Search

Background CoronaVac, an inactivated whole-virion SARS-CoV-2 vaccine, has been shown to be well tolerated with a good safety profile in individuals aged 18 years and older in phase 1/2 trials, and provided a good humoral response against SARS-CoV-2. We present the interim efficacy and safety results of a phase 3 clinical trial of CoronaVac in Turkey. Methods This was a double-blind, randomised, placebo-controlled phase 3 trial. Volunteers aged 18-59 years with no history of COVID-19 and with negative PCR and antibody test results for SARS-CoV-2 were enrolled at 24 centres in Turkey. Exclusion criteria included (but were not limited to) immunosuppressive therapy (including steroids) within the past 6 months, bleeding disorders, asplenia, and receipt of any blood products or immunoglobulins within the past 3 months. The K1 cohort consisted of health-care workers (randomised in a 1:1 ratio), and individuals other than health-care workers were also recruited into the K2 cohort (randomised in a 2:1 ratio) using an interactive web response system. The study vaccine was 3 mu g inactivated SARS-CoV-2 virion adsorbed to aluminium hydroxide in a 0.5 mL aqueous suspension. Participants received either vaccine or placebo (consisting of all vaccine components except inactivated virus) intramuscularly on days 0 and 14. The primary efficacy outcome was the prevention of PCR-confirmed symptomatic COVID-19 at least 14 days after the second dose in the per protocol population. Safety analyses were done in the intention-to-treat population. This study is registered with ClinicalTrials.gov (NCT04582344) and is active but no longer recruiting. Findings Among 11 303 volunteers screened between Sept 14, 2020, and Jan 5, 2021, 10 218 were randomly allocated. After exclusion of four participants from the vaccine group because of protocol deviations, the intention-to-treat group consisted of 10 214 participants (6646 [65.1%] in the vaccine group and 3568 [34.9%] in the placebo group) and the per protocol group consisted of 10 029 participants (6559 [65.4%] and 3470 [34.6%]) who received two doses of vaccine or placebo. During a median follow-up period of 43 days (IQR 36-48), nine cases of PCR-confirmed symptomatic COVID-19 were reported in the vaccine group (31.7 cases [14.6-59.3] per 1000 person-years) and 32 cases were reported in the placebo group (192.3 cases [135.7-261.1] per 1000 person-years) 14 days or more after the second dose, yielding a vaccine efficacy of 83.5% (95% CI 65.4-92.1; p, TUSEB, We are grateful to all participants who volunteered to be part of this study and to all members of the clinical research teams of the participating sites. We thank TUSEB for funding the study and Omega-CRO for the statistical analyses and production of figures, and providing the study protocol. We also thank the members of the data and safety monitoring board for their contributions in the safe execution of this study.

Introduction: In Turkey, the number of infected people with HIV-1 increased five-fold between the years 2011-2018. In 2019, 20202 HIV-infected individuals and 1786 AIDS patients were reported. It is necessary to update the physicians’ knowledge on medical, ethical, legal and social issues about HIV infected patients, and to increase the number of physicians following these patients. In this study, it was aimed to determine the problems experienced by infectious diseases and clinical microbiology (IDCM) physicians on diagnosis, treatment and follow-up and to increase their awareness. Materials and Methods: In the study, a questionnaire with 39 questions was sent to 400 IDCM physicians via e-mail. The results of 131 physicians in total were analyzed. In statistical comparison, chi-square test was used for categorical data, and p value below 0.05 was accepted as statistically significant in 95% confidence interval. SPSS v 21.0 program was used for statistical analysis. Results: Of the 131 physicians who participated in the study, 89 (67.9%) were females, 75.6% of these physicians stated that they follow HIV-infected patients. Average working durations of the physicians who followed and did not follow the patients were 16.0 ± 10.4 (1-44) and 11.9 ± 5.6 (2-26) years, respectively. Of the 32 physicians who did not follow the patients, 22 (68.8%) stated that they wanted to follow patients on provided conditions. The most common reasons for physicians not following HIV-infected patients are lack of access to laboratory tests (56.3% n= 18) and inadequate hospital/outpatient conditions (50.0% n= 16). Of the 99 physicians who follow HIV-infected patients, 87 (87.8%) can access all diagnostic tests, and 98 physicians (98.9%) can access at least one. The most commonly found difficulties in patient follow-up are the lack of social insurance of the patient (43.4%) and informing relatives about the disease (37.4%). Seventy-two point seven percent of the physicians have difficulties in the management of the patient group (mental disorder, drug addiction and prisoners) who has social problems. Physician-patient compliance (96%), drug posology (79.8%) and insurance problems (79.8%) were found to be the leading factors affecting treatment compliance. Conclusion: The most challenging issues of physicians following HIV-infected patients are the follow-up of patients without social insurance and the management of socially problematic patients. The most common reasons for physicians not following these patients are inaccessibility to laboratory tests and insufficient hospital conditions. Our study outputs will contribute to the education plans of infectious diseases physicians in our country. Increasing physician patient compliance is important in combating this disease.

Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diagnosis systems are developed using artificial intelligence techniques for automated detection of AF. Various machine learning and deep learning (DL) techniques have been developed for the automated detection of AF. In this review, we focused on the automated AF detection models developed using DL techniques. Twenty-four relevant articles published in international journals were reviewed. DL models based on deep neural network, convolutional neural network (CNN), recurrent neural network, long short-term memory, and hybrid structures were discussed. Our analysis showed that the majority of the studies used CNN models, which yielded the highest detection performance using ECG and heart rate variability signals. Details of the ECG databases used in the studies, performance metrics of the various models deployed, associated advantages and limitations, as well as proposed future work were summarized and discussed. This review paper serves as a useful resource for the researchers interested in developing innovative computer-assisted ECG-based DL approaches for AF detection.

Introduction: Human Immunodeficiency virus is a chronic infection that attacks the immune system of the human body, particularly CD4 T lymphocytes. Combined antiretroviral therapies are highly effective in virological suppression of human immunodeficiency virus infection. It has been shown that some retroviral therapies have a higher nephrotoxicity potential. As a result of renal injury, serum creatinine increases, and the estimated glomerular filtration rate is reduced. The aim of our study was to assess changes in kidney function during a 24-month period in HIV-positive patients who were begun on combined antiretroviral therapy. Material-method: A total of 127 HIV positive patients were enrolled. The patients were divided into five groups; patients who received no therapy were designated as Group 1; those that received Dolutegravir/Abacavir/Lamivudine combination as Group 2; those that received Elvitegravir/Cobicistat/Emtricitabine/Tenofovir Alafenamide Fumarate combination as Group 3; those that received Emtricitabine/Tenofovir Disoproxil Fumarate/Dolutegravir combination as Group 4; and those that received Emtricitabine/Tenofovir Disoproxil Fumarate/Raltegravir combination as Group 5. We compared the effects of these drugs on estimated glomerular filtration rate during a 24-month follow-up period. Results: At the 24th month of therapy, a significant difference was observed between the eGFR levels of the study groups (p:0.05). Serum creatinine level was significantly higher in Groups 4 and 5 compared to the other groups (p

Background: In chronic hepatitis B patients with or exposed to the risk of osteoporosis or renal dysfunction, switching from tenofovir disoproxil fumarate (TDF) to tenofovir alafenamide fumarate (TAF) or entecavir (ETV) may be the right choice. Objectives: This study aimed to present real-life data in terms of the efficacy and safety of a TAF/ETV treatment change while receiving TDF. Methods: This retrospective study was conducted on 344 adult patients from 10 centers. The data of patients who had changed to ETV (n = 107) and TAF (n = 237) while receiving TDF were analyzed. The data collected at 0 and 6 months of treatment were analyzed. The virological response was assessed based on undetected hepatitis B virus (HBV) DNA. Serum alanine aminotransferase (ALT) values were used to evaluate the biochemical response. For renal function, serum creatinine and phosphorus, as well as estimated glomerular filtration rate (eGFR), were recorded. Moreover, lumbar spine and hip T-scores along with the serum lipid profile were evaluated. Results: The mean age of patients was 41.14 ± 13.46 years, and 224 (65.1%) of the participants were male. The treatment arms were not significantly different in terms of demographic characteristics, comorbid diseases, infection duration, family history of HBV infection, blood platelet count, serum biomarkers, such as ALT, phosphorus, creatinine, total bilirubin, albumin, lipid profile, and HBV DNA levels at the beginning. No statistically significant difference was found between the proportion of undetectable HBV DNA of the two treatment groups after 6 months (P = 0.221). The ALT normalization in the ETV and TAF groups at the sixth month compared to the baseline levels was not significantly different (P = 0.853, P = 0.330, respectively). There was no statistically significant difference between the two treatment arms regarding changes in eGFR, creatinine, phosphorus, hip, and spine T-scores from baseline to 6 months (P = 0.296, P = 0.78, P = 0.141, P = 0.832, P = 0.947, respectively). In those who switched to TAF or ETV, low-density lipoproteins cholesterol were observed to be significantly higher after 6 months compared to baseline values (P = 0.002, P = 0.049, respectively). The TC increased significantly in the TAF group (P = 0.035). Conclusions: Our study showed that switching to ETV and TAF sustained the viral suppression and biochemical response achieved by TDF therapy. The treatment switch to TAF of ETV can control renal dysfunction and reduce bone mineral density caused by TDF.

