State Of Research Research Articles

EXAMINING CLINICAL STUDIES ABOUT CUPPING THERAPY IN TURKEY: A SYSTEMATIC REVIEW AND BIBLIOMETRIC ANALYSIS

There is insufficient quantitative data available on the characteristics of research conducted on cupping therapy. This insufficiency complicates our ability to achieve a more profound understanding of the subject. The primary aim of this work was to assess the outcome of clinical studies outputs in cupping therapy and discuss methodological gaps. Six databases PubMed, Cochrane Library, Medline, TUBITAK ULAKBIM DergiPark, Science Direct, and Google Scholar, were systematically searched from their inception to January 2024. Performance analysis was made in the study. Frequency and distributions were analyzed with SPSS 24. The software Wordclouds software was used to generate visualization. Within the scope of the research, 26 studies that met the selection criteria were accessed. It was determined that the majority of the studies (46.16%) were written in 2019-2021. The most used technique was 3 S (73.07%), and the most preferred point was du 14 point. While the top two variables are oxidative stress and heavy metal levels, the co-word analysis of keywords showed that "wet cupping therapy" was the largest single node. The findings from this bibliometric study provide the current status and productivity in clinical research of cupping therapy over the past ten years. It is seen that despite the challenges facing the field of cupping research, which include standardization, legal, and reporting quality, there has been an increase in publications to mitigate disease-related symptoms in clinical populations.

Action research in LSCM since 2010 – the emergence of design science

PurposeThe purpose of this study is the review of current status of action research (AR) and design science (DS) in logistics and supply chain management (SCM) and to compare AR and DS.Design/methodology/approachThe paper’s approach comprises the literature review of AR and DS articles.FindingsFirst, not much has changed in terms of number of published AR articles, despite frequent calls for more relevant research. One explanation is the academic system. Second, there is an increased focus on theory, which from a positive perspective is a sign of our field maturing, yet the academic system and current editorial philosophies may also contribute to this development. Third, DS is emerging as a potential replacement of AR.Research limitations/implicationsThe study provides suggestion for both authors and editors when it comes to the increased focus on theory. Comparing AR and DS, the two approaches are similar. However, while the increased focus on theory is reflected in DS, it also seems to be an inferior approach for ill-defined change-focused problems, requiring in-depth, collaborative, data-rich, longitudinal studies.Originality/valueThis paper is the first article that reviews and compares AR and DS in LSCM.

Research status of crack problem in laser brazing diamond

Research status of crack problem in laser brazing diamond

Hazardous Materials from Threats to Safety: Molecularly Imprinted Polymers as Versatile Safeguarding Platforms.

Hazards associated with highly dangerous pollutants/contaminants in water, air, and land resources, as well as food, are serious threats to public health and the environment. Thus, it is imperative to detect or decontaminate, as risk-control strategies, the possible harmful substances sensitively and efficiently. In this context, due to their capacity to be specifically designed for various types of hazardous compounds, the synthesis and use of molecularly imprinted polymers (MIPs) have become widespread. By molecular imprinting, affinity sites with complementary shape, size, and functionality can be created for any template molecule. MIPs' unique functions in response to external factors have attracted researchers to develop a broad range of MIP-based sensors with increased sensitivity, specificity, and selectivity of the recognition element toward target hazardous compounds. Therefore, this paper comprehensively reviews the very recent progress of MIPs and smart polymer applications for sensing or decontamination of hazardous compounds (e.g., drugs, explosives, and biological or chemical agents) in various fields from 2020 to 2024, providing researchers with a rapid tool for investigating the latest research status.

Analysis and Prospects of Lubrication and Friction Characteristics in Cold Strip Rolling: A Review

Lubrication and friction are the core processes in cold strip rolling, which are the key links to realizing the stable production of high‐quality strips, reflecting country advanced level in rolling technology. This review investigates the crucial properties of lubrication and friction in cold strip rolling by analyzing the mechanism, characterization methods, and influencing factors. The lubricant forms an oil film on the surface of rolls and strips based on the lubrication dynamics and tribology theory, changing the asperity contact state in the loaded roll gap, leading to changes in friction characteristics. Oil film thickness and coefficient of friction (COF) are crucial parameters for characterizing and judging the characteristics of lubrication and friction, theoretical calculations have become the core analytical method, and the study of the measurement method is still a great challenge. Parameters like rolling speed and oil viscosity affect the lubricant distribution and asperity contact in the loaded roll gap, changing the lubrication and friction state. The influence of parameters is strongly linked to the experimental or rolling environment. Prospective research trends on lubrication and friction in cold strip rolling are presented based on the status of research on lubrication and friction characterization.

