Emerging Trends in Damage Tolerance Assessment:A Review of Smart Materials and Self-Repairable Structures

The discipline of damage tolerance assessment has experienced significant advancements due to the emergence of smart materials and self-repairable structures.This review offers a comprehensive look into both traditional and innovative methodologies employed in damage tolerance assessment.After a detailed exploration of damage tolerance concepts and their historical progression,the review juxtaposes the proven techniques of damage assessment with the cutting-edge innovations brought about by smart materials and self-repairable structures.The subsequent sections delve into the synergistic integration of smart materials with self-repairable structures,marking a pivotal stride in damage tolerance by establishing an autonomous system for immediate damage identification and self-repair.This holistic approach broadens the applicability of these technologies across diverse sectors yet brings forth unique challenges demanding further innovation and research.Additionally,the review examines future prospects that combine advanced manufacturing processes with data-centric methodologies,amplifying the capabilities of these‘intelligent’structures.The review culminates by highlighting the transformative potential of this union between smart materials and self-repairable structures,promoting a sustainable and efficient engineering paradigm.

Generation of OAM Waves and Analysis of Mode Purity for 5G Sub-6 GHz Applications

This article presents the generation of Orbital AngularMomentum(OAM)vortex waves with mode 1 using Uniform Circular Array(UCA)antenna.Two different designs,namely,UCA-1(4-element array antenna)and UCA-2(8-element array antenna),were designed and fabricated using FR-4 substrate to generate OAM mode 1 at 3.5 GHz(5G mid-band).The proposed antenna arrays comprised rectangular microstrip patch elements with inset fed technique.The elements were excited by a carefully designed feeding phase shift network to provide similar output energy at output ports with desired phase shift value.The generated OAM waves were confirmed by measuring the null in the bore sight of their 2D radiation patterns,simulated phase distribution and intensity distribution.The measurement results agree well with the simulation results.Moreover,a detailed mode purity analysis of the generated OAM waves was carried out considering different factors.The investigation found that the greater the number of elements,the higher the purity of the generated OAM wave.Compared with other previous works,the proposed antenna design of this paper is very simple to design and fabricate.In addition,the proposed antennas are compact in design even at lower frequency band with very wide bandwidth to meet the requirements of 5G mid-band applications.

Chemical and Thermal Characterization of Cement Mortar Containing Ground Palm Oil Fuel Ash as a Partial Cement Replacement

This study investigates the influence of using ground palm oil fuel ash(G-POFA) from 10%-30% as cement replacement(by weight) on the cement mortar's pH under various curing conditions. These findings were supplemented by thermal gravimetric analysis(TGA). Moreover, the resistance of G-POFA blended cement mortars to water absorption and sorptivity was determined. Further, the k-value test was carried out to explain the pozzolanic and filler behavior of G-POFA and to support the results obtained from TGA. It was found that there was no significant impact of several curing conditions on the pH of mortars. The mortar with 10% G-POFA in replacement of cement(G-POFA-10) exhibited the best resistance against water absorption and sorptivity.

Multi-Objective Optimization of External Louvers in Buildings

Because solar energy is among the renewable energies,it has traditionally been used to provide lighting in buildings.When solar energy is effectively utilized during the day,the environment is not only more comfortable for users,but it also utilizes energy more efficiently for both heating and cooling purposes.Because of this,increasing the building’s energy efficiency requires first controlling the amount of light that enters the space.Considering that the only parts of the building that come into direct contact with the sun are the windows,it is essential to make use of louvers in order to regulate the amount of sunlight that enters the building.Through the use of Ant Colony Optimization(ACO),the purpose of this study is to estimate the proportions and technical specifications of external louvers,as well as to propose a model for designing the southern openings of educational space in order to maximize energy efficiency and intelligent consumption,as well as to ensure that the appropriate amount of light is provided.According to the findings of this research,the design of external louvers is heavily influenced by a total of five distinct aspects:the number of louvers,the depth of the louvers,the angle of rotation of the louvers,the distance between the louvers and the window,and the reflection coefficient of the louvers.The results of the 2067 simulated case study show that the best reflection rates of the louvers are between 0 and 15 percent,and the most optimal distance between the louvers and the window is in the range of 0 to 18 centimeters.Additionally,the results show that the best distance between the louvers and the window is in the range of 0 to 18 centimeters.

