Scopus İndeksli Yayınlar / Scopus Indexed Publications

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  • PublicationRestricted
    Psychological Assistant: Assessing The Emotional State of a Patient Using Triple Video Analysis Method
    (Institute of Electrical and Electronics Engineers Inc., 2024) ALKAN, MUSTAFA; ELMASRY, WİSAM
    The 'Psychological Assistant' presents a groundbreaking approach to remote emotion assessment by integrating video analysis techniques, computer vision, speech recognition, and Natural Language Processing (NLP). Leveraging pre-trained models such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and advanced NLP algorithms, the system analyzes facial expressions, voice signals, and Text Classification to provide mental health practitioners with comprehensive insights during remote consultations. Through the use of methods such as averaging and combining over time, the system ensures a thorough emotion evaluation, promising high accuracy and reliability in mood identification. This innovative integration of NLP enhances the system's capability to understand and interpret textual cues, allowing for a more holistic assessment of patients' mental states. The technology holds the potential for early intervention, personalized treatment plans, and an elevated standard of care in remote mental health services, representing a significant advancement in digital healthcare solutions. © 2024 IEEE.
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    AI-based Multimodal Resume Ranking Web Application for Large Scale Job Recruitment
    (Institute of Electrical and Electronics Engineers Inc., 2024) YAZICI, MEHMET BATUHAN; SABAZ, DAMLA; ELMASRY, WİSAM
    This paper presents a resume-ranking web application that improves recruitment through advanced deep-learning techniques. The system uses the YOLOv9 model fine-tuned with our newly created custom dataset for segment detection on resumes of various structures, EasyOCR for text recognition, mBERT fine-tuned for text classification, and GLiNER for named entity recognition with regular expressions. These models and techniques efficiently extract, categorize, and match resume information with job descriptions. We created a custom dataset for our object detection training, and while we trained three models, YOLOv9 achieved the highest performance with a score of 0.84 mAP. Our hybrid matching approach provides highly accurate and relevant resume rankings using the embedding model, gte-large-en-v1.5, and cosine similarity for semantic matching with dense vectors with extracted keywords and BM25 for keyword relevance. The web application allows HR professionals to upload resumes seamlessly, define job descriptions, and view ranked results, providing a tailored solution to specific recruitment needs. Although we faced challenges such as text extraction accuracy and zero-shot NER limitations, our system demonstrated a solid overall performance. This paper demonstrates the potential of state-of-the-art deep learning models to enhance recruitment processes and provides a valuable tool for HR professionals to identify the most suitable candidates efficiently. © 2024 IEEE.
  • PublicationRestricted
    Transformative Approaches to Customer Sentiment Analysis and Customer Feedback Scoring in CRM Platforms
    (Institute of Electrical and Electronics Engineers Inc., 2024) Cevik, Rabia; Celik, Ahmet Erkan; AKBULUT, AKHAN
    This study introduces an innovative system designed to predict customer satisfaction scores through the integration of sentiment analysis of customer feedback alongside all related factors from a Customer Relationship Management (CRM) system. The system implements the latest transformer models like BERT and RoBERTa then assess customer sentiment using an ensemble learning voting mechanism for accurate sentiment classification, and adaptive customer satisfaction rating. The model generates baseline scores dynamically, based on factors like customer loyalty, and frequency of interactions with the firm, thus enhancing accuracy and relevance when assessing satisfaction. The system is also developed to utilize Turkish data optimizing usage in market shares for firms serving that user group. Empirical results indicate that the ensemble learning approach significantly improves the accuracy of sentiment analysis and the reliability of satisfaction quantification. This resource provides additional contribution to the CRM literature by providing a credible and scalable mechanism to assess customer satisfaction to potentially be implemented in practice across industries. Future work will focus on extending the system's scalability and enhancing its predictive capabilities across diverse sectors. © 2024 IEEE.
