Acquisition Methodology Research Articles

Optimization of the control system for an electric power system operating on a constant-power hyperbole

For an electric power system (EPS) of the combined propulsion complex (CPC), working on a constant-power hyperbola (CPH), the strategy of managing power distribution between propulsion electric motors and own needs consumers has been improved. The study reported here aimed to reduce fluctuations in current consumption and load by optimizing voltage controllers and the rotation frequency of generator assemblies (GA). The system of EPS GA voltage and frequency stabilization was synthesized by determining, in the system of equations, the dynamics of the values of EPS links' time constants and the coefficients that correspond to control parameters. To define the characteristics of the control signals from the regulators of EPS GA rotation frequency and excitation voltage, the laws that control the speed and excitation current were calculated. After sampling the coefficients of the GA speed control regulator, the tasks for the excitation voltage controller were determined. The methodology of data acquisition was applied on the basis of a correlation between the EPS characteristics and the experimental characteristics of GA. The system of EPS dynamics equations was optimized in accordance with the structure and settings of the optimal controller and the probability of a situational error by using Spearman's rank correlation coefficient. The optimization has made it possible to reduce the likelihood of a situational error during the synchronization of GA and enable the stable operation of GA close to the mode of operation on CPH. The power controller was tested under the mode of changing the load of own needs with the power levels of EPS on CPH in the range of 50‒100 % of the rated power. The range of deviations of the current consumed with an enabled GA rotation controller was 10 % of the average value. The range of EPS power deviations with the power controller turned on was 5 %.

A March to Shape Optical Artificial Olfactory System toward Ultrasensitive Detection of Improvised Explosives

Improvised explosives (IEs) have been frequently used in explosion‐based terrorist attacks, and have aroused widespread concerns from governments to academic societies. Due to the fact that IEs mostly exist as solids, there is a pressing demand for sensors to adapt to acquire the information from the solid state. Chemical sensors usually comprise with probes designed based on the chemical properties of the analytes, and generate signals through specific chemical interactions, which would not be affected by the state of the target substance. Colorimetry and fluorescence, known for their intuitionistic (recognizable by human eyes), portable (independent of large‐scale analytical equipment), highly specific, sensitive, and environmental interference‐free characteristics, are commonly adopted optical chemical detection methods in materials analysis and environmental monitoring. Based on this, the exploration of optical artificial olfactory system, which mimics the working principle of natural olfactory system and acquires sensing signal by optical methods, has emerged to be a promising tool for IEs detection. Here, the basic working principle and the necessary components of optical artificial olfactory system are recommended, including probe molecule, array composition, pattern recognition and device construction. The potential development direction employing molecular recognition of IEs, generation, and acquisition methodology of optical signals are also prospected.

Data-Analysis Approach Aids Reservoir-Quality Prediction of Microporous Carbonates

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 19972, “Improving Reservoir-Quality Prediction of Microporous Carbonates Using a Multiscale Geophysical Data-Analysis Approach,” by Ahmad Ramdani, SPE, Viswasanthi Chandra, and Thomas Finkbeiner, SPE, King Abdullah University of Science and Technology. The paper has not been peer reviewed. Copyright 2021 International Petroleum Technology Conference. Reproduced by permission. Understanding the effect of microporosity on reservoir quality and predicting the spatial distribution of microporosity at the reservoir scale accurately is required to improve the recovery of remaining hydrocarbons from reservoirs. Using an integrated data-analysis approach applied to multiscale geological and geophysical data sets from micrometer-scale scanning-electron-microscope (SEM) imagery to decameter-scale seismic data, the authors predict the distribution of microporosity at the reservoir gridblock scale. The proposed methodology is applied in the complete paper to the Arab-D reservoir equivalent outcrop data from the Upper Jubaila formation in Saudi Arabia. Methodology This methodology is tested at an 800-m-long roadcut along the Riyadh-Mecca Highway in Saudi Arabia, stratigraphically equivalent to the Upper Jubaila Formation in the subsurface (Fig. 1). A 35-m-long core was drilled from the top of the same roadcut (dubbed the DQ-1 well), from which 106 cylindrical horizontal core plugs were obtained, drilled with an average spacing of 30 cm. This outcrop location was chosen because the pore volume in this interval is characterized by porosity with pore-diameter distribution of less than 10 µm. This outcrop provides an excellent analog to study microporosity-dominated carbonate rocks irrespective of their diagenetic origin. Moreover, the Upper Jubaila formation is a part of the Arab-D reservoir in the subsurface, which has been acclaimed as the most prolific oil reservoir in the world. Because this study used outcropped rocks, the authors acknowledge that differences in petrophysical properties with the subsurface rock formations will exist. Therefore, the study is not intended as a one-to-one analog for the Arab-D reservoir. Instead, the aim is to establish a general methodology that links micrometer-scale morphology of microcrystals that hosts microporosity with macroscale petrophysical properties and seismic attributes. Details of the methodology of seismic data acquisition and petrographic and petrophysical analysis are provided in the complete paper.

