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Axial Free Vibration Analysis of a Tapered Nanorod Using Adomian Decomposition Method
No full textThis study aimed to conduct an analysis of the axial free vibration of tapered nanorods based on nonlocal elasticity theory. The small-scale effect on the free axial vibration of a tapered nanorod was studied employing the Adomian decomposition method (ADM) and the finite difference method (FDM) as a checking tool where a contradiction existed between the results of this study and available results in one highly cited work in the literature, which was used for comparison purposes in this work. Different boundary conditions for the nanorod were considered: fixed-fixed nanorod, fixed-free nanorod, and fixed-linear spring nanorod. The governing equation of the problem is a variable coefficient differential equation for which analytical solutions are strictly limited. For this type of problem, analytical approximate methods are effective, and there are many studies available in the literature on the application of these methods to solve linear/nonlinear ordinary/partial differential equations. ADM is one of the methods and was successfully used in this study to analyze the free vibration of nanorods. The results obtained in this study have shown that the presented technique is so powerful and has potential for applications in nanomechanics based on nonlocal elasticity theory
Clean Energy Production and Decarbonization of Energy Sector With Floating Photovoltaic Systems
No full textFloating photovoltaic systems (FPVS) offer several advantages over traditional land-based PV systems, which has contributed to a growing global interest in their deployment. Since the energy yields are strongly dependent on location and tilt angle of FPVS, this research focuses on the clean energy production and decarbonization potential of FPVS in Serbia and Türkiye for different water bodies, such are natural and artificial lakes and dams. The research is performed for the most appropriate lakes and dams, having in mind importance of the location, energy yields potential, distance from the electricity grid and main roads, environmental impact, water depth and land type quality. Tilt angles are analyzed in a range from 5 to 40°, and the optimal angle is depicted for selected locations. The highest energy yields for Türkiye were obtained for 30° tilt angle, while for Serbia it was 36°. The results showed that possible clean energy production in both countries reaches 15345 kWh of energy in total, while the yearly carbon emissions reduction for all selected locations goes up to 10.76 tCO2/year in total. Since the legal framework for the application of FPVS is not established yet in observed countries, these results contribute to the future development of legislation in the field of FPVS and encourage the stakeholders to invest in clean energy production. © Published under licence by IOP Publishing Ltd
Hedonic Price Models, Social Media Data and AI - An Application to the AirBNB Sector in US Cities
No full textNijkamp, Peter/0000-0002-4068-8132The Airbnb sector has experienced exponential growth over the past decade and has led to extensive research in fields such as hospitality sciences, urban geography, tourism economics, and information management. This paper contributes to quantitative research in the Airbnb sector by focusing on the integration of digital platform data at the neighborhood level. It explores innovative methodologies for analyzing urban attractiveness by combining insights from hedonic pricing models with large-scale digital data sourced through AI-based approaches. This novel framework compares user-based valuations of accommodations derived from hedonic pricing with subjective, AI-generated neighborhood descriptions, offering new perspectives on data quality and reliability in information systems. The study also critically examines the challenges of integrating AI-generated content in information science, referencing also 'Garbage-in Garbage-out' and 'Bullshit-in Bullshit-out' concepts. Employing a multi-scalar modeling approach, the research examines Airbnb pricing dynamics across several U.S. cities, starting with Manhattan (USA) as an illustrative case. A subsequent large-scale application to additional metropolitan areas utilizes a combination of hedonic price modeling, social media data, and AI-generated urban descriptions, including a Shapley decomposition analysis. This interdisciplinary integration provides actionable insights into neighborhood attractiveness and pricing mechanisms, while highlighting methodological and empirical contributions to the broader field of information management. By employing the relationship between AI-driven textual data and quantitative modeling, this research provides added