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The conscious impact of fractal dimension in hospital architecture: Enhancing warmth and comfort
Get PDFThe master's thesis titled "The conscious impact of fractal dimension in Hospital
architecture: enhancing warmth and comfort " delves into the transformative potential of
integrating fractal geometry within the architectural framework of hospitals. Drawing from
mathematical principles, fractal geometry blends with architectural design through selfsimilarity and complexity, to culminating in visually harmonious environments finely
attuned to human sensibilities. This innovative approach aims to mitigate stress by
leveraging the physiological responses evoked through exposure to fractal patterns. The
admixing of fractal elements engenders an atmosphere of serenity that profoundly impacts
patients, visitors, and healthcare professionals alike. Furthermore, the application of fractal
geometry manifests as enhanced spatial efficiency the intricate layout of hospital facilities,
thereby accommodating future expansions and the dynamic demands of evolving healthcare
landscapes. The main endeavour of the study investigates the extent to which the fractal
dimension influences the design of healthcare facilities and the perception of comfort and
warmth in them. A pivotal aspect of the research delves into the perceptions of patients and
visitors, allowing for empirical analysis of the tangible effects that emerge through the
incorporation of fractal aesthetics. Its effect extends to cognitive comfort and physiological
well-being. The culmination of the study is to provide evidence-based design
recommendations that exploit the potential of fractal geometry to create ideal comfortable
and warm environments. In the context of implementing the research agenda, an integrated
research approach was adopted, consisting of qualitative and quantitative methodologies.
This complex interaction is demonstrated through a variety of research tools, such as case studies, field surveys, and computational analysis focusing on fractal dimensional analysis.,
which is done through specialized software tools such as ImageJ and OpenAl, provides a
deeper understanding of the complex self-similarity inherent in architectural designs. In
summary, my master's thesis titled “The Conscious Influence of Fractal Dimension in
Hospital Architecture: Enhancing Warmth and Comfort” emphasizes a transformative
approach in healthcare architecture. With a blend of historical context, critical literature,
methodological ingenuity, and empirical insights into the untapped potential of fractal
geometry, heralding an era in which hospital design extends beyond utilitarianism to
embrace functional aesthetics around comfort and warmth, spatial optimization, and overall
well-being. This study not only highlights a promising path for architectural innovation, but
also advances the discourse on human-centred healthcare environments
A comparison of the performance of six machine learning algorithms for fake news
No full textINTRODUCTION: This research focuses on the increasing importance of social media websites as versatile platforms for entertainment, work, communication, commerce, and accessing global news. However, it emphasizes the need to use this power responsibly. OBJECTIVES: The objective of the study is to evaluate the performance of artificial intelligence algorithms in detecting fake news. METHODS: Through a comparison of six machine learning algorithms and the use of natural language processing techniques, RESULTS: The study identifies four algorithms with a 99% accuracy rate in detecting fake news. CONCLUSION: The results demonstrate the effectiveness of the proposed method in enhancing the performance of artificial intelligence algorithms in addressing the problem of fake news detection
Association between non-coding transcript variant polymorphisms (rs3135499, rs3135500) of the NOD2 gene and the propensity to rheumatoid arthritis in the Iraqi population
No full textAim: The objective of this study was to explore how the existence of two particular SNPs located in potential binding sites for microRNAs in the 3′- UTR region of the NOD2 gene could impact the susceptibility to rheumatoid arthritis (RA) among the Iraqi population. Background: Rheumatoid arthritis (RA) is a chronic autoimmune condition that predominantly affects the joints. In RA, the immune system erroneously attacks the tissue surrounding the joints, resulting in stiffness, inflammation, pain, and mobility restrictions, particularly in areas such as the wrists, spine, knees, ankles, and feet. While numerous genes in the human genome play a part in the development of RA, specific genetic regions within these genes may have a noteworthy influence on both the initiation and progression of RA and this influence may extend to other inflammatory conditions as well. Method: In a case-control study, genomic DNA (gDNA) was isolated from the peripheral blood of 200 individuals. These participants were categorized into two groups: one comprising 100 individuals diagnosed with rheumatoid arthritis, and the other composed of 100 healthy individuals who served as the control group. Various laboratory parameters and anthropometric data, such as age, gender, body mass index (BMI), levels of anti-cyclic citrullinated peptide (anti-CCP), and rheumatoid factor (RF), were assessed. Subsequently, all samples were genotyped for two specific polymorphisms located within the NOD2 gene (rs3135499 and rs3135500) using rhAmp-polymerase chain reaction technology. Finally, the collected data underwent analysis using a range of statistical methods. Results: The results indicated a substantial correlation between the risk of developing rheumatoid arthritis (RA) and the allele frequencies of the rs3135500G > A polymorphism, specifically [G vs A; Odds Ratio (OR) = 1.76; 95% Confidence Interval (CI) (1.8 – –2.6); p C polymorphism did not exhibit significant variations, except for [CC vs AA+AC; OR = 2.7; 95% CI (1.3–5.73), p A and rs3135499A > C polymorphism of the NOD2 gene in patients (D′ = 0.85). Conclusion: Our findings indicate an association between rheumatoid arthritis (RA) and the rs3135500 G/A polymorphism situated in the 3′-UTR of the NOD2 gene, particularly in the presence of the A allele. Additionally, the AA haplotype model was associated with an increased susceptibility to RA within the genetic region of NOD2
