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

The reduction of PV prices and interest in energy independence accelerate the adoption of residential battery storage. This storage can support various functions of an energy system undergoing decarbonization. In this work, operative benefits of the storage from the system perspective, namely, generation cost reduction and congestion mitigation, are investigated. Germany is chosen as a case study due to its strong reliance on variable renewable energy. For the analysis, an economic dispatch model with a high spatial resolution is coupled with a Pan-European transmission grid model. It is shown that the system's generation costs are highest when the assets are used only to maximize the PV self-consumption, and the costs are lowest when the storage also reacts to the market dynamics. This amounts to 6% cost reduction. Both operation strategies result in an equal level of grid congestion and infrastructure loading. This is improved with a strategy that accounts regional peak reduction as a secondary objective. The high congestion level emphasizes the grid expansion need to keep pace with the generation and electrification expansion necessary to decarbonize other sectors. Lastly, policymakers should enable multipurpose utilization, e.g., via the introduction of market-oriented retail electricity prices with intervention options for grid operators.

Continuous Distributed Constraint Optimization Problem (C-DCOPs) are a powerful framework to model problems with continuous variables in multi-agent systems. Previous works about C-DCOP algorithms mainly use pseudo-trees to guarantee the anytime property or are without anytime property guarantees. However, there is a risk of privacy leakage in the pseudo-tree due to utility passing between agents. Therefore, based on the basic constraint graph instead of the pseudo-tree, we (i) extend the Maximum Gain Message (MGM) algorithm by combining the local search strategy to solve C-DCOPs, named Continuous MGM (C-MGM), and it’s able to guarantee the monotonicity of the solution quality; (ii) propose a Parallel C-MGM (C-PMGM) algorithm to improve the solution quality through parallel random search; and (iii) introduce the differential search into C-PMGM to design a Parallel Differential Search C-MGM (C-PDSM) algorithm, which constructs a heuristic method to speed up convergence and improve solution quality. Compared to other anytime C-DCOP algorithms using pseudo-trees, the proposed three algorithms can exhibit better performance in avoiding privacy violations. We theoretically prove that the proposed algorithms are the anytime algorithms and empirically demonstrate that our algorithms outperform the state-of-the-art C-DCOP algorithms.

The unpredictable increase in electrical demand affects the quality of theenergy throughout the network. A solution to the problem is the increase of distributedgeneration units which burn fossil fuels.While this is an immediate solution to theproblem the ecosystem gets affected by the emission of CO2.A promising solutionis the integration of Distributed Renewable Energy Sources (DRES) to the conventionalelectrical system, thus, introducing the concept of smart microgrids (SMG) that requirea safe, reliable and technically planned two-way communication system. This documentpresents a heuristic based on planning capable of providing a bidirectional communicationnear optimal route map, following the structure of an hybrid Fiber-Wireless (FiWi) with thepurpose of obtaining information of electrical parameters that help us to manage the useof energy by integrating conventional electrical system to SMG. A FiWi network is basedon the integration of wireless access and optical networks. This integration increases thecoverage and reliability at a lower cost. The optimization model is based on clusteringtechniques, through the construction of balanced conglomerates. The method is used forthe development of the clusters along with the Nearest-Neighbor Spanning Tree Algorithm(N-NST). Additionally, Optimal Delay Balancing (ODB) model will be used to minimizethe end to end delay of each grouping. In addition, the heuristic observes real designparameters such as: capacity and coverage. Using the Dijkstra algorithm, the routes arebuilt following the minimum shorter path. Therefore, this paper presents a heuristic able to plan the deployment of smart meters (SMs) through a tree-like hierarchical topology for theintegration of SMG at the lowest cost.

In the era of Industry 4.0, the focus on optimizing manufacturing processes is crucial. This study provides a comprehensive analysis of the EnPAS system, a proprietary automated control and monitoring tool developed by KAD3, implemented in the production process of Corten steel furnishings - the "Lila" chair and "Line" planter. Designed to enhance operational efficiency, EnPAS facilitates improved workflow by streamlining production stages and reducing downtime. The research design involves a comparative analysis of the workshop's production process, both before and after the implementation of the EnPAS system. Elements such as de-greasing, oxidation, and finishing stages, among others, were examined. The implementation of the EnPAS system led to a significant reduction in the total production time for both products. Further, there was a substantial decrease in waiting time and setup time. These findings unequivocally indicate the transformative impact of the EnPAS system on the manufacturing process, showcasing its potential in driving efficiency improvements, reducing production times, and increasing output. The study underscores the importance of leveraging such advanced control and monitoring tools to optimize modern manufacturing processes and does not exaggerate conclusions not backed by the research.

Boiler efficiency is called to some extent of total thermal energy which can be recovered from the fuel. Boiler efficiency losses are due to four major factors: the dry gas flux, the latent heat of steam in the flue gas, the combustion loss or the loss of unburned fuel, radiation and convection losses. In this research, the thermal behavior of boilers in gas refinery facilities is studied and their efficiency and their losses are calculated. The main part of this research is comprised of analyzing the effect of various parameters on efficiency such as excess air, fuel moisture, air humidity, fuel and air temperature, the temperature of combustion gases, and thermal value of the fuel. Based on the obtained results, it is possible to analyze and make recommendations for optimizing boilers in the gas refinery complex using response-surface method (RSM).

In this study, we focused on the eco-driving of electric vehicles (EVs). The target vehicle is an electric bus developed by our research team. Using the parameters of the bus and speed pattern optimization algorithm, we derived the EV eco-driving speed pattern. Compared to eco-driving of internal combustion engine vehicles (ICVs), we found several different characteristics. We verified these characteristics with actual vehicle driving test data of the target bus, and the results confirmed its rationality. The EV eco-driving method can improve electricity consumption by about 10% - 20% under the same average speed.

A study was conducted on the power system of a forklift equipped with PEM fuel cells. A fuel cell assembly relies on several components for proper functioning, and among these, the air compressor holds paramount importance due to its significant energy consumption when compared to other Balance of Plant components. The air supply system, in turn, plays a critical role in ensuring the stable and efficient operation of the entire fuel cell system. To enhance system efficiency, we delved into the impact of varying the stoichiometric ratio of air and air pressure on the validated and optimized power module model. This investigation was carried out under real loading conditions, replicating the conditions experienced by the power module when fuel cells are in use within a forklift. The air compressor, being a pivotal component of a PEM fuel cell system, can be oper-ated at different excess air and pressure ratios, which in turn influence both the fuel cell's performance and the overall efficiency of the power module system. Our research focused on assessing the performance of polymer electrolyte membrane (PEM) fuel cells under different load cycles, adhering to the VDI60 requirements for forklift applications. This comprehensive examination encompassed the system's minimum and maximum load scenarios, with the primary goal of optimizing excess air and pressure ratio parameters, especially under dynamic load conditions. The results revealed that higher air pressures and lower excess air ratios were con-ducive to increasing system efficiency, shedding light on potential avenues for enhancing the performance of PEM fuel cell systems in forklift applications.

With the dramatic increase in world population, continued advances in modern greenhouse agriculture and plant growth practices are expected to help overcome the global problem of future food shortages. Next generation greenhouse design practices will need to address a range of issues, ranging from energy and land use efficiency to providing plant-optimised growth techniques. In this paper, we focus on investigating the optimum irradiation spectra matched to the lettuce species (Lactuca sativa, L.), which is commonly grown in greenhouse environments, in order to develop low-emissivity glass panes that maximize the biomass productivity of glass greenhouses. This low-emissivity glass passes the solar spectral components needed for crop growth, while rejecting other unwanted radiations, leading to significant energy savings and other beneficial effects related to greenhouse climate control, in a range of climates. This is due to reducing both the solar heat gain and photosaturation, which can raise the temperature of the crops to harmful levels. Experimental results show that substantial biomass productivity improvements in lettuce (up to ~14.7%) can be attained using spectrally optimized illumination, compared with white light irradiation. We also report on the development of advanced metal-dielectric thin-film filters that produce the optimum illumination spectrum when exposed to sunlight.

