This study examines Tyler Perry's 2025 film Straw through Pierre Bourdieu's sociological theory, focusing on intersections of motherhood, mental health, and societal pressure. Despite increasing scholarly attention to Black women's cinematic representation, a significant gap exists in Bourdieusian analyses of contemporary films that centre mental health dimensions of Black motherhood, particularly in dramatic narratives addressing institutional violence. This study addresses three research questions: (RQ1) How does Straw represent the depletion of economic, social, and symbolic capital for Black single mothers within institutional fields? (RQ2) What narrative and visual strategies does the film employ to dramatize symbolic violence and its psychological consequences? (RQ3) How does Perry's film contribute to evolving cinematic representations of mental health in Black motherhood narratives? The study employs qualitative close textual analysis of key scenes across five thematic categories. Analysis includes scene transcription, visual motif identification (framing, sound, montage), Bourdieusian concept mapping, and literature triangulation. Scene selection criteria prioritized moments depicting capital exchange, institutional power dynamics, and psychological transformation. Drawing on Bourdieu's concepts of capital, habitus, field, and symbolic violence, the analysis reveals how the film dramatizes economic, social, and symbolic capital erosion endured by marginalized mothers. The findings document three distinct patterns such as the cascading institutional failures that transform routine encounters into crises, the weaponization of maternal identity through surveillance systems, and the psychological accumulation of symbolic violence leading to breaking points. This study makes three contributions: it extends Bourdieu's symbolic violence concept to cinematic mental health representation, documents emerging patterns in Black motherhood film narratives post-2020, and offers methodological innovations for integrating sociological theory with film textual analysis. The study argues that Straw exposes cumulative effects of societal neglect and stigmatization of Black motherhood, offering critical perspective on systemic barriers limiting agency and wellbeing.

Ocean plastic pollution has become one of the most urgent and destructive environmental challenges of the 21st century, threatening marine ecosystems, global biodiversity, economic sustainability, and human health. Traditional methods of monitoring marine plastic waste—such as manual observation, ship-based surveys, and laboratory sampling—are slow, geographically restricted, and incapable of providing real-time insights. As millions of tons of plastic enter the oceans every year and disperse unpredictably through water currents, there is a critical need for a more advanced and scalable monitoring strategy. This research explores the transformative role of Artificial Intelligence (AI) in the automated detection, tracking, and quantification of ocean plastic pollution. The study integrates satellite imagery, drone surveillance, oceanographic IoT sensors, and deep learning models, including CNN, YOLO, and U-Net, to classify plastic debris with high precision and generate geospatial pollution maps. Experimental analysis demonstrates that AI models achieve an average detection accuracy of more than 90%, outperforming traditional monitoring techniques that rely heavily on manual visual identification. Furthermore, machine learning forecasting mechanisms—such as LSTM—enable the prediction of future plastic accumulation hotspots, supporting proactive environmental planning rather than reactive intervention. The findings confirm that AI-based monitoring substantially reduces operational costs, increases surveillance range, and accelerates decision-making for environmental agencies. However, the study also recognizes limitations including environmental variability, lack of standardized global datasets, difficulty in detecting microplastics, and hardware implementation costs in developing nations. Despite these challenges, AI presents a highly scalable and sustainable solution for global ocean conservation. With ongoing advances in remote sensing, robotics, and cloud-based analytics, AI has the potential to become the global standard for mitigating marine plastic pollution and preserving the long-term resilience of ocean ecosystems

