Search Results (1,580)

Cyberbullying and hate speech increasingly appear in multi-modal social media posts, where images and text are combined in diverse and fast changing ways across platforms. These posts differ in style, vocabulary and layout, and labeled data are sparse and noisy, which makes it difficult to train detectors that are both reliable and deployable under tight computational budgets. Many high performing systems rely on large vision language backbones, full parameter fine tuning, online retrieval or model ensembles, which raises training and inference costs. We present a parameter efficient cross-platform multi-modal transfer learning framework for cyberbullying and hateful content detection. Our framework has three components. First, we perform domain adaptive pretraining of a compact ViLT backbone on in domain image-text corpora. Second, we apply parameter efficient fine tuning that updates only bias terms, a small subset of LayerNorm parameters and the classification head, leaving the inference computation graph unchanged. Third, we use noise aware knowledge distillation from a stronger teacher built from pretrained text and CLIP based image-text encoders, where only high confidence, temperature scaled predictions are used as soft labels during training, and teacher models and any retrieval components are used only offline. We evaluate primarily on Hateful Memes and use IMDB as an auxiliary text only benchmark to show that the deployment aware PEFT + offline-KD recipe can still be applied when other modalities are unavailable. On Hateful Memes, our student updates only 0.11% of parameters and retain about 96% of the AUROC of full fine-tuning. Full article

Measuring the fatigue crack growth rate via the crack growth experiment (a-N curve) is labor-intensive and time-consuming. A machine learning interpolation–extrapolation strategy (MLIES) aimed at enhancing the prediction accuracy of small-data models has been proposed to accelerate fatigue testing. Two specific approaches are designed by transforming a-N curve data from N to ΔN and from a to Δa (S1)/Δa/ΔN (S2) to enrich the data volume and leverage the incremental information. Thus, a simple and fast-responding single-layer neural network model can be trained based on the early-stage data points from fatigue testing and accurately predict the remaining part of an a-N curve, thereby enhancing the experimental efficiency. Through exponential data expansion and data augmentation, the trained neural network model is able to learn the underlying rules governing crack growth directly from the experimental data, requiring no explicit analytical crack growth laws. The proposed MLIES was validated on fatigue tests for aluminum alloy and titanium alloy samples under different experimental parameters. Results demonstrate its effectiveness in reducing testing time/cost by 15–32% while achieving over 30% higher prediction accuracy for the a-N curve compared to a traditional machine learning modeling approach. Our research offers a data-driven recipe for accurate crack growth prediction and accelerated fatigue testing. Full article

The aim of this research was to examine the possibility of achieving optimum texture and rheological properties of cocoa spreads produced with cocoa shell, xylitol, and stevia as sugar replacers using different vegetable fats (palm and coconut oil). Samples with different proportions of cocoa shell and sweeteners used to completely replace sugar were produced under laboratory conditions, and rheology, texture, colloidal stability, and particle size were determined. The results showed that texture properties (spreadability and firmness) and yield stress directly correlated with the type of fat used in the recipe, with the best values obtained for coconut fat. Cocoa shell addition increases Casson plastic viscosity (from approximately 2 to 7 Pas) but may reduce Casson yield stress if added in a proportion up to 25% (from approximately 26 to 18 Pa for samples produced with a combination of palm and coconut oil). Overall, samples with coconut fat had better texture properties regardless of the cocoa shell and sweetener quantities, and the sample with the lowest content of cocoa shell had the best spreadability. By careful adjustment of ingredients and the right choice of substitute fat, sustainable, nutritionally improved cocoa spreads with satisfactory texture and rheology may be produced. Full article

