Prediction of severe accident occurrence time using support vector machines

AbstractIf a transient occurs in a nuclear power plant (NPP), operators will try to protect the NPP by estimating the kind of abnormality and mitigating it based on recommended procedures. Similarly, operators take actions based on severe accident management guidelines when there is the possibility of a severe accident occurrence in an NPP. In any such situation, information about the occurrence time of severe accident-related events can be very important to operators to set up severe accident management strategies. Therefore, support systems that can quickly provide this kind of information will be very useful when operators try to manage severe accidents. In this research, the occurrence times of several events that could happen during a severe accident were predicted using support vector machines with short time variations of plant status variables inputs. For the preliminary step, the break location and size of a loss of coolant accident (LOCA) were identified. Training and testing data sets were obtained using the MAAP5 code. The results show that the proposed algorithm can correctly classify the break location of the LOCA and can estimate the break size of the LOCA very accurately. In addition, the occurrence times of severe accident major events were predicted under various severe accident paths, with reasonable error. With these results, it is expected that it will be possible to apply the proposed algorithm to real NPPs because the algorithm uses only the early phase data after the reactor SCRAM, which can be obtained accurately for accident simulations

DeeLeMa: Missing information search with Deep Learning for Mass estimation

We present DeeLeMa, a deep learning network to analyze energies and momenta in particle collisions at high energy colliders, especially DeeLeMa is constructed based on symmetric event topology, and the generated mass distributions show robust peaks at the physical masses after the combinatoric uncertainties, and detector smearing effects are taken into account. DeeLeMa can be widely used in different event topologies by adopting the corresponding kinematic symmetries

Improving Generalization of Drowsiness State Classification by Domain-Specific Normalization

Abnormal driver states, particularly have been major concerns for road safety, emphasizing the importance of accurate drowsiness detection to prevent accidents. Electroencephalogram (EEG) signals are recognized for their effectiveness in monitoring a driver's mental state by monitoring brain activities. However, the challenge lies in the requirement for prior calibration due to the variation of EEG signals among and within individuals. The necessity of calibration has made the brain-computer interface (BCI) less accessible. We propose a practical generalized framework for classifying driver drowsiness states to improve accessibility and convenience. We separate the normalization process for each driver, treating them as individual domains. The goal of developing a general model is similar to that of domain generalization. The framework considers the statistics of each domain separately since they vary among domains. We experimented with various normalization methods to enhance the ability to generalize across subjects, i.e. the model's generalization performance of unseen domains. The experiments showed that applying individual domain-specific normalization yielded an outstanding improvement in generalizability. Furthermore, our framework demonstrates the potential and accessibility by removing the need for calibration in BCI applications.Comment: Submitted to 2024 12th IEEE International Winter Conference on Brain-Computer Interfac

Increasing Prevalence of Vancomycin-Resistant Enterococcus faecium, Expanded-Spectrum Cephalosporin-Resistant Klebsiella pneumoniae, and Imipenem-Resistant Pseudomonas aeruginosa in Korea: KONSAR Study in 2001

The 5th year KONSAR surveillance in 2001 was based on routine test data at 30 participating hospitals. It was of particular interest to find a trend in the resistances of enterococci to vancomycin, of Enterobacteriaceae to the 3rd generation cephalosporin and fluoroquinolone, and of Pseudomonas aeruginosa and acinetobacters to carbapenem. Resistance rates of Gram-positive cocci were: 70% of Staphylococcus aureus to oxacillin; 88% and 16% of Enterococcus faecium to ampicillin and vancomycin, respectively. Seventy-two percent of pneumococci were nonsusceptible to penicillin. The resistance rates of Enterobacteriaceae were: Escherichia coli, 28% to fluoroquinolone; Klebsiella pneumoniae, 27% to ceftazidime, and 20% to cefoxitin; and Enterobacter cloacae, ≥40% to cefotaxime and ceftazidime. The resistance rates of P. aeruginosa were 21% to ceftazidime, 17% to imipenem, and those of the acinetobacters were ≥61% to ceftazidime, aminoglycosides, fluoroquinolone and cotrimoxazole. Thirty-five percent of non-typhoidal salmonellae were ampicillin resistant, and 66% of Haemophilus influenzae were β-lactamase producers. Notable changes over the 1997-2001 period were: increases in vancomycin-resistant E. faecium, and amikacin- and fluoroquinolone-resistant acinetobacters. With the increasing prevalence of resistant bacteria, nationwide surveillance has become more important for optimal patient management, for the control of nosocomial infection, and for the conservation of the newer antimicrobial agents

