Adaptive Version Research Articles

Deep Learning-Assisted Active Metamaterials with Heat-Enhanced Thermal Transport.

Heat management is crucial for state-of-the-art applications such as passive radiative cooling, thermally adjustable wearables, and camouflage systems. Their adaptive versions, to cater to varied requirements, lean on the potential of adaptive metamaterials. Existing efforts, however, feature with highly anisotropic parameters, narrow working-temperature ranges, and the need for manual intervention, which remain long-term and tricky obstacles for the most advanced self-adaptive metamaterials. To surmount these barriers, heat-enhanced thermal diffusion metamaterials powered by deep learning is introduced. Such active metamaterials can automatically sense ambient temperatures and swiftly, as well as continuously, adjust their thermal functions with a high degree of tunability. They maintain robust thermal performance even when external thermal fields change direction, and both simulations and experiments demonstrate exceptional results. Furthermore, two metadevices with on-demand adaptability, performing distinctive features with isotropic materials, wide working temperatures, and spontaneous response are designed. This work offers a framework for the design of intelligent thermal diffusion metamaterials and can be expanded to other diffusion fields, adapting to increasingly complex and dynamic environments.

Evaluation of feasibility phase of adaptive version of locally made bubble continuous positive airway pressure oxygen therapy for the treatment of COVID-19 positive and negative adults with severe pneumonia and hypoxaemia.

Bubble continuous positive airway pressure (bCPAP) oxygen therapy has been shown to be safe and effective in treating children with severe pneumonia and hypoxaemia in Bangladesh. Due to lack of adequate non-invasive ventilatory support during coronavirus disease 2019 (COVID-19) crisis, we aimed to evaluate whether bCPAP was safe and feasible when adapted for use in adults with similar indications. Adults (18-64 years) with severe pneumonia and moderate hypoxaemia (80 to <90% oxygen saturation (SpO2) in room air) were provided bCPAP via nasal cannula at a flow rate of 10 litres per minute (l/min) oxygen at 10 centimetres (cm) H2O pressure, in two tertiary hospitals in Dhaka, Bangladesh. Qualitative interviews and focus group discussions, using a descriptive phenomenological approach, were performed with patients and staff (n = 39) prior to and after the introduction (n = 12 and n = 27 respectively) to understand the operational challenges to the introduction of bCPAP. We enrolled 30 adults (median age 52, interquartile range (IQR) 40-60 years) with severe pneumonia and hypoxaemia and/or acute respiratory distress syndrome (ARDS) irrespective of coronavirus disease 2019 (COVID-19) test results to receive bCPAP. At baseline mean SpO2 on room air was 87% (±2) which increased to 98% (±2), after initiation of bCPAP. The mean duration of bCPAP oxygen therapy was 14.4 ± 24.8 hours. There were no adverse events of note, and no treatment failure or deaths. Operational challenges to the clinical introduction of bCPAP were lack of functioning pulse oximeters, difficult nasal interface fixation among those wearing nose pin, occasional auto bubbling or lack of bubbling in water-filled plastic bottle, lack of holder for water-filled plastic bottle, rapid turnover of trained clinicians at the hospitals, and limited routine care of patients by hospital clinicians particularly after official hours. If the tertiary hospitals in Bangladesh are supplied with well-functioning good quality pulse oximeters and enhanced training of the doctors and nurses on proper use of adapted version of bCPAP, in treating adults with severe pneumonia and hypoxaemia with or without ARDS, the bCPAP was found to be safe, well tolerated and not associated with treatment failure across all study participants. These observations increase the confidence level of the investigators to consider a future efficacy trial of adaptive bCPAP oxygen therapy compared to WHO standard low flow oxygen therapy in such patients. s Although bCPAP oxygen therapy was found to be safe and feasible in this pilot study, several challenges were identified that need to be taken into account when planning a definitive clinical trial.

