Normal Events Research Articles

An Analysis of Temporal Features in Multivariate Time Series to Forecast Network Events

Analyzing network traffic over time is crucial for understanding the changes in network activity. To properly examine network traffic patterns over time, multiple network events in each timestamp need to be converted to time series data. In this study, we propose a new approach to transform network traffic data into time series formats by extracting temporal features to analyze normal/attack patterns. The normal patterns indicate network traffic occurred without any intrusion-related activities, whereas the attack patterns denote potential threats that deviate from the normal patterns. To evaluate the features, long short-term memory (LSTM) is applied to forecast multi-step network normal and attack events. Visual analysis is also performed to enhance the understanding of key features in the network. We compared the performance differences using time scales of 60 and 120 s. Upon evaluation, we found that the temporal features extracted with the 60 s time scale exhibited better performance in forecasting future network events.

Abstract PO-011: Analysis of nuclear receptor expression in head and neck cancer

Abstract Background: The nuclear receptor (NR) superfamily contains a wide array of transcription factors responsible for cellular metabolism, inflammation, differentiation, and hormonal functions. For 45 years these receptors have been of interest in head and neck squamous cell carcinoma (HNSCC) treatment and prevention, especially retinoic acid receptor (RAR), peroxisome proliferator-activated receptor (PPAR), and vitamin D receptor (VDR) families. In the current study we examined expression patterns of these receptors in The Cancer Genome Atlas (TCGA). Methods: TCGA RNA-Seq data within the cBioportal platform comparing HNSCC samples (n=515 patients/samples) to normal tissue (n=82 patients/samples) was interrogated for significant differences in nuclear receptor expression. Genes with a z-score relative to normal samples (log RNA-Seq V2 RSEM normalized) greater than +2 or less than -2 in ≥50% of cases were considered significant. Unpaired, two-tailed t-tests were used to analyze comparisons in nuclear receptor genes’ z-scores across multiple clinical and pathologic parameters, p<0.05 was deemed significant. Statistical stringency for differential gene expression conformed to cBioportal and the University of Alabama at Birmingham’s Cancer Data Analysis Portal (UALCAN). Results: Of the 46 NR genes and 19 NR cofactors examined, ninety nine percent of tumor samples in the TCGA had some form of NR gene ‘alteration’ compared to normal tissue. These alterations predominantly encompass expression up- or down-regulation. NR genes Peroxisome Proliferator Activated Receptor Gamma (PPARγ) and Retinoic Acid Receptor Related Orphan Receptor C (RORC) (both in the thyroid hormone receptor-like family), and the NR cofactor, Nuclear Receptor Coactivator 1 (NCOA1), were differentially expressed and downregulated in tumors compared to normal tissue. Differences in specific NR expression varied significantly with HPV status (30 NRs), organ site (oral cavity vs hypopharynx & larynx; 20 NRs), tumor size (T1/2 vs. T3/4; 21 NRs), gender (male vs. female; 7 NRs); tumor staging (stage I/II vs. stage III/IV; 6 NRs); lymph node status (N0 vs. N+; 5 NRs), smoking history (smokers vs. non-smokers; 3 NRs), and patient race (White vs. Black/African American; 2 NRs). Conclusions: In prior studies of nuclear receptors in aerodigestive cancer, the retinoid receptor axis was downregulated, and a subsequent goal was stimulation and restoration of activity with natural or synthetic retinoids as a rationale for treatment. Presently, we have discovered significant decreases in PPARγ expression and are targeting this receptor with thiazolidinediones in a series of preclinical and human trials in a similar rationale to promote restoration of normal signaling and events like maturation. Here, we observed significant differences in a variety of parameters of several receptors associated with risk factors, stage, race, and gender that are observed and may become the focus of interest in future targeting efforts. Citation Format: Lindsey Mortensen, Beverly R. Wuertz, Frank G. Ondrey. Analysis of nuclear receptor expression in head and neck cancer [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-011.

The Effect of delivery method on optimality level and postpartum comfort in primiparous women

BackgroundDelivery is a normal event in a woman’s life and the most special experience. The delivery method affects the perinatal period as well as the level of postpartum comfort. AimsThis descriptive and cross-sectional study aims to determine the effect of delıvery method on optimality level and postpartum comfort in primiparous women MethodsThe study included 200 women. Data collection tools are the Introductory Information Form prepared by the researcher in line with the literature, the Turkish Adaptation of the Optimality Index, the Postpartum Comfort Scale. FindingsThe optimality index scores of the pregnant women with SVD obtained from PBI is 88.60 ± 5.71, the mean score obtained from OI is 84.02 ± 3.12, and the mean score of OI was determined to be 85.40 ± 2.98 for the groups who had SVD. ConclusionsIn accordance with the data, the optimality level of women has decreased due to non-evidence-based practices and invasive procedures during SVD. The total comfort score of the SVD group has also been negatively affected.

