Development Trend Research Articles

A review of finite control set model predictive control for linear machines

AbstractLinear machines (LMs) have become excellent candidates for linear motion applications due to their abilities of generating thrust without any transmissions, and have been widely adopted in transportation, industries etc. Among the numerous control methods of LM drive systems, finite control set model predictive control (FCS‐MPC) method has been given special attention due to its clear concept, fast response performance, and ability to handle constrained multivariate non‐linear control problems. It is widely used to improve the performance and efficiency of overall LM drive systems. Firstly, based on the three elements of FCS‐MPC, the advantages and shortcomings of the FCS‐MPC method in applications are fully investigated. The research status in FCS‐MPC has been summarised, such as multistep MPC, parameters robustness improvement, fast optimisation, and multi‐vector MPC. Then, the key issues in FCS‐MPC such as delay compensation, parameters design, and stability analysis are discussed, and their typical research methods and current research status are explained. Finally, the future development trends of FCS‐MPC in LM drive systems are discussed.

Mapping the evolution of liver aging research: A bibliometric analysis

BACKGROUND With the increasing of the global aging population, healthy aging and prevention of age-related diseases have become increasingly important. The liver, a vital organ involved in metabolism, detoxification, digestion, and immunity, holds a pivotal role in the aging process of organisms. Although extensive research on liver aging has been carried out, no bibliometric analysis has been conducted to evaluate the scientific progress in this area. AIM To analyze basic knowledge, development trends, and current research frontiers in the field via bibliometric methods. METHODS We conducted bibliometric analyses via a range of analytical tools including Python, the bibliometrix package in R, CiteSpace, and VOSviewer. We retrieved publication data on liver aging research from the Web of Science Core Collection Database. A scientific knowledge map was constructed to display the contributions from different authors, journals, countries, institutions, as well as patterns of co-occurrence keywords and co-cited references. Additionally, gene regulation pathways associated with liver aging were analyzed via the STRING database. RESULTS We identified 4288 articles on liver aging, authored by 24034 contributors from 4092 institutions across 85 countries. Notably, the years 1991 and 2020 presented significant bursts in publication output. The United States led in terms of publications (n = 1008, 25.1%), citations (n = 55205), and international collaborations (multiple country publications = 214). Keywords such as “lipid metabolism”, “fatty liver disease”, “inflammation”, “liver fibrosis” and “target” were prominent, highlighting the current research hotspots. Notably, the top 64 genes, each of which appeared in at least 8 articles, were involved in pathways essential for cell survival and aging, including the phosphatidylinositol 3-kinase/protein kinase B, Forkhead box O and p53 signaling pathways. CONCLUSION This study highlights key areas of liver aging and offers a comprehensive overview of research trends, as well as insights into potential value for collaborative pursuits and clinical implementations.

Exploring the Features and Trends of Industrial Product E-Commerce in China Using Text-Mining Approaches

Industrial product e-commerce refers to the specific application of the e-commerce concept in industrial product transactions. It enables industrial enterprises to conduct transactions via Internet platforms and reduce circulation and operating costs. Industrial literature, such as policies, reports, and standards related to industrial product e-commerce, contains much crucial information. Through a systematical analysis of this information, we can explore and comprehend the development characteristics and trends of industrial product e-commerce. To this end, 18 policy documents, 10 industrial reports, and five standards are analyzed by employing text-mining methods. Firstly, natural language processing (NLP) technology is utilized to pre-process the text data related to industrial product commerce. Then, word frequency statistics and TF-IDF keyword extraction are performed, and the word frequency statistics are visually represented. Subsequently, the feature set is obtained by combining these processes with the manual screening method. The original text corpus is used as the training set by employing the skip-gram model in Word2Vec, and the feature words are transformed into word vectors in the multi-dimensional space. The K-means algorithm is used to cluster the feature words into groups. The latent Dirichlet allocation (LDA) method is then utilized to further group and discover the features. The text-mining results provide evidence for the development characteristics and trends of industrial product e-commerce in China.

