Comprehensive Schedule Research Articles

Smart plant project scheduling optimization based on hidden Markov algorithm

ABSTRACT In order to enhance the reasonableness of project scheduling optimization process, this paper puts forward a smart plant project scheduling optimization method based on hidden Markov algorithm. First, it introduces a kind of new method to cope with conflict. It refers to that once the conflict appears in the scheduling scheme, conduct status transfer aiming at the time schedule of the conflict instead of re-refining the starting time of the project to get another feasible time schedule, and enhance the calculation efficiency of the algorithm. Second, it takes the active and passive project scheduling problems as single and comprehensive problems for formulation, wherein, the target function includes a benchmark schedule cost and project response cost, and it finds out an optimal active and passive comprehensive project scheduling strategy; finally, it conducts modeling on the above project scheduling optimization problem by aid of Markov decision-making process, designs a kind of solution based on the dynamic planning of random graph. The experiment result shows that the method is effective in the convergence precision and calculation efficiency, and it demonstrates the reasonableness of the scheduling scheme by the means of Gantt chart.

Electrothermal Comprehensive Scheduling Model Based on Electric Boiler

Currently, with the scale of wind power in the “Three Norths” region of our country is increasing constantly, the abandoning of wind phenomenon is getting worse, and the “wind-heat conflict” is becoming more evident. In this paper we establishes the combined heat and power dispatch model for power system with electronic boiler based on the “heat and power” constraint of the decoupled thermoelectric power units, aiming at the problem of large-scale wind power consumption. Considering the total coal cost and the wave range of reducing the output of the thermoelectric power unit, the operation economy of the unit is improved. Finally, the multiple objective cuckoo algorithm is used to solve the electric heating optimization scheduling model, and the analysis of the model under different scenarios is carried out. The numerical simulation results verify the economic and correctness of this model.

Increasing radiology capacity within the lung cancer pathway: centralised work-based support for trainee chest X-ray reporting radiographers.

IntroductionDiagnostic capacity and time to diagnosis are frequently identified as a barrier to improving cancer patient outcomes. Maximising the contribution of the medical imaging workforce, including reporting radiographers, is one way to improve service delivery.MethodsAn efficient and effective centralised model of workplace training support was designed for a cohort of trainee chest X‐ray (CXR) reporting radiographers. A comprehensive schedule of tutorials was planned and aligned with the curriculum of a post‐graduate certificate in CXR reporting. Trainees were supported via a hub and spoke model (centralised training model), with the majority of education provided by a core group of experienced CXR reporting radiographers. Trainee and departmental feedback on the model was obtained using an online survey.ResultsFourteen trainees were recruited from eight National Health Service Trusts across London. Significant efficiencies of scale were possible with centralised support (48 h) compared to traditional workplace support (348 h). Trainee and manager feedback overall was positive. Trainees and managers both reported good trainee support, translation of learning to practice and increased confidence. Logistics, including trainee travel and release, were identified as areas for improvement.ConclusionCentralised workplace training support is an effective and efficient method to create sustainable diagnostic capacity and support improvements in the lung cancer pathway.

Slope Shift Strategy for Automatic Transmission Vehicles Based on the Road Gradient

Motivated by the development of high-precision digital maps for advanced driver assistance system (ADAS) in recent years, this study provides a new approach to solve the problems of the conventional automatic transmission vehicle travelling on sloping roads. Based on vehicle dynamics, shift problems on hilly roads are analyzed. A novel intelligent shift strategy is proposed, which consists of a dynamic shift schedule for the uphill, a safety shift schedule for the downhill, and a comprehensive economical shift schedule for the gentle slopes. A set of driver-in-loop co-simulation tests was conducted in a driving simulator that is equipped with a MATLAB/Simulink dynamics simulation platform. The test results verified the effectiveness of the new intelligent shift strategy. With the road information provided by a high-precision digital map, busy shifting can be eliminated, and improved dynamic performance can be achieved for a vehicle travelling on the uphill roads; undesired upshift can be prevented, and engine traction resistance can be used to relieve the load of braking system when a vehicle travelling on the downhill roads; also, fuel consumption can be reduced for a vehicle travelling on a gently sloped road. Consequently, this novel intelligent shift strategy offers a reliable and effective solution for improving a vehicle’s driving performance on a hilly road.

Overexpression of p53 delivered using recombinant NDV induces apoptosis in glioma cells by regulating the apoptotic signaling pathway.

