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Pyrogallol molecules were successfully immobilized onto aminopropyl molecule functionalized MCM41 nanoparticles to obtain a fast and high gold adsorption capacity. The Taguchi statistical method was used to determine the factors affecting the gold(iii) adsorption efficiency. The effect of six factors, pH, rate, adsorbent mass, temperature, initial Au(iii) concentration and time, each with 5 levels, on the adsorption capacity was investigated by forming an L25 orthogonal. The analysis of variance (ANOVA) of each factor showed that all factors had significant effects on adsorption. pH 5, 250 rpm stirring speed, 0.025 g adsorbent mass, 40 °C temperature, 600 mg L−1 Au(iii) concentration and 15 min time were determined to be the optimum adsorption conditions. The maximum Langmuir monolayer adsorption capacity of APMCM1-Py for Au(iii) was calculated to be 168.54 mg g−1 at 303 K. The adsorption mechanism fits the pseudo-second-order kinetic model assuming the formation of a single chemical adsorption layer on the adsorbent surface. The adsorption isotherms are best represented using the Langmuir isotherm model. It exhibits a spontaneous endothermic behavior. FTIR, SEM, EDX and XRD analyses showed that mostly phenolic -OH functional groups adsorb Au(iii) ions on the APMCMC41-Py surface with their reducing character. These results enable the rapid recovery of gold ions from weakly acidic aqueous solutions by reduction of APMCM41-Py NPs. © 2023 The Royal Society of Chemistry.

In order to effectively extract the feature points of mobile network animation images and accurately reflect the main content of the video, an optimization method to extract the feature points of key frame images of mobile network animation is proposed. Firstly, the key frames are selected according to the content change degree of the animation video. The scale invariant feature transformation algorithm is used to describe the feature points of the key frame image of the animation video. The local feature points of the image are estimated by the constraint optimization method to realize the optimization extraction of the feature points of the key frame image of the mobile network animation. The efficiency of feature points extraction is analyzed from the number and effectiveness of feature points extraction, time-consuming and similarity invariance of feature points. The experimental results show that the proposed method has excellent adaptability, and can effectively extract feature points of mobile network animation image.

Underwater wireless sensor networks (UWSNs) are utilized for a wide range of monitoring and surveillance applications. Lifetime maximization and maintenance of network reliability are among the most important considerations in the deployment of UWSNs. k-connectivity is a robust approach for reinforcing reliability. However, maintaining k disjoint paths from each sensor node to the BS, inevitably, results in extra energy dissipation, which reduces the network lifetime (NLT). Yet, there is no systematic exploration to determine the extent of lifetime reduction due to the increase in the k value, in the literature, to the best of our knowledge. In this study, we create an optimization framework to be able to explore the tradeoff between NLT and k -connectivity-based reliability in UWSNs. Through the optimal solutions of the proposed optimization model for a large set of salient parameters, we characterize the interplay between lifetime and k -connectivity. Our analysis reveals that the k value to be maintained in a UWSN can affect the NLT significantly. © 2014 IEEE., Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 113E470, This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) 3001 under Project 113E470.

Academic studies on the Automatic Voltage Regulator (AVR) have focused on a linear mathematical model which lacks protective features. A more accurate model of the AVR system includes protective features as described in IEEE standards. The AVR models without protective features, namely limiters, result in less accurate control performance since the outputs of real controllers are always bounded. In the present study, the controller outputs are limited between -0.9pu and 1.0pu, and the upper bound of exciter output is limited by 3.1pu with the direction of IEEE standards. The effect of limiters on the controller performance is investigated. Two controllers with a novel sliding surface function are proposed based on a mathematical model of the AVR and its approximate reduced-order model. The proposed controllers having only two parameters are optimized with improved particle swarm optimization (PSO) algorithm. After optimization, the robustness of the controllers is compared with the results of various operating conditions identical to the previous studies. Although controller inputs and exciter outputs are limited, the maximum overshoot is measured as less than 0.1% at no-load conditions. Nominal time constants of the AVR constituents are perturbed from -50% to 50%, and ±10% load disturbance is applied to the output. The robustness of the controllers against parameter uncertainties is measured with an average overshoot at the output. Another superiority among the reported results in the literature is obtained from the proposed controllers, where the minimum average overshoot rates are obtained. In addition, when ±10% load is applied to the output of the AVR, the proposed controllers generate accurate control inputs to reject the load disturbance successfully. Furthermore, the proposed controllers keep the output within the ±5% band if there is a monotonic change at the output. All the results show that the proposed controllers with the improved PSO have drawn the best performance from the perspective of time-domain specifications in comparison with the recently reported controllers.

