Research Hotspot Research Articles

Research Progress on Removal of Oxygenates in Fischer-Tropsch Oil by Adsorption

The oxygen-containing compounds in Fischer Tropsch synthetic oil greatly affect the downstream deep processing of hydrocarbons, and effective removal is required. Compared to traditional removal technologies such as hydrogenation deoxygenation, solvent extraction, and extraction distillation, adsorption deoxygenation technology has the advantages of low cost, mild operating conditions, easy removal and recovery, and minimal impact on oil quality. Therefore, adsorption deoxygenation technology has devel‐ oped rapidly in various removal processes and has become a research hotspot in the current Fischer Tropsch oil deoxygenation. Adsorbents are the core of adsorption deoxygenation technology. Therefore, this article briefly introduces the adsorption mechanism and summarizes the research progress of adsorbents widely used in recent years, such as silica gel, alumina, molecular sieves, and metal organic frameworks, in adsorbing oxygen-containing compounds in Fischer Tropsch synthetic oils. And provide reference suggestions for further adsorption and deoxygenation directions in the future.

Advances in Adjoint Functions of Connection Number in Water Resources Complex Systems: A Systematic Review

The adjoint function of connection number has unique advantages in solving uncertainty problems of water resource complex systems, and has become an important frontier and research hotspot in the uncertainty research of water resource complex problems. However, in the rapid evolution of the adjoint function, some problems greatly limit the application of the adjoint function in the research of water resources. Therefore, based on bibliometric analysis, development, practical application issues, and prospects of the hot directions are analyzed. It is found that the development of the connection number of water resource set pair analysis can be divided into three stages: (1) relatively sluggish development before 2005, (2) a period of rapid advancement in adjoint function research spanning from 2005 to 2017, and (3) a subsequent surge post-2018. The introduction of the adjoint function of connection number promotes the continuous development of set pair analysis of water resources. Set pair potential and partial connection number are the crucial research directions of the adjoint function. Subtractive set pair potential has rapidly developed into a relatively independent and important trajectory. The research on connection entropy is comparatively less, which needs to be further strengthened, while that on adjacent connection number is even less. The adjoint function of set pair potential can be divided into three major categories: division set pair potential, exponential set pair potential, and subtraction set pair potential. The subtraction set pair potential, which retains the original dimension and quantity variation range of the connection number, is widely used in water resources and other fields. Coupled with the partial connection number, a series of new connection number adjoint functions have been developed. The partial connection number can be mainly divided into two categories: total partial connection number, and semi-partial connection number. Among these, the calculation expression and connotation of total partial connection numbers have not yet reached a consensus, accompanied by the slow development of high-order partial connection numbers. Semi-partial connection number can describe the mutual migration movement between different components of the connection number, which develops rapidly. With the limitations and current situation described above, promoting the exploration and application of the adjoint function of connection number in the field of water resources and other fields of complex systems has become the focus of future research.

Systematic Review and Prospects on Social Risks of Artificial Intelligence— Visual Analysis Based on CiteSpace Knowledge Graph

As artificial intelligence becomes more and more embedded in human society, it also creates social risks. With the help of CiteSpace software, 200 documents related to social risks of artificial intelligence searched from SSCI and SCI journals from 2000 to 2024 were sorted out to sort out the research progress and hot spots. Research shows that there are currently some highly influential scholars and institutions in the field of artificial intelligence social risks, and the number of relevant studies is increasing year by year. Risk resolution and sustainable development issues of artificial intelligence have become important research directions for the continuous development of artificial intelligence.

Review and bibliometric analysis of AI-driven advancements in healthcare

Purpose: This research intends to use literature review and bibliometric analysis methods to visually review the development status and important historical milestones of Artificial Intelligence, as well as the basic research, key topics, and future potential research hot spots of AI in the healthcare field. Methodology: Conduct in-depth analysis of AI in healthcare through bibliometrics methods such as publication activity analysis, co-occurrence analysis, and co-authorship analysis. Findings: This study outlines the development time trajectory of AI technology and its application in healthcare. Research shows that "algorithm", "machine learning", "deep learning", "controlled study", "major clinical study" and "healthcare delivery" as well as "decision support systems" are key topics for research. Gender-related research and ethical issues are areas of future focus. Research implications: The practical significance is that it can clarify and optimize the key directions of AI to improve the quality of medical decision-making, improve diagnostic accuracy and guide market investment. The originality is reflected in the comprehensive analysis of the development trajectory of AI in the medical and health field. Through a unique perspective and systematic approach, it provides an important reference for research trends and future directions in the field.