Laying hens require lighting for proper development and reproduction. There is limited research on the effects that lighting types have on birds’ welfare quality. A novel lighting source, Pulsed Alternating Wavelength System (PAWS), is being evaluated in the industry that claims to improve birds’ growth rate, decrease age at first egg, and decrease aggressive and nervous behaviors. Understanding how PAWS effects hen’s welfare, both physically and physiologically, is critical if this technology is to be adopted by industry. The project evaluated the effects of two PAWS lighting recipes on neurotransmitter turnover and welfare quality of commercial, conventionally caged laying hens. Three flocks of White leghorn hens (control [fluorescent lights] and two PAWS flocks [PAWS1 and PAWS2]) were sampled from 22 to 70 weeks of age, depending on the flock. The physical welfare of 50 hens per flock and neurotransmitter turnover of 10 hens per flock were assessed at each timepoint. The majority of welfare quality parameters were influenced by age as opposed to lighting type. No differences in dopamine turnover were observed. The hens housed under PAWS1 had reduced serotonin turnover, thus increased serotonin activity, and PAWS2 hens had improved keel bone damage scores; both indicative of improved welfare compared to control hens. The novel lighting may be beneficial to layer welfare, which may lead to increased longevity and productivity. Implementation in cage-free housing should be explored to delve into potential behavioral differences that could further influence welfare outcomes. Full article

Chronic stress (CS) contributes to male infertility, reduced testosterone levels, and impaired semen quality. CS models induced by glucocorticoids, such as dexamethasone (DEX), negatively affect sperm parameters and testicular health, notably by promoting testicular apoptosis. While individual plant extracts have been studied for their ability to mitigate stress-induced reproductive dysfunction, the preventive effect of the Tri Garn Pis (TGP) polyherbal extract in DEX-induced CS (DexCS) has not previously been investigated. This study evaluated the effects of TGP extract on testicular function, sexual behavior, and sperm quality in DexCS male mice. Seventy-two ICR mice were randomly divided into six groups: control, DexCS, TGP (50, 100, and 200) + DexCS, and TGP200. Mice received TGP (50, 100, 200 mg/kgBW) for 14 days before DEX co-treatment for 28 days. Behavioral and reproductive assessments included depression-like behavior tests, sexual behavior, sperm quality, testicular histopathology, steroidogenesis proteins (AR, CYP11A1, StAR), and apoptosis markers (Hsp70, caspase-3, caspase-9). TGP extract—which is rich in phenolics and flavonoids with antioxidant activity—improved depressive behavior, sexual performance, testicular histology, and low sperm quality. TGP also upregulated testicular StAR expression while reducing caspase-3 and caspase-9 levels. TGP prevents testicular apoptosis, sexual dysfunction, and poor sperm motility induced by DexCS. Full article

Berries are a valuable source of health-promoting substances, including vitamins, microelements, and polyphenols. Optimising the extraction efficiency of these compounds during processing is crucial to minimise their loss into the waste stream. Ultrasound technology is recognised as a sustainable and promising tool for improving extraction; however, previous literature has not sufficiently addressed the optimal point of its application in fruit puree processing, and its impact on the sensory properties of the final product has only occasionally been explored. As one of the first reports, this study aimed to determine the optimal moment for ultrasound application within a puree production scheme. In the second stage of the experiment, four recipes based on strawberry and haskap berry were tested. The results demonstrated the potential for enhancing sensory quality of puree by using an ultrasound treatment. It was found that the ultrasound-treated purees showed significantly higher pectin levels and improved rheological properties, while the content of anthocyanins and L-ascorbic acid remained mainly unchanged. This indicates that the non-thermal nature of ultrasound treatment can induce positive changes from a sensory and rheological point of view without causing the degradation of health-promoting compounds, offering a viable strategy for improving berry puree quality. Full article

