bluuebunny/crossref_metadata_2025 huggingface dataset:
Implications of Digital Operations Management Strategies for Chinese Retail-based Service Companies A Case Study of Service-oriented Companies Starbucks
Authors: Wen Xiaojing
Published: November 2024

Under the impetus of digitalization, China's retail service industry can no longer achieve higher development through traditional operation and management modes, so it is necessary to explore digital operation and management strategies that are suitable for China in the wave of the development of digital economy. This paper takes Starbucks, a global coffee chain, as an example to explore the favorable reference and inspiration provided by the digital operation management strategy to Chinese retail service enterprises. By analyzing the main reasons that motivate Chinese retail-based service enterprises to undergo digital transformation, and using Starbucks' digital transformation strategy as a case study, this paper concludes that Chinese service-oriented retail enterprises have to follow the development mode of integrating online and offline in digital transformation. China's retail-based service industry can mainly start from four major aspects in the process of digital transformation, namely, the inclusion of digital technologies, optimisation of supply chain management, innovation in marketing, and establishment a new talent training model. To promote the continuous development and innovation of the enterprise through the adoption of concrete and implementable measures.
The Impact of Live E-commerce on Supply Chain Management: Challenges, Adaptations, and Strategic Optimizations
Authors: Hou Xiaotong , Xu Tianyu , Xu Yichen
Published: November 2024

The explosive growth of live e-commerce has accelerated the transformation of supply chain management, where the efficiency and flexibility of the supply chain directly influence the success of live e-commerce. Therefore, optimizing supply chain management has become crucial for industry development. Live e-commerce, with its real-time interaction and instant purchasing characteristics, has profoundly impacted supply chain management. The rise of live e-commerce has driven supply chains to become more streamlined, personalized, and intelligent to meet consumer demands for freshness and customization of products. Additionally, the data-driven nature of live e-commerce enables better demand forecasting, reduces inventory backlogs, and enhances overall operational efficiency. This study explores the specific impacts of live e-commerce on supply chain management, including its effects on supply chain structure, logistics efficiency, inventory management, and the application of information technology. The aim is to provide strategic recommendations for supply chain optimization, helping enterprises enhance their competitiveness and promoting the healthy and sustainable development of the live ecommerce industry.
An Adaptive Cruise Control Algorithm Based on DDPG Algorithm Based on Deep Reinforcement Learning Under Variable Acceleration Conditions
Authors: Lan Yuhao
Published: November 2024

Adaptive cruise control (ACC) dynamically regulates a vehicle's speed to preserve a secure gap from the preceding vehicle, enhancing road safety. In this study, ACC is examined through the lens of deep reinforcement learning, with a focus on the Deep Deterministic Policy Gradient (DDPG) technique. The reward function takes into account the speed error, and two modesspeed control and distance controlare implemented. The proposed ACC strategy is trained and validated through simulations on the MATLAB/Simulink platform. The experimental results indicate that the reward function converges rapidly, confirming the suitability of the DDPG algorithm for automotive ACC research.
Analysis of the Implementation for the State-of-art AI Techniques in Self-driving
Authors: Xu Jiexiang
Published: November 2024

Contemporarily, AI technology has been widely adopted in various fields. In this case, this research provides an in-depth look at the application of cutting-edge Artificial Intelligence (AI) technology to autonomous driving technology. The article traces the evolution of autonomous driving from its infancy in the early 1900s to its current state of the art and highlights major milestones such as the DARPA Grand Challenge. It discusses recent advances in AI and its key role in autonomous driving, with a particular emphasis on deep learning techniques in enhancing perception and decision-making capabilities. The convergence of these AI technologies has given rise to highly sophisticated autonomous driving systems that enable vehicles to traverse complex environments safely and efficiently. In addition, this paper explores the application of distributed machine learning, in particular locally updated stochastic gradient descent (SGD), in training large-scale AI models, and highlights its advantages in terms of scalability, data privacy and communication efficiency. The research motivation of this paper stems from improving the efficiency, scalability and privacy of AI models in autonomous driving systems. At the same time, the article identifies limitations in current self-driving AI implementations, including technological maturity, reliability issues, legal and ethical concerns, and high costs, and outlines future prospects for the field.
Depression Detection Method Using Multimodal Social Media Data: An Integrative Review
Authors: Ma Jiawen
Published: November 2024

