Future Directions Research Articles

Developments of Fms-like Tyrosine Kinase 3 Inhibitors as Anticancer Agents for AML Treatment.

FMS-like tyrosine kinase 3 (FLT3) is a commonly mutated gene in acute myeloid leukemia. As a receptor tyrosine kinase (RTK), FLT3 plays a role in the proliferation and differentiation of hematopoietic stem cells. As the most frequent molecular alteration in AML, FLT3 has drawn the attention of many researchers, and a lot of small molecule inhibitors targeting FLT3 have been intensively investigated as potential drugs for AML therapy. In this paper, PubMed and SciFinder® were used as a tool; the publications about "FLT3 inhibitor" and "Acute myeloid leukemia" were surveyed from 2014 to the present with an exclusion of those published as patents. In this study, the structural characterization and biological activities of representative FLT3 inhibitors were summarized. The major challenges and future directions for further research are discussed. Recently, numerous FLT3 inhibitors have been discovered and employed in FLT3-mutated AML treatment. In order to overcome the drug resistance caused by FLT3 mutations, screening multitargets FLT3 inhibitors has become the main research direction. In addition, the emergence of irreversible FLT3 inhibitors also provides new ideas for discovering new FLT3 inhibitors.

The Seeking Proxies for Internal States (SPIS) Model of OCD - A Comprehensive Review of Current Findings and Implications for Future Directions.

The Seeking Proxies for Internal States (SPIS) model of obsessive-compulsive disorder (OCD) explains symptoms of OCD as stemming from attenuated access to internal states, which is compensated for by using proxies, which are indices of these states that are more discernible or less ambiguous. Internal states in the SPIS model are subjective states that are not accessible to others, encompassing physiological states, motivations, preferences, memories, and emotions. Compensatory proxies in OCD include fixed rules and rituals as well as seeking and relying on external information. In the present review, we outline the SPIS model and describe its basic tenets. We then use the SPIS conceptualization to explain two pivotal OCD-related phenomena - obsessive doubt and compulsive rituals. Next, we provide a detailed overview of current empirical evidence supporting the SPIS in several domains, including physiological states, emotions, sense of understanding, decision-making, and sense of agency. We conclude by discussing possible neural correlates of the difficulty in accessing internal states, focusing on the anterior insular cortex (AIC) and highlighting potential clinical implications of the model to the treatment of OCD.

Assessment of brain tumor detection techniques and recommendation of neural network.

Brain tumor classification is amongst the most complex and challenging jobs in the computer domain. The latest advances in brain tumor detection systems (BTDS) are presented as they can inspire new researchers to deliver new architectures for effective and efficient tumor detection. Here, the data of the multi-modal brain tumor segmentation task is employed, which has been registered, skull stripped, and histogram matching is conducted with the ferrous volume of high contrast. This research further configures a capsule network (CapsNet) for brain tumor classification. Results of the latest deep neural network (NN) architectures for tumor detection are compared and presented. The VGG16 and CapsNet architectures yield the highest f1-score and precision values, followed by VGG19. Overall, ResNet152, MobileNet, and MobileNetV2 give us the lowest f1-score. The VGG16 and CapsNet have produced outstanding results. However, VGG16 and VGG19 are more profound architecture, resulting in slower computation speed. The research then recommends the latest suitable NN for effective brain tumor detection. Finally, the work concludes with future directions and potential new architectures for tumor detection.

Open-Ended Online Learning for Autonomous Visual Perception.

The visual perception systems aim to autonomously collect consecutive visual data and perceive the relevant information online like human beings. In comparison with the classical static visual systems focusing on fixed tasks (e.g., face recognition for visual surveillance), the real-world visual systems (e.g., the robot visual system) often need to handle unpredicted tasks and dynamically changed environments, which need to imitate human-like intelligence with open-ended online learning ability. Therefore, we provide a comprehensive analysis of open-ended online learning problems for autonomous visual perception in this survey. Based on "what to online learn" among visual perception scenarios, we classify the open-ended online learning methods into five categories: instance incremental learning to handle data attributes changing, feature evolution learning for incremental and decremental features with the feature dimension changed dynamically, class incremental learning and task incremental learning aiming at online adding new coming classes/tasks, and parallel and distributed learning for large-scale data to reveal the computational and storage advantages. We discuss the characteristic of each method and introduce several representative works as well. Finally, we introduce some representative visual perception applications to show the enhanced performance when using various open-ended online learning models, followed by a discussion of several future directions.

