Area Of Research Research Articles

Accurate Identification for CW Direct Signal in Underwater Acoustic Ranging

The underwater channel is bilateral, heterogeneous, uncertain, and exhibits multipath transmission, sound line curvature, etc. These properties complicate the structure of the received pulse, causing great challenges in direct signal identification for ranging purposes and impacts on back-end data processing, even accurate acoustic positioning. Machine learning (ML) combined with underwater acoustics has emerged as a prominent area of research in recent years. From a statistical perspective, ML can be viewed as an optimization strategy. Nevertheless, the existing ML-based direct-signal discrimination approaches rely on independent assessment, utilizing a single sensor (beacon or buoy), which is still insufficient for adapting to the complex underwater environment. Thus, discrimination accuracy decreases. To address the above issues, an accurate CW direct signal detection approach is performed using the decision tree algorithm, which belongs to ML. Initially, the pulse parameter characteristics in the underwater multipath channel are investigated and the parameter models are built. Then, based on multi-sensor localization performance feedback, fusion characteristics for diverse pulse are created. Next, the pulse parameter characteristics are preprocessed to mitigate the impact of varying magnitudes and units of magnitude on data processing. Then, the decision tree is built to obtain the desired output results and realize accurate recognition of the ranging direct signals. Finally, the feasibility and reliability of this paper’s method are verified by computer simulation and field testing.

Cholinergic Receptor Muscarinic 1 Co-Localized with Mitochondria in Cultured Dorsal Root Ganglion Neurons, and Its Deletion Disrupted Mitochondrial Ultrastructure in Peripheral Neurons: Implications in Alzheimer's Disease.

Loss of Cholinergic Receptor Muscarinic 1 (CHRM1) has been linked to the pathogenesis of Alzheimer's disease (AD). Our recent study found significantly lower CHRM1 protein levels in AD patient cortices, linked to reduced survival. Furthermore, using knockout mice (Chrm1-/-) we demonstrated that deletion of Chrm1 alters cortical mitochondrial structure and function, directly establishing a connection between its loss and mitochondrial dysfunction in the context of AD. While CHRM1's role in the brain has been extensively investigated, its impact on peripheral neurons in AD remains a crucial area of research, especially considering reported declines in peripheral nerve conduction among AD patients. The objective was to characterize Chrm1 localization and mitochondrial deficits in Chrm1-/- dorsal root ganglion (DRG) neurons. Recombinant proteins tagged with Green or Red Fluorescent Protein (GFP/RFP) were transiently expressed to investigate the localization of Chrm1 and mitochondria, as well as mitochondrial movement in the neurites of cultured primary mouse DRG neurons, using confocal time-lapse live cell imaging. Transmission electron microscopy was performed to examine the ultrastructure of mitochondria in both wild-type and Chrm1-/- DRGs. Fluorescence imaging revealed colocalization and comigration of N-terminal GFP-tagged Chrm1 and mitochondrial localization signal peptide-tagged RFP-labelled mitochondria in the DRGs neurons. A spectrum of mitochondrial structural abnormalities, including disruption and loss of cristae was observed in 87% neurons in Chrm1-/- DRGs. This study suggests that Chrm1 may be localized in the neuronal mitochondria and loss of Chrm1 in peripheral neurons causes sever mitochondrial structural aberrations resembling AD pathology.

Domain-Specific Dictionary between Human and Machine Languages

In the realm of artificial intelligence, knowledge graphs have become an effective area of research. Relationships between entities are depicted through a structural framework in knowledge graphs. In this paper, we propose to build a domain-specific medicine dictionary (DSMD) based on the principles of knowledge graphs. Our dictionary is composed of structured triples, where each entity is defined as a concept, and these concepts are interconnected through relationships. This comprehensive dictionary boasts more than 348,000 triples, encompassing over 20,000 medicine brands and 1500 generic medicines. It presents an innovative method of storing and accessing medical data. Our dictionary facilitates various functionalities, including medicine brand information extraction, brand-specific queries, and queries involving two words or question answering. We anticipate that our dictionary will serve a broad spectrum of users, catering to both human users, such as a diverse range of healthcare professionals, and AI applications.

