Future Research Directions Research Articles

A Survey on Deep Learning Event Extraction: Approaches and Applications.

Event extraction (EE) is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, EE based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This article fills the research gap by reviewing the state-of-the-art approaches, especially focusing on the general domain EE based on deep learning models. We introduce a new literature classification of current general domain EE research according to the task definition. Afterward, we summarize the paradigm and models of EE approaches, and then discuss each of them in detail. As an important aspect, we summarize the benchmarks that support tests of predictions and evaluation metrics. A comprehensive comparison among different approaches is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area.

Nominal-phrase premodifications by non-native English speakers: The case of attributive adjective word order of Saudi EFL students at tertiary level

English nominal premodifications such as adjective word order have piqued the interest of researchers in recent years due to their learning difficulty for all language learners regardless of their linguistic backgrounds. Non-native English speakers like EFL students may find this case challenging. In light of this, the current study sought to ascertain the difficulty level of Saudi EFL students in recognising the natural order of English attributive adjectives, taking into account the most challenging order sequence, gender, and language proficiency levels. To accomplish this main objective, a designed pronominal adjective test was developed and carried out on 139 Saudi undergraduate EFL students who were purposively recruited for this study. According to the findings, Saudi EFL students had varying degrees of difficulty recognising the natural English adjective word order. While 66.2% of students had moderate difficulty choosing the appropriate adjective sequence, only 15.1% and 18.7% of participants had low and high difficulty, respectively. The most difficult was nominal premodifications with four modifiers, followed by three and two modifiers. When there were only two modifiers, most participants correctly identified incorrect adjective word ordering and provided corrections. When more modifiers existed, the percentages decreased, and the difficulty level increased. Proficiency language level and gender statistically significant differences were also discovered. Although the former revealed no differences, the latter had differences in favour of males, implying that females performed better in the test than males. Based on these findings, limitations and future research directions were proposed.

Hierarchical Design of Tissue-Mimetic Fibrillar Hydrogel Scaffolds.

Most tissues of the human body present hierarchical fibrillar extracellular matrices (ECMs) that have a strong influence over their physicochemical properties and biological behavior. Of great interest is the introduction of this fibrillar structure to hydrogels, particularly due to the water-rich composition, cytocompatibility, and tunable properties of this class of biomaterials. Here, the main bottom-up fabrication strategies for the design and production of hierarchical biomimetic fibrillar hydrogels and their most representative applications in the fields of tissue engineering and regenerative medicine are reviewed. For example, the controlled assembly/arrangement of peptides, polymeric micelles, cellulose nanoparticles (NPs), and magnetically responsive nanostructures, among others, into fibrillar hydrogels is discussed, as well as their potential use as fibrillar-like hydrogels (e.g., those from cellulose NPs) with key biofunctionalities such as electrical conductivity or remote stimulation. Finally, the major remaining barriers to the clinical translation of fibrillar hydrogels and potential future directions of research in this field are discussed.

Exploring the Influence of Word Exposure via Facebook on Young EFL Learners’ Written Form of Word Knowledge

This quantitative study aimed to investigate the impact of word exposure via Facebook on thevocabulary knowledge of Thai EFL primary school learners. Additionally, it sought to examinedifferences in receptive and productive written forms of word knowledge between differenteducation levels. It explored the relationship between participants’ vocabulary size and wordknowledge. Forty students from grades four and six participated in the study, which involveddesigning and validating tests to measure receptive and productive written forms of wordknowledge. Descriptive and inferential statistics were employed to analyze quantitative data.The findings indicated significant improvements in students’ receptive and productivevocabulary knowledge, with noticeable development in their understanding of word spelling.Furthermore, grade six learners performed better than grade four learners on receptive andproductive written form measures. Correlational analysis revealed a relationship betweenvocabulary size and knowledge of written forms, indicating their interdependency. Overall, thisstudy suggests that exposure to words through Facebook positively influences vocabularyacquisition, underscoring the importance of word exposure experiences in learning vocabulary.Additionally, it highlights the predictive role of vocabulary size in the written form of wordknowledge. This study offers implications for practitioners and provides suggestions for futureresearch directions.

Contemporary Prevalence of Oral Clefts in the US: Geographic and Socioeconomic Considerations.

