Brochhendrix0386

Salp swarm algorithm (SSA) is a recently proposed, powerful swarm-intelligence based optimizer, which is inspired by the unique foraging style of salps in oceans. However, the original SSA suffers from some limitations including immature balance between exploitation and exploration operators, slow convergence and local optimal stagnation. To alleviate these deficiencies, a modified SSA (called VC-SSA) with velocity clamping strategy, reduction factor tactic, and adaptive weight mechanism is developed. Firstly, a novel velocity clamping mechanism is designed to boost the exploitation ability and the solution accuracy. Next, a reduction factor is arranged to bolster the exploration capability and accelerate the convergence speed. Finally, a novel position update equation is designed by injecting an inertia weight to catch a better balance between local and global search. 23 classical benchmark test problems, 30 complex optimization tasks from CEC 2017, and five engineering design problems are employed to authenticate the effectiveness of the developed VC-SSA. The experimental results of VC-SSA are compared with a series of cutting-edge metaheuristics. The comparisons reveal that VC-SSA provides better performance against the canonical SSA, SSA variants, and other well-established metaheuristic paradigms. In addition, VC-SSA is utilized to handle a mobile robot path planning task. The results show that VC-SSA can provide the best results compared to the competitors and it can serve as an auxiliary tool for mobile robot path planning.In this work, we develop a mathematical model to describe the local movement of individuals by taking into account their return to home after a period of travel. We provide a suitable functional framework to handle this system and study the large-time behavior of the solutions. We extend our model by incorporating a colonization process and applying the return to home process to an epidemic.The study of DNA binding proteins (DBPs) is of great importance in the biomedical field and plays a key role in this field. At present, many researchers are working on the prediction and detection of DBPs. Traditional DBP prediction mainly uses machine learning methods. Although these methods can obtain relatively high pre-diction accuracy, they consume large quantities of human effort and material resources. Transfer learning has certain advantages in dealing with such prediction problems. Therefore, in the present study, two features were extracted from a protein sequence, a transfer learning method was used, and two classical transfer learning algorithms were compared to transfer samples and construct data sets. In the final step, DBPs are detected by building a deep learning neural network model in a way that uses attention mechanisms.Tactile-feeding wading birds, such as wood storks and white ibises, require high densities of prey such as small fishes and crayfish to support themselves and their offspring during the breeding season. Prey availability in wetlands is often determined by seasonal hydrologic pulsing, such as in the subtropical Everglades, where spatial distributions of prey can vary through time, becoming heterogeneously clumped in patches, such as ponds or sloughs, as the wetland dries out. In this mathematical modeling study, we selected two possible foraging strategies to examine how they impact total energetic intake over a time scale of one day. In the first, wading birds sample prey patches without a priori knowledge of the patches' prey densities, moving from patch to patch, staying long enough to estimate the prey density, until they find one that meets a predetermined satisfactory threshold, and then staying there for a longer period. For this case, we solve for a wading bird's expected prey intake over the course of a day, given varying theoretical probability distributions of patch prey densities across the landscape. In the second strategy considered, it is assumed that the wading bird samples a given number of patches, and then uses memory to return to the highest quality patch. Our results show how total intake over a day is impacted by assumptions of the parameters governing the spatial distribution of prey among patches, which is a key source of parameter uncertainty in both natural and managed ecosystems. Perhaps surprisingly, the foraging strategy that uses a prey density threshold generally led to higher maximum potential prey intake than the strategy for using memory to return to the best patch sampled. These results will contribute to understanding the foraging of wading birds and to the management of wetlands.Due to high requirements of storage, operation and delivery conditions, it is more difficult for cold chain logistics to meet the demand with supply in the course of disruption. And, accurate demand forecasting promotes supply efficiency for cold chain logistics in a changeable environment. This paper aims to find the main influential factors of cold chain demand and presents a prediction to support the resilience operation of cold chain logistics. After analyzing the internal relevance between potential factors and regional agricultural cold chain logistics demand, the grey model GM (1, N) with fractional order accumulation is established to forecast future agricultural cold chain logistics demand in Beijing, Tianjin, and Hebei. The following outcomes have been obtained. (1) The proportion of tertiary industry, per capita disposable income indices for urban households and general price index for farm products are the first three factors influencing the cold chain logistics demand for agricultural products in both Beijing and Tianjin. The GDP, fixed asset investment in transportation and