Bioinformatics in Russia: history and present-day landscape.

Bioinformatics has become an interdisciplinary subject due to its universal role in molecular biology research. The current status of Russia's bioinformatics research in Russia is not known. Here, we review the history of bioinformatics in Russia, present the current landscape, and highlight future directions and challenges. Bioinformatics research in Russia is driven by four major industries: information technology, pharmaceuticals, biotechnology, and agriculture. Over the past three decades, despite a delayed start, the field has gained momentum, especially in protein and nucleic acid research. Dedicated and shared centers for genomics, proteomics, and bioinformatics are active in different regions of Russia. Present-day bioinformatics in Russia is characterized by research issues related to genetics, metagenomics, OMICs, medical informatics, computational biology, environmental informatics, and structural bioinformatics. Notable developments are in the fields of software (tools, algorithms, and pipelines), use of high computation power (e.g. by the Siberian Supercomputer Center), and large-scale sequencing projects (the sequencing of 100 000 human genomes). Government funding is increasing, policies are being changed, and a National Genomic Information Database is being established. An increased focus on eukaryotic genome sequencing, the development of a common place for developers and researchers to share tools and data, and the use of biological modeling, machine learning, and biostatistics are key areas for future focus. Universities and research institutes have started to implement bioinformatics modules. A critical mass of bioinformaticians is essential to catch up with the global pace in the discipline.

The DSW to the Rescue? A Response to Feldman

Feldman's appraisal of the current status of social work research, using as its touchstone the 1991 Austin Report, is a frank and fair appraisal of how the discipline has come short of fulfilling its aspirations to become a more science-based profession. In many ways, Feldman has stood in the center of research developments within our field for the past 40 years or longer. He offers informed and thoughtful analyses of what we have accomplished and what is left undone. He asserts that part of the problem is the sluggish growth in the number of research doctorates being produced in our field. Obviously, without the production of highly trained social work researchers, the scholarly and science-based contributions to our field are limited. In this response, I contend that the burgeoning growth of the number of practice doctoral graduates, Doctor of Social Works (DSWs), may provide significant resources to augment the science of social work. In reality, the older generation of DSW graduates made highly important contributions to the science of social work, and the new generation stands poised to replicate this phenomenon.

Application of machine vision in food computing: A review

With global intelligence advancing and the awareness of sustainable development growing, artificial intelligence technology is increasingly being applied to the food industry. This paper, grounded in practical application cases, reviews the current research status and prospects of machine vision–based image recognition technology in food computing. It explores the general workflow of image recognition, applications based on traditional machine learning and deep learning methods. The paper covers areas including food safety detection, dietary nutrition analysis, process monitoring, and enterprise management model optimization. The aim is to provide a solid theoretical foundation and technical guidance for the integration and cross-fertilization of the food industry with artificial intelligence technology.

Application of modified and toughened admixtures in well cementing slurry: Progress and challenges

Cementing, as an important part of the petroleum industry, plays a crucial role in ensuring the normal production and development of oil and gas resources. However, due to the brittle nature of Portland cement, it is prone to micro-cracks when subjected to downhole impact or vibration, which affects normal operations. Moreover, conventional elastic particles have a significant impact on the performance of cement paste, prompting people to continuously research new toughening admixtures. This article reviews the mechanism of toughening additives for oil well cement, the research status of physical toughening agents (fibers/whiskers, graphene) and polymer toughening agents (latex, epoxy resin, asphalt), aiming to provide reference and guidance for the development of new toughening additives.

Statins in Mitigating Anticancer Treatment-Related Cardiovascular Disease.

