Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests

Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.

Multi-Item Fuzzy Inventory Model Involving Three Constraints: A Karush-Kuhn-Tucker Conditions Approach

In this paper, a multi-item inventory model with storage space, number of orders and production cost as constraints are developed in both crisp and fuzzy environment. In most of the real world situations the cost parameters, the objective functions and constraints of the decision makers are imprecise in nature. This model is solved with shortages and the unit cost dependent demand is assumed. Hence the cost parameters are imposed here in fuzzy environment. This model has been solved by Kuhn-Tucker conditions method. The results for the model without shortages are obtained as a particular case. The model is illustrated with numerical example.

Generalized Height-Diameter Models for Pinus montezumae Lamb. and Pinus pseudostrobus Lindl. Plantations in Michoacan, Mexico

Tree height (H) in a natural stand or forest plantation is a fundamental variable in management, and the use of mathematical expressions that estimate H as a function of diameter at breast height (d) or variables at the stand level is a valuable support tool in forest inventories. The objective was to fit and propose a generalized H-d model for Pinus montezumae and Pinus pseudostrobus established in forest plantations of Nuevo San Juan Parangaricutiro, Michoacan, Mexico. Using nonlinear least squares (NLS), 10 generalized H-d models were fitted to 883 and 1226 pairs of H-d data from Pinus montezumae and Pinus pseudostrobus, respectively. The best model was refitted with the maximum likelihood mixed effects model (MEM) approach by including the site as a classification variable and a known variance structure. The Wang and Tang equation was selected as the best model with NLS;the MEM with an additive effect on two of its parameters and an exponential variance function improved the fit statistics for Pinus montezumae and Pinus pseudostrobus, respectively. The model validation showed equality of means among the estimates for both species and an independent subsample. The calibration of the MEM at the plot level was efficient and might increase the applicability of these results. The inclusion of dominant height in the MEM approach helped to reduce bias in the estimates and also to better explain the variability among plots.

Infinitesimal dividing modeling method for dual suppliers inventory model with random lead times

As one of the basic inventory cost models, the （Q, τ）inventory cost model of dual suppliers with random procurement lead time is mostly formulated by using the concepts of ＂effective lead time＂ and ＂lead time demand＂, which may lead to an imprecise inventory cost. Through the real-time statistic of the inventory quantities, this paper considers the precise （Q, τ） inventory cost model of dual supplier procurement by using an infinitesimal dividing method. The traditional modeling method of the inventory cost for dual supplier procurement includes complex procedures. To reduce the complexity effectively, the presented method investigates the statistics properties in real-time of the inventory quantities with the application of the infinitesimal dividing method. It is proved that the optimal holding and shortage costs of dual supplier procurement are less than those of single supplier procurement respectively. With the assumption that both suppliers have the same distribution of lead times, the convexity of the cost function per unit time is proved. So the optimal solution can be easily obtained by applying the classical convex optimization methods. The numerical examples are given to verify the main conclusions.

Multi-objective linear fractional inventory model with possibility and necessity constraints under generalised intuitionistic fuzzy set environment

This study presented a multi-objective linear fractional inventory (LFI) problem with generalised intuitionistic fuzzy numbers. In modelling, the authors have assumed the ambiances where generalised trapezoidal intuitionistic fuzzy numbers (GTIFNs) used to handle the uncertain information in the data. Then, the given multi-objective generalised intuitionistic fuzzy LFI model was transformed into its equivalent deterministic linear fractional programming problem by employing the possibility and necessity measures. Finally, the applicability of the model is demonstrated with a numerical example and the sensitivity analysis under several parameters is investigated to explore the study.

Inventory Model with Supply Disruption Risk and Service Level Constraint

We develop a newsvender model with service level constraint for oneproduct and multi-supplier system, where the supplies are susceptible to disruption risks. We prove the convexity of the programming problem,and provide the necessary and sufficient conditions for the optimal order quantities. Through analysis or numerical experiments we observe the effects of the disrupt risk and the service level constraint on the optimal policy. We propose some managerial insights in managing the multi-source supply chain with disruption risks.

Research on Three-Echelon Inventory Model and Algorithm for Valuable Spare Parts in Weapon Equipment

According to the current situation of repair and supply for valuable spare parts in the weapon equipment of the PLA,using the research results of the repairable spare parts inventory theory,an inventory model of the valuable spare parts in three-echelon repair and supply system is established by expanding the classical METRIC model and theory,and the genetic algorithm is utilized to solve the model.In the algorithm,the chromosome representation and initial population production,the crossover and mutation operators are designed.By using an example,a simulation analysis is carried out to verify the model's correctness.

