bluuebunny/crossref_metadata_2025 huggingface dataset:
Algorithms and Big Data: Reshaping Global Trade and Diplomacy
Authors: Gao Jianing
Published: April 2024

This paper explores the profound impact of algorithms and big data on the modern global landscape, focusing on their roles in digitalizing trade, driving economic policies, and reshaping diplomatic relations. Through a detailed quantitative analysis, we demonstrate how these technological tools have revolutionized e-commerce, optimized supply chain management, influenced the formulation of trade policies, and redefined monetary and fiscal policy formulation. Additionally, we delve into the pivotal role of algorithms in enhancing diplomatic communication, predicting conflicts, and fostering international cooperation. Our findings reveal that the strategic application of algorithms not only offers significant efficiency gains and cost reductions but also facilitates more informed decision-making processes, contributing to more stable, efficient, and inclusive economic systems and international relations.
Continuous trading strategy based on deep reinforcement learning
Authors: Zhang Kailin
Published: April 2024

Automatic trading policy has been researched with reinforcement learning (RL). Designing a profitable and applicable policy is of great significance for research in quantitative finance. Incoprating with deep learning, typical deep reinforcement learning (DRL) algorithms such as Proximal Policy Optimization (PPO) have shown their effectiveness. To improve the practical applicability, the manner in which we train our model should better simulate the dynamic of the stock market. A sliding window training strategy is a better solution, which employes training, validation and trading procedures on dataset with a sliding window. However, this empirical strategy can still be further investigated in algorithm evaluation and the experiment designation such as choice of sliding window. In this paper, we further investigated the continuous trading strategy (CTS). We evaluated the performance of a wider range of algorithms, including PPO, Deep Deterministic Policy Gradient (DDPG), Advantage Actor-Critic (A2C), Soft Actor-Critic (SAC). Moreover, we trained our models on a longer term period. We also provide detailed observations and suggestions on experiment settings. These discussions will facilitate researchers in their future work.
GlassOnly: Transparent object dataset for object detection
Authors: Mai Xinwei
Published: April 2024

Although datasets like ImageNet have a variety of classes for object detection, there are not many samples of transparents objects like glass walls, which are fully implemented in shopping malls and houses. The ignorance of transparent objects in object detection may cause potential danger to humans as the machines would not consider glasses as obstacles in path planning. Therefore, GlassOnly collected samples from malls and apartments and built a dataset for glass walls only. The dataset sample simulates a robot walking in human living environments from a perspective of the machine itself, providing data for studying detecting transparents objects.
Dual attention-enhanced SSD: A novel deep learning model for object detection
Authors: Liu Haotian
Published: April 2024

Object detection is a fundamental task in computer vision with significant implications across various applications, including autonomous driving, surveillance, and image understanding. The accurate and efficient detection of objects within images is crucial for enabling machines to interpret visual information and make informed decisions. In this paper, we present an enhanced version of the Single Shot MultiBox Detector (SSD) for object detection, leveraging the concept of dual attention mechanisms. Our proposed approach, named SSD-Dual Attention, integrates dual attention layers into the SSD framework. These dual attention layers are strategically positioned between feature maps and prediction convolutions, enhancing the model's ability to capture informative feature representations across a wide range of object scales and backgrounds. Experimental results on the PASCAL VOC 2007 and 2012 datasets validate the effectiveness of our approach. Notably, SSD-Dual Attention achieves an impressive mean Average Precision (mAP) of 78.1%, surpassing the performance of SSD models enhanced with attention mechanisms such as SSD-ECA, SSD-CBAM, SSD-Non-local attention and SSD-SE attention, as well as the original SSD. These results underscore the enhanced accuracy and precision of our object detection system, marking a substantial advancement in the field of computer vision. Code is available at https://github.com/AlexHunterLeo/Dual-attention-Enhanced-SSD-A-Novel-Deep-Learning-Model-for-Object-Detection
Action-Aware Vision Language Navigation (AAVLN): AI vision system based on cross-modal transformer for understanding and navigating dynamic environments
Authors: Liu Jasmine
Published: April 2024

