In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015, the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the “as-it-is” scenario, the second scenario is based on milestones set for achieving goals of “Vision 2023” document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

The optimization methods are increasingly used to solve challenging problems of aeronautical engineering. Typically, the optimization methods are utilized in design of aircraft airframe or its structure. The presented study is focused on an improvement of aircraft-flight-control procedures through the numerical optimization approach. The optimization problems concern selected phases of flight of light gyroplane - a rotorcraft using an unpowered rotor in autorotation to develop lift and an engine-powered propeller to provide thrust. An original methodology of computational simulation of rotorcraft flight was developed and implemented. In this approach the aircraft-motion equations are solved step-by-step, simultaneously with the solution of the Unsteady Reynolds-Averaged Navier-Stokes equations, which is conducted to assess aerodynamic forces acting on the aircraft. As a numerical optimization method, the BFGS algorithm was adapted. The developed methodology was applied to optimize the flight-control procedures in selected stages of gyroplane flight in direct proximity of the ground, where properly conducted control of the aircraft is critical to ensure flight safety and performance. The results of conducted computational optimizations proved qualitative correctness of the developed methodology. The research results can be helpful in design of easy-to-control gyroplanes and also in the training of pilots of this type of rotorcraft.

Kelly's Criterion is well known among gamblers and investors as a method for maximizing the returns one would expect to observe over long periods of betting or investing. These ideas are conspicuously absent from portfolio optimization problems in the financial and automation literature. This paper will show how Kelly's Criterion can be incorporated into standard portfolio optimization models. The model developed here combines risk and return into a single objective function by incorporating a risk parameter. This model is then solved for a portfolio of 10 stocks from a major stock exchange using a differential evolution algorithm. Monte Carlo calculations are used to verify the accuracy of the results obtained from differential evolution. The results show that evolutionary algorithms can be successfully applied to solve a portfolio optimization problem where returns are calculated by applying Kelly's Criterion to each of the assets in the portfolio.

The process of modelling energy systems is accompanied by challenges inherently connected with mathematical modelling. However, due to modern realities in the 21st century, existing challenges are gaining in magnitude and are supplemented with new ones. Modellers are confronted with a rising complexity of energy systems and high uncertainties on different levels. In addition, interdisciplinary modelling is necessary for getting insight in mechanisms of an integrated world. At the same time models need to meet scientific standards as public acceptance becomes increasingly important. In this intricate environment model application as well as result communication and interpretation is also getting more difficult. In this paper we present the open energy modelling framework (oemof) as a novel approach for energy system modelling and derive its contribution to existing challenges. Therefore, based on literature review, we outline challenges for energy system modelling as well as existing and emerging approaches. Based on a description of the philosophy and elementary structural elements of oemof, a qualitative analysis of the framework with regard to the challenges is undertaken. Inherent features of oemof such as the open source, open data, non-proprietary and collaborative modelling approach are preconditions to meet modern realities of energy modelling. Additionally, a generic basis with an object-oriented implementation allows to tackle challenges related to complexity of highly integrated future energy systems and sets the foundation to address uncertainty in the future. Experiences from the collaborative modelling approach can enrich interdisciplinary modelling activities. Our analysis concludes that there are remaining challenges that can neither be tackled by a model nor a modelling framework. Among these are problems connected to result communication and interpretation.

The weight coefficients of the diaphragm spring depend on experiences in the traditional optimization. However, this method not only cannot guarantee the optimal solution but it is also not universal. Therefore, a new optimization target function is proposed. The new function takes the minimum of average compress force changing of the spring and the minimum force of the separation as total objectives. Based on the optimization function, the result of the clutch diaphragm spring in a car is analyzed by the non-dominated sorting genetic algorithm (NSGA-II) and the solution set of Pareto is obtained. The results show that the pressing force of the diaphragm spring is improved by 4.09%by the new algorithmand the steering separation force is improved by 6.55%, which has better stability and steering portability. The problem of the weight coefficient in the traditional empirical design is solved. The pressing force of the optimized diaphragm spring varied slightly during the abrasion range of the friction film, and the manipulation became remarkably light.

This paper investigates the potential of different fermentation feedstocks towards succinic acid production. Three alternative flowsheets were constructed and modelled in SuperPro Designer™, utilizing different fermentation substrates: crude glycerol (a by-product of the biodiesel industry), a commercial 95% glucose syrup and pure glycerol. Techno-economic analysis of the modelled plants showed that all three potential investments can be economically profitable, with the crude glycerol case as the most promising one. The succinic acid production cost for the crude glycerol scenario is estimated at 3.59 $/kg, while a thorough study of the capacity effect to the economics showed that it could drop as low as $2.6 $/kg. Parametric analysis and optimization performed on a flowsheet level, indicated opportunities for lowering the production cost down to 3 $/kg, a value that could pave the framework for a further potential decrease in the selling price of the product.

The article presents a model for optimizing biomass supply chains using the linear programming framework integrated with a Geographic Information System. Based on the distance from a given type of biomass resource, its calorific value, price, and transportation costs, the model identifies the optimal source of biomass, allowing it to cover the demand for the required total energy value with the lowest possible costs. The case study includes the Połaniec power plant in southeastern Poland and potential sources of forest biomass and agricultural straw within 100 km of the plant. Unit costs of biomass varied depending on biomass availability and energy demands. For energy demand on the level of 1 PJ yearly, the lowest unit costs of biomass (4.08 €/MJ) were when all kinds of biomass were available, and the highest (5.47 €/MJ) was when only stacked wood was available. As energy demand increased, unit costs increased, and the ability to meet this demand with just one type of biomass decreased. The energy biomass sector can utilize the model to benefit both biomass producers and their final buyers.

Preventive Maintenance (PM) is a periodic maintenance strategy, which has great results for devices in extending their life, increasing productivity, and most importantly, help avoid unexpected breakdowns and their costly consequence. A preventive maintenance scheduling (PMS) is determining the time for carrying out PM, and it represents a sensitive issue in terms of impact on production if the time for the PM process is not optimally distributed. In this study, hybrid heuristic methods were used to solve the PMS problem, as genetic algorithm and Tabu research were adopted. The optimal solution was reached in a short time compared to previous studies in which optimal methods were used, integer programming and nonlinear integer programming. Also, sensitivity analysis was applied to measure the robustness and strength of the method used, where an optimal solution was obtained for all experiments and in record time. This method can be used for power plants in privet or public sectors, to generate an optimal PMS, to save money, and to avoid water or electricity cuts.

The advancement of autonomous capabilities in maritime navigation has gained significant attention, with a trajectory moving from decision support systems to full autonomy. This push towards autonomy has led to extensive research focusing on collision avoidance, a critical aspect of safe navigation. Among the various possible approaches, Dynamic Programming is a promising tool for optimizing collision avoidance maneuvers. This paper presents a DP formulation for collision avoidance of autonomous vessels. We set up the problem framework, formulate it as a multi-stage decision process, define cost functions and constraints focusing on the actual requirements a marine maneuver must comply with, and propose a solution algorithm leveraging parallel computing. Additionally, we present a greedy approximation to reduce algorithm complexity. Through case studies, we demonstrate the effectiveness of the proposed approach in navigating complex scenarios, contributing to the future of autonomous maritime navigation. Through case studies, we show the efficacy of our approach in navigating complex scenarios.

In the realm of smart transportation and mobility, delivery organizations are continuously refining their strategies, harnessing resources and advanced analytical techniques to streamline operations, aiming to achieve optimal solutions in terms of both efficiency and cost-effectiveness. However, a persistent challenge faced by these delivery companies revolves around the effective assignment of delivery personnel to customer orders, often grappling with suboptimal assignments that lead to inefficiencies and delays. To address this issue, we propose an architecture that uses the Munkres algorithm to optimize the task assignment problem for delivery personnel with pending orders, employing different cost functions obtained with deterministic and machine learning techniques. We compared the performance of linear and polynomial regression methods to construct different cost functions represented by matrices with orders and delivery people information. Subsequently, we applied the Munkres optimization algorithm to solve the assignment problem, which optimally assigns delivery people and orders. The results demonstrate that linear regression, used to estimate distance information, can reduce estimation errors by up to 568.52 km (1.51%) for our dataset compared to other methods. Conversely, polynomial regression proves effective in constructing a superior cost function based on time information, reducing estimation errors by up to 17,143.41 minutes (11.59%) compared to alternative methods. The proposed approach aims to enhance delivery personnel allocation within the delivery sector, optimizing the efficiency of this process.

This paper introduces PatchFusion, an innovative approach for nonrigid tracking and reconstruction of deformable objects using a single RGB-D sensor. Existing methods face challenges in accurately capturing the rapid deformations of soft and flexible objects, thereby limiting their utility in diverse scenarios. Our approach overcomes this challenge by employing a dynamic patch-based framework that adapts to rapid inter-frame motions. Firstly, patch-wise rigid transformation fields for non-overlapping patches are solved via Iterative Closest Point (ICP) by incorporating geometric features as additional similarity constraints, thereby enhancing robustness and accuracy. Secondly, deformation optimization based on a nonrigid solver is applied to refine the coarse transformation fields. In order to enable simultaneous tracking and reconstruction of deformable objects, the patch-based rigid solver is designed to run in parallel with the nonrigid solver, serving as a plug-and-play module requiring minimal modifications for integration while enabling real-time performance. Following a comprehensive evaluation, PatchFusion showcases superior performance in effectively dealing with rapid inter-frame deformations when compared to existing techniques, rendering it a promising solution with broad applicability across domains such as robotics, computer vision, and human-computer interaction.

Extraction techniques of important bioactive molecules from the major concern of Seafood byproducts are getting emphasis for better valorization. Employing green extraction technologies for efficient and quality production of these bioactive molecules is also strictly required. Hence, understanding the extraction process parameters to effectively design an applicable optimization strategy could make this real. In this review, statistical optimization strategies applied for the extraction process parameters of bioactive molecules from Seafood byproducts are focused. The type of experimental designs, and techniques applied to criticize and validate the effects of independent variables on the extraction output was addressed. Dominant parameters studied were enzyme/substrate ratio, pH, time, temperature and power of extraction instruments. The yield of bioactive compounds, chitin and chitosan, proteins and peptides, enzymes and carotenoids (Astaxanthins) were the most studied responses. Efficiency and/or economic and quality considerations and their selected optimization strategies that favour the production of potential bioactive molecules were also reviewed.

The multi-objective optimization design of hydraulic climbing mechanism (the core mechanism of gantry crane) was realized by using the method of combining central composite design (CCD), Kriging and multi-objective genetic algorithm (MOGA). Firstly, the hydraulic climbing mechanism model was established and the appropriate design variables were selected. The mass, maximum deformation and maximum equivalent stress of the hydraulic climbing mechanism was taken as the optimization objectives. Through the comparative analysis of the parameters of wedge block, anchor block, anchor plate、baffle, the parameters that have the greatest impact on the optimization objectives are selected as geometric constraints. Then, according to the results of experimental design, Kriging model was used to establish the response surface optimization model of objective function. Finally, the optimal results were obtained by using MOGA method. The experimental results show that the optimization strategy is reliable, and the mass of the optimized model is reduced by 10.28%. While meeting the actual production requirements, the lightweight design of the hydraulic climbing mechanism is realized, which saves the design cost and improves the material utilization rate.

