Input Items Research Articles

Realization of Dynamic Interface and High Performance Data Retrieval

This paper introduces a method to realize dynamic interface, and designs a database storage model based on XML field technology to realize convenient data storage, any combination condition retrieval function and how to improve the retrieval speed in this kind of storage model. Usually a business system needs to provide information entry and retrieval functions, software designers have to design the appropriate entry items, input interface and retrieval functions for each business system and spend too much time on the repetitive works. And later engineers have to maintain the changing needs of the entry project, so we can apply the dynamic interface technology to achieve the customize needs of input items by the user, reducing the time of the repetitive works. Dynamic interface technology includes the realization of database storage and high performance data retrieval. This paper explores a storage model based on XML database to realize common and efficient storage and discuss on how to improve the retrieval speed in this kind of storage model.

Elastic-PPQ: A two-level autonomic system for spatial preference query processing over dynamic data streams

Paradigms like Internet of Things and the most recent Internet of Everything are shifting the attention towards systems able to process unbounded sequences of items in the form of data streams. In the real world, data streams may be highly variable, exhibiting burstiness in the arrival rate and non-stationarities such as trends and cyclic behaviors. Furthermore, input items may be not ordered according to timestamps. This raises the complexity of stream processing systems, which must support elastic resource management and autonomic QoS control through sophisticated strategies and run-time mechanisms. In this paper we present Elastic-PPQ, a system for processing spatial preference queries over dynamic data streams. The key aspect of the system design is the existence of two adaptation levels handling workload variations at different time-scales. To address fast time-scale variations we design a fine regulatory mechanism of load balancing supported by a control-theoretic approach. The logic of the second adaptation level, targeting slower time-scale variations, is incorporated in a Fuzzy Logic Controller that makes scale in/out decisions of the system parallelism degree. The approach has been successfully evaluated under synthetic and real-world datasets.

Life cycle inventory processes of the integrated steel plant (ISP) in Krakow, Poland-continuous casting of steel (CCS): a case study

The purpose of this study is to perform the life cycle inventory (LCI) to continuous casting of steel (CCS) process in integrated steel plant (ISP) in Krakow, Poland. The present LCI is representative for year 2015 by application of PN-EN ISO 14040 (2009). The system boundary was labeled as gate to gate. The background input and output data from the CCS has been inventoried as follows: consumption of energy and fuels, materials inputs, and waste. In this paper, LCI CCS process on integrated permit (secondary data), literature information, and expert consultations was developed. The functional unit (FU) is represented by 1 t of steel slabs produced by CCS. Operating parameters as well as air emissions associated with the CCS process were presented. The production data (slabs) was given. The emissions of dust, Fe, PM10 and PM2.5, and waste (iron dust, iron particles, the others) are outcomes of the CCS processes. Annually production of steel slabs in CCS plant amounted to 200,000 t. Coke oven gas, natural gas, steam, air, oxygen, casting powders for crystallizer and for intermediate tundish, refractory material, oil, lubricant, argon, and industrial water amounts were in the order of 2,750,000 Nm3/year, 770,000 Nm3/year, 2000 t/year, 90,000,000 Nm3/year, 970 t/year, 1300 t/year, 810 t/year, 30 t/year, 45 t/year, 330 t/year, and 13,175,300 m3/year, respectively. The average heat and electric energy consumptions were about 0.023 to 0.068 GJ/t and 12.0 to 17.2 kWh/t, respectively. Emissions in air of dust, Fe, PM10, and PM2.5 were in the order of 1.3, 0.1, 0.5, and 0.44 kg/h, respectively. The amounts of iron dust and the other waste were 200 t/year and 240 t, respectively. No information is available on CO2 emission. The LCI study resulted in the development of a database with an inventory of secondary data regarding CCS process referring to the year 2015. The scope of this study considered input items needed in CCS production (entry gate) to the final delivery of steel slabs (exit gate). The raw materials, supplies, and equipment to the CCS plant and infrastructure are not taken into consideration in the analysis. The FU is 1 t of steel slabs. The LCI offers environmental information consisting of the list of environmental loads. The impact assessment phase aims to present more understandable results from the inventory analysis, and life cycle impact assessment will be the direction for future research.

