Recent Research Trends Research Articles

Multi-functionality in de-doped poly 3-methylthiophene memristor device

Multi-functional devices are the recent trends in research, as they are cost-effective and reduce e-waste. In this study, detailed current-voltage measurement is performed on electro-polymerized Poly 3-methylthiophene (P3MT) thin film sandwiched between stainless steel (SS) and silver (Ag) electrodes, and the fabricated two-terminal device consisting of geometry SS/P3MT/Ag. Systematic and comparative measurements on partially doped P3MT devices with three dopants (TBAPF6, LiClO4 & TBABF4) helped in realizing multi-functionality. In all the devices the following multifunctional features are demonstrated: negative differential resistance (NDR), sweep voltage-controlled resistive switching, analog memory, and synaptic plasticity. Charge transport in all the devices is modeled based on charge accumulation, trapping, and de-trapping of injected carriers.

Neoliberal Feminism and Political Leadership: The Representation of Giorgia Meloni and Elly Schlein in Popular Culture

Building on a long tradition of studies on the intertwining of politics and popular culture, the article examines the representation of the two Italian political leaders of the moment, Giorgia Meloni and Elly Schlein, offered by women’s magazines. The study focuses in particular on two interrelated aspects: on the one hand, the extent to which the discursive strategies to talk about the leaders, their personal lives, careers and ideas contribute to strengthen the contemporary hegemony on feminism; on the other hand, the discursive strategies used to represent the leaders through a frame of competition typical of the neoliberalism. Indeed, we refer to the most recent trends in feminism research, contested among post-feminism, neoliberal feminism and celebrity feminism. Within this framework, we examined whether and how female political leaders can become icons of life, style and power in a way that is functional to the neoliberal narrative. The critical discourse analysis conducted on eight Italian women’s magazines over the course of a year makes it possible to highlight some results that contribute to the reflection on neoliberal feminism and female leadership. In particular, leaders are presented through argumentative strategies of genealogism, exceptionalism and compensation: the ways in which magazines justify their position of power refer to family relationships and character qualities, emphasizing the characteristics closest to the neoliberal model and overshadowing all others, including political ideas. Rather, leaders are deradicalized and depoliticized to favour instead a reassuring narrative in which women have broken the glass ceiling. In that way, women’s magazines renounce representing women leaders for their ideas and ideologies, and instead focus on their unique mark of difference: their gender.

Diamond-Like Carbon (DLC) Coatings : Classification, Properties, and Applications

This paper presents the most recent and important research trends on the friction and wear properties of diamond like carbon (DLC) coatings deposited on different types of materials. For this the invention, methods, techniques, and design related to this area are discussed. The different trends of tribological properties of materials under different operating conditions are observed. In this article, an attempt is made to provide an opportunity for the future researchers to identify the recent trend of these areas. It is expected that the applications of these results will contribute to the improvement of different concerned mechanical processes. This review article also includes some patents relevant to the method of producing a DLC film.

A Survey on Conversational AI Question answering System for Low Resource Language

Question-and-answer (QA) systems allow users to pose questions about the information accessible in a variety of formats, such as structured and unstructured information in natural languages. It contributes significantly to conversational artificial intelligence (AI), which has led to the creation of a novel research field on conversational question answering (CQA), in which a system is required to understand the provided context before engaging in multi-turn QA to satisfy a user's informational needs. While the majority of currently done research focuses on single-turn QA, pre-trained language models and the availability of large multi-turn QA datasets have recently made multi-turn QA more significant. This study aims to give a thorough analysis of the most recent CQA research trends, mostly based on publications that have been reviewed in recent years. Our data indicates a shift in the industry's preference for multi-turn QA over single-turn QA, which has numerous advantages for conversational AI. In order to create a solid basis for the field of CQA, this survey aims to serve as the pinnacle for the research community.

