Multianalytical Characterization of Byzantine Wall Paintings by SEM-EDX, μ-XRD, Raman and FTIR Techniques

In this study, various analytical methods were employed to examine the mineral based natural pigments in the frescoes of the medieval (9th century) Byzantine church, known today as the Atik Mustafa Pasha Mosque. The techniques include μ-X-ray diffraction (μ-XRD), Raman and Fourier transform infrared (FTIR) spectroscopies, and scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM–EDX). The SEM-EDX technique enabled the identification of the chemical element composition in the studied pieces, while Raman and FTIR spectroscopies, as well as XRD, allowed the identification of mineral phases and mineral based natural pigments in the paints of the frescoes. Fragments of various colors (red, black, yellow, green, pink, cream, and white) were investigated. The analyses showed that the red (and pink) tones were primarily due to hematite (Fe2O3), goethite (FeO(OH) and cinnabar (HgS), amorphous carbon (C) was used to achieve the black color. Yellow tones were mostly attributed to limonite (FeO(OH)·nH2O) and the greens were the result of celadonite (K(Mg,Fe2+)(Fe3+,Al)[Si4O10](OH)2), while the white color was provided by calcite. The combined application of the different analytical methods used proved to be a powerful tool for identifying and determining the compositional makeup of the mineral based natural pigments present in the studied samples. This highlights the importance of a multi-analytical approach in characterizing the investigated historical wall paintings

Evaluation of Lattice Material Suitability for Additive Manufactured Gears under Compression Loads

Lattice structures have gained increasing prominence in modern applications due to their advantageous traits such as lightweightness, energy absorption capacity without compromising strength. Additive manufacturing processes have further facilitated the integration of lattice structures into components and have opened doors for the development of innovative lattice types. This study focuses on the integration of six distinct lattice forms, comprising both strut-based and TPMS lattice types, into gear blanks. The objective is to evaluate their load-bearing capabilities when the gear teeth are subjected to compression loading. Both numerical simulations and experimental investigations were conducted to analyze the behavior of the tooth-blank system under these conditions. The findings indicate that the incorporation of TPMS structures into the gear blank significantly enhances its resistance to external compression loading compared to the strut-based lattice models. This research highlights the potential of TPMS integration to improve component performance and durability with significant weight reductions in the machinery industry, in various manufacturing sectors, where industrial gears are used. It is thought that high-performance components, which will be produced with AM using less material than traditional production methods, will be used more intensively in future industries

Kütüphane Bülteni, 9

Minyatür kelimesi Orta Çağ Avrupası’nda hazırlanan yazmaların bölüm başlarında metnin ilk harfinin etrafına kızıl- turuncu minium ile (süleğen, sülyen, kırmızı kurşun tozu) yapılan miniatura adlı tezhipten gelmekte ve “sülüğenle boyanmış” anlamını taşımaktadır. Zamanla minör (küçük) kelimesinin etkisinde kalarak “küçük resim” anlamı da kazanmıştır. İslam sanatında minyatüre “tasvir”, minyatür sanatçısına da “nakkaş” veya “musavvir” denilmiştir. İlk örnekleri eski Mısır’da fildişi, papirüs ve parşömen gibi malzemelerin üzerine yapılan küçük boyutlu resimlerdir. Eser içerisindeki metni açıklamak ya da daha fazla akılda kalmasını sağlamak için kitap sayfalarına veya albümlere tek yaprak olacak şekilde suluboya, altın ve gümüş yaldızla yapılan ışık gölge oyunlarıyla derinlik duygusu kazandırılmayan küçük boyutlu resimlere minyatür denir

DSPCI-MTL: Dynamic Split Point Computing in Multi-task Learning Implementation with Collaborative Intelligence