Chemical process control relies on a tightly controlled, narrow range of margins for critical variables, ensuring process stability and safeguarding equipment from potential accidents. The availability of historical process data is limited to a specific setpoint of operation. This challenge raises issues for process monitoring in predicting and adjusting to deviations outside of the range of operational parameters. Therefore, this paper proposes simulation-assisted deep transfer learning for predicting and optimizing the final purity and production capacity of the glycerin purification process. The proposed network is trained by the simulation domain to generate a base feature extractor, which is then fine-tuned using few-shot learning techniques on the target learner to extend the working domain of the model beyond the historical practice. The result shows that the proposed model improved prediction performance by 99% in predicting water content and 79.72% in glycerin prediction over the conventional deep learning model. Additionally, the implementation of the proposed model identified the production and product quality improvement for enhancing the glycerin purification process.

Due to their high specificity, monoclonal antibodies (mAbs) have garnered significant attention in recent decades, with advancements in production processes, such as high seeding density (HSD) strategies, contributing to improved titers. This study provides a thorough investigation of high seeding processes for mAb production in Chinese hamster ovary (CHO) cells. Using a metabolic network and flux balance analysis, we compared standard fed-batch (STD FB) with HSD cultivations, exploring supplementary lactate and cysteine, and a bolus medium enriched in amino acids. We observed high glycolytic fluxes and depletion of asparagine simultaneously with the lactate shift building kinetic models around this and other observations. An ensemble of kinetic models assessed the impact of a feeding medium enriched with additional asparagine, revealing a missing lactate shift without enhancing mAb productivity in the experiments. This research provides valuable model-based insights into cellular metabolism during HSD processes, laying the foundation for refined CHO cell-based mAb production. The proposed hypothesis on the regulations guides future experimental validation and bioprocess optimization for enhanced mAb production efficiency.

With the growing trend of urbanization and the growing number of people migrating to cities, the demand for the development and construction of new buildings as well as the infrastructure has risen, meaning that the construction industry must adapt to the trends. The growing demands with shorter deadlines for an industry already known for high costs and not delivering on time means that productivity must be increased without increasing the cost. The solution for this might lie in the application of the Lean philosophy to the construction industry. This paper analyses the application of the Lean philosophy in order to increase the productivity in the airport construction site. The paper highlights the potential for enhancing productivity in construction workplaces by concurrently fostering continuous improvement and sustainability through the implementation of the A3 methodology and Lean principles, resulting in waste reduction and increased value.

The advantage of FPGAs lies in the ability to implement a fully hardware solution for interfacing with various input/output (I/O) devices. Each block can work in parallel with all the others, simplifying the satisfaction of timing constraints. However, this hardware utilization consumes FPGA resources that could otherwise be allocated to the primary project. An alternative involves employing a small "soft-core" processor implementing I/O in software. With the goal to design and evaluate a new tiny soft-core processor optimized for FPGA resources in I/O , a novel processor named Baby8 is developed. It is an 8-bit CISC soft-core processor optimized for reduced FPGA resources including program size for 8-bit applications. The number of instructions is not large but any instruction can access arbitrary memory locations. The performance and resource utilization of the newly designed processor are evaluated and compared with a variety of other soft-core processors. The results demonstrate its competitive performance, achieving an average maximum clock frequency of approximately 57 MHz and a power consumption of around 2mW. Furthermore, it conserves nearly half of the FPGA resources in implementation.

Additive Friction Extrusion Deposition (AFED) is a recently developed additive manufacturing technique that promises high deposition rates at low forces. Due to the novelty of the process, the underlying phenomena and their interactions are not fully understood, and in particular the processing strategy and tool design are still in their infancy. This work contributes to the state of the art of AFED through a comprehensive analysis of its working principles and an experimental program including a representative sample component. The working principle and process mechanics of AFED are broken down into their individual components. Forces, their origins and effects on the process are described, and measures of process efficiency and theoretical minimum energy consumption are derived. Three geometrical features of the extrusion die are identified as most relevant to the active material flow, process forces and deposition quality: the topography of the inner and outer circular surfaces and the geometry of its extrusion channels. Based on this, the experimental program investigates seven different tool designs in terms of efficiency, force reduction and throughput. The experiments using AA 6061-T6 as feedstock show that AFED is capable of both high material throughput (close to 550 mm³/s) and reduced substrate forces: For example, the forces for a run at 100 mm³/s remained continuously below 500 N, and for a run at 400 mm³/s below 3500 N. Material flow and microstructure of the AFED are assessed from the macrosections. Significant differences are found between the advancing and retracting sides for both process effects and material flow. Banded structures show strong similarities to other solid state processes. The fabrication of the sample components demonstrates that AFED is already capable of producing industrial grade components. In mechanical testing, interlayer bonding defects result in a more brittle failure behavior in the direction of the structure, in the microstructure show strong similarities to other solid-state processes. The manufacturing of the sample components demonstrates that AFED is already capable of producing industrial grade components. In mechanical tests, interlayer bonding defects result in a more brittle failure behavior in the direction of the structure, while in the horizontal direction of the structure mechanical properties corresponding to a T4 temper have been achieved.

Neural-objective and image optimization approaches for drilling fluid rheology automation are crucial for drilling engineering optimization. A myriad of intelligent computational models are employed to predict and monitor the parameters of mud rheology and filter cake permeability posture using an artificial neural network feedforward (ANN-FF) function, a non-ANN-FF function, an image processing tool, and a model optimization tool. 498 datasets of synthetic-based mud (SBM) flat rheology from a drilling mud laboratory at Nazarbayev University were imported into a user-friendly MATLAB application using image processing and nftool. Apart from the Google TensorFlow Sequential API DNN architecture used, a Levenberg-Marquardt training algorithm with input sigmoid hidden neurons 10, 12, and 18 coupled with a linear output layer was also used to predict the SBM flow index. OBM and SBM filter cakes were processed for void spaces; however, a model study was extrapolated to maximize filtration volume Vf. The study’s findings show that the ANN-FF model employed for rheological property monitoring and prediction had higher steady and exponential levels of accuracy and a correlation coefficient of 0.96-0.99. More so, SBM and OBM image processing presented an area of void spaces of 1790M2 and 1739M2, respectively. The porosity and permeability postures of both SBM and OBM resulted in a significant void space capable of maximizing the flow index. In addition, the single-objective modelling based on a genetic algorithm did validate the experimental rheological data for flow index or filtration volume Vf optimization; the study resulted in the finding that the constrained physics-informed objective function hindered maximizing oil recovery and instead predicted possible formation damage. It is empirical to note that automating flow predictions with neural, objective, and image functions has presented an alternative novel method for non-programmers using MATLAB and Google Colabs that is capable of enhancing mud rheological parameters, drilling efficiency, and hydrocarbon recovery.

Alginate, a biopolymer promising in food, biomedical, pharmaceutical, and electronic materials industries, is characterized by its biodegradability, biocompatibility, low toxicity, and gel-forming properties. It is most abundantly found in brown algae. However, conventional dilute acid and alkali extraction methods face limitations in commercialization due to their long processing time, low throughput, and high solvent requirements. In this study, a microwave-assisted extraction (MAE) process for sodium alginate was designed to improve extraction efficiency. Solid/liquid ratio, extraction temperature, and extraction solvent concentration were major variables affecting sodium alginate extraction from Undaria pinnatifida(sea mustard). They were then statistically optimized using response surface methodology. Under optimal conditions (13.27 g/L, 91.86 ℃, 2.51 %(w/v), and 15 min), the yield was 38.41 %, which was 93.43 % of the theoretical content of sodium alginate in Undaria pinnatifida. Our work has confirmed the productivity and industrial feasibility of efficient extraction of sodium alginate from marine biomass, and we hope that it will serve as an encouraging case for the application of biopolymers as one of the desirable options for alternative petrochemicals to construct a sustainable society.

The effectiveness of Fuzzy Inference Systems (FISs) in manipulating uncertainty and nonlinearity makes them a subject of significant interest for decision-making in embedded systems. Accordingly, optimizing FIS hardware improves its performance, efficiency, and capabilities, leading to a better user experience, increased productivity, and cost savings. To be compatible with the limited power budget in most embedded systems, this paper presents a framework to realize ultra-low power FIS hardware. It supports optimizations for both conventional arithmetic as well as MSDF-computing to be highly consistent with MSDF-based sensors. In MSDF-computing FIS all the processes of fuzzification, inference, and defuzzification are done on serially coming data bits. To demonstrate the efficiency of the proposed framework, we utilized Matlab, Chisel3, and Vivado to implement it from high-level descriptions of FIS to hardware synthesis. We also developed a Scala library in Chisel3 to establish a connection between these tools, bridging the gap, and facilitating design space exploration at the arithmetic level. Furthermore, we realized an FIS for the navigation of autonomous mobile robots in unknown environments. Synthesis results show the superiority of the output of our suggested design framework in terms of resource usage as well as power and energy consumption compared to the Matlab HDL code generator output.

Discretization in time and in the state space of the system leads to the necessity to solve the parameter identification problems for dynamic systems in a limited time (at a finite time interval) using observations obtained under the influence of unknown-but-bounded noise. Finding the solution in this case is more difficult compared to traditional identification problem setting which considers random independent zero-mean noise. For system parameter identification problem under unknown-but-bounded noise, a randomized stochastic optimization algorithm is given in the paper, estimates for the mean square values of the residuals for a finite observation interval are obtained. An example of application of the given method to the problem of tuning the parameters of a multi-mirror telescope is considered.

Within the past few decades, thousands of experiments have been performed to characterize waste and biomass to estimate the bioenergy potential and product identification. There is a need to develop an integrated process model based on experimental literature, and simulation to obtain suitable products. In this study municipal solid waste (MSW) characterization and integrated process model have been developed to optimize final products in a reactor system. The process model has two modes R&D and reactor control (RC) to obtain suitable products including bio-oil, char, and gases. A database was integrated based on thermokinetics, machine learning and simulation models to optimize product efficiency. The experimental data includes thermogravimetric analysis, Fourier transform Infrared Spectroscopy, Gas chromatography, and Mass spectrometry, which are linked with pyrolysis experimental setup. Feedstock-product mapping models were incorporated into the database along with the temperature, heating rates, elemental analysis, and final product concentration, which are utilized for pyrolysis reactor setup. Product feasibility is conducted based on lifecycle cost, affordability, and product efficiency. The present work will bridge the gap between experimental study and decision making based on obtained products at several experimental conditions around the world.

Lateral vehicle control is a high importance in automated vehicles as it directly influences the vehicle’s performance and safety during operation. The Linear Quadratic Regulator (LQR) controller stands out due to its high-performance characteristics and is used in the open source for self driving functions. However, a notable limitation of the current approach is the manual calibration of LQR controllers based on the experience and intuition of the designers, leading to empirical uncertainties. To address this issue and enhance the lateral control performance, this paper concentrates on refining the LQR by employing three optimization algorithms: Artificial Bee Colony Optimization (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). These algorithms aim to overcome the reliance on empirical methods and enable a data-driven approach to LQR calibration. By comparing the outcomes of these optimization algorithms to the manual LQR controller within an offline multibody simulation as testing platform, the study highlights the superiority of the best-performing optimization approach. Following this, the optimal algorithm is implemented on a real-time system for the full vehicle level, revealing the Model-in-the-Loop and the Hardware-in-the-Loop gap up to 78,89% with lateral velocity when we use Relative Error Criterion (REC) method to validate and 2.35m with lateral displacement when considering by maximum absolute value method.

Quantum computing is an emerging field that have given a significant impact on optimization. Among the diverse quantum algorithm, the quantum gradient descent has become a prominent technique for solving unconstrained optimization problems. In this paper, we propose a quantum-spectral Polak-Ribi{\'e}re-Polyak (PRP) conjugate gradient approach. The technique is considered as a generalization of the spectral PRP method which employs a $q$-gradient that approximate the classical gradient with quadratically better dependence on the quantum variable $q$. Additionally, the proposed method reduces to the classical variant as quantum variable $q$ gets closer to $1$. The quantum search direction always satisfies the sufficient descent condition and does not depend on any line search. This approach is globally convergent with the standard Wolfe line search without any convexity assumption. Numerical experiments are conducted and compared with the existing approach to demonstrate the improvement of the proposed strategy.