Geochemistry of Geothermal Fluids in the Three Rivers Lateral Collision Zone in Northwest Yunnan, China: Relevance for Tectonic Structure and Seismic Activity

The Three Rivers Lateral Collision Zone (TRLCZ), situated at the southeastern margin of the Tibetan Plateau, is a crucial frontier where materials from the plateau flow southeastward. This study extensively investigated the hydrochemical characteristics and origin of helium and carbon isotopes in 73 thermal springs within the TRLCZ. The analysis revealed dominant processes, including carbonate and silicate interactions, resulting in elevated concentrations of HCO3− and Na+. The impact of Ca/Mg-rich minerals, particularly dolomite, influenced the cation composition. Additionally, gypsum dissolution, notably in the Lancangjiang Fault and Weixi–Qiaohou Fault, was highlighted through Ca/SO4 ratios. The positive correlation between SO42− and Cl− indicated dilution by shallow cold water, explaining the lower SO42− content in the Jingshajiang–Zhongdian Fault and Nujiang Fault compared to the Weixi–Qiaohou Fault and Lancangjiang Fault. The circulation depth of thermal spring water varied, with the northern Weixi–Qiaohou Fault exhibiting the shallowest circulation depth (~3 km), while the Jingshajiang–Zhongdian Fault and southern segments of the Nujiang Fault displayed deeper depths—ranging from 4 to 7 km. A positive correlation between the circulation depth and fault activity was also observed. The Rc/Ra ratios of free gas samples, predominantly indicating crustal origin, varied from 0.01 Ra to 0.53 Ra. Elevated Rc/Ra ratios in the research area suggested potential minor additions of mantle helium through faults and fractures. Crustal limestone was identified as the primary source of CO2-rich samples, with δ13CCO2 values ranging from −1.6‰ to −7.2‰, while trace amounts of mantle CO2 were found. The spatial distribution of the H2 concentration, CO2 concentration, He concentration, and mantle He proportions in gases indicated that higher values of He concentration and mantle He% always occur near sampling points with deeper circulation depths. However, no similar correlation was observed for H2 and CO2. Most earthquakes of magnitude 5 or greater occurred near the regions with high values of mantle source He release, highlighting the critical role of mantle fluids in the occurrence of earthquakes in the region. In this study, a fluid circulation model was developed to describe the process of fluid (water and gas) circulation migration and earthquake generation in the TRLCZ.

WS-AWRE: Intrusion Detection Using Optimized Whale Sine Feature Selection and Artificial Neural Network (ANN) Weighted Random Forest Classifier

An IDS (Intrusion Detection System) is essential for network security experts, as it allows one to identify and respond to abnormal traffic present in a network. An IDS can be utilized for evaluating the various types of malicious attacks. Hence, detecting intrusions has become a significant research area in the contemporary era, especially with the evolution of technologies. With the progress of ML (Machine Learning)-based algorithms, researchers have striven to perform optimal ID. However, most of these studies lag in accordance with their accuracy rate. Thus, to attain a high accuracy rate in ID, the present study proposes ML-based meta-heuristic algorithms, as these approaches possess innate merits of determining near-optimal solutions in limited time and are capable of dealing with multi-dimensional data. The study proposes OWSA (Optimal Whale Sine Algorithm) for selecting suitable and relevant features. With an exclusive optimization process using the SCA (Sine Cosine Algorithm), this study proposes to combine SCA with WOA (Whale Optimization Algorithm) for mitigating the demerits of both, with its hybridization thereby achieving OWSA. Following this, AWRF (Artificial Neural Network Weighted Random Forest) is proposed for classification. The main intention of this process is to propose a weight-updating process for discrete trees in the RF model. The proposed approach is motivated by avoiding overfitting and attaining stability and flexibility. This approach is assessed with regard to performance via a comparative analysis, so as to uncover the best performance of this proposed technique in ID.