A framework for configuring collaborative filtering-based recommendations derived from purchase data

This study proposes a decision support framework to help e-commerce companies select the best collaborative filtering algorithms (CF) for generating recommendations on the basis of online binary purchase data. To create this framework, an experimental design applies several CF configurations, which are characterized by different data-reduction techniques, CF methods, and similarity measures, to binary purchase data sets with distinct input data characteristics, i.e., sparsity level, purchase distribution, and item–user ratio. The evaluations in terms of accuracy, diversity, computation time, and trade-offs among these metrics reveal that the best-performing algorithm in terms of accuracy remains consistent regardless of the input-data characteristics. However, for diversity and computation time, the best-performing model varies with the input characteristics. This framework allows e-commerce companies to decide on the optimal CF configuration as a function of their specific binary purchase data sets. They also gain insight into the impact of changes in the input data set on the preferred algorithm configuration.

Energy and water nexus in power generation: The surprisingly high amount of industrial water use induced by solar power infrastructure in China

A crucial aspect of the energy and water nexus is reflected with the revelation of the surprisingly high amount of industrial water use induced by plant infrastructure of a pilot solar power generation system in China, by means of a concrete hybrid of process analysis and Input-Output analysis. With an inclusive collection of all the input items as products of the economy, this scoping distinguishes from existing accountings that either overlook the construction stage or simply cover the plant infrastructure of solar power system as some primary materials. Supported by embodied water database from input-output analysis, the industrial water use induced by each input item is fully addressed. The industrial water use induced by the solar power plant infrastructure is revealed to be over one order of magnitude higher than that in previous scoping: this magnitude approximately doubles the life cycle industrial freshwater withdrawal of typical coal-fired power generation system for per unit amount of electricity generated. Under the 2020 scenario raised in the newly issued 13th Five Year Plan for Renewable Energy Development, the annual industrial water use induced by solar thermal power infrastructure in China is accordingly estimated, in magnitude up to around 30% of the total industrial freshwater use in Beijing in the year 2014, one-fourth of that in Tianjin, and one-tenth of that in Shanxi, respectively. In the long term water-saving planning, the promotion of solar power installations is afforded further consideration.

An Empirical Study of How the Knowledge Management Is a Driver of Innovation for Software Sector SMEs in MMxico

Purpose – Knowledge Management (KM) in Innovation process (INNOV), is a powerful engine that drives the company towards competitiveness (INSEAD, 2014; WEF, 2014); however, many small and media enterprises (SMEs) in Mexico, ignore it. So, the aim of the present study is to discover the key factors of KM that are involved in the INNOV, prevailing in the field of software sector SMEs in Guadalajara (SSG), Mexico.Design/methodology/approach – This research is a documental study about KM and how is related as driver on the INNOV; to achieve this, it was designed a questionnaire splitted in two parts: the first one, corresponding to KM that involved (5) factors: KM Leadership (KMLD); KM Capture and Acquisition (KMCA); KM Training and Mentoring (KMTM); KM Policies and Strategies (KMPS); KM Communications and Rewards (KMCR) with 23 total indicators as variables. The second one, INNOV that involved (6) factors: Innovation Value Added (IVADD); Innovation Input Items (IIIT); Innovation Process (INPROC); Innovation Output Items (IOIT); Innovation Performance (IPERF) and Innovation Feedback (IFEED) with 39 total indicators as variables. So, we designed a questionnaire (62 variables), as a measurement instrument based on Likert Scale (1 to 5 interval) in order to determine the degree of agreement with well Cronbach’s Alpha confidence (0.8432). We proceeded to do a survey to the total 200 CEOs belonging to the SMEs from SSG. The results were analysed using Structural Equations Modelling (SEM) to find validity and reliability of the structure to discover by the system equations, the underlying variables and their interrelationships. Finally, we found most representative KM variables to drive the INNOV, were: KMCA (0.9095); KMCR (0.8845); KMTM (0.8815); KMLD (0.8780); KMPS (0.8235). Finally we solved the (5) hypotheses finding the relationship between the KM factors and INNOV have significant positive effects.Originality/value – It lies in the design of a construct that identify the underlying KM factors and variables sized according an exploratory and multi-correlational study to drive 1814 the INNOV. All the factors and variables were collected from the principal theories about both subjects and jointed in a solid set by SEM to find their respective correlations.Practical implications – This study, shall serve to the SSG to identify what variables and factors from KM, are able to drive the INNOV and get a better place for competitiveness.

