Future Research Directions Research Articles

Work for them or myself: a motivational approach to study the influences of supervisor bottom-line mentality on employee exhaustion

ABSTRACT Extending current bottom-line mentality research, this paper provides a new theoretical perspective for examining the how and when supervisor bottom-line mentality (i.e., supervisors who solely focus on bottom-line attainments at the expense of other priorities) is related to employee exhaustion. Drawing on self-determination theory, it is proposed that supervisor bottom-line mentality is positively related to external regulation and negatively related to intrinsic motivation, both of which can further predict employee exhaustion. Leader-member exchange is also proposed as a boundary condition that prevents the negative relationship between supervisor bottom-line mentality and intrinsic motivation but enhances the positive relationship with external regulation. Data collected at three time points from 251 employees in China provide support for most hypotheses, except the relationship between supervisor bottom-line mentality and intrinsic motivation. The proposed negative relationship was only significant after including control variables. Theoretical and practical implications are discussed and directions for future research are provided.

Measuring Linguistic Growth in Sentence-Level Writing Curriculum-Based Measures: Exploring Complementary Scoring Methods.

Picture-word writing curriculum-based measures (PW CBM-Ws) are technically sound, formative measures of descriptive, sentence-level writing but cannot estimate underlying linguistic skills. The purpose of this exploratory alternative scoring investigation was to apply metrics from language sample analysis (LSA) to PW CBM-Ws as a complementary measure of underlying language skills in beginning writers' sentence-level writing. LSA metrics were applied to 104 typically developing first through third graders' PW CBM-W samples across fall and spring semesters. Factorial analyses of variance with post hoc Bonferroni pairwise comparisons were applied after obtaining alternate-form reliability and criterion-related validity estimates. Analyses revealed reliable discrimination between grades and significant growth between fall and spring semesters for three LSA metrics: mean length of T-unit in words, mean length of T-unit in morphemes, and number of different words. While mean length of T-unit in words and morphemes demonstrated evidence of discrimination and growth in first grade only, number of different words showed evidence of reliable discrimination and growth in first and third grades. Mean length of T-unit in words, mean length of T-unit in morphemes, and number of different words showed evidence of adequate criterion-related validity, discrimination among grades, and sensitivity to growth when calculated using PW CBM-W samples to gauge underlying linguistic skills in first- and third-grade students. Implications and future directions for research are discussed. https://doi.org/10.23641/asha.25050290.

Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges

Recent years have witnessed a substantial increase in the use of deep learning to solve various natural language processing (NLP) problems. Early deep learning models were constrained by their sequential or unidirectional nature, such that they struggled to capture the contextual relationships across text inputs. The introduction of bidirectional encoder representations from transformers (BERT) leads to a robust encoder for the transformer model that can understand the broader context and deliver state-of-the-art performance across various NLP tasks. This has inspired researchers and practitioners to apply BERT to practical problems, such as information retrieval (IR). A survey that focuses on a comprehensive analysis of prevalent approaches that apply pretrained transformer encoders like BERT to IR can thus be useful for academia and the industry. In light of this, we revisit a variety of BERT-based methods in this survey, cover a wide range of techniques of IR, and group them into six high-level categories: (i) handling long documents, (ii) integrating semantic information, (iii) balancing effectiveness and efficiency, (iv) predicting the weights of terms, (v) query expansion, and (vi) document expansion. We also provide links to resources, including datasets and toolkits, for BERT-based IR systems. Additionally, we highlight the advantages of employing encoder-based BERT models in contrast to recent large language models like ChatGPT, which are decoder-based and demand extensive computational resources. Finally, we summarize the comprehensive outcomes of the survey and suggest directions for future research in the area.

