Search Results (18,527)

Berry skins are rich in phenolic compounds but are commonly discarded as low-value waste during berry wine production. The present study evaluated how primary alcoholic fermentation affects the retention and transformation of phenolics in berry skins of blackcurrant (Ribes nigrum L.), black chokeberry (Aronia melanocarpa L.), lingonberry (Vaccinium vitis-idaea L.), rowanberry (Sorbus aucuparia L.), and cranberry (Vaccinium macrocarpon L.). Non-fermented and fermented skin fractions were analysed using Folin–Ciocalteu and HPLC to determine total and individual phenolic profiles. Primary fermentation induced significant species-dependent changes in phenolic composition. Blackcurrant, lingonberry, and rowanberry skins exhibited substantial decreases in total phenolics (−66%, −26%, and −57%, respectively), driven by strong losses of flavan-3-ols and hydroxycinnamic acids. In contrast, cranberry and chokeberry skins showed net increases in phenolic content (+47% and +18%, respectively), associated with the release of bound phenolics and the appearance of new low-molecular-weight phenolic acids such as gallic acid. Across all species, fermentation enhanced biotransformation into simpler phenolics while reducing major native anthocyanins and catechins. These results demonstrate that the influence of primary fermentation on berry skins is not uniform but dictated by their inherent phenolic architecture. Berries rich in polymeric or conjugated phenolics benefit from fermentation through increased phenolic extractability. The findings provide a comparative basis for optimizing fermentation and post-processing strategies to enhance the valorization potential of berry by-products in food and nutraceutical applications. Full article

Pear black spot disease is a serious fungal disease during pear production; salicylic acid is a core signaling molecule that regulates the expression of plant disease resistance genes. To elucidate the intrinsic association between salicylic acid-induced resistance to pear black spot disease and lignin biosynthesis, in vitro plantlets of two pear cultivars, ‘Xinli No.7’ and ‘Xueqing’, were employed as experimental materials. After 60 h SA pretreatment, the leaves were inoculated with the pathogen Alternaria alternata. Leaf samples were harvested at 0, 8, 16, 24, and 48 h post-inoculation to determine phenylalanine ammonia-lyase activity, quantify lignin content, and analyze the transcript levels of genes involved in lignin synthesis. The results demonstrated that, relative to the untreated control group, SA treatment significantly enhanced phenylalanine ammonia-lyase activity and promoted lignin accumulation in both ‘Xinli No.7’ and ‘Xueqing’. Moreover, multiple key genes associated with lignin biosynthesis—including PbrPAL1, Pbr4CL1, PbrCOMT, PbrCCoAOMT, PbrCAD, and PbrPOD—were markedly upregulated, with their expression levels increasing by 3.5–15 fold. Transcript profiles of PbrHCT1, PbrHCT4, and PbrC3H1 exhibited cultivar-specific divergence between the two varieties. Notably, the susceptible cultivar ‘Xueqing’ displayed a distinct lag phase and attenuated response in the expression of all lignin-related genes compared with the other cultivar. This study provides reference for green prevention and sustainable development of pear. Full article

High-performance nylon 6,6 webbings used in critical applications degrade under solar exposure, necessitating reliable methods to assess their residual strength non-destructively. This study investigates the feasibility of using instrumental color change as a predictive indicator for the loss of breaking strength. Four colors of nylon 6,6 webbings were subjected to accelerated xenon-arc solar weathering for up to 15 days. The resulting color change was quantified using both the CIELab and CIEDE2000 formulas, and residual breaking strength was measured following ASTM D6775. A regression analysis was performed to correlate these properties. The results demonstrate that a strong predictive relationship exists, but its efficacy is highly color-dependent. Webbing with high initial chroma, namely tan (R² = 0.889) and navy (R² = 0.817), showed a strong correlation between color change and strength loss. In contrast, the models for low-chroma black and white webbings were weak and unreliable. Furthermore, the simpler CIELab (ΔE*_ab) formula provided slightly more accurate predictions than the more complex CIEDE2000 (ΔE*₀₀) metric. It is concluded that colorimetry can be a viable non-destructive tool for predicting mechanical degradation, but its application is limited to specific high-chroma materials, precluding a universal model based entirely on colorimetry. Full article

