Identification Phase Research Articles

Can constraint network analysis guide the identification phase of KnowVolution? A case study on improved thermostability of an endo-β-glucanase

Cellulases are industrially important enzymes, e.g., in the production of bioethanol, in pulp and paper industry, feedstock, and textile. Thermostability is often a prerequisite for high process stability and improving thermostability without affecting specific activities at lower temperatures is challenging and often time-consuming. Protein engineering strategies that combine experimental and computational are emerging in order to reduce experimental screening efforts and speed up enzyme engineering campaigns. Constraint Network Analysis (CNA) is a promising computational method that identifies beneficial positions in enzymes to improve thermostability. In this study, we compare CNA and directed evolution in the identification of beneficial positions in order to evaluate the potential of CNA in protein engineering campaigns (e.g., in the identification phase of KnowVolution). We engineered the industrially relevant endoglucanase EGLII from Penicillium verruculosum towards increased thermostability. From the CNA approach, six variants were obtained with an up to 2-fold improvement in thermostability. The overall experimental burden was reduced to 40% utilizing the CNA method in comparison to directed evolution. On a variant level, the success rate was similar for both strategies, with 0.27% and 0.18% improved variants in the epPCR and CNA-guided library, respectively. In essence, CNA is an effective method for identification of positions that improve thermostability.

PWM-driven model predictive speed control for an unmanned surface vehicle with unknown propeller dynamics based on parameter identification and neural prediction

This paper addresses the surge speed tracking of an unmanned surface vehicle (USV) subject to unknown surge and propeller dynamics. A two-phase on-line identification and control strategy is proposed for designing a speed tracking controller without any a priori knowledge of the model parameters in surge dynamics, propeller and drive motor. In the identification phase, an adaptive parameter estimation law is used for identifying the unknown parameters in the surge speed control system. Two-layer filters are employed to assure the convergence of estimation errors in the first learning phase. In the control phase, a pulse-width-modulation-driven (PWM-driven) adaptive model predictive speed control law is proposed where neural predictors are used to estimate the identification errors and unknown sea loads based on input–output data. The stability analysis of two neural predictors is proved on the basis of input-to-state stability. Simulation results are provided to demonstrate the efficacy of the proposed end-to-end surge speed tracking of the USV without any a priori knowledge of the surge and propeller dynamics.

An Efficient and Secure Multidimensional Data Aggregation for Fog-Computing-Based Smart Grid

The secure multidimensional data aggregation (MDA) has been widely investigated in smart grid for smart cities. However, previous proposals use heavy computation operations either to encrypt or to decrypt the multidimensional data. Moreover, previous fault-tolerant mechanisms lead to an important computation cost, and also a high communication cost when considering a separate identification phase. In this article, we propose an efficient and secure MDA scheme, named ESMA. Unlike existing schemes, the multidimensional data in ESMA are structured and encrypted into a single Paillier ciphertext and thereafter, the data are efficiently decrypted. For privacy preserving, the Paillier cryptosystem is adopted in a fog computing-based architecture, and to achieve efficient authentication, the batch verification technique is applied. Besides, ESMA is fault tolerant, i.e., even if some of the smart meters fail to send their data, the final aggregation result will not be affected. Furthermore, ESMA can be adapted to respond to other queries than the summation of data. The performance analysis demonstrates the cost efficiency of ESMA both in computation and communication and the scalability as well. For instance, with a 16-bits size for each data type and 500 reporting smart meters, 40 data types can be supported in a single Paillier ciphertext. ESMA also resists various security attacks and preserves the user's privacy.

Tool to Support Citizen Participation and Multidisciplinarity in Food Innovation: Circular Food Design

Innovations are urgently required to transform toward a more circular food system in which the food production and processing is more sustainable and the dietary patterns of consumers are more healthy and sustainable. It is needed to be more innovative in a multidisciplinary and consumer oriented way. Therefore, this paper introduces circular food design model and presents some applications. This paper presents background information regarding relevant models of product development and combines approaches and insights from different disciplines, such as consumer and food science, all present in the food system. In addition, the linkage with design thinking is addressed. Moreover, research questions are presented focused on the identification, development and optimization phase with regard to agricultural production, food storage, processing, retail and consumption. This circular food design model can support a way of thinking that will lead to multidisciplinary and citizen participating in food product development. The added value of circular food design model is; first, the model stimulates a citizen participation approach in a creative way; second, the model supports communication and collaboration among all involved disciplines. The newly developed circular food design model visualizes an iterative approach meant to be a flexible and creative tool to structure the new food development in the different phases to support value creation in the food system in order to support its transition.

