Computational Tests Research Articles

Mathematical modeling of neuroblast migration toward the olfactory bulb.

Mathematical modeling of neuroblast migration toward the olfactory bulb.

Storage assignment policies for retail warehouses processing store-specific shipment buildups

Abstract Shop-floor space in urban areas is scarce and expensive. To avoid a time-consuming zigzag movement when replenishing densely arranged shelves, store-specific shipment buildups (SSSBs) mirror the store layout in the packing pattern of the carriers (e.g., roll cages) on which merchandise arrives in brick-and-mortar sales outlets. If a carrier is pre-sorted according to the layout of its designated store, then the sales personnel can follow a clear route through the store while replenishing the shelves. However, the time savings in the stores come at the price of additional effort in the retail warehouses having to assemble SSSBs. If picker-to-parts order picking is applied to assemble SSSBs, then it is the order pickers who must zigzag through the warehouse. In this paper, we develop and test the performance of novel storage location assignment (SLA) policies that consider order frequencies and store layouts when deciding on the storage positions of stock keeping units on the shelves of a picker-to-parts warehouse. We evaluate these novel policies both with the theoretical optimum if perfect information were available and with traditional SLA policies merely based on order frequencies. These computational tests show that our novel policies can greatly reduce the picking effort during the assembly of SSSBs in picker-to-parts warehouses.

Score Test for Homogeneity of Variances in Normal Distributions

In this study, we suggest a novel test statistic based on the Score statistic for evaluating the homogeneity of variances in normal distributions. In addition to the conventional chi-square approximation of the Score statistic, we introduce a parametric bootstrap technique known as the Computational Approach Test (CAT). Through a simulation study, we evaluate the proposed test’s CAT approach (referred to as CS) and assess its performance against established methods under varying group sizes and sample sizes. The results show that, regardless of the number of groups, the CAT approach of the Score test performs well when sample sizes and variances are directly proportional, even with a minimum sample size of three. Furthermore, when sample sizes and variances are inversely proportional, the proposed test significantly outperforms alternative methods. To demonstrate the application of the discussed methods, we provide two numerical examples.

Assessing Computational Complexity in Selecting Periods for LMDI Techniques in Energy‐Related Carbon Dioxide Emissions: An Alternative Approach

ABSTRACTThe Logarithmic Mean Divisia Index (LMDI) decomposition analysis is widely employed to examine the drivers behind changes in carbon dioxide emissions related to energy consumption. This analysis has been applied using single‐period, year‐by‐year, and multi‐period time frames worldwide. However, these time frames often overlook trend changes in carbon emission time series, which may lead to inaccurate and biased identification of driving factors. This study replicates previous findings and proposes a novel multi‐period methodology for defining time frames in decomposition analysis. The proposed approach addresses the limitations of traditional methods by accounting for trend changes in the time series and performing an exhaustive search to optimally identify the most suitable time frames for LMDI‐based decomposition. The methodology comprises two stages: the first generates an exhaustive list of possible time series partitions, and the second determines the optimal partition by minimizing the total mean square error (TMSE) using sequential linear models. The results, supported by computational performance tests, demonstrate that the proposed method effectively identifies optimal time frame definitions, making it particularly suitable for annualized case studies on carbon dioxide emissions decomposition in the context of the energy transition.

Optimal location planning for battery exchange and charging stations for electric scooters in leisure areas considering stochastic user dwell times

ABSTRACT Stochastic factors significantly affect the optimal planning for locating electric scooter charging stations and scheduling battery exchanges in real operations. A plan that does not account for stochastic factors might turn out to be overly optimistic when implemented in real-world operations, where such stochastic disturbances are common. In this study, we apply mathematical programming and network flow techniques to develop a model for determining the optimal locations of battery exchange and charging stations for electric scooters, while accounting for stochastic user dwell times in leisure areas. The proposed stochastic model, designed to address a location-scheduling problem, is formulated as a mixed-integer network flow problem with side constraints. We develop a simulation-based evaluation method and conduct computational tests to assess the model’s performance. Computational tests using real data from a city in Taiwan show that the stochastic model outperforms the deterministic model and has significant potential for practical applications.

