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.

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.

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.

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.

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.

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.

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.

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.

In response to the structural optimization problem of PCB stator coreless disc motors, the orthogonal response surface method was used to optimize the motor structure, determine the basic parameters of the motor preliminarily, and conduct orthogonal experiments on the motor parameters based on the optimization design objectives, The optimization factors were the quantity of magnetic pole of the motor rotor, the ratio of main/auxiliary pole size, the thicknessof the permanent magnet, and the air gap length.The motor torque，the amplitude of magnetic density of the air gap and waveform distortion rateare used as optimization objectives. The motor parameters that make the motor torque reach the maximum, the air gap magnetic density maximum, and the waveform distortion rate minimum. Due to the use of PCB plates instead of motor stator cores in PCB coreless disc motors,The service life of the PCB board during motor operation will be reduced with the temperature rise generated by the stator winding To solve this problem, the response surface analysis of the motor is carried out to reduce the temperature rise of the stator windings during the operation of the motor. PCB board and stator winding are the main factors affecting the temperature rise of the motor.Taking the thickness of the PCB board, the hole diameter on the board, and the uneven width of the stator winding as optimization factors, the motor parameters with the lowest temperature rise of the motor winding are obtained. Simulation analysis is conducted using Ansys/Maxwell software, and the results prove the feasibility of optimization.

Hardaliye, a traditional non-alcoholic beverage, made from red grape pomace from wine production and produced by lactic acid fermentation with addition of different concentrations of whole/ground or heat-treated mustard seeds and of sour cherry leaves. Short shelf life of hardaliye limits its consumption and search in trend to process hardaliye to extend its shelf life. Thus, high hydrostatic pressure treatment of hardaliye drink with determination of changes in physicochemical and sensory properties in addition to microbial inactivation were studied according to Box-Behnken design. Maximum 5.10±0.00, 4.21±0.00, 5.38±0.59 and 5.05±0.22 log reductions were obtained in total mesophilic aerobic bacteria, total mold and yeast, Brettanomyces bruxellensis and Lactobacillus brevis by HHP. Shelf-life studies of the hardaliye samples were conducted with optimum processing parameters of 490 MPa, 29 oC for 15 min with the response variables of OD520 and inactivation of L. brevis. Both control and HHP treated samples were stored at 4 and 22 C for shelf-life studies. While control samples at 4 and 22 °C were spoiled at the 15th and 3rd days, HHP treated samples were spoiled after 108th and 228th days, respectively.

Quantum computing has attracted significant interest in the optimization community because it potentially can solve classes of optimization problems faster than conventional supercomputers. Several researchers proposed quantum computing methods, especially Quantum Interior Point Methods (QIPMs), to solve convex optimization problems, such as Linear Optimization, Semidefinite Optimization, and Second-order Cone Optimization problems. Most of them have applied a Quantum Linear System Algorithm at each iteration to compute a Newton step. However, using quantum linear solvers in QIPMs comes with many challenges, such as having ill-conditioned systems and the considerable error of quantum solvers. This paper investigates how one can efficiently use quantum linear solvers in QIPMs. Accordingly, an Inexact Infeasible Quantum Interior Point Method is developed to solve linear optimization problems. We also discuss how we can get an exact solution by Iterative Refinement without excessive time of quantum solvers. Finally, computational results with a QISKIT implementation of our QIPM using quantum simulators are analyzed.

Since the past few decades, numerous modifications and innovations have been done to design the optimal corneal biomatrix for corneal epithelial cells (CECs) or limbal epithelial stem cells (LESCs) carriers. However, researchers have yet to discover the ideal optimization strategies in the development of corneal biomatrix design and its effects on cultured CECs or LESCs. This review further discusses and summarizes recent optimization strategies to develop an ideal collagen biomatrix and its interaction with CECs and LESCs. Using PRISMA guidelines, the articles published from June 2012 to June 2022 were systematically searched using Web of Science (WoS), Scopus, PubMed, Wiley, and EBSCOhost databases. The literature search identified 444 potential relevant published articles, with 29 relevant articles selected based on the inclusion and exclusion criteria after the screening and appraising processes. The current paper highlights the physicochemical and biocompatibility (in vitro and in vivo) characterization methods, which were inconsistent throughout the different studies. Despite the variability in the methodology approach, the reviewer postulated that the modification of the collagen biomatrix improves its mechanical and biocompatibility properties toward CECs and LESCs. All findings were discussed in this review; thus, it provides a general view of up-to-date trends in this field.

In this work, we explore the extreme searching of multidimensional functions by natural gradient descent based on Dirichlet and generalized Dirichlet distributions. The natural gradient is based on describing multidimensional surface with probability distributions, which allows us to reduce changing the accuracy of gradient and step-size. In this article, we propose an algorithm of natural gradient descent based on Dirichlet and generalized Dirichlet distributions. We demonstrate that the natural gradient descent with step-size adaptation with Dirichlet and generalized Dirichlet distributions has higher accuracy and does not take a large number of iterations for minimizing test functions than gradient descent and Adam.

Reliable quantification of pulmonary arterial pressure is essential in the diagnostic and prognostic assessment of a range of cardiovascular pathologies including rheumatic heart disease, yet an accurate and routinely available method for its quantification remains elusive. This work proposes an approach to infer pulmonary arterial pressure based on scientific machine learning techniques and non-invasive, clinically available measurements. A 0-D multicompartment model of the cardiovascular system was optimized using several optimization algorithms, subject to forward-mode automatic differentiation. Measurement data were synthesized from known parameters to represent the healthy, mitral regurgitant, aortic stenosed and combined valvular disease situations with and without pulmonary hypertension. Eleven model parameters were selected for optimization based on 95 % explained variation in mean pulmonary arterial pressure. A hybrid Adam and limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer yielded the best results with input data including valvular flow rates, heart chamber volume changes and systematic arterial pressure. Mean absolute percentage errors ranged from 1.8 % to 3.78 % over the simulated test cases. The model was able to capture pressure dynamics under hypertensive conditions with pulmonary arterial systole, diastole, and mean pressure average percentage errors of 1.12 %, 2.49 % and 2.14 %, respectively. The relatively low errors highlight the potential of the proposed model to recover pulmonary pressures for diseased heart valve and pulmonary hypertensive conditions.

In this paper, we propose an efficient numerical computation method of reduced-order controller design for linear time-invariant systems. The design problem is described by linear matrix inequalities (LMIs) with a rank constraint on a structured matrix, due to which the problem is NP-hard. Instead of the heuristic method that approximates the matrix rank by the nuclear norm, we propose a numerical projection onto the rank-constrained set based on the alternating direction method of multipliers (ADMM). Then the controller is obtained by alternating projection between the rank-constrained set and the LMI set. We show the effectiveness of the proposed method compared with existing heuristic methods, by using 95 benchmark models from the COMPLeib library.

Wireline Formation Testing is an important technique in the oil field of exploration and development. Not only it can make real fluid samples of the formation directly obtained to know exactly oil existed in formation or not; but it can also show flowing pressure change to see the production capacity of the formation. So it is important measurement for formation evaluation during the drilling process and supports for activities of the exploration and development in the oil field. A numerical simulation model in this article is researched and established based on the Finite Volume Method with considering the influence of sensitive parameters such as reservoir heterogeneity, probe suction area, and mud-filtrate invasion depth during the drilling. The model is capable to design and evaluate for formation fluid sampling operations with calculating hydrocarbon content and flowing pressure. Furthermore, through case application, the performance and effect in the process of Wireline Formation Testing was demonstrated. The results indicate that this technology can be served as an effective auxiliary tool for fluid sampling operations with the function of optimizing fluid sampling measures. It can improve the accuracy of predicting indicators such as hydrocarbon content and breakthrough time during the sampling process. This study provides important backups and technical guidance for professionals in geological exploration and oilfield development.

Given the landscape of intricate and constantly evolving cyber threats, organizations demand refined strategies to deploy a Security Information and Event Management to support the management of a Cyber Security Operations Center. The dynamic nature of cyber threats complicates the efficient allocation of the location of network intrusion detection sensors, a critical component of a robust cybersecurity framework. Our research introduces an approach that integrates the precision of biomimetic optimization algorithms with the adaptability of Deep Q–Learning. By employing different biomimetic algorithms enhanced with deep learning, we aim to refine the deployment of sensors in network infrastructures, balancing the network security imperative against deployment costs. The results of computational tests demonstrate that the improved iterations leveraging Deep Q–Learning have outperformed their native counterparts. These findings underscore the importance of reinforcement learning, specifically through Deep Q–Learning, as a powerful tool to enhance the effectiveness of metaheuristics in addressing optimization challenges.

