Search Results (1,929)

Existing approaches to social media sentiment analysis typically focus on static classification, offering limited foresight into how public opinion evolves. This study addresses that gap by introducing the Multi-Feature Sentiment-Driven Forecasting (MFSF) framework, a novel pipeline that enhances sentiment trend prediction by integrating rich contextual information from text. Using state-of-the-art transformer models on the Sentiment140 dataset, our framework extracts three concurrent signals from each tweet: sentiment polarity, aspect-based scores (e.g., ‘price’ and ‘service’), and topic embeddings. These features are aggregated into a daily multivariate time series. We then employ a SARIMAX model to forecast future sentiment, using the extracted aspect and topic data as predictive exogenous variables. Our results, validated on the historical Sentiment140 Twitter dataset, demonstrate the framework’s superior performance. The proposed multivariate model achieved a 26.6% improvement in forecasting accuracy (RMSE) over a traditional univariate ARIMA baseline. The analysis confirmed that conversational aspects like ‘service’ and ‘quality’ are statistically significant predictors of future sentiment. By leveraging the contextual drivers of conversation, the MFSF framework provides a more accurate and interpretable tool for businesses and policymakers to proactively monitor and anticipate shifts in public opinion. Full article

Background/Objectives: For kidney transplantation, it is very important to provide effective post-transplantation interventions to help patients achieve continuous and efficient self-management. Therefore, we review the self-management interventions applied to kidney transplant recipients and suggest the optimal approach to increase the effectiveness of future self-management interventions. Design: Systematic review. Methods: Search terms and strategies included kidney transplantation; self-management; intervention; systematic review. We searched MEDLINE via PubMed, Excerpta Media dataBASE, Cochrane Register Controlled Trials, Cumulative Index to Nursing and Allied Health Literature, and one domestic Korean database to identify studies of self-management interventions for kidney transplant recipients aged ≥ 18 years published in English or Korean until 14 May 2025. Two reviewers independently selected related studies and extracted relevant data. Identified studies were assessed for quality and bias. Results: Of 1340 studies identified, 27 with 1912 participants met the inclusion criteria. Educational interventions were the most common self-management interventions and were provided 3 months to 1 year after kidney transplantation; most interventions were administered by nurses. Outcome variables were divided into cognitive, behavioral, affective, and health outcomes. Educational interventions were effective in improving cognitive, behavioral, and affective aspects. Some differences were observed, depending on the study. Conclusions: We recommend that nurse-involved educational interventions be included when developing self-management interventions and guidelines for kidney transplant recipients in clinical and community nursing settings. Full article

To address the significant degradation of image visibility and contrast in turbid media, this paper proposes an enhanced image dehazing algorithm. Unlike traditional polarimetric dehazing methods that exclusively attribute polarization information to airlight, our approach integrates object radiance polarization and airlight polarization for haze removal. First, sky regions are localized through multi-scale fusion of polarization and intensity segmentation maps. Second, region-specific transmittance estimation is performed by differentiating haze-occluded regions from haze-free regions. Finally, target radiance is solved using boundary constraints derived from non-haze regions. Compared with other dehazing algorithms, the method proposed in this paper demonstrates greater adaptability across diverse scenarios. It achieves higher-quality restoration of targets with results that more closely resemble natural appearances, avoiding noticeable distortion. Not only does it deliver excellent dehazing performance for land fog scenes, but it also effectively handles maritime fog environments. Full article

Data annotation serves as a critical foundation for artificial intelligence (AI) and machine learning (ML). Recently, AI agents powered by large language models (LLMs) have emerged as effective solutions to longstanding challenges in data annotation, such as scalability, consistency, cost, and limitations in domain expertise. These agents facilitate intelligent automation and adaptive decision-making, thereby enhancing the efficiency and reliability of annotation workflows across various fields. Despite the growing interest in this area, a systematic understanding of the role and capabilities of AI agents in annotation is still underexplored. This paper seeks to fill that gap by providing a comprehensive review of how LLM-driven agents support advanced reasoning strategies, adaptive learning, and collaborative annotation efforts. We analyze agent architectures, integration patterns within workflows, and evaluation methods, along with real-world applications in sectors such as healthcare, finance, technology, and media. Furthermore, we evaluate current tools and platforms that support agent-based annotation, addressing key challenges such as quality assurance, bias mitigation, transparency, and scalability. Lastly, we outline future research directions, highlighting the importance of federated learning, cross-modal reasoning, and responsible system design to advance the development of next-generation annotation ecosystems. Full article

