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Avrupa Yeşil Mutabakatı'nın Türkiye iklim politikalarına etkileri
No full text20. yüzyılda dünyayı etkisi altına alan iklim değişimi küresel boyutta krize dönüşmüş ve 21.
yüzyılın en önemli gündemi haline gelmiştir. İklim krizi özellikle son 10 yılda ekonomik ve
çevresel sorunlarla gündeme gelmiş, bu çerçevede Avrupa Birliği iklim krizi ile mücadelede
önemli bir aktör olmuş, sürdürülebilir ve çevre odaklı politikalar üretmiştir. Avrupa Birliği,
11 Aralık 2019’ de ortaya koyduğu Avrupa Yeşil Mutabakatı ile ekonomide ve enerjide
dönüşüm sağlayarak, yaşanılır bir refah toplumuna dönüşmeyi hedef almıştır. Üye devletler
ve Avrupa Birliği ile iş birliği içinde olan tüm ülkeler bu strateji paketinden doğrudan
etkilenmektedir. Türkiye de bu dönüşümün gerisinde kalmamak için yeşil ekonomiye geçiş
programını oluşturmuş, Avrupa Yeşil Mutabakatı kapsamında uyum paketini açıklamıştır.
Avrupa Birliği tam üyelik ile ilgili uyum çalışmaları durma noktasında olsa da yeni yeşil
düzen ile tam üyelik konusunda da pozitif bir etki oluşması beklenmektedir. Bu tez
çalışması, “Türkiye'nin Avrupa Yeşil Mutabakatı'na ilişkin mevzuat, politika ve kurumsal
düzenlemeleri, mutabakat hedefleriyle ne derecede uyumludur?” “Avrupa Yeşil
Mutabakatı'nın temelini oluşturan Sınırda Karbon Düzenleme Mekanizması'nın sektörel
düzeydeki olası etkileri nelerdir?” ve “Avrupa Yeşil Mutabakatı’nın Türkiye için olası
etkileri nelerdir?” sorularına yanıt arayacaktır.In the 20th century, climate change, which affected the world, turned into a global crisis,
becoming one of the most important agendas of the 21st century. Climate crisis has emerged
especially in the last decade with economic and environmental issues, and within this
framework, the European Union has become a significant actor in combating the climate
crisis, producing sustainable and environmentally focused policies. On December 11, 2019,
the EU announced the European Green Deal aiming to achieve a transformation in the
economy and energy sector towards a livable welfare society. All countries collaborating
with member states and the European Union are directly affected by this strategic package.
In order not to lag in this transformation, Turkey has developed a transition program to a
green economy and announced its compliance package within the scope of the European
Green Deal. Although the accession negotiations with the European Union have come to a
standstill, it is expected that there will be a positive impact on full membership with the new
green order. This thesis study will seek answers to the questions: "To what extent are
Turkey's legislation, policies, and institutional regulations regarding the European Green
Deal compatible with the deal's objectives? What are the potential sectoral impacts of the
Border Carbon Adjustment Mechanism, which forms the basis of the European Green Deal?
And what are the possible effects of the European Green Deal on Turkey?
Implementation of elliptic curve cryptography for internet of things (IOT) security
No full textThe Internet of Things, or IoT, is essential to the fields of industry, healthcare, and
information technology, among others. It is made up of numerous interconnected things that
are in communication with one another. Because approved objects, in addition to users, may
access data regarding the Internet of Things. For IoT applications and technology to be
widely adopted, security is a necessary component. To improve the security of the IoT data,
this study suggests an Elliptic Curve Cryptography approach. Two keys are used in Elliptic
Curve Cryptography (ECC): a public key and a private key. The user uses the private key
for encryption, and the public key is used for user identification during authentication.
