Dams design and their operation cause strong environmental alteration and therefore a long-term debate is ongoing for the scale of these projects. At the same time, the concept of Environmental Flow Assessment (EFA) is a crucial element of modified ecosystems featuring large infrastructure such as dams and reservoirs for mitigating potential environmental degradation while they operate. Nowadays, integrated scientific frameworks are required to quantify the risks caused by large infrastructure. Through the use of stochastic analysis, it is possible to quantify these uncertainties, and present a solution that incorporates long-term persistence and environmental sustainability into a balanced reservoir simulation model. In this work, an attempt is made to determine a benchmark reservoir size incorporating hydrological and ecological criteria though stochastic analysis. The primary goal is to ensure the best possible conditions for the ecosystem, and then secondarily to allow a steady supply of water for other uses. Using a synthetic timeseries based on historical inputs, it is possible to determine and preserve essential statistical characteristics of a river’s streamflow, and use these to detect the optimal reservoir capacity that maximizes environmental and local water demand reliability.

Sustainable continuous cover forestry is defined and analyzed in several ways. The differential equation representing growth of the basal areas of individual trees, motivated by fundamental biological production theory by Lohmander (2017a), is analyzed and extended in different directions. From the solution of the differential equation, the basal area and the tree diameter are obtained as explicit functions of time. The diameter is a strictly increasing function of time. In the absence of competitors, the diameter increment is shown to be a strictly decreasing function of time. Hence, the diameter increment can also be interpreted as a strictly decreasing function of the diameter. Alternative forms of adjustment of the differential equation, with consideration of competition, are defined. If the competition is strong, with large trees in the vicinity of a particular tree, then the basal area increment, and the diameter increment, are reduced. The growth of a large tree is less sensitive than the growth of a small tree, to competition from other trees. Under strong competition, the basal area increment, and the diameter increment, are strictly concave functions of the size of the tree. The unique maximum of the diameter increment occurs at a higher diameter, if the competition increases. In dynamic equilibrium, the tree size frequency distribution is stationary. If natural tree mortality can be avoided via the harvest strategy, the tree size frequency distribution is a function of the size and competition dependent growth function, and the harvest strategy. Empirical tree size frequency data are used to simultaneously estimate parameters of a size and competition dependent growth function and the applied harvest strategy, via nonlinear optimization. The properties of the estimated growth function are consistent with the corresponding properties of the production theoretically motivated hypothetical function, and the properties of the estimated harvest strategy confirm the corresponding hypotheses. The R2 of the nonlinear regression exceeds 0.97. With access to an empirically estimated equilibrium tree size distribution, it is possible to: 1. Estimate size frequency relevant parameters of tree size and competition dependent growth functions for individual trees. 2. Estimate the applied harvest strategy. 3. Explain and reproduce the empirically estimated tree size equilibrium distribution.

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

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

Remanufacturing is a way for product recovery initiative and maintaining sufficient production flow to satisfy customer demand through circular economy. Recently, manufacturers have been progressively incorporating remanufacturing processes, making their supply chain vulnerable to disruptions. Among the disruptions that usually occur is the shortage in spare parts supply, which causes unexpected delay in production and possible loss of sales. In the face of potential disruption, it is essential for remanufacturing facilities to manage their supply chains in an optimum manner as to decrease the adverse impact of disruptions to their business. In this study, a two-stage production-inventory system is analysed by developing a cost-minimisation model focusing on the recovery schedule after a disruption occurrence in sourcing spare parts for a remanufacturer’s production cycle. The developed model was solved using the branch and bound algorithm. Through numerical experiments, the results indicate that the optimal recovery schedule and the number of recovery cycles are significantly dependant on disruption time, lost sales and backorder costs. Moreover, a sensitivity analysis shows that the lost sales option seems to be more effective than the backorder option in optimising the system’s overall cost. The proposed model could assist managers in deciding the optimal production strategy while providing interesting managerial insights into vital spare parts recovery issues when disruption strikes.

The diffusion of electric vehicles (EVs) can be sustained by the presence of integrated solutions offering parking and clean power supply. The recourse to DC systems allows to better integrate EV bidirectional energy exchange, photovoltaic panels and energy storage. In this paper, a methodology for optimal techno-economic sizing of a DC-microgrid for covering EV mobility needs is carried out. It is based on the definition of different scenarios of operation, according to typical EV usage outlooks and environmental conditions. In each scenario, optimal operation is carried out by means of a specific approach for EV commitment on different stations. The sizing procedure is able to handle the modular structure of microgrid devices. The proposed approach is applied to a case study of envisaged EV service fleet for Bari port authority.

This study identifies the optimal management policy of a given energy storage system (ESS) installed in a grid-connected wind farm for maximizing the monetary benefits and provides guidelines for defining the economic value of the ESS under the optimal management policy and selecting the optimal size of the ESS based on the economic value. Considering stochastic models for wind power and electricity price, we develop a finite-horizon periodic-review Markov decision process (MDP) model to seek the optimal management policy. We also use a simple optimization model to find the optimal storage capacity and charging/discharging capacity of the ESS. By applying our analytic approach to a real-world grid-connected wind farm located in South Korea, we verify the usefulness of this study. Our numerical study shows that the economic value of the ESS is highly dependent on the management policy, wind electricity variability, and the electricity price variability. Thus, the optimal size of ESS should be carefully determined based on the locational characteristics and management policy even with limited investments. Furthermore, this study provides a meaningful policy implication on how much a subsidy the government should provide for installing ESS in a wind farm.

Integrating numerous renewable resources has been extensively researched and studied to create a dependable energy system. This study investigated the best techno-economical hybrid energy system configuration while using hydrogen energy to keep the system's energy balanced. Additionally, this study aims to determine the best hybrid system configuration for the location under investigation. A novel technique of DBM (Deterministic Balanced Method) was introduced to determine the total annual energy output for the project's lifetime. This approach is used to contrast the outcomes of other HOMER software tools. Then, using HOMER, it is possible to confirm the sizing optimization results for the suggested method by comparing the power ratings and yearly energy output. A detailed comparison between our proposed method and HOMER Pro has been conducted for validation and verification. Our method has been rigorously tested in various hybrid off-grid energy systems, ensuring its applicability and reliability across different scenarios and configurations. The results of our study indicate a high degree of similarity between the proposed approach and HOMER pro for computing annual energy power for the studied energy systems. Specifically, our analysis revealed that the difference between the approaches was within a range of 5%, indicating a high level of consistency and reliability in our method. These findings suggest that our approach can be confidently applied to determine the appropriate size of hybrid energy systems accurately. Overall, our study provides valuable insights into the development of effective sizing methodologies for hybrid energy systems. After considering the amount of solar radiation and wind speed gathered by global weather data platforms like NASA and METEONORM, this paper has also included a real case study of the Cairo International Airport's hybrid renewable energy production systems. To guarantee the viability of this study, the system design was founded on an actual load that was researched and simulated. A cluster of distributed networks was finally proposed to use the excess energy and enable energy conversion processes to be used in industry and transportation applications.

Microgrid is one of the promising green transition technologies that will provide enormous benefit to the seaport, as a solution to the major concerns in this sector, namely energy crisis, economical and environmental pollution. However, finest design of the microgrid is a challenging task considering different objectives, constraints and uncertainties involved. To ensure the optimal operation of the system, determining the right configuration framework and size for each component in the seaport microgrid at the minimum cost is a vital decision at the design stage. This paper aims to design a hybrid system of seaport microgrid with optimally sized component .The selected case study is the Port of Aalborg, Denmark. The proposed grid-connected structure consists of renewable energy sources (photovoltaic system and wind turbines), an energy storage system and cold ironing as seaport’ loads. The architecture is then optimized by utilizing HOMER to meet the maximum load demand by considering a few parameters such as solar global horizontal irradiance, temperature and wind resources. Then, the best configuration framework is analyzed in terms of economic feasibility, energy reliability and environmental impact.

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

The aim of the study was to assess the chemical and biochemical composition of heat-treated dates palm fruits (Phoenix dactylifera L.) cultivated in Algeria and stored under Modified Atmosphere Packaging (MAP). In this research, fresh dates palm fruits at harvest (Phoenix dactylifera L.) have been subjected at heat treatment at 55 ± 2°C for 20 min in a ventilated oven. The samples were packed in specific Polyethylene Terephthalate packaging (PET) bags with a thickness of 2.5 μm under MAP condition. The effect of this heat treatment on date fruits quality was investigated during 5 months of storage at room temperature storage (22 ± 1°C of temperature with 75 to 80% of relative humidity (RH), and 10 ± 1°C with 85 to 90% of RH). Chemical and biochemical composition of the samples were studied. Results indicated that during storage the Brix degree, protein, and vitamin C content changed significantly (p<0.05). The major change was observed for lipid values varied from about 0.02 to 0.06% at 10°C, and 0.04 to 0.07% at 22°C for samples not heat-treated unpacked after 5 months of storage, these results represent a highly significant difference (p<0.01). Harvesting at Tamr stage followed by treating the fruits with heat disinfestation packed in MAP, and storing at 10 ± 1°C showed to be a promising method for maintaining dates fruits storage quality.

Around 14% of the global population does not have access to electricity. About 95% of those are living in rural Sub-Saharan Africa. Often in these regions, diesel generators are the only source of electricity. The operating cost of these diesel generators is high. However, solar and wind energy are available in most of African countries. This study presents the analysis of designing an off-grid hybrid system with a wind turbine, PV, diesel generator, and battery to power a hospital, school, and 200 household village in four locations across Somalia. The research investigated the availability of wind-solar resources in selected locations. Designing of the system and economic-technical calculations were performed using HOMER. The selection of the optimum design was based on the Cost of Electricity and Net Present Cost. The results show that for Kabaal and Ceel Buur, a WT-PV-DG-Battery is the optimal system as the wind resource in these regions is high. For Saakov and Baki, a PV-DG-Battery system proves to be optimum as the wind resource is limited here. The study also evaluated the control strategy and proved that combined dispatch was the most cost-effective for these locations. The study concluded that hybrid systems are more economical than diesel systems.

One of the major concerns in structural health management (SHM) is the early detection of a growing crack. Using this, future damage due to crack propagation can be mitigated or eliminated by implementing proper repair and maintenance. Acoustic Emission (AE) is a non-destructive testing (NDT) method with potential applications for locating and monitoring fatigue cracks. The research presented in this paper focuses on SHM using AE. A novel AE signal analysis approach was proposed in order to detect crack initiation and assess small crack lengths. Experimental investigation indicated that initiation of a crack could be identified through the statistical analysis of the resulting features of the AE signals. A probabilistic AE-based model for small fatigue crack sizing was developed and the uncertainties of the model were estimated. In addition, a probabilistic model validation approach was implemented to confirm accurate estimation of the results. The outcome of this research can be used to evaluate the integrity of structures under fatigue loading. The proposed approach can also be applied as an approach to manage health and assess prognosis of structures.

