Search Results (15,244)

Domoic acid (DA) is a well-known seafood toxin produced by some species of marine phytoplankton in the genus Pseudo-nitzschia during harmful algal blooms (HABs). Acute toxic exposures induce overt clinical signs of neuroexcitotoxicity, such as seizures in mammals due to overstimulation of glutamate receptors in the central nervous system (CNS). Acute DA excitotoxicity via the CNS has been well-studied in both field poisoning events and laboratory exposure studies with rodent models, but little is known about the impacts of low-level DA exposures below those that cause outward signs of neurotoxicity; the impacts on other potential target organs, including the heart; or age-related sensitivities. Here, low-level DA exposures in young adult (9 mo) and old (24 mo) mice were conducted over multiple weeks. Mortality, cardiac function, frailty, and protein expression were quantified to assess age-related DA sensitivity and potential impacts on heart function. Echocardiography and proteome data confirm that chronic low-level DA exposure causes irreversible functional cardiomyopathy and protein remodeling in young adult mice that mimics natural cardiac aging. In addition, old mice exhibit higher mortality and frailty than young adult mice with the same low-level DA exposures. These results provide critical information for assessing potential health risks to humans who regularly consume seafood with low levels of DA. Full article

Machine learning (ML) has substantially advanced text classification by enabling automated understanding and categorization of complex unstructured textual data. However, accurately capturing nuanced linguistic patterns and contextual variations that are inherent in natural language, particularly within consumer complaints, remains a challenge. This study addresses these limitations by incorporating human-experience-trained algorithms that effectively recognize subtle semantic distinctions that are crucial for assessing consumer relief eligibility. Specifically, we propose synthetic data generation methods that leverage generative adversarial networks refined through expert labeling, complemented by featurization approaches for extracting textual representations. By combining expert-trained classifiers with high-quality synthetic data, this research demonstrates marked improvements in ML classifier performance, reduced dataset acquisition costs, and enhanced robustness across evaluation metrics in text classification tasks. Full article

The second-hand Battery Electric Vehicle (BEV) market in Italy is affected by substantial information asymmetry, particularly with regard to battery State of Health (SOH), residual value, and expected maintenance costs. This lack of transparency limits consumer confidence and reduces the potential of used BEVs to support a broader and more inclusive electric mobility transition. In this study, a data-driven decision-support framework is developed to improve the evaluation of second-hand BEVs in the Italian market. The proposed approach combines market data collected from major online platforms with historical price reconstruction and an assessment of the information asymmetries that limit user confidence in the second-hand BEV market. It also incorporates a semi-empirical SOH estimation model based on observable vehicle characteristics. The results reveal a consistent depreciation gap between BEVs and comparable internal combustion engine vehicles across different market segments and indicate that battery-related uncertainty appears to be one of the factors associated with consumer hesitation. The framework shows that combining non-invasive battery-health estimation with maintenance-related information can support a more objective assessment of used electric vehicles. Overall, the study demonstrates the potential of integrated digital and engineering-based tools to reduce uncertainty and enhance transparency in the second-hand BEV market. Full article

Shopping mobility accounts for a significant share of total travel, while the growth of e-commerce is reshaping consumer purchasing behaviour and retail dynamics. Comprehending how territorial and sociodemographic factors shape the choice between physical and digital retail channels is therefore a key issue for transport planning and sustainable urban mobility. In this context, it is important to understand how accessibility to different classes of retailers is configured and how it can impact purchasing choices. Through a discrete choice analysis, this study examines the sociodemographic and territorial determinants of purchasing behaviour, focusing on the clothing market. Four purchase alternatives are considered: medium-sized and small urban retail stores, shopping malls, online purchasing, and no purchase. This multi-alternative framework enables the direct estimation of substitution patterns not only between physical and digital retail, but also between distinct forms of physical retail. Data were collected through a survey conducted in Southern Italy, providing empirical evidence from a territorial setting that is structurally underrepresented in the existing literature. A multinomial logit model and a two-level hierarchical logit model incorporating pedestrian accessibility—measured as walking time from residence to the nearest clothing store—alongside sociodemographic and territorial attributes were calibrated to analyse alternative choice behaviour. The calibrated models show interesting results, highlighting the role of pedestrian accessibility in the choice of clothing stores in city centres. Age, income, and territorial variables further differentiate channel preferences across population segments. The findings offer relevant implications for policymakers, governance managers, urban planners, and researchers concerned with retail location, sustainable accessibility, and consumer behaviour. These insights are highly valuable for developing planning that addresses the United Nations 2030 Agenda, particularly Sustainable Development Goal 11. Full article

