Welding strength prediction in nuts to sheets joints: machine learning and ANFIS comparative analysis

This study uses machine learning algorithms and the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict welding strength in DD13 sheet metal joints with AISI 1010 nuts. The objective is to optimize industrial welding processes and improve quality control. The study investigates weld current, time, and hold time as critical input variables for joint integrity. The performance of different ML algorithms, including linear regression, random forest regression, ridge regression, Bayesian regression, K-Nearest Neighbors regression, decision tree regression, and ANFIS, are evaluated. Training and testing data consist of welding parameters and corresponding strength measurements. Performance metrics such as R2 score, mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) are used to assess the predictive capabilities. Random forest regression is the most efficient algorithm, with a high R2 score of 0.992 and minimal errors. ANFIS also exhibits comparable performance, highlighting its efficacy in this context. These findings can be useful for optimizing welding parameters in industrial settings, potentially leading to improved quality control and weld strength, particularly in automotive applications. Using ML and ANFIS, industries can make informed decisions to optimize welding processes and ensure joint integrity, ultimately meeting the rigorous demands of demanding applications. © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2024

Relationship Between Dyspnoea Scales and Quality of Life in Stroke Survivors: A Retrospective Analysis

Background and Objectives: The purpose of the study was to evaluate the relationship between different dyspnoea scales and clinical and physical parameters of stroke patients and to identify the most appropriate scale for stroke patients. Materials and Methods: This study, designed as a retrospective analysis, involved 203 patients diagnosed with stroke. Dyspnoea intensity was evaluated using four different scales: Oxygen Cost Diagram (OCD), Basic Dyspnoea Index (BDI), Modified Medical Research Council (mMRC), and Visual Analogue Scale (VAS). Respiratory muscle strength (maximal inspiratory pressure (MIP) and quality of life (Stroke Impact Scale 3.0 (SIS)) were also assessed. Results: The regression model explained only 20.2% of the variance in SIS total scores (R2 = 0.202), indicating that key predictors might be missing. Additionally, dyspnoea scales showed statistically significant but modest correlations with SIS total scores (r = 0.248–0.397), suggesting limited clinical significance. There was a statistically significant relationship between age and dyspnoea scales, except for OCD (r = −0.153, p = 0.056). A statistically significant relationship was found between the MIP and OCD scales (r = 0.290, p < 0.001) and BDI scale (r = 0.195, p = 0.014). However, only the BDI showed a statistically significant relationship with the other three dyspnoea scales in stroke patients. Conclusions: The OCD and BDI can evaluate dyspnoea ratings during day-to-day activities; therefore, these scales were significantly correlated with inspiratory muscle strength in stroke patients. Our findings suggest that while BDI and OCD are valuable tools for dyspnoea assessment in stroke patients, the overall predictive power of dyspnoea scales for quality of life is limited. Future studies should consider additional variables, such as comorbidities and rehabilitation intensity, to improve predictive accuracy and clinical relevance. © 2025 by the authors

Perrin Numbers That Are Concatenations of a Perrin Number and a Padovan Number in Base b

Let (Formula presented.) be a Padovan sequence and (Formula presented.) be a Perrin sequence. Let n, m, b, and k be non-negative integers, where (Formula presented.). In this paper, we are devoted to delving into the equations (Formula presented.) and (Formula presented.), where d is the number of digits of (Formula presented.) or (Formula presented.) in base b. We show that the sets of solutions are (Formula presented.) (Formula presented.) for the first equation and (Formula presented.) for the second equation. Our approach employs advanced techniques in Diophantine analysis, including linear forms in logarithms, continued fractions, and the properties of Padovan and Perrin sequences in base b. We investigate both the deep structural symmetries and the complex structures that connect recurrence relations and logarithmic forms within Diophantine equations involving special number sequences. © 2025 by the author

A new deep learning-based GUI design and implementation for automatic detection of brain strokes with CT images

