Search Results (63,860)

Face recognition is increasingly being adopted in industries such as education, security, and personalized services. This research introduces a face recognition system that leverages the embedding capabilities of the CLIP model. The model is trained on multimodal data, such as images and text and it generates high-dimensional features, which are then stored in a vector index for further queries. The system is designed to facilitate accurate real-time identification, with potential applications in areas such as attendance tracking and security screening. Specific use cases include event check-ins, implementation of advanced security systems, and more. The process involves encoding known faces into high-dimensional vectors, indexing them using a vector index FAISS, and comparing them to unknown images based on L2 (euclidean) distance. Experimental results demonstrate a high accuracy that exceeds 90% and prove efficient scalability and good performance efficiency even in datasets with a high volume of entries. Notably, the system exhibits superior computational efficiency compared to traditional deep convolutional neural networks (CNNs), significantly reducing CPU load and memory consumption while maintaining competitive inference speeds. In the first iteration of experiments, the system achieved over 90% accuracy on live video feeds where each identity had a single reference video for both training and validation; however, when tested on a more challenging dataset with many low-quality classes, accuracy dropped to approximately 73%, highlighting the impact of dataset quality and variability on performance. Full article

Adverse weather conditions, such as fog, degrade image quality and affect the performance of deep learning-based image processing algorithms, whereas advanced driver assistance systems (ADASs) urgently demand image clarity and large-field-of-view perception in foggy environments. Existing image dehazing methods rarely consider the non-uniform and dense distribution of particles in fog, leading to severe attenuation of background information. Image stitching, owing to the low-brightness and low-texture characteristics of ADAS scenarios and differences between sensors, faces challenges such as difficult feature point extraction and matching and poor stitching quality. To address these issues, this study proposes a non-uniform dehazing method based on Deformable Convolution v4 (DCNv4), designing a DCNv4-based transform-like network to achieve long-range dependence and adaptive spatial aggregation, combined with a lightweight Retinex-inspired Transformer for color correction and structure refinement. Meanwhile, a multi-plane scale constraint module is introduced based on the LightGlue feature matching network to improve matching accuracy and homography matrix estimation precision, and an adaptive fusion stitching method is adopted to eliminate artifacts and transition zones. Experimental results show that the proposed method effectively improves feature matching accuracy and homography matrix calculation precision, achieving Peak Signal-to-Noise Ratios (PSNRs) of 22.78 dB and 24.34 dB on the NH-HAZE and BRAS datasets, respectively, which are superior to those of existing methods. This provides a reliable environmental perception solution for autonomous driving in foggy environments, verifying its effectiveness and practicality. Full article

Background: Digitalisation and gamification are increasingly integrated into higher education, often accompanied by claims of enhanced engagement but also concerns regarding the erosion of student–teacher interaction. While prior research has focused on the effectiveness of tools or learning outcomes, less attention has been paid to how digitally mediated teaching reconfigures the interactional relations between participants. This study examined a hybrid, gamified learning setting in the MICE (Meetings, Incentives, Conferences, and Exhibitions) domain, with a particular focus on the interactional dynamics between teachers and students. Methods: The study employed a CyberSystemic interaction-observation framework to examine a four-week pilot course that combines synchronous online teaching, digital self-learning materials, and group project work. Observations were conducted by participating teachers during planning, execution, and immediate follow-up. Student perspectives were captured through a post-course survey using a 5-point Likert scale, complemented by qualitative follow-up interviews focused on prospective adaptations in future interaction cycles. Results: Interaction observations revealed high levels of student activation during time-bounded, task-oriented phases, particularly in group work and gamified activities, alongside periods of passivity during lecture-heavy phases. Survey results indicate generally positive evaluations of interactive and reflective course elements, though substantial variance exists across participants. Interaction density between teachers and students increased during execution and declined sharply afterwards, suggesting situational rather than sustained relational coupling. Conclusions: The findings indicate that gamified and digitally supported learning environments can enhance short-term engagement and operational coordination, but do not automatically stabilise student–teacher relations or learning processes over time. Within the observed timeframe, gamification appeared most effective when embedded within structured interaction and human facilitation rather than treated as a substitute for them. The study emphasises the significance of temporality and interaction design in assessing collective intelligence while highlighting how immediate feedback can inform future operational and managerial adaptation in hybrid educational systems. Full article