Gratitude research: Review and future agenda using bibliometric analysis of the studies published in the last 20 years

AbstractAlthough there is an exponential rise in the literature on gratitude research, there is no attempt to systematically identify the historical development and recent trends in gratitude research. Gratitude has been defined diversely as an emotion, an attitude, a moral virtue, a habit, a personality trait or a coping response. As an emotion, people experience gratitude when they receive something valuable from someone else. As a trait, it is defined as a tendency to recognise and respond to goodness in others. The present study uses bibliometric analysis to review gratitude publications from the last 20 years, that is from 2001 to September 2023. WoS (Web of Science) identified 2982 publications referring to gratitude in title, abstract or keywords. It comprised 2860 articles and 122 review papers. The extracted data were analysed and visualised with the help of two analytical tools, that is the WoS analysis and VOSviewer (version 1.6.16). This study elicits the number of publications and citations from 2001 to September 2023, and most cited publications, and the most influential authors, articles, publishers, universities and countries as performance analysis. Furthermore, collaboration among countries, keyword co‐occurrence and recent trend analysis are employed through science mapping. The results reveal that the major research areas of gratitude research are psychology, social sciences, business economics, psychiatry and public environmental and occupational health. The keywords co‐occurrence suggest five major research clusters: evolutionary studies of gratitude, gratitude and health, gratitude and positive psychology, gratitude among children and adolescent and mediating and moderating studies of gratitude. Also, the analysis of recent 5‐year studies highlights a clear trend of scientific explorations of gratitude against earlier trends of articles on philosophical and religious connotations of gratitude.

Problem solving in mathematics education: tracing its foundations and current research-practice trends

In tracing recent research trends and directions in mathematical problem-solving, it is argued that advances in mathematics practices occur and take place around two intertwined activities, mathematics problem formulation and ways to approach and solve those problems. In this context, a problematizing principle emerges as central activity to organize mathematics curriculum proposals and ways to structure problem-solving learning environments. Subjects’ use of concrete, abstract, symbolic, or digital tools not only influences the ways to pose and pursue mathematical problems; but also shapes the type of representation, exploration, and reasoning they engage to work and solve problems. Problem-solving foundations that privilege learners’ development of habits of mathematical practices that involve an inquiry method to formulate conjectures, to look for different ways to represent and approach problems, and to support and communicate results shed light on directions of current research trends and the relevance of rethinking curriculum proposals and extending problem-solving environments in terms of teachers/students’ consistent use of digital tools and online developments.

Global research trends in Total Body Irradiation: a bibliometric analysis.

This manuscript presents a bibliometric and visualization analysis of Total Body Irradiation (TBI) research, aiming to elucidate trends, gaps, and future directions in the field. This study aims to provide a comprehensive overview of the global research landscape of TBI, highlighting its key contributions, evolving trends, and potential areas for future exploration. The data for this study were extracted from the Web of Science Core Collection (WoSCC), encompassing articles published up to May 2023. The analysis included original studies, abstracts, and review articles focusing on TBI-related research. Bibliometric indicators such as total publications (TP), total citations (TC), and citations per publication (C/P) were utilized to assess the research output and impact. Visualization tools such as VOS Viewer were employed for thematic mapping and to illustrate international collaboration networks. The analysis revealed a substantial body of literature, with 7,315 articles published by 2,650 institutions involving, 13,979 authors. Full-length articles were predominant, highlighting their central role in the dissemination of TBI research. The authorship pattern indicated a diverse range of scholarly influences, with both established and emerging researchers contributing significantly. The USA led in global contributions, with significant international collaborations observed. Recent research trends have focused on refining TBI treatment techniques, investigating long-term patient effects, and advancing dosimetry and biomarker studies for radiation exposure assessments. TBI research exhibits a dynamic and multifaceted landscape, driven by global collaboration and innovation. It highlights the clinical challenges of TBI, such as its adverse effects and the need for tailored treatments in pediatric cases. Crucially, the study also acknowledges the fundamental science underpinning TBI, including its effects on inflammatory and apoptotic pathways, DNA damage, and the varied sensitivity of cells and tissues. This dual focus enhances our understanding of TBI, guiding future research toward innovative solutions and comprehensive care.

