Farklı Sürtünmeli Sarkaç Tipi İzolatörlerin Yapı Sismik Davranışına Etkileri

Bu çalışmada farklı sürtünme yüzeyi sayısına sahip sarkaç tipi izolatörler kullanılarak deprem yalıtımı sağlanan bir yapının zaman tanım alanında dinamik analizleri gerçekleştirilerek sürtünme yüzeyi sayısının yapı davranışı üzerindeki etkileri incelenmiştir. Bu amaçla beş katlı basit bir yapı ile birlikte tek, çift ve üç sürtünme yüzeyli sarkaç izolatörler ABAQUS sonlu eleman programı yardımıyla üç boyutlu elemanlar kullanılarak modellenmiştir. Dinamik analizler sismik yalıtımlı ve ankastre mesnetli yapılar için bir deprem kaydı dikkate alınarak gerçekleştirilmiştir. Maksimum kat göreli yer değiştirmeleri, kat ivmeleri ve kolon taban kesme kuvvetleri gibi yapı tepkileri farklı izolatör tipleri ve ankastre mesnetli durum için karşılaştırılarak yapının deprem davranışı üzerindeki etkileri incelenmiştir. Elde edilen sonuçlara göre izolatörlerin kullanıldığı yapılarda oluşan tepkilerin ankastre mesnetli yapılara göre önemli oranlarda azaldığı görülmüştür. Farklı sürtünme yüzeyi sayısına sahip izolatörler karşılaştırıldığında ise tüm bu tepkiler için üç sürtünme yüzeyli sarkaç izolatörün diğer izolatörlerinden daha iyi performans gösterdiği sonucuna varılmıştır

Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network

The strong motion earthquake could cause the building damage in case of the building not considered in the earthquake design of the building. The study aims to predict the damage-level of building due to earthquake using Artificial Neural Networks method. The building model is a reinforced concrete building with ten floors and height between floors is 3.6 m. The model building received a load of the earthquake based on nine earthquake time history records. Each time history scaled to 0,5g, 0,75g, and 1,0g. The Artificial Neural Networks are designed in 4 architectural models using the MATLAB program. Model 1 used the displacement, velocity, and acceleration as input and Model 2 used the displacement only as the input. Model 3 used the velocity as input, and Model 4 used the acceleration just as input. The output of the Neural Networks is the damage level of the building with the category of Safe (1), Immediate Occupancy (2), Life Safety (3) or in a condition of Collapse Prevention (4). According to the results, Neural Network models have the prediction rate of the damage level between 85%-95%. Therefore, one of the solutions for analyzing the structural responses and the damage level promptly and efficiently when the earthquake occurred is by using Artificial Neural Networ

Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network

The strong motion earthquake could cause the building damage in case of the building not considered in the earthquake design of the building. The study aims to predict the damage-level of building due to earthquake using Artificial Neural Networks method. The building model is a reinforced concrete building with ten floors and height between floors is 3.6 m. The model building received a load of the earthquake based on nine earthquake time history records. Each time history scaled to 0,5g, 0,75g, and 1,0g. The Artificial Neural Networks are designed in 4 architectural models using the MATLAB program. Model 1 used the displacement, velocity, and acceleration as input and Model 2 used the displacement only as the input. Model 3 used the velocity as input, and Model 4 used the acceleration just as input. The output of the Neural Networks is the damage level of the building with the category of Safe (1), Immediate Occupancy (2), Life Safety (3) or in a condition of Collapse Prevention (4). According to the results, Neural Network models have the prediction rate of the damage level between 85%-95%. Therefore, one of the solutions for analyzing the structural responses and the damage level promptly and efficiently when the earthquake occurred is by using Artificial Neural Networ

Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network

The strong motion earthquake could cause the building damage in case of the building not considered in the earthquake design of the building. The study aims to predict the damage-level of building due to earthquake using Artificial Neural Networks method. The building model is a reinforced concrete building with ten floors and height between floors is 3.6 m. The model building received a load of the earthquake based on nine earthquake time history records. Each time history scaled to 0,5g, 0,75g, and 1,0g. The Artificial Neural Networks are designed in 4 architectural models using the MATLAB program. Model 1 used the displacement, velocity, and acceleration as input and Model 2 used the displacement only as the input. Model 3 used the velocity as input, and Model 4 used the acceleration just as input. The output of the Neural Networks is the damage level of the building with the category of Safe (1), Immediate Occupancy (2), Life Safety (3) or in a condition of Collapse Prevention (4). According to the results, Neural Network models have the prediction rate of the damage level between 85%-95%. Therefore, one of the solutions for analyzing the structural responses and the damage level promptly and efficiently when the earthquake occurred is by using Artificial Neural Networ

Peningkatan Pengetahuan Melalui Penyuluhan untuk Meningkatkan Imun Tubuh di Masa Pandemi Covid-19

