Robust Design Research Articles

Robust Design of Electrical System for Nuclear Plants to Prevent a Nuclear Accident During an Emergency Situation

The Chernobyl accident taught us an important lesson regarding the significance of the emergency diesel generator (EDG), which is crucial for supplying a nuclear facility with emergency alternating-current power. A worst-case scenario would occur if the EDG failed to start functioning. In response to this problem, this research addresses the issue and suggests an optimal design for another alternative electrical source using solar energy. Until now, employing this technology presented a challenge as a solar energy system generates electrical harmonics that are transmitted from the photovoltaic (PV) grid to the electrical network. These harmonics can negatively impact the performance and operation of nuclear facilities. Consequently, this study proposes a comprehensive investigation that tackles this issue by implementing a LCL filter to mitigate the harmonics in the PV network. This paper not only provides an alternative solution in the event of a diesel generator breakdown, but also studies the effect of this solution on the station’s performance and how to improve it. A mathematical model based on the MATLAB program is developed to simulate the application of the LCL filter strategy. The proposed technique is tested through simulation with an actual case, demonstrating its reliability and high-quality outcomes.

Utilization of RESOLV with polymer to produce prazosin hydrochloride nanoparticles and optimization of the process parameters

In this study, rapid expansion of a supercritical solution into a Liquid Solvent (RESOLV) was used for the first time to produce pharmaceutical nanoparticles of Prazosin hydrochloride (PRH). The Taguchi method (robust design) was utilized to design the experiments and ensure obtaining the optimal process conditions. The pressure (15–25 MPa), temperature (308–328 K) and nozzle diameter (300–700 μm) effects on the morphology and size distribution of the resulting particles were also examined. The size of the particles decreased from about 40 μm to the range of (252–418 nm). FTIR, DLS, FESEM, XRD, DSC were used to characterize the primary and processed PRH particles. According to DSC investigations, RESOLV-produced PRH showed lower crystallinity than original PRH.

Data-driven inverse design of the perforated auxetic phononic crystals for elastic wave manipulation

Abstract In addition to the distinctive features of tunable Poisson’s ratio from positive to negative and low stress concentration, the perforated auxetic metamaterials by peanut-shaped cuts have exhibited excellent phononic crystal (PNC) behavior as well for elastic wave manipulation. Thus they have attracted much attention in vibration suppression for dynamic applications. However, traditional structural designs of the auxetic PNCs considerably depend on designers’ experience or inspiration to fulfill the desired multi-objective bandgap properties through extensive trial and error. Hence, developing a more efficient and robust inverse design method remains challenging to accelerate the creation of auxetic PNCs and improve their performance. To shorten this gap, a new machine learning (ML) framework consisting of double BPNN (back propagation neural network) modules is developed in this work to produce desired configurations of the auxetic PNCs matching the customized bandgap. The first inverse BPNN module is trained to establish a logical mapping from the bandgap properties to the structural parameters, and then the second forward BPNN module is introduced to give the new property prediction by using the design configurations generated from the former. The error between the new predictions and the desired target properties is minimized through a limited number of iterations to produce the final optimal objective configurations. The results indicate that the perforated auxetic metamaterials behave relatively wide complete bandgap and the present ML model is effective in designing them with specific bandgaps within or beyond the given dataset. The study provides a powerful tool for designing and optimizing the perforated auxetic metamaterials in dynamic environment.

Study Profile of the Tsuruoka Metabolomics Cohort Study (TMCS).

The Tsuruoka Metabolomics Cohort Study (TMCS) is an ongoing population-based cohort study being conducted in the rural area of Yamagata Prefecture, Japan. This study aimed to enhance the precision prevention of multi-factorial, complex diseases, including non-communicable and aging-associated diseases, by improving risk stratification and prediction measures. At baseline, 11,002 participants aged 35-74 years were recruited in Tsuruoka City, Yamagata Prefecture, Japan, between 2012 and 2015, with an ongoing follow-up survey. Participants underwent various measurements, examinations, tests, and questionnaires on their health, lifestyle, and social factors. This study uses an integrative approach with deep molecular profiling to identify potential biomarkers linked to phenotypes that underpin disease pathophysiology and provide better mechanistic insights into social health determinants. The TMCS incorporates multi-omics data, including genetic and metabolomic analyses of 10,933 participants, and comprehensive data collection ranging from physical, psychological, behavioral, and social to biological data. The metabolome is used as a phenotypic probe because it is sensitive to changes in physiological and external conditions. The TMCS focuses on collecting outcomes for cardiovascular disease, cancer incidence and mortality, disability and functional decline due to aging and disease sequelae, and the variation in health status within the body represented by omics analysis that lies between exposure and disease. It contains several sub-studies on aging, heated tobacco products, and women's health. This study is notable for its robust design, high participation rate (89%), and long-term repeated surveys. Moreover, it contributes to precision prevention in Japan and East Asia as a well-established multi-omics platform.

