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Train-induced vibration in bridge piers
Get PDFThe movement of trains across bridges could create forces that potentially cause significant
vibrations in supporting piers. These are important parts of the whole structure of a bridge, which
would fall without them. Knowledge about how these shakes work is so important for designing
and keeping bridges safe. Engineers have used Plaxis 3D, a tool that uses finite element analysis,
to build a model and show how a train moves. There hasn't been enough study on how train
vibrations affect bridge piers. This research will shed light on this issue. Simulations with three
different train speeds to examine their effect on a bridge pier in the middle and the most soil
settling were run. Maximum shear force and bending moments values for two specific
columns that are located in the middle and end of the platform were also recorded. It was
found that the middle column’s end settlement reached a peak then started to decline as
train’s speed increases. For the settlement of soil, maximum value was found in the part of
soil that was just over first two columns. When the speed of train grows, the soil settlement
exhibited a minor drop which can be negligible. For maximum shear force for both columns,
there was a similar trend. At first, maximum value became greater notably but then started
to fall off. For maximum bending moment, there was also a same trend for both columns.
As train’s speed climbs, an increment was observed. However, a serious amount of reduction
was seen as the train reached its maximum speed. Based on these results, we could easily
state the critical speed of the train. Even though, this research is simulation based, the outcomes of this study can give engineers and architectures an idea of how a railway bridge
can affect bridge piers and even nearby buildings.Trenlerin köprüler üzerindeki hareketi, destekleyici ayaklarda önemli titreşimlere neden
olabilecek kuvvetler yaratabilir. Köprü ayakları, bir köprünün bütün yapısının önemli
parçalarıdır; bunlar olmadan köprü çökerdi. Bu titreşimlerin nasıl çalıştığına dair bilgi,
köprülerin güvenli bir şekilde tasarlanması ve korunması için son derece önemlidir.
Mühendisler, bir trenin nasıl hareket ettiğini göstermek için sonlu elemanlar analizi kullanan
Plaxis 3D adlı bir aracı kullanarak modeller oluşturdu. Tren hareketinden kaynaklı
titreşimlerinin köprü ayakları üzerindeki etkileri üzerine yeterince çalışma yapılmamıştır. Bu
çalışma, bu konuya ışık tutacaktır. Tren hareketinin etkisini gözlemleme amacıyla ,orta
bölgede bulunan köprü ayaklarından biri ve en fazla zemin oturmasının meydana geldiği
bölgede üç farklı tren hızıyla simülasyonlar yapıldı. Platformun ortasında ve sonunda bulunan
iki belirli kolon için maksimum kesme kuvveti ve eğilme momenti değerleri de kaydedildi.
Orta kolon ayağında meydana gelen oturmanın bir zirveye ulaştığı ve ardından trenin hızı
arttıkça azalmaya başladığı tespit edildi. Zeminin maksimum oturma gösterdiği değer, ilk iki
kolonun hemen üzerindeki bölgede tespit edildi. Trenin hızı arttıkça, zemin yer değiştirmesi
hafif bir düşüş gösterdi ki bu da göz ardı edilebilir. Her iki kolon için maksimum kesme
kuvveti açısından benzer bir eğilim vardı. Başlangıçta, maksimum değer belirgin bir şekilde
arttı ama sonra düşmeye başladı. Maksimum eğilme momenti için, her iki kolon için de benzer
bir eğilim vardı. Trenin hızı arttıkça, bir artış gözlemlendi. Ancak, tren maksimum hızına ulaştığında dikkate değer bir azalma gözlemlendi. Bu sonuçlara dayanarak, trenin kritik hızını
kolayca belirtebiliriz. Bu araştırma simülasyona dayalı olsa da, bu çalışmanın sonuçları
mühendisler ve mimarlar için bir demiryolu köprüsünün köprü ayakları ve hatta yakınlardaki
binalar üzerindeki etkisini anlamalarına yardımcı olabilir
House price prediction using Artificial Neural Network (ANN) with adagrad optimizer
No full textThe real estate market is a dynamic and complex ecosystem influenced by a myriad of
factors, making accurate price predictions a formidable challenge. Understanding the
intricate relationships between variables such as location, property characteristics, economic
indicators, and market trends is essential for making informed investment decisions, in this
thesis, a comprehensive exploration of machine learning and artificial neural networks
(ANNs) has been undertaken, laying the groundwork for understanding how these powerful
computational tools can be harnessed to solve complex problems across various domains.
