Search Results (18)

Global climate change poses a significant management challenge for vital transboundary resources like the Pacific saury (Cololabis saira). To address this, we developed an innovative framework that uses high-resolution Automatic Identification System (AIS) data and deep learning to define species distribution, which then informs a robust Ensemble Species Distribution Model (ESDM). The model (TSS > 0.89, AUC > 0.97) identifies sea surface temperature (SST) and chlorophyll-a (CHL) as key habitat drivers. Projections under future climate scenarios reveal two critical threats: (1) a continuous northeastward migration of the habitat’s centroid, exceeding 400 km by 2100 under a high-emission SSP5-8.5 scenario, and (2) a drastic contraction of highly suitable habitat (suitability > 0.8), shrinking by up to 94% under the high-emission SSP3-7.0 scenario. By directly linking key oceanographic features to these climate-driven risks, this study delivers an essential scientific decision-support tool for management bodies like the North Pacific Fisheries Commission (NPFC) to develop climate-adaptive strategies. Full article

The accurate assessment and management of Blue Mackerel (Scomber australasicus) and Chub Mackerel (Scomber japonicus) resources in the high seas of the Northwest Pacific are constrained by the persistent issue of data misreporting in catch records, which arises from their high morphological similarity. This study integrates fishery logbooks and field sampling data from Chinese purse seine fleets (2014–2023), along with key oceanographic factors—six of which were finally selected after correlation analysis. We introduce, for the first time, a Zero-One Inflated Beta Model (ZOIBM) to analyze the spatiotemporal distribution of the relative abundance of these two mackerel species. Furthermore, a Generalized Additive Model (GAM) was employed to reveal the environmental mechanisms driving their niche differentiation. The results show that the ZOIBM demonstrates excellent performance (R² = 0.63, RMSE = 0.305), effectively quantifying the proportional composition of the two species in mixed catches. Spatially, high-abundance areas of Blue Mackerel were concentrated within 35–44° N, 145–160° E, with its proportion decreasing at higher latitudes. In contrast, Chub Mackerel exhibited an opposite latitudinal pattern, with its high-abundance areas covering a broader latitudinal range (35–47.5° N). The analysis of environmental drivers indicated that SST was the most critical factor for differentiation, while Chla and VO further amplified the divergence in resource utilization strategies between the species. From 2014 to 2023, the distribution centroids of both mackerel species showed significant northward and eastward shifts, and their spatial overlap has been continuously increasing. This research provides a methodological reference for the fine-scale assessment of co-occurring fish resources and offers a scientific basis for the sustainable management of the North Pacific mackerel fishery. Full article

As economically important sympatric species in the Northwest Pacific, the Japanese sardine (Sardinops melanostictus) and Chub mackerel (Scomber japonicus) exhibit significant biological interactions. Understanding the impact of interspecies competition on their habitat dynamics can provide crucial insights for the sustainable development and management of these interconnected species resources. This study utilizes fisheries data of S. melanostictus and S. japonicus from the Northwest Pacific, collected from June to November between 2017 and 2020. We integrated various environmental parameters, including temperature at different depths (0, 50, 100, 150, and 200 m), eddy kinetic energy (EKE), sea surface height (SSH), chlorophyll-a concentration (Chl-a), and the oceanic Niño index (ONI), to construct interspecific competition species distribution model (icSDM) for both species. We validated these models by overlaying the predicted habitats with fisheries data from 2021 and performing cross-validation to assess the models’ reliability. Furthermore, we conducted correlation analyses of the habitats of these two species to evaluate the impact of interspecies relationships on their habitat dynamics. The results indicate that, compared to single-species habitat models, the interspecific competition species distribution model (icSDM) for these two species exhibit a significantly higher explanatory power, with R² values increasing by up to 0.29; interspecific competition significantly influences the habitat dynamics of S. melanostictus and S. japonicus, strengthening the correlation between their habitat changes. This relationship exhibits a positive correlation at specific stages, with the highest correlations observed in June, July, and October, at 0.81, 0.80, and 0.88, respectively; interspecific competition also demonstrates stage-specific differences in its impact on the habitat dynamics of S. melanostictus and S. japonicus, with the most pronounced differences occurring in August and November. Compared to S. melanostictus, interspecific competition is more beneficial for the expansion of the optimal habitat (HIS ≥ 0.6) for S. japonicus and, to some extent, inhibits the habitat expansion of S. melanostictus. The variation in migratory routes and predatory interactions (with larger individuals of S. japonicus preying on smaller individuals of S. melanostictus) likely constitutes the primary factors contributing to these observed differences. Full article