Neuromarketing: a Tool to Understand Consumer Behaviour

This paper studies the concept of neuromarketing in consumer behaviour, emphasising the fact that consumers are not entirely rational in their decision-making process, because emotions influence their behaviour. Consequently, the neuromarketing field emerged as a way to understand the subconscious motivations of consumers. The scientific problem of this paper is to analyse how neuromarketing techniques can be effectively integrated with traditional marketing research methods to provide a comprehensive understanding of consumer behaviour. It emphasises the fact that traditional marketing research tools may not reveal all essential insights into consumer behaviour research. Therefore, market research practices should employ neuromarketing tools such as eye tracking, facial expression analysis, biometrics and neuronal activity measurements. First, six principal neuromarketing research areas are identified: branding, product design and innovation, advertising effectiveness, shopper decision-making, online experiences, and entertainment effectiveness. Next, a simplified human behaviour model, consisting of three phases, unconscious, conscious and observable, is discussed. Finally, the paper focuses on criticisms of neuromarketing and ethical issues.

Spatial-temporal characteristics and influencing factors of carbon emission in Chengdu-Chongqing area: an urban transportation perspective.

The Chengdu-Chongqing twin-city economic circle is a vital growth pole and a new power source for Chinese high-quality development. Studying the spatial-temporal characteristics of carbon emissions and the role of factors affecting them under the transportation perspective is of great significance for this region to realize the carbon peak and carbon neutrality and to formulate carbon emission reduction policies. We use the exploring spatial data analysis (ESDA) and spatial regression model combined with the STIRPAT model, and research finding: (1) The total carbon emissions in the research area gradually increased from 2014 to 2020, but the growth rate showed a significant decline in 2019. (2) There is significant spatial heterogeneity of carbon emissions in the study area; the hotspot areas of total carbon emissions are in Chongqing and Chengdu, forming a high-low aggregation of carbon emissions. Per capita carbon emissions show a high trend in the southwest and a low in the northeast. (3) From the factors of transportation perspective, highway density and private vehicles have a positive impact on carbon emissions, and urban road areas and public transportation have a very significant inhibition of carbon emissions and a spatial spillover effect. (4) Other factors, such as population size, national economic development, urbanization level, and industrial structure, all have a positive effect on carbon emissions, and disposable income has a negative effect on carbon emissions.

An Ensemble-Based Framework for Sophisticated Crop Classification Exploiting Google Earth Engine

Corn and soybeans play pivotal roles in the agricultural landscape of the United States, and accurately delineating their cultivation areas is indispensable for ensuring food security and addressing hunger-related challenges. Traditional methods for crop mapping are both labor-intensive and time-consuming. Fortunately, the advent of high-resolution imagery, exemplified by Sentinel-2A (S2A), has opened avenues for precise identification of these crops at a field scale, with the added advantage of cloud computing. This paper presents an innovative algorithm designed for large-scale mapping of corn and soybean planting areas on the Google Cloud Engine, drawing inspiration from symmetrical theory. The proposed methodology encompasses several sequential steps. First, S2A data undergo processing incorporating phenological information and spectral characteristics. Subsequently, texture features derived from the grayscale matrix are synergistically integrated with spectral features in the first step. To enhance algorithmic efficiency, the third step involves a feature importance analysis, facilitating the retention of influential bands while eliminating redundant features. The ensuing phase employs three base classifiers for feature training, and the final result maps are generated through a collective voting mechanism based on the classification results from the three classifiers. Validation of the proposed algorithm was conducted in two distinct research areas: Ford in Illinois and White in Indiana, showcasing its commendable classification capabilities for these crops. The experiments underscore the potential of this method for large-scale mapping of crop areas through the integration of cloud computing and high-resolution imagery.

