A methodology for damage evaluation of underground tunnels subjected to static loading using numerical modeling

We have proposed a methodology to assess the robustness of underground tunnels against potential failure.This involves developing vulnerability functions for various qualities of rock mass and static loading intensities.To account for these variations,we utilized a Monte Carlo Simulation(MCS)technique coupled with the finite difference code FLAC^(3D),to conduct two thousand seven hundred numerical simulations of a horseshoe tunnel located within a rock mass with different geological strength index system(GSIs)and subjected to different states of static loading.To quantify the severity of damage within the rock mass,we selected one stress-based(brittle shear ratio(BSR))and one strain-based failure criterion(plastic damage index(PDI)).Based on these criteria,we then developed fragility curves.Additionally,we used mathematical approximation techniques to produce vulnerability functions that relate the probabilities of various damage states to loading intensities for different quality classes of blocky rock mass.The results indicated that the fragility curves we obtained could accurately depict the evolution of the inner and outer shell damage around the tunnel.Therefore,we have provided engineers with a tool that can predict levels of damages associated with different failure mechanisms based on variations in rock mass quality and in situ stress state.Our method is a numerically developed,multi-variate approach that can aid engineers in making informed decisions about the robustness of underground tunnels.

Determination of uncertainties of geomechanical parameters of metamorphic rocks using petrographic analyses

Geomechanical parameters of intact metamorphic rocks determined from laboratory testing remain highly uncertain because of the great intrinsic variability associated with the degrees of metamorphism.The aim of this paper is to develop a proper methodology to analyze the uncertainties of geomechanical characteristics by focusing on three domains,i.e.data treatment process,schistosity angle,and mineralogy.First,the variabilities of the geomechanical laboratory data of Westwood Mine(Quebec,Canada)were examined statistically by applying different data treatment techniques,through which the most suitable outlier methods were selected for each parameter using multiple decision-making criteria and engineering judgment.Results indicated that some methods exhibited better performance in identifying the possible outliers,although several others were unsuccessful because of their limitation in large sample size.The well-known boxplot method might not be the best outlier method for most geomechanical parameters because its calculated confidence range was not acceptable according to engineering judgment.However,several approaches,including adjusted boxplot,2MADe,and 2SD,worked very well in the detection of true outliers.Also,the statistical tests indicate that the best-fitting probability distribution function for geomechanical intact parameters might not be the normal distribution,unlike what is assumed in most geomechanical studies.Moreover,the negative effects of schistosity angle on the uniaxial compressive strength(UCS)variabilities were reduced by excluding the samples within a specific angle range where the UCS data present the highest variation.Finally,a petrographic analysis was conducted to assess the associated uncertainties such that a logical link was found between the dispersion and the variabilities of hard and soft minerals.

A review of understanding electrocatalytic reactions in energy conversion and energy storage systems via scanning electrochemical microscopy

To address climate change and promote environmental sustainability,electrochemical energy conversion and storage systems emerge as promising alternative to fossil fuels,catering to the escalating demand for energy.Achieving optimal energy efficiency and cost competitiveness in these systems requires the strategic design of electrocatalysts,coupled with a thorough comprehension of the underlying mechanisms and degradation behavior occurring during the electrocatalysis processes.Scanning electrochemical microscopy(SECM),an analytical technique for studying surface electrochemically,stands out as a powerful tool offering electrochemical insights.It possesses remarkable spatiotemporal resolution,enabling the visualization of the localized electrochemical activity and surface topography.This review compiles crucial research findings and recent breakthroughs in electrocatalytic processes utilizing the SECM methodology,specifically focusing on applications in electrolysis,fuel cells,and metal–oxygen batteries within the realm of energy conversion and storage systems.Commencing with an overview of each energy system,the review introduces the fundamental principles of SECM,and aiming to provide new perspectives and broadening the scope of applied research by describing the major research categories within SECM.

An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine learning(ML)models effectively deal with such challenges.This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024.In addition,it analyses the effectiveness of various input parameters considered in crop yield prediction models.We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield.The total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is 125.We conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research papers.Each study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel performance.We also discuss the ethical and social impacts of AI on agriculture.However,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven models.Therefore,thorough research is required to deal with challenges in predicting agricultural output.