Monitoring and Evaluation of Coastal Ecological Carrying Capacity in the Context of Sustainable Development: A Case Study of Shandong Province

The research on coastal ecological carrying capacity holds great significance for the sustainable development of coastal areas and is a focal point of the United Nations Sustainable Development Goals (SDGs). This study coupled multi-source data and ecological analysis models to construct a multi-level evaluation system and analysis method for the coastal ecological carrying capacity of Shandong Province so as to realize the dynamic monitoring and evaluation of the coastal ecological carrying capacity of Shandong Province from 2010 to 2020. The results indicated: (1) The ecological carrying capacity of the coastal zone in Shandong Province showed a “U”-shaped development trend, with 2016 being a turning point. (2) The economic development–social support system gradually became the main force driving the overall improvement of coastal ecological carrying capacity. (3) The system coupling coordination degree of ecological carrying capacity in the coastal areas of Shandong Province showed a trend of first decreasing and then increasing, with a high level of internal coupling coordination of carrying capacity. (4) Per capita GDP, environmental protection investment, per capita water resources, and other indicators were the main factors driving the changes in the ecological carrying capacity of the coastal zone. This study aims to provide methodological reference and data support for coastal ecosystem monitoring, assessment, and climate change response.

Research Progress in Strategies for Enhancing the Conductivity and Conductive Mechanism of LiFePO4 Cathode Materials

LiFePO4 is a cathode material for lithium (Li)-ion batteries known for its excellent performance. However, compared with layered oxides and other ternary Li-ion battery materials, LiFePO4 cathode material exhibits low electronic conductivity due to its structural limitations. This limitation significantly impacts the charge/discharge rates and practical applications of LiFePO4. This paper reviews recent advancements in strategies aimed at enhancing the electronic conductivity of LiFePO4. Efficient strategies with a sound theoretical basis, such as in-situ carbon coating, the establishment of multi-dimensional conductive networks, and ion doping, are discussed. Theoretical frameworks underlying the conductivity enhancement post-modification are summarized and analyzed. Finally, future development trends and research directions in carbon coating and doping are anticipated.

Trends in Global Agricultural Carbon Emission Research: A Bibliometric Analysis

As climate change intensifies and countries actively pursue carbon peaking and carbon neutrality targets, agriculture has emerged as a significant source of carbon emissions. A comprehensive analysis of global agricultural carbon emission research can enhance the agricultural environment and achieve a mutually beneficial outcome for environmental protection and economic development. Despite the evolution of research domains and methodologies, the global context remains closely connected to the current state of the discipline. Drawing on the Web of Science core collection, this paper develops a knowledge network framework, examines the current status and hotspots of agricultural carbon emissions, forecasts future development trends, and analyzes the findings using CiteSpace visualization software. The findings indicate that the number of papers on agricultural carbon emissions has been increasing annually, with minor fluctuations; time series analysis and sustainable development have emerged as the current focal points, and relevant institutions are collaborating increasingly closely. However, cooperation among scholars requires further enhancement. Countries such as China, the United States, and Germany are the primary nations for paper publication. The hotspot analysis reveals a high frequency of keywords such as greenhouse gas emissions and climate change, indicating that research on agricultural carbon emissions has matured and the emphasis has shifted from accounting to management. This paper develops a domain knowledge framework to assist readers in understanding agricultural carbon emission patterns and provide resources for further research. Follow-up studies should enhance both comprehensiveness and breadth, promote interdisciplinary cooperation, provide a scientific foundation for policymakers, and outline future research directions.

Application of 3D printing in bacterial detection

Diseases caused by bacterial infections, especially pathogen variations and drug-resistant bacteria, are a serious threat to human health globally. Accurate and rapid identification of bacterial pathogens is essential for early diagnosis of infections and timely treatment of disease. At present, the routine diagnostic methods for identifying bacterial pathogens in clinic are bacterial culture, immunological detection, genetic analysis, etc. These methods occupy foundational position for bacterial detection that favors for clinical diagnosis of pathogen infection in daily life. Notably, 3D printing technology, together with 3D bioprinting technology, provides more options for bacterial detection due to its personalized design and manufacturing. Compared with traditional methods, 3D printing provides a more convenient, rapid and accurate bacterial detection platform, thus meeting the urgent needs of clinical and scientific research. Here, we have summarized the research progress and given a comprehensive review of 3D printing technology in bacterial detection, including bacterial detection sensors, microfluidic chips, bioprinting microarray technology, AI and spectroscopy technology, providing a scientific reference and filling in gaps in 3D printing-assistance bacterial detection. At the same time, technical advantages, challenges and future development trends will also be discussed in related healthcare fields.