Comparison of air pollution and the prevalence of allergy-related diseases in Incheon and Jeju City

PurposeA high level of air pollutants can increase the number of patients with allergy-related diseases such as asthma and allergic rhinitis (AR). To analyze the association between air pollution and allergic disease, we investigated 2 areas in Korea: Incheon, an industrial area, and Jeju, a non-industrialized area.MethodsSecond grade students at elementary schools (11 schools in Incheon and 45 schools in Jeju) were examined in a cross-sectional study. A questionnaire was used and a skin prick test was performed. The levels of NO2, CO2, O3, particulate matter (PM) PM10/2.5, formaldehyde, tVOCs, and dust mites in the classrooms and grounds were determined.ResultsThe levels of outdoor CO, PM10, and PM2.5 were significantly higher in Incheon (P<0.01). The levels of indoor CO, CO2, PM10, PM2.5 were significantly higher in Incheon (P<0.01). The prevalence rates of AR symptoms at any time, AR symptoms during the last 12 months, diagnosis of rhinitis at any time, and AR treatment during the last 12 months were significantly higher in Incheon (P<0.01). The prevalence rate of wheezing or whistling at any time, and wheezing during the last 12 months were significantly higher in Incheon (P<0.01).ConclusionWe found that the children living in Incheon, which was more polluted than Jeju, had a higher rate of AR and asthma symptoms compared to children in Jeju. To determine the effect of air pollution on the development of the AR and asthma, further studies are needed

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Recently, a huge amount of sulfur has been produced as a byproduct of petroleum refining processes in Korea. Sulfur concrete is made of modified sulfur binder instead of cement paste, which has advantages of reducing CO2 emission from cement industry as well as utilizing surplus sulfur. Also, sulfur concrete is a sustainable material that can be repetitively recycled. In this study, the physical properties of sulfur concrete are experimentally investigated. From the test results, sulfur concrete showed compressive strengths higher than at least 50MPa. Also, the unit weight, modulus of elasticity and splitting tensile strength of sulfur concrete was similar to that of Portland cement concrete (PCC). The coefficient of thermal expansion of sulfur concrete was a little larger than that of Portland cement concrete and sulfur concrete with mineral filler is helpful to lower the coefficient of thermal expansion. recycled aggregate sulfur concrete resulted in a slight reduction in the compressive strength, but sulfur concrete with recycled aggregate can achieve the high strength characteristics.clos

Visfatin Induces Sickness Responses in the Brain

BACKGROUND/OBJECTIVE: Visfatin, also known as nicotiamide phosphoribosyltransferase or pre-B cell colony enhancing factor, is a pro-inflammatory cytokine whose serum level is increased in sepsis and cancer as well as in obesity. Here we report a pro-inflammatory role of visfatin in the brain, to mediate sickness responses including anorexia, hyperthermia and hypoactivity. METHODOLOGY: Rats were intracerebroventricularly (ICV) injected with visfatin, and changes in food intake, body weight, body temperature and locomotor activity were monitored. Real-time PCR was applied to determine the expressions of pro-inflammatory cytokines, proopiomelanocortin (POMC) and prostaglandin-synthesizing enzymes in their brain. To determine the roles of cyclooxygenase (COX) and melanocortin in the visfatin action, rats were ICV-injected with visfatin with or without SHU9119, a melanocortin receptor antagonist, or indomethacin, a COX inhibitor, and their sickness behaviors were evaluated. PRINCIPAL FINDINGS: Administration of visfatin decreased food intake, body weight and locomotor activity and increased body temperature. Visfatin evoked significant increases in the levels of pro-inflammatory cytokines, prostaglandin-synthesizing enzymes and POMC, an anorexigenic neuropeptide. Indomethacin attenuated the effects of visfatin on hyperthermia and hypoactivity, but not anorexia. Further, SHU9119 blocked visfatin-induced anorexia but did not affect hyperthermia or hypoactivity. CONCLUSIONS: Visfatin induced sickness responses via regulation of COX and the melanocortin pathway in the brain