Search Results (163)

Chlorogenic acid (CA) exhibits robust lipid antioxidant activity within meat matrices. However, excess CA generates quinones that alter and damage porcine myofibrillar protein (MP). This study investigated the restorative effects of chitosan (CS) on MPs suffering from CA-induced oxidative damage. Three CA concentrations (0, 50, 100 μmol/g protein) and five CS dosages (0.125–1.0 g/g protein) were used to evaluate conformation, turbidity, surface hydrophobicity, solubility, emulsification, rheology, and gel properties. CA-oxidative damage to MP triggered protein unfolding, thiol depletion and aggregation, greatly lowering solubility, emulsifying capacity, viscoelasticity and water retention. CS exerted biphasic effects on turbidity, surface hydrophobicity, tertiary structure, and solubility only under severe CA-induced oxidative modification (100 μmol/g CA): low-to-medium CS aggravated adverse changes, while 1.0 g/g CS partially reversed such damage. For conformational, emulsion and gel parameters, CS consistently alleviated structural disorder caused by CA-induced oxidative damage across all treatments, with 1.0 g/g CS optimally mitigating α-helix loss and uneven emulsion droplets. Significant CA × CS interactions were detected for conformation, turbidity, surface hydrophobicity, solubility, emulsification and rheology (p < 0.001). Gel strength, water-holding capacity and water distribution exhibited non-significant interactions (p > 0.05), revealing independent additive effects of CA and CS on gel networks. Overall, high-dose CS partially ameliorates structural and functional defects of MP caused by CA-induced oxidative damage, which provides theoretical support for the combined application of polyphenols and polysaccharides in meat protein regulation. Full article

Microalgae are photosynthetic organisms that produce bioproducts of commercial interest and are efficient sequestering CO₂. The monitoring and control processes are areas for improvement to increase the efficiency of its production. There are sensor options for monitoring microalgae cultures, but the vast majority rely on microcontrollers, often lacking the robustness required for applications in more demanding conditions. Also, commercial systems with industrial capabilities can fit the above purpose, but they require licensing and are expensive. Therefore, this work presents the technical details of developing an open-source platform to monitor environmental variables using Modbus industrial sensors and Python used to control the photoperiod and for measuring pH, dissolved oxygen, electrical conductivity, water and air temperatures, photosynthetic photon flux density, irradiance, and turbidity in three photobioreactors containing the microalgae Chlorella vulgaris. The resulting time series showed that the platform preserved data and had a low outlier rate. pH measurements showed that during photosynthesis, the microalgae used CO₂ as their carbon source. Dissolved oxygen and culture medium temperature had an almost perfect Pearson’s anticorrelation with air-sparging. However, with aeration interruption, the correlation was $-0.804$, because dissolved oxygen depends on illumination, aeration, temperature, and biomass quantity, as shown in the time series. Full article

Accurate determination of the optical absorption coefficient, ${\mu }_a$, in turbid media is fundamental to biomedical optics and material characterization. Integrating sphere techniques, which measure total transmittance and reflectance, are a standard method for this purpose. However, the inverse models typically employed rely on the assumption of isotropic light propagation. In fiber-structured materials—a common geometry in biological tissue–this assumption often breaks down, leading to significant quantification errors. In this study, we investigated this effect using Monte Carlo simulations and proof-of-concept experiments on mechanically stretched PTFE tape. The medium was modeled as a slab of aligned dielectric cylinders embedded in an isotropic matrix, and the performance of an isotropic inverse model was compared with that of an anisotropic inverse model. The isotropic model showed substantial systematic errors in ${\mu }_a$, with a mean absolute error (MAE) of 19.3%, typical errors between approximately $-40\%$ and 50%, and outliers reaching up to 300%. In contrast, the matched anisotropic model achieved a MAE of 1.2%. Even when the structural parameters of the anisotropic model were perturbed, the MAE remained low at 1.8% for moderate perturbations and 3.9% for severe perturbations. The simulation results therefore indicate that, for the integrating sphere framework considered here, incorporating anisotropic light propagation can improve absorption retrieval more strongly than precise knowledge of all geometric details. Measurements on stretched PTFE tape showed the same qualitative trend and provide proof-of-concept experimental support for the simulation-based findings. Full article

