Exploring design space: Machine learning for multi-objective materials design optimization with enhanced evaluation strategies

Discovering optimal material designs in the design space can be significantly accelerated by leveraging machine learning (ML) models for screening candidates. However, the quality of these designs depends on the prediction accuracy of the ML models and the efficiency of the optimization algorithms used. This study comprehensively compares different ML modeling strategies, optimization algorithms and evaluation strategies. Thereby, automated ML, tree-based ML models and neural networks were compared. Various optimization algorithms were analyzed, including random search, evolutionary and swarm-based methods. In addition, different strategies for evaluating the predictive performance of the ML models were investigated, which is particularly important as these models are expected to predict design parameters that deviate significantly from the known designs in the training data throughout the optimization. Our results highlight the capability of the proposed workflow to discover material designs that significantly outperform those within the training database and approach theoretical optima. Overall, this research contributes to advancing the field of material design optimization by providing a versatile and practical workflow that introduces automated ML into material design optimization and new model error assessment strategies tailored explicitly to optimization tasks

Multifaceted superoxide dismutase 1 expression in amyotrophic lateral sclerosis patients: a rare occurrence?

Amyotrophic lateral sclerosis (ALS) is a neuromuscular condition resulting from the progressive degeneration of motor neurons in the cortex, brainstem, and spinal cord. While the typical clinical phenotype of ALS involves both upper and lower motor neurons, human and animal studies over the years have highlighted the potential spread to other motor and non-motor regions, expanding the phenotype of ALS. Although superoxide dismutase 1 (SOD1) mutations represent a minority of ALS cases, the SOD1 gene remains a milestone in ALS research as it represents the first genetic target for personalized therapies. Despite numerous single case reports or case series exhibiting extramotor symptoms in patients with ALS mutations in SOD1 (SOD1-ALS), no studies have comprehensively explored the full spectrum of extramotor neurological manifestations in this subpopulation. In this narrative review, we analyze and discuss the available literature on extrapyramidal and non-motor features during SOD1-ALS. The multifaceted expression of SOD1 could deepen our understanding of the pathogenic mechanisms, pointing towards a multidisciplinary approach for affected patients in light of new therapeutic strategies for SOD1-ALS

A simple approach to transient-state modeling of nitrogen cooling in the extrusion of light-alloy complex profiles

Nitrogen cooling has become a popular solution to reduce heat flux between the die and the profile in the hot extrusion process. However, designing effective cooling channels for complex-shape profiles poses challenges, especially when the phase transition of nitrogen significantly impacts heat transfer with solid bodies. To this end, the ability to model both the liquid and the gas phase is instrumental in devising design strategies, yet it should be combined with low computational complexity for industrial applications. The present work is aimed at employing the homogenous-flow approach as a simple, yet representative methodology to consider both phases in the simulations. One-dimensional model of nitrogen was combined with a three-dimensional extrusion model to perform the transient analysis of the whole process, mostly focused on the transition from fully gaseous to fully liquid flow. Validation using extrusion tests on seventeen AA6060 billets demonstrates the model's predictability in comparison with a fully liquid model. The average error associated with Homogeneous Flow Model was evaluated as below 10%, whereas the fully liquid approach yielded 25%. That proved the ability of the proposed model to reproduce the cooling effect, thus supporting the design of the cooling subsystem within the context of the whole extrusion tooling

Comprehensive study of SrF2 growth on highly oriented pyrolytic graphite (HOPG): Temperature-dependent van der Waals epitaxy

This study explores the molecular beam epitaxy (MBE) growth of SrF2 on highly oriented pyrolytic graphite (HOPG) highlighting the temperature-dependent variations in growth morphology, crystalline structure and electronic properties. The comprehensive characterization of SrF2/HOPG interfaces was carried out using atomic force microscopy (AFM), reflection high-energy electron diffraction (RHEED), ultraviolet photoelectron spectroscopy (UPS) and X-ray photoelectron spectroscopy (XPS). The spectroscopy data suggest that the chemical interaction of the fluoride with the substrate is weak at each deposited thickness and temperature of the substrate during the deposition, indicating a growth under a van der Waals epitaxial regime. SrF2 nanostructures deposited on HOPG depict a distinctive bulk-like character, concerning their crystallinity and composition, even at the very initial growth stage. Remarkably, temperature plays a crucial role in driving the growth patterns, moving from coalescence of dendritic islands at room temperature to induce nearly 1D rows along the step-edges of HOPG terraces at higher temperatures (400 °C)

Quercetin, the new stress buster: Investigating the transcriptional and behavioral effects of this flavonoid on multiple stressors using Lymnaea stagnalis

Growing evidence suggests that a flavonoid-rich diet can prevent or reverse the effects of stressors, although the underlying mechanisms remain poorly understood. One common and abundant flavonoid found in numerous foods is quercetin. This study utilizes the pond snail Lymnaea stagnalis, a valid model organism for learning and memory, and a simple but robust learning paradigm—operant conditioning of aerial respiration—to explore the behavioral and transcriptional effects of different stressors on snails' cognitive functions and to investigate whether quercetin exposure can prevent stress effects on learning and memory formation. Our findings demonstrate that three different stressors—severe food deprivation, lipopolysaccharide injection (an inflammatory challenge), and fluoride exposure (a neurotoxic agent)—block memory formation for operant conditioning and affect the expression levels of key targets related to stress response, energy balance, and immune response in the snails' central ring ganglia. Remarkably, exposing snails to quercetin for 1 h before stress presentation prevents these effects at both the behavioral and transcriptional levels, demonstrating the potent stress-preventive properties of quercetin. Despite the evolutionary distance from humans, L. stagnalis has proven to be a valuable model for studying conserved mechanisms by which bioactive compounds like quercetin mitigate the adverse effects of various stressors on cognitive functions across species. Moreover, these findings offer insights into quercetin's potential for mitigating stress-induced physiological and cognitive impairments

Online motion accuracy compensation of industrial servomechanisms using machine learning approaches

This paper addresses the crucial aspect of position error modeling and compensation in industrial servomechanisms with the aim to achieve accurate control and high-performance operation in industrial robots and automated production systems. The inherent complexity and nonlinear behavior of these modules, usually consisting of a servomotor and a speed reducer, often challenge traditional analytical modeling approaches. In response, the study extensively explores the design and implementation of Machine Learning (ML) algorithms to obtain a comprehensive model of the Transmission Error (TE) in rotating vector reducers, which is a main source of robot motion accuracy errors. The ML models are trained with experimental data obtained from a special purpose test rig, where the reducer is tested under different combinations of input speed, applied load and oil temperature. In the second part of the work, the resulting predictive model, tailored to capture the intricate dynamics of the analyzed reducer, is imported into a programmable logic controller to enable online compensation strategies during the execution of custom motion profiles. Experimental tests are conducted using two distinct motion profiles: one generated with a cycloidal law, typical of industrial machinery, and the other extrapolated from the joints of an industrial robot during a pick-and-place task. The results demonstrate the effectiveness of the proposed approach, enabling accurate prediction and substantial reductions (over 90%) in the overall reducer TE through the implemented predictive model

Local Lipschitz continuity for energy integrals with slow growth and lower order terms

We consider integral functionals with slow growth and explicit dependence on of the Lagrangian; this includes many relevant examples as, for instance, in elastoplastic torsion problems or in image restoration problems. Our aim is to prove that the local minimizers are locally Lipschitz continuous. The proof makes use of recent results concerning the Bounded Slope Conditions