Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (720)

Search Parameters:
Keywords = weighted mean temperature

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
20 pages, 3411 KB  
Article
Assessing the Impacts of Greenhouse Lifespan on the Evolution of Soil Quality in Highland Mountain Vegetable Farmland
by Keyu Yan, Xiaohan Mei, Jing Li, Xinmei Zhao, Qingsong Duan, Zhengfa Chen and Yanmei Hu
Agronomy 2025, 15(10), 2343; https://doi.org/10.3390/agronomy15102343 - 5 Oct 2025
Viewed by 222
Abstract
Long-term greenhouse operations face a critical challenge in the form of soil quality degradation, yet the key intervention periods and underlying mechanisms of this process remain unclear. This study aims to quantify the effects of greenhouse lifespan on the evolution of soil quality [...] Read more.
Long-term greenhouse operations face a critical challenge in the form of soil quality degradation, yet the key intervention periods and underlying mechanisms of this process remain unclear. This study aims to quantify the effects of greenhouse lifespan on the evolution of soil quality and to identify critical periods for intervention. We conducted a systematic survey of greenhouse operations in a representative area of Yunnan Province, Southwest China, and adopted a space-for-time substitution design. Using open-field cultivation (OF) as the control, we sampled and analyzed soils from vegetable greenhouses with greenhouse lifespans of 2 years (G2), 5 years (G5), and 10 years (G10). The results showed that early-stage greenhouse operation (G2) significantly increased soil temperature (ST) by 8.38–19.93% and soil porosity (SP) by 16.21–56.26%, promoted nutrient accumulation and enhanced aggregate stability compared to OF. However, as the greenhouse lifespan increased, the soil aggregates gradually disintegrated, particle-size distribution shifted toward finer clay fractions, and pH changed from neutral to slightly alkaline, exacerbating nutrient imbalances. Compared with G2, G10 exhibited reductions in mean weight diameter (MWD) and soil organic matter (SOM) of 2.41–5.93% and 24.78–30.93%, respectively. Among greenhouses with different lifespans, G2 had the highest soil quality index (SQI), which declined significantly with extended operation; at depths of 0–20 cm and 20–40 cm, the SQI of G10 was 32.59% and 38.97% lower than that of G2, respectively (p < 0.05). Structural equation modeling (SEM) and random forest analysis indicated that the improvement in SQI during the early stage of greenhouse use was primarily attributed to the optimization of soil hydrothermal characteristics and pore structure. Notably, the 2–5 years was the critical stage of rapid decline in SQI, during which intensive water and fertilizer inputs reduced the explanatory power of soil nutrients for SQI. Under long-term continuous cropping, the reduction in MWD and SOM was the main reason for the decline in SQI. This study contributes to targeted soil management during the critical period for sustainable production of protected vegetables in southern China. Full article
Show Figures

Graphical abstract

18 pages, 3000 KB  
Article
Effect of Shading Ratio on Japanese Sea Bass (Lateolabrax japonicus) and Asian Sea Bass (Lates calcarifer) Aquaculture
by Yao-Chen Lee, I-Pei Kuo, Yung-Ting Chung and Shuenn-Der Yang
Fishes 2025, 10(10), 490; https://doi.org/10.3390/fishes10100490 - 1 Oct 2025
Viewed by 220
Abstract
Floating photovoltaic arrays on ponds may alter thermal and optical conditions that are relevant to aquaculture performance. This study compared 0% and 40% surface shading in two outdoor earthen-pond trials, one with Asian sea bass (Lates calcarifer) and one with Japanese [...] Read more.
Floating photovoltaic arrays on ponds may alter thermal and optical conditions that are relevant to aquaculture performance. This study compared 0% and 40% surface shading in two outdoor earthen-pond trials, one with Asian sea bass (Lates calcarifer) and one with Japanese sea bass (Lateolabrax japonicus). Temperature was logged hourly and summarized as daily means; water quality was sampled biweekly; fish were measured repeatedly, with endpoint growth compared within species. The result shows that shading lowered pond temperature and the diurnal temperature range and reduced the number of days above species benchmark temperatures. Indicators associated with phytoplankton, including suspended solids and chlorophyll a, were lower under shading, whereas dissolved inorganic nutrients were higher. In the Japanese sea bass trial, dissolved oxygen was higher without shading. Final body weight did not differ between treatments within either trial, but survival was higher with 40% shading. Principal component analysis and permutational multivariate analysis of variance indicated a treatment signal in multivariate water quality. Because the trials occurred in different years with one pond per treatment, inference was restricted to contrasts within each species. Overall, moderate surface shading cooled ponds and altered water quality without reducing growth. Full article
(This article belongs to the Section Sustainable Aquaculture)
Show Figures