A Dataset Generation Framework for Symmetry-Induced Mechanical Metamaterials

Abstract The surge in machine learning research and recent advancements in 3D printing technologies have significantly enriched materials science and engineering, particularly in the domain of mechanical metamaterials, which commonly consist of periodic truss materials. Despite the extensive exploration of their tailorable properties, truss-based metamaterial design has predominantly adhered to cubic and orthotropic unit-cells, a limitation arising from the conventional design method, where the type of symmetry related to the designed truss-based material is determined after the design process is done. To overcome this issue, this work introduces a groundbreaking 3D truss material designing framework that departs from this constraint by employing six distinctive material symmetries (cubic, hexagonal, tetragonal, orthotropic, trigonal, and monoclinic) within the design process. This innovative approach represents a versatile paradigm shift compared to previous design approaches. Furthermore, we are able to integrate anisotropy into the design framework, thus enhancing the property space exploration capability of the proposed design framework. Probing materials property space using our design framework demonstrates its capacity to achieve a diverse range of mechanical properties, surpassing even the most extensive datasets available in the literature. The proposed method facilitates the generation of a comprehensive truss dataset, which can be represented in a trainable continuous format suitable for machine learning and data-driven approaches. This advancement paves the way for the development of robust inverse design tools for truss materials, marking a significant contribution to the mechanical metamaterial community.

A comprehensive approach to evaluate risk mitigation strategies in offshore wind farms using spherical fuzzy decision making analysis

The escalating impact of global warming, primarily exacerbated by carbon emissions from conventional energy sources, has prompted significant advancements in offshore wind energy as a pivotal avenue for sustainable development. Amid the surging interest in renewable energy, offshore wind-farms have emerged as a promising solution. To ensure their efficient and effective operation, it becomes imperative to systematically identify and mitigate potential risks. This study addresses the critical need to systematically prioritize risk reduction strategies for offshore wind farms, a problem that has not been comprehensively explored in the literature. A multi-criteria analysis has been adopted to simultaneously evaluate the contradictory criteria in the evaluation process. The novelty of this study lies in its integration of spherical fuzzy sets with multi-criteria decision-making (MCDM) techniques to handle uncertainties and evaluate contradictory criteria simultaneously. As a result of all this analysis, the most critical risks for offshore wind-farms and risk mitigation strategies that can be adopted within the determined risks have been revealed. The main findings reveal that turbine underperformance, disruption of habitats, and grid connection issues are the most critical risks. Furthermore, a combination of robust design and engineering and collaboration and partnerships emerged as the most effective risk mitigation strategies.

Robust controller design and experimental validation based on bounded uncertainty for collaborative industrial robots.

In this paper, derived from the proportional-derivative control and robust control, a novel practical robust control method based on a dynamic feedforward model is established by taking six-axis motion cooperative industrial robots as the research object. The nonlinear friction, parameter uncertainty, and external disturbance are taken into consideration while establishing the dynamic model of the cooperative industrial robot. The method includes a proportional-derivative control and a robust control. Lyapunov theory is used to analyze the proposed controller, and it is shown that this method can guarantee uniformly bounded and uniformly final bounded systems. Simulation and experiment results show that the proposed controller is better than proportional-integral-derivative control and mode-based proportional-derivative control in stability tracking performance and robustness. In addition, the CSPACE platform for rapid controller prototyping may reduce the arduous programming effort and offer a lot of ease for the trials.