Modeling method for low-capacity hot water boiler plants and their thermal loads

The paper presents a novel method for modeling autonomous low-capacity hot water boiler plants and their thermal loads. The proposed approach is based on representing the main elements of the thermal circuit (boiler, heat exchanger, heating, and domestic hot water consumers) as a group of interacting thermal masses and on using a system of differential equations to describe the heat transfer dynamics. The heat transfer coefficients between thermal masses are determined through steady-state analysis, taking into account boundary conditions on external energy flows. The computational implementation of the model was carried out in the MATLAB Simulink environment, with parameter identification based on real operational data from a boiler plant with two 1 MW boilers and 90% efficiency. In the computational experiment, both steady-state and dynamic tests were performed: heating of the boiler under nominal load and cooling after shutting off the heat carrier flow. The modeling results demonstrated a high degree of agreement with operational data (mean absolute temperature error was less than 0.5 °C), confirming the adequacy and reliability of the proposed method. The universality of the approach allows the model to be scaled to various equipment types and operating modes without significant increase in computational load. The proposed method can serve as an effective tool for engineering analysis, design, and subsequent optimization of heat supply systems, providing reductions in time and resource expenditures for experimental verification.

Promoting gender equity in an information technology course through 6E-oriented STEM instruction on Health and Well-Being

ABSTRACT Background The integration of the 6E instructional model into STEM education, focusing on Health and Well-Being, has gained attention for its potential to enhance students’ computational thinking and self-efficacy. However, limited research exists on how this approach addresses gender-specific needs in vocational education, particularly in terms of reducing gender disparities in STEM skills. Purpose This study aimed to examine the impact of integrating ‘6E-oriented STEM instruction on Health and Well-Being’ into an IT course on vocational high school students’ computational thinking abilities and self-efficacy. The research also investigated whether the curriculum differentially impacted male and female students, focusing on gender-related differences in these outcomes. Sample The study involved 67 second-year vocational high school students in Taiwan. Design and Methods A quasi-experimental design with pretest-post-test control groups was used. Data collection included computational thinking tests and self-efficacy questionnaires administered before and after the intervention. Statistical analysis, utilizing paired t-tests and ANCOVA, was conducted to assess the effectiveness of the intervention and examine gender differences. Results Findings indicate that the ‘6E-oriented STEM instruction on Health and Well-Being’ significantly enhanced students’ computational thinking abilities and self-efficacy. The curriculum’s human care themes had a particularly positive effect on female students’ computational thinking and self-efficacy. Additionally, gender differences were observed, with female students benefiting more from the curriculum’s focus on real-world health issues. Conclusion The study demonstrates the potential of the 6E instructional model to improve computational thinking and self-efficacy, particularly among female students. Future research should explore the long-term effects of this approach and its applicability in diverse educational contexts, as well as investigate how curriculum design can further address gender differences in STEM education.

Development of a Hybrid Heat Exchange Air Conditioner with a Ground Heat Exchanger Placed Downstream from the Outdoor Unit Heat Exchanger

This study presents an innovative hybrid geothermal air conditioning system that combines conventional air-based heat exchange with ground heat exchange technology. The system features a ground heat exchanger placed downstream from the outdoor unit heat exchanger, requiring minimal modifications to conventional air conditioners through the addition of bypass flow paths and a four-way valve. This design ensures that the ground heat exchanger consistently operates after the outdoor unit heat exchanger in both cooling and heating modes. The researchers evaluated the proposed system’s performance through both computational simulation (1D-CAE) and experimental testing. Simulation results demonstrated significant efficiency improvements, with the hybrid system achieving a coefficient of performance (COP) of 4.51 compared to just 1.24 for conventional air conditioners under extreme temperature conditions (38 °C). The experimental validation with a shallow-buried (20 cm) ground heat exchanger confirmed an approximately 20% COP improvement across various ambient temperatures. The main advantages of this hybrid system over conventional geothermal systems include reduced installation costs due to shorter borehole lengths, separate air conditioning units and underground piping, and compatibility with existing control systems. The design addresses skilled labor shortages while enabling large-scale demonstration operations with minimal initial investment. Future work will focus on optimizing the burial depth and conducting long-term durability testing to advance practical implementation.

