The role and significance of Magnesium in modern day research-A review

Magnesium is one of the largely available elements in the earth’s crust. It has a low structural density with high specific strength. This unique material property has forced an increase in the use of magnesium and its alloys in various applications pertaining to industrial sector,automobiles, aerospace and biomedical. Since magnesium is a highly reactive metal, it is prone to higher rate of corrosion as compared to its counterparts. Thus, it is essential to analyze the corrosion behavior of magnesium and its alloys in its applications. An appropriate process is to be followed in the design and development of magnesium alloys which overcome the limitations of magnesium and enhance the desired material properties in accordance to their applications. This review paper summarizes the importance of magnesium and its material properties. The influence of various alloying elements on the mechanical properties of magnesium is reviewed. The broad classification of Mg alloys and their behavioral trends are detailed. The corrosion behavior of magnesium and the influence of corrosion products on the material characteristics of magnesium, in aqueous medium, are discussed. The manufacturing techniques of magnesium alloys along with the secondary techniques are also covered. The various applications and the limitations of magnesium in these applications are covered. A complete section is dedicated towards detailing the recent trends of magnesium(Mg) alloys, i.e., the biodegradable nature and applications of Mg alloys. The influence of biocorrosion on Mg alloys and techniques to overcome it have been deliberated. This paper provides a thorough review on recent developments of magnesium with respect to engineering applications.

Optimal Control of Nonlinear Systems Using Experience Inference Human-Behavior Learning

Safety critical control is often trained in a simulated environment to mitigate risk.Subsequent migration of the biased controller requires further adjustments.In this paper,an experience inference human-behavior learning is proposed to solve the migration problem of optimal controllers applied to real-world nonlinear systems.The approach is inspired in the complementary properties that exhibits the hippocampus,the neocortex,and the striatum learning systems located in the brain.The hippocampus defines a physics informed reference model of the realworld nonlinear system for experience inference and the neocortex is the adaptive dynamic programming(ADP)or reinforcement learning(RL)algorithm that ensures optimal performance of the reference model.This optimal performance is inferred to the real-world nonlinear system by means of an adaptive neocortex/striatum control policy that forces the nonlinear system to behave as the reference model.Stability and convergence of the proposed approach is analyzed using Lyapunov stability theory.Simulation studies are carried out to verify the approach.

Ultra Dense Satellite-Enabled 6G Networks:Resource Optimization and Interference Management

With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks architecture.Then the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are introduced.Satellite mobile edge computing(SMEC)with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of networks.Furthermore,a scheme for interference management is presented,involving quality-of-service(QoS)and co-tier/cross-tier interference constraints.The simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks.

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic.Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air trafficflow management.Current researches mostly use traditional machine learning methods to extract features of weather scenes,and clustering algorithms to divide similar scenes.Inspired by the excellent performance of deep learning in image recognition,this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering(IDCEC),which uses the com-bination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image,retaining useful information to the greatest extent,and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area.Finally,term-inal area of Guangzhou Airport is selected as the research object,the method pro-posed in this article is used to classify historical weather data in similar scenes,and the performance is compared with other state-of-the-art methods.The experi-mental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather;at the same time,compared with the actualflight volume in the Guangz-hou terminal area,IDCEC's recognition results of similar weather scenes are con-sistent with the recognition of experts in thefield.

Analysis of Autopilot Disengagements Occurring During Autonomous Vehicle Testing

In present-day highly-automated vehicles, there are occasions when the driving system disengages and the human driver is required to take-over. This is of great importance to a vehicle's safety and ride comfort. In the U.S state of California, the Autonomous Vehicle Testing Regulations require every manufacturer testing autonomous vehicles on public roads to submit an annual report summarizing the disengagements of the technology experienced during testing. On 1 January 2016,seven manufacturers submitted their first disengagement reports:Bosch, Delphi, Google, Nissan, Mercedes-Benz, Volkswagen, and Tesla Motors. This work analyses the data from these disengagement reports with the aim of gaining abetter understanding of the situations in which a driver is required to takeover, as this is potentially useful in improving the Society of Automotive Engineers(SAE) Level 2 and Level 3 automation technologies.Disengagement events from testing are classified into different groups based on attributes and the causes of disengagement are investigated and compared in detail. The mechanisms and time taken for take-over transition occurred in disengagements are studied. Finally, recommendations for OEMs, manufacturers, and government organizations are also discussed.

