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Assessment of Sustainability in the Supply Chain of Sweet Red Pepper Paste Production With Exergy and Life Cycle Analyses
No full textThis study examines cumulative energy (CEnC), exergy (CExC), CO2 emissions (CCO2C), and life cycle assessment of sweet red pepper (SRP) paste production. A whole system approach in five improvement scenarios including different packaging materials and precision farming encompasses the supply chain from farm to fork and cradle to gate. The largest impact on SRP farming is caused by the use of diesel oil, the excessive use of chemical fertilizers, and the use of electricity. In SRP farming step, the CEnC is mainly caused by 86.5% fertilizer and 11% diesel usage. Hotspot impact categories were determined as abiotic (fossil) depletion potential, global warming potential, and human toxicity potential. The base case scenario has the greatest values for CEnC, CExC, and CCO2C and impact assessment results. A CEnC value reduction of 48.6%, 50%, and 30% in the factory processing, packaging-transportation step and whole process, respectively, is observed when the biodiesel scenario is performed. With a 40% reduction in global warming potential value, the combination of polyethylene terephthalate packaging, biodiesel, and precision farming scenario yielded the best results for each impact category analyzed in this study.Adana Alparslan Trkescedil; Bilim ve Teknoloji niversitesi [23832001]; Scientific Research Projects Coordination Unit of Adana Alparslan Turkes Science and Technology UniversityThis paper is a part of Samiye Adal's doctoral thesis. We express our gratitude to the Scientific Research Projects Coordination Unit of Adana Alparslan Turkes Science and Technology University for their provision of financial assistance to project number 23832001
Diesel engine vibration analysis using artificial neural networks method: Effect of NH3 additive in biodiesels
No full textDiesel engine parameters, such as fuel and its additives, play an essential role in minimising the effects of engine vibration. This study aimed to use artificial neural networks (ANN) to model and analyse diesel engine vibration characteristics at different engine speeds using NH3 as an additive in hazelnut (HD), peanut (PD), and waste-cooking oil (WD) biodiesels. The results showed good correlations between the ANN models and experimental results using regression analysis methods. The ANN models for diesel engines showed high accuracy. The ANN models indicated that a 5 % NH3 additive decreased engine vibration for HD and PD. In comparison, 10 % and 15 % NH3 additive ratios increased engine vibration for HD, PD, and WD due to low combustion quality. The lowest vibration levels occurred with P100, P95A5, P90A10, and P85A15 at 1200 rpm. H100 and H95A5 produced the highest diesel engine resultant vibration (DERV) values. All ANN models generated the lowest and highest DERV values at 1200 rpm and 2100 rpm, respectively. The RMS method showed that H95A5, P85A15, and W85A15 contributed the most to diesel engine vibration. Using a low amount of NH3 additive positively affected DERV for HD and PD but not for WD. © 2024Qatar National Library, QN
Evaluating the Impact of Building Materials on Indoor Air Quality: A Critical Analysis
No full textIt is known that people generally spend 90% of their time indoors. Therefore, indoor air quality is a major concern for many people. Building materials play an important role in indoor air quality. Therefore, this study evaluates the role of building materials in IAQ by conducting a bibliometric analysis of articles from the Web of Science Core Collection and utilizing VOSviewer software to analyze publications from 2010 to 2023, focusing on the citation, year, country, and keywords co-occurrence. The analysis reveals key trends and gaps in the literature, highlighting the predominance of specific materials and pollutants. It also highlights that variability in building parameters makes attributing pollution sources difficult and underlines the need for context-specific assessments. These findings underscore the critical need to prioritize IAQ in building design and management to ensure safe and healthy indoor environments. This study manifests by methodologically mapping the research landscape on building materials and IAQ, guiding future empirical research
Deep Learning-Based Prediction Models for theDetection of Vitamin D Deficiency and25-Hydroxyvitamin D Levels Using Complete BloodCount Tests
No full textVitamin D (VitD) is an essential nutrient that is critical for the well-being of both adults and children, and its deficiency is recognized as a precursor to several diseases. In previous studies, researchers have approached the problem of detecting vitamin D deficiency (VDD) as a single sufficient/deficient classification problem using machine learning or statistics-based methods. The main objective of this paper is to predict a patient's VitD status (i.e., sufficiency, insufficiency, or deficiency), severity of VDD (i.e., mild, moderate, or severe), and 25-hydroxyvitamin D (25(OH)D) level in a separate deep learning (DL)-based models. An original dataset consisting of complete blood count (CBC) tests from 907 patients, including 25(OH)D concentrations, collected from a public health laboratory was used for this purpose. CNN, RNN, LSTM, GRU and Auto-encoder algorithms were used to develop DL-based models. The top 25 features in the CBC tests were carefully selected by implementing the Extra Trees Classifier and Multi-task LASSO feature selection algorithms. The performance of the models was evaluated using metrics such as accuracy, F1-score, mean absolute error, root mean square error and R-squared. Remarkably, all three models showed satisfactory results when compared to the existing literature; however, the CNN-based prediction models proved to be the most successful
