Test Environment Research Articles

Research on Key Technologies of 260 °C/210 MPa Ultra-Deep Logging Equipment

Deep and ultra-deep oil, which are unconventional oil reservoirs have become an important field for increasing reserves and production in China’s oil and gas exploration and development, and pose higher requirements for ultra-deep logging technology and equipment. For addressing the technical challenges faced in the development of 260 °C/210 MPa ultra-high temperature and high-pressure logging equipment, based on the CPLog fast and imaging series instruments, this article originally proposes key core technologies and solutions such as 260 °C/210 MPa electromechanical sensors, 13,000 m cable communication, and large borehole correction processing. Through the design indoor testing of a BS-PT-based ultra-high temperature acoustic transducer, 260 °C/210 MPa instrument pressure-bearing housing components, and field testing of large borehole environment testing and 13,000-m cable 8000-m deep well data transmission, it will help develop a prototype of 260 °C/210 MPa ultra-high temperature and high pressure logging equipment. The key technology research of 260 °C/210 MPa logging equipment has laid the foundation for the development of ultra-deep logging equipment and provided technical support for the exploration and development of ultra-deep oil and gas at a depth of 10,000 m.

An assessment of seven closed system transfer devices in accordance with the 2015 NIOSH vapor containment performance protocol.

The purpose of this study was to assess and compare the effectiveness of seven different closed system transfer device (CSTD) product lines following the 2015 NIOSH Vapor Containment Performance Protocol, using a Gasmet™ DX5000 Terra multigas FTIR analyzer. Seven different CSTD systems were assessed using a two-task evaluation on their capacity to contain the NIOSH-specified challenge agent, 70% isopropyl alcohol (IPA). Task 1 simulated reconstitution and compounding steps while Task 2 simulated compounding and administration steps. Vial adapters, syringe adapters, IV bag adapters, and y-site adapters from each CSTD product line were tested. All tasks were performed in a custom-built environmental test chamber as outlined in the 2015 NIOSH protocol, with isopropyl alcohol (IPA) release detected and measured by a Gasmet™ analyzer. The BD PhaSeal™ CSTD product line effectively contained IPA vapor during both tasks with a task performance metric of 0.00 ppm for each task. BD PhaSeal™ Optima, Equashield®, Yukon Medical Arisure®, ICU Medical ChemoLock™, BD Texium™, and ICU Medical ChemoClave™ non-vented CSTDs demonstrated detectable and quantifiable IPA leakage during each task. The leaks occurred at different steps within the protocol, and the amount of IPA vapor detected varied. In this study, one CSTD product line (i.e., PhaSeal™) successfully contained IPA vapor below the Gasmet™ analyzer's limit of quantification (LOQ) for IPA of 0.04 ppm for both Tasks 1 and 2, demonstrating it to be a validated closed system per the study protocol. All other CSTD product lines demonstrated some level of IPA leakage with task performance metrics above the analyzer's LOQ. However, these results tell us little about how well CSTDs contain hazardous drugs (HDs), as a universal HD surrogate for effectively assessing the closedness of all types of CSTDs has yet to be identified.

Study on Self-tuning of Robot Parameters for EMC Vehicle Steering Test

With the continuous advancement of electric vehicles and smart internet technologies, ensuring vehicle safety through electromagnetic compatibility (EMC) testing in complex electromagnetic environments has become increasingly critical. However, due to the significant variability in vehicle response characteristics under electromagnetic interference, traditional PID control methods for steering robots struggle to meet the high-precision requirements of such tests. In this study, a novel fuzzy PID parameter self-tuning method is proposed, leveraging a Sparrow Search Algorithm-Back Propagation (SSA-BP) neural network. This method optimizes the fuzzy controller's quantization factor by constructing a neural network system where the expected motor angle serves as the input and the quantization factor as the output. The quantization factor is then calibrated online through iterative training.The proposed approach enables the steering robot to achieve real-time, adaptive tuning of the PID parameters for the drive motor by adjusting the steering torque according to different vehicle characteristics, thereby enhancing the robot's anti-interference capability and robustness in EMC testing. The effectiveness of this method is validated through Matlab/Simulink simulations, experiments conducted on the Sensodrive platform, and tests performed in an EMC anechoic chamber. The results indicate that the method offers substantial improvements in control accuracy and anti-interference capabilities, highlighting its strong potential for practical application.

