Optimization Time Research Articles

Optimization of tree-shaped fin structures towards enhanced discharging performance of metal hydride reactor for thermochemical heat storage based on entransy theory

Integrating fin structures into metal hydride (MH) bed can enhance heat discharging performance of the reactor for thermochemical heat storage. In this study, based on the entransy theory, the entransy dissipation rate (Eh,st) or the entransy dissipation (Eh,tr) of a simplified two-dimensional (2D) steady-state or transient heat conduction model for the MH bed was used to be an objective to optimize the tree-shaped fin (TSF) structures. Furthermore, response surface method (RSM) was adopted to explore the relationship between Eh,st or Eh,tr and structural parameters (i.e., branch number N, branch level M, length ratio α, width index β, and angle ratio γ). The results show that RSM can effectively predict influences of structural parameters of TSFs on heat discharging performance. Based on RSM with entransy theory, the obtained optimal combination parameters were N = 8, M = 3, α = 1, β = 0.5, and γ = 1, corresponding to the maximum gravimetric exergy-output rate (GEOR) of 20.98 W/kg. Furthermore, the GEOR with optimized fins was 11.5 % higher than that with unoptimized TSFs. The optimization method based on the entransy theory was effective to optimize the TSF structures by solving 2D model instead of three-dimensional model, and shorten the calculation time for optimization.

Interval Type-2 Fuzzy PID Controller Using Disassembled Gradational Optimization.

This paper presents an interval type-2 fuzzy proportional-integral-derivative (IT2F-PID) controller that is designed using a new disassembled gradational optimization (D-GO) method. A PID controller is first optimized using the D-GO method and then connected to a type-1 fuzzy logic system (T1-FLS). The parameters of the T1-FLS are optimized, and the T1-FLS is blurred into the interval type-2 fuzzy logic system (IT2-FLS). Finally, the IT2F-PID controller is formed. The proposed method is compared with the concurrent and general optimization methods. The simulation results show that the D-GO method reduces the optimization time by over 90% compared with the general method, and decreases the integral-of-time-absolute-error (ITAE) by 30%. Beyond that, compared with the concurrent optimization method, the D-GO method reduces time by over 25%, and the ITAE value by about 95%. In the normal case, model uncertainty, target uncertainty, and external disturbance, the control ability of the IT2F-PID controller designed using the D-GO method is verified via simulations using a nonlinear forced closed-loop system. The results show that the overshoot is reduced by 80% and the fluctuation is reduced by 67% compared with a traditional PID controller and an IT2F-PID controller built using the general method.

Best Practices for Electricity Generators and Energy Storage Optimal Dispatch Problems

Abstract The growing share of renewable energy sources in the energy mix and the liberalization of electricity markets has drastically affected the operation of electricity generators. Especially, in the last decade, fossil fuel-based generators have shifted their role from providing continuous base load to covering the peak demand and providing backup capacity to stabilize the grid. At the same time, a large amount of storage capacity is foreseen to be integrated into electricity grids in the coming years to shave demand peaks, mitigate price volatility, and provide services to the grid. In such a situation, in order to properly manage these crucial technologies, and thus guarantee the economic viability of the operation, it is essential to properly optimize the dispatch and define the best scheduling. This paper considers a gas turbine combined cycle and battery energy storage to study the problem of dispatch optimization of both generators and storage technologies. Different optimization algorithms have been considered and mixed integer linear programming is selected for its ability to identify the global optimum and the reduced optimization time. The impact of optimization windows (i.e., the forecast horizon of electricity prices) is also investigated. It is highlighted that an increase in forecasting ability, at least up to 36 h, guarantees more effective scheduling; on the other hand, it may require a significantly longer time. Subsequently, different approaches to account for the operation and maintenance costs at the optimization stage are assessed, and, finally, a sensitivity analysis is carried out with respect to market parameters (price average and variability) and technology features (conversion efficiency, cycle cost, etc.).

