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Despite the prevalent application of 18% Ni maraging steel in critical sectors such as aerospace and automotive due to its unique characteristics, including high ductility, yield strength, and hardenability, its machining presents enormous challenges, categorizing it as a difficult-to-machine material. The cutting tool’s geometry is crucial in machining, significantly affecting chip formation, cutting forces, power consumption, and obtainable surface quality. In particular, wiper insert technology, characterized by its multi-radius design, offers an increased contact area compared to conventional inserts, potentially enhancing the quality of the machined surface. This study explores the effectiveness of wiper inserts in the face-milling of maraging steel 350, conducting a comparative analysis across three distinct machining setups. These setups vary by alternating the number of wiper and conventional inserts within the same cutter, thereby examining the influence of insert configuration on machining outcomes. The research employs a reliable and well-established statistical approach to evaluate how different variables, such as cutting speed and feed rate, affect surface quality, power consumption, and material removal rate (MRR). It also sheds light on the material removal mechanisms facilitated by each type of insert. The findings reveal that incorporating a higher number of wiper inserts significantly enhances the surface finish but concurrently increases power consumption. Thus, the study successfully identifies an optimal set of process parameters that attain a balance between achieving superior surface quality and maintaining energy efficiency in the machining of maraging steel 350. This balance is crucial for optimizing manufacturing processes while adhering to the stringent quality and sustainability standards required in aerospace and automotive manufacturing.

Maraging steel, characterized by its superior strength-to-weight ratio, wear resistance, and pressure tolerance, is a material of choice in critical applications, including aerospace and automotive components. However, the machining of this material presents significant challenges due to its inherent properties. This study comprehensively examines the impacts of face milling variables on maraging steel’s surface quality, cutting temperature, energy consumption, and material removal rate (MRR). An experimental analysis was conducted, and the gathered data were utilized for training and testing five machine learning (ML) models: support vector machine (SVM), K-nearest neighbor (KNN), artificial neural network (ANN), random forest, and XGBoost. Each model aimed to predict the outcomes of different machining parameters efficiently. XGBoost emerged as the most effective, delivering an impressive 98% prediction accuracy across small datasets. The study extended into applying a genetic algorithm (GA) for optimizing XGBoost’s hyperparameters, further enhancing the model’s predictive accuracy. The GA was instrumental in multi-objective optimization, considering various responses, including surface roughness and energy consumption. The optimization process evaluated different weighting methods, including equal weights and weights derived from the analytic hierarchy process (AHP) based on expert insights. The findings indicate that the refined XGBoost model, augmented by GA-optimized hyperparameters, provides highly accurate predictions for machining parameters. This outcome holds significant implications for industries engaged in the machining of maraging steel, offering a pathway to optimized operational efficiency, reduced costs, and enhanced product quality amid the material’s machining challenges.

This paper presents a sight about the chemical structure deformation of poly (ethylene-co-vinyl acetate) (EVA) samples according to the change ratio of rate constant values. Spectroscopy kinetics fluorescence curves are fitted for two characteristic wavelength domains of fluorescent intensities. The short wavelengths (320-400 nm) domain show spectra overlapping, while at long wavelengths (400-800 nm) domain spectra are arranged in regular for each specific accelerated aging time. The ratio of kinetics rate constant at long wavelengths to kinetics rate constant of short wavelengths is the criterion of the degree chemical structure deformation. Molar extrinsic coefficient relies on the chemical structure change. Through absorbance measurement, EVA samples have been classified into two groups. Presence of Cyasorb additive is the key point of the ranking. The effect of three different accelerated aging of dry (115 oC), damp (85% moisture, 85 oC), and irradiated (UV, 65 oC) aging have been considered for two samples of each group over different aging time. Spectroscopy of absorbance and fluorescent for aged samples have been discussed. In general, Cyasorb adding causes higher chemical structure deformation for the EVA sample. The most effective factor is the damp aging and the less one is the UV irradiation aging, while the biggest chemical structure change of Cyasorb-free sample is produced by damp aging and the less by dry heat.

