Search Results (9,304)

The Burullus–Baltim coastal zone of Egypt’s Nile Delta represents a critical geoheritage sand-dune system functioning as the primary natural defense line against inundation of the central Nile Delta. This ecosystem is increasingly threatened by intensive black sand mining, raising concerns regarding long-term coastal sustainability. Black sand extraction disrupts dune integrity by reducing sediment density and heavy mineral content, thereby lowering resistance to wind forcing and accelerating aeolian transport. This study assesses historical dune migration and extraction-driven changes in aeolian dynamics using high-resolution satellite imagery, ERA5 wind reanalysis (1975–2024), and integrated analytical–numerical modeling, with implications for sustainable coastal management. A dominant northwesterly wind regime drives eastward and southward dune migration of 3.22 m/yr and 1.7 m/yr, respectively (2010–2025). Black sand mining since 2022 has measurably reduced heavy mineral content and bulk density, altering grain-size distribution and making dunes significantly more susceptible to wind entrainment. Coupled Bagnold and AeoLiS modeling predicts an 8.21% rise in mass transport rates and a corresponding acceleration in dune migration following extraction. These findings demonstrate that black sand mining amplifies aeolian transport and increases sand encroachment risks to nearby settlements, infrastructure, and agricultural lands. The results highlight the trade-offs between resource extraction and coastal dune ecosystem services, particularly flood protection and land stability, emphasizing the need for regulated mining, bioengineered dune stabilization, and predictive modeling to enhance the Nile Delta’s long-term resilience. Full article

Visual quality assessment is entering a new frontier as media evolve from static images to temporally dynamic videos and 3D content. These visual signals are typically captured by sensing devices such as cameras and depth sensors, whose acquisition characteristics significantly influence perceptual quality. Traditional quality models, including distortion-centric and regression-based approaches, perform well on conventional degradations but struggle to evaluate higher-level attributes such as semantic plausibility and structural coherence in modern AI-generated and multimodal scenarios. The emergence of large multimodal models (LMMs), including vision–language models (VLMs) and multimodal large language models (MLLMs), reshapes the evaluation paradigm by enabling semantic grounding, instruction-driven assessment, and explainable reasoning. This survey presents a unified perspective on visual quality assessment for sensor-captured visual data across image, video, and 3D modalities. We review conventional deep learning approaches and recent LMM-based methods, highlighting how multimodal fusion and language-conditioned reasoning transform quality assessment from scalar prediction to perceptual intelligence. Finally, we discuss key challenges and future opportunities for building efficient, robust, and sensor-aware visual quality assessment systems. Full article

Consumer interest in low-SO₂ white wines is increasing; however, such approaches may reduce compositional and sensory predictability. This study evaluates how three fermentation strategies—SO₂ addition and Saccharomyces cerevisiae ES181 inoculation (SO₂Sc), spontaneous fermentation (NoSO₂-Spont), and inoculation with S. cerevisiae ES181 without SO₂ addition (NoSO₂Sc)—shape the chemical profile, polyphenolic composition, colour, microbiological status, and sensory perception of ‘Solaris’ wines relative to the must (reference). A single batch of ‘Solaris’ must (one press run) was split into three variants and fermented under identical temperature conditions (12 ± 0.5 °C), followed by cool ageing and natural sedimentation prior to bottling. Basic oenological parameters, selected fermentation by-products, viable yeast counts, CIE Lab colour, targeted polyphenolics (phenolic acids, flavonols, flavan-3-ols, and stilbenes), PCA of by-products, and blind sensory evaluation were assessed. The NoSO₂-Spont variant showed reduced fermentation completeness (higher residual sugars and lower ethanol) and the highest volatile acidity, together with elevated glycerol and several higher alcohols, and received the lowest sensory ratings. The SO₂Sc variant yielded the most controlled outcome, with the lowest volatile acidity, the brightest colour (higher L*, lower b*), and the highest sensory acceptance. The NoSO₂Sc variant produced intermediate sensory scores and a higher total phenolic content; however, volatile acidity remained high and viable yeast counts were the greatest, indicating increased susceptibility to microbiological activity during extended pre-bottling handling. Overall, the SO₂Sc strategy provides the greatest chemical stability and sensory acceptance, whereas low-SO₂ regimes require a hurdle approach (oxygen control, residual sugar management, hygiene, and stabilisation) to limit spoilage development and post-bottling refermentation. Full article