An optimal fuzzy PID control approach for docking maneuver of two spacecraft: Orientational motion

This paper describes a scheme for a Fuzzy-Proportional Integral Derivative (FPID) controller based on genetic algorithm (GA), in a docking maneuver of two spacecraft. The docking maneuver consists of two parts: translation and orientation. Euler’s gyroscopic equation is applied to obtain governing equations of orientational phase. Here, a designed fuzzy-PID controller for stabilization purpose of orientational phase of a docking maneuver is presented based on the Single Input Fuzzy Inference Motor (SIFIMs) dynamically connected Preferrer Fuzzy Inference Motor (PFIM). This fuzzy-PID controller takes the error signal of Euler’s angles and the error of angular velocities of the chaser as its input items, and the driving force as its output. The parameters of the controller are ascertained by using a genetic algorithm. Conflicting objective functions (which their 3D pareto frontiers are obtained by Multi-objective Genetic Algorithm (MOGA)) are distance errors from the set point, angle errors from the set point, and control efforts. Optimization constraint is maximal of the momentum produced by momentum wheels. The result of optimum point demonstrates that the designed controller makes an efficient performance in the orientational phase of the chaser spacecraft. Compared to similar works, some of system parameters like settling time are improved and overshoot (as a critical parameter in docking maneuver) is decreased.

Fast Data Sort based on Searching Networks with Ring Pipeline

The paper suggests a technique for fast data sort based on a specially organized binary searching network with the following new distinctive features: 1) data sort is done within the time of data acquisition through a serial interface; 2) a new type of pipeline, which we call ring pipeline, is created 3) the delay for receiving each data item is minimized thanks to the novel ring pipeline; 4) sorted data can be transmitted almost immediately after receiving the last input item; 5) several data sets may be sorted by the same network at the same acquisition time. It is proved theoretically that the network is very fast. It was modelled and evaluated in software and the basic components were synthesized and implemented in hardware. The results have shown a significant speed-up comparing to the best known alternatives.DOI: http://dx.doi.org/10.5755/j01.eie.22.4.15920

Reordering buffer management with advice

In the reordering buffer management problem, a sequence of colored items arrives at a service station to be processed. Each color change between two consecutively processed items generates some cost. A reordering buffer of capacity k items can be used to preprocess the input sequence in order to decrease the number of color changes. The goal is to find a scheduling strategy that, using the reordering buffer, minimizes the number of color changes in the given sequence of items. We consider the problem in the setting of online computation with advice. In this model, the color of an item becomes known only at the time when the item enters the reordering buffer. Additionally, together with each item entering the buffer, we get a fixed number of advice bits, which can be seen as information about the future or as information about an optimal solution (or an approximation thereof) for the whole input sequence. We show that for any $$\varepsilon > 0$$ there is a $$(1+\varepsilon )$$ -competitive algorithm for the problem which uses only a constant (depending on $$\varepsilon $$ ) number of advice bits per input item. This also immediately implies a $$(1+\varepsilon )$$ -approximation algorithm which has $$2^{O(n\log 1/\varepsilon )}$$ running time (this should be compared to the trivial optimal algorithm which has a running time of $$k^{O(n)}$$ ). We complement the above result by presenting a lower bound of $$\varOmega (\log k)$$ bits of advice per request for any 1-competitive algorithm.