Design and simulation of photonic crystal fiber for highly sensitive chemical sensing applications

Abstract Photonic crystal fibers (PCF) have demonstrated promising capabilities for liquid sensing applications owing to their distinctive optical properties. This work presents a numerical investigation of a PCF sensor optimized for discriminating water, ethanol, and benzene samples. In the proposed configuration, there are five concentric rings of air holes in the cladding arranged in a hybrid lattice structure, while the core contains only one air hole. The optical properties of the sensor, such as refractive index, power fraction, relative sensitivity, confinement loss, effective area, and nonlinearity, were assessed through a comprehensive analysis utilizing the full vector Finite Element Method within the COMSOL Multiphysics software. All these properties have been meticulously examined through numerical investigation across a broader range of wavelengths spanning from 0.8 to 2.2 µm. The suggested model has high sensitivity, minimal confinement loss, and an exceptional nonlinear coefficient value. At a wavelength of 1.3 µm, the suggested PCF exhibits greater sensitivity of 96.84, 98.12, and 100% for water, ethanol, and benzene, respectively, and nonlinear coefficients of 13.98 W−1 km−1 for water, 13.93 W−1 km−1 for ethanol, and 14.85 W−1 km−1 for benzene, with decreased confinement loss. The created model can be utilized in several research areas, particularly in chemical sensing and bio-sensing, as well as their respective applications.

Large language models and generative AI in telehealth: a responsible use lens.

This scoping review aims to assess the current research landscape of the application and use of large language models (LLMs) and generative Artificial Intelligence (AI), through tools such as ChatGPT in telehealth. Additionally, the review seeks to identify key areas for future research, with a particular focus on AI ethics considerations for responsible use and ensuring trustworthy AI. Following the scoping review methodological framework, a search strategy was conducted across 6 databases. To structure our review, we employed AI ethics guidelines and principles, constructing a concept matrix for investigating the responsible use of AI in telehealth. Using the concept matrix in our review enabled the identification of gaps in the literature and informed future research directions. Twenty studies were included in the review. Among the included studies, 5 were empirical, and 15 were reviews and perspectives focusing on different telehealth applications and healthcare contexts. Benefit and reliability concepts were frequently discussed in these studies. Privacy, security, and accountability were peripheral themes, with transparency, explainability, human agency, and contestability lacking conceptual or empirical exploration. The findings emphasized the potential of LLMs, especially ChatGPT, in telehealth. They provide insights into understanding the use of LLMs, enhancing telehealth services, and taking ethical considerations into account. By proposing three future research directions with a focus on responsible use, this review further contributes to the advancement of this emerging phenomenon of healthcare AI.

Polynomial approaches in improving accuracy of probability distribution estimation using the method of moments

AbstractBACKGROUNDDetermination of a probability density function (PDF) is an area of active research in engineering sciences as it can improve process systems. A previously developed polynomial method‐of‐moments‐based PDF estimation model has been applied in the research to produce accurate approximations to both standard and more complex PDF. A model with a different polynomial basis than a monomial is still to be developed and evaluated. This is the work that is undertaken in this study.RESULTSA set of standard PDF (Normal, Weibull, Log Normal and Bimodal) and more complex distributions (solutions to the Smoluchowski coagulation equation and Population Balance equation) were approximated by the method‐of‐moments using Chebyshev, Hermite and Lagrange polynomial‐based density functions. Results show that Lagrange polynomial‐based models improve the fit compared to monomial based‐modeling in terms of RMSE and Kolmogorov–Smirnov test statistic estimates. The Kolmogorov–Smirnov test‐statistics decreased by 19% and the RMSE values were improved by around 85% compared to the standard monomial basis when using Lagrange polynomial basis.CONCLUSIONThis study indicates that the procedure using Lagrange polynomials with method‐of‐moments is a more reliable reconstruction procedure that calculates the approximate distribution using lesser number of moments, which is desirable. © 2024 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).

Examining the Role of Cerebral Embolic Protection Devices in Preventing Postoperative Stroke in Patients with a History of Stroke or Transient Ischemic: Insights from the National Inpatient Sample.

