Test Scenarios Research Articles

User Action Recognition Using a Fabric Pressure Distribution Sensor in an Electric Wheelchair for Soil Cultivation

Advancements in robotics technology and information and communication technology have significantly affected agriculture, especially systems that aid workers. Soil cultivation often requires prolonged bending, which results in physical strain. A commercially available work wheelchair reduces back strain, but lacks power assistance and relies on leg movements. In a previous study, we developed an electric wheelchair with a fabric pressure-distribution sensor on the seat. This sensor, combined with a fuzzy controller, aids movement by measuring the center of pressure fluctuations and reducing leg strain. However, it does not recognize user’s actions, such as standing or sitting, and requires the wheelchair to stop. This study introduced a user-action recognition system using a wheelchair seat pressure sensor. This system accurately recognizes user actions through deep learning time-series data. We evaluated data augmentation and multi-user data to enhance the performance. The results show improved prediction accuracy for action recognition in the test scenarios.

Anterior Cruciate Ligament Mechanical Response to Load in the Setting of Changes to the Medial Meniscus

The anterior cruciate ligament (ACL) is a major ligament in the knee joint, and its function is crucial for both the movement and stability of the knee. Our research takes a novel approach by investigating the effect of meniscus tears on the ACL, how such tears will impact the stress on the ACL, and its overall compensation in response to the changes in the meniscus. Hypothesis/Purpose: This study aims to investigate how the ACL compensates for the change in knee joint stability and contact pressures due to partial horizontal cleavage tears (HCTs) in the meniscus, such as partial meniscectomy and partial transplantation on knee joint stability and contact pressures. We hypothesize that HCTs will increase contact pressures and decrease joint stability, thereby inducing compensatory stress on the anterior cruciate ligament (ACL). Method: Seven freshly frozen human cadaveric knees were used in a study to investigate the effects of different meniscal conditions and surgical interventions on the meniscus itself. Four testing scenarios were established: intact knees, knees with partial horizontal cleavage tears (HCTs) of the meniscus, knees with partial meniscectomy, and knees with partial transplantation. Axial loading was applied, and the medial meniscus contact pressures were measured at 0° and 30° of flexion. Additionally, a mathematical 3D finite element model was created to evaluate the behavior of the ACL under different meniscus scenarios, which could not have been measured experimentally. Results: ACL contact pressure and stress analysis across various meniscal conditions demonstrated substantial variability. Horizontal cleavage tears (HCTs) resulted in heightened contact pressures and diminished joint stability, as evidenced by increased ACL stress attributed to compensatory mechanisms in the presence of meniscal tears. Conversely, transplantation procedures exhibited a mitigating effect, maintaining joint mechanics closer to intact conditions and minimizing alterations in ACL forces. These trends persisted at 30 degrees of knee flexion, where significant increases in ACL forces were observed in partial and complete HCT conditions. Conclusions: This study uncovers the biomechanical impacts of meniscal injuries, demonstrating how the ACL compensates for various meniscus conditions. In contrast, transplantation and repair conditions only slightly increase the stress on the ACL, putting much less strain on the ACL and supporting structures of the knee joint than an unrepaired tear.

A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous Vehicles

Real-time integrity monitoring (IM) is essential for autonomous vehicle positioning, requiring high availability and manageable computational load. This research proposes using precise point positioning real-time kinematic (PPP-RTK) as the positioning method, combined with an improved classification adaptive Kalman filter (CAKF) for processing. PPP-RTK enhances IM availability by allowing undifferenced and uncombined observations, enabling individual observation exclusion during fault detection and exclusion (FDE). The CAKF reduces FDE computational load by using a robustness test instead of traditional FDE methods, improving precision and availability in protection level estimation. Epoch-wise weighting adjustments in the robustness test create a more accurate stochastic model, aided by an adaptive unit weight variance (UWV) calculated with a sliding window, achieving a 7–28% UWV reduction. Three test scenarios with up to four simultaneous faults in code and phase observations, ranging from 1 to 200 m and 0.4 to 20 m, respectively, demonstrated successful identification and de-weighting of faults, resulting in maximum positioning errors of 6 mm (horizontal) and 11 mm (vertical). The method reduced FDE computational load by 50–99.999% compared to other approaches.

Genomic exploration of the journey of Plasmodium vivax in Latin America.

