Test Situation Research Articles

Physics-based parameters selection and machine learning based prediction of pool boiling bubble departure diameter

For forecasting the bubble departure diameter in pool boiling heat transfer, this study creates a deep-learning neural network based on physics and a variety of working fluids, manufactured surfaces, and materials subjected to different testing situations. This work analyzes nearly 5,000 data points of bubble departure diameters ranging from 0.2-28.7 mm using neural network input parameters such as saturation temperature, pressure, contact angle, surface roughness, surface tension, liquid density, gas density, wall superheat and heat flux, and other thermophysical properties, predicting their impact on the bubble departure diameter, and also uses them for training neural networks. The best Neural network which is designated as Case-4 is selected on the basis of coefficient of determination(R2), mean absolute error (MAE), and mean-square error (MSE) was used to understand the degree of influence of each input parameter and it was found that θ and Q have the greatest impact on the model. A comparison was also done to correlations proposed by researchers and it was found that neural networks have much better efficiency and accuracy than empirical formulas and thus can be an essential tool to predict the bubble diameter in the future.

Anti-Corrosion Performance of Magnesium Potassium Phosphate Cement Coating on Steel Reinforcement: The Effect of Boric Acid.

It has recently been found that magnesium potassium phosphate cement (MKPC) paste coating applied on the surface of steel reinforcement can effectively retard the onset of corrosion and suppress corrosion reactions. However, the fast-setting nature of MKPC-which is a merit in repair-can be problematic in a practical engineering process of coating the steel reinforcement with MKPC paste. To address this problem, boric acid (H3BO3) was added as a retarder in an MKPC formulation to prolong the setting time. This work investigated the impact of boric acid (at 5% by weight of MgO) on the anti-corrosion performance of MKPC paste coating through a series of electrochemical (EC) tests. The results showed that the anti-corrosion performance of MKPC paste coating for a mild steel bar could be interfered with by the presence of boric acid. In the same testing situation (immersed in 3.5 wt.% NaCl corrosion solution), the polarization resistance and corrosion current density of the group including boric acid were inferior and exceeded the corrosion thresholds prior to the control group without boric acid. Meanwhile, the time constant phase in the frequency range from 1 Hz to 10 kHz was rarely observed, implying that the presence of boric acid probably impaired the formation of the passivation layer. This decrease in anti-corrosion performance of MKPC paste coating could be related to the larger volume fraction of pores in the range from 0.1 to 10 µm that are formed during the initial stage of coating formation.

Experimental study on the mechanical properties of red sandstone with fractures under different loading rates

In order to study the effects of crack inclination angle and loading rate on rock mechanical properties, creep characteristics, and failure characteristics. Taking homogeneous red sandstone with different fracture angles as the research object, uniaxial compression tests and uniaxial compression creep tests were conducted at different loading rates. The results showed that under the same fracture angle, the loading rate was positively correlated with the peak strength, elastic modulus, instantaneous strain, creep strain, and steady-state creep rate of the sample, while negatively correlated with the peak strain. At the same loading rate, the mechanical properties and creep properties of the sample were controlled by the crack inclination angle α. With the increase of α, the peak strength, peak strain, instantaneous strain, creep strain and steady-state creep rate decreased first and then increased, and the elastic modulus increased. On the basis of rock creep testing, it is also important to establish a creep model that conforms to the actual test situation for studying rock creep characteristics. However, many models currently used cannot accurately describe the three stages of rock creep, especially the accelerated creep stage. Therefore, based on Burgers elements, this paper introduces plastic damage bodies based on damage rates and software components based on fractional calculus, A new creep model was obtained and its rationality was verified through experimental results. The results showed that the fit between the model and experimental data was above 0.97, indicating that the model can better describe the three stages of rock creep, especially reflecting the non-linear characteristics of the accelerated creep stage.

