Testing Framework Research Articles

Advancing Dementia Care in Austria: developing a Dementia Registry using a multimethod approach

Abstract Background Austria faces an increasing demand for dementia care in an ageing population. However, a lack of representative data hampers evidence-based policy development and evaluation of care. To address this gap, the Austrian National Institute of Public Health is undertaking the task of developing the Austrian Dementia Quality Registry (ÖDQR). Methods Based on the AIHTA Good Practice Framework for Dementia Registries, the development and testing of the ÖDQR has unfolded in 4 distinct phases: 1) planning; 2) registry design (including definition of quality indicators and minimum data set, recruitment, and governance); 3) definition of data collection, sources, and management; 4) pilot testing. A steering committee of government representatives and dementia care experts was convened to oversee the project. A scientific committee provided oversight and ensured methodological rigor. Working groups of national and international dementia care experts focused on standards for diagnosis and therapy, activities of daily living and quality of life, legal aspects, and technical implementation, thus contributing to the content development of the ÖDQR. Results An initial systematic literature review informed the selection of quality indicators in the working groups. A conceptual framework of 15 descriptive and 30 quality indicators was developed. Preliminary results of pilot testing indicate feasibility and content validity. As a result, the ÖDQR will be established as a longitudinal survey-based registry, based on an instrument comprising 163 variables and emphasizing data collection across care settings. Legal and technical frameworks are being developed. Conclusions The öDQR holds the promise of informing evidence-based policy and improving the quality of dementia care in Austria. Careful planning and collaboration have laid the foundation for a data repository that will inform policy and improve care for people with dementia and their caregivers. Key messages • Dementia registries require a complex development to assure validity and adherence. • The Austrian Dementia Registry aims to establish best practice by focusing on the life situation of those affected by this condition.

Indicators of the clinical utility of genetic or genomic testing: a scoping review

Abstract Background Genetic or genomic tests are the cornerstones of personalized prevention. Previous reviews primarily focused on theoretical frameworks rather than actual assessments and assessed dimensions rather than specific indicators within these dimensions. Our scoping review aimed to review the dimensions and the specific indicators measured in the published assessment reports of genetic or genomic tests. Methods We selected formal assessments of genetic and genomic test used for prevention through PubMed, Web of Science, Scopus, websites of 20 different organizations, Google, and Google Scholar. Also, through a separate analysis we identified ten comprehensive dimensions of clinical utility through an analysis of 30 theoretical frameworks for genetic and genomic tests. From the included assessments, we extracted all the indicators, clustering them based on the comprehensive dimensions. Results From 3054 unique references and 12000 grey literature search results, we collected 57 assessments, all from high income-countries (in Europe (53%) and North America (47%)). The assessment methods were HTA (42%), EGAPP (25%), ACCE (21%) and other (12%). 951 disease-specific indicators were extracted from the assessments. Analytic validity (60%), clinical validity (79%) clinical efficacy (79%), economic impact (58%) were the most common dimensions (had at least one indicator). However, only 12 assessments (21%) include indicators that compared health outcomes between tested and untested groups. Equity, acceptability, legitimacy, and personal value were evaluated in less than 15% of the documents. Conclusions Our study illustrate that, although dimensions such as equity and acceptability, are significantly emphasized in traditional evaluation frameworks, these are often not considered in the assessments. Additionally, our study has underscored a significant dearth of primary evidence concerning the clinical efficacy of these technologies. Key messages • Our study highlights the prevalence of evaluations lacking indicators for equity, acceptability, and legitimacy dimensions and a lack of direct evidence of clinical efficacy. • The catalog of indicators was openly provided as a resource for researchers and institutions working in evidence-based evaluations of personalized preventive healthcare.

A general Bayesian algorithm for the autonomous alignment of beamlines.