Study on the Application of Tactical Consciousness in Basketball Sports Training

In basketball games, tactical awareness is one of the basic qualities that athletes need to have, which has a direct impact on the effect of the tactical application of athletes. Tactical awareness can enable athletes to evaluate the competition field environment more calmly and objectively, think and choose reasonable fighting skills, and improve the ability and courage of instantaneous determination. Strengthening the tactical awareness training in basketball sports training can gradually improve the athletes’ ability to analyze the development trend of the game so that the athletes can choose reasonable tactics according to the timely situation of the game, to increase the probability of winning the basketball game. Based on basketball training, this paper discusses tactical consciousness and analyzes the specific application of tactical awareness in basketball training for reference.

Research on Multi-Scenario Simulation of Urban Expansion for Beijing–Tianjin–Hebei Region Considering Multilevel Urban Flows

With the development of urban agglomerations in China, the study of the interactions between cities has become a popular and difficult issue. Exploring the interactions between cities can help decision-makers optimize regional resource allocation and improve regional spatial patterns. Combining the urban flow model and the patch-generating land use simulation (PLUS) model, this study simulates and analyzes the process of urban expansion in the Beijing–Tianjin–Hebei (BTH) region, and investigates the impact of urban hierarchical structure differences on urban expansion. In this study, the role and influence of inter-city economy flow, transportation flow, population flow, and information flow on the development of urban agglomerations are comprehensively considered, and a multilevel urban interaction model is constructed based on a hierarchical generalized linear model (HGLM). Based on the national BTH cooperation and development strategy, a multi-scenario simulation study of urban expansion is carried out using the HGLM-PLUS model. The results indicate the following: (1) compared to the traditional PLUS model, the coupled HGLM-PLUS model, which considers multilevel urban flows, improved the overall accuracy by 0.047, the Kappa coefficient by 0.207, and the figure of merit (FoM) index by 0.051; (2) under different simulation scenarios, the development trend under the cooperation and development policy in the BTH region is more stable, demonstrating a relatively smooth urbanization expansion trend; and (3) under the BTH cooperation and development background, the total area of construction land in the BTH region is expected to be maintained at around 1,164,500 km2 by 2040. The spatial expansion pattern will present a networked expansion with the core driving development, axes and belts connecting, and clusters breaking through.

A novel spatio-temporal prediction model of epidemic spread integrating cellular automata with agent-based modeling

Since 2019, major infectious disease outbreaks have placed tremendous pressure on global public health systems, triggering extensive research on the predictive modeling of infectious diseases. Cellular Automaton (CA) is primarily used in the spatial prediction of infectious diseases to establish a model to for simulating the interaction between different regions and the infection risk to simulate the transmission process of the disease and predict its development trend. However, CA models are governed by initial fixed rules and local interactions, and often fail to capture the complex dynamics of epidemic transmission, which are influenced by factors such as public behavior and government intervention. In view of these limitations, we propose a factorial simulation model for the spatial spread of epidemics, the CA-ABM, which divides agents into three categories–public, government, and hospital agents–to comprehensively express the macro factors that affect the development of epidemics. Agent-Based Modeling (ABM) influences the transition rules of the CA through agent choices, constraints and supporting behaviors. Focusing on the COVID-19 pandemic in mainland China from February 6 to March 20, 2020, we simulate its spread. The results showed an average improvement of 8.4 % in prediction accuracy, with few errors, RMSE under 200, and R2 values over 0.9 in most provinces, demonstrating strong macro-scale stability. This approach helps regions to understand influencing factors and enables targeted infection risk assessment and prevention. In addition, scenario analysis based on CA-ABM model changes epidemic decision-making from “prediction-response” to “scenario-response” and provides theoretical reference for future epidemic management.

