Incidence trends and predictors for cost and average lengths of stay in colorectal cancer surgery

AIM:To evaluate the changing trends and outcomes of colorectal cancer(CRC)surgery performed at a large single institution in Taiwan.METHODS:This study retrospectively analyzed 778patients who received colorectal cancer surgery at E-Da Hospital in Taiwan from 2004 to 2009.These patients were from health examination,inpatient or emergency settings.The following attributes were analyzed in patients who had undergone CRC surgical procedures:gender,age,source,surgical type,tumor number,tumor size,number of lymph node metastasis,pathologic differentiation,chemotherapy,distant metastases,tumor site,tumor stage,average hospitalization cost and average lengths of stay(ALOS).The odds ratio and95%confidence intervals were calculated to assess the relative rate of change.Regression models were employed to predict average hospitalization cost and ALOS.RESULTS:The study sample included 458(58.87%)males and 320(41.13%)females with a mean age of64.53 years(standard deviation,12.33 years;range,28-86 years).The principal patient source came from inpatient and emergency room(96.02%).The principal tumor sites were noted at the sigmoid colon(35.73%)and rectum(30.46%).Most patients exhibited a tumor stage of 2(37.28%)or 3(34.19%).The number of new CRC surgeries performed per 100000 persons was12.21 in 2004 and gradually increased to 17.89 in 2009,representing a change of 46.52%.During the same period,the average hospitalization cost and ALOS decreased from$5303 to$4062 and from 19.7 to 14.4 d,respectively.The following factors were associated with considerably decreased hospital resource utilization:age,source,surgical type,tumor size,tumor site,and tumor stage.CONCLUSION:These results can be generalized to patient populations elsewhere in Taiwan and to other countries with similar patient profiles.

Proactive Caching at the Wireless Edge:A Novel Predictive User Popularity-Aware Approach

Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as possible.In an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user experience.However,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of users.Recently,techniques for caching content at the edge are becoming popular for addressing the above challenges.It is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud servers.However,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity data.To address this challenge,we introduce a novel method for content caching over MEC,i.e.,PRIME.It synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching schedules.Experimental results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.

Reliability-BasedModel for Incomplete Preventive ReplacementMaintenance of Photovoltaic Power Systems

At present,the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance,breakdown maintenance,and condition-based maintenance,which is very likely to lead to over-or under-repair of equipment.Therefore,a preventive maintenance and replacement strategy for PV power generation systems based on reliability as a constraint is proposed.First,a hybrid failure function with a decreasing service age factor and an increasing failure rate factor is introduced to describe the deterioration of PV power generation equipment,and the equipment is replaced when its reliability drops to the replacement threshold in the last cycle.Then,based on the reliability as a constraint,the average maintenance cost and availability of the equipment are considered,and the non-periodic incomplete maintenance model of the PV power generation system is established to obtain the optimal number of repairs,each maintenance cycle and the replacement cycle of the PV power generation system components.Next,the inverter of a PV power plant is used as a research object.The model in this paper is compared and analyzed with the equal cycle maintenance model without considering reliability and the maintenance model without considering the equipment replacement threshold,Through model comparison,when the optimal maintenance strategy is(0.80,4),the average maintenance cost of this paper’s model are decreased by 20.3%and 5.54%and the availability is increased by 0.2395% and 0.0337%,respectively,compared with the equal-cycle maintenance model without considering the reliability constraint and the maintenance model without considering the equipment replacement threshold.Therefore,this maintenance model can ensure the high reliability of PV plant operation while increasing the equipment availability to improve the system economy.

Direct Cost of Severe Malaria Treatment Borne by the Families of Children Aged 0 - 5 Years at the Fana Reference Health Centre, Mali

Introduction: Malaria is both a disease caused by poverty and a cause of poverty. Malaria is the leading cause of morbidity and mortality in Mali and is among the ten countries with the highest number of malaria cases and deaths. The objective was to estimate the direct economic cost borne by families in the treatment of severe malaria in children aged 0 - 5 years at the CSREF in Fana. Methodology: The study was cross-sectional, conducted from July 2017 to June 2018 with inclusion criteria and prospective data collection. The methodology was based on estimating the direct economic cost of severe malaria. Results: The sample consisted of 109 cases out of a total of 944 hospitalizations;59% of whom were boys and the 25 - 36 month age group was the most affected. The complications frequently encountered were severe anemia (50 cases) or 45.8%;convulsions (35 cases) or 32.1% and finally severe sepsis (8 cases) or 7.3%. The average direct cost was 25,324 Franc CFA (58.95 US Dollars) of which 66% represented the costs of medicines and consumables against 4% for the consultation. This cost was more than half the minimum wage in Mali. Conclusion: Despite the difficulties in estimating the cost in hospitals, the results obtained give us an estimate of the economic burden borne by families in the management of severe malaria cases among children in the district of Fana. Support is needed for parents in the fight against malaria in rural Mali.