Figure 1

23 pages, 4006 KB  
Article
Advancing Sustainable Propulsion Solutions for Maritime Applications: Numerical and Experimental Assessments of a Methanol HT-PEMFC System
by Simona Di Micco, Filippo Scamardella, Marco Altosole, Ivan Arsie and Mariagiovanna Minutillo
Energies 2025, 18(19), 5119; https://doi.org/10.3390/en18195119 - 26 Sep 2025
Viewed by 288
Abstract
The interest in analyzing alternative fuels and new propulsion technologies for shipping decarbonization is growing rapidly. This paper aims to evaluate the performance of high-temperature polymeric exchange membrane fuel cells (HT-PEMFCs) fed by reformed methanol and their potential application as a propulsion system [...] Read more.
The interest in analyzing alternative fuels and new propulsion technologies for shipping decarbonization is growing rapidly. This paper aims to evaluate the performance of high-temperature polymeric exchange membrane fuel cells (HT-PEMFCs) fed by reformed methanol and their potential application as a propulsion system for vessels. The proposed system is intended to be installed on board a 10 m long ship, designed for commercial use in the marine area of Capri Island. Numerical and experimental analyses were performed to estimate the system’s performance, and a feasibility assessment was carried out to verify its real applicability on board the reference case study. From the numerical perspective, a CFD model of the ship hull, as well as a thermochemical model of the propulsion system, was developed. From the experimental point of view, the system behavior was tested by means of a dedicated test bench. The results of the numerical models allowed for the sizing of the propulsion system and the calculation of the fuel consumption. In particular, to satisfy the ship’s power demand, two 5 kW HT-PEMFCs were needed, with a total fuel consumption of 12.7 kg over a typical daily cruise, with a methanol consumption of 1.88 kg/h during cruising at 7 knots. The feasibility analysis highlighted that the propulsion system fits the vessel’s requirements, both in terms of volume and weight. Full article
Show Figures

Figure 1

19 pages, 4745 KB  
Brief Report
Optimizing Shrimp Culture Through Environmental Monitoring: Effects of Water Quality and Metal Ion Profile on Whiteleg Shrimp (Litopenaeus vannamei) Performance in a Semi-Intensive Culture Pond
by Muhammad Farhan Nazarudin, Mohammad Amirul Faiz Zulkiply, Muhammad Hasif Samsuri, Nurul Aina Syakirah Khairil Anwar, Nur Syamimie Afiqah Jamal, Norfarrah Mohamed Alipiah, Mohd Ihsanuddin Ahmad, Norhariani Mohd Nor, Ina Salwany Md Yasin, Natrah Ikhsan, Mohammad Noor Amal Azmai and Mohd Hafiz Rosli
Water 2025, 17(19), 2818; https://doi.org/10.3390/w17192818 - 25 Sep 2025
Viewed by 337
Abstract
Water quality management is crucial for sustainable whiteleg shrimp (Litopenaeus vannamei) aquaculture, though little research has comprehensively investigated the spatiotemporal fluctuation of trace elements in tropical semi-intensive ponds. This study investigated the water quality variations and trace element concentrations in an [...] Read more.
Water quality management is crucial for sustainable whiteleg shrimp (Litopenaeus vannamei) aquaculture, though little research has comprehensively investigated the spatiotemporal fluctuation of trace elements in tropical semi-intensive ponds. This study investigated the water quality variations and trace element concentrations in an earthen pond across a 56-day culture cycle during the dry season. Physicochemical parameters (temperature, pH, salinity, dissolved oxygen, ammonia, nitrite, and nitrate) and trace elements (Cu, Zn, Mn, Fe, and Mg) were measured concurrently with shrimp growth and survival. The DO and pH readings were observed to fluctuate significantly during the mid-to-late stages of culture, with DO nearing critical thresholds (<5.0 mg L−1). A sudden increase in ammonia and nitrite levels suggested the accumulation of organic matter and a microbial imbalance. Zinc concentrations (0.28–1.00 mg L−1) approached stress-inducing levels, while magnesium remained low (10.44–10.72 mg L−1). Pearson’s correlation revealed strong positive associations between ammonia and nitrate (r = 0.95) and between DO and pH (r = 0.94), while Mg was negatively correlated with Fe (r = −0.99) and nitrite (r = −0.88). Shrimp achieved 13.43 ± 0.73 g mean weight, with 77.8% survival and an FCR of 1.08. These results provide baseline evidence that combined water quality and trace element monitoring can become an early warning framework for pond management. Future studies integrating shrimp physiology and immune responses are needed to establish direct causal relationships. Full article
(This article belongs to the Section Water, Agriculture and Aquaculture)
Show Figures