A Convolutional Neural Network-based Intelligence System for the Identification of Copra Maturity Levels

The North Sulawesi Province, widely recognized as the Coconut Waving Province owing to its substantial coconut tree population, primarily depends on copra production. This research presents a novel methodology for determining copra maturity levels by utilizing a Convolutional Neural Network (CNN) on digital photographs, classifying them into three distinct stages: raw, half-ripe, and ripe. By employing a rigorous 10-fold cross-validation technique, our models demonstrated remarkable performance. Notably, even the model with the lowest performance achieved a commendable accuracy of 87.78% during the training and validation phases. The model that exhibited the highest level of performance achieved a perfect accuracy rate of 100%. Moreover, when subjected to real-world testing situations using novel data, the model with the lowest performance exhibited a noteworthy accuracy of 83.34%. In contrast, the highest-performing model achieved a flawless accuracy of 100%. Based on the findings above, an online system has been built that leverages the most optimal model, facilitating the assessment of copra maturity in real-time. The prospects encompass the integration of this methodology into copra sorting machinery, thereby yielding advantages for both agricultural producers and industrial sectors. This research enhances copra quality control processes and promotes sustainability in the copra industry. Further research could explore refining the CNN model to accommodate a broader range of copra variations and investigating automation possibilities in copra production processes. These endeavors would advance the efficacy and applicability of copra maturity classification methods, fostering continued innovation in the industry.

Stabilometry in Relation to Hip and Knee Muscle Force in Children with Surgically Treated Unilateral Slipped Capital Femoral Epiphysis.

The main aim of our study was to analyze the stabilometric parameters in relation to hip and knee muscle force in children with unilateral slipped capital femoral epiphysis (SCFE) who had undergone surgical treatment. Another objective was to compare the stabilometry in three testing situations (eyes open, eyes closed, and head retroflexed). In total, 26 patients with unilateral right SCFE treated via in situ fixation with one percutaneous screw performed stabilometry assessments under three different situations (with their eyes open, with their eyes closed, and with their head retroflexed) and isometric muscle force assessment of the bilateral hip flexors, extensors, abductors and adductors and bilateral knee flexors and extensors. No significant differences between the right side (affected hip) and left side (non-affected hip) were recorded for all of the tested muscle groups. We found significant negative correlations between the 90% confidence ellipse area (eyes open condition) and left knee extensors (p = 0.028), right knee flexors (p = 0.041), and left knee flexors (p = 0.02), respectively. When performing the comparison between the eyes open and eyes closed situations, there were significant differences in CoP path length (p < 0.0001) and maximum CoP speed (p = 0.048); the parameters increased in the eyes closed situation. Better postural stability is acquired when assessed with eyes open or with the head retroflexed in contrast with eyes closed testing.

Uncertainty guided test-time training for face forgery detection

The rapid development of generative image modeling poses security risks of spreading unreal visual information, even though those techniques make a lot of applications possible in positive aspects. To provide alerts and maintain a secure social environment, forgery detection has been an urgent and crucial solution to deal with this situation and try to avoid any negative effects, especially for human faces, owing to potential severe results when malicious creators spread disinformation widely. In spite of the success of recent works w.r.t. model design and feature engineering, detecting face forgery from novel image creation methods or data distributions remains unresolved, because well-trained models are typically not robust to the distribution shift during test-time. In this work, we aim to alleviate the sensitivity of an existing face forgery detector to new domains, and then boost real-world detection under unknown test situations. In specific, we leverage test examples, selected by uncertainty values, to fine-tune the model before making a final prediction. Therefore, it leads to a test-time training based approach for face forgery detection, that our framework incorporates an uncertainty-driven test sample selection with self-training to adapt a classifier onto target domains. To demonstrate the effectiveness of our framework and compare with previous methods, we conduct extensive experiments on public datasets, including FaceForensics++, Celeb-DF-v2, ForgeryNet and DFDC. Our results clearly show that the proposed framework successfully improves many state-of-the-art methods in terms of better overall performance as well as stronger robustness to novel data distributions.

Noise suppression in pulsed IR thermographic NDT: Efficiency of data processing algorithms

Various types of noise, which accompany active TNDT procedures using optical heating, have been analyzed, both numerically and experimentally. An emphasis has been made on the suppression of surface clutter, which represents local areas of varying absorptivity/emissivity. The concept of signal-to-noise that is typically used in defect detection has been applied to fixed pattern noise in order to compare capabilities of data processing algorithms in reducing surface clutter. The experimental investigation has been fulfilled on a special sample containing both subsurface air-filled defects and areas with varying emissivity/absorptivity. The best suppression of the fixed pattern noise was provided by the complex wavelet transform and principle component analysis. Because of 3D heat diffusion, clutter spot boundaries are often underlined by particular data processing algorithms thus producing specific contours. The test situations where subsurface defects are located under localized clutter spots have been analyzed to demonstrate an overshadowing effect of such spots when detecting hidden defects.

