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Micro PV solar station to support home residential using multi-level inverter for stand – alone load
Get PDFThis study presents a standalone photovoltaic (PV) system for residential loads utilizing
multi-level inverter. The system is designed to optimize the power output of the PV panels
through maximum power point tracking (MPPT) control, enhance the efficiency of the
power conversion process using a multi-level inverter, and consider different weather
conditions to ensure reliable operation. The MPPT control algorithm continuously tracks
the maximum power point of the PV array, adjusting the operating point to maximize the
energy harvested from the solar panels. This enables efficient utilization of the available
solar energy and improves the overall system performance.
The multi-level inverter is employed to convert the DC power generated by the PV array
into AC power suitable for residential loads. Its advanced switching techniques and
multiple voltage levels enable reduced harmonic distortion, improved voltage quality, and
enhanced power conversion efficiency. The multi-level inverter also provides better
control over the power flow and allows for seamless integration of the PV system with the
residential loads. Moreover, the system takes into account different weather conditions,
such as variations in solar irradiance and temperature, to ensure optimal operation and
robustness. By adapting to changing environmental factors, the system can maintain stable
power generation and supply even in challenging weather conditions. Finally, the whole
system is verified by using MATLAB Simulink simulation software
Remineralization efficiency of three different agents on artificially produced enamel lesions: A micro-CT study
Get PDFObjective: This study aimed to assess the remineralization efficacy of casein phosphopeptide-amorphous calcium phosphate (CPP-ACP), silver diamine fluoride/potassium iodide (SDF/KI), and sodium fluoride with functionalized tricalcium phosphate (NaF/fTCP) on artificial early enamel lesion using laser fluorescence and micro-CT analysis.
Methodology: On extracted impacted third molars, artificial enamel lesions were prepared. Twenty-eight specimens were randomly assigned to four groups (n = 7 per group): a control group (artificial saliva), CPP-ACP (GC Tooth Mousse), SDF/KI (Riva Star), and NaF/fTCP (Clinpro White varnish). Following the manufacturer's instructions, the remineralization agents were applied to demineralized surfaces. Laser fluorescence and micro-CT were used to evaluate the remineralization efficacy of the agents and analyzes were performed during four stages: before demineralization, after demineralization, 1st day of remineralization and 30th day of remineralization. Shapiro-Wilk test, repeated measures two-way ANOVA, and Spearman correlation tests were used for statistical analysis. A significant level of p < 0.05 was established.
Results: SDF/KI significantly reduced the lesion area and lesion volume on the demineralized enamel surface after 30 days of remineralization. In the T3 period, SDF/KI increased the mineral density statistically significantly compared to the T1 period. The laser fluorescence values for all three remineralizing agents exhibited a linear decrease. A significant correlation between the fluorescence values and the mineral density was found (p = 0.01).
Conclusion: All three investigated agents were showed positive remineralization efficacy on artificial enamel lesion. However, SDF/KI, containing silver diamine fluoride and potassium iodide exhibited superior than other agents in promoting remineralization.
Clinical significance: Although all three remineralization agents showed positive remineralization efficacy on artificial enamel lesions, SDF had higher remineralization performance over the other two agents. SDF has potential to prevent progression of demineralization in treating children with high caries risk in the long-term
New data encryption method for internet of things
No full textCyberspace is a complex environment consisting of heterogeneous technologies (i.e.,
Internet of Things, Fog Computing and Cloud Computing and so forth) resulting from
interacting services, software and people on the Internet. It allows users to interact, share
information, swap ideas, engage in social or discussion forums, play games, and conduct
business, among many other activities. The biggest challenges facing cyberspace today are
Cyber attacks, which affect security and integrity services. However, many traditional
security mechanisms provide protection and security services to solve these issues.
Therefore, many researchers have been focused on solving security and integrity issues by
growing the need for effective lightweight encryption techniques that incorporate both
lightweight symmetric and asymmetrical algorithms' advantages.