An increasing number of people are suffering from depression due to rising chronic stress levels. With the advent of Web 2.0, individuals are more inclined to express their emotions on social media, offering new opportunities for depression prediction. Researchers have developed various single-modal methods for early-stage depression prediction. Recently, multimodal social media data has been utilized to enhance the accuracy of depression detection methods. These methods primarily extract multidimensional information such as text, language, and images from social media users, integrating these diverse modes to assess the risk or severity of depression. This approach significantly improves the precision of depression prediction. However, the research is still in its early stages, with challenges such as limited datasets and many areas requiring further improvement. To aid researchers in better understanding and refining multimodal approaches, we conducted a review that summarizes emerging research directions in using multimodal techniques for depression prediction on social media. Additionally, this review compares different depression detection methods, datasets, and the various modalities used in multimodal approaches, analyzing their strengths and limitations. Finally, it offers suggestions for future research.
Enhancing DF-GAN for Text-to-Image Synthesis: Improved Text-Encoding and Network Structure
Authors: Wu Yixuan , Zhou Zhaonan
Published: November 2024

Abstract. Text-to-image synthesis is one of the most challenging and popular tasks in machine learning, with many models developed to improve performance in this area. Deep Fusion Generative Adversarial Networks (DF-GAN) is a straightforward but efficient model for image generation, but it has three key limitations. First, it only supports sentence-level textual descriptions, restricting its ability to extract fine-grained features from word-level inputs. Second, the structure of the residual layers and blocks, along with key parameters, could be optimized for better performance. Third, existing evaluation metrics, such as Frchet Inception Distance (FID), tend to place undue emphasis on irrelevant features like background, which is problematic when the focus is on generating specific objects. To address these issues, we introduced a new text encoder that enhances the model having capacity to process word-level descriptions, leading to more precise and text-consistent image generation. Additionally, we optimized key parameters and redesigned the convolutional and residual network structures, resulting in higher-quality images and reduced running time. Lastly, we proposed a new evaluation theory tailored to assess the quality of specific objects within the generated images. These improvements make the enhanced DF-GAN more effective in generating high-quality, text-aligned images efficiently.
In-depth Study and Application Analysis of Multimodal Emotion Recognition Methods: Multidimensional Fusion Techniques Based on Vision, Speech, and Text
Authors: Lei Yuke
Published: November 2024

Abstract. Emotion recognition technology, pivotal in fields such as medical health, game entertainment, and human-computer interaction, benefits significantly from multimodal approaches. This paper delves into the techniques and applications of multimodal emotion recognition, focusing on fusion methods that integrate visual, speech, and text data. Emotion recognition through single modalities often faces limitations such as susceptibility to noise and low accuracy, whereas multimodal systems exhibit enhanced performance by leveraging combined data sources. The primary fusion techniques discussed include feature-level, decision-level, and model-level integrations. Feature-level fusion amalgamates multiple data types early in the processing stage, improving detection robustness. Decision-level fusion, on the other hand, involves synthesizing results from separate analyses, offering flexibility and ease of integration. Model-level fusion allows for deep interactions between modalities, potentially capturing more complex emotional states. This study confirms that multimodal emotion recognition systems generally surpass the performance of their single-modal counterparts, advocating for further exploration into sophisticated fusion techniques to boost accuracy and applicability across various domains.
Image-based Efficient Music Note Detection by Advanced Lightweighted YOLO
Authors: Shi Yuhan
Published: November 2024

In the realm of music note detection, deep learning models have demonstrated exceptional potential, enabling a wide range of applications, including automated music transcription and real-time music analysis. Despite their effectiveness, the resource-intensive nature of these models often renders them impractical for deployment in resource-limited environments. To tackle this issue, this research focuses on adapting an existing deep learning model, You Only Look Once (YOLO), to explore lightweight alternatives specifically tailored for music note detection. This work assessed the performance of the adapted model using a comprehensive dataset that includes 2,136 meticulously labeled music sheet images. Preliminary results suggest that the streamlined version of the model not only matches the accuracy of its predecessor but also boasts a substantially lower parameter count and reduced Floating Point Operations (FLOPs). These enhancements make the model an ideal candidate for music note detection across a broader spectrum of devices, including those with limited computational capabilities. This advancement opens up new possibilities for real-time music analysis and transcription in various settings, such as mobile devices and low-power embedded systems.
Advancements and Prospects in Large Integer Factorization: A Comprehensive Review of the Number Field Sieve Method
Authors: Gao Jiawei
Published: November 2024

The Number Field Sieve (NFS) stands as one of the most effective algorithms for the factorization of large integers, playing a critical role in cryptographic security, particularly in breaking RSA encryption systems. This review paper provides an in-depth analysis of NFS, highlighting its structure, key processes, and computational challenges. NFS is notably effective for integers larger than 110 digits and is recognized for its sub-exponential computational complexity, making it superior to other classical factorization algorithms. The core processes of NFSpolynomial selection, number pair sieving, matrix solving, and square root extractionare systematically examined to illustrate their roles in factorization. This study also reviews the latest advancements in NFS, including improvements in polynomial selection methods and optimizations in sieving and matrix-solving stages, which have significantly enhanced the algorithms efficiency. Moreover, the paper discusses the future prospects of NFS, emphasizing the need for further optimization to reduce computational resource demands and increase practicality in large-scale applications. The ongoing evolution of NFS continues to push the boundaries of cryptographic analysis, driving future research towards even more efficient factorization methods.
Securing the Chain: Comprehensive Analysis of Vulnerabilities and Defense Mechanisms in Blockchain Systems
Authors: Chen Junxian
Published: November 2024