Malignant glioma is the most common primary brain carcinoma in the world and has a poor survival rate. Previous studies have demonstrated that p53 dysfunction contributes to the development and severity of malignant glioma. It has also been demonstrated that Newcastle disease virus (NDV) may be a viable candidate for the treatment of various types of cancer. In the present study, a p53 oncolytic agent delivered using recombinant NDV (rNDV-p53) was constructed and its anti-tumor effects in vitro and in vivo were assessed. Glioma cell lines and a xenograft mouse model were utilized to assess the ability of p53 and rNDV to promote apoptosis and induce immunotherapy, respectively. The mechanism of rNDV-p53 in glioma therapy was investigated using quantitative polymerase chain reaction and immunohistochemistry. Tumor-specific cytotoxic T-lymphocyte (CTL) responses and lymphocyte infiltration were also analyzed in glioma-bearing models. The results of the present study demonstrate that rNDV-p53 may be a potential therapeutic agent that improves the prognosis of mice with glioma. It was revealed that rNDV-p53 inhibits glioma cell growth and aggressiveness in vitro and in vivo compared with rNDV and p53 alone. The results also demonstrated that rNDV-p53 induced glioma cell apoptosis by upregulating apoptosis-related genes. In addition, the present study demonstrated that rNDV-p53 significantly stimulated CTL responses and lymphocyte infiltration whilst increasing the number of apoptotic bodies in vivo. Furthermore, rNDV-p53 therapy inhibited tumor regression and prolonged the survival of glioma-bearing mice. In conclusion, rNDV-p53 invoked an immune response against glioma cells, which may serve as a comprehensive immunotherapeutic schedule for glioma.

The effects of interleukin 2 and rAd-p53 as a treatment for glioblastoma.

Interleukin 2 (IL-2) is an anti-cancer cytokine that stimulates T cell propagation, triggering innate and adaptive immunity. IL-2 has been used for cancer therapy and has achieved curative effects. Recombinant adenovirus p53 injection (rAd‑p53) is a gene therapeutic agent that may improve the prognosis of patients with glioblastoma (GBM). In the present study, the effect of combined IL‑2 and rAd‑p53 treatment was studied. The ability of IL‑2 to stimulate immunoregulation and the ability of p53 to induce apoptosis for GBM was researched in the GBM tumor model. In addition, the activity of IL‑2 was analyzed. The antitumor potential of IL‑2 and rAd‑p53 was studied using xenograph mice carrying GBM cells. Tumor‑specific CD4+ and CD8+ T cells were also analyzed in the GBM‑bearing models. The results demonstrated that IL‑2 and rAd‑p53 not only stimulated tumor‑specific cytotoxic T‑lymphocyte responses and increased regulatory CD4+ and cytotoxic CD8+ T cell proliferation, however additionally increased expression of apoptosis‑associated genes. The treatment with IL‑2 and rAd‑p53 resulted in tumor regression and prolonged the survival of glioma‑bearing mice. Taken together, a combination of IL‑2 and rAd‑p53 treatment combines the effects of immunotherapy and oncolytic therapy and may be a comprehensive therapeutic schedule for clinical application in future cancer therapies.

A multi-objective framework for energy resource scheduling in active distribution networks

ABSTRACTPurpose: The purpose of this paper is to investigate the impacts of electric vehicles' (EVs) charging/discharging decisions in energy resources scheduling problem of active distribution networks. Design/methodology/approach: The problem under study is modelled as a two-stage optimisation problem in which the main requirements of EV owners are introduced as an objective function of the first stage. The total energy costs and the emission factor are considered as the main criteria of the second stage. The output generation schedules of distributed generation (DG) technologies together with the charging/discharging schedule of EVs are proposed as decision variables of the energy scheduling problem. Therefore, some effective methods are presented to model the uncertainties associated with these variables. Findings: The results proved that an efficient compromise can be reached between the emission factor and the energy cost of the system. In addition, it has been emphasised on the importance of such comprehensive energy scheduling frameworks. Originality/value: This paper contributes by: (a) providing a multi-objective framework for energy scheduling of active distribution networks, (b) extracting the mathematical model of this two-stage problem and (c) employing a linearised optimisation model to reach its global optimal solution.

Metastatic glioblastoma multiforme: A rare case of long-term survival

Glioblastoma Multiforme (GBM) is the most common primary malignant brain tumor. Median survival is usually less than 1 year from the time of diagnosis, and even in the most favorable situations, a majority of patients die within 2 years. Standard therapy consists of surgical resection, radiotherapy and chemotherapy. In this case report, we described a 38 y old woman with GBM survived 7 years after a series of treatments. For the patient, the tumor was found extensively infiltrate the sagittal sinus, the surgery unable completely remove of tumor, radiotherapy, chemotherapy and individualized anti-cancer herbs therapy were further applied. After all therapy, the patient was awake, with moderate bone window tension, palpable mass, right partial hemianopia, good right limb muscle strength. In summary, comprehensive treatment schedule including thorough surgical removal of tumor, systematic radiation, standard chemotherapy and individualized anti-cancer herbs therapy can help to prolong the survival of patients with GBM and gliosarcoma.

