Search Results (1,796)

Predicting the electric field distribution inside microfluidic devices featuring an embedded array of electrical insulating pillars is critical for applications that require the electrokinetic manipulation of particles (e.g., bacteria, exosomes, microalgae, etc.). Regularly, these predictions are obtained from finite element method (FEM)-based software. This approach is costly, time-consuming, and cannot effortlessly reveal the dependency between the electric field distribution and the microchannel design. An alternative approach consists of analytically solving Laplace’s equation subject to specific boundary conditions. This path, although precise, is limited by the availability of suitable coordinate systems and can only solve for the simplest case of a single pair of pillars and not for a rectangular array of pillars. Herein, we propose and test the hypothesis that the electric field across a longitudinal path within the microchannel can be estimated from an electric circuit model of the microfluidic device. We demonstrate that this approach allows estimating the electric field for whatever pillar shape and array size. Estimations of the electric field extracted from a commercial FEM-based software were used to validate the model. Moreover, the circuit model effortlessly illustrates the relationships between the electric field and the geometrical parameters that define the microchannel design. Full article

Big Data Analytics is vital for power grids, as it empowers informed decision-making, anticipates potential operational and maintenance issues, optimizes grid management, supports renewable energy integration, ultimately reduces costs, improves customer service, monitors consumer behavior, and offers new services. This paper describes the AI–Big Data Analytics Architecture based on a data lake architecture that uses a reduced and customized set of Hadoop and Spark as a cost-effective, on-premises alternative for advanced data analytics in power systems. As a case study, a comparative analysis of electricity price forecasting models in the day-ahead market for nodes of the Mexican national electrical system using statistical, machine learning, and deep learning models, is presented. To build and select the best forecasting model, a data science and machine learning methodology is used. The results show that the Gradient Boosting and Support Vector Regression models presented the best performance, with a Mean Absolute Percentage Error (MAPE) between 1% and 4% for five-day-ahead electricity price forecasting. The implementation of the best forecasting model into the Big Data Analytics Platform allows the automation of the calculation of the local electricity price forecast per node (every 24, 72, or 120 h) and its display in a comparative dashboard with actual and forecasted data for decision-making on demand. The proposed architecture is a valuable tool that allows the future implementation of intelligent energy forecasting models in power grids, such as load demand, fuel prices, power generation, and consumption, among others. Full article

Digital marketing has become a game-changer by combining cutting-edge technologies, insights into how customers behave, and applicability across industries to change how businesses plan and how they interact with customers. Digital marketing is a key part of being competitive, sustainable, and innovative in a world where more and more people are using the internet and social media. Even though this subject is important, the study of it is still scattered, which shows that there is a need to systematically map out its intellectual structure. This research utilizes a bibliometric and topic modeling methodology, analyzing 4722 publications sourced from the Scopus database, including the string “Digital Marketing”. The authors employed Latent Dirichlet Allocation (LDA), a method from Natural Language Processing, to discern latent study themes and Vosviewer 1.6.20 for bibliometric analysis. The results explore ten main thematic clusters, such as digital marketing and blockchain, applications in the health and food industries, higher education and skill enhancement, machine learning and analytics, small and medium-sized enterprises (SMEs) and sustainability, emerging trends and ethics, sales transformation, tourism and hospitality, digital media and audience perception, and consumer satisfaction through service quality. These clusters show that digital marketing is becoming more interdisciplinary and is becoming more connected to ethical and technological issues. The report finds that digital marketing research is changing quickly because of artificial intelligence (AI), blockchain, immersive technology, and reflect it with a digital business environment. Future directions encompass the expansion of analyses to new economies, the implementation of advanced semantic models, and the navigation of ethical difficulties, thereby guaranteeing that digital marketing fosters both business progress and public welfare. Full article