The paper illustrates a general framework in which a neural network application can be easily integrated and proposes a traffic forecasting approach that uses neural networks based on graphs. The method minimizes the communication network (between vehicles and the database servers) load and represents a reasonable trade-off between communication network load and forecasting accuracy. Traffic prediction leads to the choice of less congested routes and therefore to the reduction of energy consumption. The traffic is forecasting using a LTSM neural network with a regression layer. The inputs of the neural network are sequences - obtained from graph that represent the road network - at specific moments of time that are read from traffic sensors or the outputs of neural network (forecasting sequences). The input sequences can be filtered to improve the forecasting accuracy. This general framework is based on Contiki IoT operating system that ensure support for wireless communication and efficient implementation of processes in a resource constrained system and it is particularized to implement a graph neural network. Two cases are studied: one case in which the traffic sensors are periodically read and the other case in which the traffic sensors are read when their values changes are detected. A comparison between the cases is made and the influence of filtering is evaluated. The obtained accuracy is very good, very close to the accuracy obtained in infinite precision simulation, and the computation time is low enough and the system can work in real time.

Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. In many instances, utility companies pay for both energy and peak demand. PV systems can effectively reduce energy consumption during their operational periods, while, their contribution during peak hours, particularly in the early morning and late evening, is often limited. To address this issue, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV-battery systems remains a challenge. This study proposes a novel statistical model to optimize PV-battery system sizing for enhanced peak demand reduction. The model aims to flatten 95% of peak demand days up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV-battery system successfully flattens 95% of daily peak demand up to 2000 kW, yielding a financial benefit of $812,648 over 20 years.

We consider the financial planning problem of a retiree wishing to enter a retirement village at a future uncertain date. The date of entry is determined by the retiree's utility and bequest maximisation problem within the context of uncertain future health states. In addition, the retiree must choose optimal consumption, investment, bequest and purchase of insurance products prior to her full annuitisation on entry to the retirement village. A hyperbolic absolute risk-aversion (HARA) utility function is used to allow necessary consumption for basic living and medical costs. The retirement village will typically require an initial deposit upon entry. This threshold wealth requirement leads to exercising the replication of an American put option at the uncertain stopping time. From our numerical results, active insurance and annuity markets are shown to be a critical aspect in retirement planning.

Over the centuries, honey is known for its superior usage in culinary, and for its rich nutrition and therapeutic values which are scientifically proven in the medical field. The chemical composition of honey varies depending on its botanical sources and environment. Therefore, the nutrition content in honey is highly likely to be affected by contaminants, such as heavy metals and pesticides. To ensure the quality of honey, parameters such as the heavy metal content should be within the safe range of total standard mineral and trace elements as defined by the International Food Standard (Codex Alimentarius), and pesticides should not be present at all. The high concentration of heavy metal and pesticides not only deteriorates the quality and quantity of honey, but also causes harm to the bee colony itself. In the agriculture sector, the excessive usage of pesticides and fertilizer negatively impacts the overall honey production process. Bees, a pollinating agent, bring the polluted nectar back to their beehives, eventually contaminating the honey and depreciating its value. Hence, this article will comprehensively review the activities that contribute to heavy metal and pesticide contamination, the interactions of bees as a pollinating agent, the impact of the pollutant to the colonies, and subsequently to the honey production.

The electrical motor design requires a lot of work and resources. In the field, motor designers need to decide on competitive motor sizing quickly to compete with other motor suppliers. Generally, it requires expensive FEM S/W to get accurate motor sizing results, but buying and maintaining many licenses is costly. Moreover, it needs some patience to get the result from FEM analysis. In this paper, the motor sizing technique using an existing production motor is presented to respond to urgent requests from customers without using FEM analysis. In order to enhance the fidelity of the result, magnetic flux loss by magnet coating and non-linear characteristics are considered during the motor sizing process. The proposed sizing method was verified by comparison to the FEM analysis and test result and showed reasonable confidence in them. Also, this method has the substantial advantage of outputting accurate results instantly without taking any time. It helps with the modification design of electrical motors without using expensive FEM S/W and a certain level of skill or experience.

This study explores fault-tolerant consensus in leader-following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler-Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

This article systematically studied the A-optimal designs of a high-order mixture central polynomial model, which is an advancement of the research results for the A-optimal design of the most recent third-order mixture central polynomial model and a more accurate response surface model. This article is based on the simplex center design, obtaining measurement values at various design points, and verifying them through the A-optimal equivalence theorem. We also compare the A-efficiency of designs through examples, which can be applied to the A-optimal precise design of the fourth-order mixture model.

Milk serves as a crucial source of natural bioactive compounds essential for human nutrition and health. The gastrointestinal digestion of milk proteins releases various biologically active peptides that positively impact human health and enhance physiological performance. With the growing production of cheese, Greek yogurt, and other dairy products, the volume of unutilized acid whey is significantly increasing. Acid whey, containing almost all whey proteins and approximately one-quarter of casein, presents a valuable raw material for generating peptides with potential health benefits. These peptides exhibit properties such as antioxidant, antimicrobial, anti-inflammatory, anticarcinogenic, antihypertensive, antithrombotic, opioid, mineral-binding, and growth-stimulating activities, contributing to improved human immunity and the treatment of chronic diseases. Enzymatic hydrolysis of milk proteins has been developed as a method to produce these peptides, facilitating their use in food fortification. To incorporate whey into functional beverages, it undergoes fermentation by lactic acid bacteria with proteolytic activity. To specifically enrich these beverages with peptides exhibiting targeted bioactivities, it is essential to select lactic acid bacteria strains possessing the requisite protease profiles. Additionally, the sensory properties of the final product can be enhanced using starter cultures with diverse metabolic capabilities, even in the absence of added flavoring agents. Given the imperative to develop waste-free technologies and enhance the profitability of the dairy industry, there is a growing need to efficiently utilize and transform acid whey into a broad spectrum of valuable products and food additives. This approach is justified not only by the nutritional benefits of the bioactive components in whey but also by the commercial potential of safe and health-enhancing products.

Optimal control is a critical tool for mechanical robotic systems, facilitating precise manipulation of dynamic processes. These processes are described through differential equations governed by a control function, addressing a time-optimal problem with bilinear characteristics. Our study utilizes the classical approach complemented by Pontryagin's Maximum Principle (PMP) to explore this inverse optimal problem. The objective is to develop an exact piecewise control function that effectively manages trajectory control while considering the effects of viscous friction. Our simulations demonstrate that the proposed control law markedly diminishes oscillations induced by boundary conditions. This research not only aims to delineate the reachability set but also strives to determine the minimal time required for the process. The findings include an exact analytical solution for the stated control problem.

This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, with a focus on enhancing profitability while factoring in battery degradation. The study incorporates in the optimisation framework the cost of battery capacity degradation, the effects of capacity degradation and internal resistance degradation into dynamics. We employ a continuous-time representation of the dynamics, in contrast with many other studies that use a discrete-time approximation with rather coarse intervals. We adopt an equivalent circuit model coupled with empirical degradation parameters to simulate a battery cell’s behaviour and degradation mechanisms with good support from experimental data. Utilising direct collocation methods with mesh refinement allows for precise numerical solutions to the complex, nonlinear dynamics involved. Through a detailed case study of Belgium’s day-ahead electricity market, we determine the optimal charging and discharging schedules under varying objectives: maximizing net revenues, profits considering capacity degradation, and profits considering both capacity degradation and internal resistance increase due to degradation. The results demonstrate the viability of our approach and underscore the significance of integrating degradation costs into the market strategy for battery operators, alongside its effects on the battery’s dynamic behaviour. Our methodology extends previous work by offering a more comprehensive model that empirically captures the intricacies of battery degradation, including a fine and adaptive time domain representation, focusing on the day-ahead market, and utilising accurate direct methods for optimal control. The paper concludes with insights into the potential of optimal control applications in energy markets and suggestions for future research avenues.

The integration of diverse distributed generation (DG) units, encompassing both Renewable Energy Sources (RES) and non-RES technologies delves into voltage levels, current distributions, and power flows, aiming to optimize and effectively manage the distribution network. An RES-based DGs such as photovoltaic systems and wind turbines, alongside non-RES units like fuel cells and micro turbines. This study proposes a hybrid approach the Multi-Objective Genetic Algorithm - Grey Wolf Optimizer (MOGA-GWO). By merging the strengths of multi-objective genetic algorithms with the efficiency of the Grey Wolf Optimizer, this advanced algorithm seeks semi-optimal solutions for multi-objective optimization problems. The algorithm aims to improve convergence speed without compromising solution quality, offering a promising solution for optimizing the characteristics of DG and RES integration in distribution networks compared to Cuckoo Search and Simulation Annealing Techniques. Key algorithmic operations include individual representation, initialization, fitness function computation, selection, crossover, mutation, and ending conditions. The DG optimal power flow problem is explored with a focus on minimizing generation costs, improving voltage profiles, minimizing losses, enhancing reliability, integrating renewable energy, reducing environmental impact, managing congestion, and ensuring compliance with operational constraints. Beyond traditional considerations, the study emphasizes neglected objectives and constraints, such as cyber-security concerns, real-time operation, regulatory frameworks, social acceptance, lifecycle environmental impact, grid resilience, data privacy, aging infrastructure compatibility, and economic viability.

To assure the opacity, an optimal problem is to retain the occurring events sequences as many as possible. Contrary to such problem, the other optimal problem is to preserve the minimal occurring events sequences. In the paper, based on the choosing cost, an optimal opacity-enforcing model is presented to minimize the sum of discount choosing cost, which is not only to preserve the opacity, but also to retain the secret to the maximum. To solve the model, two scenarios on opacity are considered. For the two scenarios, some algorithms are presented to achieve the optimal solution for the model by using the method of dynamic programming. And then, the solutions produced by the algorithms are proved correctly by theoretical proof. Finally, some illustrations for the algorithms are given.

The capacitated multi-item lot-sizing problem abbreviated as CLSP, is to determine the lot sizes of products in each period in a given planning horizon of finite periods meeting the product demand and resource limits in each period, and minimize the total cost consisting of production, inventory holding, and setup costs. CLSPs are often encountered in industry production settings and thus the solution is significant. In this paper, we propose a Lagrange relaxation (LR) approach for the solution of its. The approach decomposes the CLSP into several uncapacitated single-item lot-sizing problems each of which can be easily solved by dynamic programming. The feasible solutions are achieved by solving the resulting transportation problems with a proposed stepping-stone algorithm and a fix-up procedure. The Lagrange multipliers are updated by using subgradient optimization. Experimental results show that the LR approach explores high-quality solutions and has better applicability compared with other commonly used solution approaches in the literature.

In this work, we propose a variation of the cellular particle swarm optimization algorithm with differential evolution hybridization (CPSO-DE), to include constrained optimization in it, named Ts-CPD. It is implemented as a kernel of electronic design automation (EDA) tool capable of sizing circuit components considering a single-objective design with restrictions and constraints. The aim is to improve the optimization solutions in the sizing of analog circuits. To evaluate our proposal’s performance, we present the design of three analog circuits: a deferential amplifier, a two-stage operational amplifier, and a folded cascode operational transconductance amplifier. Numerical simulation results indicate that Ts-CPD can find better solutions, in terms of the design objective and the accomplishment of constraints, than those reported in previous works.

