Search Results (6,764)

This study investigated the dynamic changes in fruit color, texture and quality attributes of blueberry cultivars during a 15-day postharvest storage period to provide theoretical insights for cultivar selection, postharvest preservation and commercial evaluation. Phenotypic and quality traits, including color parameters (CIE-Lab*), texture attributes (Note: hardness represents firmness and is an indicator in the Brookfield’s texture analyzer), adhesive force and physicochemical indices, were systematically analyzed using a colorimeter, texture analyzer and conventional methods. Principal component analysis (PCA) and cluster analysis were applied to evaluate postharvest performance. Southern highbush cultivars, including ‘EB 9-2’, ‘Meadowlark’, ‘Primadonna’, ‘Eureka’ and ‘Camellia’, exhibited superior comprehensive quality, characterized by small fruit shape index, minimal scar sizes and stable hardness dynamics. During the storage period, ‘Legacy’ demonstrated optimal color stability (ΔE < 3.5 from days 0–15), while ‘EB 9-2’ showed the most significant hardness increase. Scar size, fruit shape index and flesh elasticity were identified as key indicators for analyzing shelf-life hardness variations, offering scientific guidance for cultivar selection and postharvest management. Full article

Plastics are essential in modern life but accumulate as waste. Mechanical reprocessing reduces material quality, whereas thermochemical routes require harsh conditions and are costly to upgrade. Together, these factors hinder the large-scale recovery of plastics into equivalent materials. Metal–organic frameworks provide a programmable platform where reticular design fixes porosity and positions well-defined Lewis, Brønsted, redox, and photoredox sites that can preconcentrate oligomers and align scissile bonds for activation. These attributes enable complementary pathways spanning hydrolysis, alcoholysis, aminolysis, photo-oxidation, electrocatalysis, and MOF-derived transformations, with adsorption-guided capture-to-catalysis workflows emerging as integrative schemes. In this review, we establish common figures of merit such as space–time yield, monomer selectivity and purity, energy intensity, site-normalized turnover, and solvent or corrosion footprints. These metrics are connected to design rules that involve active-site chemistry and transport through semi-crystalline substrates. We also emphasize durability under hot aqueous, alcoholic, or oxidative conditions as essential for producing polymer-grade products. Full article

This study investigated the physicochemical characteristics, functional and technological properties, and consumer acceptability of multi-millet extruded snacks using a fuzzy logic approach, with particular emphasis on key sensory parameters: color, flavor, taste, and texture. Four formulations were developed using varying proportions of proso, kodo, and foxtail millets: S1 (50% proso millet), S2 (50% kodo millet), S3 (50% foxtail millet), and S4 (33% each). Physicochemical analysis indicated minimal nutrient (fiber, ash, and protein) loss during extrusion, while technological properties such as water absorption index (WAI: 5.71 g/g), water solubility index (WSI: 5.66–6.61%), and bulk density (0.13 to 0.16 g/cm³) yielded favorable results, contributing to improved texture and taste. The observed changes in starch structure positively influenced the organoleptic qualities of the snacks. Sensory evaluation, conducted by a 20-member panel, revealed that S3 (50% foxtail millet) received the highest acceptability scores, followed by S4, S2, and S1, and was rated as ‘very good.’ Among the sensory attributes, taste emerged as the most critical factor influencing consumer preference, followed by texture, flavor, and color. This study emphasizes the importance of integrating sensory analysis with fuzzy logic modeling to systematically optimize the formulation and processing conditions. This strategy enhances product quality by aligning technological functionality with sensory appeal, offering a robust framework for the development of consumer-preferred, health-oriented snacks and reducing the risk of market failure. Full article

