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The aim of this study was to assess the effect of long-term catch crop application on the structural properties of humin, which is considered the most recalcitrant fraction of soil organic matter. Soil samples from a 30-year field experiment on triticale cultivated with and without catch crops were analysed to determine the total organic carbon content and fractional composition of humic substances. Meanwhile, humin isolated from bulk soil was analysed to determine its elemental composition and spectroscopic properties measured with UV-Vis, fluorescence, and 13C-CPMAS-NMR. It was found that catch crop farming enhanced the formation of highly reactive humus substances, like low-molecular-weight fractions and humic acids, while decreasing the humin fraction. The higher H/C and O/C atomic ratios of humin and the UV-Vis, fluorescence, and 13C-CPMAS-NMR results confirmed a higher share of oxygen-containing functional groups in humin isolated from the soil with catch crop rotation, also corroborating its greater aliphatic nature. Under the conditions of our field experiment, the results indicated that organic residues from catch crops quickly undergo the decay process and are transformed mainly into highly reactive humus substances, which can potentially improve soil health, while mineral fertilisation alone without catch crops favours the stabilisation and sequestration of carbon. [ABSTRACT FROM AUTHOR]

Automating contact-rich manipulation of viscoelastic objects with rigid robots faces challenges including dynamic parameter mismatches, unstable contact oscillations, and spatiotemporal force-deformation coupling. In our prior work, a Compliance-Aware Tactile Control and Hybrid Deformation Regulation (CATCH-FORM-3D) strategy fulfills robust and effective manipulations of 3D viscoelastic objects, which combines a contact force-driven admittance outer loop and a PDE-stabilized inner loop, achieving sub-millimeter surface deformation accuracy and ±5% force tracking. However, this strategy requires fine-tuning of object-specific parameters and task-specific calibrations, to bridge this gap, a CATCH-FORM-ACTer is proposed, by enhancing CATCH-FORM-3D with a framework of Action Chunking with Transformer (ACT). An intuitive teleoperation system performs Learning from Demonstration (LfD) to build up a long-horizon sensing, decision-making and execution sequences. Unlike conventional ACT methods focused solely on trajectory planning, our approach dynamically adjusts stiffness, damping, and diffusion parameters in real time during multi-phase manipulations, effectively imitating human-like force-deformation modulation. Experiments on single arm/bimanual robots in three tasks show better force fields patterns and thus $10\%-20\%$ higher success rates versus conventional methods, enabling precise, safe interactions for industrial, medical or household scenarios.

The use of marine protected areas (MPAs) is expanding around the world. MPAs can have a wide variety of objectives (e.g., science, conservation, food security, cultural value), and scientific guidance on how to design MPAs to achieve objectives is often based on simulation modeling. Many different models may all provide an answer to questions such as the predicted change in population biomass and fisheries catches resulting from th implementation of an MPA. When multiple levels of model complexity are all in theory capable of answering the same question, and the models cannot be confronted with data directly, the decision of what level of model complexity to use can be ad hoc. In this, paper I compare the predicted effects of MPAs on catch and biomass produced by a spatially explicit age-structured multi-species and multi-fleet (High-definition) model to the predictions generated by a two-patch surplus production (Low-definition) model, fitted to emulate the High-definition model. I found that in many cases, the predictions made by the two models were markedly different, with the Low-definition model frequently predicting substantially higher biomass benefits from MPAs than the High-definition model, and in some cases incorrectly estimating the direction (positive or negative) of the MPA effects. However, I also found that the Low-definition model has strategic value for broad classification and ranking exercises. My results show that care should be taken in selecting and interpreting the results of MPA simulation models and that research is needed to understand what models are best suited to what policy recommendations when multiple viable options exist.