Cerebral embolic protection devices (CEPD) during transcatheter aortic valve replacement (TAVR) have been shown to lower the risk of stroke during the procedure. However, their long-term and clinical effects on neuro-cognition are unknown. Therefore, we hypothesized the benefit of CEPD in TAVR patients with a prior history of stroke or transient ischemic attack (TIA). National Inpatient Sample (2019) and International Classification of Diseases, 10th Revision codes were used to identify patients undergoing TAVR with prior stroke or TIA. Propensity-matched analysis was performed to adjust for baseline characteristics and comorbidities. Primary outcome measures were postoperative stroke and all-cause mortality. Length of stay and hospital cost were secondary outcomes. Of 8450 unmatched TAVR patients with prior stroke or TIA in 2019, 1095 (13%) utilized CEPD. After propensity matching previous myocardial infarction (MI), coronary artery bypass grafting, and drug abuse were higher in the TAVR-only cohort. Postoperative stroke rate (1.4% vs 2.2%; P = 0.081) and odds [adjusted odds ratio (aOR), 0.48; 95% confidence interval (CI), 0.11-2.17; P = 0.341] were lower in the CEPD group. There was no difference in all-cause in-hospital mortality between the 2 groups (0.9% vs 1.0%). Length of stay (3 vs 2 days, P <0.001) and hospital expenditure ($172,711 vs $162,284; P = 0.002) were higher for the TAVR-only cohort. CEPD in TAVR patients with prior stroke or TIA did not show statistically significant postoperative stroke benefits. However, further larger-scale prospective studies are needed to evaluate the long-term neurocognitive benefits of CEPD in these patients. As the use of TAVR continues to expand, optimizing peri-procedural strategies such as the use of CEPD remains a critical area of research to improve patient outcomes.

PVDF-Based Piezo-Catalytic Membranes-A Net-Zero Emission Approach towards Textile Wastewater Purification.

Among the various water purification techniques, advancements in membrane technology, with better fabrication and analysis, are receiving the most research attention. The piezo-catalytic degradation of water pollutants is an emerging area of research in water purification technology. This review article focuses on piezoelectric polyvinylidene difluoride (PVDF) polymer-based membranes and their nanocomposites for textile wastewater remediation. At the beginning of this article, the classification of piezoelectric materials is discussed. Among the various membrane-forming polymers, PVDF is a piezoelectric polymer discussed in detail due to its exceptional piezoelectric properties. Polyvinylidene difluoride can show excellent piezoelectric properties in the beta phase. Therefore, various methods of β-phase enhancement within the PVDF polymer and various factors that have a critical impact on its piezo-catalytic activity are briefly explained. This review article also highlights the major aspects of piezoelectric membranes in the context of dye degradation and a net-zero approach. The β-phase of the PVDF piezoelectric material generates an electron-hole pair through external vibrations. The possibility of piezo-catalytic dye degradation via mechanical vibrations and the subsequent capture of the resulting CO2 and H2 gases open up the possibility of achieving the net-zero goal.

Advances in Additive Manufacturing of Polymer-Fused Deposition Modeling on Textiles: From 3D Printing to Innovative 4D Printing-A Review.

Technological advances and the development of new and advanced materials allow the transition from three-dimensional (3D) printing to the innovation of four-dimensional (4D) printing. 3D printing is the process of precisely creating objects with complex shapes by depositing superimposed layers of material. Current 3D printing technology allows two or more filaments of different polymeric materials to be placed, which, together with the development of intelligent materials that change shape over time or under the action of an external stimulus, allow us to innovate and move toward an emerging area of research, innovative 4D printing technology. 4D printing makes it possible to manufacture actuators and sensors for various technological applications. Its most significant development is currently in the manufacture of intelligent textiles. The potential of 4D printing lies in modular manufacturing, where fabric-printed material interaction enables the creation of bio-inspired and biomimetic devices. The central part of this review summarizes the effect of the primary external stimuli on 4D textile materials, followed by the leading applications. Shape memory polymers attract current and potential opportunities in the textile industry to develop smart clothing for protection against extreme environments, auxiliary prostheses, smart splints or orthoses to assist the muscles in their medical recovery, and comfort devices. In the future, intelligent textiles will perform much more demanding roles, thus envisioning the application fields of 4D printing in the next decade.

Adult versus adolescent idiopathic scoliosis surgery: a meta-analysis of clinical and radiographic outcomes.