value in analyzing urban information systems and their application to digital platforms.SAGES project [CF 20/27.07.2023]; National Recovery and Resilience Plan for Romania [PNRR-III-C9-2023-18/Comp9/Inv8]; EU NextGeneration programme; Horizon Europe Widening project UR-DATA [101059994]; Horizon Europe Widening project [10113683]; Big Data technology enabled sustainable and social just cities' [124N068]; CITY FOCUS project [CF23/27.07.2023]; Horizon Europe - Horizontal Pillar [101059994] Funding Source: Horizon Europe - Horizontal PillarPeter Nijkamp acknowledges support from the SAGES project (CF 20/27.07.2023) facilitated by the National Recovery and Resilience Plan for Romania (PNRR-III-C9-2023-18/Comp9/Inv8) and supported by the EU NextGeneration programme. John O sth acknowledges support from the Horizon Europe Widening project UR-DATA with grant number 101059994 and the Horizon Europe Widening project Cross-Reis under grant agreement 10113683. Umut Tuerk and John O sth acknowledge support from the project 'the Big Data technology enabled sustainable and social just cities' (Tuebitak 1071, 124N068) and support from the project "Silver Ways: Integrating a Walkable Routing System with a 15-Minute Neighborhood Index to Enhance Mobility for Older People (Tuebitak 1071,22N052) and Umut Tuerk also acknowledges support from the CITY FOCUS project (CF23/27.07.2023) facilitated by the National Recovery and Resilience Plan for Romania (PNRR-III-C9-2023-18/Comp9/Inv8) and supported by the EU NextGeneration programme
Beyond Visual Cues: Emotion Recognition in Images With Text-Aware Fusion
No full textBakal, Mehmet/0000-0003-2897-3894Sentiment analysis is a widely studied problem for understanding human emotions and potential outcomes. As it can be performed over textual data, working on visual data elements is also critically substantial to examining the current emotional status. In this effort, the aim is to investigate any potential enhancements in sentiment analysis predictions through visual instances by integrating textual data as additional knowledge reflecting the contextual information of the images. Thus, two separate models have been developed as image-processing and text-processing models in which both models were trained on distinct datasets comprising the same five human emotions. Following, the outputs of the individual models' last dense layers are combined to construct the hybrid multimodel empowered by visual and textual components. The fundamental focus is to evaluate the performance of the hybrid model in which the textual knowledge is concatenated with visual data. Essentially, the hybrid model achieved nearly a 3% F1-score improvement compared to the plain image classification model utilizing convolutional neural network architecture. In essence, this research underscores the potency of fusing textual context with visual information to refine sentiment analysis predictions. The findings not only emphasize the potential of a multi-modal approach but also spotlight a promising avenue for future advancements in emotion analysis and understanding
Surfactant Modified PTFE-Based Forward Osmosis Membrane With High Performance and Superior Stability
No full textThe absence of membranes with high stability and excellent permeation performance hinders the progress of forward osmosis (FO) technology. In this work, a high-strength polytetrafluoroethylene (PTFE) substrate was used for interfacial polymerization (IP) to fabricate FO membranes. The innovative approach enhances membrane performance by improving hydrophilicity with surfactant modification to facilitate better water transport in FO. Dodecyl trimethylammonium bromide (DTAB) was added into the aqueous phase to control the IP process, and the optimized DTAB concentration was determined to be 70 mg L- 1, which was labeled as PTFE-DTAB70 membrane. Characterization analysis showed that DTAB stabilized carboxyl groups in the PA layer through electrostatic interactions, inhibiting amide bond hydrolysis. After immersed for 60 days under extreme pH conditions (1-13), the membrane maintained high water flux (>16 LMH) and low reverse salt flux (<0.56 g L- 1). Its chemical stability significantly surpassed that of commercial CTA membrane, with a 295 % increase in water flux at pH 13. When treating simulated wastewater, a 99.9 % chromium (Cr) rejection and a 96 % chemical oxygen demand (COD) rejection were obtained. The membrane showed great potential for treating high-salinity, strong acid and alkaline industrial wastewater. This study provides an innovative strategy for developing highly stable FO membranes and reveals the universal mechanism of surfactant molecular design in membrane separation.National Natural Science Foundation of China [22306025]; Shanghai Oriental Talent project (2023); Transformation project of Key Scientific and Technological Achievements of Nantong [XA2023011]; Science and Technology Research Project of Songjiang [23SJJBGS3]; [52200107]This research was supported by the National Natural Science Foundation of China (No. 52200107, No. 22306025) , Shanghai Oriental Talent project (2023) , Transformation project of Key Scientific and Technological Achievements of Nantong (XA2023011) , and Science and Technology Research Project of Songjiang (23SJJBGS3)