Decomposition–based multi-objective differential evolution for extractive multi-document automatic text summarization

The central challenge in Automatic Text Summarization (ATS) involves efficiently generating machine-generated text summaries through optimization algorithms. An ATS is a critical component for systems dealing with textual information processing. However, the current approach faces a significant hurdle due to the computational intensity of the process, particularly when employing complex optimization techniques like swarm intelligence optimization alongside a costly ATS repair operator. While the current approach yields impressive Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics for the generated summary, it struggles with inefficiencies, mainly attributed to the substantial optimization time consumed by the ATS repair operator scheme. In order to address this, a novel solution called Decomposition-based Multi-Objective Differential Evolution (MODE/D) is proposed. It is built upon the foundation of Differential Evolution for Multi-Objective Optimization (DEMO) and the weighted sum method (WS), coupled with an innovative ATS repair operator scheme. Through experimentation on Document Understanding Conferences (DUC) datasets, the novel approach of MODE/D – WS is validated by evaluating the results using ROUGE metrics. The outcomes are twofold: a remarkable reduction in serial execution time and a noteworthy enhancement over existing techniques in the scholarly domain, as evidenced by improved ROUGE-1, ROUGE-2, and ROUGE-L scores.

Study on Two-Dimensional Exit Pupil Expansion for Diffractive Waveguide Based on Holographic Volume Grating

Diffraction gratings are becoming a preferred option for waveguide head-mounted in–out coupling devices due to their flexible optical properties and small size and light weight. At present, diffraction waveguide coupling devices for AR head-mounted displays (HMD) have difficulties such as a long development cycle and complicated processing. In this paper, we first establish a set of two-dimensional (2D) grating ray tracing models, based on which we determine the initial architecture of the dual-region two-dimensional exit pupil expansion (2D-EPE) AR-HMD holographic waveguide diffraction system. Second, we propose a honeycomb coupled grating array and optimize the optical energy utilization and brightness uniformity of the holographic waveguide and use a custom dynamic linked library (DLL) function to implement the ray tracing of the 2D grating and add a probabilistic splitting function to the DLL, which reduces the single simulation time from 11.853 min to 1.77 min. We also propose a holographic lithography device composed of holographic optical elements (HOEs) and a method for preparing HOEs. Finally, in order to obtain the diffraction efficiency preoptimized by the above DLL for the uniformity of the exit pupil brightness and light energy utilization, we inverse design with the preparation process parameters as the optimization variables and develop the adaptable electromagnetic calculation program Holo-RCWA. Using Holo-RCWA with nondominated sorting genetic algorithm II (NSGA-II), we inverse design to obtain the process parameters satisfying the diffraction efficiency distribution, and the optimization time of the whole system is reduced from 2–3 days to 10 h. This work is of great significance for AR/VR applications.

Development of hybrid surrogate model structures for design and optimization of CO2 capture processes: Part I. Vacuum pressure swing adsorption in a confined space

Design and optimization of CO2 capture processes have become a tremendously active area of research particularly in the past decade. In this context, development of intelligent techniques on the basis of first-principles models coupled with data-driven algorithms for such purpose looks very promising. In a series of works, we intend to present mechanism approaches in order to develop applicable structures for design and optimization of the CO2 capture processes of interest via hybrid models. A systematic method is presented for optimizing a process for capturing CO2 from a confined space through a Vacuum Pressure Swing Adsorption (VPSA) operation using a Hybrid Surrogate Model (HSM) and Non-dominated Sorting Genetic Algorithm (NSGA-III). The surrogate model is structured based on the VPSA process model offered in the Aspen Adsorption® environment and an artificial intelligence (AI) data-driven algorithm. The developed HSM is then used to predict the key process outputs including CO2 purity, air recovery ratio and energy consumption rate. Accordingly, the optimized parameters are re-substituted into the VPSA process simulator for further data processing. It is demonstrated that the proposed model architecture provides considerable computational efficiency for the process optimization with only 48 h to complete the corresponding evolutionary search, while the optimization time by the conventional NSGA-direct method is close to 1129 h. The optimization results also show that the CO2 purity changes from 1000 ppm to 399 ppm, the air recovery ratio remains at 93 %, and the energy consumption per unit product (ECP) decreases by 38.5 % to 99.7 kJ·Nm−3 air after an optimized air purification operation. The idea of chemical mechanism and industrial data twin modeling in this study holds substantial importance for the development of digital chemical twin systems and the process optimization of intelligent factory.

