
Published: March 2024

This brief explores the profound impact of recent technological advancements, climate crises and the COVID-19 pandemic on OSH within the retail and commerce value chain. The retail sector is witnessing an increase in contract work, temporary employment, irregular work schedules and platform workers. These transformations, coupled with the ongoing digitalization and widespread use of automated decisions and smart wearable technology in the sector, present new challenges, but also opportunities, when it comes to ensuring safety and health.In delving into those challenges, this brief not only identifies potential risks to retail workers’ safety and health but also highlights opportunities for overcoming them. It carefully examines the existing policy options that can pave the way towards safe and healthy work in the retail sector of the future. The analysis encompasses large retail chains as well as micro, small and medium-sized enterprises (MSMEs), in both the formal and informal economies, while emphasizing the diverse OSH challenges faced by workers and employers.
Insights into the ILO formalize your business (FYB) training programme in Côte d'Ivoire, the Gambia and Senegal
Authors:   International Labour Organization,
Published: March 2024

ILO Formalize Your Business (FYB) is a training programme promoting the formalization of small enterprises and the workers they employ. It was first implemented in 2021 in Côte d'Ivoire, The Gambia and Senegal in collaboration with key national stakeholders, and has, more recently, expanded to other countries. To outline how the FYB training has been implemented so far, the ILO conducted a data collection exercise in the three pilot countries. This publication includes findings from this exercise, and summarizes the feedback from trainers who participated in the Training of Trainers (ToT) as well as entrepreneurs who completed the FYB training.
Identification of Bioactive Compounds in Chrysobalanus icaco Seed Kernel Using Gas Chromatography-Mass Spectrometry
Authors: N. C. Oganezi , T. Agbaeze , S. O. Kalu
Published: March 2024

Chrysobalanus icaco seed kernel (i.e Cocoplum) is good source of oil and is utilized as spice in soups or stews in some African cuisines. The work aimed to identify compounds in methanol and dichloromethane/methanol (1:1, v/v) flour extract from its seed kernels using gas chromatography-mass spectrometry. Eleven compounds were identified from the methanol extract while thirteen compounds were identified from the dichloromethane/methanol extract. The most abundant compounds from the methanol extract were cis-13-octadecenoic acid methyl ester, cis vaccenic acid and n-Hexadecanoic acid. They had relative abundance of 28.83, 17.14 and 15.40% respectively. 2,6-octadienal-3,7-dimethyl (E) (ie α-citral), citral, cis-3-hexenyl cis-3-hexenoate and trans-2,7-dimethyl-4,6-octadiene-2-ol were the most abundant in the dichloromethane/methanol extract. These had relative abundance of 12.86, 18.92, 10.56 and 20.59% respectively. Compounds which were common to both methanol and dichloromethane/methanol extracts of C. icaco seed kernel were 2,6-octadienal-3,7-dimethyl (E), hexadecanoic acid methyl ester, n-Hexadecanoic acid, heptadecanoic acid, 16 methyl, methyl ester, cis-vaccenic acid and oleic acid. Both solvents extracted varied concentrations of compounds which fall in different classes of either being an unsaturated long chain aldehyde, fatty acids, fatty acid methyl esters, indene derivatives, monoterpenoid alchohol. The compounds identified in both extracts of Chrysobalamus icaco seed kernels have various beneficial bioactivities and sensory attributessuch as being flavor compounds which confers C.icaco seed kernel a peculiar aroma. As such, Chrysobalamus icaco seed kernel is a functional food and can serve as a good raw material for edible oil from which bioactive compounds can be isolated and utilized in relevant food systems and in the preparation of neutraceuticals and pharmaceuticals. Therefore, its use in cuisines is greatly encouraged.
Rikstäckande inventering av häckande storskarv (Phalacrocorax carbo) i Sverige 2023
Authors: Lundström Karl
Published: March 2024