Highlights from the Top 100 Most Influential Articles Regarding the Nuclear Receptor PPAR-γ: A Bibliometric Analysis.

PPAR-γ is one of three members of the PPAR group of the nuclear receptor superfamily and plays an important regulatory role as a ligand-dependent transcription factor. This study aimed to identify the top 100 most influential articles in the field of PPAR-γ. We hypothesized that a bibliometric and scientometric analysis of the PPAR-γ research field could render trends that provide researchers and funding agencies valuable insight into the history of the field, and potential future directions. A literature search of publications was carried out using the Web of Science (WOS) and Scopus database based on specific subject words on September 11, 2023. Articles were listed in descending order of the number of citations. Statistical analysis was performed on the data of the top 100 cited articles in terms of year of publication, journal, research direction, institution, author, and country. Meanwhile, co-authorship networks and co-citation networks were constructed by using VOSviewer software, and keywords were analyzed for co-occurrence. A total of 9,456 articles regarding PPAR-γ were identified and analyzed based on the WOS database, and the top 100 cited articles in the field of PPAR-γ were ranked by citation. The most cited article was published in 1998, with 2,571 citations and a density of 102.80 citations/ year. Of the 100 articles, Harvard University was the institution with the highest number of articles published. Spiegelman, B. M. was the author with the highest number of articles published. Using the VOSviewer software, we found that the most used keywords were geneexpression, activated receptor-gamma, and adipocyte differentiation. PPAR-γ, one of the most widely studied transcription factors, is an important drug target for many diseases. Therefore, screening for small molecule compounds targeting PPAR-γ remains of great value. The present study identified the top 100 most influential articles in the field of PPAR-γ, which help global researchers to better understand research perspectives and develop future research directions of PPAR-γ.

A pilot initiative to enhance quality improvement teaching with simulation.

Quality Improvement and Patient Safety (QIPS) is a recognised competency across residency programmes. Although a variety of teaching modalities exist, many do not represent the multifaceted clinical environment that trainees work in. Residents have reported challenges in linking QIPS classroom-based learning with their clinical duties. High-fidelity simulation has been used to bridge this gap within clinical skills teaching and therefore has potential to address this issue in QIPS learning. We developed and piloted four high-fidelity simulation scenarios with 15 surgical residents (Orthopaedics, General Surgery, Gynaecology and Neurosurgery). Each scenario contained elements of both latent and active safety errors. Residents were provided with a short pre-reading from an open-access resource on basic QIPS methodology and underwent a debriefing by a trained QIPS faculty. Residents were then tasked to apply their learning to their scenario to develop a QIPS-focused solution. Objective knowledge acquisition was assessed with the Quality Improvement Knowledge Assessment Tool-Revised (QIKAT-R) in conjunction with a survey based upon the Kirkpatrick Model of Learning. Overall, residents agreed that the simulation was helpful in learning QIPS methodology and agreed that they could perform fundamental QIPS tasks. The average QIKAT-R score demonstrated a trend towards improvement. High-fidelity simulation is a potential means to provide residents with hands-on experience in QIPS knowledge acquisition and application. Future directions should aim to compare the efficacy of simulation with other teaching modalities and evaluate the long-term impact of QIPS teaching on resident behaviours and motivation to take part in QIPS initiatives.

Wireless Perception of High-Speed Railway Communication: Challenges, Framework, and Future Directions

Wireless Perception of High-Speed Railway Communication: Challenges, Framework, and Future Directions

Continuous-Time Reinforcement Learning Control: A Review of Theoretical Results, Insights on Performance, and Needs for New Designs.

This exposition discusses continuous-time reinforcement learning (CT-RL) for the control of affine nonlinear systems. We review four seminal methods that are the centerpieces of the most recent results on CT-RL control. We survey the theoretical results of the four methods, highlighting their fundamental importance and successes by including discussions on problem formulation, key assumptions, algorithm procedures, and theoretical guarantees. Subsequently, we evaluate the performance of the control designs to provide analyses and insights on the feasibility of these design methods for applications from a control designer's point of view. Through systematic evaluations, we point out when theory diverges from practical controller synthesis. We, furthermore, introduce a new quantitative analytical framework to diagnose the observed discrepancies. Based on the analyses and the insights gained through quantitative evaluations, we point out potential future research directions to unleash the potential of CT-RL control algorithms in addressing the identified challenges.