A Survey of Algorithmic Methods for Competency Self-Assessments in Human-Autonomy Teaming

Humans working with autonomous artificially intelligent systems may not be experts in the inner workings of their machine teammates, but need to understand when to employ, trust, and rely on the system. A critical challenge is to develop machine agents with the capacity to understand their own capabilities and limitations, and the ability to communicate this information to human partners. Self-assessment is an emerging field that tackles this challenge through the development of algorithms that enable autonomous agents to understand and communicate their competency. These methods can engender appropriate trust and align human expectations with autonomous assistant abilities. However, current research in self-assessment is dispersed across many fields, including artificial intelligence, robotics, and human factors. This survey connects work from these disparate areas and reviews state-of-the-art methods for algorithmic self-assessments that enable autonomous agents to estimate, understand, and communicate valuable information pertaining to their competency, with focus on methods that can improve interactions within human-machine teams. To better understand the landscape of self-assessment approaches, we present a framework for categorizing work in self-assessment based on underlying algorithm type: test-based , learning-based , or knowledge-based . We synthesize common features across these approaches and discuss relevant future directions for research in this emerging space.

Storytelling as Pedagogical Practice in Support of Transcendence and Well-Being

The purpose of this article is to explore the potential of storytelling within business ethics education for facilitating transcendence and well-being. In doing so, the article focuses on five areas. First, a brief introduction to workplace spirituality and its association with well-being is provided. Second, consideration is given to the nature of storytelling and its benefits for relational-spiritual teaching. Third, the ways that storytelling (and specific examples of stories) can invite and enhance three interrelated dimensions of transcendent connection (Miller, 2015; Reed, 2018) of relevance in business ethics are described. These include the transpersonal, the interpersonal (including interpersonal transcendence across difference) and the intrapersonal. Within this discussion, the ways that each of these dimensions can contribute to well-being are identified. Fourth, a relational-spiritual, transformative learning framework within which storytelling can be practiced is provided. Fifth, directions for future research are discussed.

Patient attachment and reflective functioning as predictors for therapist in-session feelings.

Therapists' in-session feelings in psychotherapy can be seen as indications of the development of the therapeutic relationship and the therapeutic process. To manage them appropriately, it is important to know to what extent they may be influenced by patients' pretreatment characteristics. This study aims to improve the understanding of therapists' emotional reactions in the psychotherapeutic setting by investigating if patients' pretreatment mentalization ability and attachment style predicted therapist in-session feelings. In a sample of 87 therapy dyads treated with interpersonal psychotherapy and cognitive behavioral therapy for depression, patient attachment was measured using self-reported Experiences in Close Relationships (ECR) and mentalization using Reflective Functioning (RF). ECR and RF were hypothesized to predict therapist feelings measured by the Feeling Word Checklist-24 at different treatment phases over the full course of treatment. Treatment method, patient age, gender, and pretreatment depression were evaluated as potential confounders. Multilevel modeling was used to analyze the data. Lower RF in patients predicted more negative therapist feelings in the mid- to late-treatment phases and less positive feelings in the late-treatment phase. Self-reported attachment anxiety or avoidance did not predict therapist feelings. Findings indicate that patients' ability to mentalize is important to consider when conducting psychotherapy, as it can influence therapists' feelings in the therapeutic process. Limitations of the present study's approach are discussed, and directions for future research are considered. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Delving Into the Devils of Bird's-Eye-View Perception: A Review, Evaluation and Recipe.

Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry and academia. Conventional approaches for most autonomous driving algorithms perform detection, segmentation, tracking, etc., in a front or perspective view. As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance. BEV perception inherits several advantages, as representing surrounding scenes in BEV is intuitive and fusion-friendly; and representing objects in BEV is most desirable for subsequent modules as in planning and/or control. The core problems for BEV perception lie in (a) how to reconstruct the lost 3D information via view transformation from perspective view to BEV; (b) how to acquire ground truth annotations in BEV grid; (c) how to formulate the pipeline to incorporate features from different sources and views; and (d) how to adapt and generalize algorithms as sensor configurations vary across different scenarios. In this survey, we review the most recent works on BEV perception and provide an in-depth analysis of different solutions. Moreover, several systematic designs of BEV approach from the industry are depicted as well. Furthermore, we introduce a full suite of practical guidebook to improve the performance of BEV perception tasks, including camera, LiDAR and fusion inputs. At last, we point out the future research directions in this area. We hope this report will shed some light on the community and encourage more research effort on BEV perception.