A time-bounded approach for reducing runtime of well placement optimization using PSO and PSOGA algorithms

Determining the optimal drilling locations and well production/injection rates is a highly complex decision in reservoir development. The present study aims to optimize well placement and production/injection rates through the application of intelligent search algorithms. The Particle Swarm Optimization (PSO) and combined Particle Swarm Optimization-Genetic Algorithm (PSOGA) have been chosen as the optimal means to achieve this objective. The Net Present Value (NPV) of the project serves as the objective function, with decision variables including the location of wells, perforation, and flow rates. To evaluate the performance of the algorithms in optimizing well placement and flow rates, a heterogeneous reservoir model has been used. In order to reduce optimization runtime, a novel method has been presented. This method involves changing the termination condition, such that the entire optimization time is considered rather than the maximum iterations. The population size can be added to enhance algorithm convergence. Thus, modified algorithms, namely TPSO and TPSO-GA, have been introduced. The results of determining the termination condition on the TPSO-GA algorithm are quite promising. By setting a 24-h termination condition or a 70% reduction in the basic runtime, and with a population size of 30 agents, the same values of the objective function can be obtained. Furthermore, the impact of two parameters within the objective function, namely the oil price and discount rate, has also been explored.

Analysis of Some Special Functions for a Problem of Optimization of Analog Circuits

Further development of a generalized methodology for optimizing analog circuits is proposed. This methodology is based on the theory of optimal control. We have transformed the problem of minimizing the CPU time needed to optimize the circuit into the classical problem of minimizing the function in optimal control theory. In this case, we represent the process of optimizing the analog circuit as a controlled dynamic system. To analyze the properties of such a system, we propose to use the concept of the Lyapunov function of a dynamical system. The new special functions allow us to predict the CPU time for circuit optimization by analyzing the characteristics of the initial part of the process. It has been established that for any optimization strategy, there is a correlation between the behavior of these functions and the CPU time corresponding to these strategies.

Soft DAgger: Sample-Efficient Imitation Learning for Control of Soft Robots.

This paper presents Soft DAgger, an efficient imitation learning-based approach for training control solutions for soft robots. To demonstrate the effectiveness of the proposed algorithm, we implement it on a two-module soft robotic arm involved in the task of writing letters in 3D space. Soft DAgger uses a dynamic behavioral map of the soft robot, which maps the robot's task space to its actuation space. The map acts as a teacher and is responsible for predicting the optimal actions for the soft robot based on its previous state action history, expert demonstrations, and current position. This algorithm achieves generalization ability without depending on costly exploration techniques or reinforcement learning-based synthetic agents. We propose two variants of the control algorithm and demonstrate that good generalization capabilities and improved task reproducibility can be achieved, along with a consistent decrease in the optimization time and samples. Overall, Soft DAgger provides a practical control solution to perform complex tasks in fewer samples with soft robots. To the best of our knowledge, our study is an initial exploration of imitation learning with online optimization for soft robot control.

Optimal design and operation of reactive extrusion processes: Modeling, model validation, and optimization algorithm

AbstractIn this work, a methodology for the optimal design and operation of reactive extrusion processes in twin‐screw extruders is presented. As a process model, a very accurate mechanistic finite volume twin‐screw extruder model is used which is adapted to the reactive extrusion case. In the model, in contrast to other models, the pressure is described as a differential state in order to have a consistent set of equations. The model is compared to other models and advantages and disadvantages with respect to accuracy and computation time are discussed. For optimization, a memetic optimization algorithm (MA) that consists of a combination of a local optimization for the continuous operating parameters and an evolutionary algorithm (EA) for the global optimization of the discrete decision variables is employed to overcome the drawback of the longer model simulation time compared to simpler models. A reduction of optimization time by 90% for the MA compared to the EA was observed. The presented modeling and optimization methodology is validated on the widely investigated reactive extrusion process for ε‐Caprolactone. The methodology is very flexible and the underlying model has good prediction characteristics, thus it is applicable to a wide range of reactive extrusion processes of industrial interest.Highlights Simulation and optimization of reactive extrusion. Improved mechanistic twin‐screw extruder model. Novel approaches for pressure and residence time modeling. Efficient combination of local and global optimization in a memetic algorithm. Benchmarking of the optimization results and performance with literature data.