Atriyal Fibrilasyon (AFib), yaşlılarda ve hatta herhangi bir kalp hastalığı olmayan gençlerde bile görülebilen yaygın bir kalp ritim bozukluğudur. AFib; inme, kalp yetmezliği ve ani ölümlere neden olabilir. Tüm bu mevcut ve gelecekteki endişeler, dünya çapında AFib'in erken tespitinde önemli önlemlerin alınmasını gerektirir. Elektrokardiyografik (EKG) dalga formları, AFib gibi anormal kalp ritimlerini saptamak için en güvenilir yöntem olarak kabul edilmektedir. Ancak EKG sinyallerinin karmaşıklığı ve doğrusal olmaması nedeniyle bu sinyalleri manuel olarak analiz etmek zordur. Bunun yanı sıra, EKG sinyallerinin yorumlanması kişiye özgü ve uzmanlar arasında farklılık gösterebilmektedir. Bu nedenle otomatik ve güvenilir bir AFib algılama için bilgisayar destekli teşhis (BDT) sistemlerinin kullanımı önemlidir. BDT sistemleri, EKG sinyallerinin değerlendirilmesinin objektif ve doğru olmasını sağlayacak potansiyele sahiptir. Bu çalışmada, derin öğrenme yapısı kullanılarak EKG sinyallerinden otomatik AFib tespiti gerçekleştirilmiştir. Derin öğrenme algoritmalarından evrişimli sinir ağı (ESA) mimarisinin AFib sınıflandırma probleminde kullanımı için çalışma kapsamında derin bir ağ modeli tasarlanmıştır. Kullanılan verisetinde normal sinüs ritimlerinin (SR) yanısıra AFib ve Atriyal Flutter (AFL) aritmileri bulunmaktadır. AFib ve AFL sınıfları birleştirilerek model çıkışında SR ve AFib ayırımının otomatik yapılması sağlanmıştır. Önerilen model, 2222 SR ve 2218 AFib tanısı alan kişilere ait her biri 5000 örneğe sahip EKG sinyali içeren veri seti üzerinde uygulanmıştır. Çalışma kapsamında hazırlanan ESA modeli, test aşamasında sırasıyla %95.09 hassasiyet, %97.27 özgüllük ve %97.26 kesinlik değerlerine ulaşmıştır. Modelin test verileri üzerindeki doğruluk oranı %96.17 olarak elde edilmiştir., Atrial Fibrillation (AFib) is a common heart rhythm disorder that can occur in the elderly and even young people without any heart disease. AFib can result in a stroke, heart failure, or sudden death.. All of these existing and future concerns require significant efforts in the early diagnosis of AFib around the world.. Electrocardiographic (ECG) waveforms are considered the most reliable method for detecting abnormal heart rhythms such as AFib. However, due to the complexity and non-linearity of ECG signals, it is difficult to analyze these signals manually. Furthermore, the interpretation of ECG data is subjective and may differ amongst specialists.. Therefore, the use of computer-assisted diagnostic (CAD) systems is important for automatic and reliable detection of AFib. CAD systems have the potential to make objective and accurate evaluation of ECG signals. In this paper, automatic AFib detection from ECG signals was performed using deep learning system. A deep network model was proposed within the scope of this study for the use of convolutional neural network (CNN) architecture, one of the deep learning algorithms, in the AFib classification problem. The dataset used includes normal sinus rhythms (SR) as well as AFib and Atrial Flutter (AFL) arrhythmias. By combining AFib and AFL classes, automatic classification of SR and AFib is provided at the model output. The suggested model was tested on a data set of 5000 samples of ECG signals from patients with 2222 SR and 2218 AFib, respectively. The CNN model developed within the scope of this study achieved 95.09% sensitivity, 97.27% specificity and 97.26% precision values, respectively, during the test phase. The accuracy percentageof the model on the test data was 96.17%.