Biome classification influences current and projected future biome distributions

AbstractAimBiome classification schemes are widely used to map biogeographic patterns of vegetation formations on large spatial scales. Future climate change will influence biome patterns, and vegetation models can be used to assess the susceptibility of biomes to experience transitions. However, biome classification is not unique, and various classification schemes and biome maps exist. Here, we aimed to assess how the choice of biome classification schemes influences current and projected future biome patterns.LocationAfrica, Australia, Tropical Asia.Time period2000–2099.Major taxa studiedTropical vegetation.MethodsWe used the adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation in the study region. We classified vegetation into biomes using (1) a classification scheme based on the cover of functional types, (2) a cluster analysis based on the cover of functional types and (3) a cluster analysis based on trait patterns simulated by the aDGVM2. We compared the resulting biome maps to multiple observation‐based biome maps and quantified differences in projected biome changes under the RCP8.5 scenario for the different classification schemes.ResultsAs expected, biome patterns were strongly related to the scheme used for biome classification. The highest data‐model agreement was derived for a cluster analysis using 21 simulated traits. Traits related to size were most important for classification. Considering all classification schemes, the area projected to undergo biome transitions under climate change varied between 16.5% and 32.1%. Despite this variability, different schemes consistently showed that grassland and savanna areas are most susceptible to climate change, whereas tropical forests and deserts are stable. Our results demonstrate that traits simulated by aDGVM2 are appropriate to delimit biomes.Main conclusionsStudies projecting biome patterns and transitions under current and future climate should consider applying different biome classification schemes to avoid biases in such projections caused by biome classification schemes.

Development of an adaptive test of musical scene analysis abilities for normal-hearing and hearing-impaired listeners

Auditory scene analysis (ASA) is the process through which the auditory system makes sense of complex acoustic environments by organising sound mixtures into meaningful events and streams. Although music psychology has acknowledged the fundamental role of ASA in shaping music perception, no efficient test to quantify listeners’ ASA abilities in realistic musical scenarios has yet been published. This study presents a new tool for testing ASA abilities in the context of music, suitable for both normal-hearing (NH) and hearing-impaired (HI) individuals: the adaptive Musical Scene Analysis (MSA) test. The test uses a simple ‘yes–no’ task paradigm to determine whether the sound from a single target instrument is heard in a mixture of popular music. During the online calibration phase, 525 NH and 131 HI listeners were recruited. The level ratio between the target instrument and the mixture, choice of target instrument, and number of instruments in the mixture were found to be important factors affecting item difficulty, whereas the influence of the stereo width (induced by inter-aural level differences) only had a minor effect. Based on a Bayesian logistic mixed-effects model, an adaptive version of the MSA test was developed. In a subsequent validation experiment with 74 listeners (20 HI), MSA scores showed acceptable test–retest reliability and moderate correlations with other music-related tests, pure-tone-average audiograms, age, musical sophistication, and working memory capacities. The MSA test is a user-friendly and efficient open-source tool for evaluating musical ASA abilities and is suitable for profiling the effects of hearing impairment on music perception.

Enhanced Output Tracking Control for Direct Current Electric Motor Systems Using Bio-Inspired Optimization

In this paper, an efficient output reference trajectory tracking control scheme for direct current electric motor systems based on bio-inspired optimization is proposed. The differential flatness structural property of the electric motor along with dynamic tracking error compensation is suitably exploited for the backstepping control design. Off-line optimal selection of control parameters, implementing bio-inspired ant colony and particle swarm optimization algorithms, is addressed by minimizing an objective function where the decision variables are the tracking error and control input effort. A novel adaptive version of the control approach based on B-spline artificial neural networks is provided as well. The introduced flat output feedback tracking control design approach can be further extended for other differentially flat dynamic systems. Considerably perturbed, diverse velocity and position reference trajectory tracking scenarios are developed for demonstrating the acceptable closed-loop system performance. The results prove the efficient and robust tracking of the position and velocity reference profiles planned for the operation of the controlled electric motor system under variable torque disturbances using bio-inspired optimization.

Peer-supported mobile mental health for veterans in primary care: A pilot study.