To enable net zero sustainable thermal building energy this study develops an open-source thermal house model to couple solar photovoltaic (PV) and heat pumps (HP) for grid-connected residential housing. The calculation of both space heating and cooling thermal loads and the selection of HP is accomplished with a validated Python model for air-source heat pumps. The capacity of PV required to supply the HPs is calculated using an System Advisor Model integrated Python model. Self-sufficiency and self-consumption of PV and the energy imported/exported to the grid for a case study are provided, which shows that simulations based on the monthly load profile have a significant reduction of 43% for energy sent to/from the grid compared to the detailed hourly simulation and an increase from 30% to 60% for self-consumption and self-sufficiency. These results show the importance of more granular modeling and also indicate mismatches of PV generation and HP load based on hourly simulation datasets. The back-calculation PV sizing algorithm combined with HP and thermal loads presented in this study exhibited robust performance. The results indicate this approach can be used to accelerate the solar electrification of heating and cooling to offset the use of fossil fuels in northern climates.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

Reasonable allocation of production plans and saving the overall electricity cost are crucial for large manufacturing conglomerates, which is an effective way for conglomerates to realize open-source and cost-saving. This paper develops an optimization model of off-site industrial production scheduling for enterprises in response to the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's electric power and the difficulty of promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and different electricity prices in different places to optimize the scheduling of industrial production in different places. The model is embedded with two sub-models of the electricity price prediction model and distributed photovoltaic kWh cost model to complete the model parameters, in which the electricity price prediction model utilizes the LSTM neural network. The integrated model is then solved by the particle swarm algorithm. To verify the effectiveness of the model in solving the problem of off-site production scheduling on the demand side of electricity, the research team researched two off-site pharmaceutical factories belonging to the same large pharmaceutical company. Finally, the research data were substituted into the model for solving, and it was concluded that the optimization model could significantly reduce the cost of electricity consumption of the enterprise by about 7.9 %.

The state detection of power cable is very important to ensure the reliability of power supply. Traditional sensors are mostly based on electric field detection, the operation is complex, and its efficiency needs to be improved. This paper optimizes the design and development of the magnetic field detection sensor for AC power cables. Firstly, through the establishment of the magnetic field sensor model, it is determined that permalloy is the material of the magnetic core, the optimal aspect ratio of the magnetic core is 20, and the ratio of coil length to core length is 0.3. Secondly, the coil simulation model is established, and it is determined that the optimal number of turns of the coil is 11000 turns, the diameter of the enamelled copper wire is 0.08mm, and the equivalent magnetic field noise of the sensor is 0.06pT. Finally, the amplifying circuit based on negative magnetic flux feedback is designed and the sensor is assembled, and the experimental circuit is built for sensitivity test. the results show that the sensitivity of the magnetic field sensor is 327.6mV/μT. The sensor designed in this paper has the advantages of small size, high sensitivity, easy to carry and high reliability.

Pump Sizing is the process of dimensional matching an impeller and stator to provide a satisfactory performance test result and good service life during the operation of progressive cavity pumps. In this process, historical data analysis and dimensional monitoring are done manually, consuming a large amount of man-hours and requiring a deep knowledge of progressive cavity pump behavior. This work presents the use of complex networks in the construction of a prototype to recommend interference during the Pump Sizing process in a Progressive Cavity Pump. For this, data from different expert applications were used in addition to individual control Excel spreadsheets to build the database used in the prototype. From the pre-processed data, complex network techniques and the Betweenness Centrality metric were used to calculate the degree of importance of each order confirmation, as well as to calculate the dimensionality of the rotors. The model was evaluated using the mean squared error (MSE), obtaining a MSE of 0.28 for the cases where there were recommendations for order confirmations. Based on the results obtained, it was realized that there is a similarity with the dimensional defined by design engineers during the Pump Sizing process.

To control the volatility and reduce the electricity costs for indoor farming, the agrivoltaics agrotunnel introduced here uses: 1) high insulation for a building dedicated to vertical growing, 2) high-efficiency LED lighting, 3) heat pumps (HPs), and 4), solar photovoltaics (PV) provide known electric costs for 25 years. In order to size the PV array, this study develops a thermal model for agrotunnel load calculations and validates it using the Hourly Analysis Program and measured data so the effect of plants’ evapotranspiration can be included. HPs are sized, plug loads (i.e., water pump energy needed to provide for the hybrid aeroponics/hydroponics system, DC power running the LEDs hung on grow walls, and dehumidifier assisting in moisture condensation in summer) are measured/modeled. Ultimately, all models are combined to establish an annual load profile for an agrotunnel that is then used to model the necessary PV to power the system throughout the year. The results find that agrivoltaics to power an agrotunnel range from 40 to 50kW and make up an areas from 3.2 to 10.48 m²/m² of an agrotunnel footprint. Net zero agrotunnels are technically viable although future work is needed to deeply explore the economics of localized vertical food growing systems.

This research aims to apply Artificial Intelligence (AI) methods, specifically Artificial Immune Systems (AIS), to design an optimal control strategy for a multivariable control plant. Two specific industrial control approaches are investigated: I-PD (Integral-Proportional Derivative) and PI-D (Proportional-Integral Derivative) control. The motivation for using these variations of PID controllers is that they are functionally implemented in modern industrial controllers, where they provide precise process control. The research results in a novel solution to the control synthesis problem for the industrial system. In particular, the research deals with the synthesis of I-P control for a two-loop system in the technological process of a distillation column. This synthesis is carried out using the AIS algorithm, which is the first application of this technique in this specific context. Methodological approaches are proposed to improve the performance of industrial multivariable control systems by effectively using optimization algorithms and establishing modified quality criteria. The numerical performance index ISE justifies the effectiveness of the AIS-based controllers in comparison with conventional PID controllers (ISE₁ = 1.865, ISE₂ = 1.56). The problem of synthesis of the multi-input multi-output (MIMO) control system is solved, considering the interconnections due to the decoupling procedure.

This paper presents a comparative study of fundamental similarity functions for Siamese networks in semantic textual similarity (STS) tasks. We evaluate various similarity functions using the STS Benchmark dataset, analyzing their performance and stability. Additionally, we introduce a multi-objective approach for optimal threshold selection. Our findings provide insights into the effectiveness of different similarity functions and offer a straightforward method for threshold selection optimization, contributing to the advancement of Siamese network architectures in STS applications.

Alkenyl Succinic Anhydride (ASA) is a sizing agent used in papermaking to increase the water repellency of paper. Almost 60 years after the introduction of the chemical in papermaking, scientists still have differing views on how ASA interacts with cellulose. Several experiments were conducted to bring more clarity to the ASA sizing mechanism, especially on the contentious question of ASA-cellulose covalent bonding or the esterification reaction between ASA and cellulose during papermaking. Herein, research papers and patents, including experiments and results, from the1960’s to 2020 were reviewed. Our investigation revealed that the ester bond formation between ASA and cellulose is insignificant and is not a prerequisite for sizing effectiveness; the main ASA related material found in sized paper is hydrolyzed ASA or both hydrolyzed ASA and ASA salt. In addition, ASA emulsion stability and ASA emulsion retention are important for sizing efficiency improvement.

In this work, an innovative numerical approach for polylinear approximation (polylinearization) of non-self-intersecting compact sensor characteristics (transfer functions) specified either pointwise or analytically is introduced. The goal is to optimally partition the sensor characteristic, i.e. to select the vertices of the approximating polyline (approximant) along with their positions on, the sensor characteristics so that the distance (i.e. the separation) between the approximant and the characteristic is rendered below a certain problem-specific tolerance. To achieve this goal, two alternative non-linear optimization problems are solved, whose essential difference is in the adopted quantitative measure of the separation between the transfer function and the approximant. In the first problem, which relates to absolutely integrable sensor characteristics (their energy is not necessarily finite, but they can be represented in terms of convergent Fourier series), the polylinearization is constructed by numerical minimization of the L^1--metric (a distance-based separation measure), concerning the number of polyline vertices and their locations. In the second problem which covers the quadratically integrable sensor characteristics (whose energy is finite, but they do not necessarily admit a representation in terms of convergent Fourier series), the polylinearization is constructed by minimizing numerically the L^2-metric (area-, or energy-based separation measure) for the same set of optimization variables –the locations, and the number of polyline vertices.

In this study, a novel method is proposed to estimate surface-spectral reflectance from camera responses that combines model-based and training-based approaches. An imaging system is modeled using the spectral sensitivity functions of an RGB camera, spectral power distributions of multiple light sources, unknown surface-spectral reflectance, additive noise, and a gain parameter. The estimation procedure comprises two main stages: (1) selecting the local optimal reflectance dataset from a reflectance database and (2) determining the best estimate by applying a neural network to the local optimal dataset only. In stage (1), the camera responses are predicted for the respective reflectances in the database, and the optimal candidates are selected in the order of lowest prediction error. In stage (2), most reflectance training data are obtained by a convex linear combination of local optimal data using weighting coefficients based on random numbers. A feed-forward neural network with one hidden layer is used to map the observation space onto the spectral reflectance space. In addition, the reflectance estimation is repeated by generating multiple sets of random numbers, and the median of a set of estimated reflectances is determined as the final estimate of the reflectance. Experimental results show that the estimation accuracies exceed those of other methods.

One of the rapidly developing research areas in the power system is the integration of distributed generation (DG) with the distribution system. The size and location of DG sources had a considerable impact on power system networks. Artificial intelligence (AI) techniques can be used to address multidimensional problems relating to DG size and location in distribution systems. Heuristic optimization offers a reliable and effective method for solving complicated real-world problems. This work focuses on a hybrid approach that combines the two heuristic optimization methods i.e., Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for the optimal siting and sizing of DG in distribution systems. This hybrid approach combines ideas from GA and PSO and generates individuals in a new generation using both PSO mechanisms and the procedures found in GA. An extensive performance analysis of the IEEE 14 busbar standard test system is conducted to demonstrate the viability of the suggested methodologies. In the designated locations, DG is placed, and the outcomes have been verified. The results indicated that the right placement of DG injection enhances the voltage profile and lowers the distributed system’s power losses. These techniques offer unique methods for determining the location of the DG unit, demonstrating the potential of such a computational techniques to reduce computing time and complexity while simultaneously reducing human errors associated with hit-and-trail methods.

Indoor environmental comfort is fundamental to human health as people spend 90% of their time indoors. This aspect is even more crucial in hospitals, where the concept of health is closely linked to well-being, ethics, and environmental aspects. Emerging methodologies and technologies such as Digital Twin, Building Information Modeling, the Internet of Things, sensing technologies, and data analytics offer new opportunities to ensure healthier environments and more efficient building management. This paper provides an assessment of how digitalization can support decision-making processes related to maintaining high levels of indoor environmental comfort in hospital settings, particularly by analyzing how real-time data processing and the application of machine learning can promote proactive interventions in these facilities. The methodological approach was based on four phases: defining the objectives of the digital twin, identifying the input data to build and feed the digital model, defining the KPIs to evaluate the system's correct functioning, and identifying the enabling technologies to be integrated into the system to achieve the set goal. The result is a digital twin for managing the operating room and its related services, with the aim of guiding decisions based on accurate data and improving operational efficiency, levels of environmental comfort, and safety regarding the diffusion of medical gases.

The capacitated lot-sizing problem with product recovery (CLSP-RM) holds significant importance in reverse logistics but is notoriously complex (NP-hard). In this study, two techniques are introduced to confront this challenge. The first technique entails devising a linear optimization task that eliminates capacity limitations across a wide problem spectrum, yielding a remarkably accurate approximation of the optimal solution. This adaptable approach presents a potent alternative and holds potential for extension to diverse problem categories owing to its versatile nature. The second technique employs a simulation methodology utilizing Halton’s uniform random numbers to address the issue. This randomized production search method sidesteps considerations of production costs, inventory expenditures, and production order when determining production batches. The research’s novelty lies in its application of these techniques to the problem. The suggested methods undergo evaluation via a benchmark dataset of approximately 4200 instances, with comparison against solutions derived through the Gurobi solver. The results underscore the efficacy and resilience of the introduced methodology in tackling the CLSP-RM predicament.

