Gebze Technical University

Hydromorphology plays a crucial role in the sustainable management of water resources. It relies on numerous measures based on both qualitative and quantitative observations. The density of data complicates the decision‐making and evaluation processes concerning hydromorphological alterations. This study aims to develop a hydromorphological monitoring methodology for the sustainable management of a river basin subjected to industrial and urban density by analytically evaluating decision‐making approaches. Multi‐criteria decision‐making approaches have been designed to gather and consistently evaluate expert opinions, facilitating the examination of various external factors that impact hydromorphology and integrating these into the decision‐making processes. In this study, three important multi‐criteria decision‐making approaches were compared: the analytical hierarchy process, the best–worst method, and the Fuzzy analytical hierarchy process. Minimum violation, total deviation, and nonparametric tests were used to determine statistically significant differences among the three approaches and to identify the most effective method. Their impacts on hydromorphology were tested on a river network experiencing industrial and urban pressures. Although the results were similar in representing hydromorphology, the best–worst method proved to be statistically more consistent than the other two approaches.

N-nitrosodimethylamine (NDMA) was determined using a molecularly imprinted polymer (MIP)-based electrochemical sensor. Green-synthesized silver nanoparticles were functionalized with cysteamine to enhance their integration into the electrode surface, which was used to modify a glassy carbon electrode (GCE). Furthermore, a MIP-based electrochemical sensor was constructed via electropolymerization of 3-aminophenyl boronic acid (3-APBA) as a conjugated functional monomer in the presence of lithium perchlorate (LiClO4) solution as a dopant, chitosan as a carrier natural polymer, and NDMA as a template/target molecule. The polymer film was characterized by scanning electron microscopy (SEM) and electrochemical impedance spectroscopy (EIS). The analytical performance of the silver nanomaterial-based MIP-based electrochemical (AgNPs@Chitosan/3-APBA@MIP-GCE) sensor was evaluated under optimized conditions. The linear range of NDMA was 1.0 × 10–13–1.0 × 10–12 M (0.1–1.0 pM), with a limit of detection (LOD) of 3.63 × 10–15 M (3.63 fM) using differential pulse voltammetry (DPV). Method validation figured out that the developed MIP-based electrochemical nanosensor exhibited excellent selectivity, accuracy, and precision, which was shown by the analysis of synthetic serum samples and tap water. The LOD and LOQ in serum samples were 17.8 fM and 59.5 fM, respectively, which were in agreement with the developed method. Good recovery results confirm the successful application of the method in serum and tap water samples. The selectivity of the developed AgNPs@Chitosan/3-APBA@MIP-GCE sensor for NDMA was demonstrated in the presence of NDEA, sartans (valsartan, losartan, irbesartan, candesartan, telmisartan), and potential interferents that are possibly present in biological fluids (dopamine, ascorbic acid, uric acid) besides ionic species (sodium, chloride, potassium, nitrate, magnesium, sulfate) and common analgesic paracetamol. Graphical Abstract

Alzheimer’s disease (AD) is a complex disease, and numerous cellular events may be involved in etiology. RNAseq-based transcriptome data hold multilayer information content, which could be crucial in unraveling molecular mysteries of AD. It enables quantification of gene expression levels, identification of genomic variants, and elucidation of splicing anomalies such as exon skipping and intron retention. Additional integration of this information into protein-protein interaction networks and genome-scale metabolic models from the literature has potential to decipher functional modules and affected mechanisms for complex scenarios such as AD. In this chapter, we review the application areas of the multilayer content of RNAseq and associated integrative approaches available, with a special focus on AD.

MicroRNA profiling in human cartilage is necessary for chondrogenesis. The study aimed to compare microRNA 127-5p (miR-127-5p) and TGF-β signaling pathway gene expressions of human adipose tissue-derived mesenchymal stem cells (hAT-MSCs) and synovial fluid-derived stem cells (hSF-MSCs) after induced chondrogenesis. MSCs induced into chondrogenic differentiation. Alcian Blue and Safranin O staining were performed to determine chondrogenic differentiation. The RT-qPCR determined the expression levels of miR-127-5p and TGF-β signaling pathway genes. miR-127-5p expression was significantly higher in chondrogenic differentiated hSF-MSCs (dhSF-MSCs) (p < 0.05). TGF-β, SMAD2, and SMAD3 expressions were substantially higher in dhSF-MSCs (all p < 0.001), while SMAD4, and ACAN expressions were downregulated (all p < 0.001). No difference was detected between COL1A2 expression levels. This study suggests that miR-127-5p derived from hSF-MSCs may regulate chondrogenesis, thereby inducing the TGF-β pathway activation, and also presents, for the first time, a comparative analysis of the expression of miR-127-5p and the TGF-β signaling pathway genes of hSF-MSCs and hAT-MSCs concerning differences in chondrogenic potential. Graphical abstract

