Testing Processes Research Articles

Estimating cluster masses from SDSS multiband images with transfer learning

ABSTRACTThe total masses of galaxy clusters characterize many aspects of astrophysics and the underlying cosmology. It is crucial to obtain reliable and accurate mass estimates for numerous galaxy clusters over a wide range of redshifts and mass scales. We present a transfer-learning approach to estimate cluster masses using the ugriz-band images in the SDSS Data Release 12. The target masses are derived from X-ray or SZ measurements that are only available for a small subset of the clusters. We designed a semisupervised deep learning model consisting of two convolutional neural networks. In the first network, a feature extractor is trained to classify the SDSS photometric bands. The second network takes the previously trained features as inputs to estimate their total masses. The training and testing processes in this work depend purely on real observational data. Our algorithm reaches a mean absolute error (MAE) of 0.232 dex on average and 0.214 dex for the best fold. The performance is comparable to that given by redMaPPer, 0.192 dex. We have further applied a joint integrated gradient and class activation mapping method to interpret such a two-step neural network. The performance of our algorithm is likely to improve as the size of training data set increases. This proof-of-concept experiment demonstrates the potential of deep learning in maximizing the scientific return of the current and future large cluster surveys.

Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions

This research is aimed at predicting the physical stability for amorphous solid dispersion by utilizing deep learning methods. We propose a prediction model that effectively learns from a small dataset that is imbalanced in terms of class. In order to overcome the imbalance problem, our model performs a hybrid sampling which combines synthetic minority oversampling technique (SMOTE) algorithm with edited nearest neighbor (ENN) algorithm and reduces the dimensionality of the dataset using principal component analysis (PCA) algorithm during data preprocessing. After the preprocessing, it performs the learning process using a carefully designed neural network of simple but effective structure. Experimental results show that the proposed model has faster training convergence speed and better test performance compared to the existing DNN model. Furthermore, it significantly reduces the computational complexity of both training and test processes.

Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural networks

A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed using the constrained molecular dynamics model simulations, focusing on the heavy-ion collisions at the low-intermediate incident energies from several ten to one hundred MeV/nucleon in which the emissions of heavy fragments with the charge numbers larger than 3 become crucial. To make the CNN applicable in the task of the impact parameter determination at the present energy range, specific improvements are made in the input selection, the CNN construction and the CNN training. It is demonstrated from the comparisons of the deep CNN method and the conventional methods with the impact parameter-sensitive observables, that the deep CNN method shows better performance for determining the impact parameters, especially leading to the capability of providing better recognition of the central collision events. With a proper consideration of the experimental filter effect in both training and testing processes to keep consistency with the actual experiments, the good performance of the deep CNN method holds, and shows significantly better in terms of predicting the impact parameters and recognizing the central collision events, compared to that of the conventional methods, demonstrating the superiority of the present deep CNN method. The deep CNN method with the consideration of the filter effect is applied in the deduction of nuclear stopping power. Higher accuracy for the stopping power deduction is achieved benefitting from the better impact parameter determination using the deep CNN method, compared to using the the conventional methods. This result reveals the importance to select a reliable impact parameter determination method in the experimental deduction of the nuclear stopping power as well as other observables.

μFlowCal – High‐Resolution Differential Flow Microcalorimeter for the Measurement of Heats of Mixing

AbstractThis work presents the description, calibration, test and performance evaluation of a new flow microcalorimeter. The μFlowCal is a differential heat conduction, flow microcalorimeter, designed to work in isothermal conditions, in differential mode and in the absence of gas phase, requiring small amounts of sample (200 μL). Both the microcalorimeter and its operational methodology were optimized to measure the heat of mixing of fluids with moderate viscosity and volatility. The microcalorimeter has a sensitivity of 116 μV mW−1 and a level of detection of 170 nW. A very good performance (uncertainty <5 %) was obtained for the selected test processes: enthalpy of dilution of aqueous sucrose solutions and excess enthalpy of binary mixtures of decane‐octanol. The μFlowCal prototype is a new tool in micro‐scale physical chemistry and a step forward concerning signal resolution and sample volume. Nowadays, these are major requirement for the study of novel promising solvents and their mixtures.