The paper considers the influence of contaminated samples with anomalous observations on the reliability of statistical analysis results and hypothesis testing for sample homogeneity. The main focus is on visual data analysis as an effective means of preliminary research. The use of histograms, scatter plots, and density estimates allows for the visual identification of outliers, the assessment of the distribution shape, and the detection of differences between samples. The purpose of the study is to evaluate the robustness of popular statistical criteria for testing the normality of distribution in the presence of contamination in small samples. The scientific novelty lies in the quantitative study of the impact of different types of contamination on the results of popular criteria, as well as in the practical assessment of their behavior under conditions of violation of assumptions about data homogeneity. The practical novelty lies in the development of recommendations for practitioners on the selection of the optimal criterion when analyzing samples with possible anomalous observations, taking into account the stability of statistical methods. Research methods include numerical modeling of samples with controlled introduction of structural contaminants, assessment of the frequency of false rejections/acceptance of the null hypothesis, as well as comparative analysis of the results obtained using the following statistical criteria: Student's t-test for comparing the mean values of two samples; the Kolmogorov-Smirnov one-sample test to verify the conformity of the empirical distribution with the theoretical one; the Anderson-Darling criterion to verify the normality assessment; the Kolmogorov-Smirnov two-sample test to verify the homogeneity of two distributions. The results of the study showed the importance of choosing the appropriate criterion depending on the sample size and the expected level of contamination. Presenting the average values and ranges for N repetitions of the experiment allows for a visual assessment of the stability and reliability of each test in the presence of contaminated data. Based on the experiments conducted, practical recommendations are proposed for the preliminary diagnosis of samples and the selection of the optimal approach to testing hypotheses in the presence of contaminated data.

Classifying bird species presents significant challenges and often leads to ambiguous identifications. Even expert ornithologists may have differing opinions when examining bird images. This challenge tests the limits of visual identification capabilities, both for humans and computers. Although birds share common basic body structures, their shapes and appearances can vary considerably among different species. Significant variations are also observed even within the same species, due to differences in lighting conditions, backgrounds, and especially varying postures. Examples of these postures include birds in flight, aquatic birds, and birds perched partially concealed by foliage. This project aims to utilize machine learning capabilities to help birdwatching enthusiasts identify bird species based on the photographs they take.

In the fabrication of semiconductor devices, increased yield is achieved using Scanning Electron Microscopes (SEM) to measure and inspect circuit patterns. With recent decreasing scale and increasing complexity of semiconductor circuit patterns, it has become increasingly difficult to recognize patterns accurately using rule-based image processing methods. As such, we propose a method that uses semi-supervised learning for segmentation processing, to recognize which pattern level each pixel represents. With existing methods, the pseudo-labels used for training were not accurate enough, and there were issues such as inconsistent recognition of repeated-pattern layouts and mixed-up results in large unmarked areas distant from the pattern contour. Accordingly, the proposed method is able to perform highly accurate segmentation with the design of new types of loss for evaluating consistency in pattern structure at various scales. When compared with Unimatch and CAC, which are well-known high-performance segmentation methods, the accuracy relative to visual identification increased dramatically, from 10-12% to 100%. In quantitative evaluation using mean Intersection-over-Union (mIoU) at the pixel level, mean values also increased from a range between 0.45 and 0.65 to over 0.94, confirming that the proposed method is effective.

Environmental DNA (eDNA)-genetic material left behind by organisms in their ecosystems-has been increasingly positioned as an important tool for studying biodiversity, especially in marine environments. Advances in genetic methods now allow scientists to filter eDNA from seawater and use molecular tools to sequence it, generating a catalog of organisms likely present in the ecosystem without ever seeing them. Thus, eDNA sampling differs substantially from conventional biodiversity monitoring approaches that rely on visual observation-both opening new realms of monitoring while also coming into conflict with different ways of knowing and sensing marine ecosystems. How do researchers navigate the promises and pitfalls of an emerging technology such as eDNA? In this paper, I conduct a case study of an oceanographic expedition incorporating eDNA sampling for the first time to understand how researchers negotiate the perceived benefits and challenges of adopting this emergent research repertoire. Through semi-structured interviews with 30 participants, I use a valuographic approach to characterize how researchers describe the desirable outcomes they hope to achieve via eDNA monitoring. Overall, I find that researchers articulated several broad outcomes they were hoping to achieve using eDNA approaches: ocean discovery and exploration, organism measurement and identification, comparisons across time and space, and policy and management applications. However, these outcomes and their interconnections were also disputed. Researchers surfaced practical challenges such as methodological constraints and cost, epistemic tensions surrounding the shift away from visual identification, and skepticism about the validity of eDNA-based comparisons. Moreover, researchers rarely discussed broader societal or ethical implications of eDNA approaches, underscoring a gap in consideration of its role beyond scientific inquiry. By characterizing these value-driven dissonances, this study illuminates potential barriers to eDNA's widespread adoption and reveals how methodological and epistemic tensions can shape the proliferation of new scientific approaches more broadly.