The output voltage of microbial fuel cells (MFCs) needs to be controlled effectively to improve the operating efficiency of MFC system against the load variations, disturbances, and uncertainties. The output voltage control is purposed to provide the MFC with an appropriate amount of fuel required to maintain the stability of output voltage. In the applications, the MFC output voltage can be requested to track the variable reference voltage or to track the constant output voltage under changing conditions such as load change. For this purpose, two adaptive fuzzy PID (FPID) controllers powered by optimization algorithms were designed in this study to keep the output voltage value of the MFC at the desired values. This study extends the previous works by using optimization algorithms, such as particle swarm optimization (PSO) and gray wolf optimization (GWO) algorithms, to adjust fuzzy logic parameters, which are used for tuning Proportional-Integral-Derivative (PID) controllers. The optimization-based adaptive FPID controllers play an active role in efficient, fast, and robust tracking of the reference voltage value with its adaptive structure. The designed adaptive control algorithms were tested under different cases, which involve the variation of the reference voltage and load conditions. The results show that both of these controllers can effectively control the output voltage of a MFC by regulating the flow rate of the fuel. In addition, the performances of the designed controller strategies according to the results obtained vary according to the reference signal. While PSO-based FPID gives more successful results for positive step changes, GWO-based FPID gives more successful results for negative step changes. These two control strategies show more efficient, robust, successful, and faster performances compared to the traditional PID controller.

This article addresses the least-squares method, which is vital in inverse scattering problems involving the reconstruction of inaccessible rough surface profiles from the measured scattered field data. The unknown surface profile is retrieved by a regularized recursive Newton algorithm which is regularized by the Tikhonov method. The importance of the least-squares application reveals at this point, where the unknown surface profile is expressed as a linear combination of some appropriate basis functions. Thus, the problem of obtaining the unknown rough surface is reduced to finding the unknown coefficients of these functions. As an optimization problem, the choice of appropriate basis functions, as well as the number of their expansions for rough surface imaging problems are essential for the iterative solutions. The validation limits and the performances of different basis functions are presented via several numerical examples. Publisher's Version WOS:000848420400058

In order to maintain a satisfactory performance in the midst of rapid growth of mobile traffic, the mobile network infrastructure needs to be scaled. Thus there has been significant interest in scalability of mobile core networks and a variety of scaling solutions have been proposed that rely on horizontal scaling or vertical scaling. These solutions handle the scaling of the mobile core networks' elements on virtual machines (which normally take at while to create) with the help of customized modules at the cost of increased overheads. Utilizing Amazon Web Services (AWS) embedded features, we present two predictive horizontal auto-scalers for containerized and non-containerized versions of EPC that scales the two versions of the EPC according to their respective CPU utilization. Additionally, we propose an efficient task assignment scheme for AWS that aims to maximize throughput and achieve fairness among competing instances. In particular, we propose two solutions: Relaxed Optimized Solution (ROS) and a Heuristic Approach (HA). Leveraging AWS environment, we implemented and evaluated the two proposed auto-scaling models based on the attachment success rate, latency, CPU usage and RAM usage. Our findings show the superiority of container-based model over VM-based model in terms of resource utilization. The obtained results for the two proposed task assignment solutions demonstrates a significant improvement both in fairness and throughput compared to other existing solutions. 2013 IEEE. Scopus

Finding optimal solutions to production planning and scheduling problems is crucial for surviving in a competitive environment and meeting customer expectations over time. Planning can become complicated in sectors with many different products such as tire production. This study focuses on the bottleneck problem caused by a machine called a Quadruplex Extruder in a tire factory. With this machine, rubber is extruded and transformed into a tread material product, which is critically important in some essential tire features, such as low rolling resistance and brake distance. This study aims to minimize the set-up times in production by optimizing the manufacturing order of the products produced in a quadruplex extruder machine using the Ant Colony Algorithm (ACA), a well-known metaheuristic method to solve polynomial optimization problems. In addition, the second version of the Lin–Kernighan–Helsgaun (LKH-2) algorithm was adapted to this problem. Manually prepared, LKH-2 and ACA-produced schedules were compared in terms of global efficiency. As a result, it has been shown that ACA can provide fast and suitable solutions for decision makers in production planning. © 2023 Elsevier Ltd

The possibility of exploiting the enormous potential of graphene for microelectronics and photonics must go through the optimization of the graphene-metal contact. Achieving low contact resistance is essential for the consideration of graphene as a candidate material for electronic and photonic devices. This work has been carried out in an 8′′ wafer pilot-line for the integration of graphene into a CMOS environment. The main focus is to study the impact of the patterning of graphene and passivation on metal-graphene contact resistance. The latter is measured by means of transmission line measurement (TLM) with several contact designs. The presented approaches enable reproducible formation of contact resistivity as low as 660 Ω μm with a sheet resistance of 1.8 kΩ/□ by proper graphene patterning, passivation of the channel and a post-processing treatment such as annealing.