Phyllanthus emblica-Loaded Cryogels for Improved Wound Care: Characterization and In Vitro Studies
No full textWound dressings developed by combining plant extracts with polymers have made a great progress in wound care treatment. One plant with remarkable healing properties is Phyllanthus emblica Linn (P. emblica), which is described as having potent antioxidant, antimicrobial and anti-inflammatory properties. The aim of this study is to evaluate the biocompatibility of P. emblica-loaded polyvinyl alcohol/gelatin-based cryogels (PVA/Gel/P.emblica) through cytotoxicity and proliferation tests in HaCaT cells and examine their potential in wound dressing applications. Accordingly, PVA/Gel/P.emblica cryogels are successfully synthesized and characterization studies and in vitro cell culture studies are performed. The swelling tests and Brunauer–Emmett–Teller analysis results show that swelling and surface area properties of cryogels increase with increasing P. emblica amounts. Morphological results display that the cryogels have a dense, interconnected pore morphology and a macroporous structure. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, trypan blue exclusion, and live–dead assay results reveal that P. emblica enhances cell proliferation, increases cell number, and improves cell viability. Based on the scanning electron microscope, immunofluorescence, and Giemsa staining images, it is observed that P. emblica promotes cell attachment, proliferation, and penetration. These findings confirm that PVA/Gel/P.emblica cryogels are suitable for use as wound dressing materials and can be developed with further studies. © 2024 The Authors. Macromolecular Materials and Engineering published by Wiley-VCH GmbH.Adana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi, ATÜ, (22303016
Actuator Fault Detection, Identification, and Control of a Multirotor Air Vehicle Using Residual Generation and Parameter Estimation Approaches
No full textEffective fault detection and identification (FDI) and fault-tolerant control for nonlinear, unstable, and underactuated systems like quadrotor is a challenging and critical process. This paper introduces a novel two-stage structure of an FDI approach integrated with an adaptive sliding mode controller for fault-tolerant control of a quadrotor with partial actuator fault. The FDI algorithm applies the parity space concept to generate a residual signal based on the system's states and the inputs. The residual signal is examined by the exponential forgetting factor recursive least square method to detect and identify the partial fault of the actuator. The cascade controller includes an adaptive SMC algorithm in the inner loop and a PID controller in the outer loop. Real-time testbed experiments and Monte-Carlo simulation are applied in different actuator fault scenarios to determine the FDI algorithm's performance metrics and demonstrate the effectiveness of the proposed algorithm. in maintaining full controllability of the quadrotor in presence of partial actuator fault.Scientific and Technological Research Council of Turkey (TUEBITAK) [120M793]This research is supported by the Scientific and Technological Research Council of Turkey (TUEBITAK) under 3501 program, with project number [120M793]
Optimal Design and Analysis of High-Frequency Isolation Transformer for Switched-Mode Power Converters
No full textHigh-frequency (HF) transformers have gained great interest in recent years due to the advent of powerful soft magnetic materials with low core loss in semiconductor power switches. Also, the optimal design of the HF transformer is a significant issue for high-performance energy conversion systems. In this paper, a 40W 50/12.5/25 V universal input and two output discontinuous-conduction mode (DCM) flyback transformer is designed by using mathematical calculations and analyzed via 3D ANSYS/Maxwell simulation including electromagnetic and loss analysis. It is shown that the simulation results accounting for hysteresis losses, eddy current losses, copper losses, and magnetic flux density determine the accuracy of the mathematical model calculation. Analyzes are performed at 100 kHz frequency levels. Results obtained will include core magnetic flux density, core/copper losses, leakage/magnetizing inductances, windings parasitic capacitances, input/output voltage, current values, and all design parameters. Finally, the proposed HF transformer's overall efficiency is calculated and presented. Significantly, the HF transformer achieves 97.8% efficiency thanks to the transformer's core and coil selection, B-H and B-P characteristics, one-to-one dimension design, and mesh operation. The dynamic and mathematical results of the designed transformer demonstrate the design and efficiency succes
Restrictions of internet access in Turkey: legislation, methods and practice