Estrogen deficiency is associated with endothelial dysfunction, vascular inflammation, increased lipoprotein oxidation, accumulation of lipid-rich material, and platelet activation. The absence of estrogen causes physiological, metabolic, and biochemical changes that increase the risk of cardiometabolic disease development caused by a deregulation in metabolic processes such as lipid metabolism and plasma lipoprotein levels. High-density lipoprotein (HDL) has cardioprotective properties related to the quality and the quantity of its components that can be modified by some nutritional factors. Guava (Psidium guajava L.), a widely cultivated fruit in Mexico, is notable for its high polyunsaturated fatty acid and dietary fiber content in its seeds, but its effect on health is understudied. This study aimed to evaluate the effect of guava-seed supplementation on body weight, blood pressure, lipid profile, HDL composition, and paraoxonase-1 (PON1) activity in an ovariectomized rat model (OVX). Four groups with six adult female Wistar rats each were classified as a SHAM group: rats with simulated ovariectomy; OVX group: rats with ovariectomy; OVX + GS group: ovariectomized rats supplemented with 6 g of guava seeds; OVX + DGS group: ovariectomized rats supplemented with 6 g of defatted guava seeds. Biochemical parameters, size, and lipid concentration of HDL subclasses, apolipoproteins, and PON1 activity were determined. A decrease in body weight gain, systolic blood pressure, mean arterial pressure, and triglycerides in plasma was observed at the end of the experiment in the supplemented groups. The supplementation of 6 g of guava seeds for 30 days decreased biochemical parameters in ovariectomized rats; these results could be attributed to the seed composition, suggesting a protective effect against the risk of developing diseases in menopausal states. Full article

Cold-ripened fish marinades, produced mainly from Atlantic herring, represent one of the major seafood products in Northern and Central Europe. Because the shelf-life of these mildly acidified, salty products rarely surpasses 4 weeks, more than half of the commercial lots contain the preservatives sodium benzoate (E211) and potassium sorbate (E202). However, the broader technological consequences of such additives remain poorly documented. This study evaluated the impact of 0.25 (w/w) benzoate + 0.10 g/100 g sorbate on the quality of industrial-scale marinades (200 kg fish; 7 d, 4 ± 1 °C). Physicochemical traits (mass loss, pH, proximate composition, salt content, colour, and texture), enzymatic indices of ripening (cathepsins, amino-peptidases, and TCA soluble nitrogen fractions), lipid oxidation, microbial growth, and sensory attributes were analyzed. Preservatives caused only marginal changes in pH and proximate composition (0.3–3.4% w/w differences) but markedly suppressed proteolysis. Free amino acid and peptide fractions in muscle decreased by 6.0% and 8.8%, in parallel to 45% and 22% reductions in leucine- and alanine-amino-peptidase activities in muscle. In the marinating brine, the levels of total nitrogen, peptides, and free amino acids were also lower in the samples with preservatives, confirming that sodium benzoate and potassium sorbate slowed down the enzymatic ripening of the marinades. Concomitantly, peroxide, p-anisidine, and TOTOXs increased by up to 9.4, 71.3, and 33.7%, respectively, indicating accelerated lipid oxidation despite the chelating capacity of benzoate/sorbate acids. Overall sensory acceptability declined slightly (−0.15 points on a five-point scale), mainly owing to chemical off-flavours and lower juiciness. Microbial counts remained <1.0 log CFU/g in the preservative batch versus 2.1 log in the control. Benzoate–sorbate combinations effectively stabilized the microbiota of marinated herring without appreciably altering basic physicochemical traits, but they retard enzymatic ripening, diminish antioxidant peptide pools, and thereby promote lipid oxidation—collectively lowering the nutritional value. The data supports a cautious, minimal-use approach to application of chemical preservatives in cold-ripened fish products. Full article

