Priority checking RED for improving QoS in IPv6

This paper presents a priority checking random early detection (PC-RED) gateway for ensuring the quality of service (QoS) of high priority dataflow in IPv6 networks. A bit in the IP header is used in PC-RED to label the packet with the current status of the QoS that the dataflow is being treated in. The status of the QoS is determined by the difference between the packet average-dropping rate and the fixed desired limit dropping rate of the dataflow. PC-RED would perform dissimilarly to every dataflow corresponding to the different QoS status throughout congestions. PC-RED has been modeled and the parameter setting has been studied. Simulations of a TCP/IP network are used to illustrate how PC-RED affects the transfer of dataflow. The result shows remarkable contrast between the high-priority and non-priority dataflow throughput under PC- RED mechanism

PC-RED for IPv6: algorithm and performance analysis

This paper presents a Priority Checking Random Early Detection (PC-RED) gateway for ensuring the Quality of Service (QoS) of high priority dataflow in IPv6 networks. A bit in the IP header is used in PC-RED to label the current status of the QoS that the dataflow is being treated in, which is determined by the difference between the packet average-dropping rate and the fixed desired limit dropping rate of the dataflow. PC-RED would perform dissimilarly to every dataflow corresponding to the different QoS status throughout congestions. PC-RED has been modeled and the parameter setting has been studied. Simulation result shows remarkable contrast between the High-Priority and Non-Priority dataflow throughput under PC-RED mechanism

An OLS and GMM Combined Algorithm for Text Analysis for Heterogeneous Impact in Online Health Communities

The increase of doctors\u27 activity in online health communities (OHCs) plays a decisive role in their development. Although the literature on the determinants of doctors\u27 online activities has received considerable attention, the impact of illness severity on these factors remains rare. A network externality analytical framework is constructed to explain the factors (that is, responsiveness, involvement, word-of-mouth, incentives, price, titles and gender) affecting online doctors\u27 behavior, and assess whether factors differ by. By developing text analysis of 4916 doctors\u27 data from a Chinese OHC, this paper applies ordinary least squares (OLS) and General Method of Moments (GMM) to analyze whether the determinants are equal across serious, moderate, and mild illnesses. Our experiment results find that the determinants affecting doctors\u27 online service activity substantially differ across illness severity. Experiments prove the effectiveness of the proposed OLS and GMM methods and demonstrate that they are applicable in online medical field

An average queue weight parameterization in a network supporting TCP flows with RED

In this paper we use a previously developed RED (random early detection) model to analyze and develop a quantitative approach of defining one of the RED parameters, average queue weight, while the network load level varies. First, we introduce the linear control system and the pre-developed RED model. Based on this model, we next develop a proposition aiming at the RED parameter, average queue weight, and the load level only, to ensure the system stay stable. Our research is intended to provide a quantitative basis for parameterizing RED while load level varies. We present ns simulations to support our analysis

L-carnitine ameliorated fatty liver in high-calorie diet/STZ-induced type 2 diabetic mice by improving mitochondrial function

<p>Abstract</p> <p>Background</p> <p>There are an increasing number of patients suffering from fatty liver caused by type 2 diabetes. We intended to study the preventive and therapeutic effect of L-carnitine (LC) on nonalcoholic fatty liver disease (NAFLD) in streptozotocin (STZ)-induced type 2 diabetic mice and to explore its possible mechanism.</p> <p>Methods</p> <p>Thirty male Kungming mice were randomly divided into five groups: control group, diabetic group, pre-treatment group (125 mg/kg BW), low-dose (125 mg/kg BW) therapeutic group and high-dose (250 mg/kg BW) therapeutic group. The morphology of hepatocytes was observed by light and electron microscopy. LC and ALC (acetyl L-carnitine) concentrations in the liver were determined by high-performance liquid chromatography (HPLC). Moreover, liver weight, insulin levels and free fatty acid (FFA) and triglyceride (TG) levels in the liver and plasma were measured.</p> <p>Results</p> <p>Average liver LC and ALC levels were 33.7% and 20% lower, respectively, in diabetic mice compared to control mice (P < 0.05). After preventive and therapeutic treatment with LC, less hepatocyte steatosis, clearer crista and fewer glycogen granules in the mitochondria were observed. Decreased liver weight, TG levels, and FFA concentrations (P < 0.05) in the liver were also observed after treatment with LC in diabetic mice. Moreover, liver LC and ALC levels increased upon treatment with LC, whereas the ratio of LC and ALC decreased significantly (P < 0.01).</p> <p>Conclusion</p> <p>LC supplements ameliorated fatty liver in type 2 diabetic mice by increasing fatty acid oxidation and decreasing the LC/ALC ratio in the liver. Therefore, oral administration of LC protected mitochondrial function in liver.</p

LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset

As an important component of intelligent legal systems, legal case retrieval plays a critical role in ensuring judicial justice and fairness. However, the development of legal case retrieval technologies in the Chinese legal system is restricted by three problems in existing datasets: limited data size, narrow definitions of legal relevance, and naive candidate pooling strategies used in data sampling. To alleviate these issues, we introduce LeCaRDv2, a large-scale Legal Case Retrieval Dataset (version 2). It consists of 800 queries and 55,192 candidates extracted from 4.3 million criminal case documents. To the best of our knowledge, LeCaRDv2 is one of the largest Chinese legal case retrieval datasets, providing extensive coverage of criminal charges. Additionally, we enrich the existing relevance criteria by considering three key aspects: characterization, penalty, procedure. This comprehensive criteria enriches the dataset and may provides a more holistic perspective. Furthermore, we propose a two-level candidate set pooling strategy that effectively identify potential candidates for each query case. It's important to note that all cases in the dataset have been annotated by multiple legal experts specializing in criminal law. Their expertise ensures the accuracy and reliability of the annotations. We evaluate several state-of-the-art retrieval models at LeCaRDv2, demonstrating that there is still significant room for improvement in legal case retrieval. The details of LeCaRDv2 can be found at the anonymous website https://github.com/anonymous1113243/LeCaRDv2

PC-RED for IPv6: Algorithm and Performance Analysis

This paper presents a Priority Checking Random Early Detection (PC-RED) gateway for ensuring the Quality of Service (QoS) of high priority dataflow in IPv6 networks. A bit in the IP header is used in PC-RED to label the current status of the QoS that the dataflow is being treated in, which is determined by the difference between the packet average-dropping rate and the fixed desired limit dropping rate of the dataflow. PC-RED would perform dissimilarly to every dataflow corresponding to the different QoS status throughout congestions. PC-RED has been modeled and the parameter setting has been studied. Simulation result shows remarkable contrast between the High-Priority and Non-Priority dataflow throughput under PC-RED mechanism