Abstract---Face detection and recognition play pivotal roles in various applications, from attendance management to secure urban living. This paper introduces an enhanced approach that integrates latest YOLOv8 (You Only Look Once), a customdesigned deep Convolutional Neural Network (CNN), and the face_recognition library to advance face detection and recognition in both biometric and practical domains. The proposed system leverages the efficiency of YOLOv8 for real-time multi-object detection, providing a robust foundation for identifying faces in diverse and dynamic environments. YOLOv8's ability to process images at an impressive speed enhances the system's responsiveness and adaptability, crucial for real-world applications. The integration of a custom-designed deep CNN, in conjunction with the face_recognition library, serves as the backbone for intricate feature extraction. This synergy enables high-precision face recognition even in challenging scenarios. The custom model's adaptability to specific characteristics present in diverse face datasets, combined with the capabilities of the face_recognition library, enhances the system's robustness and accuracy in recognizing faces with varying attributes. To evaluate the system's performance, we conducted a various assessment using custom datasets representing real-world scenarios. The proposed system offers practicality in deployment. Its real-time capabilities make it suitable for time-sensitive applications, such as access control systems and security in urban environments. The integration of YOLOv8 with a custom deep CNN, and the face_recognition library represents a significant advancement in the field of face detection and recognition, offering a reliable and efficient solution for various challenges. As a comprehensive approach, this research contributes to the broader landscape of biometric technology, paving the way for enhanced face recognition systems applicable in various domains. The adaptability, accuracy, and efficiency demonstrated by our approach, utilizing both a custom-designed deep CNN and the face_recognition library, make it a promising candidate for integration into real-world applications, facilitating safer and more secure urban living environments and others. Index Terms- face detection, face_recognition, neural network, Yolov8, computational efficiency

The rising price of sika deer meat is increasing the risk of economic adulteration, highlighting the need for rapid and reliable authentication methods to protect both market integrity and consumers. This work presents a novel countermeasure: a polymerase chain reaction (PCR)-based nucleic acid test strip designed for the specific and visual identification of sika deer meat. Our approach commenced with the design of specific primers targeting the cytochrome C oxidase subunit I (COI) gene. To guarantee the reliability of the assay, a DNA standard plasmid was constructed to serve as an unambiguous positive control for the PCR. Under optimized conditions, results showed that authentic sika deer meat generated both test and control lines on the strip, while adulterated and negative samples produced only the control line. The assay demonstrated flawless specificity and a detection sensitivity of 1.0 ng·μL−1 for target DNA, representing a tenfold enhancement over gel electrophoresis. Furthermore, the method demonstrated a detection limit of 1% for sika deer meat in admixed samples, with a faint but visible signal observed down to 0.1% under optimized conditions. In conclusion, the developed test strip method is not only specific and sensitive but also user-friendly, positioning it as a practical and powerful tool for rapid, on-site meat authentication.

Accurate assessment of microplastic (MP) contamination in environmental samples is crucial not only for understanding the scope of this growing environmental threat but also for quantifying its magnitude and enabling proper risk assessment. However, current methodologies for MP quantification often suffer from inaccuracies due to the difficulty in distinguishing plastic particles from natural organic matter, also due to incomplete digestion of natural polymers during sample treatment. Moreover, the techniques commonly employed are highly time-consuming, further limiting their routine application. This research presents an innovative solution for optical microscopy evaluation: a sequential co-staining technique employing Nile Red (NR) and Rose Bengal (RB) to identify natural vs. synthetic polymer fragments as well as false positives. Two experiments were implemented staining natural polymers (cellulose, protein, lignin, and chitin) and synthetic polymers (Polyvinyl Chloride (PVC), Polystyrene (PS), Polyethylene Terephthalate (PET), Polypropylene (PP), Nylon (NY), High-Density Polyethylene (HDPE) and Low-Density Polyethylene (LDPE)) with the two dyes. The results showed that co-staining is an effective way of separating natural and synthetic fragments and a significant improvement in the accuracy of visual MP identification. Additionally, co-staining the same filter allows to obtain relevant time saving as well as reducing counting and identification errors, since no sample exchange is needed. Application of this novel technique will allow for more reliable monitoring of MP concentrations in various environmental matrices, leading to better-informed risk assessments and mitigation strategies.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-32829-7.