Background: Peanut is susceptible to iron (Fe) deficiency, particularly in calcareous soils. However, comparative studies on the adaptive mechanisms of different peanut cultivars to Fe deficiency remain limited. This study aimed to investigate the physiological and molecular responses of two distinct peanut cultivars to Fe deprivation and to identify the key traits contributing to differential Fe efficiency. Methods: Two peanut cultivars, LH11 and YZ9102, were cultivated under Fe-sufficient and Fe-deficient conditions, using both hydroponic and pot-based soil culture systems. Multiple parameters were assessed, including visual symptomology, biomass, tissue Fe concentration, active Fe in leaves, chlorophyll (Chl) content (SPAD value), net photosynthetic rate (Pn), Chl fluorescence (Fv/Fm), rhizosphere pH, root ferric chelate reductase (FCR) activity, and the relative expression of two Fe-acquisition-related genes (AhIRT1 and AhFRO1) via qRT-PCR. Results: Cultivar YZ9102 exhibited more severe Fe deficiency chlorosis symptoms, which also appeared earlier than in LH11, under both cultivation systems. Under Fe deficiency, YZ9102 showed significantly lower Chl content, Pn, and Fv/Fm compared to LH11. In contrast, LH11 demonstrated a greater capacity for rhizosphere acidification and maintained significantly higher root FCR activity under Fe-limited conditions. Gene expression analysis revealed that Fe deficiency induced the up-regulation of AhIRT1 and AhFRO1 in the roots of LH11, while their transcript levels were suppressed or unchanged in YZ9102. Conclusions: The peanut cultivar LH11 possesses superior tolerance to Fe deficiency compared to YZ9102. This enhanced tolerance is attributed to a synergistic combination of traits: the maintenance of photosynthetic performance, efficient rhizosphere acidification, heightened root Fe³⁺ reduction capacity, and the positive transcriptional regulation of key Fe uptake genes. These findings provide crucial insights for the selection and breeding of Fe-efficient peanut varieties for cultivation in Fe-deficient environments. Full article

With the rapid development of the Internet of Things (IoT) and Industrial IoT (IIoT), Network Intrusion Detection Systems (NIDSs) play a critical role in securing modern networked environments. Despite advances in multi-class intrusion detection, existing approaches face challenges from high-dimensional heterogeneous traffic data, severe class imbalance, and limited interpretability of high-performance “black-box” models. To address these issues, this study presents an XGBoost-based NIDSs integrating optimized strategies for feature dimensionality reduction and class balancing, alongside SHAP-based interpretability analysis. Feature reduction is investigated by comparing selection methods that preserve original features with generation methods that create transformed features, aiming to balance detection performance and computational efficiency. Class balancing techniques are evaluated to improve minority-class detection, particularly reducing false negatives for rare attack types. SHAP analysis reveals the model’s decision process and key feature contributions. The experimental results demonstrate that the method enhances multi-class detection performance while providing interpretability and computational efficiency, highlighting its potential for practical deployment in IoT security scenarios. Full article

This study examined Croatian consumer attitudes towards fresh pork from the Black Slavonian pig, focusing on the following sustainability dimensions: environmental, social, economic sustainability, and animal welfare. A survey of 410 consumers was conducted in June 2021, using an online questionnaire assessing consumption habits, breed knowledge, and socio-demographic characteristics. Factor analysis identified four key dimensions: attention to animal welfare, support for local production and biodiversity, origin and information, and price and intrinsic quality. Cluster analysis revealed three distinct consumer segments: conscious consumers (32.4%), value-oriented consumers (37.3%), and uninvolved meat consumers (30.2%). Multinomial logistic regression showed that age, region, family economic status, and place of purchase significantly predicted cluster membership (Nagelkerke R² = 0.251, classification accuracy = 52.9%), while gender, education level, and household composition did not. Conscious consumers were characterised by older age, higher income, and a preference for direct purchasing channels, while value-oriented consumers favoured supermarkets and mid-range pricing. These findings highlight the need for improved consumer education, transparent labelling, targeted marketing strategies, and enhanced policy support to promote sustainable indigenous pig breed production and conservation. Full article

