Optimising Energy Consumption in SD-DCN Networks (Software Defined-Data Center Network)

Over the last decade, the rapid growth in traffic and the number of network devices has implicitly led to an increase in network energy consumption. In this context, a new paradigm has emerged, Software-Defined Networking (SDN), which is an emerging technique that separates the control plane and the data plane of the deployed network, enabling centralized control of the network, while offering flexibility in data center network management. Some research work is moving in the direction of optimizing the energy consumption of SD-DCN, but still does not guarantee good performance and quality of service for SDN networks. To solve this problem, we propose a new mathematical model based on the principle of combinatorial optimization to dynamically solve the problem of activating and deactivating switches and unused links that consume energy in SDN networks while guaranteeing quality of service (QoS) and ensuring load balancing in the network.

Optimising repetitive transcranial magnetic stimulation for neural circuit repair following traumatic brain injury

While it is well-known that neuronal activity promotes plasticity and connectivity, the success of activity-based neural rehabilitation programs remains extremely limited in human clinical experience because they cannot adequately control neuronal excitability and activity within the injured brain in order to induce repair. However, it is possible to non-invasively modulate brain plasticity using brain stimu- lation techniques such as repetitive transcranial （rTMS） and transcranial direct current stimulation （tDCS） techniques, which show promise for repairing injured neural circuits （Henrich-Noack et al., 2013; Lefaucher et al., 2014）. Yet we are far from having full control of these techniques to repair the brain following neurotrauma and need more fundamen- tal research （Ellaway et al., 2014; Lefaucher et al., 2014）. In this perspective we discuss the mechanisms by which rTMS may facilitate neurorehabilitation and propose experimental techniques with which magnetic stimulation may be investi- gated in order to optimise its treatment potential.

Fractures in sport: Optimising their management and outcome

Fractures in sport are a specialised cohort of fracture injuries, occurring in a high functioning population, in which the goals are rapid restoration of function and return to play with the minimal symptom profile possible. While the general principles of fracture management, namely accurate fracture reduction, appropriate immobilisation and timely rehabilitation, guide the treatment of these injuries, management of fractures in athletic populations can differ significantly from those in the general population, due to the need to facilitate a rapid return to high demand activities. However, despite fractures comprising up to 10% of all of sporting injuries, dedicated research into the management and outcome of sport-related fractures is limited. In order to assess the optimal methods of treating such injuries, and so allow optimisation of their outcome, the evidence for the management of each specific sport-related fracture type requires assessment and analysis. We present and review the current evidence directing management of fractures in athletes with an aim to promote valid innovative methods and optimise the outcome of such injuries. From this, key recommendations are provided for the management of the common fracture types seen in the athlete. Six case reports are also presented to illustrate the management planning and application of sport-focussed fracture management in the clinical setting.

Pinch analysis, as a technique for optimising resource utilisation and promoting environmental sustainability:A review of recent case studies from the developing world and transition economies

Pinch analysis, as a technique to optimise the utilisation of resources, traces its beginnings to the 1970s in Switzerland and the UK ETH Zurich and Leeds University to be more precise. Over four decades down the line, this methodology has entrenched itself in research circles around the world. While the technique was developed, to begin with, for energy (heat) recovery, it has since then expanded to embrace several other fields, and enabled optimisation of resource utilisation in general. The motive behind this article is to perform a focused, selective review of recent case studies from the developing world and transition economies, having ‘pinch analysis’ in their titles and thereby as their ‘core, crux and gist’, during the period 2008-2018. The resources focused on, include heat energy, electrical energy, water, solid waste, money, time, land (surface area), storage space (volume), human resources, mass of resources in general and hydrogen, while a handful of publications have their focus on carbon dioxide (greenhouse gases in general) emissions. Multi-dimensional pinch analysis promises to be an effective tool for sustainability analysis in the years to come;most importantly in the developing world where social well-being and economic development are priorities in the years ahead, and they ought to be attained by a simultaneous truncation of the environmental footprint, in other words, an optimisation of resource utilisation as well as adverse environmental impacts. In other words, the focus ought to be on sustainable production (efficiency) and consumption (sufficiency).