Can the Infusion Teaching of Critical Thinking Improve Chinese Secondary Students’ Critical Thinking and Academic Attainment? Findings from a Randomised Controlled Trial

Critical thinking has been recognised as a core skill in the latest English curriculum standards for secondary schools in China. However, the current state of critical thinking education in China remains underdeveloped. Although some Chinese secondary schools have reported success in infusing critical thinking into regular teaching, there is little evidence of its impact as no independent robust studies have been conducted. The aim of this study was to determine whether the infusion of critical thinking in the regular curriculum has any impact on improving Chinese secondary students’ critical thinking skills and academic attainment using a more robust research design that can establish causation. Twenty-one English language teachers and 2,011 Grade 8 students from four rural secondary schools in China participated. The intervention was delivered by 11 experimental English language teachers once a week for three months. The impact of the intervention was measured by differences in modified standardised critical thinking skill tests. A process evaluation, including class observations and interviews, was also conducted to determine fidelity to the treatment. The results indicate a small positive impact of the infusion teaching on students’ critical thinking skills. Therefore, it is feasible and promising to infuse critical thinking into the English curriculum in China's secondary schools. However, the positive impact was not observed in academic attainment outcomes. The trial provides policy and educational practice recommendations to support the development of critical thinking skills among Chinese students.

JANUS: A Robust ReBCO Tape Design for Increased Performance in Magnetic Fields

JANUS: A Robust ReBCO Tape Design for Increased Performance in Magnetic Fields

Resilient and rutting response of unbound granular material and subgrade soil influenced by initial state and stress conditions

Unbound pavements with thin seals, where traffic loads are primarily supported by unbound granular layers and subgrade soil, are favoured for their low initial cost. Rutting, the primary distress mechanism, leads to rough, uncomfortable surfaces, diminishing serviceability and safety. It depends on the material's initial dry density, degree of saturation, and applied stress levels. Resilient modulus, indicating layer stiffness, is crucial for determining load transmission to underlying layers, making accurate estimation essential for pavement design. This study conducted constant radial stiffness triaxial (CRST) tests on an unbound granular material and a subgrade soil at various dry densities, saturation degrees, and stress conditions. The CRST test applies dynamic confining pressure under constant radial stiffness boundary. New models for rutting and resilient modulus were developed, relating these parameters to initial dry densities, saturation degrees, and stress states. The study also presents rutting and resilient modulus isograms on the compaction plane, aiding field compaction control and robust pavement design.

Dynamic Robust Parameter Design Using Response Surface Methodology based on Generalized Linear Model

Purpose: When designing an input-output system susceptible to noise, engineers assume a functional relation between the input and the output. The Taguchi method, which uses a dynamic, robust parameter design (RPD) to evaluate the robustness of the input-output relation against noise, is employed. This study aims to address extending the scope of use of a dynamic RPD. Methodology/Approach: A target system in a typical dynamic RPD can be interpreted as one in which the relation between the input and the output is a linear model, and the output error follows a normal distribution. However, an actual system often does not conform to this premise. Therefore, we propose a new analysis approach that can realize a more flexible system design by applying a response surface methodology (RSM) based on a generalized linear model (GLM) to dynamic RPD. Findings: The results demonstrate that 1) a robust solution can be obtained using the proposed method even for a typical dynamic RPD system or an actual system, and 2) the target function can be evaluated using an adjustment parameter. Research Limitation/implication: Further analysis is required to determine which factor(s) in the estimated process model largely contribute(s) to changes in the adjustment parameter. Originality/Value of paper: The applicability of typical dynamic RPD is limited. Hence, this study’s analytical process provides engineers with greater design flexibility and deeper insights into dynamic systems across various contexts. Category: Research paper Keywords: robust parameter design; dynamic system; generalized linear model; response surface methodology; Taguchi method

Fault reconstruction‐based robust tracking control design for periodic piecewise polynomial systems with disturbances

AbstractIn this article, we address the topics of fault reconstruction and robust fault‐tolerant tracking control for periodic piecewise polynomial uncertain systems with time‐varying delays, faults and external disturbances. In detail, the periodic piecewise polynomial learning observer system is configured with the intent to concurrently reconstruct the faults and immeasurable states of the system that is being considered. Precisely, the key intent of this study centers around in devising a robust fault‐tolerant tracking control to achieve the tracking performance of the system as intended with mixed and passivity performance. To this end, a robust fault‐tolerant tracking control is designed by incorporating the reconstructed faults and error feedback, which aids in vouching for the outcomes that were desired. Besides, by building the periodic piecewise polynomial Lyapunov–Krasovskii functional and making use of the matrix polynomial lemma and Wirtinger's inequality, certain adequate conditions are procured in the frame of linear matrix inequalities. Subsequently, the required gain values for the devised controller and the configured observer are found by using the established linear matrix inequalities. Ultimately, in order to attest the controller's intrinsic potential and relevance, numerical example along with the simulation results is supplied.