Autonomous methods to align beamlines can decrease the amount of time spent on diagnostics, and also uncover better global optima leading to better beam quality. The alignment of these beamlines is a high-dimensional expensive-to-sample optimization problem involving the simultaneous treatment of many optical elements with correlated and nonlinear dynamics. Bayesian optimization is a strategy of efficient global optimization that has proved successful in similar regimes in a wide variety of beamline alignment applications, though it has typically been implemented for particular beamlines and optimization tasks. In this paper, we present a basic formulation of Bayesian inference and Gaussian process models as they relate to multi-objective Bayesian optimization, as well as the practical challenges presented by beamline alignment. We show that the same general implementation of Bayesian optimization with special consideration for beamline alignment can quickly learn the dynamics of particular beamlines in an online fashion through hyperparameter fitting with no prior information. We present the implementation of a concise software framework for beamline alignment and test it on four different optimization problems for experiments on X-ray beamlines at the National Synchrotron Light Source II and the Advanced Light Source, and an electron beam at the Accelerator Test Facility, along with benchmarking on a simulated digital twin. We discuss new applications of the framework, and the potential for a unified approach to beamline alignment at synchrotron facilities.

Improving exercise cardiac reserve assessment using a novel cardiopulmonary and echocardiography testing protocol (CPET-ESE) based on individualised exercise intensity domains

Abstract Background Functional exercise stress echocardiography (ESE) is used to quantify changes in cardiac reserve and can improve early cardiac dysfunction diagnosis and follow-up.[1.2] Current ESE protocols do not account for individual variations in fitness and heart rate response, leading to difficulties in defining normal reference values.[2,3] Physiologically defined exercise intensity domains could be implemented as part of combined cardiopulmonary and ESE testing (CPET-ESE) to address this problem. Purpose We propose that using exercise intensity domains as part of CPET-ESE will result in cardiac reserve values not influenced by individual cardiorespiratory fitness or physical activity (PA) levels. We also hypothesise by matching work-rates and heart rates (HR), respectively, cardiac reserve will be higher in those exercising at the higher exercise intensity domain. Methods A total of 45 healthy adults (mean age 43.8 ± 14.2, range 18-65 y) underwent a CPET followed by a CPET-ESE consisting of two 6-minute stages: 1. Moderate intensity (MOD) at 90% of the work-rate at the gas exchange threshold (GET) and 2. High intensity (HIGH) at 40% of the difference between GET and peak. Global left ventricular and right ventricular free wall (LV and RV) longitudinal strain (Sl), RV fractional area change (RV-FAC), peak LV and RV TDI derived systolic (S’) and diastolic velocities (E’) were measured at each step, including rest. HIGH steps were matched 1-to-n with MOD steps from other participants based on work-rate and HR, respectively. Questionnaire derived PA was classified as low, moderate, and high active. Cardiorespiratory fitness was measured as peak oxygen uptake relative to body mass (mL·kg-1·min-1). Results After adjusting for age and sex, neither higher PA nor peak VO2 were associated with higher cardiac function parameters at either intensity (Table 1). Higher peak VO2 was associated with higher work-rate for HIGH only, while higher PA was associated with lower HR for MOD only (interaction term p<0.05). When paired by matching HR and work-rate, respectively, HIGH was associated with higher cardiac function parameters compared to MOD (all except for LV-S’ and RV-FAC when HR matched, Table 2), despite those matched from HIGH being older, less physically active and having lower peak VO2 (p<0.05 for all). Conclusions Current ESE protocols, which define submaximal exercise intensity using arbitrary work-rate or HR targets,[3] do not account for the complex differences in exercise response between healthy individuals. Cardiac function augmentation during exercise is likely a response to VO2 kinetics and this is not accurately described by either HR or work-rate alone. We derived normal reference values for cardiac reserve from implementing physiologically defined exercise intensity domains within the combined CPET-ESE testing frameworks. These values could be more appropriately used when comparing diverse clinical populations using CPET-ESE.

