Research In Field Research Articles

Toward Person-Focused Assessment and Understanding the Human Need to Be Perfect: Commentary and Introduction to the Third Special Issue on Perfectionism

In the current introductory article, we discuss the importance of balancing the variable-centered research in the perfectionism field with a person-focused approach. We examine the utility of a person-centered approach in assessment, research, and theory and the need to revisit overlooked themes central to understanding people who are extreme perfectionists. Our analysis focuses on addressing the core unaddressed issue of why perfectionists as unique individuals absolutely need to be perfect. We describe measures to assess individual differences in this need to be perfect and themes reflecting the need to be perfect that require investigation. The papers in this third special issue on perfectionism are then introduced and considered as examples of the merits of a broad approach that goes beyond trait perfectionism to also include perfectionistic self-presentation and the cognitive experience of perfectionism. We conclude by examining how certain variable-centered studies described in the current special issue yield insights about perfectionists as people when individuals are considered from a person-focused perspective.

Heavy metal sequestration from wastewater by metal-organic frameworks: a state-of-the-art review of recent progress.

Metal-organic frameworks (MOFs) have emerged as highly promising adsorbents for removing heavy metals from wastewater due to their tunable structures, high surface areas, and exceptional adsorption capacities. This review meticulously examines and summarizes recent advancements in producing and utilizing MOF-based adsorbents for sequestering heavy metal ions from water. It begins by outlining and contrasting commonly employed methods for synthesizing MOFs, such as solvothermal, microwave, electrochemical, ultrasonic, and mechanochemical. Rather than delving into the specifics of adsorption process parameters, the focus shifts to analyzing the adsorption capabilities and underlying mechanisms against critical metal(loid) ions like chromium, arsenic, lead, cadmium, and mercury under various environmental conditions. Additionally, this article discusses strategies to optimize MOF performance, scale-up production, and address environmental implications. The comprehensive review aims to enhance the understanding of MOF-based adsorption for heavy metal remediation and stimulate further research in this critical field. In brief, this review article presents a comprehensive overview of the contemporary information on MOFs as an effective adsorbent and the challenges being faced by these adsorbents for heavy metal mitigation (including stability, cost, environmental issues, and optimization), targeting to develop a vital reference for future MOF research.

The Development of Gene Editing Technology and Controversial Issues: A Discussion

The fields of genetics and biomedicine stand to benefit greatly from the innovative technique known as gene editing technology, especially with the potential uses of CRISPR-Cas9.the creation of crops resistant to disease, the treatment of genetic illnesses, and the advancement of biomedical research in fields like sickle-cell anemia and rice gene editing are all made possible by CRISPR-Cas9's capacity to precisely pinpoint and edit specific genes. Despite being beneficial to humanity, the quick development of human gene editing technology has also brought up ethical, legal, societal, and technical issues. Its management, informed consent, privacy, and social equality are among the issues that require considerable attention.In an effort to give the scientific community something to think about, this article examines the state of gene editing today, its potential for the future, and the debates surrounding its application. It concentrates on the way the CRISPR/Cas system works, how CRISPER/Cas9 gene editing is used, and the range of problems it has brought up.

An Overview of Recent Analytical Techniques for Air Quality Monitoring and Assessment

Abstract: Access to clean air, a vital necessity for life, faces severe constraints globally due to industrialization and urbanization, leading to widespread air quality deterioration. To safeguard human health and the environment from detrimental effects, the essential components of proper monitoring, assessment, and management of air quality are paramount. Conventional air quality analytical techniques such as gas chromatography/ mass spectrometry, selected ion flow tube mass spectrometry, thermal desorption/ gas chromatography, and mass spectrometry are widely used for air quality analysis. These methods, however, are laborious, necessitate sample preparation, require expansive and hazardous reagents, and have a high cost of equipment and maintenance. As such, more rapid, sensitive, specific, cost-effective, portable, user-friendly, and environmentally friendly analytical tools are required for efficient air quality monitoring and control. Over the years, various techniques have emerged to address these challenges, including mobile sensors, microbial monitoring, the Internet of Things (IoT), biomonitoring, and bio- and nanosensors in both indoor and outdoor settings. This paper offers an overview of recent advancements in air quality monitoring and assessment methods. The review encompasses sample preparations for air pollutants, data analysis methodologies, and monitoring strategies. It also delves into the crucial role of microorganisms in air quality analysis. Additionally, the paper explores the applications of the Internet of Things (IoT) and biosensors in air quality monitoring and assessment, elucidating their roles in advancing these endeavors. The paper concludes by presenting insightful perspectives on the current state of air quality monitoring techniques and outlining future directions for research and development in this critical field.