Blockchain systems, characterized by decentralization, traceability, and robust security, are increasingly adopted across various domains including finance due to their strong disaster recovery capabilities. This paper focuses on the security mechanisms underpinning Bitcoin and Ethereum, two dominant cryptocurrencies, and examines potential vulnerabilities and attack vectors targeting blockchain technologies. Specifically, it addresses attacks on consensus mechanisms, network structures, and application layers, outlining the cryptographic methods integral to blockchain security. The study delves into the types of attacks that exploit these vulnerabilities, such as forking, stale or orphaned blocks, selfish mining, and majority attacks, which threaten the integrity and consistency of blockchain systems. Additionally, it reviews the current defense mechanisms and proposes new strategies to enhance the security and resilience of these digital platforms. By exploring these security challenges comprehensively, the paper aims to provide deeper insights into the defense strategies that can fortify blockchain against such vulnerabilities, thereby enhancing its reliability and trustworthiness in digital transactions and ensuring its sustainable integration into the digital economy.
Blockchain Technology in Supply Chain Management: Current Applications and Future Prospects
Authors: Du Haoyu
Published: November 2024

Supply chain is one of the use cases where blockchain itself has proven to be a powerful technology enabling increased transparency plus traceability and security. However, as acknowledged by many that consider it today one of the major technical challenges that issues related to scalability, high cost and data privacy paper investigates what benefits will lead to implementation of supply chain as well its architecture. Examples presented include e-commerce and logistics in which fraud can be reduced while operational efficiency improved through use of blockchain technology. Research also described solutions sharding and Layer 2 handling the scalability issue without specifying security or presenting another area of research needed information. It found the use of blockchain technology in supply chain management to be highly beneficial, though Technical and regulatory challenges to successful adoption impel industry stakeholders to address them, which, apart from being far from answered without common articulated goals through standardization efforts aimed at fostering innovation capable driving transformative changes across global supply chains empowered by realizing full potential offered through DLTs like blockchains.
Path Analysis of Recycling of Urban Solid Waste Based on Multi-Objective Optimization Model
Authors: Chu Zhiao
Published: November 2024

With the acceleration of urbanization and rapid economic development, the problem of urban solid waste (SW) has become increasingly prominent. Traditional SW disposal treatment methods, mainly landfill and incineration, pose issues of environmental pollution and resource waste. This study aims to analyze the recycling pathways of urban SW and establish a multi-objective optimization model to achieve comprehensive optimization of economic, environmental, and social goals. Through data collection, quantification and standardization, establishment of a multi-objective optimization problem model, NSGA-II algorithm solution, and simulation and result analysis, this paper seeks to provide scientific decision support and technical guidance for urban SW management. Taking Wuhan as an example, this study analyzes the treatment methods of six different types of SW and seeks the optimal treatment pathways through the multi-objective optimization model. The results show that the multi-objective optimization model can effectively balance economic costs, environmental costs, and utilization efficiency in SW treatment and utilization, providing new ideas for achieving urban sustainable development and resource recycling.
Research of Modeling Skyline of Hongkong Central with OpenSCAD and Blender
Authors: Zhang Zhiyuan
Published: November 2024

Hong Kong, as an international financial center, is widely admired for its iconic Central skyline. Central is not only the heart of the economy but also a hub for global financial activities, gathering numerous international banks and financial institutions. This study aims to explore the feasibility and efficiency of using OpenSCAD for 3D modeling of some buildings in the Central district of Hong Kong, thereby providing a new tool for architectural design and urban planning.In this study, this paper selected three representative buildings for 3D modeling: the Bank of China Tower, the International Finance Centre II, and the Hong Kong Ferris Wheel. These buildings are not only distinctive in their own right but also occupy significant positions in the Central skyline. Through their modeling, we demonstrated how to generate complex geometric structures using a programmatic approach, fully reflecting the efficiency and flexibility of OpenSCAD as a modeling tool. The results show that OpenSCAD has a clear advantage in handling regular geometric bodies and repetitive structures, especially in parametric and modular model design. In addition, by using OpenSCAD's scripted modeling capabilities, we can easily create buildings with different design intentions and styles, further enriching the three-dimensional visual effects of the city.
Application of Triple Glazing Thickness Optimization Based on Artificial Fish Swarm Algorithm
Authors: Tang Dexian
Published: November 2024

