Research In Field Research Articles

HERITAGE TOURISM: A BIBLIOMETRIC ANALYSIS OVER THREE DECADES (1994-2023)

This qualitative study explores the relationship between tourism and places recognized by UNESCO as World Heritage Sites, whether tangible (WHS) or intangible (ICH). In the last two decades there has been a growth in the number of resources with UNESCO recognition. It aims to fill the research gap in the analysis of heritage tourism using bibliometric techniques. Data were collected from 886 documents in the period 1994-2023 (December) from the Web of Science database. Using the VOSviewer software, scientific maps were created showing the current and future line of research in this scientific field. The results show evidence of related research on Tourism sustainability at UNESCO sites in China and Tourism sustainability of cultural heritage for the whole period. In addition, two new research topics from 2016 are identified: Motivation and satisfaction of heritage tourists and Authenticity of intangible heritage. The results help to visualise the structure and trends of heritage tourism research, which can help researchers, policy makers and destination marketing organisations (DMOs) to gain knowledge and understanding of existing studies and current research topics.

Specified time dual-group synchronization of uncertain complex chaotic systems

Aiming at the specified time dual-group synchronization problem of multi-wing complex chaotic systems containing uncertain terms and external disturbances, a new specified-time sliding mode control scheme is proposed, which directly synchronizes the complex chaotic system without separating the real and imaginary parts of the complex chaotic system. First, a new specified time stability criterion is used to construct the integral sliding mode surface of the synchronous error system to ensure stable sliding motion within the specified time. Subsequently, a proximity controller is designed to drive the error system to reach and remain on the sliding surface within another specified time, thereby achieving specified-time synchronization. In order to realize the proposed stability concept, this paper introduces a new sliding surface and defines the corresponding control law and adaptive rate. The effectiveness of this scheme is proved through Lyapunov stability theory and specified time stability theory. Numerical simulation results show that the scheme has strong robustness to uncertainties and external disturbances, and the controller is not affected by internal uncertainties and external disturbances. Compared to other stabilization time control schemes, this scheme has a shorter synchronization time. In general, this study introduces complex variables and adopts a scheme in which sliding mode surface parameters and controller parameters can be preset to simultaneously achieve dual-group synchronization of two groups of complex chaotic systems within the complex domain. This study offers greater flexibility, presenting novel ideas and approaches for the synchronization control of complex systems. It holds significant theoretical and practical value, providing valuable references and insights for research and applications in related fields.

Activating supercooled electrolytes

Abstract While materials science research in the rechargeable battery field is usually application-oriented, the general supercooled liquid theory from glass science can drive high-performance low-temperature aqueous batteries, facilitating cross-disciplinary collaborations and simultaneous research break- throughs.

Complexity and entropy of natural patterns

Abstract Complexity and entropy play crucial roles in understanding dynamic systems across various disciplines. Many intuitively perceive them as distinct measures and assume they have a concave-down relationship. In everyday life, there is a common consensus that while entropy never decreases, complexity does decrease after an initial increase during the process of blending coffee and milk. However, this consensus is primarily conceptual and lacks empirical evidence. Here, we provide comprehensive evidence that challenges this prevailing consensus. We demonstrate that this consensus is, in fact, an illusion resulting from the choice of system characterization (dimension) and the unit of observation (resolution). By employing a complexity measure designed for natural patterns, we find that the complexity of a coffee-milk system never decreases if the system is appropriately characterized in terms of dimension and resolution. Also, this complexity aligns experimentally and theoretically with entropy, suggesting that it does not represent a measure of so-called effective complexity. These findings rectify the prevailing conceptual consensus and reshape our understanding of the relationship between complexity and entropy. It is therefore crucial to exercise caution and pay close attention to accurately and precisely characterize dynamic systems before delving into their underlying mechanisms, despite the maturity of characterization research in various fields dealing with natural patterns such as geography and ecology. The characterization/observation (dimension and resolution) of a system fundamentally determines the assessment of complexity and entropy using existing measures and our understanding.

Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures

Enzymes are crucial in numerous biological processes, with the Enzyme Commission (EC) number being a commonly used method for defining enzyme function. However, current EC number prediction technologies have not fully recognized the importance of enzyme active sites and structural characteristics. Here, we propose GraphEC, a geometric graph learning-based EC number predictor using the ESMFold-predicted structures and a pre-trained protein language model. Specifically, we first construct a model to predict the enzyme active sites, which is utilized to predict the EC number. The prediction is further improved through a label diffusion algorithm by incorporating homology information. In parallel, the optimum pH of enzymes is predicted to reflect the enzyme-catalyzed reactions. Experiments demonstrate the superior performance of our model in predicting active sites, EC numbers, and optimum pH compared to other state-of-the-art methods. Additional analysis reveals that GraphEC is capable of extracting functional information from protein structures, emphasizing the effectiveness of geometric graph learning. This technology can be used to identify unannotated enzyme functions, as well as to predict their active sites and optimum pH, with the potential to advance research in synthetic biology, genomics, and other fields.

N‐Heterocyclic Carbene–Based Catalysts for Glycerol Valorisation: Current State and Perspectives

ABSTRACTN‐Heterocyclic carbenes (NHCs) are molecules with exceptional catalytic properties in a wide range of reactions. Recently, there has been a growing interest in using them as catalysts for biomass conversion. In this review, we examine the current state of glycerol valorisation (GV) using several catalytic systems, including metallic complexes bearing NHCs as ligands, polymeric building blocks and organocatalysts. The study covers literature published in the last 15 years, detailing the catalyst scope, reaction conditions and products of GV. This analysis emphasises the importance of varying the steric and electronic properties of NHCs within the reviewed catalytic systems. Furthermore, a critical evaluation of the proposed mechanistic pathways complements these results. A comprehensive understanding of the challenges of using such catalytic systems will emerge by compiling literature on the use of NHCs for the studied reactions. Additionally, this will promote new opportunities for further research in both academic and industrial fields.

Controlling the thiourea for optimized growth of CdS nanorod arrays for improved photoelectrochemical water splitting

Energy and the environment are very important issues to secure, preserve and improve our modern lifestyle. The conversion of sunlight into hydrogen and oxygen via photoelectrochemical (PEC) water splitting is one of the most potential routes for clean energy. Cadmium sulfide (CdS) is a promising semiconductor for utilization as a photoanode. In this work, CdS has been grown via the hydrothermal method by optimizing the thiourea concentration. The growth of CdS with equal concentration of Cd2+ and S2-demonstrates the crowded hexagonal-shaped nanorod arrays with small diameter and relatively longer length, and it exhibits the highest photocurrent density due to some factors, such as high length-to-diameter ratio, large reaction area, suitable flat band potential, slow charge recombination rate, fast charge transfer, suitable conduction and valence band edges, and surface reaction kinetics. This work will be of potential to further develop improved nanocomposites of CdS nanorods for hydrogen production and research in related fields.

The influence of erosion on the dynamic process of landslide in Xinmo Village, Maoxian

A large-scale, high-speed, long-runout landslide occurred in Xinmo Village, Maoxian, Sichuan Province, China, on June 24, 2017. It was characterized by fast sliding speed, rapid volume growth, and large impact area. The dynamic process of such landslides and the influence of erosion on the dynamic process are studied by field investigation, numerical inversion and simulation. The results showed that entrainment erosion was a major factor of landslide volumetric change and a salient feature of the landslide process. An exponential equation relating the erosion rate and the deposition volume was established. Moreover, the study found that a slight change of the erosion rate (1e-4) also had a significant impact on the lateral spreading, longitudinal runout, and vertical erosion. As the erosion rate increased, the lateral spreading, longitudinal runout, and vertical erosion of this type of landslide became more obvious. By using the coefficient of variation method, it was obtained that the erosion rate had a greater influence on the vertical erosion than on the lateral spreading and longitudinal runout. In the study of the velocity of the rock avalanche under different erosion conditions, it was found that the erosion amount and the landslide velocity were not strictly linearly related. This study has important significance for understanding the dynamic process and erosion effect of rock avalanche, and provides useful references and insights for future research in related fields.

