Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning

The dynamic development of new technologies enables the optimal computer technique choice to improve the required quality in today’s manufacturing industries. One of the methods of improving the determining process is machine learning. This paper compares different intelligent system methods to identify the tool wear during the turning of gray cast-iron EN-GJL-250 using carbide cutting inserts. During these studies, the experimental investigation was conducted with three various cutting speeds vc (216, 314, and 433 m/min) and the exact value of depth of cut a_p and federate f. Furthermore, based on the vibration acceleration signals, appropriate measures were developed that were correlated with the tool condition. In this work, machine learning methods were used to predict tool condition; therefore, two tool classes were proposed, namely usable and unsuitable, and tool corner wear VBc = 0.3 mm was assumed as a wear criterium. The diagnostic measures based on acceleration vibration signals were selected as input to the models. Additionally, the assessment of significant features in the division into usable and unsuitable class was caried out. Finally, this study evaluated chosen methods (classification and regression tree, induced fuzzy rules, and artificial neural network) and selected the most effective model.     

Presentation and interpretation of food intake data: Factors affecting comparability across studies

Non-uniform, unclear, or incomplete presentation of food intake data limits interpretation, usefulness, and comparisons across studies. In this contribution, we discuss factors affecting uniform reporting of food intake across studies. The amount of food eaten can be reported as mean portion size, number of servings or total amount of food consumed per day; the absolute intake value for the specific study depends on the denominator used because food intake data can be presented as per capita intake or for consumers only. To identify the foods mostly consumed, foods are reported and ranked according to total number of times consumed, number of consumers, total intake, or nutrient contribution by individual foods or food groups. Presentation of food intake data primarily depends on a study's aim; reported data thus often are not comparable across studies. Food intake data further depend on the dietary assessment methodology used and foods in the database consulted; and are influenced by the inherent limitations of all dietary assessments. Intake data can be presented as either single foods or as clearly defined food groups. Mixed dishes, reported as such or in terms of ingredients and items added during food preparation remain challenging. Comparable presentation of food consumption data is not always possible; presenting sufficient information will assist valid interpretation and optimal use of the presented data. A checklist was developed to strengthen the reporting of food intake data in science communication.     

Variation in125i-insulin absorption and blood glucose concentration

Summary The absorption of monocomponent porcine¹²⁵I-insulin Monotard and Isophane was studied in six insulin dependent diabetic patients over a period of 12 days. The absorption of insulin was measured as the disappearance of radioactivity from sites of injection. The daily¹²⁵I-insulin doses ranged from 20 to 48 IU between patients. The insulin absorbed varied considerably within and between patients. The range of individual daily absorbed insulin varied from 19 to 104 per cent of the¹²⁵I-insulin dose. A significant correlation (p<0.05) was found between insulin absorption and blood glucose concentration. Insulin absorption rates were relatively high before all hypoglycaemic episodes and reactive hyperglycaemia was only observed when relatively low insulin absorption rates followed the hypoglycaemic attack. The results show that lability in some insulin dependent diabetics is explained by variation in insulin absorption.     

Multimodal Functional Neuroimaging by Simultaneous BOLD fMRI and Fiber-Optic Calcium Recordings and Optogenetic Control

Recent developments of optogenetic tools and fluorescence-based calcium recording techniques enable the manipulation and monitoring of neural circuits on a cellular level. Non-invasive imaging of brain networks, however, requires the application of methods such as blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI), which is commonly used for functional neuroimaging. While BOLD fMRI provides brain-wide non-invasive reading of the hemodynamic response, it is only an indirect measure of neural activity. Direct observation of neural responses requires electrophysiological or optical methods. The latter can be combined with optogenetic control of neuronal circuits and are MRI compatible. Yet, simultaneous optical recordings are still limited to fiber-optic-based approaches. Here, we review the integration of optical recordings and optogenetic manipulation into fMRI experiments. As a practical example, we describe how BOLD fMRI in a 9.4-T small animal MR scanner can be combined with in vivo fiber-optic calcium recordings and optogenetic control in a multimodal setup. We present simultaneous BOLD fMRI and calcium recordings under optogenetic control in rat. We outline details about MR coil configuration, choice, and usage of opsins and chemically and genetically encoded calcium sensors, fiber implantation, appropriate light power for stimulation, and calcium signal detection, to provide a glimpse into challenges and opportunities of this multimodal molecular neuroimaging approach.