Background: Burning Mouth Syndrome (BMS) is a nociplastic pain condition characterized by altered central nervous system pain processing, significantly impacting patient quality of life. Pharmacological management often involves amitriptyline (monotherapy) and aripiprazole (for refractory cases) in Japan. However, the therapeutic efficacy of these drugs in BMS frequently exhibits a non-sigmoid (U-shaped or bell-shaped) dose–response relationship, indicating a clinically effective dose that is often considerably lower than those used for their primary indications and challenging conventional pharmacological assumptions. Method: This paper synthesizes existing pharmacological knowledge to elucidate the mechanisms underlying the non-dose-dependent actions of amitriptyline and aripiprazole in BMS. It focuses on their specific interactions with key neurotransmitter systems and receptors, particularly N-methyl-D-aspartate (NMDA) receptors and dopamine D2 receptors, to explain the observed non-linear dose–response and the importance of identifying a personalized therapeutic window. Result: Amitriptyline demonstrates efficacy in BMS at low doses (e.g., 25 mg), primarily through its action as an NMDA receptor antagonist via calcium-dependent desensitization and open-channel block, addressing central sensitization. Its effects are distinct from its antidepressant actions, and the “serotonin paradox” highlights the complexity of serotonin’s role in pain. Aripiprazole, utilized for refractory BMS, acts as a dopamine D2 receptor partial agonist, leading to a non-linear dose–response where sustained therapeutic effect is observed at specific low doses (e.g., 1.7–1.8 mg/day). This non-linearity is attributed to partial agonism, alongside interactions with serotonin 5-HT1A and 5-HT2A receptors. The general non-dose-dependency for both drugs is further explained by phenomena such as multiple binding sites with differing affinities, receptor desensitization/downregulation, activation of counter-regulatory mechanisms, and hormesis. Discussion: The observed non-linear dose–response curves for amitriptyline and aripiprazole in BMS underscore the inadequacy of a “one-size-fits-all” treatment approach. This necessitates a shift towards personalized medicine, which considers individual patient factors including pharmacogenomics, comorbidities, age, organ function, and psychological/social profiles. The true “personalized therapeutic window” is a balance between achieving significant pain relief and minimizing adverse effects, emphasizing careful titration and patient-centered care. Conclusions: The pharmacological actions of amitriptyline and aripiprazole in BMS are not linearly dose-dependent, but rather exhibit a personalized therapeutic window driven by complex interactions with NMDA and D2 receptors and adaptive physiological responses. This intricate pharmacological landscape mandates a personalized medicine approach to optimize treatment outcomes, improve patient adherence, and enhance the quality of life for individuals suffering from this challenging nociplastic pain condition. Full article

This study presents a comprehensive latitudinal analysis of air particulate matter (PM) across an 1400 km pollution corridor spanning Chile’s central-southern zone. We systematically analyzed PM_2.5 and PM₁₀ concentrations across eight major urban centers (2014–2015), providing crucial pre-Paris Agreement baseline data for South America’s most extensive air quality monitoring network. Our analysis reveals significant pollution gradients, with Coyhaique ranking one of the world’s most severely polluted cities (95th percentile globally, WHO database) and demonstrating an extreme 86% fine particulate matter ratio that far exceeds international urban standards. Residential wood combustion (RWC) demonstrates systematic correlations with fine PM concentrations (R² > 0.96), suggesting RWC is the dominant pollution driver across multiple climate zones. The documented pollution patterns represent a concerning continental-scale environmental pattern, with 4900–6500 annual premature deaths directly attributable to PM_2.5 exposure-one of the highest per-capita pollution mortality rates in South America. This work provides a methodological framework applicable to mountain-valley pollution systems globally while addressing critical knowledge gaps in regional air quality science. The evidence indicates the need for urgent implementation of comprehensive wood combustion control strategies and positions this research as essential baseline documentation for both national air quality policy and international climate change assessment frameworks. Full article

Green transformation is becoming key for corporate sustainability in the context of global carbon neutrality goals and China’s “dual carbon” strategy (peak carbon emissions and carbon neutrality). Digital transformation, particularly supply chain digitization, plays a significant role in green transformation. Corporations could improve environmental performance through appropriate resource allocation. Much academic and practical attention is drawn to this area to motivate corporate green transformation. This research proposes to explore the incentive effect of supply chain digitization on corporate green transformation and analyze the mediation mechanism of carbon information disclosure and the regulatory effect of external investor supervision. The study samples Chinese A-share listed firms between 2012 and 2024, constructs a moderated mediation effect model, and arrives at the following conclusions: (1) The digitization of the supply chain significantly stimulates the green transformation of public firms, indicating that digital technology promotes the green development of enterprises through optimizing supply chain management and improving environmental governance efficiency; (2) Carbon information disclosure plays a partial intermediary role between supply chain digitization and corporate green transformation, that is, supply chain digitization enhances the quality of carbon information disclosure and further strengthens the willingness and ability of enterprises to undergo green transformation; (3) The positive regulatory effect of external supervision on carbon information disclosure by investors indicates that external regulatory pressure can enhance the transmission effect of carbon information disclosure on corporate green transformation; (4) Heterogeneity analysis shows that supply chain digitization has a more significant incentive effect on green transformation for manufacturing firms, state-owned enterprises, and high-polluting enterprises, indicating that industry attributes, property rights, and environmental regulation intensity affect the effectiveness of digital green transformation. Full article