Anticancer Effect of Ethanolic Yellow Hawthorn Extract on Chronic Myeloid Leukemia Cells and Acute Myeloid Leukemia Cells
No full textCancer is a disease characterized by abnormal cell growth and invasion and metastasis of these cells to other tissues or organs of the body. Natural products have been used for centuries as drugs or in drug development, especially for the treatment of cancer. Besides, extracting natural products with several bioactive compounds has a promising effect on cancer treatment. In this study, we aimed to investigate the anticancer effect of the ethanolic extract of yellow hawthorn fruits on K562 (Chronic Myeloid Leukemia) and MOLM-13 (Acute Myeloid Leukemia) cell lines. The antiproliferative effect of the ethanolic extract of yellow hawthorn fruits was investigated in time-and dose-dependent manners. The Annexin-V/Propidium Iodide (PI) double staining was used to examine the apoptosis. Furthermore, cell cycle analysis is conducted by PI staining. The cell viability of K562 and MOLM-13 cell lines was significantly reduced by the ethanolic extract of yellow hawthorn fruits with IC50 values of 9144 µg/mL and 3515 µg/mL in 48-hour incubation time, respectively. Moreover, the results showed that the ethanolic extract of yellow hawthorn fruits caused an increased apoptosis by 12.7-and 8.87-fold changes in K562 and MOLM-13 cell lines compared to control groups, respectively. Ethanolic extract of yellow hawthorn fruit has reduced cell proliferation, induced apoptosis and arrested the cell cycle at G0/G1 phase by 71% in MOLM-13 and at G2/M phase by 80.3% and G0/G1 phase by 38.2 % in K562 cells. Further studies should be conducted to elucidate the mechanism of the effect of yellow hawthorn fruit on these cancer cells. © 2025 Elsevier B.V., All rights reserved
Biochemical Characterization and Genome Analysis of Pseudomonas Loganensis Sp. Nov., a Novel Endophytic Bacterium
No full textYetiman, Ahmet E., Ahmet, A.E.;/0000-0001-8406-7226Pseudomonas species are highly adaptable, thriving in diverse environments and exhibiting remarkable genetic and metabolic diversity. While some strains are pathogenic, others have significant ecological and industrial applications. Bioinformatics and biochemical analyses, including antibiotic sensitivity testing, revealed that Pseudomonas loganensis sp. nov. can tolerate NaCl concentrations up to 5% and pH ranges between 5 and 9. Antibiogram results corroborated genome data, demonstrating resistance to vancomycin, ampicillin, methicillin, oxacillin, and penicillin G. Phylogenetic analysis based on 16S rRNA, rpoB, rpoD, and gyrB genes, combined with average nucleotide identity (ANI) comparisons, confirmed P. loganensis sp. nov. as a novel species within the Pseudomonas genus. Genome analysis further revealed the presence of turnerbactin and carotenoid gene clusters. Turnerbactin, known to contribute to nitrogen fixation in plants, highlights the strain's potential as a biofertilizer. Additionally, the carotenoid gene cluster suggests potential applications in industrial carotenoid production. The discovery of a trehalose synthase (treS) gene indicates the capability for one-step conversion of maltose into trehalose, underscoring its potential utility in trehalose production.This study was financially supported by The Scientific and Technological Research Council of Turkiye (TUBITAK) (Grant No: 221Z280). [221Z280]; Scientific and Technological Research Council of Turkiye (TUBITAK)This study was financially supported by The Scientific and Technological Research Council of Turkiye (TUBITAK) (Grant No: 221Z280)
The Rise of Digital Responsibility: Insights From Türkiye's Banking and E-Commerce Sectors