Oral cancer (OC) is a major public health problem in the Indian subcontinent. As many as 90% of all OC cases are oral squamous cell carcinomas (OSCCs), often developing from oral potentially malignant disorders (OPMDs). Although the oral cavity is freely accessible, visual identification is often challenging. Biopsy and a microscopic examination is the only confirmatory diagnostic test. Recently, the analysis of volatile organic compounds (VOCs) has emerged as a new, non-invasive, rapid, and inexpensive strategy with promising potential in clinical diagnostics. The human VOCs produced in metabolic pathways, present in body fluids and the exhaled air, can be used for monitoring several oral diseases, including OC. The aim of the present study was to determine the potential diagnostic capabilities of salivary VOCs in OC through identifying and comparing the salivary volatilomic profiles among OSCC and OPMD subjects, as well as healthy controls, using the gas chromatography-mass spectrometry (GC-MS) analysis. Unstimulated saliva samples were collected from 35 OSCC subjects, 35 OPMD subjects and 40 healthy controls. The VOCs extracted from the ZSM-5/PDMS film were condensed with 100 μL of methanol, of which 1.0 μL was subjected to the GC-MS analysis. A total of 128 salivary VOCs were detected and identified among the OSCC and OPMD subjects and the healthy controls. Twenty-five metabolites were determined to be statistically significant in differentiating among the 3 groups. Organic acids, alcohols, ketones, alkanes, and acid amides were the major classes of VOCs in the OSCC subjects, while organic acids, alcohols, ketones, acid amides, heterocyclic compounds, and phenols constituted the VOC profile in the OPMD subjects. 1-chloro-dodecane and 1-tridecanol were significant VOCs observed among the controls. The study demonstrates that salivary VOC profiling can reveal distinct metabolomic alterations in OSCC and OPMDs, with several VOCs emerging as potential tumor-specific biomarkers. While these findings highlight the promise of VOC-based screening, larger studies are needed to validate these markers and establish their clinical applicability.

ABSTRACT This study presented a minimally invasive workflow for diagnosing the structural condition of the 170–200-year-old historical Biryol House (Çamlıhemşin, Rize, Türkiye). The approach integrated non-destructive testing (NDT) screw withdrawal resistance (SWR) and stress-wave velocity with visual inspection, and Eurocode 5–based structural checks. Building-specific calibration yielded strong relationships between SWR and density (R2 ≈0.96) and between SWR and bending strength (MOR) (R2 ≈0.97), supporting in-situ estimation of mechanical properties. SWR values for members ranged roughly from 0.69 to 2.32 kN; basement beams 2B and 5B fell below minimum strength requirements defined by TS EN 338:2016 and required priority intervention. Axial compression and buckling evaluations for columns revealed that the critical limit state is the design axial strength, not buckling, and that regular monitoring and local strengthening are required, especially in lower-level columns. This study addressed research gaps by applying a comprehensive diagnostic method for historical timber structures, integrating visual identification and non-destructive testing. The results demonstrate that combining visual diagnostics with calibrated NDT enables evidence-based triage, targeted reinforcement, and monitoring for vernacular timber buildings in humid, decay-prone climates. The findings offer a replicable template for preventive conservation of historical timber structures in Türkiye and comparable regions.