This study, based on the vessel position data of pump-suction beam trawlers and the integrated species distribution model (ISDM), deeply analyzes the spatio-temporal distribution characteristics of the habitat of Antarctic krill and the contributions of key environmental factors. The Convolutional Neural Network–attention model (CNN–attention model) was used to identify the fishing status of the vessel position data of Norwegian pump-suction beam trawlers for Antarctic krill during the fishing seasons from 2021 to 2023. Variables of marine environment, including sea surface temperature (SST), sea surface height (SSH), chlorophyll concentration (CHL), sea ice concentration (SIC), sea surface salinity (SSS), and spatial factor Geographical Offshore Linear Distance (GLD) were combined and input into the ISDM for simulating and predicting the spatial distribution of the habitat. The model results show that the Area Under the Curve (AUC) and True Skill Statistic (TSS) indices for all months exceed 0.9, with an average AUC of 0.997 and a TSS of 0.973, indicating extremely high accuracy of the model in habitat prediction. Further analysis of environmental factors reveals that Geographical Offshore Linear Distance (GLD) and chlorophyll concentration (CHL) are the main factors affecting habitat suitability, contributing 34.9% and 25.2%, respectively, and their combined contribution exceeds 60%. In addition, factors such as sea surface height (SSH), sea surface temperature (SST), sea ice concentration (SIC), and sea surface salinity (SSS) have impacts on the habitat distribution to varying degrees, and each factor exhibits different suitability response characteristics in different seasons and sub-regions. There is no significant correlation between the habitat area of Antarctic krill and catch (p > 0.05), while there is a significant positive correlation between the fishing duration and the catch (p < 0.001), indicating that a longer fishing duration can effectively increase the Antarctic krill catch. Full article

To evaluate and compare the effectiveness of prediction models for Argentine squid Illex argentinus trawling grounds in the Southwest Atlantic high seas based on vessel position and fishing log data, this study used AIS datasets and fishing log datasets from fishing seasons spanning 2019–2024 (December to June each year). Using a spatial resolution of 0.1° × 0.1° and a monthly temporal resolution, we constructed two datasets—one based on vessel positions and the other on fishing logs. Fishing ground levels were defined according to the density of fishing locations, and combined with oceanographic data (sea surface temperature, 50 m water temperature, sea surface salinity, sea surface height, and mixed layer depth). A CNN-Attention deep learning model was applied to each dataset to develop Illex argentinus trawling ground prediction models. Model accuracy was then compared and potential causes for differences were analyzed. Results showed that the vessel position-based model had a higher accuracy (Accuracy = 0.813) and lower loss rate (Loss = 0.407) than the fishing log-based model (Accuracy = 0.727, Loss = 0.513). The vessel-based model achieved a prediction accuracy of 0.763 on the 2024 test set, while the fishing log-based model reached an accuracy of 0.712, slightly lower than the former, indicating the high accuracy and unique advantages of the vessel position-based model in predicting fishing grounds. Using CPUE from fishing logs as a reference, we found that the vessel position-based model performed well from January to April, whereas the CPUE-based model consistently maintained good accuracy across all months. The 2024 fishing season predictions indicated the formation of primary fishing grounds as early as January 2023, initially near the 46° S line of the Argentine Exclusive Economic Zone, with grounds shifting southeastward from March onward and reaching around 42° S by May and June. This study confirms the reliability of vessel position data in identifying fishing ground information and levels, with higher accuracy in some months compared to the fishing log-based model, thereby reducing the data lag associated with fishing logs, which are typically available a year later. Additionally, national-level fishing log data are often confidential, limiting the ability to fully consider fishing activities across the entire fishing ground region, a limitation effectively addressed by AIS vessel position data. While vessel data reflects daily catch volumes across vessels without distinguishing CPUE by species, log data provide a detailed daily CPUE breakdown by species (e.g., Illex argentinus). This distinction resulted in lower accuracy for vessel-based predictions in December 2023 and May–June 2024, suggesting the need to incorporate fishing log data for more precise assessments of fishing ground levels or resource abundance during those months. Given the near-real-time nature of vessel position data, fishing ground dynamics can be monitored in near real time. The successful development of vessel position-based prediction models aids enterprises in reducing fuel and time costs associated with indiscriminate squid searches, enhancing trawling efficiency. Additionally, such models support quota management in global fisheries by optimizing resource use, reducing fishing time, and consequently lowering carbon emissions and environmental impact, while promoting marine environmental protection in the Southwest Atlantic high seas. Full article