Investigation of Viscoelastic-Plastic Properties of Fresh Cemented Gangue Fly Ash Backfill Slurries

In underground filling mining, freshly prepared cemented gangue-fly ash backfill (CGFB) slurries are typically piped into the gobs. The rheological properties of backfill slurry during pipeline transportation have a direct impact on the transportation characteristics, which in turn affect pipeline blockage and wear. In this paper, the rheological behavior and viscoelastic-plastic properties of CGFB during pipeline transportation are investigated. The effects of different solid content and cement content on resistivity were tested experimentally, and their viscoelasticity and plasticity were analyzed. The results show that with the increase in solid phase content and cement content, the viscosity, yield stress, and energy storage modulus of the materials showed an increasing trend. The viscosity and yield stress of the material both increased, reaching 32.77% and 51.22%, respectively. It was found by the dynamic shear test that in the low-strain region, the material showed a more significant elastic nature of the solid, while in the high-strain region, the viscosity of the material gradually increased. Cement has a substantially lower resistivity than fly ash and gangue, and with the increase in solid concentration, the resistivity of the material shows an increasing trend. With the increase in cement content, the resistivity generally shows a decreasing trend, but it should be noted that the resistivity change trend may tend to stabilize after the cement content exceeds 12%. The study’s findings can aid in understanding the rheological properties of CGFB and its viscoelastic-plastic behavior during the underground filling and conveying process, which can provide a reference basis for research and application in related fields.

Cross-Study of English Education and Cognitive Science: Language Processing and Learning Mechanisms

With the acceleration of globalization and the rapid development of information technology, English, as the main language of international communication, has increasingly prominent importance in education and learning. English education, as a discipline that studies the theory and practice of English education, has been exploring more effective teaching methods and learning strategies. At the same time, cognitive science, as a discipline that studies cognitive processes such as human thinking, learning, and memory, provides a profound theoretical foundation for understanding language processing and learning mechanisms. The cross-study of English education and cognitive science helps to build a more complete theoretical framework for language processing and learning mechanisms. By integrating the theoretical achievements of the two disciplines, we can more fully understand the cognitive processes and laws of English learning, revealing the intrinsic connection and essential characteristics of language processing and learning mechanisms. This can not only enrich and develop the theoretical systems of English education and cognitive science but also provide new ideas and perspectives for research in other related fields. In addition, with the continuous development of artificial intelligence and big data technology, research on language processing and learning mechanisms can also provide technical support and innovative ideas for fields such as natural language processing and intelligent education. Therefore, the cross-study of English education and cognitive science not only has far-reaching academic value but also has broad application prospects and social significance.

Case management interventions seeking to counter radicalisation to violence and related forms of violence: A systematic review.

Case management interventions seeking to counter radicalisation to violence and related forms of violence: A systematic review.