Accurate large-scale crop mapping is critical for yield prediction, agricultural disaster monitoring, and global food security. Synthetic aperture radar (SAR), with its all-weather imaging capability, plays a vital role in remote sensing based on crop mapping studies. However, although feature-level fusion has been widely explored in remote sensing, existing VV and VH fusion approaches for rice mapping are still predominantly conducted at the data level and fail to adequately integrate their complementary information across the rice growth cycle, so the simplistic fusion methods yield features that are redundant or conflicting at field boundaries and in heterogeneous areas, thereby increasing classification errors. To address these challenges, this study proposes a novel spatiotemporal attention model (SimTA) for feature fusion to improve rice mapping. (1) A VV-VH feature-level fusion scheme is designed, integrated with a Content-Guided Attention (CGA) fusion method which effectively exploits the complementary information of the dual-polarized SAR data for achieving deep spatiotemporal dynamics fusion. (2) A Central Difference Convolution Spatial Extraction Conv (CDCSE Conv) Block is designed, enhancing sensitivity to edge variations in rice fields by combining standard and central difference convolutions. (3) To achieve efficient spatiotemporal feature integration across SAR time series, a Temporal–Spatial Attention (TSA) Block is developed, utilizing large-kernel convolutions for spatial feature extraction and a squeeze-and-excitation mechanism for capturing long-range temporal dependencies of rice time series. Extensive experiments were conducted by comparing SimTA with different models under five fusion schemes. Results demonstrate that feature-level fusion consistently outperforms other schemes, with SimTA achieving the best performance: OA = 91.1%, F1 score = 90.9%, and mIoU = 86.2%. Compared to the baseline Simple Video Prediction (SimVP), SimTA improves F1 score and mIoU by 0.8% and 2.1%, respectively. The CGA enhanced feature-level fusion further boosts SimTA’s performance to OA = 91.5% and F1 = 91.4%. SimTA bridges the gap between existing VV-VH deep fusion schemes and modern spatiotemporal modeling demands, offering a more accurate and generalizable approach for large-scale rice field mapping. Full article

Event detection is crucial for disaster response, public safety, and trend analysis, enabling real-time identification of critical events. Social media platforms provide a vast content source, offering timely and diverse event coverage compared to traditional news reports. However, challenges arise due to the informal and noisy nature of the text, along with the limited availability of ground truth data for training models. This study introduces SOCIAL (Social Media Event Classification using Integrated Artificial Learning and Natural Language Processing), a mathematically grounded framework for real-time social media event detection. SOCIAL integrates a formal representation of social media text with a customized CNN–LSTM architecture, combining convolutional operations for local feature extraction with sequential modeling to capture temporal dependencies, thereby enhancing classification accuracy. Generative AI is employed to create synthetic event-related samples, addressing data scarcity and ensuring a balanced dataset, while the design incorporates quantitative principles to guide embedding selection and model optimization. This study systematically evaluates six experimental configurations with TF-IDF and Word2Vec embeddings. The TF-IDF-based CNN–LSTM model achieved top performance with 98.59% accuracy, 98.13% precision, 99.06% recall, and 0.9719 MCC. Additionally, the $F_{0.5}$, $F_1$, and $F_2$ scores were 98.31%, 98.59%, and 98.87%, respectively, confirming the model’s strong predictive capabilities. TF-IDF integration enhanced event-specific term recognition, reducing misclassifications and improving reliability. These results demonstrate that SOCIAL is not only a fast, accurate, and scalable tool for crisis event detection, but also a formally principled framework for modeling and analyzing social media signals. Full article

Aquatic–terrestrial ecotones are highly dynamic biogeochemical hotspots where hydrological fluctuations profoundly influence microbial community structure and ecosystem functioning. However, the mechanisms underlying microbial community responses across hydrological gradients remain insufficiently understood. In this study, 16S rRNA gene sequencing was used to comparatively analyze bacterial communities in the waterward and landward zones of the drawdown area of the Danjiangkou Reservoir. The results showed that bacterial community composition differed significantly between the two zones, and waterlogging markedly increased bacterial α-diversity. Community variation was primarily associated with key environmental factors, including total phosphorus (TP), soil moisture content (SMC), and nitrate nitrogen (NO₃⁻-N). Compared with the landward zone, stochastic processes contributed more to community assembly in the waterward zone, which also exhibited higher network complexity and topological stability. In addition, several keystone taxa were identified, suggesting their potential roles in maintaining network structure and ecological stability. Functional prediction further revealed distinct metabolic potentials between zones, with enhanced anaerobic and redox-related functions in the waterward zone and predominantly aerobic metabolism in the landward zone. These findings suggest that hydrological fluctuations reshape bacterial community structure and potential ecological functions by jointly regulating water availability and nutrient dynamics. This study provides new insights into microbial ecological processes in reservoir riparian zones and offers a scientific basis for the management of aquatic–terrestrial ecotone ecosystems. Full article

