Cervicothoracic spinal cord and pontomedullary injury secondary to high-voltage electrocution: a case report

INTRODUCTION: High-voltage electrical injuries are uncommonly reported and may predispose to both immediate and delayed neurologic complications. CASE PRESENTATION: We report the case of a 43-year-old Caucasian man who experienced a high-voltage electrocution injury resulting in ischemic myelopathy and secondary paraparesis. CONCLUSION: High-voltage electrocution injuries are a serious problem with potential for both immediate and delayed neurologic sequelae. The existing literature regarding effective treatment of neurologic complications is limited. Long-term follow-up and multidisciplinary management of these patients is required

Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia

© 2019 China University of Geosciences (Beijing) and Peking University Catastrophic natural hazards, such as earthquake, pose serious threats to properties and human lives in urban areas. Therefore, earthquake risk assessment (ERA) is indispensable in disaster management. ERA is an integration of the extent of probability and vulnerability of assets. This study develops an integrated model by using the artificial neural network–analytic hierarchy process (ANN–AHP) model for constructing the ERA map. The aim of the study is to quantify urban population risk that may be caused by impending earthquakes. The model is applied to the city of Banda Aceh in Indonesia, a seismically active zone of Aceh province frequently affected by devastating earthquakes. ANN is used for probability mapping, whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering. The risk map is subsequently created by combining the probability, hazard, and vulnerability maps. Then, the risk levels of various zones are obtained. The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%. Furthermore, results show that the central and southeastern regions of the city have moderate to very high risk classifications, whereas the other parts of the city fall under low to very low earthquake risk classifications. The findings of this research are useful for government agencies and decision makers, particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh

Monotherapy Trials of New Antiepileptic Drugs

A number of clinical trials that test the efficacy and safety of the newer antiepileptic drugs (AEDs) have recently been concluded. Two dose-response trials in inpatients with refractory partial seizures and outpatients with newly diagnosed partial epilepsy established the efficacy of gabapentin as monotherapy. Lamotrigine was found to have efficacy similar to that of phenytoin and carbamazepine (CBZ) and to be better tolerated than CBZ in patients with newly diagnosed epilepsy. It was also shown to have efficacy as monotherapy in partial seizures, based on the results of an active controlled trial, and in the treatment of absence seizures, based on the results of a responder-enriched study. Topiramate as monotherapy was found to be efficacious for treatment of partial-onset seizures, based on the results of a single-center dose-response trial. A dose-response trial that tested the efficacy of tiagabine monotherapy in patients with refractory partial epilepsy was uninformative. Oxcarbazepine was found to be safe and efficacious in four comparative trials in patients with newly diagnosed epilepsy as well as in one placebo-controlled inpatient trial in patients with refractory partial seizures.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65625/1/j.1528-1157.1997.tb05201.x.pd

Value of Inpatient Diagnostic CCTV-EEG Monitoring in the Elderly

Purpose: To examine the outcome of inpatient diagnostic closed circuit TV-EEG (CCTV-EEG) monitoring in a consecutive series of elderly patients admitted to an adult epilepsy-monitoring unit (EMU) over a continuous 6-year period. Methods: Retrospective review of all admissions to a university hospital adult EMU. Those older than 60 years were identified. Patients who were monitored for status epilepticus were excluded. Data on duration of events, frequency of events, physical examination, medications, preadmission EEG, brain imaging, length of stay, and interictal and ictal EEG were obtained. Results: Of the 18 patients admitted for monitoring only, mean age was 69.5 years (range, 60–90 years). Mean length of stay was 4.3 days (range, 2–9 days). Five patients had complex partial seizures recorded. Three patients, all treated with antiepileptic drugs (AEDs), had no spells recorded, and no additional diagnostic information was gained from the admission. The other 10 patients, eight of whom had been treated with AEDs, were symptomatic during their admission, leading to a variety of neurologic but not epileptic, psychiatric, or other medical disorders, and allowing tapering of AEDs. Conclusions: In elderly patients with suspected epilepsy, CCTV-EEG is a very useful diagnostic tool. In this series of 18, 10 patients were diagnosed with potentially treatable medical illnesses not responsive to AEDs.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65172/1/j.1528-1157.1999.tb00825.x.pd

An optimization tool for production planning: A case study in a textile industry

The textile industry is an important sector of the Brazilian economy, being considered the fifth largest textile industry in the world. To support further growth and development in this sector, this document proposes a process for production analysis through the use of Discrete Event Simulation (DES) and optimization through genetic algorithms. The focus is on production planning for weaving processes and optimization to help make decisions about batch sizing and production scheduling activities. In addition, the correlations between some current technological trends and their implications for the textile industry are also highlighted. Another important contribution of this study is to detail the use of the commercial software Tecnomatix Plant Simulation 13®, to simulate and optimize a production problem by applying genetic algorithms with real production data

