Multilevel Methods Research Articles

Comprehensive Evaluation on Teachers' Knowledge Sharing Behavior Based on the Improved TOPSIS Method.

Knowledge sharing among teachers is one of the important ways to improve their teaching and research ability. From the perspectives of teachers' knowledge sharing willingness, knowledge sharing ability, knowledge sharing environment, knowledge sharing technology support, knowledge sharing effect, and so forth, this paper constructs a teacher knowledge sharing behavior evaluation model, develops a knowledge sharing behavior evaluation index system, and proposes a comprehensive teacher's knowledge sharing behavior evaluation method based on the improved entropy-TOPSIS method. This is a comprehensive evaluation method combining subjective and objective weights, which avoids the subjectivity of traditional expert evaluation methods and other multilevel and multi-index weight determination methods and makes the evaluation results more objective, accurate, and more realistic. Finally, by taking the example of evaluating knowledge sharing behavior of teachers in a university of Chongqing, China, this paper verifies the feasibility and practicability of the proposed comprehensive teachers' knowledge sharing behavior evaluation system and method.

Developmental stages associated with organizational learning in hospitals: An instrument development study.

To develop and test the Organizational Learning Instrument: Development Stages instrument, a measure of hospital units' readiness to engage in organizational learning. Cognitive interviewing, expert review and a quantitative, cross-sectional survey. Item development was informed by previous research on organizational learning. Content validity was assessed and strengthened using cycles of cognitive interviewing and expert review. The resulting instrument was distributed by email to all nurses providing direct patient care in inpatient units in 11 Magnet® hospitals. Data were collected in 2018. Intraclass correlations, using hospital unit as the grouping variable, indicated the need to use multilevel methods to analyse the survey data. Thus, coefficient omega and multilevel confirmatory factor analysis were used to estimate the instrument's reliability and construct validity, respectively. The Organizational Learning Instrument: Development Stages is a 35-item survey comprised of four scales: Identity & Ownership, Team & Respect, Accountability & Support and Reliability & Sustainability. The expert review yielded scale-level content validity scores from 0.90 to 1.0 and item-level content validity scores from 0.86 to 1.0. Survey participants were 1212 nurses, working in 99 inpatient units, across 11 Magnet® hospitals. Intraclass correlations ranged from 0.113 to 0.158. Coefficient omega reliability for the four scales was 0.981-0.993. Standardized factor loadings for the 35 items were 0.699-0.961, with acceptable model fit statistics (comparative fit index=0.980, Tucker-Lewis Fit Index=0.979, and root mean squared error of approximation=0.060). These results indicate the instrument meets or exceeds generally accepted criteria for content validity, reliability and construct validity instrument, and is suitable for further use and testing. Nurse administrators, managers and researchers now have a valid, reliable instrument to better foster and study organizational learning in hospital units. Advances in organizational learning are expected to improve a variety of patient, staff and organizational outcomes in hospital units.

Structural Damage Identification Method of Girder Bridges Based on Multilevel Data Fusion Theory

The single index based on modal strain energy and modal curvature is proved to be effective for damage localization, while its recognition accuracy is not satisfied and can not directly reflect damage degree. Therefore, multilevel data fusion methods are proposed here with three new indexes of modal strain energy dissipation rate (DR), change rate of cross-model modal strain energy (CR), and difference of modal curvature ratio (RD). Firstly, first-level, second-level, and third-level data fusion methods are deduced based on Bayes theory, weighted average criterion, and BP neural network, respectively. The first three order modes data of each index are fused in the first level, and their results are further fused between indexes in the second level, which will be applied for damage position judgement; moreover, results of the second level as input are last fused in the third level in order to predict structural damage degree. Secondly, a simply supported beam and a three-span continuous girder models are simulated to verify effectiveness of multilevel data fusion indexes. It can be concluded that the recognition results agree well with man-made damage cases whatever the positions or degrees are. Finally, a test study on a simply supported steel beam with different damage forms is carried out. The results show that the proposed multilevel data fusion methods have good abilities of sensitivity and anti-interference, are fault-tolerant, have robustness, and would provide certain effective experience for actual damage identification.

