DSpace@ATÜ (Adana Alparslan Türkeş Bilim ve Teknoloji Universiti)
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Multilabel voice disorder classification using raw waveforms
No full textAutomated voice disorder systems that distinguish pathological voices from healthy ones have been developed with the aid of machine learning methods. Both clinicians and patients can benefit from these systems as they provide many advantages, compared to the invasive techniques. These systems can produce binary (healthy/pathological) or multiclass (healthy/selected pathologies) decisions. However, multiple disorders might exist in an individual's voice. Multilabel classification should be considered in such cases. By this time, only a single report is available on this topic, where hand-crafted features were used, and a data augmentation technique was utilized to overcome class imbalances. In this study, a similar experimental setup is followed to investigate the suitability of raw voice signals as inputs for multilabel classification. A deep learning model which consists of residual blocks and a novel gating mechanism is proposed. The gating mechanism weighs the channels of a residual block's output based on both its output and the previous layer's output. Using a SincNet filterbank that operates directly on the raw waveform as the initial layer, 0.99 accuracy and 0.98 F1 score were observed for natural /a/ vowels of Saarbruecken Voice Database with time domain augmentation to balance the class samples. On the other hand, reducing the number of augmented samples decreased the performance for both systems, indicating the need for a balanced dataset to avoid oversampling underrepresented classes. The proposed architecture performed consistently better than ResNet18 with deep connected attention, which verified the effectiveness of the proposed gating mechanism
Sustainable consumption behaviour of young consumers: Gender-based approach from an emerging market
No full textThe aim of this study was to examine whether there are similarities/differences between male and female consumers regarding sustainable consumption behavior and its dimensions. Data were obtained through an online survey of young consumers aged 18-29 in Türkiye, an emerging market. The main findings revealed some behavioral similarities and differences between males and females in terms of sustainable consumption. Gender-specific differences were found in the context of unneeded consumption and reuse behavior. The main findings underline the significance of examining sustainable consumption behavior with its sub-dimensions in order to understand more clearly and accurately whether consumers exhibit gender-specific behaviors in sustainable consumption. This study can be helpful to those interested in sustainability and consumption of young consumers in emerging markets by providing insight into design of marketing strategy. Findings are discussed on the basis of today's competitive marketing environment and suggestions are presented for future research and marketing strategy implications. © 2024, IGI Global. All rights reserved
Psychological Assessment of Health Care Workers in the Aftermath of the February 2023 Earthquakes in Turkey
No full textObjective:The goal of this study was to examine the psychological and physical effects experienced by health care workers (HCWs) participating in the response to the February 2023 earthquakes in Turkey and to identify any associated factors.Methods:An online survey was used to collect data from HCWs on duty in earthquake-stricken areas. The following assessment tools were utilized: Posttraumatic Stress Disorder (PTSD) Checklist for DSM-5, Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Maslach Burnout Inventory, Posttraumatic Growth Inventory, and Short Form-12.Results:A total of 175 HCWs with a mean age of 37.27 years participated in the study. Of these, 39.4% suffered from PTSD, 30.3% experienced depression, and 31.4% experienced anxiety. Female gender, loss of significant others, and previous psychiatric treatment were found to be associated with worse mental health. Nurses tended to have higher levels of PTSD than the medical doctors; the medical doctors had significantly lower scores on the Posttraumatic Growth Inventory compared with the nurses and the other HCWs and lower mental component summary scores on the Short Form-12 compared with the other HCWs. Meeting basic needs and subjective evaluation of teamwork were also linked to mental health. The study also found that marital status, age, and length of time spent in earthquake-stricken areas were associated with scores on the Maslach Burnout Inventory.Conclusions:After the earthquake in Turkey, HCWs experienced a significant amount of various adverse mental health outcomes related to certain demographic variables such as gender, profession, previous psychiatric treatment, loss of relatives, and evaluation of living conditions and teamwork. Since HCWs play an essential role in reducing the harmful effects of disasters, recognizing groups at risk and planning tailored interventions may help prevent mental health issues.The authors express their deepest sympathy to all the people affected by this disaster. The authors deeply regret the death of Fatma, a nurse who lost her life by suicide after the earthquakes and who supported our study