WOS:000639966700013 Giriş: Türkiye’de, HIV-1 ile infekte kişi sayısı 2011-2018 yılları arasında beş kat artış göstermiş; 2019’da 20202 HIV ile infekte birey ve 1786 AIDS hastası bildirilmiştir. Hekimlerin HIV ile infekte hastalarla ilgili tıbbi, etik, hukuki ve sosyal konulardaki bilgilerinin güncellenmesi ve bu hastaları takip eden hekim sayısının artırılması gerekmektedir. Bu çalışmada infeksiyon hastalıkları ve klinik mikrobiyoloji (EHKM) hekimlerinin tanı, tedavi ve takipte yaşadıkları sorunların belirlenmesi ve farkındalıklarının artırılması amaçlanmıştır. Materyal ve Metod: Çalışmada 400 EHKM hekimine 39 soruluk anket formu elektronik posta ile gönderilmiştir. Toplamda 131 hekimin sonuçları analiz edilmiştir. İstatistiksel analizde kategorik veriler için ki kare testi kullanılmış ve %95 güven aralığında 0.05’in altındaki p değeri istatistiksel olarak anlamlı kabul edilmiştir. İstatistiksel analizler için SPSS v 21.0 programı kullanılmıştır. Bulgular: Çalışmaya katılan 131 hekimin 89’u (%67.9) kadındır, %75.6’sı (n= 99) HIV ile infekte hastaları takip ettiğini belirtmiştir. Hasta takip eden ve etmeyen hekimlerin meslekte ortalama çalışma süreleri sırasıyla 16.0 ± 10.4 (1-44) ve 11.9 ± 5.6 (2-26) yıldır. Hasta takip etmeyen 32 hekimin 22’si (%68.8) uygun koşulları olduğunda takip etmek istediğini belirtmiştir. Hekimlerin HIV ile infekte hasta takip etmemesinin en sık nedenleri takipte kullanılacak laboratuvar testlerine erişilememesi (%56.3 n= 18) ve hastane/poliklinik şartlarının yetersiz olmasıdır (%50.0 n= 16). HIV ile infekte hasta takip eden 99 hekimden 87’si (%87.8) tanı testlerinin tümüne erişebilmekte, 98 hekim (%98.9) en az birine erişebilmektedir. Hasta takibinde en çok zorlanılan konular hastanın sosyal güvencesinin olmaması (%43.4) ve hastalık konusunda yakınlarının bilgilendirilmesidir (%37.4). Hekimlerin %72.7’si sosyal özellikleri sorunlu olan hasta grubunun (mental bozukluk, ilaç bağımlılığı ve mahkûmlar) yönetiminde zorlanmaktadır. Hekim-hasta uyumu (%96), ilaç pozolojisi (%79.8) ve sigorta sorunları (%79.8) tedavi uyumunu etkileyen faktörlerin başında gelmektedir. Sonuç: HIV ile infekte hasta takip eden hekimlerin en zorlandığı konular sosyal güvencesi olmayan hastaların takibi, hasta yakınlarının bilgilendirilmesi ve sosyal olarak sorunlu hastaların yönetimidir. Hekimlerin hasta takip etmemesinin en sık nedenleri laboratuvar testlerine erişilememesi ve hastane şartlarının yetersiz olmasıdır. Çalışmamız çıktıları ülkemizdeki infeksiyon hastalıkları hekimlerinin eğitim planlarına katkı sağlayacaktır. Hekim hasta uyumunun artırılması bu hastalıkla mücadelede önemlidir Introduction: In Turkey, the number of infected people with HIV-1 increased five-fold between the years 2011-2018. In 2019, 20202 HIV-infected individuals and 1786 AIDS patients were reported. It is necessary to update the physicians’ knowledge on medical, ethical, legal and social issues about HIV infected patients, and to increase the number of physicians following these patients. In this study, it was aimed to determine the problems experienced by infectious diseases and clinical microbiology (IDCM) physicians on diagnosis, treatment and follow-up and to increase their awareness. Materials and Methods: In the study, a questionnaire with 39 questions was sent to 400 IDCM physicians via e-mail. The results of 131 physicians in total were analyzed. In statistical comparison, chi-square test was used for categorical data, and p value below 0.05 was accepted as statistically significant in 95% confidence interval. SPSS v 21.0 program was used for statistical analysis. Results: Of the 131 physicians who participated in the study, 89 (67.9%) were females, 75.6% of these physicians stated that they follow HIV-infected patients. Average working durations of the physicians who followed and did not follow the patients were 16.0 ± 10.4 (1-44) and 11.9 ± 5.6 (2-26) years, respectively. Of the 32 physicians who did not follow the patients, 22 (68.8%) stated that they wanted to follow patients on provided conditions. The most common reasons for physicians not following HIV-infected patients are lack of access to laboratory tests (56.3% n= 18) and inadequate hospital/outpatient conditions (50.0% n= 16). Of the 99 physicians who follow HIV-infected patients, 87 (87.8%) can access all diagnostic tests, and 98 physicians (98.9%) can access at least one. The most commonly found difficulties in patient follow-up are the lack of social insurance of the patient (43.4%) and informing relatives about the disease (37.4%). Seventy-two point seven percent of the physicians have difficulties in the management of the patient group (mental disorder, drug addiction and prisoners) who has social problems. Physician-patient compliance (96%), drug posology (79.8%) and insurance problems (79.8%) were found to be the leading factors affecting treatment compliance. Conclusion: The most challenging issues of physicians following HIV-infected patients are the follow-up of patients without social insurance and the management of socially problematic patients. The most common reasons for physicians not following these patients are inaccessibility to laboratory tests and insufficient hospital conditions. Our study outputs will contribute to the education plans of infectious diseases physicians in our country. Increasing physician patient compliance is important in combating this disease.

