Search Results (253)

The evolution of decentralized clinical trials, driven by advanced digital technologies, is transforming traditional clinical research. It introduces innovative methods for informed consent, remote patient monitoring, and data analysis, enhancing study efficiency, validity, and participation while reducing patient burden. Some clinical procedures can be conducted remotely, increasing trial accessibility and reducing population selection biases, particularly for cardiovascular patients. However, this also presents complex regulatory and ethical challenges. The article explores how digital platforms and emerging technologies like block chain, AI, and advanced cryptography can promote traceability, security, and transparency throughout the trial process, ensuring participant identification and documentation of each procedural step. Clear, legally compliant informed consent, often managed through electronic systems, both for research participation and data management in line with GDPR, is essential. Ethical considerations include ensuring participants understand trial information, with adaptations such as simplified language, visual aids, and multilingual support. The transnational nature of decentralized trials highlights the need for coordinated regulatory standards to overcome jurisdictional barriers and reinforce accountability. This framework promotes trust, shared responsibility, and the protection of participants rights while upholding high ethical standards in scientific research. Full article

Artificial intelligence is rapidly reshaping skill expectations across media, marketing, and journalism, however, university curricula are not evolving at a comparable speed. To quantify the resulting curriculum–skill gap in communication-related programs, two synchronous corpora were assembled for the period July 2024–June 2025: 66 course descriptions from six leading UK universities and 107 graduate-to-mid-level job advertisements in communications, digital media, advertising, and public relations. Alignment around AI, datafication, and platform governance was assessed through a three-stage natural-language-processing workflow: a dual-tier AI-keyword index, comparative TF–IDF salience, and latent Dirichlet allocation topic modeling with bootstrap uncertainty. Curricula devoted 6.0% of their vocabulary to AI plus data/platform terms, whereas job ads allocated only 2.3% (χ² = 314.4, p < 0.001), indicating a conceptual-critical emphasis on ethics, power, and societal impact in the academy versus an operational focus on SEO, multichannel analytics, and campaign performance in recruitment discourse. Topic modeling corroborated this divergence: universities foregrounded themes labelled “Politics, Power & Governance”, while advertisers concentrated on “Campaign Execution & Performance”. Environmental and social externalities of AI—central to the Special Issue theme—were foregrounded in curricula but remained virtually absent from job advertisements. The findings are interpreted as an extension of technology-biased-skill-change theory to communication disciplines, and it is suggested that studio-based micro-credentials in automation workflows, dashboard visualization, and sustainable AI practice be embedded without relinquishing critical reflexivity, thereby narrowing the curriculum–skill gap and fostering environmentally, socially, and economically responsible media innovation. With respect to the novelty of this research, it constitutes the first large-scale, data-driven corpus analysis that empirically assessed the AI-related curriculum–skill gap in communication disciplines, thereby extending technology-biased-skill-change theory into this field. Full article

The increasing complexity of modern food production demands advanced solutions for quality control (QC), safety monitoring, and process optimization. This review systematically explores recent advancements in machine learning (ML) for QC across six domains: Food Quality Applications; Defect Detection and Visual Inspection Systems; Ingredient Optimization and Nutritional Assessment; Packaging—Sensors and Predictive QC; Supply Chain—Traceability and Transparency and Food Industry Efficiency; and Industry 4.0 Models. Following a PRISMA-based methodology, a structured search of the Scopus database using thematic Boolean keywords identified 124 peer-reviewed publications (2005–2025), from which 25 studies were selected based on predefined inclusion and exclusion criteria, methodological rigor, and innovation. Neural networks dominated the reviewed approaches, with ensemble learning as a secondary method, and supervised learning prevailing across tasks. Emerging trends include hyperspectral imaging, sensor fusion, explainable AI, and blockchain-enabled traceability. Limitations in current research include domain coverage biases, data scarcity, and underexplored unsupervised and hybrid methods. Real-world implementation challenges involve integration with legacy systems, regulatory compliance, scalability, and cost–benefit trade-offs. The novelty of this review lies in combining a transparent PRISMA approach, a six-domain thematic framework, and Industry 4.0/5.0 integration, providing cross-domain insights and a roadmap for robust, transparent, and adaptive QC systems in the food industry. Full article

