Reduce Test Cost Research Articles

Electromagnetic radiation estimation at the ground plane near fifth‐generation base stations in China by using machine learning method

AbstractA novel method based on machine learning is proposed to estimate the electromagnetic radiation level at the ground plane near fifth‐generation (5G) base stations. The machine learning model was trained using data from various 5G base stations, enabling it to estimate the electric field intensity at any arbitrary radiation point when the base station provides service to different numbers of 5G terminals which are in different service modes. The inputs required for the model include the transmit power of the antenna, the antenna gain, the distance between the 5G base station and 5G terminals, terminal service modes, the number of 5G terminals and the environmental complexity around the 5G base station. Experimental results demonstrate the feasibility and effectiveness of the estimation method, with the mean absolute percentage error of the machine learning model being approximately 5.98%. This level of accuracy showcases the reliability of the approach. Moreover, the proposed method offers low costs when compared with on‐site measurements. The estimated results can be utilised to reduce test costs and provide valuable guidance for optimal site selection, thereby facilitating radio wave coverage or electromagnetic radiation regulation of 5G base stations.

A 20-GHz On-Chip Six-Port Reflectometer Using Simple Lumped Passive Devices and Bipolar Junction Transistors

This paper proposes an on-chip six-port reflectometer (SPR) fabricated in the 0.13-μm IBM BiCMOS-8HP technology. The SPR enjoys a compact circuit structure, with only four amplitude detectors as active devices, one resistive power divider, and one lumped phase shifter as passive devices. The power divider and phase shifter are responsible for manipulating the radio-frequency (RF) signals appropriately, whereas the detectors are responsible for sensing the processed signals. The chip area, which can be further reduced, is 1.25 mm in width and 1 mm in height. The SPR can perform in-situ measurement of reflection coefficients of devices under test (DUTs) and reduce testing costs of RF chips by using vector network analyzers (VNAs). The SPR demonstrates excellent performance in measuring the reflection coefficients of DUTs at around 20 GHz. The experimental results indicate that the maximum error of the measured reflection coefficients in absolute value is about -26 dB.

A Multi-Constraint Planning Approach for Offshore Test Tasks for an Intelligent Technology Test Ship

A hierarchical population task planning method is presented to enhance the test efficiency and reliability of intelligent technology test ships under various tasks and complex limitations. Firstly, a mathematical model of the vehicle path problem for multi-voyage vessel testing is developed, which aims to minimize the ship’s fixed and fuel costs, taking into account the energy and space constraints of an intelligent technology test vessel, as well as practical factors such as the dependencies and temporal relationships between test tasks. Second, to fairly minimize constraint complexity in the planning process, an offshore test task planning architecture based on the concept of hierarchical population is explored and built. This architecture separates task planning into four levels and allocates the tasks to distinct populations. Using this information, a grouping genetic algorithm is suggested based on the characteristics of the population. This algorithm uses a unique coding method to represent task clusters and narrows the range of possible solutions. The issue of the conventional grouping genetic algorithm’s vast search space is resolved. Lastly, simulation verification is carried out, and the results show that the method can effectively solve the problem of offshore test task planning for intelligent technology test ships under multi-constraint conditions. It reduces test cost and improves test efficiency.

Towards Incremental Development of Human-Agent-Robot Applications using Mixed-Reality Testbeds

Testing is essential part of the development of human-agent-robot team (HART) applications. Individual algorithms in such applications cannot be tested in isolation as their performance depends significantly on complex interactions among distributed software code, humans, hardware and the target environment. Any testing involving robots and human actors is, however, time-consuming and costly. We therefore propose an incremental development framework employing mixed-reality testbeds, which can reduce testing cost by replacing parts of the application and surrounding reality with synthetic computational models. The proposed framework introduces the concept of testbed fidelity and proposes how test reliability and cost should be managed to maximize the effectiveness of the development process. The framework is illustrated on two example applications in the domain of multi-UAV tracking and anti-maritime piracy operations.

