腹腔镜下直肠癌Miles根治术束带法断肠管与传统腹腔镜治疗的成本-效果比较

目的比较腹腔镜下直肠癌Miles根治术束带法断肠管与传统腹腔镜治疗的成本-效果。方法回顾性分析自2011年5月-2014年3月于江苏省南通市通州区人民医院（以下简称＂我院＂）进行手术治疗的15例直肠癌患者的临床资料,作为观察组,另选择2010年4月-2011年4月于我院进行手术治疗的25例直肠癌患者的临床资料,并作为对照组,其中对照组采用常规的腹腔镜下直肠癌Miles根治术治疗,观察组采用腹腔镜直肠癌Miles根治术束带法断肠管法治疗,统计并比较两组手术指标、治疗效果,并行成本-效果分析。结果对照组和观察组手术时间、术中出血量、进食时间、尿管留置时间、下床活动时间、术后住院时间、术后并发症等比较,差异无统计学意义（P〉0.05）,说明两种方式下手术特征基本一致。对照组和观察组不同方式治疗下的成本-效益分别为407.14、230.80元,说明在治疗效果基本等同的情况下,腹腔镜直肠癌Miles根治术束带法断肠管较传统腹腔镜直肠癌根治术成本明显低。结论腹腔镜直肠癌Miles根治术束带法断肠管在改善其治疗效果的同时,很大程度上降低了患者的就医成本,实现了更好地服务于患者,值得在基层医院推广应用。

Dimension-down iterative algorithm for the mixed transportation network design problem

An optimal dimension-down iterative algorithm （DDIA） is proposed for solving a mixed （continuous/ discrete） transportation network design problem （MNDP）, which is generally expressed as a mathematical programming with equilibrium constraints （MPEC）. The upper level of the MNDP aims to optimize the network performance via both the expansion of existing links and the addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium （UE） model. The idea of the proposed DDIA is to reduce the dimensions of the problem. A group of variables （discrete/continuous） are fixed to altemately optimize another group of variables （continuous/discrete）. Some continuous network design problems （CNDPs） and discrete network design problems （DNDPs） are solved repeatedly until the optimal solution is obtained. A numerical example is given to demonstrate the efficiency of the proposed algorithm.

针灸治疗产后身痛案1则

产后身痛是妇人产后常见的临床症状,以产妇在产褥期内出现的关节、肢体疼痛、麻木、酸楚、重着等为主要临床表现。本文介绍了针刺与督灸结合治疗产后身痛迁延不愈并伴有腹痛、胃胀等胃肠道反应1例,治疗后症状消失,1个月后随访未见复发。

Modeling and optimization of a multi-carrier renewable energy system for zero-energy consumption buildings

For the carbon-neutral,a multi-carrier renewable energy system(MRES),driven by the wind,solar and geothermal,was considered as an effective solution to mitigate CO2emissions and reduce energy usage in the building sector.A proper sizing method was essential for achieving the desired 100%renewable energy system of resources.This paper presented a bi-objective optimization formulation for sizing the MRES using a constrained genetic algorithm(GA)coupled with the loss of power supply probability(LPSP)method to achieve the minimal cost of the system and the reliability of the system to the load real time requirement.An optimization App has been developed in MATLAB environment to offer a user-friendly interface and output the optimized design parameters when given the load demand.A case study of a swimming pool building was used to demonstrate the process of the proposed design method.Compared to the conventional distributed energy system,the MRES is feasible with a lower annual total cost(ATC).Additionally,the ATC decreases as the power supply reliability of the renewable system decreases.There is a decrease of 24%of the annual total cost when the power supply probability is equal to 8%compared to the baseline case with 0%power supply probability.

Bi-level hybrid local search approach for three-dimensional loading problem with balancing constraints

This paper presents a bi-level hybrid local search(BHLS)algorithm for the three-dimensional loading problem with balancing constraints(3DLP-B),where several rectangular boxes with even densities but different sizes are loaded into a single cubic bin to meet the requirements of the space or capacity utilization and the balance of the center of gravity.The proposed algorithm hybridizes a novel framed-layout procedure in which the concept of the core block and its generation strategy are introduced.Once the block-loading sequence has been determined,we can load one block at a time by the designed construction heuristic.Then,the double-search is introduced;its external search procedure generates a list of compact packing patterns while its internal search procedure is used to search the core-block frames and their best distribution locations.The approach is extensively tested on weakly to strongly heterogeneous benchmark data.The results show that it has better performance in improving space utilization rate and balanced condition of the placement than existed techniques:the overall averages from 79.85%to 86.45%were obtained for the balanced cases and relatively high space-usage rate of 89.44%was achieved for the unbalanced ones.

A Semi-blind Adaptive Beamforming Algorithm for Wideband Coherent CDMA

This paper presented a novel semi blind adaptive beamforming algorithm specially designed for wideband coherent CDMA mobile communication systems with multiplexd control and data channels. The presented algorithm uses a parallel structure to exploit not only the desired user’s pseudo noise sequence but also the information from multiplexed pilot and data symbols, thus help achieve faster convergence and lower bit error rate. Monte Carlo simulation results verified the performance improvement in terms of BER.

Adaptive Resource Allocation Algorithm for Internet of Things with Bandwidth Constraint

In order to improve the transmission accuracy and efficiency of sensing and actuating signals in Internet of Things （loT） and ensure the system stability, an adaptive resource allocation algorithm is proposed, which dynami- cally assigns the network bandwidth and priority among components according to their signals＇ frequency domain characteristics. A remote sensed and controlled unmanned ground vehicle （UGV） path tracking test-bed was devel- oped and multiple UGV＇s tracking error signals were measured in the simulation for performance evaluation. Results show that with the same network bandwidth constraints, the proposed algorithm can reduce,, the accumulated and maximum errors of UGV path tracking by over 60% compared with the conventional static algorithm.