A Study on the Advancement Planning of the Design VE System

The economic efficiency review of design, etc. (hereinafter, Value Engineering (VE)) is the work designed to use the minimum life-cycle cost (LCC) and review the economy and field adjustment feasibility of design contents by function and alternative measure to secure the necessary functions of facilities. To systematically and comprehensively manage the results of the design VE done by ordering agencies and to use such results for performing similar design works, the Ministry of Land, Infrastructure, and Transport (MOLIT) has constructed and is implementing the design VE system based on the Guidelines for the Review of Economy of Design, etc. (hereinafter, VE guidelines). The design VE system, however, is outdated because of the changes that were made in the input and management items when the VE guidelines were amended and because of the inappropriate function improvement since its construction. It currently requires the input of many unnecessary items, and the information is stored chiefly in the form of text, making it inappropriate to utilize the data for designing similar construction projects. This study proposes measures for the update of the design VE system to reflect the amendment of the VE guidelines, to simplify the unnecessary input functions, to boost the search function, and to provide diverse forms of statistics. These proposed update measures are expected to assess the comprehensive analysis of the design VE performance results, to share the design VE information with the ordering agencies and design firms so as to come up with effective alternative measures, to reduce construction project costs, and to improve the quality of construction projects.

Data Mining Framework for Generating Sales Decision Making Information Using Association Rules

The rapid technological development in the field of information and communication technology (ICT) has enabled the databases of super shops to be organized under a countrywide sales decision making network to develop intelligent business systems by generating enriched business policies. This paper presents a data mining framework for generating sales decision making information from sales data using association rules generated from valid user input item set with respect to the sales data under analysis. The proposed framework includes super shop’s raw database storing sales data collected through sales application systems at different Point of Sale (POS) terminals. Apriori algorithm is famous for association rule discovery from the transactional database. The proposed technique using customized association rule generation and analysis checks the input items with sales data for validation of the input items. The support and confidence of each rule are computed. Sales decision making information about input items is generated by analyzing each of the generated association rules, which can be used to improve sales decision making policy to attract customers in order to increase sales. It is hoped that this approach for generating sales decision making information by analyzing sales data using association rules is more specific decision and application oriented as the business decision makers are not usually interested to all of the items of the sales database for making a specific sales decision.

Design of forward supply chains: Impact of a carbon emissions-sensitive demand

We explore the impacts of a carbon emissions-sensitive demand on decisions relative to the design of forward supply chains (facility location, supplier selection, production technology selection and transportation mode selection). We investigate the design of a forward SC where a set of input items (components) are purchased from a network of external suppliers and used to manufacture a finished product in one or multiple production facilities to fulfill the demand of one or multiple customers. The demand for the final product is an endogenous variable sensitive to carbon emissions per unit and it is also assumed to increase with a decrease in the per unit carbon emissions of the product. We consider the case of a single customer and extend the model to multiple customers. Based on numerical experiments conducted on a case study from the textile industry, we use the models to provide a series of insights that might be instrumental to firms and policy makers. For instance, results indicate that customers׳ environmental awareness may encourage companies to bring the area of production close to the area of consumption and to select local suppliers. It might even be optimal to dedicate a production facility to each customer in spite of the incurred additional costs. However, if the customers are very demanding (in terms of reducing carbon emissions) then the best strategy can be to design a supply chain with relatively high emissions level because satisfying customer requirements may be very expensive in this case. Furthermore, if the customers are willing to pay a higher price for the product then this can lead to reducing per unit emissions. Our results augment the research to the fields of design of forward and greener supply chains by modeling and experimenting an endogenous demand sensitive to carbon emissions.

Effective staging of fibrosis by the selected texture features of liver: Which one is better, CT or MR imaging?