Figure 1

28 pages, 4706 KB  
Article
Comparative Performance Analysis of Machine Learning-Based Annual and Seasonal Approaches for Power Output Prediction in Combined Cycle Power Plants
by Asiye Aslan and Ali Osman Büyükköse
Energies 2025, 18(19), 5110; https://doi.org/10.3390/en18195110 - 25 Sep 2025
Viewed by 420
Abstract
This study develops an innovative framework that utilizes real-time operational data to forecast electrical power output (EPO) in Combined Cycle Power Plants (CCPPs) by employing a temperature segmentation-based modeling approach. Instead of using a single general prediction model, which is commonly seen in [...] Read more.
This study develops an innovative framework that utilizes real-time operational data to forecast electrical power output (EPO) in Combined Cycle Power Plants (CCPPs) by employing a temperature segmentation-based modeling approach. Instead of using a single general prediction model, which is commonly seen in the literature, three separate prediction models were created to explicitly capture the nonlinear effect of ambient temperature (AT) on efficiency (AT < 12 °C, 12 °C ≤ AT < 20 °C, AT ≥ 20 °C). Linear Ridge, Medium Tree, Rational Quadratic Gaussian Process Regression (GPR), Support Vector Machine (SVM) Kernel, and Neural Network methods were applied. In the modeling, the variables considered were AT, relative humidity (RH), atmospheric pressure (AP), and condenser vacuum (V). The highest performance was achieved with the Rational Quadratic GPR method. In this approach, the weighted average Mean Absolute Error (MAE) was found to be 2.225 with seasonal segmentation, while it was calculated as 2.417 in the non-segmented model. By applying seasonal prediction models, the hourly EPO prediction error was reduced by 192 kW, achieving a 99.77% average convergence of the predicted power output values to the actual values. This demonstrates the contribution of the proposed approach to enhancing operational efficiency. Full article
(This article belongs to the Section F1: Electrical Power System)
Show Figures

Figure 1

28 pages, 2243 KB  
Article
Intraspecific Variation and Environmental Determinants of Leaf Functional Traits in Polyspora chrysandra Across Yunnan, China
by Jianxin Yang, Changle Ma, Longfei Zhou, Qing Gui, Maiyu Gong, Hengyi Yang, Jia Liu, Yong Chai, Yongyu Sun and Xingbo Wu
Plants 2025, 14(19), 2953; https://doi.org/10.3390/plants14192953 - 23 Sep 2025
Viewed by 414
Abstract
Plant functional traits (PFTs) serve as key predictors of plant survival and adaptation to environmental gradients. Studies on intraspecific variation in PFTs are crucial for evaluating species’ adaptation to projected climate change and developing long-term conservation strategies. This study systematically investigated PFT responses [...] Read more.
Plant functional traits (PFTs) serve as key predictors of plant survival and adaptation to environmental gradients. Studies on intraspecific variation in PFTs are crucial for evaluating species’ adaptation to projected climate change and developing long-term conservation strategies. This study systematically investigated PFT responses in Polyspora chrysandra (Theaceae, Yunnan, China) through an integrated multivariate analysis of 20 leaf functional traits (LFTs) and 33 environmental factors categorized into geographical conditions (GCs), climate factors (CFs), soil properties (SPs), and ultraviolet radiation factors (UVRFs). To disentangle complex environmental–trait relationships, we employed redundancy analysis (RDA), hierarchical partitioning (HP), and partial least squares structural equation modeling (PLS-SEM) to assess direct, indirect, and latent relationships. Results showed that the intraspecific coefficient of variation (CV) ranged from 7.071% to 25.650%. Leaf tissue density (LTD), specific leaf area (SLA), leaf fresh weight (LFW), leaf dry weight (LDW), and leaf area (LA) exhibited moderate intraspecific trait variation (ITV), while all other traits demonstrated low ITV. Reference Bulk density (RBD) and Silt emerged as significant factors driving the variation. Latitude (Lat), altitude (Alt), and mean warmest month temperature (MWMT) were also identified as key influences. HP analysis revealed Silt as the most important predictor (p < 0.05). Latent variable analysis indicated descending contribution rates: SPs (31.51%) > GCs (11.52%) > CFs (11.04%) > UVRFs (10.29%). Co-effect analysis highlighted significant coupling effects involving RBD and cation exchange capacity of clay (CECC), as well as organic carbon content (OCC) and UV-B seasonality (UVB2). Path analysis showed SPs as having the strongest influence on leaf thickness (LT), followed by GCs and UVRFs. These findings provide empirical insights into the biogeographical patterns of ITV in P. chrysandra, enhance the understanding of plant environmental adaptation mechanisms, and offer a theoretical foundation for studying community assembly and ecosystem function maintenance. Full article
Show Figures

