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Purpose: both the performance characteristics of prostate-specific membrane antigen positron emission tomography and insurance approval improves with increasing prostate-specific antigen (PSA) level causing some physicians to delay post-radical prostatectomy salvage radiation therapy (sRT) after PSA failure. Yet, it is unknown for men with at most one high-risk factor (ie, pT3/4 or prostatectomy Gleason score 8-10) whether a PSA level exists above which initiating sRT is associated with increased all-cause mortality (ACM)-risk and was investigated. Methods: using a multinational database of 25,551 patients with pT2-4N0 or NXM0 prostate cancer, multivariable Cox regression analysis evaluated whether an association with a significant increase in ACM-risk existed when sRT was delivered above a prespecified PSA level beginning at 0.10 ng/mL and in 0.05 increments up to 0.50 ng/mL versus at or below that level. The model was adjusted for age at and year of radical prostatectomy, established prostate cancer prognostic factors, institution, and the time-dependent use of androgen deprivation therapy. Results: after a median follow-up of 6.00 years, patients who received sRT at a PSA level >0.25 ng/mL had a significantly higher ACM-risk (AHR, 1.49; 95% CI, 1.11 to 2.00; P = .008) compared with men who received sRT when the PSA was & LE;0.25 mg/mL. This elevated ACM-risk remained significant for all PSA cutpoints up to 0.50 ng/mL but was not significant at PSA cutpoint values below 0.25 ng/mL. Conclusion: among patients with at most one high-risk factor, initiating sRT above a PSA level of 0.25 ng/mL was associated with increased ACM-risk., Supported by UCSF Goldberg-Benioff Program in Translational Cancer Biology

Online shopping is the most popular transaction, where the estimated transaction online is approximately USD 1.9 Trillion with an average growth from 8 -12% every year. Online shopping leads to huge consumption of paper boxes. Since, when the buyers receive the goods, they normally destroyed the paper boxes, throwing them away into a dust bin, and some were kept for nothing. Those papers wasted cause environmental impacts, huge organizational costs, and unnecessary spaces are wasted. The study investigates on activities of returning the used paper boxes through online shopping among university students in China by underpinning the Theory of Reasoned Action (TRA). The survey was conducted among 24 public universities in Guangxi, China, with 401 questionnaires accepted for analysis. The study employed regression analysis to test the hypotheses. Both independent variables have a significant relationship to return the paper wasted boxes from online shopping. This paper has its methodology contribution since a rare study focuses on all the bachelor degree students in public universities in Guangxi, China.

In real-time systems, it is crucial to guarantee that all the timing constraints are met at design stage. The WCET of each task has a significant impact on finding a feasible schedule for a set of real-time tasks. If we can reduce the WCET of each program, we are more likely to find a feasible schedule. Therefore, it is important to minimize the WCET of each program. An optimizing compiler plays a key role in reducing the WCET of a program. In this thesis, we investigate three important problems of reducing the WCET of a program executed on clustered VLIW processors, and propose novel, efficient approaches.Firstly, we investigate the problem of reducing the number of branch mispredictions for a program such that its WCET is minimized, and propose a novel branch correlation-based, hybrid branch prediction heuristic approach. Our approach consists of a static profile-based branch correlation analyzer and a dynamic branch predictor. The static profile-based branch correlation analyzer uses profiling and data dependency analysis to find precise correlations between branches of a program and identifies all the branches that do not have any impact on the WCET of the program. The dynamic predictor uses the correlation information to make online predictions. We have implemented our approach and compared it with the two state-of-the-art branch prediction approaches by using a set of benchmark suites. The experimental results show that our approach outperforms the two state-of-the-art approaches.Secondly, we investigate the problem of instruction scheduling and register allocation for a program executed on a clustered VLIW processor such that the WCET of the program is minimized, and propose a novel, unified instruction scheduling and register allocation heuristic approach. Our approach iteratively selects a set of basic blocks that may have impact on the WCET found by using WCET-aware graph reduction, performs live range splitting for a set of selected variables in those bas