C_4 Plants as Biofuel Feedstocks: Optimising Biomass Production and Feedstock Quality from a Lignocellulosic Perspective

The main feedstocks for bioethanol are sugarcane （Saccharum offic- inarum） and maize （Zea mays）, both of which are C4 grasses, highly efficient at converting solar energy into chemical energy, and both are food crops. As the systems for lignocellulosic bioethanol production become more efficient and cost effective, plant biomass from any source may be used as a feedstock for bioethanol production. Thus, a move away from using food plants to make fuel is possible, and sources of biomass such as wood from forestry and plant waste from cropping may be used. However, the bioethanol industry will need a continuous and reliable supply of biomass that can be produced at a low cost and with minimal use of water, fertilizer and arable land. As many C4 plants have high light, water and nitrogen use efficiency, as compared with C3 species, they are ideal as feedstock crops. We consider the productivity and resource use of a number of candidate plant species, and discuss biomass ＇quality＇, that is, the composition of the plant cell wall.

Optimising local council's return on investment from annual pavement rehabilitation budgets through targeting of the average pavement condition index

A high quality transportation system is necessary in a modem economy, and a road network is a common and significant, component of the system. Road systems have two major objectives： to enable the movement of passenger vehicles and the movement of freight vehicles at reasonable speeds. An important part of the transportation system and an expensive investment, a functional road network must meet both objectives to main- tain an efficient economy. In Australia, the Department of Infrastructure and Regional Development reported that, in 2011/12, the total road length was approximately 900,000 kin, and the total road expenditure was approximately $19 billion. Good policy requires that infrastructure investments provide a return on investment, thus warranting judicious management to ensure that it is maintained in a cost effective manner. Recent studies in Queensland, Australia, have identified differences between financial and engi- neering professionals in their understanding of infrastructure depreciation, condition deterioration, and future funding needs. Furthermore, the Queensland Asset Sustainability Ratio （ASR） requires clearer definitions to ensure that infrastructure remains meaningful to all users. This study proposes a separate sustainability index for road pavements （SIR） unlike the ASR that combines all type of assets. The justification is our ability to assess road condition, the high value of road assets, relative value to other infrastructure, and advanced knowledge of deterioration relative to other infrastructure. The SIR involves community consultation to target an average pavement condition index （PCI）. This study also provides an alternative method to determine the optimal target PCI for a local

Adaptive optimisation of explosive reactive armour for protection against kinetic energy and shaped charge threats

We evaluate an adaptive optimisation methodology,Bayesian optimisation(BO),for designing a minimum weight explosive reactive armour(ERA)for protection against a surrogate medium calibre kinetic energy(KE)long rod projectile and surrogate shaped charge(SC)warhead.We perform the optimisation using a conventional BO methodology and compare it with a conventional trial-and-error approach from a human expert.A third approach,utilising a novel human-machine teaming framework for BO is also evaluated.Data for the optimisation is generated using numerical simulations that are demonstrated to provide reasonable qualitative agreement with reference experiments.The human-machine teaming methodology is shown to identify the optimum ERA design in the fewest number of evaluations,outperforming both the stand-alone human and stand-alone BO methodologies.From a design space of almost 1800 configurations the human-machine teaming approach identifies the minimum weight ERA design in 10 samples.

Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition

Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation scenarios.This paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective optimisation.Through bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower level.Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the subMOPs.The bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective optimisation.Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.