PurposeTexture patterns of hepatic fibrosis are one of the important biomarkers to diagnose and classify chronic liver disease from initial to end stage on computed tomography (CT) or magnetic resonance (MR) images. Computer-aided diagnosis (CAD) of liver cirrhosis using texture features has become popular in recent research advances. To date, however, properly selecting effective texture features and image parameters is still mostly undetermined and not well-defined. In this study, different types of datasets acquired from CT and MR images are investigated to select the optimal parameters and features for the proper classification of fibrosis. MethodsA total of 149 patients were scanned by multi-detector computed tomography (MDCT) and 218 patients were scanned using 1.5T and 3T superconducting MR scanners for an abdominal examination. All cases were verified by needle biopsies as the gold standard of our experiment, ranging from 0 (no fibrosis) to 5 (cirrhosis). For each case, at least four sequenced phase images are acquired by CT or MR scanners: pre-contrast, arterial, portal venous and equilibrium phase. For both imaging modalities, 15 texture features calculated from gray level co-occurrence matrix (GLCM) are extracted within an ROI in liver as one set of input vectors. Each combination of these input subsets is checked by using support vector machine (SVM) with leave-one-case-out method to differentiate fibrosis into two groups: non-cirrhosis or cirrhosis. In addition, 10 ROIs in the liver are manually selected in a disperse manner by experienced radiologist from each sequenced image and each of the 15 features are averaged across the 10 ROIs for each case to reduce the validation time. The number of input items is selected from the various combinations of 15 features, from which the accuracy rate (AR) is calculated by counting the percentage of correct answers on each combination of features aggregated to determine a liver stage score and then compared to the gold standard. ResultsAccording to the accuracy rate (AR) calculated from each combination, the optimal number of texture features to classify liver fibrosis degree ranges from 4 to 7, no matter which modality was utilized. The overall performance calculated by the average sum of maximum AR value of all 15 features is 66.83% in CT images, while 68.14%, and 71.98% in MR images, respectively; among the 15 texture features, mean gray value and entropy are the most commonly used features in all 3 imaging datasets. The correlation feature has the lowest AR value and was removed as an effective feature in all datasets. AR value tends to increase with the injection of contrast agency, and both CT and MR images reach the highest AR performance during the equilibrium phase. ConclusionsComparing the accuracy of classification with two imaging modalities, the MR images have an advantage over CT images with regards to AR performance of the 15 selected texture features, while 3T MRI is better than 1.5T MRI to classify liver fibrosis. Finally, the texture analysis is more effective during equilibrium phase than in any of the other phased images.

Patient Safety Applications for Improving Health Care Quality

Patient safety is the fundamental thing that needs to be taken care of by medical staff when giving healthcare to patient especially in the Emergency Installation. PKU Muhammadiyah Public Hospital at Bantul has implemented Patient safety well in providing services for patients. Expected to be a reference for other hospitals to implement Patient safety as well as efforts to improve the quality of health services, but do not close the possibility there were still some deficiencies that could be made a suggestion for the PKU Muhammadiyah Bantul Hospital. There for researchers interested to review more detail how application of Patient safetyin Emergency Installation PKU Muhammadiyah Bantul Public Hospital in 2014. This research was qualitative observational study, using research subjects: The Head of the medical service, 1 Officer of Emergency Installation, the head of disaster management, 1 medical doctor, 1 nurse, and 4 patients that were taken with inclusion criteria: getting inpatient class III Hospitals in Yogyakarta, have inpatient more than 1 day.<strong> </strong>All the input items on the application of patient safety which include facilities, equipment, drugs, procedures, and activities of officers at Emergency Installation at PKU Muhammadiyah Bantul Hospital, as well as the application process is in compliance with the Guidelines of Observation Guidelines Patient Safety 2008 Survey and Guidelines for Accreditation of Hospital Emergency Services Specific Guidelines (revised edition 2007), ACT No. 44 Hospital in 2009, and patient safety procedures 2008. Patient perceived output includes five dimensions of quality (Tangible, Reliable, Responsiveness, Assurance and Empathy) all have the same perception of good and satisfying. Application on standard input, process and output is suit with Observation Guidelines for Patient Safety 2008 Survey and Guidelines for Accreditation of Hospital Emergency Services Specific Guidelines (revised edition 2007), ACT No. 44 Hospital in 2009, and patient safety procedures 2008.