Towards high-performance and robust anion exchange membranes(AEMs)for water electrolysis:Super-acid-catalyzed synthesis of AEMs

The increasing demand for hydrogen energy to address environmental issues and achieve carbon neutrality has elevated interest in green hydrogen production,which does not rely on fossil fuels.Among various hydrogen production technologies,anion exchange membrane water electrolyzer(AEMWE)has emerged as a next-generation technology known for its high hydrogen production efficiency and its ability to use non-metal catalysts.However,this technology faces significant challenges,particularly in terms of the membrane durability and low ionic conductivity.To address these challenges,research efforts have focused on developing membranes with a new backbone structure and anion exchange groups to enhance durability and ionic conductivity.Notably,the super-acid-catalyzed condensation(SACC)synthesis method stands out due to its user convenience,the ability to create high molecular weight(MW)polymers,and the use of oxygen-tolerant organic catalysts.Although the synthesis of anion exchange membranes(AEMs)using the SACC method began in 2015,and despite growing interest in this synthesis approach,there remains a scarcity of review papers focusing on AEMs synthesized using the SACC method.The review covers the basics of SACC synthesis,presents various polymers synthesized using this method,and summarizes the development of these polymers,particularly their building blocks including aryl,ketone,and anion exchange groups.We systematically describe the effects of changes in the molecular structure of each polymer component,conducted by various research groups,on the mechanical properties,conductivity,and operational stability of the membrane.This review will provide insights into the development of AEMs with superior performance and operational stability suitable for water electrolysis applications.

Oxidation of emerging organic contaminants by in-situ H_(2)O_(2) fenton system

The existence and risk of emerging organic contaminants(EOCs)have been under consideration and paid much effort to degrade these pollutants.Fenton system is one of the most widely used technologies to solve this problem.The original Fenton system relies on the hydroxyl radicals produced by Fe(Ⅱ)/H_(2)O_(2) to oxidize the organic contaminants.However,the application of the Fenton system is limited by its low iron cycling efficiency and the high risks of hydrogen peroxide transportation and storage.The introduction of external energy(including light and electricity etc.)can effectively promote the Fe(Ⅲ)/Fe(Ⅱ)cycle and the reduction of oxygen to produce hydrogen peroxide in situ.This review introduces three in-situ Fenton systems,which are electro-Fenton,Photo-Fenton,and chemical reaction.The mechanism,influencing factors,and catalysts of these three in-situ Fenton systems in degrading EOCs are discussed systematically.This review strengthens the understanding of Fenton and in-situ Fenton systems in degradation,offering further insight into the real application of the in-situ Fenton system in the removal of EOCs.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

An Assessment on the Impact of Technological Addiction on Workplace Productivity: Case of the Public Sector in Zimbabwe

With the increasing integration of technology in modern workplaces, concerns have emerged regarding the addictive nature of technology and its potential consequences on employee productivity. This research aims to investigate the impact of technological addiction on workplace productivity within the public sector of Zimbabwe. The study employed a mixed-methods approach, combining surveys, interviews, and a case study analysis, to examine the prevalence and effects of technological addiction in affecting productivity in the public sector of Zimbabwe. The findings indicate that excessive use of social media, and other digital distractions is a growing concern in the public sector, leading to decreased focus, missed deadlines, and strained teamwork. Factors such as unrestricted internet access, lack of clear usage policies, and inadequate self-regulation contribute to the problem The research outcomes also highlight the need for awareness and interventions to address social media addiction in the workplace, promote healthier technology use, and uphold productivity and employee well-being.

Comprehensive Assessment of Anxiolytic Properties in 4-HPAA Derivatives: Bridging in Vivo Validation and Molecular Docking Analyses