Global optimization is widely adopted nowadays in a variety of practical and scientific problems. In this context, a group of techniques that is widely used is that of evolutionary techniques. A relatively new evolutionary technique in this direction is that of Giant Armadillo Optimization, which is based on the hunting strategy of giant armadillos. In this paper, a number of modifications to this technique are proposed, such as the periodic application of a local minimization method as well as the use of modern termination techniques based on statistical observations. The proposed modifications have been tested on a wide - series test functions, available from the relevant literature and it was compared against other evolutionary methods.

In recent years, large language models have demonstrated remarkable performance across various natural language processing (NLP) tasks. However, deploying these models for real-world applications often requires efficient inference solutions to handle the computational demands. In this paper, we explore the utilization of CPUs for accelerating the inference of large language models. Specifically, we introduce a parallelized approach to enhance throughput by 1) Exploiting the parallel processing capabilities of modern CPU architectures, 2) Batching the inference request. Our evaluation shows the accelerated inference engine gives an 18-22x improvement in the generated token per sec. The improvement is more with longer sequence and larger models. In addition to this, we can also run multiple workers in the same machine with NUMA node isolation to further improvement in tokens/s. Table 2, we have received 4x additional improvement with 4 workers. This would also make Gen-AI based products and companies’ environment friendly, our estimates shows that CPU usage for Inference could reduce the power consumption of LLMs by 48.9% (1252 W for A100 with AMD EPYC 7V13 vs 613 W for Intel® Xeon® Gold 6538N) while providing production ready throughput & latency.

The hybrid in-band on-channel (IBOC) transmission system as a digital audio radio scheme could transmit analog FM and digital audio simultaneously. In IBOC systems, broadcasters transmit signals within the allocated channel bands, so the bandwidth allocated for digital audio transmission is a big challenge. In view of the above, the use of cooperative game theory, supported by the use of the bankruptcy game and Shapley's value, is proposed as a strategy to optimize the bandwidth allocation at each node or station, according to the demand in the service, the number of stations and the radio channel conditions in the FM band. The paper proposes a scenario under saturated traffic conditions, in order to evaluate the degree of optimization that the Shapley value can perform under clearly established traffic and channel conditions. The evaluation process yielded a quite favorable result, where it is observed that the use of cooperative game theory supported by the Shapley value can be considered as an excellent alternative when performing bandwidth optimization processes for digital broadcasting over IBOC in FM and with the possibility of being implemented in other digital radio broadcasting systems.

The global agricultural sector is increasingly pressured to adopt sustainable practices and reduce its environmental impact. In this context, greenhouses play a crucial role in enabling year-round crop production, ensuring food security, and minimizing reliance on traditional open-field farming. However, the energy consumption associated with greenhouse operations poses a significant challenge to achieving sustainability goals. As a result, there is a growing emphasis on transitioning greenhouses towards near-zero energy consumption. Near-zero energy consumption in greenhouses refers to the ambitious objective of minimizing energy usage to the greatest extent possible while maintaining optimal growing conditions for crops. This goal encompasses reducing energy consumption for heating, cooling, lighting, and other operational needs, as well as exploring renewable energy sources to power greenhouse operations. This review article offers a comprehensive overview of greenhouse energy consumption, with the main goal of analyzing the present situation, identifying key challenges, exploring potential opportunities, and proposing future perspectives for decreasing energy usage in greenhouse environments. As the focus on sustainable agricultural practices grows, the need to reduce energy consumption in greenhouses becomes increasingly important. The review critically examines current technological models and strategies applied in smart greenhouse applications, as well as the monitoring of microclimatic conditions inside the greenhouse, encompassing factors such as temperature, humidity, CO2 levels, soil quality, and crop cultivation. Moreover, it aims to present existing literature that investigates the advancement of greenhouses toward achieving significant reductions in energy consumption.

Hand gesture recognition has many valuable applications in engineering and health care. This study proposes a novel model which can accurately distinguish hand gestures using forearm muscles' surface electromyogram (sEMG) signals. A deep learning algorithm with hyper parameters impacting the final model’s accuracy and a convolutional neural network (CNN) were employed in the recognition stage. The number of convolutional layers, kernels per layer, and neurons in the dense layer were selected for optimization, while the remaining parameters, such as the learning rate, batch size, and number of epochs, were chosen based on trial and error and prior knowledge. The optimal values for the selected hyperparameters were obtained using a genetic algorithm to achieve maximum recognition accuracy. The UC2018 Dual-Myo database was used for training and testing the model based on EMG signals characterizing the activity of eight different hand gestures. The final structure of the model consisted of two convolutional layers with 131 and 28 kernels, a dense layer with 111 neurons, and a softmax layer with eight neurons. Upon optimizing the hyperparameters using the genetic algorithm, the accuracy of the proposed model increased from 91.86% to 96.4% at best and 95.3% on average in real-time applications and 99.6% in an offline mode. Future work is warranted towards improving the architecture and the computational cost.

The term STODS (Surgical Temporary Ocular Discomfort Syndrome) has been coined to describe the ocular surface perturbations induced by surgery. As one of the most important refractive elements of the eye, Guided Ocular Surface and Lid Disease (GOLD) optimization is fundamental to success in achieving refractive outcomes and mitigating STODS. Effective GOLD optimization and the prevention/treatment of STODS, requires an understanding of the molecular, cellular and anatomic factors that influence ocular surface microenviroment and the associated perturbations induced by surgical intervention. By reviewing the current understanding of STODS etiologies, we will attempt to outline a rationale for a tailored GOLD optimization depending on the ocular surgical insult. With a bench to bedside approach, we will highlight clinical examples of effective GOLD perioperative optimization can mitigate STODS deleterious effect on preoperative imaging and postoperative healing.

An essential step in solving any topology optimization problem is determining the sensitivities of the objective function and optimization constraints. Unfortunately, these sensitivities are usually derived and implemented manually. Nontrivial objective functions and constraints, especially with the involvement of material and geometric nonlinearities, need strenuous mathematical derivation, leading to error-prone implementation. Another intriguing approach to finding sensitivities is automatic differentiation. This paper uses the automatic differentiation and adjoint method to find the sensitivities for two multiphysics topology optimization problems: 1) thermoelasticity and 2) piezoelectricity. This approach is not limited to these examples and can be easily extended to other single- or multi-physics topology optimization problems.

In a one price economy, the Fundamental Theorem of Asset Pricing (FTAP) establishes that no-arbitrage is equivalent to the existence of an equivalent martingale measure. Such an equivalent measure can be derived as the normal unit vector of the hyperplane that separates the attainable gain subspace and the convex cone representing arbitrage opportunities. However, in two-price financial models (where there is a bid-ask price spread), the set of attainable gains is not a subspace anymore. We use convex optimization, and the conic property of this region to characterize the “No-Arbitrage” principle in financial models with the bid-ask price spread present. This characterization will lead us to the generation of a set of price factor random variables. Under such a set, we can find the lower and upper bounds (supper-hedging and sub-hedging bounds) for the price of any future cash flow. We will show that for any given cash flow, for which the price is outside the above range, we can build a trading strategy that provides one with an arbitrage opportunity. We will generalize this structure to any two-price finite-period financial model.

Proper calibration of eye movement signal registered by an eye tracker seems to be one of the main challenges in popularizing eye trackers as yet another user input device. Classic calibration methods taking time and imposing unnatural behavior of users have to be replaced by intelligent methods that are able to calibrate the signal without conscious cooperation with users. Such an implicit calibration requires some knowledge about the stimulus a person is looking at and takes into account this information to predict probable gaze targets. The paper describes one of the possible methods to perform implicit calibration: it starts with finding probable fixation targets (PFTs), then uses these targets to build a mapping - probable gaze path. Various possible algorithms that may be used for finding PFTs and mapping are presented in the paper and errors are calculated utilizing two datasets registered with two different types of eye trackers. The results show that although for now the implicit calibration provides results worse than the classic one, it may be comparable with it and sufficient for some applications.