Visually impaired individuals face great challenges with independently navigating dynamic environments because of their inability to fully comprehend the environment and actions of surrounding people. Conventional navigation approaches like Simultaneous Localization And Mapping (SLAM) rely on complete scanned maps to navigate static, fixed environments. With Vision Language Navigation (VLN), agents can understand semantic information to expand navigation to similar environments. However, both cannot accurately navigate dynamic environments containing human actions. To address this challenge, we propose a novel cross-modal transformer-based Action-Aware VLN system (AAVLN). Our AAVLN Agent Algorithm is trained using Reinforcement Learning in our Environment Simulator. AAVLNs novel cross-modal transformer structure allows the Agent Algorithm to understand natural language instructions and semantic information for navigating dynamic environments and recognizing human actions. For training, we use Reinforcement Learning in our action-based environment simulator. We created it by combining an existing simulator with our novel 3D human action generator. Our experimental results demonstrate the effectiveness of our approach, outperforming current methods on various metrics across challenging benchmarks. Our ablation studies also highlight that we increase dynamic navigation accuracy with our Vision Transformer based human action recognition module and cross-modal encoding. We are currently constructing 3D models of real-world environments, including hospitals and schools, for further training AAVLN. Our project will be combined with Chat-GPT to improve natural language interactions. AAVLN will have numerous applications in robotics, AR, and other computer vision fields.
Integrates Differential Gene Expression analysis and deep learning for accurate and robust prostate cancer diagnosis
Authors: Xie Y. , Xie J.
Published: April 2024

The challenge of diagnosing complex diseases and increasing human lifespan is a pressing task. Traditional methods, relying on visual characteristics like ultrasound and angiography, often struggle to detect cancer in its early stages, limiting diagnostic accuracy due to the intricate and nonlinear nature of diseases. From the perspective of gene expression, detecting cancer offers a more robust and effective approach due to its ability to directly assess the genetic activity within cells. In this study, we present the development of a prostate cancer feature selection method based on differentially expressed genes (DEGs). Utilizing datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we meticulously curated data for both model training and testing, implementing stringent filtering criteria based on p-value and fold change. Our study identifies a panel of 220 genes with substantial potential for prostate cancer detection. We then construct an ANN model for the diagnosis of the disease, whose accuracy is 0.780.01, which is more effective than other models like Ridge Classifiers, Logistic Regression, Naive Bayes Regression and Decision Trees. The average accuracy of these classifiers is 0.730.01. Notably, these genes also demonstrate exceptional performance across other various classifiers, indicating their robustness and effectiveness without dependence on specific models. The credibility is validated by comparison to random genes, and adaptability by using pancreatic cancer data from GEO. The Gene Otology analysis also verifies the feasibility of such method. This panel establishes a solid foundation for advancing clinical diagnostics of prostate cancer. This framework holds potential to significantly transform prostate cancer screening by offering strong resilience and precision across multiple classification methods.
Unraveling the characteristics of Parkinson's Disease through neuroimaging: Insights and future directions
Authors: Huang Kaiyu
Published: April 2024

Parkinson's disease (PD) is a neurodegenerative disease with a high degree of patient heterogeneity, and as of 2016 there are approximately 6.1 million PD patients worldwide. PD has a high proportion of patients with intermediate to advanced disease and a high rate of disability, and clinical diagnosis and treatment are difficult due to the lack of neuromarkers to identify disease states and the inability to quantify the effects of treatment in PD. In recent years, many researchers have explored specific changes in brain activity in PD based on electrophysiological and neuroimaging data. Electroencephalography, with its high temporal resolution and rich frequency domain information, and functional magnetic resonance imaging, with its high spatial resolution, have become the main tools to characterize the state of brain activity in PD in recent years. This paper analyses some of the available data on the characteristics of PD through two neuroimaging techniques commonly used in the disease. It concludes with the prospect of being able to establish uniform criteria for determining PD.
Enhancing automotive interior automation through face analysis techniques
Authors: Qin Hanzhen
Published: April 2024

In contemporary society, the progress and widespread adoption of automotive automation, particularly in autonomous driving technology, have been remarkable. The rapid evolution of this technology has equipped numerous vehicles with high-stability autonomous capabilities, significantly enhancing convenience for users. However, as autonomous driving continues its developmental trajectory, it is crucial to give equal attention to the advancement of interior automation technologies within vehicles. Exploring avenues that make these automated technologies smarter, safer, and more conducive to delivering heightened convenience and support to users is imperative. This realm presents a domain ripe with potential for innovation and substantial advancements that cater to the evolving needs and expectations of modern transportation.
Research on confusing responses based on ChatGPT
Authors: Xiong Huiwen
Published: April 2024