Maintaining laboratory quality assurance and safety in a pandemic: Experiences from the KEMRI-Wellcome Trust Research Programme laboratory’s COVID-19 response

Laboratory diagnosis plays a critical role in the containment of a pandemic. Strong laboratory quality management systems (QMS) are essential for laboratory diagnostic services. However, low laboratory capacities in resource-limited countries has made the maintenance of laboratory quality assurance, especially during a pandemic, a daunting task. In this paper, we describe our experience of how we went about providing diagnostic testing services for SARS-CoV-2 through laboratory reorganization, redefining of the laboratory workflow, and training and development of COVID-19 documented procedures, all while maintaining the quality assurance processes during the COVID-19 pandemic at the Kenya Medical Research Institute (KEMRI) Wellcome Trust Research Programme (KWTRP) laboratory. The KWTRP laboratory managed to respond to the COVID-19 outbreak in Kenya by providing diagnostic testing for the coastal region of the country, while maintaining its research standard quality assurance processes. A COVID-19 team comprising of seven sub-teams with assigned specific responsibilities and an organizational chart with established reporting lines were developed. Additionally, a total of four training sessions were conducted for county Rapid Response Teams (RRTs) and laboratory personnel. A total of 11 documented procedures were developed to support the COVID-19 testing processes, with three for the pre-analytical phases, seven for the analytical phase, and one for the post-analytical phase. With the workflow re-organization, the development of appropriate standard operating procedures, and training, research laboratories can effectively respond to pandemic outbreaks while maintaining research standard QMS procedures.

Clinical Practice Guidelines for Pre-Analytical Procedures of Plasma Epidermal Growth Factor Receptor Variant Testing.

Standardization of cell-free DNA (cfDNA) testing processes is necessary to obtain clinically reliable results. The pre-analytical phase of cfDNA testing greatly influences the results because of the low proportion and stability of circulating tumor DNA (ctDNA). In this review, we provide evidence-based clinical practice guidelines for pre-analytical phase procedures of plasma epidermal growth factor receptor gene (EGFR) variant testing. Specific recommendations for pre-analytical procedures were proposed based on evidence from the literature and our experimental data. Standardization of pre-analytical procedures can improve the analytical performance of cfDNA testing.

Means of measurement of heat flows in thermal vacuum research and testing of products of space engineering

We present the results of the comparative analysis of the characteristics of the domestic-made heat flux sensor PTP-1B with widely used foreign-made area sensor FOA-020. The conditions are typical to the ground-based thermovacuum research and testing of space technology products, conducted in the absence of validated converter-sensors of the aggregated heat flux within the density range of up to 2000 W/sq. m and a spectral range of 0.2 - 20 µm. Experimental studies were performed in a thermal vacuum chamber of the experimental stand TEC-2.5 at the temperature of its internal surfaces of 20 0C. Recommendations regarding the possible use of the PTP-1B sensor as a working instrument of measurements in monitoring and diagnostic systems during the processes of ground testing of space technology objects are given

A method for temperature error compensation in fiber-optic gyroscope based on machine learning

This work presents a machine-learning based method for temperature error compensation of fiber optic gyroscope (FOG). Alternative to existing methods for constructing samples by single data point, a novel method of constructing samples by sequence is proposed, which fully considers the hysteresis and delay of temperature error. Moreover, temperature experiments with three distinct temperature trends, i.e., heating, holding and cooling, are conducted. Following the construction of training and test sets, the relevant training and test processes are completed using SVR algorithm. According to the degree of approximation in predicted value relative to the real value and the degree of improvement in gyro stability, the effects of various sample construction methods on the corresponding error compensation are analyzed. Considering the root mean square error (RMSE), mean absolute value error (MAE) and improvement-factor of FOG zero-bias stability as measurement indicators, this work proposes to construct samples by sequence with temperature trend feature-extraction, which effectively improves the overall accuracy of gyroscope. Allan variance is also utilized to verify the effectiveness of our proposed method. Lastly, this study also provides a theoretical guidance and reference for temperature error compensation in other types of sensors.