Energy system models have become indispensable to shape future energy systems by providing insights into different trajectories. However, sustainable systems with high shares of renewable energy are characterized by growing cross-sectoral interdependencies and decentralized structures. To capture important properties of increasingly complex energy systems, sophisticated and flexible modelling tools are needed. At the same time open science becomes increasingly important in energy system modelling. This paper presents the Open Energy Modelling Framework (oemof) as a novel approach in energy system modelling, representation and analysis. The framework forms a toolbox to construct comprehensive energy system models and has been published open source under a free license. With a collaborative development based on open processes the framework seeks for a maximum level of participation and transparency to facilitate open science principles in energy system modelling. Based on a generic graph based description of energy systems it is well suited to flexibly model complex cross-sectoral systems and incorporate various modelling approaches. This makes the framework a multi-purpose modelling environment for modelling and analyzing different systems ranging from an urban to a transnational scale.

Quantum computers are machines that are designed to use quantum mechanics in order to improve upon classical computers by running quantum algorithms. One of the main applications of quantum computing is solving optimization problems. For addressing optimization problems we can use linear programming. Linear programming is a method to obtain the best possible outcome in a special case of mathematical programming. Application areas of this problem consist of resource allocation, production scheduling, parameter estimation, etc. In our study, we looked at the duality of resource allocation problems. First, we chose a real world optimization problem and looked at its solution with linear programming. Then, we restudied this problem with a quantum algorithm in order to understand whether if there is a speedup of the solution. The improvement in computation is analysed and some interesting results are reported.

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

In this study, a multiobjective, multiperiod, global optimization for design, sizing and dispatch of an islanded, hybrid microgrid was performed using a model built in MATLAB. The system was simulated over one year for sizing and over one day for dispatch, both using hourly time steps. The model minimized lifecycle levelized costs, emissions, lost load and dumped power while maximizing penetration of clean, renewable sources in the microgrid. This found optimal capacities of the renewable, energy storage and backup generation components which provide the best combination of affordability, sustainability, reliability and efficiency. After experimenting with several global solvers, it was determined that particle swarm optimization is most well-suited to solving the sizing optimization problem. The PV-wind microgrid using Li-ion batteries along with diesel engines was found to perform best among all the combinations considered. It was found that in spite of including additional objectives, monetary costs are the primary driver while allocating generation capacity between different renewable sources like wind versus solar PV. Furthermore, the sizing of PV, wind and battery storage depends strongly on the rating of the standby distributed generator, mainly due to reliability consideration. Generating Pareto-optimal sets revealed interesting relationships between different input variables (i.e. PV, wind and battery capacities) as well as trade-offs that arise while pursuing different objectives. Pursuing cost-minimization alone may lead to sub-optimal outcomes in terms of environmental impact, reliability and excess energy production. A sensitivity analysis was also conducted to understand the effects of various parameters like fuel price and energy storage costs on the optimal system's design and operation. Such accurate sizing programs help reduce the extent of oversizing of sub-systems during the design and planning stage, which is usually needed to achieve high reliability with distributed and decentralized energy systems like off-grid microgrids. This reduces the upfront capital investment needed to build the system, making clean electricity access affordable in the short term. The economic-environmental dispatch produced day-ahead scheduling strategies to meet the above mentioned objectives. The system was found to be relatively robust to short-term uncertainties and disturbances in renewable generation and load, although this does cause sub-optimal performance due to increased reliance on fossil fuels. It was found that dispatching of the batteries and backup generators is most critical in minimizing impacts of such events. However, the response to longer-term disturbances still remains to be assessed. The study also includes a comprehensive literature review of tools available for microgrid design as well as different optimization algorithms that have been used to solve microgrid sizing, dispatch and scheduling problems. Additionally, an overview is provided of various control strategies that can be used to improve robustness and resiliency of microgrids.

Using flexible AC transmission system (FACTS) devices in power systems while adhering to some equality and inequality constraints, researchers around the world sought to address this issue with the objectives of improving the voltage profile, reducing power losses in transmission lines, and increasing system reliability and safety. The recent development of FACTS controllers opens up new perspectives for safer and more efficient operation of electrical power networks by continuous and rapid action on power systems parameters, such as phase angle shifting, voltage injection and line impedance compensation. Thus, an improvement on voltage profile and enhancement of power transfer capability can be obtained. It is for that, the idea behind the FACTS concept is to enable the transmission system to be an active element in increasing the flexibility of power transfer requirements and in securing stability of integrated power system. It may also be effective in transient stability improvement, power oscillations damping and balancing power flow in parallel lines. The primary issue that has significantly piqued the interest of a number of researchers working in this field is the FACTS optimization problem, which involves determining the optimal type, location, and size of FACTS devices in electrical power systems. For solving this mixed integer, nonlinear and non-convex optimization problem, this paper provides an in-depth and comprehensive review of the various optimization techniques covered in published works in the field. In this review, a classification of optimization techniques in five main groups that are widely used, such as classical optimization techniques or conventional optimization approaches, Meta heuristic methods, analytic methods or sensitive index methods and mixed or hybrid methods, is summarized. In addition, a performance descriptions and comparison of these different optimization techniques are discussed in this study. Finally, some advice is offered for future research in this field.

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

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

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

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

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

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

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

Offshore grids can have a key role in the transition of the energy system to sustainability. Although they require extensive infrastructure investments, they open up for the exploitation of additional resources and may be important to provide for part of the increasing electricity demand driven by sector coupling. This paper quantifies the socio-economic value of offshore grids and identifies their major drivers, performing energy system optimization in a model application of the Northern-central European energy system and the North Sea offshore grid towards 2050. The increasing wake loss with the size of hub-connected wind farms is integrated in the modelling. We find that without sector coupling no offshore grid may develop, and that the higher the level of sector coupling, the higher the value of offshore grids. Therefore, it can be strongly stated that offshore grid infrastructure development should not be discussed as a separate political topic, but seen in connection to sector coupling.

In this paper, the computational fluid dynamics (CFD) method has been used to simulate the gas flow and the catalytic hydrogenation process of the C-2 hydrogenation adiabatic reactor in a practical ethylene production unit of a chemical plant. The laminar finite-rate model was used for the chemical reaction model. The standard two-equation model was used for the turbulence model. The exponent-function kinetic model was used for the reaction dynamics model. The simulation results were in good agreement with the actual industrial data obtained from the C-2 hydrogenation reactor, and the distributions of reaction parameters such as temperature and component concentrations in the reactor were further analyzed. Based on the validated CFD models, the structure and operating conditions of the reactor, as well as hydrogen load distribution ratios, are simulated for comparing the comprehensive performance metrics of ethylene selectivity and acetylene conversion rate; as a result, we can obtain the optimal temperature of 312 K, the optimal hydrogen/acetylene ratio of 0.10 as well as the optimal hydrogen load distribution ratio of 4:4:2, and the diameter/height ratio exerts a marginal effect on the selectivity, conversion rate and harmonic mean of the reactor.

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

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

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

Due to the demands of personal travels and entertainments, light airplanes and small business aircrafts are developing rapidly. Light airplane structure is simple; however, it lacks crashworthiness design, especially the considerations on the impact of energy absorption. Therefore, in an event of accident, significant damage to passengers will be usually incurred. Airplanes made of composite materials structurally have high specific strength and good aerodynamic configuration. These materials have become the primary choice for new airplane development. This study mainly explores the topology optimization analysis of the light aircraft’s cockpit made of carbon fiber reinforced composites. This paper compares the compression amounts in the original models of composite material and aluminum alloy fuselages with the models after optimization during the crash-landing, in order to investigate the safety of fuselages made of different materials after structural optimization under the dynamic crashing. This study found that the energy absorbed by the aluminum alloy fuselage during crash-landing is still higher than that by the carbon fiber reinforced composites fuselage. On the other hand, the aluminum alloy fuselage after topology optimization could have an energy absorption capability enhanced by 40%, as compared to the that of the original model of aluminum alloy fuselage.

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

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

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

Positive invariant set is an important concept of dynamic systems. The purpose of this paper is to study the sufficient and necessary conditions that the set of ellipsoids and the Lorenz cone are positive invariant sets of discrete time systems. By means of nonlinear programming and induced norm, the problem of positive invariance is formulated as an optimization problem, and the equivalent dual form optimization is also presented using the dual optimization method. Our results provide more alternative methods for determining the positive invariance of quadratic form convex sets from the point of view of optimization and dual optimization. The effectiveness of this method is demonstrated by numerical examples.