Hanji-derived porous carbon has been developed and utilized as a cathode material for Li-S batteries, demonstrating exceptional electrochemical performance and stability. The unique porous structure and high surface area of Hanji-based carbon enhanced S utilization and significantly improved the overall efficiency of the battery. The material exhibited excellent electrical conductivity and structural stability, effectively addressing the major challenges of Li-S batteries, such as the polysulfide shuttle effect and active material loss. In addition, flake carbon-coated separators (FCCSs) were integrated into Li-S cells to further enhance their performance, achieving a high initial specific capacity of approximately 1200 mAh/g and maintaining a capacity of 620 mAh/g after 100 cycles. In contrast, cells with conventional polypropylene separators exhibited lower initial capacities (946 mAh/g), which decreased to 366 mAh/g after 100 cycles. FCCSs also demonstrated superior capacity retention and stability under varying charge–discharge rates, maintaining a capacity of 200 mAh/g at 3 C and recovering to 730 mAh/g when the rate was 0.1 C. This study provides valuable insights into the development of sustainable and efficient Li-S battery systems, with Hanji-based carbon and FCCSs emerging as promising components for commercial applications. Full article

To analyze the impact of the marine environment on the relative abundance of Illex argentinus (high and low categories) in the southwest Atlantic, this study collected logbook data from Chinese pelagic trawlers from December 2014 to June 2024, including vessel position data and oceanographic variables such as sea surface temperature, 50 m and 100 m water temperature, sea surface salinity, sea surface height, chlorophyll-a concentration, and mixed layer depth. Vessel positions were used to enhance the logbook data quality, allowing an analysis of the annual trends in the resource center of this squid at a spatial resolution of 0.1° × 0.1° and a temporal resolution of ten days. The findings showed that the resource center is primarily located around 42° S in the north and between 45° S and 47° S in the south, with a trend of northward movement during the study period. Additionally, we constructed two ensemble learning models based on decision trees—AdaBoost and PSO-RF—aiming to identify the most critical environmental factors that affect its resource abundance; we found that the optimal model was the PSO-RF model with max_depth of 5 and n_estimators of 46. The importance analysis revealed that sea surface temperature, mixed layer depth, sea surface height, sea surface salinity, and 50 m water temperature are critical environmental factors affecting this species’ resources. Given that deep learning models generally have shorter running times and higher accuracy than other models, we developed a CNN-Attention model based on the five most important input factors. This model achieved an accuracy of 73.6% in forecasting this squid for 2024, predicting that the population would first appear near the Argentine exclusive economic zone around mid-December 2023 and gradually move east and south thereafter. The predictions of the model, validated through log data, maintained over 70% accuracy during most periods at a time scale of ten days. The successful construction of the resource abundance forecasting model and its accuracy improvements can help enterprises save fuel and time costs associated with blind searches for target species. Moreover, this research contributes to improving resource utilization efficiency and reducing fishing duration, thereby aiding in lowering carbon emissions from pelagic trawling activities, offering valuable insights for the sustainable development of this species’ resources. Full article

Despite the significant advancements facilitated by previous research in introducing a plethora of retinal biomarkers, there is a lack of research addressing the clinical need for quantifying different biomarkers and prioritizing their importance for guiding clinical decision making in the context of retinal diseases. To address this issue, our study introduces a novel framework for quantifying biomarkers derived from optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images in retinal diseases. We extract 452 feature parameters from five feature types, including local binary patterns (LBP) features of OCT and OCTA, capillary and large vessel features, and the foveal avascular zone (FAZ) feature. Leveraging this extensive feature set, we construct a classification model using a statistically relevant p value for feature selection to predict retinal diseases. We obtain a high accuracy of 0.912 and F1-score of 0.906 in the task of disease classification using this framework. We find that OCT and OCTA’s LBP features provide a significant contribution of 77.12% to the significance of biomarkers in predicting retinal diseases, suggesting their potential as latent indicators for clinical diagnosis. This study employs a quantitative analysis framework to identify potential biomarkers for retinal diseases in OCT and OCTA images. Our findings suggest that LBP parameters, skewness and kurtosis values of capillary, the maximum, mean, median, and standard deviation of large vessel, as well as the eccentricity, compactness, flatness, and anisotropy index of FAZ, may serve as significant indicators of retinal conditions. Full article