Effect of heat inactivation for the detection of severe acute respiratory syndrome-corona virus-2 (SARS-CoV-2) with reverse transcription real time polymerase chain reaction (rRT-PCR): evidence from Ethiopian study

BackgroundCoronavirus disease 2019 (COVID-19) has been a major public health importance and its specimen needs to be handled safely due to concerns of potential transmissibility to health care workers. Heat inactivation of the sample before nucleic acid isolation might permit safe testing processes. Hence, it is important to assess the effect of heat inactivation on SARS-CoV-2 RT-PCR detection in resource limited settings.MethodsAn experimental study was conducted at Ethiopian Public Health Institute (EPHI) from September 25 to October 15, 2020. A total of 188 Oro-pharyngeal swabs were collected from COVID-19 suspected cases, referred to EPHI for SARS COV-2 testing. One batch of the sample was inactivated at 56 °C heat for 30 min, and the other batch was stored at 4 °C for a similar period of time. RNA extraction and detection were done by DAAN Gene kit protocols. Abbott m2000 RT-PCR was used for amplification and detection. Data analysis was done by using SPSS version 23.0; Chi-square and Pearson correlation test for qualitative and semi-quantitative analysis were used. p-value < 0.05 was considered as statistically significant.ResultsOut of 188 total samples, 119 (63.3%) were positive and 69 (36.7%) were negative in the non-inactivated group. While, 115 (61.2%) of samples were positive and 73 (38.8) were negative in heat inactivated sample batch. Rate of positivity between groups did not have statistically significant difference (p > 0.05). The mean Cycle threshold (Ct) value difference between the two groups of ORF1a/b gene and N gene was 0.042 (95% CI − 0.247–0.331; t = 0.28; p = 0.774) and 0.38 (95% CI 0.097–0.682; t = 2.638; p = 0.010) respectively.ConclusionHeat inactivation at 56 °C for 30 min did not affect the qualitative rRT-PCR detection of SARS-CoV-2. However, the finding showed that there was statistically significant Ct value increment after heat inactivation compared to untreated samples. Therefore, false-negative results for high Ct value (Ct > 35) samples were found to be the challenge of this protocol. Hence alternative inactivation methods should be investigated and further studies should be considered.

Bulk density prediction while drilling vertical complex lithology using artificial intelligence

Because of porosity and mineralogy disparity, rocks have a broad range of bulk densities, a critical property that has an important role in rock characterization. Practically, the density of rock is estimated either via wireline logging or logging while drilling tools. However, these techniques are not always accessible, making the door open for using different predicting methods such as empirical correlations. The existing empirical correlations have substantial restrictions, which limited their accuracy and reliability. This work targets developing various artificial intelligence models to predict bulk density for complex lithology during drilling (i.e., at real-time). The artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques are used with the mechanical drilling parameters as inputs. This study uses a vertical well with 2912 data points from various lithologies including sand, carbonate, and shale to develop the models. Besides, a new empirical correlation for bulk density is developed based on the optimized ANN technique. A different dataset from the same tested field was used to validate the developed models. The obtained outcomes demonstrated that both ANN- and ANFIS-based models estimated the rock density with high fitting accuracy. The ANFIS approach results correlation coefficient (R) values of 0.99 with an average absolute percentage error (AAPE) values of 0.83 and 0.92% in training and testing processes. Whereas the ANN approach is slightly outperformed, as described by the highest R values of 0.99 and the lowest AAPE of 0.77 and 0.93% for training and testing processes. Also, a new empirical model for bulk density estimation was derived from the developed, optimized ANN-based model, which is applicable to the extracted weights and biases. The validation process indicates the robustness and reliability of the developed models with R values of 0.99 and AAPE of 0.92 and 0.96% for ANN- and ANFIS-based models, respectively. The outcomes of this work develop models with high accuracy for real-time prediction of bulk density, which can help in rock description in an adequate method with less cost, time, and errors.