Large language models (LLMs) are increasingly deployed as information systems that evolve over time, where managing internal knowledge—acquisition, retention, and removal—becomes essential. In practice, these processes are primarily realized through continual learning and machine unlearning mechanisms. Despite this, these two mechanisms are often studied in isolation, limiting both interpretability and controllability. In this work, we present a parameter-efficient knowledge management framework where continual learning and machine unlearning—despite employing distinct task-specific objectives—are integrated through a shared retention-controlled parameter evolution mechanism. We ground these structural constraints in a drift-aware design principle: under a model smoothness assumption, we establish a formal upper bound showing that Kullback–Leibler (KL) divergence on retained knowledge is controlled by the magnitude and direction of parameter updates, providing a principled rationale for combining Low-Rank Adaptation (LoRA) freezing, sparse masking, and orthogonal gradient projection into a unified constraint system. Experiments on the Task of Fictitious Unlearning (TOFU) benchmark and real-world benchmarks demonstrate effective knowledge acquisition, selective removal, and robust retention across sequential tasks with strong overall performance and stability. This work provides a practical parameter-efficient recipe and a drift-aware design principle validated on controlled interleaved benchmarks, offering insights toward reliable knowledge management in evolving deployment scenarios. Full article

This review examines biodegradable polymer-based core–shell nanoformulations encapsulating essential oils for acne treatment through the lens of physicochemical design and controlled delivery mechanisms. Acne is a common inflammatory skin disorder closely associated with sebum overproduction and microbial imbalance, while conventional therapies, although effective, may present long-term side effects. Increasing attention has therefore turned to sustainable dermatological materials derived from eco-friendly polymers combined with naturally active compounds. Recent advances show that core–shell nanostructures fabricated from biodegradable polymers function as physicochemically engineered carriers for volatile essential oils. They enhance their stability and protect them from premature degradation. They also enable controlled release governed by diffusion, polymer relaxation, interfacial interactions, and degradation kinetics. This review highlights how polymer chemistry, interfacial properties, particle morphology, and processing routes determine encapsulation efficiency, release profiles, and skin permeation behaviour. Particular emphasis is placed on structure–property–function relationships, including mass transport phenomena, thermodynamic compatibility between polymers and essential oils, surface charge, wettability, and nanostructure architecture, which collectively influence bioavailability and therapeutic performance. By integrating concepts from polymer physical chemistry, colloid and interface science, and drug delivery kinetics, these sustainable nanoformulations emerge as promising platforms for acne and sebum control. Overall, essential oil-loaded biodegradable polymeric core–shell systems represent a sustainable and scientifically grounded approach to acne management, although further physicochemical characterization, in vivo validation, and consideration of cost, technical challenges, and current limitations are required to support clinical translation. Full article

The rapid integration of artificial intelligence (AI) has heightened the need for evidence-based regulatory frameworks to effectively address its legal, ethical, and societal consequences. This research carefully analyzes the prevailing landscape of AI-related legislation in Mexico. The study conducts a comprehensive review of legislative initiatives related to AI regulation submitted to Mexican legislative bodies, encompassing those approved or pending in commissions. This process leads to the identification and categorization of outstanding initiatives across seven policy areas: Congress, Education, Health, Intellectual Property, Justice, AI Promotion, and AI Regulation. As a principal contribution, this work offers the first exhaustive mapping and thematic classification of legislative activity related to AI in Mexico. Furthermore, the analysis identifies systemic regulatory deficiencies, such as the lack of AI-specific legislation, the limited scope of existing data protection laws in relation to AI systems, and an absence of technical provisions concerning ethical design, algorithmic transparency, cybersecurity, and accountability frameworks. By showcasing these deficiencies, the study contributes a diagnostic framework for evaluating AI governance readiness in emerging economies. The findings emphasize the importance of establishing a comprehensive, technically sound, and internationally harmonized regulatory framework to reduce AI-related risks while promoting responsible innovation in Mexico. Full article

