A Combined Delay-Throughput Fairness Model for Optical Burst Switched Networks

Fairness is an important feature of communication networks. It is the distribution, allocation, and provision of approximately equal or equal performance parameters, such as throughput, bandwidth, loss rate, and delay. In an optical burst switched (OBS) network, fairness is considered in three aspects: distance, throughput, and delay. Studies on these three types of fairness have been conducted; however, they have usually been considered in isolation. These fairness types should be considered together to improve the communication performance of the entire OBS network. This paper proposes a combined delay-throughput fairness model, where burst assembly and bandwidth allocation are improved to achieve both delay fairness and throughput fairness at ingress OBS nodes. The delay fairness and throughput fairness indices are recommended as metrics for adjusting the assembly queue length and allocated bandwidth for priority flows. The simulation results showed that delay and throughput fairness could be achieved simultaneously, improving the overall communication performance of the entire OBS network

Group Scheduling for MultiChannel in OBS Networks

Group scheduling is a scheduling operation of optical burst switching networks in which the burst header packets arriving in each timeslot will schedule their following bursts simultaneously. There have been many proposals for group scheduling (such as OBS-GS, MWIS-OS and LGS), but they consider mainly to schedule the arriving bursts which have the same wavelength on an output data channel. Another suggestion is GreedyOPT which considers the group scheduling for multichannel with the support of full wavelength converters, but it is not optimal. This article proposes another approach of group scheduling which is more optimal and has a linear complexity

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

THE ROLE OF FDLS IN SCHEDULING IN OBS NETWORKS

Abstract. Scheduling in a node plays an important role in improving the efficiency of exploiting the bandwidth of optical burst switched networks. However, a burst cannot be scheduled and will be dropped if resources, including a wavelength channel and a position of scheduling an arriving burst on the channel, are not available. A solution to this problem is to use an FDL buffer to delay the appearance of the arriving burst at output, by changing the position of scheduling the arriving burst on the selected available wavelength channel, hopefully there exists an available resource at the delayed output time. This article focuses on analyzing the role of FDL buffer in scheduling and evaluating its performances basing on the simulation results on NS2

Throughput‐based fair bandwidth allocation in OBS networks

Fair bandwidth allocation (FBA) has been studied in optical burst switching (OBS) networks, with the main idea being to map the max‐min fairness in traditional IP networks to the fair‐loss probability in OBS networks. This approach has proven to be fair in terms of the bandwidth allocation for differential connections, but the use of the ErlangB formula to calculate the theoretical loss probability has made this approach applicable only to Poisson flows. Furthermore, it is necessary to have a reasonable fairness measure to evaluate FBA models. This article proposes an approach involving throughput‐based‐FBA, called TFBA, and recommends a new fairness measure that is based on the ratio of the actual throughput to the allocated bandwidth. An analytical model for the performance of the output link with TFBA is also proposed

DỰ BÁO NHU CẦU DU KHÁCH ĐẾN THỪA THIÊN HUẾ DỰA TRÊN MẠNG NƠ-RON NHÂN TẠO

Accurate tourism demand forecasting for a destination plays a vital role in advising policymakers to plan and devise strategies related to investments in facilities, infrastructure improvements and services development. There are many different approaches in tourism demand forecasting, in which the one based on time-series data has attracted the most attention due to the unstructured nature of the particular data type. Neural networks have been evaluated as a predictive method specifically suited to this type of unstructured data. This paper examines the usage of neural networks, including MLP, RBF and ELN, to forecast tourism demand using time-series data in Thua Thien Hue. The analysis and comparison based on simulation show that the RBF network gives the best forecast result with the lowest MSE, RMSE, MAE and MAPE. This result is not only consistent with previous studies but also further confirms that the spatial conversion from nonlinear to linear of the hidden layer makes RBF powerful for the non-structural data.Dự báo chính xác nhu cầu du khách đến tại một điểm đến đóng một vai trò rất quan trọng trong việc tư vấn cho các nhà chính sách để lập kế hoạch và đưa ra các chiến lược liên quan đến đầu tư cơ sở vật chất, nâng cấp hạ tầng và phát triển dịch vụ. Có nhiều cách tiếp cận khác nhau trong dự báo nhu cầu du khách, trong đó dự báo dựa trên dữ liệu chuỗi thời gian đã và đang thu hút được nhiều sự quan tâm nhất do tính chất không có cấu trúc của loại dữ liệu đặc biệt này. Mạng nơ-ron nhân tạo được đánh giá là một phương pháp dự báo đặc biệt phù hợp với loại dữ liệu không có cấu trúc này, mặc dù gần như không thể giải thích được các xử lý bên trong. Bài báo này nghiên cứu việc sử dụng mạng nơ-ron nhân tạo để thực hiện dự báo đối với dữ liệu chuỗi thời gian về nhu cầu du khách đến Thừa Thiên Huế. Có ba mô hình mạng nơ-ron nhân tạo được xem xét trong nghiên cứu này gồm: MLP, RBF và ELN. Các phân tích và so sánh dựa trên mô phỏng chỉ ra rằng mạng RBF cho kết quả dự báo tốt nhất với MSE, RMSE, MAE và MAPE thấp nhất. Kết quả này không chỉ tương đồng với các nghiên cứu trước đây mà còn khẳng định thêm rằng tính năng chuyển đổi không gian từ phi tuyến thành tuyến tính của lớp ẩn đã làm cho RBF trở nên mạnh mẽ đối với loại dữ liệu không có cấu trúc