UAV-Assisted IoT Applications, Cybersecurity Threats, AI-Enabled Solutions, Open Challenges With Future Research Directions

UAV-Assisted IoT Applications, Cybersecurity Threats, AI-Enabled Solutions, Open Challenges With Future Research Directions

Global research on sinonasal inverted papilloma over the past two decades: a bibliometric analysis.

This study aimed to investigate the global research status, hot topics, and prospects in the field of sinonasal inverted papilloma (SNIP) through bibliometric analysis. The literature on SNIP was retrieved and downloaded from the Web of Science Core Collection from 2002 to 2021. The bibliometric and visualisation networks of SNIP were constructed using VOSviewer 1.6.18, CiteSpace 6.1. R2, and a bibliometric online analysis platform. A total of 560 original articles about SNIP research were included, involving 2,457 authors from 610 institutions in 45 countries. The number of SNIP publications showed an overall rising trend, with an average annual output of 28 articles and almost 3 times as many articles published in 2020 as in 2002. The analysis of keyword burst detection indicated that EGFR mutation, malignant transformation and infection are emerging research hotspots. Moreover, EGFR mutation, KRAS mutation, malignant tumour, metallothionein 2a gene, pre-operative diagnosis, HPV-negative tumour, and expression were among the 11 key clusters of co-cited references. This study provided a comprehensive, systematic, and objective analysis and visualised knowledge map of SNIP over the past 2 decades. In particular, current hotspots and prospective trends in the field of SNIP have been identified. These results highlight the future direction of SNIP research for rhinologists.

A State-of-the-Art Review on Direct Welding of Polymer to Metal for Structural Applications: Part 1 — Promising Processes

Structural lightweighting through the effective use of multiple materials has received increasing attention for fulfilling today’s demands for environmental sustainability in transportation systems. Direct dissimilar material joining methods (versus, e.g., traditional adhesive bonding or mechanical fastening) have become increasingly desirable since they offer process simplicity, production efficiency, and hermetic sealing, among others. In this two-part article, we provide a critical assessment of the state-of-the-art research and promising direct dissimilar material joining techniques reported over the last decades, with a particular emphasis on their potential for structural applications in Part I. As such, recent advances in advanced joint design and modeling methods for enabling optimum joint design for jointability and joint performance are presented along with some detailed examples for demonstrating their potential impacts on industrial applications in Part II. Finally, recommendations on future research and development directions are outlined for supporting the industry’s drive towards multi-material lightweighting.

Automatic Context-Aware Inference of Engagement in HMI: A Survey

Engagement is the process by which participants establish, maintain, and end their perceived connection. Automatic engagement inference is one of the tasks required to develop successful human-centered HMI applications. Engagement is a multi-faceted multimodal construct requiring high accuracy in interpretating contextual, verbal and non-verbal cues, making the development of an intelligent automated engagement inference system challenging. Existing surveys concentrate on specific application settings, and a comprehensive survey covering the different engagement facets, definition and inference across various contexts is lacking. Moreover, despite the importance of context-aware modeling, the literature lacks a systematic context-aware overview on the topic. This paper presents a comprehensive survey on previous work in engagement for HMI, entailing interdisciplinary definition, engagement components, publicly available datasets, ground truth assessment, and commonly used features and methods, serving as a guide for the development of future HMI interfaces with reliable context-aware engagement inference capability. An in-depth review across embodied and disembodied interaction modes, and an emphasis on the interaction context of which engagement is studied sets apart this survey from existing ones. Our findings suggest four important directions for future research: (1) context-aware computational modeling, (2) temporal dynamics, (3) personalised computing, and (4) bias and fairness of engagement inference systems.

