Robust Testing Method Research Articles

Round Robin Study for Evaluation an in vitro skin irritation test for medical device extracts using KeraSkinTM in Korea

With the growing importance of alternative test methods that implement the 3Rs principles (Reduction, Refinement and Replacement) and the global importance of biological safety assessment data for medical devices is increasing. We have developed and optimized the ‘KeraSkin™ Skin Irritation Test (KeraSkin™ SIT) for medical device’ for regulatory application in biological evaluation according to ISO 10993-23. We conducted a round robin study to optimize and evaluate the performance of KeraSkin™ SIT for medical devices using KeraSkin™ Reconstructed Human Epidermis (RhE), which is developed and manufactured in Korea. This round robin study was performed to assess the transferability, reproducibility (within and between laboratories) and predictive capacity in 1 lead laboratory and 3 participating laboratories based on OECD Guidance Document 34. The predictive capacity, the results showed 83.3 % of sensitivity, 100 % of specificity and 91.6 % of accuracy. In conclusion, the results demonstrate that ‘KeraSkin™ SIT for medical device’ provides a robust test method for detecting irritant activity of medical device extracts and can be utilized for identifying low levels of potent irritants in medical device extracts. Therefore, it fulfills the requirements to be included as a ‘me-too’ test method to EpiDerm™ and SkinEthic™ skin irritation test in ISO 10993-23.

Lateral flow paired with RT-LAMP: A speedy solution for Influenza A virus detection in swine

Influenza A Virus in swine (IAV-S) is a zoonotic pathogen that is nearly ubiquitous in commercial swine in the USA. Swine possess sialic acid receptors that allow co-infection of human and avian viruses with the potential of pandemic reassortment. We aimed to develop a fast and robust testing method for IAV-S detection on swine farms. Two primers of the RT-LAMP assay were labeled for use in a lateral flow readout. A commercially available lateral flow kit was used to read the amplicon product. With a runtime of ∼ 45 minutes, the limit of detection for the assay is comparable with an RT-qPCR Cq less than 35, with a sensitivity of 83.5 % and a specificity of 89.6 %. This assay allows veterinarians and producers with limited access to diagnostic services to perform and detect Matrix gene amplification on-site with low equipment costs. The time from sample collection to detection is less than one hour, making this method an accessible, convenient, and affordable tool to prevent the spread of zoonotic disease.

Drug Release and Stability Study of Innovated Losartan Potassium and Rosuvastatin Calcium Fixed-dose Combination Tablet.

Patient adherence to therapy and compliance is always a challenge for care providers in the management of chronic disorders with multiple medications. Our study focused on formulating concurrently prescribed ARB (Angiotensin Receptor Blocker), i.e., losartan potassium, and a cholesterol-lowering statin derivative, i.e., rosuvastatin calcium, in a fixed-dose combination tablet. The drugs were selected based on the presence of synergism and variation in solubility characteristics. Trial batches with fixed concentrations of both active pharmaceutical ingredients (APIs) and varying quantities of different excipients were prepared by dry granulation technique and subjected to different quality control tests for tablets. Batch F5 was selected on the basis of in-process quality control data for the development of a drug release protocol. Experimental conditions were optimized. Based on the sink condition, phosphate buffer (pH 6.8) was selected as the dissolution medium. Simultaneous determination of both APIs in samples collected at predetermined time intervals was carried out using the RP-HPLC technique with acetonitrile, methanol, and water (20:25:55 v/v/v) as mobile phase. Complete dissolution of both APIs in the FDC tablet was achieved in 45 min in 900 mL of the selected medium. The in vitro drug release protocol was validated for accuracy and precision without interference with sample analysis. In this study, a validated, accurate, and robust dissolution testing method was developed for the newly formulated FDC tablet.

Microbiological Evaluation of Commercial Hand Sanitisers Available in Pakistan Using European Standard and Membrane Filtration Method.