Mathematical Modelling for a Multi-Product Inventory Routing Problem with Split Delivery

In this article, a multi-product inventory routing problem is studied. One-depot and many retailers in a finite time period are considered, and split delivery is allowed as well for the addressed problem. The objective is to minimize the overall cost including vehicle cost, inventory holding cost and transportation cost while the delivery schedule and the quantity of each product for each retailer have to be decided simultaneously. A mathematical model is presented for solving the addressed optimally and example is illustrated as well.

基于森林资源清查资料的盈江县森林生物量和生长量分析

为确定盈江县森林碳储量和碳汇潜力的变化特征及其影响因子,以便更好地分析盈江县森林生物量和生长量。基于盈江县2012年和2017年森林资源清查数据,利用生物量换算因子连续函数法和异速生长方程,评估盈江县森林碳储量和碳汇潜力的变化特征及其影响因子。结果显示:(1)盈江县森林生物量储量丰富,达85.55 t/hm^(2)。其中:栎类林最大,为168.3 t/hm^(2);核桃林最低,为7.10 t/hm^(2)。(2)不同林龄林分生物量差异较大,近熟林最高,其次分别为中龄林、成熟林、过熟林、幼龄林。(3)常绿阔叶林的林分生长量最大,其次是落叶阔叶林和针叶林。结果表明:盈江县森林生物量储量丰富且以阔叶林为主;林龄和年均气温是影响林分生长和生物量的主要因素。

基于MVC模式的医疗耗材管理系统设计

在医疗耗材管理中,整体管理方式比较滞后,造成大量耗材的浪费,为此,设计基于模型-视图-控制器(Model View Controller,MVC)模式的医疗耗材管理系统。基于MVC模式设计系统架构中的表现层。在系统硬件设计中,设计一种低功耗的处理器,由处理器芯片与电源管理单元构成。选用ASD-23型号的RFID节点,设计RFID阅读器。在系统软件设计中,设计档案管理单元、订单管理单元、查询统计单元构成管理模块,执行耗材管理的各种功能。设计供货商数据表、耗材数据表、入库表、供货商证照表等构成数据库模块。实验表明设计系统有着良好的健壮性、实时性、稳定性。

基于EOQ模型传统涉外制造企业物料库存管理研究

库存成本管理水平高低对企业经营成本有着直接影响,提高库存管理水平,有助于降低库存成本和经营成本。文章基于EOQ模型,以传统涉外制造企业DJ公司为例,选取物料面浆作为研究对象,通过对EOQ模型使用前后库存成本对比分析,发现采用EOQ模型后,面浆的库存量和库存总成本比原来有所降低。研究表明,科学合理采用EOQ模型有助于实现订货成本与维持成本最小化,降低企业经营成本,促进企业可持续发展。

黄河流域水环境问题研究现状、挑战与展望

黄河流域生态保护和高质量发展是国家重大发展战略,保护黄河是事关中华民族伟大复兴和永续发展的千秋大计.近年来,黄河流域水环境质量明显改善,但有限的水资源和脆弱的生态系统使得黄河流域水环境保护仍面临巨大的挑战.科学诊断黄河流域重大水环境问题是新时期治理黄河的前提和关键.本文围绕黄河流域水环境问题诊断这一主题,聚焦黄河流域断面水质改善的核心需求,首先分析黄河流域水环境现状,发现黄河流域整体水质持续向好,但干支流水质改善不同步,流域主要污染指标时空差异显著,中下游和部分支流污染严重;其次梳理国内外相关研究进展和主要挑战,总结断面水质提升所需的核心理论与技术,如水环境承载力评价、污染排放清单构建与水环境模型模拟等方面的应用与发展趋势,发现黄河流域存在气候变化影响下水沙变化大、现有排放清单分辨率不足、水环境模拟缺乏多尺度污染排放-断面水质响应关系研究等问题,难以支撑未来精细化水环境管理.对黄河流域水环境研究的未来发展进行了展望,建议未来在以下几个方面开展进一步研究,包括气候变化下黄河流域水环境承载力研究、黄河流域高分辨率排放清单构建、多尺度污染排放与断面水质响应关系.