Anxiety is a significant mental health issue that substantially affects an individual’s quality of life. Feelings of uneasiness, irritability, and sleep disturbances characterize it. 4-Hydroxyphenyl acetic acid (4-HPAA) is identified in brain cells as a physiological byproduct of tyramine. This study hypothesizes that 4-HPAA may regulate anxiety due to its anxiolytic properties, acting as a modulator of the GABAergic system, which plays a crucial role in the pathophysiology of anxiety disorders. Our study aims to enhance the anxiolytic effects of 4-HPAA through chemical modification to improve its pharmacokinetic properties. Three derivatives, namely Isopropyl-4-hydroxy-[phenyl] acetate (IHPA), Isopropyl-4-hydroxy-[phenyl] acetate (MPAA), and 4-methoxyphenyl acetate (MPHA), have been synthesized from 4-HPAA. This assessment will use well-established animal models, specifically the Elevated Plus-Maze (EPM) and Zero Maze (EZM) tests, selected for their validity in replicating anxiety-like symptoms in animals. Chronic caffeine administration via drinking water (0.3 g/l for 14 days) was employed to induce an anxiety state for testing purposes. IHPA and MPAA demonstrated significant anxiolyticactivity when tested in the EPM and EZM experiments. Molecular docking simulations using AutoDock Vina indicated that 4-HPAA derivatives had docking scores ranging from −5.8 to −4.8 kcal/mol, compared to the standard anxiolytic medication Diazepam, which scored −7.1 kcal/mol. These scores suggest a potential for 4-HPAA derivatives to interact effectively with the Gamma-aminobutyric acid (GABA_A) receptor. In conclusion, our in vivo and in silico analyses indicate a promising anxiolytic potential for 4-HPAA derivatives.

Perceptions of Rural Emergency Department Providers regarding Telehealth-Based Care: Perceived Competency, Satisfaction with Care and Tele-ED Patient Disposition

Introduction: This study assesses rural providers’ perceptions of their ability to deliver high quality care via telehealth compared to usual care, and whether attending providers perceive that emergency department (ED) telehealth visits influence clinical reasoning in regard to patient disposition, specifically in tele-behavioral and tele-neurological cases. Methods: A cross-sectional survey was conducted of 134 ED providers (nurses [n = 126] and physicians [n = 8]) who were working in five Midwestern critical access hospitals (response rate 83%). Descriptive, correlational and stepwise regression analyses were employed to evaluate provider perceptions of 1) competency level in telehealth delivery, 2) patient health outcomes, 3) access to continuing education in telehealth, and 4) clinical influence of telehealth visit. Evaluation of preliminary set of N = 100 telehealth cases were assessed for influence of telehealth on clinical reasoning of attending physicians regarding patient disposition. Results: The majority (67%;n = 90) of participants had at least minimal experience with telehealth care delivery, with an average of 1 - 2 visits in teleneurology, and 3 - 4 visits in telebehavioral cases. Providers rated their overall mean competency level in telehealth care delivery as 3.01/5.00 based on a 5 point “novice (1) to expert” (5) scale. Mean scores for providers perceived competency level in 7 evidence-based sub-categories for telehealth care delivery were self-reported as relatively low to mid-range values, ranging from 2.64 - 3.57/5.00. Stepwise linear regression analysis of whether all providers “would recommend telehealth to their family and friends” revealed two predictors for model of best fit (n = 81;p 2 = 0.598): 1) their perceptions of telehealth experience compared to usual care;and 2) perceptions of patient health outcomes with telehealth compared to usual care. Providers rated “neutral” to “very unlikely” that they “would recommend telehealth to family and friends” (2.75/5.00;n = 122;91%). Attending physicians reported that for a majority of cases, telehealth visits influenced patient disposition and transfer decision-making (58.4%), and the influence of telehealth visits on patient disposition was statistically significantly higher for behavioral health cases (p Discussion: This study will be followed on to inform administrators/policy makers about 1) perceived level of competency of providers who implement tele-emergency care, 2) potential importance of telehealth equipment used and teamwork between rural providers and distant specialist, and 3) how use of telehealth may enhance ability of rural ED providers to improve quality of care. Perceived influence of telehealth on patient disposition is reported to be highest for telebehavioral patients. Healthcare educators need to place a priority on addressing provider competencies in telehealth through health professions degree programs and continuing education. Further research is needed to promote application and testing of evidence-based provider competencies in telehealth, and potentially relevant health communication models, to increase providers’ perceived efficacy and competency in telehealth care delivery, thus supporting high quality patient health outcomes.

Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data center.Smart city benefitted from offloading to edge point.Consider a mobile edge computing(MEC)network in multiple regions.They comprise N MDs and many access points,in which everyMDhasM independent real-time tasks.This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)algorithm.The proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system cost.In addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted resources.The TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading decision-making.Finally,the SGO algorithm is used for the parameter tuning of the DBN model.The simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

Mathematical Modeling of Moored Ship Motion in Arbitrary Harbor utilizing the Porous Breakwater

The motion of the moored ship in the harbor is a classical hydrodynamics problem that still faces many challenges in naval operations,such as cargo transfer and ship pairings between a big transport ship and some small ships.A mathematical model is presented based on the Laplace equation utilizing the porous breakwater to investigate the moored ship motion in a partially absorbing/reflecting harbor.The motion of the moored ship is described with the hydrodynamic forces along the rotational motion(roll,pitch,and yaw)and translational motion(surge,sway,and heave).The efficiency of the numerical method is verified by comparing it with the analytical study of Yu and Chwang(1994)for the porous breakwater,and the moored ship motion is compared with the theoretical and experimental data obtained by Yoo(1998)and Takagi et al.(1993).Further,the current numerical scheme is implemented on the realistic Visakhapatnam Fishing port,India,in order to analyze the hydrodynamic forces on moored ship motion under resonance conditions.The model incorporates some essential strategies such as adding a porous breakwater and utilizing the wave absorber to reduce the port’s resonance.It has been observed that these tactics have a significant impact on the resonance inside the port for safe maritime navigation.Therefore,the current numerical model provides an efficient tool to reduce the resonance within the arbitrarily shaped ports for secure anchoring.

Research Progress and Application of Plant Branching

Plant branching development plays an important role in plant morphogenesis(aboveground plant type),the number and angle of branches are important agronomic characters that determine crop plant type.Effective branches determine the number of panicles or pods of crops and then control the yield of crops.With the rapid development of plant genomics and molecular genetics,great progress has been made in the study of branching development.In recent years,a series of important branching-related genes have been validated from Arabidopsis thaliana,rice,pea,tomato and maize mutants.It is reviewed that plant branching development is controlled by genetic elements and plant hormones,such as auxin,cytokinin and lactones(or lactone derivatives),as well as by environment and genetic elements.Meanwhile,shoot architecture in crop breeding was discussed in order to provide theoretical basis for the study of crop branching regulation.

To Blend or Not to Blend? Anxiety Buffering Effects of Essential Oil Aromas

Essential oils of pure lavender and lavender blends have been employed as potential anxiolytic aromas in aromatherapy, but a direct comparison of their effectiveness is lacking. The current study investigated the effects of aroma on induced anxiety in non-clinical adults, comparing pure lavender, a commercially available blend and a no aroma control. An experimental, quantitative, mixed factorial design with an opportunity sample of 60 participants was employed. Participants were randomly allocated to three equal groups, one tested in a room infused with lavender aroma, the second with the doTerra Peace® blend, and the third free from any aroma. Participants’ state anxiety scores were measured before and after a novel video-based anxiety induction procedure. Data analysis revealed that the anxiety induction was successful and that both aromas delivered small to medium-sized buffering effects compared to no aroma. The findings add to a small body of research in an area where the practice is global yet has limited scientific evaluation. Future studies utilising brain imaging and blood serum analysis to investigate the anxiolytic mechanism of aromas would be beneficial to further our understanding.

Fault Tree Analysis of the Reliability of Electric Vehicles in India

Innovations for electric vehicles have advanced quickly in latest decades. Large-scale business use of these vehicles is still constrained by reliability-related issues. By utilising fault tree (FT) and Monte Carlo simulation, a mathematical prototype is created that includes the reliability traits of all major electrical parts of the vehicle system, including the battery, motor, drive, controllers. The research demonstrates that by raising the component restoration rates, the vehicle’s survivability can be raised. A thorough discussion of this paradigm is provided, along with a presentation and analysis of the reliability estimations based on an electric vehicle. This research on the reliability design and maintenance of an electric vehicle can be supported by the ideas that are outlined in the paper. Additionally, the findings of this study may be helpful to those who build electric vehicle, especially when upgrading the components efficiency and planning for reliability increase.