Design and Analysis of Graphene Based Tunnel Field Effect Transistor with Various Ambipolar Reducing Techniques

The fundamental advantages of carbon-based graphene material,such as its high tunnelling probability,symmetric band structure(linear dependence of the energy band on the wave direction),low effective mass,and characteristics of its 2D atomic layers,are the main focus of this research work.The impact of channel thickness,gate under-lap,asymmetric source/drain doping method,workfunction of gate contact,and High-K material on Graphene-based Tunnel Field Effect Transistor(TFET)is analyzed with 20 nm technology.Physical modelling and electrical characteristic performance have been simulated using the Atlas device simulator of SILVACO TCAD with user-defined material syntax for the newly included graphene material in comparison to silicon carbide(SiC).The simulation results in significant suppression of ambipolar current to voltage characteristics of TFET and modelled device exhibits a significant improvement in subthreshold swing(0.0159 V/decade),the ratio of Ion/Ioff(1000),and threshold voltage(-0.2 V with highly doped p-type source and 0.2 V with highly doped n-type drain)with power supply of 0.5 V,which make it useful for low power digital applications.

Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques

One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of recovery.Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition.This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies.Additionally,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers.In this research,the tri-stage wrapper method is used because it reduces the computation time.The proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this scenario.To start,this research pre-processed the information.Next for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is used.Then,various classification methods are tested and trained.Deep learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for categorization.The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.

Minimizing Buoyancy Factor of Metallic Pressure-Hull Subjected to Hydrostatic Pressure

To increase the payload,reduce energy consumption,improve work efficiency and therefore must accordingly reduce the total hull weight of the submersible.This paper introduces a design optimization process for the pressurehull of submarines under uniform external hydrostatic pressure using bothfinite element analysis(FEA)and optimization tools.A comprehensive study about the optimum design of the pressure hull,to minimize the weight and increase the volume,to reach minimum buoyancy factor and maximum operating depth minimizing the buoyancy factor(B.F)is taken as an objective function with constraints of plate and frame yielding,general instability and deflection.The optimization process contains many design variables such as pressure-hull plate thickness,unsupported spacing,dimensions of long and ring beams andfinally the elliptical submersible pressure-hull diameters.The optimization process was conducted using ANSYS parametric design language(APDL)and ISIGHT.The Multi-Island Genetic Algorithm(G.A)is considered to conduct the optimization process.Additionally,parametric analysis is done on the pressure hull to examine the effect of different design variables on the pressure-hull design.As a result,the B.F of the proposed optimal model is reduced by an average of 31.78%compared with Reference Model(RM).Maximum von Mises stress is reduced by 27%as well.These results can be helpful for submarine pressure-hull designers.

Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).

Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system symmetrically.Therefore,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative rates.In addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were shown.The results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten iterations.Whereas in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved parameters.Thus EDLA algorithm introduces novelty concerning its performance and particular activation function.This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner.This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses aftermodification.If the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

The pH of Cement-based Materials:A Review

Cement-based materials (CBMs),such as paste,mortar and concrete,are highly alkaline with an initial high pH of approximately 12.0 to 13.8.CBMs have a high pH due to the existing oxide mineral portlandite and alkali metal contents in Portland cement.The high pH of concrete provides excellent protection and reinforces the steel bars against corrosion.The pH of concrete does not remain constant due to ageing and other defect-causing factors,such as chloride ingress,alkali leaching,carbonation,corrosion,acid attack,moisture and biodegradation process.Reducing the concrete pH has negative impact on the strength,durability and service life of concrete buildings.However,the high pH of concrete may also cause concrete structure deterioration,such as alkali silica reaction,porosity and moisture related damages in concrete structures.The pH of CBMs can be influenced by high temperatures.For instance,the extremely high volume (85%-100%) of slag-blended cement pastes shows considerable pH reduction from 12.80 to 11.34 at 800 ℃.As many concrete structure deterioration are related to concrete pH,using an accurate and reliable method to measure pH and analyse the durability of reinforced concrete structure based on pH values is extremely important.This study is a comprehensive review of the pH of CBM in terms of measurement,limitations and varying values for different CBM types.

Variables Affecting the pH Measurement of Cement Mortars

A quantitative pH measuring method has been used to measure the pH of pure and blended cement mortars.The blended cement mortars incorporating supplementary cementitious materials(SCMs)such as fly ash(FA),ground granulated ballast furnace slag(GGBFS)and palm oil fuel ash(POFA)were used.Moreover,different variables affecting the pH values of CBMs such as temperature of sample solution,quantity of sample powder,dilution ratio and temporary storage of sample during pH measuring process have been studied for all cement mortars.