This article presents an innovative methodological approach for the documentation and enhancement of ancient ironworking heritage in the Guéra region of Chad. By combining ethno-historical and archaeological surveys, 3D modeling with Blender, and the generation of images and video sequences through artificial intelligence (AI), we propose an integrated production pipeline enabling the faithful reconstruction of three types of metallurgical furnaces. Our method relies on rigorously collected field data to generate multiple and plausible representations from fragmentary information. A standardized evaluation grid makes it possible to assess the archaeological fidelity, cultural authenticity, and visual quality of the reconstructions, thereby limiting biases inherent to generative models. The results offer strong potential for integration into immersive environments, opening up perspectives in education, digital museology, and the virtual preservation of traditional ironworking knowledge. This work demonstrates the relevance of multimodal approaches in reconciling scientific rigor with engaging visual storytelling. Full article

Learned systems in the domain of visual recognition and cognition impress in part because even though they are trained with datasets many orders of magnitude smaller than the full population of possible images, they exhibit sufficient generalization to be applicable to new and previously unseen data. Since training data sets typically represent such a small sampling of any domain, the possibility of bias in their composition is very real. But what are the limits of generalization given such bias, and up to what point might it be sufficient for a real problem task? There are many types of bias as will be seen, but we focus only on one, selection bias. In vision, image contents are dependent on the physics of vision and geometry of the imaging process and not only on scene contents. How do biases in these factors—that is, non-uniform sample collection across the spectrum of imaging possibilities—affect learning? We address this in two ways. The first is theoretical in the tradition of the Thought Experiment. The point is to use a simple theoretical tool to probe into the bias of data collection to highlight deficiencies that might then deserve extra attention either in data collection or system development. Those theoretical results are then used to motivate practical tests on a new dataset using several existing top classifiers. We report that, both theoretically and empirically, there are some selection biases rooted in the physics and imaging geometry of vision that challenge current methods of classification. Full article

The restoration of forest ecosystems damaged by wildfires and pest outbreaks has become increasingly urgent. However, the public-good nature of forests, the involvement of diverse stakeholders, and the spatial variability of degradation present significant challenges to effective policy design. In particular, previous studies have largely examined these threats in isolation, and few have provided integrated economic analyses of their combined impacts. This gap underscores the need to better understand heterogeneous public preferences and their implications for restoration policy. To address this, we conducted a discrete choice experiment (DCE) with 1021 Korean citizens and applied a two-stage analytical framework combining principal component analysis (PCA) and latent class choice modeling (LCM). Five distinct preference segments were identified, each exhibiting substantial variation in willingness to pay (WTP) for restoration attributes. Policy simulations further revealed that public acceptance declines sharply at higher cost levels, highlighting the importance of setting realistic financial thresholds for broad support. While visual materials, consequentiality checks, and cheap talk scripts were employed to mitigate hypothetical bias, the limitations of external validity and potential sampling biases should be acknowledged. Our findings provide empirical evidence for tailoring restoration policies to different stakeholder groups, while also stressing the financial and institutional constraints of implementation. In particular, the results suggest that cost thresholds, citizen engagement, and awareness-raising strategies must be carefully balanced to ensure both effectiveness and public acceptance. Taken together, these insights contribute to evidence-based forest policymaking that is both economically efficient and socially acceptable, while recognizing the context-specific limitations of the Korean case and the need for comparative studies across countries. Full article