From MNIST to ImageNet and back: benchmarking continual curriculum learning

Continual learning (CL) is one of the most promising trends in recent machine learning research. Its goal is to go beyond classical assumptions in machine learning and develop models and learning strategies that present high robustness in dynamic environments. This goal is realized by designing strategies that simultaneously foster the incorporation of new knowledge while avoiding forgetting past knowledge. The landscape of CL research is fragmented into several learning evaluation protocols, comprising different learning tasks, datasets, and evaluation metrics. Additionally, the benchmarks adopted so far are still distant from the complexity of real-world scenarios, and are usually tailored to highlight capabilities specific to certain strategies. In such a landscape, it is hard to clearly and objectively assess models and strategies. In this work, we fill this gap for CL on image data by introducing two novel CL benchmarks that involve multiple heterogeneous tasks from six image datasets, with varying levels of complexity and quality. Our aim is to fairly evaluate current state-of-the-art CL strategies on a common ground that is closer to complex real-world scenarios. We additionally structure our benchmarks so that tasks are presented in increasing and decreasing order of complexity—according to a curriculum—in order to evaluate if current CL models are able to exploit structure across tasks. We devote particular emphasis to providing the CL community with a rigorous and reproducible evaluation protocol for measuring the ability of a model to generalize and not to forget while learning. Furthermore, we provide an extensive experimental evaluation showing that popular CL strategies, when challenged with our proposed benchmarks, yield sub-par performance, high levels of forgetting, and present a limited ability to effectively leverage curriculum task ordering. We believe that these results highlight the need for rigorous comparisons in future CL works as well as pave the way to design new CL strategies that are able to deal with more complex scenarios.

Sex estimation research trends in forensic anthropology between 2000 and 2022 in five prominent journals.

In forensic anthropology, osteological sex estimation methods are continuously reevaluated and updated to improve classification accuracies. Therefore, to gain a comprehensive understanding of recent trends in sex estimation research in forensic anthropology, a content analysis of articles published between 2000 and 2022 in Forensic Science International, the Journal of Forensic Sciences, the International Journal of Legal Medicine, the American Journal of Biological Anthropology, and Forensic Anthropology, was performed. The main goals of this content analysis were to (1) examine trends in metric versus morphological research, (2) examine which areas of the skeleton have been explored, (3) examine which skeletal collections and population affinities have been most frequently utilized, and (4) determine which statistical methods were commonly implemented. A total of 440 articles were coded utilizing MAXQDA and the resulting codes were exported for analysis. Statistical analyses were conducted utilizing the Cochran-Armitage and Jonckheere-Terpstra tests for trends, as well as Fisher-Freeman-Halton tests. The results demonstrate that sex estimation research published in these journals has prioritized metric over morphological methods. Further, the most utilized skeletal regions continue to be the skull and pelvis, while the most popular classification statistics continue to be discriminant function analysis and logistic regression. This study also demonstrates that a substantial portion of research has been conducted utilizing U.S. and Europe-based collections and limited populations. Based on these results, future sex estimation research must continue exploring the use of long bones and other postcranial elements, testing newer methods of analysis, as well as developing population-inclusive methods.

Current Landscape of the Robotic Tactile Array Sensors and Algorithm‐Based Performance Enhancement

The automated robots utilizing the traditional rigid tactile sensors have achieved significant success in effective task execution. Advancing into the era of service robots and humanoids that should perform sophisticated works in daily life, recent research trends in robotic tactile sensors have been shifting from the conventional rigid sensors to flexible/stretchable sensors. Over the past decade, the flexible/stretchable tactile sensors with human skin‐like mechanical compliance and stimulation perceptions have seen considerable advances. However, there are many substantial remaining challenges to produce practically useful flexible/stretchable tactile sensors such as the signal hysteresis originating from sensor material and the limited spatial resolution resulting from excessive addressing lines and signal interferences. Incorporation of learning‐based algorithms and data analysis techniques have been introduced recently and made remarkable successes in improving the challenges. Because most robotic tactile sensors are fabricated using the electronic sensing mechanisms or platforms, this article presents a concise overview on recent flexible/stretchable tactile sensors. This article introduces a brief discussion on the sensor performance parameters and sensing mechanisms, and the algorithmic approaches. In addition, it addresses existing challenges associated with current tactile array sensors and algorithms, then discusses prospects, and proposes a research direction for immediate practical uses in robots.

Ceramic Material Properties in High-Temperature Environments: Recent Trends in Finite Element Analysis Research

The finite element method is commonly utilized for high-temperature ceramics, such as cutting tools, thermal/environmental barrier coatings, and aerospace applications. Cutting performance is influenced by both internal and external factors, including heat transfer, and cutting speed. Friction coefficients and heat transfer issues reveal limitations on cutting tool efficiency, requiring accurate modeling and validation. Although oxide formation and crack failure for barrier coatings are discussed, ceramic materials and geometry must be studied. Extensive research on the nucleation and propagation of micro-crack is needed for next-generation spacecraft component applications by validating the established model based on experimental observation. Modeling and verification research improves ceramic reliability under more stress conditions.