The pursuit of energy efficiency and sustainability in the built environment has placed façade systems at the forefront of innovation in architectural design. This study proposes an integrated framework that combines generative design techniques with artificial intelligence (AI) to optimize composite façade configurations for next-generation smart buildings. Using parametric modeling, a wide design space of façade geometries and material compositions was generated, capturing trade-offs between thermal performance, daylight, structural strength, and aesthetic variability. Artificial intelligence algorithms, particularly machine learning models, are trained on simulation-derived performance datasets to rapidly predict key indicators such as energy consumption, thermal transmittance (U-value) and solar heat gain coefficients. The proposed approach achieved a predictive accuracy of 99.85%, enabling efficient exploration of optimal solutions across high-dimensional design alternatives. A multi-objective optimization strategy was further implemented to balance energy efficiency with structural and aesthetic constraints, producing façade configurations that outperform conventional designs. The findings demonstrate that integrating generative design with AI-based prediction not only accelerates the façade design process but also provides actionable pathways toward net-zero energy buildings. This research highlights the transformative potential of AI-driven generative workflows in advancing sustainable architecture and delivering intelligent, adaptive and performance-oriented façades for future urban environments. Full article

Advancement of precision agriculture increasingly relies on cost-effective and scalable technologies for real-time environmental management, particularly in greenhouse environments where vertical and spatial microclimate heterogeneity influences crop performance. This study presents the design, implementation, and experimental validation of an Android-based smartphone application edge supervisory monitoring system integrated with multi-layer wireless sensing and control nodes for real-time monitoring in a smart greenhouse. The system combined multi-layer wireless sensor nodes, wireless control nodes, a Long-Range Wide Area Network (LoRaWAN) gateway, Message Queuing Telemetry Transport (MQTT) communication, and a cloud-synchronized smartphone-based supervisory interface for visualizing environmental data, detecting defined abnormal events, and controlling actuators remotely. For feasibility tests, 54 sensing nodes and 12 actuator nodes were deployed across three vertical layers in two sections, measuring temperature, humidity, CO₂ concentration, and light intensity. Abnormality was defined as environmental threshold violations, statistical signal deviations, actuator power inconsistencies, and communication timeout events. Experimental results revealed vertical and spatial environmental variability across greenhouse sections, while real-time time-series and 3D spatial maps enabled the rapid detection of abnormal conditions. The rule-based abnormality detection engine identified out-of-range environmental values and sensor-related inconsistencies and generated immediate notifications. Smartphone profiling revealed that display and system-level processes accounted for energy consumption, with battery power reaching a peak of 3.5 W and application CPU utilization ranging from 40% to 70% during active monitoring. The results demonstrate system-level feasibility, responsiveness, and scalability under commercial greenhouse workloads, supporting future integration of predictive control and energy-efficient operation. Full article

In this pilot study, we investigate the psychological and attentional impact of biophilic urban interventions using an immersive virtual reality (VR) framework integrated with real-time eye-tracking. Specifically, it examines whether bio-esthetic enhancements can mitigate perceptual inequalities across neighborhoods of varying socioeconomic status (SES). Sixteen participants viewed original and digitally enhanced fixed-viewpoint 360° videos of Low-, Medium-, and High-SES environments while a comprehensive suite of oculomotor dynamics and psychometric responses were recorded. Results confirmed a significant Condition × SES interaction across both subjective preference (Liking) and esthetic evaluation (η²p = 0.41), suggesting a role for biophilic design as a “socio-perceptual equalizer”: while baseline ratings consistently favored High-SES areas, interventions in Low-SES contexts yielded the highest marginal gains, effectively bridging the gap with privileged environments. Eye-tracking metrics revealed that this convergence was associated with active visual engagement, with Enhanced Low-SES scenes eliciting the highest fixation counts and visual coverage. However, a critical dissociation emerged between immediate affective improvement and self-reported stress reduction. Elevated saccadic velocities in Enhanced Low-SES scenes are consistent with a state of “hard fascination” or novelty-induced arousal. This pattern implies that while biophilia elements boost positive affect, physiological restoration may be a dose-dependent process, requiring sufficient exposure duration to transition from curiosity-driven scanning to the “soft fascination” linked to stress recovery. These findings provide preliminary evidence for integrated XR analytics as a tool for evidence-based urban design and are discussed in the context of the equigenesis hypothesis. Full article