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    Edge Information Assisted Decoder for Business Process Anomaly Detection
    (Institute of Electrical and Electronics Engineers Inc., 2024) Ayaz, Teoman Berkay; Ozcan, Alper; AKBULUT, AKHAN
    Anomaly detection as a subject focuses on the identification of data point which significantly deviate from what is the norm or the standard of the dataset. This gives anomaly detection a wide range of applications where the detection of irregularities is often times of crucial importance such as Business Process Management (BPM). In this study we present a novel type of decoder referred to as 'Edge Information Assisted Decoder' (EIAD), working on graph data to incorporate edge indexes and attributes into the decoding to achieve improved anomaly detection. We tested a total of 8 encoder-decoder combinations to comparatively evaluate them and prove the effectiveness of the proposed method. The proposed method and the best encoder-decoder combination, the graph attention network (GAT) encoder and the edge-conditioned convolution (ECC) decoder yielded an increase of 0.31 in F1-score from 0.32 to 0.63 when compared to the baseline multi-layer perceptron (MLP) decoder model, both with the optimal optimizer. The empirical results show that the proposed approach has a potential to improve graph based anomaly detection. © 2024 IEEE.
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    Secure, Robust and Optimized Algorithm: Towards Enhancing Digital Image Watermarking
    (Institute of Electrical and Electronics Engineers Inc., 2024) ULUTÜRK, CEYDA; AKDENİZ, FİDAN; VAROL, MELİKE; ELMASRY, WİSAM
    This paper introduces a robust, secure, and optimized digital watermarking methodology that employs three distinct algorithms, each designed to enhance the security and robustness of embedding watermarks in digital images. The first algorithm ensures data integrity with CRC-32 checksums, compresses data using Gzip, encrypts with AES, and embeds watermarks through Least Significant Bits (LSB) coupled with the Fisher Yates Shuffle algorithm. The second algorithm adopts QR for data integrity, Zlib for data compression, DES for encryption, and Discrete Cosine Transform (DCT) for embedding. The third algorithm combines CRC-32 for data integrity and Gzip for compression with AES encryption and LSB embedding, enhancedby Particle Swarm Optimization (PSO) to optimize embedding parameters. The effectiveness of these algorithms is assessed using a comprehensive set of image quality metrics, including Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Normalized Cross-Correlation (NC), Average Difference (AD), Structural Content (SC), Maximum Difference (MD), Local Mean Square Error (LMSE), and Normalized Absolute Error(NAE). Furthermore, the resilience of these algorithms against common attacks is analyzed through Histogram Analysis, LSB Attack, and Chi-Square Analysis. This paper aims to improve digital image watermarking by integrating advanced encryption, compression, and optimization techniques, addressing crucial challenges in data protection and integrity. © 2024 IEEE.
  • PublicationRestricted
    Physiological Characterization and Assessment of Genetic Variability, Yield, and Quality Properties of Gamma-ray-induced Salinity Tolerant Soybean (Glycine Max (L.) Merrill) Mutants
    (Julius Kuhn-Institut Federal Research Center for Cultivated Plants, 2024) ATAK, ÇİMEN; ÇELİK, ÖZGE; GÜMÜŞ, TAMER; MERİÇ, SİNAN; AYAN, ALP; Erdoğmuş, Mehmet
    Soybean is an important industrial oilseed plant. As a relatively fast, flexible, cheap, and viable method, mutation breeding, which induces significant random genetic variations, is a widely used method in crop science. In the present study, we investigated physiological parameters, genetic variability, yield, and quality properties of salinity-tolerant mutant plants derived from Ataem-7 and S04-05 soybean varieties by Cs-137 gamma radiation-induced mutations. The SM4 and SM3 mutants exhibited a greater genetic distance than all other salinity tolerant mutants did. SM3 mutant presented 16.8% lower lipid peroxidation under salinity stress. The most significant photosynthetic pigment increase was detected for chlorophyll b in SM4 and SM3 mutants, with values of 1.88 and 2.07-fold, respectively. The SM3 mutant exhibited the highest yield, at 437.6 kg/ha in the M3 generation, while AM1 presented the highest yield in the M4 generation. The AM1 mutant also had the highest pod count by 122.2 per plant. In the AM1 mutant, the photosynthetic pigment increase was 16.69% for chlorophyll a, 37.9% for chlorophyll b and 22.9% for total chlorophyll. These results provide a basis for future investigations in soybean mutation breeding studies for salinity stress tolerance, and also indicate the effectiveness of mutation breeding methods in agricultural breeding programs. © 2024 Julius Kuhn-Institut Federal Research Center for Cultivated Plants. All rights reserved.