Similar to this, using the private key, the sender encrypts; if secrecy is desired, using the
public key, one can decrypt the communication. Selecting the private key is a problem with
every public key. Random selection of small values raises concerns about the overall
algorithm's security. considering the values. This study suggests using the Cuckoo Search
Algorithm to select values at rando
The investigation of the molecular changes during lipopolysaccharide-induced systemic inflammation on rat hippocampus by using FTIR spectroscopy
No full textThe aim of this study is to reveal the molecular changes accompanying the neuronal hyper-excitability during lipopolysaccharide (LPS)-induced systemic inflammation on rat hippocampus using Fourier transform infrared (FTIR) spectroscopy. For this aim, the body temperature of Wistar albino rats administered LPS or saline was recorded by radiotelemetry. The animals were decapitated when their body temperature began to decrease by 0.5°C after LPS treatment and the hippocampi of them were examined by FTIR spectroscopy. The results indicated that systemic inflammation caused lipid peroxidation, an increase in the amounts of lipids, proteins and nucleic acids, a decrease in membrane order, an increase in membrane dynamics and changes in the secondary structure of proteins. Principal component analysis successfully separated control and LPS-treated groups. In conclusion, significant structural, compositional and functional alterations occur in the hippocampus during systemic inflammation and these changes may have specific characteristics which can lead to neuronal hyper-excitability
Modeling and analysis of a multi DOF robotic arm manipulator motion with image processing control
No full textThis In this research, the precision control of the KUKA KR R900-2 robotic arm is explored
for executing welding tasks along predefined circular and square paths, with an emphasis on
image processing for navigational guidance. The primary objective is the creation and testing
of welding routes using SOLIDWORKS software, followed by the application of
MATLAB's image processing capabilities to direct and assess the accuracy of the robot's
path adherence. Two distinct control systems are evaluated: the established PID controller
and the more recent Adaptive controller. Their effectiveness is determined by their ability to
maintain welding precision and consistency, particularly with the complex shapes of the
paths. Through comprehensive simulations and analyses, the potential for integrating
computer-aided design with image processing to enhance the precision of robotic welding is
demonstrated. The anticipated findings suggest the Adaptive controller's superiority over the
PID controller, providing insights into the advancement of automated welding and the
impact of sophisticated control systems on industrial robotic applications
Classification of hand sign language using deep learning algorithm
No full textPeople with hearing disabilities face many problems, which impede many of their social life
issues in all areas of communication, so effective communication is crucial in the
development of a nation. It promotes understanding and inclusivity among all members of
the community, including those who are deaf. Good communication is key to building and
maintaining a strong, cohesive society. I used 29,000 sign language images, each class
contained 1,000 images. I built a model from scratch (ASL.model) and compared it with preexisting models (Xception, Inception, ResNet, VGG16 and MobileNet). The study of
intelligent computers that can carry out activities without direct human guidance is known
as artificial intelligence (AI), a fast-growing topic within computer science. These tasks may
include learning, decision making, and problem solving, and they are often accomplished
through the use of algorithms, data, and machine learning techniques. To find the most
appropriate classification features to be used for classification, deep learning techniques will
be employed in this thesis to create a model for classifying sign language utilizing
photographs obtained from the Kaggle depository as a training data set. Deep learning is
now widely employed across a variety of industries due to its accuracy and efficiency,
particularly for vast yet complicated data, such as photos, sounds, or text, where deep