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

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

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

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

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

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

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

Global energy scenario is becoming vital for the sustainable economic development of a region or country. Current changes in energy production, mainly due to scarcity and geopolitical factors, have proven the need for changes in energy mix towards a lower energy dependence. In that sense, a considerable amount of biomass waste is generated in many regions, because of agro-industrial activities, whose management could contribute to energy production. The aim of this work was to study the determination of combustion conditions in a biomass stove by using different raw materials as fuels, such as pine, poplar, and plum tree pellets. For that purpose, power, excess air, and biomass content were optimized, among other parameters, and fumes were analyzed with a Testo 335 analyzer, recording temperatures with temperature probes. As a conclusion, high yields were found for the optimized parameters of the studied biomass (ranging from 91.1 % for poplar pellet to 92.34 % for pine pellet), making these three biomass fuels suitable for combustion in the abovementioned stove. Also, increasing biomass flow by 25 % in the stove contributed to a higher efficiency of the process, especially in the case of plum tree pellets.

In today's human society, diesel generators (DGs) are highly applied into the human energy and electricity supply system due to its technical, operational and economic advantages. This paper proposes an intelligent nonlinear $H_2/H_{\infty}$ robust controller based on the chaos particle swarm gravity search optimization algorithm (CPSOGSA), which controls the speed and excitation of a DG. In this method, firstly, establish the nonlinear mathematical model of the DG and then design the nonlinear $H_2/H_{\infty}$ robust controller based on this. The direct feedback linearization and the $H_2/H_{\infty}$ robust control theory are combined and applied. Based on the design of the integrated controller for DG speed and excitation, the system's performance requirements are transformed into a standard robust $H_2/H_{\infty}$ control problem. The parameters of the proposed solution controller are optimized by using the proposed CPSOGSA. The introduction of CPSOGSA completes the design of an intelligent nonlinear $H_2/H_{\infty}$ robust controller for DG. The simulation is implemented in MATLAB/Simulink, and the results are compared with the PID control method. The obtained results prove that the proposed method can effectively improve the dynamic accuracy of the system and the ability to suppress disturbances and improve the stability of the system.

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

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

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

The practical application of a multi-objective optimization strategy based on evolutionary algorithms was proposed to optimize the plastics thermoforming process. For that purpose, the various steps of the process were considered individually and the optimization strategy was applied to the determination of the final part thickness distribution with the aim of demonstrating the validity of the methodology proposed. The preliminary results obtained considering three different theoretical initial sheet shapes indicates clearly that the methodology proposed is valid, as it provides solutions with physical meaning and with a great potential to be applied in real practice.

The solved research problem is oriented to the implementation of advanced methods of component design and their sophisticated use in the ever-advancing additive manufacturing. Based on the stated objectives, the research was divided into several parts. Initially, the methods and principles of design in additive manufacturing were evaluated and investigated, from which the intent was subsequently defined and the DfAM method in the component design and development process was determined. The given design method, namely topology optimization (TO), was applied to multiple development and production cycles of additive manufacturing parts to validate the research conclusions. In this paper, the procedure for a specific part or component assembly is shown. By evaluating the developed design procedure using the TO method included in the design process, the conclusions are positive in terms of both time savings and properties of the designed part such as strength and weight parameters. The given findings need to be further developed by implementing on specific product lines for increasing the efficiency of the developed design methodology.

The canonical decomposition of a bipartite graph is a new decompositionmethod that involves three operators, parallel, series and K+S. The class of weak bisblit graphs is totally decomposable with respect of these operators, and the class of bicographs is totally decomposable with respect of parallel and series operators. We prove in this paper that the class of bipartite (P_6,C_6)-free graphs is exactly the class of bipartite graphs that are totally decomposable with respect of parallel and K+S operators. We simplify the recognition algorithm of weak-bisplit graphs to adepte with bipartite (P_6,C_6)-free graphs. As a result of this adapted algorithm, we prsent efficient solutions in this class of graphs for two optimization graph problems, the first is the maximm balanced bi-clique problem and the the second is the maximum independent set problem.

The predominance of traffic lights in urban settings often induces fluctuations in traffic patterns and energy utilization among vehicles. To counteract the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs), a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD) is proposed. This strategy aims to enhance vehicle energy efficiency, traffic flow, and battery longevity, all while upholding satisfactory driving comfort. The M-EAD strategy unfolds in two distinct stages: the optimization of an eco-friendly green signal window and the refinement of speed trajectories. The initial stage tackles the optimization of traffic light green signal windows, underpinned by the minimization of travel delays via solving the shortest path problem. In the subsequent stage, a receding horizon framework takes center stage, leveraging an iterative dynamic programming algorithm to tackle the speed optimization challenge. The objective here is to curtail energy consumption and reduce battery wear by finding an optimal speed trajectory. Furthermore, the real-world efficacy of this approach is substantiated through on-road vehicle tests, attesting to its viability in actual road scenarios.

Due to the impacts from climate change, the allocation of water resources must urgently be optimized worldwide to ensure that the needs of both water managers and farmers are balanced. In this study, manager-oriented and farmer-oriented assessment models were developed for irrigation water optimization and allocation. The distance from water sources and hydraulic head were the main factors in the manager-oriented assessment model; crop value, water demand of crops, and soil type were additional factors in the farmer-oriented assessment model. The developed assessment models were used to assess irrigation water allocation in five villages in Neimen District. Cadasters at high elevation were discovered to not be suitable for cultivation of crops because of the difficulties in constructing irrigation facilities and the loss of irrigation water during transportation. The result obtained from the manager-oriented assessment system was related to the costs involved in the construction and maintenance of irrigation facilities, which indicated that cadasters located at long distances from water sources and at high elevation are unsuitable for cultivation. By contrast, the result obtained from the farmer-oriented assessment system was related to the profits of farmers and revealed that more cadasters would be suitable for cultivation if suitable crops were chosen.

Initial choice of Learning Rate is a key part of gradient based methods and has a great effect on the performance of the Deep Learning Model.This paper studies the behavior of multiple gradient based optimization algorithm which are commonly used in Deep Learning and compare their performance on various learning rate. As observed popular choice of optimization algorithms are highly sensitive to various choice of learning rates. Our goal is to find which optimizer has an edge over others for a specific setting. We look at two datasets namely MNIST and CIFAR10 for benchmarking. The results are quite surprising, and it will help us to choose a learning rate more efficiently.

The purpose of this paper is to develop a theoretical predictive model for LNG shipping routes selection process. Strategic decisions about shipping costs could be improved if a deeper knowledge about products economic value is provided. Developments made on the extraction and industrial processes related to this fossil fuel are driving the natural gas sector towards a unique globalised market. Moreover, data analytics applications as well as machine learning are topics presented as perfect catalysers for achieving an unprecedented natural gas globalised market. Additionally, this paper aims at showing the state of the art of new techniques used in transportation engineering that might have synergies with other industries (eg. commodities cost reduction, energy supply…). Finally, this paper aims to provide foundation for further research and development using more sophisticated data and algorithms that will help to close the gap between theoretical and practical scope of this techniques.

With the ongoing fifth-generation cellular network (5G) deployment, electromagnetic field exposure has become a critical concern. However, measurements are scarce, and accurate electromagnetic field reconstruction in a geographic region remains challenging. This work proposes a conditional Generative Adversarial Network to address this issue. The main objective is to reconstruct the electromagnetic field exposure map accurately according to the environment’s topology from a few sensors located in an outdoor urban environment. The model is trained to learn and estimate the propagation characteristics of the electromagnetic field according to the topology of a given environment. In addition, the conditional Generative Adversarial Network based electromagnetic field mapping is compared with simple kriging. Results show that the proposed method produces accurate estimates and is a promising solution for exposure map reconstruction.