Dispersed Wind Power Planning Method Considering Network Loss Correction with Cold Weather

In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is proposed in the paper,which takes into account the network loss correction for the extreme cold region.Firstly,an electro-thermal model is introduced to reflect the effect of temperature on conductor resistance and to correct the results of active network loss calculation;secondly,a two-stage multi-objective two-stage decentralised wind power siting and capacity allocation and reactive voltage optimisation control model is constructed to take account of the network loss correction,and the multi-objective multi-planning model is established in the first stage to consider the whole-life cycle investment cost of WTGs,the system operating cost and the voltage quality of power supply,and the multi-objective planning model is established in the second stage.planning model,and the second stage further develops the reactive voltage control strategy of WTGs on this basis,and obtains the distribution network loss reduction method based on WTG siting and capacity allocation and reactive power control strategy.Finally,the optimal configuration scheme is solved by the manta ray foraging optimisation(MRFO)algorithm,and the loss of each branch line and bus loss of the distribution network before and after the adoption of this loss reduction method is calculated by taking the IEEE33 distribution system as an example,which verifies the practicability and validity of the proposed method,and provides a reference introduction for decision-making for the distributed energy planning of the distribution network.

SCAPS 1D Simulation of a Lead-Free Perovskite Photovoltaic Solar Cell Using Hematite as Electron Transport Layer

In recent years, there has been remarkable progress in the performance of metal halide perovskite solar cells. Studies have shown significant interest in lead-free perovskite solar cells (PSCs) due to concerns about the toxicity of lead in lead halide perovskites. CH3NH3SnI3 emerges as a viable alternative to CH3NH3PbX3. In this work, we studied the effect of various parameters on the performance of lead-free perovskite solar cells using simulation with the SCAPS 1D software. The cell structure consists of α-Fe2O3/CH3NH3SnI3/PEDOT: PSS. We analyzed parameters such as thickness, doping, and layer concentration. The study revealed that, without considering other optimized parameters, the efficiency of the cell increased from 22% to 35% when the perovskite thickness varied from 100 to 1000 nm. After optimization, solar cell efficiency reaches up to 42%. The optimization parameters are such that, for example, for perovskite: the layer thickness is 700 nm, the doping concentration is 1020 and the defect density is 1013 cm−3, and for hematite: the thickness is 5 nm, the doping concentration is 1022 and the defect concentration is 1011 cm−3. These results are encouraging because they highlight the good agreement between perovskite and hematite when used as the active and electron transport layers, respectively. Now, it is still necessary to produce real, viable photovoltaic solar cells with the proposed material layer parameters.

Bayesian Classifier Based on Robust Kernel Density Estimation and Harris Hawks Optimisation

In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate probability density estimation for classifying continuous datasets. However, achieving precise density estimation with datasets containing outliers poses a significant challenge. This paper introduces a Bayesian classifier that utilizes optimized robust kernel density estimation to address this issue. Our proposed method enhances the accuracy of probability density distribution estimation by mitigating the impact of outliers on the training sample’s estimated distribution. Unlike the conventional kernel density estimator, our robust estimator can be seen as a weighted kernel mapping summary for each sample. This kernel mapping performs the inner product in the Hilbert space, allowing the kernel density estimation to be considered the average of the samples’ mapping in the Hilbert space using a reproducing kernel. M-estimation techniques are used to obtain accurate mean values and solve the weights. Meanwhile, complete cross-validation is used as the objective function to search for the optimal bandwidth, which impacts the estimator. The Harris Hawks Optimisation optimizes the objective function to improve the estimation accuracy. The experimental results show that it outperforms other optimization algorithms regarding convergence speed and objective function value during the bandwidth search. The optimal robust kernel density estimator achieves better fitness performance than the traditional kernel density estimator when the training data contains outliers. The Naïve Bayesian with optimal robust kernel density estimation improves the generalization in the classification with outliers.