The global shift toward sustainable industrial processes has increased the demand for advanced materials capable of performing under harsh conditions, with high-temperature polymer nanocomposites emerging as a key development area. This study investigates the fabrication of sustainable polysulfone (PSU)/medium-density fiberboard (MDF) nanocomposites through phase inversion, using PSU—a matrix known for its high glass transition temperature—as the base. Membranes were created by adding MDF residue at 1, 3, 5, 7, and 10 phr (parts per hundred resin). Characterization included analyzing polymer solution viscosity, ATR-FTIR, contact angle, SEM, porosity, equilibrium water content, average pore radius, tensile testing, and permeation performance. Incorporating MDF residue increased solution viscosity and affected porosity and the structure of the top layer. Mechanical testing showed MDF acted as a functional additive, improving the elastic modulus and tensile strength, and supporting overall structural stability under hydraulic stress. The membranes exhibited competitive water flux and maintained high selectivity (80–92% rejection; over 95% turbidity removal) at 1.0 and 2.0 bar. The 3 and 5 phr levels optimized performance, demonstrating that repurposing industrial waste within high-performance matrices is a practical approach for producing durable materials that meet the needs of energy systems and complex industrial separation processes. Full article

The Sichuan Basin serves as a key arena for ultra-deep natural gas exploration. The Nanhuan System Gucheng Formation, characterized by its ancient geological age and great burial depth, lacks almost any drilling data within the basin interior, and its sedimentary features and natural gas potential remain unstudied. Based on outcrop sections of the Nanhuan Gucheng Formation along the northern margin of the Sichuan Basin, sedimentological and hydrocarbon reservoir characteristics were analyzed. The study reveals: ① The lower Gucheng Formation at the Chenkou Yuyang section comprises three lithofacies: deformed-bedding conglomeratic sandstone, massive-bedded medium sandstone, and dark-gray horizontally-bedded mudstone, interpreted as deposits of turbidity channels, turbidite fan lobes, and deep-water shelf mud, respectively; ② The turbidity channel and fan sandstones exhibit dissolution pores, with porosities ranging from 8% to 12%, representing favorable reservoirs with a cumulative thickness exceeding 40 m. The deep-water shelf mud shows TOC values between 0.8% and 1.5%, serving as favorable source rocks with a cumulative thickness over 30 m. These two units are interbedded, forming an effective source-reservoir assemblage; ③ Based on the west–east outcrop transect (Zhenba Xiaoyangba, Chenkou Yuyang, and Mahuang Gou sections), the thickness of the Gucheng Formation displays a thin–thick–thin variation, interpreted as reflecting a sedimentary transition from shallow-water shelf delta to deep-water shelf/turbidite systems and back to shallow-water shelf deposits. A rift depositional model with a gentle western slope and steep eastern slope is proposed. In deep-water shelf areas, turbidite sandstone reservoirs are vertically interbedded with shelf mudstone source rocks, while in shallow-water shelf areas, deltaic sandstone reservoirs are laterally connected to source rocks. Spatially, this constitutes a hydrocarbon distribution pattern characterized by “vertical stacking and lateral connectivity,” providing valuable insights for ultra-deep natural gas exploration in the Sichuan Basin. Full article