Practical Options for Adopting Recurrent Neural Network and Its Variants on Remaining Useful Life Prediction

The remaining useful life(RUL)of a system is generally predicted by utilising the data collected from the sensors that continuously monitor different indicators.Recently,different deep learning(DL)techniques have been used for RUL prediction and achieved great success.Because the data is often time-sequential,recurrent neural network(RNN)has attracted significant interests due to its efficiency in dealing with such data.This paper systematically reviews RNN and its variants for RUL prediction,with a specific focus on understanding how different components(e.g.,types of optimisers and activation functions)or parameters(e.g.,sequence length,neuron quantities)affect their performance.After that,a case study using the well-studied NASA’s C-MAPSS dataset is presented to quantitatively evaluate the influence of various state-of-the-art RNN structures on the RUL prediction performance.The result suggests that the variant methods usually perform better than the original RNN,and among which,Bi-directional Long Short-Term Memory generally has the best performance in terms of stability,precision and accuracy.Certain model structures may fail to produce valid RUL prediction result due to the gradient vanishing or gradient exploring problem if the parameters are not chosen appropriately.It is concluded that parameter tuning is a crucial step to achieve optimal prediction performance.

The Self-Assembling Growth of Copper Nanowires for Transparent Electrodes

Long(15-40 μm), thin(diameter of 20 ± 5 nm), and well-dispersed CuNWs Cu nanowires were prepared. The high-resolution TEM and selected area electron diffraction showed that the CuNWs were single-crystalline. To investigate the growth mechanism, we examined the microstructure of these CuNWs at different reaction time. It was found that the CuNWs were actually formed through the self-assembling of Cu nanoparticles along the [110] direction. The transparent electrodes fabricated using the CuNWs achieved a high transparency of 76 % at 31±5 Ω/□.

Surface defects incorporated diamond machining of silicon

This paper reports the performance enhancement benefits in diamond turning of the silicon wafer by incorporation of the surface defect machining(SDM)method.The hybrid micromachining methods usually require additional hardware to leverage the added advantage of hybrid technologies such as laser heating,cryogenic cooling,electric pulse or ultrasonic elliptical vibration.The SDM method tested in this paper does not require any such additional baggage and is easy to implement in a sequential micro-machining mode.This paper made use of Raman spectroscopy data,average surface roughness data and imaging data of the cutting chips of silicon for drawing a comparison between conventional single-point diamond turning(SPDT)and SDM while incorporating surface defects in the(i)circumferential and(ii)radial directions.Complementary 3D finite element analysis(FEA)was performed to analyse the cutting forces and the evolution of residual stress on the machined wafer.It was found that the surface defects generated in the circumferential direction with an interspacing of 1 mm revealed the lowest average surface roughness(Ra)of 3.2 nm as opposed to 8 nm Ra obtained through conventional SPDT using the same cutting parameters.The observation of the Raman spectroscopy performed on the cutting chips showed remnants of phase transformation during the micromachining process in all cases.FEA was used to extract quantifiable information about the residual stress as well as the sub-surface integrity and it was discovered that the grooves made in the circumferential direction gave the best machining performance.The information being reported here is expected to provide an avalanche of opportunities in the SPDT area for low-cost machining solution for a range of other nominal hard,brittle materials such as SiC,ZnSe and GaAs as well as hard steels.

金属材料电弧熔丝增材制造研究现状与质量改进方法

电弧熔丝增材制造(WAAM)是一种增材制造技术,近年来在工业上有很大的发展潜力。总结目前WAAM的研究现状和面临的挑战,并提出质量改进的方法。综述WAAM在表面质量、成形精度、显微组织、力学性能、残余应力和变形、孔隙及其他缺陷等方面的研究现状。从前处理、在线处理和后处理3个方面总结消除缺陷、改善显微组织和提高力学性能的方法。WAAM的广泛应用仍然存在许多挑战,可能需要从多个角度出发来实现WAAM的工业化应用。路径规划和切片算法的开发、在线监测系统与现有WAAM设备的结合、后处理技术的复合等将是未来的重点研究方向。

Effects of fiber orientation on tool wear evolution and wear mechanism when cutting carbon fiber reinforced plastics