Simultaneous extraction of trypsin inhibitors and soy isoflavones from soybean meal was investigated using the non-destructive phytochemical extraction process, namely aqueous micellar system. The ethoxylated aliphatic alcohols Genapol X-080, Tergitol 15-S-7, and Tergitol 15-S-9, all non-toxic and biodegradable surfactants, were assessed as potential extractants. A Box-Behnken multifactorial design with the application of the Derringer desirability was used to determine the conditions that maximized the trypsin inhibitors and isoflavone extraction while minimizing the protein extraction. The optimum condition of 5% m/m of surfactant in 50 mM aqueous sodium citrate solution pH 4.5, at 45 °C for 45 min, was established for the three surfactants. The novel methodology would allow the extraction of the main soybean antinutritional factors, trypsin inhibitors, and the valuable isoflavones, preserving the nutritional quality of the treated material. This represents a sustainable alternative methodology for industrial purposes due to its low cost, biodegradability, non-toxicity, and easy scaling up.

In steel recycling, the optimization of electric arc furnaces (EAFs) is of central importance in order to increase efficiency and reduce costs. This study focuses on the optimization of electric arcs, which make a significant contribution to the energy consumption of EAFs. A three-phase equivalent circuit integrated with the Cassie-Mayr arc model is used to capture the nonlinear and dynamic characteristics of arcs, including arc breakage and ignition process. A particle swarm optimization technique is applied to real EAF data containing current and voltage measurements to estimate the parameters of the Cassie-Mayr model. A novel Arc Quality Index (AQI) is introduced in the study, which can be used to evaluate arc quality based on deviations from optimal conditions. The AQI provides a qualitative assessment of arc quality, analogous to indices such as arc coverage and arc stability. The study concludes that the AQI serves as an effective operational tool for EAF operators to optimize production and increase the efficiency and sustainability of steel production. The results underline the importance of understanding electric arc dynamics for the development of EAF technology.

This study sought to improve the nutritional, functional and biochemical properties of pearl millet through malting and fermentation. Moringa oleifera leaf powder (MLP) was used as a fortificant. Mixture design was used to find optimal proportions for each of the components that yield a high protein and or low saturated fat content. Twelve mixtures with varying ratios of fermented, and malted pearl millet flour ranging between 30–65%, and MLP between 5–15% were generated through I-Optimal mixture design. The mixtures were wet-cooked, freeze-dried and analyzed for protein and fat content. The data obtained were fitted to a linear mixture model and the search for the optimum was done using Numerical Optimization for maximising protein and minimising saturated fat. The linear model was suitable for explaining variation for total protein and saturated fat with r2 of 0.50 and 0.51, respectively. Increasing MLP increased protein content. Two final formulations were generated through the optimization process. Pearl millet’s protein content increased by up to 22% while saturated fat decreased by up to 13%. Ash content increased by 75%. Polyphenol content and oxygen radical absorbance capacity increased by 80% and 25%, respectively. Final and peak viscosity were reduced by 90% and 95%, respectively.

Distributed electric propulsion technology has great potential and advantage in the development of drones. In this paper, to study the slipstream effect of distributed propeller, the actuator disk method was used to verify a single propeller, and the calculated thrust was in good agreement with the test results. Then, based on the actuator disk method, the influence of different installation positions on the slipstream effect was studied, and the distributed propeller layout was optimized by genetic algorithm. The analysis results showed that lift of the wing will be larger when the propellers are higher than the wing. When the relative height between the propeller and the wing is zero, the drag is the lowest. The influence of disk diameter on the slipstream is that the larger the diameter is, the higher the lift force and the drag force are. The slipstream effect of the optimized propeller distribution improves the lift-drag ratio of the wing, by 108.5% in the initial lift-drag ratio.

The multiobjective optimization problem is a key research direction in the field of optimization. Based on the multivariant optimization algorithm (MOA), this paper puts forward a multiobjective multivariant optimization algorithm (MMOA). A good point set is adopted for population initialization and to ensure the homogeneity of the initial solution set and avoid prematurity; global atoms are acquired by non-dominated sorting and an external archiving strategy; a nonlinear search radius is designed to balance global and local searches and enhance the convergence precision of the algorithm. Levy Flight and Sine chaotic mapping are introduced to redesign the update formula for local atoms, increase the convergence rate of the algorithm, and boost the optimization efficiency. Comparative experiments are conducted between MMOA and NSGA-II, MOEA/D, NSWOA, MOGWO, NSMFO on multiobjective test functions and an engineering example. The simulation results show that MMOA has superior convergence and diversity to the other five algorithms.

The integration of nature-based solutions into land use optimization has emerged as a focal point of current research, primarily due to its efficacy in mitigating flooding impacts and fostering sus-tainable development in both urban and rural areas. Utilizing Shanghai Pujiang Country Park as a case study, this paper conducts a simulation analysis to assess the flood mitigation effectiveness of three distinct land use patterns (Natural scenario, Scenario N; Complete urbanization scenario, Scenario U; Country Park Planning scenario, Scenario P) under five stormwater scenarios with re-turn periods of 2, 5, 10, 20, and 50 years. The findings reveal that Scenario P exhibits superior flood mitigation performance, particularly under stormwater scenarios with a return period of less than 50 years. Building upon these results, the paper proposes recommendations for optimizing land use with a focus on mitigating the impact of flooding. This study is crucial for comprehending the mechanisms involved in urban stormwater logging mitigation through land use methods and holds significance for decision-making in land use and planning at the micro level.

Breast cancer is the most common malignancy in women worldwide. The pathogenesis of this disease is closely related to the estrogen receptor alpha subtype (ERα). Therefore, it is of great importance to develop effective inhibitors of ERα activity for the treatment of breast cancer. In this paper, we propose a novel ensemble machine learning model for quantitative structure-activity relationship of anti-breast cancer drugs, which can effectively predict drug activity in small samples with multiple characteristic variables. To avoid the problem of over-fitting caused by low-correlation independent variables, the scoring mechanism of random forest was improved by incorporating three relevance indicators, including the maximum mutual information number, Pearson correlation coefficient and distance correlation coefficient, and 20 optimal molecular descriptors were selected. The Bayesian hyperparameter optimization method was used to optimize the parameters of multiple linear regression (MLR), support vector regression (SVR), and extreme gradient boosting (XGBoost), respectively. The AdaBoost strong learner was constructed by combining the weak learner with the weighted linear addition method. The results show that the proposed ensemble learning model has the best prediction performance compared to the three basic learner models and the CNN-LSTM combination prediction model. The root mean square error was reduced by 7.60%-26.51%. The mean relative error was reduced by 6.46%-30.92%. Goodness of fit increased by 9.57%-36.94%. Finally, the biological activities of 50 candidate compounds for ERα inhibitors were predicted, and it was found that 4-[2-benzyl-1-[4-(2-pyrrolidin-1-ylethoxy)phenyl]but-1-enyl]phenol had an excellent biological activity value pIC50, which had the potential to be an ERα inhibitor. The model proposed in this paper has good prediction accuracy, which can provide an effective reference for the discovery and development of anti-breast cancer drugs.

This study aims to determine the influence of selected social and health factors on the quality of inpatient treatments when regional health organizations use stacking ensemble model. The proposed procedure is based on the logistic regression method, which is used for direct prediction in the case of good fitting data and impossibility of including aggregation of classification algorithms but also in the opposite case, for fine calibration to obtain prediction. In opposite case, the procedure uses classification algorithms and several filter methods which initially rank individual factors according to their importance to reduce the dimensionality of problem and, in such way. obtaining one optimized classification prediction. The proposed procedure is trained using one part and tested using another part of dataset from case study which enables the generalization of the solution to the set goal and is verified by the Kaggle dataset Brest cancer. Case study was conducted using the real data acquired in the region connected to the city of Nis, Republic of Serbia. The obtained results show that the proposed model achieves better results that each of methods included in this stacking ensemble-regression and classification used individually.