Spillover is a term commonly applied to the dispersal of fish and/or larvae from inside a closed area to areas open to fishing. The presence of spillover is often quantified by measuring gradients in attributes such as abundance or catch rates near the boundaries of closed areas or by measuring higher abundance inside closed areas compared to outside. It is commonly assumed that such gradients or ratios indicate that the closed area has benefitted the fishery and the total abundance of fish. We explore this assumption using a spatially explicit model of closed areas with different intensities of fishing and fish movement, and we find that such gradients will be expected any time there is higher abundance inside the closed area. However, such gradients do not necessarily indicate a benefit to the fishery either in terms of total catch or catch rate, and unless pre-closure fishing was intense, total abundance is not expected to rise significantly. We examine case studies that argue that spillover exists and leads to fishery benefits. We then evaluate the evidence for net benefits in these case studies and find those with evidence of net benefits all come from places where fishing pressure was intense. While most analysis come from quite small coastal closed areas, two studies of very large open-ocean closed areas are discussed, and we find that both suggest little overall impact on the tuna populations that support the main commercial fisheries affected by the closures in question.

Fish welfare is a crucial issue that needs to be addressed in fisheries. Thus, the scope of the fair-fish database - an online open-access platform - was expanded from aquaculture (farm branch) to fisheries (catch branch). It provides farm and catch welfare profiles (WelfareChecks) of aquatic species based on literature reviews. In the catch branch, each WelfareCheck encompasses a species in relation to a specific fishing method used to catch it, assessing 10 criteria covering welfare hazards throughout the steps of the catching process: prospection, setting, catching, emersion, release from gear, bycatch avoidance, sorting, discarding, storing, and stunning/slaughter. In each criterion, we assess the likelihood and potential of experiencing good welfare under minimal and high-standard fisheries conditions, respectively, besides the certainty level about these. A final WelfareScore is provided for each profile, which serves as a benchmark for assessing and improving fish welfare. Since its publication in 2023, we have published five WelfareChecks. The goal is to increase the number of profiles for several fished species and catching methods over time. In conclusion, the catch branch of the fair-fish database serves as an open-access source providing an overview of the welfare of a fished species given a certain catching method. It is a reliable tool that raises public awareness of fish welfare, provides scientists with insight into knowledge gaps, and offers practitioners with suggestions about how to avoid welfare risks.

Capture in nearshore fisheries is the leading threat to coastal elasmobranchs, of which more than 75 % are threatened with extinction globally. Limited knowledge of these highly dynamic fisheries impedes the design and implementation of stakeholder-inclusive policies for conservation. To address this, we developed an interdisciplinary approach, combining landing data with fishing geo-locations, Very High resolution (VHR) satellite imagery and fisher interviews to model elasmobranch catch dynamics and map areas of high catch potential. We compared how elasmobranch catch rates varied by species ecology, habitat, and fisheries characteristics in Visakhapatnam and Malvan, two regions on the east and west coasts of India, respectively. We sampled 2209 fishing trips across three oceanographic seasons from landing sites at both locations in 2022-23. We recorded 5578 elasmobranchs from >20 species of which at least 13 were categorised as ‘Threatened’. Gillnets, hook and line and trawl nets were the most common gears, but their use and catch rates varied considerably. Elasmobranchs had a higher catch risk on the eastern site (where they may be specifically targeted) and were generally larger. Catch rates were higher in shallow regions on the west coast and in the summer at both sites. Importantly, we demonstrate that drivers of elasmobranch catch were site and fishery specific, underscoring the need for more local-scale research for planning conservation actions. Our framework provides a robust method to study the highly dynamic and diverse nature of nearshore fisheries, which can inform conservation actions and, at the same, time, enable a bottom-up approach to conserving elasmobranchs.

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Accurate disease detection in fruit leaves is crucial for preserving crop health and enhancing agricultural productivity. Fruits hold significant economic value and nutritional importance, but their vulnerability to diseases severely impacts both yield and quality. This research proposes a novel Multi-level Attention DenseNet-based Deep Convolutional Neural Network (MADDCNN) model to improve fruit disease detection. The model addresses challenges such as varying lighting conditions, complex backgrounds, and overlapping symptoms, which hinder detection accuracy in existing systems. By incorporating the Catch Fish Optimization (CFO) technique, MADDCNN enhances multi-fruit classification accuracy and optimizes parameter tuning for better performance. To further enhance the detection process, the model employs the Hybrid Runge Kutta DeepLabV3+ (HRKD) method for segmenting disease-affected areas, enabling more precise identification and isolation of infected regions. This segmentation step significantly boosts classification accuracy and reliability in agricultural environments. The novelty of this research lies in the integration of MADDCNN with CFO and HRKD, enabling severity classification, adaptive optimization, and precise segmentation under varying agricultural conditions. The MADDCNN model outperforms other methods in key metrics, achieving 98.34% accuracy, 97.85% precision, 97.91% recall, and 98.12% F1 score. Furthermore, it demonstrates computational efficiency, processing each image in just 1.5 s. Overall, the MADDCNN approach offers a sustainable, efficient solution for detecting fruit diseases, addressing the challenges of varying conditions and complex symptoms, and contributing to enhanced agricultural practices.