昆明市高速公路机动车温室气体排放清单及时空性分布特征

为研究昆明市高速公路机动车的CO、CO_(2)、N_(2)O、CH_(4)温室气体排放清单,使用2021年昆明市高速公路客车交通流量数据、机动车GPS信息数据获得了高速公路网上的车型构成、车流量等基础数据,应用本土化修正后MOVES模型计算了昆明市高速公路的机动车CO、CO_(2)、N_(2)O、CH_(4)排放因子。基于实际交通流量数据、温室气体排放因子和昆明市高速公路实际道路信息,构建了昆明市高速公路机动车温室气体排放清单,并对其排放特征以及空间分布特征进行分析。结果表明:昆明市2021年高速公路机动车CO、CO_(2)、N_(2)O和CH_(4)的排放量分别为20337.1、2575677.1、33.8和72.9 t,总计CO_(2)当量为2626212.5 t。按排放标准划分,国Ⅳ排放标准的机动车是4种温室气体排放的主要贡献车型;按车辆类型划分,小型客车是CO、CO_(2)、N_(2)O排放的主要贡献车型,大型客车是CH_(4)排放的主要贡献车型;按燃料类型划分,汽油车是CO、CO_(2)、N_(2)O的主要贡献车型,柴油车是CH_(4)排放的主要贡献车型。昆明市高速公路机动车温室气体排放时间分布特征为排放强度与不同时间段的交通流量呈正相关,在24 h内呈现双高峰变化;空间分布特征为排放强度与路网密度和区域交通流量密切相关,路网密度较高和交通流量较高的区域排放强度较高。

改进PSO算法的多级可修备件库存优化

针对可修备件多级库存的METRIC模型边际优化算法效果不高问题,提出了一种改进粒子群算法来求解模型。在分析可修备件供应保障过程的基础上,建立了METRIC模型,以保障费用最低为优化目标,以系统可用度为约束条件,用改进的粒子群算法寻找备件在各级仓库间的最优分配方案。改进的粒子群算法对惯性权重和学习因子进行了动态调整,并加入罚函数提高了算法收敛速度和寻优结果,最后通过算例分析对比了边际优化算法。结果表明,改进的粒子群算法对比边际优化算法,在同样的可用度约束下,保障费用降低了5.8%,在解决装备可修备件多级库存优化中具有一定的优越性。

Models-3模式中增加移动源清单对模拟效果的影响

采用空气质量模式Models-3,在原有点源和面源污染源输入清单的基础上,加入移动污染源,对辽宁地区2004年12月24—27日大气污染物时空分布进行数值模拟。结果表明:加入移动污染源后,辽宁主要城市污染物SO2、NO2和PM10模拟效果有所提高,NO2模拟效果提高最为显著;从总体模拟效果来看,SO2和NO2模拟效果较好,而PM10模拟值相对较差。

基于LSTM-LightGBM模型的烟草存销比层次化预测方法

基于LSTM和LightGBM算法,结合零售商的地理位置和档位信息,构建了一个层次化模型,用于准确预测烟草产品的存销比。该模型首先利用LSTM网络对不同地区和档位的整体存销比进行预测。随后,将得到的整体存销比作为LightGBM的辅助输入,用于预测个体零售商销售的每一类卷烟的存销比。本模型逐步融合数据的宏观和微观特征,对某地烟草实际销售数据的实验结果表明,LSTM-LightGBM模型具有较好的预测精度。

考虑库存风险的服装行业新产品预售策略博弈研究

电子商务时代,服装行业线上销售的退货率居高不下。为缓解高退货率连带的库存风险,部分服装企业采用超长预售策略,在相对较长的预售期内次第发货满足顾客订单。在此现实背景下,文章基于报童模型,探讨商家在正常预售和超长预售中的策略选择,并求解两种策略的最优定价、产量、利润和可行条件。结果表明:当需求不确定性较大且产品残值较小,预售时长在合理的范围内且顾客更愿意等待时,应选择超长预售策略,反之选择正常预售策略;并且,超长预售策略能够显著降低库存剩余风险。研究结论对服装企业的预售决策和库存管理具有一定的现实参考价值。

集成清单算法和STIRPAT模型的能源系统碳排放影响因素研究——以四川省为例

立足燃料燃烧和区域电力交易两大单元构建能源系统碳排放核算方法,基于四川省2000—2021年的实际数据,结合拓展后的STIRPAT模型实证分析人口规模、经济发展、技术创新、能源结构和产业结构对碳排放的差异化影响效应。研究结果表明:①化石燃料燃烧是能源系统关键碳排放源,其中工业终端能源消费占据主导地位,交通运输业和居民生活碳足迹也不容忽略;区域电力交易在保障能源供应安全的同时间接推动中国低碳发展进程。②人口规模通过人群和文明效应驱动碳排放,未来影响程度取决于多孩生育政策对人口负增长趋势的扭转成效;经济发展与能源消费有待脱钩,考虑到经济上升期现状和“污染避难所”现象,其仍为碳排放关键驱动因素;经费投入增加推动节能降碳领域科研成果产出及应用,并通过技术驱动和创新补偿降低碳排放水平;能源结构高碳特征对温室效应贡献显著,立足资源禀赋的消费结构转型有助于降低排放强度;第二产业在国民经济中的主体地位带动能源系统碳排放显著增加,近年来以高新技术产业为代表的第三产业占比提升有效控制了高污染、高能耗产业的碳排放。