Intelligent device recognition of internet of things based on machine learning

With the rapid popularization and application of Internet of Things technology, smart devices have become an indispensable part of people's daily lives. Therefore, it is crucial to accurately identify these devices as their numbers continue to grow. The research aimed to introduce a lightweight method for identifying Internet of Things devices based on network flow characteristics and scheduling algorithms. This can improve device identification accuracy while maintaining high efficiency. The research constructed a comprehensive optimization algorithm selection framework to achieve performance optimization in different scenarios. This framework took into account many factors, including network traffic characteristics, device identification requirements, and system efficiency, to ensure flexible adaptation in different scenarios and optimize overall performance. Research results showed that the proposed system had an accuracy of 96.8 % at 1-minute intervals, which increased to 99.7 % at 10-minute intervals, and reached 99.9 % at both 30-minute and 60-minute intervals. In 100 experiments, the research method improved the accuracy by an average of 1.5 % compared with the baseline. In fingerprint recognition, the overall accuracy of the long short-term memory network exceeded 90 %, with an area under the curve of 0.99. Most devices had an accuracy of over 95 % in recognition, and the recall rate remained around 90 %, the effectiveness of the method proposed in the study was further verified. The method proposed in the study not only improved the accuracy and efficiency of device recognition, but also provided powerful solutions for the field of network security. This provides useful guidance for research and practical applications in related fields.

CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae

Radiomics features (RFs) studies have showed limitations in the reproducibility of RFs in different acquisition settings. To date, reproducibility studies using CT images mainly rely on phantoms, due to the harness of patient exposure to X-rays. The provided CadAIver dataset has the aims of evaluating how CT scanner parameters effect radiomics features on cadaveric donor. The dataset comprises 112 unique CT acquisitions of a cadaveric truck acquired on 3 different CT scanners varying KV, mA, field-of-view, and reconstruction kernel settings. Technical validation of the CadAIver dataset comprises a comprehensive univariate and multivariate GLM approach to assess stability of each RFs extracted from lumbar vertebrae. The complete dataset is publicly available to be applied for future research in the RFs field, and could foster the creation of a collaborative open CT image database to increase the sample size, the range of available scanners, and the available body districts.

Blockchain‐cloud integration: Comprehensive survey and open research issues

SummaryCloud computing has attracted great interest in various scientific and technical fields recently as one of the widely adopted networking technologies. Despite their many benefits and applications, it still faces many security and trust challenges, including managing and controlling services, privacy, data integrity in distributed databases, data backup, and synchronization. Moreover, due to its centralized architecture, and lack of transparency and traceability, the results of the trust assessment cannot be fully recognized by all users. However, creating a trust‐based transaction environment has become its key factor. Blockchain, with its nature of decentralization and security, can be leveraged to address these challenges and build a distributed and decentralized trust architecture, due to the underlying characteristics such as transparency, traceability, decentralization, security, immutability, and automation. This article makes a comprehensive study of how blockchain is applied to deliver security services in the cloud computing model, focusing on up‐to‐date approaches, opportunities, and future directions. This survey also discusses the benefits of the technical fusion of blockchain and cloud. It provides a classification of proposed systems based on privacy, data sharing, authentication, and access control, as well as auditing and data integrity. Finally, the main conclusions of this study will be the challenges and future directions to stimulate further research in this promising field.

Analysis of the WeWork Bankruptcy Case

This paper use case analysis and strategy analysis methods for the bankruptcy of Softbank for more than $13 billion US dollars in 2018, and in 2022 to analyze the acquisition process of WeWork acquisition, the impact of WeWork bankruptcy, and Softbank's countermeasures. The analysis of this paper shows that the business pre-bankruptcy status and key factors are the key to the WeWork bankruptcy, and the post-bankruptcy impact also needs to consider the association with the acquisition. Finally, the measures taken by WeWork for the company's bankruptcy restructuring were discussed, as well as the challenges and opportunities faced by Softbank after WeWork's bankruptcy. On the one hand, the slowdown in global economic growth and increased uncertainty have brought enormous pressure to Softbank. On the other hand, with the continuous advancement of technological innovation, Softbank Group can achieve sustained growth by investing in emerging industries. This paper provides theoretical and practical references for theoretical research and practice in related fields. This paper reminds investors to understand the company's financial situation and business model during the domestic acquisition process. Attention should also be paid to the risk management after the acquisition.