AdaptSel: Adaptive Selection of Biased and Debiased Recommendation Models for Varying Test Environments

AdaptSel: Adaptive Selection of Biased and Debiased Recommendation Models for Varying Test Environments

Development of Immersive 360° Virtual Reality Videos for Intervention with Test Anxiety

This study explores the impact of 360-degree virtual reality (VR) videos on simulating test anxiety in students who already experience it. Using a phenomenological design, participants answered questions about their immersion in the virtual environment, including the realism of visuals, emotional responses, and adaptation. Seven students (four girls and three boys) were selected based on specific criteria, and the collected data underwent content analysis. Findings indicate that 360-degree VR videos effectively simulated test environments, creating a strong sense of realism and immersion. Participants reported that VR exposure elicited test anxiety symptoms similar to those experienced in real tests, with emotional distress intensifying during VR sessions. Test-anxious individuals noted that VR exposure triggered stronger emotional responses compared to imagination-based scenarios. While VR exposure proved effective in evoking realistic experiences and emotions, it was not deemed superior to real-life exposure techniques. This suggests that 360-degree VR has potential as a tool for anxiety interventions.

Indoor Positioning Systems in Logistics: A Review

Background: Indoor Positioning Systems (IPS) have gained increasing relevance in logistics, offering solutions for safety enhancement, intralogistics management, and material flow control across various environments such as industrial facilities, offices, hospitals, and supermarkets. This study aims to evaluate IPS technologies’ performance and applicability to guide practitioners in selecting systems suited to specific contexts. Methods: The study systematically reviews key IPS technologies, positioning methods, data types, filtering methods, and hybrid technologies, alongside real-world examples of IPS applications in various testing environments. Results: Our findings reveal that radio-based technologies, such as Radio Frequency Identification (RFID), Ultra-wideband (UWB), Wi-Fi, and Bluetooth (BLE), are the most commonly used, with UWB offering the highest accuracy in industrial settings. Geometric methods, particularly multilateration, proved to be the most effective for positioning and are supported by advanced filtering techniques like the Extended Kalman Filter and machine learning models such as Convolutional Neural Networks. Overall, hybrid approaches that integrate multiple technologies demonstrated enhanced accuracy and reliability, effectively mitigating environmental interferences and signal attenuation. Conclusions: The study provides valuable insights for logistics practitioners, emphasizing the importance of selecting IPS technologies suited to specific operational contexts, where precision and reliability are critical to operational success.

Preliminary Evaluation of Nickel Silicide (NiSi12-wt%) Laser Cladding for Enhancing Microhardness and Corrosion Resistance of S355 Steel

S355 construction steel, a commonly used mild steel due to its exceptional strength, is prone to environmental degradation, especially pitting corrosion in highly corrosive marine environments. To address this vulnerability, applying a surface layer of nickel silicide (NiSi) cladding on such components could offer a solution, given that NiSi-based alloys are known for their high corrosion resistance and exceptional mechanical properties. Thus, the present study has investigated the corrosion resistance and microhardness of the NiSi12-wt% cladding deposited onto substrates of S355 steel using laser metal deposition. An accelerated ASTM G48 corrosion test and a Vickers microhardness test were conducted in a solution of 6% ferric chloride (FeCl3) solution at room and elevated temperatures to represent marine environments, with uncladded sheet substrates exposed to the same test environments as a reference. All exposed S355 steel samples, with and without cladding, underwent microhardness testing and were characterized using light optical microscopy (LOM) and low-voltage field emission scanning electron microscopy (LVFESEM). The findings indicate that the NiSi12-wt% cladding significantly enhances the corrosion resistance and mechanical properties of the S355 steel samples, showcasing its potential for use in marine and industrial environments where corrosion and mechanical wear are expected.