Nonlinear Modeling of Lithium-Ion Battery Cells for Electric Vehicles using a Hammerstein–Wiener Model

Lithium-ion batteries are a popular electrical storage choice for electric vehicles. This is motivated by their many advantages, such as their high energy density and cycling performance. This article aims to present a nonlinear model for the dynamic behavior of lithium-ion battery cells. For this purpose, we use measurements of electric vehicles at different driving cycles. This allows us to model lithium-ion battery cells using their dynamic behavior in real-world use cases. The proposed model is developed using a nonlinear Hammerstein–Wiener model to accurately predict the behavior of these cells. The proposed model prediction performance is evaluated using mean squared error (MSE), final prediction error (FPE), and goodness of fit between the Hammerstein–Wiener (H–W) model and the measured battery cell output. The results show that the model is able to predict battery cell behavior with great accuracy: The goodness of fit value shows that the presented Hammerstein–Wiener model matches the battery cell data by 93.77% in the identification phase, and by 93.74% in the validation phase for LA-92 drive cycle. In order to show the efficiency of the selected model (H–W), a comparative study is also conducted with other model types including a neural network model, a linear model and an equivalent electrical circuit model using the same measurements. The (H–W) model presented in this paper achieved better results in term of MSE compared to the other models.

An Accurate and Real-Time Commercial Indoor Localization System in LTE Networks

In this paper, a commercial high-accuracy, low-cost and real-time indoor building-level localization system is proposed, which is applicable for locating the Minimization of Drive-Tests (MDT) data in the long-term-evolution (LTE) cellular communication network system. The system utilizes MDT data containing Global Navigation Satellite Systems (GNSS) information which is easy to collect and low cost to assist indoor localization, instead of using indoor drive test (DT) data which needs high manual collection and maintenance costs. In order to compensate for the loss of location accuracy, this paper innovatively divide the online process into two phases: indoor and outdoor (IO) identification phase and indoor localization phase. A real-time and precise GMM-based unsupervised algorithm is applied to identifying if the non-GNSS MDT data is in indoor environment in IO identification phase. Then, a multi-class classification algorithm based on Bayesian classifier is used to locate indoor MDT data to the specific building. The results of experiments conducted in an in-service LTE network using more than 100 LTE base stations demonstrate that the proposed technique yields a IO identification accuracy of 90% and an indoor location accuracy of 49.3m(@67%) respectively, which provides at least 30.2% enhancement in location accuracy compared to traditional technology without DT fingerprint database. This proposed indoor localization technique is applicable in network optimization and Operation and Maintenance (O&M) to assist communication service providers to reduce their operating expense (OPEX) by locating those MDT data without GNSS information.

An Analytical Strategy Based on Multiple Complementary and Orthogonal Chromatographic and Detection Methods (Multidetector Approach) to Effectively Manage the Analytical Evaluation Threshold (AET).

To address patient safety, a drug product is chromatographically screened for organic leachables. Similarly, medical device and packaging system extracts are chromatographically screened for organic extractables as probable leachables. To protect patient health, the screening methods must respond to all potentially unsafe substances. To be efficient, analytes determined to be below the toxicologically relevant threshold are removed from consideration before the subsequent analytical tasks of identification and quantitation are performed. The analytical evaluation threshold (AET) was established for use as a toxicologically relevant threshold to differentiate between chromatographic peaks that are unlikely to be unsafe (and thus do not need safety assessment) and those that are possibly unsafe (and thus require safety assessment). In practice, the instrumental response associated with the AET is determined using surrogate standards. It is then assumed that the response strength for an unknown extractable is equivalent to that for the surrogate standard at the AET concentration (i.e., relative response factor = 1). It is an unfortunate reality that response factors vary for different compounds on nearly all detector systems. This complicates the application of the AET and can result in a failure to include potentially toxicologically relevant compounds in the identification phase of the analysis. To ensure protection, an uncertainty factor was built into the AET equation that adjusts the AET downward to address response variation. Although this mechanism does increase the protectiveness of the AET, it assumes that the available methodology and instrumentation is sufficiently sensitive to reach the new lowered AET value. However, in some cases, the response variation is so great and the resulting uncertainty factor so large that the revised AET is below the achievable sensitivity specifications of even state-of-the art, expertly operated instrumental technologies. The only option then remaining is to concentrate the samples, which can result in adverse effects on analysis quality-counteracting the perceived benefit of lowering the AET. This article demonstrates how an analytical strategy based on methods with multiple complementary and orthogonal detection techniques (a multidetector approach) mitigates the problem of response factor variation and thus eliminates the need for large uncertainty factors and the resulting lower AET values. The primary concept is that all analytes only need to be effectively detected by at least one of the combination of detectors applied, and it is this effective technique (combination of all detectors and chromatographic methods utilized) that is used to perform the AET assessment.