The purpose of this activity is that participants understand how to maintain the body's immune system during the Covid-19 pandemic and the various challenges that are being faced during the Covid-19 pandemi. Participants in this counseling activity is the students of SMP Negeri 3 Baubau, namely 43 students. This activity was held in the hall of SMP Negeri 3 Baubau on January 20, 2020 offline. Activities methods with an extension approach were Explanation of Material on how to increasing the body's immunity during the Covid-19 pandemic and conducting discussions and questions and answers, It is hoped that in this counseling, students can gain knowledge and understanding about increasing the body's immune system during the Covid-19 pandemic so that they are not easily infected with various diseases

GIS-based mapping of noise from mechanized minerals ore processing industry

Monitoring workers’ exposure to occupational noise is essential, especially in industrial areas, to protect their health. Therefore, it is necessary to collect information on noise emitted by machines in industries. This research aims to map the noise from mechanized mineral ore industry using the kriging interpolation method, and ArcGIS 10.5.1 to spatially process and analyze data. The experimental calculation result of the semivariogram showed a 0.83 range value, with an essential parameter of 1.75 sill and a spherical total theoretical model. The result shows that the main machines with the highest power consumption and the Leq value are located in the southwest position of the sampled areas with a noise map-projected to assess the workers’ noise exposure level. In conclusion, the study found that the highest noise level was generated ranged from 88 to 97 dBA and contributed to the whole sound pressure level at certain positions

Epidemiology and outcomes of hospital-acquired bloodstream infections in intensive care unit patients: the EUROBACT-2 international cohort study

Purpose: In the critically ill, hospital-acquired bloodstream infections (HA-BSI) are associated with significant mortality. Granular data are required for optimizing management, and developing guidelines and clinical trials. Methods: We carried out a prospective international cohort study of adult patients (≥ 18 years of age) with HA-BSI treated in intensive care units (ICUs) between June 2019 and February 2021. Results: 2600 patients from 333 ICUs in 52 countries were included. 78% HA-BSI were ICU-acquired. Median Sequential Organ Failure Assessment (SOFA) score was 8 [IQR 5; 11] at HA-BSI diagnosis. Most frequent sources of infection included pneumonia (26.7%) and intravascular catheters (26.4%). Most frequent pathogens were Gram-negative bacteria (59.0%), predominantly Klebsiella spp. (27.9%), Acinetobacter spp. (20.3%), Escherichia coli (15.8%), and Pseudomonas spp. (14.3%). Carbapenem resistance was present in 37.8%, 84.6%, 7.4%, and 33.2%, respectively. Difficult-to-treat resistance (DTR) was present in 23.5% and pan-drug resistance in 1.5%. Antimicrobial therapy was deemed adequate within 24 h for 51.5%. Antimicrobial resistance was associated with longer delays to adequate antimicrobial therapy. Source control was needed in 52.5% but not achieved in 18.2%. Mortality was 37.1%, and only 16.1% had been discharged alive from hospital by day-28. Conclusions: HA-BSI was frequently caused by Gram-negative, carbapenem-resistant and DTR pathogens. Antimicrobial resistance led to delays in adequate antimicrobial therapy. Mortality was high, and at day-28 only a minority of the patients were discharged alive from the hospital. Prevention of antimicrobial resistance and focusing on adequate antimicrobial therapy and source control are important to optimize patient management and outcomes.</p

Epidemiology and outcomes of hospital-acquired bloodstream infections in intensive care unit patients: the EUROBACT-2 international cohort study

Purpose In the critically ill, hospital-acquired bloodstream infections (HA-BSI) are associated with significant mortality. Granular data are required for optimizing management, and developing guidelines and clinical trials. Methods We carried out a prospective international cohort study of adult patients (≥ 18 years of age) with HA-BSI treated in intensive care units (ICUs) between June 2019 and February 2021. Results 2600 patients from 333 ICUs in 52 countries were included. 78% HA-BSI were ICU-acquired. Median Sequential Organ Failure Assessment (SOFA) score was 8 [IQR 5; 11] at HA-BSI diagnosis. Most frequent sources of infection included pneumonia (26.7%) and intravascular catheters (26.4%). Most frequent pathogens were Gram-negative bacteria (59.0%), predominantly Klebsiella spp. (27.9%), Acinetobacter spp. (20.3%), Escherichia coli (15.8%), and Pseudomonas spp. (14.3%). Carbapenem resistance was present in 37.8%, 84.6%, 7.4%, and 33.2%, respectively. Difficult-to-treat resistance (DTR) was present in 23.5% and pan-drug resistance in 1.5%. Antimicrobial therapy was deemed adequate within 24 h for 51.5%. Antimicrobial resistance was associated with longer delays to adequate antimicrobial therapy. Source control was needed in 52.5% but not achieved in 18.2%. Mortality was 37.1%, and only 16.1% had been discharged alive from hospital by day-28. Conclusions HA-BSI was frequently caused by Gram-negative, carbapenem-resistant and DTR pathogens. Antimicrobial resistance led to delays in adequate antimicrobial therapy. Mortality was high, and at day-28 only a minority of the patients were discharged alive from the hospital. Prevention of antimicrobial resistance and focusing on adequate antimicrobial therapy and source control are important to optimize patient management and outcomes