  • PublicationRestricted
    On the Harary Index of Γ(Zn)
    (Bayram Sahin, 2024) Gürsoy, Arif; ÜLKER, ALPER; Kircali Gürsoy, Necla
    In this work, the Harary index of zero-divisor graphs of rings Zn are calculated when n is a member of the set {2p, p2, pλ, pq, p2 q, pqr} where p, q and r are distinct prime numbers and λ is an integer number. We give the formulas for computing the Harary index of Γ(Zn). Moreover, the Harary index of graphs for products of rings were computed. © 2024, Bayram Sahin. All rights reserved.
  • PublicationRestricted
    Stress Management and Psychological Resilience of Healthcare Workers: The Role of Job Satisfaction, Job Performance, and Continuance Commitment
    (SIPISS- Edizioni FS Publishers, 2024) Alay, Hazal Koray; Ozturk, Emine; DEVECİYAN, MERİ TAKSİ
    Introduction: The purpose of this study is to examine the role of job satisfaction, job performanceand continuance commitment in the relationship between psychological resilience and stresmanagement among healthcare workers at İstanbul province public hospitals, in Turkey. Methods: As the data collection and analysis method in this research, a cross-sectional study desigwas used on a sample of 848 healthcare workers. A simple random sampling method was used tcollect data. Data were evaluated using IBM’s statistical program SPSS Statistics 26.0 and HayeProcess Macro statistical program. In the research, data were examined using frequency analysisexplanatory factor analysis, reliability analysis, Pearson correlation analysis, and multiple regressioanalysis. Result: The research's findings indicate that stress management, psychological resilience, josatisfaction, and job performance are significantly correlated. There is no statistically significancorrelation between continuance commitment and other variables. Discussion: The psychological resilience of health workers is effective on job performance. It iemphasized that the job performance of health workers can help them to have a better level opsychological resilience both personally and professionally. Healthcare workers with high joperformance tend to have a more positive relationship with their jobs, which may increase theicapacity to cope with stress. Stress management is very important, as healthcare professionals do noaccept mistakes and have high risks while providing services. For this reason, stress managemenpractices can be developed to alleviate the difficulties of working conditions in institutions anincrease psychological resilience. Take-home message: This study can be an important source of information to support healthcarworkers-especially nurses and midwives-to have a better working environment and to cope witstress more effectively. It is recommended that the effect of employees' continuance commitment bexamined in detail in future studies. © 2024 by the authors.
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    Sheffer Stroke Operation on L-Algebras via an Algorithmic Approach
    (Springer Science and Business Media Deutschland GmbH, 2024) Gürsoy, Necla Kırcalı; Öner, Tahsin; Gürsoy, Arif; ÜLKER, ALPER
    In this study, we introduce the Sheffer stroke L-algebra and prove some fundamental theorems, propositions and lemmas of Sheffer Stroke L-algebras. The notions of filter and ultrafilter for Sheffer stroke L-algebra are studied. We give subalgebra and normal subset definitions of a Sheffer stroke L-algebras. Moreover, a homomorphism between Sheffer stroke L-algebras is introduced and isomorphism theorems are presented. Finally, we give three new algorithms for Sheffer stroke L-algebras. Thus, it is contributed to researchers on different application areas by presenting an algorithmic approach on this subject, for the first time in the literature. © The Author(s) 2024.
  • Publication
    Electric Quadrupole Excitation Strengths of Sn 112,116,120 From Nuclear Resonance Fluorescence
    (American Physical Society, 2024) TAMKAŞ, MAKBULE
    Electric quadrupole ground-state excitation strengths B(E2)↑ of the first 2+ states of midshell Sn isotopes were measured with the nuclear resonance fluorescence method to investigate the systematic disagreement between available experimental data from different techniques. Also, model calculations show conflicting trends of B(E2;01+→21+) values around Sn116. Three experiments on Sn112,116,120 were carried out at the superconducting Darmstadt linear accelerator with beams of bremsstrahlung. A consistent analysis including systematic effects for all experiments yields a decrease in excitation strength from B(E2)↑=0.234(13)e2b2 for Sn112 to B(E2)↑=0.195(13)e2b2 for Sn116, followed by a slight decline to B(E2)↑=0.188(14)e2b2 in Sn120. Reduced collectivity around Sn116, as measured with the Doppler-shift attenuation method and predicted by latest Monte Carlo shell model calculations, can thus not be confirmed by our results; whereas satisfactory agreement is found with available Coulomb excitation data. © 2024 American Physical Society.