Ontology-Based Methodology for Knowledge Acquisition from Groupware

Groupware exist, and they contain expertise knowledge (explicit and tacit) that is primarily for solving problems, and it is collected on-the-job through virtual teams; such knowledge should be harvested. A system to acquire on-the-job knowledge of experts from groupware in view of the enrichment of intelligent agents has become one of the important technologies that is very much in demand in the field of knowledge technology, especially in this era of textual data explosion including due to the ever-increasing remote work culture. Before acquiring new knowledge from sentences in groupware into an existing ontology, it is vital to process the groupware discussions to recognise concepts (especially new ones), as well as to find the appropriate mappings between the said concepts and the destination ontology. There are several mapping procedures in the literature, but these have been formulated on the basis of mapping two or more independent ontologies using concept-similarities and it requires a significant amount of computation. With the goal of lowering computational complexities, identification difficulties, and complications of insertion (hooking) of a concept into an existing ontology, this paper proposes: (1) an ontology-based framework with changeable modules to harvest knowledge from groupware discussions; and (2) a facts enrichment approach (FEA) for the identification of new concepts and the insertion/hooking of new concepts from sentences into an existing ontology. This takes into consideration the notions of equality, similarity, and equivalence of concepts. This unique approach can be implemented on any platform of choice using current or newly constructed modules that can be constantly revised with enhanced sophistication or extensions. In general, textual data is taken and analysed in view of the creation of an ontology that can be utilised to power intelligent agents. The complete architecture of the framework is provided and the evaluation of the results reveal that the proposed methodology performs significantly better compared to the universally recommended thresholds as well as the existing works. Our technique shows a notable high improvement on the F1 score that measures precision and recall. In terms of future work, the study recommends the development of algorithms to fully automate the framework as well as for harvesting tacit knowledge from groupware.

Dynamic contrast enhanced – MRI efficiency in detecting embolization-induced perfusion defects in a rabbit model of critical-limb-ischemia

Critical limb ischemia (CLI) is a severe disease which affects about 2 million people in the US. Its prevalence is assessed at 800/100,000 population. However, no reliable tools are currently available to assess perfusion defects at the muscle tissue level. DCE-MRI is a technique that holds the potential to be effective in achieving this goal. However, preclinical studies performed with DCE-MRI have indicated low sensitivity assessing perfusion at resting state. To improve these previous results, in this work we propose new methodologies for data acquisition and analysis and we also revisit the biological model used for evaluation.Eleven rabbits underwent embolization of a lower limb. They were imaged at day 7 after embolization using DCE-MRI, performed on a 4.7 T small imaging device. Among them, n = 4 rabbits were used for MRI sequence optimization and n = 6 for data analysis after one exclusion. Normalized Areas under the curve (AUCn), and kinetic parameters such as Ktrans and Vd resulting from the Tofts-Kety modeling (KTM) were calculated on the embolized and contralateral limbs. Average and heterogeneity features, consisting on standard-deviation and quantiles, were calculated on muscle groups and whole limbs. The Wilcoxon and Fisher-tests were performed to compare embolized and contralateral regions of interests. The Wilcoxon test was also used to compare features of parametric maps. Quantiles of 5 and 95% in the contralateral side were used to define low and high outliers. A P-value <0.05 was considered statistically significant.Average features were inefficient to identify injured muscles, in agreement with the low sensitivity of the technique previously reported by the literature. However, these findings were dramatically improved by the use of additional heterogeneity features (97% of total accuracy for group muscles, P < 0.01 and 100% of total accuracy for the total limbs). The mapping analysis and automatic outlier detection quantification improvement was explained by the presence of local hyperemia that impair the average calculations. The analysis with KTM did not provide any additional information compared to AUCn.The DCE technique can be effective in detecting embolization-induced disorders of limb muscles in a CLI model when heterogeneity is taken into account in the data processing, even without vascular stimulation. The simultaneous presence of areas of ischemia and hyperemia appeared as a signature of the injured limbs. These areas seem to reflect the simultaneous presence of infarcted areas and viable peripheral areas, characterized by a vascular response that is visible in DCE.