Recently, Artificial intelligence and Machine learning have changed the nature of scientific inquiry, with chatbots moving from rule-based technology to AI technology. Open AIs ChatGPT is a prominent AI language model that has attracted great interest and attention since its launch. To better understand its role and influence in social life, it is very necessary to know its work carefully. This paper briefly introduces the development history, current situation, and future development of the ChatGPT, discusses its popular application fields, and analyzes its pros and cons. On this basis, some problems that still exist in the application are focused on. This study uses the case analysis method to emphasize the confusing responses of ChatGPT in multiple fields, telling people that while enjoying its powerful functions, should still pay attention to its side effects and risks, the most obvious one is deceptive behavior, providing users with misleading or fabricated information may further lead to other social problems. This study speculates on the future development of ChatGPT and proposes future development directions. Generally, by rationally utilizing the functions of ChatGPT, its potential in various fields can be better released, thereby promoting the advancement of conversational AI and its transformative impact on society.
Data mining in economics: Unraveling merchant transactions for strategic insights
Authors: Li Shirui
Published: April 2024

This paper explores the application of data analysis and mining techniques in the domain of economics, with a specific focus on understanding merchant transaction characteristics. The study delves into fundamental theories, including classification tasks, regression missions, and relevance analysis, showcasing the versatility of these techniques in addressing economic challenges. Neural network models, such as Multilayer Perceptron and Auto Encoder, are introduced for handling complex economic data. The research emphasizes the importance of data mining in extracting valuable insights from real-world merchant transaction data, leading to the creation of the Merchant Transaction Feature Standard Database. A detailed data preprocessing method tailored to merchant transaction data is presented, addressing issues such as missing data, noise reduction, data integration, and transformation. The unique characteristics of real merchant transaction data, including sensitivity, concentration, sparsity, and the lack of label diversity, are outlined. The study concludes by highlighting the potential benefits of employing data mining techniques in optimizing marketing, merchant management, and risk management strategies. Overall, this paper contributes to advancing the understanding and practical applications of data analysis and mining techniques in economics.
AI-driven transformation: From economic forecasting to strategic management
Authors: Li Shirui , Chen Jiani , Sun Yuhan , Wang Zhiyang , Du Jielin , He Wenchong
Published: April 2024

The convergence of Artificial Intelligence (AI) technologies and various sectors within the global economy has brought forth transformative changes, shaping decision-making processes, financial practices, and economic forecasting. This comprehensive paper explores the multifaceted applications of AI in diverse domains, ranging from economic forecasting and financial markets to accounting, auditing, strategic management, and crisis management. Through empirical analysis, case studies, and scholarly insights, we uncover how AI technologies are driving efficiencies, improving predictions, enhancing decision-making, and fostering innovation across these sectors. This study offers valuable academic contributions by delving into the specific methodologies, real-world examples, and impact assessments of AI adoption, shedding light on its implications for practitioners, policymakers, and researchers.
Brand reputation enhancement through data mining in social media
Authors: Jiang Yuxi
Published: April 2024

The rise of social media has provided a large range of data such as user behavior data, social relationship data and so on, which can be used for business analysis, and the use of analysis tools can obtain complex, extensive and continuously followed social media data to help enhance the reputation of brand. In this paper, the definition and potential relationship between data analysis, social media and brand reputation and how the analysis results can help enhance business reputation will be introduced. This paper uses the research methodology of review, collecting and analyzing a large amount of literature, and draws a series of effective conclusions, and concludes that data mining of social media can effectively help business analysis to expand brand reputation, indicating that data mining analysis brings a significant improvement in effectiveness of marketing activities[1].
Harmonizing form and function: The evolution, principles, and future of interactive design
Authors: Wang Zekun , Feng Yifan
Published: April 2024

This paper explores the evolution of interactive design, tracing its journey from the early stages of computing to the present day. It begins with a historical overview, highlighting the transition from function-focused, command-line interfaces to more accessible graphical user interfaces (GUIs) and the emergence of Human-Computer Interaction (HCI) as a vital multidisciplinary field. The paper then delves into the significant technological advancements that have reshaped interactive design, such as touch interfaces, virtual and augmented reality, and the integration of artificial intelligence. Furthermore, it discusses the paradigm shift towards user-centric design, emphasizing the importance of understanding user needs and preferences in creating intuitive, enjoyable, and inclusive digital experiences. The principles of usability, aesthetics, emotional engagement, and contextual design are explored as critical components of effective interactive design. The paper concludes by highlighting the ongoing evolution of interactive design and its impact on creating user-centered digital experiences.
AI-driven innovations in automation and urban management
Authors: Yao Keyu
Published: April 2024

This paper examines the transformative impact of Artificial Intelligence (AI) across diverse sectors, particularly focusing on industrial automation, smart homes, and intelligent cities. It delves into how AI-driven technologies enhance predictive maintenance, quality control, and supply chain optimization in industrial settings, contribute to energy management, security enhancement, and health monitoring in smart homes, and improve traffic management, waste management, and public safety in intelligent cities. Through detailed analyses and quantitative assessments, the study showcases the efficiency gains, cost reductions, and quality of life improvements facilitated by AI integration. It highlights the pivotal role of AI in addressing contemporary challenges and setting new standards for operational excellence, sustainability, and safety.
Driver fatigue detection method based on facial features using deep learning
Authors: Li Wanning
Published: April 2024