The deployment of large remotely operated vehicles (ROVs) or autonomous underwater vehicles (AUVs) typically requires support vessels, crane systems, and specialized personnel, resulting in increased logistical complexity and operational costs. In this context, lightweight and modular underwater robots have emerged as a cost-effective alternative, capable of reaching significant depths and performing tasks traditionally associated with larger platforms. This article presents a system architecture for recovering a known object using a hybrid-controlled ROV, integrating autonomous perception, high-level interaction, and low-level control. The proposed architecture includes a perception module that estimates the object pose using a Perspective-n-Point (PnP) algorithm, combining object segmentation from a YOLOv11-seg network with 2D keypoints obtained from a YOLOv11-pose model. In addition, a Natural Language ROS Agent is incorporated to enable high-level command interaction between the operator and the robot. These modules interact with low-level controllers that regulate the vehicle degrees of freedom and with autonomous behaviors such as target approach and grasping. The proposed system is evaluated through simulation and experimental tank trials, including object recovery experiments conducted in a 12 × 8 × 5 m test tank at CIRTESU, as well as perception validation in simulated, tank, and harbor scenarios. The results demonstrate successful recovery of a black box using a BlueROV2 platform, showing that architectures of this type can effectively support operators in underwater intervention tasks, reducing operational risk, deployment complexity, and mission costs. Full article

Characterizing tight reservoirs is challenging due to the complex pore structure and strong heterogeneity at various scales. Current digital rock physics often struggles to reconcile high-resolution imaging with representative sample sizes, and 3D digital cores are frequently used primarily as visualization tools rather than predictive, computable platforms. Thus, a clear methodological gap persists: high-resolution models typically lack macroscopic geological features, while existing 3D digital models are seldom leveraged for quantitative, predictive analysis. This study, based on a full-diameter core sample of a single lithology (gray-black shale), aims to bridge this gap by developing an integrated workflow to construct a high-fidelity, computable 3D model that connects the micro–nano to the macroscopic scale. The core was scanned using high-resolution X-ray computed tomography (CT) at 0.4 μm resolution. The raw CT images were processed through a dedicated pipeline to mitigate artifacts and noise, followed by segmentation using Otsu’s algorithm and region-growing techniques in Avizo 9.0 to isolate minerals, pores, and the matrix. The segmented model was converted into an unstructured tetrahedral finite element mesh within ANSYS 2024 Workbench, with quality control (aspect ratio ≤ 3; skewness ≤ 0.4), enabling mechanical property assignment and simulation. The digital core model was rigorously validated against physical laboratory measurements, showing excellent agreement with relative errors below 5% for key properties, including porosity (4.52% vs. 4.615%), permeability (0.0186 mD vs. 0.0192 mD), and elastic modulus (38.2 GPa vs. 39.5 GPa). Pore network analysis quantified the poor connectivity of the tight reservoir, revealing an average coordination number of 2.8 and a pore throat radius distribution of 0.05–0.32 μm. The presented workflow successfully creates a quantitatively validated “digital twin” of a full-diameter core. It provides a tangible solution to the scale-representativeness trade-off and transitions digital core analysis from a visualization tool to a computable platform for predicting key reservoir properties, such as permeability and elastic modulus, through numerical simulation, offering a robust technical means for the accurate evaluation of tight reservoirs. Full article

This study investigates a 26th Dynasty Ptah–Sokar–Osiris wooden statuette excavated from the Tari cemetery, Giza Pyramids area, to decode ancient Egyptian manufacturing techniques and establish evidence-based conservation strategies of such wooden objects. Using minimal sampling (1.0–2.0 mm²), integrated XRF, synchrotron-based X-ray diffraction, FTIR, and confocal microscopy distinguished original technological choices from burial-induced alterations. The 85 cm Vachellia nilotica sculpture exhibits moderate structural preservation (cellulose crystallinity index 62.9%) with partial chemical deterioration (carbonyl index 2.22). Complete pigment characterization identified carbon black, Egyptian Blue (cuprorivaite, 55 ± 5 wt %), atacamite-dominated green (65 ± 5 wt %) with residual malachite (10 ± 2 wt %), orpiment (60 ± 5 wt %), red ochre (hematite, 60% ± 5 wt %), white pigments (93 ± 5 wt % calcite), and metallic gold (40 ± 5 wt %). Confocal microscopy revealed sophisticated multi-pigment mixing strategies, with black carbon systematically blended with chromophores for nuanced color effects. Atacamite predominance over malachite provides evidence for chloride-mediated diagenetic transformation over 2600 years of burial. Consistent calcite detection (~ 20–45%) across colored layers confirms systematic ground layer application, establishing technological baseline data for 26th Dynasty Lower Egyptian workshops. Near-complete organic binder loss, severe lignin oxidation, and ongoing salt-mediated mineral transformations indicate urgent conservation needs requiring specialized consolidants, paint layer stabilization, and controlled environmental storage. This investigation demonstrates synchrotron methods’ advantages while establishing a minimally invasive framework for studying polychrome wooden artifacts. Full article