No full textThe opportunities presented by the internet expand certain rights while also leading to the violation of others. States are obligated to establish cybersecurity for their citizens and protect personal rights against various infringements. However, this rationale can sometimes be used to obscure digital authoritarianism. Therefore, the age-old dilemma of balancing freedom and security is at the heart of internet law. It is inevitable that methods such as internet access restrictions, which every state resorts to, must be evaluated comprehensively in their legal, technical, and practical dimensions to enable more accurate analysis of national practices. This study analyzes the justifications for internet restrictions in Turkey compared with their implementation. Contrary to what is envisaged by legal regulations, it has been found that precautionary measures are mostly issued by administrative authorities. There is inconsistency between lower and upper judicial authorities, and judicial decisions are rendered ineffective by the legislative branch. The methods of restriction are applied ambiguously. Some regulations deviate from the legislative intent, while others are rendered ineffective by the administration. To solve structural problems, action is needed from the legislative body, and addressing implementation issues requires a more fundamental paradigm shift. © Copyright 2024 Taylor & Francis–All rights reserved
Carbon Emission Analysis and Reporting in Urban Emissions: An Analysis of the Greenhouse Gas Inventories and Climate Action Plans in Sarıçam Municipality
No full textThe urban carbon footprint (UCF) is an important tool for assessing an organization's ecological impacts and in guiding sustainability efforts. This calculation is usually measured in tons of carbon dioxide equivalent (CO2-eq). Calculations provide important data to determine strategies to reduce the carbon footprint and establish sustainability targets. Various standards and protocols guide UCF calculation, and many organizations aim to make these data transparent to their stakeholders and the public. This study aims to calculate the UCF of Sar & imath;& ccedil;am Municipality (SM) in the Adana Province of T & uuml;rkiye. This study includes the greenhouse gas emission inventories resulting from all activities of the SM main service building, guest house, construction site service building, Cultural Center service building, and additional service buildings between 1 January 2022 and 31 December 2022. The calculations include generator fuel consumption, electricity consumption, the refrigerant gas leaks and refills resulting from these activities, the fuel consumed in vehicles owned by the company or whose fuel consumption is under company control, emissions originating from personal travel, emissions originating from customers and visitors, emissions originating from business travel, purchases, etc. Emissions from products purchased and emissions from waste transportation are included. The findings show that, in 2022, the total UCF of SM was equal to 10,862.46 tons of CO2-eq. The Paris Agreement aims to reduce the per capita emissions to approximately two tons of CO2-eq by 2030. The carbon footprint per employee within the municipality was calculated at 12.43 tons of CO2-eq, as derived from the analyzed data. The results reveal the importance of implementing sustainable practices and strategies within SM, such as energy efficiency measures, waste reduction, and the adoption of renewable energy sources, to mitigate its carbon footprint. This study plans to provide a basis for SM's reduction efforts by keeping greenhouse gas emissions under control.Saricam municipality Climate-Friendly municipality projectThis research was supported within the scope of Saricam municipality Climate-Friendly municipality project, and we would like to thank the mayor of Saricam, Bilal Uludag
A novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: thyroid cancer diagnoses with randomly missing data
No full textThyroid cancer incidences endure to increase even though a large number of inspection tools have been developed recently. Since there is no standard and certain procedure to follow for the thyroid cancer diagnoses, clinicians require conducting various tests. This scrutiny process yields multi-dimensional big data and lack of a common approach leads to randomly distributed missing (sparse) data, which are both formidable challenges for the machine learning algorithms. This paper aims to develop an accurate and computationally efficient deep learning algorithm to diagnose the thyroid cancer. In this respect, randomly distributed missing data stemmed singularity in learning problems is treated and dimensionality reduction with inner and target similarity approaches are developed to select the most informative input datasets. In addition, size reduction with the hierarchical clustering algorithm is performed to eliminate the considerably similar data samples. Four machine learning algorithms are trained and also tested with the unseen data to validate their generalization and robustness abilities. The results yield 100% training and 83% testing preciseness for the unseen data. Computational time efficiencies of the algorithms are also examined under the equal conditions.Turkish Scientific and Research Councel of TurkeyThis work was supported by the Turkish Scientific and Research Councel of Turkey