This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Sixteen features are extracted and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and that collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values being greater than 95%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.

This study fabricated piezoelectric fibers of polyvinylidene fluoride (PVDF) with graphene using near-field electrospinning (NFES) technology. A uniform experimental design table U*7 (7^4 ) was applied, considering weight percentage (1-13 wt%), distance between needle and disk collector (2.1-3.9 mm), and applied voltage (14.5-17.5 kV). We optimized the parameters using electrical property measurements and the Kriging response surface method. Adding 13 wt% graphene significantly improved electrical conductivity, increasing from 17.7 μS/cm for pure PVDF to 187.5 μS/cm. The fiber diameter decreased from 21.4 μm in PVDF/1% graphene to 9.1 μm in PVDF/13% graphene. Adding 5 wt% graphene increased the β-phase content by 6.9%, reaching 65.4% compared to pure PVDF fibers. Material characteristics were investigated using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction analysis (XRD), contact angle measurements, and tensile testing. Optimal parameters included 3.47 wt% graphene, yielding 15.82 mV voltage at 5 Hz and 5 N force (2.04 times pure PVDF). Force testing showed a sensitivity (S) of 7.67493 log(mV/N). Fibers were attached to electrodes for piezoelectric sensor applications. Results affirmed enhanced electrical conductivity, piezoelectric performance, and mechanical strength. The optimized piezoelectric sensor could be applied to measure physiological signals, such as attaching it to the throat under different conditions to measure the output voltage. The force-to-voltage conversion facilitated subsequent analysis.

The goal is the optimal synthesis of the parameters of the control law for a nonlinear satellite attitude control system under constraints related to the saturation effect of reaction wheels. The maximum stability degree of the control system is considered as a criterion for the optimality of the parameters of the control law, and the absolute values of the control torques and angular velocities of the reaction wheels, achievable within the limits of the physical characteristics of the drives, are considered as constraints. The solution to the optimal synthesis problem is based on the transformation of the nonlinear control system model into a linear model, within the framework of the problem of global asymptotic stability of the control system is also guaranteed. The problem is decomposed into a subproblems of obtaining the optimal transient process shape in the control system and considering the constraints. A method of synthesizing the optimal transient process shape based on the criterion of maximum stability degree had been developed. An analytical method of determining the optimal values of the control law parameters, based on decomposing the normalized characteristic polynomial in the linear model into three identical factors had been proposed. A method with regards to the constraints of the control torque and angular speed of the reaction wheels, based on transitioning from relative to real time is developed. An algorithm for calculation of the transition scale from relative to real time, ensuring the fulfillment of all constraints in the optimal synthesis problem, is proposed. An example demonstrating the efficiency and simplicity of the proposed methods and algorithm for targeted selection of technical characteristics of the control system, considering the maximum absolute values of angular speeds and control torques of the reaction wheels, is provided.

The objective of this study was to develop guidelines for rainwater harvesting system sizing of shopping centres in South Africa. Three generalized dimensionless relationships relating rainwater supply and demand to tank size, yield and reliability were developed based on 101 years long daily time step simulations of rainwater harvesting of 19 shopping centres located in four regions. Daily rainfalls were obtained from nearby rainfall stations and daily non-potable demands were based on the size of the retail area. The simulations revealed within-year storage behaviour with considerable variation of the yield specified as the number of days for which demand was met each year. The Weibull plotting position formula was applied on the time series of yields to obtain yield-reliability relationships. Simulation results of the hydrologically optimum systems were used to develop two of the generalized relationships and an additional one based on the dependence of the slope of the reliability – yield plots on the optimum yield was formulated to enable analysis of hydrologically non-optimal systems. Most of the relationships fitted best to the non-linear power law form with high correlation coefficients averaging 0.92 and ranging from 0.82 to 1.00. The application of the models for tank sizing and assessing system performance is demonstrated.

This study explores optimizing Synthetic Aperture Radar (SAR) satellite constellation scheduling for multi-imaging missions in densely targeted areas using an in-house developed Modified Dynamic Programming (MDP) algorithm. By employing Mixed-Integer Linear Programming (MILP) to define the mission planning problem, the research aims to maximize observation of high-value targets within restricted planning horizons. Numerical simulations, covering a wide range of target numbers and satellite configurations, reveal the MDP algorithm’s superior mission allocation efficiency, enhanced success rates, and reduced revisit times, compared to the Greedy algorithm. The findings underscore the MDP algorithm’s improved operational efficiency and planning robustness for complex imaging tasks, demonstrating significant advancements over traditional approaches.

Abstract: Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the robustness of the model. Besides, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation.

In this paper we derive a unified perspective for Optimal Transport (OT) theory and Mean Field Control (MFC) theory to analyse the learning process for Neural Networks algorithms in a high-dimensional framework. We consider Mean Field Neural Networks in the context of MFC theory, specifically the mean field formulation of OT theory that allows the development of highly efficient algorithms while providing a powerful tool in the context of explainable Artificial Intelligence.

The human microbiome is an extremely complex ecosystem considering the amount of bacterial species, their interactions, and its variability over time. Here we untangle the complexity of the human microbiome for the Irritable Bowel Syndrome (IBS) that is the most prevalent functional gastrointestinal disorder in human populations. Based on a novel information theoretic network inference model we detect species interaction networks that are functionally and structurally different for healthy and unhealthy individuals. Healthy networks are characterized by a neutral symmetrical pattern of species interactions and scale-free topology versus random unhealthy networks. We detect an inverse scaling relationship between species total outgoing information flow, meaningful of node interactivity, and relative species abundance (RSA). The top ten interacting species are also the least relatively abundant for the healthy microbiome and the most detrimental. These findings support the idea about the diminishing role of network hubs and hubs should be defined considering the total outgoing information flow rather than the node degree. Macroecologically, the healthy microbiome is characterized by the highest total species diversity growth rate, the lowest species turnover, and the smallest variability of RSA for all species. This result challenges current views that posit a universal association between healthy states and the highest absolute species diversity in ecosystems. Additionally, we show how the transitory microbiome is unstable and microbiome criticality is not at the phase transition between healthy and unhealthy states. We stress out the importance of considering interacting pairs versus single node dynamics when characterizing the microbiome and of ranking these pairs in terms of their dynamics. Interactions (i.e. species collective behavior) shape transition from healthy to unhealthy states.\\ The macroecological characterization of the microbiome is useful for diagnostic purposes and disease etiognosis, while species-specific analyses can detect species that are more beneficial leading to personalized design of pre- and pro-biotic treatments and microbiome engineering.

It is well known that fiber sizing is one of the most important component in the manufacturing of composites, and it does affect the mechanical properties including strength and stiffness. In this work, the influence of fiber sizing levels on the mechanical properties of carbon fibers (CF) is reported at room temperature by using single fiber tensile testing (Favimat+), single fiber pullout testing (SFPO), and interfacial elemental analysis using X-ray photoelectron spectroscopy (XPS). Standard modulus of CF (7±0.2 μm in diameter) were sized using two Michelman commercially available sizing formulations. The average solid content for each sizing formulation is 26.3 ± 0.2% and 34.1 ± 0.2%, respectively. HEXION RIMR 135 with curing agent RIMH 137 was used as a model thermoset epoxy matrix during the SFPO measurements. A predictive engineering fiber sizing methodology is also developed. Sizing amounts of 0.5, 1, and 2 wt. % on the surface of the fiber is achieved for both sizing formulations. For each fiber sizing level, 50 single fiber tensile testing experiments and 20 single fiber pull-out tests were conducted. The ultimate tensile strength (σult) of the carbon fibers and the interfacial shear strength (τ_app) of the single fiber composite were analyzed. it is also investigated the effect of the sizing levels on the interfacial shear stress and the O/C (Oxygen/Carbon) surface composition ratio. As a result, generalized fiber sizing and characterization methods were established. The developed methods can be used to characterize the strength and interfacial shear strength of any man-made fibers with different sizing formulations and solid contents, irrespective of the matrix i.e. thermoset or thermoplastic.

Load variations in any power system result in losses escalation and voltage drops. With the sensible and optimal allocation of distributed generators (DGs), these problems could be considerably mitigated. It has been seen in the priorly existing methods that ideally allocation of DGs has been carried out during fixed loads and constant power requirements. However, in real scenarios the loads are always variable and allocation of DGs must be done in accordance to the variations of the connected load. Therefore, the current paper addressed the aforementioned problem by distinctive optimal allocation of DGs for each variability of 24hour load horizon. However, the single exclusive solution is considered among all allocations of 24 hours. The min-max regret concept has been utilized in order to deal with such methodology. Altogether, 24 scenarios are analyzed wherein each scenario corresponds to a specific hour of the respective day. The optimal allocation of DGs in terms of their optimal sizing and placement has been carried out by using three algorithms including battle royal optimization (BRO), accelerated particle swarm optimization (APSO) and genetic algorithm (GA). The multi-objective optimization problem is evaluated on the basis of minimum value criterion of the multi-objective index (MO). MO comprises of active and reactive power losses and voltage deviation. Hence, in order to find robustness of proposed technique, Conseil international des grands réseaux electriques (CIGRE’s) MV benchmark model incorporating 14 buses has been considerably used as a test network. In the end the results of three proposed algorithms have been compared.

This paper presents experimental identification and vibration suppression of a flexible manipulator with non-collocated piezoelectric actuators and strain sensors using optimal multi-poles placement control. To precisely identify the system model, a reduced order transfer function with relocated zeros is proposed, and a first-order inertia element is added to the model to compensate the non-collocation. Comparisons show the identified model match closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved. Based on the identified model, a full-state multi-poles placement controller is designed, and the optimal locations of the closed loop poles are determined. The feasibility of the proposed controller is validated by simulations. Moreover, the controller is tested for different locations of the closed loop poles, and an excellent performance of the optimal locations of the closed loop poles is shown. Finally, the effectiveness of the proposed controller is demonstrated by experiments. Results show that the vibrations of the expected modes are significantly diminished. Besides, vibrations of the higher modes are also slightly suppressed. Accordingly, multi-mode vibrations of the manipulator are well attenuated, and the tip displacement converges quickly with the proposed method.

Photothermal therapy (PTT) can overcome cancer treatment resistance by enhancing cell membrane permeability, facilitating drug accumulation, and promoting drug release within the tumor tissue. Iron oxide nanoparticles (IONPs) have emerged as effective agents for PTT due to their unique properties and biocompatibility. Approved for the treatment of anemia, as MRI contrast agents, and as magnetic hyperthermia mediators, IONPs also offer excellent light-to-heat conversion and can be manipulated using external magnetic field for targeted accumulation in specific tissues. Optimizing parameters such as laser wavelength, power density, shape, size, iron oxidation state, functionalization, and concentration is crucial for IONPs effectiveness. In addition to PTT, IONPs enhance other cancer treatment modalities. They improve tumor oxygenation enhancing the efficacy of radiotherapy and photodynamic therapy. IONPs can also trigger ferroptosis, a programmed cell death pathway mediated by iron-dependent lipid peroxidation. Their magneto-mechanical effect can exert mechanical force on cancer cells to destroy tumors, minimizing damage to healthy tissues. This review outlines strategies for managing photothermal performance and PTT efficiency with iron oxide nanoparticles, as well as synergies with other cancer therapies.