Koshu wine, produced from the indigenous Japanese grape Vitis vinifera L. cv. Koshu exhibits a lower pH than other white wines, hindering malolactic fermentation (MLF) by lactic acid bacteria (LAB). Here, we aimed to isolate LAB strains capable of performing MLF under these challenging conditions to improve wine quality. Sixty-four Oenococcus oeni and one Lactobacillus hilgardii strain were isolated from Koshu grapes and wines that had undergone spontaneous MLF. MLF activity was assessed under varying pH, SO₂, and ethanol conditions in modified basal medium (BM) and Koshu model wine media. Expression of stress-related genes was analyzed using real-time PCR. Carbon source utilization was evaluated via API 50CH assays. All isolates degraded malic acid and produced lactic acid at 15 °C and pH 3.2 in BM without reducing sugars. Seven strains, all identified as O. oeni, demonstrated MLF activity at pH 3.0 in modified BM lacking added reducing sugars or tomato juice. Six wine-derived strains tolerated up to 12% ethanol, whereas the grape-derived strain was inhibited at 10%. In a synthetic Koshu wine model (13% ethanol, pH 3.0), wine-derived isolates exhibited higher MLF activity than commercial starter strains. In high-performing strains, mleA was upregulated, and most isolates preferred fructose, arabinose, and ribose over glucose. These findings suggest that indigenous O. oeni strains from Koshu wine possess unique stress tolerance and metabolic traits, making them promising candidates for region-specific MLF starter cultures that could enhance Koshu wine quality and terroir expression. Full article

Male-factor infertility accounts for approxiamately half of all infertility cases globally, yet therapeutic options remain limited for individuals with no retrievable spermatozoa, such as those with non-obstructive azoospermia (NOA). In recent years, artificial gametogenesis has emerged as a promising avenue for fertility restoration, driven by advances in two complementary strategies: organotypic in vitro spermatogenesis (IVS), which aims to complete spermatogenesis ex vivo using native testicular tissue, and in vitro gametogenesis (IVG), which seeks to generate male gametes de novo from pluripotent or reprogrammed somatic stem cells. To evaluate the current landscape and future potential of these approaches, a narrative, semi-systematic literature search was conducted in PubMed and Scopus for the period January 2010 to February 2025. Additionally, landmark studies published prior to 2010 that contributed foundational knowledge in spermatogenesis and testicular tissue modeling were reviewed to provide historical context. This narrative review synthesizes multidisciplinary evidence from cell biology, tissue engineering, and translational medicine to benchmark IVS and IVG technologies against species-specific developmental milestones, ranging from rodent models to non-human primates and emerging human systems. Key challenges—such as the reconstitution of the blood–testis barrier, stage-specific endocrine signaling, and epigenetic reprogramming—are discussed alongside critical performance metrics of various platforms, including air–liquid interface slice cultures, three-dimensional organoids, microfluidic “testis-on-chip” devices, and stem cell-derived gametogenic protocols. Particular attention is given to clinical applicability in contexts such as NOA, oncofertility preservation in prepubertal patients, genetic syndromes, and reprocutive scenarios involving same-sex or unpartnered individuals. Safety, regulatory, and ethical considerations are critically appraised, and a translational framework is outlined that emphasizes biomimetic scaffold design, multi-omics-guided media optimization, and rigorous genomic and epigenomic quality control. While the generation of functionally mature sperm in vitro remains unachieved, converging progress in animal models and early human systems suggests that clinically revelant IVS and IVG applications are approaching feasibility, offering a paradigm shift in reproductive medicine. Full article

Media organizations are experiencing ongoing transformation, increasingly driven by the advancement of AI technologies. This development has begun to link journalists with generative systems and synthetic technologies. Although newsrooms worldwide are exploring AI adoption to improve information sourcing, news production, and distribution, a gap exists between resource-rich organizations and those with limited means. Since ChatGPT 3.5 was released on 30 November 2022, Greek media and journalists have gained the ability to use and explore AI technology. In this study, we examine the use of AI in Greek newsrooms, as well as journalists’ reflections and concerns. Through qualitative analysis, our findings indicate that the adoption and integration of these tools in Greek newsrooms is marked by the lack of formal institutional policies, leading to a predominantly self-directed and individualized use of these technologies by journalists. Greek journalists engage with AI tools both professionally and personally, often without organizational guidance or formal training. This issue may compromise the quality of journalism due to the absence of established guidelines. Consequently, individuals may produce content that is inconsistent with the media outlet’s identity or that disseminates misinformation. Age, gender, and newsroom roles do not constitute limiting factors for this “experimentation”, as survey participants showed familiarity with this technology. In addition, in some cases, the disadvantages of specific tools regarding qualitative results in Greek are inhibiting factors for further exploration and use. All these points to the need for immediate training, literacy, and ethical frameworks. Full article