Cost-effectiveness of rapid diagnostic tests, compared to microscopic tests, for the diagnosis and treatment of gestational malaria in Colombia from an institutional perspective

BackgroundGestational malaria is associated with negative outcomes in maternal and gestational health; timely diagnosis is crucial to avoid complications. However, the limited infrastructure, equipment, test reagents, and trained staff make it difficult to use thick blood smear tests in rural areas, where rapid testing could be a viable alternative. The purpose of this study was to estimate the cost-effectiveness of rapid tests type III (Plasmodium falciparum/Plasmodium spp P.f/pan) versus microscopic tests for the diagnosis and treatment of gestational malaria in Colombia.MethodsCost-effectiveness analyses of gestational malaria diagnosis from an institutional perspective using a decision tree. Standard costing was performed for the identification, measurement and assessment phases, with data from Colombian tariff manuals. The data was collected from Health Situation Analysis, SIVIGILA and meta-analysis. Average and incremental cost-effectiveness ratio were estimated. The uncertainty was assessed through probabilistic sensitivity analysis.ResultsThe cost of rapid diagnostic tests in 3,000 pregnant women with malaria was US$66,936 and 1,182 disability adjusted life years (DALYs) were estimated. The cost using thick blood smear tests was US$50,838 and 1,023 DALYs, for an incremental cost-effectiveness of US$ 101.2. The probabilistic sensitivity analysis of rapid diagnostic tests determined that they are highly cost-effective in 70% of the cases, even below the US$1,200 threshold; also, they showed an incremental net monetary benefit of $150,000 when payer’s willingness is US$1,000.ConclusionThe use of rapid diagnostic tests for timely diagnosis and treatment of gestational malaria is a highly cost-effective strategy in Colombia, with uncertainty analyses supporting the robustness of this conclusion and the increased net monetary benefit that the health system would obtain. This strategy may help in preventing the negative effects on maternal health and the neonate at a low cost.

A New Scalable Bayesian Network Learning Algorithm with Applications to Economics

This paper proposes a new Bayesian network learning algorithm, termed PCHC, that is designed to work with either continuous or categorical data. PCHC is a hybrid algorithm that consists of the skeleton identification phase (learning the relationships among the variables) followed by the scoring phase that assigns the causal directions. Monte Carlo simulations clearly show that PCHC is dramatically faster, enjoys a nice scalability with respect to the sample size, and produces Bayesian networks of similar to, or of higher accuracy than, a competing state of the art hybrid algorithm. PCHC is finally applied to real data illustrating its performance and advantages.

Keefektifan Multimedia Pembelajaran Berbasis Courselab Pada Prinsip Kerja Engine Bidang Keahlian Kendaraan Ringan

This research is a development research that is focused on developing CourseLab-based Learning Devices on the Principle of Work Engine for 11th grade students, SMK Darussalam Makassar. The research model used in this study refers to the model developed by Dick & Carey which consists of three stages, namely (1) Identification Phase, At this stage the researcher clarifies the objectives, conducts instructional analysis of learning, and identifies initial behavior and characteristics of students; (2) Development Phase, at this stage the researcher develops courselab-based learning media with the activities of formulating objectives and benchmark reference tests, and developing learning strategies and tools, (3) testing and evaluation stages. Furthermore, the CourseLab-based learning tool that has been developed is then validated by two experts to determine whether the learning device is valid and is suitable for use. The results showed that, the learning tools that had been developed were declared valid by experts. Thus, learning tools are appropriate for self-study.