PF-MAAC: A learning-based method for probabilistic optimization in time-constrained non-adversarial moving target search

This paper investigates the multi-robot efficient search (MuRES) problem with a focus on maximizing the probability of capturing a moving target within a predefined time constraint. Given the complexity of the MuRES problem, traditional optimization algorithms often result in significant computational overhead. As a result, learning-assisted intelligent optimization, particularly reinforcement learning (RL), has emerged as a prominent research trend, providing more efficient and adaptive solutions. However, the non-additive nature of the objective to maximize capture probability complicates the direct application of canonical RL-based algorithms. To address the challenge, we propose the probabilistically factorized multi-agent actor-critic (PF-MAAC) algorithm, which serves as a lightweight solution aligned with probability theory specifically designed to handle the complexities of the maximal capture probability objective. PF-MAAC is composed of (1) a generalized temporal difference (GTD) module to establish the temporal-difference relationship of the central value function, (2) a probability-based factorization (P-FAC) module to decompose the central value function into individual ones in a probability-compliant manner, and (3) an extended policy gradient (EPG) module which updates each robot’s actor-network based on the decomposed individual value function. Comparative simulations across various MuRES test environments demonstrate that PF-MAAC outperforms state-of-the-art methods. Furthermore, we successfully deployed PF-MAAC in a real multi-robot system for moving target search in a self-constructed indoor environment, achieving the satisfactory results for different time constraints.

Statistical Testing of Random Number Generators and Their Improvement Using Randomness Extraction

Random number generators (RNGs) are notoriously challenging to build and test, especially for cryptographic applications. While statistical tests cannot definitively guarantee an RNG’s output quality, they are a powerful verification tool and the only universally applicable testing method. In this work, we design, implement, and present various post-processing methods, using randomness extractors, to improve the RNG output quality and compare them through statistical testing. We begin by performing intensive tests on three RNGs—the 32-bit linear feedback shift register (LFSR), Intel’s ‘RDSEED,’ and IDQuantique’s ‘Quantis’—and compare their performance. Next, we apply the different post-processing methods to each RNG and conduct further intensive testing on the processed output. To facilitate this, we introduce a comprehensive statistical testing environment, based on existing test suites, that can be parametrised for lightweight (fast) to intensive testing.

Next-Gen Test Automation in Life Insurance: Self-Healing Frameworks

The exponential complexity of contemporary Life Insurance applications - shaped by stringent regulatory compliance requirements, dynamic customer - centric innovation, and expansive product diversification - has magnified the challenges of maintaining traditional test automation frameworks. These frameworks often falter under the weight of frequent application updates, evolving user interfaces (UI), and fluctuating backend integrations, leading to brittle test scripts and heightened maintenance costs. Self-healing test automation frameworks offer a cutting-edge, AI-driven solution to this paradigm. By leveraging advanced machine learning (ML) algorithms and intelligent heuristics, these frameworks autonomously detect, diagnose, and remediate test failures caused by changes in application elements, workflows, or environments. Through real-time adaptation to UI modifications, API updates, and evolving test environments, self-healing frameworks ensure uninterrupted testing cycles with minimal manual intervention, significantly improving efficiency and scalability. This paper provides a comprehensive exploration of the architectural design and operational mechanisms of self- healing test automation frameworks within the context of Life Insurance applications. Core focus areas include their ability to dynamically recalibrate element locators and adapt test scripts during runtime, thereby enhancing test coverage, optimizing maintenance workflows, and reducing the overall defect detection lifecycle. Through an in-depth analysis, the paper highlights the integration of AI-powered locator optimization engines, the deployment of predictive analytics for early anomaly detection, and the orchestration of continuous self-healing pipelines in CI/CD ecosystems. Detailed case studies from the Life Insurance domain illustrate tangible benefits such as accelerated regression cycles, substantial cost reductions, and improved system resilience. Keywords: Artificial Intelligence, Machine Learning, Self – Healing, Life Insurance Applications, Quality Assurance, Test Maintenance Efficiency

Online assessment in young children: Challenges and considerations.