Research on ecological and climate impacts of offshore wind farms based on remote sensing images

Abstract. Offshore wind farm (OWF)’s impact on marine ecological environment has attracted widespread attention since its rapid development. The paper uses high-resolution satellite image data to extract the distribution of offshore wind farms (OWFs) in China based on deep learning method. Chlorophyll-a (CHL-a) concentration and sea surface temperature (SST) were used as indicators to analyze the impact of OWFs’ development. We can then draw the following conclusions: 1) From 2016 to 2022, the number of OWFs in China continues to increase, the growth rate reachs 28% in 2022. 2) The construction of OWFs presents a development trend from near sea to far sea and from shallow sea to deep sea. From 2016 to 2022, the new OWFs are mainly concentrated in sea areas with 5-50 meters water depth, accounting for 84.7 % of the total increase in OWFs. 3) The distribution density of OWFs is negatively correlated with CHL-a concentration and SST, the more OWFs, the greater the impact on CHL-a and SST. In one word, our work analyzed the development trend of OWFs in China and evaluated the impact of OWFs’ expansion on marine ecological environment and climate.

Progress and Hotspot Analysis of Bibliometric-Based Research on Agricultural Irrigation Patterns on Non-Point Pollution

With the constant advancement of irrigation technology and the continuous expansion of irrigation areas, non-point source pollution (NPS) caused by agricultural activities has posed a persistent threat to ecosystems and biological safety. Against this backdrop, it is imperative to lay scientific foundations for green, sustainable, and high-quality agricultural development through a thorough review of the relevant research progress. In this study, bibliometric methods are adopted to comprehensively analyze and visualize the current state and key literature on agricultural irrigation and NPS pollution from 2010 to July 2024. The focus of this study is specifically on summarizing the research hotspots and development trends of different irrigation methods and the mechanisms behind their impacts on NPS pollution. The results indicate that publications from the United States and China account for 63.8% of the total, but the fragmentation of research efforts remains, suggesting a necessity to strengthen international and regional collaboration. There are three institutions with the highest publication output, namely Northwest A&F University, Hohai University, and the Chinese Academy of Sciences. The subjects identified as the key areas of research on irrigation-related NPS pollution (IRR-NPS) include precision irrigation, rapid water pollution response, spatiotemporal management, interdisciplinary integration, wastewater treatment, and crop models. Regarding future research, it is necessary to focus attention on real-time precision irrigation, standardized crop models, data accuracy, spatiotemporal pollution coordination, pollution purification technology development, interdisciplinary integrated governance, and the innovative applications of soil improvement technologies. In addition to offering theoretical support and practical guidance for the management of agricultural NPS pollution, this study also provides management and technical support for policymakers, which is beneficial for advancing agricultural irrigation technology and environmental preservation.

Multi‐operating characteristic analysis and loss optimization of novel large capacity high‐speed permanent magnet generator

AbstractRecently, the development trend of large capacity generator has gradually shifted from electrical excitation generator to permanent magnet generator (PMG). As a result, the large capacity high‐speed permanent magnet generator (HSPMG) with high power density and high efficiency have received widespread attention. This paper aims to analyse the loss optimization of novel large capacity HSPMG under multi‐operating conditions. In terms of the parameter requirements, a 2.5 MW and 7000 rpm HSPMG is designed. To verify the accuracy of simulation model and the rationality of generator design, the output characteristics of large capacity HSPMG which has been combined with prototype experimental data are calculated under both no‐load and load conditions. Based on the load power characteristics of large capacity HSPMG, the optimization range of the magnetization angle under multi‐operating conditions is explored. By analysing the no‐load back electromotive force (EMF) and loss characteristics of large capacity HSPMG, the optimal magnetization angle that corresponds to the minimum loss under three conditions is obtained. The correctness of the analysis and optimization method is verified by comparing the loss distribution and electromagnetic results before and after the optimization. The research results provide a reference for the multi‐operating characteristics analysis and loss optimization of large capacity HSPMG.