Patient Safety Applications for Improving Health Care Quality

Patient safety is the fundamental thing that needs to be taken care of by medical staff when giving healthcare to patient especially in the Emergency Installation. PKU Muhammadiyah Public Hospital at Bantul has implemented Patient safety well in providing services for patients. Expected to be a reference for other hospitals to implement Patient safety as well as efforts to improve the quality of health services, but do not close the possibility there were still some deficiencies that could be made a suggestion for the PKU Muhammadiyah Bantul Hospital. There for researchers interested to review more detail how application of Patient safetyin Emergency Installation PKU Muhammadiyah Bantul Public Hospital in 2014. This research was qualitative observational study, using research subjects: The Head of the medical service, 1 Officer of Emergency Installation, the head of disaster management, 1 medical doctor, 1 nurse, and 4 patients that were taken with inclusion criteria: getting inpatient class III Hospitals in Yogyakarta, have inpatient more than 1 day.<strong> </strong>All the input items on the application of patient safety which include facilities, equipment, drugs, procedures, and activities of officers at Emergency Installation at PKU Muhammadiyah Bantul Hospital, as well as the application process is in compliance with the Guidelines of Observation Guidelines Patient Safety 2008 Survey and Guidelines for Accreditation of Hospital Emergency Services Specific Guidelines (revised edition 2007), ACT No. 44 Hospital in 2009, and patient safety procedures 2008. Patient perceived output includes five dimensions of quality (Tangible, Reliable, Responsiveness, Assurance and Empathy) all have the same perception of good and satisfying. Application on standard input, process and output is suit with Observation Guidelines for Patient Safety 2008 Survey and Guidelines for Accreditation of Hospital Emergency Services Specific Guidelines (revised edition 2007), ACT No. 44 Hospital in 2009, and patient safety procedures 2008.

Data Envelopment Analysis for Evaluating Structure of Input and Output Items

SUMMARYData envelopment analysis (DEA) is an evaluation method that allows analysts to evaluate the efficiencies of decision making units (DMUs) with multiple input and output items. When DMUs are evaluated only in terms of efficiency, analysts cannot evaluate how efficient combinations of input and output items are. In order to evaluate the efficiencies of such combinations, we propose a method that is based on DEA in order to evaluate the structure of the efficiency for input and output items based on the conventional hierarchical approach. The proposed method is composed of four steps. In Step 1, dividing the data set, we construct a hierarchical structure. In Step 2, efficiency values are calculated for each divided data set. In Step 3, nodes are integrated, and they are visualized in Step 4. Based on the visualization, analysts can evaluate the characteristics of a DMU structurally and find the second most efficient items and key items that improve efficiency when they are added to other items. A numerical example with a data set from the 2010 Quality of Life Index (35 DMUs, one input and five output items) is used to illustrate the features of the proposed method.

Integrated Assessment and Modeling of Agricultural Mechanization in Potato Production of Iran by Artificial Neural Networks

Mechanization is a concept and cannot be measured directly, so we used appropriate indicators in order to determine mechanization status for the first time in this field of study. In this paper, based on energy and power availability, four indicators, namely machinery energy ratio, mechanization index, productivity level of consumed power, and mechanization level, were selected for assessing agricultural mechanization in potato production of Iran using integrated assessment and modeling (IAM) to provide insight into the potential impacts of policy changes. To do an IAM in agricultural mechanization, we used pervasive analysis using more than 90 features in sample farms. This IAM is the first generalized model in agricultural mechanization. The main purpose of this study is presenting and showing capability of ANNs to model agricultural mechanization status and indicating best ANN model. Finally, a two hidden layer model with these features showed best performance: generalized feed-forward network with Levenberg Marquart learning rule and Bias Axon transfer function with 4–10 neurons in two hidden layers which have 27 input items for modeling four outputs. In this study, ANN models were introduced and applied to help IAM investigation which integrating production factors to have better knowledge about agricultural system of potato production in a wide region.