Research hot spots and trends in endocrine-related adverse events caused by immune checkpoint inhibitors: a bibliometric analysis and visualization research

BackgroundIn recent years, with the widespread use of immune checkpoint inhibitors (ICIs) in cancer treatment, the toxicity associated with immunotherapy of ICIs has attracted more attention from scholars. Endocrine toxicity is the most likely immune-related adverse events (irAEs) and is often irreversible, posing a significant clinical treatment challenge.MethodsIn this study, bibliometric methods were used to analyze relevant literature in screening endocrine-related adverse events caused by ICIs in the Web of Science core collection database (WoSCC) and to summarize the status, research hot spots, and future trends in this field.Results321 countries, 297 institutions, 365 authors, and 305 journals had published 671 English documents on endocrine adverse reactions of ICIs as of 1 December, 2022. The United States, Japan, and China were the top three countries with the most publications. The University of Texas MD Anderson Cancer Center, Harvard Medical School, and Memorial Sloan Kettering Cancer Center were the top three research institutions in terms of publication output. F Stephen Hodi, from the Dana-Farber Cancer Institute in the United States, contributed the largest number of publications. Frontiers in Oncology, which was the most widely distributed publication in the field. The main keywords or clusters identified that current research hotspots include the management of endocrine-related adverse events, hypophysitis, thyroid dysfunction, type I diabetes mellitus, and the impact of endocrine adverse events on survival of patients in this field.ConclusionThe basic knowledge structure of the field of endocrine-related adverse events of ICIs, including publication trends, authors, institutions, countries, keywords, journals and publications, and cited documents, was visually analyzed in this bibliometric analysis. The research results comprehensively demonstrated the hot spots and future trends in the research field, as well as its broad prospects, thus providing a reference for the researchers.

Characteristics Analysis of Influence of Multiple Parameters of Mixed Sea Waves on Delay–Doppler Map in Global Navigation Satellite System Reflectometry

Feature capture and recognition of sea wave components in radar systems especially in global navigation satellite system reflectometry (GNSS-R) using signal processing approaches or computer simulative methods has become a research hotspot in recent years. At the same time, parameter inversion of marine phenomena from the discovered characteristics plays a significant role in monitoring and forewarning the different components of sea waves. This paper aims to investigate the impact of multiple parameters, such as the wind speed, directionality variable, wave amplitude, wave length, and directions of sea wave components, on the delay waveform of the delay–Doppler map (DDM). Two types of wind waves and the 2-D sinusoidal sea surface are chosen to be analyzed. By comparing and analyzing the discrepancy of delay waveforms under different conditions, it can be concluded that the increased MSS which arises from the increase in the roughness of the sea surface can lead to the difference in the peak value or trial edges exhibited in delay waveforms. The values of delay waveforms at zero chip along the increasing direction of long-crest wind waves exhibit the periodic spikes shape, which is the opposite of the short-crest wind waves, and the fluctuation of the periodic profiles decreases with the increase in the amplitude of waves. The results and conclusions can provide a foundation for the parameter inversion, tracking, and early warning of anomalous formations of waves in bistatic radar configuration.

Analysis of Hotspots and Trends in Domestic and International Data Asset Research: A Knowledge Graph Analysis Based on CiteSpace

The article summarized 363 literature on data assets in the CNKI and WOS databases and conducted a knowledge map analysis using CiteSpace. The research found that the study of data assets at home and abroad is continuously growing, but there are differences in origins, content, and hotspots. Foreign research focuses on areas such as big data, data governance, machine learning, and the Internet, while domestic research focuses on data assets, the digital economy, data transactions, and accounting. Domestic research is gradually expanding to include data middle platforms and asset confirmation, while foreign research focuses on the Internet and artificial intelligence. This analysis helps grasp the research trends and hotspots in the field of data assets, promoting cooperation and knowledge sharing.

