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Yetişkin bireylerde benlik saygısı ile sürekli öfke ve öfke ifade tarzları arasındaki ilişki
Get PDFBu çalışma yetişkin bireylerin benlik saygısı düzeyleri ile sürekli öfke ve öfke ifade tarzları
arasındaki ilişkiyi incelemek amacıyla gerçekleştirilmiştir. Araştırmanın evrenini İstanbul
ilinde yaşayan yetişkin bireyler oluşturmaktadır. Örneklemini ise; evren grubundan tesadüfi
yöntemle seçilmiş 195’i kadın 166’sı erkek olmak üzere toplam 361 yetişkin bireyden
oluşmaktadır. Araştırmada, araştırmacı tarafından oluşturulan kişisel bilgi formu, benlik
saygılarını ölçmek amacıyla Rosenberg tarafından geliştirilmiş Rosenberg Benlik Saygısı
Ölçeği (Rosenberg Self-Esteem Scale) ve sürekli öfke ve öfke ifade tarzlarını ölçmek için,
Spielberger vd. tarafından 1983 yılında geliştirilmiş “Sürekli Öfke (SL Öfke) ve Öfke İfade
Tarzı (Öfke-Tarz)” ölçeği kullanılmıştır. Verilerin analizlerinde SPSS 25 programı
kullanılmıştır. Analiz noktasında tanımlayıcı istatistikler, korelasyon analizi ve ANOVA
analizlerinden yararlanılmıştır. Araştırma bulgularında, belirlenen hipotezlerle tutarlı bir
şekilde, benlik saygısı düzeyleri ile sürekli öfke ve öfke ifade tarzları arasında belirgin bir
ilişki bulunduğu ortaya konmuştur. Özellikle, düşük benlik saygısı (yüksek RBSÖ puanları)
olan yetişkinlerin sürekli öfke, dışa ve içe yönelik öfke düzeylerinin yüksek olduğu
görülmüştür. Öfke kontrolü ile yüksek benlik saygısı (düşük RBSÖ puanları) arasında
bulunan pozitif ilişki, yüksek benlik saygısına sahip yetişkinlerin öfke yönetiminde daha
başarılı olduğunu göstermektedir. Tek yönlü ANOVA test sonuçlarına göre de benlik saygısı
düzeylerinin sürekli öfke ve öfke ifade tarzları üzerinde anlamlı bir etkisi olduğunu ortaya
koymaktadır (p<.05). Sonuç olarak yetişkinlerde benlik saygısı arttıkça sürekli öfke, dışa ve
içe yönelik öfke düzeyleri azalırken; öfke kontrolü seviyesinin arttığı gözlemlenmiştir.This study was conducted to examine the relationship between self-esteem levels in adult
individuals and their continuous anger and anger expression styles. The population of the
research comprised adult residents of Istanbul province, with a sample of 361 individuals
randomly selected from this population, including 195 females and 166 males. To achieve
the research objectives, the researcher utilized a personal information form along with the
Rosenberg Self-Esteem Scale developed by Rosenberg to measure self-esteem. Additionally,
the "Continuous Anger (SL Anger) and Anger Expression Style (Anger-Style)" scale
developed by Spielberger et al. in 1983 was employed to measure continuous anger and
anger expression styles. Data analysis was conducted using SPSS 25 software, incorporating
descriptive statistics, correlation analysis, and ANOVA. The research findings consistently
revealed a significant relationship between self-esteem levels and continuous anger as well
as anger expression styles, in line with the formulated hypotheses. Specifically, individuals
with low self-esteem (high RSES scores) exhibited higher levels of continuous anger, both
inwardly and outwardly directed. Moreover, a positive correlation was observed between
anger control and high self-esteem (low RSES scores), indicating that individuals with high
self-esteem are more proficient in managing their anger. Furthermore, the results of the oneway ANOVA test indicated a significant impact of self-esteem levels on continuous anger
and anger expression styles (p<0.05). In conclusion, as self-esteem increases among adults,
levels of continuous anger, both inwardly and outwardly directed, decrease, while the ability
to control anger increases
Performance evaluation of a compression ignition engine running on diesel, naphtha
No full textInternal combustion engines are pivotal in modern society, extensively used in various
sectors like industry and agriculture. They're the primary engines for power generation on
land, sea, and in remote areas, powering multiple machines and electricity generation. Their
ubiquity extends to small transport vehicles. Due to their significant energy consumption,
there's a continual effort to enhance their efficiency. This involves exploring fuel economy
through innovative technologies and introducing alternative fuel additives like alcohol,
naphtha, and toluene. Engine performance evaluation hinges on key parameters like braking
power, fuel efficiency, thermal efficiency, air-fuel ratio, and engine speed, volumetric
efficiency, braking power, etc.) and the second is the emission ratios that the engine puts out
to the outside environment. The choice of fuel significantly influences the efficiency and
output of internal combustion engines. Different fuels can lead to variations in engine
performance, impacting factors such as power output, fuel consumption, and overall engine
efficiency, especially in internal combustion engines. In this thesis, a practical study was
conducted to show the effect of adding naphtha to diesel fuel in different proportions to
improve engine performance. The study was conducted on a four-stroke, two-cylinder diesel
engine using (5) types of mixtures consisting of diesel fuel in addition to naphtha, with
volume ratios of (1%, 3%, 5%, 10%) for (diesel-naphtha) mixtures. The analysis was carried