Adverse outcome pathways as a tool for optimization of the biomarker-based assessment of pollutant toxicity: A case study of cadmium in the blue mussels Mytilus edulis

Technogenic metals like cadmium (Cd) are discharged into the environment posing significant ecological risks to wildlife and contributing to a decline in biodiversity. Recent developments in toxicity testing and environmental risk assessment have shifted the focus towards prioritizing biological effects. To ensure effective environmental risk assessment, it is essential to develop sensitive sub-organismal biomarkers correlated with ecologically relevant organismal-level responses. Our study proposes a novel approach to enhance the evaluation of biomarker-based toxicity based on adverse outcome pathways (AOP) concept. We exposed the blue mussels M. edulis to Cd concentrations ranging from 200 to 600 µg l−1 (10–30 % of LC50) for 14 days, and assessed a comprehensive panel of 26 biomarkers related to molecular initiation events (Cd uptake and binding), key cellular events (oxidative stress, metabolic disturbances, and cytotoxicity), and adverse organismal outcomes (survival, energy metabolism, and reef-building capacity) in the putative AOP of Cd. Tissue Cd burdens increased linearly with exposure concentrations in the mussels. Although the condition index of the mussels remained unaffected, their biofiltration activity was suppressed at the highest Cd concentration. The negative effects of Cd exposure on the mussels' feeding were linked to reduced energetic scope for growth, impaired byssus production, increased hemocyte mortality, and altered functional traits of hemocytes. Additionally, Cd exposure induced oxidative stress, as evidenced by lower total antioxidant capacity and thiol pool, along with increased lipid peroxidation, leading to enhanced autophagy. Through Random Forest Analysis, we identified a minimal set of sensitive Cd effect biomarkers, including hemocyte mortality (contributing ∼0.32 to classification accuracy), hemocyte acid phosphatase activity, nitric oxide and metallothionein levels in the digestive gland, and whole-organism clearance rate and scope for growth (each contributing ∼0.02–0.08 to classification accuracy). These biomarkers collectively achieved a high accuracy of 90–100 % in distinguishing between control and Cd-exposed groups. Increasing the number of biomarkers did not improve classification accuracy. These findings suggest that using a smaller number of optimized biomarkers based on Adverse Outcome Pathways (AOP) can enhance the assessment of contaminant-induced negative effects while reducing testing costs in environmental risk assessments.

SPI Master Slave Communication

The serial-peripheral interface (SPI) protocol also called asynchronous serial interface specification is used for communication between single master and single/multiple slaves. With the increase in number of slaves causing high complexity of circuit creates a demand in self-test ability feature for SPI module in order to test for fault free circuits. Built-In-Self-Test (BIST) in the answer for self-test in circuits as well as it helps in reduction of maintenance and testing cost. Design of BIST embedded SPI module with single master and single slave configuration has been introduced in these paper, Here 8-bit is transferred across the module, Where the Circuit under Test (CUT) is being Self-Tested with BIST feature for its correctness. This SPI module is designed using Verilog hardware description language(HDL) using EDA playground platform for application like applications specific integrated circuit (ASIC) or system on chip (SOC). SPI stands for the Serial Peripheral Interface. It is a serial communication protocol that is used to connect low-speed devices. It was developed by Motorola in the mid-1980 for inter-chip communication. It is commonly used for communication with flash memory, sensors, real-time clock (RTC), analog-to-digital converters, and more. It is a full-duplex synchronous serial communication, which means that data can be simultaneously transmitted from both directions. The main advantage of the SPI is to transfer the data without any interruption. Many bits can be sent or received at a time in this protocol. In this protocol, devices are communicated in the master-slave relationship. The master device controls the slave device, and the slave device takes the instruction from the master device. The simplest configuration of the Serial Peripheral Interface (SPI) is a combination of a single slave and a single master. But, one master device can control multiple slave devices

Improving testing capacity for COVID-19: experiences and lessons from Senegal, Uganda, Nigeria, and the Democratic Republic of Congo.