WOS:000654928300001 PMID: 33977603 Objective Occult hepatitis B infection (OHBI) appears to have a higher prevalence in populations at high risk for hepatitis B virus (HBV) infection with concomitant liver disease. The aim was to assess the prevalence of OHBI in a sample of human immunodeficiency virus -1 positive and HBV surface antigen-negative (HIV-1+/HBsAg-) Turkish patients. Methods Ten centres in Turkey were included in the study. Patients were selected on the basis of a power calculation with a known population size of HIV-positive patients and a reported prevalence of OHBI. Gender, age, occupation, place of residence, treatment and clinical status, and laboratory results, including immunodeficiency panel, antibody tests, hemogram, biochemistry, and coagulation studies were evaluated retrospectively. Results The number of HIV-infected patients followed in these centres was 3172 and the sample population numbered 278. All 278 were HBsAg negative. The mean age of the sample was 37.2 +/- 13.1 years and 235 (84.5%) were male. All but one patient (99.6%) had been treated with antiretroviral therapy. Of the 278 patients, 169 (60.6%) were positive for Anti-HBs and 125 (44.8%) were positive for Anti-HBc IgG. HIV RNA was detected in 203/278 (73%) of the patients. Four HBV DNA (1.4%) were diagnosed with OHBI. There was no significant difference in hemogram, hemoglobin or bilirubin concentrations in those with OHBI compared with the other patients. Conclusion In a representative sample of HIV+ patients from 10 Turkish centres, the prevalence of OHBI was found to be 1.4%. In HIV positive patients, it is important to identify those with OHBI for optimal clinical management and prognosis.