Forest resources are renewable,rational exploitation and utilization of forest resources are not only conducive to sustainable development of population, but also can obtain higher economic benefits. Based on actual timber harvesting problems,a combined harvesting model of timber and non-timber with nonlinear harvesting term was established.Firstly, using the qualitative theory of ordinary differential equations, the dynamic behavior of the model is studied, and the existence and stability of the equilibrium point of the model are proved .Secondly, the optimal control solution is obtained by using the optimal control theory.Finally, the optimal harvesting strategy of timber and non-timber products is given based on the numerical simulation results, which provides a certain theoretical basis for the sustainable development of forestry ecological economy.

Due to advancement in hacking/reverse engineering tools, threat against transfer of sensitive data or highly classified information is always at risk of being intercepted by an attacker. Covert communication outwit this malicious breach of privacy act better than cryptography as it camouflage secret information inside another innocent looking information, while cryptography shows scrambled information that might arouse attention of an attacker. However, the challenges in Steganography are the modification of carrier that causes some abnormalities, which is detectable and often the methods are not optimize. This paper presents an approach in Covert communication Chanel, which utilizes mathematical concept of combination to optimize time of transmission using sets of multiple transmitter’s, and receiver’s addresses where each abstractly represents a set of bits or characters combination without modifying the address. To minimize the number of physical address to be use, a combinatorial and permutation concept of virtual address generation from physical address is introduce. The paper in addition presents some technique like relationship and their application in both re-enforcing resistivity against Steganalysis and generating combinations. Furthermore, a concept of dynamical clockwise and anti-clockwise rotation of combination over addresses after every transmission is introduce to further improve on resistivity against Steganalysis. A simple test was performed for demonstrating relay address, combination and permutation concepts. Based on test results and analysis, the method is effective as expected and it is quite easy to use as it can be implemented in different platform without much difficulties.

In this paper we analyze and propose new method and algorithm of selecting the optimal labor time as a function of skills following our main references Mirrlees (1971), Saez (2001) and Stancheva (2014). The optimal labor time is a situation when the utility function of individual reaches a maximum. One of the main differences with Saez (2001) is our algorithm for creation the skill distribution

During public health events, Active Mobility (walking and cycling) plays an important role in securing residents' travel. To enhance the resilience of urban transportation, this article integrates the travel environment elements for walking and cycling and constructs a travel environment for Active Mobility evaluation index system. This article uses survey data from the main urban area of Harbin in the context of the COVID-19 pandemic in May 2022, and combines the Gradient Boosting Decision Tree (GBDT) and Impact-Asymmetry Analysis (IAA) methods to analyze the satisfaction of the travel environment for Active Mobility. It proposes strategies for optimizing the travel environment for Active Mobility, ensuring residents' travel during public health events, guiding them to adopt Active Mobility, and improving urban transportation resilience. This research shows that Active Mobility can enhance urban transportation resilience. In addition, there are differences in resident demand for walking and cycling environments in the travel environment for Active Mobility. Residents are more concerned about the comfort of the walking environment, while they prioritize the convenience of the cycling environment.

Autonomous navigation of spacecraft necessitates innovative technologies, methods, and algorithms, particularly when orbiting in proximity of other space objects. Optimization methods are useful for autonomous spacecraft navigation, guidance, and control, but their performance is hampered by noisy multi-sensor technologies and poorly modeled system equations, and real-time on-board utilization is generally computationally burdensome. Some proposed methods use noisy sensor data to learn the optimal guidance and control solutions real-time (online), where non-iterative instantiations are preferred to reduce computational burdens. This study aims to highlight efficacy and limitations of several common methods for optimizing guidance and control while proposing a few more, where all methods are applied to the full, nonlinear, coupled equations of motion including cross-products of motion from the transport theorem. Five disparate types of optimum guidance and control algorithms are presented and compared to a classical benchmark. Comparative analysis is based on tracking errors (both states and rates), fuel usage, and computational burden. Real-time optimalization with singular switching plus nonlinear transport theorem decoupling proves superior by matching open-loop solutions to the constrained optimization problem (in terms of state and rate errors and fuel usage), while robustness is validated in the utilization of mixed, noisy state and rate sensors and uniformly varying mass and mass moments of inertia. State tracking errors are reduced one-hundred ten percent. Rate tracking errors are reduced one-hundred thirteen percent. Control utilization (e.g., fuel) is reduced eighty four percent, while computational burden in reduced ten percent simultaneously.

Driven by mounting environmental apprehensions stemming from the over dependence on fossil fuels in energy and transportation sectors, extensive exploration into alternative energy sources, particularly biomass, has stimulated profound research on harnessing the potential of computational and mathematical techniques. This comprehensive survey delves into optimal models and strategies, unveiling their pivotal role in the design of biomass gasification processes.of hydrogen through biomass gasification. In this study, we aim to provide a comprehensive overview of the computational and mathematical techniques employed in optimizing biomass gasification processes, with a specific focus on enhancing hydrogen yield. Through an extensive literature review, various models and strategies will be examined, including thermodynamic analysis, kinetic modeling, reactor design, and process optimization. By uncovering the power of these techniques, we aim to contribute to the advancement of sustainable and efficient biomass utilization for the hydrogen-based future economy.This paper aims to provide an updated and comprehensive coverage of the investigations conducted on the potential of producing hydrogen from biomass through gasification. To achieve this, we incorporate the latest works that have utilized numerical modeling, simulation, optimization techniques, process heat integration, and co-generation in their studies. By encompassing these aspects, we aim to offer a broader and more in-depth re-appraisal of the subject matter. This will ensure that readers gain a holistic understanding of the advances made in the field and the potential for sustainable hydrogen production of biomass gasification.Through a meticulous re-appraisal and analysis of each subject, we can identify their respective strengths and areas that require further research effort. By thoroughly examining numerical modeling, simulation, optimization techniques, process heat integration, and co-generation, we can assess their effectiveness and applicability in the context of biomass gasification for hydrogen production. This analysis shed light on the areas where these techniques excel, as well as pinpoint limitations or gaps in current understanding. By identifying these areas, we can highlight the need for further research and development, enabling us to make significant advancements in biomass gasification processes and pave the way for a sustainable and efficient hydrogen-based future economy..

Efficient fixed crane position analysis is critical in precast buildings construction to maximize lifting efficiency for precast elements. The primary objective is to minimize the travel distances of fixed cranes, thereby enhancing operational efficiency and cost control. This involves strategically reducing the distances between the transport truck parking, where elements are stored, the fixed crane, and the construction installation point. Despite its significance, existing studies often overlook the dynamic movement efficiency of transport trucks, particularly when dealing with multiple transport trucks and various models of fixed cranes, rendering the opti-mization of this scenario a challenging NP-hard problem susceptible to combinatorial explosion. This study presents a novel mathematical model leveraging the Genetic Algorithm (GA). The model aims to solve optimal model and position of fixed cranes, along with identifying ideal positions for transport truck parking. The study's methodology is validated and demonstrated through a project case study. Comparative analyses between the proposed model and the original planning underscore a noteworthy 6% reduction in both duration and costs when lifting elements directly from transport trucks, as opposed to establishing an on-site stock yard. Furthermore, the developed GA exhibits a substantial improvement in final solutions, achieving a remarkable 30% reduction in both operational duration and costs. This research contributes valuable insights into fixed crane position analysis of precast building construction, providing foundation for enhanced operational efficiency and cost-effectiveness in the industry.

Motivated by South Africa's need in the transition to a net-zero economy; this study 10 investigates the integration of renewable energy sources (RES) into oil refineries, considering 11 therein unique challenges and opportunities. The research focuses on optimizing RES allocation 12 using Particle Swarm Optimization (PSO), a data-driven approach that adapts to real-time 13 operational conditions. Traditional energy management systems often struggle with the inherent 14variability of RES, leading to suboptimal energy distribution and increased emissions. Therefore, 15 this study proposes a PSO-based renewable energy allocation strategy specifically designed for oil 16 refineries. It considers factors like the levelized cost of energy, geographical location, and available 17 technology. The methodology involves formulating the optimization problem, developing a PSO 18 model, and implementing it in a simulated oil refinery environment. The results demonstrate 19 significant convergence of the PSO algorithm, leading to an optimal configuration for integrating 20 RES and achieving cost reductions and sustainability goals. The optimization result of 4,457,527.00 21 Rand achieved by iterations is much better than the result of 4,829,638.88 Rand acquired using 22Linear Programming as the baseline model. The mean cost, indicating consistent performance, has 23 remained at its original value of 4,457,527.00 Rand, highlighting the convergence. Key findings 24 include the average distance measure decreasing from 4.2 to 3.4, indicating particle convergence; 25 swarm diameter decreasing from 4.7 to 3.8, showing swarm concentration on promising solutions; 26 average velocity decreasing from 7.8 to 4.25, demonstrating refined particle movement; and the 27 optimum cost function achieved at 4,457,527 Rand with zero standard deviation, highlighting 28 stability and optimal solution identification. This research offers a valuable solution for oil 29 refineries seeking to integrate RES effectively, contributing to South Africa's transition to a 30sustainable energy future.

Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980s. On the other hand, deep learning methods have boosted the capacity of machine learning algorithms and are now being used for non-trivial applications in various applied domains. But training a fully-connected deep feed-forward network by gradient-descent backpropagation is slow and requires arbitrary choices regarding the number of hidden units and layers. In this paper, we propose near-optimal neural regression trees, intending to make it much faster than deep feed-forward networks and for which it is not essential to specify the number of hidden units in the hidden layers of the neural network in advance. The key idea is to construct a decision tree and then simulate the decision tree with a neural network. This work aims to build a mathematical formulation of neural trees and gain the complementary benefits of both sparse optimal decision trees and neural trees. We propose near-optimal sparse neural trees (NSNT) that is shown to be asymptotically consistent and robust in nature. Additionally, the proposed NSNT model obtain a fast rate of convergence which is near-optimal up to some logarithmic factor. We comprehensively benchmark the proposed method on a sample of 80 datasets (40 classification datasets and 40 regression datasets) from the UCI machine learning repository. We establish that the proposed method is likely to outperform the current state-of-the-art methods (random forest, XGBoost, optimal classification tree, and near-optimal nonlinear trees) for the majority of the datasets.

Upper confidence bound multi-armed bandit algorithms (UCB) typically rely on concentration in- equalities (such as Hoeffding’s inequality) for the creation of the upper confidence bound. Intu- itively, the tighter the bound is, the more likely the respective arm is or isn’t judged appropriately for selection. Hence we derive and utilise an optimal inequality. Usually the sample mean (and sometimes the sample variance) of previous rewards are the information which are used in the bounds which drive the algorithm, but intuitively the more infor- mation that taken from the previous rewards, the tighter the bound could be. Hence our inequality explicitly considers the values of each and every past reward into the upper bound expression which drives the method. We show how this UCB method fits into the broader scope of other information theoretic UCB algorithms, but unlike them is free from assumptions about the distribution of the data, We conclude by reporting some already established regret information, and give some numerical simulations to demonstrate the method’s effectiveness.

What are relevant levels of description when investigating human language? How are these levels connected to each other? Does one description yield smoothly into the next one such that different models lie naturally along a hierarchy containing each other? Or, instead, are there sharp transitions between one description and the next, such that to gain a little bit accuracy it is necessary to change our framework radically? Do different levels describe the same linguistic aspects with increasing (or decreasing) accuracy? Historically, answers to these questions were guided by intuition and resulted in subfields of study, from phonetics to syntax and semantics. Need for research at each level is acknowledged, but seldom are these different aspects brought together (with notable exceptions). Here we propose a methodology to inspect empirical corpora systematically, and to extract from them, blindly, relevant phenomenological scales and interactions between them. Our methodology is rigorously grounded in information theory, multi-objective optimization, and statistical physics. Salient levels of linguistic description are readily interpretable in terms of energies, entropies, phase transitions, or criticality. Our results suggest a critical point in the description of human language, indicating that several complementary models are simultaneously necessary (and unavoidable) to describe it.