Exploring meta-heuristics for partitional clustering: methods, metrics, datasets, and challenges

Partitional clustering is a type of clustering that can organize the data into non-overlapping groups or clusters. This technique has diverse applications across the different various domains like image processing, pattern recognition, data mining, rule-based systems, customer segmentation, image segmentation, and anomaly detection, etc. Hence, this survey aims to identify the key concepts and approaches in partitional clustering. Further, it also highlights its widespread applicability including major advantages and challenges. Partitional clustering faces challenges like selecting the optimal number of clusters, local optima, sensitivity to initial centroids, etc. Therefore, this survey describes the clustering problems as partitional clustering, dynamic clustering, automatic clustering, and fuzzy clustering. The objective of this survey is to identify the meta-heuristic algorithms for the aforementioned clustering. Further, the meta-heuristic algorithms are also categorised into simple meta-heuristic algorithms, improved meta-heuristic algorithms, and hybrid meta-heuristic algorithms. Hence, this work also focuses on the adoption of new meta-heuristic algorithms, improving existing methods and novel techniques that enhance clustering performance and robustness, making partitional clustering a critical tool for data analysis and machine learning. This survey also highlights the different objective functions and benchmark datasets adopted for measuring the effectiveness of clustering algorithms. Before the literature survey, several research questions are formulated to ensure the effectiveness and efficiency of the survey such as what are the various meta-heuristic techniques available for clustering problems? How to handle automatic data clustering? What are the main reasons for hybridizing clustering algorithms? The survey identifies shortcomings associated with existing algorithms and clustering problems and highlights the active area of research in the clustering field to overcome these limitations and improve performance.

Interdisciplinary perspective on architectural programming: current status and future directions

PurposeArchitectural programming, as a critical phase in construction projects, has been widely recognized for its importance and advantages throughout the construction process. With the rapid development of the socioeconomic landscape, architectural programming has garnered increasing attention from various other disciplines, becoming a key trend in interdisciplinary collaboration. This study aims to provide a comprehensive understanding of the current status and future directions of architectural programming from an interdisciplinary perspective through scientometric analysis and systematic review.Design/methodology/approachThis study first collected English journal articles on architectural programming published between 1975 and 2024 from the Web of Science and Scopus databases. After an initial screening of titles and abstracts, 515 articles were selected for scientometric analysis to reveal the current state and advantages of architectural programming research in multidisciplinary fields. Subsequently, a second screening of full-text articles identified 75 journal articles for systematic review, focusing on research topics and challenges in interdisciplinary applications.FindingsThe study reveals an exponential increase in the number of papers related to architectural programming between 1975 and 2024, particularly in the last decade. Six key research topics of architectural programming in multidisciplinary fields were identified: (1) performance optimization and evaluation, (2) digitalization and automation development, (3) project management and decision support, (4) improvement of human and social welfare, (5) sustainable resources and environment and (6) educational practices of architectural programming. Additionally, the study identified the main challenges in the interdisciplinary application of architectural programming, including (1) incompatibility among disciplines, (2) limitations of data and methodologies and (3) insufficient social engagement. To address these challenges, three potential future directions were proposed: (1) establishing interdisciplinary teams and platforms, (2) enhancing multi-source data integration and digital transformation and (3) improving governance mechanisms and educational training.Originality/valueBy combining quantitative and qualitative methods, this study provides a comprehensive review of architectural programming research and applications in multidisciplinary fields, offering theoretical foundations and practical references for the future development of architectural programming. This review not only aids in understanding the overall status of current architectural programming research but also offers valuable insights and recommendations for future research directions and practical applications.

Advances in Particle Acceleration: Novel Techniques, Instruments and Applications

In the last decade, there have been significant advancements in accelerator technologies, driven by both fundamental research and practical applications in various fields, including X-ray science, medical treatments, and industrial processes [...]