The heavy environmental impact of the construction industry—responsible for 39% of world CO₂ emissions and consuming over 40% of natural resources—supports the need for evidence-based decision-making tools for sustainable material selection balancing environmental, economic, and social considerations. This research develops and evaluates an integrated decision support system that couples cradle-to-grave lifecycle assessment (LCA) with various multi-criteria decision-making (MCDM) methods to optimize climate-resilient material selection for schools. The methodology is an integration of hybrid Analytic Hierarchy Process–Technique for Order of Preference by Similarity to Ideal Solution (AHP-TOPSIS) and VIKOR techniques validated with eight case studies in hot-arid, hot-humid, and temperate climates. Environmental, economic, social, and technical performance indices were evaluated from primary experimental data and with the input from 22 international experts with climate change assessment expertise. Ten material options were examined, from traditional, recycled, and bio-based to advanced composite systems throughout full building lifecycles. The results indicate geopolymer–biofiber composite systems achieve 42% reduced lifecycle carbon emissions, 28% lower cost of ownership, and 35% improved overall sustainability performance compared to traditional equivalents. Three MCDM techniques’ cross-validation demonstrated a satisfactory ranking correlation (Kendall’s τ = 0.87), while Monte Carlo uncertainty analysis ensured framework stability across 95% confidence ranges. Climate-adaptive weighting detected dramatic regional optimization contrasts: thermal performance maximization in tropical climates and embodied impact emphasis in temperate climates. Three case studies on educational building projects demonstrated 95.8% accuracy in validation of environmental performance and economic payback periods between 4.2 and 6.8 years in real-world practice. Full article

The consumption of edible flowers has gained increasing global attention, driven by the demand for natural and functional foods. Edible flowers are consumed in various forms, including fresh, dried, or as ingredients in derived products such as infusions, dietary supplements, and honey. Their growing popularity is associated not only with their ability to enhance sensory properties, such as aroma, color, and flavor, but also with their potential health-promoting effects. Nevertheless, their consumption entails safety concerns related to possible contamination with pesticide residues, heavy metals, insects, microorganisms, and naturally occurring toxic compounds. Among these, tropane alkaloids (TAs) and pyrrolizidine alkaloids (PAs) represent major toxicological concerns. These alkaloids may be detected even in non-producing species due to cross-contamination in the field, horizontal transfer through soil, or pollination by bees that have previously visited TA- or PA-producing plants. This review addresses the risks associated with the consumption of edible flowers and flower-derived products, with particular emphasis on studies published since 2018. It provides an overview of the occurrence of TAs and PAs in fresh flowers, floral infusions, dietary supplements, and honey. Furthermore, it summarizes the analytical methodologies employed, including sample preparation and detection techniques, and compiles the reported concentrations of these alkaloids. The evidence presented highlights the need for continued investigation to establish reliable risk assessments and ensure consumer safety. Full article

Urban streets serve as essential spaces for commercial activities and social interaction, yet the mechanisms through which their landscape elements influence consumption vitality remain insufficiently explored. Focusing on Lixia District, Jinan, China, this study integrates street-view image semantic segmentation with machine learning techniques to capture the nonlinear interactions between streetscape features and consumption vitality, thereby establishing an analytical framework for examining their associations. The results show that: (1) pedestrian-friendly facilities are significantly associated with higher street-level consumption vitality, with benches and streetlights showing marked effects once their visual proportions exceed 10% and 12%, respectively; (2) the visual proportion of parking space becomes positively associated with consumption vitality when exceeding 0.15, whereas carriageway proportion shows an overall negative association; (3) the marginal effect of advertising density gradually diminishes, with billboard visibility ratios above 25% exhibiting saturated impacts; (4) when green-view visibility exceeds 30%, consumption vitality increases substantially, peaking within the 35–40% range; (5) potential synergies or trade-offs exist among streetscape elements—compared with individual factors, the combinations of benches and parking spaces, benches and billboards, as well as parking spaces and billboards, are associated with higher street-level consumption vitality. In contrast, combinations involving a larger sky view ratio are often linked to lower consumption vitality, suggesting that overly open spaces may weaken consumer attractiveness. This study not only extends the methodological toolkit for analyzing consumption vitality but also provides theoretical and practical guidance for the refined design and experiential construction of urban street environments. Full article