Protein instability in white wines is mainly caused by pathogenesis-related (PR) proteins that survive winemaking and can form haze in bottle. Because PR-protein synthesis is modulated by vine stress, this study evaluated whether unmanned aerial vehicle (UAV) multispectral imagery and NDVI-based vigor zoning can be used as early predictors of protein instability in commercial Chardonnay and Sauvignon Blanc wines. High-resolution multispectral images were acquired over two seasons (2023–2024) in two vineyards, and three vigor zones (high, medium, low) were delineated from the NDVI at the individual vine scale. A total of 180 georeferenced vines were sampled, and musts were analyzed for thaumatin-like proteins and chitinases via RP-HPLC. Separate microvinifications were carried out for each vigor zone and cultivar, and the resulting wines were evaluated for protein instability (heat test) and bentonite requirements. Low-vigor vines consistently produced musts with higher PR-protein concentrations, greater turbidity after heating, and higher bentonite demand than high-vigor vines, with stronger effects in Sauvignon Blanc. These vigor-dependent patterns were stable across vintages, despite contrasting seasonal conditions. Linear discriminant analysis using NDVI, PR-protein content, turbidity, and bentonite dosage correctly separated vigor classes. Overall, UAV NDVI–based vigor zoning provided a robust, non-destructive tool for identifying vineyard zones with increased risk of protein instability. This approach supports precision enology by enabling site-specific stabilization strategies that reduce overtreatment with bentonite and preserve white wine quality. Full article

Accurate monitoring of chlorophyll-a (Chl-a) is essential for managing coastal aquaculture, as Chl-a indicates phytoplankton biomass and water quality. This study developed a hybrid deep learning model integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and an attention mechanism (Attention) to classify Chl-a using hourly, water quality datasets collected from the GOT001 station in Si Racha Bay, Eastern Gulf of Thailand (2020–2024). A random forest (RF) identified sea surface temperature (SEATEMP), dew point temperature (DEWPOINT), and turbidity (TURB) as the most influential variables, accounting for over 90% of the accuracy. Chl-a concentrations were categorized into ecological groups (low, medium, and high) using quantile-based binning and K-means clustering to support operational classification. Model performance comparison showed that the CNN–BiLSTM model achieved the highest classification accuracy (81.3%), outperforming the CNN–LSTM model (59.7%). However, the addition of the Attention did not enhance predictive performance, likely due to the limited number of key predictive variables and their already high explanatory power. This study highlights the potential of CNN–BiLSTM as a near-real-time classification tool for Chl-a levels in highly variable coastal ecosystems, supporting aquaculture management, early warning of algal blooms or red tides, and water quality risk assessment in the Gulf of Thailand and comparable coastal regions. Full article

Ever-increasing global water shortages necessitate more advanced and cost-effective water purification methods. Herein, a novel slow filtration system using natural raw zeolite is proposed as an alternative to traditional quartz sand as a filter medium. The system demonstrates excellent performance in reducing turbidity and removing methylene blue (MB). The natural zeolite-based filtration system (filter bed depth of 70 cm) completely adsorbed 30 ppm MB at a filtration velocity of 0.2 m/h, maintaining its performance up to 2 months. The highest adsorption capacity (q_max) for MB of natural zeolite was 8.32 mg/g for the 0.3 mm fraction and 13.84 mg/g for the 0.1 mm fraction. The slow filtration process demonstrated high turbidity removal efficiencies, achieving 98.53% with the natural zeolite filter and 98.97% with the quartz sand filter, indicating the effectiveness of both media in improving water quality. This study highlights the potential of the natural zeolite-based slow filtration system as a versatile and effective water treatment solution. Full article