As a new logistics system, Physical Internet (PI) provides a solution to cope with the continuous rise of urban logistics demand by exploring same-level and cross-level paths and flattening. This research suggests a PI-based urban logistics distribution cost minimization model based on the examination of the primary elements influencing PI distribution, intending to maintain the logistic transportation process' integrity. This proposed model enhances the flexibility of goods operation and reduces the global cost of logistics distribution using the interconnected and open PI logistics distribution mode. The experimental results show that the distribution cost of the PI logistics model is significantly better than that of a traditional logistics model. The elastic and cost advantages relatively grow as the infrastructure for distribution scales up and the interruptions from emergencies occur.

The present work sets out the evaluation of composite laminates through optimization objective functions. Genetic Algorithms (GA), as well as Simulated Annealing (SA), were performed in order to determine the optimal ply orientations of a fiber-reinforced polymer laminate for three given load cases: 1) in-plane loads, 2) combined moments, and 3) in-plane loads and combined moments. It searches the optimal orientation layup, which fulfills at the same time both the maximum strain criterion and the Tsai-Wu failure criterion. Construction of an objective function based on the strains and the stresses involves a minimization process to deformations and a maximization of the safety factor. For the first load case, the initial solution is [±45/0/90/±45]s, and the best solution is [(45/135)2/452]s. For the second load case, [-45/45/45/-45/0/90]s is the initial layup sequence, then the best solution obtained is [(45/-45)2/452]s, where plies at 0° and 90º are not necessary even when axial loads are applied. For the third study case, the original layup sequence is [0/45/-45/45/0/90]s; meanwhile, the best solution calculated is [21/24/145/140/45/49]s. An interesting observation is that each pair of layers has a 5º gap. The simulations show that the qualitative results from the GA are better than the SA, but with a significantly higher computational cost. These kinds of computational tools are expected to be used as a reference guide for an optimal fiber configuration with respect to the common orientations used when composite laminates are designed for a structural application.

In the current study, a two-dimensional numerical study is carried out to investigate the performance of a novel Double-chamber Parallel Flexible Valves micropump, which utilized the electrowetting-on-dielectrics (EWOD) effect to drive the microfluid flow. By observing the flow fields, the internal circulations are seen on both the left and right side of the pump. The generation of the backflow is discussed by tracking the movement of the vortices. Only slight flow fluctuation is seen in the micropump. Based on the simulation outcomes, the structural parameters including the width of the inlet/outlet, the width of the pumping channel and the diverging angle in the micropump are analyzed, the influence of these parameters on the pumping volume and the maximum pressure have been discussed. Eventually, a group of optimal parameter combinations is given according to the results to extend the operating potential of the micropump.

Building facade design plays an essential role in enhancing energy efficiency and reducing environmental impact in high-performance building design. Balancing the conflicts among various building facade design parameters to satisfy different optimization objectives constitutes a highly complex optimization problem. The rapidly increasing number of studies demonstrates a significant interest in implementing multi-objective optimization methods to tackle building facade optimization problems. This study conducts a systematic review of optimization methods for building facade optimization (BFO). The optimization objectives and design variables are categorized based on their characteristics. The efficiency and effectiveness of optimization algorithms in addressing BFO problems are compared. Building optimization techniques and tools are showcased, along with their functions and limitations. Key findings highlight the robust feasibility and effectiveness of optimization algorithms, methods, and techniques in resolving a diverse range of BFO challenges. The limitations, challenges, and future potential of these methods are summarized and proposed.

Slime Mould Algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime molds during foraging. Numerous researchers have widely applied SMA and its variants in various domains and proved its value by the experiments in literatures. In this paper a comprehensive survey on SMA is introduced, which is based on 130 articles visa Google-scholar between 2022 and July, 2023. Firstly, the theory of SMA is described. Secondly the improved SMA variants are provided and categorized according to the approach that they are applied with. Finally, it also discusses the main applications domains of SMA such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, image segmentation and etc. This review presents some research suggestion for researcher who is interested in this algorithm.

With an increasing concern for global warming, there have been many attempts to reduce greenhouse gas emissions. About 30 % of total energy has been consumed by buildings and much attention has been paid to reducing building energy consumption. While there are many ways of reducing building energy consumption, accurate energy consumption prediction becomes more significant. As mechanical systems are the most energy-consuming components in the building, the present study developed the energy consumption prediction model for a direct-fired absorption chiller by using the ANN technique for the short term. The ANN model was optimized and validated with the actual data collected through a BAS. For the optimization, the numbers of input variables and neurons, and the data size of training were applied. By changing these parameters, the predictive performance was analyzed. In sum, the outcome of the present study can used to predict the energy consumption of the chiller as well as improve the efficiency of the energy management. The outcome of the present study can be used to develop a more accurate prediction model with a few datasets in that it can improve the efficiency of building energy management.

The ultimate aim of dynamic load balancing in cloud computing systems is to maximise the efficiency with which resources are utilised and workloads are distributed. Given that load balancing is a multi-objective process and that response time is a priority, the Harris hawk optimisation (HHO) algorithm was developed as a unique solution for dynamic load balancing. Based on burden distribution and resource utilisation, the HHO algorithm is responsible for dynamically assigning workloads to virtual machines (VMs). Through iterative interactions and position updates, the hawks investigate the solution space, determine the optimal method for dividing the work, and adapt to the ever-changing conditions of the workload. The HHO algorithm has been demonstrated to be effective and efficient in the management of dynamic load balancing via a series of experimental evaluations and comparisons with other load-balancing approaches. These discoveries have led to quicker response times and more efficient resource utilisation. Utilising the collaborative search behaviour of hawks, this technique provides a solution that is both practicable and effective for addressing load balancing concerns in dynamic scenarios.

This study investigates the applications of reconfigurable intelligent surfaces (RIS) in heterogeneous downlink networks (HetNet). Due to the network densification, the small cell base station (SBS) interferes with the macrocell users (MUE). In this paper, we utilize RIS to mitigate cross-tier interference in an HetNet via directional beamforming by adjusting the phase shift of the RIS. We consider an RIS-assisted HetNet consisting of multiple SBS nodes and MUEs that utilize both direct paths and reflected paths. Therefore, the aim of this study is to maximize the sum rate of all MUEs by jointly optimizing the transmit beamforming of the macrocell base station (MBS) and the phase shift of the RIS. An efficient RIS reflecting coefficients-based optimization (RCO) is proposed based on a successive convex approximation approach. Simulation results are provided to show the effectiveness of the proposed scheme in terms of its sum rate in comparison with the scheme HetNet without RIS and the scheme HetNet with RIS but with random phase shifts.

Data-driven models based on artificial intelligence are efficiently used to solve complex problems. The quality of groundwater is of utmost importance, as it directly impacts human health and the environment. In major parts of the world groundwater is the main source of drinking water, it is essential to periodically monitor water quality. Conventional water quality monitoring techniques involve periodical collection of water samples and analysis in the laboratory. This process is expensive, time consuming and involves lot of manual labor. The aim of our study is to build an ant colony optimized neural network for predicting groundwater quality parameters. We have proposed artificial neural network comprising of six hidden layers. The approach was validated using our groundwater quality dataset of a hard rock region in the northern part of Karnataka, India. Groundwater samples were collected by us periodically from March 2014 to October 2020 from 50 wells in this region. These samples where analysed for measuring the pH, Electrical Conductivity, Na+, Ca+, Na+, K+, Mg2+, F-, Cl- and U+. The temporal dataset was split for training, testing and validation of our model. Metrics such as R2 (Coefficient of Determination), RMSE (Root Mean Squared Error), NSE (Nash–Sutcliffe efficiencies) and MAE (Mean Absolute Error) were used to evaluate the prediction error and model performance. These performance evaluation metrics indicated the efficiency of our model in predicting the temporal variation in groundwater quality parameters. The method proposed by us can be used for prediction and the temporal frequency of sample collection can be reduced to save time and cost. The results also confirm that the combination of artificial neural network with ACO is a promising tool to optimize weights while training the network, and hence will help in reasonable prediction of groundwater quality parameters.

We present a new model formulation for a class of capacitated lot-sizing problem considering setup costs, product returns, and remanufacturing (CLSP-RM). We investigate a broad class of instances that fall into two groups, in the first group we can reformulate the problem with a relaxation and test whether the original problem is solvable. The relaxation gives near optimal solutions and the solution of this class does not give any difficulty to known solvers such as Cplex, Gurobi or Xpress. The second group of instances are of category NP and will be solved with a simple period-by-period simulation

A new set of basis functions is presented. The foundation in mathematics is established. To reconstruct a signal, it is compared to the Fourier basis functions. The M-basis functions' potential applications are also presented.

The move to lean construction is a lengthy process that may call into question established ways of construction management delivery. The objective of this article is to help businesses take the first step on this route by introducing a few lean manufacturing techniques and practices that may be used on projects over the next few weeks. Although the construction industry has come a long way since its start, the essential technology to change it has not yet found a home. The digital switch has now entered the construction industry to boost production. Artificial intelligence (AI) is a discipline of computer science described as the potential of machines to mimic intelligent human behavior by modelling traditionally complicated problems using human-inspired techniques. Due to its complexity, AI distinguishes itself from lower degrees of digitalization. The complexity of artificial intelligence involves the formation of new circumstances for human collaboration and trust. This dissertation investigates the application of artificial intelligence and the suitable interaction between humans and AI-based technologies. This study attempts to shed light on how the construction industry may close the gap between the potential and actual benefits of artificial intelligence deployment. Comparing the prospective benefits of AI implementation to the present benefits and challenges of AI deployment in the construction industry revealed the disparity.

The capacitated team orienteering problem with time windows (CTOPTW) is a NP-hard combinatorial optimization problem. In the CTOPTW, a set of customers is given each with a profit, a demand, a service time and a time window. A homogeneous fleet of vehicles is available for serving customers and collecting their associated profits. Each vehicle is constrained by a maximum tour duration and a limited capacity. The CTOPTW is concerned with the determination of a preset number of vehicle tours that begin and end at a depot, visit each customer no more than once while satisfying the time duration, time window and vehicle capacity constraints on each tour. The objective is to maximize the total profit collected. In this study we propose an iterated local search (ILS) algorithm to deal with the CTOPTW. ILS is a single solution based meta-heuristic that successively invokes a local search procedure to explore the solution space. A perturbation operator is used to modify the current local optimum solution in order to provide a starting solution for the local search procedure. As different problems and instances have different characteristics, the success of the ILS is highly dependent on the local search procedure, the perturbation operator(s) and the perturbation strength. The basic ILS uses a single perturbation operator and the perturbation strength remains the same during the optimization process. To address these issues, we use three different perturbation operators and a varying perturbation strength which changes as the algorithm progresses. The idea is to assign a larger perturbation strength in the early stages of the search in order to focus on exploring the search space. The perturbation strength is gradually decreased so that we focus more on exploitation. The computational results show that the proposed ILS algorithm is able to generate high quality solutions on the CTOPTW benchmark instances taken from the scientific literature, demonstrating its efficiency in terms of both the solution quality and computational time. Moreover, the proposed ILS produces 21 best known results and 5 new best solutions.