Plasmodium vivax is the predominant malaria parasite in Latin America. Its colonization history in the region is rich and complex, and is still highly debated, especially about its origin(s). Our study employed cutting-edge population genomic techniques to analyze whole genome variation from 620 P. vivax isolates, including 107 newly sequenced samples from West Africa, Middle East, and Latin America. This sampling represents nearly all potential source populations worldwide currently available. Analyses of the genetic structure, diversity, ancestry, coalescent-based inferences, including demographic scenario testing using Approximate Bayesian Computation, have revealed a more complex evolutionary history than previously envisioned. Indeed, our analyses suggest that the current American P. vivax populations predominantly stemmed from a now-extinct European lineage, with the potential contribution also from unsampled populations, most likely of West African origin. We also found evidence that P. vivax arrived in Latin America in multiple waves, initially during early European contact and later through post-colonial human migration waves in the late 19th-century. This study provides a fresh perspective on P. vivax's intricate evolutionary journey and brings insights into the possible contribution of West African P. vivax populations to the colonization history of Latin America.

Resistance‐Associated Substitution Testing Trends and Impact on HCV Treatment Outcomes in Canada: A CanHepC‐CANUHC Analysis

ABSTRACTResistance‐associated substitutions (RASs) are mutations within the hepatitis C (HCV) genome that may influence the likelihood of achieving a sustained virological response (SVR) with direct acting antiviral (DAA) treatment. Clinicians conduct RAS testing to adapt treatment regimens with the intent of improving the likelihood of cure. The Canadian Network Undertaking against Hepatitis C (CANUHC) prospective cohort consists of chronic HCV patients enrolled between 2015 and 2023 across 17 Canadian sites. Utilisation of RAS testing was assessed across demographics, clinical characteristics and years. SVR was described for the overall cohort and compared across populations of patients with historically negative predictors of SVR. The detection of key RASs and how this information influenced DAA selection were assessed. 2434 patients were identified with information on RAS testing. 98.3% achieved SVR. Out of the 227 patients tested for RAS, 147 (64.8%) had any detected RAS, and 84 (37.0%) had an NS5A RAS. The proportion of patients with SVR did not differ between RAS‐tested (98.3%) and non‐tested patients (98.3%; p = 0.99). SVR in those with an NS5a RAS was similar (98.6%) to the overall SVR proportion. Proportions with SVR did not differ between those with and without RAS testing in key subgroups (genotype 1a, genotype 3, prior treatment, cirrhosis). The specific DAA regimen and the addition of ribavirin were not associated with SVR outcome. RAS testing has a minimal influence on antiviral treatment selection. Going forward, there is a reduced role for RAS testing in most clinical scenarios.

Discrete Wavelet Transform Sampling for Image Super Resolution

ABSTRACT In battlefield environments, drones depend on high-resolution imagery for critical tasks such as target identification and situational awareness. However, acquiring clear images of distant targets presents a significant challenge. To address this, we propose a supervised learning approach for image super-resolution. Our network architecture builds upon the U-Net framework, incorporating enhancements to the encoder and decoder through techniques such as Discrete Wavelet Transform, Channel Attention Residual Modules, Selective Kernel Feature Fusion, Weight Normalization, and Dropout. We evaluate our model on a super-resolution dataset and compare its performance against other networks, highlighting the importance of minimizing trainable parameters for real-time deployment on resource-constrained drone platforms. The effWicacy of our proposed network is further validated through image recognition tasks and real-world scenario testing. By enhancing image clarity at extended ranges, our approach enables drones to detect adversaries earlier, facilitating proactive countermeasures and improving mission success rates

Sequential Confidence Intervals for Comparing Two Proportions with Applications in A/B Testing

This article addresses the use of fixed-width confidence intervals (FWCIs) for comparing two independent Bernoulli populations in A/B testing scenarios. Two sequential estimation procedures are proposed: one for estimating the difference in log probabilities of success and the other for log odds ratios. Both methods showcase great efficiency, as established via theoretical analysis and Monte Carlo simulations. The practical utility of these methods is demonstrated through two real-world applications: analyzing retention rates in mobile game Cookie Cats and evaluating the effectiveness of online advertising.

Integrating Information Governance as the Foundation for Information Dashboard Design Ontology