Lap joints were friction stir welded in AA 7075-T6 at 1320 rpm and using two welding speeds: 70 mm/min and 120 mm/min. The temperature profiles of the weld zone were registered during the process; furthermore, the microhardness, tensile strength, and microstructure of the joints produced were examined. This study brings new insights into the thermomechanical behavior of the material during the process. The peak temperature obtained with the lower welding speed (70 mm/min) is 10% higher than that obtained with the higher speed (120 mm/min). Moreover, a 5% increase in microhardness and a 6% increase in tensile strength were were observed increasing the welding speed. Addition-ally, the microstructure examination demonstrated that, by decreasing the welding speed, the larger interaction between the tool and the material results in a deeper stir zone due to the increased heat diffusion downwards to the material. This heat flow affects the thermal profile and influences the resulting mechanical properties of the welded joints.

Type two diabetes mellitus (T2DM) represents a chronic condition with increasing prevalence worldwide among the older population. T2DM condition increases the risk of micro and macro-vascular complications as well as the risk of geriatric syndromes as falls, fractures and cognitive impairment. The management of T2DM in the older population represents a challenge for the cli-nician, and a Comprehensive Geriatric Assessment should always be prioritized, in order to tailor the glycate haemoglobin target according to functional and cognitive status comorbidities, life ex-pectancy and type of therapy. According to the most recent guidelines, older adults with T2DM should be cathegorized in three groups: healthy patients with good functional status, patients with complications and reduced functionality and patients at the end of life; for each group the target for the glycemic control is different, also according to the type of treatment drug. The therapeutic ap-proach should always begin with lifestyle changes; after that, several lines of therapies are available, with different mechanism of action and potential effect other than glucose level reduction. Partic-ular interest is growing around sodium-glucose cotransporter-2 inhibitors, due to their effect on the cardiovascular system. In this review we evaluate the therapeutic options available for the treat-ment of older diabetic patients, to ensure a correct treatment approach

Noise reduction is the major issue in the loudspeaker for the application of the musical instruments and related areas. In this paper, a noise disturbance control of a loudspeaker with optimal and robust controllers has been done successfully. The noise of the loudspeaker has been analyzed by simply track a reference cone displacement with the actual cone displacement. Static output feedback and H infinity optimal loop shaping controllers have been used to compare the actual and reference cone displacements by using a sine wave and random cone displacement signals and a promising results have been analyzed.

Currently, maritime traffic is increasing with economic growth in several regions worldwide. However, this growth in maritime traffic has led to increased risk of marine accidents. These accidents have a higher probability of occurring in regions where geographical features, such as islands, are present. Further, the positioning of rescue ships in a particular ocean region with a high level of maritime activity is critical for rescue operations. This paper proposes a method for determining an optimal set of locations for stationing rescue ships in an ocean region with numerous accident sites in the Wando islands of South Korea. The computational challenge in this problem is identified as the positioning of numerous islands of varying sizes located in the region. Thus, the proposed method combines a clustering-based optimization method and an image processing approach that incorporates flood filling to calculate the shortest distance between two points in the ocean that detours around the islands. Experimental results indicate that the proposed method reduces the distance from rescue ships and each accident site by 5.0 km compared to the original rescue ship locations. Thus, rescue time is reduced.

The excellent properties of carbon fibers, in combination with the high price of carbon fibers, makes it interesting to recycle these fibers. Pyrolysis is already an established recycling method to recover the carbon fibers, but in this process also the sizing is removed which could result in lower mechanical properties because of bad adhesion between the fiber and matrix. In this study, recycled carbon nonwoven reinforcements made from pyrolyzed carbon fibers were pre-treated in order to improve the mechanical properties of recycled carbon polyamide and recycled carbon polypropylene composites. The pre-treatment involves applying a to the matrix adapted primer coating to the nonwoven by spraying. Via compression molding, polyamide and polypropylene composites were prepared with and without pretreatment. The tensile and interlaminar shear strength of these composites were measured to determine the effect of the pretreatment. Results revealed that both strengths could be increased by pretreatment of the carbon nonwoven and the increase was largest for the PP composites.

Introduction: The COVID-19 pandemic highlighted the necessity for rapid, efficient testing at large-scale events. Developing rapid and efficient testing methods was critical, especially for events with a high risk of virus spread. The queueing theory offers a powerful tool to manage the flow of people through testing centers. This study uses the M/M/c model to examine how different floor plan configurations impact the efficiency of RADT testing centers. Aim of the study: This research applies queueing theory to optimize the Rapid Antigen Diagnostic Test (RADT) processes at mass gatherings. The study uses the M/M/c queueing model to evaluate the dynamics of RADT centers configured in U-shaped and straight-line layouts. The primary goal is to assess the feasibility of using RADTs efficiently at events and mass gathering entrances, aiming to enhance throughput and minimize wait times. Methodology: The study involved 500 healthy participants, managed by medical staff across a U-shaped and a straight floor plans. Agile management techniques were employed to enhance operational efficiency. The process stages included queue number issuance, registration, sample collection, sample mixing, and results dissemination. Results: Both floor plans enabled the completion of RADT screening in approximately 2 minutes per participant. The U-shaped layout slightly outperformed the straight-line setup in efficiency, demonstrating its effectiveness in optimizing the testing process. Conclusion: This study confirms the feasibility of implementing Rapid Antigen Diagnostic Test (RADT) processes at mass gatherings. It was found that the U-shaped floor plan configuration is particularly effective, indicating its potential suitability for future mass testing scenarios.

Ridesharing effectively tackles urban mobility challenges by providing a service comparable to private vehicles while minimising resource usage. Our research primarily concentrates on dynamic ridesharing, which conventionally involves connecting drivers with passengers in need of transportation. The process of one-to-one matching presents a complex challenge, particularly when addressing it on a large scale, as the substantial number of potential matches make the attainment of a global optimum a challenging endeavour. This paper aims to address the absence of an optimal approach for dynamic ridesharing by refraining from conventional heuristic-based methods, commonly used to achieve timely solutions in large-scale ride-matching. We propose a novel approach that provides snapshot optimal solutions for various forms of one-to-one matching, ensuring they are generated within an acceptable timeframe for service providers. Additionally, we introduce and solve a new variant where the system itself provides the vehicles. The efficacy of our methodology is substantiated through experiments carried out with real-world data extracted from the openly available New York City taxicab dataset.

Pharmacogenomics (PGx) is the study of how variations of the patient genetic makeup could influence the selection and dosing of medications, including urate-lowering therapies (ULTs). Patients with gout present with several comorbidities, warranting the use of several long-term medications that increase their pill burden and the risk of adverse drug events. PGx testing can identify individuals who are more or less likely to benefit from a given treatment and ultimately improve adherence and reduce polypharmacy. Gout management involves using various ULTs associated with specific genetic variations that could modulate medication safety and efficacy. Individuals carrying the HLA-B*58:01 variant are at a higher risk of serious and life-threatening skin reactions from taking allopurinol. Besides, racial disparities in HLA-B distribution warrant testing for its status in high-risk populations, specifically some Asian subgroups and African Americans. G6PD-deficient individuals are prone to develop hemolytic anemia and methemoglobinemia with pegloticase and probenecid use. Patients with the less active form of the drug-metabolizing CYP2C9 are at higher risk for NSAID-related upper gastrointestinal (GI) bleeding. The drug-gene pair interactions among various gout therapies were also reviewed. Allopurinol-related genes included AOX, ABCG2, and SLC22A12. Febuxostat-related genes included UGT1A1. Benzbromarone- and probenecid- related genes included SLC22A12 and ABCB1. Colchicine-related genes included CYP2D6, ABCB1, rs6916345 G>A, and SEPHS1. Finally, the corticosteroid-related gene included HCG22; and the anakinra-related gene included IL1RA. This review synthesizes the most contemporary level of evidence for using PGx in gout pharmacotherapy, as referenced in the CPIC guidelines, FDA, PharmGKB, and current literature.

Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other swimmers. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and even the number of the leading swimmers using surface only information.

This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangement. By employing a multi-criteria decision algorithm, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare, installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in Matlab, with results validated through simulations in Dialux. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

This paper describes the optimal design of a miniature fiber-optic linear displacement sensor. It is characterized by its ability to measure displacements along a millimetric range with sub-micrometric resolution. The sensor consists of a triangular reflective grating and two fiber-optic probes. The measurement principle of the sensor is presented. The design of the sensor's triangular grating has been geometrically optimized by considering the step angle of the grating to enhance the sensor's resolution. The optimization method revealed a global optimum at which the highest resolution is obtained

Since its introduction almost 50 years ago, the concept of ‘flow’ has been descriptive of optimal experiences, also in relation to play. However, the explorative nature of play leads to some discrepancies between flow and the optimal experience of play. In this paper the differences between flow and play are explored, leading to proposing the state of ‘wonder’ (directed at exploration) as an alternative to ‘flow’. From this perspective, the study further explores how we may design toys that enable meaningful experiences of play, identifying opportunities in designing for toys as the enablers of immersed experiences of wonder.

This paper presents findings of a study on continuous feed-in management and continuous synthetic inertia contribution with wind turbines. A realistic case study, based on real measurements, is outlined. A wind turbine feeds into a weak feeder, such that its power has to be adapted to the permissible loading of this feeder. At the same time the wind turbine is to provide inertia to the grid by applying the previously published variable inertia constant controller. It is discussed that optimal grid utilisation and simultaneous inertia contribution are mandatory for the frequency control in power systems that are heavily penetrated with renewable energies. The study shows that continuous feed-in management can be combined well with continuous inertia provision. There are hardly any negative consequences for the wind turbine. The benefits for the grid are convincing, both in terms of increased system utilisation and in terms of provided inertia. It is concluded that wind turbines can enhance angular stability in a power system to a larger extent than conventional power plants.

Wind turbines (WTs) are clean a renewable energy source that has been growing in popularity in recent years. Gearboxes are complex, expensive, and critical components of WT, which are subject to high maintenance costs and several stresses including high loads and harsh environments that can lead to failure with significant downtime and financial losses. This paper focuses on the development of a digital twin-based approach for the modelling and simulation of WT gearboxes with the aim to improve their design, diagnosis, operation, and maintenance by providing insights into their behavior under different operating conditions. Pow-erful commercial computer-aided design tools (CAD) and computer-aided engineering (CAE) software are embedded into a computationally efficient multi-objective optimization framework (modeFrontier) with the aim of maximizing the power density, compactness, performance, and reliability of the WT gearbox. This entails minimizing its weight, volume, and maximum stresses and strains achieved without compromising efficiency. The 3D CAD model of the WT gearbox is carried out using SolidWorks, the Finite Element Analysis (FEA) to obtain the stresses and strains fields are modelled using Ansys Workbench, while the multibody kinematic and dynamic system is analyzed using MSC Adams. The method has been successfully applied to different case studies to find the optimal design and analyze the performance of the WT gearboxes. Simulation results can be used to identify potential failure modes and optimize gearbox design to improve performance, extend service life and relia-bility, thereby ensuring proper operation over its lifetime and reducing maintenance costs.