Figure 1

23 pages, 9288 KB  
Article
Integrating UAV-Derived Diameter Estimations and Machine Learning for Precision Cabbage Yield Mapping
by Sara Tokhi Arab, Akane Takezaki, Masayuki Kogoshi, Yuka Nakano, Sunao Kikuchi, Kei Tanaka and Kazunobu Hayashi
Sensors 2025, 25(18), 5652; https://doi.org/10.3390/s25185652 - 10 Sep 2025
Viewed by 524
Abstract
Non-destructive diameter estimation of cabbage heads and yield prediction employing Unmanned Aerial Vehicle (UAV) imagery are superior to conventional approaches, which are labor intensive and time consuming. This approach assesses spatial variability across the field, effective allocation of resources, and supports variable application [...] Read more.
Non-destructive diameter estimation of cabbage heads and yield prediction employing Unmanned Aerial Vehicle (UAV) imagery are superior to conventional approaches, which are labor intensive and time consuming. This approach assesses spatial variability across the field, effective allocation of resources, and supports variable application rates of fertilizer and supply chain management. Here, individual cabbage head diameters were estimated using deep learning-based pose estimation models (YOLOv8s-pose and YOLOv11s-pose) using high spatial resolution RGB images acquired from UAV 6 m during the cabbage-growing season in 2024. With a mean relative error (MRE) of 4.6% and a high mean average precision (mAP) 98.5% at 0.5, YOLOv11s-pose emerged as the best-performing model, verifying its accuracy for pragmatic agricultural use. The approximated diameter was then combined with climatic variables (temperature and rainfall) and canopy reflectance indices (normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green chlorophyll index (CIg)) that were extracted from the multispectral images with 6 m resolution and fed into AI models to develop individual cabbage head fresh weight. Among the machine learning models (MLMs) tested, CatBoost achieved the lowest Mean Squared Error (MSE = 0.025 kg/cabbage), highest R2 (0.89), and outperformed other models based on the Diebold–Mariano statistical test (p < 0.05). This finding suggests that an integrated AI-powered framework enhances non-invasive and precise yield estimation in cabbage farming. Full article
(This article belongs to the Section Smart Agriculture)
Show Figures