Background: Socio-economic status, living environments, and race have been implicated in the development of different congenital abnormalities. As orofacial clefting is the most common anomaly affecting the face, an understanding of its prevalence in the United States and its relationship with different determinants of health is paramount. Therefore, the purpose of this study is to determine the modern prevalence of oral-facial clefting in the United States and its association with different social determinants of health. Methods: Utilizing Epic Cosmos, data from approximately 180 US institutions were queried. Patients born between November 2012 and November 2022 were included. Eight orofacial clefting (OC) cohorts were identified. The Social Vulnerability Index (SVI) was used to assess social determinants of health. Results: Of the 15,697,366 patients identified, 31,216 were diagnosed with OC, resulting in a prevalence of 19.9 (95% CI: 19.7-20.1) per 10,000 live births. OC prevalence was highest among Asian (27.5 CI: 26.2-28.8) and Native American (32.8 CI: 30.4-35.2) patients and lowest among Black patients (12.96 CI: 12.5-13.4). Male and Hispanic patients exhibited higher OC prevalence than female and non-Hispanic patients. No significant differences were found among metropolitan (20.23/10,000), micropolitan (20.18/10,000), and rural populations (20.02/10,000). SVI data demonstrated that OC prevalence was positively associated with the percentage of the population below the poverty line and negatively associated with the proportion of minority language speakers. Conclusions: This study examined the largest US cohort of OC patients to date to define contemporary US prevalence, reporting a marginally higher rate than previous estimates. Multiple social determinants of health were found to be associated with OC prevalence, underscoring the importance of holistic prenatal care. These data may inform clinicians about screening and counseling of expectant families based on socio-economic factors and direct future research as it identifies potential risk factors and provides prevalence data, both of which are useful in addressing common questions related to screening and counseling.

Heart rate variability and psychological health: The key role of trait emotional awareness

Studies have shown that Trait Emotional Awareness (TEA) – the ability to recognize one's emotions – and Heart Rate Variability (HRV) are both negatively associated with psychological disorders. Although these studies imply that TEA is related to HRV and may explain the association between HRV and psychological disorders, there is limited research investigating this implication. Such investigation is essential to illuminate the psychophysiological processes linked to psychological disorders. The present study aims to investigate a) the association between TEA and HRV, b) the association between HRV and psychological disorders, and c) whether TEA explains the association between HRV and psychological disorders. A sample of 41 German students completed self-report questionnaires as indicators of psychological disorders, including the Hospital Anxiety and Depression Scale (HADS; Snaith & Zigmond, 1983) for anxiousness and depressiveness, as well as the somatization scale of the Hopkins Symptom Checklist (HSCL; Derogatis et al., 1976) for physical complaints. HRV was measured at baseline (resting HRV) and during exposure to a fear-provoking movie clip (reactive HRV). As hypothesized, a) TEA showed a positive association with reactive HRV, b) HRV showed negative associations with anxiousness and physical complaints, and c) TEA explained the relationships between reactive HRV and anxiousness, as well as physical complaints. Contrary to our hypothesis, we did not find any association between HRV and depressiveness. We discussed the contribution of TEA to psychophysiological health, limited generalizability of the current study, and direct future research to explore the underlying mechanisms linking TEA to health.

Financial Sentiment Analysis: Techniques and Applications

Financial Sentiment Analysis (FSA) is an important domain application of sentiment analysis that has gained increasing attention in the past decade. FSA research falls into two main streams. The first stream focuses on defining tasks and developing techniques for FSA, and its main objective is to improve the performances of various FSA tasks by advancing methods and using/curating human-annotated datasets. The second stream of research focuses on using financial sentiment, implicitly or explicitly, for downstream applications on financial markets, which has received more research efforts. The main objective is to discover appropriate market applications for existing techniques. More specifically, the application of FSA mainly includes hypothesis testing and predictive modeling in financial markets. This survey conducts a comprehensive review of FSA research in both the technique and application areas and proposes several frameworks to help understand the two areas’ interactive relationship. This article defines a clearer scope for FSA studies and conceptualizes the FSA-investor sentiment-market sentiment relationship. Major findings, challenges, and future research directions for both FSA techniques and applications have also been summarized and discussed.

