Search Results (14,661)

Against the backdrop of advancing the “dual carbon” goals (carbon peaking and carbon neutrality), the “fishery–photovoltaic complementary” model—integrating solar power generation with salt pan production—has been widely adopted in Tianjin. However, large-scale photovoltaic (PV) facility construction exerts complex impacts onsalt panns, a wetland ecosystem of unique ecological value, by blocking sunlight, altering local microclimates, and regulating water evaporation. Currently, systematic field studies on the comprehensive effects of PV facilities onsalt pans ecosystems remain scarce, particularly those focusing on impacts on primary producers and key environmental factors. Pond sediments harbor the densest and most diverse aquatic microbial communities. In this study, sediment samples were collected from four typical ponds in Tianjin’salt panan region in April, July, and September 2024. Post sample processing, multiple statistical analyses were conducted, including alpha diversity indexing, species abundance clustering, and beta diversity analysis (non-metric multidimensional scaling, NMDS). The results showed the following: (1) Microbial communities existed in both PV-equipped and non-PV areas, indicating no significant correlation between PV presence and alpha diversity indices. (2) Species and genus compositions aggregated in PV-equipped areas with generally consistent community structures, whereas they displayed high dispersion in non-PV areas. This regulatory effect of PV facilities was relatively stable, with deviations only at a few sampling sites, confirming that PV presence significantly affects community composition patterns at both species and genus levels. (3) Cluster heatmap analysis revealed distinct seasonal variations in clustering relationships between sampling stations and microbial genera. Among dominant genera, only Desulfotignum was unaffected by PV facilities or seasonal changes, while the distribution of other dominant genera was significantly influenced by PV construction. Full article

Background/Objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects the daily lives of older adults, impacting their cognitive abilities as well as speech and language communication. Early detection is crucial, as it enables timely intervention and helps improve the quality of life for those affected. While large language models (LLMs) have shown promise from spontaneous speech, most studies are unimodal and miss complementary signals across modalities. Methods: We present an LLM-powered multimodal cross-attention framework that integrates lexical (text), acoustic (speech), and visual (image) information for dementia detection using the ADReSSo 2021 picture-description dataset. Within this framework, text data are encoded using the ModernBERT, audio features are extracted using the wav2vec 2.0-base-960, and the Cookie Theft image is represented through the CLIP ViT-L/14. These embeddings are linearly projected to a shared space and then combined via Transformer-based cross-attention, yielding a fused vector for AD detection. Results: Our results show that the trimodal model achieved the best overall performance when paired with an SVC classifier, reaching an accuracy of 0.8732 and an F1 score of 0.8571, surpassing both the top-performing unimodal and bimodal configurations. For interpretability, a sensitivity analysis of modality contributions reveals that text plays the primary role, audio provides complementary improvements, and image offers modest yet stabilizing contextual support. Conclusions: These results highlight that the method of multimodal embedding fusion significantly influences performance: a cross-attention block achieves an effective balance between accuracy and simplicity, producing integrated representations that align well with interpretable downstream classifiers. Full article

On the 25 September 1987, CBS aired the first episode of Beauty and the Beast. This television fantasy romance centred on the chaste relationship between Catherine Chandler (Linda Hamilton), a New York socialite turned District Attorney investigator, and the beastly Vincent, a man with leonine features who lives in a secret commune of outcasts beneath the city, played by Ron Perlman, but designed by Rick Baker. This article examines Vincent as a core part of Beauty and the Beast’s appeal and as a sight for affective spectacle. It will argue that due to television’s ability to provide audiences with intimacy and proximity, as well as Alexia Smit’s theories of tele-affectivity, Vincent, as a character and as part of the scenography of the television show, allows for “a multisensory, situated experience”. Taking a historical materialist approach, this article will examine the initial reaction to Vincent as a character in the prerelease material and the critical reception upon the release of the first season. It will also explore ideas of responsibility in the creation of Vincent and the tension and collaboration that take place between Perlman and Baker. Full article