Regulatory T cells (Tregs) are major drivers behind immunosuppressive mechanisms and present a major hurdle for cancer therapy. Tregs are characterized by high expression of CD25, which is a potentially valuable target for Treg depletion to alleviate immune suppression. The preclinical anti-CD25 (αCD25) antibody, clone PC-61, has met with modest anti-tumor activity, due to its capacity to clear Tregs from circulation and lymph nodes but not those that reside in the tumor. Optimization of the Fc domain of this antibody clone has been shown to enhance intratumoral Treg depletion capacity. Here, we generated a stable cell line that produces optimized recombinant Treg depleting antibodies. A genome engineering strategy in which CRISPR-Cas9 was combined with homology directed repair (CRISPR-HDR) was utilized to optimize the Fc domain of the hybridoma PC-61 for effector functions by switching it from the original rat IgG1 to a mouse IgG2a isotype. In a syngeneic tumor mouse model the resulting αCD25-m2a antibody mediated effective depletion of tumor resident Tregs leading to a high effector T cell (Teff) to Treg ratio. Moreover, combination of the αCD25-m2a with αPD-L1 treatment augmented tumor eradication in mice, demonstrating the potential for αCD25 as a cancer immunotherapy.

The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems.This study examines the applicability of EOs obtained from Sentinel2 and Landsat8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel2 and Landsat8 NDVI to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different sites across the U.S Midwest. A time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI estimates in APSIM-Maize model. Then surrogate models were develop using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. Lowest RMSE for within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements.

The concept of distributed generation has made photovoltaic an integral source of energy in smart grid systems, especially in peer-to-peer energy trading frameworks that exploit excess power to fulfill the energy requirements of consumers in cost-efficient and eco-friendly manner. It is believed that P2P energy trading will dominate a significant portion of research in forthcoming power generation systems due to the excessive rise of energy demands across the globe. Despite a plethora of studies on energy optimization solutions in P2P trading, minimizing nanogrid energy trading cost and efficient energy sharing between consumers and prosumers are deemed among the challenging problems. This study overcomes essential issues overlooked by the contemporary P2P energy trading models by introducing a predictive optimization-oriented nanogrid energy trading model. The proposed study encompasses two stages: (1) predictive optimization model which harnesses BD-LSTM-based forecasted energy parameters (energy load, energy consumption, and PV generation) that are later incorporated in PSO-enabled objective function to reduce nanogrid trading cost, (2) optimal energy sharing plan is devised to decide the role of nanogrids as prosumers or consumers by emphasizing the use of PV-produced energy. The proposed model is validated on the case study containing nanogrid houses data. The simulation provides detailed experiments by comparing the energy demand and response using the proposed energy sharing model. The outcomes yield that the energy sharing plan holds a significant potential to fulfill maximum energy requirements of nanogrid house in P2P cluster and significantly reduces the energy cost compared to grid.

Innovative practices in irrigation systems can bring improvements in terms of economic efficiency and in the same time can reduce environmental impact. Investment in high tech technologies frequently involves additional costs, but an efficient water management can increase the lifetime of the equipment. The main objective of this article is to reduce the energy consumption by one thousand cubic meters pumped and automatically to increase the economic efficiency of the pumping groups. This paper develops a new operating algorithm that ensures the operation of the pumping group at safe operating intervals and in the same time identifies the equivalent pump operating points for the entire flow range and pumping height of the pumping group. This methodology is based on the principles of an Expert System to perform the optimization process of the energy consumption in pumping groups. The resulting methodology avoids the combinatorial explosion of the solutions to be analyzed and determines the point of maximum efficiency without violation of any of the system constraints under any operating condition. The proposed methodology is tested on an irrigation system that includes a pumping group with 5 pumps, showing its effectiveness in obtaining the optimal solution with a relatively low computational burden.

In this paper, we implement a new method binary Particle Swarm Optimization (PSO) for solving the kidney exchange problem, which will improve the future decisions of kidney exchange programs. Because using a kidney exchange, we can help incompatible patient-donor couples to swap donors to receive a compatible kidney. Kidney paired donation programs provide an innovative approach for increasing the number of available kidneys. Further, we implementing binary particle swarm optimization in parallel with MATLAB with one, two, three and four threads and from the computations point of view, the authors compare the performance to reduce the running time for kidney exchange to match patients as fast as possible to help clinicians. Moreover, implementing binary particle swarm optimization in solving the kidney exchange problem is an effective method. The obtained results indicate that binary PSO outperforms other stochastic-based methods such as genetic algorithm, ant lion optimization, and efficient the number of resulting exchanges.

Single cell proteins are the dead dried cells of microorganisms or purified protein isolated from microorganism’s cell culture, used as a food supplement to humans’ food and animals feed. World suffer from malnutrition particularly developing countries, due to rapid increase in population, increased the demand for protein and nutrients requirement. Bacteria is potential microorganism for SCP production due to high protein content, fast generation time, bio-active secondary metabolites production and can grow on various substrates. Actinobacteria species and strain have capability to produce biological active compounds, produced about two-thirds of antibiotics available in the market, actively used as antibiotics, antiprotozoal, antifungal, antiviral, anticancer, anticholesterol, antihelminth and immunosuppressant. Actinomycetes can be used as probiotic as well as single cell protein that will deal with antibiotic as well as protein source.

Feature selection is one of the main data preprocessing steps in machine learning. Its goal is to reduce the number of features by removing extra and noisy features. Feature selection methods must consider the accuracy of classification algorithms while performing feature reduction on a dataset. Meta-heuristic algorithms are the most successful and promising methods for solving this issue. The symbiotic organisms search algorithm is one of the successful meta-heuristic algorithms which is inspired by the interaction of organisms in the nature called Parasitism Commensalism Mutualism. In this paper, three engulfing binary methods based on the symbiotic organisms search algorithm are presented for solving the feature selection problem. In the first and second methods, several S-shaped and V-shaped transfer functions are used for binarizing the symbiotic organisms search algorithm, respectively. These methods are called BSOSS and BSOSV. In the third method, two new operators called BMP and BCP are presented for binarizing the symbiotic organisms search algorithm. This method is called EBSOS. The third approach presents an advanced binary version of the coexistence search algorithm with two new operators, BMP and BCP, to solve the feature selection problem, named EBSOS. The proposed methods are run on 18 standard UCI datasets and compared to base and important meta-heuristic algorithms. The test results show that the EBSOS method has the best performance among the three proposed approaches for binarization of the coexistence search algorithm. Finally, the proposed EBSOS approach was compared to other meta-heuristic methods including the genetic algorithm, binary bat algorithm, binary particle swarm algorithm, binary flower pollination algorithm, binary grey wolf algorithm, binary dragonfly algorithm, and binary chaotic crow search algorithm. The results of different experiments showed that the proposed EBSOS approach has better performance compared to other methods in terms of feature count and accuracy criteria. Furthermore, the proposed EBSOS approach was practically evaluated on spam email detection in particular. The results of this experiment also verified the performance of the proposed EBSOS approach. In addition, the proposed EBSOS approach is particularly combined with the classifiers including SVM, KNN, NB and MLP to evaluate this method performance in the detection of spam emails. The obtained results showed that the proposed EBSOS approach has significantly improved the accuracy and speed of all the classifiers in spam email detection.

Topology optimization is a pioneering design method that can provide various candidates with high mechanical properties. However, the high-resolution for the optimum structures is highly desired, normally in turn leading to computationally intractable puzzle, especially for the famous Solid Isotropic Material with Penalization (SIMP) method. In this paper, an efficient and high-resolution topology optimization method is proposed based on the Super-Resolution Convolutional Neural Network (SRCNN) technique in the framework of SIMP. The SRCNN includes four processes, i.e. refining, path extraction & representation, non-linear mapping, and reconstruction. The high computational efficiency is achieved by a pooling strategy, which can balance the number of finite element analysis (FEA) and the output mesh in optimization process. To further reduce the high computational cost of 3D topology optimization problems, a combined treatment method using 2D SRCNN is built as another speeding-up strategy. A number of typical examples justify that the high-resolution topology optimization method adopting SRCNN has excellent applicability and high efficiency for 2D and 3D problems with arbitrary boundary conditions, any design domain shape, and varied load.

Drying food involves complex physical atmospheric mechanisms with non-linear relations from the air-food interactions and those relations are strongly dependent on the moisture contents and the type of food. Such dependence makes it complex to design suitable dryers dedicated to a single drying process. To streamline the design of a novel compact food-drying machine, a heat pump dryer component design optimization algorithm was developed as a subprogram of a Computer Aided Engineering tool. The algorithm requires inputting food and air properties, the volume of the drying container and the technical specifications of the heat-pump off-the shelf components. The heat required to dehumidify the food supplied by the heat exchange process from condenser to evaporator, and the compressor’s requirements (refrigerant mass flow rate and operating pressures) are then calculated. Compressors can then be selected based in the volume and type of food to be dried. The algorithm is shown via a flow chart to guide the user through 3 different stages: Changes in drying air properties, Heat flow within dryer and Product moisture content. Example results of how different compressors are selected for different type of produces and quantities (Agaricus Blazei mushroom with 3 different moisture contents or fish from Thunnini tribe) conclude this article.