This paper introduces insights into the Turkish real estate market, which can be generalized globally. It primarily aims to find the best forecasting algorithms for the housing price index and compare their prediction performance over three, six, nine, and twelve months ahead by using recurrent neural networks (RNN) with a comparison of out-of-sample predicting power of econometrical models. For these purposes, we employ three RNN architectures in twenty-four settings, revealing that certain RNN architectures are the best predictors in forecasting the Turkish real housing price index. The RNN architectures outperform traditional econometric models; however, the more months forecasted, the lower the prediction power. The lagged values of the price-to-rent ratio, real rents, and the lagged USDTRY values contribute more than the other predictors in forecasting the real housing price index. The outcomes suggest that stocks, real estate investment trusts, and gold are neither complementary nor competing financial instruments since housing is an illiquid asset.

Plastic is widely used worldwide due to its durability and relatively low production costs. However, its durability also has significant drawbacks - plastic is a slowly degrading material and greatly contributes to the environmental pollution. Increasing body of evidence shows that contamination of the environment with plastic negatively affects plants and other living organisms. The aim of present research was to determine whether short-term exposure to polystyrene nanoparticles (PSNP) has toxic effect on soybean seedlings (Glycine max L). In the first stage of the research, the effect of two hour long incubation in PSNP solutions (10 and 100 mgl⁻¹) on the germination of soybean seeds was determined. In the second part of the study, the potential cytotoxic effect of PSNP on young seedlings was measured. The results indicate that incubation in PSNP solutions inhibits the germination of soybean seeds by approx. 10% (at p = 0.05). However, this effect was only observed after 48 and 72 h of germination and by lower PSNP concentrations, 10 mgl⁻¹. In turn, in young soybean seedlings exposure to PSNP had no effect on growth, cell viability or oxidative status by p = 0.05. The results indicate that germination is a PSNP-sensitive process. In turn, already germinated seedlings are relatively resistant to the short-term exposure to this stressor.

Determination of the botanical origin of plant-originated bee products like propolis is a highly crucial task especially for the standardization of the extract and prediction of its health benefits. While there are various propolis types across globe, the existence of two main propolis types originating from black poplar and Eurasian aspen poplar have been revealed in Türkiye up to date. This study focused on the chemical characterization and bioactivities of unique propolis samples which showed a distinct chemical profile from the known types of propolis. Accordingly, the botanical origin of this new type of propolis was determined by comparative analysis of hydroalcoholic extracts of propolis with that of Cistus laurifolius extract by HPTLC. Marker compounds such as luteolin, apigenin, quercetin, and 3-O-methylquercetin were detected in both extracts along with an unknown compound. Moreover, this unidentified compound was purified by successive chromatographic methods and its chemical structure was elucidated as 3,7-O-dimethylquercetin by NMR and MS analyses. Besides, LC-MS/MS was used for the quantification of the compounds. The results of bioactivity studies showed that standardized hydroalcoholic propolis extract led to a decrease in nitrite release from LPS-activated RAW264.7 cells at 10 µg/mL indicating its remarkable anti-inflammatory activity. Additionally, propolis extract showed potent anticancer activity against pancreatic cancer cell lines MIA PaCa-2 in both 2D and 3D models in a concentration-dependent manner. The strongest effect on spheroid size change was observed at the concentration of 1 mg/mL.

The Scrum method has an important place among agile software development methods, and its use worldwide has increased over the years. This study aims to measure the impact of the Scrum method on business processes and examine its relationship with employee satisfaction. Specifically, a common survey was applied to both the development team and stakeholders working with the Scrum method. Since separate studies are generally conducted for the development team or stakeholders, evaluating employees in both roles in a single research constitutes a distinctive element of the study. An in-depth examination of the effects of Scrum and its relationship with employee satisfaction will significantly contribute to the literature. Within the scope of the research, a survey was administered to the employees of a bank. In the survey, following the demographic questions, the participants were asked questions about Scrum method applications and their perspectives on the method, and then other questions were included to understand their satisfaction levels. 108 people participated in the survey, and the answers were analyzed using SPSS. Since the data did not show a parametric distribution due to the survey, the answers were analyzed using Spearman Correlation Analysis, Mann Whitney U, and Kruskal Wallis test. As a result, it has been seen that the factors that increase the satisfaction between IT teams and stakeholders the most are related to the alignment and team solidarity between the teams. In addition, it has been observed that regardless of the roles of the people in the team, seeing their work as meaningful has a significant positive impact on satisfaction. Finally, there was rarely a notable difference between demographic elements and stress and satisfaction variables, and a significant difference was only seen between the number of people in the team and stress.

