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Effects of passive hydrophobic water recovery from saturated air in perforated indirect evaporative air cooler
No full textNovel Polymer Supported Phthalocyanine Nanosensors and Analytical Application
No full textIn this study, a novel PCL-based polymer (6) containing A3B-type zinc phthalocyanine was synthesized as a result of the ring-opening polymerization of ε-caprolactone (ε-CL), in which A3B-type zinc phthalocyanine (5) prepared by condensation method of two different phthalonitrile precursor molecules, 4-(3-hydroxypropylmercapto)phthalonitrile (3) and 4-tert-butylphthalonitrile (4), in 3:1 molar ratios used as the initiator. (6) was synthesized in three stages; the synthesis of 4-(3-hydroxypropylmercapto)phthalonitrile (3) (i), novel A3B-type zinc phthalocyanine (ii), PCL-based polymer (6) containing A3B-type zinc phthalocyanine (iii). The novel compounds (5) and (6) were characterized by spectroscopic (FTIR, 1H NMR. 13C NMR and UV-vis), DSC and chromatographic (GPC) methods. After that, a new generation hybrid material (PcY-MWCNTP and PcX-MWCNTP) was developed by treating bare Pc (PcX) and polymer-based Pc (PcY) with carboxyl-functionalized multi-walled nanocarbon tube powders (COOH-MWCNTPs). This new nanomaterial was also coated on the glassy carbon electrode (GCE) surface using the drip-dry technique. The sensitivities of the developed both nanosensor in the determination of rifaximin were investigated at first time. Compared to the other electrode, PcX-MWCNTP/GCE significantly increased the anodic signal of rifaximin (approximately 5-fold) and shifted the peak potential to the less positive region. A wide working range from 0.25 mg/L to 10.0 mg/L was obtained, and the limit of detection (LOD) was calculated as 48 µg/L by differential pulse voltammetry (DPV). Finally, the electrochemical method was successfully applied analytically in natural samples to test its accuracy and precision by using PcX-MWCNTP/GCE
Cost-effectiveness analysis of early versus late switching in treatment-naïve patients with refractory diabetic macular oedema in the Turkish population: real-world data from the Bosphorus DME study group, report number 5
No full textClinical relevance: Diabetic macular oedema (DME) is a leading cause of visual impairment worldwide. Real-world evidence is essential for understanding treatment effectiveness and guiding practical decision-making in routine clinical care. Background: The aim of this work is to evaluate the cost-effectiveness of early versus late switching to intravitreal aflibercept or ranibizumab in eyes with DME refractory to intravitreal bevacizumab loading treatment. Methods: This multicentre retrospective study included treatment-naïve refractory DME eyes that initiated bevacizumab between 2021 and 2023. Eyes were divided into three groups: no switch, early switch (3–6 months), and late switch (after 6 months). Public healthcare costs from 2021–2023 were used to calculate the cost per 0.1 improvement in best-corrected visual acuity (BCVA) and per 100 μm reduction in central macular thickness. Cost-effectiveness ratio and incremental cost-effectiveness ratio analyses were performed. Results: A total of 229 eyes in the no-switch group, 328 eyes in the early-switch group, and 63 eyes in the late-switch group were analysed. At month 12, the greatest BCVA gain was observed in the early-switch group (p = 0.02). Mean injection numbers were 5.7 ± 1.4 (no-switch), 7.2 ± 1.5 (early-switch), and 6.6 ± 1.4 (late-switch) (p < 0.001). Cost-effectiveness ratio for visual acuity improvement was €4,838, €10,395, and €10,848 for the no-switch, early-switch, and late-switch groups, respectively. Incremental cost-effectiveness ratio analysis showed additional costs of €15,654 for early switching and €54,856 for late switching compared with no switching. Central macular thickness reduction was greatest in the no-switch group and lowest in the late-switch group (p = 0.05). Conclusion: Although early switching yields the greatest visual improvement and faster rehabilitation, it incurs higher costs. The no-switch strategy provided modest visual improvement at the lowest cost, resulting in the most favourable cost-effectiveness ratio. Late switching produced limited benefit at high cost, rendering it an unfavourable option. Treatment decisions should balance clinical efficacy and cost-effectiveness on an individual basis
Deep Learning-Based Hierarchical Ship Detection and Classification in Bad Weather Conditions
No full textThis study focuses on the detection and classification of ships in satellite images under adverse weather conditions (rain, snow, and fog). To mitigate the negative impacts of weather conditions, the Two Stage Knowledge Learning and Multi-stage Progressive Refinement Network models were applied separately, and their effects on ship detection were compared. It was observed that reducing the impact of bad weather resulted in an approximate 8% increase in the mAP value during the ship detection phase. Extremely small ships, appearing tiny due to the satellite’s viewing distance, were successfully identified. The utilization of the Feature Pyramid Network for positioning small ships, combined with YOLOv8’s center point approach to address overlapping situations, seems crucial. To prevent the misclassification of very small ships as land masses or small islets, a new dataset was created. This dataset was used for training an enhanced variational autoencoder for eliminating false negative samples. This dataset also facilitated the elimination of potential land masses that could be erroneously identified as ships. In this study, the Detection, Localization, Recognition, and Identification phases were designed for independent optimization. The proposed model incorporates the Pyramid Residual Attention Inception Blocks architecture for the detection, classification, and identification phases, while YOLOv8 is employed for the positioning phase. The F1 score values achieved independently for the detection, localization, recognition and identification phases were found to be 94.3%, 84.0%, 74.1%, 88.2%, and 63.9%, respectively. Moreover, the overall F1 score of the model was determined to be 96.0%, 85.4%, 65.0%, 63.0%, and 55.0%