African countries leveraged testing capacities to enhance public health action in response to the COVID-19 pandemic. This paper describes experiences and lessons learned during the improvement of testing capacity throughout the COVID-19 response in Senegal, Uganda, Nigeria, and the Democratic Republic of the Congo (DRC). The four countries' testing strategies were studied using a mixed-methods approach. Desk research on COVID-19 testing strategies was conducted and complemented by interviewing key informants. The findings were synthesized to demonstrate learning outcomes across the four countries. The four countries demonstrated severely limited testing capacities at the onset of the pandemic. These countries decentralized COVID-19 testing services by leveraging preexisting laboratory systems such as PCR and GeneXpert used for the diagnosis of tuberculosis (TB) to address this gap and the related inequities, engaging the private sector, establishing new laboratories, and using rapid diagnostic tests (RDTs) to expand testing capacity and reduce the turnaround time (TAT). The use of digital platforms improved the TAT. Testing supplies were sourced through partners, although access to global markets was challenging. Case detection remains suboptimal due to high costs, restrictive testing strategies, testing access challenges, and misinformation, which hinder the demand for testing. The TAT for PCR remained a challenge, while RDT use was underreported, although Senegal manufactured RDTs locally. Key findings indicate that regionally coordinated procurement and manufacturing mechanisms are required, that testing modalities must be simplified for improved access, and that the risk-based testing strategy limits comprehensive understanding of the disease burden. Although testing capacities improved significantly during the pandemic, case detection and access to testing remained suboptimal. The four countries could benefit from further simplification of testing modalities and cost reduction. Local manufacturing and pooled procurement mechanisms for diagnostics are needed for optimal pandemic preparedness and response.

Site-to-Site Variation in Analog Multisite Testing: A Survey on Its Detection and Correction

Parallel (multi-site) testing tests multiple chips simultaneously, increasing throughput and reducing test costs per chip. As the number of test sites is increased (to further maximize throughput), pronounced variations are often observed in the measurements from site to site, plaguing test accuracy. This paper presents a survey of recent research on detecting and correcting site-to-site variation in analog multi-site testing.

A pipette-smartphone portable on-site detection system for aflatoxin B1 detection in bread and peanut based on "Four-in-one" sea-urchin-like composite catalyst

The development of a low-cost, high-sensitivity, portable bioassay for detecting the highly toxic and thermally stable aflatoxin B1 (AFB1) is of significant importance to food safety and human health. A pipette–smartphone integrated biosensor based on a super “four-in-one” DNA sea-urchin-like composite catalyst (FSCC) was constructed for the efficient analysis of AFB1. Detection is possible via a self-made poly(methyl methacrylate) pipette, combined with a smartphone to further increase the biosensor’s portability. The combination of the two greatly simplifies experimental operations, reduces testing costs, and can minimize matrix interferences through sample pre-treatment operations. And the smartphone-APP signal readout strategy can avoid the need for a reading instrument, making it more suitable for field testing. The FSCC was developed by fully utilizing the properties of a Fe-Zr-metal–organic framework (Fe-Zr-MOF), which exhibits high catalytic activity and enables the encapsulation of abundant horseradish peroxidase in its pores, the in-situ reduction and enrichment of Pt nanoparticles on its surface, and the coordination of biotinylated DNA to achieve a high adsorption capacity of streptavidin-horseradish peroxidase. This ensured detection sensitivity, with a limit of detection = 1.57 pg/mL. The developed biosensor displays notable application potential, especially in resource-poor areas due to the advantages of low costs, simple operation, ultra-high sensitivity, portability, etc.