The growth of demand for metallic minerals has faced with the need for new techniques and improving technologies for all mine life cycle operations. Nowadays, the exploitation of old tailings and mine wastes facilities could represent a solution to this demand, with economic and environmental advantages. W-Sn Panasqueira Mine has been operating for more than 100 years. Its first processing plant “Rio” was located near Zêrere river being the mineral processing residues deposited on the top hillside on the margin of this river in Cabeço do Pião tailings dam. The lack of maintenance and monitoring of this enormous structure in the last twenty years represents high risks to the environment and population of the surrounding region. The re-mining of the tailings by hydrometallurgical methods was considered, in order to satisfy these two enounced conditions - metals demand and environmental risk, aiming for the sale of the metal to pay the environmental intervention. Field samples campaign allowed collecting data and results from laboratory tests driving to use regression optimization. The re-mining solution was studied, taking into account the technical, economic, social, and environmental aspects.

This study analyzes the convergence between artificial intelligence (AI) and management accounting in hospital contexts. Management accounting plays a central role in the effectiveness of planning, control and decision-making in healthcare institutions such as hospitals. The application of AI in this scenario offers powerful tools to improve accounting processes, optimize operational efficiency and maximize the use of available resources, resulting in an improvement in the quality of care provided to patients. Through in-depth analysis, the article explores how AI is bringing about a revolution in hospital management accounting, presenting case studies illustrating its successful implementation, outlining the tangible benefits.

This study will lead to the development of a numerical data set that will assist in the design of Savonius wind turbines. A major objective of this study is to develop a better design for Savonius turbine blades in order to increase their torque coefficients, rotational speeds, and pressure coefficients. As part of the experimental design methodology, a full dataset was generated by simulating three-dimensional scale models utilizing computational fluid dynamics (CFD) simulations that were validated by wind tunnel data. This process is multi-objective optimization for optimizing turbine performance. The twist angle, aspect ratio, and overlap ratio are all important factors in determining the optimal torque and power coefficients. The group method of data handling (GMDH) algorithm was utilized to model objective functions based on input-output data. The Pareto fronts were plotted using polynomial models obtained from the evolutionary Pareto-based optimization approach (modified NSGA-II), and the optimal commercial points were determined using TOPSIS. A comparison of three- and two-objective optimization data revealed that two-objective optimization data lie within the boundaries of a three-objective optimization problem. The torque coefficient, rotational speed, and power coefficient are all improved by 13.74%, 0.071%, and 5.32%, respectively, using multi-objective optimization. As a result of the multi-objective optimization of the turbine, some significant characteristics of objective functions were discovered.

Blasting pattern optimization is an attempt to optimize blast design parameters aiming to achieve optimum fragmentation, reduce mining operating costs as well as environmental side consequences. The present study aims to propose a multi-objective optimization model, employing artificial intelligence and metaheuristics, to simultaneously minimize the mine-to-crusher operating costs and the impact of blasting consequences including fly-rock and back-break. To achieve the purpose of the study, a multi-variable regression model was developed to model total costs from drilling to crushing. In addition to the costs, multilayer perception neural networks were implemented to predict the blast-induced back-break and fly-rock as a function of collected variables such as number of holes, hole length, burden, spacing, hole slope, stemming, blasted rock per hole, powder factor, and charge per delay. High-precise estimations for both back-break and fly-rock were achieved with the average of 99% coefficient of determination for train, test and validation data sets. Then, the developed regression model and the neural networks were used in an optimization framework, employing Grasshopper algorithm, to find the optimum blast design satisfying practical constraints. The proposed model was tested on a lead-zinc open-pit mine, where 1032 blasting patterns were recorded and analyzed. The results of the optimization model provide a Pareto set of solutions, such that any of these solutions can be implemented according to the strategy of the mining operation management team. The blast pattern with the lowest cost, result in a relatively high fly-rock and back-break, while the pattern with low fly-rock and back-break raises the cost by 20.13 % compared the minimum cost blast design.

This research is aimed at evaluating two different scenarios, firstly, appraising the impacts of employing the concepts of Total Quality Management (TQM) to the construction projects in Saudi Arabia. The results of the study were obtained through utilization of a descriptive analytical approach, where 300 questionnaires were distributed to engineering firms and companies with a response rate of 200 questionnaires, hence achieving the study sample for this research. The data gathered was analyzed by applying the Statistical Package for Social Science (SPSS) program and calculating the Relative importance index (RII) and the mean values. From the research conducted, the outcomes showed that the management’s ability to commit using TQM while applying BIM obtained a relative importance of (0.717), while the relative importance for the management’s ability to commit using TQM without the application of BIM is (0.552). The results showed that construction projects in Saudi Arabia still sustain setbacks from applying TQM concepts and suffer from the lack of administrative, scientific and technical applications. In a second scenario, a hybridized support vector regression (SVR) Harris-hawks optimization (HHO) (i.e., SVR-HHO) were used to predict the TQM. The performance accuracy of the models was checked through three different evaluation metrics namely; mean square error (MSE), correlation co-efficient (CC) and Nash-Sutcliffe efficiency (NSE). the hybridized emerging SVR-HHO outperformed the other two data driven approaches in both the training and testing stages based on the employed evaluation metrics. Overall, the obtained results showed that both the machine learning and metaheuristic approaches were capable of predicting TQM.

The increased demand for resources and energy that is developing with rising global consumption represents a key challenge for our generation. Biogas production can contribute to sustainable energy production and closing nutrient cycles using organic residues or as part of a utilization cascade in the case of energy crops. Compared to hydrogen (H2), biogas with a high methane (CH4) content can be fed into the gas grid without restrictions. For this purpose, the CH4 content of the biogas must be increased from 52 to 60 % after anaerobic digestion to more than 96 %. In this study, biological hydrogen methanation (BHM) in trickling-bed reactors (TBR) is used to upgrade biogas. Design of experiments (DoE) is used to determine the optimal process parameters. The performance of the reactors is stable under all given conditions, reaching a “low” gas grid quality of over 90 %. The highest CH4 content of 95.626 ± 0.563 % is achieved at 55 °C and 4 bar, with a methane formation rate (MFR) of 5.111 ± 0.167 m³/(m³·d). The process performance is highly dependent on the H2:CO2 ratio in the educts, which should be as close as possible to the stochiometric ratio of 4. In conclusion, BHM is a viable approach to upgrade biogas to biomethane quality and can contribute to a sustainable energy grid.

The key to building a strong bridge country lies in talent, and the foundation lies in education. The cultivation of bridge engineering talents is related to the future and destiny of building a strong bridge country. This course adopts the educational philosophy of "holographic teaching method" centered on the comprehensive development of students, cultivating "engineering thinking" guided by engineering education certification, enhancing "abilities and qualities" through professional knowledge, reconstructing the course knowledge content system through engineering philosophy thinking, and implementing the ideological and political construction of the course in a multi-level unified manner of "explicit + implicit". Based on restructuring the course content system, we will strengthen the construction of diversified course resources, integrate theory with practice throughout the entire process of the course, practice the concept of "bridge architects + bridge engineers" parallel development, and cultivate cross-border and compound talents with comprehensive qualities of "engineering + art + humanities".

The flour milling industry, a vital component of global food production, is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality, and sustainability in flour production. Accurate estimation of protein, moisture and ash content in wheat grains and flour is of paramount importance due to their direct impact on product quality and compliance with industry standards. This paper explores the application of Near-Infrared (NIR) spectroscopy as a non-destructive, efficient, and cost-effective method for measuring the aforementioned essential parameters in wheat and flour, by investigating the effectiveness of a low-cost handle NIR spectrometer. Furthermore, a novel approach using Fuzzy Cognitive Maps (FCMs) is proposed to estimate protein, moisture and ash content in grain seeds and flour, marking the first known application of FCMs in this context. Our study includes an experimental setup that assesses different types of wheat seeds and flour samples and evaluates three NIR pre-processing techniques to enhance parameter estimation accuracy. The results indicate that low-cost NIR equipment can contribute to the estimation of the under-study parameters.