AAPFE: Aligned Assembly Pre-Training Function Embedding for Malware Analysis

The use of natural language processing to analyze binary data is a popular research topic in malware analysis. Embedding binary code into a vector is an important basis for building a binary analysis neural network model. Current solutions focus on embedding instructions or basic block sequences into vectors with recurrent neural network models or utilizing a graph algorithm on control flow graphs or annotated control flow graphs to generate binary representation vectors. In malware analysis, most of these studies only focus on the single structural information of the binary and rely on one corpus. It is difficult for vectors to effectively represent the semantics and functionality of binary code. Therefore, this study proposes aligned assembly pre-training function embedding, a function embedding scheme based on a pre-training aligned assembly. The scheme creatively applies data augmentation and a triplet network structure to the embedding model training. Each sub-network extracts instruction sequence information using the self-attention mechanism and basic block graph structure information with the graph convolution network model. An embedding model is pre-trained with the produced aligned assembly triplet function dataset and is subsequently evaluated against a series of comparative experiments and application evaluations. The results show that the model is superior to the state-of-the-art methods in terms of precision, precision ranking at top N (p@N), and the area under the curve, verifying the effectiveness of the aligned assembly pre-training and multi-level information extraction methods.

Improved Modulation Strategy for Singe-Phase Cascaded H-Bridge Multilevel Inverter

In this letter, two new improved pulsewidth modulation (PWM) techniques are proposed for single-phase cascaded H-bridge inverter. Compared with the conventional modulation methods, including phase-shifted PWM (PS-PWM), level-shifted PWM (LS-PWM), discontinuous modulation (D-PWM), only PS-PWM can ensure the output quality and maintain the power balance between each module. That is why it is widely used. However, the switch with PS-PWM always operates in a high-frequency mode, resulting in the high switching losses and low efficiency. In order to solve the problem, two improved cascaded H-bridge multilevel PWM methods are proposed in this letter. With the proposed PWM methods, the power can be evenly distributed among modules, the utilization rate of switching devices is the same, while the switching times are reduced, which improves the system efficiency. Finally, the experimental tests are carried out on a digital-control single-phase cascaded H-bridge seven-level inverter. And the experimental results verify the effectiveness of the proposed solution.

Locating and testing the healthy context paradox: examples from the INCLUSIVE trial

BackgroundThe healthy context paradox, originally described with respect to school-level bullying interventions, refers to the generation of differences in mental wellbeing amongst those who continue to experience bullying even after interventions successfully reduce victimisation. Using data from the INCLUSIVE trial of restorative practice in schools, we relate this paradox to the need to theorise potential harms when developing interventions; formulate the healthy context paradox in a more general form defined by mediational relationships and cluster-level interventions; and propose two statistical models for testing the healthy context paradox informed by multilevel mediation methods, with relevance to structural and individual explanations for this paradox.MethodsWe estimated two multilevel mediation models with bullying victimisation as the mediator and mental wellbeing as the outcome: one with a school-level interaction between intervention assignment and the mediator; and one with a random slope component for the student-level mediator-outcome relationship predicted by school-level assignment. We relate each of these models to contextual or individual-level explanations for the healthy context paradox.ResultsNeither model suggested that the INCLUSIVE trial represented an example of the healthy context paradox. However, each model has different interpretations which relate to a multilevel understanding of the healthy context paradox.ConclusionsGreater exploration of intervention harms, especially when those accrue to population subgroups, is an essential step in better understanding how interventions work and for whom. Our proposed tests for the presence of a healthy context paradox provide the analytic tools to better understand how to support development and implementation of interventions that work for all groups in a population.Trial registrationCurrent Controlled Trials, ISRCTN10751359.

Efficient Algebraic Multigrid Methods for Multilevel Overlapping Coclustering of User-Item Relationships