In predictive microbiology, both primary and secondary models are widely used to estimate microbial growth, often applied through two-step or one-step modelling approaches. This study focused on developing a tool to predict the growth of Pseudomonas spp., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). The key environmental factors—temperature, water activity, and pH—served as predictor variables to model the growth of Pseudomonas spp. in culture media. To assess model performance, these machine learning approaches were compared with traditional models, namely the Gompertz, Logistic, Baranyi, and Huang models, using statistical indicators such as the adjusted coefficient of determination (R2adj) and root mean square error (RMSE). Machine learning models provided superior accuracy over traditional approaches, with R2adj values from 0.834 to 0.959 and RMSE values between 0.005 and 0.010, showcasing their ability to handle complex growth patterns more effectively. GPR emerged as the most accurate model for both training and testing datasets. In external validation, additional statistical indices (bias factor, Bf: 0.998 to 1.047; accuracy factor, Af: 1.100 to 1.167) further supported GPR as a reliable alternative for microbial growth prediction. This machine learning-driven approach bypasses the need for the secondary modelling step required in traditional methods, highlighting its potential as a robust tool in predictive microbiology.

This study presents a novel application of the Random Forest (RF) algorithm to predict delamination in ultrasonic-assisted drilling (UAD) of carbon fiber reinforced polymers (CFRPs). It performs a multi-dimensional analysis of factors including graphene nanoplatelet (GNP) addition, ultrasonic vibration, cutting tool type, and feed rate on delamination damage. The RF algorithm was chosen for its ability to handle both regression and categorical tasks. The model demonstrated strong predictive performance, achieving an R² value of 0.9445 on test data, with a root mean squared error (RMSE) of 0.32% and a mean absolute error (MAE) of 0.29% relative to the average values. Analysis of variance (ANOVA), Sobol sensitivity, and Shapley additive explanations (SHAP) analysis were used to assess the impact of input parameters. Sobol identified the cutting tool type and feed rate as the most influential factors, contributing 37.7% and 34.3% to delamination variance, aligning with ANOVA findings. SHAP further confirmed the tooling type and feed rate as key factors, with contributions of 48.75% and 32.61%. The analyses revealed that GNPs increased delamination due to higher thrust forces, while ultra-sonic vibration and high-cobalt tools reduced delamination. Optimal conditions were a feed rate of 0.08 mm/rev with an 8% cobalt tool and ultrasonic vibration, excluding GNPs.

This article examines the influence of various factors on the precision of 3D printed objects produced through Fused Filament Fabrication (FFF) using Polylactic Acid (PLA) as the primary material. While PLA is widely chosen for its compatibility with biological applications, it does suffer from limited dimensional accuracy. The study focuses on investigating the effects of four independent parameters, namely layer height, infill density, printing speed, and the number of top and bottom layers, on accuracy along the three axes of length, width, and depth. The research objective is to identify the optimal levels of these parameters that minimize dimensional errors across all axes. However, due to conflicting requirements among different dimensions, determining a comprehensive decision-making methodology presents a significant challenge. To address this issue, three distinct approaches are proposed, each offering a unique perspective and practical application. Interestingly, the outcomes of these approaches converge on a single solution. Among these parameters, printing speed emerges as the dominant factor, while, the number of layers is the least influential. Moreover, consistent shrinkage is observed in the length and width dimensions, with greater errors evident in longer dimensions. Overall, a preference for lower layer height and infill density, along with higher printing speed and increased top and bottom layers, is recommended. The study findings indicate that the most effective parameter selection for reducing errors in all dimensions involves a layer height of 0.2 mm, infill density of 60%, printing speed of 50 mm/s, and the use of four top and bottom layers.

Alzheimer's disease (AD) is the most common neurodegenerative disease, and it is currently untreatable. RNA sequencing (RNA‐Seq) is commonly used in the literature to identify AD‐associated molecular mechanisms by analysing changes in gene expression. RNA‐Seq data can also be used to detect genomic variants, enabling the identification of the genes with a higher load of deleterious variants in patients compared with controls. Here, we analysed AD RNA‐Seq datasets to obtain differentially expressed genes and genes with a higher load of pathogenic variants in AD, and we combined them in a single list. We mapped these genes on a human protein–protein interaction network to discover subnetworks perturbed by AD. Our results show that utilizing gene pathogenicity information from RNA‐Seq data positively contributes to the disclosure of AD‐related mechanisms. Moreover, dividing the discovered subnetworks into highly connected modules reveals a clearer picture of altered molecular pathways that, otherwise, would not be captured. Repeating the whole pipeline with human metabolic network genes led to results confirming the positive contribution of gene pathogenicity information and enabled a more detailed identification of altered metabolic pathways in AD.

