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Reliable prediction of biodrying efficiency using interactive regression models
No full textThe inability of municipal solid waste (MSW) to meet incineration standards often undermines the sustainability and economic feasibility of waste-to-energy applications. Biodrying offers a promising, eco-friendly pretreatment to enhance the calorific value of MSW. This study evaluated the performance of biodrying based on the final calorific value (FCV) using simple and interactive regression models. Both conventional parameters; moisture content (MC), bulk density (BD), airflow rate (AFR), and initial calorific value (ICV) and unconventional indicators; the Temperature Index (TI), Biodrying Index (BI), and oxygen consumption (L) as a measure of biodegradability were used as predictors. Besides conventional regression models (OLS), to minimize multicollinearity of the dataset with Variance Inflation Factor (VIF) of higher than 10 Ridge regression (RR) analyses were also applied. AFR was the strongest positive variable in all the tested models and achieved maximum impact in RR3 Model with value of 2189.47 at significance level of p < 0.01. In the same model, triple impact of AFR*TI*MC was strong and negative (−819.60 at p < 0.05). In both regression approaches, interactive models provided better prediction efficiencies considering higher R2 and reduced error metrics. Professionals in this sector may consider the use of RR in FCV predictions to be both an innovative and practical approach
Decoding pulvinar dysfunction in parkinson's disease dementia: Linking brain networks and structural alterations to cognitive impairment
No full textThis study aimed to examine functional connectivity and grey matter volume differences in the pulvinar sub-regions between healthy controls (HCs) and Parkinson's disease (PD) patients with dementia. Resting-state functional MRI (rs-fMRI) and T1-weighted images were collected from 20 HCs (10 males, 10 females; mean age 65.45±7.53) and 20 PD patients with dementia (9 males, 11 females; mean age 66.75±7.87). Functional data were pre-processed using SPM12 and CONN software. ROI-based rs-fMRI and grey matter volume analyses were conducted to compare functional connectivity and grey matter volume, respectively. After controlling for age, education, and gender, PD patients with dementia showed significantly lower functional connectivity of the right anterior pulvinar (PuA) to bilateral temporal regions (Cluster 1: p = 0.000919 and Cluster 2: p = 0.038627, FDR-corrected) and reduced right PuA volume compared to HCs (p = 0.044). These functional differences correlated with Unified Parkinson's Disease Rating Scale (UPDRS) scores (cluster 1 r = -0.641, p = 0.006), as well as with right PuA volume loss (cluster 1: r = 0387, p = 0.016 and cluster 2: r = 0.350, p = 0.031). The findings suggest that reduced functional connectivity and volume in the right anterior pulvinar are associated with cognitive symptoms in PD with dementia, highlighting the pulvinar's role in cognitive deficits linked to neurodegeneration
Comparison of the shear strain and the deformation test value obtained for the triangular areas affected by Kahramanmaraş earthquakes with a magnitude of Mw 7.7 and Mw 7.6 with the different GNSS networks
No full textOn Error Bounds for Parameterized Trapezoid and Newton Formulas via Conformable Fractional Operators
No full textIn this paper, we are established that the parameterized inequalities reduce to some trapezoid and Newton-type inequalities with the help of the special choices. For this, first we present a parameterized integral identity inducing fractional integrals and then prove trapezoid and Newton-type inequalities for differentiable convex functions. These inequalities are proved by using the conformable fractional integrals. By using the special cases of our main results, we also give some new and previously obtained trapezoid and Newton-type inequalities
Optimization of Industrial Hot-Stamping Parameters for 35MnB5 Steel with Balanced Strength and Toughness
No full textThe aim of this study is to optimize industrial hot-stamping parameters for 35MnB5 press-hardened steel (PHS) to obtain a balanced combination of ultra-high strength and reliable toughness. A Taguchi L27 orthogonal experimental design was used to examine the influence of austenitization temperature, soaking time, die water flow rate, press dwell time, and forming load on the mechanical and microstructural response of the steel. Hot-stamped specimens were evaluated by tensile, Vickers hardness, flexural, and impact tests, and the resulting microstructures were examined using scanning electron microscopy (SEM). The results show that austenitization temperature and soaking time are the primary parameters controlling the strength–toughness balance. At the optimized processing condition (890 °C, 8 min), the steel achieved an ultimate tensile strength of 2074.8 MPa, a hardness of 626.9 HV, a flexural strength of 3951.4 MPa, and an impact energy of 55.9 J cm−2, corresponding to a predominantly fine lath-martensitic microstructure. These results define an industrially applicable set of hot-stamping parameters for 35MnB5 steel and serve as a practical reference for the production of next-generation 2 GPa class automotive components
Prioritizing Environmental Sustainability in Local Industrial Systems
No full textThis chapter introduces the Ecoleviant Governance Model, a novel conceptual framework developed through doctoral research, designed to address the pressing need for environmentally sustainable governance in terms of crisis governance. The model can be applied to local industrial systems as well. Based on interdisciplinary inquiry the model emphasizes participatory, adaptive, and multi-scalar mechanisms of environmental decision-making. It responds to the complex ecological interdependencies and socio-economic challenges faced by industrial zones, especially in emerging economies. The chapter will first outline the model’s key components. It then discusses its empirical grounding in case studies from China and Italy. The chapter concludes with practical recommendations for policymakers and industry leaders seeking to localize sustainability efforts through integrated governance strategies
Machine learning-enhanced modelling and experimental analysis of foam-core thermoplastic composites produced via pultrusion
No full textFoam-core thermoplastic composites manufactured by pultrusion offer lightweight, recyclable structural solutions but require precise control of coupled thermal and curing phenomena to ensure uniform properties. While physics-based models can capture these thermochemical interactions, their computational cost limits their use for rapid prediction and process optimisation. This study presents an integrated experimental–numerical–machine learning framework for foam-core thermoplastic pultrusion using Elium® resin. Cure kinetics are characterised by DSC and incorporated into a validated 3D multiphysics model coupling heat transfer and polymerisation. Microscopy confirms limited resin penetration into the foam surface, forming a mechanical interlocking mechanism at the skin–core interface. A large parametric simulation campaign is used to train machine-learning surrogate models (neural networks, random forests, and gradient boosting), achieving R2>0.998 and enabling millisecond-level predictions with over 104× speed-up compared to finite-element simulations. These surrogates are employed for rapid prediction and process optimisation to identify operating windows that balance throughput, thermal control, energy efficiency, and complete curing
Analysis of the Effect of Wind Turbines Used in Electric Vehicles on Drag Power
No full textThe increasing role of electric vehicles (EVs) in future transportation systems is of significant importance for improving energy efficiency and environmental sustainability. The integration of renewable energy sources, particularly wind energy, with these vehicles has the potential to reduce energy costs and carbon emissions. This study analyzed how different wind turbine placements influence electric vehicles in terms of drag force. The aerodynamic performance impacts of various configurations were also examined. The drag force on the vehicle was determined based on the turbine location, and the optimal placement was identified. For this purpose, an electric vehicle model was designed, and simulations were conducted using ANSYS (Analysis Systems) software. Additionally, the amount of energy generated was compared with the energy loss caused by the increased drag force. Furthermore, the most efficient integration method for wind turbines in electric vehicles was determined.</p