The aim of the present paper is to reveal the influence of different fiber orientations on the tool wear evolution and wear mechanism. Side-milling experiments with large-diameter milling tools are conducted. A finite element(FE) cutting model of carbon fiber reinforced plastics(CFRP)is established to get insight into the cutting stress status at different wear stages. The results show that different fiber orientations bring about distinct differences in the extent, profile and mechanism of tool wear. Severer wear occurs when cutting 45° and 90° plies, followed by 0°, correspondingly,the least wear is obtained when θ = 135°(θ represents the orientation of fibers). Moreover, the worn profiles of cutting tools when θ = 0° and 45° are waterfall edge, while round edge occurs whenθ = 135° and a combined shape of waterfall and round edge is obtained when θ = 90°. The wear mechanisms under different fiber orientations are strongly dependent on the cutting stress distributions. The evolution of tool wear profile is basically consistent with the stress distribution on the tool surface at different wear stages, and the extent of tool wear is determined by the magnitude of stress on the tool surface. Besides, the worn edges produce an actual negative clearance angle,which decreases the actual cutting thickness and leads to compressing and bending failure of fibers beneath the cutting region as well as low surface qualities.

Driver Steering Behaviour Modelling Based on Neuromuscular Dynamics and Multi‑Task Time‑Series Transformer

Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle.In this study,a multi-task sequential learning framework is developed to pre-dict future steering torques and steering postures based on upper limb neuromuscular electromyography signals.The joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering behaviours.Regarding different testing scenarios,two driving modes,namely,both-hand and single-right-hand modes,are studied.For each driving mode,three different driving postures are further evaluated.Next,a multi-task time-series transformer network(MTS-Trans)is developed to predict the future steering torques and driving postures based on the multi-variate sequential input and the self-attention mechanism.To evaluate the multi-task learning performance and information-sharing characteristics within the network,four distinct two-branch network architectures are evaluated.Empirical validation is conducted through a driving simulator-based experiment,encompassing 21 participants.The pro-posed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both two-hand and single-hand driving modes.These findings hold significant promise for the advancement of driver steering assistance systems,fostering mutual comprehension and synergy between human drivers and intelligent vehicles.

Advanced optimization of gas turbine aero-engine transient performance using linkage-learning genetic algorithm:PartⅡ,optimization in flight mission and controller gains correlation development

PartⅠhas illustrated the procedures to apply the Linkage Learning Genetic Algorithm(LLGA)in Gas Turbine Engine(GTE)controller gains tuning and generated the optimization results for runway conditions from idle to takeoff.However,the total pressure and temperature of the engine inlet vary as the changing of altitude and Mach number,which would lead to the variation in fuel flow supply regulation.As a result,the optimized gains in runway might not be suitable for other flight conditions.In order to maintain the optimal control performance,the GTE controller gains should be adjusted according to the flight conditions.This paper extends the application of the LLGA method to other flight conditions and then simulates a complete flight mission with different gains and weather condition configurations.For this purpose,the control parameters in the Simulink model of the GTE controller are first corrected by the weather condition in altitude.Then,a typical flight mission is defined and divided into different flight segments based on the altitude and Mach number configuration.One representative point is selected from each segment as the datum point for optimization process.After this step,the LLGA method is used to find the best gains combinations for different flight conditions and the differences in optimization effects for different flight conditions are analyzed subsequently.The simulation results show that the optimization effect of the control performance of each flight condition is dependent on the value of(θδ)~(1/2)and the optimal K_(pla)in some flight conditions is approximately equal to p hd times of the Kplavalue in sea level standard condition.Finally,the complete flight mission is simulated with different gains and weather condition configurations.The simulation results show that the engine performance has been greatly improved after optimization by LLGA in the transient state and the high altitude conditions.In other steady states,the optimization effect is not very obvious.

Parametric study of turbine NGV blade lean and vortex design

The effects of blade lean and vortex design on the aerodynamics of a turbine entry nozzle guide vane （NGV） are considered using computational fluid dynamics. The aim of the work is to address some of the uncertainties which have arisen from previous studies where conflicting results have been reported for the effect on the NGV. The configuration was initially based on the energy efficient engine turbine which also served as the validation case for the computational method. A total of 17 NGV configurations were evaluated to study the effects of lean and vortex design on row efficiency and secondary kinetic energy. The distribution of mass flow ratio is introduced as an additional factor in the assessment of blade lean effects. The results show that in the turbine entry NGV, the secondary flow strength is not a dominant factor that determines NGV losses and therefore the changes of loading distribution due to blade lean and the associated loss mecha- nisms should be regarded as a key factor. Radial mass flow redistribution under different NGV lean and twist is demonstrated as an addition key factor influencin~ row efficiency.