Due to the increasing interest in blockchain technology for fostering secure, auditable, decentralized applications, a set of challenges associated with this technology need to be addressed. In this letter, we focus on the delay associated with Proof-of-Work (PoW)-based blockchains, whereby participants validate the new information to be appended to a distributed ledger via consensus to confirm transactions. We propose a novel end-to-end latency model based on batch-service queuing theory that characterizes timers and forks for the first time. Furthermore, we derive an estimation of the optimal block size analytically. Endorsed by analytical and simulation results, we show that the optimal block size approximation is a consistent method that leads to close-to-optimal performance by significantly reducing the overheads associated with blockchain applications. IEEE

7th IEEE International Symposium on Electrical and Electronics Engineering, ISEEE 2021 -- 28 October 2021 through 30 October 2021 -- -- 175357, L. Zadeh came up with the idea of Z-number to reflect human decision-making ability in environments where information is uncertain. According to his idea, a Z-number consists of a classical fuzzy part and its reliability. Although there are linguistic based studies exist in the literature, designing the reliability part is still an open issue. In this paper, Logistic Regression is used to determine reliability part. Since the reliability part contains probability information and fuzzy granular information, both statistical and probability based methods must be proposed. The features such as giving probabilistic output, being optimization based via a cost function makes the Logistic Regression one of the best methods for generating Z-number. According to the proposed method, Z-numbers and Z-number based fuzzy if-then rules are written. We tried the Z-number based classifier on Fisher Iris Dataset. The results showed us the more reliable fuzzy membership functions give the more accurate outputs as expected. Another important issue is the reliability of input information, i.e. sensor data, was not known, so the reliability based calculations could not be performed. The reliability of input data can be calculated via proposed method. © 2021 IEEE.

5h International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2021 -- 21 October 2021 through 23 October 2021 -- -- 174473 This paper aims to demonstrate the success of a recently developed metaheuristic algorithm, named slime mould algorithm (SMA), for efficient operation of a magnetic levitation system. A real proportional-integral-derivative (PID) plus second-order derivative (PIDD2) controller was utilized to demonstrate the implementation. This paper is also the first report on SMA based real PIDD2 controller tuning for control of a magnetic levitation system. The transient response analysis was performed to demonstrate the promise of the proposed approach. Besides, a comparative performance analysis in terms of transient stability was also performed by using other available and good performing approaches. The results have shown the SMA based real PIDD2 controller to be a better choice for efficient operation of a magnetic levitation system. © 2021 IEEE.

SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2021, APOG 2021 -- 12 October 2021 through 14 October 2021 -- -- 172791, Reservoir engineering constitutes a major part of the studies regarding oil and gas exploration and production. Reservoir engineering has various duties, including conducting experiments, constructing appropriate models, characterization, and forecasting reservoir dynamics. However, traditional engineering approaches started to face challenges as the number of raw field data increases. It pushed the researchers to use more powerful tools for data classification, cleaning and preparing data to be used in models, which enhances a better data evaluation, thus making proper decisions. In addition, simultaneous simulations are sometimes performed, aiming to have optimization and sensitivity analysis during the history matching process. Multi-functional works are required to meet all these deficiencies. Upgrading conventional reservoir engineering approaches with CPUs, or more powerful computers are insufficient since it increases computational cost and is time-consuming. Machine learning techniques have been proposed as the best solution for strong learning capability and computational efficiency. Recently developed algorithms make it possible to handle a very large number of data with high accuracy. The most widely used machine learning approaches are: Artificial Neural Network (ANN), Support Vector Machines and Adaptive Neuro-Fuzzy Inference Systems. In this study, these approaches are introduced by providing their capability and limitations. After that, the study focuses on using machine learning techniques in unconventional reservoir engineering calculations: Reservoir characterization, PVT calculations and optimization of well completion. These processes are repeated until all the values reach to the output layer. Normally, one hidden layer is good enough for most problems and additional hidden layers usually does not improve the model performance, instead, it may create the risk for converging to a local minimum and make the model more complex. The most typical neural network is the forward feed network, often used for data classification. MLP has a learning function that minimizes a global error function, the least square method. It uses back propagation algorithm to update the weights, searching for local minima by performing a gradient descent (Figure 1). The learning rate is usually selected as less than one. © 2021, Society of Petroleum Engineers.