User Association and Power Allocation for UAV-Assisted Networks: A Distributed Reinforcement Learning Approach

Unmanned aerial vehicles(UAVs)can be employed as aerial base stations(BSs)due to their high mobility and flexible deployment.This paper focuses on a UAV-assisted wireless network,where users can be scheduled to get access to either an aerial BS or a terrestrial BS for uplink transmission.In contrast to state-of-the-art designs focusing on the instantaneous cost of the network,this paper aims at minimizing the long-term average transmit power consumed by the users by dynamically optimizing user association and power allocation in each time slot.Such a joint user association scheduling and power allocation problem can be formulated as a Markov decision process(MDP).Unfortunately,solving such an MDP problem with the conventional relative value iteration(RVI)can suffer from the curses of dimensionality,in the presence of a large number of users.As a countermeasure,we propose a distributed RVI algorithm to reduce the dimension of the MDP problem,such that the original problem can be decoupled into multiple solvable small-scale MDP problems.Simulation results reveal that the proposed algorithm can yield lower longterm average transmit power consumption than both the conventional RVI algorithm and a baseline algorithm with myopic policies.

A generalized geometric process based repairable system model with bivariate policy

The maintenance model of simple repairable system is studied.We assume that there are two types of failure,namely type Ⅰ failure(repairable failure)and type Ⅱ failure(irrepairable failure).As long as the type Ⅰ failure occurs,the system will be repaired immediately,which is failure repair(FR).Between the(n-1)th and the nth FR,the system is supposed to be preventively repaired(PR)as the consecutive working time of the system reaches λ^(n-1) T,where λ and T are specified values.Further,we assume that the system will go on working when the repair is finished and will be replaced at the occurrence of the Nth type Ⅰ failure or the occurrence of the first type Ⅱ failure,whichever occurs first.In practice,the system will degrade with the increasing number of repairs.That is,the consecutive working time of the system forms a decreasing generalized geometric process(GGP)whereas the successive repair time forms an increasing GGP.A simple bivariate policy(T,N)repairable model is introduced based on GGP.The alternative searching method is used to minimize the cost rate function C(N,T),and the optimal(T,N)^(*) is obtained.Finally,numerical cases are applied to demonstrate the reasonability of this model.

Discount Rate of China’s New Energy Power Industry

Under the dual pressures of energy crisis and environmental pollution,China’s new energy power industry has become a focal point for environmental management and requires greater investment.In this context,as a significant input of investment projects,discount rate requires a well-calibrated evaluation because new energy power investment projects are highly capital intensive.The main objective of this paper is to evaluate the discount rate of China’s new energy power industry.First,we use Moving Average to correct the parameters of capital asset pricing model(CAPM)and weighted average cost of capital,which extends the literature on the avoidance of CAPM noise information problem.Second,we study the industry-level annual discount rates of mainly China’s new energy power industries,including hydropower,nuclear power,wind power,and photovoltaic power industries for the period of 2014-2019.The results show that discount rates in China’s new energy power industries evolved differently between the years of 2014-2019 with average annual discount rates being 7.56%,5.83%,5.60%,and 8.64%,for the hydropower,nuclear power,wind power,and photovoltaic power industries,respectively.In 2019,the four annual discount rates were highest for the photovoltaic power industry(8.66%),followed by hydropower(7.17%),wind power(5.72%),and nuclear power industry(5.26%).Forecasting to 2020 from the 2019 evaluation base period,the discount rates are 6.37%,5.00%,6.57%,and 9.05%for the photovoltaic power,hydropower,wind power,and nuclear power industries,respectively.Under the different capital structures,their forecasts for the photovoltaic power,hydropower,wind power,and nuclear power industries in 2020 are,respectively,within[4.35%,9.24%],[3.92%,7.10%],[4.58%,10.40%],[5.46%,14.81%].We also discussed more details on capital structure and forecast period of discount rates for China’s new energy power industries.Our analysis shows that it is necessary to establish a new energy power industry database and steadily promote the implementation of policies.