CATCH-1 is the first pathfinder within the Chasing All Transients Constellation Hunters (CATCH) space mission. Its payloads include the Silicon Drift Detector (SDD) system and light-weight X-ray optics. The main goal of CATCH-1 is to validate key technologies of the timing satellites in the constellation, such as the electronics of the SDD detector system, Micro-Pore Optics (MPO), and the deployable mast. The SDD detector system is required to operate with high energy resolution and dead time to detect various transients. This paper presents the design and performance of the SDD electronics system of CATCH-1. The electronics system comprises front-end electronics (FEE) and a data acquisition system (DAQ). Test results demonstrate that the energy resolution of all four detector channels are better than 150 eV at 5.9 keV, with the energy band ranging from 0.5 to 8 keV, and the dead time is better than 10μs, meeting the design specifications.

In 1974, at the age of thirty-two, Les AuCoin became the first Democrat to win a US House seat in Oregon's First District. He was one of the post-Watergate reformers who shook up an insular, autocratic Congress and led fights for affordable housing, “trickle-up” economics, wilderness protection, abortion rights, and nuclear arms control. In the 1980s, the Oregonian called him “the most powerful congressman in Oregon.” In this compelling collection of life stories, AuCoin traces his unlikely rise from a fatherless childhood in Central Oregon to the top ranks of national power. Then came a painful defeat in one of the most controversial races in US Senate history, against incumbent Bob Packwood. A fly fisher, AuCoin uses “catch and release” as a metaphor for succeeding and letting go of loss with dignity and equanimity. His memories are in turn funny, suspenseful, and revealing. AuCoin takes us to the Kremlin, pre-industrial China, the Arctic National Wildlife Refuge, and into the tortuous politics of the Northwest spotted owl crisis. He interacted with world figures like Mikhail Gorbachev, Ronald Reagan, House Speakers Tip O'Neill and Jim Wright, and Oregon legends Tom McCall and Mark Hatfield. Closer to home, AuCoin allied himself with activists like Sidney Lasseigne of the Newport Fishermen's Wives. Catch and Release offers readers a revealing glimpse behind the scenes of congressional life, as lived by the 535 souls who inhabit the US House and Senate—including the author, who assesses his own strengths and foibles with humility and candor.

Purpose. To investigate the long-term dynamics of commercial catches of Leuciscidae in the Dnipro Reservoir and to determine sustainable catch limits as an aquatic bioresource. Methodology. The study was conducted in 2024–2025 at three commercial fishing areas of the Dnipro Reservoir (central part, lower part, Samara Bay), which differed in hydrology, ecology, and the level of human impact. Classical ichthyological methods were used. The study objects were members of the family Leuciscidae: common bream, chub, asp, roach, silver bream, blue bream, sabrefish, rudd, white-eye bream, vimba, and bleak. The analysis included species composition of commercial catches, age and size structure of fish populations, condition factors, catch dynamics over long-term series, and official commercial statistics (2014–2024). Findings. It was found that Leuciscidae species account for 54.1% of total catches in the Dnipro Reservoir cascade, with common bream, roach, and bleak being the main commercial species. Populations of these species are characterized by stable abundance and biomass. Based on biological and fishing parameters, fish stocks and catch limits for the Dnipro Reservoir in 2026 were calculated. Originality. For the first time in the Dnipro Reservoir, a comprehensive analysis of Leuciscidae commercial stocks was conducted, incorporating long-term data series, spatial-temporal population characteristics, and mortality coefficients. Differentiated fishing regimes for specific reservoir areas were proposed. Practical Value. The results allow optimization of fishery management, ensuring sustainable use of resources and biodiversity conservation, reducing the risk of stock depletion, and supporting the stability of commercial fishing. The proposed recommendations were used as the basis for establishing fish catch limits in the Dnipro Reservoir in 2026.