What's the Situation With Intelligent Mesh Generation: A Survey and Perspectives.

Intelligent Mesh Generation (IMG) represents a novel and promising field of research, utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG has significantly broadened the adaptability and practicality of mesh generation techniques, delivering numerous breakthroughs and unveiling potential future pathways. However, a noticeable void exists in the contemporary literature concerning comprehensive surveys of IMG methods. This paper endeavors to fill this gap by providing a systematic and thorough survey of the current IMG landscape. With a focus on 113 preliminary IMG methods, we undertake a meticulous analysis from various angles, encompassing core algorithm techniques and their application scope, agent learning objectives, data types, targeted challenges, as well as advantages and limitations. We have curated and categorized the literature, proposing three unique taxonomies based on key techniques, output mesh unit elements, and relevant input data types. This paper also underscores several promising future research directions and challenges in IMG.

A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems.

With the widespread adoption of deep learning, reinforcement learning (RL) has experienced a dramatic increase in popularity, scaling to previously intractable problems, such as playing complex games from pixel observations, sustaining conversations with humans, and controlling robotic agents. However, there is still a wide range of domains inaccessible to RL due to the high cost and danger of interacting with the environment. Offline RL is a paradigm that learns exclusively from static datasets of previously collected interactions, making it feasible to extract policies from large and diverse training datasets. Effective offline RL algorithms have a much wider range of applications than online RL, being particularly appealing for real-world applications, such as education, healthcare, and robotics. In this work, we contribute with a unifying taxonomy to classify offline RL methods. Furthermore, we provide a comprehensive review of the latest algorithmic breakthroughs in the field using a unified notation as well as a review of existing benchmarks' properties and shortcomings. Additionally, we provide a figure that summarizes the performance of each method and class of methods on different dataset properties, equipping researchers with the tools to decide which type of algorithm is best suited for the problem at hand and identify which classes of algorithms look the most promising. Finally, we provide our perspective on open problems and propose future research directions for this rapidly growing field.

Advances in Research on Tool Wear Online Monitoring Method

Background: With the continued advancement of industrial internet technology, mechanical manufacturing is increasingly developing towards automation and intelligence. As a result, monitoring the manufacturing process has become an essential requirement for intelligent manufacturing. As one of the fundamental components of cutting processes, tools are inevitably subject to wear and damage during use. Therefore, tool wear monitoring plays a crucial role in modern manufacturing. Introduction: With the development of the manufacturing industry, the requirement for automation manufacturing is higher and higher. In the process of automatic processing, unmanned processing and adaptive processing, it is not only required to be able to know the accurate wear state of the tool in the process real-time but also required to change the milling parameters according to the wear state of the tool, in order to optimize the productivity and processing quality. The tool monitoring system can effectively reduce the operating cost of workshop production and improve the reliability of intelligent workshop and flexible production lines. Method: This article summarizes commonly used online monitoring methods mentioned by articles and patents, such as cutting force, vibration, acoustic emission, temperature, current, and power signals. Each monitoring method is analyzed in terms of its principles, advantages and disadvantages, signal acquisition equipment, and research status. The article also identifies current issues and future development directions. Results: As modern manufacturing technology continues to develop rapidly, unmanned factories have become a significant feature of the manufacturing industry. Consequently, the need for tool wear condition monitoring technology is becoming increasingly urgent. Although tool condition monitoring technology has made significant progress over the past twenty years and has been applied in actual production, several issues need to be addressed to make tool wear condition monitoring systems mo. Conclusion: This serves as a reference for theoretical research and application of online monitoring of tool wear in intelligent manufacturing systems.

FS/DS: A Theoretical Framework for the Dual Analysis of Feature Space and Data Space.

