MR imaging findings in recurrent primary osseous Ewing sarcoma

The objective of this study was to determine the value of magnetic resonance (MR) imaging in diagnosing local recurrence of Ewing sarcoma. We retrospectively reviewed radiographs, Tc^99m-methylene diphosphonate (MDP) skeletal scintigraphy, computed tomography scans, and MR studies of 11 patients who had local recurrences of osseous Ewing sarcoma following initial responses to chemotherapy and local radiation. The MR images were compared to those of a control group of nine patients who had no evidence of relapse. T₁- and T₂-weighted MR images identified 9 of the 11 recurrences. Computed tomography was diagnostic in 4 of 6 cases evaluated, Tc^99m-MDP bone scintigraphy in 4 of 11 cases, and plain radiographs in 2 of 10. MR findings at relapse included changes in signal intensity, increased extent of abnormal marrow signal on T₁- and T₂-weighted images, and identification of a new soft tissue mass. These findings suggest that MR imaging is valuable in the routine follow-up of parimary osseous Ewing sarcoma.     

Traffic-Aware Green Cognitive Radio

Cognitive Radio Network (CRN) has emerged as an effective solution to the spectrum under-utilization problem, by providing secondary users (SUs) an opportunistic access to the unoccupied frequency bands of primary users (PUs). Most of the current research on CRN are based on the assumption that the SU always has a large amount of data to transmit. This leads to the objective of SU throughput maximization with a constraint on the allowable interference to the PU. However, in many of the practical scenarios, the data arrival process of the SU closely follows an ON–OFF traffic model, and thus the usual throughput optimization framework may no longer be suitable. In this paper, we propose an intelligent data scheduling strategy which minimizes the average transmission power of the SU while maintaining the transmission delay to be sufficiently small. The data scheduling problem has been formulated as a finite horizon Markov Decision Process (MDP) with an appropriate cost function. Dynamic programming approach has been adopted to arrive at an optimal solution. Our findings show that the average transmitted power for our proposed approach can be as small as 36.5% of the power required for usual throughput maximization technique with insignificant increase in average delay.     

Size- and state-aware dispatching problem with queue-specific job sizes

We consider the dispatching problem in a size- and state-aware multi-queue system with Poisson arrivals and queue-specific job sizes. By size- and state-awareness, we mean that the dispatcher knows the size of an arriving job and the remaining service times of the jobs in each queue. By queue-specific job sizes, we mean that the time to process a job may depend on the chosen server. We focus on minimizing the mean sojourn time (i.e., response time) by an MDP approach. First we derive the so-called size-aware relative values of states with respect to the sojourn time in an M/G/1 queue operating under FIFO, LIFO, SPT or SRPT disciplines. For FIFO and LIFO, the size-aware relative values turn out to be insensitive to the form of the job size distribution. The relative values are then exploited in developing efficient dispatching rules in the spirit of the first policy iteration.     

非平稳MDP平均模型── 一致最优(G,B)-生成策略的存在性

本文考虑的是可数状态空间任意行动空间非平稳ＭＤＰ平均模型,借鉴于Ｆｅｉｎｂｅｒｇ Ｅ． Ａ（１９９４）的思想,提出了比马氏策略和 Ｆｅｉｎｂｅｒｇ Ｅ． Ａ的（ｆ,Ｂ）－生成策略和更为广泛的（Ｇ,Ｂ）－生成策略的概念,在弱遍历条件下,用概率分析的方法,证明了一致最优（Ｇ,Ｂ）－生成策略的存在性．从而将 Ｆｅｉｎｂｅｒｇ Ｅ． Ａ．（１９９４）的主要结果推广到非平衡可数状态空间情形．     

非平稳MDP平均模型及其算法

本文考虑的是Ｈｉｎｄｅｒｅｒ提出的状态空间和行动空间均业般集的非平稳ＭＤＰ平均模型，利用扩大状态空间的方法，建立了此模型的最优方程，并给出了最优方程有解及蜞 最优策略存在的条件，从最优方程出发，用概率的方法证明了最优策略的存在性，最后还提供了此模型的值迭代算法及其收敛性证明，从而推广了Ｓｍｉｔｈ。Ｌ．Ｌａｓｓｅｒｅ，Ｂ「３」及Ｌａｒｍａ＾「６」等的主要结果。     

Optimal greedy policies for stochastic control models

We introduce the notion of a greedy policy for general stochastic control models. Sufficient conditions for the optimality of the greedy policy for finite and infinite horizon are given. Moreover, we derive error bounds if the greedy policy is not optimal. The main results are illustrated by Bayesian information models, discounted Bayesian search problems, stochastic scheduling problems, single-server queueing networks and deterministic dynamic programs.     

MARKOV DECISION PROGRAMMING WITH CONSTRAINTS

ＭＡＲＫＯＶＤＥＣＩＳＩＯＮＰＲＯＧＲＡＭＭＩＮＧＷＩＴＨＣＯＮＳＴＲＡＩＮＴＳＬＩＵＪＩＡＮＹＯＮＧ（刘建庸）；ＬＩＵＫＥ（刘克）（ＩｎｓｔｉｔｕｔｅｏｆＡｐｐｌｉｅｄＭａｔｈｅｍａｔｉｃｓ，ｔｈｅＣｈｉｎｅｓｅＡｃａｄｅｍｙｏｆＳｃｉｅｎｃｅｓ，...     

Last in line

Queueing is a common praxis in banks, hospitals and transportation, just to name a few. One common performance metric is the mean sojourn time. However, humans experience waiting time in a more complex manner — they dislike being the last in line. We study queueing systems subject to such a cost structure. For the single M/G/1 queue, we derive the corresponding mean costs, value functions and admission costs, which are then applied to route customers to parallel servers.     

Synthesis of novel nano-radiotracer for in-vivo bone imaging: 99mTc- citric acid based PEG dendrimer and its conjugation with alendronate

Bone scan is the test that guide physician to diagnose diseases in the bone at the early stage, to prevent metastases to other organs. In this study, citric acid dendrimer conjugation with alendronate was synthesized. Obtained product was confirmed by FT-IR and TEM. Cytotoxicity assay at different concentrations showed no toxicity on normal cell line compared to control group. Radiolabeling process was optimized by Box-Behnken software which is a computational method to determine optimum of important radiolabeling parameters. Optimized parameter for reducing agent, dendrimer-G₂-alendronate, and time for shaking was 1 mg, 12.3 mg, and 25 min respectively. For determination of in-vivo accumulation of ^99mTc-dendrimer-G₂-alendronate, SPECT imaging was done. Images showed high accumulation of radio-tracer in the skeletal compared to ^99mTc-MDP which is the frequent bone scan agent. All in all, obtained results confirmed our hypothesis that the dendrimer-G₂-alendronate can be noteworthy nano-radiopharmaceuticals to bone cancer imaging at early stage.     

非平稳MDP--平均样本轨道最优

作者考虑的是任意状态空间，任意行动空间非平稳ＭＤＰ的平均样本轨道目标。在弱遍历条件下用鞅的极限理论，证明了最优马氏策略的存在性，推广了Ａ．Ａｒａｐｏｓｔａｔｈｉｓ，Ｖ．Ｂｏｒｋａｒ，Ｅ．Ｆ．Ｇａｕｃｈｅｒａｎｄ，Ｍ．Ｇｈｏｓｈ，Ｓ．Ｍａｒｃｕｓ（１９９３）的主要结果。