The global financial crisis of 2008, triggered by the collapse of Lehman Brothers, highlighted a banking system that was widely exposed to systemic risk. The minimization of the systemic risk via a close and detailed monitoring of the entire banking network became a priority. This is a complex and demanding task considering the size of the banking systems: in the US and the EU they include more than 10000 institutions. In this paper, we introduce a methodology which identifies a subset of banks that can: a) efficiently represent the behavior of the whole banking system and b) provide, in the case of a failure, a plausible range of the crisis dispersion. The proposed methodology can be used by the regulators as an auxiliary monitoring tool, to identify groups of banks that are potentially in distress and try to swiftly remedy their problems and minimize the propagation of the crisis by restricting contagion. This methodology is based on Graph Theory and more specifically Complex Networks. We termed this setting a “multivariate Threshold – Minimum Dominating Set” (mT–MDS) and it is an extension of the Threshold – Minimum Dominating Set methodology (Gogas e.a., 2016). The method was tested on a dataset of 570 U.S. banks: 429 solvent and 141 failed ones. The variables used to create the networks are: the total interest expense, the total interest income, the tier 1 (core) risk-based capital and the total assets. The empirical results reveal that the proposed methodology can be successfully employed as an auxiliary tool for the efficient supervision of a large banking network.

This article presents new PEM fuel cell models with hexagonal and pentagonal designs. After observing cell performance improvement in these models, we optimized them. Inlet pressure and temperature were used as input parameters, and consumption and output power were the target parameters of the multi-objective optimization algorithm. Then we used artificial intelligence techniques, including deep neural networks and polynomial regression, to model the data. Next, we employed the RSM (Response Surface Method) method to derive the target functions. Furthermore, we applied the NSGA-II multi-objective genetic algorithm to optimize the targets. Compared to the base model (Cubic), the optimized Pentagonal and Hexagonal models averagely increase the output current density by 21.819\% and 39.931\%, respectively.

Ultrasound based haptic feedback is a potential technology for human-computer interaction (HCI) with the advantages of low cost, low power consumption and controlled force. In this paper, the phase optimization for multipoint haptic feedback based on ultrasound array is investigated and the corresponding experimental verification is provided. A mathematical model of acoustic pressure is established for the ultrasound array and then a phase optimization model for an ultrasound transducer is constructed. We propose a pseudo-inverse (PINV) algorithm to accurately determine the phase contribution of each transducer in the ultrasound array. By controlling the phase difference of the ultrasound array, the multipoint focusing forces are formed leading to various shapes such as geometries and letters that can be visualized. Because the unconstrained PINV solution results in unequal amplitudes for each transducer, a weighted amplitude iterative optimization is deployed to further optimize the phase solution, by which the uniform amplitude distributions of each transducer are obtained. For the purpose of experimental verifications, a platform of ultrasound haptic feedback consisting of a Field Programmable Gate Array (FPGA), an electrical circuit and an ultrasound transducer array is prototyped. The haptic performances of single point, multiple points and dynamic trajectory were verified by controlling the ultrasound force exerted on the liquid surface. The experimental results demonstrate that the proposed phase optimization model and theoretical results are effective and feasible, and the acoustic pressure distribution is consistent with the simulation results.

Four-wire low voltage microgrids supply one-phase consumers with continuously changing electricity demand. For addressing climate change concerns, governments implemented incentive schemes for residential consumers, encouraging the installation of home PV panels for covering self-consumption needs. In the absence of sufficient storage capacities, the surplus is sold back by these entities, called prosumers, to the grid operator or in local markets, to other consumers. While these initiatives encourage the proliferation of green energy resources, and ample research is dedicated to local market designs for prosumer-consumer trading, the main concern of distribution network operators is the influence of power flows generated by prosumer surplus injection on the operating states of microgrids. The change in power flow amount and direction can greatly influence the economic and technical operating conditions of radial grids. This paper proposes a metaheuristic algorithm for prosumer surplus management that optimizes the power surplus injections using the automated control of three-phase inverters, with the aim of improving the active power losses and balancing the phase voltage profiles. A case study is performed on two real distribution networks with distinct layouts and load profiles and the algorithm shows its efficiency in both scenarios.

Energy efficiency and leakage magnetic field (LMF) are two important issues in electric vehicle inductive chargers. In this work, the maximum achievable coil efficiency and the corresponding LMF strength are formulated as functions of hardware parameters, and figure of merits (FOM) are proposed for assessing the efficiency and LMF performance of the coil assembly pair. The impacts of the coil assemblies’ geometric parameters on both FOMs are examined with the aid of finite element analysis (FEA), and measures to improve the FOMs are extracted from FEA results. A coil assembly pair is manually optimized within given dimensional limits. Compared with the initial design, the optimized one achieves higher efficiency and lower LMF strength while consuming less copper. The performance improvement is verified by FEA results and experimental data measured on an 85 kHz electric vehicle inductive charger prototype. The key measures for coil assembly optimization are summarized.

Capability assessment plays a crucial role in the demonstration and construction of equipment. To improve the accuracy and stability of capability assessment, we study the neural network learning algorithms in the field of capability assessment and index sensitivity. Aiming at the problem of over-fitting and parameter optimization in neural network learning, the paper proposes an improved machine learning algorithm—the Ensemble Learning Based on Policy Optimization Neural Networks (ELPONN) algorithm with the policy optimization and ensemble learning. This algorithm presents optimized neural network learning algorithm through different strategies evolution, and builds an ensemble learning model of multi-intelligent algorithms to assessment the capability and analyze the sensitivity of the indexes. Through the assessment of capabilities, the algorithm effectively avoids parameter optimization from entering the minimum point in performance to improve the accuracy of equipment capability assessment, which is significantly better than previous neural network assessment methods. The experimental results show that the mean relative error is 4.10%, which is better than BP, GABP, and early stopping. The ELPONN algorithm has better accuracy and stability performance, and meets the requirements of capability assessment.

Using the above results from RMS analysis the optimum values were predicted for the independent significant variables (Figure 3) the optimized levels of these variables in combination with other media variables the maximum production was predicted to be 199.90 U/mL. The predicted data were validated through confirmatory experiments performed in triplicates. A 1.29-fold increase in amylase activity against un-optimized (OVAT) medium was achieved in the present study authenticating the efficacy of RSM in process optimization (Figure 4). 2.6 Model validation and scale-up at laboratory scale (5L) bioreactor Once the parameters were standardized in the shake-flasks culture, the experiment was scaled-up to a laboratory-scale bioreactor (5 L). The yield of amylase increased by 1.01 fold (205.69 U/mL), it could be possible because the enzyme production in a bioreactor is higher than in shake-flasks culture as the various critical variable factors such as the dissolved oxygen (DO) and the pH can be optimally controlled at the desired levels [22].

Regarding the high efficiency of metaheuristic techniques in energy performance analysis, this paper scrutinizes and compares five novel optimizers, namely biogeography-based optimization (BBO), invasive weed optimization (IWO), social spider algorithm (SOSA), shuffled frog leaping algorithm (SFLA), and harmony search algorithm (HSA) for the early prediction of cooling load in residential buildings. The algorithms are coupled with a multi-layer perceptron (MLP) to adjust the neural parameters that connect the CL with the influential factors. The complexity of the models is optimized by means of a trial-and-error effort, and it was shown that the BBO and IWO need more crowded spaces for fulfilling the optimization. The results revealed that the internal parameters (i.e., biases and weights) suggested by the BBO generate the most reliable MLP for both analyzing and generalizing the CL pattern (with nearly 93 and 92% correlations, respectively). Followed by this, the IWO emerged as the second powerful optimizer with mean absolute errors of 1.8632 and 1.9110 in the training and testing phases. Therefore, the BBO-MLP and IWO-MLP can be reliably used for accurate analysis of the CL in future projects.

Adhesion systems are very important in robots for infrastructure inspection (especially in vertical walls). They present the challenge of optimizing the ratio vacuum/power consumption in battery-powered robots. In this paper a CFD (Computer Fluid Dynamics) modelling and optimization process of a robot adhesion system is carried out to determine the best performing configuration in terms of vacuum and power consumption. Analytical and numerical models were developed to predict the behaviour of the system for different configurations. The models were validated, using test rig measurements, by calibrating an arbitrary defined inlet height that simulates the leakage flow. Then, different geometric parameters were varied to determine the best performing configuration based on the vacuum/power consumption ratio value. The model presented in the paper was capable of predicting the behaviour of the system for different configurations, with a margin of error of 15% for the vacuum prediction and a 25% for the motor power calculation. Finally, the model was used to optimize parameters of the system, like the number of blades of the impeller. The adhesion system was conceived for the modular autonomous climbing legged robot ROMERIN.

The adoption of Machine Learning (ML) for building emulators for complex physical processes has seen an exponential rise in the recent years. While neural networks are good function approximators, optimizing the hyper-parameters of the network to reach a global minimum is not trivial, and often needs human knowl- edge and expertise. In this light, automatic ML or autoML methods have gained large interest as they automate the process of network hyper-parameter tuning. In addition, Neural Architecture Search (NAS) has shown promising outcomes for improving model performance. While autoML methods have grown in popularity for image, text and other applications, their effectiveness for high-dimensional, complex scientific datasets remains to be investigated. In this work, a data driven emulator for turbulence closure terms in the context of Large Eddy Simulation (LES) models is trained using Artificial Neural Networks and an autoML frame- work based on Bayesian Optimization, incorporating priors to jointly optimize the hyper-parameters as well as conduct a full neural network architecture search to converge to a global minima, is proposed. Additionally, we compare the effect of using different network weight initializations and optimizers such as ADAM, SGDM and RMSProp, to explore the best performing setting. Weight and function space similarities during the optimization trajectory are investigated, and critical differences in the learning process evolution are noted and compared to theory. We observe ADAM optimizer and Glorot initialization consistently performs better, while RMSProp outperforms SGDM as the latter appears to have been stuck at a local minima. Therefore, this autoML BayesOpt framework provides a means to choose the best hyper-parameter settings for a given dataset.

In this study, a novel task of printing speed optimization for continuous fiber composites is investigated. Using continuous fibers is an innovative approach to reinforce products made by fused filament fabrication (FFF) additive manufacturing (AM) technology. In the printing process of composites with continuous fibers, the printing speed is critical because of its significant effect on the geometric shape of the samples, especially their corners. During optimization in this research, continuous glass fiber (CGF) and polylactic acid (PLA) filaments were utilized as reinforcing phase and matrix, respectively, and were simultaneously fed into the extrusion-based polymer 3D printer to form PLA/CGF composites. The optimization was carried out by calculating the temperature changes of the deposited rasters in the presence and absence of fibers as a first step and then determining the special relationship between the printing speeds and rasters temperature changes. Finally, the optimal and the maximum printing speed was computed based on a hypothesis, which is proved by the results of high-quality printed composites with different geometric shapes.

Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems are the most widespread around the globe and are commonly found to have a ready-to-use infrastructure composed mostly of access points (APs). They provide useful information on signal strength to be processed by adequate location algorithms, which are not always capable of achieving the desired localization error only by themselves. In this sense, this paper proposes a new method to improve the accuracy of IPSs by optimizing some of their most relevant infrastructure components. Included are the arrangement of APs over the environment, the number of reference points (RPs), and the number of samples per location estimation test. A simulation environment is also proposed, in which the impact of key influencing factors on system accuracy is analyzed. Finally, a case study is simulated to validate an optimal combination of design parameters and its compliance with the requirements of localization error and the limited number of access points. Our simulation results clearly show that the desired localization accuracy, which is set as a goal, can be achieved while maintaining the factors already mentioned at minimal levels, which decreases both system deployment costs and computational effort.