out based on experimental data at different loads (1-5 kg) and at different rotational speeds
that ranged between (1250-2250) revolutions per minute. Using practical data, the thermal
performance parameters of the diesel engine and different fuel mixtures were evaluated and
analyzed. The engine performance coefficient was studied by operating it with diesel fuel
(D100) and with different percentages of mixtures (diesel - naphtha) and under similar operating conditions in terms of speed and applied load. The obtained results showed that
the specific brake consumption rate decreases with increasing load and speed stability. It
was also shown that the average specific fuel consumption increases with the increase in the
percentage of naphtha added to diesel fuel with constant speed and applied load. And it was
found that the conventional diesel fuel (D100) has the lowest average specific fuel
consumption compared to the rest of the fuel types, and the highest specific consumption is
the fuel mixture (D90N10). It was also noted that all types of fuel recorded low fuel
consumption rates at the speed (1700 and 1850) rpm minute and load 5 kg. It was also shown
that the efficiency increases with the increase in the percentage of naphtha added to the diesel
fuel until it reaches its maximum value at the mixing ratio of D95N5 compared to the rest of
the fuels. With the increase in the proportion of naphtha added to diesel fuel, then it reaches
its maximum value at the mixing ratio of D90N10 compared with the rest of the fuels. As
we can see from these results that the capacity of the mixture D90N10 at all loads is more
than the mixtures (D100, D95N5, D90N10, D92N8), respectively. The brake average
effective pressure is lowered by using naphtha as an additive with diesel fuel. The mean
brake effective pressure decreases as engine speeds increase
Machine learning and network security
No full textMachine Learning, an essential element of artificial intelligence, plays a vital role in fortifying network security. Despite its global acceptance, mastering the utilization of machine learning for network security requires substantial investment of time. Nonetheless, machine learning equips us with indispensable abilities to detect sophisticated hacker attacks proactively, often evading traditional human detection methods. Incorporating machine learning models has quickened the advancement of decision support systems in network security, boosting their speed, precision, and overall effectiveness. However, the efficacy of machine learning in this realm faces significant obstacles due to the increased susceptibility to adversarial attacks, particularly in crucial areas such as malware detection, intrusion detection, and spam filtering. The inherently adversarial nature of these applications perpetuates an ongoing battle between attackers and defenders. Recent advancements in machine learning have showcased its effectiveness in addressing complex issues, frequently rivalling or even surpassing human capabilities. Nonetheless, research indicates that machine learning models are susceptible to various attacks, posing a significant risk to both the models themselves and the systems they safeguard. Critically, these attacks operate covertly, exploiting the inherent opacity of deep learning models. While machine learning presents promising avenues for enhancing network security, it also introduces new challenges and threats that demand continuous research and innovation to counter evolving adversarial tactics
MICROMERIA MYRTIFOLIA BOISS. & HOHEN’İN BİYOLOJİK AKTİVİTELERİNİN BELİRLENMESİ
No full textObjective: Lamiaceae family has a wide variety of well-known and lesser-known plants with strong medicinal qualities. The genus Micromeria Benth. is a member of this family consisting of herbaceous plants with a variety of significant biological, phytochemical, and ethnobotanical uses. In this study, the biological activities of methanol and ethanol extracts of Micromeria myrtifolia were evaluated. Material and Method: To demonstrate the antioxidant activity DPPH radical scavenging activity and total phenolic content assays were done. The effects of the extracts on acetylcholinesterase (AChE) and monoamine oxidase-A were then assessed. Result and Discussion: Methanol extract showed the highest DPPH scavenging activity, at the dose of 10 mg/ml with a value of 96.55%. For the highest concentration that can be applicable, AChE inhibitions for the methanol and ethanol extracts were 25% and 27%, respectively. On the other hand, the inhibitory effects of the ethanol and methanol extracts of the plant on MAO-A were determined; for the ethanol extract IC50 value was found as 32.5876 ± 0.89 μg/ml, and for the methanol extract it was found as 34.6544 ± 0.76 μg/ml. It can be told that M. myrtifolia can act as a potential antioxidant. With further research and investigation, it is thought that Micromeria myrtifolia could be used as a natural source for the treatment of various neurological diseases. © 2024 University of Ankara. All rights reserved.Altınbaş University Scientific Research Fund, (PB2020-ECZ-3
Enhancing IoT Security Through Hardware Security Modules (HSMs)