Research on Stock Price Prediction and Quantitative Stock Picking Strategy Based on Deep Learning

With the continuous development of the domestic stock market and the continuous improvement of the financial system system, and at the same time, the domestic stock market gradually rises in the financial system, based on the prediction research of the domestic stock market will become more and more important. In order to solve the problems of low precision and poor accuracy of short-term stock price prediction, this paper selects the bi-directional long- and short-term memory network of attention mechanism (WOA-BiLSTM-Attenion) model under the whale optimization algorithm for stock price prediction. The modeling of bi-directional long- and short-term memory network with attention mechanism can reduce the loss of historical information and increase the influence of important information. On this basis, Whale Optimization Algorithm (WOA) is then used for hyperparameter selection to reduce human interference. The experimental results show that compared with BP, LSTM, BiLSTM, BiLSTM-Attention, the WOA-BiLSTM-Attenion model has a better effect on stock closing price prediction, with a value of 13.9446, and the value of 0.9477, which has a higher accuracy, with a view to providing certain reference for the prediction research in other fields.

Ozone Profile Retrieval Algorithm Based on GEOS-Chem Model in the Middle and Upper Atmosphere

Ozone absorbs ultraviolet radiation, which has a significant impact on research in astrobiology and other fields in that investigate the middle and upper atmosphere. A retrieval algorithm for ozone profiles in the middle and upper atmosphere was developed using the spectral data from the TROPOspheric Monitoring Instrument (TROPOMI). A priori ozone profiles were constructed through the Goddard Earth Observing System-Chem (GEOS-Chem) model. These profiles were closer to the true atmosphere in the spatial and temporal dimensions when compared to the ozone climatology. The TpO3 ozone climatology was used as a reference to highlight the reliability of the a priori ozone profile from GEOS-Chem. The inversion results based on GEOS-Chem and TpO3 climatology were compared with ground-based ozone measurements and the satellite products of the Microwave Limb Sounder (MLS) and the Ozone Mapping and Profiles Suite_Limb Profile (OMPS_LP). The comparisons reveal that the correlation coefficient R values for the inversion results based on GEOS-Chem were greater than 0.90 at most altitudes, making them better than the values based on TpO3 climatology. The differences in subcolumn concentration between the GEOS-Chem inversion results and the ground-based measurements were smaller than those between TpO3 climatology results and the ground-based measurements. The relative differences between the inversion results based on the GEOS-Chem and the satellite products was generally smaller than those between the inversion results based on TpO3 climatology and the satellite products. The mean relative difference between the GEOS-Chem inversion results and MLS is −9.10%, and OMPS_LP is 1.46%, while those based on TpO3 climatology is −14.51% and −4.70% from 20 to 45 km These results imply that using a priori ozone profiles generated through GEOS-Chem leads to more accurate inversion results.

Nudges and Prompts Increase Engagement in Self-Guided Digital Health Treatment for Depression and Anxiety: Results From a 3-Arm Randomized Controlled Trial.

Accessible and effective approaches to mental health treatment are important because of common barriers such as cost, stigma, and provider shortage. The effectiveness of self-guided treatment is well established, and its use has intensified because of the COVID-19 pandemic. Engagement remains important as dose-response relationships have been observed. Platforms such as Facebook (Meta Platform, Inc), LinkedIn (Microsoft Corp), and X Corp (formerly known as Twitter, Inc) use principles of behavioral economics to increase engagement. We hypothesized that similar concepts would increase engagement in self-guided digital health. This 3-arm randomized controlled trial aimed to test whether members of 2 digital self-health courses for anxiety and depression would engage with behavioral nudges and prompts. Our primary hypothesis was that members would click on 2 features: tips and a to-do checklist. Our secondary hypothesis was that members would prefer to engage with directive tips in arm 2 versus social proof and present bias tips in arm 3. Our tertiary hypothesis was that rotating tips and a to-do checklist would increase completion rates. The results of this study will form a baseline for future artificial intelligence-directed research. Overall, 13,224 new members registered between November 2021 and May 2022 for Evolution Health's self-guided treatment courses for anxiety and depression. The control arm featured a member home page without nudges or prompts. Arm 2 featured a home page with a tip-of-the-day section. Arm 3 featured a home page with a tip-of-the-day section and a to-do checklist. The research protocol for this study was published in JMIR Research Protocols on August 15, 2022. Arm 3 had significantly younger members (F2,4564=40.97; P<.001) and significantly more female members (χ24=92.2; P<.001) than the other 2 arms. Control arm members (1788/13,224, 13.52%) completed an average of 1.5 course components. Arm 2 members (865/13,224, 6.54%) clicked on 5% of tips and completed an average of 1.8 course components. Arm 3 members (1914/13,224, 14.47%) clicked on 5% of tips, completed 2.7 of 8 to-do checklist items, and completed an average of 2.11 course components. Completion rates in arm 2 were greater than those in arm 1 (z score=3.37; P<.001), and completion rates in arm 3 were greater than those in arm 1 (z score=12.23; P<.001). Engagement in all 8 components in arm 3 was higher than that in arm 2 (z score=1.31; P<.001). Members engaged with behavioral nudges and prompts. The results of this study may be important because efficacy is related to increased engagement. Due to its novel approach, the outcomes of this study should be interpreted with caution and used as a guideline for future research in this nascent field. RR2-10.2196/37231.