In the years following the COVID-19 pandemic, online assessment has emerged as an increasingly popular method for testing children. This review aims to review key advantages and disadvantages of online assessment in young children, specifically in the area of literacy and mathematics assessment. We first consider the recent history of online assessment and give evidence from our own experiences with online assessments in both Cebu, Philippines and Hong Kong. We then discuss the benefits of conducting assessments online. One of the key strengths of online assessments is the efficient use of resources, such as ease of staff training, increased participation and decreased costs for both participants and researchers. Difficulties related to testing environment, parental interference and exclusion of certain populations are also highlighted as focal challenges that must be considered when designing and implementing online assessment. The reliability and validity of online assessments are then specifically discussed in relation to the question of how to best ensure that online measures function as intended, stressing the importance of establishing separate norms for the various administration modes. Notably, online assessments delivered via phone screen must have separate norms than those delivered on a computer screen, as device differences may account for differences in performance. Finally, we present broader considerations for designing online assessments with young children. This includes practical tips for pilot testing, user interface design, data confidentiality and automatic scoring, both to enhance the efficiency of future online assessments and increase participation. Specifically, online assessments for children must account for their developing attentional abilities, meaning assessments should be brief, engaging and contain clear task instructions. The implementation of these considerations will support the development of more reliable, valid and inclusive assessments which will allow researchers and practitioners to support equitable and feasible online testing moving forward.

Analysis of the indoor wind environment in buildings on the Qinghai-Tibet plateau of China: A case study of the Dege Scripture Printing House

The Dege Scripture Printing House (DSPH), built in 1729, is located on the southeastern edge of the Qinghai-Tibet plateau and serves as a significant center for Tibetan Buddhism culture. Compared to typical Tibetan architecture, DSPH features unique characteristics, such as its substantial size, diverse structures, and adaptability to the local climate. The surrounding topography of DSPH primarily comprises plateaus and hilly plains, and the region experiences a continental plateau monsoon climate. This climate is characterized by cold temperatures due to high altitude, low precipitation, long sunshine hours, dry air, long winters, short summers, and intense solar radiation. To thoroughly assess the ventilation status of DSPH, a 3D scan of the building, physical environment tests, and indoor wind environment simulations were carried out. The field test results show that the unique architectural design of DSPH helps mitigate the impact of the harsh climate on indoor activities. By analyzing and evaluating the wind environment of DSPH, locally adapted climate resilience practices are summarized, and the physical properties of buildings characteristic of the Tibetan plateau are clarified. These findings provide a reference for promoting the development of local climate-adapted buildings, preserving traditional architectural culture, and promoting the integration of modern architecture with traditional culture. Furthermore, this study represents an important step toward enhancing energy efficiency and green building practices in ethnic minority settlements, contributing to sustainable development in the region.

Unsupervised machine learning in financial anomaly detection: clustering algorithms vs. dedicated methods

The article presents the application of selected clustering algorithms for detecting anomalies in financial data compared to several dedicated algorithms for this problem. To apply clustering algorithms for anomaly detection, the Determine Abnormal Clusters Algorithm (DACA) was developed and implemented. This parameterized script (DACA) allows clusters containing anomalies to be automatically detected on the basis of defined distance measures. This kind of operation allows clustering algorithms to be quickly and efficiently adapted to anomaly detection. The prepared test environment has allowed for the comparison of selected clustering algorithms. K-Means, Hierarchical Cluster Analysis, K-Medoids, and anomaly detection: Stochastic Outlier Selection, Isolation Forest, Elliptic Envelope. The research has been carried out on real financial data, in particular on the income declared in the asset declarations of the targeted professional group. The experience of financial experts has been used to assess anomalies. Furthermore, the results have been evaluated according to a number of popular classification and clustering measures. The highest result for the investigated financial problem was provided by the K-Medoids algorithm in combination with the DACA script. It is worthwhile to conduct future research on the introduced solutions as an ensemble method.

Viva voce using Objective, Integrated, Clustered Examination Software (VOICES) app: study for formative assessment of medical undergraduate students in physiology.