Ampicillin residues in animal-derived foods may cause allergic reactions and promote antimicrobial resistance in consumers; however, data on residue behavior in poultry remain limited. This study aimed to evaluate the depletion of ampicillin in muscle and skin plus fat of broiler chickens. Thirty birds were treated with ampicillin intramuscularly (20 mg kg⁻¹ every 24 h for three days) and sacrificed at 0.5, 1, 2, 5, and 9 days post-administration. Samples were analyzed by liquid chromatography coupled with tandem mass spectrometry, a method successfully validated according to Commission Implementing Regulation (EU) 2021/808, VICH GL49 and GL2. Quantification was performed by linear regression from matrix-matched calibration curves. Residue depletion was evaluated following the European Medicines Agency guidelines. Ampicillin residues in muscle were detected only during the first 24 h post-administration (6.50–8.48 µg kg⁻¹). Residues in skin plus fat remained detectable until day 5 post-administration (6.87–59.88 µg kg⁻¹). Based on this, the withdrawal period calculated for skin plus fat was 9 days considering EU maximum residue limit (MRL) and 19 days considering method limit of quantification, with 95% confidence. These results provide critical data on ampicillin residue kinetics under controlled experimental conditions, supporting risk assessments and the establishment of MRLs in broiler chickens by the Codex Alimentarius. Full article

Accelerometers embedded in consumer-grade smartwatches hold significant potential for health-related research applications, but their measurement reliability is often compromised. This limitation necessitates proper metrological characterization to ensure precision and consistency, particularly in health-related research contexts where reliable movement data are required. This study proposes a methodology for the simultaneous metrological characterization of multiple smartwatch accelerometers, enabling efficient and consistent bench-based measurement evaluation. The proposed methodology employs a seismic table to generate controlled vibrations within a frequency range of 1–8 Hz and acceleration amplitudes between 1 and 4 m/s${}^2$. Five commercial smartwatch units were tested, collecting acceleration data at sampling rate of 50 Hz. A reference accelerometer was used to assess the accuracy of smartwatch measurements, with errors and uncertainties quantified following ISO standards. Results demonstrate that simultaneous bench-based evaluation allows consistent comparison of measurement performance across devices while reducing the time required for the process. The analysis highlights variations in frequency response and amplitude accuracy across different smartwatch units, emphasizing the need for systematic metrological characterization when considering the future use of smartwatches in health-related research studies involving wearable movement monitoring. Full article

Proton exchange membrane fuel cell–battery hybrid power systems provide an effective solution to overcome the limited endurance of battery-powered multirotor unmanned aerial vehicles. However, the highly transient power demands of quadcopter platforms, combined with balance-of-plant losses and operational constraints, create significant challenges for reliable energy management. This study proposes a degradation-aware stress-mitigation model predictive control-based energy management framework to maximize mission endurance under realistic conditions. A control-oriented, physics-consistent model is developed using manufacturer polarization data from a 500 W Aerostak proton exchange membrane fuel cell. The model captures polarization behavior, balance-of-plant loads, battery dynamics, and direct current-bus power balance. The model predictive control strategy optimally allocates power by maintaining direct current-bus stability, regulating battery state-of-charge within safe limits, and constraining fuel cell power ramp rates to mitigate degradation. High-fidelity simulations are conducted under stochastic wind disturbances and mission-dependent load profiles, including takeoff, climb, cruise, and maneuvering phases. The results show continuous power delivery without unmet load demand. The hybrid system achieves a flight endurance of 220–224 min, consuming a total of 89.99 g of hydrogen at an average rate of 0.398–0.412 g/min, indicating a notable reduction under the considered operating conditions. Additionally, long-term analysis indicates that over 97% of initial endurance is preserved after 100 cycles, demonstrating robustness against fuel cell aging. An analytical real-time feasibility assessment further indicates that the control-oriented formulation is compatible with the computational resources of typical unmanned aerial vehicle-class onboard processors, while the integration of adaptive and robust predictive control techniques is identified as a direction for future work. Full article