No full text5th International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2024 -- 24 September 2024 through 27 September 2024 -- Dubrovnik -- 205021Strong security measures must be integrated in an era where data security is critical, particularly for sensitive data handled by IoT devices. In order to strengthen Internet of Things security, the use of Hardware Security Modules (HSMs) is investigated in this research. We examine the development and effectiveness of HSMs in boosting IoT security through a thorough study of the literature. Our results demonstrate the vital role that HSMs play in protecting cryptographic keys and thwarting any attacks. We explore the difficulties of incorporating HSMs into IoT environments and suggest practical approaches. This research concludes by highlighting the role that HSMs play in strengthening IoT security architecture. © 2024 IEEE
Diagnosis of Epileptic seizures and Hypoxic-ischemic encephalopathy using Artificial Intelligence based on EEG signal: A review
No full textThe brain is the nucleus for cognition and controls voluntary and involuntary activities inside the human body. Any neurological illness, regardless of its cause, will impair the brain's functionality. Certain neurological illnesses manifest symptoms as seizures. Epilepsy and Hypoxic-ischemic Encephalopathy (HIE) are the most similar disorders in symptoms, but at the neurological level, they are two completely different disorders. This difference is measured at the level of neural activity, as Electroencephalography (EEG) is one of the most distinctive tools used to measure neural activity in the brain. Experts use EEG to diagnose disorders through recorded brain activity, including seizures, but the diagnosis process consumes much time and effort. Adopting Artificial Intelligence (AI) techniques to extract the patterns of brain illnesses is a more efficient process for diagnosing disorders because it depends on computing and, thus, has high accuracy in diagnosing brain illnesses. In this research, we reviewed the most effective stages and methods adopted by researchers to diagnose brain disorders based on EEG and artificial intelligence techniques
A Comprehensive Survey of Predicting stock market prices: an analysis of traditional statistical models and machine-learning techniques
No full textThe stock market has witnessed a remarkable surge in popularity in recent years, attracting investors from all walks of life. However, predicting stock values remains a daunting task due to financial markets' inherent unpredictability and complexity. Despite these challenges, the stock market offers a dynamic and ever-changing platform for traders to invest in shares, with the potential for significant gains and losses. For investors, accurate forecasting of stock prices is crucial as it provides invaluable insights into a company's financial health and growth prospects. With this information, investors can make informed decisions, mitigate risks, and capitalize on lucrative opportunities in the market. As a result, extensive research has been dedicated to developing effective prediction methods, leveraging various mathematical models and machine-learning techniques. This research paper delves into the realm of stock market prediction, explicitly focusing on evaluating different machine-learning styles. The primary objective is to comprehensively analyze and compare the performance of these techniques in forecasting stock market behavior. By understanding the strengths and limitations of each method, investors, financial analysts, and market participants can gain critical knowledge to optimize their trading strategies and decision-making processes. To achieve this goal, the study explores an array of machine-learning algorithms, ranging from traditional linear regression models to sophisticated deep-learning approaches. These algorithms leverage historical stock market data, macroeconomic indicators, company financials, and sentiment analysis, among other factors, to predict future price movements and market trends. In addition to performance comparison, the research paper examines the impact of various factors that influence the effectiveness of these machine-learning techniques. Factors such as data quality, feature engineering, model selection, hyperparameter tuning, and market conditions play pivotal roles in the accuracy of predictions. Understanding these factors will aid in refining the model-building process and enhancing overall forecasting capabilities. The study encompasses an extensive dataset spanning multiple stock markets and periods, ensuring robustness and reliability in the findings. Performance evaluation metrics, including mean squared error, accuracy, precision, recall, and F1 score, will be employed to assess the predictive power of the machine-learning techniques objectively. Furthermore, the paper investigates the potential of ensemble methods, combining the strengths of multiple models to achieve enhanced prediction accuracy. Ensemble techniques, such as bagging, boosting, and stacking, have proven effective in diverse domains and are expected to demonstrate their value in stock market prediction. By the end of this research, readers will have a comprehensive understanding of the landscape of machine-learning techniques applied to stock market prediction. The findings will offer insights into which methods are most suitable for different market conditions and will aid in establishing best practices for effective and reliable stock market forecasting. In conclusion, this research paper serves as a valuable resource for investors, financial analysts, and researchers, thoroughly assessing machine-learning techniques' efficacy in predicting stock market behavior. It contributes to the growing body of knowledge in financial technology. It underscores the critical role of data-driven decision-making in navigating the complexities of the modern stock market