Brain stroke is a disease that can occur in almost any age group, especially in people over 65. There are two main types of strokes: ischemic stroke and hemorrhagic stroke. Blockage of brain vessels causes ischemic stroke, while rupture of blood vessels in or around the brain causes hemorrhagic stroke. According to the World Health Organization, 5 million people die annually from this disease; 85% of these 5 million are paralyzed by ischemic stroke and 15% by hemorrhagic stroke. For these reasons, especially in the early diagnosis and treatment of ischemic stroke, patients can lead to more comfortable lives. In this study, we evaluate the effectiveness of real-time object detection algorithms for the detection of brain strokes in computed tomography images and propose an artificial intelligence-supported system for busy physicians to quickly analyze computed tomography images. The aim of this study is to compare the performance of YOLOv7, YOLOv8, and YOLOv9 models in the detection of ischemic and hemorrhagic strokes from brain computed tomography images, to compare the performance of YOLO-based algorithms known as real-time object detection networks in segmentation with other segmentation networks known as U-Net and U-Net variants and Mask-RCNN algorithms, and to develop a system that doctors can use to analyze stroke computed tomography images. In this study, 6951 anonymized brain computed tomography slices obtained from the Turkish Ministry of Health were used. The YOLOv7, YOLOv8, and YOLOv9 models are trained using deep learning algorithms. Model training and testing processes were performed with the PyTorch deep learning framework and CUDA acceleration. The dataset consists of anonymized brain computed tomography images collected between 2019 and 2020. Experimentally, three different studies were conducted using ischemic stroke-health images, hemorrhagic stroke-health images, and ischemic stroke–hemorrhagic stroke-health images together for comparison with the literature. In all studies, the YOLOv9-Seg model is successful. In the ischemic stroke-health images study, 99.50% segment [email protected] success was achieved; in the hemorrhagic stroke-health images study, 99.49% segment [email protected] success; and in the ischemic stroke–hemorrhagic stroke-health images study, %99.71 segment [email protected] success was achieved. The YOLOv7 and YOLOv8 models also exhibited high accuracy rates but lagged behind the YOLOv9-Seg model. These findings suggest that the YOLOv9-Seg model is the most suitable model for the detection and segmentation of ischemic and hemorrhagic strokes in brain CT images. The real-time processing capability of the model will help to make fast and accurate decisions in emergency medical interventions. In addition, this study has shown that YOLO-based models can be used effectively in health data. It is thought that the use of the model in clinical applications will make significant contributions to early diagnosis and treatment processes. © The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024

Production of Highly Efficient Pt/C for PEM Fuel Cell Applications

PEM fuel cell technologies have emerged as promising candidates for advancing sustainable energy solutions, primarily due to their exceptional efficiency and minimal environmental impact. However, the widespread commercialization of fuel cells is hindered by the high cost and limited availability of platinum catalysts, which play a critical role in facilitating electrochemical reactions. This research mainly focused on investigating innovative solutions aiming to mitigate platinum loading while simultaneously preserving or potentially enhancing their performance. To this end, the impact of two distinct surfactants, Tween 40 and Tween 80, was examined to assess their influence on the synthesis and characteristics of platinum nanoparticles immobilized on carbon supports. Subsequently, their electrochemical activities were compared. The catalysts were synthesized using the polyol method with the incorporation of surfactants, and their performance was compared with that of Pt/C catalysts without surfactants. TGA analysis indicated a significant reduction of approximately 12% in the Pt content of the catalyst synthesized using Tween 80 surfactant. However, CV analysis revealed a remarkable increase of 85% in the ECSA for the same catalyst. Furthermore, significant improvements in the performance of this catalyst were also observed in the single-cell test setup. The high performance achieved with a lower Pt content in the catalyst layer highlights its potential for large-scale commercialization. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025

Kolza (Brassica napus L.) Tohumuna Borik Asit ve Gibberellik Asit Ön Uygulamalarının Kuraklık Stresine Karşı Etkisinin İncelenmesi