Figure 1

24 pages, 5321 KB  
Article
Analysis of Spatiotemporal Variability and Drivers of Soil Moisture in the Ziwuling Region
by Jing Li, Yinxue Luo, Zhanbin Li, Guoce Xu, Mengjing Guo and Fengyou Gu
Sustainability 2025, 17(17), 8025; https://doi.org/10.3390/su17178025 - 5 Sep 2025
Viewed by 838
Abstract
Understanding soil moisture’s spatiotemporal variations and the factors influencing it is crucial for the restoration and growth of vegetation across the Loess Plateau, particularly in the Ziwuling region. This study employs soil moisture remote sensing data, complemented by information on soil properties, environmental [...] Read more.
Understanding soil moisture’s spatiotemporal variations and the factors influencing it is crucial for the restoration and growth of vegetation across the Loess Plateau, particularly in the Ziwuling region. This study employs soil moisture remote sensing data, complemented by information on soil properties, environmental conditions, and topography, to examine soil moisture variability within the Ziwuling region between 2001 and 2020. Using trend analysis, geographic detectors, and multi-scale geographic weighting techniques, this research aims to elucidate the effects of driving factors on soil moisture’s spatiotemporal patterns. The findings indicate the following: (1) Over the study period, the mean soil moisture in the Ziwuling region exhibited a relatively stable declining trend, with an annual decrease of −0.00047 m3/(m3·a). Spatially, higher soil moisture levels were observed in the south-central area, while lower levels occurred in the northern, western, and eastern peripheries. (2) Geoprobe analysis illustrated that the normalized difference vegetation index (NDVI) had the most notable effect on the spatial distribution of soil moisture in the region. As a direct indicator of vegetation cover, NDVI strongly affects soil moisture distribution through ecological and hydrological processes. Following NDVI, average annual potential evapotranspiration and annual precipitation were identified as the next most influential factors. The combined effect of these factors on soil moisture surpassed that of individual factors, with the interaction between NDVI and annual precipitation being particularly pronounced, predominantly controlling the spatial variability of soil moisture in the Ziwuling region. (3) Different factors exhibited varying effects on soil moisture levels. Notably, slope and elevation consistently had negative impacts, whereas variables such as soil texture (loam and sand), land use, temperature, precipitation, NDVI, and slope aspect showed bidirectional influences. This study offers a comprehensive analysis of the spatiotemporal variability of soil moisture and its controlling factors in the Ziwuling region, ultimately offering a scientific basis to support ecological restoration and sustainable development initiatives on the Loess Plateau. Full article
Show Figures

Figure 1

19 pages, 2495 KB  
Article
Integrated Assessment of Climate-Driven Streamflow Changes in a Transboundary Lake Basin Using CMIP6-SWAT+-BMA: A Sustainability Perspective
by Feiyan Xiao, Yaping Wu, Xunming Wang, Ping Wang, Congsheng Fu and Jing Zhang
Sustainability 2025, 17(17), 7901; https://doi.org/10.3390/su17177901 - 2 Sep 2025
Viewed by 636
Abstract
Estimating the impacts of climate change on streamflow in the Xiaoxingkai Lake Basin is vital for ensuring sustainable water resource management and transboundary cooperation across the entire Xingkai Lake Basin, a transboundary lake system shared between China and Russia. In this study, 11 [...] Read more.
Estimating the impacts of climate change on streamflow in the Xiaoxingkai Lake Basin is vital for ensuring sustainable water resource management and transboundary cooperation across the entire Xingkai Lake Basin, a transboundary lake system shared between China and Russia. In this study, 11 Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSP245 and SSP585) were used to drive the Soil and Water Assessment Tool Plus (SWAT+) model. Streamflow projections were made for two future periods: the 2040s (2021–2060) and the 2080s (2061–2100). To correct for systematic biases in the GCM outputs, we applied the Delta Change method, which significantly reduced root mean square error (RMSE) in both precipitation and temperature by 3–35%, thereby improving the accuracy of SWAT+ simulations. To better capture inter-model variability and enhance the robustness of streamflow projections, we used the Bayesian Model Averaging (BMA) technique to generate a weighted ensemble, which outperformed the simple arithmetic mean by reducing uncertainty across models. Our results indicated that under SSP245, greater increases were projected in annual streamflow as well as in wet and normal-flow seasons (e.g., streamflow in normal-flow season in the 2080s increased by 13.0% under SSP245, compared to 7.0% under SSP585). However, SSP585 produced a much larger relative amplification in the dry season, with percentage changes relative to the historical baseline reaching up to +171.7% in the 2080s, although the corresponding absolute increases remained limited due to the low baseline flow. These findings quantify climate-driven hydrological changes in a cool temperate lake basin by integrating climate projections, hydrological modeling, and ensemble techniques, and highlight their implications for understanding hydrological sustainability under future climate scenarios, providing a critical scientific foundation for developing adaptive, cross-border water management strategies, and for further studies on water resource resilience in transboundary basins. Full article
Show Figures