This study examined the effects of Bacillus probiotic strains on red abalone Haliotis rufescens reproductive performance. We supplemented plant- and fish-based feeds and compared them to fresh giant kelp Macrocystis pyrifera as a control diet. Over 180 days, abalone fed the plant–probiotic diet reached higher female gonadal maturation, with 56% of females attaining the maximum Visual Gonad Index (VGI 3). Additionally, plant-based treatment showed a female-biased sex ratio (1.5:1 female-to-male ratio, F:M) compared with the kelp control treatment (0.8:1 F:M). These results suggest that probiotics can improve nutrient utilization from soybean meal and may enhance the bioavailability of phytoestrogens and other bioactive compounds, contributing to reproductive outcomes. Although the mechanisms remain to be confirmed, this approach provides a promising strategy to reduce reliance on fishmeal and wild macroalgae while supporting faster reproductive cycles in abalone aquaculture. Future research should focus on biochemical validation, molecular pathways, and multigenerational trials to ensure the long-term safety and sustainability of probiotic–plant-based feeds. Full article

Rhodopsin, the prototypical Class A G protein-coupled receptor (GPCR) and visual pigment of rod photoreceptors, has long served as a structural and mechanistic model for GPCR biology. Mutations in rhodopsin are the leading cause of autosomal dominant retinitis pigmentosa (adRP), making this receptor a critical therapeutic target. In this review, we summarize the chemical, structural, and biophysical features of small-molecule modulators of this receptor, spanning both classical retinoid analogs and emerging non-retinoid scaffolds. These ligands reveal recurrent binding modes within the orthosteric chromophore pocket as well as peripheral allosteric and bitopic sites, where they mediate folding, rescue trafficking, photocycle modulation, and mutant stabilization. We organize ligand performance into a three-tier framework linking binding affinity, cellular rescue potency, and stability gains. Chemotypes in tier 2, which show sub-micromolar to low-micromolar activity with broad mutant coverage, emerge as promising candidates for optimization into next-generation scaffolds. Across scaffolds, a recurring minimal pharmacophore is evident by a contiguous hydrophobic π-surface anchored in the β-ionone region, coupled with a strategically oriented polar handle that modulates the Lys296/Glu113 microenvironment, offering tractable design vectors for non-retinoid chemotypes. Beyond the chromophore binding pocket, we highlight opportunities to exploit extracellular loop epitopes, cytoplasmic microswitch clefts, dimer/membrane interfaces, and ion co-binding sites to engineer safer, state-biased control with fewer photochemical liabilities. By integrating rhodopsin photobiophysics with environment-aware, multi-state medicinal chemistry, and by addressing current translational challenges in drug delivery, this review outlines a rational framework for advancing rhodopsin-targeted therapeutics toward clinically credible interventions for RP and related retinal degenerations. Full article

Activation functions play a crucial role in ensuring training stability, convergence speed, and overall performance in both convolutional and attention-based networks. In this study, we introduce two novel activation functions, each incorporating a sine component and a constraint term. To assess their effectiveness, we replace the activation functions in four representative architectures—VGG16, ResNet50, DenseNet121, and Vision Transformers—covering a spectrum from lightweight to high-capacity models. We conduct extensive evaluations on four benchmark datasets (CIFAR-10, CIFAR-100, MNIST, and Fashion-MNIST), comparing our methods against seven widely used activation functions. The results consistently demonstrate that our proposed functions achieve superior performance across all tested models and datasets. From a design application perspective, the proposed functional periodic structure also facilitates rich and structurally stable activation visualizations, enabling designers to trace model attention, detect surface biases early, and make informed aesthetic or accessibility decisions during interface prototyping. Full article