Fatigue driving can result in delayed driver reactions and lack of concentration, increasing the risk of traffic accidents. Therefore, finding effective fatigue detection methods is crucial for accident prevention and ensuring public safety. Traditional driver fatigue detection methods based on facial features are limited by their reliance on manually designed features and classifiers, leading to low accuracy, slow detection speed, and inefficient performance. Hence, methods based on deep learning have gradually become a trend in fatigue-driving detection research. This paper focuses on driver fatigue detection methods based on facial features using deep learning. In this study, we analyze numerous research outcomes on how to accurately extract, analyze, and integrate various facial features, such as eye features, mouth features, facial expressions, and head postures, to precisely determine the driver's fatigue state. Then, we compare the latest deep learning-based technologies. This paper provides an information-rich repository to help researchers select more promising designs, that can drive the widespread development of fatigue-driving detection technologies.
Enhancing language acquisition through personalized learning: The role of collaborative filtering and recommender systems in TESOL
Authors: Guo Yeqin
Published: April 2024

This paper explores the transformative potential of Collaborative Filtering (CF) and Recommender Systems (RS) in Teaching English to Speakers of Other Languages (TESOL). By leveraging data-driven insights from learner interactions, these technologies offer personalized learning experiences that significantly enhance language acquisition, engagement, and retention. Through empirical evidence and quantitative analyses, we demonstrate the positive impact of CF and RS on learners' proficiency, vocabulary acquisition, and communicative competence. The integration of CF and RS into TESOL not only facilitates adaptive learning pathways but also addresses practical implementation challenges, including privacy, ethical concerns, and technological barriers. This study underscores the efficacy of personalized learning recommendations in creating more engaging, efficient, and effective language learning environments.
Video anomaly detection based on hybrid attention mechanism
Authors: He Ruifeng , Xie Mingtian , He Aixing
Published: April 2024

To improve the ability of video anomaly detection models to extract normal behavior features of samples and suppress abnormal behaviors, this paper proposes an unsupervised video anomaly detection model, which takes advantage of spatio-temporal feature fusion, storage module, attention mechanism, and 3D autoencoder model. The model utilizes autoencoder to capture scene feature maps to enhance anomaly feature extraction. These maps are merged with the original video frames, forming fundamental units constituting continuous sequences serving as the model's input. Moreover, the attention mechanism is integrated into the 3D convolutional neural network to strengthen the network's capability in extracting channel and spatial features from videos. Experimental validation is performed on a publicly accessible campus dataset, illustrating the model's superior accuracy in anomaly detection.
Research on clustering algorithms based on the Iris dataset
Authors: Song Jiayang
Published: April 2024

In the burgeoning field of machine learning, clustering algorithms play a quintessential role in uncovering hidden patterns and structures within data. This study commences by highlighting the critical importance of machine learning and the expansive application of clustering algorithms across various disciplines. It then provides a brief background, tracing the development history of clustering algorithms and elucidating the unique characteristics and methodologies inherent to different algorithms. Through empirical analysis conducted on the Iris dataset, this research evaluates the performance of the K-means, hierarchical clustering, and DBSCAN algorithms, leveraging experimental charts and datasets for a nuanced assessment. The comparative analysis reveals distinct advantages and disadvantages of each algorithm, facilitating a balanced discussion on their practical implications. The conclusion synthesizes these findings, offering insights into the comparative merits of the algorithms and suggesting avenues for future research. This investigation aims to deepen the comprehension of the application challenges and opportunities presented by clustering algorithms, thus offering a guiding framework for future explorations in the field.
Enhancing large-span structure design and maintenance through the synergy of CAD, BIM, and IoT-AI technologies
Authors: Xing Jinquan
Published: April 2024