This review presents a critical and comparative analysis of carbon-based electrochemical sensing platforms for the determination of heavy metal ions in water, with emphasis on Pb2+, Cd2+, and Hg2+. The growing discharge of industrial and mining effluents has led to persistent contamination of aquatic environments by toxic metals, creating an urgent need for sensitive, rapid, and field-deployable analytical technologies. Carbon-based nanomaterials, including graphene, carbon nanotubes (CNTs), and MXene, have emerged as key functional components in modern electrochemical sensors due to their high electrical conductivity, large surface area, and tunable surface chemistry. Based on reported studies, typical detection limits for Pb2+ and Cd2+ using differential pulse voltammetry (DPV) on glassy carbon and thin-film electrodes are in the range of 0.4-1.2 µg/L. For integrated thin-film sensing systems, limits of detection of 0.8-1.2 µg/L are commonly achieved. MXene-based platforms further enhance sensitivity and enable Hg2+ detection with linear response ranges typically between 1 and 5 µg/L, accompanied by clear electrochemical or optical signals. Beyond conventional electrochemical detection, this review specifically highlights self-sustaining visual sensors based on MXene integrated with enzyme-driven bioelectrochemical systems, such as glucose oxidase (GOD) and Prussian blue (PB) assembled on ITO substrates. These systems convert chemical energy into measurable colorimetric signals without external power sources, enabling direct visual identification of Hg2+ ions. Under optimized conditions (e.g., 5 mg/mL GOD and 5 mM glucose), stable and distinguishable color responses are achieved for rapid on-site monitoring. Overall, this review not only summarizes current performance benchmarks of carbon-based sensors but also identifies key challenges, including long-term stability, selectivity under multi-ion interference, and large-scale device integration, while outlining future directions toward portable multisensor water-quality monitoring systems.

This study develops an innovative approach that integrates a colorimetric sensor array (CSA) composed of phenylalanine-modified Mn3O4 nanozymes with advanced algorithms, aiming to detect the anthrax biomarker 2,6-pyridine dicarboxylic acid (2,6-PDA) and six other structural analogs. The nanozymes exhibit tunable oxidase-like activity, catalyzing the oxidation of the chromogenic substrate 3,3',5,5'-tetramethylbenzidine (TMB) to produce blue-colored oxTMB, resulting in quantifiable color changes modulated by the PDAs' interactions. With the assistance of multivariate statistical analysis, the CSA effectively discriminates among the seven structural analogs, enabling the rapid quantitative detection of 2,6-PDA with a detection limit (LOD) of 0.015 ± 0.002 μM and allowing for visual monitoring of anthrax biomarker levels in fetal bovine serum. Moreover, by employing the deep learning YOLOv8 algorithm to visually analyze and train the linear discriminant analysis (LDA) plots obtained from the CSA, the sensor array is able to automatically classify and detect 2,6-PDA and its six structural analogs without the need for visual identification. The results show that the model's mean average precision (mAP) on the validation data set reaches 0.98-0.99, and its average confidence on the training data set can reach 0.90-0.93 (on a scale of 0 to 1). Compared to traditional manual analysis, YOLOv8 algorithm assistance significantly reduces detection time and labor costs while maintaining the accuracy of detection results, providing technical support for the application of sensor arrays in complex environments.

Purpose: To evaluate current strategies for enterprise development in order to determine their viability under sanctions pressure. To develop recommendations for a sustainable long-term strategy that will be appropriate for a volatile external economic environment. To enhance methods for strategic planning. Methods: Analysis and synthesis, comparative analysis, economic and statistical techniques, along with expert evaluations. Results: The current strategic development approaches have been analyzed. The repercussions of saPurpose: To evaluate current strategies for enterprise development in order to determine their viability under sanctions pressure. To develop recommendations for a sustainable long-term strategy that will be appropriate for a volatile external economic environment. To enhance methods for strategic planning. Methods: Analysis and synthesis, comparative analysis, economic and statistical techniques, along with expert evaluations. Results: The current strategic development approaches have been analyzed. The repercussions of sanctions on the national economy have been thoroughly examined. Recommendations for establishing a sustainable long-term strategy in a volatile external economic environment along with effective strategic planning tools applicable across various enterprises have been formulated. Practical significance: Employing a scenario approach to evaluate the current enterprise strategy will enable the formulation of a comprehensive strategic development plan for industrial enterprises, factoring in the risks and uncertainties associated with sanctions pressure. Representing the development strategy as a “Decision Tree” will facilitate the visual identification and analysis of the enterprise’s strengths and weaknesses, thereby enabling the formulation of the most effective strategy for the enterprise’s advancement.nctions on the national economy have been thoroughly examined. Recommendations for establishing a sustainable long-term strategy in a volatile external economic environment along with effective strategic planning tools applicable across various enterprises have been formulated. Practical significance: Employing a scenario approach to evaluate the current enterprise strategy will enable the formulation of a comprehensive strategic development plan for industrial enterprises, factoring in the risks and uncertainties associated with sanctions pressure. Representing the development strategy as a “Decision Tree” will facilitate the visual identification and analysis of the enterprise’s strengths and weaknesses, thereby enabling the formulation of the most effective strategy for the enterprise’s advancement.