Environmental monitoring system design based on STM32 platform

This study addresses the current societal demand for environmental monitoring by designing an environmental monitoring system based on the STM32 platform. This system assesses and monitors environmental conditions in real-time by tracking parameters such as CO, PM2.5, temperature, humidity, and light intensity. It holds significant value in preventing air pollution and improving indoor air quality. The system employs four types of sensors: the DHT11 digital temperature and humidity sensor, the BH1750FV light sensor, the GP2Y1010AUOF optical dust sensor, and the MQ-7 CO sensor to collect environmental data, which is then processed by the STM32F103C8T6 controller. This system is characterized by its real-time capabilities, high precision, and low power consumption, making it highly practical and valuable for widespread application. The paper provides a detailed discussion of sensor selection, measurement algorithms, and system design and implementation, offering valuable insights for research and applications in related fields.

Revolutionizing Cancer Treatments through Stem Cell-Derived CAR T Cells for Immunotherapy: Opening New Horizons for the Future of Oncology

Recent advances in cellular therapies have paved the way for innovative treatments of various cancers and autoimmune disorders. Induced pluripotent stem cells (iPSCs) represent a remarkable breakthrough, offering the potential to generate patient-specific cell types for personalized as well as allogeneic therapies. This review explores the application of iPSC-derived chimeric antigen receptor (CAR) T cells, a cutting-edge approach in allogeneic cancer immunotherapies. CAR T cells are genetically engineered immune cells designed to target specific tumor antigens, and their integration with iPSC technology holds immense promise for enhancing the efficacy, safety, and scalability of cellular therapies. This review begins by elucidating the principles behind iPSC generation and differentiation into T cells, highlighting the advantage of iPSCs in providing a uniform, inexhaustible source of CAR T cells. Additionally, we discuss the genetic modification of iPSC-derived T cells to express various CARs, emphasizing the precision and flexibility this affords in designing customized therapies for a diverse range of malignancies. Notably, iPSC-derived CAR T cells demonstrate a superior proliferative capacity, persistence, and anti-tumor activity compared to their conventionally derived counterparts, offering a potential solution to challenges associated with conventional CAR T cell therapies. In conclusion, iPSC-derived CAR T cells represent a groundbreaking advancement in cellular therapies, demonstrating unparalleled potential in revolutionizing the landscape of immunotherapies. As this technology continues to evolve, it holds the promise of providing safer, more effective, and widely accessible treatment options for patients battling cancer and other immune-related disorders. This review aims to shed light on the transformative potential of iPSC-derived CAR T cells and inspire further research and development in this dynamic field.

Prompt Engineering Paradigms for Medical Applications: Scoping Review.

Prompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in the medical domain due to its specialized terminology and language technicity. Clinical natural language processing applications must navigate complex language and ensure privacy compliance. Prompt engineering offers a novel approach by designing tailored prompts to guide models in exploiting clinically relevant information from complex medical texts. Despite its promise, the efficacy of prompt engineering in the medical domain remains to be fully explored. The aim of the study is to review research efforts and technical approaches in prompt engineering for medical applications as well as provide an overview of opportunities and challenges for clinical practice. Databases indexing the fields of medicine, computer science, and medical informatics were queried in order to identify relevant published papers. Since prompt engineering is an emerging field, preprint databases were also considered. Multiple data were extracted, such as the prompt paradigm, the involved LLMs, the languages of the study, the domain of the topic, the baselines, and several learning, design, and architecture strategies specific to prompt engineering. We include studies that apply prompt engineering-based methods to the medical domain, published between 2022 and 2024, and covering multiple prompt paradigms such as prompt learning (PL), prompt tuning (PT), and prompt design (PD). We included 114 recent prompt engineering studies. Among the 3 prompt paradigms, we have observed that PD is the most prevalent (78 papers). In 12 papers, PD, PL, and PT terms were used interchangeably. While ChatGPT is the most commonly used LLM, we have identified 7 studies using this LLM on a sensitive clinical data set. Chain-of-thought, present in 17 studies, emerges as the most frequent PD technique. While PL and PT papers typically provide a baseline for evaluating prompt-based approaches, 61% (48/78) of the PD studies do not report any nonprompt-related baseline. Finally, we individually examine each of the key prompt engineering-specific information reported across papers and find that many studies neglect to explicitly mention them, posing a challenge for advancing prompt engineering research. In addition to reporting on trends and the scientific landscape of prompt engineering, we provide reporting guidelines for future studies to help advance research in the medical field. We also disclose tables and figures summarizing medical prompt engineering papers available and hope that future contributions will leverage these existing works to better advance the field.