Efficient natural convection-cooled heat sinks are vital to the future of electronics cooling due to their low energy demand in the absence of an external pumping agency in comparison to other cooling methods. The present study is aimed to identify the most effective fin design for enhancing heat transfer in natural convection applications. Initially, a baseline case with rectangular fins was considered in the present study and it was optimized with respect to fin spacing. This optimized baseline case is then validated against the semi-empirical correlation proposed by Elen-baas (1942) [2]. Upon good agreement, the validated model is used for comparative analysis of different heat sink configurations with rectangular, trapezoidal, curved, and angled fins. The optimised fin spacing obtained for the baseline case is also used for the other heat sink configura-tions and then the fin designs are further optimized for better performance. However, for the an-gled fin case, the optimized configuration proposed by Zhang et al. (2020) [3] is adopted in the present study. This study is carried out with Ansys Fluent for a Rayleigh number of 2.4 × 10^6. The proposed novel curved fin design with a shroud defined as Case C4 showed a 4.1% decrease in the system’s thermal resistance with an increase in the heat transfer coefficient of 4.4% when compared to the optimized baseline fin case. The obtained results are further non-dimensionalized with proposed scaling in terms of the baseline case for the two novel heat sink cases (trapezoidal, curved).

A real-time two-way direct load control (TWDLC) of central air-conditioning chillers in wide area is proposed to provide demand response. The proposed TWDLC scheme is designed to optimize the load shedding ratio of every customer under control to ensure the target load to be shed is met at every scheduling period. In order to overcome the load reduction uncertainties of TWDLC, an innovative solution is proposed by applying a certain degree of loosening on the constraint of the actual shed load. Fuzzy linear programming is utilized to solve the optimization problem with fuzzy constraints. The proposed fuzzy linear programming problem is solved by delicately transforming it into a regular liner programming problem. A selection scheme used to obtain the feasible candidates set for load shedding at every sampling interval of TWDLC is also designed along with the fuzzy linear programming.

To assess the degree to which it has met its commitments under the Paris Agreement, Morocco is called upon to carry out carbon assessments and transparent evaluations. Within the forestry sector, little is known about the role of Morocco’s forests in contributing to carbon uptake. With this aim, we applied for the first time in literature the 3-PG model to Cedrus atlantica ((Endl.) Manetti ex Carrière, 1855), which represents about 131.800 ha of Morocco’s forest area (i.e. Azrou forest). Through the Differential Evolution - Markov Chains (DE-MC) we tested and assessed the sensitivity and calibrated the 3-PG model. This process-based model provided significant results regarding the carbon sequestration capacity. The results showed the following: i- Parameters related to stand properties, canopy structure, and processes, as well as biomass partitioning, are the most important or sensitive for the performance of the model; ii- The DE-MC method optimized the values of the 3-PG parameters which was confirmed by the means of Gelman-Rubin convergence test; iii- According to the predictions of the calibrated 3-PG, the Net Primary Production in the pure Azrou forest varies between 0.32 and 7.88 tC.ha−1.yr−1, it is equal in average to 4.9 tC.ha−1.yr−1, which given the total area corresponds to 7078 tC.yr−1.

Since the outbreak of the COVID-19 pandemic, patients infected with COVID-19 may experience abnormal heart rates, posing potential health risks. This study proposes a non-contact method for measuring heart rate (HR) and heart rate variability (HRV) to effectively reduce the risk of infection and assist healthcare professionals in more accurate diagnosis and treatment. In this study, except for the different image acquisition method, other research processes and methods are similar to the research methods in [15]. The image acquisition method is changed from forehead image to palm image. The new non-contact measurement performance of the proposed method can effectively not only avoid infection concerns but also obtain heart rate and HRV quickly and conveniently, providing higher accuracy for physiological parameters, root mean square error (RMSE), and mean absolute percentage error (MAPE) compared to those in recently published papers.

A class of controlled plants, whose dynamics is governed by a vector system of ordinary differential equations with a partially known right-hand side, is considered. The state variables and their velocities are assumed to be measurable. The aim is to design a controller which minimizes a loss function under certain constraints which arguments is the current state of the controlled plant. The designed control action is admitted to be a function of the current sub-gradient only, which supposed to be measurable on-line. The control design is based on ASG (Average Sub-Gradient method) — version of Integral Sliding Mode (ISM) concept, aimed to minimize on average a given convex (not obligatory strongly convex) cost function of the current state under a set of given constraints. An optimization type algorithm is developed and analyzed using ideas of SDM technique. The main results consist in proving the reachability of the "desired regime" (nonstationary analogue of sliding surface) from the beginning of the process and obtaining an explicit upper bound for the averaged loss function decrement, that is, the averaged in time functional convergence is proven and the rate of such convergence is estimated.

Efficient commencement of clinical trials is pivotal for their successful execution. The purpose of this study was to determine and examine the factors that contribute to trial start-up delays. The study assessed 20 clinical trials' start-up stages in order to identify relevant variables. Results showed that trials with an emphasis on devices, trials that did not outsource, trials that utilized less auxiliary services, and trials that employed interventional designs had quicker start-up times than observational studies. Furthermore, approval times were substantially shortened by using a centralized Institutional Review Board (IRB) rather than a local IRB. Furthermore, compared to studies that successfully enrolled subjects, those who failed to do so required more time to complete contracts and budgets and secure IRB clearance. This study underscores the diverse impact of clinical trial features on start-up timelines. Understanding these factors is crucial for optimizing the initiation process of clinical trials.

We consider a general problem of optimal allocation of limited resources in a wireless telecommunication network. The users are divided into several different groups (or classes), which correspond to different levels of service. The network manager must satisfy these different users requirements. This approach leads to a convex optimization problem with balance and capacity constraints. We present several decomposition type methods to find a solution of this problem, which exploit its special features. We suggest to apply first the dual Lagrangian method with respect to the total capacity constraint, which gives the one-dimensional dual problem. However, calculation of the value of the dual cost function requires solution of several optimization problems. Our methods differ in approaches of solution of these auxiliary problems. We compare the performance of the suggested methods on several series of test problems. They show rather satisfactory convergence. Nevertheless, proper decomposition technique enhance the convergence essentially.

Indonesia's SUMBAGUT 150 kV transmission of High Voltage Alternating Current Network (HVAC) system has considerable power losses. These power losses are a critical problem in the transmission network system. Meanwhile, this study provides one solution to reduce power losses using a High Voltage Direct Current (HVDC) network system. Determining the location to convert HVAC into HVDC is very important. The authors use Particle Swarm Optimization (PSO) to get the optimal location on the 150 kV SUMBAGUT HVAC transmission network system. The study results showed that before using the HVDC network system, the power losses were 122.26 MW. Meanwhile, power losses with one transmission HVDC in the "Paya Pasir-Sei Rotan" are 84.16 MW, "Porsa-P. Siantar" 90.83 MW, "Paya Pasir-Paya Geli" 104.14 MW. Then power losses with two transmission HVDC in "Paya Pasir-Sei Rattan" and "Porsa-P. Siantar" is 71.24 MW, "Paya Pasir-Sei Rotan" and "Paya Pasir-Paya Geli" 77.46 MW, "Porsa-P. Siantar" and "Paya Pasir-Paya Geli" 78.52 MW. The last result, power losses with three transmission HVDC in "Paya Pasir-Sei Rotan," "Porsa-P. Siantar," and "Paya Pasir-Paya Geli" lost 64.57 MW.

A universally applicable method for the prediction of the isobaric heat capacities of the liquid and solid phase of molecules at 298.15 K is presented, derived from their “true” volume. The molecules’ “true” volume in A³ is calculated on the basis of their geometry-optimized structure and the Van-der-Waals radii of their constituting atoms by means of a fast numerical algorithm. Good linear correlations of the “true” volume of a large number of compounds encompassing all classes and sizes with their experimental liquid and solid heat capacities over a large range have been found, although noticeably distorted by intermolecular hydrogen-bond effects. To take account of these effects, the total amount of 1303 compounds with known experimental liquid heat capacities has been subdivided into three subsets consisting of 1102 hydroxy-group-free compounds, 164 monoalcohols/monoacids, and 36 polyalcohols/polyacids. The standard deviations for Cp(liq,298) were 20.7 J/mol/K for the OH-free compunds, 22.91 J/mol/K for the monoalcohols/monoacids and 16.03 J/mol/K for the polyols/polyacids. Analogously, 797 compounds with known solid heat capacities have been separated into a subset of 555 OH-free compounds, 123 monoalcohols/monoacids and 119 polyols/polyacids. The standard deviations for Cp(sol,298) were calculated to 23.14 J/mol/K for the first, 21.62 J/mol/K for the second, and 19.75 J/mol/K for the last subset. A discussion of structural and intermolecular effects influencing the heat capacities as well as of some special classes, in particular hydrocarbons, ionic liquids, siloxanes and metallocenes, has been given. In addition, the present method has successfully been extended to enable the prediction of the temperature dependence of the solid and liquid heat capacities in the range between 250 and 350 K.

