Sakarya University

This study analyses building foundation models with and without expansion joints, investigating the effects of deep soil mixing (DSM) on settlement and the effect of expansion joints on stress distribution. Numerical models were developed to evaluate the impact of DSM parameters, including diameter, length, and spacing, as well as the influence of mini-piles and anchorage systems on the support structure, and showed that increasing the diameter and length of DSM columns significantly reduced settlements, achieving reductions of 81-94% in models with expansion joints and 77-94% in models without expansion joints. In models with expansion joints and without joints, closely spaced DSM applications (1.3 m) have significantly reduced settlement and also prevented non-uniform settlement. Expansion joints limit stress interaction between blocks, interrupt stress continuity, and thus prevent the propagation of secondary stresses arising from non-uniform settlement, temperature effects, and stiffness differences. The relationship between DSM length and settlement is similar to the relationship between DSM diameter and settlement. For this study, a DSM length of 12 meters and a DSM diameter of 0.4 meters were determined as the ideal DSM parameters. DSM configuration with 12 meter length, 0.4 m diameter, and 1.3 m spacing was identified as the most efficient solution in terms of both performance and cost for this project. These findings indicate that expansion joints are a secondary parameter in settlement control under DSM-improved soil conditions, that soil improvement properties should be prioritised in design evaluations, and that expansion joints are an important criterion in terms of stress distribution.

Nanomaterials (NMs) are increasingly utilized in drug delivery, diagnostic imaging, and therapeutic applications. However, their widespread use raises concerns regarding potential neurotoxicity, particularly for metal and metal oxide nanoparticles. Accumulating evidence indicates that these nanoparticles induce neurotoxicity through interconnected mechanisms, including excessive reactive oxygen species generation, activation of neuroinflammatory pathways, mitochondrial dysfunction, and disruption of blood–brain barrier integrity. These molecular events collectively lead to synaptic impairment, neuronal apoptosis, and progressive cognitive and behavioral deficits, with toxicity severity influenced by dose, exposure duration, and age. Given that in vitro models often fail to capture complex systemic interactions such as nanoparticle biodistribution, blood–brain barrier dynamics, and neuroimmune responses, this review places particular emphasis on in vivo studies to provide a more physiologically relevant understanding of nanoparticle-induced neurotoxicity. Importantly, a growing body of in vivo evidence demonstrates that natural bioactive compounds can mitigate these effects by targeting key pathogenic pathways, including oxidative stress, inflammation, and mitochondrial dysfunction, while preserving neuronal integrity. These findings highlight the therapeutic potential of natural bioactives as protective agents against nanoparticle-induced neurotoxicity and as candidates for broader neuroprotective strategies. This review summarizes the mechanistic basis of metal and metal oxide nanoparticle neurotoxicity and critically evaluates the protective role of natural bioactive compounds, with a focus on evidence derived from animal models.

This study uses experimental data to demonstrate the accuracy of deep learning (DL) modeling for automotive air conditioning (AAC) systems. The experimental AAC system uses a variable capacity compressor and R1234yf as the refrigerant. The experimental system is equipped with different control and data acquisition systems to obtain the best data. The experimental data is obtained at different compressor speeds and condenser and evaporator inlet air flow rates, temperatures, and relative humidity values. Data obtained according to various conditions were collected according to the system’s steady state. The AAC system with a variable capacity compressor and R1234yf was tested 108 times for DL application. The obtained data were evaluated according to the compressor discharge temperature, evaporator outlet airflow temperature, refrigerant mass flow rate, compressor power, cooling capacity, and coefficient of performance. To construct a dependable DL model for the AAC system utilizing R1234yf, the dataset was partitioned into training (72.22%) and testing (27.78%) subsets. Linear Interpolation-based Data Augmentation was employed to address data shortage and enhance generalization, augmenting the training dataset to 1008 patterns. The optimal model performance was achieved with HLNN set to 35 and NHL set to 4. Exceptional prediction accuracy was attained in all instances, with R 2 values surpassing 0.9998 and minimal error metrics (e.g. MSE = 0.000027 for refrigerant mass flow rate, MSE = 0.00113 for coefficient of performance). The results indicate that the DL model, improved by data augmentation, yields precise, generalizable predictions, and diminishes the necessity for substantial physical experimentation in AAC system analysis.