IL-10 and IL-1β Serum Levels, Genetic Variants, and Metabolic Syndrome: Insights into Older Adults' Clinical Characteristics.

Populational aging is marked by chronic noncommunicable diseases, such as metabolic syndrome (MetS). IL-10 and IL-1β are pleiotropic cytokines with multiple biological effects linked to metabolic disorders. This cross-sectional study assessed 193 participants' IL-10 and IL-1β serum levels regarding their role in developing MetS, clinical characteristics, and their IL1B rs1143627 and IL10 rs1800890 variants' genotype frequencies in a population over 60. IL-10 levels correlated weakly with HDL levels and fat mass and inversely with triglycerides, glucose, glycated hemoglobin, and estimated average blood glucose levels. IL-10 levels were also indirectly influenced by the patient's T2DM duration, lean mass amount, and bone mineral content. Participants with altered HDL, elevated serum glucose, raised HbA1c levels, or those over 80 had reduced serum IL-10 levels compared to those with normal levels or other age groups, respectively. Women also had higher serum IL-10 levels than men. Dissimilarly, IL-1β levels correlated directly only with the number of total leukocytes and segmented neutrophils, showing only significant variations with self-reported alcohol consumption. Our study also found that those with the IL10 AA genotype (lower IL-10 levels) had a significantly higher risk of developing MetS. These findings may help direct future research and more targeted therapeutic approaches in older adults.

Feature selection for sentiment analysis using hybrid multiobjective evolutionary algorithm

As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model’s efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it’s crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach.

A Story Of Strength And Struggle: Exploring The Motivations And Hurdles Of Mompreneurs In Peshawar, Khyber Pakhtunkhwa

The mompreneurs of Peshawar, a bustling city in Khyber Pakhtunkhwa province, are the focus of this research. Bringing attention to these remarkable women in the hopes that we learn from their experiences helps us better grasp the balancing act of business and family life. Using Kimberlé Crenshaw's concept of intersectionality, we investigate how Peshawar's mopreneurs' experiences are shaped by their gender, parenting, marital status, and socioeconomic level. We relied on in-depth qualitative phenomenological interviews to get to the bottom of these women's real-life experiences. The following seven themes emerged from the investigation: navigating financial independence, balancing roles and identities, intersectional challenges, societal expectations and support, identity and empowerment, resilience and determination, and flexible work schedules. Despite the many challenges faced by mompreneurs in Peshawar, our research emphasizes the intricate nature of this entrepreneurial endeavor. However, we need more comprehensive theories of identity and motivation to help us make sense of the myriad factors that contribute to and hinder their success. Examining mompreneurship in the specific social and cultural context of Peshawar, Pakistan, this study provides valuable insights. It also highlights the importance of thoroughly looking into Pakistan and South Asia. Ultimately, we hope our findings will help direct future research on mompreneurship and fill in the gaps in the existing literature.

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.

Ethnobotanical study of Zhuang medicinal herbs of Ardisia: variety systematization, traditional uses, phytochemistry, pharmacology, clinical application, and toxicity.

This study aimed to systematize the Zhuang medicinal herbs of Ardisia (ZMHA) in China, to clarify the traditional use in Zhuang medicine and the dynamics of international research on phytochemistry, pharmacology, clinical application, and toxicity. There are 25 species of ZMHA, approximately 938 compounds from the different part, including triterpenoids, phenolics, volatile oils, etc. Pharmacological activity studies have also shown that this genus has anti-tumour, anti-inflammatory, anti-oxidant, anti-bacterial, anti-microbial, etc., and significant effects on respiratory, digestive, urinary, and musculoskeletal system diseases without toxic side effects. The Ardisia has a medicinal history of nearly a thousand years, mainly for treating diseases of the injuries, musculoskeletal, and symptomatic system in Zhuang medicine. Some plants, such as A. crenata, A. gigantifolia, and A. japonica, are also commonly used in folk Zhuang medicine formulas, to treat musculoskeletal, injury, respiratory, and urinate systems disease. These diseases are related to inflammation. These could provide a new direction for future new drug development research. Therefore, species identification and resource investigation should be strengthened, and conducted in vitro mechanism, in vivo pharmacology, clinical efficacy, and toxicology studies and establish a perfect quality standard system.