The impact of K-beauty entrepreneurship on technical competency and business performance—Focusing on the CEO

This study investigates the relationships among entrepreneurship, technical competency, and business performance, focusing on CEOs in the beauty service industry in the Busan area. A total of 215 survey responses were collected, with 213 valid responses selected for final analysis after excluding 2 unsuitable responses. The key findings of the study are as follows: First, entrepreneurship was found to partially influence technical competency. Second, technical competency was found to influence business performance. Third, entrepreneurship was found to partially influence business performance. Fourth, technical competency was found to partially mediate the relationship between entrepreneurship and business performance. Based on these results, the study systematically analyzes and explains the causal relationships among the entrepreneurship of CEOs in the beauty service industry, their technical competency, and business performance. It also seeks to provide useful reference materials for strengthening the innovation and competitiveness of CEOs in the beauty service industry and establishing a theoretical foundation for future research in related fields.

Process of Phenomenology as Qualitative Research Method in English

Research methodology is a pronounced science that contributes to the study of research in many fields. The literature on research methodology shows how it serves in several ways as it is multi-dimensional and theoretical alongside the features of experimental literature. Based on different philosophies, it involves two significant methods of choice, qualitative and quantitative, with a range of research strategies from simple to complex. Fundamentally, the strategies of the qualitative method are of much use in the study of English literature and to some extent in the studies of English language and Applied linguistics since the latter also deal with empirical study. The current research method, phenomenology, is one of the critical approaches of the qualitative method, and it is all about philosophical experiences. The study of phenomenology in English is necessary as it has been a widely used method in the fields of medical science, philosophy, computers, and business but not still more in English and Second language acquisition studies; therefore, the main motive behind the study is to investigate how congenial the phenomenology research method is to exploit it in English language and literature studies. Concerning the abovementioned facts, the present study will include the conceptualization, periodical developments, classification, and the milestones associated with phenomenological research studies to analyze and extract as a research method in English. The study also endeavors to identify philosophy and language as two important parameters and emphasizes that they are equally necessary to use phenomenology as a research method in English.

Social assessment of the population for the transition to Value- based healthcare model

Background: The advanced experience of implementing a value-based model in the practice of Moscow healthcare allowed us to identify a number of the most significant practices of its effectiveness, among which support for making an informed decision by the patient, as well as measuring satisfaction and analyzing the needs of patients were noted. In this regard, the transition of the entire national healthcare system to a value-based model requires the development of approaches to assessing the population’s compliance with such a transformation, taking into account socio-economic, demographic and psychological criteria. Aim: To consider possible predictors of the readiness of a social group of the population older than working age to transition to a value-based model of national health care. Materials and methods: A review of fundamental and modern works in the problem field was carried out. The empirical basis of the work was made up of data from the official website of the Federal Statistics Service with geographic coverage of the entire Russian Federation. Results: Analysis of data from a subjective assessment of the population's health on a national sample of the Russian Federation demonstrates the internal readiness of the population for a value-based healthcare model, despite the negative trends of aging. In the age groups 55-59, 60-64, 65-69, 70-74, 75-79, 80 and older, there is a general trend in assessing their health - the majority of respondents rated it as “satisfactory,” while the number of ratings was “very bad” is minimal. The positive dynamics of the formation of information and technical competence of the population 65+, who are experiencing difficulties in integrating into digital health services, is shown. Conclusion: The transition of national healthcare to a value-based model involves the active involvement of the patient and the formation of patient experience in assessing the outcome of medical care. In this regard, it is necessary to take into account the patient’s personal socio-psychological compliance profile, the elements of which determine the horizons of promising research in the problem field under consideration. As a possible patient profile module, a subjective assessment of health and an assessment of information technology competencies in a social group older than working age, whose work growth is determined by demographic realities, are used.