Microplastic quantification in marine vegetated ecosystems remains analytically demanding, yet little is known about the environmental footprint of the laboratory procedures required to isolate and measure these particles. This study applies Life Cycle Assessment (LCA) to laboratory analytical workflows for microplastics quantification, focusing exclusively on sample preparation and analytical procedures rather than natural environmental absorption or fate processes, in two ecologically relevant matrices: (i) pelagic algae (Sargassum) and (ii) seagrass biomass. Using the openLCA 2.5 and the ReCiPe Midpoint (H) v1.13 methods, the analysis integrates foreground inventories of digestion, filtration, drying, and spectroscopic identification, combined with background datasets from OzLCI2019, ELCD 3.2 and USDA. Results show substantially higher impacts for the algae scenario, particularly for climate change, human toxicity, ionising radiation and particulate matter formation, largely driven by longer digestion times, increased reagent use and higher energy demand during sample pre-treatment. Conversely, the seagrass scenario exhibited lower burdens per functional unit due to reduced organic complexity and shorter laboratory processing requirements. These findings highlight the importance of matrix-specific methodological choices and the influence of background datasets on impact profiles. This study provides the first benchmark for the environmental performance of microplastic analytical workflows and underscores the need for harmonised, low-impact laboratory protocols to support sustainable monitoring of microplastic pollution in marine ecosystems. Full article

Fermentation is a complex biochemical process that transforms brewer’s wort into beer. Beer fermentation is driven by yeast and is influenced by process parameters such as the content of fermentable sugars in wort, temperature, and pH. Traditional methods of modelling this process rely heavily on empirically tuned kinetic models. However, these models tend to be recipe-specific and often require retuning when processes change. This paper proposes a data-driven approach using a Long Short-Term Memory (LSTM) network, a type of recurrent neural network, to model beer fermentation dynamics. By training the LSTM model on real-world fermentation data (1305 fermentations across ales, IPAs, lagers, and mixed-culture beers), including variables such as apparent extract (derived from specific gravity), temperature, and pH, we demonstrate that this technique can accurately predict key fermentation trajectories and support process monitoring and optimisation. When evaluated on representative medoid fermentations as one-step-ahead roll-outs over 0–300 h, the model produces accurate predictions with low errors and minimal residuals. These results show that the LSTM-based model provides accurate and robust predictions across beer styles and operating conditions, offering a practical alternative to traditional mechanistic kinetic models. This work highlights the potential of LSTM networks to enhance our understanding, monitoring, and control of fermentation processes, providing a scalable and efficient tool for both research and industrial applications. The findings suggest that LSTM models can be effectively adapted to model other fermentation processes in beverage production, opening new possibilities for advancing food science and engineering. Full article

Background: White spot lesions (WSLs) are characterized by enamel demineralization. Minimally invasive treatments using infiltrating resins, such as the commercially available Icon^®, are recommended. The need for such treatments justifies ongoing research into developing materials that can address existing limitations regarding strength, durability, and biocompatibility. Objectives: This study aimed to synthesize and characterize four novel nanobiomaterials by evaluating their physicochemical properties and biocompatibility compared to the commercial material Icon^®. Materials and methods: The recipes for the experimental nanobiomaterials NB3, NB6, NB3F, and NB6F contain varying proportions of TEGDMA, UDMA, HEMA, Bis-GMA, and HAF-BaF2 glass. Mechanical and physicochemical characteristics were evaluated, such as flexural strength, measured using the three-point test; water absorption and solubility; fluoride release; polymerization conversion; and residual monomers, assessed using High-Performance Liquid Chromatography (HPLC). In vitro cell viability was assessed via colorimetry using human dysplastic oral keratinocytes (DOKs). Results: NB6 and NB6F demonstrated the greatest polymerization potential. NB3 exhibited the lowest water absorption and solubility due to its hydrophobic nature. Additionally, the inclusion of UDMA enhanced the strength and elasticity of NB3 when compared to NB6. Among the samples with fluoride additives (NB3F and NB6F), the highest fluoride release on day 7 occurred with the material lacking UDMA. In contrast, the NB3F sample containing UDMA released the least amount of fluoride on the same day. In quantitative terms, NB3 and NB6F exhibited the lowest levels of residual monomers, whereas NB6 showed the highest levels. Both NB3 and NB6 were significantly better tolerated by the cells, showing higher cell viability compared to the commercial material Icon^®. Conclusions: The materials’ mechanical and physicochemical properties varied with component proportions, enabling identification of a suitable formulation for targeted clinical applications. Biocompatibility tests showed that the experimental NB3 and NB6 were better tolerated than Icon^®. Furthermore, the incorporation of filler particles improved the mechanical strength of the experimental nanobiomaterials. Full article