This study aimed to determine the presence of relevant infectious and parasitic agents (IPAs) in managed honeybees from Central Italy and the Republic of Kosovo and Albania to assess the overall health status of local apiaries by determining the contamination levels and co-occurrence. Therefore, pathogens and parasites such as Paenibacillus larvae, Melissococcus plutonius, Vairimorpha apis, V. ceranae, the acute bee paralysis virus (ABPV), black queen cell virus (BQCV), chronic bee paralysis virus (CBPV), deformed wing virus variants DWV-A and DWV-B, and the parasitoid flies Megaselia scalaris and Senotainia tricuspis were detected by quantitative polymerase chain reaction (qPCR) and reverse transcriptase qPCR (RT-qPCR) in clinically healthy adult honeybees collected from 187 apiaries in the Abruzzo and Molise regions of Central Italy, 206 apiaries in the Republic of Kosovo in 2022 and 2023 and 18 apiaries in Albania in 2022. The percentages of positive samples and contamination for V. ceranae, P. larvae and DWV-B were significantly higher in the Republic of Kosovo and Albania, while the percentages of samples positive for M. plutonius, CBPV, DWV-A, and the parasitoid flies were higher in Central Italy. Additionally, P. larvae and some viruses showed significantly different occurrence rates between the two years in Italy and the Republic of Kosovo. The co-occurrence of IPAs also differed between the two geographic areas. Their varying distribution could depend on epidemiological dynamics, climatic factors, and management practices specific to each country, whose relative impact should be defined to guide targeted interventions to reduce honeybee mortality. Full article

Photovoltaic Systems have been a long-standing challenge to integrate with electrical Power Grids due to the randomness of solar irradiance. Deep Learning (DL) has potential to forecast solar irradiance; however, black-box DL models typically do not offer interpretation, nor can they easily distinguish between deterministic astronomical cycles, and random meteorological variability. The objective of this study was to develop and apply a new Physics-Aware Deep Learning Framework that identifies and utilizes physical attributes of solar irradiance via Fourier-based signal decomposition. The proposed method decomposes the time-series into polynomial trend, Fourier-based seasonal component and stochastic residual, each of which are processed within different neural network paths. A wide variety of architectures were tested (Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN)), at multiple historical window sizes and forecast horizons on a diverse dataset from a three-year span. All of the architectures tested demonstrated improved accuracy and robustness when using the physics aware decomposition as opposed to all other methods. Of the architectures tested, the GRU architecture was the most accurate and performed well in terms of overall evaluation. The GRU model had an RMSE of 78.63 W/m² and an $R^2$ value of 0.9281 for 15 min ahead forecasting. Additionally, the Fourier-based methodology was able to reduce the maximum absolute error by approximately 15% to 20%, depending upon the architecture used, and therefore it provided a way to reduce the impact of the larger errors in forecasting during periods of unstable weather. Overall, this framework represents a viable option for both physically interpretive and computationally efficient real-time solar forecasting that provides a bridge between Physical Modeling and Data-Driven Intelligence. Full article

Obesity is a major global health concern, being associated with insulin resistance and multiple metabolic disorders. Gremlin 1 (GREM1), a bone morphogenetic protein (BMP) antagonist, is increasingly recognized as a key regulator of adipose tissue dysfunction and impaired thermogenesis in obesity. Orlistat, a lipase inhibitor that reduces dietary fat absorption, is one of the most commonly used pharmacological agents for obesity management. White tea has demonstrated antioxidant and anti-obesity properties in experimental models. The aim of this study was to evaluate the effects of white tea on metabolic parameters (HOMA-IR, BMP4, Gremlin1) and GREM1 expression in rats made obese by a high-fat diet (HFD). A total of 40 male Sprague-Dawley rats were randomized into five groups: a standard diet group (STD); a high-fat diet group (HFD); an HFD + orlistat group (ORL); an HFD + 50 mg/kg white tea group (WT50); and an HFD + 150 mg/kg white tea group (WT150). Obesity was induced by feeding the rats a 45% high-fat diet for 3 weeks. Serum insulin, glucose and HOMA-IR levels were measured. Levels of GREM1 and BMP4 in serum and retroperitoneal adipose tissue were assessed. White tea supplementation significantly reduced weight gain and HOMA-IR compared to the HFD group. GREM1 mRNA expression in visceral adipose tissue decreased markedly in the WT50 and WT150 groups (p = 0.002 and p = 0.017, respectively). Serum GREM1 levels were significantly lower in the white tea-treated groups than in the HFD group (p = 0.011). Tissue BMP4 levels were only significantly reduced in the WT50 group (p = 0.005), indicating a non-linear dose–response pattern. There was a negative correlation between serum BMP4 levels and weight gain (rho = –0.440, p = 0.015). White tea was associated with improvements in metabolic parameters in an HFD-induced obesity model. These observations suggest a potential association between white tea bioactives and adipose tissue-related molecular pathways implicated in obesity. Given the short intervention duration and the exploratory design of this animal study, the findings should be interpreted with caution. Full article