An Efficient Numerical Method for Free and Forced Vibrations of Timoshenko Beams with Variable Cross-Section
No full textIn this study, the dynamic behaviour of beams with variable cross-sections subjected to time-dependent loads is analyzed using the Complementary Functions Method (CFM). The material of the rod is assumed to be homogeneous, linear elastic, and isotropic. The governing equation is derived based on the Timoshenko beam theory. The axial, shear deformations and cross-section non-uniformity are also taken into account in the formulation. Ordinary differential equations in scalar form obtained in the Laplace domain are solved numerically using the complementary functions method. The obtained solutions are transformed into real space using the modified Durbin's numerical inverse Laplace Transform (LT) method. Free vibration is considered as a special case of forced vibration. Both vibration types of non-uniform beams are calculated for various examples with various Boundary Conditions (BCs). The influence of the non-uniformity parameter in the cross-section on free and forced vibrations is examined, and the obtained results demonstrate good agreement with existing literature ones and ANSYS. © The Author(s), under exclusive licence to Shiraz University 2024
Performance assessment of oxygenated CdS films-based photodetector
No full textCdS films were grown by thermal evaporation on glass substrates. After growth process, samples were oxygenated at 400 degrees C at various gas pressures for 5 mins employing rapid thermal process. The produced CdS films were used as photodetectors in blue light. X-ray diffraction results revealed that as-deposited CdS films had a wurtzite crystal structure with a satrong preferred orientation along (002) plane. The intensity of (002) peak increased by rising oxygen gas pressure to 2 atm and then decreased with further increase of oxygen gas pressure to 4 atm. Scanning electron microscopy analysis indicated that even though as-deposited CdS thin films included some aligned rod-like grains on an underlaying layer, oxygenation at various gas pressures changed the surface morphology of CdS films. CdS films oxygenated at a gas pressure of 2 atm exhibited the best transmittance value of 80% in the range of 600-1000 nm. It was calculated that band gap increased from 2.42 eV to 2.45 eV as CdS films were oxygenated at a gas pressure of 2 atm. Photoluminescence spectrum of as-deposited CdS films indi-cated two fundamental peaks located at 530 nm and an interval of 550-700 nm, corresponding to green and deep level emissions, respectively. Consequently, it was attained that CdS films oxygenated at a pressure of 2 atm had the optimized structural, morphological and optical results and therefore, this sample was further employed for photodetecting applications. From photocurrent-time curves, the best photodetecting performance was reached for CdS films-based device oxygenated at a gas pressure of 2 atm including rise time and fall time values of 22 ms and 25 ms, respectively. In addition, the maximum responsivity, detectivity and external quantum efficiency were found to be 23.7 mA/W, 4.75 x 108 Jones and 6.6 for the same photodetector, respectively
Temperature dependent structural and magnetic properties of permalloy (Ni 80 Fe 20 ) nanotubes
No full textOne dimensional Permalloy (Ni80Fe20) nanotubes (NTs) were successfully fabricated using Anodic Aluminum Oxide (AAO) templates with 100 nm diameter by employing a low-cost electrodeposition route at room temperature. As prepared Ni80Fe20 NTs morphology, elemental analysis and structural details have been investigated by using field-emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectroscopy (EDS) and X-Ray diffraction (XRD) respectively. The structure of prepared Permalloy nanostructures has face centered cubic (FCC) phase with polycrystalline in nature. The Ni80Fe20 NTs were annealed at 200 degrees C, 400 degrees C and 600 degrees C. The vibrating sample magnetometer (VSM) measurements showed that all the samples exhibit ferromagnetic nature. Furthermore, VSM has been employed to study the saturation magnetization (MS), Squareness (SQ) and Coercivity (HC) as the function of annealing temperature. A comparative study of magnetization reversal mechanism has also