We investigated the electrical properties of Graphene Sheet by employing the B3LYP/DFT at SIESTA – trunk - 462 of program, and calculated by employing the LDA calculations using the Gollum software . We showed that the studied Graphene Sheet has small energy band gap. Graphene Sheet has high values of ionization potential and electron affinity. It has small value of electrochemical hardness and large value of electronic softness. The calculated density of states and the observed nonzero density of states indicates a finite number of states. The I-V characteristic and conductance curves showed high value of conductivity. Pure Graphene Sheet is suggest that may make it a suitable for thermoelectric applications.

Low-threshold mechanosensory C-fibres, C-tactile afferents (CTs), respond optimally to sensations associated with a human caress. Additionally, CT-stimulation activates brain regions associated with processing affective states. This evidence has led to the social touch hypothesis, that CTs have a key role in encoding the affective properties of social touch. Thus, to date, the affective touch literature has focussed on gentle stroking touch. However, social touch interactions involve many touch types, including static, higher force touch such as hugging and holding. This study aimed to broaden our understanding of the social touch hypothesis by investigating relative preference for static vs dynamic touch and the influence of force on these preferences. Additionally, as recent literature has highlighted individual differences in CT-touch sensitivity, this study investigated the influence of affective touch experiences and attitudes, autistic traits, depressive symptomology and perceived stress on CT-touch sensitivity. Directly experienced, robotic touch responses were obtained through a lab-based study and vicarious touch responses through an online study where participants rated affective touch videos. Individual differences were determined by self-report questionnaire measures. In general, static touch was preferred over CT-non-optimal stroking touch, however, consistent with previous reports, CT-optimal stroking (velocity 1–10 cm/s) was rated most pleasant. However, static and CT-optimal vicarious touch were rated comparably for dorsal hand touch. For all velocities, 0.4N was preferred over 0.05N and 1.5N robotic touch. Participant dynamic touch quadratic terms were calculated for robotic and vicarious touch as a proxy CT-sensitivity measure. Attitudes to intimate touch significantly predict robotic and vicarious quadratic terms, as well as vicarious static dorsal hand touch ratings. Perceived stress negatively predicted robotic static touch ratings. This study has identified individual difference predictors of CT-touch sensitivity. Additionally, it has highlighted the context dependence of affective touch responses and the need to consider static, as well as dynamic affective touch. [ABSTRACT FROM AUTHOR]

Cancer of the colon (colorectal cancer, or CRC) is the third most frequent malignancy in the world and the fourth leading cause of cancer-related death. Recent research has focused on the link between high-risk human papillomavirus (HPV) infections and the onset/development of several different types of cancer in humans. As a result, scientists are now paying more attention to HPV and CRC. In a variety of malignant tumors, P63 is overexpressed. This includes non-Hodgkin lymphoma and breast carcinoma, as well as lung, bladder, and prostate cancers. However, in accordance with the existence of many P63 isoforms in malignant tumors, the actions of P63 in these malignancies remain a source of debate. P63 immunohistochemistry expression in CRC tissues is being investigated as a possible etiological link between high-risk HPV types and CRC. This retrospective study intended to investigate if there was an etiological link between high-risk HPV types and CRC. It has utilized 92 chosen formalin-fixed and paraffin-embedded tissue block samples. The collected samples were divided into 62 blocks of colorectal adenocarcinoma mass tissues and 30 non-malignant colorectal tissues used as a control group. Chromogenic in situ hybridization (CISH) was employed to discover HPV DNA16/18 in colorectal tissues. The overall proportion of positive HPV16/18 DNA- CISH detection in the mass CRC group was 44.4%, whereas HPV16/18 DNA was obtained at 80.0% in the non-malignant control group. The overall proportion of positive P63-ISH detection in the CRC group was also 70.4%, whereas P63 was 73.3% in the non-malignant control group. The link between HPV infection and P63 expression in CRC might point to the importance of these molecules in the progression of CRC. [ABSTRACT FROM AUTHOR]