In response to the growing demand for energy-efficient building designs in cold northern regions, this study explores the optimization of triple glazing thickness to maximize the transmission of solar energy within the wavelength range of 300-2000 nm. The optimization employs the Artificial Fish Swarm Algorithm (AFSA), which is used to iteratively determine the optimal combination of glass thicknesses. A transmittance model of solar radiation through triple glazing is established, where the thicknesses of the three glass layers are treated as variables. The study shows that the optimized thickness combination significantly enhances the indoor heating effect by maximizing solar energy incidence, offering theoretical support and practical guidance for energy-saving building designs in cold climates. Parameter settings of the AFSA, including perception range, step size, and crowding factor, are analyzed to understand their influence on optimization performance. The results suggest that AFSA efficiently addresses complex optimization problems, demonstrating its potential in optimizing material properties for energy-efficient applications. Future directions include multi-objective optimization considering additional factors such as thermal insulation and cost, algorithmic improvements to enhance search accuracy, and experimental validation of the results in real-world conditions. This research provides a scientific foundation for integrating advanced optimization algorithms into the sustainable design of building materials.
A Comparative Study of AStar, LPA* and DStarLite Path Planning Algorithms Based on Matlab
Authors: Huang Zixuan
Published: November 2024

In the realm of robotics and autonomous systems, path planning is a pivotal component that determines the efficacy and safety of navigational tasks. With the proliferation of autonomous vehicles, drones, and mobile robots, the need for efficient and adaptive path planning algorithms has become increasingly acute. This paper studies AStar, LPA and DStarLite path planning algorithms based on Matlab platform, and compares their performance through simulation experiments. AStar algorithm is simple and widely applicable, but it has some shortcomings in path smoothness and computational efficiency. LPA improves path smoothness by introducing dynamic cost updating, but it may sacrifice some computational efficiency. The DStarLite algorithm performs well in dynamic environments with an efficient incremental update strategy that maintains high path smoothness and low computational costs. The experimental results show that DStarLite is the fastest in most cases, LPA* and DStarLite are superior to AStar in path smoothness. Future research may explore combining the advantages of each algorithm to develop more efficient, flexible and robust path planning algorithms to cope with complex and changeable actual scenarios.
An Empirical Exploration of the Effectiveness of Word Embedding Techniques for Emotion Recognition
Authors: Wang Puan
Published: November 2024

Emotion recognition is an important research direction in the field of natural language processing. It is widely used in social media monitoring, product feedback analysis, and medical diagnosis, and it has significant practical value. The task of emotion recognition aims to recognize the emotional orientation of text through analysis. Word embedding technology is a key component in emotion recognition tasks; it helps the model capture sentiment relationships within the context by converting words into dense vectors, which is crucial for the model's performance. This paper is based on the Internet Movie Database (IMDB) and investigates the performances of different word embedding methods, including Rand, Static, Non-static, and Multi-channel, in sentiment recognition tasks. It also evaluates their effects on various neural network architectures. The experimental results show that random initialization performs best, especially in complex networks, demonstrating strong classification capability and training efficiency. This indicates that deep learning models have strong adaptive capabilities in sentiment recognition tasks. Even without the help of pre-trained word vectors, the model can still effectively capture the sentiment information in text.
Research on the Application of Terahertz Communication Technology in 5G and Future Networks
Authors: Bai Lu
Published: November 2024

Abstract: As the limitations of 5G in spectrum scarcity and massive data transmission capability become evident, the vision of 6G technology emerges, aiming for global coverage, abundant spectrum resources, ultra-high-speed data transmission, minimal latency, and high reliability. Terahertz (THz) communication technology, with its rich spectrum resources, is seen as a key to achieving 6G objectives. This paper explores the application and challenges of THz communication in 5G and future networks, analyzes its basic theory, key technologies, channel characteristics, and modeling, and discusses its prospects and challenges in 6G and beyond. The paper provides an overview of THz communication, including its position in the electromagnetic spectrum, bandwidth potential, and advantages of short wavelengths. It discusses THz-related technologies, particularly the roles of THz electronics, photonics, and plasmonics in device development, and their contributions to 6G advancements. Future research directions are outlined, including optimizing transmission windows, applying AI in wireless communication, and the potential of 6G as a new generation of intelligent digital infrastructure. While THz communication shows great potential in 6G networks, it faces technical and economic challenges. This paper provides a solid theoretical foundation for understanding THz technology, but future work requires deeper exploration in experimental validation, technical detail research, and cost-benefit analysis.
Research on the Regression Analysis of Real Estate Investment and Economic Growth in Chongqing
Authors: Jiang Chenye
Published: November 2024