Accurate and timely prediction of Napa cabbage (Brissica rapa subsp. Perkinensis) fresh weight is crucial for optimizing harvest timing, crop management, and supply chain logistics, contributing to food security and price stabilization. Traditional manual sampling methods are labor-intensive and imprecise. This study addresses this challenge by developing a comprehensive (artificial intelligence) AI-powered model for predicting Napa cabbage fresh weight using unmanned aerial vehicle (UAV)-based multi-sensor data. High-resolution RGB, multispectral, and thermal infrared (TIR) imagery were collected over a Napa cabbage field throughout the 2020 growing season. Various vegetation indices, crop features (vegetation fraction, crop height model), and water stress indi-cators (CWSI) were extracted from the imagery. Three AI algorithms—deep neural network (DNN), support vector machine (SVM), and random forest (RF)—were trained and evaluated, with the DNN model consistently outperforming the others. The DNN model achieved the highest accuracy (R² = 0.86 for training, 0.82 for testing; root mean square error (RMSE) = 0.432 kg for training, 0.465 kg for testing) during the mid-to-late rosette growth stage (DAP 35-42), highlighting this period as crucial for fresh weight estimation due to stable leaf area and well-developed canopy structure. The model tended to underestimate the weight of Napa cabbages exceeding 5 kg, potentially due to limited samples and saturation effects of vegetation indices. However, the overall error rate was less than 5%, demonstrating the feasibility and effectiveness of this approach. Spatial analysis revealed that the model accurately captured the variability in Napa cabbage growth across different soil types and irrigation conditions, particularly reflecting the positive impact of drip irrigation on the sandy loam plot. Bias analysis indicated the DNN model's tendency to overestimate smaller Napa cabbages (2 kg), suggesting areas for future refinement. This study demonstrates the potential of UAV-based multi-sensor data and AI algorithms for accurate and non-invasive prediction of Napa cabbage fresh weight. The developed DNN model offers a promising tool for optimizing harvest timing, improving crop management practices, and en-hancing supply chain efficiency. Future research should focus on refining the model for specific weight ranges and diverse environmental conditions, as well as extending its application to other crops, to further advance precision agriculture and contribute to sustainable food production.

Unmanned Aerial Vehicles (UAVs) in particular multirotors are becoming the {\it de facto} tool for aerial sensing and remote inspection. In large industrial facilities, a UAV can transmit an online video stream to inspect difficult to access structures, such chimneys, deposits and towers. However, the communication range is limited, constraining the UAV operation range. This limitation can be overcome with relaying UAVs placed between the source UAV and the control station, creating a line of communication links. In this work we assume the use of a digital data packet network technology, namely WiFi, and tackle the problem of defining the exact placement for the relaying UAVs that creates an end-to-end channel with maximal delivery of data packets. We consider asymmetric communication links and we show an increase as large as $15$\% in end-to-end packet delivery ratio when compared to an equidistant placement. We also discuss the deployment of such a network and propose a fully distributed method that converges to the global optimal relay positions taking, on average, 1.4 the time taken by a centralized method.

The Standard Genetic Code (SGC) exists in every organism known on Earth. SGC evolution via early unique codon assignment, then later wobble, yields coding resembling the near-universal code. Below, later wobble also creates an optimal route to accurate codon assignment. This assignment time matches a previous mean time for ordered codes, exhibiting ≥ 90% of SGC order. Accurate evolution is also accessible, sufficiently frequent to appear in populations of 10³ to 10⁴ codes. SGC-like coding capacity, code order and assignments therefore arise together, in one attainable evolutionary intermediate. Examples, which plausibly resemble coding at evolutionary domain separation, are characterized.

For the time being, public sector innovation gains new and complex forms of expression: managerial, institutional, technological or communication. This fact is also due to national and international important bodies’ interest for using innovation as resource and tool for public sector development. Characterised by complexity and adaptation, the innovative processes in the public sector embrace the form of medium and long term innovation strategies, holding high key socio-economic impact on the social utility of public sector innovation. The optimality of innovation strategies becomes a tool for improved decisions in public sector management, providing the methodology for their evaluation related to the objectives of development in the public sector.

Pencil-beam Doppler scatterometers are a promising remote sensing tool for measuring ocean vector winds and currents from space. While several point designs exist in the literature, these designs have been constrained by the hardware they inherited, and the design is sub-optimal. Here, I present guidelines to optimize the design of these instruments starting from the basic sensitivity equations. Unlike conventional scatterometers or pencil-beam imagers, appropriate sampling of the Doppler spectrum and optimizing the radial velocity error lead naturally to a design that incorporates a pulse-to-pulse separation and pulse length that vary with scan angle. Including this variation can improve radial velocity performance significantly and the optimal selection of system timing and bandwidth is derived. Following this, optimization of the performance based on frequency, incidence angle, antenna length, and spatial sampling strategy are considered. It is shown that antenna length influences the performance most strongly, while the errors depend only on the square root of the transmit transmit power. Finally, a set of example designs and associated performance are presented.

In this paper we present the numerical and experimental results of a design optimization of electromagnetic cantilevers. In particular, a cost-effective technique of evolutionary computing enabling the simultaneous minimization of multiple criteria is applied. A set of optimal solutions are subsequently fabricated and measured. The designed structures are fabricated in arrays, which makes the comparison and measurements of the sensor properties reliable. The microfabrication process, based on the silicon on insulator (SOI) technology, is proposed in order to minimize parasitic phenomena and enable efficient electromagnetic actuation. Measurements on the fabricated prototypes assessed the proposed methodological approach.

This study develops a progressive optimal fault-tolerant control method based on insufficient fault information. By combining passive and active fault-tolerant control manners during the process of fault diagnosis, insufficient fault information is fully used, and optimal fault-tolerant control effect is achieved. In addition, the fault-tolerant control method based on guaranteed robust cost control is introduced. The proposed progressive optimal fault-tolerant control method considers two aspects. First, as the amount of fault information continually increases, the performance index of the progressive optimal fault-tolerant controller improves. Second, at each moment, based on the corresponding insufficient fault information and prior knowledge, optimal fault-tolerant control is achieved according to current fault information. The process of progressive optimal fault-tolerant control converges to active fault-tolerant control when the fault is completely identified and the optimal fault-tolerant controller is no longer reconfigured until no more useful fault information can be provided. Furthermore, a progressive optimal fault-tolerant control algorithm based on the grid segmentation in the parameter uncertainty domain and the selection of different auxiliary center points is introduced. The simulation results verify the feasibility of the proposed algorithm and the validity of the proposed theory.

The spread of high-performance personal computers, frequently equipped with powerful Graphic Processing Units (GPU), raised the interest on a set of techniques able to extract models of electromagnetic phenomena (and devices) directly from available examples of the desired behavior. Such approaches are collectively referred to as Machine Learning (ML). A typical representative ML approach is the so called “Neural Network” (NN). Using such data-driven models allows evaluating the output in a much shorter time when a theoretical model is available, or allows predicting the behavior of the systems and devices when no theoretical model is available. With reference to a simple yet representative benchmark electromagnetic problem, some of the possibilities and the pitfalls of the use of NN for the interpretation of measurements (inverse problem) or to obtain the required measurements (optimal design problem) are discussed. The investigated aspects include the choice of the NN model; the generation of the dataset(s); the selection of hyperparameters (hidden layers, training paradigm). Finally, the capabilities in the handling of ill-posed problems are critically revised.

Malaria is a critical fevered illness caused by Plasmodium parasites transmitted among people through the bites of infected female Anopheles mosquitoes. Public awareness about the disease is important for the control of disease. This article proposes a mathematical model to study the dynamics of malaria disease transmission with the influence of awareness-based control interventions. We found two equilibria of the model, namely the disease-free and endemic equilibrium. Disease-free equilibrium is stable globally if basic reproduction number (R0) is less than unity (R0<1). Some basic mathematical properties of the proposed model, such as nonnegativity and boundedness of solutions, the feasibility of the equilibrium points and their stability properties, have been studied. Finally, we adopted optimal control to minimize the cost of disease control and solve the problem by formulating Hamiltonian functional. The optimal use of insecticides for controlling the mosquito population is determined. Numerical simulations have been provided for the confirmation of the analytical results. We conclude that media awareness with optimal control approach is best for cost-effective malaria disease management.

A very high parking occupancy can negatively influence the traffic performance of an area by causing very long cruising times. A very low parking occupancy, on the other hand, is inefficient from a space utilization perspective. Thus, this paper proposes a framework to compute the optimal parking occupancy rate over a given time horizon based on a macroscopic traffic and parking model. This rate is set high enough to ensure an efficient usage of the parking infrastructure. However, it should also guarantee a high likelihood of finding parking in order to eliminate the drivers’ time wasted in cruising for parking and the added congestion it causes. The model outputs are based on small data collection efforts and low computational costs, and they can be generated without complex simulation software using a simple numerical solver. Multiple vehicle types are included into our methodology allowing us to generate insights about the optimal parking occupancy with or without differentiated parking (i.e., parking for specific vehicles, such as fuel and electric vehicles). In times of a modal shift towards electric vehicles, cities can use our model to evaluate how much parking supply (with battery charging opportunities) they would like to dedicate to electric vehicles in order to achieve optimal traffic and parking results, and whether a differentiated or semi-differentiated parking policy is desirable. We illustrate our framework in a case study of a central area within the city of Zurich, Switzerland, showing the traffic and parking impacts (e.g., average searching time for parking, total revenue created by parking fees, optimal parking occupancy rate) for different proportions of fuel and electric vehicles in the parking demand and/or supply. Our results confirm that optimal occupancy rates are between and for most realistic scenarios. We then discuss how these rates might change depending on various demand and supply relationships, and according to different parking policies. We show that equal proportions between electric vehicles in the demand and their parking spaces in the supply lead to the best traffic performance in the area. We also provide the tools for cities to analyze their loss in performance if they do not react, e.g., to an increasing demand for electric vehicles over time. Moreover, we illustrate how some of these risks can be mitigated by having more flexible parking policies, e.g., allowing electric vehicles to use parking spaces for fuel vehicles.

Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists need support from automated tools for precise opinion. Automated detection of the affected lungs nodule is difficult because of the shape similarity among healthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer. In this article, we propose a framework to precisely detect lungs cancer by classifying it between benign and malignant nodules. The framework is tested using the subset of the publicly available at the Lung Image Database Consortium image collection (LIDC-IDRI). Multiple techniques including filtering and noise removing are applied for pre-processing. Subsequently, the OTSU and the semantic segmentation are used to accurately detect the unhealthy lungs nodules. In total, 13 nodules features were extracted using Principal Components Analysis (PCA) algorithm. Four optimal features are selected based on the classification performance. In the classification phase, 9 different classifiers are used along with two types of validation schemes i.e. train test holdout validation with 70-30 data split and 10 fold cross-validation. Our experiments show that the proposed system provides 99.23\% accuracy using logic boost classifier.