The city of Aigion, located in the northwestern Peloponnese, flourished as an important city-state especially during the Hellenistic period (323–32 BC). This is evidenced by abundant archaeological remains, including kilns, waste pits, and pottery workshop facilities. Among the ceramic goods produced by local workshops are various types of stamped and unstamped transport amphorae. Also, recent discoveries, approximately 15 km southeast in the village of Trapeza Diakopto, have uncovered a distinctive type of amphora—identified as Type B of the Corinthian–Corcyraean or Ionian–Adriatic tradition—from destruction layers dated to the 4th and early 3rd centuries BC. This study examines the technological attributes and provenance of transport amphorae from both sites through integrated petrographic and mineralogical analyses, drawing on 27 samples from Aigion and 17 from Trapeza. Petrographic analysis, focusing on compositional and textural characteristics, identified three distinct ceramic recipes (petrographic fabric groups AIG-1, AIG-2, and AIG-3) associated with amphora types I, II, and III at Aigion. Samples from Trapeza were grouped into two main fabric categories (TR1 and TR2a/b), along with a notable number of singletons. Moreover, petrographic observations combined with X-ray powder diffraction (XRPD) analysis provided insights into the firing technologies used. The results indicate that many amphorae from both Aigion and Trapeza were fired at temperatures below 850 °C, while others were fired at higher temperatures, ranging from approximately 900 °C to 1100 °C. The combined petrographic and mineralogical evidence illuminates local ceramic production techniques and interregional exchange patterns, contributing to a broader understanding of amphora manufacture and distribution in the northwestern Peloponnese from the Late Classical to the Late Hellenistic period. Full article

This study explores how artificial intelligence can promote accessibility and inclusiveness in digital culinary environments. Centred on the Receitas +Power platform, the research adopts an exploratory, multidimensional case study design integrating qualitative and quantitative analyses. The investigation addresses three research questions concerning (i) user empowerment beyond recommendation systems, (ii) accessibility best practices across disability types, and (iii) the effectiveness of AI-enabled inclusive solutions. The system was developed following user-centred design principles and WCAG 2.2 standards, combining generative AI modules for recipe creation with accessibility features such as voice interaction and adaptive navigation. The evaluation, conducted with 87 participants, employed the System Usability Scale complemented by thematic qualitative feedback. Results indicate excellent usability (M = 80.6), high reliability (Cronbach’s α = 0.798–0.849), and moderate positive correlations between usability and accessibility dimensions (r = 0.45–0.55). Participants highlighted the platform’s personalisation, clarity, and inclusivity, confirming that accessibility enhances rather than restricts user experience. The findings provide empirical evidence that AI-driven adaptability, when grounded in universal design principles, offers an effective and ethically sound pathway toward digital inclusion. Receitas +Power thus advances the field of inclusive digital gastronomy and presents a replicable framework for human–AI co-creation in accessible web technologies. Full article

Due to their simplicity and ease of visualization, lattice models can be useful to illustrate basic concepts in thermodynamics. The recipe to obtain classical thermodynamic expressions from lattice models is usually based on invoking the thermodynamic limit, and the ideal gas law can easily be obtained as the density of non-interacting particles vanishes. We present a lattice-based analysis that shows that, when a gas consisting of non-interacting particles evolves towards mechanical equilibrium with the environment, the ideal gas law can be obtained with no recourse to unnecessary assumptions regarding the size or particle density of the lattice. We also present a statistical mechanical analysis that considers a quantized volume and reproduces the process obtained for the discrete lattice model. We show how the alternative use of a well-known and accessible model (the non-interacting lattice gas) can give microscopic insights into thermal systems and the assumptions that underlie the laws used to describe them, including local vs. global equilibrium, irreversible processes, and the sometimes subtle difference between physical assumptions and mathematically convenient approximations. Full article