Bu çalışmada, polietilen glikol’un (PEG) kolza (Brassica napus L.) tohumlarına ön uygulama olarak Gibberellik Asit (GA3) ve Borik Asit’in (BA) çimlenme ve fide gelişimi üzerine etkileri incelenmiştir. Araştırma Sakarya Uygulamalı Bilimler Üniversitesi Ziraat Fakültesi Tarla Bitkileri laboratuvarında gerçekleştirilmiştir. Denemede tohumlar ekim öncesi GA3 ve BA’nın beş farklı konsantrasyonu (kontrol, 0.50, 1.00, 1.50, 2.00 mg l-1) ile ön muameleye alınmış ve daha sonra dört farklı PEG (6000) konsantrasyonu (kontrol, -0.4, -0.8, 1.2 Mpa) ile kuraklık stresi uygulamasına tabi tutulmuş. Deneme Tesadüf Parselleri Faktöriyel Deneme Desenine göre 3 tekerrürlü olarak yürütülmüştür. Çalışmada çimlenme hızı, çimlenme gücü, fide uzunluğu, kök uzunluğu, su içeriği, fide yaş ve kuru ağırlığı özellikleri incelenmiştir. Denemede kuraklık stresinin artışıyla, çimlenme hızı ve çimlenme gücünde düşüşler ve diğer fide özelliklerinde olumsuz etkiler tespit edilmiştir. Ancak araştırma sonuçlarına göre çimlenme ve fide özelliklerini incelediğimizde, genel olarak -0.4 Mpa PEG stresinde 1.5 ile 2.00 mg l-1 BA uygulaması kuraklık stresine karşı olumlu sonuçlar verdiği görülmüştür. Sonuç olarak, PEG stresi koşullarında kolza tohumlarına Borik Asit uygulamalarının, bitki düzenleyici gruplara alternatif olarak fide gelişim dönemlerinde fayda sağlayabileceği neticesine varılmıştır

Investigation of variability in monthly minimum and maximum temperature with trend methods in Khyber Pakhtunkhwa, Pakistan

Global warming is an inherent phenomenon that has a substantial impact on both the ecosphere and the human population. Understanding and strategizing to mitigate the effects of global warming is of utmost importance. Extensive research regarding climatological factors has been conducted in a recent timeframe. The predominant approach employed in such investigations is trend analysis. This study employs the Trend Polygon Star Concept, Three-Dimensional Innovative Trend Analysis (3D-ITA), and Innovative Polygon Trend Analysis (IPTA) due to their effectiveness in visualizing, analyzing, and understanding complex temperature data in the context of climate change. The IPTA Process is a method used to compare the raw and processed data sets of two data series. To illustrate the test's findings, the Trend Polygon Star Concept splits the two-month data set interval on a graph—the IPTA output—into four parts. Thus, this research assesses monthly minimum and maximum temperature data using this two-polygon methodology. This data capture spans four decades (1981–2020). As an outcome of the work, polygon graphics were generated. Furthermore, the IPTA method has been used to compute the trend slopes and lengths of temperature data. A list was prepared to provide all the values for the x- and y-axis of graphs created using the Trend Polygon Star Concept Method. The findings of both research methodologies were reviewed for a particular station. Additionally, when the arithmetic mean analysis of monthly maximum temperatures was examined, a rising trend was detected in most months. In contrast, the lowest temperature series revealed no movement in most of the months. When the standard deviation graph was studied, it was discovered that all ten zones had transitions between two months. Using the 3D-ITA strategy, 40% of the entire region was found to have stable trends, whereas 60% of the region examined had unstable trends. © 2025 The Author(s

Effect of the use of methanol, toluene and nitromethane mixtures as fuel additives in a gasoline engine on engine performance and exhaust emissions

The increasing concerns about environmental sustainability and the depletion of fossil fuels have led to increased research into alternative fuel blends that can improve engine performance while reducing harmful emissions. This study investigates the effects of various fuel blends (methanol, toluene, and nitromethane) added to gasoline in a single-cylinder, four-stroke gasoline engine. The aim is to analyze the effects on key engine performance parameters, including thermal efficiency, brake-specific fuel consumption (BSFC), exhaust gas temperature and emissions such as carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx) and carbon dioxide (CO2). It was observed that the highest gain in BSFC value was achieved with a 9.70 % decrease in the tests conducted with G90M10 fuel compared to G100 under full load conditions, and accordingly, the highest gain in BTE value was achieved with a 17.14 % increase in the tests conducted with G90M10 fuel. When BSFC and BTE data are examined, it is determined that the highest positive effect compared to G100 is provided by G90M10 fuel, followed by G90M5N5 and G90T5N5 fuel mixtures, respectively. It is determined that the G90T5N5 fuel mixture provides the highest reduction in HC and CO emissions compared to G100 at full load conditions, with reductions of 56.8 % and 60.92 %, respectively. However, it is observed that the same fuel mixture increases NOx emissions by 102.53 % compared to G100 for the same conditions. The results show that lambda values (air–fuel ratio) increase with engine load in all fuel types and have a mixture closer to the stoichiometric ratio at higher loads, increasing fuel efficiency, reducing CO and HC emissions, and increasing combustion temperatures and NOx formation. The study results reveal that fuel compositions must be carefully optimized to balance performance gains with environmental impact. © 202