Progesterone control of myometrial contractility

During pregnancy, the primary function of the uterus is to be quiescent and not contract, which allows the growing fetus to develop and mature. A uterine muscle layer, myometrium, is composed of smooth muscle cells (SMCs). Before the onset of labor contractions, the uterine SMCs experience a complex biochemical and molecular transformation involving the expression of contraction-associated proteins. Labor is initiated when genes in SMCs are activated in response to a combination of hormonal, inflammatory and mechanical signals. In this review, we provide an overview of molecular mechanisms regulating the process of parturition in humans, focusing on the hormonal control of the myometrium, particularly the steroid hormone progesterone. The primary reason for discussing the regulation of myometrial contractility by progesterone is the importance of the clinical problem of preterm birth. It is thought that the hormonal mechanisms regulating premature uterine contractions represent an untimely triggering of the normal events occurring during term parturition. Yet, our knowledge of the complex and redundant hormonal pathways controlling uterine contractile activity leading to delivery of the neonate remains incomplete. Finally, we introduce recent animal studies using a novel class of drugs, Selective Progesterone Receptor Modulators, targeting progesterone signaling to prevent premature myometrial contractions.

Variation of b-Value of Normal, Reverse and Strike-Slip Faulting Earthquakes with Focal Depth

A crucial factor in evaluating seismic activity, seismotectonics, and seismic hazard assessment is the distribution of earthquake sizes (b-value). The goal of this research is to explore the relation between depth and the b-constant values of normal, inverse, and strike-slip faulting earthquakes. The earthquake catalog used in this work was extracted from the Global Centroid Moment Tensor Catalog (GCMTC) and covers the period from 2008 to 2017. The focal depth of the selected tremors ranges from zero to 700 km. The magnitude of completeness for normal and reverse faulting events is 5.2 and 5.1 for strike-slip faulting events. The value of the b- constant was estimated using the maximum likelihood technique. The findings of regression analysis showed that there is an insignificant negative poor correlation relation between the b- value of the normal and strike-slip faulting events and depth, while that relation is an insignificant positive poor correlation for reverse faulting. The depth of the turning point for the b-constant value of normal and reverse faulting earthquakes is near the mantle seismic discontinuities 410 and 520 km. The depth of inflection points of b-constant value strike-slip faulting earthquakes is near to lithosphere-asthenosphere boundary (LAB) at depth of 250km and the mantle seismic discontinuity of 520km.

Validation of Satellite Rainfall Product (GPM-IMERG) an Bali and Nusa Tenggara: A Comparison of Normal Seasons, El Nino and La Nina Events

Bali and Nusa Tenggara are regions where monsoonal wind changes and strange interactions between the ocean and atmosphere influence rainfall. The purpose of this research is to evaluate Integrated Multi-Satellite Retrievals for GPM (IMERG) rainfall data using in-situ observations from Bali and Nusa Tenggara, Indonesia, while considering seasonal variations and the El Nino-Southern Oscillation (ENSO) phenomenon. The study combines rainfall data from synoptic stations with rain gauge measurements over ten years, from January 2012 to December 2021, to obtain more accurate verification results. The study's findings indicate that, apart from the transitional seasons, IMERG data provides substantial estimates of monthly rainfall accumulation with low error values for both light and heavy rainfall. The study also reveals that the islands' complexity and topography can impact each province's validation values. The verification results show excellent accuracy in flat terrain areas and moderate elevations, while performance decreases in regions with high altitudes. These findings are significant because IMERG data can estimate rainfall for regions lacking monitoring stations during specific seasons and active ENSO conditions. Thus, this information can serve as a valuable tool to address the issue of data unavailability in hard-to-access areas and contribute to optimizing water resource management and weather-related disaster mitigation. Keywords: Validation, Rainfall, IMERG, ENSO

Adam-Dingo optimized deep maxout network-based video surveillance system for stealing crime detection