Abstract: Chinas economic expansion during the last 30 years has been mostly driven by the real estate sector. However, since 2020, the Chinese market has experienced a lack of energy, particularly within the real estate industry. Therefore, its imperative to reanalyze the connection between them at this point in time. A clear understanding of the relationship between real estate investment and economic growth is essential for the maintenance of sustainable economic growth in the future. his essay quantitatively examines the relationship between real estate investment and economic growth in Chongqing Municipality over a 30-year period, utilizing co-integration theory, the Augmented Dickey-Fuller Test, Granger causality test, and regression analysis.The regression analysis demonstrates that real estate investment in Chongqing is a substantial catalyst for GDP growth and is essential for fostering economic development. However, in recent years, the housing market has become saturated, which means that it can no longer be used as a major factor to drive economic growth..At this point, there is an urgent need to explore new ways to maintain sustained economic growth.
Review on Data Visualisation Techniques
Authors: Nie Shiyou
Published: November 2024

The ascendancy of data visualization methodologies has rendered these instruments indispensable for contemporary decision-making processes within the epoch of exponential data proliferation. This review takes an in-depth look at various data visualisation techniques, providing an in-depth analysis of their applications, advantages and disadvantages. Data visualisation is an important tool for transforming large and complex datasets into intuitive graphical representations, enabling users to discover patterns, correlations and actionable insights more effectively. This review encompasses foundational visualization strategies including bar charts, line graphs, and scatter plots, in addition to sophisticated techniques such as heat maps, tree diagrams, and network diagrams, which are particularly efficacious for the representation of larger, more complex data sets. It also explores the growing importance of interactive visualisations, including dashboards and geospatial maps, which provide users with real-time data interaction and deeper exploration. In addition, emerging trends such as artificial intelligence, virtual reality and big data visualisation were seen as important advances that will shape the future of the field. Challenges such as data distortion, cognitive overload and accessibility issues were also analysed, highlighting the importance of choosing the appropriate technology based on the characteristics of the data, the user's task and the audience's needs to enable more effective communication and decision-making.
Text Classification based on Spiking Neural Network with LSTM Conversion
Authors: Zhou Shaoheng
Published: November 2024

In recent years, spiking neural network (SNN) have attracted much attention due to their low energy consumption and have achieved remarkable results in the fields of vision and information processing. However, the application of SNNs in the field of natural language processing (NLP) is still relatively small. Given that current popular large-scale language models rely on huge arithmetic and energy consumption, it is of great practical importance to explore SNN-based approaches to implement NLP tasks in a more energy-efficient way. This paper investigates the conversion method from LSTM network to SNN and compares the performance of the original LSTM network with the converted SNN in a text classification task. The paper experimentally compares the accuracy of different LSTM-based models using different methods on the same dataset. The experimental results show that the converted SNN is able to achieve similar performance to the original LSTM network with significantly lower power consumption in text classification tasks on multiple datasets.
Building Energy Consumption Prediction and Optimization
Authors: Fan ZiLing , Pei ChiangSu , Zhao Yan , Chen Shurong , Kang Chengwei
Published: November 2024

As the proportion of building energy consumption in global energy consumption is significant and continues to rise, accurate prediction and optimization of building energy consumption are of great significance. This research aims to develop high-precision prediction models and innovative optimization strategies to address the problems of insufficient accuracy of existing prediction methods and poor optimization effects. By collecting and integrating data through multiple channels, a hybrid model architecture combining LSTM, CNN, and the attention mechanism is selected, and an automatic search and optimization method for hyperparameters is adopted. In terms of data processing, multi-source heterogeneous data fusion, data cleaning and enhancement based on spatiotemporal features, and innovative feature engineering are carried out. In the experiments, the dataset is divided considering the dynamic use of buildings and environmental changes, and indicators related to energy costs are introduced for evaluation. The research successfully constructed a model for accurately predicting building energy consumption, proposed effective optimization strategies, reduced energy consumption, and ensured comfort, providing a basis for practical applications through sensitivity analysis and robustness tests.
Molecular Docking-Aided Identification of Natural Bioactive Molecules as Potential Cancer Cell Proliferation Inhibitors
Authors: Hassan Iqra , Gul Sameena , Zaman Aqsa , Zafar Erum , Khan Muhammad
Published: November 2024

Cancer is the second leading cause of death worldwide. Uncontrolled proliferation of cells is a hallmark of cancer development and progression. Ki-67 (a marker of proliferation Kiel-67) and Proliferating Cell Nuclear Antigen (PCNA) are two major proliferations, diagnostic and prognostic biomarkers as these are over expressed in cancerous cells. Pharmacological inhibition of Ki-67 and PCNA could effectively inhibit the growth of cancer cells. Objective: To identify Sesquiterpene Lactones (SLs) as potential inhibitors of Ki-67 and PCNA to reduce cancer burden. Methods: The inhibitory potential of SLs, namely sulfocostunolide A, sulfocostunolide B, ilicol, eucalyptone, and ascleposide E, were investigated using Molecular Docking (MD) analysis. MD analysis and visualization of ligand-protein complexes were performed using softwares such as MGL tools, BIOVIA Discovery Studio visualizer and LigPlot plus. Additionally, drug likeness and pharmacokinetic properties of SLs were assessed via pkCSM and ADMET analysis. Results: Results showed that eucalyptone with binding energy of -8.1 kcal/mol with Ki-67 while sulfocostunolide B with -6.4 kcal/mol binding energy with PCNA are the most potent proliferative inhibitors of Ki-67 and PCNA. ADMET properties, MD studies and toxicity prediction shows that current investigated ligands bind effectively with Ki-67 and PCNA without showing any toxicity. Conclusions: Current study concludes that eucalyptone with Ki-67 and sulfocostunolide B with PCNA made stable complexes and can be considered as novel inhibitors. In addition to that, these suggested ligands have also shown effective drug likeness and ADMET profile. Further, in-vitro and in-vivo studies are required to validate these findings.
A review of different structures of power amplifiers to improve linearity and efficiency
Authors: Ziraksaz Fazel
Published: November 2024