The market for microfluidic chips is experiencing significant growth; however, their development is hindered by a complex design process and low efficiency. Enhancing microfluidic chips’ design quality and efficiency has emerged as an integral approach to foster their advancement. Currently, the existing structural design schemes lack careful consideration regarding the impact of chip area, microchannel length, and the number of intersections on chip design. This inadequacy leads to redundant chip structures resulting from the separation of layout and wiring design. This study proposes a structural optimization method for microfluidic chips to address these issues utilizing a simulated annealing algorithm. The simulated annealing algorithm generates an initial solution in advance using the fast sequence pair algorithm. Subsequently, an improved simulated annealing algorithm is employed to obtain the optimal solution for the device layout. During the wiring stage, an advanced wiring method is used to designate the high wiring area, thereby increasing the success rate of microfluidic chip wiring. Furthermore, the connection between layout and routing is reinforced through an improved layout adjustment method, which reduces the length of microchannels and the number of intersections. Finally, the effectiveness of the structural optimization approach is validated through six sets of test cases, successfully achieving the objective of enhancing the design quality of microfluidic chips.

Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

Because of the rapid advancement in the use of photovoltaic (PV) energy systems, it has become critical to look for ways to improve the energy generated by them. The extracted power from the PV modules is proportional to the output voltage. The relationship between output power and array voltage has only one peak under uniform irradiance, whereas it has multiple peaks under partial shade circumstances (PSC). There is only one global peak (GP) and many local peaks (LPs), where the typical maximum power point trackers (MPPT) may become locked in one of the LPs, significantly reducing the PV system's generated power and efficiency. The metaheuristic optimization algorithms (MOAs) solved this problem, albeit at the expense of the convergence time, which is one of these algorithms' key shortcomings. Most MOAs attempt to lower the convergence time at the cost of the failure rate and the accuracy of the findings because these two factors are interdependent. To address these issues, this work introduces the dandelion optimization algorithm (DOA), a novel optimization algorithm. The DOA's convergence time and failure rate are compared to other modern MOAs in critical scenarios of partial shade PV systems to demonstrate the DOA's superiority. The results obtained from this study showed substantial performance improvement compared to other MOAs, where the convergence time is reduced to 0.4 s with zero failure rate compared to 0.9 s, 1.25 s, and 0.43 s for other MOAs under study. The optimal number of search agents in the swarm, optimal initialization of search agents, and optimal design of the dc-dc converter is introduced for optimal MPPT performance.

Geopolymer cement has been popularly studied nowadays compared to ordinary Portland cement because it demonstrated superior environmental advantages due to its lower carbon emissions and waste material utilization. This paper focuses on the formulation of geopolymer cement from nickel-laterite mine waste (NMW) and coal fly ash (CFA) as geopolymer precursors, and sodium hydroxide (SH) and sodium silicate (SS) as alkali activators. Different mix formulations of raw materials are synthesized to produce a geopolymer based from an I-optimal design and obtained different compressive strengths. A mixed formulation of 50% NMW and 50% CFA, SH-to-SS ratio of 0.5, and an activator-to-precursor ratio of 0.429 yielded the highest 28-day unconfined compressive strength (UCS) of 22.10 ± 5.40 MPa. Furthermore, using an optimized formulation of 50.12% NMW, SH-to-SS ratio of 0.516, and an activator-to-precursor ratio of 0.428, a UCS value of 36.30 ± 3.60 MPa was obtained. The result implies that the synthesized geopolymer material can be potentially used for concrete structures and pavers, pedestrian pavers, light traffic pavers, and plain concrete.

Due to production space and operating environment requirements, mine production equipment often breaks down, which seriously affects the mine’s production schedule. To ensure the smooth completion of the haulage operation plan under abnormal conditions, a model of the haulage equipment rescheduling plan based on the random simulation of equipment breakdowns is established in this paper. The model aims to accomplish both the maximum completion rate of the original mining plan and the minimum fluctuation of the ore grade during the rescheduling period. This model is optimized by improving the wolf colony algorithm and changing the location update formula of the individuals in the wolf colony. Then, the optimal model solution can be used to optimize the rescheduling of the haulage plan by considering equipment breakdowns. The application of the proposed method in an underground mine revealed that the completion rate of the mine’s daily mining plan reached 83.40% without increasing the number of the equipment, while and the ore quality was stable. Moreover, the improved optimization algorithm converged fast and was characterized by high robustness.

To realize structural vibration control,a two parameters H2 optimization design was proposed to optimize the tuning ratio and damping ratio for electromagnetic tuned mass damper (EMTMD). The control effect of this two parameters optimization design is better than that of classical tuned mass damper (TMD).For this two parameters optimization,the most important thing is that the inductance of the coil can be set very small and the external load resistance can be positive ,which can avoid the use of complex negative impedance circuit. If Ref.[6] were designed according to the H2 optimization of two parameters, the EMTMD can be used for multi-modal vibration control of structures without connecting negative inductance and negative resistance spontaneously.

The extensive utilization of rebar during the construction of the project generates immense rebar waste, leading to increased construction costs and significant greenhouse gas emissions. Various approaches have been explored focusing on the minimization of rebar-cutting waste, such as optimizing cutting patterns, lap splice position, and special-length rebar utilization. Nonetheless, reducing rebar usage by minimizing the number of splices remains uninvestigated. In response to these issues, a two-stage optimization algorithm was developed, prioritizing special-length rebar to achieve near-zero rebar-cutting waste of less than 1% and concurrently reduce rebar usage, thereby promoting sustainable construction practices. The two-stage algorithm presented in this study involves the optimization of the lap splice position for the continuous rebar with a reduction of splices number. Furthermore, it integrates a special-length minimization algorithm for the additional rebars. Applying the algorithm to beam structures in a small-sized factory building project led to a notable reduction of 29.624 tons of rebar, equivalent to 12.31% of the total purchased quantity. Greenhouse gas emissions were reduced by 102.68 tons, and associated costs decreased by USD 30,256. A rebar-cutting waste of 0.99% which is near zero was achieved. These findings highlight the significant potential of the proposed algorithm in reducing rebar waste and facilitating sustainable construction practices. In addition, the application of the proposed algorithm in reinforced concrete construction projects will amplify the associated advantages accordingly.

This study aims to better understand students who attend the Basic Vocational Training Cycles (FPB) regarding the measures to ensure diversity. This quantitative research project approximates the perception of students concerning their passage through their previous studies and their satisfaction and goals after finishing the school year. The sample con-sists of 352 students from Cordoba (Spain). A questionnaire has been used which follows the CIPP model. After the exploratory factor analysis was completed with the different groups of items and their descriptive analysis, various tests were carried out to consider the hypotheses (Pearson's correlation (r), one-factor analysis of variance and repeated ANOVA measures). The results indicate that the educational interest of the students is ac-ademic and professional. Likewise, there is no relationship detected between the appear-ance of socio-professional goals and average academic levels and the attributions with re-spect to repetitions of previous courses, although these goals vary depending on the satis-faction shown by the students with respect to the cycles. We conclude that the course of the FPB influences the adoption of decisions regarding academic-professional projects.

Multidisciplinary Design Optimization (MDO) is the most active field in the design of current complex system engineering, which is possessed with such two difficulties as subsystem information exchange and analytical and computational complexity of systems. Therefore, an improved collaborative optimization algorithm based on ANN (artificial neural network) response surface was proposed dependent on the consistency constraint algorithm and concurrent subspace algorithm. As an optimization method with secondary structure, it satisfied only local constraints in discipline layer, but provided a coordinated mechanism for interdisciplinary conflict in system layer. Finally, it was applied in the multidisciplinary design optimization of autonomous underwater vehicle (AUV). As shown from the result, the MDO convergence stability and reliability of low resistance, low noise and high maneuvering performance of the AUV shape can be ensured by the improved collaborative optimization algorithm, thus verifying the effectiveness of the algorithm.