A new integrated approach for landslide data balancing and spatial prediction based on generative adversarial networks (GAN)

Landslide susceptibility mapping has significantly progressed with improvements in machine learning techniques. However, the inventory / data imbalance (DI) problem remains one of the challenges in this domain. This problem exists as a good quality landslide inventory map, including a complete record of historical data, is difficult or expensive to collect. As such, this can considerably affect one’s ability to obtain a sufficient inventory or representative samples. This research developed a new approach based on generative adversarial networks (GAN) to correct imbalanced landslide datasets. The proposed method was tested at Chukha Dzongkhag, Bhutan, one of the most frequent landslide prone areas in the Himalayan region. The proposed approach was then compared with the standard methods such as the synthetic minority oversampling technique (SMOTE), dense imbalanced sampling, and sparse sampling (i.e., producing non-landslide samples as many as landslide samples). The comparisons were based on five machine learning models, including artificial neural networks (ANN), random forests (RF), decision trees (DT), k-nearest neighbours (kNN), and the support vector machine (SVM). The model evaluation was carried out based on overall accuracy (OA), Kappa Index, F1-score, and area under receiver operating characteristic curves (AUROC). The spatial database was established with a total of 269 landslides and 10 conditioning factors, including altitude, slope, aspect, total curvature, slope length, lithology, distance from the road, distance from the stream, topographic wetness index (TWI), and sediment transport index (STI). The findings of this study have shown that both GAN and SMOTE data balancing approaches have helped to improve the accuracy of machine learning models. According to AUROC, the GAN method was able to boost the models by reaching the maximum accuracy of ANN (0.918), RF (0.933), DT (0.927), kNN (0.878), and SVM (0.907) when default parameters used. With the optimum parameters, all models performed best with GAN at their highest accuracy of ANN (0.927), RF (0.943), DT (0.923) and kNN (0.889), except SVM obtained the highest accuracy of (0.906) with SMOTE. Our finding suggests that RF balanced with GAN can provide the most reasonable criterion for landslide prediction. This research indicates that landslide data balancing may substantially affect the predictive capabilities of machine learning models. Therefore, the issue of DI in the spatial prediction of landslides should not be ignored. Future studies could explore other generative models for landslide data balancing. By using state-of-the-art GAN, the proposed model can be considered in the areas where the data are limited or imbalanced

SPIRONOLACTONE FOR NONRESOLVING CENTRAL SEROUS CHORIORETINOPATHY: A RANDOMIZED CONTROLLED CROSSOVER STUDY.

PURPOSE: To evaluate the effect of spironolactone, a mineralocorticoid receptor antagonist, for nonresolving central serous chorioretinopathy. METHODS: This is a prospective, randomized, double-blinded, placebo-controlled crossover study. Sixteen eyes of 16 patients with central serous chorioretinopathy and persistent subretinal fluid (SRF) for at least 3 months were enrolled. Patients were randomized to receive either spironolactone 50 mg or placebo once a day for 30 days, followed by a washout period of 1 week and then crossed over to either placebo or spironolactone for another 30 days. The primary outcome measure was the changes from baseline in SRF thickness at the apex of the serous retinal detachment. Secondary outcomes included subfoveal choroidal thickness and the ETDRS best-corrected visual acuity. RESULTS: The mean duration of central serous chorioretinopathy before enrollment in study eyes was 10 ± 16.9 months. Crossover data analysis showed a statistically significant reduction in SRF in spironolactone treated eyes as compared with the same eyes under placebo (P = 0.04). Secondary analysis on the first period (Day 0-Day 30) showed a significant reduction in subfoveal choroidal thickness in treated eyes as compared with placebo (P = 0.02). No significant changes were observed in the best-corrected visual acuity. There were no complications related to treatment observed. CONCLUSION: In eyes with persistent SRF due to central serous chorioretinopathy, spironolactone significantly reduced both the SRF and the subfoveal choroidal thickness as compared with placebo

Computationally efficient ontology selection in software requirement planning

Understanding the needs of stakeholders and prioritizing requirements are the vital steps in the development of any software application. Enabling tools to support these steps have a critical role in the success of the corresponding software application. Based on such a critical role, this paper presents a computationally efficient ontology selection in software requirement planning. The key point guiding the underlying design is that, once gathered, requirements need to be processed by decomposition towards the generation of a specified systems design. A representational framework allows for the expression of high level abstract conceptions under a single schema, which may then be made explicit in terms of axiomatic relations and expressed in a suitable ontology. The initial experimental results indicate that our framework for filtered selection of a suitable ontology operates in a computationally efficient manner