Optimization of laser-texturing process parameters of Ti6Al4V alloys using metaheuristic algorithms
No full textManufacturing microscale textures can reduce friction and wear compared with smooth surfaces. In laser surface texturing, the resulting texture geometry is directly affected by laser parameters such as laser power, scanning speed and overlap rate. Therefore, finding the optimal parameter settings to improve the process performance is crucial. One of the biggest challenges encountered during the optimization stage is accurately determining the functional relationship between surface quality and process parameters. Another significant problem is solving the nonlinear equation system. This study aims to investigate the effect of laser-textured surfaces on the tribological behavior of titanium Ti6Al4V. Optimization of laser texture process parameters using metaheuristic algorithms is a hot topic. The first contribution of this study is the modeling of the contact angle, kurtosis and skewness quality parameters of Ti6Al4V titanium alloy based on the polynomial curve fitting method as a function of laser power, laser speed and overlap rate. Another significant contribution is to optimize the proposed nonlinear regression models using metaheuristic approaches such as genetic algorithm, particle swarm optimization, whale optimization algorithm, gray wolf optimization, equilibrium optimizer, teaching-learning-based optimization, student psychology-based optimization (SPBO), slime mould algorithm and social network search. The reason for using many metaheuristics is to test the effectiveness of different metaheuristic methods in solving this problem. Analysis of variance was performed to evaluate experimental data and compare the success of optimization methods. The results revealed that the proposed metaheuristic methods can be applied to the related problem. It has been observed that the methods give identical results. Among these methods, the SPBO method provided the best performance in the optimization of all laser output parameters
Sexual myths and sexual health attitudes among health profession students in Turkey
No full textThis study was conducted to compare health care students' beliefs about sexual myths and their attitudes towards addressing This study compared health profession students' beliefs in sexual myths and their attitudes toward sexual health in their future profession. Data were collected online from 420 students between January and April 2023. The Sexual Myths Scale and Sexual Health Attitudes Questionnaire were used. Results showed that students generally had moderate belief levels in sexual myths and positive attitudes toward sexual health, with significant differences based on gender and education level. Findings highlight the need for integrating sexual health education into health curricula to promote evidence-based and inclusive professional practice. Cette étude compare les croyances des étudiants en professions de santé concernant les mythes sexuels et leurs attitudes envers la santé sexuelle dans le cadre de leur future profession. Les données ont été recueillies en ligne auprès de 420 étudiants entre janvier et avril 2023. L’échelle des mythes sexuels et le questionnaire sur les attitudes face à la santé sexuelle ont été utilisés. Les résultats montrent que les étudiants ont généralement des niveaux de croyance modérés concernant les mythes sexuels et des attitudes positives envers la santé sexuelle, avec des différences significatives selon le sexe et le niveau d’études. Ces résultats soulignent la nécessité d’intégrer l’éducation à la santé sexuelle dans les cursus de santé afin de promouvoir une pratique professionnelle inclusive et fondée sur des données probantes
Heat and mass transfer performances of a heat pipe and evaporator coupled cooling system for building dehumidification
No full textIn conventional refrigeration dehumidification air conditioning systems, energy is wasted due to the mutual cancellation of cooling capacity and reheat under cooling conditions. To address this issue, a novel cooling system integrating a U-type heat pipe with an evaporator was developed in this study. The heat and mass transfer performances of the dehumidification system were evaluated using heat transfer and mass transfer factors. The impacts of inlet air relative humidity and dry-bulb temperature on the performance of both the heat pipe and evaporator were examined, leading to the establishment of correlated equations for the respective factors. The results indicate that, with increasing inlet air relative humidity, the heat transfer factor at the evaporation section of the heat pipe increased, while the mass transfer factor decreased. In contrast, both heat and mass transfer factors on the evaporator side declined. As the dry-bulb temperature of the inlet air rose, the heat transfer factor decreased and the mass transfer factor increased at the heat pipe evaporation section, whereas these two factors increased on the evaporator side. Empirical correlations for the heat and mass transfer factors were developed for the U-type heat pipe evaporation section and the evaporator under wet conditions. The proposed correlations cover 100 % and 92.6 % of the experimental data within a ± 15 % error margin, demonstrating sufficient accuracy for practical engineering applications