End User Involvement on Performance of Consultative Group on International Agricultural Research Institutes in Kenya

Purpose: The purpose of the study was to assess the influence of end user involvement on performance of consultative group on international agricultural research institutes in Kenya.Methodology: This research study adopted a descriptive research design approach. The researcher prefers this method because it allows an in-depth study of the subject. The target population were procurement 152 officers in the consultative group on international agricultural research institutes in Kenya. This study used census as a rule of thumb since the population of procurement officers in consultative group on international agricultural research institutes is less than 200. The study combined two methods in its data collection that is, questionnaires and key informant interviews. After data collection, quantitative data was coded using Statistical Package for Social Science (SPSS) version 20. Data was analyzed through descriptive statistical methods such as means, standard deviation, frequencies and percentage. Inferential analyses were used in relation to correlation analysis and regression analysis to test the relationship between the four explanatory variables and the explained variableResults and conclusion: The findings of the study indicated that need identification phase, design phase, implementation phase and maintenance phase have a positive relationship with performance of consultative group on international agricultural research institutes in Kenya.Unique contribution to theory, policy and practice: The study recommended that institutions should embrace need identification phase, design phase, implementation phase and maintenance phase so as to improve performance of consultative group on international agricultural research institutes and further researches should to be carried out in other institutions to find out if the same results can be obtained.

Human-Large Carnivores Co-existence in Europe – A Comparative Stakeholder Network Analysis

Improving human co-existence with large carnivores (LC) is considered necessary for reaching one of the goals of the EU Council Directive on the conservation of natural habitats and of wild fauna and flora (1992). This study is part of the EU LIFE project EuroLargeCarnivores, providing a scientific analysis of current stakeholder networks of the project partners (mainly WWF offices), a necessary foundation for “Improving human co-existence with large carnivores in Europe through communication and transboundary cooperation”. We conducted systematic participatory and transdisciplinary primary research in 14 European countries. The transdisciplinary research design consists of three phases: stakeholder identification (Phase 1), participatory stakeholder-mapping (Phase 2a), a comparative network analysis (Phase 2b), and an Individual Stakeholders’ Perception Survey (Phase 3). We use the realistic method based on perceptions of the stakeholders involved. Phase 1 identifies 10 relevant Stakeholder Categories and specific agents. Phase 2a provides distinct comprehensive regional stakeholder maps with a special focus on the quality of multilateral relationships and stakeholders which are not yet actively involved in the networks. Phase 2b concludes with a comparative network analysis. The composition, density and quality of stakeholder networks as well as the interconnectivity of the project partners differ substantially. We reveal common denominators across Europe, varying relationships between stakeholder categories, and the potential positive role of foresters and veterinarians, for example. The results indicate the need for a more comparable implementation of EU regulations at national level, and for regional adaptations of support strategies for distinct stakeholders and networks. Phase 3 provides complementary insights into the involvement of the 10 Stakeholder Categories and their attitudes to large carnivore management. It also tests the institutional representation of membership in formal organizations. We challenge the perception of distinct stakeholder categories and whether involving institutional representatives in networking activities is sufficient. Based on current conflict constellations and best practice examples, we conclude with recommendations for strategic stakeholder engagement to: a) broaden and strengthen the stakeholder networks to b) improve human-human conflict management in the context of expanding large carnivore populations and their management.

Review of the Research Landscape of Multi-Criteria Evaluation and Benchmarking Processes for Many-Objective Optimization Methods: Coherent Taxonomy, Challenges and Recommended Solution

Evaluation and benchmarking of many-objective optimization (MaOO) methods are complicated. The rapid development of new optimization algorithms for solving problems with many objectives has increased the necessity of developing performance indicators or metrics for evaluating the performance quality and comparing the competing optimization algorithms fairly. Further investigations are required to highlight the limitations of how criteria/metrics are determined and the consistency of the procedures with the evaluation and benchmarking processes of MaOO. A review is conducted in this study to map the research landscape of multi-criteria evaluation and benchmarking processes for MaOO into a coherent taxonomy. Then contentious and challenging issues related to evaluation are highlighted, and the performance of optimization algorithms for MaOO is benchmarked. The methodological aspects of the evaluation and selection of MaOO algorithms are presented as the recommended solution on the basis of four distinct and successive phases. First, in the determination phase, the evaluation criteria of MaOO are collected, classified and grouped for testing experts’ consensus on the most suitable criteria. Second, the identification phase involves the process of establishing a decision matrix via a crossover of the ‘evaluation criteria’ and MaOO’, and the level of importance of each selective criteria and sub-criteria from phase one is computed to identify its weight value by using the best–worst method (BWM). Third, the development phase involves the creation of a decision matrix for MaOO selection on the basis of the integrated BWM and VIKOR method. Last, the validation phase involves the validation of the proposed solution.