Amaç: Aşı tereddüdü hem Türkiye hem de dünya için toplumsal tehdit oluşturan bir tutumdur. Bu konuda Türkçeye validasyonu yapılmış bir ölçek bulunmamaktadır. Bu çalışmanın amacı Opel ve ark. tarafından 2011 yılında geliştirilip 2013 yılında revize edilen ve ‘Parent Attitudes About Childhood Vaccines Survey ‘isimli ölçeğin Türkçeye dil validasyonunun yapılmasıdır. Gereç ve Yöntem : Çalışma bir dil validasyonu çalışması olarak gerçekleştirilmiştir. Opel DJ ve arkadaşları tarafından geliştirilen ölçek için ‘Seri yöntem’ kullanılarak dil validasyonu çalışması yapılmıştır. Ölçeğin çeviri, gözden geçirme, geri çeviri, açıklığın ve aynılığın değerlendirmesi ile çevirisi sağlanmış ve Modifiye Lawshe yöntemiyle kapsam geçerlilik analizi yapılarak son hali verilmiştir. Uzmanların ölçek maddelerine yönelik içerik uygunluğu yönünden belirtmiş oldukları görüşlere göre her madde için kapsam geçerlilik oranı ve kapsam geçerlilik indeksi hesaplanmıştır. Bulgular. Tüm ölçek soruları için kapsam geçerlilik oranı kritik değer olan 0,455’in üzerinde saptanmıştır. Ölçeğin kapsam geçerlilik indeksi değeri ise 0,630 olup ölçek sorularının kapsam geçerliliğinin istatistiksel olarak anlamlı olduğu tespit edilmiştir. Çıkarımlar: Çocukluk çağı aşılarına yönelik ebeveyn tutumları ölçeğinin dil validasyonunun tamamlanması ve kapsam geçerlilik testlerinden istatistik olarak anlamlı sonuçlar alınmış olması bu ölçeğin Türkçe anlaşılırlığının sağlandığını göstermektedir. Sonrasında yapılacak geçerlilik ve güvenilirlik çalışmaları ile ölçeğin Türkiye’de aşı tereddüdü konusunda ebeveyn tutumlarını ölçebilir hale gelmesi hedeflenmektedir. Böylece daha geniş kitlelerde çalışılarak aşı tereddüdü ve mücadelesi konusunda yapılacak çalışmalara katkı sağlayacağı düşünülmektedir.

In this paper, a new generation Internet-based Power Quality Monitoring System (IPQMS) that transmits real-time power quality (PQ) data over the internet has been developed. The monitoring system has the ability to measure PQ parameters in accordance with the related standards. This monitoring system includes many hardware and software designs and presents an efficient structure. PQ parameters are determined by signal processing algorithms that are applied to the current and voltage signals continuously obtained from the electrical power network. These signal processing algorithms are performed as embedded functions in the FPGA device. The PQ data obtained from the measurement points are transmitted to a server by UDP/IP communication protocol that is implemented in the FPGA device. The monitoring, reporting and permanently storing tasks are accomplished with the real-time automation software and web applications that running on the server computer. With its innovative software and hardware designs, the proposed monitoring approach presents a very useful monitoring structure that can be used in the PQ field.

Millions of people have been lost due to HIV/AIDS after complete presentation of the disease. Mortality rates decrease significantly with effective treatments. However, HIV/AIDS is an important infectious disease and a concern for public health. Therefore, knowing fatality rates and mortality rates are very important in patient follow-up. In this study, it was aimed to calculate the fatality rate in patients followed up in a university hospital for eleven years.

Hybrid renewable energy systems considered as candidate solutions for environmental problems. Solar energy and wind energy are important components of these systems, but both of them cause fluctuations in the grid. Batteries of Electric Vehicles (EVs) can be connected to the grid to create an ancillary resource. These systems known as Vehicle to Grid (V2G) and they could be the most suitable solution to the fluctuation problems of renewable resources. In this paper, economic analysis of V2G is investigated from various perspectives. The findings of this study indicate that battery aging costs of a V2G system can be minimized with an intelligent energy management system, and these systems are both economically and environmentally better alternatives to fossil-fuelled generators.