Under 2023 genomfördes en rikstäckande inventering av häckande storskarv (Phalacrocorax carbo) i Sverige, den första sedan 2012. Genom att samordna inventeringar som redan var inplanerade med kompletterande insatser i områden där inga inventeringar var planerade erhölls en god geografisk täckning. Arbetet har utförts av ett stort antal personer och organisationer med lokalkännedom, expertis och långvarig erfarenhet av inventeringar av häckande storskarv. En kombination av metoder har använts för att få så detaljerade resultat som möjligt. Resultaten från riksinventeringen 2023 visar att den häckande populationen av storskarv i Sverige har ökat markant sedan den förra inventeringen 2012. År 2023 räknades 74 767 bon i 224 kolonier, jämfört med 40 958 bon och 169 kolonier år 2012. Kolonierna var flest och störst vid kusterna och häckningar förekom i samtliga län med havskust, men med ytterst få kolonier och bon i Norrbottens och Västerbottens län. Inga häckningar förekom i Jämtlands och Dalarnas län. De största kolonierna fanns längs Östersjöns kuster (i Kalmar, Södermanlands, Stockholms, Gotlands och Östergötlands län). Ökningen av antalet bon sedan 2012 var störst i Kalmar, Blekinge, Västra Götalands och Östergötlands län. I Gotlands län samt bland de insjöhäckande storskarvarna i Örebro, Värmlands, Kronobergs och Jönköpings län hade antalet bon minskat sedan 2012. I Mälaren och Vättern hade antalet kolonier och bon ökat sedan 2012, men i Vänern hade både antalet kolonier och antalet bon minskat. Tätare intervall mellan riksinventeringar rekommenderas och mer ingående undersökningar och analyser behövs för att bättre kunna kombinera resultat om antalet bon som samlats in med olika metoder. Vilken metod som är bäst lämpad varierar mellan kolonier och är bland annat beroende av koloniernas storlek, ålder och fördelningen mellan mark-, busk- och trädhäckande fåglar samt hur stor noggrannhet man eftersträvar. Val av metod beror även på vilka inventeringsresurser som finns tillgängliga och avvägningar mellan kvalitet på data och risk för störningar av häckande fåglar. Dessutom rekommenderas bättre övervakning av häckningsframgången och olika typer av störningar. Omfattningen av häckande storskarvar av underarten atlantstorskarv på svenska västkusten och förekomst av eventuella hybrider mellan atlantstorskarv och mellanskarv bör undersökas. Slutligen bör poängteras att återkommande inventeringar av häckande storskarv ger en bild av populationens utveckling och geografiska fördelning över år, men säger ingenting om den geografiska fördelningen av storskarv i Sverige under andra årstider, utanför häckningssäsongen.
Cirkulär bioekonomi i svensk trädgårdsnäring : goda affärer och innovation
Authors: Blix Germundsson Lisa , Johansson Gunnar A , Windfäll Elin
Published: March 2024

För att introducera begreppet cirkulär bioekonomi i svensk trädgårdsnäring har LRF Trädgård bedrivit projektet ”Goda affärer, cirkulär och innovativ trädgårdsodling” under åren 2020 - 2023. Syftet var att utveckla en arbetsmodell för affärsutveckling och innovation inom cirkulär bioekonomi. Resultaten visar att arbetsmodellen är ett bra sätt att stödja företag att nå större hållbarhet och lönsamhet, samt att stärka samarbetet mellan företag, rådgivare och forskare inom cirkulär bioekonomi.
Світові моделі системи охорони здоров'я, адаптація в Україні та вплив на тривалість життя
Authors: Філіпова Н. В. , Левчук Ю. М.
Published: March 2024

Система охорони здоров'я постійно зазнає змін і стикається з різними викликами. Ці виклики включають демографічні зміни, такі як старіння населення, зростання поширеності хронічних захворювань, а також необхідність інтеграції інноваційних технологій у медицину. Враховуючи ці аспекти, вивчення та порівняння різних моделей державного регулювання охорони здоров'я дозволяє ідентифікувати ефективні підходи до реформування національних систем охорони здоров'я, зокрема в Україні. Проведено огляд чотирьох основних моделей охорони здоров'я, які застосовуються в різних країнах світу: моделі Бісмарка, Беверіджа, національного здоров'я, та моделі прямої оплати медичних послуг. Автори дослідження виходять з необхідності адаптації та оптимізації національних систем охорони здоров'я з метою підвищення їх доступності, ефективності та якості в контексті викликів сучасності, таких як глобальні пандемії, старіння населення та зростання хронічних захворювань. Значна увага приділяється аналізу фінансування, управління та способів надання медичних послуг в кожній з моделей, а також розглядаються їх переваги та недоліки. Особливий акцент поставлено на моделях Бісмарка та Беверіджа як на найбільш поширених та ефективних систем у світовій практиці охорони здоров'я. Дослідження також охоплює вплив зазначених моделей на загальний стан здоров'я населення та середню тривалість життя. Аналізуючи досвід різних країн, автори пропонують рекомендації щодо можливостей удосконалення української системи охорони здоров'я, зокрема через інтеграцію та адаптацію найкращих практик зарубіжних моделей, що особливо актуально в умовах сьогодення в Україні. Оскільки військовий стан потребує нових особливих підходів для формування майбутньої політики у сфері охорони здоров'я, спрямованої на забезпечення універсального доступу до високоякісних медичних послуг для всього населення України.
A Fixed-Bed Study on the Feedsorption of BSA Using PKC: Toward the Sustainable Agrosorption of Protein-rich Waste for Enhancing Low Nutritional-value Feed
Authors: Khayat Mohd Ezuan , Shukor Mohd Yunus
Published: March 2024