Proposal of Technique for Analysis of Complementary Frequency Selective Surfaces

AbstractA specific class of frequency selective surface (FSS) is the complementary frequency selective surface (CFSS) that has interesting characteristics such as high angular stability, multiple transmission and/or reflection bands, and the possibility of miniaturization. The analysis and design of this sort of structure are commonly performed using commercial software, which demands a high computational effort, impacting a longer optimization time. The equivalent circuit model combined with a cascading technique emerges as an alternative method to the use of these softwares, in the optimization of the physical dimensions of these structures, as they model the behavior of a CFSS with low computational effort and optimization time, in addition to being able to be implemented in various programming languages. Thus, this work proposes a CFSS analysis technique that combines the equivalent circuit method with the ABCD matrix. To the best of our knowledge, this is the first reported research on approximate techniques for CFSS analysis. The chosen geometry was the circular ring, due to high angular stability and polarization independence. The modeling of the equivalent circuit for patch and aperture geometries is presented. Some structures are simulated, and the results are compared with results obtained with the HFSS software. Finally, two prototypes are built to validate the analyses performed.

An Efficient Self‐supporting Infill Structure for Computational Fabrication

AbstractEfficiently optimizing the internal structure of 3D printing models is a critical focus in the field of industrial manufacturing, particularly when designing self‐supporting structures that offer high stiffness and lightweight characteristics. To tackle this challenge, this research introduces a novel approach featuring a self‐supporting polyhedral structure and an efficient optimization algorithm. Specifically, the internal space of the model is filled with a combination of self‐supporting octahedrons and tetrahedrons, strategically arranged to maximize structural integrity. Our algorithm optimizes the wall thickness of the polyhedron elements to satisfy specific stiffness requirements, while ensuring efficient alignment of the filled structures in finite element calculations. Our approach results in a considerable decrease in optimization time. The optimization process is stable, converges rapidly, and consistently delivers effective results. Through a series of experiments, we have demonstrated the effectiveness and efficiency of our method in achieving the desired design objectives.

Optimizing PID Gains of a Vehicle using the state-of-the-art Metaheuristic Methods

PID controllers are important control methods that are widely used in industrial processes. Proper tuning of PID gains is critical for achieving the state-of-the-art system performance. Therefore, optimizing PID gains is an important research topic in the field of control engineering. In this study, PID controller gains are automatically tuned using metaheuristic optimization methods. These methods use an iterative approach to calculate optimal values of PID controller gains based on different optimization techniques. The interaction between artificial intelligence and control systems requires a multidimensional approach across different disciplines. In the study, we perform Particle Swarm Optimization, Gray Wolf Optimization, Whale Optimization Algorithm, Firefly Algorithm, Harris Hawks Optimization, Artificial Hummingbird Algorithm and African Vulture Optimization Algorithm to determine PID gains. In the simulation, step input is applied to the dynamic equation of the unmanned free-swimming submersible vehicle. The fitness function is determined with respect to controller integral square error, settling time value, and maximum percent overshoot value. We also evaluate the optimization time of the selected algorithms based on the fitness function. Experimental results present that Artificial Hummingbird Algorithm, Gray Wolf Optimization and Particle Swarm Optimization achieve significant performance. This underlines that using metaheuristic methods in PID gain optimization increase overall system performance.

Is Robust Optimization Essential When Planning Pencil Beam Scanned Proton Therapy for Intracranial Lesions?

Creating intensity modulated proton therapy (IMPT) plans usually involves a robust optimization step to account for uncertainties in proton range and positioning instead of using the PTV margins typically seen in photon IMRT planning. Because robust optimization adds significant planning time per iteration, and proton planning typically involves many iterations to obtain an optimal plan, this project evaluates whether a PTV approach can be used to more efficiently create plans for intracranial lesions by comparing plan quality from both approaches to determine if they produce equivalent plans. Five patients with intracranial lesions treated with IMPT at our center were randomly chosen for this study. 3-4 beams were used to treat CTVs ranging between 46 and 246 cc. Patients were treated on a Mevion S250i single-vault proton machine with Hyperscan. Static blocks (7mm conformed to the CTV) were used for all cases and plans were created in a treatment planning system using robustness criteria of 3mm (position uncertainty) and 3.5% (range uncertainty). For each patient, the CTV was uniformly grown by 3 mm to create a PTV. The optimization criteria used in the clinical plan were used as baseline to create two plans - one using robust optimization for CTV coverage and one without robust optimization for PTV coverage. A script was used to time two otherwise identical optimization runs. All plans were normalized so that the prescription was delivered to 95% of the CTV. The plan was robustly evaluated under conditions of 3.5% range uncertainty and 2mm positional uncertainty. For each nominal plan, the CTV dose heterogeneity and conformity as well as the following dose metrics were collected: CTV D99% and D98%, Normal Brain V100%, V90%, V80%, V50% as well as overall plan Dmax (to 0.03cc). For each plan, the minimum CTV D99 and maximum Brain Dmax were also collected for the robust evaluation scenarios. All collected metrics were compared between the robust CTV-based and non-robust PTV-based plans using paired t-tests with 5% significance. For the five cases investigated here, all dosimetric metrics investigated were not significantly different between the CTV-based and PTV-based plans except for the plan maximum dose (CTV-based: 104.6% Rx - 110.0% Rx, PTV-based: 105.6% Rx-110.6% Rx, p = 0.029). The optimization times were also significantly different, averaging 1532 s for CTV-based plans versus 252 s for the PTV-based plans (p = 0.004). For the plans investigated here, non-robust PTV planning approach creates plans of very similar quality to a robust CTV-based plan, while having significantly shorter planning times.