One in four veteran primary care patients suffers from a mental health condition; however, most do not receive any treatment for these problems. Mobile health (mHealth) can overcome barriers to care access, but poor patient engagement limits the effectiveness and implementation of these tools. Peers may facilitate patient engagement with mHealth. We designed a protocol for peers to support implementation of mobile mental health tools in primary care and tested the feasibility, acceptability, and clinical utility of this approach. Thirty-nine patients across two Veterans Affairs sites who screened positive for depression during a primary care visit and were not currently in mental health treatment were enrolled. Participants were scheduled for four phone sessions with a peer over 8 weeks and introduced to five mobile apps for a range of transdiagnostic mental health issues (stress, low mood, sleep problems, anger, and trauma). Pre/post phone interviews using quantitative and qualitative approaches assessed participants' self-reported app use, satisfaction with the intervention, symptom change (stress, anxiety, depression, insomnia), and progress with personal health goals. On average, patients reported using 3.04 apps (SD = 1.46). Per the Client Satisfaction Questionnaire, global satisfaction with the intervention was high (M = 25.71 out of 32, SD = 3.95). Pre to post participants reported significant improvements in their level of stress, based on a quantitative measure (p = .008), and 87% reported progress on at least one personal health goal. Findings support the feasibility, acceptability, and clinical utility of peer-supported mobile mental health for veterans in primary care. A randomized controlled trial of an adaptive version of this intervention is recommended. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Greedy versus optimal analysis of bounded size dictionary compression and on-the-fly distributed computing

Scalability and robustness are not an issue when compression is applied for massive data storage, in the context of distributed computing. Speeding up on-the-fly compression for data transmission is more controversial. In such case, a compression technique merging together an adaptive and a non-adaptive approach has to be considered. A practical implementation of LZW (Lempel, Ziv and Welch) compression, called LZC (C indicates the Unix command ’compress’), has this characteristic. The non-adaptive phases work with bounded size prefix dictionaries built by LZW factorizations during the adaptive ones. Both phases employ the greedy approach. We show a worst case analysis of the greedy approach with respect to the optimal solution decodable by the LZC decompressor and give worst case examples for which the solution cost produced by the distributed implementations reaches the Θ(d) theoretical upper bound to the optimal cost approximation factor, where d is the dictionary size. We prove that for the worst case examples of the totally adaptive version of LZW compression the approximation factor is Θ(d). Such analysis suggests that parallelization of on the fly compression is not suitable for highly disseminated data since the non-adaptive phases are too far from optimal. In order to improve the compression effectiveness, we suggest to apply LZMW (Lempel, Ziv, Miller and Wegman) factorization to LZC compression (LZCMW) during the adaptive phases. Such factorization was originally thought with a dictionary bounded by the least recently used strategy. We introduce LZCMW in order to have non-adaptive phases.

Ship roll stabilization using an adaptive fractional-order sliding mode controller

The current study designs an adaptive fractional-order sliding mode controller (AFOSMC) to diminish the undesirable effects caused by the ship roll motions. The ship roll dynamics combined with the fin actuator are employed for the controller design. The design steps are given as follows. At first, a non-integer sliding surface is utilized to design a fractional-order sliding mode controller (FOSMC). Then, an appropriate Lyapunov-based adaptation mechanism is designed to construct the adaptive version of the FOSMC. A Lyapunov candidate function is adopted to attain the ship roll stabilization through this adaptive control approach. The designed controller is robust to uncertain parameters in the ship roll dynamics and the fin actuator. Moreover, the effect of sea waves is reduced, as well. The robust performance of the AFOSMC is shown through numerical simulations.

Impact of Engaging with Intelligent Tutoring System Lessons Prior to Class Start

A study was conducted using the Generalized Intelligent Framework for Tutoring (GIFT) intelligent tutoring system (ITS) software and an Army Schoolhouse course. Existing materials were provided and used to create 9 computer-based GIFT lessons that included adaptive tutoring versions of the materials. These lessons included a Pre-Test, 7 content lessons (on the areas of Detect, Engage, and Assess), and a Post-Test. Six participants completed the Pre-Test, Post-Test, and all lessons. Overall Post-Test scores were significantly higher than Pre-Test scores, and remediation also had a positive impact on Post-Test scores. This study demonstrates that completing self-paced ITS lessons prior to initial class instruction can lead to positive learning impacts prior to the start of a class.