Various digital data sets that encode user-item relationships contain a multilevel overlapping cluster structure. The user-item relation can be encoded in a weighted bipartite graph and uncovering these overlapping coclusters of users and items at multiple levels in the bipartite graph can play an important role in analyzing user-item data in many applications. For example, for effective online marketing, such as placing online ads or deploying smart online marketing strategies, identifying co-occurring clusters of users and items can lead to accurately targeted advertisements and better marketing outcomes. In this paper, we propose fast algorithms inspired by algebraic multigrid methods for finding multilevel overlapping cocluster structures of feature matrices that encode user-item relations. Starting from the weighted bipartite graph structure of the feature matrix, the algorithms use agglomeration procedures to recursively coarsen the bipartite graphs that represent the relations between the coclusters on increasingly coarser levels. New fast coarsening routines are described that circumvent the bottleneck of all-to-all similarity computations by exploiting measures of direct connection strength between row and column variables in the feature matrix. Providing accurate coclusters at multiple levels in a manner that can scale to large data sets is a challenging task. In this paper, we propose heuristic algorithms that approximately and recursively minimize normalized cuts to obtain coclusters in the aggregated bipartite graphs on multiple levels of resolution. Whereas the main novelty and focus of the paper lies in algorithmic aspects of reducing computational complexity to obtain scalable methods specifically for large rectangular user-item matrices, the algorithmic variants also define several new models for determining multilevel coclusters that we justify intuitively by relating them to principles that underlie collaborative filtering methods for user-item relationships. Experimental results show that the proposed algorithms successfully uncover the multilevel overlapping cluster structure for artificial and real data sets. Summary of Contribution: This paper develops new and efficient computational methods for finding the multilevel overlapping cocluster structure of feature matrices that encode user-item relationships. We base our approach on the use of pairwise similarity measures between features, seeking clusters of points that are similar to each other and dissimilar from the points outside the cluster. We approximately solve the problem of finding optimal overlapping coclusters on multiple levels by employing a framework that is based on efficient multilevel methods that have been used previously to solve sparse linear systems and to cluster graphs. Our main contribution is that we extend these methods in efficient manners to find coclusters in the bipartite graphs that encode common and important user-item relationships or social network relations. The novel methods that we propose are inherently scalable to large problem sizes and are naturally able to uncover overlapping coclusters at multiple levels, whereas existing methods generally only find coclusters at the fine level. We illustrate the algorithm and its performance on some standard test problems from the literature and on a proof-of-concept real-world data set that relates LinkedIn users to their skills and expertise.

Analisis Karakterisasi Penyakit pada Tanaman Pisang Menggunakan Kamera Termal dengan Metode Tresholding

Abstract — Banana is a fruit plant that is widely produced in Indonesia. Unfortunately, this plant is verysusceptible to diseases which can reduce the quality and quantity of the crop. This paper proposes diseasedetection in banana plants using a thermal camera. The detection is carried out using image processingtechniques with multilevel thresholding methods. The image is captured using a thermal camera, then theimage is preprocessed to suit what is desired. After that, so that the position is the same as the image takenusing a digital camera, the image produced by the thermal camera is carried out by an image registrationprocess. The image processing result is compared with the ground truth image obtained from a digitalcamera to determine the effectiveness of the proposed method. The effectiveness of the proposed method ismeasured using the parameters Recall, Precision, F-measure, and Accuracy. The effectiveness of theproposed method is effective enough to produce a recall value of 76.65%, Precision 58.38%, F-measure64.68%, and Accuracy 62.87%. Keywords— Banana Plant Disease, Thermal Image, RGB Image, Multi Thresholding, EffectivenessMeasurement.

Conducting and Evaluating Multilevel Studies: Recommendations, Resources, and a Checklist

Multilevel methods allow researchers to investigate relationships that expand across levels (e.g., individuals, teams, and organizations). The popularity of these methods for studying organizational phenomena has increased in recent decades. Methodologists have examined how these methods work under different conditions, providing an empirical base for making sound decisions when using these methods. In this article, we provide recommendations, tools, resources, and a checklist that can be useful for scholars involved in conducting or assessing multilevel studies. The focus of our article is on two-level designs, in which Level-1 entities are neatly nested within Level-2 entities, and top-down effects are estimated. However, some of our recommendations are also applicable to more complex multilevel designs.

RESEARCH METHODOLOGY FOR THE STUDY OF MECHANISMS OF IMPLEMENTATION OF PARTICIPATORY DEMOCRACY UNDER THE MILITARY SITUATION IN UKRAINE

