Search Results (725)

Animal by-products (ABPs), comprising both edible and inedible components, offer significant nutritional, economic, and environmental value. However, their utilization differs markedly across global jurisdictions due to cultural preferences, regulatory frameworks, and technological capacities, which collectively shape consumption patterns and determine integration into food systems or diversion to industrial applications. While consumer reliance on offal remains high in the Global South, driven by tradition, affordability, and nutritional needs, its acceptance in the Global North is markedly lower, often limited by cultural aversion and perceived risks. Drawing from published evidence and primary survey data, this review examines regional consumption trends, industrial utilization pathways, and emerging valorization opportunities for ABPs. Globally, industrial use of ABPs is increasingly shifting toward advanced bioprocessing, integration within circular bioeconomy models, and high-value applications in nutraceutical, pharmaceutical, and bio-industrial sectors. An online cross-sectional survey (n = 358) conducted across Africa, North America, Europe, and Asia revealed strong regional disparities in offal consumption, with higher acceptance in parts of Africa and Asia and more selective use in Europe and North America. Respondents also indicated clear support for non-food valorization pathways, particularly animal feed, fertilizer, and energy production, alongside pharmaceutical and cosmetic applications. These findings align with the literature, where industrial valorization pathways such as collagen and gelatin extraction, rendering, and bioenergy production dominate. This review synthesized the jurisdictional disparities in consumption, regulation, technological capability, and industrial applications while highlighting emerging technological opportunities for high-value valorization. Recommendations emphasize consumer education, regulatory refinement, technological innovation, and sustainable practices to enhance the economic and environmental benefits of ABP utilization within a circular bioeconomy framework. Full article

Background/Objectives: Soy flour has been proposed as a functional ingredient to improve the protein and fiber content of foods; however, its metabolic and sensory effects, particularly in individuals at elevated risk for metabolic disease, remain insufficiently characterized. This randomized, repeated-measures study examined whether substituting refined wheat or corn flour with soy flour influences postprandial glucose and insulin plasma concentrations, appetite ratings, and product acceptability in adults with overweight or obesity. Methods: Participants (N = 17) attended at least three separate visits during which they consumed, in random order, a food matrix with 0% (control), 10%, or 30% soy flour substitution. Food matrices included breads (n = 10), tortillas (n = 10), and arepas (n = 8); some participants completed more than one matrix. Postprandial plasma glucose and insulin concentrations were measured at baseline and at 15, 30, 60, 90, and 120 min post-ingestion. Subjective hunger, satiety, and product liking were assessed using a 10 cm visual analog scale. Results: Compared with the control condition, substituting 30% of refined flour with soy flour significantly reduced the area under the concentration–time curve for postprandial glucose for breads (p = 0.03) and arepas (p = 0.04), and reduced plasma glucose concentrations at 90–120 min for tortillas (p = 0.0009). In contrast, postprandial insulin concentrations and subjective hunger and satiety ratings did not differ across substitution levels or food matrices (all p > 0.05). Importantly, even 30% soy flour substitution maintained product liking. Conclusions: Incorporating up to 30% soy flour may improve postprandial glycemic responses without compromising overall liking, supporting its potential as a practical food reformulation strategy to improve metabolic health in populations at increased risk of metabolic disease. Full article

As a key task in remote sensing analysis, semantic segmentation of remote sensing images (RSI) underpins many practical applications. Despite its importance, obtaining dense pixel-wise annotations remains labor-intensive and time-consuming. Unsupervised domain adaptation (UDA) offers a promising solution by utilizing knowledge from labeled source domains for unlabeled target domains, yet its effectiveness is often compromised by significant distribution shifts arising from variations in imaging conditions. To address this challenge, we propose a depth-aware adaptation network (DAAN), a novel two-branch network that explicitly leverages complementary depth information from a digital surface model (DSM) to enhance cross-domain remote sensing segmentation. Unlike conventional UDA methods that primarily focus on semantic features, DAAN incorporates depth data to build a more generalized feature space. This network introduces three key components: an adaptive feature aggregator (AFA) for progressive semantic-depth feature fusion, a gated prediction selection unit (GPSU) that selectively integrates predictions to mitigate the impact of noisy depth measurements, and misalignment-focused residual refinement (MFRR) module that emphasizes poorly aligned target regions during training. Experiments on the ISPRS and GAMUS datasets demonstrate the effectiveness of the proposed method. In particular, DAAN achieves an mIoU of 50.53% and an F1 score of 65.75% for cross-domain segmentation on ISPRS to GAMUS, outperforming models without depth information by 9.17% and 8.99%, respectively. These results demonstrate the advantage of integrating auxiliary geometric information to improve model generalization on unlabeled remote sensing datasets, contributing to higher mapping accuracy, more reliable automated analysis, and enhanced decision-making support. Full article

