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RNN-based integrated system for real-time sensor fault detection and fault-informed accident diagnosis in nuclear power plant accidents
No full textSensor faults in nuclear power plant instrumentation have the potential to spread negative effects from wrong signals that can cause an accident misdiagnosis by plant operators. To detect sensor faults and make accurate accident diagnoses, prior studies have developed a supervised learning-based sensor fault detection model and an accident diagnosis model with faulty sensor isolation. Even though the developed neural network models demonstrated satisfactory performance, their diagnosis performance should be reevaluated considering real-time connection. When operating in real-time, the diagnosis model is expected to indiscriminately accept fault data before receiving delayed fault information transferred from the previous fault detection model. The uncertainty of neural networks can also have a significant impact following the sensor fault features. In the present work, a pilot study was conducted to connect two models and observe actual outcomes from a real-time application with an integrated system. While the initial results showed an overall successful diagnosis, some issues were observed. To recover the diagnosis performance degradations, additive logics were applied to minimize the diagnosis failures that were not observed in the previous validations of the separate models. The results of a case study were then analyzed in terms of the real-time diagnosis outputs that plant operators would actually face in an emergency situation
On-the-fly energy release per fission model in STREAM with explicit neutron and photon heating
No full textThe on-the-fly energy release per fission (OTFK) model is implemented in STREAM to continuously update the Kappa values during the depletion calculation. The explicit neutron and photon energy distribution, which has not been considered in previous STREAM versions, is incorporated into the existing on-the-fly model. The impacts of the modified OTFK model with explicit neutron and photon heating in STREAM on the power distribution, fuel temperature, and other core parameters during depletion with feedback calculations are studied using several problems from the VERA benchmark suit. Overall, the explicit heating calculation provides a better power map for the feedback calculations particularly when strong gamma emitters are present. Generally, the fuel temperature decreases when neutron and photon heating is employed because fission neutrons and gamma rays are transported away from their points of generation. This energy release model in STREAM indicates that gamma energy accounts for approximately 9.5%???10% of the total energy released, and approximately 2.4%???2.6% of the total energy released will be deposited in the coolant for the VERA 5, NuScale, and Yonggwang Unit 3 2D cores
20 K H2Physisorption on Metal-Organic Frameworks with Enhanced Dormancy Compared to Liquid Hydrogen Storage
No full textLiquid hydrogen (LH2) is the best way of transporting hydrogen, as its high volumetric energy density translates into a significant reduction in hydrogen transportation and refueling operations expenses. However, the phase transformation from liquid to gaseous hydrogen, due to heat leakage of the LH2 vessel, causes a considerable volume change, results in boil-off losses, and makes long-term storage/transportation problematic. These boil-off losses are a severe drawback for continental transportation through truck tube trailers having evaporative losses of about 3???15% per day (depending on the volume). Herein, hydrogen storage by cryo-adsorption using metal???organic frameworks (MOFs) is proposed as an alternative to reduce boil-off losses and enhance dormancy during continental transportation. The stronger van der Waals interaction operating between adsorbate and adsorbent leads to superdense H2 adsorption, which compensates for the space occupied by the adsorbent skeleton and results in a volumetric storage capacity comparable to that of LH2 tanks (???96%). Depending on the textural properties of MOFs, H2 desorption can start from 45 K, resulting in an extended dormancy time of the tank system. In addition, the observation of hindered rotational transition (J: 0 ??? 1) signal in neutron scattering analysis indicates that H2 are firmly attached and highly immobile on the adsorption sites. The hindered rotation by adsorption at 20 K on MOFs also suggests that the intermolecular separation is less than the bulk liquid (even solid) phase
Phase- and composition-controlled synthesis
No full textA general method by controlling reaction kinetics is proposed to synthesize 67 kinds of two-dimensional crystal with custom-made phases and compositions, in particular, Fe- and Cr-based (layered and non-layered) chalcogenides and phosphorous chalcogenides, which show interesting ferromagnetism and superconductivity properties
Effective Indexes and Classification Algorithms for Supervised Link Prediction Approach to Anticipating Technology Convergence: A Comparative Study
No full textThis article conducts a comparative analysis to investigate the effects of different classification algorithms and structural proximity indexes on the performance of the supervised link prediction approach to anticipating technology convergence at different forecast horizons. For this, we identify relationships between technologies of interest for different time periods and compute 10 structural proximity indexes among unconnected technologies at each period. We develop a set of classification models that identify potential convergence among unconnected technologies where each model is configured differently by a classification algorithm and a combination of the proximity indexes. We compare the performance of the classification models to investigate effective combinations of classification algorithms and proximity indexes at different forecast horizons. The empirical analysis on Wikipedia articles about artificial intelligence technology indicates that random forest outperforms others in short-term forecasting while support vector machine outperforms others in mid-term forecasting. We also identify structural proximity indexes that produce higher performance when combined with the most effective algorithm at each forecast horizon. The results of this article are expected to offer guidelines for choosing classification algorithms and indexes when applying the supervised link prediction approach in anticipating technology convergence
Coordination modulated passivation for stable organic-inorganic perovskite solar cells