A review on the optimal scheduling of byproduct gases in steel making industry

The scheduling of byproduct gases is an important topic in the optimal energy management of steel enterprises. For the past two decades, intensive studies have been implemented on this topic. Because the byproduct gases account for approximately 30% of total energy consumption in steel enterprises, the efficient and effective usage of them is beneficial for both the energy efficiency improvement and the production cost reduction in steel industry. However, due to the complexity of steel-making process, it is difficult to build a comprehensive and reasonable scheduling model to optimise the byproduct gas usage. In this paper, current methods and progresses in byproduct gas scheduling are reviewed and some interesting findings are discussed.

Modeling and Valuation of Residential Demand Flexibility for Renewable Energy Integration

The share of renewable generation (RG) in the energy mix has seen constant growth in recent years. RG is volatile and not (fully) controllable. Consequently, the alignment of stochastic demand with supply, which is fundamental for ensuring grid stability, becomes more difficult. The utilization of demand side flexibility as well as RG portfolio design are attractive opportunities to avoid excessive investments in conventional power plants and costs for balancing power. This paper provides a comprehensive centralized scheduling model to exploit demand flexibility from residential devices. We analyze the monetary value of households for demand response (DR) by determining the potential of various current and possible available future end consumer devices to reduce generation costs of a flexibility aggregator in a microgrid with a large share of RG. Furthermore, we identify key characteristics affecting the value of demand flexibility and derive recommendations for an aggregator’s RG portfolio structure. Our simulation results indicate that electric vehicles, stationary batteries, and storage heaters are the most promising devices for residential DR. Furthermore, we show that the potential of a device to directly utilize intermittent RG is largely influenced by the composition of the renewable energy source portfolio.

The Japanese Health Care System: An Analysis of the Funding and Reimbursement System

The modern Japanese health care system was established during the Meiji period (1868-1912) using the example of Germany. In this paper, the funding and remuneration of health services and products in Japan are described. The focus lies on the mechanisms used to implement health policy goals and to control costs. Selective literature search. All permanent residents in Japan are enrolled in one of more than 3,000 compulsory health funds. Employees and public servants are covered through company or government-related health insurance schemes. Independent workers, the unemployed and the pensioners are usually assigned to health insurance plans managed by local city governments. The elderly over 75 years are insured through special health funds managed at the prefectural level. To correct the fiscal disparities among the health insurance programs, a risk adjustment is realized by compensatory financial transfers between the funds and substantial subsidies from the central and local governments. The statutory benefits package that is identical for all insurance plans is regulated in a single comprehensive schedule. All the covered health services and products are listed with the fees and compensations, and the conditions for the service providers to be remunerated are also stated. This fee and compensation schedule is regularly revised every 2 years under the leadership of the Ministry of Health, Labor and Welfare. The revisions are intended to contain health expenditures and to set incentives for the achievement of health policy goals. The funding of the Japanese health care system and the risk adjustment mechanisms among health funds are well established and show a rather static character. The short- and mid-term development of the system is mainly controlled on the side of the expenditures through the unique and comprehensive fee and compensation schedule. The regular revisions of this schedule permit to react at relatively short notice to evolving situations, and through a policy of small improvements, target an optimization of the system as a whole.

Optimal Scheduling and Management on Correlating Reliability, Performance, and Energy Consumption for Multiagent Cloud Systems

In large-scale cloud computing environments, a multiagent cloud system (MACS) can be developed for effectively managing cloud infrastructure. In principle, a MACS has two essential components, a global agent (GA) and multiple local agents (LAs). The GA dispatches user requests to multiple LAs, while the LAs make optimal resource management strategies for serving user requests received. To develop optimal request scheduling strategies and resource management strategies for the MACS, multiple correlated metrics, particularly reliability, performance, and energy consumption need to be comprehensively considered. In this paper, a reliability-performance-energy correlation model is first proposed. The new model captures significant effects of random resource failures and recovery on MACS performance and energy consumption to ensure high fidelity and precise evaluation. A net profit optimization model depicting the tradeoff between performance and energy consumption is then developed based on the proposed theoretical model. Furthermore, an approach for optimization is proposed, where LAs can perform dynamic resource management for reaching optimum in the net profit metric. A genetic algorithm is also designed and implemented for searching the solution of global request scheduling for the GA. Numerical examples illustrate that the MACS can achieve efficient and comprehensive scheduling and management capability based on cooperation between the GA and LAs.