With the increasing demand for high-quality imaging in consumer electronics, image aesthetics assessment (IAA) has been widely applied to electronic cameras and display devices. Although the deformable attention mechanism has been introduced into IAA due to its perceptual capabilities, enabling models to refine attention regions by learning interest points and their corresponding offsets, existing methods often lack guidance from aesthetic composition features during the offset generation process, which limits their performance in aesthetic evaluation tasks. To address this issue, we propose a graph neural network (GNN)-guided deformable attention module that incorporates composition information into the generation of interest points by modeling image features as graphs and applying the GNN to guide interest point selection. In addition, we design an improved transformer model that employs neighborhood attention to further enhance IAA performance. We evaluate the proposed model on two aesthetic datasets, AVA and TAD66K, and the experimental results demonstrate its effectiveness in improving overall model performance. Full article

Integrated Demand Response (IDR) enhances the operational flexibility of Integrated Energy Systems (IES) and promotes renewable energy integration. However, limited interaction between the Integrated Energy Operator (IEO) and users during actual energy transactions can lead to biases in IDR planning, compromising user response effectiveness. To address this, this paper proposes a method for revising IDR stimulus parameters in IES based on gradient descent within a Stackelberg game framework. First, an IDR model based on consumer psychology principles is constructed to establish an IES Stackelberg game, in which the IEO acts as the leader and the load aggregator acts as the follower. Subsequently, during the game, the IEO utilizes users’ energy consumption strategies to adjust the stimulus threshold parameters of the dead zone and saturation zone along the negative gradient direction, thereby updating its decision for the next round. Furthermore, a response adjustment mechanism is designed to refine the IDR plan, enhancing its feasibility. Finally, comparative analyses across diverse scenarios demonstrate that the proposed method reduces deviations in planned IDR, thereby enhancing the low-carbon performance and renewable energy integration capacity of IES. Full article

Oral mucosal melanoma (OMM) is a rare and aggressive malignancy that differs markedly from cutaneous melanoma in terms of epidemiology, genetic characteristics, clinical presentation, and treatment response. Despite advances in understanding OMM pathogenesis and the development of novel therapeutic strategies, early diagnosis remains challenging due to its low prevalence, anatomically concealed locations, and frequent multifocality. This review emphasizes the importance of the early detection of OMM using non-invasive imaging methods—specifically dermoscopy and reflectance confocal microscopy (RCM)—and explores their potential role in guiding treatment decisions, preventing disease progression, and improving prognosis. A narrative review of the PubMed database was conducted using the terms “oral melanoma,” “oral melanoma dermoscopy,” and “oral melanoma reflectance confocal microscopy.” Seventy-two relevant review articles were included. In addition, two illustrative clinical cases from our practice are presented to demonstrate the diagnostic value of non-invasive imaging techniques. Although biopsy and histopathology remain the diagnostic gold standards, they are invasive, time-consuming, and may be poorly tolerated, particularly in patients with multifocal lesions. Dermoscopy and RCM provide real-time, high-resolution imaging that enables the detection of early tissue abnormalities not visible to the naked eye. These techniques show good correlation with clinical and histopathological findings, thereby enhancing diagnostic accuracy and facilitating follow-up without the need for repeated biopsies. In our cases, they were instrumental in identifying recurrence and guiding clinical management. However, several limitations should be considered, including restricted accessibility, anatomical constraints, and the requirement for specialized training and expertise. Non-invasive imaging techniques may support clinicians in the early recognition and evaluation of suspicious oral lesions; however, histopathologic examination remains essential for definitive diagnosis. Wider implementation and further technological refinement are needed to optimize their integration into clinical practice. Full article