Scandate cathodes have garnered significant attention for their exceptional low-temperature, high-current-density emission characteristics. However, their widespread deployment in vacuum electronic devices is currently hindered by stringent vacuum requirements and susceptibility to ion bombardment. To enhance the engineering applicability of scandate cathodes, this study employs first-principles density functional theory (DFT) to model the surface microstructures of strontium (Sr)–scandium (Sc) co-doped systems. Guided by simulation predictions regarding surface elemental ratios, corresponding emission active materials and cathode samples were fabricated. A systematic comparison between theoretical calculations and experimental measurements reveals a critical trade-off: while increasing Sr content enhances structural stability (indicated by lower formation energies), it concurrently increases the work function. Consequently, an optimal Sr doping level of approximately 2 wt% is identified, which significantly improves emission current density without compromising stability. Cathodes fabricated with this optimized composition were tested in a practical electron gun configuration. Results demonstrate that under low-temperature conditions (1000 °C) and wide-pulse operation (2 ms), the cathode achieves an emission current density of 21.57 A/cm². These findings validate the efficacy of simulation-guided material design and highlight the potential of Sr-doped scandate cathodes for high-power microwave applications. Full article

The development of high-performance composite supercapacitors remains challenging because the specific capacitance of composite electrodes is jointly governed by electronic percolation, ion accessibility, and interfacial contact, all of which are strongly affected by the balance among active materials, conductive agents, and binders. Traditional equivalent circuit modeling and empirical trial-and-error methods are often inadequate for describing these non-linear relationships and optimizing electrode design. To address this limitation, we establish a physics-guided and interpretable machine learning (ML) framework for predicting the specific capacitance of composite electrodes. Unlike traditional methods that rely on macroscopic mass fractions, our approach constructs a feature space comprising ten descriptors, including two newly introduced binder-related proxy descriptors—Binder-to-Conductive Ratio (BCR) and Specific Binder Loading (SBL)—to better represent the influence of binder content. By systematically evaluating 17 machine learning algorithms on a high-fidelity dataset, we identify the XGBoost model, optimized via Bayesian optimization, as the best predictor, achieving a coefficient of determination (R²) of 0.981 and a low mean absolute percentage error (MAPE) of 14.49%. Importantly, interpretability analysis using Shapley Additive Explanations (SHAP) provides physically interpretable statistical insights, revealing that high BCR suppresses specific capacitance through an insulating barrier effect, whereas lattice distortion in the filler material promotes ion transport. This study offers a robust, data-driven framework for optimizing composite electrode performance, demonstrating the potential of interpretable ML models for the rational design of advanced energy-storage materials. Full article

Cocoa fermentation is a key post-harvest process that determines the chemical composition and functional quality of cocoa beans. This study evaluated the effect of enzyme-assisted fermentation, using bromelain and laccase, on the phenolic compounds, methylxanthines and antioxidant capacity of CCN-51 cocoa beans from northern Peru. Fresh cocoa beans were fermented in wooden boxes under ambient conditions with different enzymatic treatments based on a factorial design. Samples were collected at 0, 2, 4 and 6 days of fermentation to determine total phenolic content (TPC), total flavonoid content (TFC), antioxidant activity (DPPH, ABTS and FRAP), and the concentrations of theobromine, caffeine, catechin and epicatechin by UHPLC-MS. Significant changes in phenolic composition and antioxidant activity were observed during fermentation (p < 0.05), with higher values in enzyme-treated samples, particularly at day 4. Principal component analysis indicated that phenolic compounds and antioxidant activity were the main variables responsible for sample differentiation. Response surface methodology showed that bromelain had the strongest influence on most responses. Optimization using a desirability function predicted an optimal enzymatic condition of 52.19 g of bromelain and 18 g of laccase per 5 kg of cocoa beans to maximize bioactive compounds. These findings highlight that enzyme-assisted fermentation is a promising strategy to enhance cocoa functional quality. Full article