Tracking Control of Uncertain Neural Network Systems with Preisach Hysteresis Inputs: A New Iteration-Based Adaptive Inversion Approach

To describe the hysteresis nonlinearities in smart actuators, numerous models have been presented in the literature, among which the Preisach operator is the most effective due to its capability to capture multi-loop or sophisticated hysteresis curves. When such an operator is coupled with uncertain nonlinear dynamics, especially in noncanonical form, it is a challenging problem to develop techniques to cancel out the hysteresis effects and, at the same time, achieve asymptotic tracking performance. To address this problem, in this paper, we investigate the problem of iterative inverse-based adaptive control for uncertain noncanonical nonlinear systems with unknown input Preisach hysteresis, and a new adaptive version of the closest-match algorithm is proposed to compensate for the Preisach hysteresis. With our scheme, the stability and convergence of the closed-loop system can be established. The effectiveness of the proposed control scheme is illustrated through simulation and experimental results.

E-Babylab: An open-source browser-based tool for unmoderated online developmental studies.

The COVID-19 pandemic massively changed the context and feasibility of developmental research. This new reality, as well as considerations about sample diversity and naturalistic settings for developmental research, highlights the need for solutions for online studies. In this article, we present e-Babylab, an open-source browser-based tool for unmoderated online studies targeted for young children and babies. e-Babylab offers an intuitive graphical user interface for study creation and management of studies, users, participant data, and stimulus material, with no programming skills required. Various kinds of audiovisual media can be presented as stimuli, and possible measures include webcam recordings, audio recordings, key presses, mouse-click/touch coordinates, and reaction times. An additional feature of e-Babylab is the possibility to administer short adaptive versions of MacArthur-Bates Communicative Development Inventories (Chai et al. Journal of Speech, Language, and Hearing Research, 63, 3488-3500, 2020). Information pages, consent forms, and participant forms are customizable. e-Babylab has been used with a variety of measures and paradigms in over 12 studies with children aged 12 months to 8 years (n = 1516). We briefly summarize some results of these studies to demonstrate that data quality, participant engagement, and overall results are comparable between laboratory and online settings. Finally, we discuss helpful tips for using e-Babylab and present plans for upgrades.

RETRACTED ARTICLE: A Computational Model for Predicting Customer Behaviors Using Transformer Adapted with Tabular Features

The recent decade has seen an explosion in the e-commerce industry with the support of modern technologies (e.g., artificial intelligence) to maximize conversion rates. Many recommendation systems, therefore, have been developed to predict the customer behaviors to take appropriate actions. Customization of promotions or items for distinct groups of online customers considerably contributes to enhancing the sales. The purchasing records are essential information that can be used to investigate the customer behaviors. These kinds of information, however, are mainly expressed in tubular forms. Several computational models were developed using conventional machine learning algorithms to deal with that data type. However, these approaches struggle with large-volume and high-dimensional data, feature engineering, and high computational cost. In our study, we propose a deep learning model based on the Feature Tokenizer Transformer architecture to predict the customer purchasing intention. This novel architecture is a simplified adaptive version of the Transformer tabular data. The results demonstrated that our model showed better performance compared to conventional machine learning models. Furthermore, the model’s stability was also confirmed by the results of multiple repeated experiments.

Adaptive lasso and Dantzig selector for spatial point processes intensity estimation

Lasso and Dantzig selector are standard procedures able to perform variable selection and estimation simultaneously. This paper is concerned with extending these procedures to spatial point process intensity estimation. We propose adaptive versions of these procedures, develop efficient computational methodologies and derive asymptotic results for a large class of spatial point processes under an original setting where the number of parameters, i.e. the number of spatial covariates considered, increases with the expected number of data points. Both procedures are compared theoretically, in a simulation study, and in a real data example.