A carbonized interlayer effectively helps to improve the electrochemical performance of lithium–sulfur (Li–S) batteries. In this study, a simple and inexpensive carbon intermediate layer was fabricated using a traditional Korean paper called “hanji”. This carbon interlayer has a fibrous porous structure, with a specific surface area of 91.82 m² g⁻¹ and a BJH adsorption average pore diameter of 26.63 nm. The prepared carbon interlayer was utilized as an intermediary layer in Li–S batteries to decrease the charge-transfer resistance and capture dissolved lithium polysulfides. The porous fiber-shaped carbon interlayer suppressed the migration of polysulfides produced during the electrochemical process. The carbon interlayer facilitates the adsorption of soluble lithium polysulfides, allowing for their re-utilization in subsequent cycles. Additionally, the carbon interlayer significantly reduces the polarization of the cell. This simple strategy results in a significant improvement in cycle performance. Consequently, the discharge capacity at 0.5 C after 150 cycles was confirmed to have improved by more than twofold, reaching 230 mAh g⁻¹ for cells without the interlayer and 583 mAh g⁻¹ for cells with the interlayer. This study demonstrates a simple method for improving the capacity of Li–S batteries by integrating a functional carbon interlayer. Full article

Fine and ultrafine particulate matter are consequences of air pollution in industrialized nations. The use of natural materials for filters produces fewer side effects for humans and the environment, and due to their structural characteristics, they have the potential to effectively filter out fine particles. In this study, we developed an indoor, fine-dust removal filter using Hanji, a traditional Korean paper made from natural materials derived from mulberry trees. We impregnated activated carbon (AC) into the Hanji filter and conducted air permeability and efficiency experiments to determine the improvement in indoor air quality. The Hanji filter showed a removal efficiency of 80.4% within the first minute and 99.1% efficiency by the 38th minute, maintaining an efficiency of >99% thereafter. The dust removal efficiency of the AC-embedded Hanji filter proved superior. The dust adhesion was ~20 g/m². An AC-embedded Hanji filter has the potential to remove not only fine dust but also volatile substances. The use of natural filters is both effective and sustainable. Full article

In this study, we address the challenge of surface roughness in CoCrMo alloys, typically used in artificial knee joints, which can initiate a cascade of biological responses causing inflammation, osteolysis, joint instability, and increased susceptibility to infection. We propose the application of a chemical mechanical polishing (CMP) technique, using an ecologically responsible slurry composed of 4 wt% SiO₂, 0.3 wt% H₂O₂, 1.0 wt% glycine, and 0.05 wt% benzotriazole. Our innovative approach demonstrated significant improvements, achieving a material removal rate of 30.9 nm/min and reducing the arithmetic mean roughness from 20.76 nm to 0.25 nm, thereby enhancing the nanoscale surface quality of the artificial knee joint alloy. The smoother surface is attributed to a decrease in corrosion potential to 0.18 V and a reduction in corrosion current density from 9.55 µA/cm² to 4.49 µA/cm² with the addition of BTA, evidenced by electrochemical tests. Furthermore, the preservation of the phase structure of the CoCrMo alloy, as confirmed by XRD analysis and elemental mapping, ensures the structural integrity of the treated surfaces. These outcomes and our simulation results demonstrate the effectiveness of our CMP method in engineering surface treatments for artificial knee joints to optimize friction behavior and potentially extend their lifespans. Full article

Noise pollution is often overlooked and invisible, but it significantly impacts the quality of human life. One of the most straightforward solutions to mitigate noise pollution is by using sound-absorbing materials. Recently, research trends to develop sound absorbing green materials, typically derived from agricultural by-products, have witnessed an uptick. This paper summarizes the sound-absorbing properties of various green materials found in the literature, including coconut fiber, kenaf fiber, rice bran, rice husk, rice straw, Hanji (a traditional Korean paper), tea-leaf fiber, mandarin peel, pineapple-leaf fiber, corn husk, peanut shell, sugar palm trunk, Yucca gloriosa fiber, fruit stones, wood barks, flax fiber, and nettle fiber. Natural fibers can be made by compressing the raw material or manufacturing them into fibrous materials or composites. The key variables that determine sound absorption performance are the thickness and density of the green material, as well as the presence of an air back cavity. Generally, thicker materials exhibit better sound absorption performance in the low- and mid-frequency range. Moreover, higher density is associated with better sound absorption performance at the same thickness. Additionally, increasing the distance between the sound-absorbing material and the air back cavity enhances sound absorption performance at low frequencies. Thus, these physical variables, rather than the specific materials used, primarily influence sound absorption capabilities. Therefore, various green materials, such as fibers, granules, and porous materials, can be effective sound absorbers if their thickness, density, and air back cavity are properly controlled. Full article