Understanding how rising temperatures driven by climate change affect biodiversity and ecosystem functioning is particularly relevant in fragile island environments. Terceira Island, part of the Azorean Archipelago (Portugal) in the mid-Atlantic Ocean, was selected for an in situ experiment to assess the effects of simulated warming on arthropod diversity and grass productivity in intensively managed pastures. Over one year, we examined the influence of a simulated increase in temperature via Open Top Chambers (OTCs) on ground-dwelling arthropod assemblages and plant biomass. We used several statistical methods to analyze both individual variables and overall community patterns to explore the relationships between treatments, grass biomass, and arthropod community structure. Although the OTC treatment did not result in any significant changes to the analyzed variables, an increase in diversity indices was observed over the course of the experiment. Distance-based redundancy analysis revealed significant effects of both OTC treatment and altitude on arthropod community composition. Models showed that the analyzed variables only partially explained the observed changes. Nevertheless, additional factors, such as vegetation structure and microclimatic variation, may also contribute to the observed patterns and warrant further investigation. These results underscore the importance of long-term, multifactorial approaches to understanding interactions between warming, plant productivity, and arthropod diversity. Full article

Solid-state lighting, especially light-emitting diodes (LEDs), is revolutionizing indoor lighting due to its energy efficiency, long lifespan, low heat output, and enhanced color rendering. LEDs can quickly adjust light intensity, enabling the development of visible light communication (VLC) technology. However, the modulation bandwidth of phosphor-converted white LEDs commonly used for illumination is limited, potentially affecting the speed of the VLC links. This paper presents a receiver-side equalization technique to overcome bandwidth limitations in VLC links due to LEDs. The proposed approach utilizes a novel transimpedance amplifier with an embedded T-network shunt-feedback equalizer (TIA-TE) to introduce adjustable high-frequency peaking in the TIA’s frequency response. By incorporating this peaking, the system’s bandwidth is extended without sacrificing important performance parameters like gain, noise, or power dissipation. The TIA-TE is followed by a main amplifier and a standalone continuous-time linear equalizer (CTLE) for further signal conditioning, while a 50 Ω buffer interfaces the receiver with measurement equipment. Post-layout simulations in a 0.35 µm CMOS process validate the approach. Using a 4 pF photodiode, the system bandwidth was initially limited by the LED’s 3 MHz modulation bandwidth. The proposed TIA-TE extends the bandwidth to 8.4 GHz without sacrificing the gain or power dissipation. The subsequent CTLE further extends the bandwidth to 14 MHz. The receiver front end achieves a mid-band transimpedance of 110 dBΩ and an input-referred noise current of 7.2 nA_rms, while dissipating 2.48 mW (excluding the 50 Ω buffer). Simulated 28 Mb/s NRZ eye diagrams demonstrate the feasibility of the proposed TIA-TE architecture for LED-limited VLC links. Full article

Background: In 2024, Reddit, an emerging social media platform, saw a 50% increase in monthly users to nearly 100 million. Reddit has also emerged as a significant space for discussions about health conditions, including epilepsy, which affects about 50 million people globally. Purpose: This study aims to explore trends in the volume, timing, themes, emotional tone, and sentiment of posts on the r/Epilepsy subreddit from 1 December 2023 to 31 December 2024. Methods: We collected 25,222 original English-language posts from r/Epilepsy using Reddit’s Application Programming Interface (API). Data extraction was restricted to English-language submissions to ensure compatibility with sentiment and thematic analyses. We analyzed post volume and timing using chi-square tests and Poisson regression. Emotional tone was measured using TextBlob (version 0.19.0), while compound sentiment scores were calculated via VADER (Valence Aware Dictionary and Sentiment Reasoner) (NLTK version 3.9.1). A Pearson correlation assessed agreement between sentiment and emotional tone, with statistical significance set at p < 0.05. Thematic analysis was conducted using a KMeans clustering algorithm (scikit-learn version 1.6.1) to identify recurring discussion topics. Results: Total monthly posts steadily increased, with the highest number (2175) in December 2024. Peak posts in descending order were in December 2024, August 2024, and November 2024. Posts were not evenly distributed across the week, with a significant peak on Mondays (χ² = 86.75, p < 0.001) and Poisson regression confirming higher activity early in the week (p = 0.001). Emotional tones fluctuated, with positive sentiments in January and October 2024, and negative sentiments in March and August 2024. KMeans clustering identified five main themes: treatment experiences, community engagement, personal experiences, solidarity, and subreddit gratitude. Manual validation of a random subset of posts demonstrated moderate concordance between automated sentiment classification and human ratings. Conclusions: This study highlights temporal patterns, sentiment dynamics, and thematic structure in online discussions on epilepsy. Social media may offer valuable, real-time insights into patient-centered concerns and community engagement, which can inform healthcare professionals and advocacy groups in supporting individuals affected by epilepsy. Future studies may compare trends of epilepsy discussions across various social media platforms, such as X and Instagram, to further understand online patient experiences. Full article