Amaç: Ülkemizde 2017 verileriyle 17.000 kişinin HIV (insan immün yetmezlik virüsü) ile enfekte olduğu bilinmektedir. Hastalığın tedavisinde ilaç uyumu en önemli noktalardandır. HIV/AIDS tedavisinde hasta eğitimi çok önemlidir. Bu çalışmanın amacı eğitim müdahalesi yapılan hasta grubunda ilaç uyumunun değerlendirilmesidir.Yöntemler: Çalışma retrospektif verilerin analiz edildiği bir müdahale çalışması olarak gerçekleştirilmiştir. MayısTemmuz 2017 tarihleri arasında Dicle Üniversitesi Tıp Fakültesi Enfeksiyon Hastalıkları ve Klinik Mikrobiyoloji bölümünde izlenmekte olan 107 HIV/AIDS hastası çalışmaya dahil edilmiştir. Hastalığa özgü detaylı eğitim almayı kabul eden hastalar 1. Grup olarak değerlendirilmiştir. Eğitim almak istememiş olan 25 hasta ise (2. Grup/Kontrol grubu) standart poliklinik bilgilendirmesi almıştır. Her iki grubun eğitimleri aynı hekim tarafından verilmiştir. Hastaların tedavinin 3. ve 6. aylarındaki ilaç uyumları tablet sayma yöntemiyle değerlendirilmiştir. Bulgular: Tedavi almakta olan 107 hastanın yaş ortalaması 28.6 yıldır. Hastaların 86’sı (%80,4) erkektir. Hastaların eğitim durumları incelendiğinde %8,4’ü (n=9) sadece okuryazar, %67,3’ü (n=72) ilk ve orta öğretim mezunu olarak tespit edilmiştir. Hastaların öğrenim durumları ile 3. ve 6. aydaki ilaç uyumları arasında istatistiksel olarak anlamlı bir fark tespit edilmemiştir (sırasıyla p=0,159 ve p=0,274). Günlük tablet sayısından bağımsız olarak, ilaç uyumu, eğitim verilenlerde istatistiksel olarak anlamlı düzeyde yüksek saptanmıştır ( p

Deep reinforcement learning (DRL) exhibits a promising approach for controlling humanoid robot locomotion. However, only values relating sensors such as IMU, gyroscope, and GPS are not sufficient robots to learn their locomotion skills. In this article, we aim to show the success of vision based DRL. We propose a new vision based deep reinforcement learning algorithm for the locomotion of the Robotis-op2 humanoid robot for the first time. In experimental setup, we construct the locomotion of humanoid robot in a specific environment in the Webots software. We use Double Dueling Q Networks (D3QN) and Deep Q Networks (DQN) that are a kind of reinforcement learning algorithm. We present the performance of vision based DRL algorithm on a locomotion experiment. The experimental results show that D3QN is better than DQN in that stable locomotion and fast training and the vision based DRL algorithms will be successfully able to use at the other complex environments and applications.

Face recognition is of great importance to real world applications such as video surveillance, human machine interaction and security systems. As compared to traditional machine learning approaches, deep learning based methods have shown better performances in terms of accuracy and speed of processing in image recognition. This paper proposes a modified Convolutional Neural Network (CNN) architecture by adding two normalization operations to two of the layers. The normalization operation which is batch normalization provided acceleration of the network. CNN architecture was employed to extract distinctive face features and Softmax classifier was used to classify faces in the fully connected layer of CNN. In the experiment part, Georgia Tech Database showed that the proposed approach has improved the face recognition performance with better recognition results.

This paper offers an overview of essential concepts in deeplearning, one of the state of the art approaches in machine learning, in termsof its history and current applications as a brief introduction to the subject.Deep learning has shown great successes in many domains such as handwritingrecognition, image recognition, object detection etc. We revisited the conceptsand mechanisms of typical deep learning algorithms such as Convolutional NeuralNetworks, Recurrent Neural Networks, Restricted Boltzmann Machine, andAutoencoders. We provided an intuition to deep learning that does not relyheavily on its deep math or theoretical constructs.&nbsp

In this paper, a new S-Transform and Extreme Learning Machine (ST–ELM)-based event recognition approach for the purpose of classifying power quality (PQ) event signals automatically has been proposed. In this approach, the distinctive features of the PQ event signals have been obtained with the S-Transform-based feature extraction. The feature vector obtained with feature extraction has been applied as input to the ELM classifier. Ten different classification procedures were determined within the framework of this study to assess the performance of the ELM classifier on PQ event data. Real PQ event data and synthetic PQ event data obtained from MATLAB/Simulink environment have been used in these procedures. Also, three different PQ event data sets, which are formed by adding noises of 20, 30 and 50 dB to the synthetic PQ event data respectively, have been used in order to assess the performance of the proposed approach on noisy conditions. According to the results of performance evaluations, the proposed ST–ELM-based PQ event recognition system has a very high performance of recognizing PQ event data. Besides, classification of noisy data showed that the proposed approach is robust at recognizing noisy data. The performance of the ST–ELM-based recognition system on PQ data shows that this approach has an effective recognition structure that can be used in real power systems.