Citrus diseases severely impact fruit yield and quality. To facilitate in-field, non-destructive, and rapid detection of citrus leaf physiological and pathological conditions, this study proposes a classification method for citrus leaf physiological and pathological statuses that integrates visible/near-infrared multispectral technology with deep learning. First, a handheld spectrometer was employed to acquire spectral images of five sample categories—Healthy, Huanglongbing, Yellow Vein Disease, Magnesium Deficiency and Manganese Deficiency. Mean spectral data were extracted from regions of interest within the 350–2500 nm wavelength range, and various preprocessing techniques were evaluated. The Standard Normal Variate (SNV) transformation, which demonstrated optimal performance, was selected for data preprocessing. Next, we innovatively introduced an adaptive spectral positional encoding mechanism into the Transformer framework. A lightweight, learnable network dynamically optimizes positional biases, yielding the ASTransformer (Adaptive Spectral Transformer) model, which more effectively captures complex dependencies among spectral features and identifies critical wavelength bands, thereby significantly enhancing the model’s adaptive representation of discriminative bands. Finally, the preprocessed spectra were fed into three deep learning architectures (1D-CNN, 1D-ResNet, and ASTransformer) for comparative evaluation. The results indicate that ASTransformer achieves the best classification performance: an overall accuracy of 97.7%, underscoring its excellent global classification capability; a Macro Average of 97.5%, reflecting balanced performance across categories; a Weighted Average of 97.8%, indicating superior performance in classes with larger sample sizes; an average precision of 97.5%, demonstrating high predictive accuracy; an average recall of 97.7%, showing effective detection of most affected samples; and an average F1-score of 97.6%, confirming a well-balanced trade-off between precision and recall. Furthermore, interpretability analysis via Integrated Gradients quantitatively assesses the contribution of each wavelength to the classification decisions. These findings validate the feasibility of combining a handheld spectrometer with the ASTransformer model for effective citrus leaf physiological and pathological detection, enabling efficient classification and feature visualization, and offer a valuable reference for disease detection of physiological and pathological conditions in other fruit crops. Full article

Visual grounding for remote sensing (RSVG) focuses on localizing specific objects in remote sensing (RS) imagery based on linguistic expressions. Existing methods typically employ pre-trained models to locate the referenced objects. However, due to the insufficient capability of cross-modal interaction and alignment, the extracted visual features may suffer from semantic drift, limiting the performance of RSVG. To address this, the article introduces a novel RSVG framework named the text–visual interaction multilevel feature alignment network (TVI-MFAN), which leverages a text–visual interaction attention (TVIA) module to dynamically generate adaptive weights and biases at both spatial and channel dimensions, enabling the visual feature to focus on relevant linguistic expressions. Additionally, a multilevel feature alignment network (MFAN) aggregates contextual information by using cross-modal alignment to enhance features and suppress irrelevant regions. Experiments demonstrate that the proposed method achieves 75.65% and 80.24% (2.42% and 3.1% absolute improvement) accuracy on the OPT-RSVG and DIOR-RSVG dataset, validating its effectiveness. Full article

This study proposes a novel, scalable framework for the automated classification and synthesis of survey literature by integrating state-of-the-art Large Language Models (LLMs) with robust ensemble voting techniques. The framework consolidates predictions from three independent models—GPT-4, LLaMA 3.3, and Claude 3—to generate consensus-based classifications, thereby enhancing reliability and mitigating individual model biases. We demonstrate the generalizability of our approach through comprehensive evaluation on two distinct domains: Question Answering (QA) systems and Computer Vision (CV) survey literature, using a dataset of 1154 real papers extracted from arXiv. Comprehensive visual evaluation tools, including distribution charts, heatmaps, confusion matrices, and statistical validation metrics, are employed to rigorously assess model performance and inter-model agreement. The framework incorporates advanced statistical measures, including k-fold cross-validation, Fleiss’ kappa for inter-rater reliability, and chi-square tests for independence to validate classification robustness. Extensive experimental evaluations demonstrate that this ensemble approach achieves superior performance compared to individual models, with accuracy improvements of 10.0% over the best single model on QA literature and 10.9% on CV literature. Furthermore, comprehensive cost–benefit analysis reveals that our automated approach reduces manual literature synthesis time by 95% while maintaining high classification accuracy (F1-score: 0.89 for QA, 0.87 for CV), making it a practical solution for large-scale literature analysis. The methodology effectively uncovers emerging research trends and persistent challenges across domains, providing researchers with powerful tools for continuous literature monitoring and informed decision-making in rapidly evolving scientific fields. Full article