The rapid expansion of concrete production has intensified the depletion of natural aggregate (NA) resources, necessitating sustainable alternatives in the construction industry. Recycling construction and demolition (C&D) waste offers a solution to enhance environmental sustainability and resource efficiency. Most existing studies have mainly focused on first-generation RCAs (RCA1), with little work on second-generation RCAs (RCA2), especially fine fractions. This study examined the properties of recycled concrete aggregates (RCAs) across first and second recycling cycles, focusing on their upcycling potential. Therefore, commercially sourced NAs and RCA1 were compared with lab-produced RCA2, both coarse and fine, derived from further recycling of first-generation recycled aggregate concrete (RAC1). Comprehensive tests assessed morphology and physical, mechanical, and microstructural properties to provide a clear insight into how RCA2 differs from RCA1. Average sphericity for coarse RCA1 was 0.81, an 8% decrease from NA’s 0.88, while RCA2 had an average sphericity of 0.76, a 14% decrease. The results revealed a progressive decline in aggregate quality with each cycle. RCA1 exhibited water absorption of 9.53% (fine) and 5.55% (coarse), while RCA2 showed higher absorption at 13.16% (fine) and 6.88% (coarse). RCA1’s crushing value was 25.9%, a 41% rise over NA’s 18.09%, while RCA2’s reached 29.2%, a 61% increase. Coarse RCA2 contained 51.03% attached old mortar, 50% more than the 33.95% in RCA1. Fine RCA2 showed significant performance reductions, limiting these aggregates to non-structural downcycling applications. Microstructure analyses confirmed RCA2’s porous structure, attributed to increased adhered old mortar, including multiple weak interfaces, and numerous microcracks compared to RCA1, necessitating careful consideration when using coarse RCA2 for upcycling in sustainable construction. Full article

Hypergraphs are powerful tools for modeling complex systems because they naturally encode higher-order interactions. However, most existing hypergraph representation-learning methods still struggle to capture such high-order structures, particularly in heterogeneous hypergraphs, which results in suboptimal performance on structure-sensitive tasks such as node classification. This paper presents WCRW-MLP, a new framework that integrates a Weighted- and Clustering-Biased Random Walk (WCRW) with a multi-layer perceptron. WCRW extends second-order random walks by introducing node-pair co-occurrence weights and triadic-closure clustering bias, enabling the walk to favor structurally significant and locally cohesive regions of the hypergraph. The resulting walk sequences are processed with Skip-gram to obtain high-quality structural embeddings, which are then concatenated with node attributes and fed into an MLP for classification. Experiments on several real-world hypergraph benchmarks show that WCRW-MLP consistently surpasses state-of-the-art baselines, validating both the efficacy of the proposed biasing strategy and the overall framework. These results demonstrate that explicitly modeling co-occurrence strength and local clustering is crucial for effective hypergraph embedding. Full article