6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- -- 176826, Classification problems have an important role in the field of machine learning and data mining. Classification problems are used in different areas such as disease diagnosis, estimation of bank customers, drug studies, sentiment analysis. Many classification algorithms have been developed in the literature and these algorithms have many different parameter inputs. In this study, it is aimed to increase the classification success by using hyperparameter optimization algorithms. K-nearest neighbor, support vector machines, decision tree and gradient boosting classification algorithms were applied to the frequently used 'heart and iris' datasets in the literature. Grid search and random search algorithms, which are hyperparameter optimization algorithms, are applied to these selected classification algorithms. As a result of the experimental studies, it has been observed that the accuracy of all classification algorithms increases when hyperparameter optimization algorithms are applied. The parameter values that give the best results are shown. © 2021 IEEE

Stochastic methods attempt to solve problems that cannot be solved by deterministic methods with reasonable time complexity. Optimization algorithms benefit from stochastic methods; however, they do not guarantee to obtain the optimal solution. Many optimization algorithms have been proposed for solving problems with continuous nature; nevertheless, they are unable to solve discrete or binary problems. Adaptation and use of continuous optimization algorithms for solving discrete problems have gained growing popularity in recent decades. In this paper, the binary version of a recently proposed optimization algorithm, Battle Royale Optimization, which we named BinBRO, has been proposed. The proposed algorithm has been applied to two benchmark datasets: the uncapacitated facility location problem, and the maximum-cut graph problem, and has been compared with 6 other binary optimization algorithms, namely, Particle Swarm Optimization, different versions of Genetic Algorithm, and different versions of Artificial Bee Colony algorithm. The BinBRO-based algorithms could rank first among those algorithms when applying on all benchmark datasets of both problems, UFLP and Max-Cut. Publisher's Version WOS:000787281000010

Network Function Virtualization (NFV)-based 5G networks deliver specific services to a end-users through the creation of Service Function Chains (SFCs), which are composed of a number of Virtualized Network Functions (VNFs) interconnected via a set of virtual links (vLinks). VNFs consume computational resources of the network's servers, while vLinks utilize the communicational resources of the network. The efficient utilization of network resources remains a challenge in NFV-based networks. To this end, in this paper, we propose an analytical framework for the Admission Ratio (AR) calculation in NFV-based converged optical-wireless 5G networks. The proposed methodology employs a network slicing architecture in which different network slices formend-to-end logically isolated networks and each slice delivers a specific service type to the users through its SFC(s). In the proposed analysis,we not only take into account the occupancy distribution in both the network's computational and communicational domains (servers and fiber links), butwe also consider theSFCestablishment AR by taking into account different sub-service-classes belonging to different slices. The accuracy of the model is evaluated through the comparison of analytical and simulation results and was found satisfactory. Furthermore, the proposed model is employed for the determination of the optimal (minimum) capacity for all SFCs elements (VNFs and vLinks), in a way that users belonging to a specific slice experience a predefined value of AR as minimum. Additionally, our calculations deploy recursive formulas,which have a low computational complexity, as opposed to time-consuming simulations, without requiring the application of complex optimization algorithms. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Network Function Virtualization (NFV)-based 5G networks deliver specific services to a end-users through the creation of Service Function Chains (SFCs), which are composed of a number of Virtualized Network Functions (VNFs) interconnected via a set of virtual links (vLinks). VNFs consume computational resources of the network's servers, while vLinks utilize the communicational resources of the network. The efficient utilization of network resources remains a challenge in NFV-based networks. To this end, in this paper, we propose an analytical framework for the Admission Ratio (AR) calculation in NFV-based converged optical-wireless 5G networks. The proposed methodology employs a network slicing architecture in which different network slices formend-to-end logically isolated networks and each slice delivers a specific service type to the users through its SFC(s). In the proposed analysis,we not only take into account the occupancy distribution in both the network's computational and communicational domains (servers and fiber links), butwe also consider theSFCestablishment AR by taking into account different sub-service-classes belonging to different slices. The accuracy of the model is evaluated through the comparison of analytical and simulation results and was found satisfactory. Furthermore, the proposed model is employed for the determination of the optimal (minimum) capacity for all SFCs elements (VNFs and vLinks), in a way that users belonging to a specific slice experience a predefined value of AR as minimum. Additionally, our calculations deploy recursive formulas,which have a low computational complexity, as opposed to time-consuming simulations, without requiring the application of complex optimization algorithms. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Forming nickel-based superalloy aero-engine components is a challenging process, largely because of the risk of high degree of springback and issues with formability. In the forming tests conducted on alloy 718 at room temperature, open fractures are observed in the drawbead regions, which are not predicted while evaluating the formability using the traditional forming-limit diagram (FLD). This highlights the importance of an accurate prediction of failure during forming as, in some cases, may severely influence the springback and thereby the accuracy of the predicted shape distortions, leading the final shape of the formed component out of tolerance. In this study, the generalised incremental stress-state dependent damage model (GISSMO) is coupled with the isotropic von Mises and the anisotropic Barlat Yld2000-2D yield criteria to predict the material failure in the forming simulations conducted on alloy 718 using LS-DYNA. Their effect on the predicted effective plastic strains and shape deviations is discussed. The failure and instability strains needed to calibrate the GISSMO are directly obtained from digital image correlation (DIC) measurements in four different specimen geometries i.e. tensile, plane strain, shear, and biaxial. The damage distribution over the drawbeads is measured using a non-linear acoustic technique for validation purposes. The numerical simulations accurately predict failure at the same regions as those observed in the experimental forming tests. The expected distribution of the damage over the drawbeads is in accordance with the experimental measurements. The results highlight the potential of considering DIC to calibrate the GISSMO in combination with an anisotropic material model for forming simulations in alloy 718.