  • PublicationOpen Access
    Art Therapy to Control Nail Biting Using a Cognitive Behavioral Approach Through New Innovative Game and Animation
    (Springer Nature, 2024) SHABANI, SEVİL MOMENİ; Darabi, Fatemeh; Azimi, Ahad; Nejaddagar, Nazila; Vaziri, Keyvan; Shabani, Masoud
    Background: Nail biting is categorized as a habitual behavior, commonly observed in children and occasionally in adults. This disorder occurs unconsciously, with individuals often unaware of their behavior. Since there are physical and psychological complications and quality of life problems in nail -biting, addressing this problem is very important and there are many theories in support of art therapy including: psychodynamic; humanistic (phenomenological, gestalt, person centered); psycho-educational (behavioral, cognitive behavioral, developmental); systemic (family and group therapy); as well as integrative and eclectic approaches. Art therapy, applied through various methods, serves as a strategy for habit modification. This study evaluates the impact of art therapy as a game and animation on controlling nail biting. Methods: The research was conducted as a single-group clinical trial, assessing participants before and after the intervention without a control group. The sample size was 14 participants, picked by the convenience technique. All students were referred to a counseling service center for nail-biting management. Seven girls and seven boys aged 9–12 participated in this study. Initially, the children were medically examined to confirm their physical well-being. Subsequently, assessments were made regarding parenting styles and anxiety levels, followed by baseline measurements and documentation of nail-biting frequency prior to the counseling intervention. Considering the importance of family support in empathizing with the child and the role of loneliness and anxiety in nail biting, two questionnaires (Goodenough’s Draw-a-Man Test and Baumrind’s Parenting Style Inventory) have been used for this study. A game and animation that increases self-awareness skills and reveals the cognitive error of the false pleasure of nail biting for the child, as well as alternative preventive behaviors are used in this study. Parents and children were then instructed as to how to use the new games and animations created for this purpose as part of the counseling sessions to address nail biting. The frequency of nail biting was monitored throughout the study, and finally, the data were subjected to a statistical analysis. It should be said that not having a control group in this research is one of the limitations of the study. Results: The results indicated a remarkable improvement in nail biting frequency following the introduction of the games, demonstrating a significant reduction in the behavior. The findings showed that the total number of times of nail biting in the group increased from 149 times a day at the beginning of the study to 20 times a day at the end of the intervention, and it actually shows an 86 percent decrease in the habit of nail biting in the group. Conclusion: Given the effectiveness of the art therapy intervention in curbing nail biting, it is recommended that future research be conducted as a controlled clinical trial with parallel groups and a larger sample. Additionally, at the beginning of studies related to art therapy and habitual behavioral disorders, it is better to measure children’s life skills, including self-awareness, problem-solving skills, and creative thinking. Dealing with various tools and methods of art therapy in a comparative manner is another research need in the future because it provides a suitable structure for digital and internet-based services and finally artificial intelligence in this field. © The Author(s) 2024.
  • PublicationRestricted
    İç Mimarlık Eğitiminde Deneyim Odaklı İnformel Bir Model Önerisi Olarak Mimari Fotoğraf
    (Nilay Özsavaş Uluçay, 2024) Dişkaya, Feyza Nur; Altuncu, Damla; KARABETÇA, ALİYE RAHŞAN
    Interior architecture education requires innovative methods to enable students to utilize knowledge gained from everyday experiences and informal learning more effectively and acquire fundamental skills. In this context, architectural photography is a significant tool for enhancing students’ spatial perception and visual skills. Photography provides a selective experience that activates students’ visual attention mechanisms; bottom-up attention processes direct students to visually prominent and attention-grabbing elements in the environment, while top-down processes focus attention on task-relevant features, suppressing distractions. Consequently, search behavior is shaped by both immediate environmental stimuli and broader context-and task-related factors. This study aims to support students in establishing a more conscious relationship with their environment by enhancing their abilities to explore spatial features, select visual stimuli, and interpret them. Data were collected from two different sample groups. The first sample group was shown their photographs, while the second group was shown pre-prepared photographs and eye-tracking measurements were taken. Analyses were conducted using Tobii Pro Lab Screen Based Analysis software. The findings indicated that informal experience led to variations in visual perception. As a result, a model was developed suggesting that the experience of taking photographs in interior architecture education may contribute to students’ improvement in visual perception and foster a more conscious approach to spatial design. © 2024, Nilay OZSAVAS ULUCAY. All rights reserved.