A systematic review of the impact of precision teaching and fluency-building on teaching children diagnosed with autism

Precision teaching (PT) is a holistic system that allows the monitoring of behaviour change on a specially designed Standard Celeration Chart (SCC). In instructional settings, precision teachers aim for fluent responding (i.e., accuracy and speed) from their learners. Thus PT, in these settings, includes fluency-building (FB) strategies. FB strategies can be used without graphing the learner's performance on the SCC. Behavioural fluency is linked to high skills retention, endurance, stability, and generalisation to other skills and environments. A systematic literature review was carried out of PT methodology for skill acquisition of children diagnosed with autism. A systematic literature review of fluency-building procedures used in the process of skill acquisition of children diagnosed with autism was also performed. Reichow, Volkmar, and Cicchetti's (2008) assessment tool was used to establish the empirical validity of the 13 studies identified in this review. Results suggest that while there is emerging evidence for the positive outcomes, the evidence base is rather small. The implications for the use of PT and fluency-building procedures to support individuals diagnosed with autism are discussed.

Measurement of Unsteady Aerodynamic Data in Turbomachi-nery Periodic Flows

The objective of this work is to demonstrate the feasibility and advantage of an alternative approach to aerodynamic data acquisition in high-speed highly-loaded turbine engine rotating components. Two methodologies of data acquisition using fast sensing pressure probes are described here. The conventional approach is based on using separate probes for detection of unsteady total and static pressures and the flow direction. This method is labor intensive, requiring either multiple access ports on the investigated engine or repetitive test runs for each individual probe if only one access port is available. An alternative method, using only one Pitot-cylinder probe, can be used to acquire total pressure and flow direction data. The Pitot-cylinder probe basically functions like a controlled flow direction follower. The data reduction procedure for this probe relies on strict periodicity of the investigated flow during a test run. A detailed explanation of data reduction procedure and examples of achieved results are presented.

Methodology for Configuration Management in the framework of Integrated Logistic Support at COTECMAR

The main objective of this research is to propose a methodology for configuration management in COTECMAR in conjunction with the Integrated Logistic Support (ILS) describing the elements that make up the ILS and its activities throughout the life cycle of the system in the framework of a naval program, which is based on standards issued by different authors of both military and civilian nature, taking into account the methodology of acquisition of military systems adopted by the Navy and COTECMAR. Also, a proposal for the design of a computer tool for configuration management was developed. As a final point, it was described how the ILS will evolve in COTECMAR considering the concept of shipyard 4.0 and how the development of the ILS will allow COTECMAR to be a world class shipyard.

RGBD Infant Head Reconstruction for Cranial Vault Asymmetry Estimation

We present an RGBD infant head reconstruction method with a mobile phone depth sensor on a novel dataset. Acquiring 3D models from infants enables many important medical tasks such as automatic cranial asymmetry classification for plagiocephaly therapy progress estimation. Existing methods for 3D infant head reconstruction employ synchronized multi-view configurations or hand-held laser scanning methods making their widespread employment difficult. In contrast, RGBD reconstruction methods either rely on static scenes failing on this task due to rapid infant head movements or employ dynamic methods lacking the high fidelity surface reconstructions required for accurate cranial measurements. We propose a domain-specific 3D reconstruction method augmenting static RGBD methods focusing on the rigid parts of the head and exploiting scene knowledge about the data acquisition methodology. We evaluate our approach using provided ground truth anthropometric measurements of the biparietal diameter and report competitive accuracy.