Technical Note: Realization and uncertainty analysis for an adjustable 3D structured breast phantom in digital breast tomosynthesis.

Projection imaging phantoms are often optimized for 2-dimensional image characteristics in homogeneous backgrounds. Therefore, evaluation of image quality in tomosynthesis (DBT) lacks accepted and establishedphantoms. We describe a 3D breast phantom with a structured, variable background. The phantom is an adaptable and advanced version of the L1 phantom by Cockmartin etal. Phantom design and its use for quality assurance measurements for DBT devices are described. Four phantoms were compared to assess theobjectivity. The container size was increased to a diameter of 24cm and a total height of 53.5 mm. Spiculated masses were replaced by five additional non-spiculated masses for higher granularity in threshold diameter resolution. These patterns are adjustable to the imaging device. The masses were printed in one session with a base layer using two-component 3D printing. New materials compared to the L1 phantom improved the attenuation difference between the lesion models and the background. Four phantoms were built and intra-human observer, inter-human observer and inter-phantom variations were determined. The latter assess the reproducibility of the phantom production. Coefficients of variance (V) were calculated for all threevariations. The difference of the attenuation coefficients between the lesion models and the background was 0.20cm-1 (with W/Al at 32kV, equivalent to 19-20keV effective energy) compared to 0.21cm-1 for 50/50 glandular/adipose breast tissue and cancerous lesions. PMMA equivalent thickness of the phantom was 47.0mm for the Siemens Mammomat Revelation. For the masses, the for the intra-observer variation was 0.248, the averaged inter-observer variation, was 0.383. for phantom variance was 0.321. For the micro-calcifications, was 0.0429, 0.0731 and 0.0759. Position, orientation and shape of the masses are reproducible and attenuation differences appropriate. The phantom presented proved to be a candidate test object for qualitycontrol.

Effects of Sleep Deprivation on Performance during a Change Signal Task with Adaptive Dynamics.

Augmented cognition, which refers to real-time modifications to a human-system interface to improve performance and includes dynamic task environments with automated adaptations, can serve to protect against performance impairment under challenging work conditions. However, the effectiveness of augmented cognition as a countermeasure for performance impairment due to sleep loss is unknown. Here, in a controlled laboratory study, an adaptive version of a Change Signal task was administered repeatedly to healthy adults randomized to 62 h of total sleep deprivation (TSD) or a rested control condition. In the computerized task, a left- or right-facing arrow was presented to start each trial. In a subset of trials, a second arrow facing the opposite direction was presented after a delay. Subjects were to respond within 1000 ms of the trial start by pressing the arrow key corresponding to the single arrow (Go trials) or to the second arrow when present (Change trials). The Change Signal Delay (CSD)-i.e., the delay between the appearance of the first and second arrows-was shortened following incorrect responses and lengthened following correct responses so that subsequent Change trials became easier or harder, respectively. The task featured two distinct CSD dynamics, which produced relatively stable low and high error rates when subjects were rested (Low and High Error Likelihood trials, respectively). During TSD, the High Error Likelihood trials produced the same, relatively high error rate, but the Low Error Likelihood trials produced a higher error rate than in the rested condition. Thus, sleep loss altered the effectiveness of the adaptive dynamics in the Change Signal task. A principal component analysis revealed that while subjects varied in their performance of the task along a single dominant dimension when rested, a second inter-individual differences dimension emerged during TSD. These findings suggest a need for further investigation of the interaction between augmented cognition approaches and sleep deprivation in order to determine whether and how augmented cognition can be relied upon as a countermeasure to performance impairment in operational settings with sleep loss.