Investigation of nano-fluids in shell and tube heat exchangers using CFD simulation
Get PDFThis master thesis examines how nano-fluids can improve the way heat exchangers work,
focusing on the use of Al₂O₃ nano-fluids with a 2% volume fraction. Heat exchangers play
a crucial role in many industries. This research investigates whether nano-fluids, with their
special properties from tiny particles, can work better than common fluids like water in
transferring heat. To find this out, the study uses computer simulations, mainly using the
CAD software, SOLIDWORKS. This software was used to design the specific shell and tube
heat exchangers for the study and to run the computer simulations. The goal of these
simulations is to see how nano-fluids behave, especially when they mix with other fluids,
such as cold water, inside the heat exchanger. The main goal of this thesis is to compare how
well nano-fluids and regular fluids transfer heat. It also aims to give suggestions on how to
best use these fluids in heat exchangers. By understanding the benefits and challenges of
nano-fluids, this research hopes to suggest better and more energy-saving ways to transfer
heat in industrial settings
A study of scalable solutions for smart contracts performance
No full textThe emergence of blockchain technology has ushered in a transformative era of decentralized and trustless systems, challenging conventional centralized models reliant on intermediaries. At the core of this revolution lies the concept of smart contracts, self-executing agreements encoded on a blockchain, automating contractual processes without intermediaries. Smart contracts offer numerous advantages, including automation, cost reduction, transparency, and enhanced security. However, their widespread adoption faces challenges, with a paramount one being the limitation of scalability within blockchain networks. This paper studies existing scalable solution of blockchain, compares the existing techniques and discusses the best scalable solution depending on the requirement of the system. It assesses existing research, case studies and unlocking the full potential of smart contracts in the realm of blockchain technology
Islanding detection in distributed generation grids using recurrent neural networks
Get PDFThis research focuses on modeling and simulating islanding detection in distributed
generation grids using Recurrent Neural Networks (RNN). This study offerings just how
responsive influence production plus concentration can be present used to share islanding
detection in intermediate power distribution across many micro-girds with rule-based
mathematical model. Currently, just in high-level voltage substation, large energy plant life,
responsive energy is under control. In this study, the objective to use responsive ability close
to utilization positions by incorporating multi micro-grids across our distributed nets. Some
of the vital benefit of the R.N.N addition to the grid is shifting the structure nets from the
one-way to a two-way. This part of work out indicates how the M.G on the trucks provide
to monitor the energy at the trucks throughout a question or else link severed, just by what
means the system damages willpower decline by incorporating effective energy for islanding
detection with an accuracy of 98.79%, distributed generation D.G is definite equally “an
electrical power source that is either on the customer side of the meter or directly connected
to the distribution network”. Thanks to technology innovation, distributed generation (DG)
plants can now use a variety of resources, including hydropower, biomass, wind, tidal,
distribution, and micro-generation. The word "distributed" refers to a decentralized power
supply that is produced closer to the point of demand. The distributed generation (DG) plants
use smaller scale energy floras associated with the dispersal networks, in contrast to the
typical "centralized" systems, which create electricity from large-scale power plants and then
send it to the client across great distances. Due to the connection of DG plants with enormous capacity, such as the power plants, the traditional distribution net and accompanying
operation face certain challenges. D.G is the new knowledges, and which come up with a
incredibly nice running, competence, also better in reducing the power price since as of this
suppliers we be able to give the momentum to the method. By way of it presented with
influence stream formation thru detaching outlines after the influence net in what way the
energy about trucks willpower remain la-di-da directly and diminish its energy cost to below,
finished the wanted cost that is non with general structure and other structure take of counter
with unwelcome state