The fatigue crack initiation and growth characteristics in 7050-T7451 aluminum alloy butt joints subjected to different stress ratios and owing to friction stir welding (FSW) were investigated using fatigue tests for stress ratios of 0.1, 0.3, and 0.5. The difference between the fatigue crack initiation in the base material (BM) and FSW joints, related to coarse secondary phases, was explored using scanning electron microscopy (SEM). Accordingly, Al₂₃CuFe₄, Al₇Cu₂Fe, and Al₂Mg₃Zn₃ were the preferred joint crack initiation locations, whereas Mg₂Si was the major fracture initiation point of the parent material, and cracks tended to propagate along dense, coarse secondary phases, becoming more pronounced for larger cracks. In addition, as the stress ratio increased, non-Mg₂Si phase fracture initiation points appeared in the BM. Meanwhile, the quantity of non-Mg₂Si phases in the joints continued to increase, and the crack initiation sites became increasingly concentrated in the TMAZ-HAZ region. Full article

Mineral scale refers to the hard crystalline inorganic solid deposit from the water phase. Although scale formation is very common in the natural environment, deposited scale particles can seriously threaten the integrity and safety of various industries, particularly oilfield productions. Scale deposition is one of the three most serious water-related production chemistry threats in the petroleum industry. The most commonly adopted engineering approach to control the scale threat is chemical inhibition by applying scale inhibitor chemicals. Aminophosphonates and polymeric inhibitors are the two major groups of scale inhibitors. To address the drawbacks of conventional inhibitors, scale inhibitor colloidal materials have been prepared as an alternative delivery vehicle of inhibitors for scale control. Quite a few studies have reported on the laboratory synthesis and testing of scale inhibitor colloidal materials composed mainly of pre-precipitated metal-aminophosphonate solids. However, limited research has been conducted on the preparation of polymeric inhibitor-based colloidal materials. This study reports the synthesis approach and laboratory testing of novel polystyrene sulfonate (PSS) based inhibitor colloidal material. PSS was selected in this study due to its high thermal stability and calcium tolerance with no phosphorus in its molecule. Both precipitation and surfactant surface modification methods were employed to prepare a barium-PSS colloidal inhibitor (BaPCI) material with an average diameter of several hundred nanometers. Experimental results indicate that the prepared BaPCI material has a decent migration capacity in the formation medium, and this material is superior to the conventional PSS inhibitor in terms of inhibitor return performance. The prepared novel BaPCI material has a great potential to be adopted for field scale control where environmentally friendly, thermal stable, and/or calcium tolerating requirements should be satisfied. This study further expands and promotes our capacity to fabricate and utilize functional colloidal materials for mineral scale control. Full article

People have traditionally relied on fish to supply their major food and healthcare needs all across the world. However, there has been little focus on the traditional dietary, cultural identity, and integrity of traditional food systems in many rural Himalayan populations. The current study looks into the use of fish in traditional food and foraging practices, as well as its role in local traditional medicine and cultural practices. Semi-structured interviews and group discussions were used to collect data in the years 2020–2021. The data were analyzed using various statistical indices such as the fidelity level (FL), rank order priority (ROP), and relative popularity level (RPL). The data were then classified through a heat map, and ordination techniques were used to refine them even further. The current study identified 20 fish species of five families, with Cyprinidae (70%) as the dominant family. Three unique traditional fish preservation techniques were documented, namely: (1) smoking, (2) sun drying, and (3) pickling. The smoked fish are locally called fari, sun-dried fish are known as hugaad, and pickled fish are gaad anchaar. As a result of rapid modernization, fish has become a symbol of social class in the valley. In total, 17 diseases were identified, with joint pain being treated by the greatest number of species (N = 10). Flesh (43%) was the most commonly used part to treat various diseases. The mode of application of various fishbody parts for treating a variety of diseases was most commonly peroral (65%), followed by topical (35%). Schizopyge niger demonstrated the highest level of fidelity (82%) for joint pain. Schizopygeniger, Schizothorax curvifrons, Catla catla, Schizothorax esocinus, Schizothorax labiatus, and Schizothorax plagiostomus were the most popular (relative popularity level = 1.0). The rank order priority of six species was above 55 (Schizothorax plagiostomus (59.18), Labeo dyocheilus (61.99) Schizothorax labiatus (64.28), Schizothorax esocinus (68.36), Schizothorax curvifrons (73.19), and Schizopyge niger (82)). Two principal groups of six ethno-zoological categories (medicine, food, black magic, poultry, agricultural, and recreational) were identified using cluster analysis. Fish are also important as a source of livelihood and are closely associated with a local ethnic group known as Hanji/Haanz, who have extensive knowledge of ecology of the local fish species. Our research will contribute to filling a knowledge gap in the Kashmir Himalayan region, with policy implications for the protection and preservation of high-quality traditional knowledge for future generations. The findings of this documentation study can be used as an ethnopharmacological foundation for selecting fish in future pharmaceutical research. Full article