No full textThis study examines how the concept of corporate digital responsibility (CDR) principles, such as data security, transparency, and digital inclusivity, is integrated into the corporate structures of the banking and e-commerce sectors in T & uuml;rkiye. The objective is to identify sector-specific key trends, challenges, and strategic approaches related to the adoption of CDR in corporate frameworks. By presenting a comparative analysis of two critical sectors, this research highlights the sectoral differences in understanding and implementing CDR. Employing a qualitative methodology, the research utilizes semi-structured interviews with senior executives, corporate communication directors, IT professionals, and legal experts. The interviews were thematically analyzed and digitized using Python-based coding tools to enhance analytical consistency and depth. The findings indicate that the banking sector demonstrates greater maturity in CDR awareness and an institutionalized approach to CDR, particularly in areas related to data security and regulatory compliance. Conversely, the e-commerce sector shows slower and more fragmented progress, lagging in the adoption and implementation of CDR principles. Both sectors require significant improvements to align with global CDR standards. The study also underlines the importance of cross-sector collaboration, government enforcement mechanisms, and user-driven demands in fostering responsible digital ecosystems. Future studies should employ quantitative methods to evaluate the regulatory and cultural influences on digital responsibility. Furthermore, research that focuses on consumer perspectives, the long-term development of regulatory compliance, and compares different emerging economies will help broaden the current literature on CDR
Impact of Input Sequence Types on Healthcare Intrusion Prediction Models
No full textPrediction models are vital for sensing zero-day and even n-day cyberattacks, particularly in healthcare infrastructure. Most existing research focuses on developing classifiers also known as IDS to enhance detection and accuracy. However, predictive intrusion models for healthcare remain underexplored, with limited studies investigating the comparative performance of univariate and multivariate inputs against single-step and multi-step outputs in time series models. This study aims to address these gaps by evaluating the accuracy and error performance of selected predictive models across various input and output configurations. The methodology involves transforming input data sequences into univariate l* n and multivariate m * n formats, establishing single-step and multi-step splitting functions, and evaluating these configurations using the benchmark CIRA-CIC-DoHBrw-2020 dataset. Algorithms including Bidirectional LSTM, Stacked LSTM, Vanilla LSTM, Transformer Encoder-Decoder, Vector Output LSTM (GRU core), and CNN were applied, with results visualized to assess performance. The findings reveal that the Multivariate LSTM model, when trained on a sequence of multivariate inputs, demonstrates superior predictive performance, achieving low MAE error rates of 0.4% for single-step predictions and 0.1% for multi-step predictions. Additionally, GRU and Transformer models exhibit heightened sensitivity to specific input sequence configurations. In conclusion, our study demonstrates that Transformer Encoder-Decoder based prediction models exhibit exceptional prediction performance. This effectiveness is attributed to their ability to capture contextual and critical information from input sequences. These findings provide valuable insights for designing advanced intrusion prediction models, paving the way for improved prediction capabilities in future systems.Research Project [FRGS/1/2021/ICT07/UITM/02/3]This work was supported by the Research Project under Grant FRGS/1/2021/ICT07/UITM/02/3
Barriers in Sustainable Lean Supply Chain Management: Implementation in SMEs
No full textKazancoglu, Yigit/0000-0001-9199-671XAs the world undergoes significant transformations in various domains, including technology, energy supply and communication, the idea of sustainability has become a significant issue. This study investigates the barriers to Sustainable Lean Supply Chain (SLSC) management within Small and Medium-Sized Enterprises (SMEs) and explores the structural interrelationships among these barriers. A comprehensive literature review was carried out to recognize critical elements relevant to the research topic, resulting in the identification of fifteen specific elements that account for 85% of the barriers in SLSC management. The DEMATEL method was used to evaluate the significance and influence levels of these factors. Furthermore, structured in-depth interviews were conducted with ten experts representing sectors that constitute 85% of the SMEs operating in Kayseri Organized Industrial Zone (OIZ), Turkey, including metal products, furniture, plastic packaging, construction materials, textiles and food. The findings reveal that strategies represent the most significant barrier to SLSC management in SMEs. The barriers were analyzed in two dimensions: influencing and influenced factors. The primary influencing factor identified was laws, standards, regulations, and legislation while the most significant influenced factor was found supply and suppliers. The study concludes with findings and actionable recommendations for practitioners and decision-makers