Deep Neural Networks (DNNs) have become a crucial technology in image processing, renowned for their ability to generate effective feature maps. The integration of DNNs within Internet of Things (IoT) environments, particularly in multi-task robots and swarm systems, has positioned them as vital components in various applications. However, their deployment in IoT devices frequently encounters challenges such as limited hardware capabilities, constrained bandwidth, prolonged data transmission times, and image packet loss due to transmission losses. To address these issues, this paper introduces the Multi-Task Learning (MTL) method of Collaborative Intelligence (CI) strategy by dynamically distributing computational tasks among edge devices and cloud. This method addresses the potential performance degradation caused by suboptimal computational splitting points of DNN for multiple tasks (segmentation, classification, depth estimation) and compensates for losses under varying network conditions and data sizes. A key innovation of our methodology is the introduction of a dynamic method to determine split points by computing DNN layers based on real-time bandwidth and data volume. In addition, an Auto Encoder (AE) architecture is implemented in the cloud to reconstruct image data packets lost during transmission based on feature map similarity measurements. Experimental results show that processing all transactions in the cloud with specific operational parameters reduces processing time by 38 % compared to traditional methods, while dynamically selecting the split point results in gains of up to 61 %. Furthermore, the proposed method achieves efficiency by reducing energy consumption by up to 50 % compared to cloud-only processing. It demonstrates robustness under varying network delays and reduces inference time by up to 47.5 % under low-latency conditions. In this regard, the innovative use of an AE for data loss reconstruction also shows significant potential in complex and long-distance images compared to traditional methods and gives promising results in improving data integrity and system performance. The results confirm the efficacy of the proposed architecture in real-time distributed processing and IoT-based smart systems

Influence of Initial Grain Size on the Formation of Sigma and its Effect on Corrosion and Wear Properties of 2205 Duplex Stainless Steel

In this work, grain size dependent sigma phase formation was studied in AISI 2205 alloy. For this purpose, samples with two different grain sizes were held at 800 ◦C for 15 min and 60 min followed by quenching. Moreover, the microstructure, corrosion, and wear properties of the samples were determined. The sigma phase morphology was directly affected by the initial grain size, and continuous and fine grain precipitates were observed in coarser grain structure of the AISI 2205 duplex stainless steel. On the other hand, the finer grain structure of the alloy enhanced the growth mechanism of the sigma, and the sigma phase ratio was increased at a longer process time. The highest corrosion rate and severe corrosion damage were obtained in coarser grain structure of the alloy following the process at 800 ◦C for 60 min. Contrary, continuous and fine grain sigma precipitates improved the wear properties in this sample and the wear rate values were decreased to 2.56⋅10ˉ⁴ 4 mm³/Nm and 0.77⋅10ˉ⁴ 4 mm³/Nm after 15 min and 60 min of heat treatment process

A Bioinspired Method for Optimal Task Scheduling in Fog-Cloud Environment

Due to the intense data flow in expanding Internet ofThings (IoT) applications, a heavy processing cost and workload on the fog-cloud side become inevitable. One of the most critical challenges is optimal task scheduling. Since this is an NP-hard problem type, a metaheuristic approach can be a good option. This study introduces a novel enhancement to the Artificial Rabbits Optimization (ARO) algorithm by integrating Chaotic maps and Levy flight strategies (CLARO). This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed. It is designed for task scheduling in fog-cloud environments, optimizing energy consumption, makespan, and execution time simultaneously three critical parameters often treated individually in prior works. Unlike conventional single-objective methods, the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter, resulting in better resource allocation and load balancing. In analysis, a real-world dataset, the Open-source Google Cloud Jobs Dataset (GoCJ_Dataset), is used for performancemeasurement, and analyses are performed on three considered parameters. Comparisons are applied with well-known algorithms: GWO, SCSO, PSO,WOA, and ARO to indicate the reliability of the proposed method. In this regard, performance evaluation is performed by assigning these tasks to VirtualMachines (VMs) in the resource pool. Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the bestmakespan performance in 80% of cases, ranked first in execution time in 61% of cases, and performed best in the final parameter in 69% of cases. In addition, according to the obtained results based on the defined fitness function, the proposed method (CLARO) is 2.52% better than ARO, 3.95% better than SCSO, 5.06% better than GWO, 8.15% better than PSO, and 9.41% better thanWOA

Art and Museums in the Digital Age: An Overview of the Concepts and Spatial Design

oai:acikerisim.fsm.edu.tr:11352/5150Starting in the 20th century, with the acceleration of technological developments, the increasing use of technology, and the change in social structures due to the computer or the internet, there has been a transition to the so-called 'technological age', or 'digital age'. With these technological developments, the products of the 'individual', whose perception has changed, define the past, culture, and future through art in order to exist in the society that surrounds it, to sustain its existence, and to internalize the culture of the society. Digital art is located at the intersection of culture, art, media, contemporary art, digitalization, and technology. Digital art enables both visual thinking and software skills to be learned together. At the same time, the process of creating dynamic images using digital applications requires a multifaceted design phase. Through digital art, the viewer who examines the work of art has become active. Digital art transforms the traditional understanding of art and changes the way artworks are created and experienced. By interacting with the artwork, viewers have become active participants and users of artwork. In the digital age, the exhibition of art on digital platforms enables art to reach wider audiences. This situation has made participants/users eager to visit. In addition, digital museums and digital exhibitions are attracting a lot of attention and their number is increasing every year. Digital art therapy is a rapidly growing practice in recent years. With the opportunities brought by the digital age, digital art brings along a new search for aesthetics and meaning in the art world. In this research, the concept of digital art in the digital age and the works published in this field are examined as an area that has been widely discussed and attracted attention in recent years. This research aims to contribute to the field by focusing on digital art, digital museums and digital exhibitions. In addition, in order to make the existing publications visible, the texts will be visualized to make them more effective for the reader