Impact of yoga on the central and peripheral vascular function among desk-based workers: A single-centered trial study

Background The aim of this study was to observe and analyze vascular function in ‘prolonged sitting’, followed by a yoga asana routine and pranayama intervention. Participants in this study include those who work from desks in offices. The study required the participants to attend on three separate days at random, and they had to finish a computerized test on each day. On the first day, participants were required to complete a computer test while sitting still for four hours (with the exception of washroom breaks). The next day, they underwent a computerized test along with a pranayama intervention. Finally, on the last day, they underwent a computerized test along with a yoga asana intervention. At the start of the study and after two and four hours, we measured the diameter and velocity of the common carotid artery (CCA) and superficial femoral artery (SFA). Methods The study was a within-subjects prospective single-center trial conducted in the Department of Radio-Diagnosis and Imaging, Kasturba Medical Hospital, Manipal, India, between September 2022 and January 2023. Participants were asked to do one of the following ‘activities’ over successive weeks: Week 1 – Prolonged sitting; Week 2 – Pranayama intervention; and Week 3 – Yoga asana intervention during prolonged sitting. The baseline and follow-up variables of pulse velocity, endothelial thickness, and shear rate were assessed for normality through a Shapiro-Wilk Test. Results Our sample included 11 participants with moderate physical activity, five with high physical activity and one with low physical activity. Yoga asana intervention comprised participants sitting continuously for four hours, with a yoga asana intervention being provided every hour, lasting for 10 minutes. Conclusions Yoga asana improves vascular functions in prolonged sitting conditions. This routine can promote the concept of interrupted sitting and ways to reduce it with efficient yoga asana practice without changing the work culture and provide better physical relief. Trial registration Clinical Trials Registry – India ( CTRI/2022/09/045628), date of registration: 19/09/2022(CTRI/2022/9/045628)https://ctri.nic.in/Clinicaltrials/main1.php?EncHid=16349.27799,

Geometry as a Persisting Backbone Metalanguage to Architecture

Albeit Architecture is not mere Geometry, much of Architecture is about Geometry. Either in real shapes, or in representational models, or in theoretical terms. Three simple questions should suffice to illustrate the point. What would a cathedral be, without its geometric feature? What would a BIM model collapse in, without its geometric framework? What would architectural theory be based on, without its reference to geometry of space? That is, looking through an ideal infrared lens, Geometry is revealed everywhere in the architectural field, from the dawn of time, and throughout the differenttechnological eras. And not only in the architectural field, as René Descartes officially highlighted, establishing Geometry at the base of the nexus between logical (λόγος) and visual (γραφή) thinking1, in the last decades widely reconfirmed, also at the computational test. This dual nature, making Geometry working as a metalanguage2, so beneficial in agglutinating the multi-disciplinary field of Architecture, is even more to be considered when facing the challenges promised by AI (fig. 1), either in research or in the professional world, and especially in Geometry education, which is still our priority mission. Then, a further question seems to emerge. What would AI based architectural design be without Geometry? This paper aims at discussing some of the implications related to these topics, including some simple generative tests made with Midjourney and ComfyUI, and an instant tip from Chat GPT.

