Heterogeneous Image Knowledge Driven Visual Perception

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous images.Facing the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

Biological Interaction and Imaging of Ultrasmall Gold Nanoparticles

Ultrasmall gold nanoparticles(AuNPs)typically includes atomically precise gold nanoclusters(AuNCs)and AuNPs with a core size below 3 nm.Serving as a bridge between small molecules and traditional inorganic nanoparticles,the ultrasmall AuNPs show the unique advantages of both small molecules(e.g.,rapid distribution,renal clearance,low non-specific organ accumulation)and nanoparticles(e.g.,long blood circulation and enhanced permeability and retention effect).The emergence of ultrasmall AuNPs creates significant opportunities to address many challenges in the health field including disease diagnosis,monitoring and treatment.Since the nano–bio interaction dictates the overall biological applications of the ultrasmall AuNPs,this review elucidates the recent advances in the biological interactions and imaging of ultrasmall AuNPs.We begin with the introduction of the factors that influence the cellular interactions of ultrasmall AuNPs.We then discuss the organ interactions,especially focus on the interactions of the liver and kidneys.We further present the recent advances in the tumor interactions of ultrasmall AuNPs.In addition,the imaging performance of the ultrasmall AuNPs is summarized and discussed.Finally,we summarize this review and provide some perspective on the future research direction of the ultrasmall AuNPs,aiming to accelerate their clinical translation.

A new evolutional model for institutional field knowledge flow network

Purpose:This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model(IKM).The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.Design/Methodology/Approach:The IKM model enhances the preferential attachment and growth observed in scale-free BA networks,while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network.To compare its performance,the BA and DMS models are also employed for simulating the network.Pearson coefficient analysis is conducted on the simulated networks generated by the IKM,BA and DMS models,as well as on the actual network.Findings:The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network.It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm.The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.Research Limitations:This study has some limitations.Firstly,it primarily focuses on the evolution of knowledge flow networks within the field of physics,neglecting other fields.Additionally,the analysis is based on a specific set of data,which may limit the generalizability of the findings.Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.Practical Implications:The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions.It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations.The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.Originality/value:This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks.The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions.Moreover,the model has the potential to be applied to other knowledge networks,which are formed by knowledge organizations as node units.

Nutrition Awareness and Knowledge and Body Mass Index in Adolescents in Public Junior Secondary Schools

Background: Non-communicable diseases are increasing among adolescents. The decision about diet is the foundation of eating habits that could persist to adulthood. Dietary decisions, which usually are hard to change later in life, make nutrition education at school paramount to prevent obesity and NCDs, and promote healthy eating. Objectives: To assess level of nutrition awareness and knowledge of healthy eating and food intake behaviors and association with Body Mass Index (BMI) and age. Methods: A cross sectional study that included measures such as age, gender, socioeconomic status, BMI, and nutrition knowledge was conducted in 264 respondents from 18th June 2015 to 9th July 2015. The nutrition knowledge questionnaire was composed of 24 questions divided into food nutrients, food contents, healthiest foods, and energy expenditure and nutrition benefits. CDC BMI chart for 2-20-year-olds was used to plot respondent’s weight and height. Results: The mean age of the respondents was 14.3 years (SD 0.79). Most of the respondents (153/252, 53.6%) had a low socio-economic status as categorized by the present study. The mean (SD) BMI was 20.08 (3.90). Most respondents (56.3%, 142/252) failed the knowledge test and scored below 50% and only two respondents (0.8%) had excellent nutrition knowledge. The mean percentage achieved was 46.1% (SD 15.91) ranging from 8.3% to 87.5%. There was a significant correlation between nutrition knowledge and BMI (p = 0.001). Conclusion: The study shows that adolescents do not have adequate nutrition knowledge, placing them at risk for developing non-communicable diseases later in life. Nutrition education programs are urgently needed for teachers, parents, and children.

Combining Deep Learning with Knowledge Graph for Design Knowledge Acquisition in Conceptual Product Design

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product design.This study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge graph.Specifically,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product design.Moreover,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction module.Experimental comparison verified the effectiveness and accuracy of the proposed knowledge extraction model.The case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

A Survey of Knowledge Graph Construction Using Machine Learning

Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.