A multi‑targeted tyrosine kinase inhibitor lenvatinib for the treatment of mice with advanced glioblastoma.

Glioblastoma is the most aggressive primary brain tumor that originates from the glial cells in adults. Aberrant angiogenesis is essential for malignant glioblastoma tumorigenesis, development and metastasis. Lenvatinib is a multi‑targeted anticancer agent that targets of receptor tyrosine kinases including vascular endothelial growth factor receptor1 and2, fibroblast growth factor receptor 1, platelet‑derived growth factor receptorβ and v‑kit Hardy‑Zuckerman 4 feline sarcoma viral oncogene homolog. In the present study, the therapeutic effects of lenvatinib as a treatment for glioblastoma were investigated invivo and invitro. The maximum dose toxicity (MDT) and treatment‑associated adverse events of lenvatinib were identified by cytotoxicity assay in experimental mice. Increasing levels of the pro‑apoptosis genes caspase‑3, -8, -9 and-10 following lenvatinib treatment were determined by reverse transcription‑quantitative polymerase chain reaction, and apoptosis of the malignant gliomas cells was analyzed by FACS. Invivo treatment with lenvatinib for BV‑2 bearing male BALC/c nude mice was assessed via tumor growth suppression and long‑term observation of survival. Subsequent cytotoxic T lymphocyte responses were further analyzed to determine the invivo efficacy of lenvatinib treatment in mice with glioblastoma. The MDT of lenvatinib was identified as 0.24mg, with relatively few side effects and improved efficacy in mice. Lenvatinib (0.24mg) significantly increased apoptosis in BV‑2, C6, BC3H1 and G422 glioma cell lines. Tumor growth was significantly inhibited and tumor‑bearing mice demonstrated an improved survival rate following treatment with lenvatinib. In conclusion, lenvatinib provided an effective treatment outcome, and the results of the present study may help to achieve a comprehensive therapeutic schedule for clinical application.

Solving comprehensive dynamic job shop scheduling problem by using a GRASP-based approach

There are many dynamic events like new order arrivals, machine breakdowns, changes in due dates, order cancellations, arrival of urgent orders etc. that makes static scheduling approaches very difficult. A dynamic scheduling strategy should be adopted under such production circumstances. In the present study an event driven dynamic job shop scheduling mechanism under machine capacity constraints is proposed. The proposed method makes use of the greedy randomised adaptive search procedure (GRASP) by also taking into account orders due dates and sequence-dependent set-up times. Moreover, order acceptance/rejection decision and Order Review Release mechanism are integrated with scheduling decision in order to meet customer due date requirements while attempting to execute capacity adjustments. We employed a goal programming-based logic which is used to evaluate four objectives: mean tardiness, schedule unstability, makespan and mean flow time. Benchmark problems including number of orders, number of machines and different dynamic events are generated. In addition to event-driven rescheduling strategy, a periodic rescheduling strategy is also devised and both strategies are compared for different problems. Experimental studies are performed to evaluate effectiveness of the proposed method. Obtained results have proved that the proposed method is a feasible approach for rescheduling problems under dynamic environments.

Immunisation coverage in the rural Eastern Cape - are we getting the basics of primary care right? Results from a longitudinal prospective cohort study.

Immunisations are one of the most cost-effective public health interventions available and South Africa (SA) has implemented a comprehensive immunisation schedule. However, there is disagreement about the level of immunisation coverage in the country and few studies document the immunisation coverage in rural areas. To examine the successful and timely delivery of immunisations to children during the first 2 years of life in a deeply rural part of the Eastern Cape Province of SA. From January to April 2013, a cohort of sequential births (N=470) in the area surrounding Zithulele Hospital in the OR Tambo District of the Eastern Cape was recruited and followed up at home at 3, 6, 9, 12 and 24 months post birth, up to May 2015. Immunisation coverage was determined using Road-to-Health cards. The percentages of children with all immunisations up to date at the time of interview were: 48.6% at 3 months, 73.3% at 6 months, 83.9% at 9 months, 73.3% at 12 months and 73.2% at 24 months. Incomplete immunisations were attributed to stock-outs (56%), lack of awareness of the immunisation schedule or of missed immunisations by the mother (16%) and lack of clinic attendance by the mother (19%). Of the mothers who had visited the clinic for baby immunisations, 49.8% had to make multiple visits because of stock-outs. Measles coverage (of at least one dose) was 85.2% at 1 year and 96.3% by 2 years, but 20.6% of babies had not received a second measles dose (due at 18 months) by 2 years. Immunisations were often given late, particularly the 14-week immunisations. Immunisation rates in the rural Eastern Cape are well below government targets and indicate inadequate provision of basic primary care. Stock-outs of basic childhood immunisations are common and are, according to mothers, the main reason for their children's immunisations not being up to date. There is still much work to be done to ensure that the basics of disease prevention are being delivered at rural clinics in the Eastern Cape, despite attempts to re-engineer primary healthcare in SA.