Inclusive education aims to provide equitable access to quality education for all students. While its principles are widely accepted, implementing inclusive education in subjects such as physical education presents specific challenges and requires distinct teacher competencies. Because physical education promotes physical activity, social interaction, and well-being, inclusive practices in this area are essential for the quality of life and social participation of students with disabilities. This study examined the perceptions, experiences, and needs of physical education teachers in Türkiye regarding inclusive education. A qualitative design using directed content analysis was employed. Of the 257 PE teachers who completed the initial survey, 151 volunteered for a written interview. From these, 35 teachers with inclusive teaching experience were purposively selected. Data were collected through a demographic questionnaire and a semi-structured written interview, and analyzed using a predetermined conceptual framework. The directed content analysis identified four main themes. For Perceptions of Inclusion, 91.4% of teachers defined inclusion as participation, belonging, and equal opportunity. For Teacher Competence, 85.7% reported that self-efficacy, professional experience, and positive attitudes were key to their inclusive practices. Contextual Factors were the main barrier, with 94.3% citing inadequate infrastructure, limited equipment, and lack of institutional support. Regarding Practice and Outcomes, 71.4% said they used adaptive strategies despite these challenges, resulting in improved student motivation, self-confidence, and social integration. These outcomes are associated with better well-being and quality of life, especially for students with disabilities. The study concludes that although physical education teachers in Türkiye have a strong understanding of inclusion and use adaptive strategies to support students, their efforts are constrained by systemic barriers. Effective inclusive physical education therefore requires support that addresses infrastructural, resource, and institutional limitations, not just teacher competence. Removing these barriers is essential for inclusive physical education to improve quality of life, social inclusion, and well-being for all students, especially those with disabilities, in accordance with public health goals.

Abstract This work explores the application of state-of-the-art techniques of time series forecasting to the delignification process in the pulp and paper industry, aiming to enhance sustainability and efficiency. While traditional machine learning models, such as Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), have been widely used, recent advancements in time series architectures provide significant improvements in prediction accuracy. This study adopts cutting-edge time series architectures and integrates them with explainability techniques (Explainable Artificial Intelligence, XAI) to analyze critical features and their temporal saliency, providing insights into the most influential variables in the delignification process. For a sequence length of 96, Crossformer attains the lowest error of 0.625±0.032, while for a sequence length of 24, LMS-AutoTSF achieves the lowest error of 0.281±0.0001. Additionally, we perform simulation analyses using the identified important features to evaluate the effects of input parameter changes–such as temperature or H-factor adjustments–on correlated variables and the target Kappa number. To model these interdependencies and generate realistic input scenarios, we employ a Conditional Variational Autoencoder (CVAE), which enables the adjustment of one input feature while automatically adapting correlated features in a coherent and interpretable manner. This allows for counterfactual simulations that help operators understand the dynamic impact of process modifications on the overall system behavior. By leveraging advanced forecasting models, XAI-driven feature analysis, and CVAE-based simulation studies, we aim to improve prediction accuracy, optimize resource usage, and enhance operational efficiency. This work underscores the potential of combining modern time series forecasting, explainability techniques, and generative modeling to advance delignification processes, contributing to a more sustainable future for the pulp and paper industry. We further extend our experimental analysis to another industrial pulp dataset comprising a large number of instances, where Crossformer achieves the lowest error of 0.283 ± 0.112, followed by LMS-AutoTSF with the second-lowest error of 0.367 ± 0.136, across average prediction lengths of 12, 24, and 48.