The market for electric single-person vehicles in Sweden has undergone significant changes, shifting from a rental-dominated model to increasing private ownership. This transformation has resulted in both benefits and challenges, including improved accessibility, evolving consumer behaviour, and increased accident rates, particularly among young users. This study, commissioned by the Swedish government, presents a comprehensive mapping of the availability, usage, and consequences of private electric scooters. Through market surveys, user studies, and accident data analysis, we provide insights into regulatory gaps, consumer awareness, and safety concerns. Our findings highlight the need for clearer communication of existing regulations and improved consumer education to ensure the safe and responsible use of electric single-person vehicles. Full article

High dynamic range (HDR) image reconstruction from a single low dynamic range (LDR) input remains an important problem for computational photography, particularly when practical deployment on consumer-grade hardware is considered. With the increasing availability of hardware supporting HDR, public demand for capturing and viewing HDR images has grown significantly. Recent research has explored deep learning-based approaches to reconstruct HDR images from low dynamic range (LDR) inputs by extracting regional pixel features or leveraging the camera response function (CRF) for model training. Many of these approaches employ Convolutional Neural Network (CNN) architectures and utilize skip connections to preserve learned information. Nevertheless, the configuration-level effects of data augmentation in HDR reconstruction remain insufficiently discussed. Existing CNN-based approaches, such as HDRCNN, HDRUNet, and ExpandNet, have demonstrated promising reconstruction ability, but they may involve a heavy backbone architecture, a long training time, or a limited discussion of how preprocessing configurations affect reconstruction performance. This study presents an engineering-oriented HDR reconstruction framework derived from HDRCNN, focusing on practical efficiency, structural fidelity, and training feasibility. The proposed framework introduces three modifications: (1) a configuration-level comparison of composite data augmentation settings, including unsharp masking, denoising, Gaussian blur, and brightness–contrast adjustment; (2) the replacement of the original VGG16 backbone with a ResNet50-based encoder enhanced with attention blocks and squeeze-and-excitation (SE) blocks for improved multi-scale feature extraction and channel-wise recalibration; and (3) the integration of mixed-precision training with cosine annealing learning-rate scheduling to reduce computational cost. Experimental results on the SI-HDR dataset show that the best composite augmentation configuration improves PSNR from 19.05 dB to 22.10 dB and SSIM from 0.6444 to 0.7714 without increasing the training time. Compared with the original VGG16-based HDRCNN setting, the ResNet50-based model reduces training time while improving SSIM from 0.2705 to 0.8512. Under the adopted comparison protocol, the proposed model achieves the shortest training time and slightly higher PSNR than HDRUNet, while HDRUNet retains a higher SSIM. This indicates a trade-off among pixel-wise fidelity, structural similarity, and computational efficiency. The current evaluation is limited by a small test setting, composite rather than operation-level augmentation analysis, and the use of PSNR and SSIM only; therefore, future work should include full benchmark evaluation, additional perceptual/HDR-specific metrics, and controlled component-level ablation studies. Full article

