Search Results (5)

The non-enzymatic conjugation of peptides through the Maillard reaction has gained attention as an effective method to enhance biological functions. This study focuses on two conjugate mixtures: crude soy protein hydrolysate (SPH) conjugated with mannose (SPHM) and crude soy protein hydrolysate conjugated with allulose (SPHA). These two mixtures were products of the Maillard reaction, also known as non-enzymatic glycation. In vitro experiments were conducted to evaluate the antioxidant, anti-pancreatic lipase, inhibition of Bovine Serum Albumin (BSA) denaturation, and anti-angiotensin converting enzyme (ACE) activities of these conjugated mixtures. The results indicate that conjugated mixtures significantly enhance the antioxidant potential demonstrated via the DPPH and FRAP assays. SPHA exhibits superior DPPH scavenging activity (280.87 ± 16.39 µg Trolox/mL) and FRAP value (38.91 ± 0.02 mg Trolox/mL). Additionally, both conjugate mixtures, at a concentration of 10 mg/mL, enhance the BSA denaturation properties, with SPHM showing slightly higher effectiveness compared to SPHA (19.78 ± 2.26% and 5.95 ± 3.89%, respectively). SPHA also shows an improvement in pancreatic lipase inhibition (29.43 ± 1.94%) when compared to the SPHM (23.34 ± 3.75%). Furthermore, both the conjugated mixtures and rare sugars exhibit ACE inhibitory properties on their own, effectively reducing ACE activity. Notably, the ACE inhibitory effects of the individual compounds and their conjugate mixtures (SPHM and SPHA) are comparable to those of positive control (Enalapril). In conclusion, SPHM and SPHA demonstrate a variety of bioactive properties, suggesting their potential use in functional foods or as ingredients in supplementary products. Full article

This review paper addresses the critical need for structural prognostics and health management (SPHM) in aircraft maintenance, highlighting its role in identifying potential structural issues and proactively managing aircraft health. With a comprehensive assessment of various SPHM techniques, the paper contributes by comparing traditional and modern approaches, evaluating their limitations, and showcasing advancements in data-driven and model-based methodologies. It explores the implementation of machine learning and deep learning algorithms, emphasizing their effectiveness in improving prognostic capabilities. Furthermore, it explores model-based approaches, including finite element analysis and damage mechanics, illuminating their potential in the diagnosis and prediction of structural health issues. The impact of digital twin technology in SPHM is also examined, presenting real-life case studies that demonstrate its practical implications and benefits. Overall, this review paper will inform and guide researchers, engineers, and maintenance professionals in developing effective strategies to ensure aircraft safety and structural integrity. Full article

The development of serial or parallel manipulator robots is constantly increasing due to the need for faster productivity and higher accuracy. Therefore, researchers have turned to combining both mechanisms, sharing the advantage from serial to parallel or vice versa. This paper proposes a new configuration design for a serial-parallel hybrid manipulator (SPHM) using the industrial robotic KUKA Kr6 R900 and 3-DOF parallel spherical mechanism. The Kr6 R900 has six degrees of freedom (6-DOF) divided into three joints for translation (x, y, z) and another three joints for orientation (A, B, C) of the end-effector and the 3-DOF parallel spherical mechanism with three paired links. On the contrary, each limb of the parallel spherical mechanism consists of revolute–revolute–spherical joints (3-RRS). This mechanism allows translation movement along the Z-axis and orientation movements about the X- and Y- axes. The new hybrid will enrich the serial manipulator in movement flexibility and expand the workspace for serial and parallel manipulator robots. In addition, a complete conceptual design is presented in detail for the new robot configuration with a schematic and experimental setup. Then, a comprehensive mathematical model was derived and solved. The forward, inverse kinematics, and workspace analyses were derived using the graphical solution. Additionally, the new hybrid manipulator was tested for path planning. Moreover, an experimental setup was prepared to test the selected path. Finally, the new robot configuration can enlarge the workspace of both manipulators and the selected path matched to the experimental test. Full article

Advances in the manufacturing industry have led to modern approaches such as Industry 4.0, Cyber-Physical Systems, Smart Manufacturing (SM) and Digital Twins. The traditional manufacturing architecture that consisted of hierarchical layers has evolved into a hierarchy-free network in which all the areas of a manufacturing enterprise are interconnected. The field devices on the shop floor generate large amounts of data that can be useful for maintenance planning. Prognostics and Health Management (PHM) approaches use this data and help us in fault detection and Remaining Useful Life (RUL) estimation. Although there is a significant amount of research primarily focused on tool wear prediction and Condition-Based Monitoring (CBM), there is not much importance given to the multiple facets of PHM. This paper conducts a review of PHM approaches, the current research trends and proposes a three-phased interoperable framework to implement Smart Prognostics and Health Management (SPHM). The uniqueness of SPHM lies in its framework, which makes it applicable to any manufacturing operation across the industry. The framework consists of three phases: Phase 1 consists of the shopfloor setup and data acquisition steps, Phase 2 describes steps to prepare and analyze the data and Phase 3 consists of modeling, predictions and deployment. The first two phases of SPHM are addressed in detail and an overview is provided for the third phase, which is a part of ongoing research. As a use-case, the first two phases of the SPHM framework are applied to data from a milling machine operation. Full article