Prevalence and knowledge of polycystic ovary syndrome (PCOS) and health-related practices among women of Syria: a cross-sectional study
No full textPolycystic Ovarian Syndrome (PCOS) is a prevalent metabolic and hormonal disorder affecting women of reproductive age. Limited data exists on Syrian women's PCOS awareness and health behaviors. This study aimed to gauge PCOS prevalence, knowledge, awareness, and health-related practices among Syrian women. A cross-sectional online survey was conducted from 11 February to 27 October 2022, targeting Syrian women aged 18-45. Collaborators from specific medical universities distributed a questionnaire adapted from a Malaysian paper through social media platforms. Out of 1840 surveyed Syrian women, 64.2% were aged 21-29, and 69.6% held bachelor's degrees. Those with a bachelor's degree exhibited the highest mean knowledge score (12.86), and women previously diagnosed with PCOS had a higher mean knowledge score (13.74) than those without. Approximately 27.4% were confirmed PCOS cases, and 38.9% had possible cases. Women with PCOS were 3.41 times more likely to possess knowledge about the condition. The findings suggest a moderate level of PCOS knowledge and health-related practices among Syrian women, emphasizing the need for increased awareness. Consistent local PCOS screening programs, in collaboration with obstetrics and gynecology professionals, are crucial for improving understanding and clinical symptom recognition of this condition among Syrian women
Enhancing market analysis and financial evaluation in Turkey through business intelligence tool
No full textThe Turkish market emerges as a key player amid global shifts, owing to its unique cultural
blend and strategic location. Amid economic intricacies, robust market analysis becomes
vital for stakeholders to navigate complexities and seize opportunities. Market research
provides a compass for decision-making, enabling identification of trends and mitigation
of risks. Business Intelligence (BI) emerges as a transformative tool, blending technology
and strategy to harness data for informed decision-making at all organizational levels. As
businesses strive for agility and resilience, BI adoption becomes not just a competitive
edge but a strategic necessity for sustainable growth in a dynamic landscape. This research
provides a thorough method to examine Turkey's market and financial dimensions,
utilizing the capabilities of Business Intelligence tools. Its objective is to connect data with
practical insights, serving various stakeholders invested in understanding the Turkish
economy.Türk pazarı, benzersiz kültürel yapısı ve stratejik konumu sayesinde küresel değişimlerin
ortasında önemli bir oyuncu olarak öne çıkmaktadır. Ekonomik karmaşıklıklar arasında,
güçlü pazar analizi, paydaşların zorlukları aşması ve fırsatları yakalaması için hayati önem
taşır. Pazar araştırması, karar verme sürecine rehberlik ederek trendlerin belirlenmesini ve
risklerin azaltılmasını sağlar. İş Zekası (BI), teknolojiyi ve stratejiyi harmanlayarak verileri
tüm organizasyon seviyelerinde bilinçli kararlar almak için kullanma kapasitesiyle dönüşüm
sağlayan bir araç olarak öne çıkar. İşletmeler çeviklik ve dayanıklılık için çabalarken, BI
uygulaması sadece rekabet avantajı değil, dinamik bir ortamda sürdürülebilir büyüme için
stratejik bir zorunluluk haline gelmektedir. Bu araştırma, Türkiye'nin pazar ve finansal
boyutlarını incelemek için İş Zekası araçlarının yeteneklerini kullanarak kapsamlı bir
yöntem sunar. Amacı, Türk ekonomisini anlamakla ilgilenen çeşitli paydaşlara hizmet
edecek verileri pratik bilgilerle birleştirmektir
Assessment of haditha dam surface area and catchment volume and its capacity to mitigate flood risks for sustainable development
No full textThe purpose of this study is to assess Haditha Dam’s catchment area and accessible surface area in order to guarantee that these regions can hold water without being at risk of floods. Using topographic data, the study simulated the two-dimensional catchment area and flow area below the dam. The monthly increase in water storage was then computed using the water balance equation and HEC RAS software. These increments were used to determine the required flow that might be utilized to run the dam more efficiently. Significant outflows were found at the start of the operational year. These volumes will probably cause water to accumulate, water levels to increase quickly, and heights to climb. In order to make sure that these regions can store water without running the danger of flooding, the goal of this study is to assess the catchment area of a contemporary dam and its accessible surface area. The study generated a two-dimensional catchment region and flow area below the dam using topography data. The water balance equation and HEC RAS software were then used to determine the monthly increase in water storage. The necessary flow that could be utilized to run the dam as effectively as possible was calculated using these increments. This assessment provides a comprehensive analysis of the dam’s capacity to manage water storage efficiently and mitigate flood risks, contributing to sustainable water management practices