learning algorithms are taught using massive, labeled data sets. In this thesis, we used deep
learning to classify a total of twenty-nine sign language-related classes. We put forth a fresh framework for categorizing sign language. The suggested model was put into practice,
trained, verified, and tested. The model passed the test with a 99.97% success rate. To assess
the effectiveness of our suggested model (ASL.model) with that of these methods, we also
employed five pre-trained models (Xception, Inception, ResNet, VGG16, and MobileNet)
of assisting the performance of deep learning algorithms that use Convolutional Neural
Networks (CNNs). The five pre-trained models had F1-score levels of 100%, 99.51%,
99.87%, 100%, and 99.68%, respectively. The model Xception and VGG16 outperformed
all others in terms of testing accuracy but when the testing time was smaller than 1.45
seconds, the suggested model outperformed all others in terms of time.İşitme engelli bireyler, iletişimin her alanında sosyal yaşamdaki pek çok sorunu sekteye
uğratan pek çok sorunla karşı karşıyadır; bu nedenle etkili iletişim, bir ulusun gelişmesinde
büyük önem taşımaktadır. Sağır olanlar da dahil olmak üzere toplumun tüm üyeleri arasında
anlayış ve kapsayıcılığı teşvik eder. İyi iletişim, güçlü ve uyumlu bir toplum inşa etmenin
ve sürdürmenin anahtarıdır. 29.000 işaret dili görseli kullandım, her sınıfta 1.000 görsel
vardı. Sıfırdan bir model (ASL.model) oluşturdum ve bunu önceden var olan modellerle
(Xception, Inception, ResNet, VGG16 ve MobileNet) karşılaştırdım. Doğrudan insan
rehberliği olmadan faaliyetleri gerçekleştirebilen akıllı bilgisayarların incelenmesi,
bilgisayar biliminde hızla büyüyen bir konu olan yapay zeka (AI) olarak biliniyor. Bu
görevler öğrenmeyi, karar vermeyi ve problem çözmeyi içerebilir ve genellikle algoritmalar,
veriler ve makine öğrenimi teknikleri kullanılarak gerçekleştirilir. Sınıflandırma için
kullanılacak en uygun sınıflandırma özelliklerini bulmak amacıyla, Kaggle deposundan elde
edilen fotoğrafları eğitim veri seti olarak kullanarak işaret dilini sınıflandırmaya yönelik bir
model oluşturmak amacıyla bu tezde derin öğrenme teknikleri kullanılacaktır. Derin
öğrenme, doğruluğu ve verimliliği nedeniyle artık çeşitli endüstrilerde yaygın olarak
kullanılıyor; özellikle derin öğrenme algoritmalarının çok büyük, etiketli veri kümeleri
kullanılarak öğretildiği fotoğraflar, sesler veya metinler gibi çok büyük ancak karmaşık
veriler için. Bu tezde, işaret diliyle ilgili toplam yirmi dokuz dersi sınıflandırmak için derin
öğrenmeyi kullandık. İşaret dilini kategorize etmek için yeni bir çerçeve ortaya koyduk. Önerilen model uygulamaya konuldu, eğitildi, doğrulandı ve test edildi. Model testi %99,97
başarı oranıyla geçti. Önerilen modelimizin (ASL.model) bu yöntemlerle etkinliğini
değerlendirmek için ayrıca Evrişimli algoritmaları kullanan derin öğrenme algoritmalarının
performansına yardımcı olacak önceden eğitilmiş beş model (Xception, Inception, ResNet,
VGG16 ve MobileNet) kullandık. Sinir Ağları (CNN'ler). Önceden eğitilmiş beş modelin F1
puanı seviyeleri sırasıyla %100, %99,51, %99,87, %100 ve %99,68'di. Xception ve VGG16
modeli, test doğruluğu açısından diğerlerinden daha iyi performans gösterdi ancak test süresi
1,45 saniyeden küçük olduğunda önerilen model, zaman açısından diğer tüm modellerden
daha iyi performans gösterdi
Direct-to-implant retropectoral dual plane approach with autologous inferior-based dermal flap: does spy-elite laser angiographic system reduce complication rates?
No full textPurpose: The study aims to investigate the complications and long-term outcomes associated with retropectoral DTI breast reconstruction with IDF utilizing the SPY-Elite laser angiographic system.
Material and method: This retrospective study was conducted from June 2017 to January 2023. We examined 52 patients (85 breasts) treated with a direct-to-implant retropectoral dual plane approach with IDF implant coverage. Informed consent was duly obtained from every participant. Inclusion criteria dictated that patients should have medium to large breasts and a second or third degree of ptosis, as per the Regnault ptosis scale. During the intraoperative evaluation, the mastectomy flaps and IDF were assessed with the SPY-Elite laser angiographic system using near-infrared imaging. We recorded patient demographics, characteristic data, and complications.