Point-Sim: A Lightweight Network for 3D Point Cloud Classification

Analyzing point clouds with neural networks is a current research hotspot. In order to analyze the 3D geometric features of point clouds, most neural networks improve the network performance by adding local geometric operators and trainable parameters. However, deep learning usually requires a large amount of computational resources for training and inference, which poses challenges to hardware devices and energy consumption. Therefore, some researches have started to try to use a nonparametric approach to extract features. Point-NN combines nonparametric modules to build a nonparametric network for 3D point cloud analysis, and the nonparametric components include operations such as trigonometric embedding, farthest point sampling (FPS), k-nearest neighbor (k-NN), and pooling. However, Point-NN has some blindness in feature embedding using the trigonometric function during feature extraction. To eliminate this blindness as much as possible, we utilize a nonparametric energy function-based attention mechanism (ResSimAM). The embedded features are enhanced by calculating the energy of the features by the energy function, and then the ResSimAM is used to enhance the weights of the embedded features by the energy to enhance the features without adding any parameters to the original network; Point-NN needs to compute the similarity between each feature at the naive feature similarity matching stage; however, the magnitude difference of the features in vector space during the feature extraction stage may affect the final matching result. We use the Squash operation to squeeze the features. This nonlinear operation can make the features squeeze to a certain range without changing the original direction in the vector space, thus eliminating the effect of feature magnitude, and we can ultimately better complete the naive feature matching in the vector space. We inserted these modules into the network and build a nonparametric network, Point-Sim, which performs well in 3D classification tasks. Based on this, we extend the lightweight neural network Point-SimP by adding some trainable parameters for the point cloud classification task, which requires only 0.8 M parameters for high performance analysis. Experimental results demonstrate the effectiveness of our proposed algorithm in the point cloud shape classification task. The corresponding results on ModelNet40 and ScanObjectNN are 83.9% and 66.3% for 0 M parameters—without any training—and 93.3% and 86.6% for 0.8 M parameters. The Point-SimP reaches a test speed of 962 samples per second on the ModelNet40 dataset. The experimental results show that our proposed method effectively improves the performance on point cloud classification networks.

A Bibliometric Study of English Textbook Analyses (1979‒2022)

In recent years, the field of English textbook analysis has garnered increasing attention from scholars worldwide, emerging as a burgeoning research hotspot. However, this field currently lacks a comprehensive and systematic analysis. This study adopts a bibliometric approach, using raw data from Scopus to review research on English textbooks from 1979 to 2022. Overall, research in this field has shown a steady upward trend, with a significant acceleration in growth over the past five years. The results indicate that scholars from Asia, especially East Asia, have made the most significant contributions to this field. Most studies typically utilize methods such as corpus approach and critical discourse analysis to explore themes such as ideology, gender, culture, and language teaching in English textbooks, thereby making this research field a pivotal lens for comprehending global language education and societal dynamics, fostering mutual understanding and communication across diverse regions worldwide. Through these studies, the field of English textbook analysis not only advances language education but also provides significant insights and support for educational reforms and societal development on a global magnitude.

Construction of an Intelligent Identification Model for Drugs in Near Infrared Spectroscopy and Research on Drog Classification based on Improved Deep Algorithm

Near-infrared spectroscopy has important applications in drug and food identification. Combining machine learning with near-infrared spectroscopy to achieve intelligent identification of drugs has become a research hotspot in recent years. To solve the problem of machine learning’s inefficiency in classifying small-scale data, a drug identification model based on near-infrared spectroscopy combined with a random fading depth belief network is proposed. Aiming at the problem that the training time of the machine learning algorithm is too long, the extreme learning machine is used to replace the back propagation algorithm to optimize the stack sparse auto-encoder network. Additionally, the stack sparse auto-encoder algorithm based on extreme learning machine algorithm is constructed. The study found that the precision of the Dropout Deep Belief Network model was 99.12%, which was higher than the other three models. Additionally, the area under the curve value of the Dropout Deep Belief Network model was 0.87, which was 0.04 higher than the binary whale optimization algorithm model, 0.26 higher than the factor decomposition machine and depth neural network model, and 0.05 higher than the random forest network model. The sparse auto-encoder algorithm based on the extreme learning machine algorithm achieved a precision of 99.72%. The study proposes two algorithm models that can effectively identify drugs using near-infrared spectroscopy. This has a positive impact on the medical industry and the safety of patients' lives and health.

Visual analysis on the study status and trends of acupuncture and moxibustion for Alzheimer's disease.