Autism Spectrum Disorder (ASD) is estimated to affect about 1% of children globally. While there is currently no cure, early detection and targeted interventions can significantly enhance the well-being and daily functioning of children with ASD. This paper presents an intelligent, content-based recommender system designed to suggest personalized activities aligned with each child’s preferences and developmental needs. The proposed system integrates social stories, educational videos, and interactive exercises supported by machine learning techniques to foster communication, social interaction, emotional regulation, and cognitive development—while reducing the need for constant parental supervision. Unlike traditional content-based systems, our approach incorporates the child’s emotional state (mood) to provide more diverse and context-aware recommendations, avoiding the filter bubble effect and enhancing personalization and engagement. A key contribution of this work lies in its focus on personalized and interactive learning experiences, made possible through the combination of multiple assistive technologies. Additionally, the study addresses the problem of data scarcity by providing a publicly available dataset to facilitate further research in ASD-focused intelligent systems. Preliminary feedback from therapists and parents indicates that the system holds strong potential to substantially improve the educational, communicative, and emotional skills of children with ASD. These promising results motivate future large-scale empirical evaluations to validate its effectiveness and establish it as a valuable tool for ASD intervention and inclusive education. Full article

 The present study aims to provide a comprehensive and integrated analysis of the potential of offshore renewable energy resources in the maritime sector located at the Danube mouth area in the Black Sea, one of the most complex and dynamic hydrological and climatic systems in Eastern Europe. In the current context of climate change, the Danube mouths are of strategic importance due to the specific morphology of the area and the high potential for harnessing multiple renewable sources such as wind, wave, and solar energy. Therefore, this research supports sustainable development and adaptation to climate change. At the same time, predicted climate change may increase the frequency of extreme events, such as storms, sudden changes in water levels, and increased wave heights, which can affect navigational safety, ecosystem integrity, and coastal infrastructure. Thus, this research seeks not only to identify the energy potential of renewable resources but also to assess their risks and vulnerabilities. Using a wide range of data types, three time periods were studied for the main Danube mouth: Sulina and St. George. Both Sulina and St. George present future wind and wave intensification trends, especially in high-emission scenarios, without significant changes in the dominant direction. St. George remains the area with the more intense regime, while Sulina has more moderate episodes, but with a slightly more evident increase in the frequency of 6–12 m/s winds. At the same time, solar radiation shows a slight increase in recent years, especially in the summer season. Harnessing these resources has the potential to, for example, power coastal communities and offshore installations, providing clean and reliable energy while reducing greenhouse gas emissions.  Full article

Yersinia enterocolitica is a foodborne pathogen that forms biofilms on surfaces, enhancing its survivability and increasing bacterial resistance, which poses a significant challenge to public health. Therefore, developing effective strategies to inhibit biofilm formation is crucial. Lipoic acid (LA) is a compound with antibiofilm properties. This study investigates the effects of LA on biofilm formation by Y. enterocolitica BNCC 108930 (a standard strain from the BeNa Culture Collection). Biofilm formation, maturation, removal, and cell viability were evaluated by crystal violet staining, extracellular polysaccharide assay, Methylthiazolyldiphenyl-tetrazolium bromide assays, motility, and quorum sensing (QS) assays. The results indicate that LA interferes with the early stages of biofilm formation by compromising cell membrane integrity and reducing cellular adhesion. Furthermore, 2.5 mg/mL of LA reduced biofilm biomass (with a 48 h treatment inhibition rate of 51.46 ± 1.29%) and extracellular polysaccharide production (with a relative inhibition rate of 30.09 ± 1.8%), while significantly reducing the metabolic activity of bacteria within the biofilm (inhibition rate over 85%) compared to the untreated group. Confocal laser scanning microscopy and field emission gun scanning electron microscopy confirm that LA induces a sparse biofilm structure, reduced aggregation, and decreased biofilm thickness to 21.33 ± 2.27 μm. Motility and QS assays demonstrate that LA affects flagellar motility and the secretion of N-acyl homoserine lactones. Transcriptome analysis revealed downregulation of genes involved in the QS system and biofilm formation (e.g., lsrA, lsrC, lsrD, lsrR, and oppA), as well as upregulation of genes related to bacterial chemotaxis and flagellar assembly (e.g., RS19655, RS15590, fliE, fliJ, fliP, fliA, and fliK). These alterations suggest that LA inhibits Y. enterocolitica biofilm formation by affecting intercellular communication and flagellar motility. This study highlights the antibiofilm properties of LA, providing a theoretical basis for potential applications in microbial and biofilm control. Full article