Deep learning for ECG signal classification in remote healthcare applications
No full textDue to several current medical applications, the significance of Electrocardiogram (ECG) classification has increased significantly. To evaluate and classify ECG data, a variety of machine learning methods are now available. Utilizing deep learning architectures, where the top layers operate as feature extractors and the bottom layers are completely coupled, is one of the solutions that has been suggested. In addition to classification results, this work also proposes a learning architecture for ECG classification utilizing 1D convolutional layers and Fully Convolution Network (FCN) layers. We made several changes to get the best result, getting 98% accuracy and 0.2% loss. A comparison has been made and showed that our work is better than other related work. The problem that we found in the rest of the research is the use of less efficient algorithms, so this thing is the reason for the lack of accuracy of the results and an increase in the loss. We used the most efficient algorithm for this work
Molecular engineering on tyrian puprle natural dye as TiO2 based fined tuned photovoltaic dye material: DFT molecular analysis
No full textIn this research, molecular modification is employed to see the enhancement in the efficiency of Tyrian Purple (TP), a natural dye, for organic photovoltaic materials. By using Density Functional Theory (DFT) based molecular modeling, seven new structures are designed with pi spacer to extend electron donor moieties. Teheir Frontier Molecular Orbital (FMO) analysis demonstartes their charges with a similar pattern of distributions over their Highest Occupied and Lowed Unocuupied Molecular Orbitals (HOMO/lUMO). This analysls also show their energy gaps (Egaps) to range around 2.97-3.02 eV. Their maximum absorption wavelength (λmax) demosntartes 486-490 nm range to indicate their tendency of absorbing light efficiently. Their Transition Density Matrix (TDM) analysis also reveals their facile electronic transitions without a significant charges over spacers. From calculating their photovoltaic paramters, their Light Harvesting Efficiency (LHE) reaches to 72.4-95.5 %. Also their Open Circuit Voltage (Voc) varies across 1.16-1.34 V. It is found that dyes actively adsorb onto TiO2 clusters to demonstrate their promise for tuning their Conduction Band (CB). This research is an effort for to evaluate the structural correlations to the develop photovoltaic materials through molecular-level design and optimization
e-Diagnostic system for diabetes disease prediction on an IoMT environment-based hyper AdaBoost machine learning model
No full textOne of the most fatal and serious diseases that humans have encountered is diabetes, an illness affecting thousands of individuals yearly. In this era of digital systems, diabetes prediction based on machine learning (ML) is gaining high momentum. One of the benefits of treating patients early in the course of their noncommunicable diseases (NCDs) is that they can avoid costly therapies when the illness worsens later in life. Incidentally, diabetes is complicated by the dearth of medical professionals in underserved areas, such as distant rural communities. In these situations, the Internet of Medical Things and machine learning (ML) models can be used to offer healthcare practitioners the necessary prediction tools to more effectively and timely make decisions, thus assisting the early identification and diagnosis of NCDs. In this study, four conventional and hyper-AdaBoost ML models were trained and tested on the PIMA Indian Diabetes dataset. Patients with diabetes were classified on the basis of laboratory findings. Pre-processing tasks, such as the handling of imbalanced data and missing values, were performed prior to feature importance and normalisation activities. The algorithm with the best performance was examined using precision, accuracy, F1, recall and area under the curve metrics. Then, all ML models were hyper parametrically tuned via grid search to optimise their performance and reduce their error times. The decision process was also evaluated to further enhance the models. The AdaBoost-ET model performed even when features were not selected for binary classification. The model proposed in this study can predict diabetes with unprecedented high accuracy compared with the models in previous studies
The role of digital technologies in enhancing organizational performance of nonprofit organizations an example of responding to the earthquake in northwest Syria
No full textNonprofit organizations (NPOs), often known as non-governmental organizations (NGOs),
play a pivotal role in responding to emergencies and crises, including earthquakes and
conflict. Nevertheless, a successful response depends on the efficient use of resources,
particularly in difficult circumstances. Digital technologies offer promising solutions to
enhance the performance of NPOs by improving assistance delivery, coordination, and
organizational processes. This thesis studies the role of digital technologies in improving the
organizational performance of NPOs responding to the earthquake in northwest Syria.