Aim/Purpose: This paper addresses the lack of a standardized approach to information dashboard design and the absence of integrated Information Governance (IG) principles in this context. Background: This study addresses the critical role of IG principles in ensuring dashboards are reliable, secure, and effective. By integrating IG principles into the work-in-progress Information Dashboard Design Ontology (IDDO), the study aims to enhance the systematic development of dashboards, addressing real-world issues such as data governance failures in Healthcare.gov, security breaches in CRM systems and risk reporting gaps in the JPMorgan Chase “London Whale” incident. Methodology: A narrative literature review (NLR) method was employed to synthesize insights from three established IG models and prior studies. NLR was chosen for its flexibility in exploring emerging topics, such as the integration of IG with dashboard design, where existing literature is scarce. The study acknowledges the limitations of NLR, including potential selection bias, while emphasizing its value in identifying foundational insights and aligning IG principles with dashboard design needs. Contribution: This study bridges the gap between IG and information dashboard design by proposing eight relevant IG principles for integration into the work-in-progress IDDO, providing a novel framework for governance-driven dashboard design. Findings: The study derived eight IG principles aligned with common design principles: Quality, Security, Compliance, Integrity, Availability, Transparency, Accountability, and Effectiveness. Recommendations for Practitioners: Practitioners should incorporate these IG principles into their dashboard design processes to enhance data quality, compliance, user trust, and decision-making support. Recommendation for Researchers: Researchers should explore the practical application of these IG principles across various organizational contexts and assess their impact on dashboard effectiveness and user experience. Impact on Society: This approach offers a potential solution to data quality, compliance, and user trust issues in enterprise information systems, which could lead to improved organizational decision-making and enhanced end-user experiences. Future Research: Future studies should focus on the practical integration of the identified IG principles into the IDDO framework, including empirical testing and validation in real-world scenarios.

On the Experimental Antenna Measurements: Study and Investigations

This study focuses on the experimental measurement and analysis of antenna characteristics, aiming to provide a deeper understanding of antenna performance in real-world conditions. The investigation involves systematic assessment of key parameters such as radiation patterns, gain, impedance, and bandwidth. Using a variety of measurement techniques and equipment, including network analyzers, signal generators, and anechoic chambers, the study evaluates different types of antennas under various test scenarios. The experimental results are compared with theoretical predictions to identify discrepancies and refine antenna design methodologies. The findings of this study offer valuable insights into the practical performance of antennas, highlighting the importance of precise measurements in the design and optimization of antenna systems for communication and radar applications. The results also underscore the necessity of accounting for environmental and operational factors in antenna performance assessments.

Optimizing Irrigation Infrastructure Management with Web-Based Technologies and OpenStreetMap Integration

Irrigation is essential for increasing agricultural productivity in Indonesia, directly impacting national food security and farmer welfare by ensuring the sustainability of irrigation systems. Despite its importance, significant challenges persist, including damage to 46% of the irrigation infrastructure across 7.1 million hectares, with responsibilities divided among central, provincial, and city governments. These challenges are compounded by issues such as inadequate information access, limitations in real-time monitoring, and poor stakeholder coordination. To address these issues, integrating technological tools such as Geographic Information Systems (GIS) and platforms such as OpenStreetMap (OSM) is essential. These technologies offer real-time information, facilitate land change tracking, and enable spatial mapping, which supports proactive management and planning while improving decision making through data-driven insights. The adoption of Rapid Application Development (RAD) and Decision Support Systems (DSS) further enhances the efficiency of irrigation system management. The RAD methodology ensures swift development cycles with user interaction, while DSS provides structured decision-making capabilities. This approach supports robust system design, user interface development, and geographic data visualization. In practice, efficient data collection and stakeholder participation enable the system to effectively report and track irrigation conditions, promoting public participation, and fostering long-term infrastructure sustainability. The developed online monitoring system demonstrates its effectiveness by providing interactive map features and transparent reporting platforms, verified through successful implementation of testing scenarios. This system not only improves infrastructure management in areas like Batu City, but also sets a precedent for scalable participatory irrigation solutions, creating a foundation for future enhancements.

The effect of misclassification on sample size for one and two-sample tests with binary endpoints

ABSTRACT In recent years, an increasing number of publications on the analysis of binary data have applied methods that take misclassification into account. However, potential misclassification is often ignored in study design due to the lack of sample size formulas or software. This may lead to a considerable loss of power in studies that only account for misclassification at the analysis stage. We argue that analyses correcting for misclassification should be used in combination with appropriate sample size adjustment in the design phase of the studies. We illustrate the importance of this by comparing the required sample sizes with and without misclassification, and provide an appropriate sample size procedure implemented as an R function for the one-sample and two-sample tests for binary endpoints. The sample size is calculated from the presumed binomial parameters (p 0 and p a for one-sample and p 1 and p 2 for two-sample tests), the required power, and the probabilities of correct classification, sensitivity (Se), and specificity (Sp). Our results show that misclassification may drastically affect the necessary sample size in both testing scenarios.