Data-driven approach in investigating and predicting unconfined compressive strength of cemented paste backfill

Eight machine learning (ML) models including 6 shallow ML including XGB, GB, RF, LGB, SVR, KNN and 2 hybrid ML models consisting of XGB_P (Particle Swarm Optimization) and XGB_R (Random-Restart Hill Climbing) were trained on the dataset collected from the literature for predicting unconfined compressive strength (UCS) of Cemented paste backfill (CPB). The dataset includes 359 samples and 8 input variables including of D50, Cu, SiO2, Al2O3, Fe2O3, Cement/tailing, Solid content, and Curing age. Three shallow ML models with the best performance are XGB, GB, and RF. The model with the best predictive performance is XGB with R2 = 0.9889, RMSE= 0.1344 MPa, MAE= 0.0925 MPa, and MAPE= 0.3761% observed on the testing dataset. The obtained XGB model has higher performance than the GB model with R2 = 0.9273 proposed in the previous investigation for predicting UCS of CPB. Shapley Additive Explanation (SHAP) and Partial Dependence Plot 2D are used for quantify the input variables effect on the UCS of CPB. The most important influenced input variables are cement/tailing, curing age, solid content, and Fe2O3. The cement/tailing feature has the greatest influence on the head variable and has a positive effect on the UCS of CPB. Fe2O3 content is the feature that is inversely related to the UCS of CPB. The modification of Fe2O3 content has a relatively large influence not only on the development of UCS of CPB, the smaller the Fe2O3 content, the higher the UCS of CPB. The results of this study are the basis for the establishment of Machine learning-based soft computing and the basis for selecting the mix design of CPB. That makes it easier for engineers to design CPB's component, as well as reduce testing costs and construction time.

Development of a Maturity Model for Software Quality Assurance Practices

The advancements in the technology landscape and software development in recent years mandate paying attention to Software Quality Assurance (SQA) because it is becoming significantly important and complex. SQA is a set of activities within the software development lifecycle that aims at reducing development and testing costs, improving the quality of the software systems, and increasing customer satisfaction. Thus, the objective of this paper is to build a SQA maturity model, particularly in the telecommunication industry. To achieve this, this research identified perspectives and factors based on a comprehensive literature review and experts’ inputs using Hierarchical Decision Modeling (HDM) as the methodology. The proposed model consists of five perspectives, which are requirements validation, testing, software change management control, technology, and organization and culture with every perspective containing relevant factors. The factors and perspectives are validated and quantified using SQA inputs from subject matter experts. The findings of this study suggest that requirements validation is the most important perspective. Two case studies were conducted to identify the maturity score for each case, demonstrate the practicality of the research model, identify areas of deficiencies, and propose corrective actions. This paper provides an in-depth look at software quality factors and their relative importance, targeting to help SQA practitioners understand and assess their SQA practices.

Generation of New Low-Complexity March Algorithms for Optimum Faults Detection in SRAM

Memory BIST implements March test techniques extensively for testing embedded memories on a chip. A high-complexity test algorithm like the March MSS (18N) can guarantee the detection of all unlinked static faults in SRAM. In contrast, March algorithms with lower complexity can be used to reduce test costs and chip area overhead. Still, they have poor coverage of faults identified in the nanometer process technologies. Subsequently, a balance between the fault coverage and the test cost is necessary. This paper presents a method to generate new March algorithms that provide optimum coverage on faults introduced by the nanometer process technologies. It was achieved by developing automated software to generate the new Data Background sequence and rearrange the existing March algorithms’ test operations to remove redundancies and enable the sensitization and detection of the intended faults while preserving their complexities. Comprehensive fault detection analyses were conducted to assess their fault coverages and to find any removable redundant test operations. The proposed method produced new March AZ1 and March AZ2 algorithms, with 13N and 14N complexity, respectively, that provide optimum coverage of the targeted faults. They were successfully implemented in the Memory BIST controllers, and their functionalities were validated via simulations.

Research on three-state reliability evaluation method of high reliability system based on multi-source prior information.