The publication reveals the methodological aspects of research on the development and functioning of mechanisms of participatory democracy in Ukraine after the military aggression of the Russian Federation. It is noted that on the one hand, the introduction of military situation in Ukraine has limited a number of constitutional rights of citizens and forms of social participation of citizens, but on the other hand, participatory democracy continues to function effectively at the national and local levels in most territories of our state controlled by Ukraine. Public councils continue to function under ministries and central executive bodies, the most important drafts of normative legal decisions undergo public discussions, and electronic appeals to the President of Ukraine demonstrate the interest of citizens in many issues related to Ukraine’s opposition to Russian aggression. Participatory democracy (local initiatives, public hearings, general meeting of residents, participatory budgets) is even more popular in territorial communities. It is noted that the methodology of public administration science research was formed relatively recently, but takes into account the historical achievements of the methodology of other socio-humanitarian research in philosophy, law, economics, and sociology. Under this methodology it is proposed to understand the systems of interdependent and multilevel (philosophical, general scientific, special) methods and their groups, which are complex differentiated tools for understanding the phenomena, processes, statuses and phenomena of public administration, including the essence, content, mechanisms and forms of participatory potential public (state) governance. The importance of transformation of methodology of research of questions of the theory and practice of development of democracy in Ukraine in the conditions of martial law is substantiated. In particular, because of strengthening of this methodology with predictive methods and modeling methods. The updated methodology should create effective cognitive tools for the use of the potential of participatory democracy in the formation of national resistance to Russian aggression. The next important task of transformation of research methodology of participatory democracy in state management is the formation of perspective theoretical models of deepening of democratic transformations in Ukraine during the post-war reconstruction of our state.

Multilevel quadratic spline integration

In this paper we present new quadratures based on both quasi-interpolation and multilevel methods by using bivariate quadratic B-spline functions, defined on simple and multiple knot type-2 triangulations, improving classical quadratures, based on quasi-interpolating splines. We also prove some symmetry properties that simplify their expression, study their approximation performances, propose some numerical results and a comparison with other known multilevel spline quadratures.

Differential exponential entropy-based multilevel threshold selection methodology for colour satellite images using equilibrium-cuckoo search optimizer

Recently, the entropic based multilevel threshold selection methods use 2D histogram, which is constructed using the local averages, leading to a loss of edges. Further, the computation of the entropy using the diagonal pixel values only leads to a loss of information. Nevertheless, traditional 2D histogram based multilevel thresholding methods suffer from efficiently retaining the spatial correlation information. In addition, the conventional entropy uses logarithmic function, which has inherent problems, thereby, reducing the accuracy at some situations. To solve these issues, a differential exponential entropy (DEE) -based multilevel threshold selection methodology is proposed. To suppress the high magnitude peaks in the 2D histogram, the normalized local variance is used while the construction. A novel objective function is suggested to compute the DEE. A new Equilibrium-Cuckoo Search Optimizer (ECSO) is suggested to maximize the DEE. For testing, standard benchmark functions are used. The results are compared with the physics-based Equilibrium Optimizer (EO) and the nature-inspired Cuckoo Search Algorithm (CSA). Different benchmark colour satellite images are acquired from the Landsat Image Gallery database for the experiment. The performances are compared with the state-of-the-art methods. Different metrics such as PSNR, SSIM and FSIM are used for the image quality assessment. A statistical analysis is presented in terms of the Box plots. Our proposed DEE-ECSO outperforms the other techniques. The suggested algorithm would be useful for segmentation of the brain MR images for biomedical engineering applications.

Analysis Of Banana Plant Disease Characterization Using Thermal Camera With Tressolding Method

Banana is a fruit plant that is widely produced in Indonesia. Unfortunately, this plant is very susceptible to diseases which can reduce the quality and quantity of the crop. This paper proposes disease detection in banana plants using a thermal camera. The detection is carried out using image processing techniques with multilevel thresholding methods. The image is captured using a thermal camera, then the image is preprocessed to suit what is desired. After that, so that the position is the same as the image taken using a digital camera, the image produced by the thermal camera is carried out by an image registration process. The image processing result is compared with the ground truth image obtained from a digital camera to determine the effectiveness of the proposed method. The effectiveness of the proposed method is measured using the parameters Recall, Precision, F-measure, and Accuracy. The effectiveness of the proposed method is quite effective because it produces parameter values above 80%, namely the recall value of 86,59%, the Precision of 99,1%, the F-measure of 92%, and the accuracy of 89,78%.