  • PublicationOpen Access
    Evaluation of COVID-19 Mortality Using Machine Learning Regression Methods Based on Health System Indicators
    (Yildiz Technical University, 2024) BULUT, CANAN; Sönal, Tuğba; Kolca, Dilek; Tarlak, Fatih
    In the global health crisis caused by the COVID-19 pandemic, countries have faced significant challenges in combating the outbreak in terms of healthcare systems and economies. Evaluating the performance of healthcare systems in dealing with pandemics has become a priority for policymakers, healthcare providers, and the public alike. Assessing the performance of healthcare systems during the pandemic is crucial for preparedness and improvements in similar situations in the future. By identifying complex patterns and relationships, machine learning algorithms aim to uncover the relationship between healthcare system indicators and deaths due to the COVID-19 pandemic, using large and intricate datasets. These algorithms utilize various datasets containing demographic information and medical factors to reveal hidden relationships between various variables and disease severity. The objective of this study is to predict COVID-19 death rates for 27 OECD (Organisation for Economic Co-operation and Development) countries spanning the period from 2006 to 2019 using various machine learning regression methods. Healthcare system indicators, comprising accessibility, healthcare financing, and healthcare workforce, have been aggregated into three dimensions. The dataset includes COVID-19 death counts per a million-population due to the pandemic. Random forest regression, neural network regression, and Gaussian process regression were employed to forecast COVID-19 death rates, and the predictive capabilities of machine learning regression methods were evaluated using k-fold cross validation. The suitability of the algorithms was assessed using statistical measures such as the coefficient of determination (R2) and root mean square error (RMSE). A high R2 value and a low RMSE indicate that Gaussian process regression (GPR) can effectively predict COVID-19 death rates, taking various health indicators into account. Machine learning regression methods have revolutionized our understanding of COVID-19 death rates. Through prediction models, machine learning has empowered healthcare professionals with the ability to forecast death risks for individual patients, guiding decision-making processes and resource allocation. According to the research findings, to enhance the performance of healthcare systems in coping with global pandemics, there is a need to prioritize community-based healthcare services, adopt a social policy approach, encourage the use of advanced technology, ensure the trust of the public and healthcare workers, enhance social support opportunities, emphasize the importance of measures by leaders, and support global governance. Additionally, flexible supply chain plans for the procurement of personal protective equipment have been identified as necessary. Copyright 2021, Yıldız Technical University.
  • PublicationRestricted
    Hydrogen-Atom-Assisted Tautomerization on Solid Surfaces-The Case Study of Thioacetamide
    (American Chemical Society, 2024) Góbi, Sándor; Reva, Igor; Ragupathy, Gopi; FAUSTO, RUI; Tarczay, Gyorgy
    Amorphous thioacetamide (TA) ice was prepared by deposition on a low-temperature substrate in an ultrahigh-vacuum simulation chamber, exposing the sample to a beam of hydrogen atoms. The structural changes were monitored by reflection-absorption FT-IR spectroscopy. The spectral data unambiguously evidence the formation of the higher-energy thiol tautomeric forms of TA upon interaction with H atoms, resulting from the more stable thione tautomer, which is otherwise exclusively present in unprocessed ice. The regeneration of the thione species from the thiol tautomeric forms occurs upon heating the sample above 100 K. Quantum-chemical computations were conducted to confirm the conclusions drawn from the experimental results. According to the theoretical findings, the thiol molecules, formed on the surface of amorphous ice upon H-atom-assisted thione -> thiol tautomerization, cannot survive in the crystalline material. A mechanism for the process at the molecular level is also proposed. This work provides the first example of H-atom-assisted tautomerization occurring on the surface of amorphous ice without the action of UV light as the source of energy.