New Advanced Strategy for Controlling the Charging and Discharging of a Storage Unit in a Microgrid Using a Finite Control Set Predictive Model

Storage units are critical in microgrids to ensure stable operation, making optimal and robust control during charging and discharging essential. In this work, we propose a novel finite control set model predictive control (FCS-MPC) strategy to manage the charging and discharging of a battery in a DC microgrid powered by renewable energy. Unlike traditional Proportional-Integral (PI) controllers, the FCS-MPC approach optimizes control actions in real-time, considering system constraints and providing enhanced dynamic response. This work demonstrates the superiority of FCS-MPC in controlling a bi-directional DC/DC converter under various test scenarios, ensuring continuous power supply to a 15W DC load. Simulations in Matlab/Simulink validate the proposal, showing that the FCS-MPC delivers higher efficiency and better performance compared to the PI controller.

Comparison of EfficientNetB0 and EfficientNetB7 Models in Classifying Malaria Based on Blood Cells

Malaria is a disease caused by the bite of malaria mosquitoes, which spreads through blood. Malaria mosquitoes will spread the Plasmodium parasite through their bites. Early malaria identification is essential so the disease can be prevented immediately. Through data science, which utilizes the CNN model, the classification of blood infected with parasites can be predicted accurately. This research uses data obtained from Kaggle website with 27,558 image samples. The data is divided into two classes, parasite-infected and uninfected, which are then divided again into two types. The first class is training data divided into 80% of the total data and the other 20% as validation data. This research used two test scenarios to obtain a more effective classification model. The first scenario uses Hyperparameter Tuning and the EfficientNetB0 model with classification results of 95%. Meanwhile, the classification achievement for scenario two was 99% by utilizing EfficientNetB7.

Securing Real-Time Data Transfer in Healthcare IoT Environments with Blockchain Technology

The increasing number of Internet of Things (IoT) devices in healthcare applications, particularly during emergencies, necessitates safe protocols for transmitting real-time data. Medical data are essential for healthcare applications, and reliance on IoT devices to control information flow necessitates the consideration of five critical areas. This work addresses the security challenges associated with the transmission and storage of copyrighted healthcare data, as well as the inadequacy of the present methods in facilitating real-time data transfer given the volume of data and network conditions. This research provides a theoretical framework for the secure and immediate offloading of computations in IoT healthcare systems. The objective is to implement secure communication and networking technologies to ensure the security and integrity of medical data, maintain confidentiality, and facilitate real-time transmission of information. The proposed framework is simulated in MATLAB for system model implementation. A blockchain network sandbox was established with the delegated proof-of- stake (DPoS) consensus method, supplemented by proof-of-work (PoW) and proof-of-validation (PoV) for enhanced security. To assess the efficacy of this framework, multiple test scenarios focused on the number of nodes, the volume of data, and the conditions of network connectivity. The results demonstrated the system's efficacy in facilitating the offloading of real-time data in IoT healthcare applications. The aforementioned study demonstrated that the framework exhibited rapid transaction processing, efficient resource use, and energy conservation while also enhancing secure data transmission across various network conditions. The findings confirm that the proposed architecture can effectively and securely transmit real-time data in IoT healthcare applications without jeopardizing data authenticity, privacy, or integrity. The system's ability to address security challenges and manage substantial data volumes under varying settings indicates that it can be effectively deployed in healthcare systems, particularly in critical situations.

Emotion Recognition Using PPG Signals of Smartwatch on Purpose of Threat Detection.

This paper proposes a machine learning approach to detect threats using short-term PPG (photoplethysmogram) signals from a commercial smartwatch. In supervised learning, having accurately annotated training data is essential. However, a key challenge in the threat detection problem is the uncertainty regarding how accurately data labeled as 'threat' reflect actual threat responses since participants may react differently to the same experiments. In this paper, Gaussian Mixture Models are learned to remove ambiguously labeled training, and those models are also used to remove ambiguous test data. For the realistic test scenario, PPG measurements are collected from participants playing a horror VR (Virtual Reality) game, and the proposed method validates the superiority of our proposed approach in comparison with other methods. Also, the proposed filtering with GMM improves prediction accuracy by 23% compared to the method that does not incorporate the filtering.

Leveraging physics-informed neural networks for wavefield analysis in laser ultrasonic testing

ABSTRACT Laser Ultrasonic (LU) technology has emerged as a pivotal non-destructive testing method, offering a unique capability to visualise ultrasonic wavefields and identify defects without causing structural damage. However, challenges arise in certain testing scenarios where direct laser irradiation of the sample surface is hindered, resulting in incomplete LU wavefield datasets. This limitation poses a significant obstacle in accurately assessing material integrity and defect detection. This paper explores the application of Physics-Informed Neural Networks (PINNs) for LU wavefield reconstruction and prediction. PINNs are employed to reconstruct wavefields from incomplete data and predict wavefield behaviour at different time instances. Results demonstrate PINNs’ effectiveness in accurately reconstructing wavefields, with correlation coefficients exceeding 0.94 between reconstructed and actual wavefields. Additionally, PINNs show promise in predicting LU wavefield data, albeit with slightly reduced accuracy beyond the training range. Moreover, PINNs effectively reduce noise in wavefield data, enhancing clarity and reliability. This study lays groundwork for further exploration of PINNs in LU defect detection.