To evaluate the antibacterial efficacy of various commercially available alcohol-based hand sanitisers (ABHS) using European standard (EN 1500) method and perform ABHS testing with membrane filtration method. A Cross-sectional observational study. Place and Duration of the Study:Quality Control Section of the Microbiology Laboratory, The Aga Khan University Hospital, Karachi, Pakistan, from February to April 2023. Efficacy of 14 commercially and widely accessible hand sanitisers was defined as reducing micro-organism growth. It was determined using the EN 1500 European standard test and membrane filtration method. Majority (92.8%) ABHS showed a significant bacterial reduction except one ABHS tested with the EN 1500 method. Only six ABHS products were tested through the membrane filtration method because high viscosity of hand sanitisers was causing damage to filter membranes. Continued vigilance in evaluating hand sanitiser's efficacy through robust testing methods is essential to ensure public health and prevent the dissemination of misleading products that may compromise hand hygiene practices. Hand sanitisers, European standard, Membrane filtration method, Antibacterial efficacy.

Reprint of: Robust inference on correlation under general heterogeneity

Considerable evidence in past research shows size distortion in standard tests for zero autocorrelation or zero cross-correlation when time series are not independent identically distributed random variables, pointing to the need for more robust procedures. Recent tests for serial correlation and cross-correlation in Dalla, Giraitis, and Phillips (2022) provide a more robust approach, allowing for heteroskedasticity and dependence in uncorrelated data under restrictions that require a smooth, slowly-evolving deterministic heteroskedasticity process. The present work removes those restrictions and validates the robust testing methodology for a wider class of innovations and regression residuals allowing for heteroscedastic uncorrelated and non-stationary data settings. The updated analysis given here enables more extensive use of the methodology in practical applications. Monte Carlo experiments confirm excellent finite sample performance of the robust test procedures even for extremely complex white noise processes. The empirical examples show that use of robust testing methods can materially reduce spurious evidence of correlations found by standard testing procedures.

Enhanced identification of endocrine disruptors through integration of science-based regulatory practices and innovative methodologies: The MERLON Project.

The prevalence of hormone-related health issues caused by exposure to endocrine disrupting chemicals (EDCs) is a significant, and increasing, societal challenge. Declining fertility rates together with rising incidence rates of reproductive disorders and other endocrine-related diseases underscores the urgency in taking more action. Addressing the growing threat of EDCs in our environment demands robust and reliable test methods to assess a broad variety of endpoints relevant for endocrine disruption. EDCs also require effective regulatory frameworks, especially as the current move towards greater reliance on non-animal methods in chemical testing puts to test the current paradigm for EDC identification, which requires that an adverse effect is observed in an intact organism. Although great advances have been made in the field of predictive toxicology, disruption to the endocrine system and subsequent adverse health effects may prove particularly difficult to predict without traditional animal models. The MERLON project seeks to expedite progress by integrating multispecies molecular research, new approach methodologies (NAMs), human clinical epidemiology, and systems biology to furnish mechanistic insights and explore ways forward for NAM-based identification of EDCs. The focus is on sexual development and function, from foetal sex differentiation of the reproductive system through mini-puberty and puberty to sexual maturity. The project aims are geared towards closing existing knowledge gaps in understanding the effects of EDCs on human health to ultimately support effective regulation of EDCs in the European Union and beyond.

The incentive effects of the macro tax burden on economic growth: A negative or positive incentive effect? Analysis based on panel data

Utilizing macroeconomic panel data from 2010 to 2022 of 31 provinces in China, this paper employed the fixed effect model, the random effect mode, the robust test method, the system GMM estimation method, the instrumental variables method (IV), and subregional heterogeneity analysis to conduct an empirical investigation into the effect of China's small-caliber macro tax burden, large-caliber macro tax burden and the burden of various major taxes and fees on economic growth. The research revealed that the small-caliber macro tax burden exerts a positive incentive effect on economic growth, while the large-caliber macro tax burden acts in the opposite way. The tax burden of VAT and CIT is relatively moderate, which can stimulate a positive incentive effect on economic growth. However, the tax burden of total nontax income and individual income tax is too heavy and overburdens negatively economic growth. Moreover, this paper reveals regional heterogeneity in the effects of China's small-caliber and large-caliber macro tax burdens and the burden of major taxes and fees on economic growth. Drawing from the findings of this study and considering the specific context of China, this paper offers several policy recommendations for optimizing China's tax system.