The research history, hot spots and frontier trends of acupuncture and moxibustion for Alzheimer's disease (AD) were explored using knowledge graph technology. The articles on acupuncture and moxibustion for AD were searched from 6 databases, i.e. CNKI, VIP, Wanfang, SinoMed, Pubmed and Web of Science, from January 1st, 1993 to January 1st, 2023. Using CiteSpace6.2.R2 Advance and VOSviewer V1.6.19 softwares, the knowledge map was graphed and the visual analysis was performed. A total of 1 228 Chinese and 309 English articles were included. The high-frequency keywords were generally divided into the keywords of clinical diseases (AD, dementia), those of therapeutic methods (electroacupuncture, acupuncture-moxibustion and acupuncture) and those of mechanism study (β-amyloid, mice). Thirteen keyword clusters were formed among the articles of Chinese version, e.g. acupuncture-moxibustion, dementia, acupuncture and electroacupuncture; and 8 clusters were obtained among English articles, e.g. electroacupuncture, drug therapy and hippocampus. The high-frequency keywords of acupoints included Baihui (GV 20), Dazhui (GV 14), Yintang (GV 24+), Zusanli (ST 36), Fenglong (ST 40), etc. Six clusters of "acupuncture techniques → acupoints" were obtained for the treatment of AD with acupuncture and moxibustion. The therapeutic methods and modes of AD with acupuncture and moxibustion are constantly progressed, the development of clinical research tends to the evaluation of novel therapeutic mode and clinical effect, and the mechanism of acupuncture and moxibustion for the effect on AD are more deeply explored. Among the various therapeutic methods, acupuncture-moxibustion, acupuncture and electroacupuncture have been early predominant; while, many novel methods are gradually displayed later, such as music electroacupuncture and hydro-acupuncture. In recent 30 years, among Chinese and English articles for the studies of AD treated with acupuncture and moxibustion, the theme of them focuses on the two aspects, the observation of clinical effect and the mechanism research. It is found that the clinical therapeutic methods have been gradually improved and the mechanism exploration been deepened.

Trends, progress and future directions of nanomaterial based sensors: a bibliometric overview

Purpose Nanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc. Design/methodology/approach In this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021. Findings Among these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development. Research limitations/implications The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development. Originality/value This is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

Knowledge Graph Analysis for Chronic Diseases Nursing based on Visualization Technology and Literature Big Data

The use of knowledge graph analysis for chronic disease nursing based on visualization technology and literature big data is an unexplored area of research in this field of study. To uncover research hotspots and developmental trends in the field of chronic disease nursing, and to provide a scholarly reference, we employed mathematical and statistical methods along with CiteSpace literature visualization analysis software for quantitative analysis of extensive literature data from the Web of Science Core Collection. We examined aspects such as publication trends, journals, author collaborations, research institutions, national and regional distributions, keyword co-occurrence, clustering, time zones, emergence, literature co-citations, and more. These analyses identified the current hotspots and future directions for research. Notably, scholars' interest in chronic disease nursing exhibited a consistent upward trajectory. In particular, the field of artificial intelligence technology application in nursing yielded $3,610$ published papers in $141$ journals with more than or equal to $10$ published papers on the topic, accounting for $58.41 \%$ of the total number of published papers in this field of study. Furthermore, the top three publishers were the “Journal of Clinical Nursing,” “Journal of Advanced Nursing,” and “BMC Health Services Research.” Among authors, Hu, Frank B., Willett, Walter C., and Rimm, Eric B., ranked as the top three, and 12 authors had more than 10 publications. The most active research institutions included Harvard University, Harvard Medical School, Brigham & Women’s Hospital, University of California System, University of London, US Department of Veterans Affairs, Veterans Health Administration (VHA), Harvard T. H. Chan School of Public Health, University of Sydney, and the University of Toronto. The United States, Australia, England, China, Canada, Netherlands, Spain, Italy, Sweden, and Germany emerged as the leading countries in terms of research output, while emerging hotspots encompassed topics such as incidence, rheumatoid arthritis, qualitative research, burnout, kidney transplantation, critical illness, COVID-19, Sars-COV-2, public health, and the well-being of medical staff. These findings present valuable insights for prospective research endeavors.

Construction of hydrological and hydrodynamic models based on ARM architecture processor – A case study of inner Harbor area of Macao Peninsula

Abstract Green computing is the current research hotspot; adapting the professional computing model to the low-power ARM architecture processor is a research trend. This article constructs a hydrological–hydrodynamic model by choosing SWMM and TELEMAC-2D as submodules, taking stormwater grates and inspection wells as water flow exchange nodes. To make the coupling model run on the ARM architecture processor, the container technology is selected for model adaptation, and the adapted coupling model is named the ARM hydrological–hydrodynamic model (AHM). To verify the reasonableness and accuracy of the model, taking the inner harbor of Macao Peninsula as the study area, a numerical simulation study on the evolution of waterlogging was carried out in various scenarios. The analysis showed that the maximum flow velocity was concentrated in the streets with low topography in the city center, and the areas of standing water were distributed in a point-like manner. Risk distribution maps during different storm recurrence periods were also constructed. Finally, the direct economic losses caused by Typhoon Mangkhut were calculated based on the model results and compared with the statistical values, with an error of only 2.3%, and the direct flooding losses in the inner harbor area under different rainfall scenarios were derived accordingly.

