IN VIVO ANTIDIABETIC SCREENING OF KABAU (ARCHIDENDRON BUBALINUM (JACK) I. C NIELSEN) SEEDS

Objective: Study described the screening potential antidiabetic activity of kabau seed extract and fraction. Methods: The powdered crude drugs weighing 1349.32 grams were extracted with a solvent with solvents with escalating polarity by using soxhletation. The solvents used were n-hexane, ethyl acetate, and 96% ethanol. Screening activity using three variations on doses on the three extracts using the glucose test tolerance method, then the alloxan induction and high-fat feed induction testing methods using selected doses, decreasing blood glucose levels using the GOD PAP enzyme and decreasing MDA levels and increased levels of the enzyme SOD. Extracts that have potential antidiabetic activity are fractionated using liquid-solid fractionation; then the fraction is screened for antidiabetic activity using the glucose test tolerance method. Results: Screening for antidiabetic activity on the three extracts using the glucose test tolerance method showed that the ethanol extract at a dose of 250 mg/kg BW. The three extracts were then screened for the next mechanism using the alloxan induction method and high-fat feed induction, the decrease in blood sugar levels by the GOD-PAP method showed a good decrease in the ethanol extract by 202.94±2 mg/dl, the three extracts showed good less significant, in the SOD enzyme method, the ethanol extract gave a good value such as the positive control value. Testing on fraction can decrease in blood sugar; the results showed that the ethanol extract and methanol fraction gave a small AUC 0-150 (32695,3 and 33167,71), where the value was close to the result of the glibenclamide 30238,48. Conclusion: The antidiabetic activity of the extract showed that the ethanol extract was better with the glucose test tolerance method, with alloxan induction animal models and high-fat feed induction. In the methanol fraction derived from 96% ethanol extract, it provides a good reduction in blood sugar levels in the screening method with a glucose test tolerance

Hypoglycemic activity test on smooth pigweed (Ammaranthus Hybridus L) leaf water extract on male Wistar rats

Introduction: Type 2 diabetes mellitus consists of an array of dysfunction characterized by hyperglycemia. The activity of smooth pigweed (Amaranthus hybridus L.) leaves water extract on male Wistar rats. Objectives: This research was started by supplying simplicia, making smooth pigweed leaves water extract, and testing the hypoglycemic activity of smooth pigweed leaves water extract on male Wistar rats. Methods: The glucose tolerance method was used to determine the hypoglycemic activity of smooth pigweed leaves water extract. Male white rats were divided into five groups of six rats each: a positive control group (0.5% of tragacanth suspension), a comparison group (Diabinese suspension at a dose of 22.5 mg/kg body weight (bw)), and three test groups at doses of 50 mg/kg bw, 100 mg/kg bw, and 150 mg/kg bw. Results and conclusions: The most significant hypoglycemic activity was seen with the dose of 150 mg/kg bw in comparison with the control group at 90 minutes.

Server and network load balancing

Software Defined Networking (SDN), is an emerging networking technology. This thesis aims to develop a new Server and Network Load balancing scheme in content delivery datacenters using SDN-based architecture. The scheme, called Server and Network Load Balancing (SNLB), tends to distribute the traffic load more evenly across the network. The SNLB achieves even distribution of flows on the links and servers by utilizing real-time network statistics. Furthermore, SNLB classifies the network flows into mice (flows with small bandwidth) and elephant (flows with large bandwidth) flows and performs load balancing on these two classes of flows separately. A detailed comparison of SNLB with Global first fit, Round robin and Load based balancing is presented. Other objectives achieved in this thesis are the designs of overload traffic handling technique and Fault tolerance method. The overload traffic handling technique activates and de-activates servers according to the traffic load; the fault tolerance method can reduce the impact on network performance during the network fault.

Server and network load balancing

Software Defined Networking (SDN), is an emerging networking technology. This thesis aims to develop a new Server and Network Load balancing scheme in content delivery datacenters using SDN-based architecture. The scheme, called Server and Network Load Balancing (SNLB), tends to distribute the traffic load more evenly across the network. The SNLB achieves even distribution of flows on the links and servers by utilizing real-time network statistics. Furthermore, SNLB classifies the network flows into mice (flows with small bandwidth) and elephant (flows with large bandwidth) flows and performs load balancing on these two classes of flows separately. A detailed comparison of SNLB with Global first fit, Round robin and Load based balancing is presented. Other objectives achieved in this thesis are the designs of overload traffic handling technique and Fault tolerance method. The overload traffic handling technique activates and de-activates servers according to the traffic load; the fault tolerance method can reduce the impact on network performance during the network fault.

A Data-Driven Methodology to Improve Tolerance Allocation Using Product Usage Data

Industry 4.0, the fourth industrial revolution, puts forward new requirements for the sustainable service of products. With the recent advances in measurement technologies, global and local deformations in inaccessible areas can be monitored. Product usage data such as geometric deviation, position deviation, and angular deviation that lead to product functional performance degradation can be continuously collected during the product usage stage. These technologies provide opportunities to improve tolerance design by improving tolerance allocation using product usage data. The challenge lies in how to assess these deviations for identifying relevant field factors and reallocate the tolerance value. In this paper, a data-driven methodology based on the deviation for tolerance analysis is proposed to improve the tolerance allocation. A feature graph of a mechanical assembly is established based on the assembly relationship. The node representation in the feature graph is defined based on the unified Jacobian-torsor model and the node label is calculated by a synthetic evaluation method. A novel hierarchical graph attention networks (HGAT) is proposed to investigate hidden relations between nodes in the feature graph and calculate labels of all nodes. A modification necessity index (MNI) is defined for each tolerance between two nodes based on their labels. An identification of the to-be-modified tolerance method is proposed to specify the tolerance analysis target. A deviation difference matrix is constructed to calculate the MNI of each tolerance for identifying the to-be-modified tolerance value with high priorities for product improvement. The effectiveness of the proposed methodology is demonstrated through a case study for improving tolerance allocation of a press machine.