Task-Efficiency Oriented V2X Communications: Digital Twin Meets Mobile Edge Computing

Digital Twin (DT) has emerged as an enabling technology for the sixth generation (6G) vehicle-to-everything (V2X) communications. However, there are two crucial issues on leveraging DT for 6G V2X communications. First, what kind of DT capabilities can be combined with the 6G V2X networks? Second, how to transform the DT capabilities into the practical V2X network performance gain? Motivated to solve these problems, this article investigates the DT capabilities under a DT and mobile edge computing empowered 6G V2X network architecture. Specifically, three DT capabilities are presented: (1) Strengthening the human-machine interaction via driving behavior analysis; (2) Improving the traffic safety via knowledge-based vehicle fault diagnosis; (3) Analyzing the spatial-temporal traffic characteristics via data aggregation. Furthermore, we investigate two case studies for illustrating how the DT capabilities can be utilized to perform the task-efficiency oriented V2X network scheduling. In the first case study, the driver behavior analysis result is combined with the V2X channel scheduling strategy. In the second case study, a deep reinforcement learning based vehicle merging decision is devised in the DT domain. Then, a coalition based V2X channel scheduling strategy is proposed to help accomplish the vehicle merging decision task. Finally, we evaluate the performance of our proposed task-efficiency oriented V2X channel scheduling schemes and highlight the future research directions.

A Comprehensive Survey on Community Detection With Deep Learning.

Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis. However, beyond the classic spectral clustering and statistical inference methods, there have been significant developments with deep learning techniques for community detection in recent years--particularly when it comes to handling high-dimensional network data. Hence, a comprehensive review of the latest progress in community detection through deep learning is timely. To frame the survey, we have devised a new taxonomy covering different state-of-the-art methods, including deep learning models based on deep neural networks (DNNs), deep nonnegative matrix factorization, and deep sparse filtering. The main category, i.e., DNNs, is further divided into convolutional networks, graph attention networks, generative adversarial networks, and autoencoders. The popular benchmark datasets, evaluation metrics, and open-source implementations to address experimentation settings are also summarized. This is followed by a discussion on the practical applications of community detection in various domains. The survey concludes with suggestions of challenging topics that would make for fruitful future research directions in this fast-growing deep learning field.

Systematic literature review on the holistic integration of e-learning in universities: Policy, human, financial, and technical perspectives

This systematic literature review (SLR) explores the integration of e-learning in universities, emphasizing a comprehensive approach that intertwines various mainstream perspectives. Despite numerous studies on e-learning implementation evaluation, few have holistically considered financial, human, technical, and policy factors. This review used PRISMA guidelines and sources from Scopus, Google Scholar, ERIC, SAGE, and ProQuest. Of 26 analyzed studies, seven core themes emerged: Policy, financial, technical, human, institutional factors, others, and an integrated perspective, further distilled into 13 sub-themes. Findings highlight the importance of an integrative framework for evaluating e-learning, underscoring the interplay between macro and institutional policies. Additionally, the authors recommend cross-national comparisons and data synthesis from stakeholders, including students, educators and directors, to fully grasp e-learning implementation dynamics. Distinctively, it adopts an integrated perspective, filling the research gap by emphasizing overlooked financial considerations and presenting a comprehensive view through an SLR. By drawing insights from human, finance, technical, and policy perspective, the study provides a multidimensional lens on e-learning. This forward-looking approach not only captures the current state of e-learning integration but also charts future research directions, establishing its originality and significance in higher education.

Cardiotoxicity of Doxorubicin in Acute Leukemia

Abstract: Doxorubicin, an anthracycline chemotherapeutic agent, is widely utilized in the treatment of acute leukemia due to its potent anti-neoplastic properties. However, its clinical utility is limited by the occurrence of dose-dependent cardiotoxicity, which may manifest as cardiomyopathy, heart failure, or arrhythmias. This review provides an overview of the mechanisms underlying doxorubicin-induced cardiotoxicity, including oxidative stress, mitochondrial dysfunction, and DNA damage. Additionally, it discusses risk factors predisposing patients to cardiotoxicity, such as cumulative dose, age, pre-existing cardiac conditions, and concomitant use of other cardiotoxic agents. Furthermore, this review highlights various strategies for the prevention and management of doxorubicin-induced cardiotoxicity, including the use of cardioprotective agents, cardiac monitoring protocols, and lifestyle modifications. Finally, future directions for research aimed at minimizing cardiotoxicity while preserving the antineoplastic efficacy of doxorubicin in acute leukemia treatment are discussed.