In our previous works, it is clearly addressed that optimisation of fin profile is of vital importance in terms of the rate of heat transfer from a hot surface, and the optimisation procedure depends on several factors. Within the scope of this research, a longitudinal cylindrical fin profile is under interest for the optimisation research. The purpose is to investigate the effects of longitudinal parabolic perforations on the fin parameters such as temperature distribution, effectiveness and efficiency, in which the fin surface is cooled by natural convection and radiation. Different concavity levels are considered to form parabolic perforations. The rate of heat transfer from fin surface is numerically correlated with the fin mass with respect to different concavity levels. According to results, heat transfer from unit fin mass is enhanced with the new designs. The outcome of the study can be used to optimise the needs for particular applications by making a decision between heat loss and weight options. That is, the increase in the concavity level of the perforation results in a lighter and cheaper design, but yielding a lower heat loss. However, heat transfer from unit mass is still enhanced. © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group.

For atomic frequency standards in which fluctuations of the local oscillator (LO) frequency are the dominant noise source, we examine the role of the servo algorithm that predicts and corrects these frequency fluctuations. We derive the optimal linear prediction algorithm, showing how to measure the relevant spectral properties of the noise and optimise servo parameters while the standard is running, using only the atomic error signal. We find that, for realistic LO noise spectra, a conventional integrating servo with a properly chosen gain performs nearly as well as the optimal linear predictor. Using simple analytical models and numerical simulations, we establish optimum probe times as a function of clock atom number and of the dominant noise type in the local oscillator. We calculate the resulting LO-dependent scaling of achievable clock stability with atom number for product states as well as for maximally-correlated states., 16 pages, 7 figures. Added discussion of servo's spectral response and of phase-stabilization vs frequency stabilization. Results unchanged from first version

Funding: This work was supported by the EU Horizon 2020 project, TeamPlay, Grant Number 779882, and UK EPSRC Discovery, Grant Number EP/P020631/1. This paper presents a new technique for introducing and tuning parallelism for heterogeneous shared-memory systems (comprising a mixture of CPUs and GPUs), using a combination of algorithmic skeletons (such as farms and pipelines), Monte–Carlo tree search for deriving mappings of tasks to available hardware resources, and refactoring tool support for applying the patterns and mappings in an easy and effective way. Using our approach, we demonstrate easily obtainable, significant and scalable speedups on a number of case studies showing speedups of up to 41 over the sequential code on a 24-core machine with one GPU. We also demonstrate that the speedups obtained by mappings derived by the MCTS algorithm are within 5–15% of the best-obtained manual parallelisation. Publisher PDF