The current work investigates how adding a battery of optimal capacity to a grid-connected photovoltaic (PV) system can improve its economic feasibility. Also, the effect of different parameters on the feasibility of the PV system was evaluated. The OBC was determined for different saving targets of the annual electricity consumption of the chosen building. For this aim, real electricity consumption data of a residential building in Landskrona, Sweden, was used as energy consumption profile. Solar World SW325XL, which is a monocrystalline solar panel, was selected as PV panels. The calculations were performed under the metrological and economic conditions of southern Sweden. Different working parameters (WP)were considered (prices of the battery, feed-in tariffs, and saving targets). The performed calculations show that the optimal battery capacity (OBC), in which the payback time (PBT) of the system is maximized, strongly depends on the WP. The proper selection of the battery can considerably increase the economic feasibility of the PV system in southern Sweden. However, in some cases, using battery can have a negative impact on the PBT of the system. The results show that the electricity price, the module price, the inverter price, and the inverter lifetime have the highest effect on the PBT.

The present work aims at the comprehensive application of stochastic and optimization tools with the support of Information and Communication Technologies (ICT) through a case study in a logistics process for electronic goods; simulation and Response Surface Methodology (RSM) are applied for this purpose. The problem to be evaluated is to define an optimal distribution cost for products shipped to wholesale customers located in different cities in Mexico from a manufacturing plant in Tijuana, Mexico. The factors under study are the product allocation for each distribution center, finished good inventory level and on time deliveries, which are supposed to be significant to get the objective. The methodology applied for this problem considers the design of a discrete event simulation model to represent virtually the real life of logistics process, which is considered a complex system due to different activities are interrelated to carry it out. This model is used to execute the different experiments proposed by the RSM. The results obtained from simulation model were analyzed with the RSM to define the mathematical model that allows identifying the parameters of the factors in order to optimize the process. The findings prove how the ICT facilitate the application of stochastic tools with the purpose of process optimization.

Several optimization models of irreversible reverse cycle machines have been developed based on different optimization criteria in the literature, most of them using linear heat transfer laws at the source and sink. This raises the issue how close to actual operation conditions they are, since the heat transfer law on the phase-change processes is dependent on ΔT3. This paper addresses this issue by proposing a general model for study and optimization of thermal machines with two heat reservoirs applied to a Carnot-like refrigerator, with non-linear heat transfer laws and internal and external irreversibility. The optimization was performed using First and Second Law of Thermodynamics and Lagrange multipliers method. Thus, several constraints were imposed to the system, also different objective functions were considered, allowing finding the optimum operating conditions, as well as the limited variation ranges of the system parameters. Results show that the nature of the heat transfer laws affects the optimum values of system parameters for obtaining maximum performances and also their magnitude. Sensitivity studies with respect to system several parameters are presented. The results contribute to the understanding of the system limits in operation under different constraints and allow choosing the most convenient variables in given circumstances.

Forming a clustered network structure has been proposed as a solution to increase network performance, scalability, stability and manageability in an ad hoc network. A good clustering algorithm aims to select cluster heads among available nodes so that a number of specific constraints are satisfied; thus the cluster head selection problem is a multiobjective optimization problem. This paper proposes an algorithm on the basis of ant colony optimization (ACO) to be used to solve this problem. The proposed algorithm is a simple, one hop cluster formation algorithm, to form a clustered structure with the minimum number of clusters. The centralized ACO-based clustering algorithm is evaluated and compared with other clustering algorithms in ad hoc networks in terms of cluster density.

Bipedal locomotion remains one of the major open challenges of humanoid robotics. The common approaches are based on simple reduced model dynamics to generate walking trajectories, often neglecting the whole-body dynamics of the robots. As motions in nature are often considered as optimal with respect to certain criteria, in this work we present an optimal control based approach that allows us to generate optimized walking motions using a precise whole-body dynamic model of the robot, in contrast with the common approaches. The optimal control problem is formulated to minimize a set of desired objective functions with respect to physical constraints of the robot and contact constraints of the walking phases. We apply the method with combinations of different objective criteria to the model of a reduced version of the iCub humanoid robot of 15 internal DOF. The obtained trajectories are executed on the real robot and we carry out a discussion on the differences between the outcomes of this approach with the classic approaches.

Dump design and scheduling are critical elements to effective mine planning, especially if several of them are required in large-scale open pit mines. Infrastructure capital and transportation costs are considerable from an early stage in the mining project, and through the life-of-mine as these dumps gradually become immense structures. Delivered mining rates, as well as certain spatial and physical constraints, provide a set of parameters of mathematical and economic relationship that creates opportunities for modelling and thus facilitates the measuring and optimization of ultimate dump design by using programming and empirical techniques while achieving economic objectives. This paper presents a methodology to model and optimize the design of a mine dump by minimizing the total haulage costs. The proposed methodology consists on: (i) Formulation of a dump model based on a system of equations relying on multiple relevant parameters; (ii) Solves by minimizing the total cost using linear programming and determines a ‘preliminary’ dump design; (iii) Through a series of iterations, modifies the ‘preliminary’ footprint by projecting it to the topography and creates the ultimate dump design. Finally, an example application for a waste rock dump illustrates this methodology.

Entanglement is at the heart of quantum technologies such as quantum information and quantum metrology. Providing larger quantum Fisher information (QFI), entangled systems can be better resources than separable systems in quantum metrology. QFI topic is a very active research area and it has many possible usage areas in quantum information domain. In this study, we review quantum Fisher information research with both from theoritical and application perspective.

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

Trajectory Optimization (TO) is the sequence of processes that are considered in order to produce the best path that mends the overall performance or reduces the consumption of the resources where the restriction system remains maintained. In this survey, an inclusive review of the latest advancements in modeling and optimization of trajectory generation in robotic applications will be discussed broadly. In recent times, numerous studies have employed optimal control techniques involving direct and indirect methods in order to convert the authentic Trajectory Optimization problem into a constrained parameter optimization problem. Moreover, a huge variety of optimization algorithms such as Genetic Algorithms (GA), Simulated Annealing (SA), Sequential Quadratic Programming (SQP), and Particle Swarm Optimization (PSO) are used aiming to find the optimal solutions for trajectory planning. It is observable that, minimizing the jerk, energy consumption, and execution time among the most widely used design objectives, on the other hand, the robot’s joints configurations and the motor torques are the most used design variables. This paper aims to review the fundamental techniques and their coincident robotic applications in the field of trajectory optimization aiming to afford some steering for related researchers.

In this paper, a multi-patch weakly singular isogeometric boundary element method (WSIGABEM) for two-dimensional elastostatics is proposed. Since the method based on the weakly singular boundary integral equation, the quadrature techniques, dedicated for the weakly singular and regular integrals, are applied in the method. A new formula for the generation of collocation points is suggested to take the full advantage of the multi-patch technique. The generated collo-cation points are essentially inside the patches without any correction. If the boundary conditions are assumed to be continuous in every patch, no collocation point lies on the discontinuous boundaries, thus simplifying the implementation. The multi-patch WSIGABEM is verified by the simple examples with analytical solutions. The feature of the present multi-patch WSIGABEM is investigated by the comparison with the traditional IGABEM. Furthermore, the combination of the present multi-patch WSIGABEM and the particle swarm optimization algorithm results in a shape optimization method in two-dimensional elastostatics. By changing some specific control points and their weights, the shape optimizations of the fillet corner, the spanner and the arch bridge are verified to be effective.