Past, Present, and Future of New Applications in Utilization of Eddy Currents

Eddy currents are an electromagnetic phenomenon that represent an inexhaustible source of inspiration for technological innovations in the 21st century. Throughout history, these currents have been a subject of research and technological development in multiple fields. This article delves into the fascinating world of eddy currents, revealing their physical foundations and highlighting their impact on a wide range of applications, ranging from non-destructive evaluation of materials to levitation phenomena, as well as their influence on fields as diverse as medicine, the automotive industry, and aerospace. The nature of eddy currents has stimulated the imaginations of scientists and engineers, driving the creation of revolutionary technologies that are transforming our society. As we progress through this article, we will cover the main aspects of eddy currents, their practical applications, and challenges for future works.

A ternary gate-connected threshold switching thin-film transistor

Multi-valued logic has been a significant focus of research in various fields with the advancement of information technology. One approach to realizing ternary logic is integrating of a threshold switching (TS) device with a transistor, but this method often entails a complex fabrication process. This work suggests a ternary gate-connected threshold switching thin-film transistor (TS-TFT) by serially connecting the TS device with a bottom-gate thin-film transistor (TFT). The fabrication process is simplified with a structure that shares electrodes and insulators. Different threshold voltages from TS and TFT devices produce stable multiple states. The Pt/HfO2/TiN TS device has an electronic trapping/detrapping switching mechanism that exhibits low power consumption and high reliability. With the superior electrical performance of an amorphous indium gallium zinc oxide TFT, the TS-TFT has stable endurance. Furthermore, pulse switching and ternary inverter are demonstrated from the practical point of view.

Football Player Classification

Clustering is an essential tool in data mining research and applications. It is the subject of active research in many fields of study, such as computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning. Several clustering techniques have been proposed and implemented, and most of them successfully find excellent quality or optimal clustering results in the domains mentioned earlier. However, there has been a gradual shift in the choice of clustering methods among domain experts and practitioners alike, which is precipitated by the fact that most traditional clustering algorithms still depend on the number of clusters provided a priori. These conventional clustering algorithms cannot effectively handle real-world data clustering analysis problems where the number of clusters in data objects cannot be easily identified. Also, they cannot effectively manage problems where the optimal number of clusters for a high-dimensional dataset cannot be easily determined. Therefore, there is a need for improved, flexible, and efficient clustering techniques. Recently, a variety of efficient clustering algorithms have been proposed in the literature, and these algorithms produced good results when evaluated on real-world clustering problems. This study presents an up-to-date systematic and comprehensive review of traditional and state-of-the-art clustering techniques for different domains. This survey considers clustering from a more practical perspective. It shows the outstanding role of clustering in various disciplines, such as education, marketing, medicine, biology, and bioinformatics. It also discusses the application of clustering to different fields attracting intensive efforts among the scientific community, such as big data, artificial intelligence, and robotics. This survey paper will be beneficial for both practitioners and researchers. It will serve as a good reference point for researchers and practitioners to design improved and efficient state-of-the-art clustering algorithms.