Advanced optimization of gas turbine aero-engine transient performance using linkage-learning genetic algorithm:PartⅠ,building blocks detection and optimization in runway

This paper proposes a Linkage Learning Genetic Algorithm(LLGA)based on the messy Genetic Algorithm(mGA)to optimize the Min-Max fuel controller performance in Gas Turbine Engine(GTE).For this purpose,a GTE fuel controller Simulink model based on the Min-Max selection strategy is firstly built.Then,the objective function that considers both performance indices(response time and fuel consumption)and penalty items(fluctuation,tracking error,overspeed and acceleration/deceleration)is established to quantify the controller performance.Next,the task to optimize the fuel controller is converted to find the optimization gains combination that could minimize the objective function while satisfying constraints and limitations.In order to reduce the optimization time and to avoid trapping in the local optimums,two kinds of building block detection methods including lower fitness value method and bigger fitness value change method are proposed to determine the most important bits which have more contribution on fitness value of the chromosomes.Then the procedures to apply LLGA in controller gains tuning are specified stepwise and the optimization results in runway condition are depicted subsequently.Finally,the comparison is made between the LLGA and the simple GA in GTE controller optimization to confirm the effectiveness of the proposed approach.The results show that the LLGA method can get better solution than simple GA within the same iterations or optimization time.The extension applications of the LLGA method in other flight conditions and the complete flight mission simulation will be carried out in partⅡ.

Review of hybrid electric powered aircraft,its conceptual design and energy management methodologies

The paper overviews the state-of-art of aircraft powered by hybrid electric propulsion systems.The research status of the design and energy management of hybrid aircraft and hybrid propulsion systems are further reviewed.The first contribution of the review is to demonstrate that,in the context of relatively underdeveloped electrical storage technologies,the study of mid-scale hybrid aircraft can contribute the most to both theoretical and practical knowledge.Meanwhile,the profits and potential drawbacks of applying hybrid propulsion to mid-scale hybrid airplanes have not been thoroughly illustrated.Secondly,as summed in the overview of design methodologies,the multi-objective optimization transcends the single-objective one.The potential of the hybrid propulsion system can be thoroughly evaluated in only one optimization run,if several objectives optimized simultaneously.Yet there are few researches covering the conceptual design of hybrid aircraft using multi-objective optimization.The review of the most popular energy management strategies discloses the third research gap—current methodologies favoured in hybrid ground vehicles do not consider the aircraft safety.Additionally,both non-causal and causal energy management are needed for performing a complicated flight mission with several sub-tasks.

Mycogenic Silver Nanoparticles From Endophytic Trichoderma atroviride with Antimicrobial Activity

There is an increasing interest in developing nanoparticles with diverse biologic activities.To this end,we prepared 10 to 15 nm silver nanoparticles(AgNP)from native isolates of Trichoderma atroviride.Within this study,endophytic fungi hosted four medicinal plants in Saint Katherine Protectorate,South Sinai,Egypt have been isolated by surface sterilization technique on four isolation media.Ten species,based on their frequency of occurrence,out of twenty recovered taxa were tested for their capability to synthesize extracellular AgNPs.Trichoderma atroviride hosted Chiliadenus montanus was found to be the best candidate for the production of mycogenic AgNPs among all examined species.The mycosynthesized AgNPs were compared with chemically synthesized and characterized using Ultraviolet-visible(UV-vis)spectroscopy,Raman spectroscopy,X-ray diffraction(XRD)and high-resolution transmission electron microscopy(HRTEM)techniques.The HRTEM result showed the distribution of spherical AgNPs ranging from 10 to 15 nm.Trichoderma atroviride isolate was subjected to sequencing for confirmation of phenotypic identification.The internal transcribed spacer(ITS)1-5.8 s-ITS2 rDNA sequences obtained were compared with those deposited in the GenBank Database and registered with accession number MH283876 in the NCBI Database.Antibacterial,anticandidal and antifungal effects of chemically and mycosynthesized AgNPs were examined at various concentrations in vitro against six pathogenic bacteria and 4 pathogenic fungi by agar well diffusion technique.Standard antibiotics;Gentamicin,Amoxicillin,Clotrimazole,and Nystatin at 5μg/disk were taken as positive controls,while 5%DMSO was used as the negative control.Our data revealed that the application of mycogenic AgNPs at a concentration of 100 ppm resulted in maximum inhibition of pathogenic bacteria and fungi.These data suggest that AgNPs from native isolates of Trichoderma atroviride(MH283876)offer a source of rapid synthesis of eco-friendly,economical biomaterials that show antimicrobial activities.