Development of a USE/d-SPE and targeted DIA-Orbitrap-MS acquisition methodology for the analysis of wastewater-derived organic pollutants in fish tissues and body fluids

Pharmaceuticals (PhACs) are partially removed during wastewater treatment and end up in the receiving waters. As a result, aquatic biota is continuously exposed to a wide range of potentially hazardous contaminants such as PhACs. Therefore, fish could be a good bio indicator to give a direct measure of the occurrence of PhACs in the aquatic environment. In this study, a robust analytical method has been optimized and validated for the determination of 81 organic compounds, mainly PhACs, in seven wild fish tissue types (liver, muscle, pancreas, kidney, skin, heart, and brain) and two body fluids (plasma and bile). Solid samples extraction was performed combining a procedure based on bead beating tissue homogenization plus ultrasound extraction followed by dispersive solid-phase extraction (dSPE) clean-up using zirconia and C18 sorbents for solid matrices, whereas bile and plasma were diluted.The key aspects of this method are:• The method facilitated the simultaneous extraction of 81 PhACs of a wide range of polarity (logP from -4.9 to 5.6) in tissues with variable lipid content.• The validation was performed using Cyprinus carpio at 20 ng g−1 and 200 ng g−1 for solid tissues, 50 ng mL−1 and 500 ng mL−1 for plasma, and 100 ng mL−1 and 1000 ng mL−1 for bile. Analyte detection was performed in LC-HRMS Q-Exactive Orbitrap system with full scan and targeted data-independent acquisition (DIA) mode for high-confidence and sensitive quantitation in either (+) or (-) ESI mode.• The majority of compounds presented recoveries between 40% and 70% and relative standard deviations (RSD) below 30%.

A Survey of Smart Home IoT Device Classification Using Machine Learning-Based Network Traffic Analysis

Smart home IoT devices lack proper security, raising safety and privacy concerns. One-size-fits-all network administration is ineffective because of the diverse QoS requirements of IoT devices. Device classification can improve IoT administration and security. It identifies vulnerable and rogue items and automates network administration by device type or function. Considering this, a promising research topic focusing on Machine Learning (ML)-based traffic analysis has emerged in order to demystify hidden patterns in IoT traffic and enable automatic device classification. This study analyzes these approaches to understand their potential and limitations. It starts by describing a generic workflow for IoT device classification. It then looks at the methods and solutions for each stage of the workflow. This mainly consists of i) an analysis of IoT traffic data acquisition methodologies and scenarios, as well as a classification of public datasets, ii) a literature evaluation of IoT traffic feature extraction, categorizing and comparing popular features, as well as describing open-source feature extraction tools, and iii) a comparison of ML approaches for IoT device classification and how they have been evaluated. The findings of the analysis are presented in taxonomies with statistics showing literature trends. This study also explores and suggests undiscovered or understudied research directions.

Call for Papers: “Real-World Bio-Psycho-Social Assessments”

Call for Papers: “Real-World Bio-Psycho-Social Assessments”

A methodology and clinical dataset with ground-truth to evaluate registration accuracy quantitatively in computer-assisted Laparoscopic Liver Resection

ABSTRACT Augmented Reality (AR) can assist Laparoscopic Liver Resection (LLR) by registering a preoperative 3D model with laparoscopic images. Evaluating the accuracy of the registration methods requires measuring Target Registration Error (TRE). Previous work evaluates TRE on simulated, phantom and animal data but not on clinical data. Our contribution is a methodology for groundtruth acquisition using Laparoscopic Ultrasound (LUS) in clinical LLR, two evaluation criteria, a four-patient dataset and an evaluation of two existing registration methods. The LUS is coregistered with the laparoscope and its observations are made transferable to 3D laparoscope coordinates. The average position error in our LUS registered images is estimated to be about 1 mm, which is far better than the measured errors for the state-of-the-art registration methods, making our dataset relevant for their evaluation. We ran a preliminary evaluation of two registration methods. The dataset is available publicly at http://igt.ip.uca.fr/ab/code_and_datasets/datasets/llr_reg_evaluation_by_lus.