Heterogeneous Weighted Voting-Based Ensemble (HWVE) for Root-Cause Analysis

Root-cause analysis has been one of the major requirements of the current information-rich world due to the huge number of opinions available online. This paper presents a heterogeneous weighted voting-based ensemble (HWVE) model for root-cause analysis. The proposed model is composed of an aspect extraction and filtering module, a model-based sentiment identification module, and a ranking module. Domain-based aspect ontologies are created using the available training data and is used for categorization. The input data is passed to the HWVE model for opinion identification and is in-parallel passed to the significance identification phase for aspect identification. The identified aspects are combined with their corresponding sentiments and ranked based on their ontological occurrence levels to provide the final categorized root-causes. Experiments were performed with the five-product dataset, and comparisons were performed with recent models. Results indicate that the proposed model exhibits improved performances of 5%-13% in terms of F-measure when compared to other models.

Physical education classes based on sports education: a systematic review

This article aimed to summarize the evidence from studies on how participation in physical education (PE) classes based on sports education (EDE) has been related to physical activity (PA) in adolescents, especially regarding participation in moderate to vigorous physical activity (MVPA), psychological and social factors. A systematic review was carried out that included eight electronic databases (PubMed, Scopus, Web of Science, LILACS, SPORTDiscus, PsycINFO, ERIC, and COCHRANE). An initial search reached 162 titles, remaining 149 after removing duplicates. Another ten studies were included based on the reference lists, totaling 159 studies in the identification phase. In the screening, 89 references were excluded, remaining 70 references for full-text analysis. In this stage, 53 references were excluded, resulting in 17 references that met the inclusion criteria. The studies were carried out between 2002 and 2017, mostly in the United States. Most studies reported improvements in psychological and social factors, especially motivation. The data on the participation of adolescents in MVPA were contradictory and inconclusive, making it difficult to understand the model's contribution to students' PA practice.

Upgraded Kalman Filtering of Cutting Forces in Milling

Advanced piezoelectric dynamometers with a wide frequency bandwidth are required for cutting force measurement in high-speed milling and micromilling applications. In many applications, the signal bandwidth is limited by the dynamic response of the mechanical system, thus compensation techniques are necessary. The most effective compensation techniques for a full 3D force correction require an accurate and complex identification phase. Extended Kalman filtering is a better alternative for input force estimation in the presence of unknown dynamic disturbances. The maximum bandwidth that can be currently achievable by Kalman filtering is approximately 2 kHz, due to crosstalk disturbances and complex dynamometer’s dynamics. In this work, a novel upgraded Kalman filter based on a more general model of dynamometer dynamics is conceived, by also taking into account the influence of the force application point. By so doing, it was possible to extend the frequency bandwidth of the device up to more than 5 kHz along the main directions and up to more than 3 kHz along the transverse directions, outperforming state-of-the-art methods based on Kalman filtering.

Economic Evaluations of Interventions for Snakebites: A Systematic Review.

BackgroundSnakebite is an often-neglected event with a high rate of mortality and is concentrated in poor areas. We aimed to assess the economic impact and health effects of the implementation of interventions for snakebites through a systematic review of the scientific literature.MethodsThirty search strategies were conducted in seven databases, applying PRISMA’s identification, screening, selection, and inclusion phases. The reproducibility of the selection of studies and the extraction of information were guaranteed. The methodological quality was evaluated using the Consolidated Health Economic Evaluation Reporting Standards. Qualitative synthesis and meta-analysis were performed for determining the average cost-effectiveness (ACE) for each death and disability-adjusted life years (DALY) avoided.ResultsSix cost-effectiveness studies were included for the supply of antivenom (AV), taken as outcomes on days of hospitalization or in ICU, death and DALYs avoided. All studies only included institutional costs, and majority of them did not specify the analytical model or economic evaluation parameters and did not perform uncertainty analyses. The management protocol standardization with interdisciplinary attention improves ACE of AV. Cost-effectiveness ratio (CER) of treatment with AV was USD 1253 (constant value for the year 2017, adjusted by purchasing power parity) for each death avoided and USD 51 per DALY avoided.ConclusionHigh cost-effectiveness of the AV treatment for snakebites was evidenced, which shows that the allocation of resources for this event should be a healthcare priority in addition to implementation of strategies that improve the access to, opportunity, and quality of hospital and pre-hospital care and reduce the cost of AV.