A high reliability system has the characteristics of complexity, modularization, high cost and small sample size. Throughout the entire lifecycle of system development, storage and use, the high reliability requirements and the risk analysis form a direct contradiction with the testing expenses. In order to ensure the system, module or component maintains good reliability status and effectively reduces the cost of sampling tests, it is necessary to make full use of multi-source prior information to evaluate its reliability. Therefore, in order to evaluate the reliability of highly reliable equipment under the condition of a small sample size correctly, the equipment reliability evaluation model should be built based on multi-source prior information and form scientific computing methods to meet the needs of condition evaluation and fund assurance of high reliability system. In engineering practice, high reliability system or module gradually develops from normal state to failure state, generally going through three working states of "safety-potential failure-functional failure". Firstly, the historical test data under the three states can be used for the data source for the reliability evaluation of the system at the current stage, which supplements the deficiency of the field data; secondly, due to the lack of accurate judgment on the working state of a high reliability system or modules and analysis of the health status, the unnecessary maintenance may aggravate the evolution speed from potential failure to functional failure; thirdly, when high reliability system or module operates under overload or harsh conditions, the potential failure will be worsened to a certain extent. Aiming at the difficulty of multi-state system reliability evaluation, a reliability evaluation method based on non-information prior distribution is proposed by fusing multi-source prior information, which provides ideas and methods for reliability evaluation and optimization analysis of high reliability system or module. The results show that the three-state reliability evaluation method proposed in this article is consistent with the actual engineering situation, providing a scientific theoretical basis for preventive maintenance of high reliability system. At the same time, the research method not only helps evaluate the reliability state of a high reliability system accurately, but also achieves the goal of effectively reducing test costs with good economic benefits and engineering application value.

Reliability Analysis and Applications of Generalized Type-II Progressively Hybrid Maxwell–Boltzmann Censored Data

Today, the reliability or quality practitioner always aims to shorten testing duration and reduce testing costs without neglecting efficient statistical inference. So, a generalized progressively Type-II hybrid censored mechanism has been developed in which the experimenter prepays for usage of the testing facility for T units of time. This paper investigates the issue of estimating the model parameter, reliability, and hazard rate functions of the Maxwell–Boltzmann distribution in the presence of generalized progressive Type-II hybrid censored data by making use of the likelihood and Bayesian inferential methods. Using an inverse gamma prior distribution, the Bayes estimators of the same unknown parameters with respect to the most commonly squared-error loss are derived. Since the joint likelihood function is produced in complex form, following the Monte-Carlo Markov-chain idea, the Bayes’ point estimators as well as the Bayes credible and highest posterior density intervals cannot be derived analytically, but they may be examined numerically. Via the normal approximation of the acquired maximum likelihood and log-maximum-likelihood estimators, the approximate confidence interval bounds of the unknown quantities are derived. Via comprehensive numerical comparisons, with regard to simulated root mean squared-error, mean relative absolute bias, average confidence length, and coverage probability, the actual behavior of the proposed estimation methodologies is examined. To illustrate how the offered methodologies may be used in real circumstances, two different applications, representing the failure time points of aircraft windscreens as well as the daily average wind speed in Cairo during 2009, are explored. Numerical evaluations recommend utilizing a Bayes model via the Metropolis-Hastings technique to produce samples from the posterior distribution to estimate any parameter of the Maxwell–Boltzmann distribution when collecting data from a generalized progressively Type-II hybrid censored mechanism.