Selective conversion of CO_(2)to CO using earth abundant tin modified copper gas diffusion electrodes

Earth-abundant copper-tin(CuSn)electrocatalysts are potential candidates for cost-effective and sustainable production of CO from electrochemical carbon dioxide reduction(eCO_(2)R).However,the requirement of highoverpotential for obtaining reasonable current,low Faradaic efficiencies(FE)and low intrinsic catalytic activities require the optimisation of the CuSn nanoarchitecture for the further advancement in the field.In the current work,we have optimised Sn loading on Cu gas diffusion electrodes(GDEs)by electrochemical spontaneous precipitation.Samples with various Sn loadings were tested in a three-chamber GDE reactor to evaluate their CO_(2)reduction performances.The best performance of 92%CO Faradaic efficiency at a cathodic current density of 120 mA cm^(-2)was obtained from the 20 min Sn deposited Cu_(2)O sample operated at-1.13 V vs.RHE.The electrocatalyst had~13%surface coverage of Sn on Cu GDE surface,and had Sn in oxide form and copper in metallic form.The catalyst also showed stable performance and was operable for>3 h under chronoamperometric conditions.The surface of the GDE reduces from Cu2O to Cu during eCO_(2)R and goes further reconstruction during the eCO_(2)R.This study demonstrates the potential of Cu-Sn for selective CO production at high current densities.

Emerging trends in expansive soil stabilisation: A review

Expansive soils are problematic due to the performances of their clay mineral constituent, which makes them exhibit the shrink-swell characteristics. The shrink-swell behaviours make expansive soils inappropriate for direct engineering application in their natural form. In an attempt to make them more feasible for construction purposes, numerous materials and techniques have been used to stabilise the soil. In this study, the additives and techniques applied for stabilising expansive soils will be focused on,with respect to their efficiency in improving the engineering properties of the soils. Then we discussed the microstructural interaction, chemical process, economic implication, nanotechnology application, as well as waste reuse and sustainability. Some issues regarding the effective application of the emerging trends in expansive soil stabilisation were presented with three categories, namely geoenvironmental,standardisation and optimisation issues. Techniques like predictive modelling and exploring methods such as reliability-based design optimisation, response surface methodology, dimensional analysis, and artificial intelligence technology were also proposed in order to ensure that expansive soil stabilisation is efficient.

An Overview of Self-piercing Riveting Process with Focus on Joint Failures, Corrosion Issues and Optimisation Techniques

Self-piercing riveting(SPR)is a cold forming technique used to fasten together two or more sheets of materials with a rivet without the need to predrill a hole.The application of SPR in the automotive sector has become increasingly popular mainly due to the growing use of lightweight materials in transportation applications.However,SPR joining of these advanced light materials remains a challenge as these materials often lack a good combination of high strength and ductility to resist the large plastic deformation induced by the SPR process.In this paper,SPR joints of advanced materials and their corresponding failure mechanisms are discussed,aiming to provide the foundation for future improvement of SPR joint quality.This paper is divided into three major sections:1)joint failures focusing on joint defects originated from the SPR process and joint failure modes under different mechanical loading conditions,2)joint corrosion issues,and 3)joint optimisation via process parameters and advanced techniques.

Modelling-based Optimisation of the Direct Synthesis of Dimethyl Ether from Syngas in a Commercial Slurry Reactor

In the present study, we developed a multi-component one-dimensional mathematical model for simulation and optimisation of a commercial catalytic slurry reactor for the direct synthesis of dimethyl ether （DME） from syngas and CO2, operating in a churn-turbulent regime. DME productivity and CO conversion were optimised by tuning operating conditions, such as superficial gas velocity, catalyst concentration, catalyst mass over molar gas flow rate （W/F）, syngas composition, pressure and temperature. Reactor modelling was accomplished utilising mass balance, global kinetic models and heterogeneous hydrodynamics. In the heterogeneous flow regime, gas was distributed into two bubble phases： small and large. Simulation results were validated using data obtained from a pilot plant. The developed model is also applicable for the design of large-scale slurry reactors.