Certain anticancer therapies inevitably increase the risk of cardiovascular events, now the second leading cause of death among cancer patients. This underscores the critical need for developing effective drugs or regimens for cardiovascular protection. Statins possess properties such as antioxidative stress, anti-inflammatory effects, antifibrotic activity, endothelial protection, and immune modulation. These pathological processes are central to the cardiotoxicity associated with anticancer treatment. There is prospective clinical evidence confirming the protective role of statins in chemotherapy-induced cardiotoxicity. Numerous preclinical studies have demonstrated that statins can ameliorate heart and endothelial damage caused by radiotherapy, although clinical studies are scarce. In the animal models of trastuzumab-induced cardiomyopathy, statins provide protection through anti-inflammatory, antioxidant, and antifibrotic mechanisms. In animal and cell models, statins can mitigate inflammation, endothelial damage, and cardiac injury induced by immune checkpoint inhibitors. Chimeric antigen receptor (CAR)-T cell therapy-induced cardiotoxicity and immune effector cell-associated neurotoxicity syndrome are associated with uncontrolled inflammation and immune activation. Due to their anti-inflammatory and immunomodulatory effects, statins have been used to manage CAR-T cell therapy-induced immune effector cell-associated neurotoxicity syndrome in a clinical trial. However, direct evidence proving that statins can mitigate CAR-T cell therapy-induced cardiotoxicity is still lacking. This review summarizes the possible mechanisms of anticancer therapy-induced cardiotoxicity and the potential mechanisms by which statins may reduce related cardiac damage. We also discuss the current status of research on the protective effect of statins in anticancer treatment-related cardiovascular disease and provide directions for future research. Additionally, we propose further studies on using statins for the prevention of cardiovascular disease in anticancer treatment.

Technology Progress in High-Frequency Electromagnetic In Situ Thermal Recovery of Heavy Oil and Its Prospects in Low-Carbon Situations

Heavy oil resources are abundant globally, holding immense development potential. However, conventional thermal recovery methods such as steam injection are plagued by high heat loss, substantial carbon emissions, and significant water consumption, making them incompatible with carbon reduction goals and the sustainable socioeconomic development demands. A new method of high-frequency electromagnetic in situ heating, which targets polar molecules, can convert electromagnetic energy into heat so as to achieve rapid volumetric heating of the reservoir. This method has the potential to overcome the drawbacks of traditional techniques. Nevertheless, it faces significant drawbacks such as limited heating range and inadequate energy supply during later production stages, which necessitates auxiliary enhancement measures. Various enhancement measures have been reported, including nitrogen injection, hydrocarbon solvent injection, or the use of nano-metal oxide injections. These methods are hindered by issues such as pure nitrogen being easy to breakthrough, high costs, and metal pollution. Through extensive literature review, this article charts the evolution of high-frequency electromagnetic in situ heating technology for heavy oil and the current understanding of the coupled heat and mass transfer mechanisms underlying this technology. Moreover, based on a profound analysis of the technology’s progression trends, this work introduces a new direction: CO2-N2 co-injection as an enhancement strategy for high-frequency electromagnetic in situ heavy oil recovery. There is promising potential for the development of new technologies in the future that combine high efficiency, low carbon emissions, environmental friendliness, economic viability, and energy conservation. Furthermore, some research prospects in low-carbon situations and challenges for the new technology in future are presented in detail. All in all, the contribution of the paper lies in the summarizing of some main drawbacks of current enhanced electromagnetic in situ thermal recovery methods, and presents a novel research direction of using CO2-N2 co-injection as an enhancement strategy based on its current research status in low-carbon situations.

Systematic Literature Review on Pipeline Transport Losses of Hydrogen, Methane, and Their Mixture, Hythane

The transition to cleaner energy sources necessitates an in-depth understanding of the transport characteristics, losses, and opportunities associated with various gaseous fuels, including hydrogen, methane, and their mixtures, such as hythane. Hydrogen (H2), the most abundant element in the universe, is increasingly recognized as a viable alternative to fossil fuels, primarily due to its potential to reduce carbon footprints as a cleaner energy source. Gradually gaining prominence in the energy market, it is displacing other fuels such as methane. In some transport systems, hydrogen is mixed with methane (CH4) in order to reduce the carbon footprint while using the same existing production equipment. As more and more large methane consumers are implementing this mixture, we would like to see how the research has followed the market trend. An up-to-date research, development, and implementation status review is critical. This study aims to identify the main indicators of H2 and CH4 transport losses in pipes, providing a review of the state of the art in the specific literature. To deliver this, a systematic literature review (SLR) was carried out using preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, pinpointing the research trends and results in peer review-published articles over a period of twelve years (2012–2024). Findings: this review identifies and points out, in numbers, the boundaries of the 2012–2024 timeline research.