storage, and the proportion of tertiary industry are three major influential factors in Hebei. (2) Agricultural cold chain demand in Beijing and Hebei will grow sustainably in 2021-2025, while the trend in Tianjin remains stable. In conclusion, regional developmental differences should be considered when planning policies for the Beijing-Tianjin-Hebei cold chain logistics system.Reaction networks are widely used models to describe biochemical processes. Stochastic fluctuations in the counts of biological macromolecules have amplified consequences due to their small population sizes. This makes it necessary to favor stochastic, discrete population, continuous time models. The stationary distributions provide snapshots of the model behavior at the stationary regime, and as such finding their expression in terms of the model parameters is of great interest. The aim of the present paper is to describe when the stationary distributions of the original model, whose state space is potentially infinite, coincide exactly with the stationary distributions of the process truncated to finite subsets of states, up to a normalizing constant. The finite subsets of states we identify are called copies and are inspired by the modular topology of reaction network models. PP1 research buy With such a choice we prove a novel graphical characterization of the concept of complex balancing for stochastic models of reaction networks. The results of the paper hold for the commonly used mass-action kinetics but are not restricted to it, and are in fact stated for more general setting.Harris Hawks Optimization (HHO) algorithm is a kind of intelligent algorithm that simulates the predation behavior of hawks. It suffers several shortcomings, such as low calculation accuracy, easy to fall into local optima and difficult to balance exploration and exploitation. In view of the above problems, this paper proposes an improved HHO algorithm named as QC-HHO. Firstly, the initial population is generated by Hénon Chaotic Map to enhance the randomness and ergodicity. Secondly, the quantum correction mechanism is introduced in the local search phase to improve optimization accuracy and population diversity. Thirdly, the Nelder-Mead simplex method is used to improve the search performance and breadth. Fourthly, group communication factors describing the relationship between individuals is taken into consideration. Finally, the energy consumption law is integrated into the renewal process of escape energy factor E and jump distance J to balance exploration and exploitation. The QC-HHO is tested on 10 classical benchmark functions and 30 CEC2014 benchmark functions. The results show that it is superior to original HHO algorithm and other improved HHO algorithms. At the same time, the improved algorithm studied in this paper is applied to gas leakage source localization by wireless sensor networks. The experimental results indicate that the accuracy of position and gas release rate are excellent, which verifies the feasibility for application of QC-HHO in practice.By upgrading medical facilities with internet of things (IoT), early researchers have produced positive results. Isolated COVID-19 patients in remote areas, where patients are not able to approach a doctor for the detection of routine parameters, are now getting feasible. The doctors and families will be able to track the patient's health outside of the hospital utilizing sensors, cloud storage, data transmission, and IoT mobile applications. The main purpose of the proposed research-based project is to develop a remote health surveillance system utilizing local sensors. The proposed system also provides GSM messages, live location, and send email to the doctor during emergency conditions. Based on artificial intelligence (AI), a feedback action is taken in case of the absence of a doctor, where an automatic injection system injects the dose into the patient's body during an emergency. The significant parameters catering to our project are limited to ECG monitoring, SpO2 level detection, body temperature, and pulse rate measurement. Some parameters will be remotely shown to the doctor via the Blynk application in case of any abrupt change in the parameters. If the doctor is not available, the IoT system will send the location to the emergency team and relatives. In severe conditions, an AI-based system will analyze the parameters and injects the dose.Hepatitis B is a disease that damages the liver, and its control has become a public health problem that needs to be solved urgently. In this paper, we investigate analytically and numerically the dynamics of a new stochastic HBV infection model with antiviral therapies and immune response represented by CTL cells. Through using the theory of stochastic differential equations, constructing appropriate Lyapunov functions and applying Itô's formula, we prove that the disease-free equilibrium of the stochastic HBV model is stochastically asymptotically stable in the large, which reveals that the HBV infection will be eradicated with probability one. Moreover, the asymptotic behavior of globally positive solution of the stochastic model near the endemic equilibrium of the corresponding deterministic HBV model is studied. By using the Milstein's method, we provide the numerical simulations to support the analysis results, which shows that sufficiently small noise will not change the dynamic behavior, while large noise can induce the disappearance of the infection. In addition, the effect of inhibiting virus production is more significant than that of blocking new infection to some extent, and the combination of two treatment methods may be the better way to reduce HBV infection and the concentration of free virus.