Evolution of International Pediatric Endoscopic Practice Changes During the Coronavirus Disease 2019 Pandemic.

ABSTRACTObjectives:The coronavirus disease 2019 (COVID-19) pandemic has drastically altered endoscopic practices. We initially reported the international impact of COVID-19 on pediatric endoscopic practice. This follow-up study aimed to assess changes 7 months following the initial survey to delineate practice change patterns as the pandemic evolved.Methods:Pediatric gastroenterologists who responded to the initial survey were re-surveyed seven months later using Research Electronic Data Capture (REDCap). The survey recorded information on changes in pediatric endoscopic practice patterns, including COVID-19 screening and testing processes and personal protective equipment (PPE) utilization. Additionally, endoscopists’ risk tolerance of COVID-19 transmission was evaluated.Results:Seventy-five unique institutions from 21 countries completed surveys from the 145 initial responses (51.7% response rate). Procedural volumes increased at most institutions (70.7%) and most were performing previously postponed cases (90.7%). Ninety-seven percent of institutions were performing pre-endoscopy screening with 78.7% testing all patients. Many institutions (34.7%) have performed procedures on COVID-19 positive patients. There was significantly less PPE reuse (P < 0.05) and fewer institutions recommending full PPE for all endoscopies (43.2% vs 59.2%, P = 0.013). Overall, pediatric endoscopists’ risk tolerance of COVID-19 transmission is low.Conclusions:This is the first survey to highlight the evolution of pediatric endoscopic practices related to the COVID-19 pandemic, underscoring the need for ongoing pandemic-related guidance for pediatric endoscopic practice.

Domain adaptation for epileptic EEG classification using adversarial learning and Riemannian manifold

The epileptic electroencephalography (EEG) classification technique has been extensively adopted in epilepsy diagnosis and management due to its powerful capacity in distinguishing brain dysfunction. Since EEG patterns vary significantly from patient to patient, conventional studies usually perform the training and testing processes on the same subject. However, the challenging issue regarding domain shift among different individuals remains unsolved, which leads to low popularization of clinical application. To alleviate such problem, a domain adaptation model is proposed in this paper. This method intends to learn the universal feature space between different patients via constructing an adversarial autoencoder. By performing an adversarial training procedure, the aggregated posterior of the embedding space is matched with a Riemannian manifold-based prior that contains cross-domain information. This variational inference process can increase the generalization ability while circumventing the overfitting to source domains. Moreover, a distance measure is developed to align the distributions among various domains using the Riemannian mean. It can make the extracted features present higher domain adaptability by minimizing domain gaps in hidden space. Two common classification tasks, seizure prediction and seizure detection, are utilized for model evaluation. In the tests performed on the CHB-MIT scalp EEG dataset, the proposed model achieves a sensitivity of 82.2%, a false alarm rate (FPR) of 0.13 h−1 for seizure prediction and a sensitivity of 86.4%, an FPR of 0.08 h−1 for seizure detection. Experimental results indicate that this method can effectively reduce the domain disparity compared with state-of-the-art baselines.

Performance of self-healing geopolymer paste produced using Bacillus subtilis

This study examines the effects of the usage of bacteria as a self-healing agent on the geopolymer paste (GP) sample's characteristics. Examining the microbial self-healing capacity of geopolymer binders, which have many advantages over traditional Portland cement, is seen as an important and necessary step because of frequently studying innovative approaches on geopolymer samples. To carry out this investigation, GP samples produced using ground blast furnace slag (GBFS) were activated only with Na2SiO3. Bacillus subtilis was selected as the healing agent for the production of GP samples. As a result of the preliminary tests in which different variables (curing environment, sample content) were examined, it was decided what the sample content to be used in the main test processes. The bacterial suspensions were prepared at ratios of 107 and 109 CFU/mL. Bacterial samples prepared at two different cell densities were added to the mixture at 1, 2 and 3% by weight of the binder. The GP samples that cured under laboratory conditions until the end of the 7th day, were subjected to healing process in three different curing environments (water, air and precipitation medium consisting of urea, yeast extract and Ca(NO3)2·4H2O After the healing process was completed, the compressive strength, rheological behaviour, geopolymerization kinetics, physical properties, microstructural and visual examinations were performed. Experimental findings demonstrated that the self-healing mechanism resulting from the metabolic activity of Bacillus subtilis can be successfully applied in geopolymer composites in terms of high durability and mechanical properties.