Fisher Meets Bayes: The Value of Randomisation for Bayesian Inference of Causal Effects

SummaryFor a Bayesian agent with beliefs about the relationship between covariates and potential outcomes, deterministically selecting an assignment that yields optimal covariate balance rationally dominates randomisation. However, randomisation—by enabling control over the probabilities of erroneous causal conclusions due to unknown covariate imbalances—offers insurance against the possibility that an agent's beliefs may be misleading. For the most part, such rational justifications for optimum assignment have presupposed the framework of Bayesian inference, while such epistemic justifications for randomisation have presupposed the framework of significance testing. In this paper, I build on a conception of balance that seems inextricable from the significance testing framework, Fisherian balance, to show that it implies an analogous epistemic justification for randomisation within the framework of Bayesian inference. Consequently, for the choice between optimum and random assignment, this paper shows that epistemic justifications need not be wedded to significance testing nor must Bayesian inference be wedded to rational justifications.

Hardware automatic test scheme and intelligent analyze application based on machine learning model

Abstract Hardware testing has always been the core of hardware development, and improving the performance and efficiency of hardware testing is very important for hardware development. Because hardware quality management is insufficient, many large hardware tools were developed using manual workshop technology in the past and could hardly be maintained. This can lead to the cancellation of the project, causing major personnel and property losses. Improving hardware quality and ensuring security are very complex problems. Hardware testing is usually conducted through manual and automatic testing, and the limitations of manual testing are increasingly obvious. So hardware automatic testing technology has attracted people’s attention in recent years. It has become an important research direction in the field hardware testing and can overcome many problems of traditional testing methods. Strict test rules, based on standards and scores, provide a fully automated test process. With the continuous improvement of network technology, the functions and scope of hardware are constantly enriched and expanded. With the acceleration of hardware updates and development, this has brought a heavy burden to the previous hardware testing work. The purpose of this article was to study the application of machine learning technology in the field of hardware automatic testing and provide an appropriate theoretical basis for optimizing testing methods. This article introduced the research methods of hardware automatic testing technology, introduced three automatic testing framework models, and summarized the application of machine learning in hardware testing. It included hardware security and reliability analysis, hardware defect prediction, and source-based research. Then, this article studied the defect prediction model and machine learning algorithm and constructed a hardware defect prediction model based on machine learning based on the theory. First, the data were preprocessed, and then, the Stacking method was used to build a comprehensive prediction model, and four prediction results evaluation indicators were established. In the experiment part, the defect prediction results of the hardware automatic test model were studied. The results showed that the hardware defect prediction model based on machine learning had higher accuracy, recall rate, F_measure and area under curve. Compared with other models, the average accuracy of the hardware defect prediction model in this article was 0.092 higher, which was more suitable for automatic hardware testing and analysis.

Design and Testing of a Web-Based Student Information Management System

Politeknik Piksi Ganesha is an institution of higher learning located in Bandung, Indonesia, specializing in engineering and technological curricula. The research's general objective is to describe the design and implementation of the web-based Student Information Management System (SIMS) to manage student data at Politeknik Piksi Ganesha Bandung. The functional and non-functional specifications are represented using Unified Modeling Language (UML) based on broad data collection through observation, interviews, and a literature review. It also entails black-box testing, which measures the system's performance against set criteria, focusing on the output without the details of its internal workings. The significant findings indicate that SIMS enhances highly administrative processes, accuracy in data, and good communication among stakeholders by applying clear UML diagrams of the system. This successful system development meets the institution's requirements and adds to the bar for other developments within the educational technology sector. The result has also emphasized the need for practical design methodologies and testing frameworks to develop reliable information systems to improve decision-making in academic institutions. The research has contributed significantly to the discipline because it has shown how different technological innovations can alleviate the problems surrounding managing student information at higher education levels.

Research on Electric Vehicle Powertrain Systems Based on Digital Twin Technology

As a critical component of electric vehicles, the powertrain has a significant impact on the overall performance of vehicles. In addressing the challenge of lengthy testing cycles, this study develops a para model of the powertrain, utilizing digital twin (DT) technology, thereby establishing a framework for simulation testing of multi-controller intermodulation. We establish functional definition coverage testing by designing specific functional requirement use cases, and we validate the failure mechanism via fault injection use cases. The results indicate that the DT testing platform can effectively simulate the operational interactions among various controllers within the powertrain system. In comparison to traditional field testing, the digital twin-based testing methodology offers enhanced operational efficiency and allows for the examination of testing conditions that are impractical to implement in real vehicles, particularly in the context of fault injection testing, thus facilitating the early detection of potential safety risks within the system. The advancement of this technical solution holds significant practical implications for the future mass production and development of electric vehicles.