Detecting and Preventing Common Web Application Vulnerabilities: A Comprehensive Approach

Web applications are becoming very important in our lives as many sensitive processes depend on them. Therefore, it is critical for safety and invulnerability against malicious attacks. Most studies focus on ways to detect these attacks individually. In this study, we develop a new vulnerability system to detect and prevent vulnerabilities in web applications. It has multiple functions to deal with some recurring vulnerabilities. The proposed system provided the detection and prevention of four types of vulnerabilities, including SQL injection, cross-site scripting attacks, remote code execution, and fingerprinting of backend technologies. We investigated the way worked for every type of vulnerability; then the process of detecting each type of vulnerability; finally, we provided prevention for each type of vulnerability. Which achieved three goals: reduce testing costs, increase efficiency, and safety. The proposed system has been validated through a practical application on a website, and experimental results demonstrate its effectiveness in detecting and preventing security threats. Our study contributes to the field of security by presenting an innovative approach to addressing security concerns, and our results highlight the importance of implementing advanced detection and prevention methods to protect against potential cyberattacks. The significance and research value of this survey lies in its potential to enhance the security of online systems and reduce the risk of data breaches.

Service-oriented model-based fault prediction and localization for service compositions testing using deep learning techniques

As service-oriented computing systems become more buoyant and complex, the occurrence of faults dramatically increases. Fault prediction plays a crucial role in the service-oriented computing paradigm, aiming to reduce testing cost while maximizing testing quality to utilize testing resources effectively and increase the reliability of service compositions. Although various fault prediction techniques were considered in software testing, service-oriented systems were less fortunate, in which most of the studies have focused on single web services testing rather than service compositions. Moreover, mainly the detection of faulty/non-faulty services was addressed, ignoring the estimate of faults count, their severity, as well as predicting when and where such faults would occur. In this paper, a multilateral model-based fault prediction and localization approach is proposed using deep learning techniques for web service compositions testing rather than single web service testing, which uniquely predicts not only faulty services, but also their count and severity level, location of faults, and time at which faults would occur. Three deep learning models are investigated: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and a proposed hybrid model based on both CNN and RNN. The proposed approach is language-independent, as it adopts process metrics rather than code metrics to overcome the code unavailability concern of services. The experimental analysis adopted main performance metrics on multiple public datasets to evaluate its efficiency and effectiveness. The results indicated that the hybrid CNN_RNN model achieves an average accuracy range of 84%–95.7%, where the RNN and CNN models individually achieve 75%–90% and 70%-79.3% respectively. Thus, the hybrid model increases the accuracy level by 5%–10% and 15%–20%, while achieving the least mean square error of 30% and 60% compared to the RNN and CNN models respectively. In terms of time, the RNN model consumes less average time as of 30–50 ms for the different datasets of variant sizes compared to the CNN and hybrid CNN_RNN models that consume 79–102 and 177–224 ms respectively. Thus, RNN model consumes around 50%–80% less time than those of the CNN and hybrid models respectively.

Fire safety risks of external living walls and implications for regulatory guidance in England

External living walls (LWs) have aesthetic and environmental appeal, but these characteristics must not compromise fire safety. A review of legislation indicates there are no specific fire regulations or test standards for LWs in England. Furthermore, the 2013 UK Green wall guidance document (GWGD) contradicts current guidance in Approved Document B (ADB) for certain categories of buildings, yet ADB cites GWGD as “best practice”. We suggest the recommended reaction to fire testing methodology for LW systems (single burning item (SBI) EN13823/ignitability EN ISO11925-2 tests) is inappropriate for assessing their fire performance. Despite some limitations, the BS8414 full-scale test could be used to assess LW installations. While not identified in the GWGD or specifically recommended within ADB as a suitable test method for LWs, it is arguably more appropriate than reduced scale SBI testing, primarily because it accommodates full LW modules with planting, and uses a more appropriate fire size. To reduce testing costs, we propose the use of CFD fire modelling, or a modified SBI test to identify candidate LW products likely to pass BS8414 testing. Given the inherent variable nature of LWs and their associated fire properties, LW maintenance is considered essential for on-going compliance with fire safety requirements.