The nondestructive capture and direct identification of circulating tumor cells (CTCs) have emerged as the critical technologies in modern oncology for oncology research, clinical diagnosis, and personalized therapy optimization. However, technical challenges still exist due to the extreme rarity and significant phenotypic heterogeneity. Herein, we present an innovative biomimetic detection platform engineered by integrating platelet membrane-camouflaged magnetic beads (PM-MBs) with rolling circle amplification (RCA)-synthesized aptamer-based polyvalent antibody mimics (PAMs), which is abbreviated as PM-PAM platform. This dual-functional PM-PAM platform demonstrates exceptional CTC capture efficiency (>90%) while enabling instrument-free visual detection of trophoblast cell surface antigen 2 (Trop-2) expression through metal-organic framework (MOF) nanozyme-based colorimetric signals. Triple-negative breast cancer (TNBC) patients undergoing sacituzumab govitecan (SG) therapy suggests CTCs can be successfully detected. Meanwhile, concurrent visual assessment of Trop-2 expression levels on captured cells can be realized. With the unique advantages of cell viability preservation, 100% diagnostic specificity, modular design adaptability, and cost-effectiveness, the PM-PAM platform establishes a new paradigm for liquid biopsy applications. This technology also shows the particular promise for monitoring antibody-drug conjugate (ADC) treatment efficacy through dynamic Trop-2 expression analysis in breast cancer management.

Habitat imprinting is the phenomenon where exposure to cues in the natal habitat increases the probability of choosing a habitat with similar cues later in life. It is considered a key behavioural mechanism that decreases the costs associated with habitat selection. The similarity of breeding to natal habitats can be especially beneficial when choosing the first breeding site and when the choice has long-term consequences due to high site fidelity. Habitat imprinting in breeding habitat selection has rarely been documented in wild animals living in unmanipulated environments and is challenging to study in long-lived species with delayed maturity. We used a combination of genetic and visual identification to identify 354 white-tailed eagles Haliaeetus albicilla hatched between 1991 and 2015 that were subsequently documented breeding between 2001 and 2023 along the Baltic Sea coast or in the inland environments. We examined (a) the similarity of natal and breeding habitats and (b) the effects of natal dispersal distance on this similarity. Furthermore, (c) we were interested in breeding habitat selection and tested whether eagles showed a preference for natal-like habitats among suitable territories that were at the time still unoccupied. We found that breeding habitats were similar to natal habitats, independent of natal dispersal distance. Eagles were also more likely to choose a natal-like breeding site among available alternative sites. These results indicate that habitat imprinting is a possible driving mechanism in the habitat selection of long-lived animals with delayed maturity and has important implications for conservation actions such as eagle reintroduction programmes.