Extrusion-based three-dimensional (3D) bioprinting has enabled the fabrication of complex, cell-laden constructs; however, process parameter selection remains largely empirical and system-specific. As biofabrication workflows scale in complexity and translational ambition, trial-and-error optimization increasingly limits reproducibility, transferability, and informed decision-making. In this work, a formal, optimization-oriented design framework is proposed to structure extrusion-based bioprinting as a constrained, multivariable design problem. Rather than introducing a system-specific predictive model, the framework organizes process parameters, material descriptors, scaffold architecture, and biological feasibility into a unified formulation based on objective functions and admissible constraints. Symbolic coupling relationships are employed to make parameter dependencies, trade-offs, and constraint interactions explicit without imposing restrictive assumptions on material behavior or biological response. A demonstrative computational case study is presented to illustrate how qualitative predictive reasoning emerges through constraint-driven design space analysis and multi-objective considerations. The framework reveals how feasible operating regions are shaped by competing biological, mechanical, and manufacturing limitations, emphasizing robustness-aware parameter selection over isolated optimization. The proposed approach is intended as a transferable methodological foundation that supports structured reasoning, experimental planning, and future integration with numerical models, data-driven tools, and closed-loop biofabrication systems. Full article

Duplex-phase low-density steels are attracting interest for lightweight structural applications, as reducing vehicle mass is an effective route to lower fuel consumption and emissions. This review summarizes recent progress in alloy design, processing, microstructure control, and performance of duplex-phase low-density steels. The roles of major alloying elements are discussed in terms of phase stability and precipitation tendency, followed by an overview of typical processing routes from melting to hot and cold rolling and subsequent heat treatments used to tailor phase fractions and defect structures. Strengthening mechanisms are reviewed with emphasis on precipitation control, including the beneficial contribution of fine intragranular κ′ precipitates and the ductility penalty associated with coarse intergranular κ* films, as well as the use of B2-based particles for high specific strength. Deformation behavior is then discussed in terms of transformation-/twinning-induced plasticity (TRIP/TWIP), planar versus wavy slip, and strain partitioning between ferrite and austenite. Finally, key challenges are outlined, including quantitative interface-based mechanism description, gaps in service property data, stable industrial production and compositional uniformity, and the development of forming and welding windows for engineering implementation. Full article

Virtual agents powered by large language models are increasingly deployed in digital mental health services, yet the influence of avatar appearance on users’ emotional, cognitive, and physiological responses remains insufficiently understood. This study was conducted between March and April 2024 and examined how three avatar designs—animal-like, human-like, and object-like—shape affective experience, user evaluation, autonomic activity, and attentional allocation during virtual doctor interactions. Forty-two participants completed a within-subjects experiment involving self-reported affect ratings, multidimensional user-experience assessments, heart rate variability (HRV) measures, and eye-tracking indicators. The avatar type did not yield statistically significant differences in changes in positive or negative affect across conditions. However, physiological data revealed clear divergences. The animal-like avatar elicited the strongest parasympathetic activation, reflected by significant increases in the root mean square of successive differences (RMSSD) and high-frequency (HF) power, whereas the object-like avatar produced a sympathetic-dominant response. Across six user-experience dimensions, the animal-like avatar consistently received the highest evaluations. Eye-tracking results showed faster first fixation and a longer face-directed fixation duration for the animal-like avatar, indicating stronger social attention. The human-like avatar demonstrated slightly delayed initial fixation, consistent with subtle yet nonsignificant uncanny-valley tendencies. These findings underscore the critical role of avatar visual design in shaping emotional safety, engagement, and social processing in virtual mental-health interactions. Full article