Results: A total of 52 patients, aged 27 to 63, underwent 85 mastectomies using a direct-to-implant retropectoral approach with inferior dermal flap. The average age of the patients was 48, and their average body mass index was 30.8, with a range of 28 to 43. The distance from the nipple to the inframammary fold varied between 14 and 24 cm. The implants used had an average size of 275 cc, ranging from 250 to 650 cc. Textured anatomic implants with either moderate plus or high profile were used in all cases. The sternal notch to nipple distance for these patients ranged from 24 to 38 cm. During the evaluation using the SPY-Elite laser angiographic system, insufficient distal marginal perfusion was detected in five out of 85 inferior dermal flaps, measuring between 2 and 5 cm2. These areas were subsequently debrided, and the reconstructions were successfully completed, representing 5.8% of cases. No instances of necrosis related to IDF have been observed. There have been no failed assessments conducted by SPY ICG. In total, the complication rate was 15.2%, with minor complications occurring in 8.2% of the breasts (7 out of 85) and major ones in 7% (6 out of 85). The subjects were monitored for an average of 14 months, the duration ranging from 12 to 24 months.
Conclusion: Inferior dermal flaps have considerable advantages, such as a natural autologous blood supply, a more realistic tissue thickness and texture, lower costs, and better tolerance to post-reconstruction radiation. Moreover, using the IDF technique and assessing the perfusion of IDF and mastectomy flaps through the SPY-Elite laser angiographic system appears to be a dependable, efficient way to achieve good cosmetic results in one operation, eliminating the need for additional surgeries.
Level of evidence iv: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine Ratings, please refer to Table of Contents or online Instructions to Authors www.springer.com/00266
Advanced AI-based cyber security network to reduce the cyber crimes in the world
No full textIn the current digital epoch, cybersecurity emerges as a paramount concern, given the
relentless advancement of cyber threats that pose formidable challenges to global digital
infrastructures. This thesis embarks on a comprehensive exploration, centering on advanced
neural network models, particularly emphasizing the Convolutional Neural Network (CNN),
to address the imperative need for resilient cyber defense mechanisms. Employing
meticulous experimentation and analysis utilizing the 'Cyber Security Indexes' dataset, this
study meticulously evaluates the performance of these models across a spectrum of cyberattack types. The findings illuminate the CNN model's robustness and efficacy, portraying
its potential in fortifying cybersecurity measures and countering the evolving landscape of
threats. Throughout this exploration, with a focus on achieving a 96% accuracy threshold,
the study outlines the implications of these advancements in fostering a more secure digital
landscape on a global scale. The comprehensive insights drawn from this research
collectively underscore the pivotal role of the CNN model in fortifying cybersecurity
defenses, offering a beacon of hope in mitigating the escalating cyber threats prevalent in
today's digital milieu
Video yardımlı torakoskopik cerrahide serratus anterior düzlem blok’unun ameliyat sonrası analjezik etkinliğinin farklı blok tipleri ile karşılaştırılması: Randomize kontrollü çalışmaların sistematik derleme ve meta-analizi
No full textBackground: The study aimed to compare the analgesic efficacy of single-shot serratus anterior plane block (SAPB) for video-assisted thoracoscopic surgery (VATS) with other regional block techniques. Methods: In this meta-analysis, randomized controlled trials published in the PubMed, Scopus, Web of Science, ClinicalKey, and PROSPERO electronic databases between March 24, 2014 and March 24, 2024 comparing the analgesic efficacy of SABP with other regional blocks in adult patients undergoing VATS were reviewed. Results: Nine randomized controlled trials consisting of a total of 537 participants (287 males, 250 females; mean age: 55.2±13.1 years) were included in this meta-analysis. Serratus anterior plane block was compared with erector spinae plane block (ESPB), local infiltration anesthesia (LIA), and thoracic paravertebral block (TPVB). The postoperative 24-h cumulative opioid consumption was statistically significantly higher in SAPB than in ESPB (standardized mean difference [SMD]=1.98; 95% confidence interval [CI], 0.23 to 3.73; Z=2.22; p=0.03; I2=97%; random effects model) and TPVB (SMD=0.63; 95% CI, 0.31 to 0.96; Z=3.84; p<0.001; I2=0%; fixed effects model) and lower than in LIA (SMD=–1.77; 95% CI, –2.24 to –1.30; Z=7.41; p<0.001; I2=0%; fixed