Enhancing Drug-Target Interaction Predictions Using a Divisive Computational Framework

Computational prediction of drug-target interactions (DTIs) is crucial for drug discovery. However, the sparse distribution of DTIs and the imbalance in the number of interactions among targets pose challenges. This study proposes a divisive computational framework. Firstly, it includes a novel preprocessing algorithm that adjusts the interaction matrix based on the number of interactions of a target and its neighbors, enhancing DTI predictions for targets with fewer interactions. Additionally, a new divisive computational testing method is introduced, which evaluates targets with similar numbers of interactions separately, ensuring that the results are not disproportionately influenced by targets with a large number of interactions. Furthermore, a weighted global testing method is proposed to provide a more comprehensive assessment of the enhanced prediction capabilities, which reduces the negative impact of low-interaction targets on the overall evaluation and offers a more balanced perspective on the algorithm's effectiveness. Experimental results demonstrate the efficacy of the proposed framework, where the means of AUCs in the divisive computational framework are respectively 9.45%, 10.64%, 4.21%, 7.04%, 3.67%, and 6.50% higher than those in the traditional framework on six DTI datasets.

Branch-and-Cut Algorithms for Colorful Components Problems

We tackle three optimization problems in which a colored graph, where each node is assigned a color, must be partitioned into colorful connected components. A component is defined as colorful if each color appears at most once. The problems differ in the objective function, which determines which partition is the best one. These problems have applications in community detection, cybersecurity, and bioinformatics. We present integer nonlinear formulations, which are then linearized using standard techniques. To solve these formulations, we develop exact branch-and-cut algorithms, embedding various improving techniques, such as valid inequalities, bounds limiting the number of variables, and warm-start and preprocessing techniques. Extensive computational tests on benchmark instances demonstrate the effectiveness of the proposed procedures. The branch-and-cut algorithms can solve reasonably sized instances efficiently. To the best of our knowledge, we are the first to propose an exact algorithm for solving these problems. History: Accepted by Russell Bent, Area Editor for Network Optimization: Algorithms & Applications. Funding: The work of M. Cerulli was partially funded by project “SEcurity and RIghts in the CyberSpace” [Grant PE00000014] under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0927 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0927 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Inference on powers of the scale parameters for two exponential populations under equality restriction on location parameter

This article focuses on the problems of point estimation, interval estimation, and hypothesis testing on the powers of the scale parameter for two exponential populations with a common location. In the case of point estimation, we derive the maximum likelihood estimator (MLE), the uniformly minimum variance unbiased estimator (UMVUE), and two plug-in type estimators based on the modified MLE and UMVUE of the common location parameter. An Inadmissibility result is proved for the class of affine equivariant estimators. Several interval estimators, such as an approximate interval based on the asymptotic normality theory of the MLE, bootstrap intervals, and intervals based on the generalized variable method, have been derived in the case of interval estimation. In the case of hypothesis testing, we derive several test procedures, such as the parametric bootstrap likelihood ratio test (PBLRT), the computational approach tests, and tests based on the generalized p-value approach. A simulation study in each case has been done to compare the performances of the estimators and test procedures. Finally, we conclude our remarks with an application using real-life datasets.

Dataset on Programming Competencies Development Using Scratch and a Recommender System in a Non-WEIRD Primary School Context

The ability to program has become an essential competence for individuals in an increasingly digital world. However, access to programming education remains unequal, particularly in non-WEIRD (Western, Educated, Industrialized, Rich, and Democratic) contexts. This study presents a dataset resulting from an educational intervention designed to foster programming competencies and computational thinking skills among primary school students aged 8 to 12 years in Milagro, Ecuador. The intervention integrated Scratch, a block-based programming environment that simplifies coding by eliminating syntactic barriers, and the CARAMBA recommendation system, which provided personalized learning paths based on students’ progression and preferences. A structured educational process was implemented, including an initial diagnostic test to assess logical reasoning, guided activities in Scratch to build foundational skills, a phase of personalized practice with CARAMBA, and a final computational thinking evaluation using a validated assessment instrument. The resulting dataset encompasses diverse information: demographic data, logical reasoning test scores, computational thinking test results pre- and post-intervention, activity logs from Scratch, recommendation histories from CARAMBA, and qualitative feedback from university student tutors who supported the intervention. The dataset is anonymized, ethically collected, and made available under a CC-BY 4.0 license to encourage reuse. This resource is particularly valuable for researchers and practitioners interested in computational thinking development, educational data mining, personalized learning systems, and digital equity initiatives. It supports comparative studies between WEIRD and non-WEIRD populations, validation of adaptive learning models, and the design of inclusive programming curricula. Furthermore, the dataset enables the application of machine learning techniques to predict educational outcomes and optimize personalized educational strategies. By offering this dataset openly, the study contributes to filling critical gaps in educational research, promoting inclusive access to programming education, and fostering a more comprehensive understanding of how computational competencies can be developed across diverse socioeconomic and cultural contexts.