ADCS Design for a Sounding Rocket with Thrust Vectoring

This paper addresses the development of an attitude determination and control system (ADCS) for a sounding rocket using thrust vector control (TVC). To design the ADCS, a non-linear 6 degrees-of-freedom (DoF) model for the rocket dynamics and kinematics is deduced and implemented in simulation environment. An optimal attitude controller is designed using the linear quadratic regulator (LQR) with an additional integral action (LQI), and relying on the derived linear, time-varying, state-space representation of the rocket. The controller is tested in the simulation environment, demonstrating satisfactory attitude tracking performance, and robustness to model uncertainties. A navigation system is designed, based on measurements available on-board, to provide accurate real-time estimates on the rocket’s state and on the aerodynamic forces and moments acting on the vehicle. These aerodynamic estimates are used by an adaptive version of the controller that computes the gains in real time after correcting the state-space model. Finally, the ADCS is the result of the integration of the attitude control and navigation systems, with the complete system being implemented and tested in simulation, and demonstrating satisfactory performance.

Applying multidimensional computerized adaptive testing to the MSQOL-54: a simulation study

BackgroundThe Multiple Sclerosis Quality of Life-54 (MSQOL-54) is one of the most commonly-used MS-specific health-related quality of life (HRQOL) measures. It is a multidimensional, MS-specific HRQOL inventory, which includes the generic SF-36 core items, supplemented with 18 MS-targeted items. Availability of an adaptive short version providing immediate item scoring may improve instrument usability and validity. However, multidimensional computerized adaptive testing (MCAT) has not been previously applied to MSQOL-54 items. We thus aimed to apply MCAT to the MSQOL-54 and assess its performance.MethodsResponses from a large international sample of 3669 MS patients were assessed. We calibrated 52 (of the 54) items using bifactor graded response model (10 group factors and one general HRQOL factor). Then, eight simulations were run with different termination criteria: standard errors (SE) for the general factor and group factors set to different values, and change in factor estimates from one item to the next set at < 0.01 for both the general and the group factors. Performance of the MCAT was assessed by the number of administered items, root mean square difference (RMSD), and correlation.ResultsEight items were removed due to local dependency. The simulation with SE set to 0.32 (general factor), and no SE thresholds (group factors) provided satisfactory performance: the median number of administered items was 24, RMSD was 0.32, and correlation was 0.94.ConclusionsCompared to the full-length MSQOL-54, the simulated MCAT required fewer items without losing precision for the general HRQOL factor. Further work is needed to add/integrate/revise MSQOL-54 items in order to make the calibration and MCAT performance efficient also on group factors, so that the MCAT version may be used in clinical practice and research.

Cognitively Enhanced Versions of Capuchin Search Algorithm for Feature Selection in Medical Diagnosis: a COVID-19 Case Study.

Feature selection (FS) is a crucial area of cognitive computation that demands further studies. It has recently received a lot of attention from researchers working in machine learning and data mining. It is broadly employed in many different applications. Many enhanced strategies have been created for FS methods in cognitive computation to boost the performance of the methods. The goal of this paper is to present three adaptive versions of the capuchin search algorithm (CSA) that each features a better search ability than the parent CSA. These versions are used to select optimal feature subset based on a binary version of each adapted one and the k-Nearest Neighbor (k-NN) classifier. These versions were matured by applying several strategies, including automated control of inertia weight, acceleration coefficients, and other computational factors, to ameliorate search potency and convergence speed of CSA. In the velocity model of CSA, some growth computational functions, known as exponential, power, and S-shaped functions, were adopted to evolve three versions of CSA, referred to as exponential CSA (ECSA), power CSA (PCSA), and S-shaped CSA (SCSA), respectively. The results of the proposed FS methods on 24 benchmark datasets with different dimensions from various repositories were compared with other k-NN based FS methods from the literature. The results revealed that the proposed methods significantly outperformed the performance of CSA and other well-established FS methods in several relevant criteria. In particular, among the 24 datasets considered, the proposed binary ECSA, which yielded the best overall results among all other proposed versions, is able to excel the others in 18 datasets in terms of classification accuracy, 13 datasets in terms of specificity, 10 datasets in terms of sensitivity, and 14 datasets in terms of fitness values. Simply put, the results on 15, 9, and 5 datasets out of the 24 datasets studied showed that the performance levels of the binary ECSA, PCSA, and SCSA are over 90% in respect of specificity, sensitivity, and accuracy measures, respectively. The thorough results via different comparisons divulge the efficiency of the proposed methods in widening the classification accuracy compared to other methods, ensuring the ability of the proposed methods in exploring the feature space and selecting the most useful features for classification studies.