Using Scorecards to Tune Ethos Directive Templates: An ARTIA Cervix Dosimetric Planning Study

The Adaptive Radiation Therapy Individualized Approach (ARTIA) Cervix clinical trial uses predefined clinical directive templates (CDT) combined with RapidPlan DVH estimations (DVHe) to guide plan optimization in the Ethos treatment planning system. The dosimetric scorecard tool (DST) quantifies improvements in plan quality. This is the first study to utilize the DST to tune an Ethos CDT to improve resulting plan quality. Iterative replanning was used to modify the draft CDT (CDT-1) in Ethos to generate a new CDT (CDT-2) that maximized the clinical consensus scorecard's total score compared toCDT-1. CDT-2 was established, and resulting plans were compared with and without a DVHe. Additional fixed field IMRT beam geometries were compared between CDT-1 and CDT-2, both with DVHe. After obtaining favorable results when comparing CDT-1 verses CDT-2 for two test cases, 10 additional cases were retrospectively identified and tested. For the initial test cases, CDT-2 decreased OAR doses without compromising PTV coverage (No DVHe). While both plans met the protocol target guidelines and OAR constraints, the scorecard was able to quantify the improvement with CDT-2 on a test case with a score of 166.1 (78.7%) vs CDT-1, 163.87 (77.6%). When CDT-2 was combined with the DVHe, it still marginally outperformed CDT-1: 168.73 (76%) versus 166.13 (74.8%). Plan quality was further improved by increasing the total number of fields to 19. Combining CDT-2 and DVHe with a 19-field geometry resulted in the greatest benefit at 184.6 (83.2%). This scored higher than the ARTIA-Cervix defined delivery technique of CDT-1 and DVHe, with a 9-field geometry 166.1 (74.8%). The study was expanded to a separate analysis on 10 new cases. The 19-field approach was superior for all 10 cases and CDT-2 achieved a higher score in 7/10 cases. When comparing 9 versus 19 fields, the total optimization and calculation time increased by an average of 1.9 minutes while the beam delivery time increased by an average of 2.8 minutes (+/- 0.1). The average MU/field was 174.3 (total 1568.3) and 129.9 (total 2468) for 9 and 19 fields, respectively. Two test plans were re-optimized and calculated with Ethos 1.1 maintenance release (MR) 1 with both 9 and 19 fields. For case 1, MR 1 resulted in an 8.4% and 6.9% decrease in MU and scored -0.6% and +0.5% for 9 and 19 fields, respectively. For case 2, MR 1 resulted in a 0.8% and 3.1% decrease in MU and scored -3.4% and -0.3% for 9 and 19 fields, respectively. The scorecard allows for easy evaluation of the dosimetric impact of other planning parameters (beam arrangements and use of DVHe) to identify the best approach. Using a scorecard to finely-tune a CDT is expected to improve planning efficiency, decrease intra-institutional plan quality and variability, improve the average calculated plan quality and benefit CBCT-guided ART.

Optimizing the Cooling System of High-Speed Train Environmental Wind Tunnels Using the Gene-Directed Change Genetic Algorithm.