Chlorophyll-a (Chl-a) concentration is a key parameter for assessing the degree of eutrophication and the algal bloom risk in water bodies. Accurate and robust monitoring of Chl-a is crucial for effective water quality management of inland and coastal optically complex Case-II waters. This study proposes a stratified integrated framework that combines optical water type (OWT) classification and multi-index blending models and evaluates the capability of ZY-1 02E hyperspectral imagery in the retrieval of Chl-a concentration in Case-II waters. This research is based on ZY-1 02E hyperspectral remote sensing images and ground synchronous measurement data from four typical water bodies in China (Dongpu Reservoir, Nanyi Lake, Tangdao Bay, and Moon-lake Reservoir). Using Fuzzy C-Means (FCM) clustering combined with spectral feature analysis, three different OWTs were identified, and the bands sensitive to Chl-a for each water type were recognized. Subsequently, the most suitable semi-empirical indices (BR, TBI) were selected, and a new suspended matter correction index (SMCI) was constructed by integrating spectral bands and TSM data specifically for high-turbidity waters to facilitate the retrieval of Chl-a concentration. The RMSE and MAPE of the model constructed based on the unclassified dataset were 3.1586 μg·L⁻¹ and 30.82%, respectively. When the stratified ensemble method based on optical water type classification was employed, the overall RMSE and MAPE were reduced to 1.5832 μg·L⁻¹ and 16.36%. The results demonstrate that this hierarchical ensemble framework significantly improved the retrieval accuracy of Chl-a concentration. An uncertainty assessment of the Chl-a retrieval model for highly turbid waters incorporating SMCI was conducted using the Monte Carlo method, revealing a mean coefficient of variation of 0.0567 and a coverage rate of 95.65% for the 95% confidence interval, indicating high predictive stability and reliability of the model. This study emphasizes the importance of the integrated framework strategy that combines OWTs classification and multi-index blending models for accurate and robust remote sensing estimation of Chl-a concentration under optically complex environmental conditions. It confirms the application potential of ZY-1 02E hyperspectral data in monitoring Chl-a in inland and near-coastal waters at medium and small scales. Full article

During long-range imaging, the turbid medium in the atmosphere absorbs and scatters light, resulting in reduced contrast, a narrowed dynamic range, and obscure detail information in remote sensing images. The prior-based method has the advantages of good real-time performance and a wide application range. However, few of the existing prior-based methods are applicable to the dehazing of panchromatic images. In this paper, we innovatively propose a prior-based dehazing method for panchromatic remote sensing images through statistical histogram features. First, the hazy image is divided into plain image patches and mixed image patches according to the histogram features. Then, the features of the average occurrence differences between adjacent gray levels (AODAGs) of plain image patches and the features of the average distance to the gray-level gravity center (ADGG) of mixed image patches are, respectively, calculated. Then, the transmission map is obtained according to the statistical relation equation. Then, the atmospheric light of each image patch is calculated separately based on the maximum gray level of the image patch using the threshold segmentation method. Finally, the dehazed image is obtained based on the physical model. Extensive experiments in synthetic and real-world panchromatic hazy remote sensing images show that the proposed algorithm outperforms state-of-the-art dehazing methods in both efficiency and dehazing effect. Full article

This study develops a geomorphological identification methodology for wall reefs in the microcontinental environment of Wangiwangi Island, Indonesia, using medium-resolution Landsat 8 satellite imagery and morphological analysis based on Maxwell’s geomorphological framework. The uniqueness of the wall reef landform lies in the fact that the lagoon elongates on limestone, resulting in a habitat and ecosystem that develops differently from those of other shelf reefs, namely, platform reefs and plug reefs. Using Optimum Index Factor (OIF) optimization and RGB image composites, four reef types were successfully identified: cuspate reefs, open ring reefs, closed ring reefs, and resorbed reefs. A field check was conducted at fifteen observation sites, which included measurements of depth, turbidity, and water quality parameters, as well as an in situ benthic habitat inventory. The analysis results showed a strong correlation between image composites, geomorphological reef classes, and ecological conditions, confirming the successful adaptation of Maxwell’s classification to the Indonesian reef system. This hybrid integrated approach successfully maps the distribution of reefs on a complex continental shelf, providing an essential database for shallow-water spatial planning, ecosystem-based conservation, and sustainable management in the Coral Triangle region. Policy recommendations include zoning schemes for protected areas based on reef landform morphology, strengthening integrative monitoring systems, and utilizing high-resolution imagery and machine learning algorithms in further research. Full article

In this paper, we present a set of experimental data (SESD) from Capsicum annuum with two different pigmentations, obtained using a self-made computed tomography spectrometer (CTIS), which adapt to the optical model of radiative transfer. An optical model is based on the directional-hemispheric reflectance and transmittance of a turbid medium with plane-parallel layers. To estimate the fruit’s primary pigments (Chlorophyll, Carotenoids, Capsanthin, and Capsorubin), we use the optical model combined with a numerical search and optimization method based on a robust and efficient multi-objective genetic algorithm (GA), allowing us to find the closest solution to the global minimum; and the inverse problem is solved by obtaining the best fit of the analytical function defined in the SESD optical model. Values of pigment concentrations retrieved with the proposed GA show a total difference of 2.51% for green pepper and 5.60% for red pepper compared with those reported in the literature. Full article