Gas Detection Systems (GDSs) are critical safety technologies deployed in semiconductor wafer fabrication facilities to monitor the presence of hazardous gases. A GDS receives input from gas detectors equipped with consumable gas sensors, such as electrochemical (EC) and metal oxide semiconductor (MOS) types, which are used to detect toxic, flammable, or reactive gases. However, over time, sensors degradations, accuracy drift, and cross-sensitivity to interference gases compromise their intended performance. To maintain sensor accuracy and reliability, routine manual calibration is required—an approach that is resource-intensive, time-consuming, and prone to human error, especially in facilities with extensive networks of gas detectors. This systematic review (PROSPERO on 11th October 2025 Registration number: 1166004) explored minimizing or eliminating the dependency on manual calibration. Findings indicate that using properly calibrated gas sensor data can support advanced data analytics and machine learning algorithms to correct accuracy drift and improve gas selectivity. Techniques such as Principal Component Analysis (PCA), Support Vector Machines (SVMs), multivariate regression, and calibration transfer have been effectively applied to differentiate target gases from interferences and compensate for sensor aging and environmental variability. The paper also explores the emerging potential for integrating calibration-free or self-correcting gas sensor systems into existing GDS infrastructures. Despite significant progress, key research challenges persist. These include understanding the dynamics of sensor response drift due to prolonged gas exposure, synchronizing multi-sensor data collection to minimize time-related drift, and aligning ambient sensor signals with gas analytical references. Future research should prioritize the development of application-specific datasets, adaptive environmental compensation models, and hybrid validation frameworks. These advancements will contribute to the realization of intelligent, autonomous, and data-driven gas detection solutions that are robust, scalable, and well-suited to the operational complexities of modern industrial environments. Full article

Atrial Fibrillation (AF) is a common yet often undiagnosed cardiac arrhythmia with serious clinical consequences, including increased risk of stroke, heart failure, and mortality. In this work, we present a novel Embedded Edge system performing real-time AF detection on a low-power Microcontroller Unit (MCU). Rather than relying on full Electrocardiogram (ECG) waveforms or cloud-based analytics, our method extracts Heart Rate Variability (HRV) features from RR-Interval (RRI) and performs classification using a compact Long Short-Term Memory (LSTM) model optimized for embedded deployment. We achieved an overall classification accuracy of 98.46% while maintaining a minimal resource footprint: inference on the target MCU completes in 143 ± 0 ms and consumes 3532 ± 6 μJ per inference. This low power consumption for local inference makes it feasible to strategically keep wireless communication OFF, activating it only to transmit an alert upon AF detection, thereby reinforcing privacy and enabling long-term battery life. Our results demonstrate the feasibility of performing clinically meaningful AF monitoring directly on constrained edge devices, enabling energy-efficient, privacy-preserving, and scalable screening outside traditional clinical settings. This work contributes to the growing field of personalised and decentralised cardiac care, showing that Artificial Intelligence (AI)-driven diagnostics can be both technically practical and clinically relevant when implemented at the edge. Full article

Electricity expansion planning is inherently subject to uncertainty, shaped by climatic, regulatory, and economic risks. In Brazil, this challenge is compounded by recurrent crises that have repeatedly reduced electricity demand. This study proposes a complementary decision-support approach to make planning more resilient to such crises. Using Brazil’s official optimization models (NEWAVE), we introduce two analytical elements: (i) a regret-minimization screen for choosing between conservative and optimistic demand trajectories and (ii) a flexibility stress test that evaluates the cost impact of compulsory-dispatch shares in generation portfolios. Key findings show that conservative demand projections systematically minimize consumer-cost regret when crises occur, while portfolios with lower compulsory-dispatch shares reduce total system cost and improve adaptability across 2000 hydro inflow scenarios. These results highlight that crisis-robust planning requires combining cautious demand assumptions with flexible supply portfolios. Although grounded in the Brazilian context, the methodological contributions are generalizable and provide practical guidance for other electricity markets facing deep and recurrent uncertainty. Full article