Safety-involved co-optimization of speed trajectory and energy management for fuel cell-battery electric vehicle in car-following scenarios

Vehicle connectivity technologies has propelled integrated optimization of vehicle’s motion and power splitting becoming a hotspot in eco-driving control research. However, the security issues and power sources life loss of fuel cell-battery hybrid electric vehicle (FCHEV) are still challenging due to disturbances and power sources degradation. To address these problems, in this paper, control barrier function (CBF) based multi-objective energy management strategy (EMS) for FCHEV in car-following process is proposed. Firstly, the state of health models of fuel cell and battery are established to reflect the relationship between power sources degradation and energy consumption. Secondly, multi-objective model predictive control (MPC) based EMS framework is developed by comprehensive considering tracking performance, comfort, fuel consumption and power sources life loss. Thirdly, to robustly cope with disturbances and uncertainties, discrete-time CBFs are designed to enforce safety-critical constraints related to safety issues of both vehicle dynamics and powertrain operation in MPC. Finally, comprehensive simulations in extreme and long driving cycle testing scenarios show the proposed strategy can prevent vehicles from entering unsafe states, while improving fuel economy by 9.95%, reducing power sources life loss by 6.53%.

A Survey of Scenario Generation for Automated Vehicle Testing and Validation

This survey explores the evolution of test scenario generation for autonomous vehicles (AVs), distinguishing between non-adaptive and adaptive scenario approaches. Non-adaptive scenarios, where dynamic objects follow predetermined scripts, provide repeatable and reliable tests but fail to capture the complexity and unpredictability of real-world traffic interactions. In contrast, adaptive scenarios, which adapt in real time to environmental changes, offer a more realistic simulation of traffic conditions, enabling the assessment of an AV system’s adaptability, safety, and robustness. The shift from non-adaptive to adaptive scenarios is increasingly emphasized in AV research, to better evaluate system performance in complex environments. However, generating adaptive scenario is more complex and faces challenges. These include the limited diversity in behaviors, low model interpretability, and high resource requirements. Future research should focus on enhancing the efficiency of adaptive scenario generation and developing comprehensive evaluation metrics to improve the realism and effectiveness of AV testing.

Predict Students' Dropout and Academic Success with XGBoost

The attrition rate of students in higher education is a worldwide issue that profoundly affects both individuals and institutions. Students who fail to complete their studies often encounter economic and social difficulties, while educational institutions suffer a deterioration in reputation and operational efficacy. This paper proposes the creation of a prediction model utilizing the XGBoost algorithm to assess students' academic progress and dropout risk. The model incorporates several elements, such as academic, demographic, and socio-economic, to yield comprehensive insights into students' educational trends. This research utilizes the Predict Students' Dropout and Academic Success dataset, comprising 4,424 data points and 36 attributes. The data underwent normalization via StandardScaler and was divided into five scenarios for training and testing, ranging from a 50:50 to a 90:10 split. The evaluation of the model was conducted utilizing accuracy, precision, recall, and F1-Score criteria. The findings indicate that the model attains peak performance in the 80:20 scenario, exhibiting 88% precision and an 81% F1-Score, signifying an ideal equilibrium between predictive accuracy and risk identification capability. This study demonstrates that XGBoost can serve as a dependable predictive instrument to aid decision-making in the education sector. These findings establish a foundation for formulating targeted interventions aimed at enhancing student retention. Subsequent study may investigate the use of real-time data and sophisticated models to enhance predictive accuracy.

A method for finding web application vulnerabilities using the ChatGPT API

This paper presents a method for automating web application testing using the ChatGPT API, designed to help ethical hackers identify vulnerabilities. The goal of the research is to develop an approach that improves the efficiency and accuracy of pentesting, focusing on the automation of processes that are traditionally performed manually. The proposed method is based on the capabilities of the GPT model to generate test requests and analyze server responses, which allows detecting potential vulnerabilities without the need for detailed analysis of the source code. The presented results demonstrate the advantages of using GPT models for generating complex test scenarios and analyzing web application responses, which helps identify potential threats. The results of the experiments showed an increase in the accuracy of vulnerability detection by 15-20% and a reduction in testing time by 35% compared to traditional methods. The proposed approach is promising for implementation in the practice of ethical hacking and cyber security.