Tracking and Sensor Coverage of Spatio-temporal Quantities Using a Swarm of Artificial Foraging Agents

Using a network of mobile sensors to track and map a dynamic spatio-temporal process in the environment is one of the current challenges in multi-agent systems. In this work, a distributed probabilistic multi-agent algorithm inspired by the bacte- rium foraging behavior is presented. The novelty of the algorithm lies in being capable of tracking and mapping a spa- tio-temporal quantity without the need of machine learning, estimation algorithms or future planning. This is unlike most current techniques that rely heavily on machine learning to estimate the distribution as well as the profile of spatio-temporal quantities. The experimental studies carried out in this work show that the algorithm works well by following the concentration gradient of a dynamic plume created under diffusive conditions. Furthermore, the algorithm is inherently capable of finding the source of a diffusive spatio-temporal quantity as well as performing environmental exploration. It is computationally tractable for simple agents, shown to adapt to its environment and can deal successfully with noise in sensor readings as well as in robot dynamics.

Aerodynamic analysis of insect-like flapping wings in fan-sweep and parallel motions with the slit effect

In this study,the aerodynamic performance of flapping wings using a parallel motion was investigated and compared with the insect-like‘‘fan-sweep’’motion,and the effect of adding a slit to the wings was analyzed.First,numerical simulations were performed to analyze the wing aerodynamics of two flapping motions with equivalent stroke amplitudes over a range of pitching angles based on computational fluid dynamics(CFD).The simulation results indicated that flapping wings with a rapid and short parallel motion achieved better lift and efficiency than those of the fan-sweep motion while maintaining the same aerodynamic characteristics regarding stall delay and leading-edge vortices.For a parallel motion with a pitching angle of 25◦and 100 mm stroke amplitude,the wings generated an average lift of 8.4 gf with a lift-to-drag ratio of 1.06,respectively,which were 1.8%and 26%greater than those of the fan-sweep motion with a corresponding 96◦stroke amplitude.This situation was reversed when the pitching angle and stroke amplitude were increased to 45◦and 144◦for the fan-sweep motion,which was equivalent to the parallel motion with a 150 mm stroke amplitude.The slit effect in the parallel motion was also evaluated,and the CFD results indicated that a slit width of 1 mm(1/50 wing chord)increased the lift of the wing by approximately 27%in the case of the 150 mm stroke amplitude.Further,the slit width slightly influenced the lift and aerodynamic efficiency.

Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks

Reinforcement Learning(RL)techniques are being studied to solve the Demand and Capacity Balancing(DCB)problems to fully exploit their computational performance.A locally gen-eralised Multi-Agent Reinforcement Learning(MARL)for real-world DCB problems is proposed.The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management(ATFM)region to quickly obtain a satisfactory solution.In this method,agents of all flights in a scenario form a multi-agent decision-making system based on partial observation.The trained agent with the customised neural network can be deployed directly on the corresponding flight,allowing it to solve the DCB problem jointly.A cooperation coefficient is introduced in the reward function,which is used to adjust the agent’s cooperation preference in a multi-agent system,thereby controlling the distribution of flight delay time allocation.A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated.Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method.From a statis-tical point of view,it is proven that the proposed method is generalised within the scope of the flights and sectors of interest,and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods.The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.

Optimal three-dimensional impact time guidance with seeker’s field-of-view constraint

This paper proposes a new three-dimensional optimal guidance law for impact time control with seeker’s Field-of-View(FOV) constraint to intercept a stationary target. The proposed guidance law is devised in conjunction with the concept of biased Proportional Navigation Guidance(PNG). The guidance law developed leverages a nonlinear function to ensure the boundedness of velocity lead angle to cater to the seeker’s FOV limit. It is proven that the impact time error is nullified in a finite-time under the proposed method. Additionally, the optimality of the biased command is theoretically analyzed. Numerical simulations confirm the superiority of the proposed method and validate the analytic findings.