Environmental wind tunnels play a crucial role in the research and development of high-speed railways. However, constructing and operating these wind tunnels requires significant resources, especially with respect to the cooling system, which serves as a vital subsystem. The cooling system utilizes an air compression refrigeration cycle and consists of multiple components. The efficient operation of these components, along with the adoption of appropriate strategies, greatly enhances the efficiency of the wind tunnel refrigeration system. Despite this, the existing methods for evaluating the refrigeration system do not fully capture the energy consumption of an air compression refrigeration system during practical use. To address this issue and effectively evaluate the wind tunnel refrigeration system, we propose using an exergoeconomic evaluation coefficient with experimental cycles to establish the system. This method incorporates the use of frequency coefficients and related parameters. By employing the newly developed evaluation coefficient as an objective function, we utilize the adaptive value-sharing congestion genetic algorithm to optimize the wind tunnel for high-speed trains. Furthermore, we compare the advantages and disadvantages of different optimization schemes. Traditional optimization methods prove inefficient because of the system's numerous variables and the presence of multiple peaks in the objective function. Inspired by the biogenetic breeding method, we introduce an optimization approach based on a specific gene mutation. This innovative method significantly reduces optimization time and improves efficiency by approximately 17%.

Research on Hyperspectral Modeling of Total Iron Content in Soil Applying LSSVR and CNN Based on Shannon Entropy Wavelet Packet Transform

The influence of some seemingly anomalous samples on modeling is often ignored in the quantitative prediction of soil composition modeling with hyperspectral data. Soil spectral transformation based on wavelet packet technology only performs pruning and threshold filtering based on experience. The feature bands selected by the Pearson correlation coefficient method often have high redundancy. To solve these problems, this paper carried out a study of the prediction of soil total iron composition based on a new method. First, regarding the problem of abnormal samples, the Monte Carlo method based on particle swarm optimization (PSO) is used to screen abnormal samples. Second, feature representation based on Shannon entropy is adopted for wavelet packet processing. The amount of information held by the wavelet packet node is used to decide whether to cut the node. Third, the feature bands selected based on the correlation coefficient and the competitive adaptive reweighted sampling (CARS) algorithm using the least squares support vector regression (LSSVR) are applied to the soil spectra before and after wavelet packet processing. Finally, the Fe content was calculated based on a 1D convolutional neural network (1D-CNN). The results show that: (1) The Monte Carlo method based on particle swarm optimization and modeling multiple times was able to handle the abnormal samples. (2) Based on the Shannon entropy wavelet packet transformation, simple operations could simultaneously preserve the spectral information while removing high-frequency noise from the spectrum, effectively improving the correlation between soil spectra and content. (3) The 1D-CNN with added residual blocks could also achieve better results in soil hyperspectral modeling with few samples.

High-efficiency expression of a novel antimicrobial peptide I20 with superior bactericidal ability and biocompatibility in Pichia pastoris and its efficiency enhancement to aquaculture

One of the major constraints on aquaculture is the susceptibility of farmed fish to diseases or slow growing. Antimicrobial peptide I20, as a novel feed additive, is used to cope with these limiting factors. In the current study, the Pichia pastoris expression system was used to produce I20 in large quantities, thereby reducing its application cost. In terms of molecular strategy, the I20 expression codon was optimized according to the preference of P. pastoris codon. Through traditional strategy optimization (expression strain, time, methanol concentration, temperature, and pH), the expression level of I20 in shaking bottles was 21.09 ± 1.10 mg/L. Surprisingly, the expression level of I20 in a 20 L high-cell-density cultivation was 514.78 ± 22.07 mg/L after methanol induction of 72 h. Comprehensive analyses using a series of fluorescence and microscopy revealed that I20 killed bacteria by increasing bacterial membrane permeability and binding bacterial DNA. I20 effectively killed a variety of pathogenic bacteria (Aeromonas hydrophila, Aeromonas veronii, Elizabethkingia miricola, Flavobacterium columnare, Edwardsiella ictaluri, Vibrio parahaemolyticus, Streptococcus agalactiae, Nocardia seriolae, Escherichia coli, and Staphylococcus aureus). I20 was found to have excellent biocompatibility after both feeding and injection studies. Grass carp were fed I20 diet supplemented with 200 mg/Kg I20 for 8 weeks. I20 can effectively promote intestinal villus length, secretion of digestive enzymes, and expression of growth genes to improve growth performance. I20 significantly promotes immune response to reduce tissue damage and bacterial load, enhancing survival after Aeromonas hydrophila infection. In conclusion, we systematically optimized the expression of antimicrobial peptide I20 in P. pastoris, and achieved a satisfactory efficiency for producing antimicrobial peptide I20 by integrating several strategies. The results herein showed that I20-feed promoted growth performance and disease resistance in fish, suggesting a feed supplement with great potential applications for aquaculture.