Stabilized asynchronous fast adaptive composite multigrid using additive damping

SummaryMultigrid solvers face multiple challenges on parallel computers. Two fundamental ones read as follows: Multiplicative solvers issue coarse grid solves which exhibit low concurrency and many multigrid implementations suffer from an expensive coarse grid identification phase plus adaptive mesh refinement overhead. We propose a new additive multigrid variant for spacetrees, that is, meshes as they are constructed from octrees and quadtrees: It is an additive scheme, that is, all multigrid resolution levels are updated concurrently. This ensures a high concurrency level, while the transfer operators between the mesh levels can still be constructed algebraically. The novel flavor of the additive scheme is an augmentation of the solver with an additive, auxiliary damping parameter per grid level per vertex that is in turn constructed through the next coarser level—an idea which utilizes smoothed aggregation principles or the motivation behind AFACx: Per level, we solve an additional equation whose purpose is to damp too aggressive solution updates per vertex which would otherwise, in combination with all the other levels, yield an overcorrection and, eventually, oscillations. This additional equation is constructed additively as well, that is, is once more solved concurrently to all other equations. This yields improved stability, closer to what is seen with multiplicative schemes, while pipelining techniques help us to write down the additive solver with single‐touch semantics for dynamically adaptive meshes.

Systematization of absorptive capacity microprocesses for knowledge identification in project management

PurposeThis study aims to systematize the acquisition phase of absorptive capacity microprocesses that contribute to project management (PM) knowledge identification.Design/methodology/approachAn exploratory and descriptive qualitative research was adopted. The first stage consisted of building the conceptual framework based on four systematic-literature-reviews. The data collection process in the second phase involved in-depth interviews, which are adequate to understand the interviewee’s reality. The sample composition consisted of 15 respondents who are PM professionals with an average of 15 years of experience. Each interviewee was chosen based on their expertise and ability to transmit the entire management process of several projects. The data were analyzed using the Atlas. Tecnology information software following the grounded theory technique with three coding cycles: open, axial and selective.FindingsBased on the results, the authors organized the microprocesses into three groups: events, social interaction and the use of tools and techniques.Research limitations/implicationsThe primary limitation of the study was the number of respondents. Future studies will be able to identify other microprocesses and evaluate their role in the knowledge identification process.Practical implicationsThis study presents a systematization of microprocesses in knowledge identification, as it occurs in the context of PM. Based on the results of this study, organizations will be able to choose the microprocesses that best fit their operations and activities according to the complexity, innovation and/or criticality of their projects.Originality/valueThe systematic literature review revealed a gap in the knowledge identification phase of knowledge management as it pertains to PM. Thus, this study presents a systematization of how knowledge identification occurs in the context of PM.

ELEMENTARY SCHOOL TEACHERS’ EXPERIENCES REGARDING THE PREREFERRAL PROCESS

<p>Informing teachers about identifying students at risk is necessary and important for effective application of interventions in the pre-referral process. The purpose of this study was to determine the adaptations that elementary school teachers did for their students at risk in the pre-referral process and to determine these teachers’ experiences in the process. In this study, the phenomenological design was used to determine the elementary school teachers’ views about the pre-referral process and their experiences regarding the process. In the present study, which was conducted to determine elementary school teachers’ experiences and views about the pre-referral process, three themes were obtained as a result of the analysis of the data collected via the interviews. The themes were as follows: teachers’ experiences and views about the identification phase, instructional adaptations in the pre-referral process and overall thoughts and suggestions regarding the pre-referral process. Informing teachers about the pre-referral process and about what to do in this process will not only help decrease the number of students involved in referral process by increasing the in-class adaptations but also allow keeping the students in the same class who will be able to continue their education with their peers with help of in-class interventions.</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0651/a.php" alt="Hit counter" /></p>