Streamlined integrated protein isoelectric focusing using microfluidic paper-based device

An innovative integrated paper-based microdevice was developed for protein separation by isoelectric focusing (IEF), allowing for robust design thanks to a 3D-printed holder integrating separation channel, reservoirs, and electrodes. To reach robustness and precision, the optimization focused on the holder geometry, the paper nature, the reservoir design, the IEF medium, and various focusing parameters. A well-established and stable pH gradient was obtained on a glass-fiber paper substrate with simple sponge reservoirs, and the integration of the electrodes in the holder led to a straightforward system. The separation medium composed of water/glycerol (85/15, v/v) allowed for reducing medium evaporation while being an efficient medium for most hydrophobic and hydrophilic proteins, compatible with mass spectrometry detection for further proteomics developments. To our knowledge, this is the first report of the use of glycerol solutions as a separation medium in a paper-based microdevice. Analytical performances regarding pH gradient generation, pI determination, separation efficiency, and resolution were estimated while varying the IEF experimental parameters. The overall process led to an efficient separation within 25 min. Then, this methodology was applied to a sample composed of saliva doped with proteins. A minimal matrix effect was evidenced, underscoring the practical viability of our platform. This low-cost, versatile and robust paper-based IEF microdevice opens the way to various applications, ranging from sample pre-treatment to integration in an overall proteomic-on-a-chip device.

Neural network extrapolation to distant regions of the protein fitness landscape

Machine learning (ML) has transformed protein engineering by constructing models of the underlying sequence-function landscape to accelerate the discovery of new biomolecules. ML-guided protein design requires models, trained on local sequence-function information, to accurately predict distant fitness peaks. In this work, we evaluate neural networks’ capacity to extrapolate beyond their training data. We perform model-guided design using a panel of neural network architectures trained on protein G (GB1)-Immunoglobulin G (IgG) binding data and experimentally test thousands of GB1 designs to systematically evaluate the models’ extrapolation. We find each model architecture infers markedly different landscapes from the same data, which give rise to unique design preferences. We find simpler models excel in local extrapolation to design high fitness proteins, while more sophisticated convolutional models can venture deep into sequence space to design proteins that fold but are no longer functional. We also find that implementing a simple ensemble of convolutional neural networks enables robust design of high-performing variants in the local landscape. Our findings highlight how each architecture’s inductive biases prime them to learn different aspects of the protein fitness landscape and how a simple ensembling approach makes protein engineering more robust.

A novel logistic regression-embedded 0–1 mixed-integer probabilistic robust design model to obtain optimum sustainable factor settings

Quality by design (QbD) focuses on satisfying the needs of products or processes when aiming for variance reduction. On the other side, sustainability is associated with carrying on to conduct so over time. The research gap is also addressed in sustainability and QbD in this paper. Next, robust design (RD) is a suitable approach to QbD to reduce the process or product variation regarding quantitative and qualitative design factors. For a number of situations, a binomial random response variable is considered to decrease the variability of the production process. Thus, this paper is four-fold. First of all, a modified distance-based criterion is developed to generate the design matrix when dealing with quantitative and qualitative design factors. Second, a logistic regression technique is presented for modeling responses when the random response variable is binomial. Third, a new logistic regression-based 0–1 mixed-integer probabilistic RD model is proposed to find optimum sustainable design factors. Fourth, an Industry 4.0 associated case study is performed in order to design sustainable process development, and the comparison and verification studies are also presented to show the effectiveness of the proposed probabilistic model. Further, managerial insights are provided based on the sustainable development goals (SDGs).