To enhance the early-age properties of steel slag cement mortar and promote the resource utilization of metallurgical solid waste, in this study, nano-SiO₂ (KH-NS) was modified using a KH550 silane coupling agent. The hydration kinetics and microstructure evolution were systematically analyzed by means of a macroscopic performance test (setting time and compressive strength) and multi-scale microscopic characterization (characterized by Fourier Transform Infrared Spectroscopy, Scanning Electron Microscopy, X-ray Diffraction, Thermogravimetry-Differential Thermal Analysis, and isothermal calorimetry). The influence mechanism of its content on the early performance of the steel slag cement system was systematically studied. Research findings indicate that at a given dosage, increasing the proportion of KH-NS results in a shorter setting time for steel slag mortar. When the KH-NS dosage reaches 1.5%, the initial and final setting times of steel slag mortar decrease by 24.21% and 21.20%, respectively. The addition of KH-NS effectively enhances the compressive strength of mortar, with a particularly pronounced effect on early strength prior to 14 h of curing. At a KH-NS dosage of 1.5%, the onset of the accelerated phase of hydration heat release in steel slag cement mortar is advanced by 2.5 h. Mechanistic studies indicate that KH-NS accelerates cement hydration by promoting C₃S dissolution and C-S-H gel nucleation through interactions between surface silanol groups (Si-OH) and amino groups (-NH₂). Furthermore, KH-NS refines the pore structure via a micro-aggregate filling effect, reducing the number of harmful pores and improving the pore size distribution. KH-NS continuously consumes Ca(OH)₂ through pozzolanic reactions to generate C-S-H, with its reactivity increasing with higher dosage. Research confirms that KH-NS significantly enhances the early strength and density of steel slag mortar, providing both theoretical justification and technical support for developing low-carbon building materials based on solid waste with high dosage. Full article

This study investigates the influence of Product Transparency (PT) and Pricing Strategy (PS) on customer behavior, specifically examining Market Competition (MC) as a moderating factor. Grounded in Signal Theory, the Theory of Planned Behavior (TPB), and Social Exchange Theory (SET), we propose a model where customer trust and perceived value mediate the impact of firm strategies on purchase decisions and customer retention. Using a two-wave time-lagged design with anonymous respondent matching, data were analyzed from e-commerce consumers in China and Pakistan using PLS-SEM multi-group analysis. The findings reveal that market competition undermines the positive relationships in the model, with a greater impact in China than in Pakistan. In general, the results suggest that market competition negatively influences the efficiency of product transparency and pricing strategies in shaping customer trust, perceived value, customer retention, and purchase decisions, which is why firms should be able to adjust these strategies to the levels of market competition and country context. This research provides critical theoretical insights into signal translation and offers practical guidance for international e-commerce managers to refine their customer relationship strategies in highly competitive digital environments. Full article

This study employs a constructivist grounded theory approach based on 69 in-depth interviews conducted between March 2022 and December 2023 to examine socio-cognitive dynamics in Korea’s bottled water and household water purifier markets. The study addresses a gap in prior research by explaining how product meanings and stakeholder strategies co-evolve across adjacent “safe-water” markets under regulatory and sustainability pressures. Drawing on qualitative data from 69 stakeholders, including producers (n = 30), consumers (n = 19), and institutional experts (n = 20), we analyze how distrust, risk perception, and health consciousness reshape conceptual systems and market strategies. These shifts drive innovation across markets, including new technologies, service models, and branding strategies. The findings show that socio-cognitive stabilization arises through iterative interactions among institutional shocks, producer reinterpretation, and consumer adaptation. In the bottled water market, the meanings of “natural purity” became materially embedded in packaging, mineral labeling, and brand narratives. In the purifier sector, “technological reliability” was institutionalized through service-based maintenance systems and visible quality control technologies. These processes developed within asymmetric communicative environments shaped by corporate branding capacity and media amplification. This study refines socio-cognitive market theory by specifying boundary conditions under institutional distrust in developed economies. Although Republic of Korea possesses advanced drinking water infrastructure comparable to that of other developed economies, public confidence in tap water has periodically weakened following highly salient contamination incidents and regulatory transitions. This paradox provides a theoretically informative context for examining how product meanings and stakeholder behaviors mutually adapt over time. Although environmental impact metrics were not directly measured, the findings suggest that sustainability policies must address socio-cognitive trust dynamics alongside regulatory instruments such as plastic levies, certification schemes, and transparent risk communication. Full article