Ecological network analysis reveals complex responses of tree species life stage interactions to stand variables

Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.

Reviewer Acknowledgements

The editors of International Journal of Ophthalmology gratefully acknowledge the members of IJO Editorial Board and reviewers from 57 countries and regions who participated in the peer-reviews and provided their valuable comments between Nov.1^(st),2022 and Oct.31^(st),2023.

Global systematic review and meta-analysis of knowledge, attitudes, and practices towards dengue fever among the general population

Objective:To determine the global level of knowledge,attitudes,and practices towards dengue fever among the general population.Methods:To complete this systematic review and meta-analysis,a thorough search for pertinent English-language literature was undertaken during the study's extension until October 2023.The search used Google Scholar,Scopus,PubMed/MEDLINE,Science Direct,Web of Science,EMBASE,Springer,and ProQuest.A quality assessment checklist developed using a modified Newcastle-Ottawa Scale for the cross-sectional study was used to evaluate the risk of bias in the included papers.Inverse variance and Cochran Q statistics were employed in the STATA software version 14 to assess study heterogeneity.When there was heterogeneity,the Dersimonian and Liard random-effects models were used.Results:59 Studies totaling 87353 participants were included in this meta-analysis.These investigations included 86278 participants in 55 studies on knowledge,20196 in 33 studies on attitudes,and 74881 in 29 studies on practices.The pooled estimates for sufficient knowledge,positive attitudes,and dengue fever preventive behaviors among the general population were determined as 40.1%(95%CI 33.8%-46.5%),46.8%(95%CI 35.8%-58.9%),and 38.3%(95%CI 28.4%-48.2%),respectively.Europe exhibits the highest knowledge level at 63.5%,and Africa shows the lowest at 20.3%.Positive attitudes are most prevalent in the Eastern Mediterranean(54.1%)and Southeast Asia(53.6%),contrasting sharply with the Americas,where attitudes are notably lower at 9.05%.Regarding preventive behaviors,the Americas demonstrate a prevalence of 12.1%,Southeast Asia at 28.1%,Western Pacific at 49.6%,Eastern Mediterranean at 44.8%,and Africa at 47.4%.Conclusions:Regional disparities about the knowledge,attitude and preventive bahaviors are evident with Europe exhibiting the highest knowledge level while Africa has the lowest.These findings emphasize the importance of targeted public health interventions tailored to regional contexts,highlighting the need for region-specific strategies to enhance dengue-related knowledge and encourage positive attitudes and preventive behaviors.

Assessment of Knowledge, Attitude and Practices among Healthcare Workers in a Tertiary Care Hospital on Needle Stick Injury

Purpose: Needle-stick injury (NSI) is one of the most potential occupational hazards for healthcare workers because of the transmission of blood-borne pathogens. As per recent data, around 30 lakh healthcare workers sustain Needle stick injuries each year. This study was conducted to assess healthcare workers’ knowledge, attitude and practices regarding needle stick injury. Materials & Methods: A cross-sectional study was conducted in a Tertiary Care Hospital over the period of 3 months. The study population consisted of Intern Doctors, Post Graduate resident Doctors, Staff Nurses, laboratory technicians of Government Medical College and New Civil Hospital, Surat (n = 300). The data were collected using a self-administered questionnaire via the means of Google Forms. Questionnaire was made with prior review literature. The data obtained were entered and analysed in Microsoft Excel. Results: The prevalence of NSI in our study was 46%, with a higher prevalence among the PG residents (72%). Overall scores regarding knowledge and attitude were better in PG residents (knowledge score > 7 in 71% and Attitude Score > 7 in 68% of PG Residents). Even though the PG residents scored highest in the knowledge category, the majority of them suffered needle stick injuries as a result of poor practice scores. Among those who had NSI (n = 139/300), 70% of study participants had superficial injuries, only 9% reported the incident, 18% got medical attention within 2 hours of the incident, and 7% followed up to recheck their viral markers status. Most incidents of NSI were due to hypodermic needles while recapping needles. Conclusion: Exposure to needle stick injuries and their underreporting remains a common problem. It is imperative that healthcare workers receive regular training on the proper handling of sharp objects. We can also draw the conclusion that preventing NSIs requires putting knowledge into practice.