The appropriate selection of renovation plaster properties is essential for ensuring the durability and effectiveness of conservation works. This study focused on the design and characterization of cement-based renovation mortars modified with synthetic zeolites with different Si/Al ratios. It was assumed that high-silica zeolites would provide more favorable mechanical and hygric performance than low-silica types. Owing to their porous structure and pozzolanic reactivity, zeolites proved to be effective additives, enhancing both the microstructure and functionality of the mortars. The modified mixtures exhibited increased total porosity, higher capillary absorption, and improved moisture transport compared with the reference mortar based on CEM I 52.5R. Dynamic vapor sorption tests confirmed that the zeolite-containing mortars achieved Moisture Buffer Values (MBV) above 2.0 g/m², which corresponds to the “excellent” moisture buffering class. Electrical resistivity measurements further demonstrated the relationship between denser microstructure and enhanced durability. At the frequency of 10 kHz, the electrical resistivity of the reference mortar reached 43,858 Ω·m, while mortars with 15% ZSM-5 and 15% Na-A achieved 62,110 Ω·m and 21,737 Ω·m. These results show that the addition of high-silica zeolite promotes the formation of a denser and more insulating matrix, highlighting the potential of this method for non-destructive quality assessment. The best overall performance was observed in mortars containing the high-silica zeolite ZSM-5. A 35% replacement of cement with ZSM-5 increased compressive strength by 10.5% compared with the reference mortar R (4.3 MPa). Frost resistance tests showed minimal mass loss (0.03% at 15% and 1.79% at 35% replacement), and ZSM-5 mortars also maintained integrity under salt crystallization. These improvements were attributed to the reaction of reactive SiO₂ and Al₂O₃ from the zeolites with Ca(OH)₂, leading to the formation of additional C-S-H. A higher Si/Al ratio promoted a denser, fibrous C-S-H morphology, as confirmed by SEM, which explains the improved strength and durability of mortars modified with ZSM-5. Full article

Low Earth orbit (LEO) satellites, characterized by low altitudes, high velocities, and strong ground signal reception, have become an essential and dynamic component of modern global navigation satellite systems (GNSS). However, orbit decay induced by atmospheric drag poses persistent challenges to maintaining stable trajectories. Frequent orbit maneuvers, though necessary to sustain nominal orbits, introduce significant difficulties for precise orbit determination (POD) and navigation augmentation, especially under complex operational conditions. Unlike most existing methods that rely on Two-Line Element (TLE) data—often affected by noise and limited accuracy—this study directly utilizes onboard GNSS observations in combination with real-time precise ephemerides. A novel time-series indicator is proposed, defined as the geometric root-mean-square (RMS) distance between reduced-dynamic and kinematic orbit solutions, which is highly responsive to orbit disturbances. To further enhance robustness, a sliding window-based adaptive thresholding mechanism is developed to dynamically adjust detection thresholds, maintaining sensitivity to maneuvers while suppressing false alarms. The proposed method was validated using eight representative maneuver events from the GRACE-FO satellites (May 2018–June 2022), successfully detecting seven of them. One extremely short-duration maneuver was missed due to the limited number of usable GNSS observations after quality-control filtering. To examine altitude-related applicability, two Sentinel-3A maneuvers were also analyzed, both successfully detected, confirming the method’s effectiveness at higher LEO altitudes. Since the thrust magnitudes and durations of the Sentinel-3A maneuvers are not publicly available, these cases primarily serve to verify applicability rather than to quantify sensitivity. Experimental results show that for GRACE-FO maneuvers, the proposed method achieves near-real-time responsiveness under long-duration, high-thrust conditions, with an average detection delay below 90 s. For Sentinel-3A, detections occurred approximately 7 s earlier than the reported maneuver epochs, a discrepancy attributed to the 30 s observation sampling interval rather than methodological bias. Comparative analysis with representative existing methods, presented in the discussion section, further demonstrates the advantages of the proposed approach in terms of sensitivity, timeliness, and adaptability. Overall, this study presents a practical, efficient, and scalable solution for real-time maneuver detection in LEO satellite missions, contributing to improved GNSS augmentation, space situational awareness, and autonomous orbit control. Full article