This study introduces an improved ANFIS based MPPT method to maximize the power extraction capability of the FC-connected system. The proposed method is tested in a stand-alone system that consists of an FC in the power rating of 1.9 kW, a boost dc-dc converter, local consumer load, and a processor unit. The energy transfer between FC and load is handled through the adjustment of a duty cycle of the dc-dc converter. In this context, the output voltage of FC is controlled by the duty cycle to track the MPP. The proposed method called GA-ANFIS computes optimum reference voltages to control the FC output voltage optimally. The GA-ANFIS uses a reduced-size training dataset extracted by GA to train the ANFIS in comparison with conventional ANFIS. Unlike the existing methods, the proposed method tracks the MPP by merely monitoring FC voltage during operation. Besides, it performs precise MPP tracking by considering pressure & temperature variations. Thus, the proposed method provides reduced computational load owing to its current features. The performance of the proposed method is compared with the traditional methods like ANFIS and PI. The power extraction ratings and efficiency values validate the viability and effectiveness of the proposed method (>98%).

This study explores the use of carbon nanotubes (CNTs) and nanoclays in cement mortars. The paper presents modelling and optimisation of the compressive and flexural strengths of cement mortars containing CNTs and the compressive, tensile and flexural strengths of cement mortars including nanoclays. The CNT ratio, CNT aspect ratio and functionalisation effects are optimised, as are the montmorillonite nanoclay ratio and temperature effect. Mechanical strengths are modelled using two cascade feed-forward neural networks (NNs), designated CSNN-CNTs and CSNN-CLY for CNTs and nanoclays, respectively. The trained NN CNT and nanoclay models both successfully replicated experiments with significant accuracy. Inputs to the NN models were different mix combinations, optimised using a genetic algorithm to achieve optimal strength results. The optimisation process for mortars with CNTs revealed increases of 20·96% and 54·27% for compressive and flexural strengths, respectively. Similarly, optimisation of the mortar with nanoclays achieved increases of 63·7%, 199% and 133% for compressive, tensile and flexural strengths, respectively. These results demonstrate that the NN integrated genetic algorithm based optimisation is an effective way of determining the best recipe for CNT and nanoclay mortars for optimum strength.

An active aerodynamic feeding system developed at the IFA offers a large potential regarding output rate, reliability and neutrality towards part geometries. In this paper, the procedure of a genetic algorithm's into the feeding system's control is shown. The genetic algorithm automatically identifies optimal values for the feeding system's parameters which need to be adjusted when setting up for new workpieces. The general functioning of the automatic parameter identification is confirmed during tests on the convergence behaviour of the genetic algorithm. Thereby, a trade-off between the adjustment time of the feeding system and the solution quality is revealed. © 2016 The Authors. DFG/NY 4/51-1

This paper presents a layup optimisation algorithm for composite marine propellers, including the hygrothermal effects, using Non-Uniform Rational B-Splines (NURBS) based FEM coupled with real-coded Genetic Algorithm (GA). The use of Iso-Geometric Analysis (IGA) enables accurate representation of complex marine propeller blades, coupled with GA for both continuous and mixed integer ply angle optimisation. The optimisation scheme was further investigated multi-objective, multi-material and multiple layer thickness optimisation scenarios. Furthermore, the IGA-FEM solver was constructed based on constitutive equations that take into account the hygrothermal effects that must be addressed to enable the optimisation of non-symmetric layups. Such non-symmetric layup patterns are proposed to be used to create extension-twist coupling to gain further control in layup optimisation. The optimisation technique is presented here as a method to widen the efficiency envelope of marine propellers. However, the same approach can potentially be adopted to cater many other practical applications such as composite wind turbine blades, aircraft propellers and other general composite ply angle optimisation scenarios. The paper discusses the proposed framework for optimisation of marine propeller, numerical tools used in the process and results under different conditions. � 2014 Elsevier B.V.

In this study, advanced exergoeconomic analysis of solar-biomass hybrid energy systems for the northern part of Cameroon is conducted. Three concentrating solar power (CSP) technologies, parabolic trough collector (PTC), linear Fresnel reflector (LFR) and solar tower (ST), hybridised with biomass-fired (BF) technology are investigated. It is found that exergy destruction from the solar field alone is responsible for the most of the whole exergy destructions (86.3% for PTC-BF, 92.2% for ST-BF and 85.4% for LFR-BF). The results show that LFR-BF hybrid system is the best cost-efficient system while ST-BF hybrid system is the worst cost-efficient system considering initial investment, avoidable-endogenous exergy destruction, cost associated with avoidable-endogenous exergy destruction and total cost required for the optimisation. Copyright © 2020 Inderscience Enterprises Ltd.