Congestion Control Algorithms for the Internet – A Secondary Publication

In the last five years, there has been a V-shaped recovery in the number of papers on congestion controlalgorithms on the Internet. In this paper, congestion problems on the Internet are discussed, such as congestion collapse and bufferbloat from the perspective of the necessity of congestion control algorithms. The typical congestion control algorithms are introduced, and the research areas and methods of congestion control algorithms are described. Recent research trends and future prospects of congestion control algorithms are also presented.

Employee Attrition Prediction using Machine Learning Models: A Review Paper

Employee attrition refers to the decrease in staff numbers within an organization due to various reasons. As it has a negative impact on long-term growth objectives and workplace productivity, firms have recognized it as a significant concern. To address this issue, organizations are increasingly turning to machine-learning approaches to forecast employee attrition rates. This topic has gained significant attention from researchers, especially in recent times. Several studies have applied various machinelearning methods to predict employee attrition, producing different resultsdepending on the employed methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple studies applying machine-learning models to predict employee attrition to date. Therefore, this study aims to fill this gap by providing an overview of research conducted on applying machine learning to predict employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted, summarized, and classified. Most studies agree on conducting comparative experiments with multiple predictive models to determine the most effective one.From this literature survey, the RF algorithm and XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms. Additionally, the application of deep learning to employee attrition prediction issues also shows promise. While there are discrepancies in the datasets used in previous studies, it is notable that the dataset provided by IBM is the most widely utilized. This study serves as a concise review for new researchers, facilitating their understanding of the primary techniques employed in predicting employee attrition and highlighting recent research trends in this field. Furthermore, it provides organizations with insight into the prominent factors affecting employee attrition, as identified by studies, enabling them to implement solutions aimed at reducing attrition rates.

Recent trends in ginseng research.

Ginseng, the dried root of Panax ginseng, contains ginsenosides and has long been used in Korea, China, and Japan to treat various symptoms. Many studies on the utility of ginseng have been conducted and in this paper we investigate recent trends in ginseng research. P. ginseng studies were collected from scientific databases (PubMed, Web of Science, and SciFindern) using the keywords "Panax ginseng C.A. Meyer", "ginsenosides", "genetic diversity", "biosynthesis", "cultivation", and "pharmacology". We identified 1208 studies up to and including September 2023: 549 studies on pharmacology, 262 studies on chemical components, 131 studies on molecular biology, 58 studies on cultivation, 71 studies on tissue culture, 28 studies on clinical trials, 123 reviews, and 49 studies in other fields. Many researchers focused on the characteristic ginseng component ginsenoside to elucidate the mechanism of ginseng's pharmacological action, the relationship between component patterns and cultivation areas and conditions, and gene expression.

A review of cancer data fusion methods based on deep learning

With advancements in modern medical technology, an increasing amount of cancer-related information can be acquired through various means, such as genomics, proteomics, imaging, and pathology. However, these datasets come from diverse sources and possess heterogeneity and complexity in terms of data types, formats, and quality, which pose challenges for cancer diagnosis, treatment, and prognosis evaluation. Data fusion has emerged as an effective approach to address data heterogeneity and enhance data information value. Integrating cancer data of different origins and types can improve diagnostic accuracy and deepen our profound understanding of cancer. Currently, data fusion methods based on deep learning have gained considerable attention in cancer research. This study presents a comprehensive review of the most recent research and development trends of deep learning-based data fusion in cancer, with emphasis on the advancements of various data fusion methods based on heterogeneous data types (including specific methodologies, their pros, and cons), which offer substantial support for enhancing the precision of diagnosing and treating cancer. Furthermore, we present an overview of prevalent cancer data types and fusion approaches and analyze the general modeling methodologies based on deep learning. We further discuss the challenges and future directions, aiming to provide assistance and guidance for researchers endeavoring to devise deep learning solutions in the sphere of cancer research.

Therapeutic potential and roles of dietary seaweeds in food: A systematic review

Food security is a significant global concern as a result of the burden that the fast-expanding global population has placed on finite resources, such as arable land for farming. Additionally, the hunt for more nutritious food alternatives has been prompted by a growing understanding of health, nutrition, and diet. Seaweeds are simple-to-grow aquatic plants that are underused for food and medicine. Recent studies have focused on gaining access to and using these readily available natural resources for nutrition and medicinal objectives. This review systematically evaluated trends of recent research on seaweed, for therapeutic possibilities and product development. After examining several documents, this analysis discovered that seaweeds are incredibly beneficial algae for both nutrition and medicine, and may be further explored in innovative food products, in meeting the increasing global need for food.