Modeling the impact of BDA-AI on sustainable innovation ambidexterity and environmental performance

Data has evolved into one of the principal resources for contemporary businesses. Moreover, corporations have undergone digitalization; consequently, their supply chains generate substantial amounts of data. The theoretical framework of this investigation was built on novel concepts like big data analytics—artificial intelligence (BDA-AI) and supply chain ambidexterity’s (SCA) direct impacts on sustainable supply chain management (SSCM) and indirect impacts on sustainable innovation ambidexterity (SIA) and environmental performance (EP). This study selected employees of manufacturing industries as respondents for environmental performance, sustainable supply chain management, big data analytics, artificial intelligence, and supply chain ambidexterity. The results from this study show that BDA-AI and SCA significantly affect SSCM. SSCM has significant associations with SIA and EP. Finally, SIA has a significant impact on EP. According to the results indicating the indirect impacts, BDA-AI has significant indirect relationships with SIA and EP by having SSCM as the mediating variable. Furthermore, SCA has significant indirect associations with SIA and EP, with SSCM as the mediating variable. Additionally, both BDA-AI and SCA have significant indirect associations with EP, while SIA and SSCM are mediating variables. Finally, SSCM has an indirect association with EP while having SIA as a mediating variable. The findings of this paper provide several theoretical contributions to the research in sustainability and big data analytics artificial intelligence field. Furthermore, based on the suggested framework, this study offers a number of practical implications for decision-makers to improve significantly in the supply chain and BDA-AI. For instance, this paper provides significant insight for logistics and supply chain managers, supporting them in implementing BDA-AI solutions to help SSCM and enhance EP.

Advancements in Inflammatory Bowel Disease Management: From Traditional Treatments to Monoclonal Antibodies and Future Drug Delivery Systems

Inflammatory bowel disease (IBD) is a chronic gastrointestinal inflammatory disorder with two main subtypes: ulcerative colitis (UC) and Crohn’s disease (CD). The pathogenesis involves genetic predisposition, dysbiosis, and immune dysregulation. Complications include perianal lesions, strictures, fistulas, perforations, and an increased risk of colon cancer. Clinical classification ranges from mild to fulminant and recurrent disease, with common symptoms such as abdominal discomfort, rectal bleeding, diarrhea, and weight loss. Extraintestinal manifestations include arthritis, erythema nodosum, pyoderma gangrenosum, and uveitis. Conventional treatments using aminosalicylates, corticosteroids, and immunomodulators have limitations. Biologics, introduced in the 1990s, offer improved efficacy and specificity, targeting factors like TNF-α, integrins, and cytokines. Monoclonal antibodies play a crucial role in IBD management, aiming to reduce relapses, hospitalizations, and surgeries. In conclusion, this review is aimed at summarizing the latest knowledge, advantages, and drawbacks of IBD therapies, such as small molecules, biologics, and monoclonal antibodies, to provide a basis for further research in the IBD field.

Long-lived carriers-promoted photocatalytic deuteration of halides with D2O as the deuterium source over Cu doped quantum dots

Deuterium labeling is a highly valuable yet challenging subject of research in various scientific fields. Conventional deuteration methods often involve harsh reaction conditions and suffer from limited reactivity and selectivity. Herein, we report a visible light–driven C–X (X = halogen) to C–D (D = deuterium) exchange strategy over copper-doped cadmium sulfide quantum dots (Cu-CdS QDs) under mild conditions, eliminating the need for noble metal catalysts and expensive deuterium sources. The conversion of aryl halides into deuterated products using Cu-CdS QDs reaches up to 99%, which is four times higher than that achieved using pristine CdS QDs. The substantial enhancement in the photocatalytic activity of the QDs can be primarily attributed to the generation of long-lived charge carriers (approximately 6 μs) induced by Cu doping. Mechanistic studies reveal that the Cu dopants considerably retard the recombination of photoinduced carriers by creating intermediate energy levels that serve as hole trapping centers in CdS QDs, thereby improving the electron utilization efficiency in energetically demanding photoreduction reactions. Additionally, the introduction of Cu increases the energy offset between the conduction band of CdS QDs and molecular acceptors, facilitating the electron transfer process. Upon visible light irradiation, a series of aryl halides can be efficiently converted into the desired deuterated compounds using D2O as the deuterium source. This work demonstrates that regulating charge carrier dynamics in ultrasmall QD-based photocatalysts is a promising strategy for promoting organic transformations.