Comparison of Progressive Resolution Optimizer and Photon Optimizer algorithms in RapidArc delivery for head and neck SIB treatments.

The aim of this study is to analyze and verify characterization of two different algorithms using simultaneous integrated boost (SIB) in head and neck (H&N) plans. In our study 15 patients were selected, who received radiation therapy by using Eclipse volumetric modulated arc therapy (VMAT) Progressive Resolution Optimizer (PRO) algorithm 15.1. The same cases were re-optimized using a Photon Optimizer (PO) algorithm 15.6.A total of 30 treatment plans (15 PRO-VMAT plans and 15 PO-VMAT plans) were produced in the present study. All plans were created using double full arcs, keeping the identical constraints, cost functions and optimization time. Plan evaluation was done using planning target volume (PTV) parameters (D98%, D95%, D50%, D2% mean dose and V105%), homogeneity index (HI), conformity index (CI), Monitor unit (MU) per degree with control points (CP), organ at risk (OAR) doses and gamma verification (Portal dosimetry and ArcCHECK) values were evaluated. Treatment was delivered in Varian Truebeam 2.5, energy 6 MV with Millennium 120 multileaf collimator (MLC). The PTV coverage (D95%) for PRO and PO were 98.7 ± 0.8 Gy, 98.8 ± 0.9 Gy, HI were 0.09 ± 0.02 and 0.09 ± 0.02, CI were 0.98 ± 0.01 and 0.99 ± 0.01. Monitor units (MU) for PRO and PO were 647.5 ± 137.9, 655.2 ± 138.4. The Portal dose results were [3%, 3mm (%) & 1 %, 1 mm (%)] for PO and PRO 100 ± 0.1, 95.1 ± 1.4 and 100 ± 0.1, 95.2 ± 1.3. For ArcCHECK were 99.9 ± 0.1, 94.7 ± 3.0 and 99.9 ± 0.1, 93.5 ± 3.9, respectively. Results showed that PTV coverage and OAR doses were comparable. For individual patients CI and HI of PO showed slightly higher values than PRO. MUs for PO were slightly increased as compared to PRO. MU per degree with each individual control points generated by PO showed a high degree of modulation compared to PRO. Hence, new PO optimizer can produce a comparable degree of plan while using the same PRO objectives.

Direct waste heat recovery from a solid oxide fuel cell through Kalina cycle, two-bed adsorption chiller, thermoelectric generator, reverse osmosis, and PEM electrolyzer: 4E analysis and ANN-assisted optimization

The present study investigates an innovative model for the utilization of redundant heat from a solid oxide fuel cell (SOFC). The proposed system comprises a Kalina cycle (KC), a two-bed adsorption chiller (AC), a thermoelectric generator (TEG), reverse osmosis (RO), and a proton exchange membrane electrolyzer (PEME). A parametric analysis is conducted to evaluate the impact of key variables on the performance of the system. Various metrics are employed, including energy, exergy, economic, and environmental (4E). The environmental analysis reveals that waste heat recovery from SOFC decreases CO2 emissions by 36.41 kg per megawatt output at the base condition. At a fuel (methane) consumption rate of 0.0743 kg/s, the system produces 2206.49 kW of power, 166.09 kW of cooling load, 404.68 m3/day of fresh water, and 18.04 kg/day of hydrogen with reported energy efficiency and exergy efficiency values of 63.91% and 51%, respectively. Besides around 60.55% of the overall exergy destruction in the entire system is attributed to the air preheater, afterburner, and water preheater of the SOFC. The system is optimized using a genetic algorithm (GA) in three separate optimization issues, aiming to minimize the total cost rate while maximizing other objective functions, such as exergy efficiency, freshwater production, and cooling load. A modern optimization model using artificial neural networks (ANNs) is employed to reduce optimization time. By considering the overall cost rate and exergy efficiency as the objective functions, the optimal point is achieved at 58.03 $/h and 59.92%, respectively. The optimum values of 120.99 $/h and 1346.30 m3/day for the objective functions of total cost rate and freshwater production, respectively, are obtained in the second issue. In the third optimization problem, the evaluated values at the optimal point are 272.6 kW and 70.69 $/h for cooling load and total cost rate, respectively. Sensitivity analysis indicates that the changes in current density significantly affect the exergy efficiency, cost rate, net power, and cooling load, while variations in the fuel utilization factor coefficient considerably influence freshwater production.