The integration of Computer-Aided Design (CAD), Building Information Modeling (BIM), and Internet of Things (IoT) with Artificial Intelligence (AI) represents a transformative shift in the architectural and engineering practices for large-span structures. This paper delves into the quantitative and qualitative enhancements brought about by these technologies, focusing on their pivotal roles in design precision, structural analysis, project management, and maintenance strategies. CAD's evolution from simple drafting to complex 3D modeling has significantly reduced design time and errors, while its integration with structural analysis software has improved load distribution accuracy and material cost efficiency. BIM's contribution to design, construction, and sustainability emphasizes optimized resource allocation and energy-efficient practices. Furthermore, IoT and AI's role in real-time monitoring and predictive maintenance underpins a proactive approach to structural health, ensuring longevity and safety. Through statistical analysis, mathematical modeling, and case studies, this paper highlights the synergistic impact of these technologies on reducing project timelines, enhancing collaboration, and fostering innovation in large-span structure engineering. The findings advocate for a multidisciplinary approach, combining engineering expertise with advanced computational tools to achieve superior outcomes in the construction and maintenance of large-span structures.
Validation of the applicability of the ideal gas equation of state and its correction based on molecular simulation and statistical methods
Authors: Zhang Junkai
Published: April 2024

The ideal gas equation of state is a theoretical model devised to simplify the behaviour of real gases, although its usefulness is limited in numerous realistic scenarios. The experimental subject of this study is the Ar atomic gas, and data is gathered using molecular simulation techniques to assess the suitability and scope of the ideal gas equation of state. Simultaneously, statistical techniques such as linear regression and polynomial regression are employed to construct a novel model. Additionally, the ideal gas equation of state is adapted under specific circumstances, leading to the proposition of a fresh empirical gas equation of state. The study determined that the ideal gas equation of state can be applied to Ar atomic gases within the temperature range of 300-500 K and gas densities ranging from 0.1-0.6 g/cm^3. However, when examining higher gas densities, specifically at temperatures of 300 K and densities exceeding 0.6 g/cm^3, a new empirical gas equation was derived. This equation demonstrates that the pressure of Ar atomic gas is influenced by the 1st and 6th power terms of its density.
Transforming construction: The growing role of mechatronics in building our infrastructure
Authors: Shao Peichen
Published: April 2024

Mechatronics, the synergistic integration of mechanical engineering, electrical engineering, control engineering, and computer science, is progressively transforming the construction industry. This paper explores how mechatronic technologies are being applied across the construction lifecycle to enhance productivity, quality, safety, sustainability, and cost-effectiveness. Core mechatronic technologies changing construction include automated material handling systems, advanced construction robotics, sensor networks for monitoring, laser scanning for accurate modeling and control, and building information modeling (BIM) software. Mechatronics offers multifaceted benefits spanning from enhanced labor productivity and construction velocity to superior quality control, safety, and process optimization. However, realizing the immense potential of mechatronics in construction requires overcoming key challenges including high upfront costs, lack of technical skills, organizational resistance, integration difficulties, and reliability concerns. With thoughtful leadership and implementation, mechatronics promises to revolutionize construction, shifting it towards more automated, streamlined, and optimized techniques. This paper delineates the tremendous potential of mechatronics in construction regarding benefits, technologies, and challenges. The outlook is promising for intelligent mechatronic systems to profoundly enhance productivity, quality, and safety as construction progresses towards greater automation and optimization.
Application of triblock copolymers in lithium-ion batteries based on solid electrolyte
Authors: Lin Shihao
Published: April 2024

In the future, all-solid Li-ion batteries are expected to gain widespread acceptance in larger markets due to their high safety and excellent electrochemical performance. However, it is important to acknowledge their drawbacks, including the inadequate compatibility between the electrode and electrolyte interface and the low ionic conductivity at room temperature. This paper reviews the specific methods and the latest research progress of triblock copolymers to solve the above problems. Block copolymerization is an effective way to enhance the efficiency and performance of electrolyte. Its advantage is that two or more monomers with different properties can be polymerized into the same structure, which is conducive to the ionic conductivity of polymer electrolyte. In the latest research results, straight-chain block copolymers have been synthesized, which have better physical and chemical properties compared to traditional comb block copolymers. However, the electrochemical properties of the straight-chain block copolymer electrolyte and the stability of the interface between the electrode and the solid electrolyte are rarely reported. Therefore, it is particularly important to systematically study the electrochemical properties and interface properties of block copolymer electrolytes. In the future, more attention should be drawn to not only improving the properties of SPEs but also building a stable interface with low interface resistance.
Advancements and challenges in nanomaterial-based medical implants
Authors: Cheng Yu
Published: April 2024