How do different amounts of straw as well as compost in the home pen affect the rooting motivation of growing-finishing pigs?

Rooting is a strongly motivated, species-specific behaviour of pigs. Most housing systems do not provide appropriate materials that enable the full expression of this behaviour, and it remains unclear whether straw is suitable to entirely fulfil the rooting motivation of pigs. We therefore investigated the suitability of small (minimal) and large (deep) amounts of straw as well as large amounts of compost to satisfy rooting motivation in pigs. Fifty-seven growing-finishing pigs were housed in three pens, each providing permanent access to one of the three treatment substrates. Eight pigs per group were tested individually in a classical preference test (PT) and another eight pigs in a conditioned place preference test (CPPT). In the tests, pigs could show their preference to consume freely available feed (“feed”) or feed hidden in sawdust (“root”). In the CPPT, feed was only present during training but not during testing. Pigs were exposed to the test situation twice, with approximately 72 kg and 115 kg BW. In both tests, the following variables were measured and used as outcome variables in linear mixed effect models: first decision to choose one of the two stimuli (“feed” or “root”), duration of time spent in proximity to “root”, number of changes between stimuli, and latency to the first decision. Overall, the pigs’ first decision (by tendency; P = 0.076) and the duration in proximity to “root” (P = 0.034) varied among treatments: Pigs housed with minimal straw tended to be more likely to choose “root” first (posthoc comparison; P = 0.090) and spent more time in proximity to “root” (P = 0.030) than pigs housed with compost, whereas pigs housed with deep straw were intermediate. Interestingly, the patterns of response to the treatment differed depending on the behavioural tests for both, first decision (interaction; P = 0.032) and duration in proximity to “root” (interaction; by tendency; P = 0.006). In addition, pigs in the PT changed more often between stimuli than pigs in the CPPT (P < 0.001). There was a tendency for an interactive effect between test and treatment for latency to first decision (interaction; P = 0.082), though pairwise comparisons did not reveal any differences. We concluded that in this study housing with permanent access to compost satisfied rooting motivation in pigs more than housing with minimal amounts of straw.

Human physiological responses to different types of human-dog interactions: A randomised crossover study

Previous studies of human-dog interventions vary in terms of type of interaction, which is rarely quantified, leading to contradictory findings and limited comparability. To uncover the influence of different types of interactions, the present study investigated if it was possible to detect differences in immediate physiological measurements of healthy humans during different standardised types of interaction with a dog. Thirty-three healthy participants (women = 25, men = 8, >18 years) were exposed to four different test situations with standardised types of interaction intensity with a dog in random order: no dog present (CONTROL), looking at a dog (VISUAL), petting a dog (TACTILE) or performing tricks with a dog (ACTIVE). Each test situation lasted 10 min with a 30-min break between each. Heart rate (HR), heart rate variability (HRV) and skin conductance (tonic level (SCL) and peak counts (SCR)) were continuously recorded. Blood pressure (BP) and salivary cortisol (s-cortisol) were measured before and after each test situation. Linear Mixed Models were applied. HR, HRV, BP, SCL and SCR increased with increased interaction with the dog (for all: p < 0.001). HRV increased with decreased HR (p = 0.002), increased SCL (p = 0.027), and SCR (p < 0.001) depending on the type of interaction. Generally, s-cortisol increased with increased HR (p = 0.042), SCL increased with increased SCR (p < 0.001), and SCR increased with increased HRV (p = 0.013), depending on type of interaction. The physiological measurements HR, HRV, BP, SCL and SCR are influenced by different types of dog interaction, and thus it is important to quantify and report the type of interaction in human-dog interaction studies. (ClinicalTrials.gov ID:NCT04696419).

Preliminary Studies on Changes in Static Plantar Pressure and Stabilometry in Patients with Ankylosing Spondylitis Undergoing an Exercise Program.