In this paper, a new optimal feature selection based power quality event recognition system is proposed for the classification of power quality events. While Apriori algorithm is capable of processing categorical data, an effective feature vector, which represents distinctive features of digital power quality event data, has been obtained by means of the proposed k -means based Apriori algorithm feature selection approach. The proposed k -means based Apriori algorithm feature selection approach is presented with a power quality event recognition system. In the power quality event recognition system, normalization and segmentation processes have been applied to three-phase event voltage signals. Using 9-level multiresolution analysis, wavelet transform coefficients of the event signals have been obtained. By applying nine different feature extraction processes to these coefficients, a 90 dimensional feature vector belonging to three-phase event voltage signals has been extracted. Optimal feature vector has been obtained by applying the k -means based Apriori algorithm feature selection approach to the obtained feature vector, which has been applied as the last step to the input of the least squares support vector machine classifier and recognition performance results have been obtained. Real power quality event data have been used to evaluate the performance of the proposed feature selection approach and power quality event recognition system. According to the results, the proposed k -means based Apriori algorithm feature selection approach and power quality event recognition system are efficient, reliable and applicable and classify three-phase event types with a high degree of accuracy.

Autonomous driving requires reliable and accurate detection and recognition of surrounding objects in real drivable environments. Although different object detection algorithms have been proposed, not all are robust enough to detect and recognize occluded or truncated objects. In this paper, we propose a novel hybrid Local Multiple system (LM-CNN-SVM) based on Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) due to their powerful feature extraction capability and robust classification property, respectively. In the proposed system, we divide first the whole image into local regions and employ multiple CNNs to learn local object features. Secondly, we select discriminative features by using Principal Component Analysis. We then import into multiple SVMs applying both empirical and structural risk minimization instead of using a direct CNN to increase the generalization ability of the classifier system. Finally, we fuse SVM outputs. In addition, we use the pre-trained AlexNet and a new CNN architecture. We carry out object recognition and pedestrian detection experiments on the Caltech-101 and Caltech Pedestrian datasets. Comparisons to the best state-of-the-art methods show that the proposed system achieved better results.

Disturbances occurring on voltage signals following switching events or faults on power systems are defined as power quality problem. Power quality problems result in significant financial losses in the power systems in addition to faulty operation or breakdown of sensitive loads connected to the power system. Therefore, power quality problems should be swiftly detected and eliminated. This paper proposes a real-time embedded detection system for the purpose of identification of power quality disturbances. The detection system developed has been created using the wavelet transform in the Field Programmable Gate Array (FPGA) media. The general structure of FPGA-based detection system comprises two fundamental parts: hardware and software. Hardware structure comprises signal input card, FPGA and computer components. The software stage includes FPGA and graphical interface software. An experimental setup of the power system has been created in laboratory environment in order to test the FPGA-based detection system, determine accuracy rates and assess its success. Various power quality disturbances have been created over this model in wide parameter intervals. When the results obtained for the real-time power quality disturbance detection system developed under this study are examined, it has been seen that hardware and software designs are quite effective, fast and of high achievement performance.

In this study, an FPGA based online monitoring system was developed for detection of power quality disturbances. The developed system has the capability to instantly detect commonly seen PQ disturbances such as voltage sag, voltage swell and interruption occurring on real-time three-phase voltage signals obtained from the electric power system. The data of the detected disturbance events are transferred to the visual software prepared in the computer environment by means of the UDP/IP communication module embedded in the FPGA. This PQ disturbance detection system was tested upon installation at three different measuring points across the campus of Tunceli University. It was seen from the test results examined that the detection system successfully identified the PQ disturbance events.