State of the art advancement in rational design of g-C3N4 photocatalyst for efficient solar fuel transformation,environmental decontamination and future perspectives

Recently, the graphite based heterogeneous photocatalysts has attained tremendous research attention in various environmental applications. Among them, the graphitic carbon nitride (g-C₃N₄) is categorized as a unique solar active particle with its outstanding intrinsic properties i.e., adequate band configuration, excellent light absorptivity and thermo-physical durability, which make it highly useful and reliable for revenue transformation and ecological concerns. Considering the intrinsic potential of g-C₃N₄ in photocatalysis, so far, no report has been done in literature for its extraordinary configuration, morphological characteristics and perspective tuning for said applications. To overcome this research gap, our primary emphasis of this review regarding photocatalysis is to provide layout as well as the advancement of visible-light-fueled materials as highly stabilized and extremely effective ones for pragmatic implementation. Thus, this existing comprehensive assessment conducts a systematic survey over visible light driven non-metal novel g-C₃N₄. The major advancement of this evaluation is the fabrication of well-designed nanosized g-C₃N₄ photocatalysts with unique configurable frameworks and compositions. Furthermore, alternative techniques in order to customize the analogue band configuration and noticeable cultivation such as metal (cation), nonmetal (anion) doping, worthy metal activating, and alloy initiation with certain semiconductors are discussed in detail. In addition to this, g-C₃N₄ photocatalytic functionalities towards photocatalytic hydrogen evolution, CO₂ photoreduction, biological metal ions deterioration as well as bacterial sanitization are also presented and discussed in detail. Therefore, we believe that such a pivotal compact assessment can provide a roadmap in several perspectives on the currently underway obstacles in the innovation of effective g-C₃N₄ catalytic design processes. Moreover, this critical assessment will ultimately serve as a useful supplement in the research area of g-C₃N₄ nanosized photocatalysts and for the researchers working on its key aspects in diverse range of natural, chemistry, engineering and environmental applications.     

基于空气质量模拟的江苏省大气污染物排放清单比较研究

大气污染物排放清单是空气质量模拟和空气污染治理的重要依据.本研究比较分析了两套覆盖江苏省的2017年大气污染物排放清单，即分别由上海市环境科学研究院、江苏省环境科学研究院编制的"长三角清单"和"江苏省清单"，并结合区域空气质量模型CMAQ评估不同清单对长三角地区2017年1、4、7、10月的空气质量模拟的影响.清单比较结果表明，除二氧化硫（SO₂）以外，江苏省清单估算的各污染物排放量较长三角清单低.通过与观测数据比较，发现两套清单对SO₂、氮氧化物（NO_x）、臭氧（O₃）和细颗粒物（PM_2.5）的模型模拟性能均较好.江苏省清单与长三角清单两者的模拟结果空间分布接近，其中江苏省清单模拟的PM_2.5和O₃在长三角多数地区略低于长三角清单的模拟结果（1月O₃除外）.江苏省清单与长三角清单均能够用于空气质量模式模拟，可为江苏地区的细颗粒物和光化学烟雾污染的控制策略制定提供参考.     

随机库存分配问题的一种求解方法

讨论了在VMI管理思想下对具有随机需求特性的多客户库存分配问题 ,重点论述了有效近似算法的设计过程 ,最后基于一组模拟数据给出了一个算例。     

Spare parts inventory management with demand lead times and rationing

We study an inventory system that consists of two demand classes. The orders in the first class need to be satisfied immediately, whereas the orders in the second class are to be filled in a given demand lead time. The two classes are also of different criticality. For this system, we propose a policy that rations the non-critical orders. Under a one-for-one replenishment policy with backordering and for Poisson demand arrivals for both classes, we first derive expressions for the service levels of both classes. The service level for the critical class is an approximation, whereas the service level for the non-critical class is exact. We then conduct a computational study to show that our approximation works reasonably, the benefits of rationing can be substantial, and the incorporation of demand lead time provides more value when the demand class with demand lead time is the critical class. The research is motivated by the spare parts service system of a major capital equipment manufacturer that faces two types of demand. For this company, the critical down orders need to be satisfied immediately, while the less critical maintenance orders can be satisfied after a fixed demand lead time. We conduct a case study with 64 representative parts and show that significant savings (as much as 14% on inventory on hand) are possible through incorporation of demand lead times and rationing.     