Renewable energy curtailment has emerged as a growing challenge on the Korean mainland grid as photovoltaic (PV) and wind power capacity continues to expand toward national carbon neutrality targets. Plus demand response (Plus DR), in which electricity consumers increase consumption during curtailment periods, has been introduced as a demand-side mitigation measure. Buildings represent a potential resource for Plus DR participation. However, existing studies have primarily focused on load-reduction DR, and Plus DR availability by building use type under curtailment conditions has not been systematically quantified. This study estimates Plus DR availability of building loads by use type—department store, hotel, general commercial, public facility, apartment, and school—based on representative building load profiles, PV generation data, and 2025 curtailment occurrence data from the Korean mainland grid. Curtailment events were concentrated in the 10:00–16:00 window with peak frequency at 12:00 (80 events). The combined Plus DR availability across the six use types averaged 290.3 kW during curtailment hours, peaking at 300.9 kW at 14:00. The estimated Plus DR availability operated primarily through the load-increase pathway (additional grid consumption) rather than the surplus absorption pathway (reduced PV export). Surplus generation was observed only in the school at 13:00 (0.77 kW). These results provide a quantitative basis for identifying suitable building types and curtailment-responsive time windows for building-based Plus DR program design on the Korean mainland, and may serve as a reference for mainland DR market development. Full article

Background/Objectives: Consumption of ultra-processed foods (UPF) has been linked to poorer diet quality and adverse health outcomes. Although Chile ranks among the highest consumers of UPFs in Latin America, studies using primary dietary data, especially among older adults, are scarce. This study aimed to describe the food intake of Chilean older adults according to the degree of food processing, and to explore the association between UPF intake and sociodemographic, economic and health factors. Methods: A cross-sectional study of 434 non-institutionalized older adults (≥60 years) living in the Metropolitan Region of Chile was conducted. Dietary intake was assessed using interviewer-administered 24h recall, with a second assessment 8–15 days later in a random subsample (n = 60). Foods were classified according to the NOVA system into minimally processed foods (MPFs), culinary ingredients, processed foods (PF), or UPF. Usual energy intake was estimated using the MSM. Sociodemographic (sex, age, area), economic (income, education, health system), and health-related variables (chronic conditions, sedentary lifestyle, tobacco use) were collected through home-visit questionnaires. Anthropometric and functional measurements were taken by trained nutritionists. The association between UPF intake and studied variables was evaluated using multivariate fractional probit regression, with mean marginal effects presented. Results: Most of the participants were women (86.2%), aged 70–79 years (47.9%), and residents of urban areas (76.3%). Most of their calories came from MPF (45.7%), followed by PF (25.5%) and UPF (16.6%). Higher UPF intake was associated with living in an urban area (+3.8%; 95% CI 1.2–6.3%), higher education (+3.5%; 95% CI 1.1–6.0%), and being affiliated with the private health system (+9.1%; 95% CI 4.1–14.0%). Conclusions: In this community-based sample of Chilean older adults, UPF intake was associated with socioeconomic factors but not health status. Full article

Image sensors produce high-dimensional visual data for classification algorithms. Deep Neural Networks (DNNs) achieve high accuracy but require large labeled datasets and computational and energy resources, limiting their use in embedded systems. Active Learning (ALrn) can reduce labeling effort by selecting samples based on informativeness scores, but it remains computationally expensive, especially for high-dimensional images. This work presents a hardware-accelerated approach for the instance selection stage based on a query strategy in uncertainty-based ALrn for image classification using a novel in-line top-k selection algorithm that avoids conventional sorting and reduces memory and computational requirements. The algorithm is implemented on an Xilinx ZYNQ-7000 System on Chip (SoC) using a Field Programmable Gate Array (FPGA)-based accelerator operating at 110 MHz, interfacing with an embedded Advanced RISC Machine (ARM) processor for data acquisition and communication via the Python Productivity for Zynq (PYNQ) framework. Experiments on diverse multiclass datasets demonstrate correctness within an ALrn setting, showing negligible performance deviation in the learning curves compared to software baselines. The accelerator achieves speedup of 231.7× and 22.9× over software baseline and optimized software implementation of the proposed algorithm, respectively, in query-strategy computation while consuming only 0.473 W, substantially lower than conventional Central Processing Unit (CPU)- and Graphics Processing Unit (GPU)-based platforms. These results demonstrate the efficiency and extensibility of the proposed accelerator across alternative ALrn designs and hardware platforms, where the computational cost of instance selection scales with the size of the unlabeled pool. Full article