A WSN (Wireless Sensor Network) consists of a set of low-cost, low-power sensor nodes which are distributed in a specific area to monitor the environment. Sensor nodes are typically battery powered and not practical to recharge or replace after the deployment. Therefore, maximizing the network lifetime is an important goal when designing a WSN. Sensor nodes communicate with each other by using radio signals. Experiments show that most energy of a sensor node is consumed in communication. Consequently, it is important to minimize the energy consumption of sensor nodes in communication. This thesis investigates three lifetime-aware data collection problems in WSNs. Firstly, we investigate the latency and network lifetime-aware, cluster-based data collection problem in a heterogeneous WSN with one base station and propose a novel approach taking link quality into account. Our approach consists of a polynomial-time heuristic for constructing clusters, a polynomial-time heuristic and an ILP-based (Integer Linear Programming) algorithm for constructing a routing tree for inter-cluster communication.Secondly, we investigate the lifetime-aware clustering and DAG-based (Directed Acyclic Graph) routing problem in a heterogeneous WSN with one base station and propose a novel efficient approach taking link quality into account. Our approach consists of a polynomial-time clustering heuristic and a routing DAG construction algorithm. The clustering heuristic constructs clusters based on a precise energy model for each sensor node. The routing DAG construction algorithm formulates the DAG construction problem into an LP (Linear Programming) problem. Lastly, we investigate the problem of collecting sensed data using a mobile base station in a WSN with unreachable regions such that the network lifetime is maximized and the total tour length is minimized. We propose a polynomial-time heuristic, an ILP-based heuristic, an MINLP-based (Mixed-Integer Non-Linear Programming) algorithm, a

In this paper, we propose using Random forests, a machine learning approach, to build house price estimation model. Compared to benchmark linear regression model, random forests model can better capture hidden nonlinear relations between the price and features of houses and gives an overall better estimation. Numerical experiments have been done on North Virginia house price data, which can strongly demonstrate our findings.

Based on real data, a new parameterized model of the Main Drift Chamber response is proposed. In this model, we tune the ratio of good hits and the residual distribution separately. The difference between simulation and data in track reconstruction efficiency reduces from 1% to 0.5%, and the momentum resolution agreement improves significantly.

VLIW (Very Long Instruction Word) processors issue and execute multiple operations in parallel, on different functional units at each processor cycle. A major problem with VLIW processors is that a single register file hampers the scalability of the processor. Clustering is an efficient technique for improving the scalability and energy consumption of VLIW processors. In a clustered VLIW processor, each cluster has its own functional units and local register file with fewer registers and ports. Clusters are connected by an inter-cluster communication network. An optimising compiler plays a key role in improving the ILP (Instruction Level Parallelism) for clustered VLIW processors. Instruction scheduling and register allocation are two important parts in an optimising compiler for clustered VLIW processors. These two parts are closely related and have a significant impact on the ILP. This thesis studies instruction scheduling and register allocation on clustered VLIW processors, and presents a set of efficient techniques for these problems.Firstly, we study the problem of scheduling a set of operations of a basic block on a clustered VLIW processor where for any two functional units, the two subsets of operations executed on these two functional units are either the same or disjoint. The objective is to minimise the schedule length for all the operations in the basic block. We propose a novel priority-based, phase coupled heuristic. When computing the operation priorities, our heuristic considers not only the inter-operation latencies, but also the processor resource constraints. After computing the priorities, our heuristic schedules each operation on a functional unit of a cluster by considering different cases. Simulation results show that our heuristic significantly improves over two previous heuristics, UAS and Integrated, for the basic blocks taken from the MediaBench II benchmark suite based on six processors.Secondly, we study the problem of scheduling a set

The purpose of this thesis is to develop a toolkit to visualize bag calculations for teaching. We take full advantage of interaction techniques in Computer Science to achieve it, which could lead to a modern and impressive way for teaching. In this thesis, the developed toolkit is going to show the bag calculations and corresponding animations interactively and aesthetically which make new learners easier to acquire the concept of multiset.