Umbu is a tropical fruit with high content of bioactive compounds. However, maturation causes significant losses on nutrient density reducing the nutritional value of this highly appreciated fruit. Thus, the objective of the present work was to encapsulate the Umbu fruit bioactive compounds using spray drying and identify the variables affecting the biomolecules preservation. A Box-Behnken experimental design with 3 factors was set varying inlet temperature, atomization flow rate, and maltodextrin concentration for process otimization. Then the powder physicochemical and chemical properties were characterized, and results were modeled using a polynomial equation. Results revealed that the droplet size and maltodextrin concentration had a significant influence on the conservation of the biomolecules. Drying kinetics favoring fast formation of a particle crust increases encapsulation efficiency. Bioactive compounds retention was achieved by increasing maltodextrin even at high temperatures, where a matrix is formed hindering chemical degradation. Process optimization was validated and revealed low inlet temperatures (122ºC), high atomization flow rate (5kg/h) and high maltodextrin concentration (20%) to be the most desirable conditions for bioactive compounds retention.

Nature-inspired algorithms are very popular tools for solving optimization problems inspired by nature. However, there is no guarantee that optimal solution can be obtained using a randomly selected algorithm. As such, the problem can be addressed using trial and error via the use of different optimization algorithms. Therefore, the proposed study in this paper analyzes the time-complexity and efficacy of some nature-inspired algorithms which includes Artificial Bee Colony, Bat Algorithm and Particle Swarm Optimization. For each algorithm used, experiments were conducted several times with iterations and comparative analysis was made. The result obtained shows that Artificial Bee Colony outperformed other algorithms in terms of the quality of the solution, Particle Swarm Optimization is time efficient while Artificial Bee Colony yield a worst case scenario in terms of time complexity.

Nature-inspired algorithms are very popular tools for solving optimization problems inspired by nature. However, there is no guarantee that optimal solution can be obtained using a randomly selected algorithm. As such, the problem can be addressed using trial and error via the use of different optimization algorithms. Therefore, the proposed study in this paper analyzes the time-complexity and efficacy of some nature-inspired algorithms which includes Artificial Bee Colony, Bat Algorithm and Particle Swarm Optimization. For each algorithm used, experiments were conducted several times with iterations and comparative analysis was made. The result obtained shows that Artificial Bee Colony outperformed other algorithms in terms of the quality of the solution, Particle Swarm Optimization is time efficient while Artificial Bee Colony yield a worst case scenario in terms of time complexity.

In tracking-by-detection paradigm for multi-target tracking, target association is modeled as an optimization problem that is usually solved through network flow formulation. In this paper, we proposed combinatorial optimization formulation and used a bipartite graph matching for associating the targets in the consecutive frames. Usually, the target of interest is represented in a bounding box and track the whole box as a single entity. However, in the case of humans, the body goes through complex articulation and occlusion that severely deteriorate the tracking performance. To partially tackle the problem of occlusion, we argue that tracking the rigid body organ could lead to better tracking performance compared to the whole body tracking. Based on this assumption, we generated the target hypothesis of only the spatial locations of person’s heads in every frame. After the localization of head location, a constant velocity motion model is used for the temporal evolution of the targets in the visual scene. Qualitative results are evaluated on four challenging video surveillance dataset and promising results has been achieved.

Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in market conditions prediction and network constraints. Thereafter, the importance of energy systems with flexible dispatch is reinforced, ensuring energy storage as an essential asset for these systems to be able to balance production and demand. In order to do so, such systems should participate in whole-sale energy markets, enabling competition among all players, including conventional power plants. Consequently, an effective dispatch schedule considering market and resource uncertainties is crucial. In this context, an innovative dispatch optimization strategy for schedule planning of renewable systems with storage is presented. Based on an optimization algorithm combined with a machine learning approach, the proposed method develops a financial optimum schedule with the incorporation of uncertainty information. Simulations performed with a concentrated solar power plant model following the proposed optimization strategy demonstrate promising financial improvement with a dynamic and intuitive dispatch planning method, emphasizing the importance of uncertainty treatment on the enhanced quality of renewable systems scheduling.

The purpose of this study was to investigate the effect of chitosan (CS) and Alginate (Alg) polymers concentrations and CaCl2 concentration on metronidazole (MET) drug loading (LE), size particles and zeta potential. Nanocomposites were prepared by ionotropic pregelation method. A (21 *31 *21) *3= 36 full factorial design (FFD) was used to predict statistical equation and responses. The MET-CS-AlgNPs nanocomposites were characterized by X-ray diffraction, Fourier-transform infrared spectroscopy, thermal gravimetric analysis, scanning electron microscope and in vitro drug release studies. All data indicated the presence of drug into MET-CS-AlgNPs nanocomposites. The release profile of MET-CS-AlgNPs nanocomposites was found to be sustained

Since large power transformers are custom-made, and their design process is a labor-intensive task, their design process is split into different parts. In tendering, the price calculation is based on the preliminary design of the transformer. Due to the complexity of this task, it belongs to the most general branch of discrete, non-linear mathematical optimization problems. Most of the published algorithms are using a copper filling factor based winding model to calculate the main dimensions of the transformer during this first, preliminary design step. Therefore, these cost optimization methods are not considering the detailed winding layout and the conductor dimensions. However, the knowledge of the exact conductor dimensions is essential to calculate the thermal behaviour of the windings and make a more accurate stray loss calculation. The paper presents a novel, evolutionary algorithm-based transformer optimization method which can determine the optimal conductor shape for the windings during this examined preliminary design stage. The accuracy of the presented FEM method was tested on an existing transformer design. Then the results of the proposed optimization method have been compared with a validated transformer design optimization algorithm.

The purpose of this work is to define optimal growth conditions for batch culture of the cyanobacterium Arthrospira maxima and the microalgae Chlorella vulgaris, Isochrysis galbana and Nannochloropsis gaditana. Thus, we study the effect of three variables on algae growth: i.e., inoculum:culture medium ratio, light:darkness photoperiod and type of culture medium, including both synthetic media and wastewaters. The results showed that the initial inoculum volume did not affect the amount of biomass at the end of the growth (14 days), whereas an excess (18 h) or defect (6 h) in the number of hours of light is determinant for its development. The contribution of nutrients from different culture media modified the growth of the different species. A. maxima was favoured in seawater enriched with Guillard's F/2 as well as C. vulgaris and N. gaditana but in fresh water medium. I. galbana had the greatest growth in the marine environment enriched with Walne’s media. Nitrate was the limiting growth reagent at the end of the exponential phase of growth for C. vulgaris and N. gaditana, while iron was for A. maxima and I. galbana. All species demonstrated their capability to grow in effluents from a wastewater treatment plant and they efficiently consume nitrogen, especially the three microalgae species.

A new design scheme is proposed for twisting the walls of a microchannel, and its performance is demonstrated numerically. The numerical study was carried out for a T-shaped microchannel with twist angles in the range of 0 to 34π. The Reynolds number range was 0.15 to 6. The T-shaped microchannel consists of two inlet branches and an outlet branch. The mixing performance was analyzed in terms of the degree of mixing and relative mixing cost. All numerical results show that the twisting scheme is an effective way to enhance the mixing in a T-shaped microchannel. The mixing enhancement is realized by the swirling of two fluids in the cross section and is more prominent as the Reynolds number decreases. The twist angle was optimized to maximize the DOM, which increases with the length of the outlet branch. The twist angle was also optimized in terms of the relative mixing. The two optimum twisting angles are generally not coincident. The optimum twist angle shows a dependence on the length of the outlet branch but it is not affected much by the Reynolds number.

In this paper, we propose two novel methods for robot-world/hand-eye calibration and provide a comparative analysis against six state-of-the-art methods. We examine the calibration problem from two alternative geometrical interpretations, called hand-eye and robot-world-hand-eye, respectively. The study analyses the effects of specifying the objective function as pose error or reprojection error minimization problem. We provide three real and three simulated datasets with rendered images as part of the study. In addition, we propose a robotic arm error modeling approach to be used along with the simulated datasets for generating a realistic response. The tests on simulated data are performed in both ideal cases and with pseudo-realistic robotic arm pose and visual noise. Our methods show significant improvement and robustness on many metrics in various scenarios compared to state-of-the-art methods.

We proposed a deep learning based chemometric data analysis technique. We trained L2 regularized sparse autoencoder end-to-end for reducing the size of the feature vector to handle the classic problem of curse of dimensionality in chemometric data analysis. We introduce a novel technique of automatic selection of nodes inside hidden layer of an autoencoder through pareto optimization. Moreover, linear regression, ϵ-SVR , and Gaussian process regressor are applied on the reduced size feature vector for the regression. We evaluated our technique on orange juice and wine dataset and results are compared against state-of-the-art methods. Quantitative results are shown on Normalized Mean Square Error (NMSE) and the results show considerable improvement in the state-of- the-art.

Bauxite residue is the voluminous by-product of alumina production after Bayer process. Its high alkalinity causes disposal problems and harmful environmental impacts. However, the residue contains significant amounts of valuable elements such as rare earth elements including scandium. Greek bauxite residue contains a high amount of scandium close to its main resources. Taking into account scandium limited availability coupled with its high demand in modern technology, bauxite residue could be considered as a potential resource for scandium recovery. In this study, the optimization of scandium extraction from bauxite residue with sulfuric acid is investigated using Taguchi methodology. Based on previous studies acid molarity, leaching time, solid/liquid ratio and reaction temperature were selected as control parameters for the selective Sc recovery. Method optimization targeted the highest concentration of scandium combined with the lowest concentration of iron without taking into account applications constraints. The predicted values resulted by Taguchi methodology were affirmed by a confirmation experiment conducted at optimal conditions. Regression analysis provided the respective equations to be applied on several conditions depending on different applications.

Municipal landfills play a vital role in disposing of the solid waste from a community. One of the biggest challenges landfill management face is when the available storage space is being reduced faster than the rate that was planned for. This shortening of the landfill lifecycle impacted the city of Wilmington, Delaware when it was found that their landfill had lost 11 years of its projected lifespan and would reach fully capacity within the next decade. In order to remedy the situation six alternative solutions were devised, with each having different costs (capital, operating, and transport) and varying lengths of landfill extension. This research used the principle of optimization via a LINDO program to determine which solution would achieve the longest landfill extension for the minimum total cost. Other constraints placed on the program include reducing the waste being sent to the landfill and to achieve a minimum of 25 additional years for the landfill. The calculated solution produced a cost reduction of $232.64 million dollars and 3.2 additional years of operation from the option that was eventually chosen by the landfill authority.

This paper provides an overview of the new CPAM Project on Additive Manufacturing (AM) in design and simulation, focusing on topology & lattice structure optimization for a light-weighting advantage. This industry/academia collaboration project aims to utilize existing hardware and software tools, and investigate the practical limits of the technologies, providing eventual guidelines for general use. This will provide a solid foundation for the practical use of metal AM optimized solid and latticed structures especially for Ti6Al4V parts. Two case studies are demonstrated here, one a purely topology optimized design, and one also incorporating lattice optimized design, both from Ti6Al4V and load-bearing components, to be utilized in the Nelson Mandela University (NMU) Eco-Car Project in competition, late in 2018. This paper presents the Design for Additive Manufacturing (DfAM) process, the challenges met iro applying a DfAM design mindset, and a unique final voxel-based smoothing step finishing off the design process. Detailed structural integrity assessment of these parts are included - the question remains: can Additive Manufacturing help win the race?