Some aspects affecting the response of RC compartment structures subjected to internal blast loads

Protective compartments are typically used to protect some specific structures from internal explosions,such as industrial buildings that contain devices that may explode in certain circumstances.This research investigates how the response of reinforced concrete(RC)compartment structures subjected to internal blast loads are affected by the following aspects:introduction of material nonlinearity in the analysis,reinforcement ratio,and aspect ratio of the compartment.To achieve this goal,a calibrated and sophisticated FE numerical model is introduced,and a parametric study for the intended aspects is carried out.A discussion of the results and conclusions are offered,which show the role of each aspect in the dynamic performance of the compartment structures.The main conclusions are as follows:introduction of material nonlinearity in this type of analysis and for these structures is very important and significant in obtaining accurate outputs that are similar to actual behavior;the reinforcement ratio has a significant effect on the response and its effect varies depending on the thickness of the compartment;in general,increasing the reinforcement ratio enhances the behavior and reduces the stresses in the compartment;and the aspect ratio of the compartment does not show a clear pattern on the response of such structures under internal blast loads.

Synthesis and characterization of polymeric responsive CMC/Pectin hydrogel films loaded with Tamarix aphylla extract as potential wound dressings

The fourth most predominant overwhelming type of trauma is burn injuries worldwide.Ideal wound healing dressings help in the wound healing process in a lower time with less pain.Commonly used dry wound dressing,like absorbent gauze or absorbent cotton,possess limited therapeutic effects and require repeated use,which further exaggerates patients’suffering.In contrast,hydrogels films present a promising alternative to improve healing by guaranteeing a moisture balance at the wound site.The aim of the current study was to synthesize Tamarix aphylla(T.aphylla)extract-loaded hydrogel film with Na-CMC and pectin and to study their wound healing properties.The Na-CMC/Pectin hydrogels films were synthesized and characterized for HPLC analysis,FTIR,surface morphology,rheology,tensile strength,swelling behavior,drug release kinetics,and in vivo wound healing in an animal model.FTIR confirmed the existence of strong interaction between both polymers but no interaction with the extract.SEM photographs showed successful embedding of extract in small pores of hydrogel film and showed smooth and homogenous morphology.Rheological and texture profiles indicated that hydrogels behaved as strong gels.Swelling and erosion were dependent on the amount of the CMC.HPLC showed drug content of three selected formulation(A3,E3 and S3)as 85±0.1%,82.5±0.4%and 80±0.3%,respectively.The release of the drug from the hydrogel was controlled by a Fickian diffusion mechanism.In vivo wound healing activity of hydrogel film confirmed that T.aphylla extract successfully promoted healing rate by significantly reducing(P<0.05)the size of wound closure compared to the control group,evidenced by intensive collagen formation in histopathological and biochemical analysis.The capability of these hydrogels for burn wounds could be valuable for medical uses as a new window of safe and effective medication.

Formation of Co-implanted Silicon Ultra-Shallow Junctions for Low Thermal Budget Applications

We present a systematic study to create ultra-shallow junctions in n-type silicon substrates and investigate both pre-and post-annealing processes to create a processing strategy for potential applications in nano-devices.Starting wafers were co-implanted with indium and C atoms at energies of 70 keV and 10 keV,respectively.A carefully chosen implantation schedule provides an abrupt ultra-shallow junction between 17 and 43 nm with suppressed sheet resistance and appropriate retained sheet carrier concentration at low thermal budget.A defect doping matrix,primarily the behavior and movement of co-implant generated interstitials at different annealing temperatures,may be engineered to form sufficiently activated ultra-shallow devices.

An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip(MPSoC)has become an integral chip design issue for multiprocessor systems.The performance measurement of computational systems is changing with the advancement in technology.Due to shrinking and smaller chip size power densities onchip are increasing rapidly that increasing chip temperature in multi-core embedded technologies.The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor(CMOS)circuits because high on-chip temperature adversely affects the life span of the chip.In this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first(EA-EDF)scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip(SOC).Dynamic power management(DPM)enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task migration.Task migration avoids peak temperature values in the multicore system.High utilization factor(ui)on central processing unit(CPU)core consumes more energy and increases the temperature on-chip.Our technique switches the core bymigrating such task to a core that has less temperature and is in a low power state.The proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature core.The effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and works.The simulation results show the improvement in performance by optimizing 4.8%on u_(i) 9%,16%,23%and 25%at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can schedule more tasks to make an energy-efficient processor by controlling and managing the energy consumption of MPSoC.

Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for Homogeneous MPSoCs

Increasing the life span and efficiency of Multiprocessor System on Chip(MPSoC)by reducing power and energy utilization has become a critical chip design challenge for multiprocessor systems.With the advancement of technology,the performance management of central processing unit(CPU)is changing.Power densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip size.When energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor(CMOS)circuits and reduces the speed by 10%–15%because excessive on-chip temperature shortens the chip’s life cycle.In this paper,we address the scheduling&energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling(EA-EDF)based technique formultiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor systemon-chip while lowering energy and power consumption.The selection of core andmigration of tasks prevents the system from reaching itsmaximumenergy utilization while effectively using the dynamic power management(DPM)policy.Increase in the execution of tasks the temperature and utilization factor(u_(i))on-chip increases that dissipate more power.The proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple CPUs.The performance of the EA-EDF algorithm was evaluated by an extensive set of experiments,where excellent results were reported when compared to other current techniques,the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%–4.7%on a utilization of 6%,36%&46%at 520&624 MHz operating frequency when particularly in comparison to other energy-aware methods for MPSoCs.Tasks are running and accurately scheduled to make an energy-efficient processor by controlling and managing the thermal effects on-chip and optimizing the energy consumption of MPSoCs.

Intelligent System Application to Monitor the Smart City Building Lighting

A smart city incorporates infrastructure methods that are environmentally responsible,such as smart communications,smart grids,smart energy,and smart buildings.The city administration has prioritized the use of cutting-edge technology and informatics as the primary strategy for enhancing service quality,with energy resources taking precedence.To achieve optimal energy management in themultidimensional system of a city tribe,it is necessary not only to identify and study the vast majority of energy elements,but also to define their implicit interdependencies.This is because optimal energy management is required to reach this objective.The lighting index is an essential consideration when evaluating the comfort indicators.In order to realize the concept of a smart city,the primary objective of this research is to create a system for managing and monitoring the lighting index.It is possible to identify two distinct phaseswithin the intelligent system.Once data collection concludes,the monitoring system will be activated.In the second step,the operation of the control system is analyzed and its effect on the performance of the numerical model is determined.This evaluation is based on the proposed methodology.The optimized resultswere deemed satisfactory because they maintained the brightness index value(79%)while consuming less energy.The intelligent implementation system generated satisfactory outcomes,which were observed 1.75 times on average.

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images.Hyperspectral remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic spectrum.In order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and quantitativemonitoring.Particularly,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on vegetation.With this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing Images.The presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images.To accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature vectors.Besides,Elman neural network(ENN)model is applied to allot proper class labels into the input HSI.Finally,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this article.The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects.Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue functions.Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions.The latest advancements in deep learning(DL)approaches permit the design of effectual OD approaches.This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing Images.The proposed AEODCNN-VD model focuses on the identification of vehicles accurately and rapidly.To detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline model.In addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in RSIs.The features from the Inception model are passed into theMFPNformultiway andmultiscale feature fusion.Finally,the fused features are passed into bounding box and class prediction networks.For enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an ecosystem.The performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

Challenge-Response Emotion Authentication Algorithm Using Modified Horizontal Deep Learning

Face authentication is an important biometric authentication method commonly used in security applications.It is vulnerable to different types of attacks that use authorized users’facial images and videos captured from social media to perform spoofing attacks and dynamic movements for penetrating secur-ity applications.This paper presents an innovative challenge-response emotions authentication model based on the horizontal ensemble technique.The proposed model provides high accurate face authentication process by challenging the authorized user using a random sequence of emotions to provide a specific response for every authentication trial with a different sequence of emotions.The proposed model is applied to the KDEF dataset using 10-fold cross-valida-tions.Several improvements are made to the proposed model.First,the VGG16 model is applied to the seven common emotions.Second,the system usability is enhanced by analyzing and selecting only the four common and easy-to-use emotions.Third,the horizontal ensemble technique is applied to enhance the emotion recognition accuracy and minimize the error during authen-tication processes.Finally,the Horizontal Ensemble Best N-Losses(HEBNL)is applied using challenge-response emotion to improve the authentication effi-ciency and minimize the computational power.The successive improvements implemented on the proposed model led to an improvement in the accuracy from 92.1%to 99.27%.