Peer Pressure: A Comprehensive Literature Review of the Last Two Decades

Peer pressure has long been recognized as a significant influence on individuals' behaviors, particularly in adolescence and young adulthood. This paper provides a detailed review of the literature over the past two decades on the peer pressure. Peer pressure remains a pervasive influence on individuals' behaviors, particularly during adolescence and emerging adulthood. Drawing upon a diverse array of studies, this review synthesizes findings on the definition, types, mechanisms, consequences, and interventions related to peer pressure. Peer pressure has long been recognized as a significant influence on individuals' behaviors, attitudes, and decision-making processes, particularly during adolescence. The review encompasses various dimensions of peer pressure, including its psychological, social, and cultural aspects, as well as its implications for health, education, and social development. The article also examines recent trends, advancements, and challenges in understanding and addressing peer pressure, offering insights for future research and intervention strategies. By examining recent research trends and emerging perspectives, this review offers insights into the complexities of peer influence and highlights avenues for future research. Through an examination of empirical studies, theoretical frameworks, and key findings, this review aims to elucidate the complex interplay between peer pressure.

Recent Research Trends in Neuroinflammatory and Neurodegenerative Disorders.

Neuroinflammatory and neurodegenerative disorders including Alzheimer's disease (AD), Parkinson's disease (PD), traumatic brain injury (TBI) and Amyotrophic lateral sclerosis (ALS) are chronic major health disorders. The exact mechanism of the neuroimmune dysfunctions of these disease pathogeneses is currently not clearly understood. These disorders show dysregulated neuroimmune and inflammatory responses, including activation of neurons, glial cells, and neurovascular unit damage associated with excessive release of proinflammatory cytokines, chemokines, neurotoxic mediators, and infiltration of peripheral immune cells into the brain, as well as entry of inflammatory mediators through damaged neurovascular endothelial cells, blood-brain barrier and tight junction proteins. Activation of glial cells and immune cells leads to the release of many inflammatory and neurotoxic molecules that cause neuroinflammation and neurodegeneration. Gulf War Illness (GWI) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are chronic disorders that are also associated with neuroimmune dysfunctions. Currently, there are no effective disease-modifying therapeutic options available for these diseases. Human induced pluripotent stem cell (iPSC)-derived neurons, astrocytes, microglia, endothelial cells and pericytes are currently used for many disease models for drug discovery. This review highlights certain recent trends in neuroinflammatory responses and iPSC-derived brain cell applications in neuroinflammatory disorders.

Building Stock Models for Embodied Carbon Emissions—A Review of a Nascent Field

Building stock modeling emerges as a critical tool in the strategic reduction of embodied carbon emissions, which is pivotal in reshaping the evolving construction sector. This review provides an overall view of modern methodologies in building stock modeling, homing in on the nuances of embodied carbon analysis in construction. Examining 23 seminal papers, our study delineates two primary modeling paradigms—top-down and bottom-up—each further compartmentalized into five innovative methods. This study points out the challenges of data scarcity and computational demands, advocating for methodological advancements that promise to refine the precision of building stock models. A groundbreaking trend in recent research is the incorporation of machine learning algorithms, which have demonstrated remarkable capacity, improving stock classification accuracy by 25% and urban material quantification by 40%. Furthermore, the application of remote sensing has revolutionized data acquisition, enhancing data richness by a factor of five. This review offers a critical examination of current practices and charts a course toward an environmentally prudent future. It underscores the transformative impact of building stock modeling in driving ecological stewardship in the construction industry, positioning it as a cornerstone in the quest for sustainability and its significant contribution toward the grand vision of an eco-efficient built environment.

The Circular Economy: Recent debates and research trends

The Circular Economy is one of the most prominent cross-disciplinary and cross-sectoral concepts to emerge in recent decades. It has permeated academia, policymaking, business, NGOs, and the general public, leading to numerous applications of the concept, some of which only partially overlap. In this article, we review recent debates and research trends in the Circular Economy, outlining the ten most common groups of its conceptualizations using the PRISMA (Preferred Items for Systematic Reviews and Meta-Analysis) method. We then propose a post disciplinary and transnational research program on the Circular Economy that would not only combine hard and soft sciences in unprecedented ways but also have important practical applications, such as developing tools to embed the Circular Economy in natural, technical, economic, and socio-cultural settings.