Machine Learning Applied to Lithium‐Ion Battery State Estimation for Electric Vehicles: Method Theoretical, Technological Status, and Future Development

ABSTRACTLithium‐ion batteries (LIBs) are extensively utilized in electric vehicles due to their high energy density and cost‐effectiveness. LIBs exhibit dynamic and nonlinear characteristics, which raise significant safety concerns for electric vehicles. Accurate and real‐time battery state estimation can enhance safety performance and prolong battery lifespan. With the rapid advancement of big data, machine learning (ML) holds substantial promise for state estimation. This paper systematically reviews several common ML algorithms, detailing the basic principles of each and illustrating their structures with flowcharts. We compare the advantages and disadvantages of various methods. Subsequently, we discuss feature extraction techniques employed in recent studies for estimating state of charge (SOC), state of health (SOH), state of power (SOP), and remaining useful life (RUL), as well as the application of these ML methods in state estimation. Finally, we discuss the challenges associated with using ML methods for state estimation and outline future development trends.

Advances in Catalytic Hydrogenation of Liquid Organic Hydrogen Carriers (LOHCs) Using High‐Purity and Low‐Purity Hydrogen

AbstractLiquid organic hydrogen carriers (LOHCs) are emerging as a promising solution for global hydrogen logistics. The LOHC process involves two primary chemical reactions: hydrogenation for hydrogen storage and dehydrogenation for hydrogen reconversion. In the exothermic hydrogenation reaction, hydrogen‐lean compounds are converted to hydrogen‐rich compounds, storing hydrogen from various sources such as water electrolysis, fossil fuel reforming, biomass processing, and industrial by‐products. Conversely, hydrogen is extracted from hydrogen‐rich compounds through an endothermic dehydrogenation reaction and supplied to several hydrogenation utilization offtakers. This review article discusses the development trends in catalytic hydrogenation processes for various LOHC materials, including benzene, toluene, naphthalene, biphenyl‐diphenylmethane, benzyltoluene, dibenzyltoluene, and N‐ethylcarbazole. It introduces references for catalytic hydrogenation processes utilizing both high‐purity and low‐purity (alternatively, mixed) hydrogen feedstocks, with particular emphasis on low‐purity hydrogen applications. The direct storage of hydrogen with minimal purification, using by‐product hydrogen and mixed hydrogen from hydrocarbon and biomass reforming, is crucial for the economic viability of this hydrogen carrier system.

Optimizing mechanical properties and pioneering biodegradable polymer blends for superior energy-absorbing structures used in sport helmets

Replacing elements made of conventional plastics (like polystyrene) with biodegradable substitutes is part of the trend of sustainable development and waste reduction. The manuscript covers issues related to the design, manufacturing and testing of sports helmet protective inserts made of biodegradable material. The FEM numerical simulations carried out by the authors allowed to determine the optimal desirable mechanical properties (Re = 8.5–65 MPa, E = 500–8000 MPa for 30 × 30 mm inserts; Re = 10.5–60 MPa, E = 500–7500 MPa for 48 × 48 mm inserts; Re = 13–95 MPa, E = 400–8500 MPa for 55 × 55 mm inserts) and geometric parameters (wall thickness equal to 0.2–0.5 mm, height of 20 mm), ensuring the formation of a plastic fold, which is the most effective energy-absorbing mechanism. The conducted quasi-static compression, bending and dynamic tensile strength tests allowed to determine blends with appropriate proportions of durable PLA with more plastic PBAT, PBS and TPS that meet the established criteria: PLA50PBAT50, PLA30PBAT70 and PLA30TPS70.