The study began by delving into the fundamentals of machine learning, categorizing it into
its primary types, and discussing its applications in clustering, dimensionality reduction, and
learning association rules. These sections highlighted the versatility and breadth of machine
learning techniques in uncovering patterns and simplifying the complexities inherent in vast
datasets. Further, a transition was made into a focused discussion on linear regression,
including its simplest form and the more sophisticated gradient boosting method. This
progression underscored the evolution of machine learning from basic predictive modelling
to more advanced, iterative improvement techniques capable of handling nonlinear
relationships with exceptional accuracy and efficiency
MFF-LSTM: designing a multi-scale feature fusion-based long short term memory with divergent features for fake news detection system
No full textThe rise in the usage of media has led to a significant increase in the raise of false
information, making it imperative to combat this issue and reduce our reliance on such
unreliable sources. Fake news can mislead people, spread rumors, and even impact the
positions of political leaders. Detecting fake news has become crucial in this digital era, with
direct messaging platforms and social media playing a major role in its proliferation. Various
innovative techniques have been suggested to determine fake news, making the endeavor
both intriguing and challenging. Hence, this synopsis aims to develop the adaptive learning
model with multiscale feature fusion for fake news detection. The proposed system
constitutes “text collection, text pre-processing, feature extraction and detection”. Initially,
the text input is collected from the benchmark datasets, which is then followed by the text
stage of pre-processing. Here, the pre-processed text is obtained that is fed into the feature
extraction phases. The three feature extraction techniques such as “Bidirectional Encoder
Representations from Transformers (BERT), Term Frequency-Inverse Document Frequency
(TF-IDF) and GloVe Embedding” are employed to provide the feature set 1, 2 and 3. Finally,
these resultant features are given to “Multiscale Feature Fusion based Long Short Term
Memory (MFF-LSTM)”, where the features are fused together in multiscale manner and
detection is taken place by LSTM. Therefore, the system evaluation is done by considering
the distinct measures and compared among traditional approaches. Hence, the
recommended model attains the desired results to detect the fake news that helps to evade
the exploration of any false information
The design of ventilation system and blowing in gas turbine compartment
Get PDFThe design of an efficient ventilation system for gas turbine compartments is a crucial issue
in the gas turbine industry. The proper ventilation system design must maintain acceptable
temperatures inside the compartment and also eliminate any possible gas leakage. This thesis
study deals with a 3D numerical simulation of the thermal and flow fields within a gas
turbine enclosure. A blowing system to cool the turbine casing and the exhaust frame is also
considered in the simulation. Studying the enclosure ventilation system together with the
turbine blowing system is considered a very practical and important matter and has not been
discussed in another research previously. So, the originality of our research is distinguished
by focusing on this side. The ventilation flow rates, and the duct cross section are selected
based on a real-life situation. Software program is used to solve the governing equation
together with the k-ω SST turbulence model and obtain the velocity and temperature
contours within the enclosure.
The results showed that the average velocity within the enclosure, except the region adjacent
to the side walls and roof, has been about 5.5 m/s. Furthermore, the average temperature
within the enclosure has been less than 100 oC . These values for velocity and temperature
are within acceptable ranges and almost identical to the real operation data. Finally, the
designed blowing system of the turbine casing and the exhaust frame has caused a significant
reduction in the temperature from 450 oC in the interior to 200 oC at the surface. In
conclusion, the design ventilation system, with the selected specifications, could
successfully provide proper ventilation to both gas turbines and auxiliary compartments
Bulanık mantık ile reklam kampanyaları için teklif optimizasyon modelinin geliştirilmesi
Get PDFTurizm sektöründeki rekabetin internet ortamına taşınması ile önce internet ortamından
rezervasyon yapılan online seyahat acenteleri (OTA) ardından da birçok otel meta arama
motoru (metasearch) geliştirildi. Günümüzde kullanıcılar, bu metasearch’ler sayesinde bir
otelin birçok farklı OTA’daki fiyatlarını karşılaştırabilir hatta bu metasearch’ten ilgili linke
tıklayarak rezervasyon yapabilir.