Empirical Analysis of Widely Used Website Automated Testing Tools

In today's software development, achieving product quality while minimising cost and time is critical. Automated testing is crucial to attaining these goals by lowering inspection efforts and discovering faults more effectively. This paper compares widely used automated testing tools, such as Selenium, Appium, Java Unit (JUnit), Test Next Generation (TestNG), Jenkins, Cucumber, LoadRunner, Katalon Studio, Simple Object Access Protocol User Interface (SoapUI), and TestComplete, based on functionality, ease of use, platform compatibility, and integration capabilities. Our findings show that no single tool is inherently superior, with each excelling in certain areas such as online, mobile, Application Programming Interface (API), or performance testing. While Selenium and Appium are the dominant online and mobile testing frameworks, TestComplete and Katalon Studio offer complete, user-friendly cross-platform testing solutions. Despite the benefits of automation, obstacles such as tool maintenance, scalability, and cost issues remain. The report finishes with advice for picking the best tool for the project and offers potential approaches for enhancing testing frameworks, such as AI-driven optimisation, cloud-based testing, and greater Continuous Integration/ Continuous Deployment (CI/CD) integration. This study offers useful information for developers and testers looking to optimise their testing methods and increase software quality.

Exploring machine learning to study and predict the chloride threshold level for carbon steel reinforcement

Chloride-induced corrosion of steel reinforcing bar (rebar) is the primary cause of deterioration in reinforced concrete structures, posing a significant infrastructure challenge. The chloride threshold level (CTL) of rebar, which represents the critical amount of chloride needed to initiate active corrosion, is crucial in corrosion and service life prediction models. However, substantial uncertainties and a multitude of influencing factors, along with the absence of a universally accepted testing framework, hinder the achievement of a consistent CTL range for service life models and complicate comparisons of published values. This study addresses these challenges by developing multiple machine learning models to predict CTL, considering 21 carefully selected features. A comprehensive database of 423 data points was compiled from an exhaustive literature review. Seven machine learning models—linear regression, decision tree, random forest, K-nearest neighbors, support vector machine, artificial neural network, and an ensemble model—were developed and optimized. The ensemble model achieved superior prediction performance, with a mean absolute error of 0.218 % by weight of binder, root mean square error of 0.321 %, and a coefficient of determination of 0.751 on unseen CTL data. Partial dependence plots generated using the support vector machine model quantified the effect of each feature on CTL. The random forest model identified SiO₂ binder content and exposed rebar area to chlorides as the most influential factors. The study also examined the impact of supplementary cementitious materials (SCMs), finding that only blast furnace slag positively affected CTL.

Dyna-PINN: Physics-informed deep dyna-q reinforcement learning for intelligent control of building heating system in low-diversity training data regimes

This paper introduces Dyna-PINN, a novel physics-informed Deep Dyna-Q (DDQ) reinforcement learning (RL) approach, designed to address the data-intensive training requirements and model-agnostic nature of the conventional model-free RL methods. The DDQ approach blends model-based and model-free elements to enhance both learning and decision-making processes. By utilizing a physics-informed neural network (PINN) based model, our method enriches the learning process with physical information, enhancing the agent's planning capabilities and leading to faster learning compared to conventional model-free RL methods like Deep Q-Network (DQN) in scenarios with low-diversity training data availability. Our results demonstrate that Dyna-PINN has 50% greater sample efficiency than DQN and outperforms rule-based control in terms of thermal discomfort. Due to physics incorporation, the Dyna-PINN implements a more logical and interpretable control policy. It shows consistently good performance compared to all control variants across low-diversity data scenarios, i.e., 6 weeks of building data, and in higher-diversity data regimes, i.e., 6 months of building energy data, demonstrating the value of physics incorporation into the RL training. Additionally, we present two other DDQ-based techniques, RC-DDQ and NN-DDQ, exploring the synergy between neural networks and physical data in intelligent control designs for building energy systems. Rigorous controller testing is performed using the Building Optimization and Testing Framework (BOPTEST), a high-fidelity simulator that closely represents a real building's operation. Through comprehensive comparisons and realistic simulations, our study underscores the effectiveness of incorporating physics-informed approaches into RL-based control strategies, paving the way for more efficient and robust building energy management systems.