Background/Objectives: Studies have reported that patients suffering from ankylosing spondylitis (AS) have decreased postural stability in comparison to healthy subjects. Our study aims to compare static plantar pressure and stabilometry parameters in AS patients who performed an 8-week exercise program (spine motion and flexibility exercises; stretching of hamstring, erector spine, and shoulder muscles; control abdominal and diaphragm breathing exercises and chest expansion exercises), in three different testing conditions (eyes open, eyes closed, and head retroflexed). Methods: Plantar pressure (the loading of the first and fifth metatarsal heads (MT1, MT5) and calcaneus) and stabilometry (CoP path length, 90% confidence ellipse area, and maximum CoP speed) were recorded in 28 AS patients (age 56.64 ± 10.3 years; body mass index 29.4 ± 4.9 kg/m2) at the beginning of rehabilitation and after 8 weeks. At first evaluation, there were significant differences (p < 0.05) for the foot loading sites (MT1, MT5, and calcaneus), both for the right and left feet, when comparing eyes open with the other two testing situations. Results: After rehabilitation, significant differences were recorded between eyes-open and head-retroflexed conditions for MT1 (p = 0.03 for right; p = 0.004 for left) and calcaneus (p = 0.014 for right; p = 0.011 for left). A significantly higher CoP path length was registered in both initial and final assessments when tested with eyes closed. The maximum CoP speed had increased values at both evaluations when tested with head retroflexed. Conclusions: The CoP path length decreased after the physical exercise program, with a better postural stability after rehabilitation.

Relation of general-perceptual cognitive abilities and sport-specific performance of young competitive soccer players.

In soccer, skill is significantly influenced by motor skills and physical constitution. In addition, perceptual-cognitive abilities are thought to affect sport-specific performance. Nevertheless, there are hardly any studies investigating the relationship of general cognitive abilities with sport-specific performance. Therefore, the aim of this study was to analyze relationships between general perceptual-cognitive abilities and soccer-specific performance in competitive youth sports. Thirty highly talented male youth soccer players aged 12-14years completed various perceptual-cognitive (selective attention, cognitive flexibility, inhibition, working memory, peripheral perception, and choice response) and sport-specific on-field tests. Cognitive abilities were assessed using a computer-based test system. Soccer-specific performance skills were evaluated by two sport-specific on-field tests. The relation between perceptual-cognitive abilities and soccer-specific performance was examined using a correlation analysis as well as a four-stage regression analysis. Overall, the expression of general perceptual-cognitive abilities was found to have an impact on performance in soccer-specific test situations, particularly cognitive flexibility and selective attention. Our results suggest that general cognitive tests could be an important tool for the evaluation of cognitive abilities in soccer. This study brings together key approaches in expertise research and makes a significant contribution to a better understanding of expertise in soccer.

When Large Language Models Meet Optical Networks: Paving the Way for Automation

Since the advent of GPT, large language models (LLMs) have brought about revolutionary advancements in all walks of life. As a superior natural language processing (NLP) technology, LLMs have consistently achieved state-of-the-art performance in numerous areas. However, LLMs are considered to be general-purpose models for NLP tasks, which may encounter challenges when applied to complex tasks in specialized fields such as optical networks. In this study, we propose a framework of LLM-empowered optical networks, facilitating intelligent control of the physical layer and efficient interaction with the application layer through an LLM-driven agent (AI-Agent) deployed in the control layer. The AI-Agent can leverage external tools and extract domain knowledge from a comprehensive resource library specifically established for optical networks. This is achieved through user input and well-crafted prompts, enabling the generation of control instructions and result representations for autonomous operation and maintenance in optical networks. To improve LLM’s capability in professional fields and stimulate its potential on complex tasks, the details of performing prompt engineering, establishing domain knowledge library, and implementing complex tasks are illustrated in this study. Moreover, the proposed framework is verified on two typical tasks: network alarm analysis and network performance optimization. The good response accuracies and semantic similarities of 2400 test situations exhibit the great potential of LLM in optical networks.