基于能值分析的无公害设施蔬菜生产系统效率和可持续性评价

PDF HTML阅读 XML下载 导出引用 引用提醒 基于能值分析的无公害设施蔬菜生产系统效率和可持续性评价 DOI: 10.5846/stxb201306041318 作者: 作者单位: 中国农业大学农学与生物技术学院,石家庄信息工程职业学院,中国农业大学农学与生物技术学院,中国农业大学农学与生物技术学院,中国农业大学农学与生物技术学院,中国农业大学农学与生物技术学院,河北省景县津龙公司 作者简介: 通讯作者: 中图分类号: 基金项目: 国家"十二五"科技支撑计划项目(2012BAD14B03) Efficiency and sustainability evaluation of a pollution-free vegetable production system based on emergy analysis Author: Affiliation: College of Agronomy and Biotechnology, China Agricultural University,,,,College of Agronomy and Biotechnology, China Agricultural University,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:发展无公害蔬菜是提高我国蔬菜品质和安全性、增强蔬菜产业竞争力的重要举措。针对蔬菜生产高投入、高产出的特点,采用能值分析方法对无公害设施蔬菜生产系统两种蔬菜栽培模式进行系统效率和可持续性评价,并与一般蔬菜生产模式进行对比。结果表明,无公害蔬菜生产模式能值利用效率、可更新比例、净能值产出率、食品安全性和可持续发展能力分别比一般蔬菜生产系统平均提高24.3%、24.9%、36.0%、98.2%和3.87倍,环境负载率平均下降64.7%,系统投入的能值货币价值平均降低20.0%。在此基础上,通过情景分析表明,如果进一步综合采用收集降雨用于棚内蔬菜灌溉、沼气发电供给蔬菜灌溉利用、通过标准化管理降低人力投入,可以使现有的无公害生产方式的能值利用效率提高3.4%-10.8%,可持续性提高1.2%-31.7%。该研究也表明,将无公害蔬菜生产纳入我国以沼气为纽带的循环农业发展模式中,实现适度的产业结合与规模化经营,能够较好的提高无公害蔬菜生产的生态经济效益和可持续发展能力。 Abstract:Substantial development is needed in the production of pollution-free vegetables to ensure the quality, safety, and competitiveness of vegetables in China. In contrast to customary vegetable production, pollution-free production uses organic fertilizers and biological pesticides instead of chemical fertilizers and chemical pesticides. This mode of production improves the safety of the vegetables and reduces environmental pollution, and the government is encouraging its use throughout China. Nevertheless, there is still considerable scope for research into efficient and sustainable vegetable production. Emergy evaluation is an effective tool in analyzing the efficiency and sustainability of an ecological system, which addresses the weakness of traditional energy analysis and expresses different forms of energy using a common physical basis, namely, solar emergy. It also has the advantage of taking into consideration aspects such as natural resources, labor, and ecosystem services that are generally left uncalculated by other evaluation methods. This paper evaluated the efficiency and sustainability of a pollution-free vegetable production system based on emergy analysis, and results were compared with those of a customary vegetable production system. At the same time, the renewability factor of each input item was introduced in this study to improve analysis of the types of energy input. The emergy input structure and energy-based indices such as emergy efficiency (UEV), renewability (ΦR), net emergy yield ratio (EYR), environmental loading rate (ELR), product safety index based-emergy (PSI), emergy sustainability index (ESI), and emergy-based monetary value (EmRMB) were analyzed to estimate the efficiency and sustainability of the pollution-free vegetable system, and to determine the key points affecting the ecological performance of the system. The results showed that the total energy input of the two modes of the pollution-free vegetables production system were 4.97×1016 sej/year and 4.12×1016 sej/year, respectively. Compared with the customary vegetable production system, the UEV, EYR, ΦR, EPSI, and ESI values of the pollution-free vegetable production system rose on average by 24.3%, 24.9%, 36.0%, 98.2%, and 387.0%, respectively; and on average the ELR value declined by 64.7% and the EmRMB by 20.0%. At the same time, we found that that labor input and irrigation were the core factors that affected the efficiency and sustainability of the system. The scenario analysis revealed that the emergy efficiency and sustainability of the current pollution-free vegetable production system could be improved by 3.4%-10.8% and by 1.2%-31.7%, respectively, by creating an efficient system capable of adjusting to changes such as collection of rainfall to irrigate vegetables in a greenhouse, usage of electricity from a biogas project to irrigate, and reducing the labor input by applying standardized management. From an economic point of view, the reduction in purchased inputs and the higher price of pollution-free vegetables can be beneficial to the farmer. Moreover, the study demonstrates that the introduction of pollution-free vegetable production into recycling agriculture and the usage of biogas slurry as the main fertilizer have positive economic and ecological effects. Consequently, there is a considerable prospect for the development of pollution-free vegetables in China. The results also indicated that emergy evaluation is an effective method to analyze the vegetable ecological system and could provide a useful tool in the development of pollution-free vegetable production in China. 参考文献 相似文献 引证文献