Prediction Of Customers at Bank Rakyat Indonesia Using Backpropagation Algorithm

This study aims to see the development of the number of BRI bank customers in the following year. With this prediction, it is hoped that it can help bank bri in making policies related to customers who save more easily. The data source is obtained from Bank Rakyat Indonesia (BRI). In this study, researchers used the Backpropagation Algorithm. Backpropagation Algorithm is an algorithm that functions to reduce the error rate by adjusting the weight based on the desired output and target. The benefit of this research is to determine the increase or decrease in savings customers at the BRI bank in the following year. And artificial neural networks using the backpropogation algorithm can be applied in analyzing the increase in the number of bank bri customers by determining the best architectural model from a series of training and testing processes carried out.

COVID-19 Testing Crisis Management Through a Public-Private Partnership in Sindh, Pakistan.

As the coronavirus disease (COVID-19) pandemic spread, meeting the testing needs to control the spread of infection became a major challenge worldwide. In Pakistan, the lack of the requisite infrastructure and training compounded the acute shortage of testing kits and other consumables. Against this backdrop and to urgently improve province-wide access to high-quality COVID-19 polymerase chain reaction (PCR) testing with rapid turnaround times, the Government of the Sindh (GoS) province of Pakistan entered into a public-private partnership with Indus Hospital & Health Network (IHHN). Under this partnership, the GoS undertook sample collection and Indus Hospital in Karachi, Sindh, centralized testing. We describe the implementation strategies adopted by the partnership, as well as the challenges, opportunities, and lessons learned. Notably, up to 40% and 22% of total COVID-19 PCRs done in Sindh in the first 2 months of the pandemic, respectively, were performed at Indus Hospital in Karachi, though this percentage declined gradually as other centers caught up with their testing capacities. The rapid scaling up was achieved through a combination of mechanisms and factors including building on preexisting partnerships between the GoS and IHHN, pooling resources and harnessing distinct and complementary roles, relocating existing resources, introducing automation and information technology system changes, establishing risk mitigation strategies, and introducing quality measures within testing processes. The primary outcome of the partnership was rapid province-wide access to quality COVID-19 PCR testing with short turnaround times and at no cost to the patient. Furthermore, implementation of the partnership goals established new mechanisms as well as strengthened existing ones to enable rapid response to the future global health security challenges in Sindh, Pakistan.

Ide-cabe: chili varieties identification and classification system based leaf

Identifying good quality chili varieties can be done by observing their leaves. It is required for seed testing and certification processes. Currently, a manual leaf identification method is used in which human experts inspect a wide range of leaves every one to two months. An automatic method could increase the identification process. Deep learning has proven to be a prominent method for image classification. We investigate the performance of deep CNN models, as: AlexNet, VGG16, Inception-v3 and DenseNet-121; to classify chili variety. In this paper, we took images of leaves aged 10 days. A preprocessing strategy was taken to enrich the dataset and to boost the classification performance and to assess the proposed models’ quality. From this study, we acquired 12 classes of chili leaves dataset. We acquired performance accuracy ranging from 70.18% to 78.37%. Further, the classification results by DenseNet-121 obtained the highest accuracy of 78.37% and recall of 74.83%. The classifiers investigated in this study perform well despite the relatively small number of our dataset. These results encourage the application of such an approach in practice.