This paper outlines a methodology to compile geo-referenced observational data of Australian birds acting as pollinators of Strelitzia sp. (Bird of Paradise) flowers and dispersers of their seeds. Given the absence of systematic published records, a crowdsourcing approach was employed, combining data from natural history platforms (e.g., iNaturalist, eBird), image hosting websites (e.g., Flickr) and, in particular, social media. Facebook emerged as the most productive channel, with 61.4% of the 301 usable observations sourced from 43 ornithology-related groups. The strategy included direct solicitation of images and metadata via group posts and follow-up communication. The holistic, snowballing search strategy yielded a unique, behavior-focused dataset suitable for analysis. While the process exposed limitations due to user self-censorship on image quality and completeness, the approach demonstrates the viability of crowdsourced behavioral ecology data and contributes a replicable methodology for similar studies in under-documented ecological contexts. Full article

This research examines how influencer information spreads and is accepted by consumers, focusing on a Tunisian sample of social media users, and how these effects percolate into consumers’ perception of the quality of cosmetic products. Drawing on the Information Adoption Model (IAM), this study develops a conceptual framework adapted to the social media landscape, particularly the TikTok platform. To test this framework, we conducted a survey targeting 285 consumers using a non-random sampling frame, primarily through Facebook and Instagram. The findings suggest that consumers perceive influencer information as useful when they believe it is credible and of high quality. Interestingly, while high-quality information tends to lead to influencer information adoption, credibility alone does not guarantee adoption. Additionally, our study emphasizes the role of influencer information usefulness in driving its adoption. One notable discovery is the link between influencer information adoption and consumers’ perceptions of the quality of cosmetic products. However, this correlation does not hold equally for both genders, thus suggesting a moderation effect between gender and influencer information processing in this context. Full article

The rise of virtual production has created an urgent need for both efficient and high-fidelity 3D face generation schemes for cinema and immersive media, but existing methods are often limited by lighting–geometry coupling, multi-view dependency, and insufficient artistic quality. To address this, this study proposes a cross-modal 3D face generation framework based on single-view semantic masks. It utilizes Swin Transformer for multi-level feature extraction and combines with NeRF for illumination decoupled rendering. We utilize physical rendering equations to explicitly separate surface reflectance from ambient lighting to achieve robust adaptation to complex lighting variations. In addition, to address geometric errors across illumination scenes, we construct geometric a priori constraint networks by mapping 2D facial features to 3D parameter space as regular terms with the help of semantic masks. On the CelebAMask-HQ dataset, this method achieves a leading score of SSIM = 0.892 (37.6% improvement from baseline) with FID = 40.6. The generated faces excel in symmetry and detail fidelity with realism and aesthetic scores of 8/10 and 7/10, respectively, in a perceptual evaluation with 1000 viewers. By combining physical-level illumination decoupling with semantic geometry a priori, this paper establishes a quantifiable feedback mechanism between objective metrics and human aesthetic evaluation, providing a new paradigm for aesthetic quality assessment of AI-generated content. Full article

Objectives: The present cross-sectional analysis aimed to investigate whether Large Language Model-based chatbots can be used as reliable sources of information in orthodontics by evaluating chatbot responses and comparing them to those of dental practitioners with different levels of knowledge. Methods: Eight true and false frequently asked orthodontic questions were submitted to five leading chatbots (ChatGPT-4, Claude-3-Opus, Gemini 2.0 Flash Experimental, Microsoft Copilot, and DeepSeek). The consistency of the answers given by chatbots at four different times was assessed using Cronbach’s α. Chi-squared test was used to compare chatbot responses with those given by two groups of clinicians, i.e., general dental practitioners (GDPs) and orthodontic specialists (Os) recruited in an online survey via social media, and differences were considered significant when p < 0.05. Additionally, chatbots were asked to provide a justification for their dichotomous responses using a chain-of-through prompting approach and rating the educational value according to the Global Quality Scale (GQS). Results: A high degree of consistency in answering was found for all analyzed chatbots (α > 0.80). When comparing chatbot answers with GDP and O ones, statistically significant differences were found for almost all the questions (p < 0.05). When evaluating the educational value of chatbot responses, DeepSeek achieved the highest GQS score (median 4.00; interquartile range 0.00), whereas CoPilot had the lowest one (median 2.00; interquartile range 2.00). Conclusions: Although chatbots yield somewhat useful information about orthodontics, they can provide misleading information when dealing with controversial topics. Full article