LUBRICATION PROPERTIES OF MXenes AND THEIR COMPOSITES: A REVIEW

Friction and wear phenomena are common problems in sliding equipment, so materials with excellent antifriction and wear resistance properties are getting increasingly more attention. Two-dimensional (2D) materials, such as graphene and molybdenum disulfide, have always been the research hotspots on account of their special structures and excellent tribological properties. In 2011, MXene, an important member of the 2D material family, was launched. With excellent properties such as ultra-high strength, rich surface end groups, and relatively low cost, MXenes have great potential as lubricant additives. Although these properties have proved to be the reason why MXenes are impressive, the preparation of MXenes composites and the understanding of their mechanical and tribological mechanisms remain to be further investigated. In addition, the abundant surface end groups of MXenes nanosheets make them relatively easy to be functionalized, which has attracted widespread attention. This review aims to comprehensively understand the tribological properties of MXenes nanolayers and to summarize and sort out recent research progress. First, the preparation of MXenes is reviewed, and then the latest research progress of MXenes in liquid lubrication, solid lubrication, and solid–liquid hybrid systems since the advent of MXenes is summarized. Finally, some current problems are presented and an outlook on the development of MXenes is given.

A High-Stretching, Rapid-Self-Healing, and Printable Composite Hydrogel Based on Poly(Vinyl Alcohol), Nanocellulose, and Sodium Alginate

Hydrogels with excellent flexibility, conductivity, and controllable mechanical properties are the current research hotspots in the field of biomaterial sensors. However, it is difficult for hydrogel sensors to regain their original function after being damaged, which limits their practical applications. Herein, a composite hydrogel (named SPBC) of poly(vinyl alcohol) (PVA)/sodium alginate (SA)/cellulose nanofibers (CNFs)/sodium borate tetrahydrate was synthesized, which has good self-healing, electrical conductivity, and excellent mechanical properties. The SPBC0.3 hydrogel demonstrates rapid self-healing (<30 s) and achieves mechanical properties of 33.92 kPa. Additionally, it exhibits high tensile strain performance (4000%). The abundant internal ions and functional groups of SPBC hydrogels provide support for the good electrical conductivity (0.62 S/cm) and electrical response properties. In addition, the SPBC hydrogel can be attached to surfaces such as fingers and wrists to monitor human movements in real time, and its good rheological property supports three-dimensional (3D) printing molding methods. In summary, this study successfully prepared a self-healing, conductive, printable, and mechanically superior SPBC hydrogel. Its suitability for 3D-printing personalized fabrication and outstanding sensor properties makes it a useful reference for hydrogels in wearable devices and human motion monitoring.

Global trends of acupuncture clinical research on analgesia from 2010 to 2023: a bibliometric and visualization analysis

ObjectiveAcupuncture, acknowledged as a potent non-pharmacological therapy, is frequently employed to alleviate pain. Despite its widespread use, there has been a lack of overarching bibliometric analysis of clinical research on acupuncture analgesia. We aimed to summarize current patterns, hotspots, and development trends in this field through bibliometric analysis.MethodsThis study evaluates academic publications retrieved from the Web of Science database (2010.01–2023.09) concerning acupuncture analgesia in clinical settings. All primary and secondary studies on humans were included. To track global developmental trends, we employed several software for analyzing annual publication volumes, countries/regions, institutions, authors, cited authors, journals, cited journals, references, and keywords and to draw collaborative networks and reference co-citation network maps.ResultsThe final search encompassed 7,190 relevant studies, including 1,263 randomized controlled trials (RCTs) and 1,293 systematic reviews and meta-analyses. The results indicated a gradual increase in the number of annual publications on acupuncture analgesia in clinical practice. Among countries and institutions, China (2,139) and Chengdu University of Traditional Chinese Medicine (258) ranked first. Liang FR (89 articles) was the most prolific author, while MacPherson H (604) was the most cited author. MEDICINE (455) was the most productive journal, and Pain (2,473/0.20) ranked first in both the frequency and centrality of cited journals. Notably, the most frequently cited reference was a systematic review of individual patient data on acupuncture carried out for chronic pain that was published by Vickers Andrew J in 2012 (156). Burst analysis identified frontier research areas for 2010–2020, encompassing network meta-analysis, case reports, dry needling, lumbar disc herniation, cancer, post-herpetic neuralgia, insomnia, and bibliometric analysis.ConclusionThis study outlines current trends and potential future research hotspots in clinical acupuncture analgesia over the past decade. Findings emphasize the necessity for enhanced international collaboration to improve research output and translation.