The violation of Bell's theorem is a very simple way to see that there is no underlying classical interpretation of quantum mechanics. The measurements made on the photons shows that light signal (information) could travel between them, hence completely eliminating any chance that the result was due to anything other than entanglement. Entanglement has been studied extensively for understanding the mysteries of non-classical correlations between quantum systems. It was found that violation of Bell's inequalities could be trivially calculated and for sets of nonmaximally entangled states of two qubits, comparing these entanglement measures may lead to different entanglement orderings of the states. On the other hand, although it is not an entanglement measure and not monotonic under local operations, due to its ability of detecting multipartite entanglement, quantum Fisher information (QFI) has recently received an intense attraction generally with entanglement in the focus. In this work, we visit violation of Bell's inequalities problem with a different approach. Generating a thousand random quantum states and performing an optimization based on local general rotations of each qubit, we calculate the maximal QFI for each state. We analyze the maximized QFI in comparison with violation in Bell's inequalities and we make similar comparison of this violation with commonly studied entanglement measures, negativity and relative entropy of entanglement. We show that there are interesting orderings for system states.

Background: While outcomes of chronic phase chronic myeloid leukemia (CP-CML) patients aged over 65 years have been extensively evaluated in real-life experiences, limited data exist for the very elderly population (i.e., aged ≥ 75 years), especially for next-generation tyrosine kinase inhibitors (TKIs). In this retrospective study, we sought to evaluate the safety and efficacy of TKIs in this particular setting of patients. Methods: We conducted a retrospective analysis of a multicenter cohort of 123 newly diagnosed CP-CML very elderly patients. Results: The median age at diagnosis was 80 years (range: 75-96). In the first-line, 86.1% of patients received imatinib, 7.1% dasatinib, 5.6% nilotinib, and 0.81% received bosutinib. A total of 31 patients (25.2%) switched to second-line therapy, nine patients to a third-line and one patient to a fourth line of therapy. Resistance to treatment was the primary reason for switching therapy in both the first (64.5%) and second lines (77.7%). At diagnosis, reduced doses were administered in 36.5% of patients, in 61.2% in the second line, and in all patients in subsequent lines of therapy. In first-line setting, 71.9% of patients achieved an early molecular response (EMR, i.e. 3-month BCR::ABL1 IS <10%); at 6, 12, and 24 months, MR3 was reached by 35.7%, 55.7%, and 75.0% of patients, with 16.6%, 35.7%, and 51.7% achieving DMR at the same timepoints. Treatment-free remission (TFR) was successfully attempted in 11 patients. During the follow-up period, adverse events (AEs) were observed in 78.8% of patients, including 22 cases of cardiovascular AEs. Toxicity grade ≥ 3 was more commonly observed in patients treated with standard doses of TKIs compared to reduced doses (p=0.033). Overall, median follow-up was 46.62 months (range: 1.8-206.2), and 43 patients died due to non CML related causes. Three patients died due to disease progression to advanced (n=1) and blastic (n=2) phases. The 5-year overall survival (OS) for the entire cohort was 71.9% (95% CI: 0.63-0.81), with no significant difference between patients treated with standard doses of TKI compared to those treated with reduced doses (p=0.35). Conclusions: TKIs appear to be safe and effective even in very elderly CML patients, and dose optimization strategies yield satisfactory molecular responses for adequate disease control with an improved safety profile.

In recent years, the demand for coffee has increased tremendously. During the production process, green coffee beans are traditionally screened manually for defective beans before they are packed into coffee bean packages; however, this method is not only time-consuming but also increases the rate of human error due to fatigue. Therefore, this paper proposed a lightweight deep convolutional neural network (LDCNN) for the quality detection system of green coffee beans, which combined depthwise separable convolution (DSC), squeeze-and-excite block (SE block), skip block, and other frameworks. To avoid the influence of low parameters of the lightweight model caused by the model training process, rectified Adam (RA), lookahead (LA), and gradient centralization (GC) were included to improve efficiency; the model was also put into the embedded system. Finally, the local interpretable model-agnostic explanations (LIME) model was employed to explain the predictions of the model. The experimental results indicated that the accuracy rate of the model could reach up to 98.38% and the F1 score could be as high as 98.24% when detecting the quality of green coffee beans. Hence, it can obtain higher accuracy, lower computing time, and lower parameters. Moreover, the interpretable model verified that the lightweight model in this work is reliable, providing the basis for screening personnel to understand the judgment through its interpretability, thereby improving the classification and prediction of the model.

Although the concept of additive manufacturing has been proposed for several decades, momentum of selective laser melting (SLM) is finally starting to build. In SLM, density and surface roughness, as the important quality indexes of SLMed parts, are dependent on the processing parameters. However, there are few studies on their collaborative optimization in SLM to obtain high relative density and low surface roughness simultaneously in the previous literature. In this work, the response surface method was adopted to study the influences of different processing parameters (laser power, scanning speed and hatch space) on density and surface roughness of 316L stainless steel parts fabricated by SLM. The statistical relationship model between processing parameters and manufacturing quality is established. A multi-objective collaborative optimization strategy considering both density and surface roughness is proposed. The experimental results show that the main effects of processing parameters on the density and surface roughness are similar. It is noted that the effects of the laser power and scanning speed on the above objective quality show highly significant, while hatch space behaves an insignificant impact. Based on the above optimization, 316L stainless steel parts with excellent surface roughness and relative density can be obtained by SLM with optimized processing parameters.

Hot stamping dies include cooling channels to treat the formed sheet. The optimum cooling channels of dies & molds should adapt to the shape and surface of the dies, so that a homogeneous temperature distribution and cooling are guaranteed. Nevertheless, cooling ducts are conventionally manufactured by deep drilling, attaining straight channels unable to follow the geometry of the tool. Laser Metal Deposition (LMD) is an additive manufacturing technique capable to fabricate nearly free-form integrated cooling channels and therefore shape the so-called conformal cooling. The present work investigates the design and manufacturing of conformal cooling ducts, which are additively built up on hot work steel and then milled in order to attain the final part. Their mechanical performance and heat transfer capability has been evaluated, both experimentally and by means of thermal simulation. Finally, conformal cooling conduits are evaluated and compared to traditional straight channels. The results show that LMD is a proper technology for the generation of cooling ducts, opening the possibility to produce new geometries on dies & molds and, therefore, new products.

This study analyzes the convergence between artificial intelligence (AI) and management accounting in hospital contexts. Management accounting plays a central role in the effectiveness of planning, control and decision-making in healthcare institutions such as hospitals. The application of AI in this scenario offers powerful tools to improve accounting processes, optimize operational efficiency and maximize the use of available resources, resulting in an improvement in the quality of care provided to patients. Through in-depth analysis, the article explores how AI is bringing about a revolution in hospital management accounting, presenting case studies illustrating its successful implementation, outlining the tangible benefits.

The visual perception of leisure spaces between residences in cold regions is important for public health. To compensate for the existing researches` lack of ignoring the cold snow season`s influence, this study selected two types of outdoor leisure space environments in non-snow and snow seasons as research objects. An eye tracker combined with a Semantic Difference (SD) questionnaire was used to verify the feasibility of the application of virtual reality technology, screen out leisure gaze characteristics, and reveal the environmental factors related to leisure visual perception. In snow season, spatial aspect ratio (SAR), building elevation saturation (BS), and grass proportion in the field of view (GP) showed strong correlation to the leisure visual perception scores (W). In non-snow season, apart from the above three factors, roof height difference (RHD), tall tree height (TTH) and color contrast (HC) also influenced W obviously. The effects of factors on W were revealed in immersive virtual en-vironment (IVE) orthogonal experiments and genetic algorithm (GA) and k-nearest neighbor algorithm (KNN) were combined to optimized the environmental factors. The optimized threshold ranges in the non-snow season environment were SAR: 1.82‒2.15, RHD: 10.81 m‒20.09 m, BS: 48.53‒61.01, TTH: 14.18 m‒18.29 m, GP: 0.12‒0.15, and HC: 18.64‒26.83. In the snow season environment, the optimized threshold ranges were SAR: 2.22‒2.54, BS: 68.47‒82.34, and GP: 0.1‒0.14.