Ceramic materials have been utilized for centuries across a range of industries due to their chemical stability and porous microstructure. One prominent application is water filtration, where ceramics offer an effective medium for removing contaminants. Ceramic filters can operate under either pressure-driven or gravity-driven mechanisms. While traditional fabrication techniques, such as pottery, have been historically employed to produce ceramic filters, these methods are limited by user skills, lack of reproducibility, and geometric constraints. In contrast, modern additive manufacturing techniques provide enhanced precision, repeatability, and customization. This study employs extrusion-based 3D printing to fabricate gravity-driven ceramic filters with tailored geometries to meet specific performance requirements. The use of 3D printing allows for efficient production of uniform filters with optimized internal structures. The selected ceramic material, derived from natural sources, offers environmental compatibility, as it is both sustainable and biodegradable. The fabricated filters were evaluated for their effectiveness in treating water. The filtration tests showed significant improvements in water quality, including reduced turbidity, color, iron, manganese, and total and calcium hardness. pH increased from 6.23 to 7.26, and conductivity dropped from 7.43 mS to 4.5 mS, indicating effective ion removal. These findings highlight the potential of 3D-printed ceramic filters as an environmentally friendly and effective solution for decentralized water purification applications. Full article

The rapid advancement of underwater wireless communication technologies is critical to unlocking the full potential of marine resource exploration and environmental monitoring. This paper reviews recent progress in three primary modalities: underwater acoustic communication, radio frequency (RF) communication, and underwater optical wireless communication (UWOC), each designed to address specific challenges posed by complex underwater environments. Acoustic communication, while effective for long-range transmission, is constrained by ambient noise and high latency; recent innovations in noise reduction and data rate enhancement have notably improved its reliability. RF communication offers high-speed, short-range capabilities in shallow waters, but still faces challenges in hardware miniaturization and accurate channel modeling. UWOC has emerged as a promising solution, enabling multi-gigabit data rates over medium distances through advanced modulation techniques and turbulence mitigation. Additionally, bio-inspired approaches such as electric field communication provide energy-efficient and robust alternatives under turbid conditions. This paper further examines the practical integration of these technologies in underwater platforms, including autonomous underwater vehicles (AUVs), highlighting trade-offs between energy efficiency, system complexity, and communication performance. By synthesizing recent advancements, this review outlines the advantages and limitations of current underwater communication methods and their real-world applications, offering insights to guide the future development of underwater communication systems for robotic and vehicular platforms. Full article

A comparative study was conducted to investigate membrane fouling control and treatment performance using natural surface water as the feed source. The evaluated processes included: (1) direct filtration–tubular ceramic membrane (DF-TCM, control); (2) coagulation–tubular ceramic membrane (C-TCM); and (3) coagulation–tubular ceramic membrane with concentrate recycling (C-TCM-CR). Experimental results demonstrated that under constant flux operation at 75 L/(m²·h) for 8 h, the C-TCM-CR process reduced the transmembrane pressure (TMP) increase by 83% and 35% compared to DF-TCM and C-TCM, respectively. Floc size distribution analysis and cake layer characterization revealed that the C-TCM-CR process enhanced coagulation efficiency and formed high-porosity cake layers on membrane surfaces, thereby mitigating fouling development. Notably, the coagulation-assisted processes demonstrated improved organic matter removal, with 13%, 10%, and 10% enhancement in COD_Mn, UV₂₅₄, and medium molecular weight organics (2000–10,000 Da) removal compared to DF-TCM, along with a moderate enhancement in fluorescent substances removal efficiency. All three processes achieved over 99% turbidity removal efficiency, as the ceramic membranes demonstrate excellent filtration performance. Full article