SummaryUnderstanding how the brain represents meaning in real-world contexts is essential for both fundamental neuroscience and clinical applications. Brain encoding and decoding models from naturalistic stimuli provide a powerful window into semantic representations. Yet, existing approaches rely on a constrained scanning environment, or on conventional fNIRS, which has been limited to sparse sampling and/or block-design paradigms. Here, we tested whether high-density diffuse optical tomography (HD-DOT), an advanced high-density tomographic optical imaging method, can support semantic encoding and decoding using naturalistic movies. We collected 3.5 hours of naturalistic movie viewing data from six participants using stimuli labeled with 1,708 categories. Encoding models robustly predicted voxel-level responses, yielding single semantic category maps consistent with prior fMRI studies. In complementary decoding analyses, we showed that DOT responses captured sufficient semantic content to identify which clips participants viewed. To assess organization across individuals, we identified a shared low-dimensional semantic space that captures common semantic dimensions. Finally, clustering analyses revealed interpretable higher-order semantic dimensions like social and animate agents, objects vs natural organisms, and textural scenes, consistently mapped across the cortex. These findings demonstrate that DOT can recover distributed, high-dimensional semantic representations from naturalistic movies, bridging fMRI-level semantic mapping with the accessibility of optical imaging.

MuCoSA: Multi-contextual similarity assessment for histopathology image search

Older typical-hearing adults without a cochlear implant (CI) have been found to exhibit greater multisensory benefits when identifying audiovisual speech than younger normal-hearing adults. The greater multisensory benefits demonstrated by older non-CI users can compensate for unisensory auditory and visual speech deficits, allowing them to identify audiovisual speech at a degree of accuracy like that of younger normal-hearing adults. Although most new CI recipients are 65 yrs of age and older, the reliance of older CI users on such multisensory benefits is unknown. The goal of the current investigation was to evaluate age-related differences in cross-sensory and multisensory benefits in audiovisual speech identification in aging CI users and to examine how they differ from age-matched non-CI users. Twenty middle-aged-to-older CI users (50 to 83 yrs of age) and 35 age-matched non-CI users completed an auditory-visual speech identification task, identifying 288 disyllabic words presented either auditory-alone, visual-alone, or audiovisually. CI users identified speech stimuli streamed directly through their CI device in quiet and in noise (Gaussian) at a +10 and +5 dB signal to noise ratio (SNR). Non-CI users identified speech stimuli delivered through earphones in noise at -5, 0, and +5 dB SNR conditions. Different noise conditions were used for CI users and non-CI users to avoid ceiling and floor effects. From visual, auditory, and audiovisual performance, psychometrics for the visual enhancement of auditory speech (VE), the auditory enhancement of visual speech (AE), and auditory-visual multisensory enhancement (AVE) were calculated. Group differences (in the overlapping +5 dB SNR condition) and effects of age and noise were tested using linear regression and linear mixed-effects regression models. Both CI users and non-CI users demonstrated canonical differences in visual, auditory, and audiovisual speech identification. VE and AVE were greater for CI users than for non-CI users. AVE increased with the age of older CI users and non-CI users, consistent with age-group differences in AVE we observed in a previous study of non-CI users. The results of the current investigation suggest that CI users, like age-matched non-CI users, rely on multisensory integration more as they age. Older CI users may benefit more from audiovisual input than older non-CI users. These perceptual benefits grant older CI users the capacity to identify audiovisual speech to a degree of accuracy closer to that of older non-CI users, despite deficits in the auditory perception of CI users. As a result, the successful use of a CI device may partially depend on the ability of a CI user to integrate information they see with information available from their device, and older CI users may depend on visual input more to successfully use their CI.

This study presents a preliminary assessment of opisthobranch sea slug diversity in the coastal waters of Erdemli-Akkum, located in Mersin Bay (Eastern Mediterranean, Türkiye). During exploratory scuba dives conducted between 2014 and 2015, seven species belonging to different opisthobranch groups were observed and photographed across a range of marine biotopes, including rocky substrates, sandy bottoms, and macroalgal assemblages. The recorded taxa include Goniobranchus annulatus, Flabellina rubrolineata, Flabellina affinis, Cratena peregrina, Elysia viridis, Aplysia depilans, and Syphonota geographica. These species represent a broad ecological and morphological diversity within the subclass Heterobranchia. The findings contribute to the growing faunistic inventory of opisthobranchs in the Eastern Mediterranean and highlight the ecological richness of the Akkum coastal area. The use of in situ photography proved valuable for documentation and visual identification, supporting future taxonomic and ecological studies. Keywords: Eastern Mediterranean; Mersin Bay; Nudibranchs; Opisthobranchia; Photographic records