The significance of assessment in driving learning is undeniable, particularly in oral assessments like viva voce where students express themselves verbally. However, traditional viva voce examinations' challenges include reliability, objectivity, content validity, time constraints, varying question difficulty levels, inconsistency in questioning among students, the influence of the "halo" effect, and issues with the test environment. Thus the study aimed to evaluate the effectiveness of Objective, Integrated, Clustered Examination Software (VOICES) compared to traditional viva voce methods. Using a mixed-method design with 103 students, questions were framed using VOICES software created by the National Informatics Center (NIC), Chhattisgarh State Center, Government of India, and students responded through voice messages. Faculty graded students based on personalized videos created by the app. Then, feedback was given, and a focused group discussion was conducted for the students. Traditional viva voce examinations were conducted as part of internal assessments, and the marks obtained from these assessments were compared to evaluate their effectiveness in comparison to the VOICES app. There was no significant difference in the marks obtained using both methods indicating that viva voce using an oral assessment app is equally effective. Students responded positively to the VOICES method. The formative oral assessment conducted through the use of the VOICES app successfully addresses the challenges typically associated with traditional viva voce examinations.NEW & NOTEWORTHY Traditional viva voce examination encounters challenges including reliability, objectivity, content validity, time constraints, varying question difficulty levels, inconsistency in questioning among students, the influence of the "halo" effect, and issues with the test environment. Formative oral assessment conducted through the use of the Objective, Integrated, Clustered Examination Software (VOICES) app developed by the National Informatics Center (NIC) successfully addresses the challenges typically associated with traditional viva voce examinations.

Does testing environment matter for virtual school students?

Standardized test performance among students in full-time virtual schools generally lags behind that of students in traditional, face-to-face schools. One largely unexplored potential cause is the requirement to complete tests at unfamiliar facilities rather than from home, the normal learning environment for these students. The COVID-19 pandemic enabled some full-time virtual school students to test from home, providing a unique opportunity to assess how testing environment might influence outcomes. Using data from an educational management organization (EMO) running virtual schools in 21 U.S. states, we test whether full-time virtual school students perform better on standardized exams administered at home (n=524) relative to those testing at an unfamiliar testing site (n=1,443) in 2020-21. Home testing is associated with nearly one fifth of a standard deviation improvement in math and ELA performance even after controlling for prior test performance. Moreover, tests taken from home more strongly correlate with other measures of academic performance than do tests taken at designated facilities, indicating they better capture academic progress. Analyses investigating whether home testing enables cheating are inconclusive.

Biodegradable Plastics from Marine Biomass: A Solution to Marine Plastic Pollution

The growing demand for plastics has raised environmental concerns due to their non-biodegradable nature. Sustainable solutions are urgently required to decrease plastic pollution. This study explored the potential of Sargassum wightii, a seaweed found in Malaysia, as a sustainable material for bioplastic films. The seaweed-based bioplastic was produced using an extraction-based method where alginate was formed using NaOH, followed by mixing sodium alginate with isopropanol and potato starch. The bioplastic was then characterized using various analytical techniques, including Thermogravimetric Analysis (TGA), Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), and Fourier-Transform Infrared (FTIR) spectroscopy. Physical properties such as density and moisture content, along with environmental tests like water absorption and biodegradability, were evaluated. TGA analysis indicated that 31.12% of the sample remained as residue. FTIR spectroscopy identified the presence of bioactive compounds, with a prominent alcohol group peak at 3358cm-1. XRD analysis revealed a peak at 23.1°, indicating crystallinity within the sample. The moisture content of the bioplastic film was found to be 21.16%. The water absorption test demonstrated the film's hydrophilic nature, showing a 60% increase in weight. A soil burial test for biodegradability confirmed a 40% reduction in weight over 21 days, indicating a reasonable degradation rate. These findings suggest that seaweed holds promise as an alternative raw material for bioplastic production, contributing to more sustainable materials and reducing reliance on non-biodegradable plastics.

Effect of Ischemic Preconditioning on Endurance Running Performance in the Heat.