The service conditions of underground coal mine equipment are poor, and it is difficult to accurately extract the fault characteristics of rolling bearings. In order to better improve the accuracy of fault identification of rolling bearings, a fault detection method based on multiscale permutation entropy and SOA-SVM is proposed. First, the whale optimization algorithm is used to select the modal analysis number K and the penalty factor α of the variational mode decomposition algorithm. Then, the vibration signal of rolling bearings is dissolved according to the optimized variational mode decomposition algorithm, and the multi-scale permutation entropy of the main intrinsic mode function is calculated. Finally, the feature values of the matrix are entered into the SVM algorithm optimized by the seagull optimization algorithm to obtain the classification result. The experimental results based on the published rolling bearing datasets of Western Reserve University show that the identification success rate of the proposed method can reach 98.75%. The fault detection of the rolling bearings can be completed accurately and efficiently.

The capital allocation framework proposed by [] presents capital allocation principles as solutions to particular optimization problems and provides a general solution of the quadratic allocation problem via a geometric proof. However, the widely used haircut allocation principle is not reconcilable with that optimization setting. In this paper we provide an alternative proof of the quadratic allocation problem based on the Lagrange multipliers method to reach the general solution. We show that the haircut allocation principle can be accommodated to the optimization setting with the quadratic optimization criterion if one of the original conditions is relaxed. Two examples are provided to illustrate the accommodation of this allocation principle.

In the rescue operations the full time of action plays important role. It is a sum of planning, travel, and manipulation (in the action place) phases times. The time minimization of first two phases by autonomous vehicle for remote action is considered in the paper. For known a priori map the path planning consists of local optimal decision collected next in the general algorithm of the optimal path. Such approach significantly reduces time of path planning. The robot features and known sparse obstacles reduce the allowable robot speeds. The time of travel is calculated from allowable velocity profile. So, it can be used to estimate the travel performance. Genetic algorithm and random search-based methods for path finding with travel time optimization are exploited and compared in the paper. All the proposed time optimisation solutions of rescue operation are checked during computer simulations and results of simulation are presented.

Non-pneumatic tires have gained a lot of attention due to their advantages, including not having to worry about getting a flat tire. In this type of tire, metamaterial cells replace the pneumatic part of the tire. In general, metamaterials are formed by stacking cells together. These cells are obtained by various methods including topology optimization. Therefore, in this research, to achieve a metamaterial cell suitable for a non-pneumatic tire with the objective function of increasing compressive strength and bending fatigue life, optimization for three types of geometry including (1) square plane, (2) rectangular plane, and (3) entire circumference of tire and three types of material including polylactic acid (PLA), thermoplastic polyurethane (TPU) and void have been done. Optimization has been implemented by using MATLAB code in 2D mode. The cubic cell is obtained from extruding the optimized response of the square plane. From the optimized response of the rectangular plane, the cylindrical cell and from extruding the optimized response of the entire circumference of tire to the tire cross-section size, the main geometry of the tire has been calculated. Finally, to check the quality of 3D printing in producing cells and how they are connected, the optimal cell fabricated by the fused deposition modeling (FDM) method has been observed using a field-emission scanning electron microscopy (FE-SEM). The results show that in the optimization of the square plane, the sample with the minimum remaining weight constraint equal to 40% and in the optimization of the rectangular plane and the entire circumference of tire, the sample with the minimum remaining weight constraint equal to 60% was selected as the optimal sample. From checking the quality of 3D printing, it has been concluded that PLA and TPU materials are completely connected.

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

A method was developed to perform shape optimization of a tidal stream turbine hydrofoil using a multi-objective genetic algorithm. A bezier curve parameterized the refrence hydrofoil profoil NACA 63815. Shape optimization of this hydrofoil maximized its lift-to-darg ratio and minimized its pressure coefficient, thereby increasing the turbines power output power and improving its cavitation characteristics. The Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) was employed to perform the shape optimization. A comparative study of two-and three-dimensional optimizations was carried out. The effect of varing the angle of attack on the quality of optimized results was also studied. predictions based on two-dimensional panel method results was also studied. Preditions based on a two-dimensional panel method and on a computational fluid dynamics code were compared to experimental measurments.

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

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

The application of evolutionary optimization algorithms in problem solving is currently gaining wide popularity. Use of Differential Evolution (DE) algorithm in obtaining analytically approximate solution of unstable second order initial value Ordinary Differential Equation (ODE) is presented in this work. The methodology involves solving an associated problem of optimization with con-strains to get an analytically approximate solution for the ODE under consider-ation. Three test cases were used to demonstrate the efficiency of our method. In comparison with other methods discussed in the literature, our method gave significant improvement on the accuracy of the obtained results.

Concerning fan systems with an air pipe connecting air intake and a closed outlet, aerodynamic noise cannot be directly transmitted from the fan inlet and outlet to the outside. At this moment, the volute vibrational radiation noise induced casing surface vibration is the major noise component. The main factors affecting the fan vibrational noise are analyzed through theoretical derivation, then a vibrational noise optimization control method for the volute casing is proposed that considered the influence of vibro-acoustic coupling, taking the panel thickness of the volute (front-panel thickness [FT], side-panel thickness [ST], and back-panel thickness [BT]) as design variables, and the acoustical power of the volute surface and the total mass of the volute as the optimal target function. The optimization method is mainly divided into three main parts: the first was based on the simulation of unsteady flow of the fan to obtain the vibrational noise source; the second, using the design of experimental (DOE) method and the proposed numerical simulation of fluid-structure-acoustic coupling method to obtain the designing space, then the radical-based function (RBF) method is used to construct the approximate surrogate model instead of the simulation model previously mentioned, which was used to provide the basic mathematical model for the optimization of the next part; the third part, implementing the low vibrational noise optimization for the fan volute, applied the single-target (taking volute radiated acoustical power as the target function) and the multi-target (taking the volute radiated acoustical power and volute total mass as the target function) methods. In addition, the fan aerodynamic performance, volute casing surface fluctuations, and vibration response were validated by experiments, showing good agreement. It is of utmost importance that the dynamic pressure measurements and vibrational tests on the volute casing verify the accuracy of the numerical calculation. The optimization results showed that the vibrational noise optimization method proposed in this study can effectively reduce the vibration noise of the fan, obtaining a maximum value of noise reduction of 7.3 dB. The optimization identified in this paper provides a significant reference for the design of a low-vibrational-noise volute.

With the accelerating urbanization process, the population increasingly concentrates in urban areas. In view of the special situation in China and a series of problems in the process of rapid urbanization, there were no reasonable measures for optimizing the population pattern. This study explored the distribution pattern of the Chinese population and proposed an optimization plan for the population distribution using GIS analysis. The main findings were as follows. (1) From 2010 to 2015, the distribution of population density in China presented a pattern of high in the southeast and low in the northwest based on the county-level administrative regions. The population still showed a tendency to migrate to the southeast of the country based on the “Hu Huanyong Line”. (2) There was a great difference in the land use efficiency in terms of population and economic production in China. The economic concentration in China was higher than the population concentration. In the areas where population and economic production were aggregated, GDP per capita and land use efficiency were higher. (3) Based on the land use efficiency in terms of population and economic production, the optimized urbanization plan of “1+4+11” for China’s urbanization was put forward, namely, one national-level aggregated area of population and economic production, 4 regional-level aggregated areas of population and economic production, and 11 local regionally aggregated areas of population and economic production. This optimization plan for urbanization represents an attempt to explore the direction of China’s urbanization, and it can be used to optimize the spatial development pattern and provide scientific guidance for the new urbanization plan.

For an enterprise, the most critical aspect of development is resource management, which has a significant impact on all aspects of the enterprise. Therefore, enterprises must pay attention to resource management allocation, which can better promote the sustainable development of the enterprise and obtain optimal benefits. In the production and development of modern enterprises, the acquisition of benefits also involves the allocation of resources in enterprise management. This paper proposed a benefit optimization scheduling algorithm based on a macroeconomic prediction model under auction mechanism and a grid resource scheduling algorithm driven by a benefit function to allocate resources reasonably. This article used macroeconomic forecasting models to fully understand the resource needs of various departments and the resources held by enterprises, and rationally allocate various resources in various departments. This can improve the work efficiency of various departments, thereby reducing the cost of the enterprise, and achieving optimal benefits for the enterprise. The experimental results in this paper showed that the cost of resource management allocation for the benefit optimization scheduling algorithm and the grid resource scheduling algorithm based on the benefit function driven under the auction mechanism was 105.6 yuan and 46.8 yuan respectively when the task volume was 125 under the multi user environment. The time allocated for resource management was 36.6s and 18.9s respectively. It can be seen that the efficiency function driven grid resource scheduling algorithm had a lower cost and time for resource management allocation, so the efficiency function driven grid resource scheduling algorithm can achieve enterprise efficiency optimization.