Figure 1

18 pages, 2070 KB  
Article
Structural Water Accommodation in Co3O4: A Combined Neutron and Synchrotron Radiation Diffraction and DFT Study
by Mariangela Longhi, Mauro Coduri, Paolo Ghigna, Davide Ceresoli and Marco Scavini
Inorganics 2025, 13(9), 288; https://doi.org/10.3390/inorganics13090288 - 27 Aug 2025
Viewed by 548
Abstract
Spinels like Co3O4 have acquired relevance because of their photocatalytic, electrocatalytic, optical and magnetic properties. In this context, we investigated the defect structure evolution of compounds synthetized using the nitrate precursor method and after annealing cycles at temperatures ranging from [...] Read more.
Spinels like Co3O4 have acquired relevance because of their photocatalytic, electrocatalytic, optical and magnetic properties. In this context, we investigated the defect structure evolution of compounds synthetized using the nitrate precursor method and after annealing cycles at temperatures ranging from 260 to 650 °C by means of thermogravimetric analysis (TGA), neutron powder diffraction (NPD), X-ray powder diffraction (XRPD) coupled to Pair Distribution Function (PDF) analysis, and Density Functional Theory (DFT) calculations. Deuterated and hydrogenated precursors were adopted to produce the samples for NPD and XRPD experiments, respectively. TGA measurements displayed weight losses, the extent of which increased on lowering the preparation annealing temperature, suggesting that the adopted wet synthesis introduces structural water in the sample. Both XRPD and NPD revealed the presence of vacancies in tetrahedral cobalt sites (VCo1) whose concentration at RT decreases on raising the annealing temperatures, while octahedral cobalt and oxygen sites were fully occupied in all the samples. In addition, the VCo1 presence induces a shrinking of the volume of the CoO4 tetrahedra. The combination of DFT calculation and diffraction revealed that deuterium/hydrogen ions (Di/Hi), introduced during the synthesis by the nitrate precursor balanced the VCo1. Finally, DFT calculations revealed that (Di/Hi) in Co3O4 forms hydroxyl groups. Full article
(This article belongs to the Section Inorganic Solid-State Chemistry)
Show Figures

Graphical abstract

20 pages, 3513 KB  
Article
New Strategy for the Degradation of High-Concentration Sodium Alginate with Recombinant Enzyme 102C300C-Vgb and the Beneficial Effects of Its Degradation Products on the Gut Health of Stichopus japonicus
by Ziqiang Gu, Feiyu Niu, Peng Yang, Wenling Gong, Hina Mukhtar, Siyu Li, Yanwen Zheng, Yiling Zhong, Hanyi Cui, Jichao Li, Haijin Mou and Dongyu Li
Mar. Drugs 2025, 23(9), 339; https://doi.org/10.3390/md23090339 - 25 Aug 2025
Viewed by 802
Abstract
High viscosity of alginate means a relatively low substrate concentration, which limits the efficiency of hydrolysis, resulting in one of the main challenges for the large-scale production of alginate oligosaccharides (AOS). In this study, a pilot-scale degradation product (PSDP) of the recombinant enzyme [...] Read more.
High viscosity of alginate means a relatively low substrate concentration, which limits the efficiency of hydrolysis, resulting in one of the main challenges for the large-scale production of alginate oligosaccharides (AOS). In this study, a pilot-scale degradation product (PSDP) of the recombinant enzyme 102C300C-Vgb was produced for the first time at a substrate concentration of up to 20% sodium alginate. The optimal conditions for SA digestion were: enzyme dosage of 25 U/g, enzymatic temperature of 45 °C, enzymatic pH of 7.0, and enzymatic time of 24 h. Under these conditions, the yield of enzymatic hydrolysis was consistently in the range of 69% to 70%. The average molecular weight (Mw) of PSDP was 1496.36 Da, mainly containing oligosaccharides with degrees of polymerization ranging from 2 to 4. The low-Mw PSDP was subsequently applied in the diet of sea cucumber Stichopus japonicus. The results showed that the body wall weight of S. japonicus increased significantly after 40 days of feeding with a 0.09% PSDP-supplemented diet. Furthermore, PSDP-supplemented diet significantly increased the thickness of the serosal and submucosal layers and the width folds of mucosa of the sea cucumber gut. The abundance of pathogenic bacteria was reduced effectively, and that of beneficial bacteria increased significantly after being fed with PSDP. The results demonstrated that PSDP can serve as a digestive health enhancer for sea cucumbers, promoting their healthy growth. Full article
(This article belongs to the Section Biomaterials of Marine Origin)
Show Figures