The applications of magnetic particles in anti-counterfeiting and anti-absorbing coatings and other functional materials are becoming increasingly widespread. However, due to their high density, the magnetic particles rapidly settle in organic resin media, significantly affecting the quality of the related products. Thereby, reducing the density of the particles is essential. To achieve this goal, high-density magnetic particles were coated onto the surface of hollow silica using anion–cation composite technology. Further, the silane coupling agent N-[3-(trimethoxysilyl)propyl]ethylenediamine was bonded to the surface of magnetic particles to form an amino-covered interfacial layer with a pH value of 9.28, while acrylic acid was polymerized and coated onto the surface of hollow silica to form a carboxyl-covered interfacial layer with a pH value of 4.65. Subsequently, the two materials were compounded to obtain a low-density composite magnetic material. The morphologies and structural compositions of the magnetic composite materials were studied by FTIR, SEM, SEM-EDS, XRD, and other methods. The packing densities of the magnetic composite materials were compared using the particle packing method, thereby solving the problem of magnetic particles settling in the resin solution. Full article

The corpus luteum (CL) is a transient gland that can directly influence follicular dynamics and oocyte quality. The objective of this study was to evaluate the influence of the absence or presence of a small (≤3 mm), medium (4–8 mm), or large (>8 mm) CL in slaughterhouse ovaries on in vitro embryo production. Cumulus–oocyte complexes (COCs) were collected from each group of ovaries and matured in TCM-199 medium, plus hormones and fetal bovine serum. Fertilization was performed with fresh semen from a Katahdin ram of known fertility. Embryo development was carried out in commercial sequential media for 72 and 96 h, until the blastocyst stage. The number of follicles (2–6 mm in diameter) and COCs were influenced by the presence of CL, which was higher (p < 0.05) in the Large CL group (5.51 ± 0.33 and 3.62 ± 0.27) compared to the Without CL group (4.54 ± 0.19 and 2.62 ± 0.14, respectively), with no difference between the CL sizes. Likewise, the diameter and area of the COCs were higher in the Small CL group of ovaries compared to the Without CL group. In the Large CL group of ovaries, 9% more morulae (p < 0.05) were obtained compared to the Without CL group; in the Medium CL group, 13% more blastocysts were obtained compared to the Without CL group. However, in the hatching capacity and diameter of blastocysts, no statistical difference was evident (p > 0.05). In conclusion, the presence and size of the CL in the ovaries of slaughtered sheep influence the productive efficiency of embryos in vitro under the conditions in which the present study was carried out. Full article

Single or combined heat pump technologies are generally used to dry fruits and vegetables, with combined heat pump technologies offering superior performance. This review summarizes the applications of single and combined heat pump drying technologies for fruits and vegetables in China and globally, discusses their current advantages and disadvantages, and outlines future development directions for heat pump-based drying methods. Future research should focus on improving combined heat pump technologies and enhancing the performance of single heat pump drying systems to enhance the effectiveness and feasibility of these technologies for drying fruits and vegetables. Improved technologies would also help meet the increasing demand for high-quality food and social development. Moreover, changes in the mechanisms of key indicators, such as mechanical and thermodynamic properties, should be continuously monitored while drying various fruits and vegetables. Future research into combined heat pump technologies should focus on determining the conversion methods between pairs of drying technologies and identifying the most effective drying technology combinations. Future research into single heat pump technologies should focus on improving the performance levels of core components, such as compressors and drying media. Full article

Large language models (LLMs), as one of the most advanced achievements in the field of natural language processing (NLP), have made significant progress in areas such as natural language understanding and generation. However, attempts to achieve the widespread use of these models have met numerous challenges, encompassing technical, social, ethical, and legal aspects. This paper provides a comprehensive review of the various challenges associated with LLMs and analyzes the key issues related to these technologies. Among the challenges discussed are model interpretability, biases in data and model outcomes, ethical concerns regarding privacy and data security, and their high computational requirements. Furthermore, the paper examines how these challenges impact the applications of LLMs in fields such as healthcare, law, media, and education, emphasizing the importance of addressing these issues in the development and deployment of these models. Additionally, solutions for improving the robustness and control of models against biases and quality issues are proposed. Finally, the paper looks at the future of LLM research and the challenges that need to be addressed for the responsible and effective use of this technology. The goal of this paper is to provide a comprehensive analysis of the challenges and issues surrounding LLMs in order to enable the optimal and ethical use of these technologies in real-world applications. Full article