Hybrid paper/PDMS microfluidic device integrated with RNA extraction and recombinase polymerase amplification for detection of norovirus in foods.

HuNoV belongs to the family of Caliciviridae and is a leading cause of acute gastroenteritis that can be transmitted through contaminated foods. HuNoV causes around one out of five cases of acute gastroenteritis that lead to diarrhea and vomiting, placing a substantial burden on the healthcare system worldwide. HuNoV outbreaks can occur when food is contaminated at the source (e.g., wild mussels exposed to polluted water), on farms (e.g., during crop cultivation, harvesting, or livestock handling), during packaging, or at catered events. The research outcomes of this study expand the approaches of HuNoV testing, adding value to the framework for routine testing of food products. This microfluidic device can facilitate the monitoring of HuNoV outbreaks, reduce the economic loss of the agri-food industry, and enhance food safety.

United Voices Group-Singing Intervention to Address Loneliness and Social Isolation Among Older People With HIV During the COVID-19 Pandemic: Intervention Adaption Study.

People living with HIV experience HIV stigma alongside a spectrum of aging-related health conditions that accelerate their vulnerability to the ill effects of loneliness and social isolation. Group-singing interventions are efficacious in improving psychosocial well-being among older people in the general population; however, the social curative effects of group singing have not been explored in relation to HIV stigma. By promoting group identification, bonding, and pride, group singing may reduce loneliness, social isolation, and other negative impacts of HIV stigma among older people living with HIV. Access to group-singing programs may be enhanced by technology. While group singing has been extensively studied in older adults, group-singing interventions have not been adapted for older people living with HIV to target loneliness and social isolation in the context of HIV stigma. The objective of this study was to describe the systematic development of a group-singing intervention to reduce loneliness and social isolation among older people living with HIV. In the San Francisco Bay Area between February 2019 and October 2019, we engaged older people living with HIV in a rigorous, 8-stage, community-engaged intervention adaptation process using the Assessment, Decision, Adaptation, Production, Topical Experts, Integration, Training, and Testing (ADAPT-ITT) framework. On the basis of a formative assessment of the needs and preferences of older people living with HIV, we selected an evidence-based group-singing intervention for older adults and systematically adapted the intervention components by administering them to a community advisory council (n=13). The result was United Voices, a 12-week hybrid (web-based and in-person) group-singing intervention for older people living with HIV. United Voices comprises 12 web-based (ie, via Zoom [Zoom Video Communications]) rehearsals, web-based and in-person drop-in helpdesk sessions, and a professionally produced final concert recording. Through an iterative process and in consultation with stakeholders and topic experts, we refined and manualized United Voices and finalized the design of a pilot randomized controlled trial to evaluate the feasibility and acceptability of the intervention protocol and procedures. The findings provide insights into the barriers and facilitators involved in culturally tailoring interventions for older people living with HIV, implementing intervention adaptations within web-based environments, and the promise of developing hybrid music-based interventions for older adults with HIV.

High-dimensional Biomarker Identification for Scalable and Interpretable Disease Prediction via Machine Learning Models.

Omics data generated from high-throughput technologies and clinical features jointly impact many complex human diseases. Identifying key biomarkers and clinical risk factors is essential for understanding disease mechanisms and advancing early disease diagnosis and precision medicine. However, the high-dimensionality and intricate associations between disease outcomes and omics profiles present significant analytical challenges. To address these, we propose an ensemble data-driven biomarker identification tool, Hybrid Feature Screening (HFS), to construct a candidate feature set for downstream advanced machine learning models. The pre-screened candidate features from HFS are further refined using a computationally efficient permutation-based feature importance test, forming the comprehensive High-dimensional Feature Importance Test (HiFIT) framework. Through extensive numerical simulations and real-world applications, we demonstrate HiFIT's superior performance in both outcome prediction and feature importance identification. An R package implementing HiFIT is available on GitHub ( https://github.com/BZou-lab/HiFIT ).