Radiation Hardness Assurance by Redundancy in Raspberry Pi Zero W Computation Metrics via Total Ionizing Dose 60Co Testing for Spacecraft Applications.

This work showed the outcome from conducting formal total ionizing dose (TID) testing with a traditional 60Co source on commercial Raspberry Pi computer boards. Two Raspberry Pi boards inside a Gammacell 220 irradiator with continuous biased operations, voltage monitoring, and live computational testing and data extraction were exposed to dose rates of 2.61 ± 0.09 mGy(Si) s-1. The results showed potential tolerance doses approaching 500 Gy(Si). Computational tests included memory, calculation capability, voltage rail output, and I/O. Memory degradation was not observed alongside voltage rail and Input/Output. Individual test result deviations for CPU performance slightly increased in likelihood throughout accumulation of dose. Particular observed weakness included the SD card with potential strengths being use of redundancy and novel spot-shielding opportunities.

Exploring computational thinking, executive functions, visuospatial skills and experiences with toys in early childhood: Protocol for a controlled trial.

The research aims to evaluate the effect of a robotics-based computational thinking program on executive functions and visuospatial skills in preschool children. Additionally, the study will explore the relationship between these three variables and early experiences with toys. The study will be a cluster-randomized controlled trial with pre- and post-intervention measures. Participants will be preschool children aged 5 to 6 years from both public and private institutions. The intervention will involve a new educational robotics device called ROVERSA and will consist of eight sessions distributed over four weeks, with the experimental group placed on a waitlist. To assess executive functions and visuospatial skills, standardized and validated neuropsychological measures will be used, including the Flanker task, the NIH Toolbox Card Sorting Test, the Corsi Block Test, the NEPSY-II Mental Rotation Test, the Pyramid of Mexico from the ENI-2 battery, and the Computational Thinking Test (CTt). Data analysis will begin with a hypothesis tested through ANOVA-MR, and linear regressions will be implemented to assess the relationship between variables. It is expected that the results will contribute to the discussion on teaching computational thinking and educational robotics as early learning experiences and their impact on the cognitive development of children.

Teaching molecular biology to medical university preparatory department foreign students

The article outlines the features of teaching molecular biology to foreign students of the preparatory department. At the preparatory department at St. Petersburg State Pediatric Medical University, students from more than 30 countries are trained, who upon admission differ significantly in their level of training. To assess the initial level of knowledge of students in molecular genetics, the department conducts computer testing, the analysis of the results of which serves as the basis for a further differentiated approach to teaching students. To improve the efficiency of mastering theoretical material and problem solving skills, various methodological approaches are proposed. Students are helped to overcome the difficulties of using the Russian language when studying biology by biological thematic mini-dictionaries prepared by teachers of the department for each section studied. The textbook “Cell division. Genetics. Molecular Biology” is addressed to the students of the preparatory department, it includes test items and problems, provides examples of problem solving with diagrams and drawings. Having students complete assignments using an interactive whiteboard and the ActivInspire application significantly expands the teacher’s capabilities. To familiarize students with high-tech methods that are promising in medicine, various visual materials are being developed, including animations, which in an accessible form introduce future doctors to advanced technologies. Visualization of molecular genetic processes through original author’s presentations, animations, diagrams and drawings facilitates the perception of information by foreign students and contributes to the adaptation of students of foreign software to studying.