The tilapia sector is advancing due to breakthroughs in aquaculture techniques and genetic enhancements. Comprehending sensory qualities is crucial for producers striving to meet market demands efficiently. As consumer preferences play a significant role in shaping the market, enhancing the sensory attributes of both farmed and wild red tilapia will be key to ensuring their success in the competitive aquaculture industry. One of Malaysia’s most prominent aquaculture projects is the Como River Aquaculture Project located in Kenyir Lake, where tilapia fish farming, trademarked as Red Kenyir™, is conducted. Thus, this study aimed to evaluate the nutrient analysis of raw and five sensory attributes (appearance, texture, smell, taste, overall quality) of filets from Red Kenyir™ and wild red tilapia (Oreochromis sp.). Red Kenyir™ were fed three different commercial diets (A, B, and C) from fingerling to adulthood, while wild tilapia (W) was sourced from the market. Proximate and nutritional analyses were conducted based on the standard food analysis protocol by AOAC/AOCS. To the best of our knowledge, this is the first study to comprehensively document the nutrient analysis of raw and consumer sensory perception of cooked Red Kenyir™ aquaculture tilapia in direct comparison with wild red tilapia. The sensory evaluation was conducted using a consumer preference test. Statistical analysis was performed using SPSS. Nutrient analysis showed that Red Kenyir™ tilapia had lower fat (0.25–1.37 g/100 g vs. 4.30 g/100 g) and lower energy (77.38–113.46 kcal/100 g vs. 132.79 kcal/100 g) levels. Protein levels varied across groups (19–26.54 g/100 g vs. 22.95 g/100 g). The tryptophan content of the Red Kenyir™ tilapia samples ranged between 0.13 and 0.23 g/100 g, while the wild tilapia contained 0.19 mg/100 g. Sensory evaluation with 36 panelists revealed no significant differences in appearance, texture, or smell (p > 0.05). However, wild tilapia scored slightly higher in taste (4.14) than Red Kenyir™ (3.54–3.71) for steamed preparation (p < 0.05). In conclusion, these findings suggest that variations in the nutritional composition of Red Kenyir™ do not affect the sensory experience for consumer acceptance, making it a sustainable alternative for customers. Full article

Floods are among the most destructive natural disasters, and accurate flood susceptibility mapping (FSM) is crucial for disaster prevention and mitigation amid climate change. The Poyang Lake basin, characterized by complex flood formation mechanisms and high spatial heterogeneity, poses challenges for the application of FSM models. Currently, the use of machine learning models in this field faces several bottlenecks, including unclear model applicability, limited sample quality, and insufficient machine interpretation. To address these issues, we take the 2020 Poyang Lake flood as a case study and establish a high-precision flood inundation sample database. After feature screening, the performance of three hybrid models optimized by Particle Swarm Optimization (PSO)—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) is compared. Furthermore, the Shapley Additive exPlanations (SHAP) framework is employed to interpret the contributions and interaction effects of the driving factors. The results demonstrate that the ensemble learning models exhibit superior performance, indicating their greater applicability for flood susceptibility mapping in complex basins such as Poyang Lake. The RF model has the best predictive performance, achieving an area under the receiver operating characteristic curve (AUC) value of 0.9536. Elevation is the most important global driving factor, while SHAP local interpretation reveals that the driving mechanism has significant spatial heterogeneity, and the susceptibility of local depressions is mainly controlled by the terrain moisture index. A nonlinear phenomenon is observed where the SHAP value was negative under extremely high late rainfall, which is preliminarily attributed to the “spatial transfer that is prone to occurrence” mechanism triggered by the backwater effect, highlighting the complex nonlinear interactions among factors. The proposed “high-precision sampling, model comparison, SHAP explanation” framework effectively improves the accuracy and interpretability of FSM. These research findings can provide a scientific basis for smart flood control and precise flood risk management in basins. Full article

The Australian pork industry has been seeking a rapid and non-destructive way to predict pork chemical components and eating quality. In this study, near-infrared spectroscopy (NIRS) and nuclear magnetic resonance (NMR) were applied to fresh pork Longissimus thoracis et lumborum (LTL) and Semimembranosus (SM) with the aim to build prediction models for intramuscular fat (IMF) content, collagen content and solubility, pH, and sensory attributes, namely tenderness, juiciness, liking of flavor and overall liking as well as investigate the effects of chemical components on pork eating quality. Results showed that the NIRS output, which was a predicted IMF content calibrated for the IMF of lamb, correlated with the chemically analyzed IMF content across both muscles. In LTL, NMR parameter p_2f was weakly correlated with IMF and pH. For the LTL, NMR parameters p₂₁ and p₂₂ were related to sensory tenderness, while T₂₂ was correlated with the liking of flavor. In both muscles, the collagen content and pH were related to all sensory attributes, and IMF was related to the liking of flavor. The chemical properties of SM were weakly correlated with those of LTL. The NIRS and NMR weakly predicted the pork chemical components and sensory properties, but more studies are required to improve the accuracy. Full article