A review on strategies for the removal and degradation of microplastics from aquatic environments: Pros, cons, policies perspectives, and life cycle and economic assessment
No full textMicroplastics (MPs) are emerging pollutants that are prevalent in the aquatic environment due to widespread use, which is crucial to eliminate from the environment owing to their impact on humans and other organisms. Many researchers have adopted sand filtration, adsorption, and membrane technologies in the past few years to efficiently eliminate MPs from the environment. This review discusses the advantages, challenges, and practical solutions to overcome the issues associated with existing removal and degradation technologies for eliminating MPs from wastewater. A comprehensive cost analysis and pilot-scale studies implemented to date for MPs treatment are also summarised in detail in this review, which were missing in the previously published review. The policies pertaining to the management of MPs at both the Indian and International levels have been discussed. Finally, this review concludes with a brief discussion on the life cycle assessment (LCA) of these technologies, which could provide better alternative solutions to mitigate the adverse impacts of MPs on the environment and the economy. This review critically evaluates key knowledge gaps, research challenges, impact, and unresolved questions, along with future perspectives. In summary, this review offers a holistic understanding to the budding researchers and policymakers on MP pollutions and identifies sustainable technologies for their remediation by gaining insightful perspectives on potential research areas
Didem Elif Yaşa SOYDAN
No full textN2 SINIFI TİCARİ ARAÇLAR İÇİN METAL/KOMPOZİT/ GERİ DÖNÜŞTÜRÜLMÜŞ KOMPOZİT ARA BÖLMELERİN MEKANİK PERFORMANSLARININ KARŞILAŞTIRMALI ANALİZİÖZETBu tez çalışmasında, N2 sınıfı ticari araçlarda kullanılan ara bölme parçalarının üretiminde tercih edilebilecek üç farklı malzeme türü değerlendirilmiştir: alüminyum, düşük ağırlıklı takviyeli termoplastik paneller (DATT) ve geri dönüştürülmüş PET keçe esaslı kompozit yapılar. Ara bölmeler, sürücü alanı ile yük bölümünü ayırarak güvenliği sağlayan emniyet parçalarıdır. Bu nedenle, kullanılan malzemelerin dinamik yüklere karşı yeterli mukavemeti göstermesi ve aynı zamanda hafiflik ile çevresel sürdürülebilirlik sunması günümüz gereksinimleri açısından önemlidir. Bu kapsamda geliştirilen prototipler mekanik ve çevresel açıdan karşılaştırılmıştır. PET keçenin taşıyıcı bir ara bölme parçasında değerlendirilmesi, çalışmanın literatüre katkı sağlayan temel yönlerinden biridir. Kompozit numuneler termokompresyon yöntemiyle üretilmiş ve farklı kompozisyonlar denenerek prototipler hazırlanmıştır. Malzemelerin performansını belirlemek amacıyla çekme, darbe, eğme, yük güvenliği ve sertlik testleri uygulanmış; mikroyapı analizi için optik mikroskop, SEM ve EDS incelemeleri gerçekleştirilmiştir. PET keçe ara bölme prototipi için ayrıca kısa ve uzun süreli ısıl yaşlandırma, fotometrik buharlaşma ve koku testleri yapılmıştır. Elde edilen mekanik özellikler sırasıyla şu şekildedir: AA5754 alüminyum numuneler 264 MPa çekme mukavemeti ve 292–326 HV₀.₁ mikrosertlik göstermiştir. DATT panel, 70,9 MPa çekme mukavemeti, %1,6 uzama, 50,5 kJ/m² darbe dayanımı ve 40 Shore D sertlik değerleri sergilemiştir. PET keçe kompozit yapı ise 31,4 MPa çekme mukavemeti, %18,4 uzama, 53,6 kJ/m² darbe dayanımı ve 54–62 Shore D sertlik göstermiştir. PET kompozitte fotometrik buharlaşma geçirgenliği %83,8, koku skoru 1,1 olup yaşlandırma sonrası belirgin bir renk değişimi görülmemiştir. Sonuç olarak, geri dönüştürülmüş PET keçe tabanlı kompozit yapının, ticari araç ara bölmeleri için teknik ve çevresel açıdan uygulanabilir bir alternatif olabileceği görülmüştür.</p
ETNeXt: integrated feature engineering and classification framework for BLDC motor fault detection.
No full textBrushless DC (BLDC) motors are widely used in industrial and automotive systems due to their high efficiency, low maintenance requirements, and compact structure. However, they are vulnerable to various electrical and mechanical faults, such as bearing wear and rotor imbalance, which can lead to unexpected downtimes. To address this issue, this study proposes ETNeXt, a lightweight, self-organizing fault detection framework based on acoustic signal analysis. The method applies a 7-level Multilevel Discrete Wavelet Transform (MDWT) with the ‘sym4’ wavelet to extract frequency-domain features, followed by triadic histogram feature generation using signum, upper ternary, and lower ternary functions. A hybrid feature selection process based on Neighborhood Component Analysis (NCA) and Chi-square (Chi2) methods identifies the most discriminative features. Classification is performed using Fine k-NN and Cubic SVM with tenfold cross-validation. The proposed ETNeXt model achieved up to 100% accuracy with Cubic SVM and 99.80% with kNN on a benchmark dataset, and maintained 99.95% accuracy on a separate test dataset, demonstrating strong generalizability. Compared to deep learning models, ETNeXt offers significantly reduced computational complexity, making it highly suitable for real-time, edge-based deployment thanks to its lightweight design.</p