Nanomaterials have emerged as a groundbreaking technology with transformative applications in medicine, particularly in development of medical implants. In an era where conventional implant materials face limitations in terms of biocompatibility and long-term effectiveness, nanomaterials offer a promising avenue for innovation. This comprehensive review essay focuses on exploring advancements and challenges in employing specific types of nanomaterials, namely carbon nanotubes, graphene, and nanocomposites, to enhance the functionality of medical implants. By synthesizing findings from current literature and case studies, this essay establishes that nanomaterials offer substantial improvements in various dimensions, including mechanical strength, biocompatibility and drug delivery capabilities of implants. Carbon nanotubes have demonstrated exceptional tensile strength and flexibility, which can extend the functional lifespan of implants. Graphene, due to unique electronic properties, can be tailored for specific applications like electrical stimulation in neural implants. Nanocomposites provide a balanced combination of mechanical strength and biocompatibility, have shown potential in controlled drug release to mitigate post-operative complications. However, despite these advancements, there are substantial challenges to be addressed. Regulatory approval processes for nanomaterial-based implants are complex, often requiring extensive clinical trials that can prolong market introduction. The potential risks and long-term effects of nanomaterials in human tissues are also not fully understood, requiring further in-depth studies. The implications of this research are profound, as the innovations in nanomaterial-based implants have potential to revolutionize medical treatments and patient outcomes. The study underscores the urgency for further research and clinical trials to accelerate adoption of these promising technologies in mainstream healthcare.
Using roof garden to alleviate urbanization problems - A case study of Chengdu city
Authors: Liu Ziyan
Published: April 2024

The accelerated process of urbanization has led to significant urbanization issues such as noise pollution, air pollution, and the urban heat island effect. Roof gardens, as an effective way to create green spaces within cities, have considerable potential to address these urbanization problems. Taking Chengdu City as an example, this paper presents a simple design for a roof garden, focusing on the infrastructure, layout, plant species, and materials of the roof garden. The roof garden was improved after establishing its potential to mitigate urban noise by initial qualitative analysis. Firstly, specific vegetation selections are made which include deciduous trees such as Cephalotaxus Harrington and shrubs like Bauhinia blakeana. which can adapt to Chengdu's climate while effectively capturing dust particles. Secondly, a water storage layer was installed, and the angle of the pavilion roof was adjusted which allows the roof garden to regulate rainwater runoff effectively and minimize potential problems as possible. Although the current roof garden design is still in the theoretical stage and requires further field research and design, it holds tremendous potential in dealing with urban issues due to its unique advantages and policy support.
Comparative analysis of greywater recycling and rainwater harvesting as supplementary water sources for conventional urban and tourist resort water supplies
Authors: Cao Shuhong
Published: April 2024

Freshwater scarcity combined with high water demand from rapid urbanization, population growth, and changing consumption has resulted in increasing stress on urban water supply systems. Seasonal fluctuations in the population of tourist destinations are especially evident in tourist resorts. Greywater recycling and rainwater harvesting have been proposed as the most widely used valuable strategies to address water scarcity. By taking the conventional water supply system and the water supply of tourist resorts as examples, this study systematically compares and analyzes the advantages and limitations of greywater recycling and rainwater harvesting by enumerating the stress of water supply in different systems, discussing the benefits of both strategies and comparing the differences when using them for tourist destinations. The results showed that pumping systems or elevated tanks used to meet the water supply needs of high-rise buildings pose energy challenges. The water use characteristics of tourist resorts cause it to be closely related to the seasonal influx of tourists. Greywater recycling is more effective than rainwater harvesting in mitigating tourist resorts' water shortage problem. Suggestions for ways to implement these strategies for different regions are also given to make informed decisions about water distribution efforts. This study provides a reference for sustainable urban water resource management.
Navigating the nitrogen challenge: Strategies for wastewater management and sustainable resource recovery
Authors: Li Zixuan
Published: April 2024

Water scarcity and the impact of nitrogen pollution from wastewater have emerged as pressing global concerns. This study delved into the hazards posed by nitrogen pollution, focusing on its sources, composition, and detrimental effects on both the environment and the agricultural sector. The primary research methodology involved a comparative analysis of various nitrogen control and recovery technologies, highlighting the technical effects and future application prospects of ammonia vapor stripping and adsorption nitrogen recovery. Findings revealed that while ammonia vapor stripping offers operational simplicity, it is energy-intensive and requires careful byproduct management. In contrast, adsorption nitrogen recovery provides a more energy-efficient method, though challenges like selectivity in the presence of contaminants persist. The study concludes by suggesting an integrated approach that combines both methods, optimized based on wastewater characteristics, and underscores the importance of byproduct valorization and the development of selective adsorbents. The ultimate goal is to ensure sustainable environmental solutions while maximizing resource optimization.
The advantages of sponge cities in urban flood prevention and control
Authors: Bian Ziming
Published: April 2024