Design of Evolutionary Algorithm Based Unequal Clustering for Energy Aware Wireless Sensor Networks

Wireless Sensor Networks(WSN)play a vital role in several real-time applications ranging from military to civilian.Despite the benefits of WSN,energy efficiency becomes a major part of the challenging issue in WSN,which necessitate proper load balancing amongst the clusters and serves a wider monitoring region.The clustering technique for WSN has several benefits:lower delay,higher energy efficiency,and collision avoidance.But clustering protocol has several challenges.In a large-scale network,cluster-based protocols mainly adapt multi-hop routing to save energy,leading to hot spot problems.A hot spot problem becomes a problem where a cluster node nearer to the base station(BS)tends to drain the energy much quicker than other nodes because of the need to implement more transmission.This article introduces a Jumping Spider Optimization Based Unequal Clustering Protocol for Mitigating Hotspot Problems(JSOUCP-MHP)in WSN.The JSO algorithm is stimulated by the characteristics of spiders naturally and mathematically modelled the hunting mechanism such as search,persecution,and jumping skills to attack prey.The presented JSOUCPMHP technique mainly resolves the hot spot issue for maximizing the network lifespan.The JSOUCP-MHP technique elects a proper set of cluster heads(CHs)using average residual energy(RE)to attain this.In addition,the JSOUCP-MHP technique determines the cluster sizes based on two measures,i.e.,RE and distance to BS(DBS),showing the novelty of the work.The proposed JSOUCP-MHP technique is examined under several experiments to ensure its supremacy.The comparison study shows the significance of the JSOUCPMHP technique over other models.

Elucidating the clinical and immunological value of m6A regulatormediated methylation modification patterns in adrenocortical carcinoma

N6-methyladenosine methylation(m6A)is a common type of epigenetic alteration that prominently affects the prognosis of tumor patients.However,it is unknown how the m6A regulator affects the tumor microenvironment(TME)cell infiltration in adrenocortical carcinoma(ACC)and how it affects the prognosis of ACC patients yet.The m6A alteration patterns of 112 ACC patients were evaluated,furthermore,the association with immune infiltration cell features was investigated.The unsupervised clustering method was applied to typify the m6A alteration patterns of ACC patients.The principal component analysis(PCA)technique was taken to create the m6A score to assess the alteration pattern in specific malignancies.We found two independent patterns of m6A alteration in ACC patients.The TME cell infiltration features were significantly in accordance with phenotypes of tumor immune-inflamed and immune desert in both patterns.The m6Ascore also served as an independent predictive factor in ACC patients.The somatic copy number variation(CNV)and patients prognosis can be predicted by m6A alteration patterns.Moreover,the ACC patients with high m6A scores had better overall survival(OS)and higher efficiency in immune checkpoint blockade therapy.Our work demonstrated the significance of m6A alteration to the ACC patients immunotherapy.The individual m6A alteration patterns analysis might contribute to ACC patients prognosis prediction and immunotherapy choice.

Three-Dimensional Multiferroic Structures under Time-Harmonic Loading

Magneto-electro-elastic(MEE)materials are a specific class of advanced smart materials that simultaneouslymanifest the coupling behavior under electric,magnetic,and mechanical loads.This unique combination ofproperties allows MEE materials to respond to mechanical,electric,and magnetic stimuli,making them versatile forvarious applications.This paper investigates the static and time-harmonic field solutions induced by the surface loadin a three-dimensional(3D)multilayered transversally isotropic(TI)linear MEE layered solid.Green’s functionscorresponding to the applied uniform load(in both horizontal and vertical directions)are derived using the FourierBessel series(FBS)system of vector functions.By virtue of this FBS method,two sets of first-order ordinarydifferential equations(i.e.,N-type and LM-type)are obtained,with the expansion coefficients being Love numbers.It is noted that the LM-type system corresponds to the MEE-coupled P-,SV-,and Rayleigh waves,while the N-typecorresponds to the purely elastic SH-and Love waves.By applying the continuity conditions across interfaces,the solutions for each layer of the structure(from the bottom to the top)are derived using the dual-variable andposition(DVP)method.This method(i.e.,DVP)is unconditionally stable when propagating solutions throughdifferent layers.Numerical examples illustrate the impact of load types,layering,and frequency on the response ofthe structure,as well as the accuracy and convergence of the proposed approach.The numerical results are usefulin designing smart devices made of MEE solids,which are applicable to engineering fields like renewable energy.