B-patterns are algorithmically-computed sequences of field-work tracks for agricultural vehicles completely covering an area that do not follow any pre-determined standard motif, as in the conventional field-work patterns, but are a result of an optimisation process under one or more selected criteria. Here, an initial approach for the assessment of the benefits derived from the implementation of B-patterns is presented. This approach is based on the comparison of B-patterns against conventional field work patters, by simulating the operational performance of the different patterns on template fields for different combinations of field and machinery characteristics. The assessment of the benefits of B-patterns showed reductions in non-working distance up to 58.65% and increases in the area capacity, i.e. the processed area per time unit, were up to 19.23% when compared with different types of conventional field-work patterns.

WOS: 000455100100005, In this paper, a steam cycle assisted solar tower power system is designed and parametrically optimised for Iskenderun region by considering the weather conditions. After the optimisation, exergy-based analysis of the compounds and overall plant is performed for the best-performing cycle conditions. As a result of the annual-based monthly parametric optimisation, the design parameters, working performance, energy and exergy efficiencies of system are compared to obtain the best-performing cycle conditions which are found in July at the tower outlet pressure and temperature of 100 bar and 1,000 degrees C. The net power production and thermal efficiency of the system at the best-performing conditions are calculated as 47,046.60 kW and 45.814%, respectively. After the exergetic analysis of compounds and overall system, the highest exergetic efficiency is evaluated in 2nd turbine (T-2) as 99.23% and the lowest exergetic efficiency is found in 1st heat exchanger (HeX-1) as 74.16%. Moreover, the overall exergetic efficiency is calculated as 47.19%.

Probabilistic testing techniques that sample input data at random from a probability distribution can be more effective at detecting faults than deterministic techniques. However, if overly large (and therefore expensive) test sets are to be avoided, the probability distribution from which the input data is sampled must be optimised to the particular software-under-test. Such an optimisation process is often resource-intensive. In this paper, we present a prototypical cloud platform-and architecture-that permits the optimisation of such probability distributions in a scalable, distributed and robust manner, and thereby enables cost-effective probabilistic testing.

The ARM-DSP based ANPR system described is designed for commercial applications where the need for low power, low prices and real time systems is vital. A single FPGA can also be added as a plug-in to the ARM-DSP based hardware SoC, depending on the extra resources needed for the application. The overall results have shown that it is possible to use cheaper off-the-shelf ARM-DSPs and FPGAs multicore processors for standalone ANPR systems through device and algorithm optimisation to achieve real-time performance at higher recognition rate using efficient algorithms. 2013 IEEE. Scopus

Automated storage and retrieval systems (AS/RS) are playing an important role in modern manufacturing systems such as flexible manufacturing systems (FMS). The integration of AS I RS systems and FMS is very complex owing to the complexity of the individual elements. Stochastic coloured petri nets (SCPN) can be used to model, simulate and analyse such a system efficiently and realistically. In this paper, SCPN based simulation, aided by the Taguchi method of design of experiments, has been used for analysing the influence of important factors on makespan, unproductive travel time, and the mean flow time, and is supported by an example. ? 1996 Springer-Verlag London Limited.

The paper presents different determination and optimization methods for verification intervals of technical devices for monitoring and measurement based on the requirements of some widely used international standards, e.g. ISO 9001, ISO/IEC 17020, ISO/IEC 17025 etc., maintained by various organizations implementing measuring devices in practice. Comparative analysis of the reviewed methods is conducted in terms of opportunities for assessing the adequacy of interval(s) for calibration of measuring devices and their optimization accepted by an organization – an extension or reduction depending on the obtained results. The advantages and disadvantages of the reviewed methods are discussed, and recommendations for their applicability are provided.

Software development organisations are under heavy pressure to complete projects on time, within budget and with the appropriate level of quality, and many questions are asked when a project fails to meet any or all of these requirements. Over the years, much research effort has been spent to find ways to mitigate these failures, the reasons of which come from both within and outside the organisation's control. One possible risk of failure lies in human resource management and, since humans are the main asset of software organisations, getting the right team to do the job is critical. This paper proposes a procedure for software project managers to support their project scheduling and team staffing activities - two areas where human resources directly impact software development projects and management decisions - by adopting a genetic algorithm approach as an optimisation technique to help solve software project scheduling and team staffing problems.