The growing demand for lightweight, high-performance, and fire-safe polymer materials has accelerated research into advanced flame-retardant composites. Traditional experimental approaches to designing sustainable flame-retardant biodegradable polymer composites still rely heavily on empirical formulation and iterative testing, which are time-consuming and costly, and they often struggle to capture the coupled effects of chemical composition, processing conditions, and material performance. Recent advances in artificial intelligence (AI) provide opportunities to address these challenges by learning formulation–structure–performance relationships from curated datasets and by translating materials chemistry and flame-retardant mechanisms into data-ready descriptors and targets. This review summarizes recent progress of AI-assisted approaches to design sustainable flame-retardant biodegradable polymer composites, emphasizing machine learning, deep learning, and active learning methods for predicting and optimizing key fire performance metrics, including limiting oxygen index and heat release-related parameters. Biodegradable-specific limitations, including narrow processing window, thermal degradation, and moisture sensitivity, are discussed in the content of descriptor selection and constraint-aware optimization, together with the role of interpretable/explainable models in supporting experimentally actionable guidance. Current challenges such as limited data availability, protocol variability, model transferability, and interpretability are highlighted, and emerging solutions, including data harmonization, standardized fire testing, and physics-informed models are outlined. AI-assisted strategies are expected to play a central role in accelerating efficient, sustainable, halogen-free, and performance-driven development of next-generation flame-retardant biodegradable polymer composites. Full article

Reforming processes are key technologies for the production of hydrogen and synthesis gas from hydrocarbon feedstocks, with steam reforming and dry reforming being the most extensively studied routes. Steam reforming remains the dominant industrial process due to its high efficiency and economic viability; however, its associated CO₂ emissions raise environmental concerns, partially mitigated through an integration with carbon capture and storage technologies. Dry reforming has emerged as an attractive alternative, although it requires high operating temperatures and suffers from catalyst deactivation. Catalyst design is therefore critical for improving process efficiency and stability. Supported metal catalysts, particularly Ni-based systems, are widely employed, with the support material playing a decisive role in metal dispersion, resistance to sintering and coking, and reaction selectivity. Microporous and mesoporous silica-based materials, including zeolites and ordered mesoporous silicas, offer tunable structural and surface properties that enhance catalytic performance. The novelty of this work lies in its holistic approach to reforming catalysis, where the catalytic performance is not discussed solely in terms of active metals, but is systematically correlated with the surface properties, chemical composition, and structural features of silica-based supports. Moreover, this study expands the perspective to alternative and less-explored feedstocks. By considering multiple fuels and support types, the study provides new design guidelines for developing more efficient and sustainable reforming catalysts. Full article

Urban regeneration is increasingly expected to integrate environmental resilience, social equity, and cultural heritage alongside economic objectives. This narrative review examines how nature-based solutions (NbS) can be embedded within regeneration strategies through ecological landscape planning and design. A structured search of peer-reviewed literature and policy reports identified 34 academic studies and 13 reports that were coded and synthesised into three thematic areas: (i) NbS typologies and applications, including urban forests, blue–green infrastructure, and landscape-led regeneration; (ii) governance frameworks addressing equity, participation, anti-displacement safeguards, and cultural sensitivity; and (iii) methodological advances such as Geographic Information Systems (GIS)-based spatial analysis, multi-criteria decision frameworks, microclimate modelling, and participatory co-design tools. The review finds that NbS can enhance climate adaptation, biodiversity, and community wellbeing, yet implementation often remains fragmented because of governance barriers and uneven policy integration. Strengthening participatory processes, embedding culturally informed design principles, and incorporating anti-displacement measures are essential to ensure socially just outcomes. Strategic instruments, particularly Strategic Environmental Assessment (SEA), combined with GIS and multi-criteria tools, can support more coherent long-term decision-making. Future research should prioritise cross-sectoral policy coordination, long-term monitoring, and inclusive governance to ensure that NbS-driven regeneration contributes to equitable, resilient, and culturally grounded urban futures. Full article