Southeast Asia's environmental challenges: emergence of new contaminants and advancements in testing methods.

Emerging contaminants, including pharmaceuticals, personal care products, microplastics, and per- and poly-fluoroalkyl substances, pose a major threat to both ecosystems and human health in Southeast Asia. As this region undergoes rapid industrialization and urbanization, the increasing presence of unconventional pollutants in water bodies, soil, and various organisms has become an alarming concern. This review comprehensively examines the environmental challenges posed by emerging contaminants in Southeast Asia and recent progress in toxicity testing methods. We discuss the diverse range of emerging contaminants found in Southeast Asia, shedding light on their causes and effects on ecosystems, and emphasize the need for robust toxicological testing methods. This review is a valuable resource for researchers, policymakers, and environmental practitioners working to mitigate the impacts of emerging contaminants and secure a sustainable future for Southeast Asia.

Will Environmental Regulations lead to China's outward Foreign Direct Investment?: A Quasi Natural Experiment Based on China's Carbon Emission Trading Pilot Policy

Foreign direct investment (OFDI) is an important part of China's "going global" strategy. With the continuous expansion and rapid development of OFDI in China, domestic environmental problems are becoming increasingly severe. Therefore, China has established a pilot policy of Carbon Emission Trading System (ETS) to address environmental issues. This article takes the implementation of ETS pilot policies as a quasi natural experiment, uses the double difference method (DID model) to empirically test whether ETS pilot policies will lead to OFDI, and uses the composite control method for robustness testing to supplement and expand the conclusions of the DID model. In addition, incorporate the "Pollution Shelter Hypothesis" and "Porter Hypothesis" into the same framework to explore the impact mechanism of ETS pilot policies on OFDI. Based on the conclusions drawn, it is recommended to further improve the design of carbon trading systems in pilot areas and establish a national carbon emission trading system to enhance the quality of China's outward foreign direct investment.

Characterization Technique for a Doppler Radar Occupancy Sensor

Occupancy sensors are electronic devices used to detect the presence of people in monitored areas, and the output of these sensors can be used to optimize lighting control, heating and ventilation control, and real-estate utilization. Testing methods already exist for certain types of occupancy sensors (e.g., passive infrared) to evaluate their relative performance, allowing manufacturers to report coverage patterns for different types of motion. However, the existing published techniques are mostly tailored for passive-infrared sensors and therefore limited to evaluation of large motions, such as walking and hand movement. Here we define a characterization technique for a Doppler radar occupancy sensor based on detecting a small motion representing human breathing, using a well-defined readily reproducible target. The presented technique specifically provides a robust testing method for a single-channel continuous wave Doppler-radar based occupancy sensor, which has variation in sensitivity within each wavelength of range. By comparison with test data taken from a human subject, we demonstrate that the mobile target provides a reproducible alternative for a human target that better accounts for the impact of sensor placement. This characterization technique enables generation of coverage patterns for breathing motion for single-channel continuous wave Doppler radar-based occupancy sensors.

How does digital life influence the health service use among rural residents? Evidence from China.