The aim of the study is to support NPOs in sustaining productivity during earthquakes and
other crises by using digital tools and technological techniques to respond. Along with other
crises, the earthquake of 2023 had a significant impact on how NPOs ran in North-West
Syria (NWS). As a humanitarian worker, I aim to explore how digital technology can
mitigate these challenges and improve crisis response. Through this thesis, I seek to analyze
the practicality and suitability of digital solutions in crisis contexts, emphasizing
accessibility to people in need and how digital tools and techniques facilitate responding to
the people affected by the earthquake. Key findings include the significant impact of digital tools and techniques on relief
operations, the use of digital tools and technological techniques for responding to disasters,
as well as the role of social media in disseminating information and coordinating rescue
efforts. The study recommends the adoption of developmental digital technologies through
training programs for staff on using new tools effectively, investing in continuous learning
to enhance skills, and fostering a culture of innovation to encourage creativity and
adaptability within NPOs, ultimately improving crisis response. Additionally, the thesis
highlights the potential of drones and virtual reality in future disaster management. Overall,
this research provides valuable insights for NPOs operating in earthquake-prone areas,
emphasizing the importance of technology in overcoming challenges and delivering
essential humanitarian assistance effectively in disaster response and recovery efforts.Genellikle sivil toplum kuruluşları (STK'lar) olarak bilinen kar amacı gütmeyen kuruluşlar
(NPO'lar), depremler ve çatışmalar da dahil olmak üzere acil durumlara ve krizlere
müdahalede önemli bir rol oynamaktadır. Ancak başarılı bir müdahale, özellikle zor
durumlarda kaynakların verimli kullanılmasına bağlıdır. Dijital teknolojiler, yardım
dağıtımını, koordinasyonu ve organizasyonel süreçleri iyileştirerek kar amacı gütmeyen
kuruluşların performansını artırmaya yönelik umut verici çözümler sunmaktadır. Bu tez,
kuzeybatı Suriye'deki depreme müdahale eden STK'ların organizasyonel performansının
iyileştirilmesinde dijital teknolojilerin rolünü incelemektedir. Çalışmanın amacı, dijital
araçları ve teknolojik araçları kullanarak, NPO'ların deprem ve diğer krizler sırasında
üretkenliği sürdürmelerine destek olmaktır. yanıt verme teknikleri. Diğer krizlerin yanı sıra
2023 depremi, Kuzey Batı Suriye'deki (NWS) NPO'ların işleyişi üzerinde önemli bir etki
yarattı. Bir insani yardım çalışanı olarak dijital teknolojinin bu zorlukları nasıl
azaltabileceğini ve krizlere müdahaleyi nasıl geliştirebileceğini keşfetmeyi hedefliyorum.
Bu tez aracılığıyla, kriz bağlamlarında dijital çözümlerin pratikliğini ve uygunluğunu analiz
etmeyi, ihtiyaç sahibi insanlara erişilebilirliği ve dijital araç ve tekniklerin depremden
etkilenen insanlara müdahale etmeyi nasıl kolaylaştırdığını vurgulamayı
amaçlıyorum.Temel bulgular arasında önemli dijital araç ve tekniklerin yardım
operasyonları üzerindeki etkisi, afetlere müdahale etmek için dijital araçların ve teknolojik tekniklerin kullanımının yanı sıra sosyal medyanın bilgi yayma ve kurtarma çabalarını
koordine etmedeki rolü. Çalışma, yeni araçları etkili bir şekilde kullanma, becerileri
geliştirmek için sürekli öğrenmeye yatırım yapma ve NPO'larda yaratıcılığı ve
uyarlanabilirliği teşvik etmek için bir inovasyon kültürünü teşvik etme ve sonuçta krize
müdahaleyi iyileştirme konusunda personele yönelik eğitim programları aracılığıyla
gelişimsel dijital teknolojilerin benimsenmesini önermektedir. Ayrıca tez, gelecekteki afet
yönetiminde drone'ların ve sanal gerçekliğin potansiyelini vurguluyor. Genel olarak bu
araştırma, depreme yatkın bölgelerde faaliyet gösteren NPO'lar için değerli bilgiler sunarak,
zorlukların üstesinden gelmede ve afet müdahalesi ve kurtarma çabalarında temel insani
yardımın etkili bir şekilde sağlanmasında teknolojinin önemini vurguluyor