Frequent occurrence of extreme precipitation is highly susceptible to flooding. The urbanization process has exacerbated the current situation of urban flooding. Sponge city technology has an important role in solving the problem of urban flooding. Since 2012, sponge city construction has been gradually launched in China. However, there are problems and challenges in China's sponge city construction. In this context, this paper explains the concept and advantages of sponge city. It attributes the main factors causing urban flooding to natural and social factors and discusses them separately. This paper uses the sponge city concept to construct the method of urban flood prevention and control system, and gives how to solve the problem of good articulation within the system. It also categorizes the existing sponge cities in China and puts forward the misunderstandings and deficiencies of the current state of construction of sponge cities. This study finds that sponge city construction is an important method to address the issue of urban flooding. It is also a necessary project to comply with the situation of urban development and plays a guiding role in the future urban construction project.
Analysis on degassing at microgravity condition
Authors: Li Siai
Published: April 2024

For the future diversity and sustainability of the International Space Station, the manned flight time will be greatly increased, and the water required will be greatly increased as well. It is particularly important to deal with resources in more detail. In the process of treating drinking water, tiny air bubbles may be created. Bubbles can form in the Water Recovery and Management System (WRMS) on the ISS due to gas dissolution, microgravity environment, chemical reactions, and microbial metabolism. Water under pressure can dissolve gases. In the ISS, if water comes from a high-pressure environment to a lower one, gas may come out of solution, forming bubbles. Also, in the microgravity environment of the ISS, bubbles can remain suspended in the water, interfering with the systems sensors and pumps. Some processes in the water recovery system can produce gases as byproducts, such as electrolysis used in the Oxygen Generation System of the ISS. Finally, some microbial metabolic processes generate gases which can form bubbles in the water. According to four reasons given above, the passage will first introduce the current situation about dealing with bubbles in ISS, then providing some future directions of the development of the bubble-remove techniques.
Analysis of sewage treatment process and effect - Take Maidao sewage plant in Qingdao as an example
Authors: Liu Jiaxiu
Published: April 2024

The purpose of this paper is to analyze the current situation of urban sewage treatment, understand the process of sewage treatment, conduct in-depth analysis of it combined with actual cases, and put forward effective suggestions for improvement. Nowadays, with the development of science and technology, many cities have built advanced sewage plants, as an example as Qingdao, which is a serious water shortage city in Shandong Province of China. More specifically, a field interview and a field trip in Mai Dao sewage plant provide the actual treatment process and real data of the plant. It provides with a comprehensive process and actual removal rate of the plant, so we can have a deeper understanding of the current status of urban sewage treatment processes. Additionally, an analysis of the effect of every devices in the process supports the effectiveness of the plant. Furthermore, this paper gives information about current situations of reuse water process and offers some suggestions to the current treatment process and the use of outflow water.
The significance of materials informatics on material science
Authors: Gong Siyu
Published: April 2024

In 2011, U.S. President Barack Obama proposed the Material Genome Project. Want to high high-speed and low-cost methods to develop material science, which promoted the rapid development of materials informatics. Materials informatics is an interdisciplinary field that employs the principles of informatics to enhance the comprehension, utilization, and advancement of materials within the realm of materials science. Materials informatics can take multiple approaches and influence many aspects of new material development. Material informatics will cause great changes in the material industry and promote the rapid development of materials. A variety of materials informatics schemes have been developed to analyze materials, and there have been many successful cases worth applying. This article introduces the basic concepts and main research fields of materials informatics and describes four main steps in materials informatics: data collection, data processing, the establishment of a database, construction of a model. The main applications of materials informatics include simulation and prediction of material properties, development of new materials, material optimization and performance improvement. Thinking about the difficult problems in the field of materials informatics for application.
Factor analysis and countermeasures for global warming based on data science methods
Authors: Man Yuning
Published: April 2024

In recent years, the frequent and intensifying occurrence of extreme weather events such as heatwaves, floods, and storms worldwide has led scientists and policymakers to be increasingly concerned about the phenomenon of global warming. The global average temperature has steadily risen over the past few decades, and 2015-2022 has been identified as the warmest years on record. Taking global warming as the research object, the main problem is to find out the factors affecting climate change. The article predicts the global average temperature of the future and builds a model to analyse the relationship between global average temperature, time, location and the factors affecting climate change. The prediction models of ARMIA and EEMD-LSTM were built, and the model with the best fit was obtained after several iterations of tuning and trial optimization. The results showed that Flood, Storm, and Extreme temperature, had a high correlation with the global average temperature. The data of greenhouse gas indicators such as CO2, NO, and CH4, and catastrophes such as earthquakes, Volcanic activity, and wildfires have objective effects on global warming. Random forest regression model pairs were developed, and analysis of the importance of each component to the model showed that CO2 concentration and CH4 concentration had significant effects on global average temperature. Propose measures that can curb or slow down global warming.
Three solid electrolyte interphases of aqueous zinc batteries
Authors: Chen Kewei
Published: April 2024