Graphical abstract

25 pages, 4050 KB  
Article
A Multi-Objective Optimization Study of Supply Air Parameters in a Supersonic Aircraft Cabin Environment Combined with Fast Calculation
by Guo Yu, Sajawal Nazar, Fei Li, Yuxin Wu, Zhu He and Xiaodong Cao
Atmosphere 2025, 16(9), 1005; https://doi.org/10.3390/atmos16091005 - 25 Aug 2025
Viewed by 561
Abstract
Supersonic cabins are characterized by high heat flux and high occupant density, which can adversely affect passenger comfort, health, and energy efficiency. This study proposed a multi-objective optimization framework for determining supply air parameters in a supersonic aircraft cabin, evaluating the performances of [...] Read more.
Supersonic cabins are characterized by high heat flux and high occupant density, which can adversely affect passenger comfort, health, and energy efficiency. This study proposed a multi-objective optimization framework for determining supply air parameters in a supersonic aircraft cabin, evaluating the performances of different optimization methods. The optimization focused on three design objectives: thermal comfort (PMV), air freshness (air age), and the temperature differential between the supply and exhaust air. Two fast calculation methods—Proper Orthogonal Decomposition (POD) and Artificial Neural Networks (ANN)—were compared alongside two optimization algorithms: Multi-Objective Genetic Algorithm (MOGA) and Pareto search. The results indicate that the POD method has a smaller relative root mean square error compared to the ANN method. The relative root mean square error of the ANN method in predicting PMV is 2.7 times higher than the POD method and 3.9 times higher in air age prediction. The Pareto search algorithm outperformed MOGA in computational efficiency, generating 3.3 times more Pareto-optimal solutions in less time. The entropy weight method was used to assign weight for both optimization algorithms, revealing that neither algorithm achieved universally optimal performance across all objectives. Therefore, selecting the best solution requires aligning optimization outcomes with specific design priorities. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
Show Figures

Figure 1

20 pages, 21489 KB  
Article
A GRU-Enhanced Kolmogorov–Arnold Network Model for Sea Surface Temperature Prediction Derived from Satellite Altimetry Product in South China Sea
by Rumiao Sun, Zhengkai Huang, Xuechen Liang, Siyu Zhu and Huilin Li
Remote Sens. 2025, 17(16), 2916; https://doi.org/10.3390/rs17162916 - 21 Aug 2025
Cited by 1 | Viewed by 854
Abstract
High-precision Sea Surface Temperature (SST) prediction is critical for understanding ocean–atmosphere interactions and climate anomaly monitoring. We propose GRU_EKAN, a novel hybrid model where Gated Recurrent Units (GRUs) capture temporal dependencies and the Enhanced Kolmogorov–Arnold Network (EKAN) models complex feature interactions between SST [...] Read more.
High-precision Sea Surface Temperature (SST) prediction is critical for understanding ocean–atmosphere interactions and climate anomaly monitoring. We propose GRU_EKAN, a novel hybrid model where Gated Recurrent Units (GRUs) capture temporal dependencies and the Enhanced Kolmogorov–Arnold Network (EKAN) models complex feature interactions between SST and multivariate ocean predictors. This study integrates GRU with EKAN, using B-spline-parameterized activation functions to model high-dimensional nonlinear relationships between multiple ocean variables (including sea water potential temperature at the sea floor, ocean mixed layer thickness defined by sigma theta, sea water salinity, current velocities, and sea surface height) and SST. L2 regularization addresses multicollinearity among predictors. Experiments were conducted at 25 South China Sea sites using 2011–2021 CMEMS data. The results show that GRU_EKAN achieves a superior mean R2 of 0.85, outperforming LSTM_EKAN, GRU, and LSTM by 5%, 25%, and 23%, respectively. Its average RMSE (0.90 °C), MAE (0.76 °C), and MSE (0.80 °C2) represent reductions of 31.3%, 27.0%, and 53.2% compared to GRU. The model also exhibits exceptional stability and minimal Weighted Quality Evaluation Index (WQE) fluctuation. During the 2019–2020 temperature anomaly events, GRU_EKAN predictions aligned closest with observations and captured abrupt trend shifts earliest. This model provides a robust tool for high-precision SST forecasting in the South China Sea, supporting marine heatwave warnings. Full article
Show Figures