Molecularly Imprinted Ratiometric Fluorescent Sensors for Analysis of Pharmaceuticals and Biomarkers

Currently, analyzing pharmaceuticals and biomarkers is crucial for ensuring medication safety and protecting life and health, and there is an urgent need to develop new and efficient analytical techniques in view of the limitations of traditional analytical methods. Molecularly imprinted ratiometric fluorescent (MI-RFL) sensors have received increasing attention in the field of analytical detection due to their high selectivity, sensitivity and anti-interference ability, short response time, and visualization. This review summarizes the recent advances of MI-RFL sensors in the field of pharmaceuticals and biomarkers detection. Firstly, the fluorescence sources and working mechanisms of MI-RFL sensors are briefly introduced. On this basis, new techniques and strategies for preparing molecularly imprinted polymers, such as dummy template imprinting, nanoimprinting, multi-template imprinting, and stimulus-responsive imprinting strategies, are presented. Then, dual- and triple-emission types of fluorescent sensors are introduced. Subsequently, specific applications of MI-RFL sensors in pharmaceutical analysis and biomarkers detection are highlighted. In addition, innovative applications of MI-RFL sensors in point-of-care testing are discussed in-depth. Finally, the challenges of MI-RFL sensors for analysis of pharmaceuticals and biomarkers are proposed, and the research outlook and development trends of MI-RFL sensors are prospected.

Chinese Patent Review of Integrated Management System for Agricultural Machinery

Background: With the rapid development of China's economy and the accelerating process of agricultural mechanization, agricultural machinery has become increasingly popular in agricultural production activities. At the same time, problems in agricultural machinery management have gradually been exposed, hindering the development of modern agriculture. Objective: This study aimed to discuss the development trend of agricultural management and modern agriculture by analyzing and discussing the related patents of agricultural machinery management systems. Methods: The development of agricultural machinery management was summarized, and the representative patents related to agricultural machinery management systems, agricultural machinery automatic control systems, and agricultural machinery terminals were analyzed. Results: By analyzing the existing patents of the agricultural machinery management system, the paper summarized their applications in agricultural management and obtained their future development trend. Conclusion: In agricultural machinery management, we should actively adopt complementary strategies to solve various problems and improve the management level of agricultural machinery. The continuous improvement of the agricultural machinery management system is of great significance to the automation of agricultural production and the modernization of agricultural management in China.

The method of driver fatigue detection in intelligent driving

Abstract. Fatigue driving is one of the main causes of traffic accidents, and currently available methods for detecting driver fatigue include sensor detection and intelligent driving assistance. The importance of driver fatigue detection lies in timely detection of the driver condition, which helps reduce fatigue driving accidents. The research on fatigue detection technology has also provided more sensors and data processing techniques for intelligent driving systems, promoting the improvement and optimization of intelligent driving systems. This article reviews the technologies available for fatigue detection, summarizes existing research on driver fatigue detection, analyzes various modal detection methods and the use of AI in fatigue monitoring, and proposes future research and development trends for fatigue detection systems. This paper discussed three parts of fatigue monitoring technology: sensor technology, data processing technology, and algorithm optimization, and analyzed their advantages, disadvantages, and future development trends. The conclusion is that fatigue monitoring technology should use the advantages of different sensors and combine them with intelligent driving. The accuracy of this technology still needs to be improved.

Advancements in Contemporary Pharmacological Innovation: Mechanistic Insights and Emerging Trends in Drug Discovery and Development

Developing a new drug and bringing it to the market is a complex and time-consuming process that involves multiple phases of drug discovery and development. However, recent advancements in various technologies, such as multi-omics, genome editing, Artificial Intelligence (AI), and Machine Learning (ML), have significantly improved this process. These technologies have made the process more accurate, less time-consuming, and cost-effective compared to the conventional methods of drug discovery and development. In the current age, discovering and developing drugs is a collaborative effort that involves scientific breakthroughs, technological advancements, and regulatory oversight. The pharmaceutical industry is constantly innovating new techniques, fostering interdisciplinary collaboration, and prioritizing patient-centered approaches. In this review, we explore the latest and most updated information about using advanced technologies in drug discovery. The review begins by briefly explaining the conventional drug discovery and development process, and then delves into the applications of multi-omics, genome editing technology, systems biology, artificial intelligence, and machine learning.