Successfully Publishing With Undergraduate Coauthors in Psychology: Insights From Faculty With Top Track Records

Research in psychology and other fields has demonstrated the benefits of faculty-undergraduate collaborative research and shown that these research experiences can culminate in publication. To date, there has not yet been an in-depth investigation of the factors that enable faculty to successfully publish with undergraduate coauthors. In the present study, we used blended reflexive qualitative and quantitative methods to understand faculty perspectives on the topic by recruiting 38 faculty with extensive experience publishing with undergraduate coauthors from institutions in three categories: predominantly undergraduate, masters-granting, and doctoral-granting. These faculty responded to an open-ended survey regarding their experiences publishing with undergraduates. Although these faculty find great joy and meaning in publishing with undergraduates, they report facing obstacles such as a lack of student training, high rates of student turnover, and lack of time to dedicate to the process. Based on our results, we recommend that if institutions seek to increase their rates of faculty-student publication, they should dedicate resources, especially time, for student-faculty collaboration. In addition, departments should offer advanced training in scientific writing to facilitate student success in publication.

Unsteady Flows and Component Interaction in Turbomachinery

Unsteady component interaction represents a crucial topic in turbomachinery design and analysis. Combustor/turbine interaction is one of the most widely studied topics both using experimental and numerical methods due to the risk of failure of high-pressure turbine blades by unexpected deviation of hot flow trajectory and local heat transfer characteristics. Compressor/combustor interaction is also of interest since it has been demonstrated that, under certain conditions, a non-uniform flow field feeds the primary zone of the combustor where the high-pressure compressor blade passing frequency can be clearly individuated. At the integral scale, the relative motion between vanes and blades in compressor and turbine stages governs the aerothermal performance of the gas turbine, especially in the presence of shocks. At the inertial scale, high turbulence levels generated in the combustion chamber govern wall heat transfer in the high-pressure turbine stage, and wakes generated by low-pressure turbine vanes interact with separation bubbles at low-Reynolds conditions by suppressing them. The necessity to correctly analyze these phenomena obliges the scientific community, the industry, and public funding bodies to cooperate and continuously build new test rigs equipped with highly accurate instrumentation to account for real machine effects. In computational fluid dynamics, researchers developed fast and reliable methods to analyze unsteady blade-row interaction in the case of uneven blade count conditions as well as component interaction by using different closures for turbulence in each domain using high-performance computing. This research effort results in countless publications that contribute to unveiling the actual behavior of turbomachinery flow. However, the great number of publications also results in fragmented information that risks being useless in a practical situation. Therefore, it is useful to collect the most relevant outcomes and derive general conclusions that may help the design of next-gen turbomachines. In fact, the necessity to meet the emission limits defined by the Paris agreement in 2015 obliges the turbomachinery community to consider revolutionary cycles in which component interaction plays a crucial role. In the present paper, the authors try to summarize almost 40 years of experimental and numerical research in the component interaction field, aiming at both providing a comprehensive overview and defining the most relevant conclusions obtained in this demanding research field.

Knowledge Management

Research in the KM field has been a point of attraction for innovation and sustainability. The need for research and the effort by the researchers are important to be analysed to know the overall status of contributions and contributors. The purpose of this paper is to identify trends in Knowledge management research and forecast future trends through bibliometric analysis. The study also aims to identify the highest contribution of articles by the authors, the institutions, the journals, and the countries. Microsoft Excel and VOSViewer software were used for the analysis of the data extracted from the Scopus database for the period 2003–2022. It found Bontis. N. of Canada stood out as the highest contributing author in KM research; the Hong Kong Polytechnic University of China proved to be the top contributing institution in the field; the Journal of Knowledge Management ranks first amongst the most contributing journals in the field; and the United States was the highest contributing country. Furthermore, the study found four clusters based on the co-occurrence of keywords. “Artificial Intelligence,” “Big Data,” and “knowledge hiding” are the budding areas in the field.