This review paper presents a comprehensive study of commonly used power amplifier (PA) structures. In recent years, with the development of modern wireless telecommunications and their dramatic challenges, new requirements are needed. In addition, some applications, like cell phones and tablets, for example, need new considerations, especially in terms of power consumption. Also, linearity is another major factor in designing a PA. Furthermore, fabrication technologies such as complementary metal-oxide semiconductors (CMOS), silicon on insulators (SOI), gallium nitride (GaN), gallium arsenide (GaAs), etc. play a crucial role in terms of power consumption. Therefore, it is necessary for PAs to meet these considerations. This paper reviews design considerations, fabrication technologies, and common PA structures, including envelope tracking (ET), envelope elimination and restoration (EER), Doherty, linear amplification with nonlinear components (LINC), feedback, and feedforward linearization techniques with their pros and cons. This review focuses on the significant achievements, techniques, structures, and characteristics of each. Also, this review focuses on the significant achievements, techniques, structures, and characteristics of each. Also, this paper tries to provide a brief overview of the various methods with the advantages and disadvantages of each. This review paper tries to make readers familiar with common structures so that readers know the advantages and disadvantages of each and choose the desired structure based on their priorities.
Analyzing device-to-device communication performance with amplify-and-forward relaying amid co-channel interference
Authors: Tran Huu Quy
Published: November 2024

Currently, the pressing issue of spectrum limitation, driven by the increasing demand for wireless communication services, has led to the challenge of co-channel interference (CCI) due to the reuse of frequencies in wireless networks. To address this, non-orthogonal multiple access (NOMA) has emerged as a solution. This report conducts a thorough evaluation of NOMA’s system performance over independent and non-identical Rayleigh fading channels in device-to-device (D2D) communications networks, where CCI is significant. The analysis includes examining the probability density function (PDF) and cumulative distribution function (CDF) of the upper SINR threshold. Communication channels are defined as independent and non-identical Rayleigh fading channels, and probability expressions are formulated to assess system failure likelihood for two users. Additionally, Monte-Carlo simulations are conducted to validate the proposed theoretical mathematical expressions.
Research on interdisciplinary project-based geography fieldwork in education for sustainable development
Authors: Hua Ruiyan , Guo Fengtao , Duan Yushan , Fan Hanxin , Zhang Jiaqi
Published: November 2024

The ability to solve real-world problems for a sustainable future has become a worldwide consensus, and interdisciplinary competencies and project-based learning (PBL) have become the focus of curriculum reform in China. This study investigates the effectiveness of interdisciplinary PBL fieldwork in geography education for sustainable development, focusing on the perceptions of students from a junior high school in Chongming, Shanghai, of the interdisciplinary effectiveness of PBL fieldwork. Over a one-month pilot program, the results suggest that the new fieldwork approach did not achieve the expected benefits. The significant gains of students can be grouped into four perspectives, namely, understanding of nature and classroom knowledge; problem-solving skills and environmental action; scientific spirit, environmental awareness, and interest in geography; and understanding of local needs and sustainable development issues, which increased their interest in learning. The students generally accepted the ability to collect information and data and the thinking ability of circular development. The influencing factors of activity effectiveness include the time and difficulty of the activity, cognitive and knowledge levels, learning habits of students, student participation, and teaching experience of teachers. The study offers valuable insights for improving fieldwork in other regions and for future research.
Recent development of nanotechnology-based approaches for gynecologic cancer therapy
Authors: Gedda Gangaraju , Park Yoo-Jin , Pang Myung-Geol
Published: November 2024