Scientific evidence is fundamental to uncovering insights about health, including food and nutritional claims. Substantiating such claims requires robust scientific procedures that often include clinical studies, biochemical analyses, and the examination of multiple forms of data. The growing capabilities of artificial intelligence (AI) and large language models (LLMs) present new opportunities for analyzing food–health relationships and supporting health claim validation. Yet, applying these technologies to the food and nutrition domain raises challenges that differ from those encountered in broader biomedical text mining (TM). In this perspective, we review key issues, including the complexity and heterogeneity of food-related data, the scarcity of food-specific language models and standardized resources, difficulties in interpreting nuanced and often contradictory evidence, and requirements for integrating AI tools into regulatory workflows. We compare modern LLM approaches with traditional TM methods and discuss how each may complement the other. Our position is that, despite their promise, current AI and LLM tools cannot yet reliably handle the subtleties of food–health evidence without substantial domain-specific refinement and human expert oversight. We advocate for hybrid approaches that combine the precision of established TM techniques with the analytical breadth of LLMs, supported by harmonized ontologies, multidimensional evaluation frameworks, and human-in-the-loop validation, particularly in regulatory contexts. We also highlight the importance of public education, transparent communication standards, and coordinated cross-disciplinary efforts to ensure these technologies serve broader goals of food safety, consumer trust, and global health. Full article

Identifying real-world saturation points and grid-hosting capacity in mixed-use urban Renewable Energy Communities (RECs) requires dynamic spatial evaluation. To address this, this paper introduces a novel simulation framework that integrates GIS spatial analysis with an iterative heuristic selection algorithm. The proposed method evaluates the energetic interaction between a primary generation node and surrounding consumers, utilizing a dynamic function to calculate the collective Self-Consumption Rate (SCR). Applied to the Flaminio Stadium in Rome, the model incrementally aggregates users to determine the optimal cluster size for economic feasibility. The results demonstrate that the heuristic selection algorithm successfully refined the community from an initial pool of 854 buildings to an optimal cluster of 734. This targeted selection eliminated energy surplus and achieved a near-perfect collective SCR of 99.8%. Furthermore, by strategically reducing the required installed PV capacity by 52.6%, the initial capital investment dropped from € 89.9 million to € 42.6 million, significantly de-risking the project while maintaining a competitive payback period of approximately 13 years. Ultimately, this study presents a scalable spatial optimization tool that empowers decision makers to transform large-scale urban infrastructure into the energetic and economic engines of district wide decarbonization Full article

Background/Objective: Helping Early Adolescents Live Their Healthiest Youth (HEALTHY) is a four-session, smoothie-based nutrition education program grounded in Social Cognitive Theory (SCT) and Choice Architecture, designed to promote nutrition security among rural adolescents. This study examined students’ experiences with the program, including perceptions of likeability and perceived learning, as well as the theoretical mechanisms shaping engagement. Methods: A mixed-methods evaluation was conducted in two rural middle schools where the programming was delivered. Process indicators were assessed using brief paper-based surveys administered after each program session. Quantitative items captured likability and willingness to consume smoothies again (at home or school lunch), and qualitative open-ended responses were analyzed inductively. Post-program focus groups were conducted with a subsample of participants (N = 18) and analyzed deductively using a coding framework aligned with SCT constructs. Results: Across sessions, students (N = 360) reported high smoothie likeability, with fewer than 15% indicating dislike of any recipe. Willingness to consume smoothies again remained high, with affirmative responses ranging from 72% to 94% at home and 79% to 97% at school lunch. Inductive thematic analysis indicated that 53% of survey responses reflected session-aligned nutrition knowledge, along with themes related to acceptability and suggestions for improvement. Focus group findings reflected multiple SCT constructs, including knowledge awareness, self-efficacy, and goal-setting, as well as environmental influences regarding engagement, consistent with Choice Architecture. Conclusions: Findings indicate that the HEALTHY program was well received by rural adolescents and reflected key theoretical mechanisms underlying its design. Student feedback guides future program refinement. Full article