The anchovy (Engraulis japonicus) is a highly abundant but underutilized fish resource in China, primarily due to its extreme post-harvest perishability. This study expanded the utilization of anchovy by developing a blended surimi from anchovy and golden threadfin bream, an I-optimal mixing design experiment was performed to optimize the formulation, and the effects of soy protein isolate (SPI), egg white powder (EWP), and yeast protein (YP) on the gel properties were investigated. The results of sensory evaluation and model prediction indicated that SPI had the most pronounced positive effect on the sensory characteristics of the gels, especially improving the elasticity, followed by EWP. Furthermore, the SPI-rich sample exhibited superior gel strength and chewiness, which was attributed to the increased β-sheet structure and the highest content of disulfide bonds in the protein network. And the water hold capacity of SPI-rich sample increased by 6.0%. The YP-rich group showed the strongest hydrophobic interactions and exhibited a significant enhancement in water hold capacity of 7.7%, which also provided a notable improvement in gel strength. The results showed that EWP contributed to the smoothness of the surimi, but it had no significant impact on water distribution, water-holding capacity, or the content of disulfide bonds within the gel network. Moreover, the EWP-rich group exhibited reduced the gel strength, hardness, and chewiness of the gel, resulting in the lowest overall sensory score of the surimi. Therefore, the optimal composite ratio was determined to be SPI:EWP:YP = 5.45%:2.55%:2.00%. These findings provided a precise blending strategy for developing high-quality surimi products from anchovy, offering a viable technical pathway for the value-added utilization of this resource. Full article

Quality by design (QbD) is a systematic approach focused on achieving consistent, predictable quality based on predefined objectives. Unlike traditional methods, QbD prioritizes risk assessment and management, which significantly enhances the robustness of the analytical method. In this study, we initiated factor screening using a three-factor, two-level design to evaluate three independent variables: flow rate, pH, and mobile phase composition. To further investigate the interaction of these variables, we employed Central Composite Design (CCD). This allows us to apply response surface methodology to the Critical Analytical Attributes (CAAs), specifically retention time, peak area, and symmetry factor, by conforming to the method’s robustness. The combination of pregabalin and duloxetine hydrochloride (HCl) dosage form was determined using a straightforward, exact, specific, and accurate reverse-phase HPLC approach. The results showed retention times of 3.265 min and 4.318 min for duloxetine HCl and pregabalin, respectively. Pregabalin demonstrated linearity from 100 to 200 μg/mL (R² = 0.998), whilst duloxetine HCl demonstrated linearity between 20 and 120 μg/mL (R² = 0.997). Lower LOD values of 0.925 µg/mL and 0.853 μg/mL and LOQ values of 2.809 μg/mL and 2.587 μg/mL of pregabalin and duloxetine HCl, respectively, suggest good sensitivity for the technique. The drug content of the commercial formulation may thus be determined using the recommended method. This technique can be used for standard quality control studies to simultaneously estimate pregabalin and duloxetine HCl. The novelty of the present studies lies in the development of a robust RP-HPLC method for simultaneous estimation of pregabalin and duloxetine HCl using a systematic AQbD approach, enhancing robustness, reproducibility, and reliability, making it highly suitable for routine quality control and regulatory applications. Full article

Atmospheric corrosion of carbon and low-alloy steels causes direct economic losses that are estimated at around 3.4% of the global GDP, and its accurate multi-year prediction is essential for protective coating selection, service-life estimation, and infrastructure maintenance scheduling. In this study, machine learning (ML) algorithms, including gradient boosting regressor (GBR), eXtreme gradient boosting (XGBoost), random forest (RF), support vector regression (SVR), and ridge regression, were trained on a 600-sample physics-grounded dataset to predict the cumulative atmospheric corrosion loss (µm) of low-alloy steels over 1–10 years of exposure. The dataset was constructed using the exact ISO 9223:2012 dose–response function (DRF) for a first-year corrosion rate and the ISO 9224:2012 power-law multi-year kinetic model (C(t) = C₁·t0.5), spanning ISO 9223 corrosivity categories C2–CX across 11 environmental and material input features. All models were evaluated on the original (untransformed) corrosion scale under an 80/20 train/test split and five-fold cross-validation. Gradient boosting achieved the best overall performance with test set R² = 0.968, CV-R² = 0.969, RMSE = 10.58 µm, MAE = 5.99 µm, and MAPE = 12.6%. XGBoost was a close second (R² = 0.958, CV-R² = 0.960). RF achieved an R² of 0.944. SHAP (SHapley Additive exPlanations) analysis identified SO₂ deposition rate, exposure time, relative humidity, Cl⁻ deposition rate, and temperature as the five most influential predictors. The dominance of the SO₂ deposition rate (mean |SHAP| = 26.37 µm) and the high second-place ranking of exposure time (13.67 µm) are fully consistent with the ISO 9223:2012 dose–response function and ISO 9224:2012 power-law kinetics, respectively, while among the material features, Cu and Cr contents showed the strongest negative SHAP contributions, confirming their corrosion-inhibiting roles in weathering steels. These results establish a physics-consistent, interpretable ML benchmark exceeding R² = 0.90 for multi-year cumulative corrosion loss prediction and provide a quantitative tool for alloy screening, coating selection in aggressive atmospheric environments, and service-life planning. Full article