Mooring System Optimisation and Effect of Different Line Design Variables on Motions of Truss Spar Platforms in Intact and Damaged Conditions

This paper presents the effect of mooring diameters, fairlead slopes and pretensions on the dynamic responses of a truss spar platform in intact and damaged line conditions. The platform is modelled as a rigid body with three degrees-of-freedom and its motions are analysed in time-domain using the implicit Newmark Beta technique. The mooring restoring force-excursion relationship is evaluated using quasi-static approach. MATLAB codes DATSpar and QSAML, are developed to compute the dynamic responses of truss spar platform and to determine the mooring system stiffness. To eliminate the conventional trial and error approach in the mooring system design, a numerical tool is also developed and described in this paper for optimising the mooring configuration. It has a graphical user interface and includes regrouping particle swarm optimisation technique combined with DATSpar and QSAML. A case study of truss spar platform with ten mooring lines is analysed using this numerical tool. The results show that optimum mooring system design benefits the oil and gas industry to economise the project cost in terms of material, weight, structural load onto the platform as well as manpower requirements. This tool is useful especially for the preliminary design of truss spar platforms and its mooring system.

Multiresolution Isogeometric Topology Optimisation Using Moving Morphable Voids

A general and new explicit isogeometric topology optimisation approach with moving morphable voids(MMV)is proposed.In this approach,a novel multiresolution scheme with two distinct discretisation levels is developed to obtain high-resolution designs with a relatively low computational cost.Ersatz material model based on Greville abscissae collocation scheme is utilised to represent both the Young’s modulus of the material and the density field.Two benchmark examples are tested to illustrate the effectiveness of the proposed method.Numerical results show that high-resolution designs can be obtained with relatively low computational cost,and the optimisation can be significantly improved without introducing additional DOFs.

Nature Conservation versus Scenic Quality:A GIS Approach towards Optimized Tourist Tracks in a Protected Area of Northwest Yunnan,China

Development of appropriate tourism infrastructure is important for protected areas that allow public access for tourism use.This is meant to avoid or minimize unfavourable impacts on natural resources through guiding tourists for proper use.In this paper,a GIS-based method,the least-cost path(LCP) modelling,is explored for planning tourist tracks in a World Heritage site in Northwest Yunnan(China),where tourism is increasing rapidly while appropriate infrastructure is almost absent.The modelling process contains three steps:1) selection of evaluation criteria(physical,biological and landscape scenic) that are relevant to track decision; 2) translation of evluation criteria into spatially explicit cost surfaces with GIS,and 3) use of Dijkstra's algorithm to determine the least-cost tracks.Four tracks that link main entrances and scenic spots of the study area are proposed after optimizing all evaluation criteria.These tracks feature lowenvironmental impacts and high landscape qualities,which represent a reasonable solution to balance tourist use and nature conservation in the study area.In addtion,the study proves that the LCP modelling can not only offer a structured framwork for track planning but also allow for different stakeholders to participate in the planning process.It therefore enhances the effectivenss of tourism planning and managemnt in protected areas.

Optimisation of laser welding parameters for welding of P92 material using Taguchi based grey relational analysis

Creep strength enhanced ferritic(CSEF) steels are used in advanced power plant systems for high temperature applications. P92(Cr–W–Mo–V)steel, classified under CSEF steels, is a candidate material for piping, tubing, etc., in ultra-super critical and advanced ultra-super critical boiler applications. In the present work, laser welding process has been optimised for P92 material by using Taguchi based grey relational analysis(GRA).Bead on plate(BOP) trials were carried out using a 3.5 k W diffusion cooled slab CO_2 laser by varying laser power, welding speed and focal position. The optimum parameters have been derived by considering the responses such as depth of penetration, weld width and heat affected zone(HAZ) width. Analysis of variance(ANOVA) has been used to analyse the effect of different parameters on the responses. Based on ANOVA, laser power of 3 k W, welding speed of 1 m/min and focal plane at-4 mm have evolved as optimised set of parameters. The responses of the optimised parameters obtained using the GRA have been verified experimentally and found to closely correlate with the predicted value.? 2016 China Ordnance Society. Production and hosting by Elsevier B.V. All rights reserved.

Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally expen-sive.To tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)approach.Firstly,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN.Secondly,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates.Thirdly,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost.The SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.