An adaptive operation strategy for on-demand control of DMFC system is proposed as an alternative method to enhance the voltage stability. Based on a single-cell DMFC stack, a newly simplified semi-empirical model is developed from the uniform-designed experimental results to describe the I-V relationship. Integrated with this model, the multi-objective optimization method is utilized to develop an adaptive operation strategy. Although the voltage instability is frequently encountered in unoptimized operations, the voltage deviation is successfully decreased to a required level by adaptive operations with operational adjustments. Moreover, the adaptive operations are also found to be able to extend the range of operating current density or to decrease the voltage deviation according to ones requirements. Numerical simulations are implemented to investigate the underlying mechanisms of the proposed adaptive operation strategy, and experimental adaptive operations are also performed on another DMFC system to validate the adaptive operation strategy. Preliminary experimental study shows a rapid response of DMFC system to the operational adjustment, which further validates the effectiveness and feasibility of the adaptive operation strategy in practical applications. The proposed strategy contributes to a guideline for the better control of output voltage from operating DMFC systems.

Shale gas production is associated with significant usage of fresh water and discharge of wastewater. Consequently, there is a necessity to create the proper management strategies for water resources in shale gas production and to integrate conventional energy sources (e.g., shale gas) with renewables (e.g., solar energy). The objective of this study is to develop a design framework for integrating water and energy systems including multiple energy sources, cogeneration process, and desalination technologies in treating wastewater and providing fresh water for shale gas production. Solar energy is included to provide thermal power directly to a multi-effect distillation plant (MED) exclusively (to be more feasible economically) or indirect supply through a thermal energy storage system. Thus, MED is driven by direct or indirect solar energy, and excess or direct cogeneration process heat. The proposed thermal energy storage along with the fossil fuel boiler will allow for the dual-purpose system to operate at steady-state by managing the dynamic variability of solar energy. Additionally, electric production is considered to supply a reverse osmosis plant (RO) without connecting to the local electric grid. A multi-period mixed integer nonlinear program (MINLP) is developed and applied to discretize operation period to track the diurnal fluctuations of solar energy. The solution of the optimization program determines the optimal mix of solar energy, thermal storage, and fossil fuel to attain the maximum annual profit of the entire system. A case study is solved for water treatment and energy management for Eagle Ford Basin in Texas.

The design of curved surface sliders (CSS) based on the elastic response spectrum is done by iteration to find the combination of friction coefficient and displacement capacity which satisfies the condition that the maximum horizontal CSS force is equal to the horizontal force of the structure. Although this CSS design is valid it does not necessarily minimize structural acceleration. This paper therefore describes the optimum CSS design for minimum structural acceleration. All valid CSS designs and the optimum CSS design are represented by their associated trajectory in the elastic response spectrum plane which visualizes the optimization problem. The results demonstrate that the optimum CSS design is not obtained at maximum tolerated effective damping ratio. The subsequent sensitivity analysis describes how much the structural acceleration increases if the actual friction coefficient of the real CSS deviates from its optimum design value. The analysis points out that the increase in structural acceleration is approximately one order of magnitude smaller than the deviation in friction. The sensitivity data may be used by structural engineer to determine tolerable deviations in friction coefficient which still results in acceptable structural accelerations.

In order to coordinate the damping performance and energy regenerative performance of energy regenerative suspension, this paper proposes a structure of vehicle semi-active energy regenerative suspension with electro-hydraulic actuator (EHA). In light of the proposed concept, a specific energy regenerative scheme is designed and the mechanical properties test is carried out. Based on the test results, the parameter identification for the system model is conducted using recursive least squares algorithm. On the basis of system principle, the nonlinear model of the semi-active energy regenerative suspension with EHA is built. Meanwhile, LQG control strategy of the system is designed. And then the influence of the main parameters of EHA on the damping performance and energy regenerative performance of suspension is analyzed. Finally, the main parameters of EHA actuator are optimized via genetic algorithm. The test results show that when sinusoidal is input at the frequency of 2Hz and the amplitude of 30mm, the spring mass acceleration RMS value of optimized EHA semi-active energy regenerative suspension is reduced by 22.23% and energy regenerative power RMS value is increased by 40.51%, which means while meeting the requirements of certain vehicle ride comfort and driving safety, energy regenerative performance is improved significantly.

In this paper a variable’s involved in assessing the quality of a distributed generation system are reviewed, aiming to minimize the electric power losses (unused power consumption) and optimize the voltage profile. To provide this assessment, several experiments have been made to the IEEE 34-bus test case and various actual test cases with the respect of multiple DG units. The possibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been verified. Finally, four algorithms were trailed: simulated annealing (SA), hybrid genetic algorithm (HGA), genetic algorithm (GA) and variable neighbourhood search. The HGA algorithm was found to produce the best solution at a cost of longer processing time.

The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for 15-5PH Stainless steel by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of machining variables such as concentration of cutting fluid , temperature of cutting fluid , feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error and material removal rate, the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is shown that the most significant factor is cutting speed, followed by feed rate, concentration of cutting fluid, radial depth of cut and temperature of cutting fluid. Finally, confirmation tests were carried out to make a comparison between the experimental results and developed model. Experimental results have shown that machining performance in the turn-boring process can be improved effectively through this approach.

As with many of the non-deterministic search algorithms, particularly those are analogous to complex biological systems, there are a number of inherent difficulties, and the Bees Algorithm (BA) is no exception. Basic versions and variations of the BA have their own drawbacks. Some of these drawbacks are a large number of parameters to be set, lack of methodology for parameter setting and computational complexity. This paper describes a Grouped version of the Bees Algorithm (GBA) addressing these issues. Unlike its conventional version, in this algorithm bees are grouped to search different sites with different neighbourhood sizes rather than just discovering two types of sites, namely elite and selected. Following a description of the GBA, the results gained for 12 benchmark functions are presented and compared with those of the basic BA, enhanced BA, standard BA and modified BA to demonstrate the efficacy of the proposed algorithm. Compared to the conventional implementations of the BA, the proposed version requires setting of fewer parameters, while producing the optimum solutions much faster.

To enable the deployment of battery-electric vehicles (BEV) as passenger cars in the private transport sector, a suitable charging infrastructure is crucial. In this paper a methodology for efficient spatial distribution of charging infrastructure is evaluated by investigating a scenario with a market penetration of BEVs of 100 percent (around 1.3 million vehicles). It aims towards the development of various charging infrastructure scenarios - including public and private charging - which are suitable to cover the charging demand. Therefore, these scenarios are investigated in detail with focus on number of public charging points, their spatial distribution, the available charging power and the necessary capital costs. For the creation of those charging infrastructure scenarios a placement model is developed. It uses the data of a MATSim (Multi-Agent Transport Simulation) traffic simulation of the metropolitan area of Berlin to evaluate and optimize different distributions of charging infrastructure. The model uses a genetic algorithm and the principle of multi-objective optimization. The capital cost of the charging points and the mean detour car drivers must cover additionally are used as optimization criteria. Using these criteria should lead to cost efficient infrastructure solutions which provide high usability at the same time. The main advantage of the method selected is that multiple optimal solutions with different characteristics can be found and suitable solutions can be selected by using other criteria subsequently. The optimized charging infrastructure solutions show capital costs between 624 and 2950 million euro. Users must cover an additionally mean detour of 254m to 590m per charging process to reach an available charging point. According to the results a suitable ratio between charging points and vehicles is between 11:1 and 5:1. A share of fast charging infrastructure (>50kW) of less than ten percent seems to be sufficient, if it is situated at main traffic routes and highly frequented places.

Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the very recent state–of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

Based on big data and artificial intelligence technology, the judicial big data intelligent service platform provides users with efficient and accurate legal consulting services. It has many functions such as intelligent question and answer, case analysis, and law inquiry. It also analyzes the application value of the intelligent legal consulting service platform in the field of judicial big data. By building an intelligent service platform, the rapid processing and analysis of massive judicial data has been realized, and the efficiency and quality of judicial work have been significantly improved. In addition, it emphasizes the importance of data management and quality control in the application of judicial big data, and puts forward specific measures to strengthen data management and quality control. With the continuous advancement of technology and the expansion of application fields, the platform is expected to achieve deep integration with other legal services, expand new application fields, and provide users with more comprehensive and efficient legal services. At the same time, it points out the problems that need to be paid attention to in the future development, such as data security, privacy protection and so on. Through in-depth analysis and research on judicial big data, the platform can provide strong support for judicial decision-making and promote the improvement of judicial justice and efficiency.

Public transportation scheduling aims to optimize the allocation of transportation resources, enhance transportation efficiency, and increase passenger satisfaction, which is crucial for building a sustainable urban transportation system. As a complement to public transportation, shared bikes provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, such as peak usage periods in the morning and evening and significant fluctuations in demand across different areas, the optimization of shared bike dispatch can better meet user needs, thereby reducing vehicle vacancy rates and increasing operating revenue. Addressing this issue, this paper designs a comprehensive decision-making approach for spatio-temporal demand prediction and bike dispatch optimization. For demand prediction, we designed a T-GCN based bike demand prediction model. In terms of dispatch optimization, we considered factors such as dispatch capacity, distance restrictions, and dispatch costs, and designed an optimization solution based on genetic algorithms. Finally, this paper validates the approach using shared bike operating data, shows that T-GCN can effectively predict short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution verifying the model’s effectiveness. The shared bike dispatch approach proposed in this paper, which combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

Intelligence is a human construct to represent the ability to achieve goals. Given this wide berth, intelligence has been defined countless times, studied in a variety of ways and represented using numerous measures. Understanding intelligence ultimately requires theory and quantification, both of which have proved elusive. I develop a framework – the Theory of Intelligences (TIS) – that applies across all systems from physics, to biology, humans and AI. TIS likens intelligence to a calculus, differentiating, correlating and integrating information. Intelligence operates at many levels and scales and TIS distils these into a parsimonious macroscopic framework centered on solving, planning and their optimization to accomplish goals. Notably, intelligence can be expressed in informational units or in units relative to goal difficulty, the latter defined as complexity relative to system (individual or benchmarked) ability. I present general equations for intelligence and its components, and a simple expression for the evolution of intelligence traits. The measures developed here could serve to gauge different facets of intelligence for any step-wise transformation of information. I argue that proxies such as environment, technology, society and collectives are essential to a general theory of intelligence and to possible evolutionary transitions in intelligence, particularly in humans. I conclude with testable predictions of TIS and offer several speculations.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.

Visual Sensor Networks (VSNs) have been the focus of numerous studies, emphasizing the quality of the information they generate. The primary goal of these networks is to capture data from an area, extract the pertinent information from the visual scene, and facilitate communication. However, the presence of objects in the study area can impacted the network's efficiency, linking the quality of acquired data directly to the positioning of the cameras. Strategically positioning multiple cameras in a potentially complex area, while ensuring effective communication between nodes, presents a challenging issue in VSNs applications. This paper introduces a specific method to enhance the deployment of visual sensors in indoor environments. The research addresses the theme of multi-objective optimization of VSN deployment, integrating various criteria: maximizing overall visual coverage, maximizing the observation of zones of interest, maximizing observation redundancy of zones of interest and ensuring communication connectivity for data transfers. To tackle this problem, we simulate the observed environment incorporating cameras, obstacles, and considering zones of interest. Camera positioning is performed while considering visual constraints and the scene's characteristics. The quality of positioning is evaluated based on the defined objective functions. Initially, a comparative study is conducted with a state-of-the-art approach, integrating the first three objective functions. Subsequently, we incorporate the fourth objective function to ensure data transfers. The proposed method demonstrates efficient camera network deployments in indoor areas, considering global coverage and observation redundancy. In contrast to the state-of-the-art, these results are achieved in areas with visual obstacles while ensuring communication connectivity between the different cameras.