Numerical magnitude can bias spatial attention, typically facilitating faster responses to the left for small numbers and to the right for large numbers—an effect traditionally attributed to egocentric spatial mappings. However, in everyday environments, individuals often share space with others, raising the question of whether such spatial–numerical associations can spontaneously reorganize based on another person’s visual perspective. To investigate this, we employed a digit-primed visual detection paradigm in which participants judged the location (left, right, up, or down) of a briefly presented peripheral probe following centrally displayed digits. If numerical magnitude implicitly guides attention, probe detection should be faster when its location is congruent with the digit-induced spatial bias. Critically, in the avatar condition, a task-irrelevant avatar was positioned on the participant’s left side, such that the avatar’s horizontal (left–right) axis corresponded to the participant’s vertical (up–down) axis—an axis along which egocentric numerical biases are typically absent. If participants spontaneously adopted the avatar’s perspective, numerical cues might induce attentional biases along this axis. Results revealed two simultaneous effects: a canonical egocentric SNARC-like effect (small–left, large–right) and a novel allocentric effect (small–up, large–down) emerged along the vertical axis, implicitly aligned with the avatar’s left–right spatial orientation. Numerical extremity enhanced the egocentric SNARC-like effect but had no effect in the allocentric case, pointing to a distinct mechanism rooted in embodied spatial perspective. These findings suggest that numerical magnitude can implicitly map onto both egocentric and allocentric spatial frames, reflecting a implicit and embodied mechanism of social understanding. Full article

Cognitive bias can prompt inconsistency and error in visual comparisons of forensic patterns. We tested whether bias can likewise impede attempts to identify unknown criminal perpetrators via facial recognition technology (FRT). Participants (N = 149) completed two simulated FRT tasks. In each, they compared a probe image of a perpetrator’s face against three candidate faces that FRT allegedly identified as possible matches. To test for contextual and automation biases, each candidate was randomly paired with either extraneous biographical information or a biometric confidence score, respectively. As predicted, participants rated whichever candidate’s face was paired with guilt-suggestive information or a high confidence score as looking most like the perpetrator’s face, even though those details were assigned at random. Furthermore, candidates randomly paired with guilt-suggestive information were most often misidentified as the perpetrator. These findings indicate a clear need for procedural safeguards against cognitive bias when using FRT in criminal investigations. Full article

In real-world visual recognition tasks, long-tailed distribution is a pervasive challenge, where the extreme class imbalance severely limits the representation learning capability of deep models. Although supervised learning has demonstrated certain potential in long-tailed visual recognition, these models’ gradient updates dominated by head classes often lead to insufficient representation of tail classes, resulting in ambiguous decision boundaries. While existing Supervised Contrastive Learning variants mitigate class bias through instance-level similarity comparison, they are still limited by biased negative sample selection and insufficient modeling of the feature space structure. To address this, we propose Rebalancing Supervised Contrastive Learning (Reb-SupCon), which constructs a balanced and discriminative feature space during model training to alleviate performance deviation. Our method consists of two key components: (1) a dynamic rebalancing factor that automatically adjusts sample contributions through differentiable weighting, thereby establishing class-balanced feature representations; (2) a prototype-aware enhancement module that further improves feature discriminability by explicitly constraining the geometric structure of the feature space through introduced feature prototypes, enabling locally discriminative feature reconstruction. This breaks through the limitations of conventional instance contrastive learning and helps the model to identify more reasonable decision boundaries. Experimental results show that this method demonstrates superior performance on mainstream long-tailed benchmark datasets, with ablation studies and feature visualizations validating the modules’ synergistic effects. Full article