The Healthy China strategy is an important development objective of the 14th Five-Year Plan and Vision 2035 in China, while health service use in rural China has been a weak link in this strategy. Nowadays, people's health service use will be influenced by digital technology due to the arrival of the Digital Age, and that is the reason why our interest is to discuss the effect of digital life on health service use among rural residents. We use the data from the China Family Panel Studies (CFPS) 2020 to examine the effect of digital life on health service use among rural residents, and we use Instrumental Variables method to control the endogenous problem and use KHB model to discuss the mechanism of this effect. It was found that digital life has increased the health service use among rural residents significantly, and this result has been verified by robust test and Instrumental Variables method. Besides, digital life can increase health service use through the information channel effect and the health literacy effect indirectly. Moreover, digital life has a more significant impact on the residents with low social capital, low physical capital and low social trust, which represents the inclusivity of digital life. The results of our paper will be helpful to examine the effect of the digital policy on promoting the health service use in rural China, and our findings will provide evidence of how to use digital life to enhance health service use among rural residents. Based on this, the government should take measures to eliminate the digital divide between urban and rural areas by promoting the level of digital life among rural residents, paying more attention to the digital literacy development among them, and forging ahead toward the great goal of the Healthy China under the Digital Age.

Robust confidence intervals for the process capability index Cpk with bootstrap improvement

The process capability index (PCI), C pk , one of the widely used tools for assessing the capability of a manufacturing process, expresses the deviation of the process mean from the midpoint of the specification limits. The C pk is known to perform well under the general assumption that the experimental data are normally distributed without contamination. Under this assumption, the sample mean and sample standard deviation are used for the estimation of the PCI. However, the sample mean and sample standard deviation are quite sensitive to data contamination and this will result in underperformance of C pk . Therefore, in this article, we propose alternatives to the conventional method by replacing the sample mean and sample standard deviation with robust location and scale estimators. We also propose a method for constructing a robust PCI C pk confidence interval which lends itself to robust statistical hypothesis testing. The robust hypothesis testing methods based on this confidence interval are shown to be quite efficient when the data are normally distributed yet also outperform the conventional method when data contamination exists.

Use of machine learning to predict pathological complete response using multi-modal data from I-SPY2 clinical trials.

592 Background: The objective of the current analysis of the I-SPY 2 data from multiple breast cancer clinical trials for stages II and III cancers was to look at the possibility of developing a machine learning algorithm to predict pathological complete response using multimodal data. Methods: Imagining, clinical and biomarkers data form the I-SPY 2 trial was used in this analysis. Four different experiments were designed for to assess different data pre-processing and machine learning methods mainly deep neural networks (DNN) and random forest (RF) classifiers. Experiment 1 and 3 use DNNs while Experiment 2 and 4 used RFs; more over the first two experiments used data that was continuous in nature from the point of view of pre-performed imaging analysis while in later two experiments we had binarized all the variables using a median cutoff for high vs low values. The variables used were treatment recieved, HER2/neu receptor status, hormone receptor status, age, ethnicity, gynecological history, MRI feature data. All models were evaluated using robust testing methods that included sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), accuracies (training and test sets), area-under the receiver-operator curve and F1-Score. Results: From a total of 985 patients with clinical data we used 384 that had multi-feature MRI data. Time series data for the initial scan (T0) and a scan 3 weeks later (T1). The final comparative analysis showed the best model among the 4 experiment the best performing algorithms for this analysis was random forests using median cutoffs with a best F1-score (0.43), specificity (86.4%), PPV (47.8%), accuracies (train sets, 100%; test sets, 75%), and AUROCs (0.78, 0.56-0.73). Conclusions: The use of machine learning to predict pathological complete response using multimodal data shows good potential as a digital biomarker. However, these results need further validation before a clinical tool is available for the clinicians. [Table: see text]

Characterization method of the Joule heating efficiency of electric textiles and influence of boundary conditions

Joule heating textiles are available on the market for a variety of applications. However, their market growth is limited by challenges in terms of quality, for instance with the need to provide a reliable account of the heating to be expected, prevent the occurrence of overheating leading to burns and fires, and ensure the long-term performance when exposed to use conditions such as abrasion and laundering. Standard test methods are a key component to solve these issues of efficiency, safety, and durability. Yet, they mostly remain to be established. In this research, a test method was developed for the characterization of the Joule heating efficiency of electric textiles. It involves measuring the temperature of a heating textile using a thermocouple affixed to its surface while it is powered for an hour. The value of the surface temperature that would ultimately be reached by the heating textile after an infinite heating time and the time for the temperature to enter a slow increase regime are determined by fitting an equation to the temperature-time data. These two parameters provide a quantitative mean of comparison between different heating textiles/conditions. This test method was used to analyze the effect of different experimental conditions on the heating efficiency of four heaters corresponding to different technologies of Joule heating textiles and make recommendations in terms of conditions for a standardized test protocol. These results give some insights towards the development of a robust and universal test method for the quantitative assessment of the Joule heating efficiency of electrical textiles that will ultimately be proposed for standardization to help support the growth of the e-textile industry.