江西省某三级甲等医院2型糖尿病患者次均住院费用新灰色关联分析

目的分析江西省某三级甲等医院2型糖尿病患者次均住院费用的构成以及影响次均住院费用的主要因素,为有效控制此类患者的住院费用及减轻疾病经济负担提供依据。方法选取江西省某三级甲等医院2015—2018年1928例2型糖尿病患者的住院费用明细,利用新灰色关联分析法对患者住院各项费用的关联度、贡献率以及变动率等指标进行分析。结果2015—2018年,次均住院费用的构成比和关联系数最大的影响因素是治疗费,而结构贡献率最高的因素是药品费;与次均住院费用关联度最大的前三项目顺位分别是治疗费(0.9650)、药品费(0.9100)和检查费(0.8125);结构贡献率最高的前三项目顺位是药品费(30.00%)、护理费(11.69%)和诊察费(10.61%),三者累计结构贡献率达到52.30%。结论治疗费、药品费和检查费是影响患者次均住院费用最主要的因素,但服务性项目的收费较低。建议着重控制药品费用的增长,适当减轻检查类项目对费用的影响,严格规范临床路径,合理提高服务性项目费用的比例,进一步体现医护人员的劳务价值,构建糖尿病精细化管理体系,丰富健康管理内涵。

Multi-robot task allocation for exploration

The problem of allocating a number of exploration tasks to a team of mobile robots in dynamic environments was studied. The team mission is to visit several distributed targets. The path cost of target is proportional to the distance that a robot has to move to visit the target. The team objective is to minimize the average path cost of target over all targets. Finding an optimal allocation is strongly NP-hard. The proposed algorithm can produce a near-optimal solution to it. The allocation can be cast in terms of a multi-round single-item auction by which robots bid on targets. In each auction round, one target is assigned to a robot that produces the lowest path cost of the target. The allocated targets form a forest where each tree corresponds a robot’s exploring targets set. Each robot constructs an exploring path through depth-first search in its target tree. The time complexity of the proposed algorithm is polynomial. Simulation experiments show that the allocating method is valid.

Assessment of global solar resource development

With the increasing severity of environmental problems,many countries have set energy transition targets to promote the realization of the Paris Agreement.There has been a global consensus on utilizing solar energy resources as alternatives to conventional sources to support this energy transition.In this regard,analyzing the“location,”“quantity,”and“quality”of global solar energy resources will not only assist an individual country to efficiently utilize these resources but also promote the realization of large-scale intercontinental resource utilization and complementation.This study established the basic database,model methods,and platform tools for global solar energy assessment,Then,a global solar energy resource assessment was conducted,which included the theoretical reserves(TRs),technical installed potential capacity(TPIC),and average development cost(ADC).A comparative analysis of the assessment results for all continents was also performed.After that,based on big data analysis and geographic information system(GIS)calculations,the distribution characteristics of the global solar power TPIC were calculated with the two core indicators,namely the capacity factor and ADC.Furthermore,a data-driven quantitative evaluation of the refined development potential of solar energy resources was performed.Finally,the reasonableness and coincidence analysis of the resource assessment results were verified using data from global and specifically Chinese photovoltaic(PV)bases.

Optimization on bicriterion policies for M/G/1 system with second optional service

We compare the optimal operating cost of the two bicriterion policies, <p,T> and <p,N>, for an M/G/1 queueing system with second optional service, in which the length of the vacation period is randomly controlled either by the number of arrivals during the idle period or by a timer. After all the customers are served in the queue exhaustively, the server immediately takes a vacation and may operate <p,T> policy or <p,N> policy. For the two bicriterion policies, the total average cost function per unit time is developed to search the optimal stationary operating policies at a minimum cost. Based upon the optimal cost the explicit forms for joint optimum threshold values of (p,T) and (p,N) are obtained.

Reliability Analysis of Multi-State Repairable System with Dynamic Transition Probabilities

Inadequate maintenance decisions lead to incremental overall costs. In order to minimize costs in maintenance of the multi-state repairable system, we model a preventive maintenance(PM) scheme of the multistate repairable system using non-Markov process. The periodically decreasing reliability model of the non-Markov dynamic system with dynamic transition probabilities is established to satisfy the probability change. The diesel engine system is taken as an example to illustrate the model. The reliability of the diesel engine is analyzed and its PM scheme is worked out. RENO software is used to simulate the diesel engine system. The maintenance cost of components and the optimal PM interval data of the system are obtained by using the minimal average cost as the objective function. The adaptability of PM is judged, and the optimal PM scheme is presented.

THE OPTIMAL REPLACEMENT FOR ADDITIVE DAMAGE MODELS IN DISCRETE SETTING

A system receives shocks at successive random points of discrete time, and each shock causes a positive integer-valued random amount of damage which accumulates on the system one after another. The system is subject to failure and it fails once the total cumulative damage level first exceeds a fixed threshold. Upon failure the system must be replaced by a new and identical one and a cost is incurred. If the system is replaced before failure, a lower cost is incurred.On the basis of some assumptions, we specify a replacement rule which minimizes the longrun (expected) average cost per unit time and possesses the control limit property, Finally, an algorithm is discussed in a special case.