In this thesis, we will implement and design a lightweight encryption method which has the
following characteristics (Key less, Encryption & Integrity, Text & Number End-to-End
Encryption, Reduce Traffic and processing overhead). In addition, the proposed system
provide the data integrity via applying HASH 256 function to generate HASH value.
The proposed lightweight encryption algorithm focuses on the optimal use of the resources
of Internet of Things devices, so that it greatly saves all of (Processor, Memory, Energy,
Time, and Bandwidth (no need to distribute keys)) on the other hand, giving high security,
especially against the crypto analyser. In addition The proposed lightweight encryption algorithm has the ability to manipulated both text and numbers for English and Arabic
languages.
Also, to achieving data integrity in proposed system within the Internet of Things
environment, 4hexa decimal digit from HASH value were used instead of original 64 hexa
decimal digit HASH value to lower the bandwidth of the network, processing, and storage
Detection of malicious SQL injections using SVM and KNN algorithms
Get PDFIn this age of technology, cyber-security plays a critical role in safeguarding our sensitive
data and information. Our concern for years has been the creation of A more robust system
that is able to anticipate and prevent cyberattacks, as there are countless cyberattacks that
occur every day. The goal of this research work is to create methods that can effectively
identify and stop SQL Injection Attacks. An SQL Injection attack is a kind of cyberattack in
which malevolent SQL requests are used to manipulate internal data and retrieve data from
the back-end database that was not supposed to be visible. A database is even more open to
various types of assaults as a result of SQL Injection Attack. Since the majority of businesses
keep their data in SQL-based back databases, all of their data is vulnerable to a
straightforward attack if the databases are not well secured. The goal of this research is to
identify the optimal machine learning approach for SQL Injection Attack prediction and
prevention in order to build a model. This project includes a brief description of our work
plan, our experiments, and the outcomes of our studies. The support vector machine and knearest-neighbors’ techniques have been used to an algorithm, and the accuracy ratings of
the SVM and KNN classifiers exceeded 99%
Exploring factors affecting English language teacher wellbeing: insights from positive psychology
No full textEven in the face of adversities such as pandemics, teachers worldwide have demonstrated perseverance and resilience in achieving educational objectives. The outburst of the COVID-19 pandemic and the resulting psychological and social pressures coupled with physical health issues disturbed the balance of resources and challenges that define teacher wellbeing. To document the possible influences of the recent pandemic on English as a Foreign Language (EFL) teacher wellbeing, this project shifts its focusing lens toward teachers in private language centers in Iran. Following convenience sampling, seven teachers were recruited for this study and interviewed online, and their wellbeing was canvassed from several angles. The findings of the MAXQDA analysis indicated that the comorbidity of the pandemic and already -existing economic stagnation cast a dark shadow on teacher wellbeing in Iran. Moreover, financial pressures on EFL teachers severely influence the work -life balance in the context of this study. The findings also revealed that teachers ' intrinsic motivation and the satisfaction they gained from learner achievements boosted their wellbeing by creating positive emotions. Recommendations are made based on positive psychology for the stakeholders (i.e., teacher trainers, supervisors, parents, and teachers) to help teachers flourish post -pandemic
Is ChatGPT reliable and accurate in answering pharmacotherapy-related inquiries in both Turkish and English?
No full textIntroduction: Artificial intelligence (AI), particularly ChatGPT, is becoming more and more prevalent in the healthcare field for tasks such as disease diagnosis and medical record analysis. The objective of this study is to evaluate the proficiency and accuracy of ChatGPT in different domains of clinical pharmacy cases and queries.
Methods: The study NAPLEX® Review Questions, 4th edition, pertaining to 10 different chronic conditions compared ChatGPT's responses to pharmacotherapy cases and questions obtained from McGraw Hill's, alongside the answers provided by the book's authors. The proportion of correct responses was collected and analyzed using the Statistical Package for the Social Sciences (SPSS) version 29.
