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This paper is inspired by traditional rural fishing methods and proposes a new metaheuristic optimization algorithm based on human behavior: Catch Fish Optimization Algorithm (CFOA). This algorithm simulates the process of rural fishermen fishing in ponds, which is mainly divided into two phases: the exploration phase and the exploitation phase. In the exploration phase, there are two stages to search: first, the individual capture stage based on personal experience and intuition, and second, the group capture stage based on human proficiency in using tools and collaboration. Transition from independent search to group capture during the exploration phase. Exploitation phase: All fishermen will surround the shoal of fish and work together to salvage the remaining fish, a collective capture strategy. CFOA model is based on these two phases. This paper tested the optimization performance of CFOA using IEEE CEC 2014 and IEEE CEC 2020 test functions, and compared it with 11 other optimization algorithms. We employed the IEEE CEC2017 function to evaluate the overall performance of CFOA. The experimental results indicate that CFOA exhibits excellent and stable optimization capabilities overall. Additionally, we applied CFOA to data clustering problems, and the final results demonstrate that CFOA’s overall error rate in processing clustering problems is less than 20%, resulting in a better clustering effect. The comprehensive experimental results show that CFOA exhibits excellent optimization effects when facing different optimization problems. CFOA code is open at https://github.com/Meky-1210/CFOA.git.

In the Barents Sea commercial snow crab (Chionoecetes opilio) fishery, an increase in catch efficiency of the conical pots is important for the profitability of the industry. Light emitting diodes (LEDs) have previously been tested for increasing catch efficiency of the snow crab pots. These earlier experiments have shown varying results ranging from large increase in snow crab catches to no significant effect. These experiments have used different pot soaking times; however, the soaking time might affect the impact of LEDs on catch efficiency. In commercial snow crab fishery, the pot soak time is varying which has not been considered in earlier experiments testing the effect of LEDs. Therefore, this study examined whether pot soaking time can explain the observed differences in relative catch efficiency of snow crab pots with and without LEDs with soak times ranging from 2 to 14 days in the Barents Sea snow crab fishery. For target sizes of snow crab (≥95 mm carapace width), results indicated an increase in catch efficiency between 10 and 30% for pots with LEDs with exception of one experiment using six days soak time. However, experimental results were subjected to large uncertainties and, except from one experiment with five days soak time, the estimated increases were nonsignificant. Furthermore, the pot soak time was not found to impact the effect of white LEDs on capture efficiency.

Gillnets are one of the most commonly used fishing gears in both marine and inland waters. Different colour filament nettings are often used in gillnets. However, the effect of gillnet colour on catch efficiency is unclear for most fisheries. Therefore, in this study we investigated the effect of gillnet colour on the catch efficiency in pearlspot (Etroplus suratensis) fishery in Vembanad lake, India. Gillnet colours tested were transparent, green and blue. Results showed that in this fishery, the catch efficiency of gillnets of the three colours tested differed significantly. The highest catch efficiency of pearlspot was shown by green gillnets compared to transparent or blue netting. Specifically, green gillnets on average were estimated to be 74% more efficient compared to transparent gillnets. For the green compared to the blue gillnets, the catch efficiency was estimated to be higher by 81%. These results demonstrate that gillnet colour can be an important factor significantly affecting the catch efficiency of this fishing gear.

Fibrin formation and mechanical stability are essential in thrombosis and hemostasis. To reveal how mechanical load impacts fibrin, we carried out optical trap-based single-molecule forced unbinding experiments. The strength of noncovalent A:a knob-hole bond stabilizing fibrin polymers first increases with tensile force (catch bonds) and then decreases with force when the force exceeds a critical value (slip bonds). To provide the structural basis of catch–slip-bond behavior, we analyzed crystal structures and performed molecular modeling of A:a knob-hole complex. The movable flap (residues γ 295 to γ 305) containing the weak calcium-binding site γ 2 serves as a tension sensor. Flap dissociation from the B domain in the γ -nodule and translocation to knob ‘A’ triggers hole ‘a’ closure, resulting in the increase of binding affinity and prolonged bond lifetimes. The discovery of biphasic kinetics of knob-hole bond rupture is quantitatively explained by using a theory, formulated in terms of structural transitions in the binding pocket between the low-affinity (slip) and high-affinity (catch) states. We provide a general framework to understand the mechanical response of protein pairs capable of tension-induced remodeling of their association interface. Strengthening of the A:a knob-hole bonds at 30- to 40-pN forces might favor formation of nascent fibrin clots subject to hydrodynamic shear in vivo.

As an analog of intersection graphs among digraphs, catch digraphs were introduced by Hiroshi Maehara in 1984. Later on, Prisner focused his research on interval catch digraphs, which he identified as diasteroidal triple free digraphs. It has been used to solve a variety of real-world challenges, such as networking, telecommunication operations, traffic control, and location problems. Oriented catch digraphs are those catch digraphs that have exactly one edge direction attached to each edge of the corresponding underlying graph. In this article, we consider two important subclasses of oriented catch digraphs, namely, oriented interval catch digraphs and oriented circular-arc catch digraphs. First, we characterize those oriented interval catch digraphs whose underlying graphs are trees in terms of forbidden subdigraphs. Next, we provide a characterization of connected proper oriented interval catch digraphs whose underlying graphs are chordal, by determining the complete list of forbidden induced subdigraphs. Another interesting result is characterizing oriented circular-arc catch digraphs, which are tournaments.

The distribution of wind-driven rain on building facades significantly affects their thermal performance and durability. Accurately and efficiently predicting the wind-driven rain catch ratio on wall surfaces is crucial for building performance evaluation. This study proposes a novel computational approach to rapidly predict the wind-driven rain catch ratio on urban building facades. A predictive model was developed using extensive numerical simulations combined with machine learning algorithms. Specifically, the model replaces traditional numerical simulations by learning the influence of wind field characteristics and building geometry on raindrop catch ratios across different sizes. The research results indicate that the machine learning models can effectively substitute conventional simulation methods for wind-driven rain predictions. Notably, the Artificial Neural Network model achieved a prediction accuracy comparable to numerical simulations (RMSE: 0.009, MAE: 0.006) while being over 300 times faster. The inlet wind speed at roof height emerged as the most influential feature, and the model exhibited strong generalization performance across varying wind directions. This method is simple, efficient, and well-suited to support wind-driven rain analysis, experimental measurements, and urban energy consumption studies in residential building contexts.