This study explains the electrochemical application using a glassy carbon electrode (GCE) and boron-doped diamond electrode (BDDE) to determine upadacitinib (UPA), which is a selective inhibitor of Janus kinase 1 (JAK1) protein from the standard solution and serum sample. Electrochemical oxidation behavior was evaluated regarding pH and scan rate effect, and the oxidation mechanism was found to be a mixture of adsorption/diffusion for BDDE and diffusion-controlled for GCE. The possible oxidation mechanism was explained in detail using indole, zolmitriptan, ruxolitinib, and tofacitinib. UPA was determined using GCE and BDDE in pH 4.7 acetate buffer solution (ABS). The linear concentration range was 8.0 × 10⁻⁷ – 6.0 × 10⁻⁵ M for GCE and 4.0 × 10⁻⁶ – 6.0 × 10⁻⁵ M for BDDE in standard solution. The obtained limit of detection (LOD) values are 1.91 × 10⁻⁸ M and 3.69 × 10⁻⁸ M for GCE and BDDE, respectively. Furthermore, the feasibility and accuracy of both electrodes were demonstrated through commercial human serum sample application and recovery studies, and the recovery% value was between 100.76% and 102.96%. For the precision evaluation, the repeatability and reproducibility values for GCE were 0.96–1.36 (standard solution-serum) and 1.48–1.99 (standard solution-serum); for BDDE were 0.42–0.85 (standard solution-serum) and 1.13–1.37 (standard solution-serum), respectively. Compared to the literature, this study stands out as an easier, environmentally friendly, affordable, and sensitive electrochemical option for UPA determination using GCE and BDDE.

Machine-learning interatomic potential models based on graph neural network architectures have the potential to make atomistic materials modeling widely accessible due to their computational efficiency, scalability, and broad applicability. The training datasets for many such models are derived from density-functional theory calculations, typically using a semilocal exchange-correlation functional. As a result, long-range interactions such as London dispersion are often missing in these models. We investigate whether this missing component can be addressed by combining a graph deep learning potential with semiempirical dispersion models. We assess this combination by deriving the equations of state for layered pnictogen chalcohalides BiTeBr and BiTeI and performing crystal structure optimizations for a broader set of V–VI–VII compounds with various stoichiometries, many of which possess van der Waals gaps. We characterize the optimized crystal structures by calculating their x-ray diffraction patterns and radial distribution function histograms, which are also used to compute Earth mover’s distances to quantify the dissimilarity between the optimized and corresponding experimental structures. We find that dispersion-corrected graph deep learning potentials generally (though not universally) provide a more realistic description of these compounds due to the inclusion of van der Waals attractions. In particular, their use results in systematic improvements in predicting not only the van der Waals gap but also the layer thickness in layered V–VI–VII compounds. Our results demonstrate that the combined potentials studied here, derived from a straightforward approach that neither requires fine-tuning the training nor refitting the potential parameters, can significantly improve the description of layered polar crystals.

The study aims to investigate how COVID-19 pandemic affects the probability of being overweight or obese in the case of NEET in Türkiye. This study delves into the comprehensive dataset provided by the Turkish Health Research Surveys from 2014 to 2022. The primary focus is on individuals aged 15–29, with a specific exploration of the repercussions of the COVID-19 pandemic on health conditions. The investigation centers on deciphering the intricate connections between NEET (Not in Education, Employment, or Training) status, pandemic ramifications, and the likelihood of being overweight or obese. Leveraging probit models and meticulous control for variables such as age, gender, regional fixed effects, and completed education levels, our findings indicate significant associations. Notably, the NEET subgroup demonstrates a 5.8% increase in the probability of being obese or overweight. We find suggestive evidence that the pandemic contributes to a marginal increase of 1.2%. A particularly intriguing revelation surfaces as being NEET significantly amplifies the likelihood of overweight or obesity in young females. Education plays a vital role in preventing overweight or obesity, as higher education levels correlate with a lower likelihood of obesity. Moreover, we analyze different age groups, our results show that being NEET is positively associated with the probability of overweight or obesity for females aged 19–24 and 25–29 in the post-pandemic period, indicating higher sensitivity to the pandemic among NEET females.