Environmental contamination is becoming an increasingly evident risk to human health worldwide. The small, free-living nematode Caenorhabditis elegans (C. elegans) has become a compelling model organism for environmental toxicity studies in recent years, owing to its numerous advantages, including its transparent body, small size, well-characterized biology, genetic tractability, short lifespan, and ease of culture. Several assays have been developed using C. elegans to enable a better understanding of toxicant effects, from whole-animal to single-cell levels. While these methods can be extremely useful, they can be time-consuming and cumbersome to perform on a large scale. Recent advances in microfluidics have adapted many of these assays to enable high-throughput analysis of C. elegans, greatly reducing time and resource consumption while increasing efficiency and scalability. Further integration of these microfluidic platforms with machine learning expands their analytical capabilities and accuracy, revolutionizing what can be achieved with this model organism. This article will review the physiological basis of C. elegans as a model organism for environmental toxicity studies, and recent advances in integrating microfluidics and machine learning which could lead to using C. elegans as a promising living biosensor for environmental sensing. Full article

The identification and classification of textiles play a crucial role in archaeometric studies, in the vicinity of their technological, economic, and cultural significance. Traditional textile analysis is closely related to optical microscopy and observation, while other microscopic, analytical, and spectroscopic techniques prevail over fiber identification for composition purposes. This protocol can be invasive and destructive for the artifacts under study, time-consuming, and it often relies on personal expertise. In this preliminary study, an alternative, macroscopic approach is proposed, based on texture and surface textile characteristics, using low-magnification images and deep learning models. Under this scope, a publicly available, imbalanced textile image dataset was used to pretrain and evaluate six computer vision architectures (ResNet50, EfficientNetV2, ViT, ConvNeXt, Swin Transformer, and MaxViT). In addition to accuracy, energy efficiency and ecological footprint of the process were assessed using the CodeCarbon tool. The results indicate that two of the convolutional neural network models, Swin and EfficientNetV2, both deliver competitive accuracies together with low carbon emissions, in comparison to the transformer and hybrid models. This alternative, promising, sustainable, and non-invasive approach for textile classification demonstrates the feasibility of developing a custom, heritage-based image dataset. Full article

Flavonoids, a ubiquitous class of plant secondary metabolites, are increasingly pivotal as chemical markers for ensuring the quality, safety, and efficacy of herbal medicines (HMs). Their broad distribution, biological activities, and detectability make them ideal for this role. This comprehensive review critically examines current trends and analytical perspectives regarding flavonoids in HM quality control. We first explore advanced quality control strategies that move beyond single-compound quantification, including chemical fingerprinting, metabolomics, network pharmacology, and the innovative concept of Q-markers. The review then provides an in-depth analysis of the analytical techniques central to flavonoid analysis, from the routine use of HPTLC and HPLC-UV to advanced hyphenated systems like UHPLC-QTOF-MS, highlighting their applications in authentication, standardization, and adulteration detection. Furthermore, we emphasize the growing importance of modern data analysis workflows, particularly the integration of chemometrics and molecular networking, for interpreting complex datasets and identifying robust, bioactivity-relevant markers. By synthesizing recent research (2017–2024), this work underscores a paradigm shift towards holistic, multi-marker approaches and data-driven methodologies. It concludes that the synergistic application of advanced analytical techniques with sophisticated data modeling is essential for the future of HM quality control, ensuring reliable and standardized herbal products for global consumers. Full article