Study of the Relationship Between New Ionic Interaction Parameters and Salt Solubility in Electrolyte Solutions Based on Molecular Dynamics Simulation

Studying the relationship between ionic interactions and salt solubility in seawater has implications for seawater desalination and mineral extraction.In this paper,a new method of expressing ion-to-ion interaction is proposed by using molecular dynamics simulation,and the relationship between ion-to-ion interaction and salt solubility in a simulated seawater water-salt system is investigated.By analyzing the variation of distance and contact time between ions in an electrolyte solution,from both spatial and temporal perspectives,new parameters were proposed to describe the interaction between ions:interaction distance(ID),and interaction time ratio(ITR).The best correlation between characteristic time ratio and solubility was found for a molar ratio of salt-to-water of 10:100 with a correlation coefficient of 0.96.For the same salt,a positive correlation was found between CTR and the molar ratio of salt and water.For type 1-1,type 2-1,type 1-2,and type 2-2 salts,the correlation coefficients between CTR and solubility were 0.93,0.96,0.92,and 0.98 for a salt-to-water molar ratio of 10:100,respectively.The solubility of multiple salts was predicted by simulations and compared with experimental values,yielding an average relative deviation of 12.4%.The new ion-interaction parameters offer significant advantages in describing strongly correlated and strongly hydrated electrolyte solutions.

Interatomic Interaction Models for Magnetic Materials:Recent Advances

Atomistic modeling is a widely employed theoretical method of computational materials science.It has found particular utility in the study of magnetic materials.Initially,magnetic empirical interatomic potentials or spinpolarized density functional theory(DFT)served as the primary models for describing interatomic interactions in atomistic simulations of magnetic systems.Furthermore,in recent years,a new class of interatomic potentials known as magnetic machine-learning interatomic potentials(magnetic MLIPs)has emerged.These MLIPs combine the computational efficiency,in terms of CPU time,of empirical potentials with the accuracy of DFT calculations.In this review,our focus lies on providing a comprehensive summary of the interatomic interaction models developed specifically for investigating magnetic materials.We also delve into the various problem classes to which these models can be applied.Finally,we offer insights into the future prospects of interatomic interaction model development for the exploration of magnetic materials.

Resistive field generation in intense proton beam interaction with solid targets

The Brown-Preston-Singleton(BPS)stopping power model is added to our previously developed hybrid code to model ion beam-plasma interaction.Hybrid simulations show that both resistive field and ion scattering effects are important for proton beam transport in a solid target,in which they compete with each other.When the target is not completely ionized,the self-generated resistive field effect dominates over the ion scattering effect.However,when the target is completely ionized,this situation is reversed.Moreover,it is found that Ohmic heating is important for higher current densities and materials with high resistivity.The energy fraction deposited as Ohmic heating can be as high as 20%-30%.Typical ion divergences with half-angles of about 5°-10°will modify the proton energy deposition substantially and should be taken into account.

Investigation on the Knowledge, Attitude and Practice of Oral Health among Medical College and University Undergraduate

Objectives: This study aims to investigate the status of knowledge, attitude and practice (KAP) of oral health among medical undergraduate students, and provide reference for implementing oral health interventions. Methods: A total of 528 undergraduate students enrolled in Fuzhou Medical College from February 2023 to September 2023 were selected as the research subjects. Their oral health KAP were investigated, and the oral health behavior habits of different types of medical students were compared, and possible influencing factors were analyzed. Results: The total awareness rate of oral health knowledge among medical students is 77.0%, with an average score of 3.85 ± 1.16 points. The overall positive rate of oral health attitudes among medical students is 80.0%, with an average score of 3.19 ± 0.72 points. The total qualified rate of oral health behavior is 65.9%, with an average score of 4.61 ± 1.23 points. The scores of oral health knowledge, attitudes, and behaviors among medical students are related to gender, major, smoking status, and oral health status. The frequency of brushing teeth in the female group was higher than that in the male group, while the habit of brushing teeth before bedtime and the frequency of timely replacement of toothbrushes when deformed were lower, with statistical significance (p 0.05). The frequency of timely replacement of toothbrushes varies among medical students from different majors, and the difference is statistically significant (p 0.05). People who have a habit of eating hot and cold food have a higher frequency of brushing their teeth every day, and the difference is statistically significant (p 0.05). Non smokers have a better habit of brushing their teeth before bedtime and a higher frequency of timely replacement when their toothbrush deforms, with a statistically significant difference (p 0.05). The frequency of using fluoride toothpaste or medicated toothpaste, having a habit of unilateral chewing, and timely replacement of toothbrushes when deformed in patients with existing oral problems is higher than that of those without oral problems, and the difference is statistically significant (p 0.05). Conclusion: The knowledge, attitude, and behavior of oral health among medical students in this school are above average. Students with different genders, dietary and smoking habits, and oral health status have different oral health behavioral habits. It is recommended to include oral health education in mandatory courses for various medical majors.