Estimation of Covid-19 Prevalence Dynamics from Pooled Data

Abstract Estimating the prevalence of a disease, such as COVID-19, is necessary for evaluating and mitigating risks of its transmission. Estimates that consider how prevalence changes with time provide more information about these risks but are difficult to obtain due to the necessary survey intensity and commensurate testing costs. Motivated by a dataset on COVID-19, from the University of Notre Dame, we propose pooling and jointly testing multiple samples to reduce testing costs. A nonparametric, hierarchical Bayesian model is used to infer population prevalence from the pooled test results without needing to retest individuals from pools that test positive. This approach is shown to reduce uncertainty compared to individual testing at the same budget and to produce similar estimates compared to individual testing at a much higher budget through simulation studies and an analysis of COVID-19 infections at Notre Dame.

Attitudes towards booster, testing and isolation, and their impact on COVID-19 response in winter 2022/2023 in France, Belgium, and Italy: a cross-sectional survey and modelling study.

European countries are focusing on testing, isolation, and boosting strategies to counter the 2022/2023 winter surge due to SARS-CoV-2 Omicron subvariants. However, widespread pandemic fatigue and limited compliance potentially undermine mitigation efforts. To establish a baseline for interventions, we ran a multicountry survey to assess respondents' willingness to receive booster vaccination and comply with testing and isolation mandates. Integrating survey and estimated immunity data in a branching process epidemic spreading model, we evaluated the effectiveness and costs of current protocols in France, Belgium, and Italy to manage the winter wave. The vast majority of survey participants (N=4594) was willing to adhere to testing (>91%) and rapid isolation (>88%) across the three countries. Pronounced differences emerged in the declared senior adherence to booster vaccination (73% in France, 94% in Belgium, 86% in Italy). Epidemic model results estimate that testing and isolation protocols would confer significant benefit in reducing transmission (17-24% reduction, from R=1.6 to R=1.3 in France and Belgium, to R=1.2 in Italy) with declared adherence. Achieving a mitigating level similar to the French protocol, the Belgian protocol would require 35% fewer tests (from 1 test to 0.65 test per infected person) and avoid the long isolation periods of the Italian protocol (average of 6 days vs. 11). A cost barrier to test would significantly decrease adherence in France and Belgium, undermining protocols' effectiveness. Simpler mandates for isolation may increase awareness and actual compliance, reducing testing costs, without compromising mitigation. High booster vaccination uptake remains key for the control of the winter wave. The European Commission, ANRS-Maladies Infectieuses Émergentes, the Agence Nationale de la Recherche, the Chaires Blaise Pascal Program of the Île-de-France region.

QSAR Models for the Prediction of Dietary Biomagnification Factor in Fish.

Xenobiotics released in the environment can be taken up by aquatic and terrestrial organisms and can accumulate at higher concentrations through the trophic chain. Bioaccumulation is therefore one of the PBT properties that authorities require to assess for the evaluation of the risks that chemicals may pose to humans and the environment. The use of an integrated testing strategy (ITS) and the use of multiple sources of information are strongly encouraged by authorities in order to maximize the information available and reduce testing costs. Moreover, considering the increasing demand for development and the application of new approaches and alternatives to animal testing, the development of in silico cost-effective tools such as QSAR models becomes increasingly important. In this study, a large and curated literature database of fish laboratory-based values of dietary biomagnification factor (BMF) was used to create externally validated QSARs. The quality categories (high, medium, low) available in the database were used to extract reliable data to train and validate the models, and to further address the uncertainty in low-quality data. This procedure was useful for highlighting problematic compounds for which additional experimental effort would be required, such as siloxanes, highly brominated and chlorinated compounds. Two models were suggested as final outputs in this study, one based on good-quality data and the other developed on a larger dataset of consistent Log BMFL values, which included lower-quality data. The models had similar predictive ability; however, the second model had a larger applicability domain. These QSARs were based on simple MLR equations that could easily be applied for the predictions of dietary BMFL in fish, and support bioaccumulation assessment procedures at the regulatory level. To ease the application and dissemination of these QSARs, they were included with technical documentation (as QMRF Reports) in the QSAR-ME Profiler software for QSAR predictions available online.