Evaluating Performance of Circular Staplers Using Comparative Test Methods for Evidence-Based Surgery.

Circular stapler anastomosis is a common surgical procedure. Despite technological advancements, anastomotic leak remains a postoperative concern. Assessment of new technologies is impeded by variations in test methods and analysis, precluding outcome reproducibility and direct comparisons of results across studies. The development of robust and reproducible preclinical test methods is critical to accelerating stapling technology advancements. Leak pressure, staple line perfusion and security, and device removal force were quantified for triple-row (Tri-staple EEA, TriEEA) and double-row staplers (Echelon Circular Powered, ECP). Leak and perfusion testing were performed in vivo. Device removal force and staple line security testing were performed with synthetic medium using an Instron. Data were analyzed using unpaired student's t-test or Kruskal-Wallis test, with statistical significance defined as P < .05. Leak pressure was 73% higher in TriEEA vs ECP (P = .016). TriEEA staple line failure force was lower than ECP at 40 and 50mmHg (P = .001 and P = .023, respectively). Perfusion to the staple line was higher (148%) for TriEEA than for ECP (P = .003) and the force required to remove the device from its stapled anastomosis was 78% lower for TriEEA than for ECP (P < .001). This report addresses a primary limitation in stapling research by presenting novel methodologies which enhance clinical relevance and provide sufficient detail for reproduction by independent investigators. These methods are applied to a comparison between triple-row and double-row staplers to demonstrate utility of new test methods in assessing key technology design features.

Vibroacoustic modulation method with arbitrary probe wave frequency based on machine learning algorithms

ABSTRACT The vibro-acoustic modulation (VAM) method assesses the integrity of a structure using modulation of a high-frequency probe wave and a low-frequency pump wave and measuring the modulation index. A large body of studies found that the modulation index could be used for the defection of contact-type defects in different materials. However, it was also found that the performance of this method is dependent on the probe wave frequency selection. To enhance the robustness of this method, researchers made efforts to modify the VAM method raising the complexity of the equipment and related operator skills, creating needs for extra preparation or procedures. All of these limits VAM potential for industrial application. To develop a robust, efficient, and fast VAM-based non-destructive testing method, the machine learning algorithm was employed. The algorithm utilises amplitude of the multiple sidebands of the modulation spectrum as feature variables and the structural integrity as target variables for training and testing the machine learning model. The experimental result shows that the fatigue crack could be detected with an arbitrary probe wave frequency using the applied machine learning approach.

Nano Flex Screen Protectors for 2D Material Piezotronics

We propose nano flex film, a commercially available mobile screen protector, as a prospective flexible substrate for piezotronic devices. Reliable fabrication methods on flexible substrates and robust testing methods are essential to explore piezotronic properties in emerging 2D materials. Unlike conventional rigid substrates, flexible substrates are challenging to handle during fabrication and subsequent testing. The fabrication process on the proposed flexible substrate is as seamless as on rigid substrates. The optimized methods to fabricate 2D material devices on nano flex films are presented. Inorganic dielectric materials such as silicon dioxide (SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) offer good adhesion, better lifetime, and optical contrast to the 2D material flakes compared to polymer alternatives. The devices made on nano flex films with SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> have shown almost three-fold improvement in open circuit voltage generated (50 mV) and strain gauge values (1997 at 0.44% strain). Also, a simple setup proposed for strain-dependent electrical measurements enables us to characterize the piezotronic properties of different 2D materials without wire bonding or probe needles.