Gynecological cancer is a life-threatening malignancy among women. Traditional therapies, including chemotherapy, often face challenges in terms of chemotherapeutic drug solubility and resistance, specificity, tumor site targeting, and toxicity to healthy tissues, leading to shortened efficacy and unfavorable patient outcomes and survival rates in patients with gynecologic malignancies. Recently, nanotechnology-based therapeutic methods such as targeted drug delivery and phototherapies have emerged as an appropriate alternative to overcome issues associated with traditional therapeutic methods. Specifically, nanomaterials and nanomaterial-based methods enhance the delivery of therapeutic/targeting agents to tumor sites and cellular uptakes and improve the tumor-suppressing effect. This review aims to provide an overview and future perspective on the potential impact of nanotechnology-based therapeutic methods for effective therapies for gynecologic cancer.
ESTRATÉGIAS DO SUS NO MANEJO DE GESTANTES COM HIV NO AMAZONAS: UM RELATO DE EXPERIÊNCIA
Authors: Bezerra Anne Lethicia Mendonça , Oliveira Caio Santos , Conceição Glaycy Anny Andrade , Santiago Gabriela Breval de Oliveira , Nishi Lara Akemi Braga , Figueiredo Ana Flavia Martins , Tavares Clarice Santos , Silvai Aurélia Cristina Andrade da , Souza Sergio Henrique Silva de , Diniz Alena Mileo Monteiro , Marques Brenda Araújo , Oliveira Suzane Lima de , Evaristo Jucyette da Silva Gomes , Souza Celsa da Silva Moura
Published: November 2024

Este relato de experiência descreve práticas da disciplina de Saúde Coletiva II e tem por objetivo principal apresentar a linha de cuidado do Sistema Único de Saúde (SUS) concernente ao manejo de gestantes vivendo com HIV (Vírus da Imunodeficiência Humana). Nesse sentido, as práticas contemplaram níveis de atenção do sistema de saúde, rodas de conversa e uma visita à Diretoria de Vigilância Epidemiológica, Ambiental, Zoonoses e da Saúde do Trabalhado (DVAE), tudo com a finalidade de obter dados sobre disseminação do vírus em nível macro (Brasil) e micro (Manaus, Amazonas). Ademais, o estudo teve a intenção de investigar a implementação das políticas públicas no que concerne à prevenção da transmissão vertical do HIV e ao acesso à saúde da grávida e do lactente.
IMPACTO DAS PRECIPITAÇÕES INTENSAS NA CIDADE DE CACULÉ-BA, ENTRE OS ANOS DE 2019 E 2023
Authors: Silva Aline Azeredo da , Sampaio Andrecksa Viana Oliveira , Lima Espedito Maia , Oliveira Edvaldo
Published: November 2024

Esse estudo tem por objetivo analisar os impactos causados pelas precipitações intensas na cidade de Caculé-BA, entre os anos de 2019 e 2023, por meio do levantamento dos dados mensais e anuais das precipitações durante o período estudado, assim como, informações obtidas nos meios de comunicação. A fundamentação teórico-conceitual desta pesquisa, baseou-se nos estudos desenvolvidos por Monteiro (1975, 2003), sobre o Sistema Clima Urbano (S.C.U.). Os dados de precipitação foram coletados no site do Instituto Nacional de Meteorologia (INMET), da estação automática de Guanambi, e as ocorrências foram levantadas em blogs. Mediante as informações obtidas, foi possível analisar as ocorrências de eventos extremos (alagamentos, inundações) na cidade e suas implicações. Diante dos resultados, ficou evidente a necessidade de implementar estratégias de mitigação e adaptação que fortaleçam a resiliência da cidade, perante tais eventos.
Evaluating point-of-care testing for anemia diagnosis in pigs: Blood collection location disparities, repeatability, and validity
Authors: McClellan Katlyn , Levesque Crystal , Weaver Eric
Published: November 2024

The HemoCue 201 was used to compare hemoglobin (HbC) across blood sampling sites. Tail docking samples had lower HbC than both ear and mammary vein samples (P = .001). Both point-of-care and laboratory HbC testing methods showed agreement, with biases of 0.2 g/dL (ear) and -0.45 g/dL (jugular).
Сучасні тенденції модернізації управлінських рішень в сфері обліку пошкодженого та знищеного житла в Україні
Authors: Трампольська К. О.
Published: November 2024