Building on the carbon reduction targets agreed in the Paris Agreements, many nations have renewed their efforts toward achieving carbon neutrality by the year 2050. In line with this ambitious goal, nations are seeking to understand the appropriate combination of technologies which will enable the required reductions in such a way that they are appealing to investors. Around the globe, solar and wind power lead in terms of renewable energy deployment, while carbon capture and storage (CCS) is scaling up toward making a significant contribution to deep carbon cuts. Using Japan as a case study nation, this research proposes a linear optimization modeling approach to identify the potential contributions of renewables and CCS toward maximizing carbon reduction and identifying their economic merits over time. Results identify that the combination of these three technologies could enable a carbon dioxide emission reduction of between 55 and 67 percent in the energy sector by 2050 depending on resilience levels and CCS deployment regimes. Further reductions are likely to emerge with increased carbon pricing over time. The findings provide insights for energy system design, energy policy making and investment in carbon reducing technologies which underpin significant carbon reductions, while identifying potential regional social co-benefits.

Background: With the trend of intelligent display, the interface design of in-vehicle HUD is an expanding research field; Methods: To solve the subjectivity and uncertainty in the optimization of HUD interface design schemes, this paper proposed a hybrid scheme evaluation and optimization method based on entropy weight and VIKOR. The entropy weight method was used to reduce the subjectivity the decision-maker's weighting and obtain the objective weight of each indicator; The VIKOR method was used to obtain the best ranking of alternative schemes, and then the optimal interface design scheme was selected; Results: The evaluation of in-vehicle HUD interface design schemes were taken as an example for verification and calculation. The results showed that this method considers the subjectivity and uncertainty of the decision-making process in the optimization of design scheme, which can effectively improve the objectivity and accuracy of the evaluation results, and provide a reference for designers to optimize interface design schemes.

Optimizing the NACA0015 airfoil which is widely applied in small-scale vertical axis wind turbine to make it has a better aerodynamic performance. In the optimization process, using CST parameterization method to perturb the airfoil geometry, the thickness and camber of the airfoil are selected as the constraint, and the value of the maximum tangential force coefficient is chosen as the objective function, the genetic algorithm based on non-dominated sorting (NSGA-II)is selected as an optimization method, calculates the aerodynamic performance of the airfoil by applying the approach of combining XFOIL program and Viterna-Corrigan post-stall mode ,and establishes the optimizing process by the optimization software modefrontier for NACA0015 airfoil’s muti-point optimization, validate the airfoil’s performance with CFD finally. The result illustrates that, by comparing with the NACA0015 airfoil, the optimized airfoil’s lift to drag ratio is improved over a wide range of attack angles, the stall performance is more gentle. The maximum lift coefficient, the maximum lift-drag ratio and the maximum tangential force coefficient are increased by 7.5%,9 and 8.87%, respectively. The optimized airfoil has a wide variable condition performance, more suitable for the operating conditions of a vertical axis wind turbine. Finally, predict the rotor efficiency with optimized airfoil and NACA0015 airfoil for different tip speed ratios and different solidities with multiple streamtube model, the result shows the rotor with optimized airfoil has a higher efficiency.

The enhanced multi-objective symbolic discretization for time series (eMODiTS) method employs a flexible discretization scheme using different value cuts for each non-equal time interval, which requires a computational cost for evaluating each objective function. Therefore, surrogate models were implemented to minimize this disadvantage. Nevertheless, each solution found by eMODiTS is a different-sized vector, so the surrogate model must be able to handle data sets under this characteristic. Consequently, this work's contribution lies in analyzing the surrogate models' implementation on the time series discretization, where each potential scheme is a real-number different-sized vector. For this reason, the surrogate model proposed was k-nearest Neighbors for regression with Dynamic Time Warping as a distance measure. Results suggest our proposal finds a suitable approximation to the final eMODiTS solutions with a functions evaluation reduction rate between 15% and 95%. However, according to Pareto front performance measures, the proposal Pareto front is competitive compared to the eMODiTS Pareto front, reaching an average Generational Distance (GD) between 0.0447 and 0.0536. Moreover, the average Hypervolume Ratio (HVR) ranges between 0.334 and 0.3891. Finally, our proposal compared against SAX-based methods presents a similar behavior regarding classification tasks and statistical tests.

Real-time data processing and decision-making are increasingly crucial in various applications, driven by the continuous influx of data streams. Event-driven AI workflows within serverless computing environments offer a promising approach to handle these real-time demands efficiently. This paper presents a framework for simulating and analyzing the performance characteristics of such workflows. Our proposed approach utilizes simulated data with varying event rates and durations to investigate the impact on key performance metrics like latency, throughput, and resource utilization. This enables a comprehensive evaluation of the inherent trade-offs within event-driven AI systems. The key findings reveal a trade-off between latency and throughput. As the event rate increases, average processing latency generally increases while average throughput increases. Resource utilization remains relatively stable across different event rates in the simulated scenarios (e.g., 75.55\% at 2 events/second, 74.51\% at 10 events/second). This framework provides a valuable tool for understanding the performance characteristics of event-driven AI workflows and optimizing resource allocation strategies.

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.

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.

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.

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.

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.

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.

The minimum dominating tree (MDT) problem consists of finding a minimum weight sub-graph from an undirected graph, such that each vertex not in this sub-graph is adjacent to at least one of the vertices in it, and the sub-graph is connected without any ring structures. This paper presents a Dual Neighborhoods Search (DNS) algorithm for solving the MDT problem, which integrates several distinguishing features, such as two neighborhoods collaboratively working for optimizing the objective function, a fast neighborhood evaluation method to boost the searching effectiveness, and several diversification techniques to help the searching process jump out of the local optimum trap thus obtaining better solutions. DNS improves the previous best-known results for 4 public benchmark instances while providing competitive results for the remaining ones. Several ingredients of DNS are investigated to demonstrate the importance of the proposed ideas and techniques.

Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) methods. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variation of environmental conditions. Smart optimization algorithms (SOA) using metaheuristic optimization algorithms can avoid this limitation inherent in the conventional MPPT methods. The problem of slow convergence of the SOA is avoided in this paper using a novel strategy called Swarm Size Reduction (SSR) utilized with Particle Swarm Optimization (PSO) algorithm, specifically designed to achieve short convergence time (CT) while maintaining exceptional tracking accuracy. The novelty of the proposed MPPT method introduced in this paper is the dynamic reduction of the swarm size of the PSO for improved performance of the MPPT of the PV systems. This adaptive reduction approach allows the algorithm to efficiently explore the solution space of PV systems and rapidly exploit to identify the global maximum power point (GMPP) even under fast fluctuation of uneven solar irradiance and temperature. This pioneering ultra-fast MPPT method represents a significant advancement in PV system efficiency and promotes the wider adoption of solar energy as a reliable and sustainable power source. The novel adaptive PSO algorithm also opens new possibilities for further innovations in optimization-driven applications, extending its potential impact on a diverse range of real-world challenges. The results obtained from this proposed strategy are compared with several optimization algorithms to validate its superiority.

This paper presents research on the application of trajectory design, optimization, and control to an orbital transfer from Mars-Phobos Distant Retrograde Orbits to the surface of Phobos. Given a Distant Retrograde Orbits and a landing location on the surface of Phobos, landing trajectories for which total Δv for a direct 2-burn maneuver is minimized are computed. This is accomplished through the use of Particle Swarm Optimization in which the required Δv and time of flight are optimization parameters. The non-uniform gravitational environment of Phobos is considered in the computation. Results show how direct transfers can be achieved with Δv in the order of ∼30 m/s.

The twisted Wang transform distribution family, defined as the composition of parameter shifted inverse CDF function with an original CDF function, is found to be most suitable for matching low shape factor distributions, characterizing hard to fit or to simulate reinsurance portfolio losses for some perils from our previous study. Among them, the best form for matching a hard-to-fit empirical loss distribution for a specific peril, is the Exponential Fractional Extra Power 0 Distribution in (0,1) with CDF:.The simplest yet still a good form of this family is the Transformed Hyperbolic Tangent Distribution with CDF:,which has analytical formulas for the moments. The twisted Wang transform distribution family is compared and confirmed to be superior to all other well-known distribution families through extensive numerical optimization practice, distribution forms guesses, and computer-aided exploration experiments.

Microgrids are becoming popular nowadays because they provide clean, efficient, and low-cost energy. To use the stored energy in times of emergency or peak loads, microgrids require bulk storage capacity. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to generate electricity. Batteries play a variety of essential roles in daily life and are used at peak hours and during a time of emergency. There are different types of batteries i.e., lion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem, that limits modern technologies such as electric vehicles, etc. It is important to know different battery features such as battery life, battery throughput, and battery autonomy to get optimal battery sizing for microgrids. Mixed-integer linear programming (MILP) is an established technique for the integration and optimization of different energy sources and parameters for optimal battery sizing. A new MILP based dataset is introduced in this work. Support vector machine (SVM) is the machine learning application used to estimate the optimum battery size. The impact of feature selection algorithms on the proposed machine learning-based model is evaluated. The performance of the six best-performing feature selection algorithms is analyzed. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. Ranker search shows the best performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488 and root mean squared error of 0.0525.