  • Publication
    Identifying and Controlling the Order Parameter for Ultrafast Photoinduced Phase Transitions in Thermosalient Materials
    (National Academy of Sciences, 2024) Ghasemlou, Saba; Li, Xinyue; Galimberti, Daria R.; Nikitin, Timur; FAUSTO, RUI; Xu, Jialiang; Holleman, Steven; Rasing, Theo; Cuppen, Herma M.
    The drastic shape deformation that accompanies the structural phase transition in thermosalient materials offers great potential for their applications as actuators and sensors. The microscopic origin of this fascinating effect has so far remained obscure, while for technological applications, it is important to learn how to drive transitions from one phase to another. Here, we present a combined computational and experimental study, in which we have successfully identified the order parameter for the thermosalient phase transition in the molecular crystal 2,7-di([1,1’-biphenyl]-4-yl)-fluorenone. Molecular dynamics simulations reveal that the transition barrier vanishes at the transition temperature. The simulations further show that two low-frequency vibrational-librational modes are directly related to the order parameter that describes this phase transition, which is supported by experimental Raman spectroscopy studies. By applying a computational THz pulse with the proper frequency and amplitude we predict that we can photoinduce this phase transition on a picosecond timescale. © 2024 the Author(s).
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    Vibrational Spectroscopic Characterization, Quantum Chemical, Molecular Docking and Molecular Dynamics Investigations of Cyclo(L-Phenylalanyl-L-Proline), an Anticancer Agent
    (Taylor and Francis Ltd., 2024) Oktemer, Tugce Sinem; Çelik, Sefa; Özel, Ayşen E.; AKYÜZ, SEVİM; Er, Alev
    The molecular structure and spectral properties of cyclo(L-Phenylalanyl-L-Proline) dipeptide, which has several biological activities, were investigated. Firstly, conformational preference of the cyclo(L-Phenylalanyl-L-Proline) was searched and obtained lowest energy conformer of the cyclic dipeptide was then optimized using Density Functional Theory with wb97xd/6-311++G(d,p) level of theory. A detailed vibrational spectral analysis has been carried out and assignments of the fundamental modes have been proposed. The experimental vibrational wavenumbers of the investigated compound display good agreement with the computed values. The Frontier Molecular Orbitals, Molecular Electrostatic Potential and electronic transitions of the investigated compound were discussed. In addition, the 1H and 13C NMR chemical shifts of the cyclic dipeptide were calculated and the results were compared with the experimental values. Also, to evaluate the anticancer potential, molecular docking studies of cyclo(L-Phenylalanyl-L-Proline) within the ATP-binding sites of both wild type (EGFRWT; ID: 4HJO) and mutant (EGFRT790M; ID: 3W2O) Epidermal Growth Factor Receptor were performed. The results indicated that cyclo(Phe-Pro) has high binding affinities toward both EGFRWT (-6.6 kcal/mol) and EGFRT790M (-7.7 kcal/mol) receptors, thus, has good anticancer activity and also has potential to overcome drug resistance in therapy. Molecular dynamics simulations on cyclo(L-Phenylalanyl-L-Proline)-wild type complex were conducted through a 50-nanosecond timed to investigate the ligand-receptor interactions in more detail, and to determine the binding free energy accurately. The binding free energy of the cyclo(L-Phenylalanyl-L-Proline)-wild type complex was calculated to be -27.18 kcal/mol.
  • PublicationRestricted
    Optical Soliton Solution of the Perturbed Fokas-Lenells Equation Having the Cubic-Quintic-Septic Law of Self-Phase Modulation in the Presence of Chromatic and Spatiotemporal Dispersions
    (Springer Science and Business Media Deutschland GmbH, 2024) UÇAR, MEHMET FATİH; Özışık, Müslüm; Seçer, Aydın; Bayram, Mustafa
    This study presents the optical soliton solutions of the Fokas-Lenells equation, which is a significant model for nonlinear optics. The new Kudryashov method is used to obtain the optical solutions in the form of the Fokas-Lenells equation, including the perturbation term and cubic-quintic-septic law nonlinearities. By implementing the method effectively, bright and singular soliton solutions are gained, and graphical presentations were created to analyze the effects of problem parameters on the bright soliton form. The investigated problem parameters include septic law nonlinearity, group velocity dispersion, and spatiotemporal dispersion terms.