This study presents a method for extracting lignin and hemicellulose from black liquor using organic acids (citric, malic, and acetic) in comparison to the traditional sulfuric acid method. We investigated and compared the influence of the acid type on the structural properties of the resulting precipitates in the context of their potential applications. The lignin fractions were characterized for their chemical structure (ATR-FTIR, NMR), thermal stability (TGA), morphology and surface elemental composition (SEM-EDS), bulk elemental composition (C, H, N, S), and molecular weight distribution (GPC). The hemicellulose fractions were analyzed for their molecular weight (GPC), surface elemental composition (EDS), and chemical structure (ATR-FTIR). These analyses revealed subtle differences in the properties of the individual materials depending on the extraction method. We showed that organic acids, particularly citric acid, can effectively precipitate lignin with yields comparable to the sulfuric acid method (47–60 g/dm³ vs. 50 g/dm³). Simultaneously, this method produces lignin with higher purity (regarding sulfur content) and an increased content of carboxyl groups. This latter aspect is of particular interest due to the enhanced potential of lignin’s adsorption functions towards metal ions. AAS analysis confirmed that lignin precipitated with citric acid showed better adsorption efficiency towards heavy metals compared to lignin precipitated with sulfuric acid, especially for Cu²⁺ ions (80% vs. 20%) and Cr³⁺ ions (46% vs. 2%). This enhanced adsorption efficiency of the isolated lignins, combined with the environmental benefits of using organic acids, opens a promising perspective for their application in water treatment and environmental remediation. Furthermore, the presented research on the valorization and reuse of paper industry by-products fully aligns with the fundamental principles of the Circular Economy. Full article

This paper is a theoretical contribution that introduces a new reasoning framework for SAT solving based on resolvable networks (RNs). RNs provide a graph-based representation of propositional satisfiability in which clauses are interpreted as directed reaches between disjoint subsets of Boolean variables (nodes). Building on this framework, we introduce a novel RN-based SAT solver, called RN-Solver, which replaces local assignment-driven branching by global reasoning over token distributions. Token distributions, interpreted as truth assignments, are generated by monotone CNF–DNF dualization applied to white (all-positive) clauses. New white clauses are derived via resolution along private-pivot chains, and the solver’s progression is governed by a taxonomy of token distributions (black-blocked, terminal, active, resolved, and non-resolved). The main results establish the soundness and completeness of the RN-Solver. Experimentally, the solver performs very well on pigeonhole formulas, where the separation between white and black clauses enables effective global reasoning. In contrast, its current implementation performs poorly on random 3-SAT instances, highlighting both practical limitations and significant opportunities for optimization and theoretical refinement. The presented RN-Solver implementation is a proof-of-concept which validates the underlying theory rather than a state-of-the-art competitive solver. One promising direction is the generalization of strongly connected components from directed graphs to resolvable networks. Finally, the token-based perspective naturally suggests a connection to token-superposition Petri net models. Full article

Whole-genome sequencing is a powerful approach for exploring genomic diversity in livestock species. Chickens (Gallus gallus) are an important food source worldwide, and in Taiwan, poultry production contributes substantially to the livestock industry. Taiwan’s commercial red- and black-feathered country chickens dominate this category and play a crucial role in local poultry production. However, fundamental genomic information on their population structure remains limited. To address this gap, this study generated whole-genome sequencing data from red-feathered country chickens originating from four major breeding farms. Genetic diversity analyses revealed uniformly low genetic diversity across all farms. Runs of homozygosity (ROH) analyses indicated predominantly historical inbreeding, with farm-specific differences in recent inbreeding patterns. Population structure analyses revealed clear clustering of individuals according to farm origin, indicating distinct line structures among breeding farms. These results provide the first comprehensive genomic overview of Taiwan’s commercial red-feather country chickens and offer valuable reference information for future breeding strategies and the development of new lines. Full article