A Review of Brain Tumor Detection Techniques Using YOLOv8

Abstract: Brain tumors, whether benign or malignant, pose significant risks, especially in children and young adults, impacting both life expectancy and quality of life. This review addresses the critical importance of early detection and treatment of brain tumors. With a focus on artificial intelligence, particularly object detection using the YOLOv8 model, the paper explores the potential for accurate and efficient brain tumor detection from magnetic resonance images (MRI). The YOLOv8 model is known for its real-time performance, efficiency, and high accuracy, making it a promising tool in the field of medical image analysis. The paper presents a method for brain cancer detection and localization, discusses experimental results, reviews the state-of-theart literature, and outlines future research directions.[1-22]

Deciphering adolescent emptiness: A novel interpretation of other-contribution theory and intervention strategies

Deciphering adolescent emptiness: A novel interpretation of other-contribution theory and intervention strategies

Blockchain adoption in supply chains: implications for sustainability

This study aims to systematically review the current academic literature on blockchain and supply chain management (SCM) to explore the antecedents and sustainable outcomes of blockchain adoption and identify factors that influence blockchain adoption at different stages of implementation in supply chains (SC). This review follows the 6 steps and 14 decisions of conducting a systematic literature review (SLR) to comprehensively review 69 papers published in Academic Journal Guide (AJG) 3 and above journals between 2008 and 2023. Based on the content analysis of the selected papers, this study identifies antecedents and outcomes for blockchain adoption. This study further identifies influential factors in each blockchain implementation stage. A conceptual framework is developed to facilitate the conceptual development of blockchain adoption for sustainability in SCs. This study extends the prior studies of blockchain adoption in SCM with the consideration of how blockchain affects the SCM from the aspects of economic, social, and environmental. This review identifies four major themes in the topic of blockchain implementation and SCM, thus assisting researchers in avoiding focusing on the investigation of mature and saturated topics. The research gaps are identified, and future research directions are recommended for scholars, thereby enabling the comprehensive development of blockchain adoption in SCs.

Practical Implication of Forensic Accounting, Insight from Academicians and Practitioners: A Qualitative Perspective

The primary goal of this research is to assess the importance and value of forensic accounting in Pakistan's present accounting curricula. It describes the many theoretical and professional implications of forensic accounting, along with their distinctions. This study employs a qualitative research methodology, utilizing 12 semi-structured interviews with academicians and practitioners to further examine and delve into the subject matter. The fundamental procedures for analyzing qualitative data include coding, transcribing the information, grouping the code into more general categories and themes, and interpreting the outcomes. NVIVO software is employed for data transcription. To examine if forensic accounting is required and important, the study identifies 12 semi-structured interviews for academics and practitioners. Future research directions are explored along with the study's limitations and policy implications. Such research has not been carried out in Pakistan in recent years. This study, which investigates forensic accounting in detail, is significant for Pakistani professionals.

LITERATURE REVIEW: BASIC STATISTICS IN EDUCATIONAL RESEARCH

This paper provides a comprehensive exploration of the role and significance of basic statistics in educational research. Drawing upon historical foundations and theoretical frameworks, the study elucidates the evolution of statistical methodologies and their application in understanding complex educational phenomena. Key pioneers such as Francis Galton and Karl Pearson are highlighted for their foundational contributions, laying the groundwork for statistical analysis in education. The paper examines various statistical techniques, including descriptive and inferential statistics, correlation analysis, regression analysis, and analysis of variance (ANOVA), showcasing their relevance in analyzing diverse aspects of teaching, learning, and educational outcomes. Moreover, the integration of statistical methodologies into educational research is examined across domains such as educational management, policy-making, and teaching practice, emphasizing their role in informed decision-making, resource allocation, and policy formulation. Despite the benefits, challenges such as sample bias and misapplication of statistical tests are acknowledged, underscoring the importance of methodological rigor and statistical literacy among researchers and practitioners. Looking ahead, the paper discusses future directions in educational research, including the integration of advanced statistical techniques and efforts to promote statistical literacy. Overall, this paper offers a comprehensive framework for understanding the complexities of basic statistics in educational research, highlighting its transformative potential in driving positive change and innovation within the education sector.