No full textDespite the recent exceptional rise in power conversion efficiency of perovskite solar cells (PSCs), surface defects and ion migration related instability are still present in PSCs. The chain length and binding energy of the passivation material play important roles in defect passivation, ion migration, moisture stability, and device-performance improvement. We synthesized three sulfonated ammonium compounds and investigated the ef-fect of post-passivation with these compounds on ion-migration and stability. New materials with high binding energy include octylamine (OA) functionalized with sulfanilic acid (OAS), p-toluenesulfonic acid (OAT), and camphorsulfonic acid (OAC). The passivation improves power conversion efficiency (PCE) from 21.06% for the control to 24.37% for the devices treated with OAC. The champion device's hysteresis index decreased to 0.01 compared to 0.11 for the control device, which is the lowest reported so far. Furthermore, the passivated perovskite films retain over 85% of their initial PCE under 60% relative humidity for 1,600 h, and the device with OAC maintains over 90% of its initial operational long-term device stability without encapsulation for 600 h under 1 sun-illumination
Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green
No full textThis study focuses on the potential capability of numerous machine learning models, namely CatBoost, Gra-dientBoosting, HistGradientBoosting, ExtraTrees, XGBoost, DecisionTree, Bagging, light gradient boosting ma-chine (LGBM), GaussianProcess, artificial neural network (ANN), and light long short-term memory (LightLSTM). These models were investigated to predict the photocatalytic degradation of malachite green from wastewater using various NM-BiFeO3 composites. A comprehensive databank of 1200 data points was generated under various experimental conditions. The ten input variables selected were the catalyst type, reaction time, light intensity, initial concentration, catalyst loading, solution pH, humic acid concentration, anions, surface area, and pore volume of various photocatalysts. The MG dye degradation efficiency was selected as the output variable. An evaluation of the performance metrics suggested that the CatBoost model, with the highest test coefficient of determination (0.99) and lowest mean absolute error (0.64) and root-mean-square error (1.34), outperformed all other models. The CatBoost model showed that the photocatalytic reaction conditions were more important than the material properties. The modeling results suggested that the optimized process conditions were a light in-tensity of 105 W, catalyst loading of 1.5 g/L, initial MG dye concentration of 5 mg/L and solution pH of 7. Finally, the implications and drawbacks of the current study were stated in detail
Assembling good citizenship under Korean COVID-19 surveillance
No full textThe Korean COVID-19 surveillance system generates public discourses on good citizenship regarding civic duty, human rights, and social equality by releasing the travel history information of COVID-19 patients to the public. Through digital ethnography, this article examines how good citizenship is constructed in three material contexts???things, places, and mobility???associated with COVID-19 patients??? movements. From the new materialist perspective, citizenship is an outcome of the socio-material assemblage of things, people, and institutions. We view travel history information as ???actants??? that affect people???s behaviors, especially, mom caf??s mothers??? conception of good citizenship. This citizenship involves materiality related to face masks and COVID-19, a sense of place in terms of crowded places, and the physical mobility of COVID-19 patients. The disclosure policy on travel history information plays a vital role in overweighing civic duty over human rights and social equality by decontextualizing the life of COVID-19 patients due to concerns over privacy infringement. This policy spawns a uniform citizenship that overlooks the economic and cultural differences among COVID-19 patients. Ultimately, irregular employees, sexual minorities, and delivery workers carry a greater burden in complying with the civic duties of the general population. ?? 2022 Informa UK Limited, trading as Taylor & Francis Group
RAMP: response-aware multi-task learning with contrastive regularization for cancer drug response prediction
Get PDFThe accurate prediction of cancer drug sensitivity according to the multiomics profiles of individual patients is crucial for precision cancer medicine. However, the development of prediction models has been challenged by the complex crosstalk of input features and the resistance-dominant drug response information contained in public databases. In this study, we propose a novel multidrug response prediction framework, response-aware multitask prediction (RAMP), via a Bayesian neural network and restrict it by soft-supervised contrastive regularization. To utilize network embedding vectors as representation learning features for heterogeneous networks, we harness response-aware negative sampling, which applies cell line???drug response information to the training of network embeddings. RAMP overcomes the prediction accuracy limitation induced by the imbalance of trained response data based on the comprehensive selection and utilization of drug response features. When trained on the Genomics of Drug Sensitivity in Cancer dataset, RAMP achieved an area under the receiver operating characteristic curve > 89%, an area under the precision-recall curve > 59% and an F1 score > 52% and outperformed previously developed methods on both balanced and imbalanced datasets. Furthermore, RAMP predicted many missing drug responses that were not included in the public databases. Our results showed that RAMP will be suitable for the high-throughput prediction of cancer drug sensitivity and will be useful for guiding cancer drug selection processes. The Python implementation for RAMP is available at https://github.com/hvcl/RAMP
Effectiveness of nanoparticles-based ultrahydrophobic coating for concrete materials
No full textMoisture interaction and extreme weather may complicate the service life and increase the maintenance cost of various building materials. This paper investigates the performance of protective surface coatings applied to the most common building material, concrete. A novel synthesis route for producing ultrahydrophobic surface coatings is demonstrated to enhance the impermeability of concrete. The concrete specimens were chemically modified with silica sol, which was synthesized by hydrolysis of tetraethoxysilane (TEOS) under alkaline conditions, followed by treatment with hexadecyltrimethoxysilane (HDTMS) solution. The concrete specimens coated with proposed micro-composite coating were tested for hydrophobicity and self-cleaning characteristics in terms of contact angle and sliding angle at various water exposure conditions and periods of exposure. The permanency and efficiency of proposed coatings was further tested after exposure to alternate wet-dry cycles and highly saline environment. The modified specimens exhibited a contact angle of 121?? ??? 135?? and a sliding angle of 9?? ??? 22?? at various exposure conditions, depicting superior hydrophobicity. The overall findings of this study could aid in maintaining the intended longevity and performance of various concrete materials