Under a dual-carbon background, promoting substantive green innovation in manufacturing enterprises has become a central topic in green transition research. This paper constructs an evolutionary game model involving manufacturing enterprises and consumers under market mechanisms and government intervention to analyze the evolutionary patterns and stability conditions of their strategic choices. Using case data and numerical simulations, it explores the role of government guidance in addressing market failures and fostering green innovation in manufacturing. The findings reveal the following: (1) Under market mechanisms, system evolution is influenced by multiple factors. If enterprises prioritize short-term gains by accelerating symbolic green innovation, consumer trust erodes, leading to a shift toward traditional consumption and ultimately driving the system toward market failure. (2) Under government intervention, incentive subsidies must reach a specific threshold to effectively guide manufacturers toward substantive green innovation. Such subsidies also lower the marginal cost of low-carbon consumption, enhancing consumer willingness to purchase green products. Furthermore, government regulation demonstrates positive promoting effects on the green behaviors of both manufacturers and consumers, with a more pronounced impact on the former. (3) The policy combination of incentive subsidies and government supervision significantly shapes evolutionary trajectories through a synergistic mechanism of “reward incentives and regulatory rigidity.” Policy mismatches may trap the system in market failure. Only when subsidy intensity sufficiently compensates for innovation costs and regulatory capacity exceeds enforcement efficiency thresholds can the system stably evolve toward a substantive green innovation, low-carbon consumption state, fostering a virtuous cycle of supply–demand synergy. Full article

Global food production must reduce environmental impact while meeting rising demand for dietary protein. Plant-based meats aim to preserve the sensory and cultural role of animal meat while lowering greenhouse gas emissions, land use, and health risks. Advances in protein structure and flavor chemistry have improved product quality, yet consumers continue to prioritize taste and texture over sustainability, and systematic large-scale consumer surveys are scarce. It remains unclear how plant-based products rank against animal benchmarks and which product attributes most strongly influence overall liking. Here we show, in a large-scale blinded in-person sensory evaluation across 14 product categories, 2684 consumers, more than 11,000 product evaluations and 800,000 data points, that plant-based products still trail animal benchmarks at the category average level but approach parity in selected formats. Plant-based unbreaded chicken filets, chicken nuggets, and burgers achieved mean overall liking scores of 5.1, 4.9, and 5.2, differing from the animal benchmarks by only $\Delta$ = 0.1, 0.2, and 0.3 points on a seven-point scale. For unbreaded chicken filets and burgers, 48% and 47% of the participants rated the plant-based product the same as or better than the animal benchmark. Categories with higher sensory parity captured 5–14% market share compared with less than 1% for low-parity categories. Penalty analysis identified savoriness, aftertaste, juiciness, and tenderness as the strongest determinants of liking. These findings show that sensory parity is technically achievable but not yet consistent across product types. By publicly sharing all the sensory, preference, and market-linked data, we establish an open benchmark for alternative protein performance to democratize research and accelerate principled data-driven innovation. All the data are freely available at https://www.nectar.org/sensory-research/2025-taste-of-the-industry. Full article

The integration of renewable energy and rising electricity demand strain system flexibility. While price-based demand response (PBDR) improves flexibility through pricing signals, its efficacy hinges critically on accurate consumer modeling. Recognizing this pivotal role, this paper provides a comprehensive review of consumer models in PBDR and their applications to electric vehicles (EVs). First, a unified conceptual framework is presented, delineating the energy, information and financial flows among the system operator (SO), load aggregators (LAs), and end-users, and highlighting the central position of consumer modeling. Second, existing modeling approaches are systematically classified into four categories, namely rule-based, optimization-based, data-driven, and hybrid, to facilitate the selection of appropriate models by researchers and stakeholders for diverse scenarios. Furthermore, the application and adaptation of these models to EVs are critically analyzed, accounting for unique vehicular constraints. Subsequently, a systematic summary of the key characteristics and existing research gaps is provided. Finally, key directions for future research are proposed accordingly, aimed at incorporating bounded rationality into behavioral models, developing individualized consumer modeling coupled with user-specific dynamic pricing, and extending consumer modeling to residential multi-energy prosumers in integrated energy systems. Full article