effects model). Active pain scores 2 h postoperatively were statistically significantly lower in SAPB than in LIA (SMD=–2.90; 95% CI, –5.29 to –0.50; Z=2.37; p=0.02; I2=93%; random-effects model). At 12 h postoperatively, both passive pain scores (SMD=0.37; 95% CI, 0.07 to 0.66; Z=2.41; p=0.02; I2=0%; fixed effects model) and active pain scores (SMD=0.55; 95% CI, 0.25 to 0.85; Z=3.60; p<0.001; I2=0%; fixed effects model) were statistically significantly lower in ESBP than in SAPB. There was no difference between SAPB and the other groups in terms of the incidence of postoperative nausea and vomiting. Conclusion:After a comprehensive evaluation of postoperative analgesic effects, it appears that ESBP and TPVB may be better than SABP, and SABP may be better than LIA for analgesia of patients undergoing VATS. Further studies are required to determine the optimal regional analgesia technique in VATS
Therapeutic and prophylactic effects of fulvic acid on a breast cancer model established by MCF-7 cell line in SCID mice
No full textArticle Number : 5871444Introduction. There is still minimal scientific understanding of effects of fulvic acid (FA) on breast cancer. We investigated the prophylactic, therapeutic, and combined effects of FA in a breast cancer model created using MCF-7 cell line in severe combined immunodeficiency disease (SCID) mice. Results. Four experimental groups were established as the control group (Group C), prophylaxis group (Group P), therapeutic group (Group T), and prophylaxis + therapeutic group (Group P + T). Tumor growth was observed by the in vivo imaging system and macroscopically in mammary glands of all mice (100%) of Group C, microscopically in only one mouse of Group P (12.5%), in four mice in Group T (50%), but only one animal (12.5%) in Group P + T. Immunohistochemistry (IHC) showed that p53 staining was significantly higher in tissues of Group C compared to other groups (P 0.05). Bcl-2 staining was significantly higher in Group C compared to Group P + T (P = 0.015) and higher in Group P + T compared to Group T (P = 0.021) but no significant difference was found between Group P and others (P > 0.05). Bax staining was significantly higher in Group C compared to others (P 0.05). Conclusion. Prophylactic FA treatment can prevent tumor formation by inducing variations in the expression of p53, BcL-2, and Bax proteins in mammary glands of SCID mice before tumor formation. This suggests that FA may be a powerful inhibitory candidate for the prevention of tumorigenesis in breast cancer
Advancing medical imaging: detecting polypharmacy and adverse drug effects with Graph Convolutional Networks (GCN)
Get PDFPolypharmacy involves an individual using many medications at the same time and is a frequent healthcare technique used to treat complex medical disorders. Nevertheless, it also presents substantial risks of negative medication responses and interactions. Identifying and addressing adverse effects caused by polypharmacy is crucial to ensure patient safety and improve healthcare results. This paper introduces a new method using Graph Convolutional Networks (GCN) to identify polypharmacy side effects. Our strategy involves developing a medicine interaction graph in which edges signify drug-drug intuitive predicated on pharmacological properties and hubs symbolize drugs. GCN is a well-suited profound learning procedure for graph-based representations of social information. It can be used to anticipate the probability of medicate unfavorable impacts and to memorize important representations of sedate intuitive. Tests were conducted on a huge dataset of patients' pharmaceutical records commented on with watched medicate unfavorable impacts in arrange to approve our strategy. Execution of the GCN show, which was prepared on a subset of this dataset, was evaluated through a disarray framework. The perplexity network shows the precision with which the show categories occasions. Our discoveries demonstrate empowering advance within the recognizable proof of antagonistic responses related with polypharmaceuticals. For cardiovascular system target drugs, GCN technique achieved an accuracy of 94.12%, precision of 86.56%, F1-Score of 88.56%, AUC of 89.74% and recall of 87.92%. For respiratory system target drugs, GCN technique achieved an accuracy of 93.38%, precision of 85.64%, F1-Score of 89.79%, AUC of 91.85% and recall of 86.35%. And for nervous system target drugs, GCN technique achieved an accuracy of 95.27%, precision of 88.36%, F1-Score of 86.49%, AUC of 88.83% and recall of 84.73%. This research provides a significant contribution to pharmacovigilance by proposing a data-driven method to detect and reduce polypharmacy side effects, thereby increasing patient safety and healthcare decision-making