With the increasing demand for enhanced safety performance of rechargeable batteries, water-based zinc-ion batteries have witnessed rapid development due to their exceptional safety characteristics. And, zinc batteries have been widely used in a diverse of various fields such as electric vehicles and electric bicycles. However, the dendrite issue and limited cycle life of the negative electrode pose significant challenges that hinder further advancements in this technology, which greatly limits the development of zinc batteries. For this reason, extensive research has been conducted on various solid electrolyte interphases related to the battery anode. By comprehensively understanding and comparing three materials of solid electrolyte interphase (SEI) utilizing similar controlled negative ion techniques, it can validate the accuracy of this theoretical approach. The utilization of these three materials results in a substantial improvement in battery performance compared to the original battery, particularly in terms of service life; however, each material also exhibits distinct advantages and drawbacks.
Comparison and investigation of edible hydrophobic coatings on beeswax and carnauba wax oil gels
Authors: Zhang Zixi
Published: April 2024

In our daily life, it often uses all kinds of liquid food, such as cola, fruit juice and other drinks. Whenever you finish using these, you will find that there will inevitably be residual liquid at the bottom of the bottle that cannot be poured out, resulting in a considerable waste. However, in recent years, the emergence of lubricant infused surface (LIS) can be used to solve this problem. In this research, LIS is prepared by forming an oleogel with shellac and linseed oil, which has the advantage that the experimental raw materials require low cost, the materials are easily available, and do not cause health hazards to human beings. To gain a deeper understanding of the mechanism of LIS antifouling, the effects of optimal Brazilian carnauba wax content and beeswax content on the surface morphology, water contact angle and sliding angle, chemical functional groups, transmittance, and oil-holding rate of LIS were investigated, and the effects of liquid foodstuffs antifouling were tested with optimal Brazilian carnauba wax and beeswax content, respectively.
Effects of microorganisms on nitrogen and phosphorus removal from wastewater
Authors: Shao Ziwen
Published: April 2024

Nitrogen and phosphorus are key to plant growth. The balance of nitrogen and phosphorus is crucial in nature. Excessive nitrogen and phosphorus content in wastewater has caused great harm to the environment. Traditional nitrogen and phosphorus removal methods can no longer meet the treatment standards for nitrogen and phosphorus. This article introduces the hazards of nitrogen and phosphorus, and points out the harm to nature and human beings if they are not up to standard in wastewater treatment plants. Through the analysis and discussion of several methods, the advantages of microbial nitrogen and phosphorus removal are explained. This article further summarizes several representative new microorganisms and microbial processes through the types and mechanisms of microbial nitrogen and phosphorus removal. Anammox bacteria can accept other electron pools to increase nitrogen removal efficiency. Denitrifying phosphorus-removing bacteria can achieve energy saving and carbon reducing effects through endogenous denitrification. Besides, the enhanced biological phosphorus removal process can allow denitrifying phosphorus accumulating bacteria to absorb phosphorus in an anaerobic, anoxic, aerobic alternating environment. The moving bed biofilm reactor (MBBR) abandons the disadvantages of the traditional biofilm method, while retaining the advantages of the traditional biofilm method such as shock load resistance and low sludge production. These new microorganisms and new microbial processes have become important applications in future nitrogen and phosphorus removal processes due to their advantages of high efficiency, low energy, and no secondary pollution. This article is conducive to the promotion of microbial-based wastewater treatment and can provide suggestions for the sustainable development of wastewater treatment.
SUDS: New solution for urban flooding
Authors: Zhang Yiyang
Published: April 2024

Climate change causing extreme weather events across the world. The excising urban drainage system facing the great stress of managing heavy precipitation events and caused urban flooding. The specialists in the urban designing field are searching for more effective way of managing flooding events. Sustainable Urban Drainage System is kind of drainage system design which simulating nature rainwater managing practices. In this research, the SUDS is analyzed mainly from three aspects: Flood managing ability, economic benefits, and ecological benefits. This research reviews the existing SUDS examples and research basing on the SUDS designing strategies. Research has found that the SUDS is more than capable of managing stormwater but also can generate both monetary and indirect economic benefit. Furthermore, the nature feature of SUDS facilities can provide ecological benefits like providing habitats for animals, improving hydrological feature, and increase comfortability for residents. Thus, SUDS can be an ideal solution for the new urban drainage systems.