Culturally-informed neuropsychological evaluations of patients with spina bifida: Application of the ECLECTIC framework to a complex pediatric medical condition

Objective: The pediatric patient populations in the United States are becoming increasingly diverse in culture. In addition to medical factors, a variety of sociocultural factors (including educational systems, language, immigration status, etc.) can impact a child’s cognitive development, performance on traditional neuropsychological measures, provider interactions, differential diagnosis, and recommendations. A culturally-informed neuropsychological evaluation aims to understand the impact of sociocultural factors and integrate them into the assessment approach. Fujii’s ECLECTIC framework (acronym for Education & Literacy, Culture/Acculturation, Language, Economic Issues, Communication Style, Testing Situation, Intelligence Conceptualization, and Context of Immigration) can be used to guide culturally-informed neuropsychological evaluations of children with complex medical conditions. Method: Using the ECLECTIC framework, this paper describes clinical considerations and offers practical solutions for providing culturally-informed pediatric neuropsychology evaluations. Four diverse patients with spina bifida are reviewed. Results: The case presentations highlight the application of the ECLECTIC framework within a medically complex pediatric patient population. Conclusions: Spina bifida is one example of a pediatric medical population in which both medical and sociocultural factors can impact cognitive development and the evaluation process. The ECLECTIC framework defines a variety of sociocultural factors that can influence cognitive development and multiple aspects of a pediatric neuropsychological evaluation. Pediatric neuropsychologists should use the ECLECTIC framework to provide culturally-informed evaluations for our increasingly diverse patient populations.

Quantitative electroencephalography in cerebral amyloid angiopathy

ObjectiveWe investigated whether quantitative electroencephalography (qEEG) correlates with cognition and cortical superficial siderosis (cSS) in cerebral amyloid angiopathy. MethodsWe included patients with sporadic (sCAA) and hereditary Dutch-type CAA (D-CAA). Spectral measures and the phase lag index (PLI) were analyzed on qEEG. Cognition was assessed with the MoCA and cSS presence was scored on 3T-MRI. Linear regression analyses were performed to investigate these qEEG measures and cognition. Independent samples T-tests were used to analyze the qEEG measure differences between participants with and without cSS. ResultsWe included 92 participants (44 D-CAA; 48 sCAA). A lower average peak frequency (β[95 %CI] = 0.986[0.252–1.721]; P = 0.009) and a higher spectral ratio (β[95 %CI] = −0.918[−1.761–−0.075]; P = 0.033) on qEEG correlated with a lower MoCA score, irrespective of a history of symptomatic intracerebral hemorrhage (sICH). The PLI showed no correlation to the MoCA. qEEG slowing was not different in those with or without cSS. ConclusionsSpectral qEEG (but not PLI) reflects cognitive performance in patients with CAA with and without a history of sICH. We found no association between qEEG slowing and cSS. SignificanceqEEG could be a valuable biomarker, especially in challenging cognitive testing situations in CAA, and a potential predictive tool in future studies.

Effects of School-Based Neurofeedback Training on Attention in Students with Autism and Intellectual Disabilities.

In this study we aimed to assess the influence of school-based neurofeedback training on the attention of students with autism and intellectual disabilities. We assessed 24 students of a special education center who attended neurofeedback training sessions during the schoolyear; we also assessed 25 controls from the same center. We used two computer tasks to assess sustained attention in simple and cognitively demanding test situations, and we used a pen-and-paper task to assess selective attention. Each student who took part in the study was tested at the beginning and at the end of the schoolyear. Students from the experimental group significantly improved their performance in the task related to sustained attention to simple stimuli. No performance improvement related to neurofeedback treatment was observed in either sustained attention in cognitively demanding situations or selective attention. School-based neurofeedback training may improve sustained attention to simple stimuli in students with developmental disabilities.

AI-driven adaptive learning for enhancing business intelligence simulation games

This study investigates how automated adaptable learning (BSG) may improve business simulating games (BSG) and its transformation possibilities. The rapid increase of information, combined with the intricate nature of today's company contexts, means that classic BSG education techniques frequently fail to adequately prepare students for the difficulties they will face in everyday life. This study provides a revolutionary architecture that continually evolves learning paths, responses, and testing situations based on specific student progress and desires. It does this by utilizing modern artificial intelligence methods, including machine learning. This technique seeks to maximize users' preservation of expertise, ability to make decisions, and capacity for creative thinking through tailoring their education. In summary, our study raises possibilities for better and more interesting educational opportunities throughout the modern age by adding to the continuing conversation about the relationship between AI, training, and corporate strategies.