Real-time learning of predictive recognition categories that chunk sequences of items stored in working memory.

How are sequences of events that are temporarily stored in a cognitive working memory unitized, or chunked, through learning? Such sequential learning is needed by the brain in order to enable language, spatial understanding, and motor skills to develop. In particular, how does the brain learn categories, or list chunks, that become selectively tuned to different temporal sequences of items in lists of variable length as they are stored in working memory, and how does this learning process occur in real time? The present article introduces a neural model that simulates learning of such list chunks. In this model, sequences of items are temporarily stored in an Item-and-Order, or competitive queuing, working memory before learning categorizes them using a categorization network, called a Masking Field, which is a self-similar, multiple-scale, recurrent on-center off-surround network that can weigh the evidence for variable-length sequences of items as they are stored in the working memory through time. A Masking Field hereby activates the learned list chunks that represent the most predictive item groupings at any time, while suppressing less predictive chunks. In a network with a given number of input items, all possible ordered sets of these item sequences, up to a fixed length, can be learned with unsupervised or supervised learning. The self-similar multiple-scale properties of Masking Fields interacting with an Item-and-Order working memory provide a natural explanation of George Miller's Magical Number Seven and Nelson Cowan's Magical Number Four. The article explains why linguistic, spatial, and action event sequences may all be stored by Item-and-Order working memories that obey similar design principles, and thus how the current results may apply across modalities. Item-and-Order properties may readily be extended to Item-Order-Rank working memories in which the same item can be stored in multiple list positions, or ranks, as in the list ABADBD. Comparisons with other models, including TRACE, MERGE, and TISK, are made.

The national clinical database as an initiative for quality improvement in Japan.

The JCVSD (Japan Cardiovascular Surgery Database) was organized in 2000 to improve the quality of cardiovascular surgery in Japan. Web-based data harvesting on adult cardiac surgery was started (Japan Adult Cardiovascular Surgery Database, JACVSD) in 2001, and on congenital heart surgery (Japan Congenital Cardiovascular Surgery Database, JCCVSD) in 2008. Both databases grew to become national databases by the end of 2013. This was influenced by the success of the Society for Thoracic Surgeons’ National Database, which contains comparable input items. In 2011, the Japanese Board of Cardiovascular Surgery announced that the JACVSD and JCCVSD data are to be used for board certification, which improved the quality of the first paperless and web-based board certification review undertaken in 2013. These changes led to a further step. In 2011, the National Clinical Database (NCD) was organized to investigate the feasibility of clinical databases in other medical fields, especially surgery. In the NCD, the board certification system of the Japan Surgical Society, the basic association of surgery was set as the first level in the hierarchy of specialties, and nine associations and six board certification systems were set at the second level as subspecialties. The NCD grew rapidly, and now covers 95% of total surgical procedures. The participating associations will release or have released risk models, and studies that use ‘big data’ from these databases have been published. The national databases have contributed to evidence-based medicine, to the accountability of medical professionals, and to quality assessment and quality improvement of surgery in Japan.