While the natural history of adolescent idiopathic scoliosis (AIS) and adult idiopathic scoliosis (AdIS) is well documented in the literature, the impact of age on postoperative outcomes remains an active area of research. We performed a systematic review and meta-analysis to compare patients undergoing surgery for AIS and AdIS with respect to: (1) postoperative Cobb correction, (2) perioperative variables, and (3) postoperative complications. A systematic literature search was performed in accordance with preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. studies published between 2002 and 2022, retrospective, and comparing AIS vs. AdIS patients undergoing deformity surgery. The primary outcome was postoperative Cobb correction. Secondary outcomes included estimated blood loss (EBL), operative time, total instrumented levels, length of stay (LOS), and postoperative complications. Random-effects models were performed according to the method of DerSimonian and Laird. Of 190 identified articles, 14 fit the inclusion criteria. A total of 1788 patients were included, 1275(71.3%) with AIS, and 513(28.7%) with AdIS. There was a significant age difference between AIS and AdIS (15.3 vs. 36.7years, mean difference (MD) = 21.3years, 95%CI = 14.3-28.4,p < 0.001). Mean postoperative Cobb percentage correction was reported in 5 articles and was significantly higher in AIS (68.4%) vs. AdIS (61.4%) (MD = -7.2, 95%CI = -11.6,-2.7,p = 0.001). EBL was not significantly different between AIS and AdIS (695.6mL vs 817.7mL,p = 0.204). Furthermore, no difference was found in operative time (MD = 37.9min,95%CI = -10.7;86.6,p = 0.127), total instrumented level (MD = 0.88,95%CI = -0.7,2.4,p = 0.273), and LOS (MD = 0.5, 95%CI = -0.2;1.2, p = 0.188). Four articles reported postoperative complications in AIS vs AdIS, with no difference in neurological deficit, instrumentation-related complications, and medical complications. AIS patients had better radiographic correction compared to AdIS. Though no difference was found in perioperative outcomes and complications, these findings emphasize the importance of counseling patients regarding the optimal timing of surgical correction.

Emotional responses to narrative content: A comparative study on consumer food choice intentions

Influencing consumer food choices has evolved as a multidisciplinary research area that bridges marketing, food science, and sensory experiences. However, little is known about the impact of mixed reality (MR) or interactive content narratives in MR on influencing consumers to make specific food choices. Drawing on narrative transportation theory, the current research examines the effect of MR content narratives on consumer food choice intentions and the underlying mechanism of emotional response. A single-factor between-subjects experiment with four conditions (narrative format: mixed reality, video, audio, text) was conducted with 175 participants. Results revealed a significant indirect effect of media format on food choice intention through emotional response. More specifically, our results demonstrated that, as compared to MR, audio and video narrative formats had negative effects on choice of plant-based foods due to lower levels of consumer happiness and liveliness. Our results also demonstrated that, as compared to MR, audio-based narratives had a positive effect on choice of plant-based foods due to higher levels of consumer distress. The study contributes to literature in immersive technologies and consumer choice by providing empirical evidence of the role of emotions in food choice intentions that can be triggered through mixed reality technology. This can assist restaurateurs and food marketers in leveraging immersive technologies to stimulate the food experience and choice.

Bibliometric analysis of publications on osteoarticular brucellosis

Objectives: The aim of this investigation was to undertake a thorough bibliometric analysis of publications between 1991 and 2022 to scrutinize and comprehend the research landscape of osteoarticular brucellosis, a zoonotic infection that affects bones and joints. Methods: We scrutinized the distribution of publications by various criteria, including country, institution, author, and journal. Furthermore, we executed citation analysis, established collaboration networks, and performed keyword co-occurrence analysis. Results: Our examination discovered 432 documents on this topic indexed in the Web of Science database, with a noticeable surge in publications over time. Turkey, the United States, and Iran were the leading nations in terms of research output. The University of Buenos Aires emerged as the most productive institution. The primary research areas were General Internal Medicine, Infectious Diseases, and Rheumatology. The primary beneficiary of this research was Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT). Conclusions: This study furnishes valuable insights into worldwide research endeavors on osteoarticular brucellosis. These insights can steer future research directions, emphasizing the necessity for sustained collaboration and funding support to tackle this significant public health issue.