Portuguese ceramic tile (azulejo) production has evolved significantly since its beginnings in the 16th century. While historic tiles reflect well-established traditional techniques and styles, modern and contemporary works began to explore new aesthetic and material possibilities, introducing textures, surface effects, and experimental approaches that challenge conventional conservation methods. This study examines a contemporary Portuguese tile panel dated from 1987, featuring decorative effect glazes with crater and crazing textures, which were characterized and reproduced. Analytical techniques, including optical microscopy, micro-X-ray fluorescence spectrometry, and Raman spectroscopy in microscopic mode, were employed to characterize material composition and formation mechanisms. Results showed that the crater-effect glazes were achieved with a silica-rich glaze recipe with MnO₂ and ZrO₂. The crazing effect developed in regions where unmelted crystalline silica induced internal stresses within a lead-silicate glaze, contributing to localized degradation. Experimental reproductions of the glazes, guided by analytical data, were conducted to better understand their formation and inform conservation strategies. The results provide essential insights for the technical assessment, documentation, and preservation of contemporary ceramic artworks featuring decorative effect glazes and contribute to the broader field of cultural heritage conservation. Full article

Background & Aim: Sweets and appetizers are an integral part of the Gulf Cooperation Council (GCC) region’s cultural heritage but are often high in refined carbohydrates, sugars, and fats, contributing to the rising burden of obesity and type 2 diabetes. Qatar, as one of the fastest-developing GCC nations, exemplifies these nutrition-related challenges. Therefore, this study aimed to systematically develop a culturally adapted meal-planning exchange list for 34 commonly consumed Qatari and GCC sweets and appetizers to support nutrition counseling and diabetes management. Methods: This study is primarily methodological and developmental in scope, employing a descriptive observational design in which the units of analysis were the 34 selected traditional dishes. Standardized recipes were compiled for each dish, and serving sizes were determined. Macronutrient content (carbohydrates, protein, fat) was analyzed, variability across dishes was assessed, and nutrient data were validated against food processor software data. Results: The nutrient comparison analysis revealed strong correlations between collected nutrient data sources (r = 0.81–0.85, p < 0.05) and significant variability in macronutrient profiles. Fried and sugar-syrup-based items presented higher fat and carbohydrate content, while legume- and vegetable-based dishes contributed additional protein and fiber, demonstrating the dual role of traditional foods as both nutrient rich and energy dense. Conclusions: The developed exchange list provides a practical tool for culturally relevant nutrition guidance. It enables dietitians to plan individualized meals, promoting moderation, portion control, and adherence to dietary recommendations, thereby supporting diabetes and weight management initiatives across the GCC. Full article

To optimize and model the fermentation process of Beauveria bassiana, adsorbed carrier solid-state fermentation (ACSSF) was used with rice husk as the inert support. The sample pretreating method was improved by combining homogenization and ultrasonic treatment after dry crushing; the large particles (100–1000 μm in size) were broken and the content of small particles (2–100 μm in size) increased, and the relative standard deviation of the biomass detection method was as low as 3.32% (intra-day) and 3.75% (inter-day). The most suitable carbon source—cassava starch—and the most suitable nitrogen source—corn steep liquor powder (CSLP)—were screened from multiple carbon and nitrogen sources. Through single-factor optimization and an artificial neural network combining genetic algorithm optimization, the optimal recipe including cassava starch 0.0314 g·cm⁻³, CSLP 0.004885 g·cm⁻³ and water 0.2630 g·cm⁻³ was obtained, and the highest biomass yield was verified as 0.1379 g·cm⁻³, which was 45.0% higher than the original recipe before the optimization (0.0951 g·cm⁻³). The modeling of microbial growth was based on the Logistic model and executed by nonlinear regression with the R² value as high as 0.9525 and absolute value of the residues completely under 0.003 g·cm⁻³, which validated not only the feasibility of modeling the growth kinetics of B. bassiana using total biomass content, but also the reliability of the improved biomass pretreating and determination method. Full article