Cardiovascular disease has a way of slipping into everyday life almost unnoticed until it suddenly becomes serious, which is why early prediction has turned into such a central concern in healthcare. The difficulty is not only that the condition is widespread, but that it grows out of a messy mix of biological, behavioural, and lifestyle factors that rarely line up in a simple, linear way. Traditional screening tools try to tame this complexity with fixed rules and risk scores, yet they often miss faint, interacting patterns in large datasets. Machine learning models, by contrast, learn directly from patient data and can sometimes pick up relationships that are too subtle or too tangled for conventional methods to capture. In this project, the focus is on whether a Radial Basis Function Neural Network can meaningfully support cardiovascular risk prediction. RBF networks sit in an interesting middle space: they are flexible enough to approximate complex decision boundaries, but they remain more interpretable than many deep architectures that are used in clinical prediction tasks. Using a publicly available dataset with common clinical variables such as age, blood pressure, cholesterol, glucose, and lifestyle indicators like smoking, alcohol use, and physical activity, the model was trained to classify individuals as having cardiovascular disease or not. Working through preprocessing, tuning the RBF parameters, and evaluating the predictions highlighted both the promise and the limits of this relatively simple architecture. Given the stakes in healthcare, such models need to be treated with caution, but they still offer a useful glimpse of how early detection might be supported in practice rather than replaced outright.

In this paper, the optimized weighted K-Nearest Neighbors (WKNN) algorithm was utilized for an efficient two-class automatic banana quality classification: good and bad. The seven-feature-based dataset was normalized, and then it was split into three parts: training, validation, and testing. Feature weights were incrementally adjusted based on errors over 70 training epochs evaluated on the validation set such that informative features were assigned more weight. When converged, the final KNN classifier with k=5 neighbors was tested on a separate test batch. The model achieved 98.10%, 97.98%, 98.23%, 97.97%, and 98.10% accuracy, precision, sensitivity, specificity, and F1-score for the good quality class, respectively. The error decreased from 0.260 to 0.191 in training, and the weights of size and weight dominated. Our results demonstrate that the proposed WKNN has the potential for day-to-day banana grading with high reliability and uniformity in the automated inspection systems.

Wood is a natural composite of strong cellulose fibres embedded in a lignin matrix that is resistant to compression. It is a complex material, serve as raw material for many chemical products, including pulp and paper. The pulp properties of Douglas fir wood were examined in this study to meet the need of wood for paper production in order to reduce overexploitation of the most used species. Wood samples obtained from a 9-years old tree were chipped for the pulp experiment using the Kraft pulping method. The experiment was subjected to usage of two different alkali charges (NaOH 15% and 17%) and three sulphidity levels (Na2S 23%, 25% and 27%). Data were analysed using descriptive statistics and ANOVA at α0.05. The results show that the highest pulp yield of 51.41% was obtained in this study under active alkali of 15%, sulphidity of 27%, time of 190 minutes and temperature of 170oC. A strong negative correlation coefficient of -0.936 was observed between kappa number and pulp yield. Generally, the pulp properties observed in this study indicated the suitability of Douglas fir for paper production as it has produced high yield and a good pulp quality.

The increasing complexity of modern software systems has exacerbated the challenges of debugging, often leading to significant financial and time costs. This paper explores the effectiveness of Spectrum-Based Fault Localization (SBFL) as an automated debugging technique using the Flacoco tool. The research evaluates the tool's performance on the IntroClassJava benchmark dataset, analyzing the suspiciousness scores of Java programs with known defects. The results indicate that Flacoco significantly reduces the search space for developers by highlighting potentially faulty code elements. However, discrepancies in the accuracy of fault localization reveal limitations related to test case quality, underscoring the importance of high-quality, comprehensive test cases in the debugging process. This study contributes to enhancing the practical application of SBFL in industrial software development and demonstrates the potential of Flacoco for more efficient and accurate fault localization.

Peat soils are soils with low load-bearing, high compressibility and very saturated in water, which requires stabilization before their use as a foundation layer. This study examines the effect of the addition of Evocrete additives and an Evocrete-lime combination on improving the physical and mechanical properties of fibrous peat soils. Analyses include water content, density, organic matter content, ash content, fiber content, pH, as well as mechanical tests such as compaction test, simple compressive strength test (UCS), and UU triaxial test. Different proportions of Evocrete were used, from 5% to 25%, as well as a combination of 25% Evocrete and lime, at percentages ranging from 5% to 25%. The results showed that the addition of Evocrete increased dry density, shear strength, and compressive strength. The combination of Evocrete and lime allowed for a more significant increase, particularly for an Evocrete content of 25% and a lime content of 15% to 20%. In conclusion, Evocrete effectively improves the mechanical properties of peat soils and optimal performance is achieved through the Evocrete-lime combination.

Building energy performance is traditionally evaluated under the implicit assumption that structural conditions remain invariant during building operation. While structural dynamics are rigorously analyzed for safety and serviceability, their potential influence on operational energy demand has remained largely unexplored. This study addresses this gap by introducing the STRIDE framework (Structural Influence on Dynamic Energy Demand), a fully coupled, time-dependent methodology that integrates nonlinear structural dynamic analysis with transient building energy simulation. Using synchronized time-history modeling, the framework resolves structural response and energy demand on compatible temporal scales, enabling explicit investigation of their interaction. The results demonstrate that structural dynamic behavior produces measurable, nonlinear, and regime-dependent effects on time-varying energy demand, particularly influencing peak demand intensity and short-term demand variability rather than cumulative consumption alone. The findings confirm that static or sequential energy modeling approaches underestimate demand dynamics in buildings with pronounced structural response. By repositioning structural systems as active contributors to operational energy performance, the study advances performance-based building analysis and provides a transferable methodological foundation for integrated structural–energy research.

The building sector faces growing pressure to reduce total energy consumption across the entire life cycle of buildings. While previous research has predominantly focused on operational energy reduction, increasing evidence shows that structural embodied energy plays a critical role in determining long-term energy performance, particularly under uncertain conditions. This study develops an uncertainty-aware, decision-oriented life cycle energy framework that integrates structural embodied energy and operational energy to evaluate their combined impact on total building energy demand. A set of alternative structural material scenarios is assessed using life cycle energy analysis, incorporating variability in operational energy demand and building service life. The results reveal that embodied energy dominates total life cycle energy during early service periods, while operational energy becomes decisive only beyond specific threshold conditions. Importantly, uncertainty significantly alters comparative performance, challenging deterministic rankings of structural solutions. The findings demonstrate that no single structural system is universally optimal and that robust, moderate embodied energy strategies offer more resilient performance across uncertain futures. The study provides a practical analytical foundation for energy-conscious structural decision-making in early-stage building design.