An enormous amount of video data is generated every day as a result of the widespread deployment of security cameras due to recent technological advancements. Analyzing such videos for identifying anomalies manually is an extremely challenging issue. Stealing is an issue occurring worldwide resulting in casualties and huge financial damages yearly. Efficient techniques to identify this anomaly haven’t been much researched, and hence here a novel technique is proposed. A hybrid optimized deep learning technique is proposed for detecting stealing crime from videos obtained by surveillance cameras. Firstly, the videos are summarized using Video summarization (VSUMM) method and then video is given input to the Deep Maxout Network. A deep maxout network whose weights are updated with the help of the introduced Adam-Dingo Optimizer for identifying the stealing crime event or normal event. The devised Adam-Dingo optimized Deep Maxout network is tested for its performance with metrics, such as True Positive Rate (TPR), True Negative Rate (TNR), and accuracy and is found to have attained values of 0.940, 0.936 and 0.945, respectively. Identifying stealing crime at different weather condition can be the future work. Identifying different crime events and reporting to control room can be the future work of proposed system.

Axonal transport during injury on a theoretical axon.

Neurodevelopment, plasticity, and cognition are integral with functional directional transport in neuronal axons that occurs along a unique network of discontinuous polar microtubule (MT) bundles. Axonopathies are caused by brain trauma and genetic diseases that perturb or disrupt the axon MT infrastructure and, with it, the dynamic interplay of motor proteins and cargo essential for axonal maintenance and neuronal signaling. The inability to visualize and quantify normal and altered nanoscale spatio-temporal dynamic transport events prevents a full mechanistic understanding of injury, disease progression, and recovery. To address this gap, we generated DyNAMO, a Dynamic Nanoscale Axonal MT Organization model, which is a biologically realistic theoretical axon framework. We use DyNAMO to experimentally simulate multi-kinesin traffic response to focused or distributed tractable injury parameters, which are MT network perturbations affecting MT lengths and multi-MT staggering. We track kinesins with different motility and processivity, as well as their influx rates, in-transit dissociation and reassociation from inter-MT reservoirs, progression, and quantify and spatially represent motor output ratios. DyNAMO demonstrates, in detail, the complex interplay of mixed motor types, crowding, kinesin off/on dissociation and reassociation, and injury consequences of forced intermingling. Stalled forward progression with different injury states is seen as persistent dynamicity of kinesins transiting between MTs and inter-MT reservoirs. DyNAMO analysis provides novel insights and quantification of axonal injury scenarios, including local injury-affected ATP levels, as well as relates these to influences on signaling outputs, including patterns of gating, waves, and pattern switching. The DyNAMO model significantly expands the network of heuristic and mathematical analysis of neuronal functions relevant to axonopathies, diagnostics, and treatment strategies.

Utilizing Midwifery-Led Care Units (MLCU) for Enhanced Maternal and Newborn Health in India: An Evidence-Based Review.

The allocation of the midwife-led care unit (MLCU), a midwifery-led care model in which midwives carry out eminent roles to enrich maternal and newborn outcomes with minimal standard interventions, has appeared to be productive in furthering the quality of care and positive childbirth experiences. In the present article, we review the investments needed in MLCUs for their inclusion into the public health system by describing their advantages, the latest trends in maternal mortality, the roles of midwives, the relevant background, and the current advances in midwifery practices in India. Midwifery-led care is directed by a philosophy that considers pregnancy and childbirth as normal physiological events for women. Making use of a midwife, especially in low-risk pregnancies, extends satisfactory and cost-effective care. The Government of India has begun to introduce midwifery services to the country to improve the quality, righteousness, and worthiness in the provision of care and to offload higher-level hospitals. The year 2020 was designated as the "Year of the Nurse and the Midwife" by the WHO, highlighting the importance of nurses' and midwives' roles in sustaining quality health care. Further, the acceptability among clinicians and the public is crucial for the future advancement and implementation of MLCUs in India.

Anomaly Awareness

We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns about normal events while being made aware of the anomalies through a modification of the cost function. We show how this method works in different Particle Physics situations and in standard Computer Vision tasks. For example, we apply the method to images from a Fat Jet topology generated by Standard Model Top and QCD events, and test it against an array of new physics scenarios, including Higgs production with EFT effects and resonances decaying into two, three or four subjets. We find that the algorithm is effective identifying anomalies not seen before, and becomes robust as we make it aware of a varied-enough set of anomalies.

The role of prior trauma exposure and subsequent posttraumatic stress disorder in reactions to the COVID-19 pandemic: A qualitative study.

