Evaluation of High-Frequency Measurement Errors from Turned Surface Topography Data Using Machine Learning Methods.

Manufacturing processes in industry applications are often controlled by the evaluation of surface topography. Topography, in its overall performance, includes form, waviness, and roughness. Methods of measurement of surface roughness can be roughly divided into tactile and contactless techniques. The latter ones are much faster but sensitive to external disturbances from the environment. One type of external source error, while the measurement of surface topography occurs, is a high-frequency noise. This noise originates from the vibration of the measuring system. In this study, the methods for reducing high-frequency errors from the results of contactless roughness measurements of turned surfaces were supported by machine learning methods. This research delves into optimizing filtration methods for surface topography measurements through the application of machine learning models, focusing on enhancing the accuracy of surface roughness assessments. By examining turned surfaces under specific machining conditions and employing a variety of digital filters, the study identifies the Gaussian regression filter and spline filter as the most effective methods at a 22.5 µm cut-off. Utilizing neural networks, support vector machines, and decision trees, the research demonstrates the superior performance of SVMs, achieving remarkable accuracy and sensitivity in predicting optimal filtration methods.

Analytical Approach for Forecasting the Load Capacity of the EN AW-7075-T6 Aluminum Alloy Joints Created Using RFSSW Technology.

To ensure the high reliability of aircraft structures, the Refill Friction Stir Spot Welding (RFSSW) process must be characterized by a high load capacity of the welds and a small standard deviation of the load capacity spread. This allows us to obtain uniform functional properties in the connections, ensuring the high quality of the process. This work aims to select the most favorable technological parameters for the welding process of EN AW-7075-T6 Alclad aluminum alloy sheets, which are used for the production of aircraft structures. The best networks were calculated using the Statistica 13.3 program. The obtained results were compared with the results of previous investigations. It has been shown that a model using neural networks allows for the determination of connection parameters with much greater accuracy than the classical model. The maximum error in estimating the load capacity of the connection for the mathematical model was 6.13%, and the standard deviation was 14.51%. In the case of neural networks, the maximum error value did not exceed 1.55%, and the standard deviation was 3.74%. It was shown that, based on the neural model, it is possible to determine the process parameters that ensure the required quality capacity of the process, ensuring a probability of obtaining the required load capacity of the connections amounting to P = 0.999935 with a defect rate of 0.0065%. This possibility is not provided by the classical model due to its large error in estimating the process spread and the high sensitivity of the process input parameters to the output parameters.

Chaperones-A New Class of Potential Therapeutic Targets in Alzheimer's Disease.

The review describes correlations between impaired functioning of chaperones and co-chaperones in Alzheimer's disease (AD) pathogenesis. The study aims to highlight significant lines of research in this field. Chaperones like Hsp90 or Hsp70 are critical agents in regulating cell homeostasis. Due to some conditions, like aging, their activity is damaged, resulting in ß-amyloid and tau aggregation. This leads to the development of neurocognitive impairment. Dysregulation of co-chaperones is one of the causes of this condition. Disorders in the functioning of molecules like PP5, Cdc37, CacyBP/SIPTRAP1, CHIP protein, FKBP52, or STIP1 play a key role in AD pathogenesis. PP5, Cdc37, CacyBP/SIPTRAP1, and FKBP52 are Hsp90 co-chaperones. CHIP protein is a co-chaperone that switches Hsp70/Hsp90 complexes, and STIP1 binds to Hsp70. Recognition of precise processes allows for the invention of effective treatment methods. Potential drugs may either reduce tau levels or inhibit tau accumulation and aggregation. Some substances neuroprotect from Aß toxicity. Further studies on chaperones and co-chaperones are required to understand the fundamental tenets of this topic more entirely and improve the prevention and treatment of AD.

ADAM33's Role in Asthma Pathogenesis: An Overview.

Asthma is a complex chronic respiratory disease characterized by airway hyperresponsiveness, inflammation, and obstruction. Many genes have been identified as associated with asthma but none with such substantial significance as the ADAM33 gene due to its role in airway remodeling and bronchial hyperresponsiveness. This review summarizes the current knowledge on the genetic and functional aspects of ADAM33 in asthma pathogenesis. We highlight its genetic variants associated with asthma susceptibility and severity, as well as the functional effects of ADAM33 on airway remodeling, smooth muscle cell proliferation, and its interplay with environmental factors. Additionally, we discuss the potential clinical implications of ADAM33 as a therapeutic target for asthma management.

Molecular cross-talk between long COVID-19 and Alzheimer's disease.