Bu çalışmada; Türkiye’nin önde gelen OTA’larından Tatil sepeti’nin, dünyanın önde gelen
metasearch’lerinden Trivagodaki kampanyalarına, bulanık mantık modeli ile reklam teklifi
önerisi verdik. Tatilsepeti’nin kampanyalarının dönüşüm miktarını ve görünürlüğünü
maksimize etmeyi, tıklama başına maliyeti (CPC) optimum bir pozisyonda tutmayı
amaçladık. Kullanıcı kısıtı ve maliyet kısıtı ile kampanyaların yönetilmesini daha güvenli
hale getirdik. Seçilen kampanyaların gelir, maliyet ve tıklama sayıları tarihsel olarak
karşılaştırıldığında optimizasyon modelimizin başarılı sonuçlar verdiği tespit edilmiştir.With the transfer of the competition in the tourism sector to the Internet, online travel
agencies (OTA), which can be booked from the Internet, and then many hotel meta search
engines (metasearch) were developed. Nowadays, users can compare the prices of a hotel
in many different OTAs thanks to these metasearch and even make a reservation by
clicking on the corresponding link from this metasearch.
In this study; We have given an advertising proposal proposal to the campaigns of Tatil
sepeti, one of the leading OTAs in Turkey, and Trivago, one of the leading metasearchs in
the world, using a fuzzy logic model. We aimed to maximize the conversion amount and
visibility of Tatil sepeti's campaigns and to keep the cost per click (CPC) in an optimum
position. We made the management of campaigns safer with user and cost constraints.
When the revenue, cost and number of clicks of the selected campaigns are compared
historically, it has been determined that our optimization model gives successful results
Genetic evaluation of the patients with clinically diagnosed inborn errors of immunity by whole exome sequencing: results from a specialized research center for immunodeficiency in Türkiye
No full textOpen access funding provided by the Scientifc and Technological Research Council of Türkiye (TÜBİTAK). The study received support from the “Sucak Candan Biseyler” Foundation and the Clinical Immunology Society, which provided the necessary Whole Exome Sequencing (WES) kits for the research.Molecular diagnosis of inborn errors of immunity (IEI) plays a critical role in determining patients' long-term prognosis, treatment options, and genetic counseling. Over the past decade, the broader utilization of next-generation sequencing (NGS) techniques in both research and clinical settings has facilitated the evaluation of a significant proportion of patients for gene variants associated with IEI. In addition to its role in diagnosing known gene defects, the application of high-throughput techniques such as targeted, exome, and genome sequencing has led to the identification of novel disease-causing genes. However, the results obtained from these different methods can vary depending on disease phenotypes or patient characteristics. In this study, we conducted whole-exome sequencing (WES) in a sizable cohort of IEI patients, consisting of 303 individuals from 21 different clinical immunology centers in Türkiye. Our analysis resulted in likely genetic diagnoses for 41.1% of the patients (122 out of 297), revealing 52 novel variants and uncovering potential new IEI genes in six patients. The significance of understanding outcomes across various IEI cohorts cannot be overstated, and we believe that our findings will make a valuable contribution to the existing literature and foster collaborative research between clinicians and basic science researchers
A comparative study of classification algorithms for sentiment analysis of COVID-19 vaccine opinions using machine learning
No full textThe task of analyzing textual data and classifying them into positive, negative, or neutral
emotions within the domain of natural language processing is a multifaceted undertaking.
The primary objective of this study is to employ machine learning algorithms in order to
classify opinions pertaining to the coronavirus disease and vaccines. In this study, four
algorithms were employed, namely Random Forest (RF), Gradient Boosting Classifier
(GBC), Logistic Regression (LR), and Decision Tree (DT). The RF and GBC algorithms
demonstrated a commendable accuracy rate of 89%, while the LR and DT algorithms yielded
a slightly lower accuracy rate of 87%. The findings derived from this research can provide
valuable guidance to policymakers in effectively addressing potential barriers that may
impede the successful execution of vaccination campaigns. The analysis of the Kaggle data,
which encompasses a wide range of commentaries related to the pandemic and vaccines,
underscores the urgent need for prompt measures to attain herd immunity against Covid-19.
This imperative objective holds significant importance in effectively managing the
transmission of the virus and mitigating its adverse consequences on the well-being of the
general population. The task at hand necessitates the acknowledgment and resolution of
public apprehensions, as well as the establishment of trust and assurance in the vaccination
initiative. This study presents an analysis of the machine learning techniques employed and
conducts a comparative evaluation of their significance. The forthcoming research endeavors
to create an application that will be capable of categorizing sentiments and opinions
pertaining to diseases and vaccines. It is imperative for governments and organizations to comprehend the obstacles linked to the worldwide COVID-19 vaccination endeavor in order
to develop efficacious strategies. Nevertheless, it is crucial to acknowledge that the scope of
the analysis was restricted to tweets written in the English language. This limitation may
potentially undermine the credibility and generalizability of the findings pertaining to overall
sentiment. Additional investigation could be conducted to examine more extensive Twitter
datasets employing deep learning models in order to gain a deeper comprehension of the
general public's attitudes towards COVID-19 vaccines
Nucleosome assembly protein 1-like 1 (NAP1L1) in gastric cancer patients: a potential biomarker with diagnostic and prognostic utility
No full textBackground: The nucleosome assembly protein 1-like 1 (NAP1L1) is suggested to have an oncogenic role in several tumors based on its overexpression. However, its diagnostic and prognostic role in gastric cancer remains unclarified. This study aimed to evaluate the diagnostic and prognostic utility of NAP1L1 in gastric cancer patients.