Palm Kernel Cake (PKC), a by-product of Malaysia's palm oil industry, stands out as a sustainable and cost-efficient feed ingredient. However, its use is nutritionally limited for monogastric animals due to high fiber content and anti-nutritional factors. This groundbreaking study explores the innovative process of 'feedsorption'—a term we have coined under the broader concept of 'agrisorption'—to enhance the nutritional value of PKC. By adsorbing protein-rich agricultural, poultry, and farm animal wastes, represented by bovine serum albumin (BSA), we aim to elevate PKC's protein content. Through detailed experimentation involving varying bed depths (1 cm to 2 cm) and initial BSA concentrations (100 to 500 µg/mL) within fixed-bed columns, our findings reveal that increased bed depths significantly prolong breakthrough and exhaustion times, highlighting improved adsorption efficiency. Yet, depths beyond 1 cm pose a risk of clogging. Higher BSA concentrations were found to accelerate breakthrough, indicating a stronger driving force capable of overcoming mass transfer resistance. The Modified Dose Response (MDR) model outperformed the Thomas model in accurately predicting breakthrough curves across different conditions. This study not only confirms the feasibility of feedsorption to bolster the nutritional profile of low-quality feed using protein-rich waste but also introduces a promising avenue for enhancing sustainable livestock nutrition.
Szolnok – Milléri-holtág part I-II. több korszakú lelőhely őrlőkő leletei archeometriai vizsgálatának előzetes eredményei : Új adatok a domoszlói piroxénandezit nyersanyag régészeti elterjedéséhez
Authors: Péterdi Bálint , Czifra Szabolcs
Published: March 2024

The multi-period archaeological site Szolnok – Milléri-holtág part I-II is located in the north-eastern vicinity of Szolnok, approximately 5 km from the town, on the high bank of the Milléri-holtág. The total excavated area is almost 27 000 m2 . The preventive excavation was performed by the staff of the Hungarian National Museum in two campaigns (in 2014 and 2015) connected with the M4 motorway constructions. Most excavated settlement features belonged to a Middle Iron Age (Scythian Period) and a Late Iron Age (Celtic Period) rural settlement with scattered structures. Besides these, other settlement features were found from the Neolithic, Late Bronze Age and Medieval Period (Árpád Period). It has to be mentioned that in the excavated part of the Scythian Period settlement, only circular and oval structures were found. The rectangular features – typical for other settlements of the period – were absent in Milléri-Holtág part I-II. However, the archaeological finds were typical: ceramic assemblages were characterised by long-lasting forms (pots, bowls) of the Iron Age household ceramics. The assemblages of the Late Iron Age features were very similar to the vessel types of the earlier period. The partition of the artefacts can only be made with the help of some characteristic forms and ornamentations that appeared in a close unit. The ground stone assemblage of the site (primarily fragments of grinding stones) was analysed with petrographic and geochemical methods. Based on these investigations, the finds' most frequent raw material type could be identified with the Domoszló-type pyroxene andesite from the Mátra Mountains, studied intensively in the past few years. Other types of andesite were present in the ground stone assemblage in smaller amounts. The identification of the source areas of these different andesite raw material types needs more exploration, not just in the territory of Hungary, but also in some areas beyond the borders, e.g. Subcarpathia (Zakarpatska Oblast, Ukraine), Eastern-Slovakia, Transylvania (Romania).