Research In Field Research Articles

Current trends and future directions in surgery: A brief scoping review

Surgery is undergoing rapid transformation due to advancements in technology, innovative techniques, and a shift toward patient-centered care. This review explores current trends and future directions in the field of surgery, focusing on five major areas: minimally invasive surgery (MIS), robotic-assisted surgery, personalized medicine, enhanced recovery after surgery (ERAS) protocols, and the use of telemedicine. MIS techniques, including laparoscopy and NOTES, continue to evolve, offering benefits such as reduced recovery times and fewer complications. Robotic-assisted surgery has advanced with systems such as the da Vinci Surgical System, which enhances surgical precision and has expanded into new specialties. Personalized medicine, driven by genomics and bioinformatics, aims to tailor surgical interventions to individual patient profiles, improving treatment outcomes. ERAS protocols represent a significant shift toward optimizing perioperative care, with a focus on multimodal pain management and early mobilization to improve recovery. Additionally, the rise of telemedicine and remote monitoring has facilitated preoperative and postoperative care, mostly in the context of the COVID-19 pandemic. The future of surgery promises continued technological advancements, with potential developments including enhanced robotic systems, AI integration, and expanded applications of ERAS principles. However, challenges such as high costs, the need for ongoing training, and the integration of new technologies into healthcare systems must be addressed to fully realize these advancements. This review provides an overview of these trends and highlights areas for future research and development in the surgical field.

Promotion focus, but not prevention focus of teachers and students matters when shifting towards technology-based instruction in schools

Implementing technology-enhanced learning and teaching in schools causes major changes for both teachers and students. Research in other fields shows that individuals’ regulatory orientation (i.e., promotion and prevention focus) predicts how people deal with such fundamental changes. Our research ties in with a school trial where classes were equipped with tablets, yielding a shift from predominantly analog to technology-enhanced learning and teaching. In this context, we investigated whether the regulatory orientation of teachers (N = 109) and students (N = 1,127) related to how they dealt with this change. As expected, teachers with a stronger promotion focus were more likely to perceive the changing teaching situation as a (positively connotated) challenge and to intend to use the tablets in class. Further, students with a strong promotion focus when tablet-based teaching was introduced showed higher technology-related self-efficacy and higher motivation to use digital devices after a few months of using tablets in class. Prevention focus was unrelated to teachers’ and students’ attitudes, behavioral intentions, or motivation. Thus, the concept of regulatory orientation helps explain how teachers and students respond to changes in school, whereby particularly promotion focus appears to be relevant for change processes due to digital transformation in education.

The Multiple Dimensions of Space: Spatial Narrative Types and Socio-Cultural Mapping in New Century Chinese Novels

This study explores how spatial narratives in new-century Chinese novels reflect and engage with China's rapidly changing socio-cultural landscape. Since the mid-1990s, China's accelerated marketization and globalization have profoundly altered traditional lifestyles and public aesthetics, significantly influencing literary creation. How do contemporary Chinese novelists employ spatial narratives to represent these changes, and what insights do these narratives offer into modern Chinese society? Employing textual analysis grounded in Lefebvre's spatial theory and Soja's Thirdspace concept, this paper examines the manifestations and functions of physical, psychological, and social spaces in new-century Chinese novels. Findings reveal diverse and complex spatial narratives: fragmented physical spaces reflecting globalization's impact, psychological spaces highlighting identity recognition anxiety, and social spaces exposing modern society's alienation and class disparities. This study contributes to a deeper application of spatial narrative theory in Chinese literary research, offering new analytical tools for understanding modern literature and its reflection of social changes. It provides insights into the characteristics and trends of new-century Chinese social culture, promoting further research and theoretical development in related fields.

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.

Ten Years of the Synthetic Biology Summer Course at Cold Spring Harbor Laboratory.

The Cold Spring Harbor Laboratory (CSHL) Summer Course on Synthetic Biology, established in 2013, has emerged as a premier platform for immersive education and research in this dynamic field. Rooted in CSHL's rich legacy of biological discovery, the course offers a comprehensive exploration of synthetic biology's fundamentals and applications. Led by a consortium of faculty from diverse institutions, the course structure seamlessly integrates practical laboratory sessions, exploratory research rotations, and enriching seminars by leaders in the field. Over the years, the curriculum has evolved to cover essential topics such as cell-free transcription-translation, DNA construction, computational modeling of gene circuits, engineered gene regulation, and CRISPR technologies. In this review, we describe the history, development, and structure of the course, and discuss how elements of the course might inform the development of other short courses in synthetic biology. We also demonstrate the course's impact beyond the lab with a summary of alumni contributions to research, education, and entrepreneurship. Through these efforts, the CSHL Summer Course on Synthetic Biology remains at the forefront of shaping the next generation of synthetic biologists.

Complexity and entropy of natural patterns.

Complexity and entropy play crucial roles in understanding dynamic systems across various disciplines. Many intuitively perceive them as distinct measures and assume that 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.