Study of the landscape vocabulary of the Baise Uprising under the perspective of vocabulary

The Baise Uprising is an important name card of the red cultural resources in Guangxi. It is taking comrade in Baise and the fighting course of the seventh Red Army as the main line, and plays an important position in the history of the Chinese revolution.In this paper, it expounds the research status and progress of landscape vocabulary, and analyzes the landscape, signs and venues of the old site of Baise uprising from the perspective of vocabulary vocabulary.It aims to boost and strengthen the mass communication of red culture on the basis of comprehensively combing the landscape vocabulary of the old site of Baise Uprising.

Ratiometric Fluorescent Probes for Copper Ion Detection and Cuproptosis Studies

Abstract: This study reviews the latest progress of ratiometric fluorescent probes of copper ions from 2022 till now. As a key trace element in biological systems, copper ions play an important role in a variety of biological processes. However, the abnormal change in copper ion concentration is closely related to a variety of diseases, so the development of highly sensitive and highly selective copper ion detection methods is of great significance for scientific research and clinical diagnosis. In this paper, we first introduced the classification of copper ion fluorescence probes, including molecular sensors based on five-membered azocycles, six-membered azocycles, imide groups (R2C=NR'), oxygen and nitrogen functional groups (such as ethers, aldehydes, ketones, esters, carboxylic acids and hydroxyl groups) and group ⅥA elements. Subsequently, the application of nanosensors in the detection of copper ions is further discussed, especially the nanosensors in which copper is used as a reactant, catalyst or alternative reaction. Finally, the current research status of copper ion fluorescence probes is summarized, and the future development direction is prospected in order to provide reference and inspiration for the design of new high-efficiency copper ion detection tools.

Research status and challenges related to thermal-magnetic instability of niobium-tin high-field superconducting coils

Research status and challenges related to thermal-magnetic instability of niobium-tin high-field superconducting coils

Visual analysis of coal mine safety using CiteSpace V

AbstractTo intuitively and systematically grasp the development status and trend of safety evaluation research in China's coal mining operations, we consulted relevant literature in the fields of “coal mine” and “safety evaluation” collected by China Knowledge Network, and deployed CiteSpace V software to summarize and analyze research pertaining to safety evaluation in China's coal mines from three aspects: research institutions, authors, and hot keywords. With respect to research institutions, the results show that although many researchers have conducted in‐depth evaluations of coal mine safety, most of these studies were independent, and two‐way information exchange and cooperation between research institutions remains scarce. With respect to authors, most cooperation between authors has been limited to team members, and relatively stable research teams have been formed. However, most of these research teams are independent. With respect to hot keywords, the trends of coal mine safety evaluation studies exhibit continuous change, with an overall increase in richness. According to the frequency burst times of important keywords, “entropy weight method” and “mining with pressure” are expected to remain important keywords in future evaluations of coal mine safety in China.

Research status of hormone replacement therapy on mood and sleep quality in menopausal women

Menopausal syndrome is a common disease of clinical women, which refers to a series of physical and mental symptoms caused by the fluctuation or reduction of sex hormones before and after menopause. Many of these patients have sleep and mood abnormalities that affect their health and quality of life. At present, the understanding of it is gradually improving. This paper mainly analyzes its background and current treatment.

Advances in Highly Ductile Concrete Research.