Fishery production surveys constitute the basis for assessing and managing fishery resources. A well-defined and reasonable sample size is essential for the accuracy and precision of survey outcomes. In this study, we aggregated production surveys from major economic fishing ports in the northern South China Sea from 2008 to 2018, totaling 36 499 forms. It was assumed that these data accurately reflected the catch per unit effort (CPUE) of Decapterus maruadsi employing various fishing gear. We focused on optimizing the investigations by analyzing the CPUE of D. maruadsi across five distinct fishing operations: otter trawl, twin trawl, light purse seine, gillnet, and light falling net. We organized the survey data by fishing type and stratified them according to engine power and survey time. We used a proportional allocation for the sample sizes and stratified random sampling without replacement for the simulations. We utilized computer simulations to resample the CPUE of D. maruadsi derived from five different fishing operation types, employing the relative estimation error (REE) and relative bias (RB) as evaluation metrics. We aimed to analyze the relationship between the CPUE of D. maruadsi and sample size in the northern South China Sea. The port catch sampling survey yielded production information for different fishing operation types, with each survey form reflecting the CPUE data for a single voyage. Because of the variability of the CPUE for D. maruadsi among different fishing operation types and across seasons within the same operation type, this study categorized the survey forms by operation type and season. We calculated the CPUE for each operation type in the different seasons and used these values as the true values for comparison. We consolidated the survey data from various fishing gears across different power ranges and computed the CPUE for these forms. Furthermore, we employed CPUE as a metric to compare the fishing capacity and efficiency of the different fishing gear targeting the species of interest. We observed seasonal variations in the CPUE estimates for D. maruadsi across different fishing operation types. By averaging the CPUE estimates over the four quarters, we discovered that the light purse seine method had the highest CPUE estimate at 3.577 kg/(kW·d), whereas the gillnet method had the lowest CPUE estimate at 0.143 kg/(kW·d). The results of this study revealed differences in the distribution range of REE values for catch rate estimates among different fishing operation types; however, the overall trend of change was similar. Particularly, with an increase in sample size, the boxplot of REE values for CPUE estimates of each fishing gear showed a gradually decreasing trend, whereas the RB values exhibited decreasing dispersion and tended to stabilize. Notably, the distribution range of REE values for the light purse seine and gill net methods was relatively smaller than that of other fishing gear. We found that the minimum sample sizes required for estimating CPUE varied among different fishing operation types, and the rules for determining these minimum sample sizes also differed. Otter trawl, pair trawl, and light purse seine determined the minimum sample size based on REE ≤ 10%, whereas gillnets and light falling nets (except in winter) determined the minimum sample size based on REE ≤ 5%. We also found that, as the sample size reached a specific threshold, the impact of increasing the number of survey forms on the estimation accuracy of the average catch rates gradually decreased. In the summer, when the sample size reached 600, the REE values for twin trawl, light purse seine, and light falling net were below 10%; when the sample size reached 800, the REE values for the otter trawl decreased to within 10%; and when the sample size increased to 1 200, the REE value for the gill net decreased to within 10%, whereas the REE values for other operation types remained below 5%. As the sample size continued to expand, the impact on sampling accuracy became increasingly minimal. In general, when the sample size reached a certain threshold, the changes in REE and RB tended to stabilize, and the redundant portion of the sample size could be optimized. Even with a reduced sample size, estimation accuracy could be ensured to a certain extent. In this study, the minimum acceptable sample size for CPUE estimation varied across fishing operation types. Assuming that the survey data from 2008 to 2018 accurately represented fishery production and considering an REE of less than 10%, the minimum number of survey trips required for CPUE estimation of D. maruadsi by operation type and season were as follows: otter trawl (91, 68, 59, and 86), twin trawl (41, 41, 82, and 52), light purse seine (164, 87, 95, and 57), gillnet (218, 218, 245, and 191), and light falling net with attractors (100, 81, 64, and 43). On average, these corresponded to 76 trips for the otter trawl, 54 for the twin trawl, 218 for the gillnet, 101 for the light purse seine, and 72 for the light falling net with attractors. In this study, we optimized sample size using the mean CPUE of D. maruadsi as the survey target, and the evaluation results may serve as a reference for catch surveys in northern South China Sea fishing ports.