Diversity in Religiosity Undermines Conventional Personal Morality Across the Globe: Evidence From 90 Nations, 300,000+ Individuals

AbstractIn societies where the populace exhibits a wide range of religiosity, social conservatives (religiously devout or socially traditional) feel their beliefs and way of life threatened, even where others in their society (secular, or socially liberal) have no desire to threaten them, or to discriminate against them, or even to proselytize. Examples include devout English Pilgrims in liberal 16th century Holland and devout Muslims in liberal 21st century Western Europe. We suggest that this is because diversity in religiosity itself poses a threat to conventional personal morality (attitudes on abortion, divorce, euthanasia, suicide, prostitution). The consequences of societal diversity in religiosity (the centrality of religion to one's life) for individuals’ endorsement of conventional personal morality have been neglected in prior research. This paper shows that diversity in religiosity at the national level undermines individuals’ endorsement of conventional personal morality, net of an individual's own religiosity, net of the average levels of religiosity and socioeconomic development in the individual's society, and net of key individual‐level controls. Data are pooled from the World Values Surveys/European Values Surveys, 1981–2008, with 90 countries, 200+ surveys, and 300,000+ individual respondents. Analysis is by multilevel methods (variance components models with fixed effects and random intercepts, estimated by generalized least squares [GLS]).

Parallel space-time adaptive numerical simulation of 3D cardiac electrophysiology

The bidomain equations form the state-of-the-art model of cardiac electrophysiology and describe normal or pathological propagation of the excitation wave through cardiac tissue. If based on mechanistic cell models and applied to anatomically realistic geometries, their discretization requires very fine resolutions both in space and time and renders the numerical solution an extremely challenging computational problem. In this paper, we address this challenge by combining space-time adaptive discretization with dynamic load balancing for parallel computing. We use multilevel finite element methods for the spatial adaptation of the mesh, embedded Rosenbrock time integration for an adaptive choice of step sizes, and non-overlapping domain decomposition for the parallelization. In order to further reduce the computational costs, we exploit the sparsity of local matrices during the assembly of global FEM matrices which ensure an optimal usage of memory. In numerical tests, we demonstrate the feasibility of our approach that a substantial reduction factor of computing time is achieved w.r.t the static grid computation and a good parallel efficiency is achieved.

On Multilevel and Control Variate Monte Carlo Methods for Option Pricing under the Rough Heston Model

The rough Heston model is a form of a stochastic Volterra equation, which was proposed to model stock price volatility. It captures some important qualities that can be observed in the financial market—highly endogenous, statistical arbitrages prevention, liquidity asymmetry, and metaorders. Unlike stochastic differential equation, the stochastic Volterra equation is extremely computationally expensive to simulate. In other words, it is difficult to compute option prices under the rough Heston model by conventional Monte Carlo simulation. In this paper, we prove that Euler’s discretization method for the stochastic Volterra equation with non-Lipschitz diffusion coefficient E[|Vt−Vtn|p] is finitely bounded by an exponential function of t. Furthermore, the weak error |E[Vt−Vtn]| and convergence for the stochastic Volterra equation are proven at the rate of O(n−H). In addition, we propose a mixed Monte Carlo method, using the control variate and multilevel methods. The numerical experiments indicate that the proposed method is capable of achieving a substantial cost-adjusted variance reduction up to 17 times, and it is better than its predecessor individual methods in terms of cost-adjusted performance. Due to the cost-adjusted basis for our numerical experiment, the result also indicates a high possibility of potential use in practice.

Abstract 9394: Establishing Validity of a Midlife Black Women's Wellness Program

Introduction: Expert ratings and qualitative feedback are essential for establishing validity of materials developed for interventions. Validation of intervention content should be established prior to initiation of trials. In collaboration with a community research advisory board, the purpose of this study was to analyze expert ratings and qualitative feedback for the Midlife Black Women’s Stress Reduction Wellness (B-SWELL) intervention materials. Methods: The B-SWELL is a culturally tailored 8-week program that leverages stress reduction to lower cardiovascular disease (CVD) risk in midlife Black women by promoting the adoption of the AHA’s Life’s Simple 7 healthy lifestyle behaviors (LS7). Using community participatory methods, the LS7 metrics and B-SWELL binder were culturally tailored and targeted to midlife Black women. A multilevel mixed methods approach was used to establish content and face validity for the B-SWELL binder made up of eight modules. Twelve experts consisting of midlife Black women (n=6), researchers (n=3), and integrative health specialists (n=3) were purposefully recruited to rate each B-SWELL module and the overall binder for content accuracy, topic relevance, stress relevance, cultural appropriateness, feasibility, usefulness, ease of use, and appeal using a 5-point Likert scale (1 strongly disagree to 5 strongly agree). Open ended questions were integrated with ratings to obtain insight into areas needing improvement. Results: Combined mean expert ratings for individual B-SWELL modules were high with means ranging between 4.43 and 4.66. Group ratings differed for individual B-SWELL modules and the B-SWELL binder with midlife Black women having the highest mean ratings (4.71, 4.97), followed by researchers (4.56, 4.73), and integrative health specialists (4.11, 4.40). Narrative data provided details to guide refinement. Conclusions: The B-SWELL intervention materials exhibited strong evidence of content and face validity. Testing feasibility of the B-SWELL intervention in a future study is warranted, ultimately leading to a well-powered randomized control clinical trial to test efficacy of the B-SWELL in lowering CVD risk in midlife Black women through adoption of the LS7 healthy lifestyle behaviors.