Maritime data not only identify financial resources that can significantly enhance the efficiency of shipping companies but also play a pivotal role in promoting eco-friendly and sustainable shipping practices. However, active corporate strategies are required to include maritime data essential for the commercial operation of autonomous ships in the form of corporate assets by transparent evaluating the data. The aim of this study is to review previous studies on da-ta-valuation models, identify a valuation model suitable for maritime business, consider the characteristics of maritime data. The analytic hierarchy process (AHP) method is used to derive priorities for valuing maritime data assets, with emphasis placed on the characteristics of mari-time data, followed by the character of the future-oriented maritime-data market and the da-ta-valuation model. The results obtained that the most important factors in selecting a maritime data-valuation model by analyzing the AHP method were identified as the characteristics of the maritime data(M1). The next most important factors analyzed were the features of the mari-time-data market(M2) and the features of the maritime data-valuation model(M3) in order. In addition, the characteristics of maritime data were preferentially considered among the main criteria, and the market approach (A2) was selected as the most optimal valuation model. The potential impact of this implementation could contribute to the establishment of an intelligent technology market by estimating the value of data and developing a platform for maritime data trading, allowing for more efficient data sharing and utilization by maritime autonomous sur-face ship(MASS).

Ethanolic extracts of propolis collected from Apis mellifera hives have been shown to reduce Nosema ceranae infection in honey bee workers. This study aimed to determine the propolis concentration that optimizes the control of N. ceranae infection in honey bee workers. Newly emerged worker bees were individually fed 2 μL of 50% sucrose solution (w/v) containing 105 N. ceranae spores per bee, and treated with 0%, 25%, 50%, and 75% ethanolic extracted propolis in 50% sucrose solution (v/v). The treated bees were maintained in cages at 34 ± 2˚C and 55 ± 5% RH throughout the study. The number of dead bees was counted and removed from the cages daily for 30 days to generate survival curves. The number of N. ceranae spores per bee (infectivity) was quantified using a hemocytometer, and the number of infected bees per hundred bees (infection rate) was determined. The survival rates of N. ceranae infected bees fed 25%, 50%, and 75% propolis (NP1, NP2, NP3, respectively) were significantly higher than those of infected bees not fed propolis (NO). Furthermore, the infectivity and infection rates of NP2 and NP3 groups were significantly lower than those of the NO group. The infectivity and infection rates of the NP1 group were not different than those of the NO group. Our data suggest that 50% and 75% propolis extracts are appropriate concentrations for treating N. ceranae infection in honey bees. Ethanolic extracted propolis can be an alternative treatment for N. ceranae infection in Western honey bees.

Our study essentially concerns the dynamic behavior of an SIRS epidemic model in discrete time. Two equilibrium points are obtained; one is disease-free while the other is endemic. We are interested in the endemic fixed point as well as its asymptotic stability. Depending on the parameters which are estimated using the data from US Department of health and SIRS modelling with optimization, two Flip and Transcritical bifurcations appear. We illustrate their diagrams, as well as their bifurcation curves using the method of Carcasses \cite{carcasses1993determination,carcasses1995singularities} for the Flip bifurcation and by an implicit function deduced from such an equation for the Transcritical bifurcation. We use the scanning of the parametric plane to have a global view of the behavior of the model and to highlight the zones of stability of the existing singularities. A superposition of the bifurcation curves with the parametric plane can show the overlap of the curves with the boundaries of the stability domains, which confirms the smooth running of the simulation and its correspondence with the theory, we finish this article with constrained optimal control applied to infection rate and recruitment rate for an SIRS discrete epidemic model. Pontryagin's maximum principle is used to determine these optimal controls. Finally using COVID-19 data in the USA, we obtain results that demonstrate the effectiveness of the proposed control strategy to mitigate the spread of the pandemic.

We evolve our past models PYR, Loukeris, et al. (2016) to provide a competitive system whose infiltration of categorical information and fundamentals into advanced higher moments, supports a more objective Portfolio Selection, aided by Intelligent Computing. The system of Portfolia Yield Reactives (PYR) searches for hidden prototypes into big data of accounting and financial statements, restricting malicious patterns such as hoax, noise, manipulation, incorporated to a novel optimal portfolio selection method.

Sleep is an essential component of a healthy lifespan, influencing both physical and mental well-being along with good quality of life. Adequate sleep quality and quantity are associated with a host of health advantages, including enhanced immune function, learning and memory, emotional well-being, and cardiovascular health. Chronic sleep deprivation, or poor sleep quality, is associated with a variety of negative health outcomes, including an increased risk of obesity, diabetes, hypertension, depression, cancer, and even premature death. One compelling argument in favor of sleep as a predictor of a healthy lifespan is its effect on cellular repair and regeneration. During sleep, the body goes through important activities such as tissue repair, muscular growth, and memory consolidation. Sleep also helps the brain eliminate toxins and metabolic waste, which contributes to its optimal functioning. Without adequate sleep, these important processes are jeopardized, resulting in accelerated aging and greater susceptibility to chronic illnesses. Furthermore, sleep is essential for maintaining hormone balance, particularly those involved in appetite regulation and metabolism. Sleep disturbances can disrupt the regulation of these hormones, resulting in weight gain, metabolic diseases, and, ultimately, a shorter life span. Finally, prioritizing optimal sleep is pivotal for increasing longevity and general health. Emphasizing the importance of sleep hygiene and treating sleep-related dysfunctions should be part of the modern approach to extending healthy life.

A mathematical model that incorporates human attacks on dogs is presented to explore the dynamics of rabies transmission. The model divided the infection rate into two categories: dog-to-dog transmission rates during the prodromal phase (βdP) and the furious phase (βdF). It has been determined that the model is well-posed and that all of the solutions are positive, as well as the feasibility and positivity of the model's solutions. Both the basic reproduction number (R0) and the effective reproductive number (Re) are computed, and it is demonstrated that the model has a unique disease-free equilibrium that is globally asymptotically stable whenever Re < 1. To identify the model's most sensitive parameters and the ones that intervention efforts should focus on, sensitivity analysis of the effective reproduction number is carried out. We can construct an optimal control system using the state equations, the adjoint equations, and the optimality condition that determines the controls by applying Pontryagin's Maximum/Minimum principle. To help decision-makers in allocating funds for rabies interventions, cost-effectiveness analyses are conducted. To determine the degree to which the intervention strategies are advantageous and cost-effective, this study performs a cost-effective analysis of one or all possible combinations of the optimal rabies control strategies (pre-exposed and post-exposed vaccination for both dog and human populations) for the four different transmission settings. After arranging the techniques to increase effectiveness (total infections prevented), a cost-effective analysis using the Incremental Cost Effectiveness Ratio (ICER) was conducted. The result supports the function that the four intervention options are performing to either completely eradicate or significantly reduce rabies disease among the populations. The dynamical behavior of the system is studied through simulations using the ode45 numerical technique from MATLAB.

In view of the floating rectangular aerostatic guideway problem of low load capacity and stiffness, this paper puts forward a kind of closed rectangular aerostatic guideway and its optimized design. Firstly, the analytic calculation formula of a closed rectangular aerostatic guideway was deduced. The use of MATLAB software for the numerical simulation and the parameter influence analyses was carried out in terms of the gas supply pressure on load capacity and stiffness. A simulation model of the rectangular air bearing was also built using the Fluent software, and the model was used for simulation calculations. Secondly, the static performance of the rectangular air bearing was analyzed by analyzing the distribution position and number of orifices on its load capacity and stiffness. On this basis, the response surface optimization design method is used to analyze the variation law of the load capacity and stiffness of the air bearing under different restrictor structural parameters. Finally, the optimal design parameters for the rectangular air bearing are obtained. The simulation results show that the optimized rectangular air bearing designed in this paper has a good load capacity and stiffness of 644.58N and 63.405N N∙μm^(-1).

A proxy war, between a coalition of countries, BLUE and a country, RED, is considered. RED wants to increase the size of the RED territory. BLUE wants to involve more regions in trade and other types of cooperation. GREEN is a small and independent nation that wants to become a member of BLUE. RED attacks GREEN and tries to invade. BLUE decides to give optimal arms support to GREEN. This support can help GREEN in the war against RED and simultaneously reduce the military power of RED, which is valuable to BLUE, also outside this proxy war, since RED may confront BLUE also in other regions. The optimal control problem of dynamic arms support, from the BLUE perspective, is defined in general form. The objective function is a weighted sum of the present value of the free GREEN territory and the present value to BLUE of the net loss of military resources in the RED army. The net loss of RED at a particular point in time is a function of the location of the front line and the size of the mobile GREEN forces behind the RED line. First, it is assumed that the expected net loss is proportional to the force ratio behind the red front line. It is proved that the net loss function is a strictly concave quadratic function of x, the location of the front line. It is also proved that the unique maximum of the net loss function occurs at the same front location, also if the net loss function is proportional to the strength ratio behind the RED lines, raised to any strictly positive exponent. From the BLUE perspective, there is an optimal position of the front. This position is a function of the weights in the objective function and all other parameters. Optimal control theory is used to determine the optimal dynamic BLUE strategy, conditional on a RED strategy, which is observed by BLUE military intelligence. The optimal arms support strategy for BLUE is to initially send a large volume of arms support to GREEN, to rapidly move the front to the optimal position. Then, the support should be almost constant during most of the war, keeping the war front location stationary. In the final part of the conflict, when RED will have almost no military resources left and tries to retire from the GREEN territory, BLUE should strongly increase the arms support and make sure that GREEN rapidly can regain the complete territory and end the war

Fuel consumption, subsequent emissions and safe operation of class 8 vehicles are of prime importance in recent days. It is imperative that the vehicle operates in its true optimal operating region given a variety of constraints such as road grade, load, gear shifts, Battery State of charge (for hybrid vehicles), etc. In this paper a research study is conducted to evaluate the fuel economy and subsequent emission benefits when applying predictive control to a mild hybrid line haul truck. The problem is solved using a combination of dynamic programming with back tracking and model predictive control. The specific fuel saving features that are studied in this work are dynamic cruise control, gear shifts, vehicle coasting and torque management. These features are evaluated predictively as compared to a reactive behavior. The predictive behavior of these features are a function of road grade. The result and analysis shows significant improvement in fuel savings along with NOx benefits. Out of the control features dynamic cruise (predictive) control and dynamic coasting showed the most benefits while predictive gear shifts and torque management (by power splitting between battery and engine) for this architecture did not show fuel benefits but provided other benefits in terms of powertrain efficiency.

: Solar sails use radiation from the sun to generate thrust without any fuel or propellant. Since this is a form of propulsion that has theoretically infinite use, we would like to test its capability on long-term missions by simulating a spacecraft equipped with solar sails to the Sun-Earth L5 Lagrange point. To control the sail angle, which is the main form of control we have over the sail’s performance, we will devise a form of optimal control based on Pontryagin’s Minimum Principle. Simulating the dynamics in MATLAB SIMULINK, we find that such a control method relies on iterating over initial conditions for the co-states to find the necessary parameters for the trajectory to reach the desired point. Therefore, an autonomous control scheme that uses this form of optimal control will need a way to numerically find said initial conditions in order to find the control angle needed at any point in time, which may be computationally intensive.

This research aims to analyse the sustainability of retirement system and relevant optimal path to achieve sustainable post-retirement livings in Malaysia context. Focus of this study is put on Employees Provident Fund (EPF) which is mainly constituted by employees from private sector. EPF is a fully funded defined-contribution by both employees and employers. In this research, main variables used are contribution rate and retirement age based on relevant indexation of pension. For a sustainable retirement living, retirement benefits are expected to be in a maximisation function. Attempts to find sustainable retirement fund are via step-by-step mathematical calculation using formulas in Microsoft Excel and verification of the result with Excel Solver via Generalised Reduced Gradient (GRG) algorithm. Eventually, two-third retirement decision model is used to justify the sustainability of retirement fund other than justification using monthly household expenditure.