Reducing cake fat while maintaining aeration, crumb softness, and consumer acceptance remains challenging because fat crystals contribute to interfacial stabilization and structure development. This study evaluated an interfacial processing strategy in which oil dispersion is refined by pre-emulsification to evaluate whether refining oil dispersion by pre-emulsification modulates the functional impact of lipase (via in situ formation of surface-active lipolysis products). A D-optimal design (16 formulations) quantified the effects of fat type (shortening vs. sunflower oil), fat level (100% vs. 50%), pre-emulsification (absent/present), and lipase dose (0, 50, 100 ppm; flour basis) on batter and baked-cake quality. Responses included moisture, color, volume/visual structure, texture and hedonic sensory evaluation for selected formulations. Lipase improved structure and texture, with the strongest benefits in reduced-fat samples, where hardness-related parameters decreased and volume/crumb refinement improved. Pre-emulsification modulated lipase performance in a formulation-dependent manner, indicating significant interactions. In sensory tests, the combined approach improved low-fat acceptance compared with the low-fat control. Overall, pre-emulsification-enabled lipase action offers a route to recover key quality attributes in low-fat cakes without conventional emulsifiers. Full article

The Atlantic sharpnose (Rhizoprionodon terraenovae), blacknose (Carcharhinus acronotus), blacktip (Carcharhinus limbatus), and bonnethead (Sphyrna tiburo) sharks are commonly encountered large mobile consumers found in the estuaries along the western North Atlantic coast. The bulk of the dietary data for these species has been coarsely recorded at a broad taxonomic level (e.g., “teleost fish”). Here, we used DNA metabarcoding of fecal DNA collected using non-lethal cloacal swabs to identify the species of prey contributing to the diets of these shark species and measure the degree of trophic overlap. Samples were collected from 24 Atlantic sharpnose, 33 blacknose, six blacktip, and 17 bonnethead sharks in the summer of 2020. Based on previous dietary research on these shark species, we targeted teleost fishes and crustaceans using two previously published primer sets. From the 80 sharks sampled off the coast of North Carolina, we identified 38 prey taxa, with 82% classified to the species level and all assigned to at least the genus and family levels. The most common prey taxa found in the diet of the bonnethead was Atlantic blue crab (Callinectes sapidus; 44.75%, based on percent of occurrence) followed by penaeid shrimp (Penaeus spp.; 24.41%), mantis shrimp (Squilla empusa; 20.34%), and spot (Leiostomus xanthurus; 4.75%). Atlantic sharpnose and blacknose sharks had the largest Levin’s niche overlap, with both species relying on the same two most frequently consumed prey taxa: penaeid shrimp (Atlantic sharpnose: 33.33%, percent of occurrence, and blacknose: 34.78%) and spot (Atlantic sharpnose: 32.70% and blacknose: 22.32%). Bonnetheads and blacktips had the least amount of overlap between all shark species, where blacktips primarily consumed menhaden (Brevoortia spp.; 58.62%) and penaeid shrimp (26.44%). Our findings highlight the value of DNA metabarcoding in refining our understanding of predator diets, moving beyond broad taxonomic classifications to identify species-level prey associations and trophic interactions. As coastal habitats undergo increasing alteration due to anthropogenic impacts, such information is crucial for fisheries management, helping to identify key prey dependencies and anticipate potential ecosystem shifts. Full article

Moroccan lben is a traditional spontaneously fermented milk widely consumed across the Maghreb. In this review, we synthesize data on spontaneously fermented milks from Morocco and the wider Maghreb–Middle Eastern region to infer the likely microbiota of Moroccan lben, with particular emphasis on dominant lactic acid bacteria such as Lactococcus lactis, Streptococcus thermophilus, Leuconostoc mesenteroides and lactobacilli sensu lato, alongside yeasts including Kluyveromyces marxianus and Saccharomyces cerevisiae. These communities drive a staged fermentation in which early mesophilic lactic acid bacteria (LAB) rapidly acidify the milk and initiate coagulation, intermediate heterofermentative LAB and yeasts generate key aroma compounds and mild effervescence, and late acid-tolerant lactobacilli contribute to flavor refinement and microbiological stability. We summarize how these bacteria and fungi collectively shape physicochemical, sensory and safety attributes through pH reduction, organic acid and bacteriocin production, proteolysis, and volatile formation, and discuss potential nutritional and health-related effects associated with bioactive peptides and putative probiotic strains. Finally, we identify major research gaps, including the need for high-resolution, culture-dependent and culture-independent studies, systematic safety assessments, and rational design of starter and adjunct cultures that reproduce traditional sensory profiles while improving process control. Full article