A growing body of literature suggests that the COVID-19 pandemic is a traumatic stressor capable of causing posttraumatic stress symptoms. People with a history of trauma, particularly those with posttraumatic stress disorder (PTSD), may be particularly vulnerable to the negative mental health impacts of the pandemic. However, qualitative research exploring potential differences in the lived experiences of and reactions to COVID-19 between people with and without PTSD is lacking. Semistructured interviews were conducted with 31 women (n = 15 women with probable PTSD, n = 16 women without probable PTSD) recruited from an ongoing U.S.-based cohort study. Themes were identified using inductive thematic analysis. The majority of women with PTSD described their level of fear or perceived safety related to COVID-19 as a major factor influencing their mental health during the pandemic. In contrast, women without PTSD indicated that their level of distress was largely driven by pandemic-related restrictions on normal activities and family events. Many women with PTSD also described feeling anger or frustration toward people they perceived as not taking the COVID-19 pandemic seriously. Only one participant without PTSD expressed similar feelings. This study found notable differences in reactions to the COVID-19 pandemic between people with and without PTSD, with findings that are likely relevant to future disasters. These findings can inform the development of preparedness policies for future disasters, pandemics, or other collective traumas to prevent distress and improve mental health, particularly for vulnerable populations such as individuals with preexisting PTSD. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Evaluation of the Circular Economy Potential in Improving Sustainability in the Construction Industry in the UAE

The civil engineering and construction industry is usually highlighted as primary contributing factors for resource consumption and scarcity. Many researchers perceive that as a normal event with the respect to the high demand for construction that comes concerning the growing world population. As a consequence of this demand, a need for resources and energy efficiency becomes extremely critical and vital. The circular economy could be presented as one of the potential factors to regulate the construction industry with a circular business model. The reason that the professionals highly suggest this mechanism is due to its suitability with the vibrant and dynamic nature of the construction industry that usually causes a communication gap between the main stakeholders. The literature shows that this could be a result of a lack of collaboration and creating unnecessary complex steps that affect the productivity and durability of the business. Hence, a need for a holistic and integrated framework is necessary to regulate and manage the flow work of resources and energy efficiency process. In this study, the Sustainability Development Goals (SDGs) will be highlighted as major key performance indicators to ensure the accuracy and feasibility of the circular economy. Through this study, the survey and the interviews showed that three areas could be considered as challenging to be applied to circular economy in construction in The United Arab Emirates (UAE): regulatory factors, economic factors, and technological factors.

Numerical simulation technologies for safety evaluation in plant lifecycle optimization method, ARKADIA for advanced reactors

This paper describes the development of Advanced Reactor Knowledge- and AI-aided Design Integration Approach through the whole plant lifecycle (ARKADIA) which provides best possible solutions for the design, safety measures, maintenance, and decommissioning of advanced nuclear reactors. ARKADIA consists of state-of-the-art numerical simulation methods, the artificial intelligence technologies, and a knowledge base that includes experiences from the development and operation of the fast reactors and the related research and development. Two subsystems of ARKADIA: ARKADIA-Design and ARKADIA-Safety, are currently being developed individually as the first target of sodium-cooled fast reactors. In a subsequent development phase, the two subsystems will be integrated into a single system, ARKADIA, that is applicable to advanced reactors with a variety of concepts, coolants, configurations, and output levels. ARKADIA-Design offers the functions to support design optimization both in normal operating conditions and design basis events mainly during a conceptual design stage in the fields of core design, plant structure design including thermal-hydraulics analysis, and maintenance plan optimization. The main technology of ARKADIA-Design is based on the multi-level simulation approach by the coupled analysis with numerical analysis codes according to the user’s requirements. For instance, the coupled simulation of neutronics, core deformation, and core thermal hydraulics could evaluate the negative feedback effect of the core deformation reactivity during the plant transient. ARKADIA-Safety is developed for the automatic optimization of severe accident management and their feedback to a plant design. ARKADIA-Safety includes the thermal-hydraulics and multi-physics code, SPECTRA, for in- and ex-vessel integrated severe accident simulation. The basic capability to evaluate severe accident progress was demonstrated through the analysis of a hypothetical loss of reactor level event in a sodium-cooled fast reactor.