The magnetic navigation system (MNS) with gradient and uniform saddle coils is one of the effective systems to manipulate various medical magnetic robot due to its compact structure and uniformity of the magnetic field and field gradient. However, it has limited operating time and large inductance effect due to the high current density and numerous turns of coils, respectively. In this paper, we propose an optimization methodology of the MNS considering the current density and inductance of the coils. We introduce objective functions, constraints, and design variables of the MNS. Then, the MNS was designed by an optimization algorithm. The constructed MNS was compared with the conventional MNS, and it could generate 22 % stronger magnetic field or field gradient while the maximum three-dimensional rotating magnetic field was improved by 42 %. We also demonstrated an unclogging performance of a helical robot was improved by 54 % with the constructed MNS.

This paper considers a new approach to the modeling of the vacuum infusion process at the manufacturing three-dimensional composite parts of complex shape. The developed approach and numerical methods are focused on the reliable prediction with the needed accuracy and elimination the unrecoverable defect of composite structure such as the dry spots. The paper presents some experimental results, which demonstrate two cases of dry spots formation in large aircraft composite panels, and analyses the reasons of these defects arising. Our numerical technique is based on the vacuum infusion of the liquid resin into porous preform as the two phase flow, which is described by the phase field equation coupled with the Richards equation describing the fluid motion in unsaturated soils with spatially varied pressure dependant porosity and saturation. This problem statement allowed to correctly reconstruct the resin front motion and formation of inner and outer dry spots depending on its movement. For the rapid detection of preform zones that are suspicious for defect formation two indicators calculated during process simulation are proposed and tested at the numerical experiments. The auxiliary program tool has been developed in MATLAB environment to correctly detect the times of formation, localization and dimensions of the arising dry spots by using the results of the finite element model simulation.

The beautiful and stable posture is essential for ballet. Especially the pirouette which dancers balance themselves on their one leg is one of the most impressive postures. During the pirouette motion, loads from the rotation and the body weight are concentrated to the one leg. Proper ballet shoes can reduce the muscle burden and improve the stability. Four types of shoes; bare feet, running shoes, pointe shoes, and dance sneakers are analyzed. The motion capture system and SIMM are used for the analysis of muscle burden and stability during pirouette. The major 6 muscles in the leg (beceps femoris long head, beceps femoris short head, soleus, rectus femoris, gastrocnemius medialis, and gastrocnemius lateralis) are analyzed, and the effect of rotating velocity is considered. Dance sneakers are outstanding for improving the stability and lessening the muscle burdens in all conditions for beginners. That comes from the design feature of the divided shoe soles and protecting the ankle of the axis leg. With the results and analysis, the direction for design optimization of ballet shoes is suggested. Consequently, this research is about the verification of sports equipment using computer simulation with the musculoskeletal model from a scientific viewpoint of biomechanics.

To study the fermentation process of biocatalyzing decanoic acid to synthesize trans-2-decenoic acid, this study was based on the engineering E.coli constructed in the laboratory in the early stage and studied the effect of seed liquid culture time, culture temperature, inoculum amount, induction temperature, dissolution effects of substrate solvent, metal ions, and substrate loading on the yield of trans-2-decenoic acid. Based on single-factor experimental optimization, the Box–Behnken design was used to conduct response surface testing using the substrate feeding concentration, inducer concentration, and MnCl2 concentration as response variables and trans-2-decenoic acid production as the response value. The optimal fermentation process was determined through experiments: seed culture time 20 h, culture temperature 37°C, inoculation amount 1%, induction temperature 30°C, substrate flow amount 0.15 g/L, inducer concentration 5.60 g/L, MnCl2 concentration was 0.10 mM. Under these conditions, the average production of trans-2-decenoic acid was 1.982 ± 0.110 g/L. Compared with the basic LB medium, the production of trans-2-decenoic acid increased by 1.042 g/L; compared with the previous period, the yield of trans-2-decenoic acid studied increased by 1.501 ± 0.110 g/L, which provides a reliable basis for further research on the fermentation process of biocatalytic decanoic acid synthesis of trans-2-decenoic acid.

Bacteria a Microscopic organisms are the most inexhaustible and flexible of microorganisms and constitute a huge division of the whole living earthly biomass, certain microorganisms were found to amass metallic components at a high limit Was Known as Bacterial Bio-sorption Due to their little size, capacity to become under controlled conditions, and their Accommodation to an extensive variety of ecological situations; Potent metal bio-sorbents among microorganisms, at low pH esteems, cell divider ligands are protonated and contend essentially with metals for official. With expanding pH, more ligands, such as amino and carboxyl groups, could be exposed, leading to attraction between these negative charges and the metals, and consequently increment bio-sorption onto the cell surface. Starting with Isolation and identification of heavy metal-resistant bacteria from rock Ore. Studying Factors Affecting Uranium Bio-sorption, Optimization of bacterial growth conditions and optimum for metal uptake by free and immobilized bacterial cells and Desorption ratio of uranium ions adsorbed by Coli. /alginate, All this evidence suggest that functions groups Represented in our study are responsible for metal uptake in our bacterial biomass beside change in peaks position which assigned for it's groups confirm bio-sorption of metal ions from waste due to ions charge interaction comparing with immobilized we found increase in no of binding sites indicate that immobilized bacterial have high efficiency for metal up take which also change in peaks position which assigned for its groups confirm bio-sorption of metal ions from waste due to ions charge interaction, Where the high bio-sorption yield obtained by bacteria, the Uranium & heavy metal bioremediation process expects microorganisms to be joined to a strong surface.

Renewable energy sources (RES) will play a crucial role in future sustainable energy systems. In scenarios analyzing future energy system designs, a detailed spatial and temporal representation of renewable-based electricity generation is essential. For this, sufficiently representative weather data are required. Most analyses performed in this context use the historical data of either one specific reference year or an aggregation of multiple years. In contrast, this study analyzes the impact of different weather years based on historical weather data from 1980 through 2015 in accordance with the design of an exemplary future energy system. This exemplary energy system consists of on- and offshore wind energy for power-to-hydrogen via electrolysis, including hydrogen pipeline transport for most southwestern European countries. The assumed hydrogen demand for transportation needs represents a hypothetical future market penetration for fuel cell-electric vehicles of 75%. An optimization framework is used in order to evaluate the resulting system design with the objective function of minimizing the total annual cost (TAC) of the system. For each historical weather year, the applied optimization model determines the required capacities and operation of wind power plants, electrolyzers, storage technologies and hydrogen pipelines to meet the assumed future hydrogen demand in a highly spatially- and temporally-detailed manner, as well as the TAC of the system. Following that, the results of every individual year are compared in terms of installed capacities, overall electricity generation and connection to the transmission network, as well as the cost of these components within each region. The results reveal how sensitive the final design of the exemplary system is to the choice of the weather year. For example, the TAC of the system changes by up to 20% across two consecutive weather years. Furthermore, significant variation in the optimization results regarding installed capacities per region with respect to the choice of weather years can be observed.

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

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

Desalinators are traditionally driven by fossil-fuels but in order to avoid greenhouse emissions, renewable energy must be used. In this paper, a coupling between multi-stage flash distillation apparatus and a parabolic trough solar collector is analyzed. The purpose of this study is to determine the economic feasibility of the system, considering four cities of Saudi Arabia and three different potable water productions. To avoid solar energy intermittency and unavailability at night, thermal storage is implemented. Whereas other researchers made parametric analyses, in this paper, the authors developed a mathematical program which was optimized with the help of GAMS software, where the capital cost of the plant was the objective function. After that, a life cycle cost analysis was carried out for each scenario. Depending on the region and water production, the costs of drinking water vary from 2.26 to 3.93 US$/m³, and from 7440 to 23825 tons of carbon dioxide emissions are avoided. As a consequence of the approach used, low costs are obtained; even though no auxiliary heater is implemented and the lowest irradiance conditions are considered. The results of this study reflect that the proposed process is competitive with respect to the traditional one.