Apatite-wollastonite glass-ceramics containing B2O3 and Na2O: Potential bioactive material for tissue protection during radiation therapy procedures

In this study, an attempt to expand available data and functionality of apatite-wollastonite glass ceramics (AW GCs) in medical therapy and bone engineering by estimating and analysing the physical, structural, fast neutron and gamma interaction properties of B2O and Na2O doped AW GCs is presented. The pristine (AW) and (20 wt% B2O3 and 30 wt% Na2O) doped AW GC (AW-B20-N30) samples were prepared using the cold isostatic press method. The samples were subject of structural and physical characterisation through experimental procedures, while their radiation interaction parameters were obtained following standard theoretical models. Samples’ densities were calculated as 2.917 and 2.613 g/cm3, while the Vickers hardness was 553 and 518 HV for AW and AW-B20-N3, respectively. The structure of the samples revealed that Na2O formed the brianite phase inserted in the apatite structure. The mass and linear attenuation coefficients fluctuated within the ranges, 0.0232-13.6853 cm2/g and 0.0676-39.92 cm-1 for AW and 0.021-8.313 cm2/g and 0.055-21.7223 cm-1 for AW-B20-N30, respectively. The half- and tent-value layers increased from about 0.02 to 10.25 cm and 0.06 to 34.05 cm for AW; for AW-B20-N30, the increase is from 0.032 to 12.61 cm and 0.11 to 41.88 cm, respectively. AW was more effective for shielding photons and fast neutrons, and had lower gamma buildup factors compared to AW-B20-N30. The study showed doping AW with B2O and Na2O could be optimised to get equivalent bone material in radiation studies. The AW GCs also showed better shielding effectiveness compared to some traditional shields and could therefore be applied for shielding tissues outside the target volume in radiation therapy. © 202

Smartphone addiction among elderly individuals: its relationship with physical activity, activities of daily living, and balance levels

Background: The growing use of smartphones among elderly individuals, driven by social and informational needs, may lead to smartphone addiction, potentially impacting their daily lives. This study aimed to determine whether there is a difference in physical activity, activities of daily living, and balance levels between elderly individuals with and without smartphone addiction. Methods: This descriptive and cross-sectional study included 94 elderly individuals. Data were obtained using the Smartphone Addiction Scale-Short Version (SAS-SV), the Physical Activity Scale for the Elderly (PASE), the Lawton Instrumental Activities of Daily Living Scale (Lawton IADL), the Fullerton Advanced Balance Scale (FAB-T), and the Timed Up and Go Test (TUG). The participants were divided into two groups according to their SAS-SV scores: those with (n = 45) and those without (n = 49) smartphone addiction. Results: When the groups with and without smartphone addiction were compared, there was a significant difference between the groups in terms of Lawton IADL (t = 4.223, p < 0.001), total PASE (t = 7.791, p < 0.001), PASE work-related activity (t = 2.541, p = 0.013), household activity (t = 3.598, p = 0.001), and leisure activity (t = 7.063, p < 0.001). Structural equation modeling showed that Lawton IADL (β = -0.320, p < 0.001), PASE total (β = -0.518, p < 0.001), and PASE work-related activity (β = -0.211, p = 0.033), household activity (β = -0.300, p = 0.002), and leisure time activity (β = -0.483, p < 0.001) subscales had a direct negative predictive effect on SAS-SV. FAB-T had a direct positive predictive effect on total PASE (β = 0.186, p = 0.030) and work-related activity subscales (β = 0.197, p = 0.046). FAB-T had a direct positive predictive effect on Lawton IADL (β = 0.247, p = 0.009), but a direct negative effect on TUG (β = -0.541, p < 0.001). Conclusions: The study determined that smartphone addiction was directly related to the maintenance of physical activity and daily living activities in elderly individuals but did not lead to a change in balance status. Future studies should consider including potential confounders, such as baseline physical fitness, socioeconomic status, and cognitive impairment, in structural equation modeling to provide more comprehensive insights. © The Author(s) 2025