Ensemble anomaly score for video anomaly detection using denoise diffusion model and motion filters

Video anomaly detection is a crucial task that aims to differentiate between normal and abnormal events. The current mainstream approach involves constructing an anomaly score based on the reconstruction error from a prediction model trained on normal frame sequences. However, this approach is limited by its deterministic nature, which may cause the anomaly score to be sensitive to underlying noise in the video. To address this limitation, this paper proposes an ensemble anomaly score constructed using a series of stochastic reconstructions of the original prediction. Specifically, we introduce the denoise diffusion model as a perturbation-denoise tool. First, the original prediction undergoes a perturbation process through a diffusion process. Then, a denoise diffusion model trained on normal predictions is used to directly reconstruct a series of noise-free predictions from the perturbed versions with different noise levels. Finally, an ensemble of all the reconstruction errors is used to provide a more generic and regularized anomaly score. Furthermore, we introduce motion filters into the detection pipeline to improve the modeling accuracy of the image distribution. The proposed method is evaluated on public datasets, and experimental results demonstrate its effectiveness, particularly in detecting performance under out-of-distribution (OOD) conditions.

Reinforcement Learning-Based Adaptive Feature Boosting for Smart Grid Intrusion Detection

Intrusion detection systems (IDSs) are crucial in the security monitoring for the smart grid with increasing machine-to-machine communications and cyber threats thereafter. However, the multi-sourced, correlated, and heterogeneous smart grid data pose significant challenges to the accurate attack detection by IDSs. To improve the attack detection, this paper proposes Reinforcement Learning-based Adaptive Feature Boosting, which aims to leverage a series of AutoEncoders (AEs) to capture critical features from the multi-sourced smart grid data for the classification of normal, fault, and attack events. Multiple AEs are utilized to extract representative features from different feature sets that are automatically generated through a weighted feature sampling process; each AE-extracted feature set is then applied to build a Random Forest (RF) base classifier. In the feature sampling process, Deep Deterministic Policy Gradient (DDPG) is introduced to dynamically determine the feature sampling probability based on the classification accuracy. The critical features that improve the classification accuracy are assigned larger sampling probabilities and increasingly participate in the training of next AE. The presence of critical features is increased in the event classification over the multi-sourced smart grid data. Considering potential different alarms among base classifiers, an ensemble classifier is further built to distinguish normal, fault, and attack events. Our proposed approach is evaluated on the two realistic datasets collected from Hardware-In-the-Loop (HIL) and WUSTIL-IIOT-2021 security testbeds, respectively. The evaluation on the HIL security dataset shows that our proposed approach achieves the classification accuracy with 97.28%, an effective 5.5% increase over the vanilla Adaptive Feature Boosting. Moreover, the proposed approach not only accurately and stably selects critical features on the WUSTIL-IIOT-2021 dataset based on the significant difference of feature sampling probabilities between critical and uncritical features, i.e., the probabilities greater than 0.08 and less than 0.01, but also outperforms the other best-performing approaches with the increasing Matthew Correlation Coefficient (MCC) of 8.03%.

Outlining cardiac ion channel protein interactors and their signature in the human electrocardiogram

Protein–protein interactions are essential for normal cellular processes and signaling events. Defining these interaction networks is therefore crucial for understanding complex cellular functions and interpretation of disease-associated gene variants. We need to build a comprehensive picture of the interactions, their affinities and interdependencies in the specific organ to decipher hitherto poorly understood signaling mechanisms through ion channels. Here we report the experimental identification of the ensemble of protein interactors for 13 types of ion channels in murine cardiac tissue. Of these, we validated the functional importance of ten interactors on cardiac electrophysiology through genetic knockouts in zebrafish, gene silencing in mice, super-resolution microscopy and patch clamp experiments. Furthermore, we establish a computational framework to reconstruct human cardiomyocyte ion channel networks from deep proteome mapping of human heart tissue and human heart single-cell gene expression data. Finally, we integrate the ion channel interactome with human population genetics data to identify proteins that influence the electrocardiogram (ECG). We demonstrate that the combined channel network is enriched for proteins influencing the ECG, with 44% of the network proteins significantly associated with an ECG phenotype. Altogether, we define interactomes of 13 major cardiac ion channels, contextualize their relevance to human electrophysiology and validate functional roles of ten interactors, including two regulators of the sodium current (epsin-2 and gelsolin). Overall, our data provide a roadmap for our understanding of the molecular machinery that regulates cardiac electrophysiology.