With the surge of data-driven analysis techniques, there is a rising demand for enhancing the exploration of large high-dimensional data by enabling interactions for the joint analysis of features (i.e., dimensions). Such a dual analysis of the feature space and data space is characterized by three components, 1) a view visualizing feature summaries, 2) a view that visualizes the data records, and 3) a bidirectional linking of both plots triggered by human interaction in one of both visualizations, e.g., Linking & Brushing. Dual analysis approaches span many domains, e.g., medicine, crime analysis, and biology. The proposed solutions encapsulate various techniques, such as feature selection or statistical analysis. However, each approach establishes a new definition of dual analysis. To address this gap, we systematically reviewed published dual analysis methods to investigate and formalize the key elements, such as the techniques used to visualize the feature space and data space, as well as the interaction between both spaces. From the information elicited during our review, we propose a unified theoretical framework for dual analysis, encompassing all existing approaches extending the field. We apply our proposed formalization describing the interactions between each component and relate them to the addressed tasks. Additionally, we categorize the existing approaches using our framework and derive future research directions to advance dual analysis by including state-of-the-art visual analysis techniques to improve data exploration.

Future Directions for Developing Non-dopaminergic Strategies for the Treatment of Parkinson's Disease.

The symptomatic treatment of Parkinson's disease (PD) has been dominated by the use of dopaminergic medication, but significant unmet need remains, much of which is related to non-motor symptoms and the involvement of non-dopaminergic transmitter systems. As such, little has changed in the past decades that has led to milestone advances in therapy and significantly improved treatment paradigms and patient outcomes, particularly in relation to symptoms unresponsive to levodopa. This review has looked at how pharmacological approaches to treatment are likely to develop in the near and distant future and will focus on two areas: 1) novel non-dopaminergic pharmacological strategies to control motor symptoms; and 2) novel non-dopaminergic approaches for the treatment of non-motor symptoms. The overall objective of this review is to use a 'crystal ball' approach to the future of drug discovery in PD and move away from the more traditional dopamine-based treatments. Here, we discuss promising non-dopaminergic and 'dirty drugs' that have the potential to become new key players in the field of Parkinson's disease treatment.

Artificial Intelligence in The Management of Neurodegenerative Disorders.

Neurodegenerative disorders are characterized by a gradual but irreversible loss of neurological function. The ability to detect and treat these conditions successfully is crucial for ensuring the best possible quality of life for people who suffer from them. The development of effective new methods for managing and treating neurodegenerative illnesses has been made possible by recent developments in computer technology. In this overview, we take a look at the prospects for applying computational approaches, such as drug design, AI, ML, and DL, to the treatment of neurodegenerative diseases. To review the current state of the field, this article discusses the potential of computational methods for early disease detection, quantifying disease progression, and understanding the underlying biological mechanisms of neurodegenerative diseases, as well as the challenges associated with these approaches and potential future directions. Moreover, it delves into the creation of computational models for the individualization of care for neurodegenerative diseases. The article concludes with suggestions for future studies and clinical applications, highlighting the advantages and disadvantages of using computational techniques in the treatment of neurodegenerative diseases.

3D-printed gradient scaffolds for osteochondral defects: Current status and perspectives.

Articular osteochondral defects are quite common in clinical practice, and tissue engineering techniques can offer a promising therapeutic option to address this issue.The articular osteochondral unit comprises hyaline cartilage, calcified cartilage zone (CCZ), and subchondral bone.As the interface layer of articular cartilage and bone, the CCZ plays an essentialpart in stress transmission and microenvironmental regulation.Osteochondral scaffolds with the interface structure for defect repair are the future direction of tissue engineering. Three-dimensional (3D) printing has the advantages of speed, precision, and personalized customization, which can satisfy the requirements of irregular geometry, differentiated composition, and multilayered structure of articular osteochondral scaffolds with boundary layer structure. This paper summarizes the anatomy, physiology, pathology, and restoration mechanisms of the articular osteochondral unit, and reviews the necessity for a boundary layer structure in osteochondral tissue engineering scaffolds and the strategy for constructing the scaffolds using 3D printing. In the future, we should not only strengthen the basic research on osteochondral structural units, but also actively explore the application of 3D printing technology in osteochondral tissue engineering. This will enable better functional and structural bionics of the scaffold, which ultimately improve the repair of osteochondral defects caused by various diseases.