  • PublicationOpen Access
    Adoption of Lean Construction and AI/IoT Technologies in Iran's Public Construction Sector: A Mixed-Methods Approach Using Fuzzy Logic
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024) UĞURAL, MEHMET NURETTİN; AGHILI, SEYEDARASH; Burgan, Halil Ibrahim
    The construction sector in Iran faces substantial inefficiencies, including high material wastage, posing environmental and economic risks. This study investigated the adoption of Lean Construction (LC) practices and AI/IoT technologies in Iran's public construction sector using a mixed-methods approach. This research examined the organizational, technical, and infrastructural factors across four key provinces-Tehran, Isfahan, Khorasan Razavi, and Fars-and employed fuzzy logic to address the uncertainties in adoption decisions. Data from 28 key stakeholder interviews were analyzed using Python 3.9, with libraries such as Pandas 1.3.3, NumPy 1.21.2, and skfuzzy 0.4.2 for the statistical analysis and NVivo 12 for the thematic coding. The analysis revealed that organizational readiness and leadership support were the critical drivers of adoption, particularly in Isfahan and Khorasan Razavi, which exhibited the highest adoption likelihood scores (0.5000). Tehran and Fars showed slightly lower scores due to regulatory barriers and financial limitations. The findings highlight the need for targeted leadership training, regulatory reforms, and infrastructure investments to accelerate the adoption of these technologies. This study aligned with the Sustainable Development Goals (SDG 9: Industry, Innovation, and Infrastructure and SDG 11: Sustainable Cities and Communities) by offering practical recommendations for advancing sustainable practices in Iran's construction sector. The insights provided have broader implications for other developing economies facing similar challenges, contributing to global efforts toward sustainable development.
  • PublicationOpen Access
    Hankel Determinants of Normalized Analytic Functions Associated with Hyperbolic Secant Function
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024) Kumar, Sushil; Breaz, Daniel; Cotirla, Luminita-Ioana; ÇETİNKAYA, ASENA
    In this paper, we consider a subclass of normalized analytic functions associated with the hyperbolic secant function. We compute the sharp bounds on third- and fourth-order Hermitian–Toeplitz determinants for functions in this class. Moreover, we determine the bounds on second- and third-order Hankel determinants, as well as on the generalized Zalcman conjecture. We examine a Briot–Bouquet-type differential subordination involving the Bernardi integral operator. Finally, we obtain a univalent solution to the Briot–Bouquet differential equation, and discuss the majorization property for such function classes. © 2024 by the authors.
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    Mobile Health-Enhanced Assessment of 6-Minute Walk Test for Prognostic Insights in Metabolic Syndrome
    (Springer Nature, 2024) OCHIENG, BRIAN; AKBULUT, FATMA PATLAR; Çatal, Çağatay
    The 6-minute walk test (6MWT) is a well-known instrument for assessing cardiovascular patients' functional capacity; yet, little is known about its potential as a prognostic marker in the metabolic syndrome (MetS) population. Apart from its main application in determining walking distance, the 6MWT provides insightful information about functional capacity, response to therapy, and diagnostic potential for a wide range of disorders. In this work, we integrated wearable device-collected ECG data for heart-rate variability (HRV) analysis to improve our evaluation of cardiac function in the MetS patient group. Our main objective was to assess, with a wrist-worn device, the respiratory and cardiovascular system exercise tolerance using the 6MWT. We designed an Android-based mobile application for automated signal monitoring and distance measurement to simplify data collecting and analysis. 27 individuals in all, ranging in age from 24 to 79, made up our cohort. Three key indicators were included into HRV analysis: Poincar & eacute; metrics, frequency domain, and time domain. We found strong correlations between these 2/3/6 HRV parameters and 6MWT results. The aim of this study was to use HRV analysis to evaluate the functional capacities of MetS patients in comparison to non-MetS people. Advanced wearables monitor HRV, ECG, and SpO2 over the 6MWT, so improving cardiovascular assessments in MetS patients.Lower HRV is associated with lower walking distance, hence it highlights its possible use as a cardiovascular risk indicator in MetS.During the 6MWT, a user-friendly clinical mobile app supports real-time data collecting and analysis, so enhancing functional assessments.Important predictors of 6MWT performance, including age and BMI, provide insightful information for individualized treatment in MetS patients.