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The radiative perturbation of the Earth's energy balance caused by all aerosols, the direct radiative effect (DRE), and anthropogenic aerosols, the direct radiative forcing (DRF), remain major sources of uncertainty in climate projections. Here we propose a method for determining DRE and DRF that makes use of the High Spectral Resolution Lidar (HSRL)-retrieved aerosol loading and derived aerosol types (i.e. dust, marine, urban, smoke, etc.) in combination with aerosol-type specific optical properties. As the global spatiotemporal distributions of HSRL-derived aerosol types are not currently available, the methodology is tested here using a global 3-D model of atmospheric chemistry (GEOS-Chem) along with Creating Aerosols from CHemistry (CATCH) algorithm-generated aerosol types analogous to ones derived by HSRL. In this method, the Rapid Radiative Transfer Model for General Circulation Models (RRTMG) is used to perform radiative transfer calculations with the single scattering albedo (SSA) and asymmetry parameter (g) of atmospheric particles assigned based on the aerosol type in each grid box. Average GEOS-Chem/CATCH-derived all-sky DRE and DRF across the North American domain are estimated to be −1.98 W/m² and − 0.77 W/m², respectively between mid-January and early February 2013 and − 4.20 W/m² and − 1.41 W/m² respectively between mid-July and early August 2014. Sensitivity studies revealed that the scheme may produce up to about ±0.42 W/m² and ± 0.21 W/m² uncertainty in DRE and DRF, respectively, related to variability in aerosol type-specific optical properties. This study presents a new way of determining DRE and DRF estimates once global retrievals of aerosol intensive parameters by HSRL become available.

Due to the increasing demand for fish consumption, sustainable fishery become more and more challenging. To prevent from overfishing, massive data in open sea fishing have been collected and analyzed to achieve efficient management of fishery. Still, it is extremely difficult for fishers and fishery managers to exploit available data for accurate prediction, because of their limited data processing capacities, and the overall lack of adequate database systems [1].The goal of this work is therefore to analyze the relationship between data collected from all sensors installed on-board fishing vessels and catch weight, to better support generating a map showing likely fishing effort allocation. To do so, we train neural networks to predict catch weight using all available data from sensors on fishing vessels. The raw data are pre-processed using random sampling techniques to be fed into a neural network for training. A multi-layer perceptron (MLP) neural network is proposed as the baseline. We propose a data augmentation method and a training strategy in order to optimize the prediction accuracy of the model. Our data augmentation method conducts random sampling of the original data multiple times, which reduces the root mean square error (RMSE) by 15.8%, as compared with the results obtained by the model trained without data augmentation. Our training strategy works well to further optimize the prediction accuracy of the model trained with an augmented dataset, which significantly decreased the RMSE by 11. 2%. To the best of our knowledge, this is the first study on the catch weight prediction using neural networks.

The article reports on the television adaptation of "The Catch," the first novel by poet Yrsa Daley-Ward, published in summer 2025.

Seattle locals and tourists alike flock to Pike Place each day to watch the fish fly. But Pike Place Fish is about more than just crowd-pleasing entertainment --- there is an underlying philosophy at work that has made the market both wildly successful and internationally renowned. In an unusual combination of oral biography and practical business blueprint, Crother and the fishmongers take you behind the scenes at the famous market to illustrate the PPF philosophy and reveal the underlying assumptions that have made PPF the international phenomenon it is today. Catch! shows that to be'ordinary'is to be a victim of circumstances, responding to whatever comes one's way. To be'great'is to realize that anyone can live an extraordinary life. At Pike Place Fish, everyone is responsible for transforming themselves from ordinary to great by creating their own reality. The authors explore the issues of goals --- financial, personal and humanitarian --- and intention, showing how the crew itself creates these goals and works towards them in collaboration. Most importantly, Catch! examines the power of possibility and shows how you can achieve greatness in your own life. Catch! explores such guiding principles as coaching and acknowledgment that are lacking in many businesses, and shows that you, too, can be the prime mover in your own experience.

This paper analyzes the dynamics of occurrence of four sturgeon species from the family Acipenseridae (Russian sturgeon Acipenser gueldenstaedtii, sterlet A. ruthenus, stellate sturgeon A. stellatus, and beluga Acipenser (Huso huso) in IUU catches from the Ural (Zhaiyk in Kazakh) River in the Republic of Kazakhstan. Data on the arrested IUU catches of the annual quantity of sturgeons from 2012 to 2018 are given that demonstrate fluctuations in the amount of Ural River sturgeons from this river, as well as the male dominance in their populations. Biological parameters of Ural sturgeons (sex ratio, age, weight, length, and length–weight ratio) during the period of maximum catches in 2018 are given.

Several contemporary programming languages, including Java, have exception management as a crucial built-in feature. By employing try-catch blocks, it enables developers to handle unusual or unexpected conditions that might arise at runtime beforehand. If exception management is neglected or applied improperly, it may result in serious incidents like equipment failure. Exception handling mechanisms are difficult to implement and time expensive with the preceding methodologies. This research introduces an efficient Long Short Term Memory (LSTM) technique for handling the exceptions automatically, which can identify the locations of the try blocks and automatically create the catch blocks. Bulky java code is collected from GitHub and splitted into several different fragments. For localization of the try block, Bidirectional LSTM (BiLSTM) is used initially as a token level encoder and then as a statement-level encoder. Then, the Support Vector Machine (SVM) is used to predict the try block present in the given source code. For generating a catch block, BiLSTM is initially used as an encoder, and LSTM is used as a decoder. Then, SVM is used here to predict the noisy tokens. The loss functions of this encoder-decoder model have been trained to be as small as possible. The trained model then uses the black widow method to forecast the following tokens one by one and then generates the entire catch block. The proposed work reaches 85% accuracy for try block localization and 50% accuracy for catch block generation. An improved LSTM with an attention mechanism method produces an optimal solution compared to the existing techniques. Thus the proposed method is the best choice for handling the exceptions.