Does non-financial information matter? Mapping and clustering literature on the value relevance of comprehensive disclosure

The paper aims to contribute to understanding the value relevance of compre-hensive disclosure, covering different types of information provided in addi-tion to traditional financial reporting. This research is based on an initial sam-ple of 361 documents available on the Scopus database as of December 2022. The study adopts a bibliometric analysis based on the similarity visualization technique on a final sample of 179 articles using the VOSViewer software, and it also performs a content analysis on a subsample of 49 articles. The study mapped and clustered research networks, providing a systematic literature overview. A steady increase of interest in the topic investigated has been observed in re-cent years, driven by the growing attention investors and other users deserve on the role of extended information in disclosing how the firm creates value. The network analysis reveals the existence of four main research streams re-ferring to the following topics: Intangibles, Intellectual Capital Disclosure (ICD) and corporate reputation; ESG and Sustainability disclosure; Environ-mental disclosure and capital market effects; Integrated Reporting and Value Creation. Results highlight prominent research fields and emerging trends, which calls for in-depth consideration of the value relevance of comprehen-sive disclosure within the academic and political space. This article adds to prior research on the topic investigated, combining quantitative and qualita-tive methods to systematize literature. In contrast to other review publications that rely primarily on specific reporting tools, our study refers to different types of reports, providing a systematic picture of the state of the art and fu-ture directions of literature on the value relevance of non-financial infor-mation.

Gossiping the (recipient's) day away: The impact of supervisor-directed gossip on recipients' rumination, sleep quality, vitality, and work outcomes.

Despite gossip research's predominant focus on gossipers and gossip targets, existing theoretical views and the limited yet important empirical studies converge to suggest that gossip benefits its recipients. Our research builds on conservation of resources theory to shift this consensus by examining the negative effects of supervisor-directed gossip on recipients. We theorize that hearing negative supervisor-directed gossip triggers both cognition- and affect-focused rumination, which consume resources, and we develop a research question around the moderating role of hearing positive supervisor-directed gossip. Furthermore, we propose that the aforementioned effects have cascading implications for recipients' work behaviors the following day. In a 15-day experience sampling investigation of 122 workers, we found that on days when employees hear negative gossip about the supervisor, they are more likely to engage in cognition- and affect-focused rumination, and hearing positive supervisor-directed gossip strengthens these positive relationships. In addition, we found that cognition- and affect-focused rumination lead to poor sleep quality and diminished next-morning vitality, which in turn results in reduced work engagement and supervisor-directed organizational citizenship behavior. We conclude by discussing the implications and future directions of our work. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Trustworthy Recommender Systems

Recommender systems (RSs) aim at helping users to effectively retrieve items of their interests from a large catalogue. For a quite long time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, and various types of biases. As a result, it has become clear that the focus on RS accuracy is too narrow, and the research must consider other important factors, particularly trustworthiness. A trustworthy recommender system (TRS) should not only be accurate but also transparent, unbiased, fair, and robust to noise and attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, there is a lack of a systematic overview and discussion of the literature in this novel and fast-developing field of TRSs. To this end, in this article, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs.

Functional parcellation of the cingulate gyrus by electrical cortical stimulation: a synthetic literature review and future directions

BackgroundThe cingulate gyrus (CG), a brain structure above the corpus callosum, is recognised as part of the limbic system and plays numerous vital roles. However, its full functional capacity is...

Review and Analysis of RGBT Single Object Tracking Methods: A Fusion Perspective

Visual tracking is a fundamental task in computer vision with significant practical applications in various domains, including surveillance, security, robotics, and human-computer interaction. However, it may face limitations in visible light data, such as low-light environments, occlusion, and camouflage, which can significantly reduce its accuracy. To cope with these challenges, researchers have explored the potential of combining the visible and infrared modalities to improve tracking performance. By leveraging the complementary strengths of visible and infrared data, RGB-infrared fusion tracking has emerged as a promising approach to address these limitations and improve tracking accuracy in challenging scenarios. In this article, we present a review on RGB-infrared fusion tracking. Specifically, we categorize existing RGBT tracking methods into four categories based on their underlying architectures, feature representations, and fusion strategies, namely feature decoupling based method, feature selecting based method, collaborative graph tracking method, and traditional fusion method. Furthermore, we provide a critical analysis of their strengths, limitations, representative methods, and future research directions. To further demonstrate the advantages and disadvantages of these methods, we present a review of publicly available RGBT tracking datasets and analyze the main results on public datasets. Moreover, we discuss some limitations in RGBT tracking at present and provide some opportunities and future directions for RGBT visual tracking, such as dataset diversity, unsupervised and weakly supervised applications. In conclusion, our survey aims to serve as a useful resource for researchers and practitioners interested in the emerging field of RGBT tracking, and to promote further progress and innovation in this area.