Methods: A total of 85 patients [mean (SD) age: 60.9 (1.6) years, 49.4% were males] with newly-diagnosed gastric cancer and 40 healthy individuals [mean (SD) age: 60.7 (1.7) years, 52.5% were males] were included. Data on patient demographics (age, gender), TNM stages and tumor size, and the serum NAP1L1 levels were recorded.
Results: Serum NAP1L1 levels were significantly higher in gastric cancer patients than in control subjects [12 (9.5-13.8) vs. 1.8 (1.5-2.4) ng/mL, p 4 vs. <4 cm (p < 0.001), M1 vs. M0 stage (p < 0.001), N2 vs. N0 and N1 stage (p < 0.001), and T4 vs. lower T stage (p < 0.001) were associated with significantly higher serum NAP1L1 levels in gastric cancer patients.
Conclusions: Our findings revealed for the first time that serum levels for NAP1L1 were overexpressed in the gastric cancer, as also correlated with the disease progression. NAP1L1 seems to be a potential biomarker for gastric cancer, providing clinically important information on early diagnosis and risk stratification
Pan-European survey on medication adherence management by healthcare professionals
Get PDFAims: While medication adherence (MA) is a key prerequisite for achieving optimal clinical and economic outcomes, nonadherence is highly prevalent. Assessing how healthcare professionals (HCPs) in Europe manage MA, focusing on measurement, reporting and interventions, is the subject of this study.
Methods: A cross-sectional study was conducted among 40 European countries and quantitative analysis was conducted via an online survey. The multi-language online survey was created using Webropol 3.0 survey and reporting tool. Descriptive statistics and chi-squared tests were applied.
Results: In total, 2875 HCPs (pharmacists: 39.9%; physicians: 36.7%; nurses: 16.4%) from 37 European countries participated. The most used methods for MA assessment were direct communication with patients (86.4%) and referring to personal patient records (56.7%) (P < 0.0001). Physicians (74.9%) and nurses (58.8%) were more aware of problems related to MA in contrast to pharmacists (48.6%) (P < 0.001). Almost all HCPs (92.6%) indicated that MA-enhancing interventions involved mainly direct communication with nonadherent patients (93.3%) and their caregivers (55.7%). Medication review and related optimization of therapy were mainly performed in Western European countries (46.8%). Technological solutions were ranked as one of the less applied approaches (10-15%) (P < 0.001).
Conclusions: HCPs in all European regions recognize MA management as an integral element of overall patient-centred care. More efforts are needed to ensure timely, adequate and relevant MA assessment, reporting and improvement and involvement of all HCPs, especially among pharmacists who were generally less aware of MA issues. Promotion and use of digital technological solutions should be the focus of current and future clinical practice to optimize MA management processes
Design optimization and simulation of a 3D printed cable-driven continuum robot using IKM-ANN and nTop software
No full textThe first aspect of the paper focuses on presenting the innovative design of a new continuum robot, which was initially conceptualized using SolidWorks and then brought to life through 3D printing. This section illustrates the construction process, detailing the wiring method and the separator between each section of the robot. A key feature of the newly proposed design is the ball-like shape on the upper side of each disk, allowing each disk to rotate freely and gracefully in conjunction with the next one. To further enhance the design, the disk was optimized using nTopology software, an AI-based solution that reduces weight while maintaining performance. This modern engineering tool proved to be instrumental in addressing engineering challenges effectively. Subsequently, both the original and optimized disks were fabricated using 3D printing technology. In addition to the physical construction, the study employed an Artificial Neural Network (ANN) coupled with Particle Swarm Optimization (PSO) to simulate the developed model by solving its inverse kinematic model. The findings from this research have paved the way for a new continuum robot design that can be trained using the ANN-PSO method. Furthermore, the powerful nTopology tool was demonstrated to be capable of skillfully optimizing any given components without sacrificing performance