Robust bioprocess design and evaluation of commercial media for the serial expansion of human induced pluripotent stem cell aggregate cultures in vertical-wheel bioreactors

BackgroundWhile pluripotent stem cell (PSC) therapies move toward clinical and commercial applications at a rapid rate, manufacturing reproducibility and robustness are notable bottlenecks in regulatory approval. Therapeutic applications of PSCs require large cell quantities to be generated under highly robust, well-defined, and economically viable conditions. Small-scale and short-term process optimization, however, is often performed in a linear fashion that does not account for time needed to verify the bioprocess protocols and analysis methods used. Design of a reproducible and robust bioprocess should be dynamic and include a continuous effort to understand how the process will respond over time and to different stresses before transitioning into large-scale production where stresses will be amplified.MethodsThis study utilizes a baseline protocol, developed for the short-term culture of PSC aggregates in Vertical-Wheel® bioreactors, to evaluate key process attributes through long-term (serial passage) suspension culture. This was done to access overall process robustness when performed with various commercially available media and cell lines. Process output variables including growth kinetics, aggregate morphology, harvest efficiency, genomic stability, and functional pluripotency were assessed through short and long-term culture.ResultsThe robust nature of the expansion protocol was demonstrated over a six-day culture period where spherical aggregate formation and expansion were observed with high-fold expansions for all five commercial media tested. Profound differences in cell growth and quality were revealed only through long-term serial expansion and in-vessel dissociation operations. Some commercial media formulations tested demonstrated maintenance of cell growth rates, aggregate morphology, and high harvest recovery efficiencies through three bioreactor serial passages using multiple PSC lines. Exceptional bioprocess robustness was even demonstrated with sustained growth and quality maintenance over 10 serial bioreactor passages. However, some commercial media tested proved less equipped for serial passage cultures in bioreactors as cultures led to cell lysis during dissociation, reduction in growth rates, and a loss of aggregate morphology.ConclusionsThis study demonstrates the importance of systematic selection and testing of bioprocess input variables, with multiple bioprocess output variables through serial passages to create a truly reproducible and robust protocol for clinical and commercial PSC production using scalable bioreactor systems.

Component tests and numerical simulations of 3D steel frame structures for progressive collapse

This paper presents a comprehensive study on three-dimensional steel frame structures subjected to progressive collapse, drawing insights from a full-scale steel frame substructure test. The investigation encompasses connection component tests and numerical simulations, focusing on extended end plate and double-angle cleat connections employed in the substructure test. The mechanical properties of the connection components were tested, forming a basis for defining component properties in subsequent connection models. Finite element (FE) models of the test substructure were developed, utilizing hybrid elements for the steel frame part, which include beam, connector, and spring elements based on the component method. To simulate reinforced concrete slabs, a combination of refined solid elements and simplified shell elements was employed. The former accurately captures detailed failure modes, while the latter efficiently simulates the collapse behavior of large-scale steel frame structures. Validation of the established models against test results encompassed load-displacement responses, internal forces in structural members, and failure modes. The validated FE models were then utilized to analyze and discuss the contributions of various structural components in resisting progressive collapse. Specific focus was placed on the development of load-resisting mechanisms in the floor slab and the influence of beam-column connection types on structural behavior. The paper explores the role of bracing systems in a building in resisting progressive collapse. Additionally, it evaluates the effectiveness of the restraint systems used in the test, shedding light on their ability to accurately reflect real restraint effects from the surrounding structure. The findings presented herein contribute valuable insights to the understanding of progressive collapse behavior in steel frame structures, with implications for robustness design of multi-story steel buildings.

Non-genetic factors and breast cancer: an umbrella review of meta-analyses

BackgroundPrevious research has found associations between various non-genetic factors and breast cancer (BrCa) risk. This study summarises and appraises the credibility of the available evidence on the association between non-genetic factors and BrCa risk.MethodsWe conducted an umbrella review of meta-analyses. Medline, Scopus, and the Cochrane databases were systematically searched for meta-analyses examining non-genetic factors and BrCa incidence or mortality. The strength of the evidence was graded in four categories (i.e., weak, suggestive, highly suggestive, convincing).ResultsA total of 781 meta-analyses from 280 publications were evaluated and graded. We included exposures related to anthropometric measurements, biomarkers, breast characteristics and diseases, diet and supplements, environment, exogenous hormones, lifestyle and social factors, medical history, medication, reproductive history, and pregnancy. The largest number of examined associations was found for the category of diet and supplements and for exposures such as aspirin use and active smoking. The statistically significant (P-value < 0.05) meta-analyses were 382 (49%), of which 204 (53.4%) reported factors associated with increased BrCa risk. Most of the statistically significant evidence (n = 224, 58.6%) was graded as weak. Convincing harmful associations with heightened BrCa risk were found for increased body mass index (BMI), BMI and weight gain in postmenopausal women, oral contraceptive use in premenopausal women, increased androstenedione, estradiol, estrone, and testosterone concentrations, high Breast Imaging Reporting and Data System (BIRADS) classification, and increased breast density. Convincing protective factors associated with lower BrCa risk included high fiber intake and high sex hormone binding globulin (SHBG) levels while highly suggestive protective factors included high 25 hydroxy vitamin D [25(OH)D] levels, adherence to healthy lifestyle, and moderate-vigorous physical activity.ConclusionsOur findings suggest some highly modifiable factors that protect from BrCa. Interestingly, while diet was the most studied exposure category, the related associations failed to reach higher levels of evidence, indicating the methodological limitations in the field. To improve the validity of these associations, future research should utilise more robust study designs and better exposure assessment techniques. Overall, our study provides knowledge that supports the development of evidence-based BrCa prevention recommendations and guidance, both at an individual level and for public health initiatives.Trial registrationPROSPERO CRD42022370675.