Graphical abstract

22 pages, 3265 KB  
Article
A Novel Multi-Core Parallel Current Differential Sensing Approach for Tethered UAV Power Cable Break Detection
by Ziqiao Chen, Zifeng Luo, Ziyan Wang, Zhou Huang, Yongkang He, Zhiheng Wen, Yuanjun Ding and Zhengwang Xu
Sensors 2025, 25(16), 5112; https://doi.org/10.3390/s25165112 - 18 Aug 2025
Viewed by 499
Abstract
Tethered unmanned aerial vehicles (UAVs) operating in terrestrial environments face critical safety challenges from power cable breaks, yet existing solutions—including fiber optic sensing (cost > USD 20,000) and impedance analysis (35% payload increase)—suffer from high cost or heavy weight. This study proposes a [...] Read more.
Tethered unmanned aerial vehicles (UAVs) operating in terrestrial environments face critical safety challenges from power cable breaks, yet existing solutions—including fiber optic sensing (cost > USD 20,000) and impedance analysis (35% payload increase)—suffer from high cost or heavy weight. This study proposes a dual innovation: a real-time break detection method and a low-cost multi-core parallel sensing system design based on ACS712 Hall sensors, achieving high detection accuracy (100% with zero false positives in tests). Unlike conventional techniques, the approach leverages current differential (ΔI) monitoring across parallel cores, triggering alarms when ΔI exceeds Irate/2 (e.g., 0.3 A for 0.6 A rated current), corresponding to a voltage deviation ≥ 110 mV (normal baseline ≤ 3 mV). The core innovation lies in the integrated sensing system design: by optimizing the parallel deployment of ACS712 sensors and LMV324-based differential circuits, the solution reduces hardware cost to USD 3 (99.99% lower than fiber optic systems), payload by 18%, and power consumption by 23% compared to traditional methods. Post-fault cable temperatures remain ≤56 °C, ensuring safety margins. The 4-core architecture enhances mean time between failures (MTBF) by 83% over traditional systems, establishing a new paradigm for low-cost, high-reliability sensing systems in terrestrial tethered UAV cable health monitoring. Preliminary theoretical analysis suggests potential extensibility to underwater scenarios with further environmental hardening. Full article
(This article belongs to the Section Sensor Networks)
Show Figures

Figure 1

18 pages, 8210 KB  
Article
Multi-Model Analyses of Spatiotemporal Variations of Water Resources in Central Asia
by Yilin Zhao, Lu Tan, Xixi Liu, Ainura Aldiyarova, Dana Tungatar and Wenfeng Liu
Water 2025, 17(16), 2423; https://doi.org/10.3390/w17162423 - 16 Aug 2025
Viewed by 606
Abstract
Over the past 70 years, Central Asia has emerged as a globally recognized water security hotspot due to its unique geographic location and uneven distribution of water resources. In arid and semi-arid regions, understanding runoff dynamics under climate change is essential for ensuring [...] Read more.
Over the past 70 years, Central Asia has emerged as a globally recognized water security hotspot due to its unique geographic location and uneven distribution of water resources. In arid and semi-arid regions, understanding runoff dynamics under climate change is essential for ensuring regional water security. This study addresses the data-sparse Central Asian region by applying the ISIMIP3b multi-scenario analysis framework, selecting three representative global hydrological models. Using model intercomparison, trend analysis, and geographically weighted regression, we assess the spatiotemporal evolution of runoff from 1950 to 2080 and investigate the spatial heterogeneity of runoff responses to precipitation and temperature. The results show that under the historical scenario, all models consistently identify similar spatial pattern of runoff, with higher values in southeastern mountainous regions and lower values in western and central regions. However, substantial differences exist in runoff magnitude, with regional annual means of 10, 26, and 68 mm across the three models, respectively. The spatial disparity of runoff distribution is projected to increase under higher SSP scenarios. During the historical period, most of Central Asia experienced a slight decreasing trend in runoff, but the overall trends were −0.022, 0.1, and 0.065 mm/year, respectively. In contrast, future projections indicate a transition to increasing trends, particularly in eastern regions, where trend magnitudes and statistical significance are notably greater than in the west. Meanwhile, the spatial extent of significant trends expands under high-emission scenarios. Precipitation exerts a positive influence on runoff in over 80% of the region, while temperature impacts exhibit strong spatial variability. In the WaterGAP2-2e and MIROC-INTEG-LAND models, temperature has a positive effect on runoff in glaciated plateau regions, likely due to enhanced snow and glacier melt under warming conditions. This study presents a multi-model framework for characterizing climate–runoff interactions in data-scarce and environmentally sensitive regions, offering insights for water resource management in Central Asia. Full article
(This article belongs to the Section Water and Climate Change)
Show Figures

Figure 1

Back to TopTop