Cosmic Muon Rates and Neutron Background in a Backpack Radiation Detection System

Radiation detection systems for security applications are often equipped with neutron sensors and required to promptly respond with an alarm if the neutron flux exceeds the background level. As the ambient neutron flux is essentially caused by cosmic radiation, this background could be evaluated by assessing the rate of muons crossing the gamma-ray detector which is comprised in such systems anyway. A study performed with a commercial Backpack Radiation Detection System (BRD) demonstrates the feasibility of this approach, supposed the gamma spectrometer is capable of discriminating muon signals from energetic events caused by the gamma-ray cascades following neutron captures. This requires a dynamic range exceeding the usual 3-4 MeV. The instrument used provided standard spectroscopy up to 10 MeV and facilitated charge-loss compensated spectroscopy up to 1 GeV, which allowed a separate assessment of normal gamma events (< 3 MeV), neutron-capture gamma events (3-7 MeV), and muon events (> 10 MeV). In this case, the rate of neutron-capture gamma events can be used as a neutron indicator on its own, while the muon rate signals the ambient neutron background level.

Real time control of nature-based solutions: Towards Smart Solutions and Digital Twins in Rangsit Area, Thailand

The intensity and frequency of hydro-meteorological hazards have increased due to fast-growing urbanisation activities and climate change. Hybrid approaches that combine grey infrastructure and Nature-Based Solutions (NBSs) have been applied as an adaptive and resilient strategy to cope with climate change uncertainties and incorporate other co-benefits. This research aims to investigate the feasibility of Real Time Control (RTC) for NBS operation in order to reduce flooding and improve their effectiveness. The study area is the irrigation and drainage system of the Rangsit Area in Thailand. The results show that during the normal flood events, the RTC system effectively reduces water level at the Western Raphiphat Canal Station compared to the system without RTC or with additional storage. Moreover, the RTC system facilitates achieving the required minimum volume and increasing the volume in the retentions. These findings highlight the potential of using RTC to improve the irrigation and drainage system operation as well as NBS implementation to reduce flooding. The RTC system can also assists in equitable water distribution between Klongs and retention areas, while also increasing the water storage in the retention areas. This additional water storage can be utilized for agricultural purposes, providing further benefits. These results represent an essential starting point for the development of Smart Solutions and Digital Twins in utilizing Real-Time Control for flood reduction and water allocation in the Rangsit Area in Thailand.

Calculation of boron concentration dependent reactivity versus moderator density curve with application on ATWS analysis for Maanshan PWR plant

A method used for determining the moderator density coefficient (MDC) based on the effective multiplication factor (keff) calculated from the SIMULATE-3 code is developed in this study. The calculation is performed at various water density and boron conditions for Maanshan unit 1 cycle 27. The calculated MDC data are fitted as a second-order polynomial function of density, modified with a factor of linear function of boron concentration. The fitted equation is then integrated and normalized to zero at rated density to represent the reactivity as a function of moderator density and boron concentration. A reactivity vs. density table under different boron concentrations can then be generated based on this function. The table generated is used as an input for the ATWS analysis to be performed using the RETRAN-02 thermal–hydraulic analysis code. As an illustration, a table based on a boron concentration of 1500 ppm is generated and used as an input for the analysis of the potential limiting ATWS case, i.e., Loss of Normal Feedwater Flow Event. The ASME emergency level limit of reactor coolant system pressure less than 220.63 bar is used as the acceptance criterion for the current ATWS analysis. It is known that the critical boron concentration at hot full power for each operating fuel cycle varies with the burnup through the cycle. Hence, the calculated ATWS results at different boron concentrations are equivalent to that calculated at different cycle burnup conditions. The cycle burnup point after which the calculated ATWS maximum is within the ASME limit can be determined based on the sensitivity analyses considering different boron concentrations.

Microgrids and distribution system resilience assessment: A multi-objective robust-stochastic optimization approach considering social behavior using a deep learning modeling

In this paper, a new multi-objective two-stage robust-stochastic (MOTSRS) optimization approach for assessing microgrids and distribution system resilience is proposed. A novel approach based on deep learning is presented to model social behavior, specifically Social Reactions to Disasters (SRDs), in assessing microgrid and distribution system resilience. It is initially demonstrated that the proposed robust-stochastic approach is efficient for assessing the resilience of microgrids and distribution systems. A new resilience criterion is also proposed to be used for sensitivity analyses at the conservative level of the model. Subsequently, the effect of SRDs on system resilience is investigated through a supervised deep regression method. First, a large dataset is generated through a proposed nested Monte Carlo simulation. A numerical feature and five binary social behavior features are included in the dataset. Next, a feedforward neural network is utilized to predict the load during both normal and disaster circumstances based on the six features. The proposed supervised deep regression model with mean squared errors between 2e-5 and 2e-6 is capable of predicting electrical load during normal and disaster events. It is concluded that the resilience metrics of MGs, as well as expected operation costs and regret values, are influenced by the SRD effect.