The Future of Healthcare Data Governance AI-Enabled ETL Testing Frameworks on Data Warehousing Testing automation using ML, AI and NLP

The healthcare sector enhances the integration of artificial intelligence, natural language processing and machine learning in data governance for developing automation procedures. Data management offered AI-powered ETL testing with the adoption of an automation framework for resolving data governance issues . This insight promotes operational efficiency with assurance of data quality that empowers data security by ML, AI and NLP frameworks. The framework allows healthcare institutions to arrange unstructured data in effective insights into patient care and treatment procedures. Advanced analytics leads to the development of predictions on patient outcomes in treatment procedures. The automated process of ETL improves efficient information collection by NPL with the creation of predictive models in monitoring data management by ML

Rapid acquisition of battery impedance across multiple scenarios using DRT analysis

Quick and accurate electrochemical impedance spectroscopy (EIS) measurement has always been challenging. This paper proposes a reliable and fast measurement method, which, based on battery characteristics, utilizes a distribution of relaxation times (DRT) extraction algorithm to construct a characteristic spectrum and map it to a binary sequence. The proposed method for constructing characteristic sequences is applicable to battery measurements in multiple scenarioes. The paper additionally introduces a hybrid reconstruction filtering (HRF) method that amalgamates the DRT reconstruction technique and non-parametric estimation techniques. The filtering method does not require preset parameters, resulting in high accuracy and applicability. Simultaneously, combining the characteristic sequence with HRF ultimately obtains a smooth and complete EIS curve under varying states of charges (SOCs), temperatures, and capacities. The proposed testing framework obtains impedance from 0.1 Hz to 2000 Hz <13.2 s. The overall measurement error for batteries with different internal resistances does not exceed 2 %. This work provides a reliable characteristic optimization sequence for large-scale battery impedance measurements, offering a method that is both simple and highly versatile.

Mutation‐Guided Metamorphic Testing of Optimality in AI Planning

ABSTRACTAutonomous systems such as space‐ or underwater‐exploration robots or elderly people assistance robots often include an artificial intelligence (AI) planner as a component. Starting from the initial state of a system, an AI planner automatically generates sequential plans to reach final states that satisfy user‐specified goals. Generating plans having a minimum number of intermediate steps or taking the least time to execute is usually strongly desired, as these plans exhibit minimal costs. Unfortunately, testing if an AI planner generates optimal plans is almost impossible because the expected cost of these plans is usually unknown. Based on mutation adequacy test suite selection, this article proposes a novel metamorphic testing framework for detecting the lack of optimality in AI planners. The general idea is to perform a systematic but non‐exhaustive state space exploration from the initial state and to select mutant‐adequate states to instantiate new planning tasks as follow‐up test cases. We then check a metamorphic relation between the automatically generated solutions of the AI planner for these new test cases and the cost of the initial plan. We implemented this metamorphic testing framework in a tool called MorphinPlan. Our experimental evaluation shows that MorphinPlan can detect non‐optimal behaviour in both mutated AI planners and off‐the‐shelf, configurable planners. It also shows that our proposed mutation adequacy test selection strategy outperforms three alternative test generation and selection strategies, including both random state selection and random walks through the state space in terms of mutation scores.