Проблема обліку пошкодженого та зруйнованого житла стає все більш актуальною для України через стрімке зростання його кількості. Метою дослідження є визначення ключових тенденцій модернізації управлінських рішень в сфері обліку пошкоджених та знищених об’єктів житлової нерухомості. Особливу увагу приділено Реєстру майна, пошкодженого та знищеного внаслідок бойових дій, терористичних актів, диверсій, спричинених збройною агресією Російської Федерації проти України, який становить основу електронного обліку пошкодженого та знищеного житла в Україні. В процесі дослідження були застосовані загальні та спеціальні методи наукового пізнання, статистичний метод та метод порівняння. Вперше сформульоване визначення управлінських рішень в сфері обліку пошкодженого або знищеного житла як послідовність оцінок, висновків та управлінських дій посадових осіб державних органів, установ, органів місцевого самоврядування, які стали результатом комплексного вивчення особливостей обліку пошкодженого або знищеного житла внаслідок бойових дій, та втілюють собою вибір найкращого варіанту вирішення проблем, які перешкоджають збиранню, формуванню, обліку, зберіганню та охороні даних про об’єкти власності з метою захисту та дотримання прав осіб, житло яких зазнало руйнації. Зроблено висновок, що основними тенденціями модернізації управлінських рішень в сфері обліку пошкодженого та знищеного житла в Україні є цифровізація, інноваційність, високотехнологічність, автоматизація та інтеграція з іншими програмними комплексами. Зауважено, що продовження наукового дослідження основних сучасних тенденцій модернізації та трансформації управлінських рішень щодо обліку пошкодженого та знищеного житла матиме важливе значення для повоєнної інфраструктурної відбудови України.
King's College criteria and the Clichy-Villejuif criteria require adjustments for assessing acute liver failure due to yellow fever
Authors: Athanasio Bruno da Silva , Andrade Antonio Marcio de Faria , Costa Vivian Vasconcelos , Castro Juliano Felix , Garcia Silverio Leonardo Macedo , Teixeira Mauro Martins , Souza Daniele da Gloria , Vidigal Paula Vieira Teixeira , Lima Cristiano Xavier
Published: November 2024

BACKGROUND

Acute liver failure (ALF) is a severe condition characterized by rapid deterioration of liver function in individuals without preexisting liver disease. Liver transplantation (LT) is the most impactful treatment. Yellow fever (YF) is an infectious disease that primarily affects the liver and has a high mortality rate. However, LT can be a viable option for treating rare cases with extensive liver involvement. However, the criteria for assessing the severity of ALF and determining the indications for transplantation have not been specifically validated for cases caused by YF.

AIM

To present necessary adjustments to established scoring systems for ALF secondary to YF.

METHODS

This was an observational, retrospective, single-center study. Fourteen consecutive patients with confirmed ALF due to YF were monitored in the intensive care unit by a specialized liver transplant team during a three-month epidemic outbreak in Brazil. During hospitalization, general supportive therapeutic measures were implemented, and the patients were regularly assessed using the King's College criteria and the Clichy-Villejuif criteria to determine the severity of liver failure. LT is considered a viable measure for patients with signs of end-stage liver failure.

RESULTS

Eight of 14 (57%) patients developed severe neurological alterations within the first 96 hours after hospital admission. Four patients underwent emergency LT, and despite a moderate viral infection of the graft after transplantation, the 5-year survival rate was 50%. Although the King's College criteria and the Clichy-Villejuif criteria are the main scoring systems for ALF, they are insufficient for predicting the risk of mortality in this context, primarily because of low serum bilirubin levels in the final stage of the disease and significant disparities between coagulation abnormalities and patient severity.

CONCLUSION

To ensure good applicability in cases of YF-induced ALF, the authors suggest adaptations to the King's College and Clichy-Villejuif criteria.
Efficacy of antimicrobials in preventing resistance in solid organ transplant recipients: A systematic review of clinical trials
Authors: Ardila Carlos M , Yadalam Pradeep K , Ramírez-Arbelaez Jaime
Published: November 2024

BACKGROUND

In the absence of effective antimicrobials, transplant surgery is not viable, and antirejection immunosuppressants cannot be administered, as resistant infections compromise the life-saving goal of organ transplantation.

AIM

To evaluate the efficacy of antimicrobials in preventing resistance in solid organ transplant recipients.

METHODS

A systematic review was conducted using a search methodology consistent with the preferred reporting items for systematic reviews and meta-analyses. This review included randomized clinical trials that evaluated the efficacy of antimicrobial agents (prophylactic or therapeutic) aimed at preventing antimicrobial resistance. The search strategy involved analyzing multiple databases, including PubMed/MEDLINE, Web of Science, EMBASE, Scopus, and SCIELO, as well as examining gray literature sources on Google Scholar. A comprehensive electronic database search was conducted from the databases’ inception until May 2024, with no language restrictions.

RESULTS

After the final phase of the eligibility assessment, this systematic review ultimately included 7 articles. A total of 2318 patients were studied. The most studied microorganisms were cytomegalovirus, although vancomycin-resistant enterococci, Clostridioides difficile , and multidrug-resistant Enterobacterales were also analyzed. The antimicrobials used in the interventions were mainly maribavir, valganciclovir, ganciclovir, and colistin-neomycin. Of concern, all clinical trials showed significant proportions of resistant microorganisms after the interventions, with no statistically significant differences between the groups (mean resistance 13.47% vs 14.39%), except for two studies that demonstrated greater efficacy of maribavir and valganciclovir (mean resistance 22.2% vs 41.1% in the control group; P < 0.05). The total reported deaths in three clinical trials were 75, and there were 24 graft rejections in two studies.

CONCLUSION

All clinical trials reported significant proportions of antimicrobial-resistant microorganisms following interventions. More high-quality randomized clinical trials are needed to corroborate these results.