Classroom resource allocation is a challenging problem in educational institutions. The use of cloud computing can aid in the optimization of this process by allowing resource providers to allocate resources to users in a timely and efficient manner. In this paper, we propose a modified Salp Swarm Algorithm (SSA) to optimize the allocation of classroom resources. The proposed approach involves generating a population of salps representing potential solutions to the resource allocation problem and updating the positions of the salps iteratively based on a fitness function and mathematical equations. We conducted simulations of the proposed algorithm on various numbers of tasks and sources to evaluate its performance. The results indicate that the modified SSA algorithm can efficiently allocate classroom resources, achieving lower completion time of tasks (makespan) with lower costs. This paper contributes to the literature by introducing an effective approach to solve the classroom resource allocation problem.

Evolutionary algorithms and swarm intelligence algorithms find applicability in reinforcement learning of neural networks due to their independence from gradient-based methods. To achieve successful training of neural networks using these algorithms, careful considerations must be made to select appropriate algorithms due to the availability of various algorithmic variations. In Part1, 2 and 3, the author previously reported experimental evaluations on Evolution Strategy, Genetic Algorithm, and Differential Evolution for reinforcement learning of neural networks, utilizing the Acrobot control task. This article constitutes Part4 of the series of comparative research. In this study, Particle Swarm Optimization is adopted as an instance of major swarm intelligence algorithms. The experimental result shows that PSO performed worse than all of DE, GA and ES. The difference between PSO and DE was statistically significant (p<.01). In addition, PSO exhibited lower capability in exploring solutions in high-dimensional search spaces than DE, GA, and ES did. A larger swarm size compensated for the weakness of PSO in global exploration, thus making itself more beneficial than a larger number of swarm search iterations.

This study focuses on the optimal deployment problem, and determines the optimal size of a military force to send to the battle field. The decision is optimized, based on an objective function, that considers the cost of deployment, the cost of the time it takes to win the battle, and the costs of killed and wounded soldiers with equipment. The cost of deployment is modeled as an explicit function of the number of deployed troops and the value of a victory with access to a free territory, is modeled as a function of the length of the time it takes to win the battle. The cost of lost troops and equipment, is a function of the size of the reduction of these lives and resources. An objective function, based on these values and costs, is optimized, under different parameter assumptions. The battle dynamics is modeled via the Lanchester differential equation system based on the principles of directed fire. First, the deterministic problem is solved analytically, via derivations and comparative statics analysis. General mathematical results are reported, including the directions of changes of the optimal deployment decisions, under the influence of alternative types of parameter changes. Then, the first order optimum condition from the analytical model, in combination with numerically specified parameter values, is used to determine optimal values of the levels of deployment in different situations. A concrete numerical case, based on documented facts from the Battle of Iwo Jima, during WW II, is analyzed, and the optimal US deployment decisions are determined under different assumptions. The known attrition coefficients of both armies, from USA and Japan, and the initial size of the Japanese force, are parameters. The analysis is also based on some parameters without empirical documentation, that are necessary to include to make optimization possible. These parameter values are motivated in the text. The optimal solutions are found via Newton- Raphson iteration. Finally, a stochastic version of the optimal deployment problem is defined. The attrition parameters are considered as stochastic, before the deployment decisions have been made. The attrition parameters of the two armies have the same expected values as in the deterministic analysis, are independent of each other, have correlation zero, and have relative standard deviations of 20%. All possible deployment decisions, with 5000 units intervals, from 0 to 150000 troops, are investigated, and the optimal decisions are selected. The analytical, and the two numerical, methods, all show that the optimal deployment level is a decreasing function of the marginal deployment cost, an increasing function of the marginal cost of the time to win the battle, an increasing function of the marginal cost of killed and wounded soldiers and lost equipment, an increasing function of the initial size of the opposing army, an increasing function of the efficiency of the soldiers in the opposing army and a decreasing function of the efficiency of the soldiers in the deployed army. With stochastic attrition parameters, the stochastic model also shows that the probability to win the battle is an increasing function of the size of the deployed army. When the optimal deployment level is selected, the probability of a victory is usually less than 100%, since it would be too expensive to guarantee a victory with 100% probability. Some of many results of relevance to the Battle of Iwo Jima, are the following: In the deterministic Case 0 analysis, the optimal US deployment level is 66200, the time to win the battle is 30 days and 14000 US soldiers are killed or wounded. If the marginal cost of the time it takes to win a victory doubles, the optimal deployment increases to 75400, the time to win a victory is reduced to 26 days, and less than 12000 soldiers are killed or wounded. In the stochastic Case 0 analysis, the optimal US deployment level is 65000, the expected time to win the battle is 46 days and almost 25000 US soldiers are expected to be killed or wounded. If the cost per killed or wounded soldier increases from 0 to 5 M $US, the optimal deployment level increases to 75000. Then, the victory is expected to appear after 35 days and 19900 US soldiers are expected to be killed or wounded.

Water networks are critical infrastructure components, ensuring the continuous supply of high-quality drinking water to consumers. To secure such water supply, regular maintenance, including the replacement of deteriorating pipelines, is essential. In this study, a methodology has been developed for determining optimal pipeline replacement solutions in water supply systems at water utilities with limited data availability. Hydraulic analysis has been conducted on the segment of 25 km of the water supply network using the free software EPANET. Applying water network optimization, eight pipeline replacement projects according to 13 pre-defined criteria have been identified and evaluated. The paper outlines the methods for evaluating the criteria, including defining specific quantitative limits. The Analytical Hierarchical Process (AHP) method was used in the paper to determine the weights of the criteria. The reason for applying this method refers to problems that involve a set of criteria with a mixed structure, including both quantitative and qualitative aspects. Also, the paper describes the steps of the multi-criteria optimization method VIKOR, used to select the optimal project. This paper, considered as case studie, describes a method, i.e. application of a new principles and an innovative way to solve a problem for developing countries.

In the context of the energy transition, the integration of land use considerations into energy planning is increasingly vital, especially in scenarios where land availability is a constraint. This study addresses the challenge of incorporating land-use aspects into Energy System Optimization Models (ESOMs), with a focus on the unique context of Pantelleria Island. It aims to bridge the gap in methodologies for renewable energy potential assessment and model integration, considering the critical role of land pricing and availability. It combines geospatial data aggregation with model adaptation to include detailed land use aspects. Findings highlight the substantial impact of land costs on renewable energy planning, with land pricing significantly altering model outcomes. This research offers key insights for sustainable energy planning and underscores the importance of considering land use in energy transition strategies.

This article is devoted to overcoming the contradiction between the problem of maximizing the extraction of wind energy as the goal of optimizing wind turbines, and the use of traditional methods based on criteria for the aerodynamic quality of the blades. A modified technique is considered in which the optimization criterion is the directly extracted flow power. It is based on pulse theory methods that use specific power as an optimization criterion. A comparative analysis of the energy efficiency of different types of turbines is carried out, and the effects of blade inversion are considered. A method for calculating multi-rotor turbines based on the concept of uniform power distribution is presented. The compatibility of proprietary and traditional methods is considered by comparing the results of numerical experiments with calculated and experimental data from sources. Optimization computational algorithms generate data from numerical experiments and determine optimal parameter configurations for the turbines under consideration. It is shown that orthogonal Darrieus turbines in high-speed mode provide energy efficiency comparable to collinear turbines, and multi-rotor turbines with power uniformly distributed over the working section are not inferior in energy efficiency to basic turbines, with linear blade velocities halved.

Improper design of friction pendulum bearings can lead to poor seismic reduction performance and may result in the failure of local vulnerable components. And the parameter design of friction pendulum bearings mainly relies on experience and verification calculations at present. This paper proposes an adaptive genetic algorithm considering the overall evolution state of the population, adjusting crossover and mutation probabilities adaptively based on individual fitness and population diversity. Compared to traditional algorithms, it exhibits better global search capabilities and convergence efficiency. Combining the improved genetic algorithm with finite element models, a parameter optimization method is proposed. The parameters of friction pendulum bearings are optimized, providing reference for the design of seismic isolation devices.

The digital economy (DE) has become a major force driving the optimization and upgrading of industrial structures (ISOU), and research on the driving mechanism has yet to be verified. To this end, the driving influence of DE on the ISOU is targeted, and the role played by economic resilience (ER) and innovation is explored. Based on the panel data of 31 provinces in China from 2011 to 2020, the driving influence of the DE on the ISOU is analyzed using a two-way fixed-effects model. The empirical study finds that the DE presents a positive driving effect on both industrial structure advancement and rationalization, and the driving effect is still significant after the addition of control variables and the robust-type test with one period of lag; the DE presents regional differences in ISOU, with the driving effect of the DE on industrial structure advancement in the eastern region having a more significant performance than that of the central and western regions, and the driving influence on the rationalization of industrial structure in the eastern and western regions exceeds that in the central region; the impact of DE on the ISOU has a mediating role through innovation, ER has a moderating role in the process, and innovation as a partial mediator also has a threshold effect, and finally countermeasure suggestions are put forward.

This paper presents an efficient topology optimization approach considering fail-safe requirements in the framework of the solid isotropic material with penalization method. Existing methods either are computationally extremely expensive or do not consider fail-safe requirements in the optimization. The current approach not only obtains the fail-safe optimization structure, but also significantly improves the computational efficiency of fail-safe topology optimization. In this method, the worst-case compliance is considered to minimize as the optimization objective, using the Kreisselmeier–Steinhauser function to approximate the non-differentiable max-operator. The sensitivity is derived by the adjoint method, and a general fail-safe optimality criteria method is developed to update design variables. The damage scenarios greatly reduces in the process of failsafe strategy optimization by employing the density-based filter method. Finally, the effectiveness and computational efficiency of this method for fail-safe optimization is verified by several numerical examples.

Space exploration presents vast prospects for scientific, industrial, and economic progress. This paper introduces the MeSat mission as a pioneering approach to Mars exploration. MeSat aims to deepen our understanding of Martian conditions and resources by employing an optimized Earth-to-Mars trajectory, enabling a comprehensive study of the Martian atmosphere and surface. The mission comprises a Cargo Microsatellite hosting three 6U CubeSats and two 3U CubeSats, deployed into four separate Mars orbits forming a constellation. Each CubeSat carries distinct payloads: a THz radiometer for water detection, a high-resolution surface camera, a top-tier spectrometer, and a Fourier Transform Spectrometer (FTS) for wind speed readings. This paper focuses on two pivotal innovations: the development of a 183GHz radiometer payload for detecting water on Mars and a optimal mission design algorithm that analyzes a fuel-efficient low-thrust trajectory from Earth to Mars. Regarding the THz payload for water detection, a subharmonic Schottky diode-based mixer is meticulously designed to efficiently down-convert radio signals. The down-converted signals are carefully analyzed using a customized Do-It-Yourself (DIY) spectrum meter, which facilitated the capture and processing of acquired data. To provide the necessary Local Oscillator signals for the mixer, a Tripler employing three-anode Schottky diodes is developed. Additionally, the Tripler requires a signal generator for its entry frequency, which is addressed by developing a DIY signal generator. Together, these components, comprising the Schottky diode mixer, Tripler, and DIY circuits, collectively form the complete THz payload, demonstrating exceptional power efficiency with a total consumption of only 3.7W. The study employs a dual-step hybrid optimization algorithm (PSO-Homotopy) to analyze fuel-efficient low-thrust trajectories from Earth to Mars, incorporating the Ephemeris dynamics model to account for gravitational perturbations in the entire solar system. In practical mission design, crucial factors like hyperbolic excess velocity, diverse opportunities for Earth launch and Mars rendezvous, varied propulsion systems, and Time of Flight (TOF) play vital roles in trajectory optimization.