Parallel Optimal Calibration of Mixed-Format Items for Achievement Tests

When large achievement tests are conducted regularly, items need to be calibrated before being used as operational items in a test. Methods have been developed to optimally assign pretest items to examinees based on their abilities. Most of these methods, however, are intended for situations where examinees arrive sequentially to be assigned to calibration items. In several calibration tests, examinees take the test simultaneously or in parallel. In this article, we develop an optimal calibration design tailored for such parallel test setups. Our objective is both to investigate the efficiency gain of the method as well as to demonstrate that this method can be implemented in real calibration scenarios. For the latter, we have employed this method to calibrate items for the Swedish national tests in Mathematics. In this case study, like in many real test situations, items are of mixed format and the optimal design method needs to handle that. The method we propose works for mixed-format tests and accounts for varying expected response times. Our investigations show that the proposed method considerably enhances calibration efficiency.

The Impact of College Students’ Emotional Intelligence and Achievement Motivation on Test Anxiety: A Case of 3 Colleges at Chongqing

Test anxiety is a special psychological reaction characterized by worry and tension under the excitation of test situations. In the environment of China's test-oriented education, there is a common phenomenon of test anxiety among students. Test anxiety is particularly acute in the context of the current coronavirus pandemic. It not only affects students' learning, but also has an important impact on their mental health, so the problem of test anxiety cannot be ignored. This study starts from the factors that affect test anxiety, explores the relationship between emotional intelligence, achievement motivation, and test anxiety of college students under the current background of the new coronavirus epidemic, and on this basis, discusses the influence of emotional intelligence and achievement motivation on test anxiety. In this study, the Emotional Intelligence Scale (EIS), Test Anxiety Scale (TAS) and Achievement Motivation Scale (AMS) were used to survey college students in 3 Chongqing universities through questionnaires, and descriptive statistics, variance analysis, correlation analysis, regression Analysis and other methods were used to explore the relationship and influence of college students' emotional intelligence and achievement motivation on test anxiety, and to analyze the influence mechanism of college students' emotional intelligence and achievement motivation on test anxiety.

MSSPN: Automatic first-arrival picking using a multistage segmentation picking network

Picking the first arrival of prestack gathers is an indispensable step in seismic data processing. To enhance the efficiency of seismic data processing, some deep-learning-based methods for first-arrival picking have been developed. However, when applying currently trained models to data that significantly differ from the training set, the results are often suboptimal. We refer to this predictive scenario as cross-survey picking. Therefore, further improving model generalization for accurate cross-survey picking has become an urgent problem. To overcome the problem, we develop a multistage picking method called multistage segmentation picking network (MSSPN), which breaks down the complex picking task into four stages. In the first stage, we develop a coarse segmentation network to recognize a rough trend of first arrivals. Second, a robust trend estimation method is developed in the second stage to further obtain a tighter range of first arrivals. Third, a refined segmentation network is conducted in the third stage to pick high-precision first arrivals. Finally, we develop a velocity constraint-based postprocessing strategy to remove the outliers of network pickings. Extensive experiments indicate that MSSPN outperforms current state-of-the-art methods under the cross-survey test situation in terms of the metrics of accuracy and stability. Particularly, MSSPN achieves 94.64% and 89.74% accuracy under the cross-survey field cases of the median and low signal-to-noise ratio data, respectively.

혼합형 문항으로 구성된 단계적 적응검사에서 교차 라우팅 방법의 효과 탐색

Multistage testing among several adaptive testing types has been more frequently applied into&#x0D; practice because it can accurately and efficiently estimate abilities with relatively short tests. However,&#x0D; it has been also criticized due to its low level of adaptability. To address this issue, the intersectional&#x0D; routing method was proposed and its effectiveness has been partly confirmed. This study aims to&#x0D; explore the effectiveness of the intersectional routing method with mixed-format MST by applying the&#x0D; bi-factor MIRT model, and the UIRT model, extending previous researches. Based upon the results of&#x0D; this study, when the module length was short (8 items), and the correlation between sections was&#x0D; high (0.9), the accuracy of ability estimates using the intersectional routing was higher than the&#x0D; typical multistage testing design regardless of the IRT model. The application of the intersectional&#x0D; routing technique decreased the differences of ability estimate accuracy in accordance with panel&#x0D; designs. Considering the high inter-correlation values between sections in actual tests (ranging from 0.6&#x0D; to 0.9), the application of the intersectional routing method into multistage testing could be a choice&#x0D; for broadening options in test designs and obtaining more accurate ability estimates. The results also&#x0D; indicate the necessity of selecting an appropriate IRT model based on test characteristics in&#x0D; multidimensional adaptive testing situations.