Effect of Thermal Treatment on Meat Proteins with Special Reference to Heterocyclic Aromatic Amines (HAAs)

Meat is one of the most imperative protein sources available with respect to its production and consumption. It is the richest source of some valuable nutrients like proteins, essential amino acids, polyunsaturated fatty acids, vitamins, and minerals like iron, zinc, and selenium. Thermal treatment produces conformational changes in protein structure as well as flavor, texture, and appearance, and chemical properties of the ingredients are also changed. Heterocyclic aromatic amines (HAAs), potent mutagens/carcinogens, are formed during the cooking of meat at high temperature. The review paper highlights the effects of various cooking methods, i.e., pan-frying, deep-frying, charcoal grilling, and roasting, on the formation of HAAs. The levels of HAAs produced in cooked meats vary depending upon the cooking method, time of cooking, and the type of meat being cooked. Metabolic behavior of HAAs is very unique, they interfere in the activity of many enzymes, modify the metabolic pathways, and lead to the adduct formation of DNA. The application of black pepper and several other spices during processing may reduce the formation of these (HAAs) mutagenic compounds.     

Empirical Analysis of Software Success Rate Forecasting During Requirement Engineering Processes

Forecasting on success or failure of software has become an interesting and, in fact, an essential task in the software development industry. In order to explore the latest data on successes and failures, this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework? What human factors contribute to success or failure of a software? What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work? In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors, misinterpretation, and miscommunication of requirements and decision-making processes play their roles in software success forecasting. We discussed a potential relationship between forecasting of software success or failure and the development processes. We noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study, and they were more likely to rate software success forecasting linking to the development processes. Our analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success rate. The results of this empirical study also validated that requirements’ misinterpretation and miscommunication were the main causes behind software systems’ failure. It has been seen that reliable, relevant, and trustworthy sources of information help in decision-making to predict software systems’ success in the software industry. This empirical study highlights a need for other software practitioners to avoid such bias while working on software projects. Future investigation can be performed to identify the other human factors that may impact software systems’ success.     

Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation

Several pests feed on leaves, stems, bases, and the entire plant, causing plant illnesses. As a result, it is vital to identify and eliminate the disease before causing any damage to plants. Manually detecting plant disease and treating it is pretty challenging in this period. Image processing is employed to detect plant disease since it requires much effort and an extended processing period. The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases, including Phytophthora infestans, Fusarium graminearum, Puccinia graminis, tomato yellow leaf curl. Therefore, this work uses the Support vector machine (SVM) classifier to detect and classify the plant disease using various steps like image acquisition, Pre-processing, Segmentation, feature extraction, and classification. The gray level co-occurrence matrix (GLCM) and the local binary pattern features (LBP) are used to identify the disease-affected portion of the plant leaf. According to experimental data, the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy.     

Synthesis of Mg-stabilized nitrogen-rich alpha-sialons along Si3N4–0.5Mg3N2:3AlN line via field-assisted sintering

The present study reports for the first time the synthesis and evaluation of magnesium (Mg)-doped nitrogen-rich (N-rich) sialon ceramics exploring the possible existence of Mg-stabilized single-phase alpha-sialon (on the Mg-alpha-sialon plane). Mg-stabilized N-rich sialons, with the general formula of Mg_xSi_12−2xAl_2xN₁₆ having x value in the range of 0.2–2.2 for the composition laying along the Si₃N₄–0.5Mg₃N₂:3AlN line, were synthesized at 1500°C, using nano-size starting powder precursors and field-assisted (or spark plasma) sintering technique. Consolidated sialon ceramic samples were characterized for their microstructure, phase stability regime, and physical and mechanical properties. Although a relatively low synthesis temperature was adopted, well densified sialon samples were achieved; however, the densification of the samples became difficult with a higher x value (containing high Mg₃N₂/AlN content). Contrary to what was expected to take place, a single-phase Mg-doped sialon was not obtained near the N-rich line (on Mg-alpha-sialon plane). Such distinctive behavior in Mg-doped sialons was supposed to be due to the formation of a highly stable Mg-containing aluminum nitride polytype phase, which consumed most of the high-temperature transient liquid phase. Mg-doped N-rich sialon sample having the maximum amount of alpha phase depicted a remarkable hardness (HV₁₀) of 21.4 GPa and a fracture toughness of 3.5 MPa m^1/2.     

Line extraction in handwritten documents via instance segmentation

International Journal on Document Analysis and Recognition (IJDAR) - Extraction of text lines from handwritten document images is important for downstream text recognition tasks. It is challenging...     

Authority gradients between rail network controllers,train crew and track workers in Australia and New Zealand: motivations and behaviours

Cognition, Technology & Work - The underlying relationship dynamics leading to authority gradients between frontline rail workers are being overlooked despite rail incident investigation...