Semi-supervised classifier ensemble model for high-dimensional data

To complete the challenging task of high-dimensional data classification with limited labeled samples, we propose two semi-supervised learning models, namely the random subspace classifier ensemble model (SSRS) and its adaptive version (ASSRS). Considering the unique characteristics of high-dimensional data, SSRS selects subspaces of sample and feature dimensions and then reduces the dimensions of each subspace. To improve SSRS performance further, we designed a sample-labeling auxiliary algorithm, adaptive sample subspace algorithm, and adaptive weight voting rule for ASSRS to increase the proportion of labeled samples, obtain a suitable sample subspace for each feature subspace, and acquire a relative optimal weight for each base classifier. Experiments revealed that the performances of SSRS and ASSRS were better than those of other competitive algorithms and that the performance of ASSRS was stronger than that of SSRS. Additionally, we can accurately label samples in datasets where the proportion of labeled samples is relatively low by using SSRS and ASSRS. Because analysts are facing large numbers of high-dimensional datasets with limited labels, it is important to make accurate predictions based on a limited proportion of labeled data.

Computerized Block Games for Automated Cognitive Assessment: Development and Evaluation Study

Cognitive assessment using tangible objects can measure fine motor and hand-eye coordination skills along with other cognitive domains. Administering such tests is often expensive, labor-intensive, and error prone owing to manual recording and potential subjectivity. Automating the administration and scoring processes can address these difficulties while reducing time and cost. e-Cube is a new vision-based, computerized cognitive assessment tool that integrates computational measures of play complexity and item generators to enable automated and adaptive testing. The e-Cube games use a set of cubes, and the system tracks the movements and locations of these cubes as manipulated by the player. The primary objectives of the study were to validate the play complexity measures that form the basis of developing the adaptive assessment system and evaluate the preliminary utility and usability of the e-Cube system as an automated cognitive assessment tool. This study used 6 e-Cube games, namely, Assembly, Shape-Matching, Sequence-Memory, Spatial-Memory, Path-Tracking, and Maze, each targeting different cognitive domains. In total, 2 versions of the games, the fixed version with predetermined sets of items and the adaptive version using the autonomous item generators, were prepared for comparative evaluation. Enrolled participants (N=80; aged 18-60 years) were divided into 2 groups: 48% (38/80) of the participants in the fixed group and 52% (42/80) in the adaptive group. Each was administered the 6 e-Cube games; 3 subtests of the Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV; Block Design, Digit Span, and Matrix Reasoning); and the System Usability Scale (SUS). Statistical analyses at the 95% significance level were applied. The play complexity values were correlated with the performance indicators (ie, correctness and completion time). The adaptive e-Cube games were correlated with the WAIS-IV subtests (r=0.49, 95% CI 0.21-0.70; P<.001 for Assembly and Block Design; r=0.34, 95% CI 0.03-0.59; P=.03 for Shape-Matching and Matrix Reasoning; r=0.51, 95% CI 0.24-0.72; P<.001 for Spatial-Memory and Digit Span; r=0.45, 95% CI 0.16-0.67; P=.003 for Path-Tracking and Block Design; and r=0.45, 95% CI 0.16-0.67; P=.003 for Path-Tracking and Matrix Reasoning). The fixed version showed weaker correlations with the WAIS-IV subtests. The e-Cube system showed a low false detection rate (6/5990, 0.1%) and was determined to be usable, with an average SUS score of 86.01 (SD 8.75). The correlations between the play complexity values and performance indicators supported the validity of the play complexity measures. Correlations between the adaptive e-Cube games and the WAIS-IV subtests demonstrated the potential utility of the e-Cube games for cognitive assessment, but a further validation study is needed to confirm this. The low false detection rate and high SUS scores indicated that e-Cube is technically reliable and usable.