The increasing demand for multi-ingredient dietary supplements (MIDS), driven by diverse consumer health needs, has introduced analytical challenges in product testing and quality control. These challenges stem from complex ingredient interactions, formulation variability, and the diverse physicochemical properties of the individual components. To examine these issues and explore practical solutions, this study employed semi-structured focus group interviews with 33 industry professionals and 10 analytical experts from academic and industry. Professionals reported major obstacles including the degradation or loss of trace components, interferences among ingredients, analytical difficulties with specific dosage forms, and the lack of standardized testing protocols. To mitigate these challenges, professionals reported implementing various combination strategies including substituting problematic raw materials and modifying analytical instruments and pretreatment procedures, in order to improve test reproducibility. These measures were developed internally and varied significantly across companies, reflecting the absence of a unified analytical framework for MIDS testing. Building on these insights, the analytical experts proposed systematic improvements including developing matrix-specific pretreatment protocols and optimized extraction strategies as well as regulatory harmonization to enhance analytical reliability and reproducibility. These findings provide critical insights into current field practices and inform the development of standardized methodologies for the analysis and quality assurance of MIDS. Full article

Background: Phytochemicals play a crucial role in determining the sensory qualities and nutritional value of plant-based foods. They influence flavour perception by interacting with aroma, taste, and texture. Terpenes, phenolic compounds, and flavonoids are particularly important as they contribute to the characteristic sensory profiles of foods while offering antioxidant, anti-inflammatory and anticancer properties that support the prevention of diet-related chronic diseases. Methods: A systematic literature search was conducted in PubMed, Web of Science, Scopus, and EMBASE, complemented by Google Scholar. The search focused on peer-reviewed articles, reviews, and meta-analyses published within the last two decades, prioritising studies on phytochemicals, their biosynthesis, the molecular mechanisms of flavour formation, and their functional properties in plant-based foods. Keywords included ‘phytochemicals’, ‘flavour development’, ‘flavonoids’, ‘terpenes’, ‘phenolics’, ‘plant foods’, ‘molecular pathways’, and ‘food processing’. Relevant studies providing mechanistic insights were selected. Results: Terpenes, phenolic compounds, and flavonoids modulate sensory attributes by interacting with taste and olfactory receptors, and they contribute to antioxidant and anti-inflammatory mechanisms. Food processing influences the stability, bioavailability, and efficacy of these compounds, thereby affecting flavour and health-promoting potential. Modern analytical techniques enable the detailed characterisation of these compounds and their sensory and functional roles. Conclusions: By integrating insights from sensory science and nutrition, this review emphasises the dual importance of phytochemicals in enhancing consumer acceptance and promoting health. Understanding their mechanisms and how they respond to processing can inform the development of plant-based foods that are enjoyable and nutritious. Full article

Background: Despite guidelines recommending lower alcohol limits for individuals who flush, some still drink at unhealthy levels. This study investigates whether drinking motives are differentially associated with alcohol consumption based on self-reported flushing status among U.S. Asian young adults. Asian American youth report alcohol use at rates comparable to other high-risk groups, identifying the need to understand factors shaping these behaviors. Methods: The current analysis drew participants from a longitudinal multi-cohort study examining the emotional and behavioral health of college students. Freshmen were recruited, all aged 18 years and older, to complete a baseline survey and follow up surveys over a four-year period. The analytic sample (Mean age = 19.4; 70.5% female) included 244 students who self-identified as Asian. Participants self-reported whether they experience facial flush when consuming alcohol and rated their endorsement of various drinking motives. Negative binomial regression models tested main effects and interaction effects between flushing status (flushers, non-flushers) and drinking motives (coping, enhancement, conformity, social). Results: Facial flushing moderated enhancement, conformity, and social drinking motives, but not coping. Among flushers, enhancement and social motives were more strongly associated with greater alcohol consumption. Among non-flushers, conformity motives were stronger and associated with greater drinking, at a trend level. Overall, flushing or higher coping motives were associated with lower alcohol consumption. Peer drinking was associated with higher consumption in both flushing-status groups. Conclusions: The current study extends prior international research on drinking motives and flushing status to U.S. Asian young adults. Findings support the need for prevention strategies that address individual drinking motives and the modeling of alcohol use by peers. Reducing alcohol use among individuals who experience alcohol-induced flushing is a public health priority, given their heightened risk for alcohol-related cancers and other negative health outcomes. Full article