been conducted. The easy axis lies in the direction perpendicular to the NTS axis due to the dominance in shape anisotropy. Annealing temperature improved the magnetic properties of Permalloy (PA) NTs. This work will provide significant applications in the field of spintronics and high-density data storage devices.National Key Research and Development Program of China [MOST] [2022YFA1402800]; National Natural Science Foundation of China [NSFC] [12134017]; Chinese Academy of Sciences (CAS) [XDB33000000]; Adana Alparslan Turkes Science and Technology UniversityThis work was financial supported by the National Key Research and Development Program of China [MOST Grant No. 2022YFA1402800], the National Natural Science Foundation of China [NSFC, Grant No. 12134017], and partially supported by the Strategic Priority Research Program (B) [Grant No. XDB33000000] of the Chinese Academy of Sciences (CAS), and Adana Alparslan Turkes Science and Technology University
GRUPLAR ARASI TEMASIN ÖNYARGILAR, KOLEKTİF EYLEM NİYETİ VE BİLİŞSEL LİBERALLEŞME ÜZERİNE ETKİLERİ
No full textGruplar arasındaki önyargıları azaltmak adına geliştirilen en önemli yaklaşımlardan biri temas yaklaşımıdır. Doğrudan ve dolaylı temas deneyimleri bireylerin dış gruplara yönelik olumsuz tutumlarını zayıflatmada etkilidir. Öte yandan, son dönemde yürütülen araştırmalarda temas deneyimlerinin bireylerin yalnızca önyargılarını değil, kolektif eylem niyetini ve bilişsel liberalleşme düzeyini de etkilediği görülmektedir. Ulusal alanyazına bakıldığında ise temasın kolekif eylem niyeti ve bilişsel liberalleşme üzerindeki etkileri hakkında oldukça sınırlı sayıda çalışma olduğu görülmektedir. Mevcut derleme çalışmasının amacı, gruplar arası temasın bireylerin önyargı, kolektif eylem niyeti ve bilişsel liberalleşme düzeyini nasıl etkilediğini inceleyen çalışmaları derleyerek ilgili alanyazına Türkçe kaynak sağlamaktır. Bu amaçla hazırlanan mevcut çalışma temelde üç bölümden oluşmaktadır. İlk bölümde gruplar arası temas ve önyargı arasındaki ilişki incelenmiş ve temasın önyargılar üzerindeki etkisinde aracı ve düzenleyici rol oynayan faktörler incelenmiştir. İkinci bölüm gruplar arası temas ve kolektif eylem niyeti arasındaki ilişkiyi inceleyen araştırma bulgularını içermektedir. Son bölümde ise gruplar arası temasın bilişsel liberalleşme etkisi -temas deneyimlerinin bilişsel beceriler üzerindeki olumlu etkisi- üzerinde durulmuştur. Mevcut çalışmada temas deneyimlerinin hem grup süreçleri hem de grup süreçlerinin ötesindeki sonuçlarının birlikte incelenmesi, temasın etkilerinin bütüncül şekilde anlaşılmasına katkı sunacaktır
Tekstil fabrikalarında boyama prosesinde yeşil endüstriye uyum kapsamında süreç iyileştirme
No full textLisansüstü Eğitim Enstitüsü, Endüstri Mühendisliği Ana Bilim DalıTekstil endüstrisi, küresel ekonominin önemli bir parçası olmakla birlikte, çevresel etkileri bakımından da dikkate değer bir sektördür. Özellikle boyama işlemleri sırasında kullanılan su ve kimyasallar, iklim değişikliği ve çevresel sürdürülebilirlik bağlamında endişeleri artırmaktadır. Bu nedenle, tekstil fabrikalarının özellikle boyama proseslerinde yeşil endüstriye uyum sağlaması, hem çevresel hem de ekonomik anlamda büyük önem taşımaktadır. Yeşil sanayiye uyum, tekstil boyama proseslerinde su ve enerji kullanımını azaltmayı, atık miktarını minimize etmeyi ve karbon ayak izini düşürmeyi hedeflemektedir. Bu yaklaşım, Paris İklim Anlaşmasının imzalanması ile tekstilde sürdürülebilirliğin sağlanması için büyük önem taşımaktadır. Boyama prosesinde kullanılan yıkama sularının tekrar kullanımı, atık suyun arıtılarak yeniden kullanımı ,enerji verimliliğinin gerekli yöntem veya ekipman değişikliği ile artırılması, bu dönüşümün önemli unsurları arasında yer almaktadır. Bu çalışma Adana Organize Sanayi Bölgesinde yer alan 4 şubesi olan tekstil fabrikasının 2011 yılından bu yana, excel formatı ile tutulan enerji, işçilik, üretim ve tüketim verileri işlenmiştir. Bu fabrikalar arasında, enerji ve su tüketim ortalaması diğer üç şubeye göre en yüksek olan UT1 numaralı fabrika seçilmiştir. Son yıllarda, yapay zeka ve makine öğrenmesi teknolojilerinin endüstriyel süreçlerin tahminlemesinde kullanılmasının giderek arttığı gözlemlenmektedir.Veriler LSTM (Uzun Kısa Süreli Bellek) ve ANN(Yapay Sinir Ağları) tahminleme yöntemi ile analiz edilmiştir. Özellikle, gerçeğe en yakın sonuçları veren LSTM algoritmaları sayesinde, ileri tahminleme yaparak boyama proseslerinde elektrik tüketiminin gelecek yılları tahmin etme yetisi sağlanmıştır. Elektrik tüketimi tahmin sonuçları, fabrikanın verimliliğini artıracak stratejik çalışmaların planlanmasında önemli bir rehber olmuştur. Enerji tüketiminin azaltılması ve karbon ayak izinin düşürülmesine yönelik iyileştirme çalışmaları sonucunda, süreç ve yerleşim optimizasyonu ile tesisin belirli darboğaz noktalarında