Results: When tested in English, ChatGPT had substantially higher mean scores than when tested in Turkish. The average accurate score for English and Turkish was 0.41 ± 0.49 and 0.32 ± 0.46, respectively, p = 0.18. Responses to queries beginning with "Which of the following is correct?" are considerably more precise than those beginning with "Mark all the incorrect answers?" 0.66 ± 0.47 as opposed to 0.16 ± 0.36; p = 0.01 in English language and 0.50 ± 0.50 as opposed to 0.14 ± 0.34; p < 0.05in Turkish language.
Conclusion: ChatGPT displayed a moderate level of accuracy while responding to English inquiries, but it displayed a slight level of accuracy when responding to Turkish inquiries, contingent upon the question format. Improving the accuracy of ChatGPT in languages other than English requires the incorporation of several components. The integration of the English version of ChatGPT into clinical practice has the potential to improve the effectiveness, precision, and standard of patient care provision by supplementing personal expertise and professional judgment. However, it is crucial to utilize technology as an adjunct and not a replacement for human decision-making and critical thinking
Epilepsy disease detection by EEG signal recognition using AI technique and IoTs
Get PDFThis exploration work focuses on the bettered soothsaying of Geomagnetic storms in the
environment of disaster operation. Geomagnetic storms, caused by disturbances in Earth's
magnetosphere, can have significant impacts on critical architectures and communication
systems. Accurate soothsaying of these storms is essential for effective disaster preparedness
and response strategies. In this study, we propose an approach grounded on Long ShortTerm Memory(LSTM) models to read geomagnetic exertion. We gather and preprocess
applicable time series data, employ data disquisition and analysis ways, and develop a
acclimatized LSTM model. The model is trained and estimated using colorful performance
criteria, including loss functions and the Root Mean Squared Error(RMSE). Our results
demonstrate the effectiveness of the LSTM model in landing temporal dependences and
patterns, leading to accurate prognostications. Furthermore, we discuss the practical
applicability of our approach in disaster management scenarios and highlight avenues for
future research. The outcomes of this research provide valuable insights and contribute to
enhancing the forecasting of Geomagnetic storms, ultimately improving disaster
management strategies and resilience in the face of these natural phenomena.Bu keşif çalışması, afet operasyonu ortamında Jeomanyetik fırtınaların daha iyi kehanet
edilmesine odaklanıyor. Dünyanın manyetosferindeki bozuklukların neden olduğu
jeomanyetik fırtınaların kritik mimariler ve iletişim sistemleri üzerinde önemli etkileri
olabilir. Bu fırtınalara ilişkin doğru kehanet, etkili afet hazırlığı ve müdahale stratejileri için
çok önemlidir. Bu çalışmada, jeomanyetik eforu okumak için Uzun Kısa Süreli Bellek
(LSTM) modellerine dayanan bir yaklaşım öneriyoruz. Uygulanabilir zaman serisi verilerini
topluyor ve ön işliyoruz, veri inceleme ve analiz yollarını kullanıyoruz ve iklimlendirilmiş
bir LSTM modeli geliştiriyoruz. Model, kayıp fonksiyonları ve Ortalama Karekök Hata
(RMSE) dahil olmak üzere renkli performans kriterleri kullanılarak eğitilir ve tahmin edilir.
Sonuçlarımız, LSTM modelinin zamansal bağımlılıkların ve kalıpların inişinde etkinliğini
göstererek doğru tahminlere yol açtığını göstermektedir. Ayrıca, yaklaşımımızın afet
yönetimi senaryolarına pratik uygulanabilirliğini tartışıyor ve gelecekteki araştırmalar için
yolları vurguluyoruz. Bu araştırmanın sonuçları değerli bilgiler sağlıyor ve Jeomanyetik
fırtınaların tahmininin geliştirilmesine, sonuçta afet yönetimi stratejilerinin ve bu doğal
olaylar karşısında dayanıklılığın geliştirilmesine katkıda bulunuyor
Turkish-Russian relations after the Syrian Civil War
No full textThis thesis seeks to examine and make sense of the evolving nature of Turkish-Russian ties
since 2011. Moreover, the thesis uses qualitative research design to explore and analyses
Turkish-Russian relations in the aftermath of the Syrian Civil War, through the utilization
of document analysis and thematic analysis. Consequently, the thesis concluded that
Turkish-Russian relations can benefit from different opportunities in the future.