EANM dosimetry committee series on standard operational procedures: a unified methodology for 99mTc-MAA pre- and 90Y peri-therapy dosimetry in liver radioembolization with 90Y microspheres

The aim of this standard operational procedure is to standardize the methodology employed for the evaluation of pre- and post-treatment absorbed dose calculations in 90Y microsphere liver radioembolization. Basic assumptions include the permanent trapping of microspheres, the local energy deposition method for voxel dosimetry, and the patient–relative calibration method for activity quantification.The identity of 99mTc albumin macro-aggregates (MAA) and 90Y microsphere biodistribution is also assumed. The large observed discrepancies in some patients between 99mTc-MAA predictions and actual 90Y microsphere distributions for lesions is discussed. Absorbed dose predictions to whole non-tumoural liver are considered more reliable and the basic predictors of toxicity. Treatment planning based on mean absorbed dose delivered to the whole non-tumoural liver is advised, except in super-selective treatments.Given the potential mismatch between MAA simulation and actual therapy, absorbed doses should be calculated both pre- and post-therapy. Distinct evaluation between target tumours and non-tumoural tissue, including lungs in cases of lung shunt, are vital for proper optimization of therapy. Dosimetry should be performed first according to a mean absorbed dose approach, with an optional, but important, voxel level evaluation. Fully corrected 99mTc-MAA Single Photon Emission Computed Tomography (SPECT)/computed tomography (CT) and 90Y TOF PET/CT are regarded as optimal acquisition methodologies, but, for institutes where SPECT/CT is not available, non-attenuation corrected 99mTc-MAA SPECT may be used. This offers better planning quality than non dosimetric methods such as Body Surface Area (BSA) or mono-compartmental dosimetry. Quantitative 90Y bremsstrahlung SPECT can be used if dedicated correction methods are available.The proposed methodology is feasible with standard camera software and a spreadsheet. Available commercial or free software can help facilitate the process and improve calculation time.

Variable separations for sources and streamers in marine seismic acquisition: a novel towing concept to improve survey efficiency

This paper presents a novel acquisition methodology aimed at shallow targets and/or shallow water environments that require good near offset coverage and an improved crossline distance between sources and streamers. The proposed acquisition methodology features variable separations between adjacent sources and between adjacent streamers and builds on recent advances achieved by the seismic industry in towing the sources wide. This methodology results in better near offset coverage while improving survey efficiency, up to approximately 28% when compared to conventional configurations. A theoretical case study is presented to explain how to design this new towing concept and demonstrate the expected gains when compared to similar configurations.

Modeling of spiral wound membranes for gas separations. Part III: A nonisothermal 2D permeation model

In the present study, a previously proposed mathematical model is improved with inclusion of the energy balance, considering the Joule–Thomson effect and the heat exchange between the retentate and permeate streams, in order to explain the temperature effects observed in actual industrial sites, which requires attention to potential problems and failures, such as gas condensation that damages the dense polymeric membrane, reducing separation efficiency. The proposed model was validated with real industrial data and was able to represent the phenomena at different operation conditions, as characterized through extensive sensitivity analyses. In order to do this, a methodology for data acquisition, pre-treatment, characterization, and data reconciliation was employed. Also, an algorithm based on Oriented-Object Programming (OOP) was proposed and implemented to simulate the real membrane separation offshore unit, considering the different configuration levels of the membrane separation setup for the first time: trains, banks, tubes, elements, and leaves. It is shown that the proposed model constitutes a promising tool for design of membrane separation equipment at industrial scale, thanks to its flexibility, model robustness, and the efficiency of the numerical method, which allows the full mapping of dew points on the membrane leaf, which can be used to prevent undesired swelling and ageing of the membrane material.

GRASP-125: A Dataset for Greek Vascular Plant Recognition in Natural Environment

Plant identification from images has become a rapidly developing research field in computer vision and is particularly challenging due to the morphological complexity of plants. The availability of large databases of plant images, and the research advancements in image processing, pattern recognition and machine learning, have resulted in a number of remarkably accurate and reliable image-based plant identification techniques, overcoming the time and expertise required for conventional plant identification, which is feasible only for expert botanists. In this paper, we introduce the GReek vAScular Plants (GRASP) dataset, a set of images composed of 125 classes of different species, for the automatic identification of vascular plants of Greece. In this context, we describe the methodology of data acquisition and dataset organization, along with the statistical features of the dataset. Furthermore, we present results of the application of popular deep learning architectures to the classification of the images in the dataset. Using transfer learning, we report 91% top-1 and 98% top-5 accuracy.