Isogeometric multilevel quadrature for forward and inverse random acoustic scattering

We study the numerical solution of forward and inverse time-harmonic acoustic scattering problems by randomly shaped obstacles in three-dimensional space using a fast isogeometric boundary element method. Within the isogeometric framework, realizations of the random scatterer can efficiently be computed by simply updating the NURBS mappings which represent the scatterer. This way, we end up with a random deformation field. In particular, we show that it suffices to know the deformation field’s expectation and covariance at the scatterer’s boundary to model the surface’s Karhunen–Loève expansion. Leveraging on the isogeometric framework, we employ multilevel quadrature methods to approximate quantities of interest such as the scattered wave’s expectation and variance. By computing the wave’s Cauchy data at an artificial, fixed interface enclosing the random obstacle, we can also directly infer quantities of interest in free space. Adopting the Bayesian paradigm, we finally compute the expected shape and variance of the scatterer from noisy measurements of the scattered wave at the artificial interface. Numerical results for the forward and inverse problems validate the proposed approach.

Multilevel quadrature for elliptic problems on random domains by the coupling of FEM and BEM

Elliptic boundary value problems which are posed on a random domain can be mapped to a fixed, nominal domain. The randomness is thus transferred to the diffusion matrix and the loading. While this domain mapping method is quite efficient for theory and practice, since only a single domain discretisation is needed, it also requires the knowledge of the domain mapping. However, in certain applications, the random domain is only described by its random boundary, while the quantity of interest is defined on a fixed, deterministic subdomain. In this setting, it thus becomes necessary to compute a random domain mapping on the whole domain, such that the domain mapping is the identity on the fixed subdomain and maps the boundary of the chosen fixed, nominal domain on to the random boundary. To overcome the necessity of computing such a mapping, we therefore couple the finite element method on the fixed subdomain with the boundary element method on the random boundary. We verify on one hand the regularity of the solution with respect to the random domain mapping required for many multilevel quadrature methods, such as the multilevel quasi-Monte Carlo quadrature using Halton points, the multilevel sparse anisotropic Gauss–Legendre and Clenshaw–Curtis quadratures and multilevel interlaced polynomial lattice rules. On the other hand, we derive the coupling formulation and show by numerical results that the approach is feasible.

Optimization of Multi Two-Level Batch Testing

As the COVID-19 pandemic continues to spread, rapid testing could help curb asymptomatic transmission. Thus, it is paramount that the most efficient testing method be identified and implemented, as to reduce the strain on the medical community. This project introduces a novel batch testing method called multi two-level batch testing, which was hypothesized to increase the efficiency of batch testing in terms of minimizing the number of tests performed for a given population. While Dorfman’s two-level and Li’s multi-level batch testing methods already exist, this method offers a novel strategy distinct from existing methods. A Java simulation was created to iteratively compute the number of tests required for each testing method at various percentages of population infection rate, batch sizes, and other parameters specific to each method. Based on this simulation, it can be shown that the multi two-level procedure is more efficient than both the two-level and the multi-level procedures at an infection rate of 0.01, which is the anticipated rate at the University of Florida during the Spring 2021 semester. Additionally, at infection rates between 0.05 and 0.30, the multi two-level batch testing method slightly outperforms multi-level. When the infection rate exceeds 0.30, all methods are unviable and begin to require more tests than necessary to test each person in the population individually. If laboratories implement multi two-level batch testing, they may reduce costs and labor. Additionally, the novel batch testing procedure can be applied to other diseases and future pandemics.