Dual Orthogonally Polarized Lasing Assisted by Imaginary Fermi Arcs in Organic Microcavities.

The polarization control of micro- and nanolasers is an important topic in nanophotonics. Up to now, the simultaneous generation of two distinguishable orthogonally polarized lasing modes from a single organic microlaser remains a critical challenge. Here, we demonstrate simultaneously orthogonally polarized dual lasing from a microcavity filled with an organic single crystal exhibiting selective strong coupling. We show that the non-Hermiticity due to polarization-dependent losses leads to the formation of real and imaginary Fermi arcs with exceptional points. Simultaneous orthogonally polarized lasing becomes possible thanks to the eigenstate mixing by the photonic spin-orbit coupling at the imaginary Fermi arcs. Our work provides a novel way to develop linearly polarized lasers and paves the way for the future fundamental research in topological photonics, non-Hermitian optics, and other fields.

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.

Comparative bibliometric analysis of artificial intelligence-assisted polyp diagnosis and AI-assisted digestive endoscopy: trends and growth in AI gastroenterology (2003-2023).

Artificial intelligence is already widely utilized in gastroenterology. This study aims to comprehensively evaluate the research hotspots and development trends within the field of AI in gastroenterology by employing bibliometric techniques to scrutinize geographical distribution, authorship, affiliated institutions, keyword usage, references, and other pertinent data contained within relevant publications. This investigation compiled all pertinent publications related to artificial intelligence in the context of gastrointestinal polyps and digestive endoscopy from 2003 to 2023 within the Web of Science Core Collection database. Furthermore, the study harnessed the tools CiteSpace, VOSviewer, GraphPad Prism and Scimago Graphica for visual data analysis. The study retrieved a total of 2,394 documents in the field of AI in digestive endoscopy and 628 documents specifically related to AI in digestive tract polyps. The United States and China are the primary contributors to research in both fields. Since 2019, studies on AI for digestive tract polyps have constituted approximately 25% of the total AI digestive endoscopy studies annually. Six of the top 10 most-cited studies in AI digestive endoscopy also rank among the top 10 most-cited studies in AI for gastrointestinal polyps. Additionally, the number of studies on AI-assisted polyp segmentation is growing the fastest, with significant increases in AI-assisted polyp diagnosis and real-time systems beginning after 2020. The application of AI in gastroenterology has garnered increasing attention. As theoretical advancements in AI for gastroenterology have progressed, real-time diagnosis and detection of gastrointestinal diseases have become feasible in recent years, highlighting the promising potential of AI in this field.

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.

Research on Glacier Changes and Their Influencing Factors in the Yigong Zangbo River Basin of the Tibetan Plateau, China, Based on ICESat-2 Data

The intense changes in glaciers in the southeastern Tibetan Plateau (SETP) have essential impacts on regional water resource management. In order to study the seasonal fluctuations of glaciers in this region and their relationship with climate change, we focus on the Yigong Zangbo River Basin in the SETP, extract the annual and seasonal variations of glaciers in the basin during 2018–2023, and analyze their spatio-temporal characteristics through the seasonal-trend decomposition using the LOESS (STL) method. Finally, combining the Extreme Gradient Boosting (XGBoost) model and the Shapley additive explanations (SHAP) model, we assess the comprehensive impact of meteorological factors such as temperature and snowfall on glacier changes. The results indicate that glaciers in the Yigong Zangbo River Basin experienced remarkable mass loss during 2018–2023, with an average annual melting rate of −0.83 ± 0.12 m w.e.∙yr−1. The glacier mass exhibits marked seasonal fluctuations, with increases in January–March (JFM) and April–June (AMJ) and noticeable melting in July–September (JAS) and October–December (OND). The changes over these four periods are 2.12 ± 0.04 m w.e., 0.93 ± 0.15 m w.e., −1.58 ± 0.19 m w.e., and −1.32 ± 0.17 m w.e., respectively. Temperature has been identified as the primary meteorological driver of glacier changes in the study area, surpassing the impact of snowfall. This study uses advanced altimetry data and meteorological data to monitor and analyze glacier changes, which provides valuable data for cryosphere research and also validates a set of replicable research methods, which provides support for future research in related fields.

Research on the application and prospects of variable reluctance inductive sensors

Variable reluctance inductive sensors are extensively utilized in the industrial field due to its unique working mechanism and good performance characteristics. Based on the working principle of Variable Reluctance Inductive Sensors, this paper reviews its development history, application in industrial automation and medical industry. Through the analysis of variable reluctance inductive sensors, variable reluctance inductive sensors have the advantages of high sensitivity, low cost and reliability which can support its wide application in precision measurement, position detection and motion control, but it is susceptible to the interference of the external magnetic field and the influence of the temperature which limits its development space and scope. However, the advancement of new materials, intelligent technologies and multi-mode integration has broadened the potential of future development of sensors and created more challenges for the development of variable reluctance inductive sensors. The update of new research conditions also provides a valuable reference of research and application 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.