Knowledge of Hospital Acquired Infections (HAIs) among Medical Students in a Tertiary Hospital in Jos North Local Government Area, Plateau State, Nigeria

Background: Hospital Acquired Infections (HAIs) remain a common cause of death, functional disability, emotional suffering and economic burden among hospitalized patients. Knowledge of HAIs is important in its prevention and control. This study seeks to assess the knowledge of Hospital Acquired Infections (HAIs) among medical students in a Tertiary Hospital in Jos North Local Government Area, Plateau State, Nigeria. Methods: This was a descriptive cross-sectional study done in October 2019 among clinical medical students using a Multistage sampling technique. Data was collected using a self-administered structured questionnaire and analyzed using the IBM SPSS 20 (Statistical Package for the Social Sciences). Ethical approval was granted by Bingham University Teaching Hospital, Ethics Committee, Jos, Plateau State. Results: A total of 219 students in the clinical arm of the College of Medicine and Health Sciences were selected. A higher proportion (97.7%) of respondents knew about Hospital Acquired Infections and 85.4% knew that Hospital Acquired infections occur in the hospital, and (86.3%) considered patients contagious with half (58.9%) considered patients as the most important source of HAIs, followed by care givers (13.2%), then doctors including medical students and interns (10.0%) and lastly nurses (8.7%). The majority of respondents (70.8%) considered Surgical Wound Infections to be the most commonly occurring HAI, followed by UTIs (69.9%), RTIs (61.2%), BSIs (37.0%) and others (0.9%). The clinical thermometer was the instrument that most commonly transmits HAIs (82.6%), then followed by stethoscope (62.1%), white coats (53.9%), and blood pressure cuff (51.1%). Most respondents knew the infectious substances, like blood (96.3%), nasal discharge (82.6%), saliva (85.3%), and faeces (79.4%) transmitted HAIs, 72.6% of the respondents said that they were aware of the recommended hand washing techniques by WHO. Conclusion: The majority of students 91.3% had good knowledge while 8.7% had poor knowledge of HAIs. Lower classes had more respondents with poor knowledge. This finding was statistically significant (p = 0.002, Chi-square 12.819). Students are encouraged to keep up the level of knowledge they have about HAIs. These students can help improve the knowledge of those whose knowledge level is low. Government and NGOs should support sponsorship for capacity-building events targeted at HAIs for healthcare workers and medical students.

A cross-sectional study to assess medication safety,knowledge,attitude,and practices regarding nutrition and medication among pregnant women

Objective:To assess pregnant women's knowledge,attitude,and practice regarding nutrition and medication usage,analyse the prescribing pattern,and categorize them based on the Food and Drug Administration(FDA)guidelines.Methods:A cross-sectional study was conducted with 264 pregnant women in the obstetrics and gynaecology department of a tertiary care hospital from October 2022 to August 2023.A knowledge,attitude,and practice(KAP)questionnaire was prepared in English language by the researchers and validated by an expert panel consisting of 12 members.The validated questionnaire was then translated into regional languages,Kannada and Malayalam.The reliability of the questionnaire was assessed with test-retest method with a representative sample population of 30 subjects(10 subjects for each language).The subjects'knowledge,attitude,and practice were evaluated using the validated KAP questionnaire.The safety of the medication was assessed using the FDA drug safety classification for pregnancy.Results:The mean scores for nutritional and medication usage knowledge,attitude,and practice were 4.14±1.15,4.50±1.09,and 3.00±1.47,respectively.Among 30 prescribed medications,3 belong to category A(no risk in human studies),8 belong to category B(no risk in animal studies),18 belong to category C(risk cannot be ruled out)and 1 drug is not classified.A significant association was observed between medication knowledge and practice(r=0.159,P=0.010).Conclusions:Most of the study population knows the need to maintain good dietary and medication practices during pregnancy.Counselling pregnant women regarding diet and medication usage is crucial in maternal care.