Power-based Shift Schedule for Pure Electric Vehicle with a Two-speed Automatic Transmission

This paper introduces a comprehensive shift schedule for a two-speed automatic transmission of pure electric vehicle. Considering about driving ability and efficiency performance of electric vehicles, the power-based shift schedule is proposed with three principles. This comprehensive shift schedule regards the vehicle current speed and motor load power as input parameters to satisfy the vehicle driving power demand with lowest energy consumption. A simulation model has been established to verify the dynamic and economic performance of comprehensive shift schedule. Compared with traditional dynamic and economic shift schedules, simulation results indicate that the power-based shift schedule is superior to traditional shift schedules.

Optimal Power Scheduling for a Cooperative Network of Smart Residential Buildings

This paper attempts to contribute to the development of a new concept focused on a cooperative control scheme for a smart network of residential buildings (SNRB) and to demonstrate advantages of interconnections and coordination among a set of smart residential buildings (SRBs), by taking advantages of fluctuations of stochastic renewable sources and loads, and exploiting the operational flexibilities of thermal loads defined as the required hot water and desired buildings temperature. A comprehensive finite-horizon scheduling optimization problem is formulated to optimally control a SNRB using model predictive control (MPC) method, which integrates both forecasts and newly updated information. The cooperation is reached through a bidirectional communication infrastructure in the SNRB, where the master controller (MC) is available at the network level and in charge of coordinating and managing power in the SNRB. A MPC-based algorithm is used for the future scheduling of power exchanges, charge/discharge state of each energy storage device (ESD), the state of each micro-CHP and the charging state of each plug-in electric vehicle (PEV) available in the network. The MPC strategy is tested through case studies where the influences of both network topology and thermal loads on the operation of each SRB are analyzed via numerical results.

New Approach of Comprehensive Shift Schedule Based on Electric Drive Automated Mechanical Transmission

According to different control objectives, shift schedules of dynamical and economical mode for electric-drive AMT vehicles is presented and a comprehensive shift schedule model is established with precise traction motor torque and speed experimental data. The state function and objective functional are established by using dynamic programming and variety of optimal shift schedules are solved for different control weights. The simulation results indicate that the vehicle performance with comprehensive shift schedule is better than the traditional method.

Management of teaching processes using the Share point platform: A case study from the University of Split School of Medicine.

To develop a software tool that will combine teaching timetables with the generation of reports on teaching load. The University of Split School of Medicine project team and the external experts from the company LAMA, LLC. determined necessary functionalities and developed the software platform as an extension of the existing software solutions already in use by the Croatian academic community. By combining comprehensive scheduling functionality with planned and performed teaching activities we determined the teaching load and realized automatic generation of payments for adjunct lecturers. The implementation required perfecting of the human resources services, brought about a manifold alleviation of the work of the entire school's administration and substantially increased the effectiveness of the quality management. The software is currently managing 54,676 teaching hours, 841 teaching staff member, 111 teaching rooms, 8 study programs, and 645 courses. The program resolved several administrative problems of the school and is an example of successful implementation of IT technology in medical school management.

Transparent Real-Time Task Scheduling on Temporal Resource Partitions

The Hierarchical Real-Time Scheduling (HiRTS) technique helps improve overall resource utilization in real-time embedded systems. With HiRTS, a computation resource is divided into a group of temporal resource partitions, each of which accommodates multiple real-time tasks. Besides the computation resource partitioning problem, real-time task scheduling on resource partitions is also a major problem of HiRTS. The existing scheduling techniques for dedicated resources, like schedulability tests and utilization bounds, are unable to work without changes on temporal resource partitions in most cases. In this paper, we show how to achieve maximal transparency for task scheduling on Regular Partitions, a type of resource partition introduced by the Regularity-based Resource Partition (RRP) Model. We show that several classes of real-time scheduling problems on a regular partition can be transformed into equivalent problems on a dedicated single resource, such that comprehensive single-resource scheduling techniques provide optimal solutions. Furthermore, this transformation method could be applied to different types of real-time tasks such as periodic tasks, sporadic tasks and aperiodic tasks.