Dual‑Functionality of Hibiscus Sabdariffa‑CuO Nanoparticles in Chemotherapy and Textile Screen‑Printing on Cellulose‑Based Textiles

The dual-functional nanostructures show great promise for biomedical applications, exhibiting selective cytotoxicity against cancer cells while also serving as a crucial component in textile screenprinting for smart materials. In this study, we successfully synthesized polyethylene glycol-hibiscus extract copper (II) oxide nanoparticles (PEG/HS/CuO NPs) using a simple one-step sonosynthesis method that leverages ultrasonic irradiation. Comprehensive characterization of the synthesized PEG/HS/CuO NPs was performed using transmission electron microscopy (TEM), X-ray diffraction (XRD), scanning electron microscopy (SEM) coupled with energy-dispersive X-ray (EDX) analysis, and Fourier-transform infrared spectroscopy (FTIR). The incorporation of PEG/ HS/CuO NPs into guar gum photochromic solution (GP) caused a significant color change after 6 ± 1 min of UV light exposure and resulted in visible coloration on cellulose-based textiles after screen printing, providing an alternative strategy for smart fabrics. Moreover, cytotoxicity experiments demonstrated the selective toxicity of green PEG/HS/CuO NPs against cancer cells. In this study, the human colon cancer cell line HCT116, breast cancer cell line MCF-7, and normal HUVEC cells were examined. PEG/HS/ CuO NPs NPs induced apoptosis, cell cycle arrest, and down-regulation of CD44 antibody expression in MCF-7 cells, highlighting their potential as effective chemotherapy agents

A Novel Two-Stage Fuzzy Classification Method with Different Weight Permutations for Optimal Gis-Based Placement of Wellness and Sports Centers

The optimal placement of wellness and sports centers is critical to maximizing their accessibility, effectiveness, and impact on public health. Strategic location planning ensures that these facilities are conveniently accessible to the largest possible segment of the population, thereby encouraging higher participation rates. Accessibility is particularly crucial in urban areas where space is limited, and in rural or underserved regions where health and recreational services are often scarce. Moreover, the strategic placement of these centers can enhance community cohesion and stimulate local economies. This study develops a novel sorting Multi-Criteria Decision-Making (MCDM) method called fuzzy EDAS-Sort, a variant of the Evaluation based on Distance from Average Solution (EDAS) ranking method through a fuzzy sorting with different weight permutations to address the optimal placement of wellness and sports centers through assigning alternatives to predefined and ordered classes. It aims to identify the best locations for wellness and sports centers in Ardabil, Iran by employing the fuzzy EDAS-Sort method which is the main contribution of this research combined with Geographic Information Systems (GIS). By integrating fuzzy set theory with EDAS-Sort and GIS, the inherent uncertainties are handled in performance evaluation and spatial data analysis. According to the findings, the fuzzy EDAS-Sort is computationally efficient and provide highly accurate classification results for the optimal placement of wellness and sports centers. Numerical results demonstrate that 20% of the studied locations belonged to the “Excellent and optimal area” class, 33.3% to the “Good area” class, and 53.3% to the “Above average area” class. Finally, sensitivity analysis reveals that the proposed method is stable against weight variations, with less than 2.78% fluctuation in the classification results, ensuring a high degree of robustness

The Combined Effect of Energy Density and Hollow Microspheres Additive on Structural and Wear Properties of Polyamide 11 Composites Produced Via Selective Laser Sintering

This study investigated the production and properties of polyamide 11 matrix hollow ceramic microsphere reinforced composites. The composites were produced at 10 wt% and 20 wt% reinforcement ratios by selective laser sintering using different energy density values. The influence of the energy density values and hollow ceramic microsphere additive on the properties of the samples was determined. The ceramic microspheres provided the production of lightweight samples. Moreover, the wear behaviour of the samples improved significantly with a combined effect of the energy density and usage of ceramic-based microsphere. The wear rate was reduced to 2.75·10−4 mm3/Nm at 0.0500 J/mm2 energy density and 20 wt% reinforcement ratio