More water utilities are adopting Aquifer Storage and Recovery (ASR) to balance long-term water supply and demand. Due to large implementation and operation costs, ASR projects need to be optimized, particularly for energy use, which is a major operating expense. This study examines the relationships among energy use, recharge, and recovery at two ASR projects in the western United States. The major finding is an economy of scale for recovery processes, but not for gravity-fed recharge processes. The economy of scale found is as follows: the energy intensity recovered decreases with volume. This suggests it is more energy-efficient to recover large volumes of water in one interval instead of recovering smaller volumes at more frequent intervals. The H2Oaks recovery process experienced a 78% decrease in energy intensity from 0 to 50,000 m3 recovered, while the Sand Hollow site experienced a 43% decrease in energy intensity from 0 to 50,000 m3 recovered. Statistical analyses of the recovery process showed p values lower than 0.0001, R2 values between 0.43 and 0.57, and a RMSE value between 0.55 and 2.1, indicating the presence of a moderate correlation between energy and volume. This economy of scale has been observed in multiple instances in water and wastewater treatment. This finding not only has applications to ASR but also all recovery or recharge wells, whether or not they are paired with each other. Furthermore, this study confirms the need for more reliable and accessible energy data to fully understand the implications of the energy–water nexus.

Among the plethora of financial management tools available, stock investments stand out as the most recognizable and widely accepted option. However, investors often make buying and selling decisions based on unverified information provided by stock analysts. Since the credibility of this information remains uncertain, it frequently results in investment losses. Ordinary investors relying solely on rumors often miss out on prime opportunities for buying and selling stocks. In the current volatile investment landscape, various investment strategies cater to different situations. If individuals can effectively identify opportunities within this complex environment and dynamically adapt suitable investment strategies at any given time, substantial returns can be achieved. The ability of investors to promptly detect shifts in the current stock market environment and make accurate decisions regarding stock selection, timing, and capital allocation strategies is of utmost importance. This study presents a dynamic framework for investment decision support, which utilizes evolutionary strategies to determine the optimal timing for key strategic tools such as stock selection, timing, and capital allocation. By leveraging historical data for training and testing, this approach aims to assist investors in adapting to the ever-changing stock market environment. If investors can successfully identify and implement an appropriate investment strategy to aid in making accurate decisions, it is possible to achieve significant excess returns.

This paper studies the dividend optimization problem in the entropy regularization framework by following the same continuous-time reinforcement learning setting as in Wang et al. (2020). The exploratory HJB is established and the optimal exploratory dividend policy is a truncated exponential distribution. We show that, for suitable choices of the maximal dividend paying rate and the temperature parameter, the value function of the exploratory dividend optimization problem could be significantly different from the value function in the classical dividend optimization problem. In particular, the value function of the exploratory dividend optimization problem could be classified into three cases based on its monotonicity. Numerical examples are also presented to show the impact of temperature parameter on the solution.

In light of the hazardous effects of global warming brought on by climate change, bioethanol production is one of the key alternatives in which the world has expressed a great deal of interest. Two factors—indigenous production and price—are crucial for bioethanol to be appealing in Africa and, for that matter, Ghana. The objective of this research is to produce bioethanol from cheap lignocellulosic materials using onsite enzymes produced from cassava peels degrading fungi. Due to Ghana's prominent position as one of the leading cassava producers in the world, the study focused on utilizing cassava peels, which are underutilized in the country. The research was conducted in multiple stages. In the first stage, the peels were prepared by washing, drying, and manually crushing them using mortar and pestle. The second stage involved analyzing the chemical composition of the peels, including starch and insoluble fibre, which were determined as Acid Detergent Fiber (ADF), Neutral Detergent Fiber (NDF), and Acid Detergent Lignin (ADL). The results showed significant levels of cellulose (39.78%) and starch (31.21%), indicating that cassava peels are valuable raw materials for bioethanol production. The content of hemicellulose (21.11%) and lignin (3.84%) were also determined. In the third stage, using a Petri dish, three fungi (ICPF1, ICPF2, and ICPF3) were isolated from two different cassava peels. ICPF1 was only identified from fresh cassava peels (FCP), while all three fungi were identified from decayed cassava peels (DCP). Morphologically, these fungi were identified as Aspergillus niger, Aspergillus flavus, and Rhizopus stolonifer. The fourth stage focused on optimizing the enzyme activity of the three isolates for potential applications, with A. niger demonstrating the highest enzyme activity with a diameter of zone of clearance of 16 mm. Stage five involved optimizing the production of the onsite enzyme in a 50ml flask using A. niger, basal salt medium (BSM), and cassava peels as a substrate. The DNSA method was used to measure the absorbance of maltose and glucose at 540 nm for various substrate concentrations (1%, 3%, 5%, 8%, and 10%) at specific intervals of 2, 4, 6, 8, and 10 days with a spore concentration of 2.1 x 105 cells/ml. The maltose and glucose concentrations were calculated as 7.138mg/ml and 6.398 mg/ml, respectively, and the corresponding enzyme activity was determined as 4.759U/ml and 4.265U/ml. The optimal conditions of Day 4 and a substrate concentration of 10%, along with a fixed temperature of 30°C and a pH of 5.5, were used to prepare the onsite enzymes in a 500 ml flask for the fermentation process. The onsite enzymes were used for saccharification and Saccharomyces cerevisiae for fermentation under simultaneous saccharification and fermentation (SSF) process. A mixture of 20 ml of onsite enzymes and 1.5 g of S. cerevisiae were added to substrate concentrations of 5%, 10%, and 20%, and the ethanol concentration was analyzed daily for 7 days using Gas Chromatography (GC). The highest ethanol concentration (1.316%) was observed on Day 5 with a substrate concentration of 20%, while the lowest concentration (0.123%) was recorded on Day 1 with a substrate concentration of 5%.

The future of intelligent manufacturing machines involves autonomous selection of process parameters to maximize productivity while maintaining quality within specified constraints. To effectively optimize process parameters, these machines need to adapt to existing uncertainties in the physical system. This paper proposes a novel framework and methodology for feedrate optimization that is based on a physics-informed data-driven digital twin with quantified uncertainty. The servo dynamics are modeled using a digital twin, which incorporates the known uncertainty in the physics-based models and predicts the distribution of contour error using a data-driven model that learns the unknown uncertainty on-the-fly by sensor measurements. Using the quantified uncertainty, the proposed feedrate optimization maximizes productivity while maintaining quality under desired servo error constraints and stringency (i.e., the tolerance for constraint violation under uncertainty) using a model predictive control framework. Experimental results obtained using a 3-axis desktop CNC machine tool and a desktop 3D printer demonstrate significant cycle time reductions of up to 38% and 17% respectively, while staying close to the error tolerances compared to the existing methods.

With the rapid growth of large language models (LLMs), there is increasing demand for memory and computation for LLMs. Recent efforts on post-training pruning of LLMs aim to reduce the model size and computation, yet the performance is still sub-optimal. In this paper, we present a plug-and-play solution for post-training pruning of LLMs. The proposed solution has two innovative components: 1) **Relative Importance and Activations** (RIA), a new pruning metric that jointly considers the weight and activations efficiently on LLMs; and 2) **Channel Permutation**, a new approach to maximally preserve important weights under N:M sparsity. The proposed two components can be readily combined to further enhance the N:M structuredly pruned LLMs. Our empirical experiments show that RIA alone can already surpass all existing post-training pruning methods on prevalent LLMs, e.g., LLaMA ranging from 7B to 65B. Furthermore, N:M structured pruning with channel permutation can even outperform the original LLaMA2 70B on zero-shot tasks, together with practical speed-up on specific hardware.

Optical power splitters play a crucial role as the fundamental building blocks for many integrated optical devices. They should have low losses, broad bandwidth, and a high tolerance to fabrication errors. Adiabatic optical power splitters inherently possess these qualities while being compatible with foundry processes well-suited for mass-production. The long device lengths of adiabatic power splitters, however, are a limiting factor to achieve compact device sizes, which must be reduced. Here, we use a polynomial taper profile optimization algorithm to design 1×2 and 2×2 adiabatic power splitters with significantly shorter lengths than their adiabatic counterparts. The best performing 1×2 and 2×2 power splitters have 20 μm and 16 μm coupling lengths, respectively. Our designs have minimum feature sizes ranging from 140 nm to 200 nm, and our measurements averaged across 9 different chips show excellent consistency in performance for devices with 180 nm and 200 nm minimum features. Both the 1×2 and 2×2 adiabatic optical power splitters have excess losses less than 0.7 dB over a 100 nm bandwidth, with a standard deviation lower than 0.3 dB. Furthermore, our measurements show greater than 20 dB extinction ratios over 130 nm bandwidth. We also demonstrate the design of 1×2 power splitters with arbitrary splitting ratios, where splitting ratios ranging from 50:50 to 94:6 were achieved with standard deviations between 2% and 6%.

Portugal, in line with the European Union, is aiming for carbon neutrality by 2050 (Net 1 Zero), which implies a transition to sustainable energy sources. Climate change is all too evident, extreme weather periods are occurring in a cyclical manner, with greater brevity to such an extent that the grid operator must deal with production scenarios where it can no longer rely on hydroelectric production given the recurring drought situation. This situation increases dependence on thermal production using natural gas and imports. This has significant economic implications. Portugal has exploited its onshore wind potential, reaching an installed capacity of 5.671 MW by 2022. However, the expansion of onshore wind energy is limited to reinforcing existing infrastructure. To overcome these challenges, it is necessary to expand the exploitation of offshore wind potential that is already underway. This article proposes the location of offshore wind production platforms along the Portuguese coast. This allows for an analysis of offshore production and its optimization according to the minimum cost per MWh in the face of extreme scenarios, i.e., in periods of extreme drought where hydroelectric production capacity is practically non-existent. The model is fed by using market price indications and the amount of energy needed for the following day. Using forecast data, the model adapts offshore wind production for the following day according to the minimization of the average market price. The results of the simulations allow us to conclude that despite the high cost of offshore technology (in deep waters), in extreme climate scenarios, it enables cost reduction and a clear decrease in imports.

This article explores the relationship between convex functions defined on integers ($\mathbb{Z}$) and their extension to real numbers ($\mathbb{R}$). We introduce key definitions and investigate the hypothesis that there exists a unique convex curve within this family of functions, leading to a proof by contradiction. Our findings highlight the preservation of convexity as functions transition from integers to real numbers.

Liquidity providers for asset-pair pools with constant value distri- bution, such as Balancer pools, experience some impermanent loss whenever there is price divergence in the tokens’ fiat values. In this paper, we model this impermanent loss for geometric mean invariant pools by deriving a function of two parameters. We analyze our function graphically, showing that unevenly distributed pools with large weighting disparities best mitigate the risk of impermanent loss. We conclude by providing a general protocol for liquidity providers to choose a suitable pool based on their risk tolerance and profit goals.

Material Extrusion is a popular additive manufacturing technology due to its low cost, wide market availability, ability to construct complex parts, safety, and cleanliness. However, optimizing the process parameters to obtain the best possible mechanical properties has not been extensively studied. This paper aims to develop ensemble learning-based models to predict the ultimate tensile strength, Young's modulus, and strain at break of PLA and PLA-CF 3D-printed parts, using printing temperature, printing speed, and layer thickness as process parameters. Additionally, the study investigates the impact of process parameters and material selection on the mechanical properties of the printed parts and uses Genetic Algorithms for multi-objective optimization according to user specifications. The results indicate that process parameters and material selection significantly influence the mechanical properties of the printed parts. The Genetic Algorithm successfully identifies optimal parameter values for the desired mechanical properties. Moreover, this work is the first to model Process-Structure-Properties relationships for an additive manufacturing process and to use a multi-objective optimization approach for multiple mechanical properties, utilizing ensemble learning-based algorithms and Genetic Algorithms.