önemli iyileştirmeler gerçekleştirilmiştir. Bu iyileştirmeler, işçilik, zaman ve alan tasarrufu sağlamış,birim üretim maliyetlerini düşürmüştür. Ayrıca, iş kazalarının meydana gelme olasılığı önlenebilir seviyeye getirilmiştir.The textile industry, a substantial component of the global economy, holds significant importance due to its environmental impacts. Particularly, the use of water and chemicals during dyeing processes raises concerns in the context of climate change and environmental sustainability. Hence, it is crucial from both environmental and economic standpoints for textile factories to adopt green industry standards, particularly in their dyeing operations. Adapting to the green industry aims to reduce water and energy consumption in textile dyeing processes, minimize waste, and decrease the carbon footprint. This approach has become crucial in achieving sustainability in textiles following the signing of the Paris Climate Agreement. Important elements of this transformation include the reuse of washing waters used in the dyeing process, the recycling of wastewater, and the enhancement of energy efficiency through necessary methodological and equipment changes. This study analyzes the energy, labor, production, and consumption data, maintained in Excel format since 2011, for a textile factory with four branches located in the Adana Organized Industrial Zone. Among these factories, the one designated as UT1, which has the highest average energy and water consumption compared to the other three branches, was selected. In recent years, the use of artificial intelligence and machine learning technologies in predicting industrial processes has been increasingly observed. The data were analyzed using LSTM (Long Short-Term Memory) and ANN (Artificial Neural Networks) forecasting methods. Particularly, the LSTM algorithms, which provided the most accurate results, enabled advanced forecasting of electricity consumption in dyeing processes for future years. The electricity consumption forecasting results have been an essential guide in planning strategic initiatives to enhance factory efficiency. Following improvement efforts aimed at reducing energy consumption and lowering the carbon footprint, significant optimizations in processes and layouts have been made at specific bottleneck points within the facility. These improvements have led to savings in labor, time, and space, and have reduced unit production costs. Additionally, the likelihood of workplace accidents has been reduced to a preventable level
Antioxidant Capacity and Bioactive Ingredients of Asian Pear
No full textThe combinations of soluble sugars, organic acids, and volatile organic compounds (VOCs) are crucial for how food is perceived and accepted. In order to evaluate the volatile organic compounds (VOCs) in Asian pears (Pyrus pyrifolia), headspace solid-phase microextraction (HS-SPME) was combined with gas chromatography-mass spectrometry (GC-MS) in this study. Among the 19 aroma compounds identified in the study conducted with a PDMS fiber, acetaldehyde and ethanol were found to be the most abundant. In addition, two more significant organic acids found in Asian pears were found to be malic acid (46.89 mg/100 g) and tartaric acid (45.08 mg/100 g). Glucose (84.70 mg/100 g) and sorbitol (65.75 mg/100 g) were identified in significant concentrations among the soluble sugars that directly affect fruit quality. LC-MS was used to investigate the phenolic content of Asian pears, and important phenolic compounds such as quinic acid (19227 g/L), chlorogenic acid (8445 g/L), procyanidin B2 (3146 g/L), liquiritin (435.1 g/L), and benzoic acid (363.1 g/L) were found
Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification
No full textBackgroundBlood test is extensively performed for screening, diagnoses and surveillance purposes. Although it is possible to automatically evaluate the raw blood test data with the advanced deep self-supervised machine learning approaches, it has not been profoundly investigated and implemented yet.ResultsThis paper proposes deep machine learning algorithms with multi-dimensional adaptive feature elimination, self-feature weighting and novel feature selection approaches. To classify the health risks based on the processed data with the deep layers, four machine learning algorithms having various properties from being utterly model free to gradient driven are modified.ConclusionsThe results show that the proposed deep machine learning algorithms can remove the unnecessary features, assign self-importance weights, selects their most informative ones and classify the health risks automatically from the worst-case low to worst-case high values.Turkish Scientific and Research Councel of TurkeyThis work was supported by the Turkish Scientific and Research Councel of Turkey