Nevertheless, there are still a number of obstacles that challenge the future of cooperation
between Turkey and Russia
Improved performance and cost algorithm for scheduling IoT tasks in fog-cloud environment using gray wolf optimization algorithm
No full textToday, the IoT has become a vital part of our lives because it has entered into the precise details of human life, like smart homes, healthcare, eldercare, vehicles, augmented reality, and industrial robotics. Cloud computing and fog computing give us services to process IoT tasks, and we are seeing a growth in the number of IoT devices every day. This massive increase needs huge amounts of resources to process it, and these vast resources need a lot of power to work because the fog and cloud are based on the term pay-per-use. We make to improve the performance and cost (PC) algorithm to give priority to the high-profit cost and to reduce energy consumption and Makespan; in this paper, we propose the performance and cost-gray wolf optimization (PC-GWO) algorithm, which is the combination of the PCA and GWO algorithms. The results of the trial reveal that the PC-GWO algorithm reduces the average overall energy usage by 12.17%, 11.57%, and 7.19%, and reduces the Makespan by 16.72%, 16.38%, and 14.107%, with the best average resource utilization enhanced by 13.2%, 12.05%, and 10.9% compared with the gray wolf optimization (GWO) algorithm, performance and cost algorithm (PCA), and Particle Swarm Optimization (PSO) algorithm
Visual quality assessment of interior space through colors
No full textThis study uses a mixed-methods approach to examine the effects of color on interior space
visual quality. Specifically, quantitative color dominance analysis and qualitative user input
are combined. K-means clustering analysis was used to identify prominent colors and
evaluate their distribution within each living room image collection, which consisted of 100
images chosen for their varied colors. Concurrently, an assessment is given to 87 individuals
to find out how they feel about the colors in terms of aesthetic appeal, comfort, functionality,
and overall satisfaction with the colors. The results of the color analysis are compared with
user input to detect trends that guide design choices. The study offers a nuanced
understanding of the effect of color dominance and harmony on the aesthetic and functional
dynamics of interior spaces, providing valuable insights for enhancing design practices in
the fields of architecture and interior design.Bu çalışmada, rengin iç mekan görsel kalitesi üzerindeki etkilerini incelemek için karma bir
yöntem yaklaşımı kullanılmıştır. Özellikle, niceliksel renk baskınlığı analizi ve niteliksel
kullanıcı girdisi birleştirilmiştir. K-ortalamalar kümeleme analizi, öne çıkan renkleri
belirlemek ve bunların çeşitli renkleri için seçilen 100 görüntüden oluşan her bir oturma
odası görüntü koleksiyonu içindeki dağılımını değerlendirmek için kullanılmıştır. Eş
zamanlı olarak, 87 kişiye estetik çekicilik, konfor, işlevsellik ve renklerden genel
memnuniyet açısından renkler hakkında nasıl hissettiklerini öğrenmek için bir
değerlendirme verilmiştir. Renk analizinin sonuçları, tasarım seçimlerini yönlendiren
eğilimleri tespit etmek için kullanıcı girdileri ile karşılaştırılmıştır. Çalışma, renk hakimiyeti
ve uyumunun iç mekanların estetik ve işlevsel dinamikleri üzerindeki etkisine dair incelikli
bir anlayış sunarak, mimarlık ve iç tasarım alanlarındaki tasarım uygulamalarını geliştirmek
için değerli içgörüler sağlamaktadır