What urban spatial structure is more conducive to reducing carbon emissions? A conditional effect of population size

Clarifying what urban spatial structure (US) is more conducive to reducing emissions and conserving energy provides scientific references for government departments and decision-makers to optimize and realize their “carbon reduction” goal. In light of the mixed results of previous studies, our study explored the effect of monocentric US (MUS) and polycentric US (PUS) on carbon emissions in China by using panel regression models and robust test methods based on an US quantification through remotely sensed nighttime light data. According to the findings, population size regulates the effect of US on carbon emissions. For cities with a population size less than 70.71 × 104–98.58 × 104, a MUS is more conducive to reducing carbon emissions, whereas for cities with a population size large than 70.71 × 104–98.58 × 104, a PUS performs better on carbon emission reduction. Further quantitative analyses reveal that the conditional effect of population size on the US—carbon emissions relationship is dependent on the relative magnitudes of the three mediating factors, e.g., the transportation, residents' lives, and industrial production and manufacturing. Our study determines the MUS and PUS most conducive to carbon emission reduction for different population sizes, which may lay the groundwork for a low-carbon urban planning system.

High-Speed Railway Network Development, Inter-County Accessibility Improvements, and Regional Poverty Alleviation: Evidence from China

The rapid expansion of the high-speed railway (HSR) network in China has significantly shortened the space–time distance between cities. China is striving to enter an anti-poverty era, which is increasing the importance of research on the poverty reduction effect created by upgrading transportation infrastructure, in particular, HSR development. Describing the characteristics of accessibility and the mechanisms by which that accessibility reduces poverty could provide the insights needed for determining suitable anti-poverty paths. By using data for 2341 counties and equivalents in China during 2007–2018, this study analyses the railway accessibility improvements and the poverty reduction effect created by HSR development. On average, HSR in China contributed to a significant increase in potential economic accessibility (317.8%) and a decrease in weighted average travel time (39.9%) for counties. Based on accessibility calculations, the Theil index was used to measure the disparity level of regional accessibility and regional poverty measured based on the income of rural residents. The results indicate that HSR leads to an increase in inequality in terms of travel time and potential economic accessibility at a national level. Pearson coefficients reveal a strong correlation between disparities in accessibility and in rural income among provinces. Furthermore, using the full sample, and sub-samples of poor and non-poor counties in China, the association between regional accessibility and poverty was examined by using two-way fixed effect models and spatial econometric models. The estimated results show that a 1% improvement in potential economic accessibility leads to an aggregate rural income improvement of 0.03–0.17%; the ratio of rural income to urban income increases by 0.04–0.12% and a larger effect is observed in poor counties. The weighted average travel time reduction also leads to improvement in rural income and reduction in the urban–rural income gap. The empirical results obtained by different robust test methods, including different sample groups, different estimated methods and accessibility indicators, are shown to be robust. These findings can help transportation departments formulate poverty-alleviation-oriented transportation planning and investment policies and inform future policies for countries planning to construct HSRs.

A Methodology to Design and Evaluate HRI Teaming Tasks in Robotic Competitions

As social robots become more prominent in our lives, their interaction with humans takes an increasing role, and new collaborative scenarios emerge. This development brings the need to realize robust test methods enabling the design and evaluation of Human-Robot Interaction (HRI) teaming tasks to prove functionality and promote adoption. In this article, we present a general-purpose and repeatable methodology for conducting studies in collaborative HRI in the range of robotic competitions. The methodology includes a step-by-step approach to design HRI teaming tasks tailored to be enacted in a robotic competition and to evaluate the performance of social robots to execute the designed tasks, exploring the relationship between robots’ performance and user perceptions based on the feedback of the users participating to such tasks. We assess the feasibility of the methodology to design and evaluate an HRI teaming task in the context of “Smart CIties RObotics Challenges” (SciRoc) competition, which targets at investigating the impact of social of robots in smart cities.