The long COVID (coronavirus disease), a multisystemic condition following severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, is one of the widespread problems. Some of its symptoms affect the nervous system and resemble symptoms of Alzheimer's disease (AD)-a neurodegenerative condition caused by the accumulation of amyloid beta and hyperphosphorylation of tau proteins. Multiple studies have found dependence between these two conditions. Patients with Alzheimer's disease have a greater risk of SARS-CoV-2 infection due to increased levels of angiotensin-converting enzyme 2 (ACE2), and the infection itself promotes amyloid beta generation which enhances the risk of AD. Also, the molecular pathways are alike-misregulations in folate-mediated one-carbon metabolism, a deficit of Cq10, and disease-associated microglia. Medical imaging in both of these diseases shows a decrease in the volume of gray matter, global brain size reduction, and hypometabolism in the parahippocampal gyrus, thalamus, and cingulate cortex. In some studies, a similar approach to applied medication can be seen, including the use of amino adamantanes and phenolic compounds of rosemary. The significance of these connections and their possible application in medical practice still needs further study but there is a possibility that they will help to better understand long COVID.

Integrated Approach for Safety Culture Factor Evaluation from a Sustainability Perspective.

Traditionally, sustainable development has been seen as a combination of three pillars: economic, social and environmental development. In recent years, another one has been added to these three pillars, namely culture, as being indispensable in achieving sustainable development. This study proposes an integrated approach for the identification and classification of safety culture factors in the company in a sustainability context. The research design was based on the assumption that safety culture is part of organizational culture that should support the development of corporate sustainability. Firstly, the identification of the safety culture factors (SCFs) based on the literature review was presented. Then, the ISM method was used to identify the interaction between SCFs and to develop the hierarchical structure of these factors. In the next step, ISM was integrated with the MICMAC method to cluster the factors based on driving power and dependence power into four categories. Finally, safety culture factors with high driving power were rated using the fuzzy TOPSIS method from the sustainability dimension perspective. This approach was used in an automotive industry company to improve and develop the company's practices aimed at implementing a sustainable development strategy. A sensitivity analysis was also carried out to monitor the robustness of the approach.

Locating Chart Choice Based on the Decision-Making Approach.

Modern manufacturing engineering requires quick and reasonable solutions during the production planning stage, ensuring production efficiency and cost reduction. This research aims to create a scientific approach to the rational choice of a locating chart for complexly shaped parts. It is an important stage during the manufacturing technology and fixture design process. The systematization of the designed and technological features of complexly shaped parts and the definition of the features that impact a locating chart create the fundamentals for justification. A scientific approach has been developed using the complex combination of the part's features and a decision-making approach using the example of bracket-type parts. The matrix of design and technological features of parts was developed including steel AISI 3135 and cast iron DIN 1691. The classification of locating charts for bracket-type parts was defined. A mathematical model of the rational choice of the locating chart according to the structural code of the workpiece was verified in case studies from the practice. As a result, a decision-making approach was applied to the rational choice of the locating chart for any bracket-type part. The proposed solutions improve the production planning stage for machine building, automotive, and other industries.

Modelling the Influence of Slide Burnishing Parameters on the Surface Roughness of Shafts Made of 42CrMo4 Heat-Treatable Steel.

This article presents the results of tests aimed at determining the effect of slide burnishing parameters on the surface roughness of shafts made of 42CrMo4 heat-treatable steel. The burnishing process was carried out using tools with polycrystalline diamond and cemented carbide tips. Before burnishing, the samples were turned on a turning lathe to produce samples with an average surface roughness Ra = 2.6 µm. The investigations were carried out according to three-leveled Hartley's poly selective quasi D (PS/DS-P: Ha3) plan, which enables a regression equation in the form of a second-order polynomial to be defined. Artificial neural network models were also used to predict the roughness of the surface of the shafts after slide burnishing. The input parameters of the process that were taken into account included the values of pressure, burnishing speed and feed rate. Overall, the burnishing process examined leads to a reduction in the value of the surface roughness described by the Ra parameter. The artificial neural networks with the best regression statistics predicted an average surface roughness of the shafts with R2 = 0.987. The lowest root-mean-square error and mean absolute error were obtained with all the network structures analysed that were trained with the quasi Newton algorithm.

Fatigue Reliability Analysis Method of Reactor Structure Considering Cumulative Effect of Irradiation.

The influence of irradiation should be considered in fatigue reliability analyses of reactor structures under irradiation conditions. In this study, the effects of irradiation hardening and irradiation embrittlement on fatigue performance parameters were quantified and a fatigue life prediction model was developed. Based on this model, which takes into account the cumulative effect of a neutron dose, the total fatigue damage was calculated according to Miner's linear cumulative damage law, and the reliability analysis was carried out using the Monte Carlo simulation method. The case results show that the fatigue life acquired by taking into account the cumulative effect of irradiation was reduced by 24.3% compared with that acquired without considering the irradiation effect. Irradiation led to the increase of the fatigue life at low strains and its decrease at high strains, which is in accordance with the findings of an irradiation fatigue test. The rate of increase in the fatigue life decreased gradually with the increase of the neutron dose. The irradiation performance parameters had a small influence on fatigue reliability, while the fatigue strength coefficient and the elastic modulus had a great influence on the fatigue reliability. Compared with the current method, which uses a high safety factor to determine design parameters, a fatigue reliability analysis method taking into account the cumulative effect of irradiation could be more accurate in the reliability analysis and life prediction of reactor structures.