Factors of tacit knowledge acquisition and its mechanism of action on academic research performance: an empirical research from Chinese universities

PurposeThis study examines the relationship between higher education improvement and tacit knowledge importance. In this context, the scarcity of empirical and theoretical studies on acquiring tacit knowledge to enhance academic performance in higher education suggests that this research area holds significant importance for experts and policymakers. Consequently, this study aims to explore the factors that influence academic research performance at Chinese universities by acquiring tacit knowledge.Design/methodology/approachTo achieve the study aims, the current approach utilizes the research technique based on the socialization, externalization, internalization and combination (SECI) model and knowledge management (KM) theory. To analyze the study objective, the authors collected data from post-graduate students at Chinese universities and analyzed it using structural equation modeling (SEM) to test the model and hypotheses.FindingsThe results indicated that social interaction, internalization and self-motivation have a positive impact on academic research performance through the acquisition of tacit knowledge. Furthermore, the findings suggest that academic researchers can acquire more knowledge through social interaction than self-motivation, thereby advancing research progress.Originality/valueThis study addresses the critical issues surrounding the acquisition of tacit knowledge and presents a comprehensive framework and achievements that can contribute to achieving exceptional academic performance.

Multi-Modal Attentive Prompt Learning for Few-shot Emotion Recognition in Conversations

Emotion recognition in conversations (ERC) has emerged as an important research area in Natural Language Processing and Affective Computing, focusing on accurately identifying emotions within the conversational utterance. Conventional approaches typically rely on labeled training samples for fine-tuning pre-trained language models (PLMs) to enhance classification performance. However, the limited availability of labeled data in real-world scenarios poses a significant challenge, potentially resulting in diminished model performance. In response to this challenge, we present the Multi-modal Attentive Prompt (MAP) learning framework, tailored specifically for few-shot emotion recognition in conversations. The MAP framework consists of four integral modules: multi-modal feature extraction for the sequential embedding of text, visual, and acoustic inputs; a multi-modal prompt generation module that creates six manually-designed multi-modal prompts; an attention mechanism for prompt aggregation; and an emotion inference module for emotion prediction. To evaluate our proposed model’s efficacy, we conducted extensive experiments on two widely recognized benchmark datasets, MELD and IEMOCAP. Our results demonstrate that the MAP framework outperforms state-of-the-art ERC models, yielding notable improvements of 3.5% and 0.4% in micro F1 scores. These findings highlight the MAP learning framework’s ability to effectively address the challenge of limited labeled data in emotion recognition, offering a promising strategy for improving ERC model performance.

A review of isotretinoin in the treatment of frontal fibrosing alopecia.

Frontal fibrosing alopecia (FFA) is characterized by scarring alopecia of the frontotemporal scalp and facial papules. Isotretinoin is a vitamin A-derived retinoid discovered in 1955 and approved for treating nodulocystic acne. This drug can also affect facial papules and frontotemporal hair loss in patients with FFA. In this article, we conducted a review of the available studies investigating the use of oral isotretinoin for FFA treatment. Our study provides insights into the efficacy and safety of isotretinoin as a potential treatment option for FFA and highlights areas for future research. In this study, we aimed to investigate the potential advantages and disadvantages of isotretinoin as a treatment for FFA. To identify all relevant articles, we developed a comprehensive search strategy and conducted a thorough search of three major databases: PubMed, Embase, and Science Direct. We retrieved a total of 82 articles from the search results. Two independent reviewers then screened each of the 82 articles based on our inclusion and exclusion criteria, resulting in the identification of 15 articles that were deemed relevant to our study. Across the 15 articles, 232 patients who suffered from FFA were involved. Nearly 90% of patients experienced a significant reduction of symptoms after receiving oral isotretinoin at 10-40 mg daily. We conclude that isotretinoin can positively affect facial papules and help suppress hair loss.