Climate change affects flood frequency and intensity throughout the world, leading to a research gap in the traditional management framework. Furthermore, traditional frameworks often rely on complex hydrological patterns and one-way communication, demonstrating urgent needs for adaptive and two-way communication approaches. For this purpose, the current systematic literature review (SLR) fills this gap by analyzing the widely reported literature on the role of an artificial intelligence (AI)-based framework. This SLR provides conceptual and theoretical insight into the potential role of generative AI and an OpenAI-based theoretical framework for effective flood management. Therefore, 77 peer-reviewed articles published between 2010 and 2025 in reputed sources such as ScienceDirect, Springer Nature, MDPI, Wiley, and others were analyzed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. According to the results of this paper, four hypothetical applications of generative AI are described, namely: (i) a knowledge translator to provide simplified hydrological information, (ii) a decision-support assistant that aids real-time strategic analysis, (iii) a community engagement tool to increase the participation and understanding of people, and (iv) an interface to harmonize and synthesize various sources of information. The discussion indicates that there is a lot of potential in terms of generative AI improving the inclusiveness, real-time sensitivity, and cost-effectiveness of flood risk management practice. Nevertheless, the research also presents significant issues that are connected to data integrity, algorithm bias, digital equity, and ethical governance. The results indicate that generative AI has a significant potential of developing robust, more accessible, and more communicative flood risk management systems, and that additional studies on the responsible and ethical use of the technology are necessary. Full article

The assessment of flood defense structures is essential for community resilience and disaster prevention. Within these structures, the potential for erosion and piping mechanisms poses critical risks, often leading to severe infrastructure damage. Breach initiation and growth are the main causes of dam and levee failure, which is directly affected by the phreatic line. This study introduces a natural element method (NEM) formulation with Sibson interpolation specifically tailored to directly estimate the phreatic line in homogeneous earthen embankments, avoiding conventional mesh generation and reducing preprocessing effort. The main innovation is the combination of a mesh-free NEM scheme with an iterative free-surface update dedicated to phreatic line tracking, rather than full embankment flow field simulation. Comparative analyses and validation against existing data emphasize the method’s strength. Validation against piezometric data from a railway embankment in Cumbria (UK) and the IJkdijk full-scale test levee (Netherlands) shows average relative errors below 2% and maximum errors under 10%, demonstrating that the proposed NEM approach can reproduce observed phreatic levels with high accuracy using relatively few nodes. These results indicate that the method provides an accurate and practically attractive tool for phreatic line assessment in flood defense structures, suitable for integration into levee and embankment safety evaluations. Full article

Coastal settlements in Bangladesh are geographically flood-prone areas. This physical nature erodes the size and shape of those settlement boundaries over time. Such changes leave communities vulnerable in terms of securing a living place and livelihoods. However, the research arena rarely addresses the long-term changing aspects of settlement and the local governance responses to vulnerability. To examine this situation, this study explored settlement transformation patterns and governance challenges, using the case study of Chattogram Division in Bangladesh from 2005 to 2025. It applied a mixed-methods approach. The analysis, using the technique of Multi-temporal Landsat imagery with Random Forest classification, revealed complex settlement trajectories. It showed built-up areas expanded significantly between 2005 and 2015 but shrank by 2025, reflecting both hazard exposure and displacement pressures. Union-level analysis identified 62 coastal unions with high to very high settlement change. Conducting field surveys in selected Juidandi and Kalamarchhara unions through focus group discussions with communities and interviews with local officials highlighted recurring inundation, permanent land loss affecting thousands of households, and persistent disruptions to livelihoods. This study also found moderate emergency responses in selected unions; however, strategic planning for relocation, health, and well-being of communities is insufficient. Continuous resource constraints and poor coordination with communities and line organizations made local implementation less effective, which blurs the effectiveness of disaster risk reduction policies. These findings underscore the necessity of union-level governance capacity building, integrating community-based adaptation with formal interventions, and developing spatially differentiated relocation strategies to enhance the resilience of climate-vulnerable coastal settlements. Full article