Estimation of COVID-19 vaccine effectiveness against infections and severe outcomes using routine surveillance data in Kosovo, July-September 2021.

COVID-19 vaccines have proven effective against severe outcomes in many settings, yet vaccine effectiveness (VE) estimates remain lacking for Kosovo. We aimed to estimate VE against COVID-19 infections, hospitalisations, and deaths for one and two vaccine doses during the fourth pandemic wave in July-September 2021, the period when vaccination initially became widely available. We analysed routine surveillance data to define cases and vaccination status as partially (one dose) or completely (two doses) vaccinated. We used the screening method to calculate the proportion of cases with the outcomes vaccinated (PCV). The proportion of the population vaccinated (PPV) was based on numbers vaccinated and the Kosovo population estimate on 30/09/2021. Between July-September 2021, 51,804 COVID-19 cases were reported in Kosovo with 9.3% of cases partially and 3.4% completely vaccinated. Estimated vaccine effectiveness for one dose was 93.1% (95%CI:92.9-93.2%) for infections, 90.3% (95%CI:88.8-91.7%) for hospitalisations, and 90.3% (95%CI:88.4-92.1%) for deaths. Estimated vaccine effectiveness for two doses was 97.8% (95%CI:97.6-97.9%) for infections, 94.5% (95%CI:93.3-95.6%) for hospitalisations, and 94.2% (95%CI: 93.7-96.5%) for deaths. This study provides real-world evidence for COVID-19 vaccine effectiveness in Kosovo using routine administrative data sources and the screening method. COVID-19 vaccine effectiveness against infections and severe outcomes in Kosovo was higher with two vaccine doses than one dose, which is in accordance with findings from other study designs and settings. Using the screening method in our study reflects an important initial methodology for estimating vaccine effectiveness with routine surveillance that may be particularly important for low- and middle-income settings with less robust surveillance systems or fewer opportunities to conduct more robust vaccine effectiveness study designs.

Exploring the nuclear proteins, viral capsid protein, and early antigen protein using immunoinformatic and molecular modeling approaches to design a vaccine candidate against Epstein Barr virus

The available Epstein Barr virus vaccine has tirelessly harnessed the gp350 glycoprotein as its target epitope, but the result has not been preventive. Right here, we designed a global multi-epitope vaccine for EBV; with special attention to making sure all strains and preventive antigens are covered. Using a robust computational vaccine design approach, our proposed vaccine is armed with 6–16 mers linear B-cell epitopes, 4–9 mer CTL epitopes, and 8–15 mer HTL epitopes which are verified to induce interleukin 4, 10 & IFN-gamma. We employed deep computational mining coupled with expert intelligence in designing the vaccine, using human Beta defensin-3—which has been reported to induce the same TLRs as EBV—as the adjuvant. The tendency of the vaccine to cause autoimmune disorder is quenched by the assurance that the construct contains no EBNA-1 homolog. The protein vaccine construct exhibited excellent physicochemical attributes such as Aliphatic index 59.55 and GRAVY − 0.710; and a ProsaWeb Z score of − 3.04. Further computational analysis revealed the vaccine docked favorably with EBV indicted TLR 1, 2, 4 & 9 with satisfactory interaction patterns. With global coverage of 85.75% and the stable molecular dynamics result obtained for the best two interactions, we are optimistic that our nontoxic, non-allergenic multi-epitope vaccine will help to ameliorate the EBV-associated diseases—which include various malignancies, tumors, and cancers—preventively.