Research Hotspots, Research Frontiers, and Management Significance: A Bibliometric Analysis and Review of Global Food Waste of Students Research Based on CiteSpace

Reducing food waste in the student population is important for promoting sustainable economic, social, and ecological development. In this paper, with the help of CiteSpace software (versions 6.1.R6 and 6.2.R4), we visually analyze the literature related to the food waste of students in the WoS core collection database. It is found that (1) scholars are paying increasing attention to the field of student food waste research, with the United States being the country with the largest total amount of research in this field, the greatest academic influence, and the most frequent cooperation with other countries. (2) Research in this field can be broadly divided into three phases: the starting period (2000–2010), the exploration period (2011–2015), and the development period (2016–2023). The research on student food waste involves multiple fields of knowledge, such as statistics, behavior, psychology, management, nutrition, etc. It is divided into three research hotspots, namely student food waste quantity measurement, student food waste influencing factors, and student food waste behavioral interventions, as well as three cutting-edge themes, namely student food waste in developing countries, student food waste and dietary intake and nutritional health, and student food waste from the perspective of behavioral science. (3) Scholars from different countries/regions have different research focuses. Research in the United States focuses on the development of student food waste interventions and the investigation of students’ and teachers’ knowledge, attitudes, and barriers to intervention in student populations; European scholars focus on researching this area from a behavioral perspective; developing countries in Asia focus on quantifying the level of food waste among college students and exploring the impact of demographic factors on student food waste; and developed countries in Asia focus on surveys of teachers, dietitians, and other parties to explore the impact of food education on food waste and propose food education interventions based on the recommendations of various parties. Based on the above findings, future research directions are proposed to provide references for subsequent research on food waste among students.

A Comparative Review of Recent Research Progress in Prefabricated Buildings in China and Other Countries

Prefabricated building construction has emerged as a transformative technology in construction engineering and the building industries. However, owing to its research characteristics, the relevant literature on prefabricated buildings is diverse and fragmented. This study offers a comparative review of relevant 21st century literature on prefabricated buildings using VOSviewer1.6.18 software. The research progress and future opportunities about prefabricated buildings were comprehensively analyzed, which provides recommendations for its subsequent development. Through keyword searches on the Web of Science, 3214 documents were identified, and an overall analysis of co-citations and co-authorship was conducted. Additionally, a comparative co-occurrence analysis highlighted the differences between China and other countries. Further elaboration of research hotspots is provided, and three future research directions are proposed: (1) energy conservation and reducing the environmental impact of prefabricated buildings, (2) improving the performance of prefabricated building components, and (3) deepening the understanding of the behavior of prefabricated structures under seismic and dynamic conditions. This study provides practitioners and scholars in the field of construction engineering with a comprehensive overview of the literature on prefabricated buildings and paves the way for future advancements in the industry. The findings of this study can be used to promote prefabricated buildings in the architecture, engineering, and construction industries.

A Bi-Level Optimal Scheduling Strategy for Microgrids for Temperature-Controlled Capacity and Time-Shifted Capacity, Considering Customer Satisfaction

Since microgrids can effectively integrate renewable energy, energy storage devices, and controllable loads, this advantage promotes the rapid development and application of microgrid technology. However, with the high proportion of renewable energy access, only considering how energy is optimally distributed in microgrids can no longer meet the actual demand. How to aggregate user-side controllable loads to form regulation resources has become a research hotspot, and the users, as a passive party in the load scheduling process, should also be an important consideration in their perception of the use of electricity. First, a control model for temperature-controlled loads and a time-shift model for time-shiftable loads are developed. Then, the comprehensive electricity satisfaction model of users is established, and the two-layer optimal scheduling model of microgrids considering users’ satisfaction is proposed, with users as the upper layer and microgrids as the lower layer, and the two-layer model is transformed into a single-layer model according to the KKT condition for solving. Finally, the effect of the weighting factor for satisfaction on the economy is discussed through the analysis of examples, which verifies the effectiveness of the two-layer model.