B-128 Delivering Predictable Operational efficiency gains at our core central reference laboratory with strategic deployment of navify® Lab Operation informatics automation solution

Abstract Background Metropolis Healthcare Limited (‘MHL’) operates a network of 200 clinical labs in India, our Global Reference Lab (GRL) in Mumbai processes over 25000 tests in a 17 hour daily operation. We are an ISO15189 and CAP accredited laboratory. Our strategic goals have been to bring operational standardization across the network &amp; Improve speed of service with a predictable 90 percentile production TAT. To achieve this goal, we chose digitalization of our laboratory operation with the deployment of navify ® Lab Operations informatics automation solution in. This poster explains our journey of planning, implementing, and delivering on our goal of predictable operational efficiency. The digital solution is at the forefront of all digitalization IQC processes, auto validation and auto approval of patient results passing through a framework of autoverification rules and complete traceability of processes. Methods The poster will explain the scope, planning and implementation approach of this digital automation project at Global reference Lab in 5 stepsHL7 order structure for communication between LIS and navify ® Lab Operations22 analyzers from 11 different manufacturers being connected to the Informatics solution. the corresponding testingTest &amp; IQC (Internal Quality Control) Master with specific ranges and rules10 steps AV (Auto verification) Rules decision framework of concentric sieves across lab disciplines (155 AV Algorithms have been configured at GRL)Structured user acceptance testing framework before Go Live. Results This poster discusses the outcomes that we have seen on deployment of this digital solution.Over 25,000 test results are subject AV engine daily with AV rate of 75 % of all results in Biochemistry, Immunoassays.66 % of all Complete Blood Count (CBC) reports 95 % of all HbA1c results 93 % of all ESR resultsOver 1750 QC results are subject to QC rules dailyThere is an improvement of 4 hours observed in production TAT Conclusions Laboratory Informatics solution has enhanced lab performance in terms of TAT &amp; efficiency of review. The staff satisfaction (both technical and medical) has increased and we see an impact on production outcome. The poster will also show an incremental effectiveness analysis by comparing key performance areas such as predictability in production, staff time saved, before and after technology deployment.

Biomonitoring emerging hazards of pharmaceuticals in river water using gut microbiome and behavioural Daphnia magna responses

A cost-effective Daphnia magna testing framework was applied to identify emerging hazards such as neurological and cardiovascular defects as well as antibiotic resistant genes (ARGs), related to pharmaceuticals present in waste water treated (WWTP) effluent discharged into rivers. D. magna juveniles were exposed during 48 h to water samples from three rivers in the vicinity of Barcelona (NE Spain), Besós, Llobregat and Onyar, upstream and downstream of WWTP discharging points. The analyses included measuring levels of 80 pharmaceutical residues in water samples by HPLC-MS, determination of the loads of different clinically relevant antibiotic resistant genes (ARGs) in both water samples and exposed animals, and assessment of toxic effects in feeding, heartbeat responses, and behavioural indicators. ARG prevalence in water, but not in gut microbiomes, was associated with the presence of bactericides in water. These results suggest that their levels were high enough to put a selective pressure over river microbial populations, but that Daphnia guts were not easily populated by environmental bacteria. Toxic effects were found in 20 to 43% of water samples, depending on the river, and related to water quality parameters and to pollutant levels. For example, heartbeats were correlated with salinity, whereas feeding impairment did so with high loads of suspended solids. In contrast, behavioural alterations were associated to the concentration of neuroactive chemicals. Accordingly, we hypothesize that measured neuroactive chemicals have caused the observed effects. If this also applies to local invertebrate populations, the environmental consequences may be severe and unpredictable.

Intellectual Property Management and Legal Protection Mechanism of Blockchain-based Crowdsourced Testing

While promoting the vigorous development of crowdsourced testing services, Internet technology has also generated problems such as non-standard intellectual property management, unclear ownership of rights, and lack of trustworthiness. Based on the characteristics of being decentralized, traceable and tamper-proof of blockchain, IPFS, SHA256 algorithm, and digital certificate technology, this paper puts forward a new intellectual property management framework for crowdsourced testing to meet the requirements of intellectual property traceability of crowdsourced testing, right confirmation and query, and proposes the idea of applying on the chain and off chain decomposition and integration technology to address the storage performance bottleneck problem caused by data expansion on the chain, thus realizing a trusted intellectual property management system of crowdsourced testing. Meanwhile, this paper puts that the intellectual property of crowdsourced testing cannot be fully maintained only by technical means, and corresponding legal support means should be supplemented, including improving the laws and regulations on judicial and administrative protection of intellectual property, and properly handling the legal risk control of intellectual property of crowdsourced testing.