RepDNet:A re-parameterization despeckling network for autonomous underwater side-scan sonar imaging with prior-knowledge customized convolution

Side-scan sonar(SSS)is now a prevalent instrument for large-scale seafloor topography measurements,deployable on an autonomous underwater vehicle(AUV)to execute fully automated underwater acoustic scanning imaging along a predetermined trajectory.However,SSS images often suffer from speckle noise caused by mutual interference between echoes,and limited AUV computational resources further hinder noise suppression.Existing approaches for SSS image processing and speckle noise reduction rely heavily on complex network structures and fail to combine the benefits of deep learning and domain knowledge.To address the problem,Rep DNet,a novel and effective despeckling convolutional neural network is proposed.Rep DNet introduces two re-parameterized blocks:the Pixel Smoothing Block(PSB)and Edge Enhancement Block(EEB),preserving edge information while attenuating speckle noise.During training,PSB and EEB manifest as double-layered multi-branch structures,integrating first-order and secondorder derivatives and smoothing functions.During inference,the branches are re-parameterized into a 3×3 convolution,enabling efficient inference without sacrificing accuracy.Rep DNet comprises three computational operations:3×3 convolution,element-wise summation and Rectified Linear Unit activation.Evaluations on benchmark datasets,a real SSS dataset and Data collected at Lake Mulan aestablish Rep DNet as a well-balanced network,meeting the AUV computational constraints in terms of performance and latency.

Isotope Tracking of Surface Water Groundwater Interaction in the Beninese Part of the Iullemeden Aquifer System

The Kandi basin is located in northeast Benin (West Africa). This study is focused on the estimation of water fluxes exchanged between the river Niger (and its tributaries) and the transboundary Iullemeden Aquifer System. In that framework, an innovative approach based on the application of the Bayesian Mixing Model (MixSIAR) analysis on water isotopes (oxygen-18, deuterium and tritium) was performed. Moreover, to assess the relevance of the model outputs, Pearson’s correlation and Principal Component Analysis (PCA) have been done. A complex relationship between surface water and groundwater has been found. Sixty percent (60%) of groundwater samples are made of more than 70% river water and rainwater;while 31.25% of surface water samples are made of about 84% groundwater. To safeguard sustainable water resources for the well-being of the local communities, surface water and groundwater must be managed as a unique component in the Kandi basin.

Numerical investigation of the effects of soil-structure and granular material-structure interaction on the seismic response of a flat-bottom reinforced concrete silo

In this work,a numerical study of the effects of soil-structure interaction(SSI)and granular material-structure interaction(GSI)on the nonlinear response and seismic capacity of flat-bottomed storage silos is conducted.A series of incremental dynamic analyses(IDA)are performed on a case of large reinforced concrete silo using 10 seismic recordings.The IDA results are given by two average IDA capacity curves,which are represented,as well as the seismic capacity of the studied structure,with and without a consideration of the SSI while accounting for the effect of GSI.These curves are used to quantify and evaluate the damage of the studied silo by utilizing two damage indices,one based on dissipated energy and the other on displacement and dissipated energy.The cumulative energy dissipation curves obtained by the average IDA capacity curves with and without SSI are presented as a function of the base shear,and these curves allow one to obtain the two critical points and the different limit states of the structure.It is observed that the SSI and GSI significantly influence the seismic response and capacity of the studied structure,particularly at higher levels of PGA.Moreover,the effect of the SSI reduces the damage index of the studied structure by 4%.