Managing logical channel configurations in complex satellite architectures

As a result of the increased switching complexity of many communications satellites, often transponders are available with a variety of configuration options offering flexibility in power, bandwidth, and coverage. Many of these configurations share resources that prevent them from being simultaneously active, making it a challenge to understand and manage the satellite capacity inventory. In this paper we define the logical channel configuration (LCC) as an unambiguous unit of the inventory and present a novel method of modelling the interdependencies between these LCCs using linear algebra. In particular, a projector matrix is defined which can be used to update the activation state of all LCCs when the state of one or more of these is changed. As a result, a number of valid LCC scenarios can be easily generated and evaluated for optimal use of the satellite resources. Copyright © 2005 John Wiley & Sons, Ltd.     

Optimal production-inventory policy for make-to-order versus make-to-stock based on the M/Er/1 queuing model

This paper analyzes a multi-product production / inventory system where demands for each item arrive according to a Poisson process and the production time for each product has an Erlang distribution. The paper proposes an optimality condition that specifies whether each product should be produced make-to-stock or make-to-order. In the event a product should be produced make-to-stock, an approach for computing the optimal base-stock level is proposed. Numerical examples are given for illustrative purpose.     

Hybrid Approach for Addressing Uncertainty in Risk Assessments

Parameter uncertainty is a major aspect of the model-based estimation of the risk of human exposure to pollutants. The Monte Carlo method, which applies probability theory to address model parameter uncertainty, relies on a statistical representation of available information. In recent years, other uncertainty theories have been proposed as alternative approaches to address model parameter uncertainty in situations where available information is insufficient to identify statistically representative probability distributions, due in particular to data scarcity. The simplest such theory is possibility theory, which uses so-called fuzzy numbers to represent model parameter uncertainty. In practice, it may occur that certain model parameters can be reasonably represented by probability distributions, because there are sufficient data available to substantiate such distributions by statistical analysis, while others are better represented by fuzzy numbers (due to data scarcity). The question then arises as to how these two modes of representation of model parameter uncertainty can be combined for the purpose of estimating the risk of exposure. This paper proposes an approach (termed a hybrid approach) which combines Monte Carlo random sampling of probability distribution functions with fuzzy calculus. The approach is applied to a real case of estimation of human exposure, via vegetable consumption, to cadmium present in the surficial soils of an industrial site located in the north of France. The application illustrates the potential of the proposed approach, which allows the uncertainty affecting model parameters to be represented in a way that is consistent with the information at hand. Also, because the hybrid approach takes advantage of the “rich” information provided by probability distributions, while retaining the conservative character of fuzzy calculus, it is believed to hold value in terms of a “reasonable” application of the precautionary principle.     

On a responsive replenishment system: a fuzzy logic approach

In today's competitive business environment, it is important that customers are able to obtain their preferred items in the shops they visit, particularly for convenience store chains such as 7–Eleven where popular items are expected to be readily available on the shelves of the stores for buyers. To minimize the cost of running such store chains, it is essential that stocks be kept to a minimum and at the same time large varieties of popular items are available for customers. In this respect, the replenishment system needs to be able to cope with the taxing demands of minimal inventory but at the same time keeping large varieties of needed items. This paper proposes a replenishment system which is able to respond to the fluctuating demands of customers and provide a timely supply of needed items in a cost–effective way. The proposed system embraces the principle of fuzzy logic which is able to deal with uncertainties by virtue of its fuzzy rules reasoning mechanism, thereby leveraging the responsiveness of the entire replenishment system for the chain stores. To validate the feasibility of the approach, a case study has been conducted in an emulated environment with promising results.     

Web环境下的销售管理系统

针对企业销售管理现状，提出了基于库存的销售管理模型。通过建立基于Web的销售管理中心，企业可及时提供价廉物美、具有个性的产品，并有效地改善售前售后服务质量。     

An empirical InSAR-optical fusion approach to mapping vegetation canopy height

Exploiting synergies afforded by a host of recently available national-scale data sets derived from interferometric synthetic aperture radar (InSAR) and passive optical remote sensing, this paper describes the development of a novel empirical approach for the provision of regional- to continental-scale estimates of vegetation canopy height. Supported by data from the 2000 Shuttle Radar Topography Mission (SRTM), the National Elevation Dataset (NED), the LANDFIRE project, and the National Land Cover Database (NLCD) 2001, this paper describes a data fusion and modeling strategy for developing the first-ever high-resolution map of canopy height for the conterminous U.S. The approach was tested as part of a prototype study spanning some 62,000 km² in central Utah (NLCD mapping zone 16). A mapping strategy based on object-oriented image analysis and tree-based regression techniques is employed. Empirical model development is driven by a database of height metrics obtained from an extensive field plot network administered by the USDA Forest Service-Forest Inventory and Analysis (FIA) program. Based on data from 508 FIA field plots, an average absolute height error of 2.1 m (r = 0.88) was achieved for the prototype mapping zone.