Optimizing Combustion Flow Dynamics in Rotating Detonation Engines: A Numerical and Experimental Investigation

This study investigates the design and performance of a small-scale Rotating Detonation Engine (RDE) through Computational Fluid Dynamics (CFD) and experimental testing. Building on recent advancements in detonation wave stabilization [3] and thermodynamic efficiency limits [1], we optimize an annular combustion chamber (8 mm gap, titanium/Inconel construction) with 12 staggered pintle injectors (45°). High-fidelity LES simulations [2] validate a detonation wave speed of ~2000 m/s and 42% higher combustion efficiency over conventional engines. Challenges in flow instability and thermal management are addressed via manual control adjustments and passive cooling, aligning with findings from AFRL’s RDE testing [5]. The results demonstrate RDEs’ potential for hypersonic propulsion, with future directions emphasizing AI-driven control [4] and additive manufacturing. Keywords: Rotating Detonation Engine (RDE), Pressure-gain combustion, CFD analysis, Pintle injectors, Thermal management

Airfoil Shape Optimization for Improved Aerodynamics

This project focuses on the optimization of a custom airfoil by systematically analyzing the effects of camber percentage (2%– 6%), camber position (1– 6), and angle of attack (6°) while maintaining a fixed thickness of 12%. The primary objective is to maximize the lift- to-drag ratio (L/D) to enhance aerodynamic efficiency and stability. A parametric investigation was conducted using both unilabiate and bivariate analyses to evaluate the influence of individual and combined aerodynamic parameters on airfoil performance. To achieve this, XFOIL, a high- fidelity aerodynamic analysis tool, was integrated with MATLAB for automated batch processing, enabling efficient computation of lift coefficient (Cₗ), drag coefficient (C𝒹), and the corresponding L/D ratios. The parametric study revealed that variations in camber and its position significantly affect aerodynamic characteristics, offering critical insights for the design of optimized airfoils applicable to aircraft wings, UAVs, and wind turbine blades. In addition to parametric analysis, this study explores advanced optimization techniques, with a focus on evolutionary algorithms such as the Genetic Algorithm (GA). The GA framework was employed to systematically search for airfoil configurations that yield optimal L/D ratios by iteratively refining candidate solutions based on selection, crossover, and mutation operations. Future work will incorporate Reynolds number effects and validate the optimization results using computational fluid dynamics (CFD) simulations and experimental testing for enhanced accuracy and practical applicability

Integrating ResNet-50 and Vision Transformer Architectures for Robust and Efficient Tomato Fruit Ripeness Classification

This article introduces a new hybrid deep learning framework that combines ResNet-50 and Vision Transformer (ViT) models to classify tomato fruits by ripeness and quality. The hybrid model takes advantage of the ResNet-50's ca-pability to extract features in local spatial regions. Additionally, it combines ResNet-50 with ViT's ability to establish a global contextual relationship be-tween elements with self-attention. The framework overcomes the limitations of utilizing a single model. The framework was trained and evaluated, using a balanced and diverse dataset. It consists of four tomato classes, ripe, unripe, unevenly ripened, and damaged, collected under controlled conditions in the field. The experiments conducted aggressively in the study demonstrated a high classification accuracy of 98% with a hybrid framework compared to individual ResNet-50 and ViT models. The results in addition to the general performance were validated further using precision, recall, the area under the curve (AUC) and the F1-score. The study also included a computational efficiency test to look for accuracy versus multiple time and spatial resource costs. The research suc-cessfully addresses the challenges with dataset diversity, computational costs, and real-time feature deployments through the conceptualization of strategies (data augmentation, transfer learning) to improve generalization. The hybrid framework is expected to provide similar design principles and measures for use with other agricultural products. It can help in real-time sorting applications in variable real-world scenarios in agricultural operations. The research pro-vides a pathway for developing smarter, scalable, and sustainable solutions in food quality and safety systems within precision agriculture. Future work will incorporate deployment and optimize the framework for edge devices in a re-al-world variable farm environment.