In recent years, high-ductility concrete (HDC) has gradually become popular in the construction industry because of its excellent ductility and crack resistance. Concrete itself is a kind of building material with poor tensile properties, and it is necessary to add a large number of steel bars to improve its tensile properties, which increases the construction cost of buildings. However, most of the research studies on high-ductility concrete are scattered. In this paper, the basic mechanical properties of high-ductility concrete and the effects of dry and wet cycles, freeze-thaw cycles, and salt erosion on the durability of high-ductility concrete are obtained by comprehensive analysis. The results show that the tensile properties of HDC can be significantly improved by adding appropriate fiber. When the volume fraction of steel fiber is 2.0%, the splitting tensile strength of concrete is increased by 98.3%. The crack width threshold of concrete chloride erosion is 55-80 μm, and when the crack width threshold is exceeded, the diffusion of CL-1 will be accelerated, and the HDC can control the crack within the threshold, thereby improving the durability of the concrete. Finally, the current research status of high-ductility concrete is analyzed, and the future development of high-ductility concrete is proposed.

Decoding machine learning in nursing research: A scoping review of effective algorithms.

The rapid evolution of artificial intelligence (AI) technology has revolutionized healthcare, particularly through the integration of AI into health information systems. This transformation has significantly impacted the roles of nurses and nurse practitioners, prompting extensive research to assess the effectiveness of AI-integrated systems. This scoping review focuses on machine learning (ML) used in nursing, specifically investigating ML algorithms, model evaluation methods, areas of focus related to nursing, and the most effective ML algorithms. The scoping review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. A structured search was performed across seven databases according to PRISMA-ScR: PubMed, EMBASE, CINAHL, Web of Science, OVID, PsycINFO, and ProQuest. The quality of the final reviewed studies was assessed using the Medical Education Research Study Quality Instrument (MERSQI). Twenty-six articles published between 2019 and 2023 met the inclusion and exclusion criteria, and 46% of studies were conducted in the US. The average MERSQI score was 12.2, indicative of moderate- to high-quality studies. The most used ML algorithm was Random Forest. The four second-most used were logistic regression, least absolute shrinkage and selection operator, decision tree, and support vector machine. Most ML models were evaluated by calculating sensitivity (recall)/specificity, accuracy, receiver operating characteristic (ROC), area under the ROC (AUROC), and positive/negative prediction value (precision). Half of the studies focused on nursing staff or students and hospital readmission or emergency department visits. Only 11 articles reported the most effective ML algorithm(s). The scoping review provides insights into the current status of ML research in nursing and recognition of its significance in nursing research, confirming the benefits of ML in healthcare. Recommendations include incorporating experimental designs in research studies to optimize the use of ML models across various nursing domains. The scoping review demonstrates substantial clinical relevance of ML applications for nurses, nurse practitioners, administrators, and researchers. The integration of ML into healthcare systems and its impact on nursing practices have important implications for patient care, resource management, and the evolution of nursing research.

Parametric optimization and comparative study of machine learning and deep learning algorithms for breast cancer diagnosis.

Breast Cancer is the leading form of cancer found in women and a major cause of increased mortality rates among them. However, manual diagnosis of the disease is time-consuming and often limited by the availability of screening systems. Thus, there is a pressing need for an automatic diagnosis system that can quickly detect cancer in its early stages. Data mining and machine learning techniques have emerged as valuable tools in developing such a system. In this study we investigated the performance of several machine learning models on the Wisconsin Breast Cancer (original) dataset with a particular emphasis on finding which models perform the best for breast cancer diagnosis. The study also explores the contrast between the proposed ANN methodology and conventional machine learning techniques. The comparison between the methods employed in the current study and those utilized in earlier research on the Wisconsin Breast Cancer dataset is also compared. The findings of this study are in line with those of previous studies which also highlighted the efficacy of SVM, Decision Tree, CART, ANN, and ELM ANN for breast cancer detection. Several classifiers achieved high accuracy, precision and F1 scores for benign and malignant tumours, respectively. It is also found that models with hyperparameter adjustment performed better than those without and boosting methods like as XGBoost, Adaboost, and Gradient Boost consistently performed well across benign and malignant tumours. The study emphasizes the significance of hyperparameter tuning and the efficacy of boosting algorithms in addressing the complexity and nonlinearity of data. Using the Wisconsin Breast Cancer (original) dataset, a detailed summary of the current status of research on breast cancer diagnosis is provided.