Apples (Malus domestica Borkh.) are among the most widely consumed fruits worldwide and represent a significant dietary source of phenolic compounds. Efficient extraction is a critical step for the isolation, characterization, and quantification of phenolic compounds. The extraction yield and composition are strongly influenced by multiple parameters, including solvent type and concentration, temperature, extraction time, solid-to-liquid ratio, and the presence and concentration of acidifying agents. This study aimed to optimize an ultrasound-assisted extraction (UAE) procedure using response surface methodology (RSM) to evaluate the effects of extraction temperature, solvent-to-sample ratio (SSR) and citric acid concentration on total phenolic content (TPC) and total flavonoid content (TFC). Statistical analysis showed that SSR and temperature were the most influential factors affecting phenolic recovery, while citric acid concentration exerted a secondary, interaction-driven effect. Optimization using a desirability function identified the operating conditions that maximized phenolic and flavonoid recovery: 55 °C, 10 mL/g SSR and 0.2% citric acid concentration. Model predictions were validated experimentally, confirming the reliability of the approach for TPC and TFC. Chlorogenic acid and flavan-3-ols, including monomers, such as catechin and epicatechin, and polymers such as procyanidins, were identified. Overall, the proposed approach provides a statistically supported framework for phenolic compound analysis in apples. Full article

The starch and moisture content of saffron corms are critical indicators of their flowering potential and yield. This study investigated the use of rapid, minimally invasive VNIR reflectance spectroscopy measurement to assess these parameters. The measurements were used to develop predictive models through four machine learning algorithms (PLSR, RF, SVR, and GPR). Spectral data were obtained from 130 fresh corm samples. Wavelength analysis identified key starch-sensitive intervals (~930–1000 nm and ~1150–1220 nm) and a broad moisture-sensitive region (~900–1350 nm). Among the evaluated models, the combination of the multiplicative scatter correction pre-processing method and Gaussian process regression (MSC-GPR) demonstrated the optimal predictive performance for water content (R² = 0.92, RMSE = 0.71%, RPD = 4.56, RPIQ = 5.37), and the combination of the MSC method and partial least squares regression (PLSR-MSC) demonstrated moderate performance for starch content (R² = 0.73, RMSE = 28.7 mg g⁻¹, RPD = 2.14, RPIQ = 2.81, dry weight). These results demonstrate the viability of VNIR spectroscopy as a minimally invasive tool for the pre-planting assessment of saffron corm quality under laboratory conditions. The method provides a laboratory-based framework for corm screening and selection, with potential for future adaptation to field settings using portable spectrometers following expanded calibrations and advanced modeling techniques. Full article

An integrated approach combining metabolomics, network pharmacology, and molecular docking was employed to systematically explore the serum-absorbed components of jujube, their potential targets, and regulatory pathways. UPLC-MS/MS was used to characterize the absorbed components, while network pharmacology was applied to predict potential targets associated with alcoholic liver disease (ALD). A total of 10 absorbed components and 323 common targets were identified. Among the key components, quercetin, (-)-epigallocatechin, and methyl gallate exhibited strong binding affinities to eight core targets, including AKT serine/threonine kinase 1 (AKT1) and mitogen-activated protein kinase 1 (MAPK1), with quercetin showing the highest content. Jujube intervention significantly altered the serum metabolic profiles of healthy rats, with distinct differences observed between the control and jujube-treated groups. Bioinformatics analysis revealed that the differential metabolites were mainly enriched in the diterpenoid biosynthesis pathway. These findings provide a systematic and preliminary characterization of the serum-absorbed components of jujube, their potential ALD-related targets, and their regulatory effects on serum metabolism in healthy rats. This study provides a preliminary theoretical reference and direction for further research on the potential role of jujube in ALD. Full article