This study presents the substantiation, development, and analysis of a technique for planning the autonomous vehicle (AV) motion reference parameters. The trajectory plan, speed and acceleration distributions, including other AV's kinematic parameters, are determined using sequential optimization. The study objectives are based on an analysis of the fundamental problems of AV motion planning summarized in this area's latest publications. The proposed approach combines the basic principles of the finite element method (FEM) and nonlinear optimization with nonlinear constraints. First, the generalization on representing an investigated function by finite elements (FE) is briefly described. A one-dimension FE with two nodes and three degrees of freedom (DOF) in a node was chosen as the basic one, corresponding to the 5th-degree polynomial. Next, a method for determining the motion trajectory is presented. The following are considered: formation of a restricted space for the AV's allowable maneuvering, the geometry of motion trajectory and its relation with vehicle steerability parameters, cost functions and their influences on the desirable trajectory's nature, compliance of nonlinear restrictions of the node parameters with the motion area boundaries. At the second stage, a technique for optimizing AV speed and acceleration redistribution is presented. The model considers possible combinations of cost functions, conditions of limiting the kinematic parameters with the tire slip critical speed, maximum speed level, maximum longitudinal acceleration, and critical lateral acceleration. In the simulation section, several variants of trajectories were searched and compared. Several versions of distributing the longitudinal speed and acceleration curves are determined, and their comparative analysis is fulfilled. At the end of the paper, the advantages and drawbacks of the proposed technique are noted. The conclusion is made regarding the options for improving the method in further studies.

Acid fracturing simulation is used widely to optimize carbonate reservoirs and improve acid fracturing treatment performance. In this study, a method was used to minimize the risk of the acid fracturing treatment. First, optimal fracture geometry parameters with UFD methods are calculated. After that, design components change as long as fracture geometry parameters reach their optimal values. The results showed a high flow rate needed to achieve optimal fracture geometry parameters with increasing acid volume. Sensitivity analysis was performed on controllable and reservoir parameters. It observed that a high flow rate should be applied for a low fluid viscosity to achieve the optimization goals. Straight acid reaches optimal conditions at a high flow rate and low volume. These conditions for retarded acids appear only at a low flow rate and high volume. The study of the acid concentration for gelled acid showed that as it increased, the flow rate and volume increased. Besides, for low permeability formation, a large fracture half-length and small fracture width are desirable. In this case, a higher flow rate will be required. The sensitivity analysis showed that the optimum flow rate and acid volume increase and decrease for the high Young's modulus. The effect of closure stress was also investigated and observed for a sample with high closure stress, low flow rate, and high acid volume are required.

Metaheuristic algorithms have been frequently using to tackle optimization problems, however such algorithms in the analysis of health-related data is not commonly used as developing metaheuristic algorithms that work well on health-related data is a difficult task due to complexity of the health data in particular genomics and epigenetics data. One of the important tasks in genomics is to predict genomic elements that are incorporating together to regulate a disease-related genes. Predicting such elements are important as they can be used to develop a personalized cure. In this study, we present for the first time, a multi-object simulated annealing algorithm to identify enhancer-promoter like interactions from Hi-C (chromosome conformation capture) data. These regulatory elements can potentially play vital roles as promoters and/or enhancers in appearance and exacerbation of the regulation of gene.s To evaluate the efficiency of the proposed method, we applied our proposed method and traditional methods on the Hi-C data from mice and compared together. Our results show that the interacting elements identified by our new method are more likely to be functional. The source code of the method is publicly available.

: The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are through synthesizing multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

Forest planners have traditionally used expected growth and yield coefficients to predict future merchantable timber volumes. However, because climate change affects forest growth, the typical forest planning methods using expected value of forest growth can lead to sub-optimal harvest decisions. We proposed in this paper to formulate the harvest planning with growth uncertainty due to climate change problem as a multistage stochastic optimization problem and use sample average approximation (SAA) as a tool for finding the best set of forest units that should be harvested in the first period even though we have a limited knowledge of what future climate will be. The objective of the harvest planning model is to maximize the expected value of the net present value (NPV) considering the uncertainty in forest growth and thus in revenues from timber harvest. The proposed model was tested on a small forest with 89 stands and the numerical results showed that the approach allows to have superior solutions in terms of net present value and robustness in face of different climate scenarios compared to the approach using the expected growth and yield. The SAA methods requires to generate samples from the distribution of the random parameter. Our results suggested that a sampling scheme that focuses on generating high number of samples in distant future stages is favorable compared to having large sample sizes for the near future stages. Finally, we demonstrated that, depending on the level of forest growth change, ignoring this uncertainty can negatively affect forest resources sustainability.

The problem of scheduling is an area that has attracted a lot of attention from researchers for many years. Its goal is to optimize resources in the system. The assigning task to the lecturer is an example of the timetabling problem, a class of scheduling. This study introduces a mathematical model to assign fixed tasks (the time and required skills to be fixed) to university lecturers. Our model is capable of generating a calendar that maximizes faculty expectations. The formulated problem is in the form of a multi-objective problem that optimal makes decisions require the trade-off presence of trade-offs between two or more conflicting objectives. To solve this, we use the Compromise Programming approach to multi-objective programming. We then proposed the new version of the Genetic Algorithm to solve the introduced model. Finally, the model and algorithm tested with real scheduling data collected at the Computing Fundamental Department, FPT University, Hanoi, Vietnam.

There exist numerous high-dimensional problems in the real world which cannot be solved through the common traditional methods. The metaheuristic algorithms have been developed as successful techniques for solving a variety of complex and difficult optimization problems. Notwithstanding their advantages, these algorithms may turn out to have weak points such as lower population diversity and lower convergence rate when facing complex high-dimensional problems. An appropriate approach to solve such problems is to apply multi-agent systems along with the metaheuristic algorithms. The present paper proposes a new approach based on the multi-agent systems and the concept of agent, which is named Multi-Agent Metaheuristic (MAMH) method. In the proposed approach, several basic and powerful metaheuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Crow Search Algorithm (CSA), Farmland Fertility Algorithm (FFA), are considered as separate agents each of which sought to achieve its own goals while competing and cooperating with others to achieve the common goals. In overall, the proposed method was tested on 32 complex benchmark functions, the results of which indicated effectiveness and powerfulness of the proposed method for solving the high-dimensional optimization problems. In addition, in this paper, the binary version of the proposed approach, called Binary MAMH (BMAMH), was executed on the spam email dataset. According to the results, the proposed method exhibited a higher precision in detection of the spam emails compared to other metaheuristic algorithms and methods.

In this effort we propose a data-driven learning framework for reduced order modeling of fluid dynamics. Designing accurate and efficient reduced order models for nonlinear fluid dynamic problems is challenging for many practical engineering applications. Classical projection-based model reduction methods generate reduced systems by projecting full-order differential operators into low-dimensional subspaces. However, these techniques usually lead to severe instabilities in the presence of highly nonlinear dynamics, which dramatically deteriorates the accuracy of the reduced-order models. In contrast, our new framework exploits linear multistep networks, based on implicit Adams-Moulton schemes, to construct the reduced system. The advantage is that the method optimally approximates the full order model in the low-dimensional space with a given supervised learning task. Moreover, our approach is non-intrusive, such that it can be applied to other complex nonlinear dynamical systems with sophisticated legacy codes. We demonstrate the performance of our method through the numerical simulation of a two-dimensional flow past a circular cylinder with Reynolds number Re = 100. The results reveal that the new data-driven model is significantly more accurate than standard projection-based approaches

In theory and applications, it is often inevitable to work with projectors onto convex sets, where a linear transform is involved. In this article, a novel projector is presented, which generalizes previous results in that it admits a broader family of linear transforms, but on the other hand it is limited to box-type convex sets in the transformed domain. The new projector has an explicit formula and it can be interpreted within the framework of proximal optimization. The benefit of the new projector is demonstrated on an example from signal processing, where it was possible to speed up the convergence of a signal declipping algorithm by a factor of more than two.