The Mediterranean Sea is a biodiversity and climate change hotspot. The increase in seawater temperature affects marine ecosystems causing marine species to change their distribution and abundance. Such changes lead to alterations in community composition, often characterized by an increase in warm-affinity species over time, known as tropicalization of temperate seas. Monitoring programmes are useful for understanding the consequences of the ongoing transformations driven by ocean warming. In this study, underwater visual censuses (UVC) were conducted for fish and benthic communities at 24 stations of the Balearic Archipelago in 2022 and 2025. The comparison between both periods revealed an increase in the frequency of warm-affinity species, including the fishes Sparisoma cretense (Teleostea, Scaridae) and Caranx crysos (Teleostea, Carangidae); the invertebrates Telmatactis cricoides (Cnidaria, Actiniaria) and Hermodice carunculata (Annelida, Polychaeta, Amphinomidae) and the algae Penicillus capitatus (Chlorophyta, Ulvophyceae). Our findings highlight the importance of monitoring programmes to identify evidence of processes such as tropicalization and to provide timely information to respond to shifting marine ecosystems. Full article

Veterinary education is academically demanding and emotionally intensive, affecting student performance, well-being, and long-term professional development. This narrative review synthesizes current evidence on academic stressors, admissions predictors, coping mechanisms, and institutional responses in veterinary training. Cognitive indicators such as Grade Point Average (GPA) and standardized test scores reliably predict early performance in pre-clinical biomedical courses. However, these measures do not adequately capture essential non-cognitive attributes, including resilience, adaptability, motivation, and communication skills, which are critical for sustained success in clinical environments. Holistic admission approaches show promise but remain inconsistently validated across institutions. Academic stress in veterinary programs arises from heavy curricular loads, frequent high-stakes assessments, financial pressures, and transitions into clinical training. Persistent stress exposure is associated with anxiety, depressive symptoms, and burnout risk. Evidence suggests that structured wellness initiatives, peer mentoring, and resilience-building programs can mitigate these effects when embedded systematically within the curriculum. Current literature is largely cross-sectional and geographically concentrated in Western educational contexts, limiting causal inference and generalizability. Longitudinal, multi-institutional research linking admissions profiles to academic trajectories and psychological outcomes is needed. Integrating cognitive and non-cognitive evaluation with sustained institutional support may enhance retention, academic performance, and professional preparedness in veterinary education. Full article

This study examines how individuals experience AI-generated natural landscapes and whether visually simulated, non-immersive environments activate perceptual, emotional, and restorative mechanisms typically associated with real or immersive nature. Grounded in a critical realist epistemology, AI-generated nature is conceptualized not as a substitute for natural environments, but as a distinct experiential domain in which the transferability of established environmental and restoration theories can be empirically tested. A total of 1021 participants evaluated AI-generated landscapes using a 36-item instrument derived from environmental psychology, digital media studies, and restoration research. Exploratory factor analysis identified six experiential dimensions—perceived naturalness, sense of presence, affective attunement, emotional resonance, restorative quality, and cognitive restoration—which were examined using confirmatory factor analysis and structural equation modeling. While the measurement model showed excellent fit, structural relationships among constructs were consistently weak, indicating systematic non-support of theoretically expected pathways. The findings show that AI-generated landscapes can elicit salient perceptual, affective, and cognitive responses that remain largely unintegrated, limiting their capacity to produce coherent restorative outcomes. The study delineates boundary conditions for applying environmental and restoration theories to visually simulated AI landscapes and supports positioning synthetic nature as a complementary element in sustainable tourism experience design and digital environmental communication. Full article