Utilising a systematic review-based approach to create a database of individual participant data for meta- and network meta-analyses: the RELEASE database of aphasia after stroke

ABSTRACT Background: Collation of aphasia research data across settings, countries and study designs using big data principles will support analyses across different language modalities, levels of impairment, and therapy interventions in this heterogeneous population. Big data approaches in aphasia research may support vital analyses, which are unachievable within individual trial datasets. However, we lack insight into the requirements for a systematically created database, the feasibility and challenges and potential utility of the type of data collated. Aim: To report the development, preparation and establishment of an internationally agreed aphasia after stroke research database of individual participant data (IPD) to facilitate planned aphasia research analyses. Methods: Data were collated by systematically identifying existing, eligible studies in any language (≥10 IPD, data on time since stroke, and language performance) and included sourcing from relevant aphasia research networks. We invited electronic contributions and also extracted IPD from the public domain. Data were assessed for completeness, validity of value-ranges within variables, and described according to pre-defined categories of demographic data, therapy descriptions, and language domain measurements. We cleaned, clarified, imputed and standardised relevant data in collaboration with the original study investigators. We presented participant, language, stroke, and therapy data characteristics of the final database using summary statistics. Results: From 5256 screened records, 698 datasets were potentially eligible for inclusion; 174 datasets (5928 IPD) from 28 countries were included, 47/174 RCT datasets (1778 IPD) and 91/174 (2834 IPD) included a speech and language therapy (SLT) intervention. Participants’ median age was 63 years (interquartile range [53, 72]), 3407 (61.4%) were male and median recruitment time was 321 days (IQR 30, 1156) after stroke. IPD were available for aphasia severity or ability overall (n = 2699; 80 datasets), naming (n = 2886; 75 datasets), auditory comprehension (n = 2750; 71 datasets), functional communication (n = 1591; 29 datasets), reading (n = 770; 12 datasets) and writing (n = 724; 13 datasets). Information on SLT interventions were described by theoretical approach, therapy target, mode of delivery, setting and provider. Therapy regimen was described according to intensity (1882 IPD; 60 datasets), frequency (2057 IPD; 66 datasets), duration (1960 IPD; 64 datasets) and dosage (1978 IPD; 62 datasets). Discussion: Our international IPD archive demonstrates the application of big data principles in the context of aphasia research; our rigorous methodology for data acquisition and cleaning can serve as a template for the establishment of similar databases in other research areas.

Subsoil Recognition for Road Investment Supported by the Integration of Geodetic and GPR Data in the Form of a Point Cloud

During road construction investments, the key issue affecting the structure’s safety is accurate subsoil recognition. Identifying subsoil variability zones or natural voids can be performed using geophysical methods, and ground-penetrating radar (GPR) is recommended for this task as it identifies the location and spatial range karst formations. This paper describes the methodology of acquisition and processing of GPR data for ground recognition for road investment. Additional subsoil research was performed after karst phenomena were identified in the investment area, formations not revealed by geological recognition from earlier studies during the pre-design stage. Mala Ramac CU II radar with a 250 MHz antenna and a Leica DS2000 with 250 and 700 MHz antennas with real-time geopositioning were used to obtain the data. Regarding GPR data postprocessing, we present a method of converting spatial visualization into a point cloud that allows for GPR and geodetic data integration and confrontation. This approach enabled us to determine the locations of control trenches, the results of which were used for material validation, which is necessary to improve the reliability of subsoil recognition. The results showed a high correlation between the recorded GPR signals and the subsoil structure. Additionally, differences in the quality of results for measurements conducted before laying supporting layers with slag and on the completed road structure surface are illustrated.

Is COVID-19 forcing ESL instructors to return to more traditional teaching in the second language acquisition methodology?

This paper is part of PhD research conducted by two experienced ESL instructors from the International University of Sarajevo. The aim was to see whether the instructors at the English Language School (ELS) at the International University of Sarajevo (IUS) were turning to some more traditional teaching methods and techniques due to the COVID-19 pandemic and emergency online instruction. Four experienced English Language School instructors were interviewed and special attention was paid to the following aspects of teaching: class activities, feedback, teacher talking time and student-instructor rapport. The results showed that despite being fully experienced with and knowledgeable about modern teaching methods and techniques, more traditional methods seemed to be preferred to modern teaching methods when it came to online instruction. However, to prove this even further, some large-scale and more empirical studies would have to be conducted in the future./ Keywords: in-person instruction, online instructions, modern methodology, traditional methodology, class activities, feedback, teacher talking time, student-instructor rapport