The paper presents results of a FEA-based tool design optimisation for a clinching process with an open multiple-part die. The studied materials are the bake-hardening steel CR240BH (1.5 mm) on the punch side and the die-cast aluminium alloy AlSi10MnMg (2.95 mm) on the die side. The objective of the optimisation was to minimise probability of cracks that appear at the outer circumference of the bottom of the clinch joint in AlSi10MnMg. In the framework of the optimisation, it was possible to minimise crack probability by varying geometrical parameters of the tools though at the cost of slightly worse but still tolerable geometrical parameters of the clinch joint. German Federation of Industrial Research Associations (AiF), Cologne German Federal Ministry of Economic Affairs and Energy (BMWi) Volkswagen AG BTM Blechverbindungstechnik GmbH

The emergence of huge container ships together with the scarcity of yard land put an enormous pressure on authorities to adopt advanced technologies to increase the throughput of current container terminals. Automated Container Terminals (ACT), using advanced material handling technologies, are recently considered as candidates for increasing the performance of manually operated yards. In this paper, an ACT that employs Linear Motor Technology is designed. A multi attribute decision making method is employed to find the optimum number of conveyor shuttles in the terminal. Simulations demonstrate a double increase in the throughput of ACT compared to the similar one with manual operations. © 2001 EUCA. 289 294 Sponsors: Directorate General XII of the European Commission Faculdade de Engenharia da Universidade do Porto Conference code: 112066

Date of Conference: 10–12 July, 1997 Conference name: 7th International Workshop on Logic Programming Synthesis and Transformation Workshop, LOPSTR’97 Schema-based logic program transformation has proven to be an eective technique for the optimisation of programs. This paper results from the research that began by investigating the suggestions in [11] to construct a more general database of transformation schemas for optimising logic programs at the declarative level. The proposed transformation schemas fully automate accumulator introduction (also known as descending computational generalisation), tupling generalisation (a special case of structural generalisation), and duality laws (which are extensions to relational programming of the rst duality law of the fold operators in functional programming). The schemas are proven correct. A prototype schema-based transformation system is evaluated. © Springer-Verlag Berlin Heidelberg 1998.

Compact closed-form mathematical expressions are presented for the effective permittivity, characteristic impedance and attenuation constant of coplanar waveguides with thick electrodes and high-frequency dispersion. Excellent agreements, for a wide range of frequencies, up to 100 GHz and for conductor thicknesses up to 40 m are found with full-wave finite-element results from commercial software (HFSS) in a margin of geometric parameter values and frequencies of interest for the design and optimisation of high-speed electro-optical modulators. The formulae are simple and specially tailored to the narrow gap widths and thick electrodes typical of electro-optical modulator applications. Other approaches to closed-form formulae for CPW parameters are also compared and analysed.

Artificial intelligence has brought new features to unmanned aerial vehicles. Path planning, trajectory optimization and obstacle avoidance are some of the features that artificial intelligence brings to drones. In this study, path planning optimization and collision avoidance with artificial intelligence for an unmanned aerial vehicle are emphasized. Path planning and collision avoidance simulations were performed using MATLAB software under ideal conditions and under noisy conditions. It is shown that the unmanned aerial vehicle finds the shortest path on maps with various obstacles. © 2021 IEEE.

Application of deep learning techniques for breast cancer classification using histopathology images has gained interest during recent years. In this study, an open-source convolutional neural network (CNN) model developed for breast cancer classification model is optimized by performing sensitivities on various CNN parameters such as data balancing, activation functions and adding/removing CNN layers. Some of the parameters are less-sensitive in affecting model's performance. The results show that by balancing the number of positive and negative samples, accuracy of the model can be improved. However, some additional work is required to reach to that point. Furthermore, the computation time is reduced by almost 30% by increasing the learning rate from 0.01 to 0.05 while the training and validation accuracy and loss are comparable to that of the original CNN model. © 2021 IEEE.

Underwater wireless sensor networks (UWSNs) are utilized for a wide range of monitoring and surveillance applications. Lifetime maximization and maintenance of network reliability are among the most important considerations in the deployment of UWSNs. k-connectivity is a robust approach for reinforcing reliability. However, maintaining k disjoint paths from each sensor node to the BS, inevitably, results in extra energy dissipation, which reduces the network lifetime (NLT). Yet, there is no systematic exploration to determine the extent of lifetime reduction due to the increase in the k value, in the literature, to the best of our knowledge. In this study, we create an optimization framework to be able to explore the trade-off between NLT and k-connectivity based reliability in UWSNs. Through the optimal solutions of the proposed optimization model for a large set of salient parameters, we characterize the inter-play between lifetime and k-connectivity. Our analysis reveals that the k value to be maintained in a UWSN can affect the NLT significantly. IEEE