A Model for Design and Implementation of a Laboratory Information-Management System Specific for Molecular Pathology Laboratory Operations

The Molecular Pathology Section, Cleveland Clinic (Cleveland, OH), has undergone enhancement of its testing portfolio and processes. An Excel 2013- and paper-based data-management system was replaced with a commercially available laboratory information-management system (LIMS) software application, a separate bioinformatics platform, customized test-interpretation applications, a dedicated sample-accessioning service, and a results-releasing software application. The customized LIMS solution manages complex workflows, large-scale data packets, and process automation. A customized approach was required because, in a survey of commercially available off-the-shelf software products, none met the diverse and complex needs of this molecular diagnostics service. The project utilized the expertise of clinical laboratorians, pathologists, genetics counselors, bioinformaticians, and systems analysts in partnering with software-engineering consultants to design and implement a solution. Concurrently, Agile software-building best practices were formulated, which may be emulated for scalable and cost-effective laboratory-authored software.

Pan-Sharpening Based on CNN+ Pyramid Transformer by Using No-Reference Loss

The majority of existing deep learning pan-sharpening methods often use simulated degraded reference data due to the missing of real fusion labels which affects the fusion performance. The normally used convolutional neural network (CNN) can only extract the local detail information well which may cause the loss of important global contextual characteristics with long-range dependencies in fusion. To address these issues and to fuse spatial and spectral information with high quality information from the original panchromatic (PAN) and multispectral (MS) images, this paper presents a novel pan-sharpening method by designing the CNN+ pyramid Transformer network with no-reference loss (CPT-noRef). Specifically, the Transformer is used as the main architecture for fusion to supply the global features, the local features in shallow CNN are combined, and the multi-scale features from the pyramid structure adding to the Transformer encoder are learned simultaneously. Our loss function directly learns the spatial information extracted from the PAN image and the spectral information from the MS image which is suitable for the theory of pan-sharpening and makes the network control the spatial and spectral loss simultaneously. Both training and test processes are based on real data, so the simulated degraded reference data is no longer needed, which is quite different from most existing deep learning fusion methods. The proposed CPT-noRef network can effectively solve the huge amount of data required by the Transformer network and extract abundant image features for fusion. In order to assess the effectiveness and universality of the fusion model, we have trained and evaluated the model on the experimental data of WorldView-2(WV-2) and Gaofen-1(GF-1) and compared it with other typical deep learning pan-sharpening methods from both the subjective visual effect and the objective index evaluation. The results show that the proposed CPT-noRef network offers superior performance in both qualitative and quantitative evaluations compared with existing state-of-the-art methods. In addition, our method has the strongest generalization capability by testing the Pleiades and WV-2 images on the network trained by GF-1 data. The no-reference loss function proposed in this paper can greatly enhance the spatial and spectral information of the fusion image with good performance and robustness.

Creating and implementing objective structured clinical exams for use in pediatric nursing education in Ghana: Reflections and lessons learned

The use of the Objective Structured Clinical Exam (OSCE) to measure the clinical competency of health care professionals is well established in many training institutions globally because of their high validity and reliability. In the context of a pediatric nursing partnership program in Ghana (the SickKids-Ghana Pediatric Nursing Education Partnership [PNEP]), we aimed to develop a national, accredited, competency-based curriculum. The integration of the OSCE into the curriculum was novel in the Ghanaian setting. It served as a standardized method for student assessment of higher clinical competencies, including both skills and attitudes, compared to conventional methods. Using mixed-methods, we previously found that the PNEP curriculum effectively increased graduates’ knowledge, confidence, and clinical skill in pediatric nursing. This manuscript aims to reflect on the development of the OSCE stations, case scenarios, and rating materials, with particular reference to the methodological approach and lessons learned. We further reflect on the feedback from faculty and students pertaining to the usefulness of the OSCE as an assessment tool and the inclusion of standardized patients to assess communication skills. Adaptations required to safely conduct the OSCEs in the context of the COVID-19 pandemic have also been highlighted. Our findings throughout the OSCE development, implementation, and testing processes in Ghana could aid and inform similar tool development for nursing education institutions in the West African region and other resource-limited settings.

The Need to Incorporate Diversity, Equity, and Inclusion: Reflections from a National Initiative Measuring Fruit and Vegetable Intake

The Need to Incorporate Diversity, Equity, and Inclusion: Reflections from a National Initiative Measuring Fruit and Vegetable Intake