Ischemic preconditioning (IPC) is a strategy that may enhances endurance performance in thermoneutral environments. Exercising in the heat increases thermoregulatory and cardiovascular strain, decreasing endurance performance. The current study aimed to determine whether IPC administration improves endurance performance in the heat. In a randomized crossover design, 12 healthy subjects (V̇O2max: 54.4 ± 8.1 mL·kg-1·min-1) underwent either IPC administration (220 mmHg) or a sham treatment (20 mmHg), then completed a moderate-intensity 6-min running (EX1) and a high-intensity time-to-exhaustion running test (EX2) in a hot environment (35 °C, 50 % RH). Cardiac function, oxygen consumption (V̇O2), and core body temperature (TCORE) were measured. During EX2, IPC administration increased the total running time in the heat compared to the sham treatment (IPC: 416.4 ± 61.9 vs. sham 389.3 ± 40.7 s, P = 0.027). IPC administration also increased stroke volume (IPC: 150.4 ± 17.5 vs. sham: 128.2 ± 11.6 ml, P = 0.008) and cardiac output (IPC: 27.4 ± 1.7 vs. sham: 25.1 ± 2.2 ml min-1, P = 0.007) during 100% isotime of EX2. End-exercise V̇O2 (IPC: 3.72 ± 0.85 vs. sham: 3.54 ± 0.87 L·min-1, P = 0.017) and slow phase amplitude (IPC: 0.57 ± 0.17 vs. sham: 0.72 ± 0.22 L·min-1, P = 0.016) were improved. When compared with the baseline period, an increase in TCORE was less in the IPC condition during EX1 (IPC: 0.18 ± 0.06 vs. sham: 0.22 ± 0.08 °C, P = 0.005) and EX2 (IPC: 0.87 ± 0.10 vs. sham: 1.03 ± 0.10 °C, P < 0.001). IPC improves high-intensity endurance performance in the heat by 6.9 %. This performance benefit could be associated with improved cardiac and thermoregulatory function engendered by IPC administration.

Diffusion characteristics of re-suspended PM2.5 within a radiant floor heating room under various ventilation modes

Few studies have revealed the effects of re-suspension of particulate matter deposited by radiant floor heating on indoor environments. The present study utilized Computational Fluid Dynamics (CFD) to investigate the impact of radiant floor heating and ventilation mode on indoor airflow patterns, as well as the dispersion and migration of re-suspended particulate matter, based on a turbulence model validated by an environmental test cabin. The Archimedes number (Ar) was employed to represent the strength of interaction between the thermal buoyancy force generated by floor radiation and the inertial force of the supply airflow. The results indicates that as Ar increases, the supply airflow exhibits a downward deflection, thereby forming a stable vortex below to prevent the diffusion of heat and re-suspended PM2.5 generated from the heated floor. As the Ar increases, the re-suspended PM2.5 deposited on the surrounding walls increased significantly, and the indoor PM2.5 concentration gradually decreased. When the Ar exceeds 6.03, the increase in Ar will no longer affect the indoor re-suspension PM2.5 diffusion, deposition characteristics, and removal efficiency. Therefore, it is recommended the critical Ar (Arcrit) of 6.03 for a radiant floor heating room under various ventilation modes, beyond which the indoor flow structure and distribution characteristics of re-suspended PM2.5 no longer change with increasing Ar. This study is helpful to further understand the diffusion and distribution characteristics of re-suspended particulates in floor heating and ventilation rooms, and provide technical reference for improving indoor air quality.

Evaluating the Integrity of Surface Coatings on a Pipe-shaped Aluminum Alloy through Environmental Testing

Evaluating the Integrity of Surface Coatings on a Pipe-shaped Aluminum Alloy through Environmental Testing

Neural Cell Interactions with a Surgical Grade Biomaterial Using a Simulated Injury in Brain Organotypic Slices

Tissue engineering research for neurological applications has demonstrated that biomaterial-based structural bridges present a promising approach for promoting regeneration. This is particularly relevant for penetrating traumatic brain injuries, where the clinical prognosis is typically poor, with no available regeneration-enhancing therapies. Specifically, repurposing clinically approved biomaterials offers many advantages (reduced approval time and achieving commercial scaleup for clinical applications), highlighting the need for detailed screening of potential neuromaterials. A major challenge in experimental testing is the limited availability of neuromimetic, technically accessible, cost-effective, and humane models of neurological injury for efficient biomaterial testing in injury-simulated environments. Three dimensional (3D) organotypic brain slices bridge the gap between live animal models and simplified co-cultures and are a versatile tool for studies on neural development, neurodegenerative disease and in drug testing. Despite this, their utility for investigation of neural cell responses to biomaterial implantation is poorly investigated. We demonstrate that murine brain organotypic slices can be used to develop a model of penetrating traumatic brain injury, wherein a surgical-grade biomaterial scaffold can be implanted into the lesion cavity. Critically, the model allowed for examination of key cellular responses involved in CNS injury pathology/biomaterial handling: astrogliosis, microglial activation and axonal sprouting. The approach offers a technically simple and versatile methodology to study biomaterial interventions as a regenerative therapy for neurological injuries.