The Feasibility of Composted Agricultural Castoffs as Substitute for Peat in Pot Flower Production

Zhang Qixiang,Liu Qingchao,Wang Kuiling,Liu Qinghua,Pan Huitang,Liang Shule 한국원예학회 2006 한국원예학회 기타간행물 Vol.- No.-

Germplasm Resources and Breeding of Primula in China

Zhang Qixiang,Liang Shule,Liu Qingchao,Pan Huitang,Jin Xiaoxia,Zhang Yanli,Zhang Ruili 한국원예학회 2006 한국원예학회 기타간행물 Vol.- No.-

Four-year changes in central fatness, risk of diabetes, and metabolic control in older adults: a cohort study with mediation analysis

( Xue Cai ),( Dan Luo ),( Shuling Liu ),( Ruxue Li ),( Yanhui Lu ),( Mingzi Li ),( Shanhu Qiu ) 대한내과학회 2022 The Korean Journal of Internal Medicine Vol.37 No.1

Background/Aims: Older adults are vulnerable to central obesity, while the association of changes in central fatness with risk of diabetes and metabolic control has not been investigated among this particular population. This study was aimed to address these issues. Methods: A total of 1,815 adults aged ≥ 60 years without diabetes at baseline were followed for 4 years. Incident diabetes was ascertained based on plasma glucose, hemoglobin A1c, medical history, and/or the use of anti-diabetic drugs. Central fatness was assessed by waist circumference (WC), waist-height ratio (WHtR), and body roundness index (BRI). Logistic regression analyses were used to assess the association of changes in central fatness with risk of diabetes, along with dose-response and mediation analyses. Results: During the 4-year follow-up, 177 participants developed diabetes. The risk of diabetes was increased by 42%, 41%, and 40% per 1 standard deviation increases in WC, WHtR, and BRI, respectively, in multivariable-adjusted models (all p < 0.01). Moreover, these relationships were all linearly-shaped (all p_nonlinearity ≥ 0.11). Increases in WC, WHtR, and BRI correlated with increases in hemoglobin A1c, triglycerides-and-glucose index, triglycerides, white blood cell, and C-reactive protein (all p ≤ 0.04). Yet only changes in hemoglobin A1c and triglycerides-and-glucose index were identified as the possible mediators for risk of diabetes, with their mediating effect being about 35% and 21%, respectively. Conclusions: Increases in central fatness were related to elevated risk of diabetes, and this association might be partly explained by the worsening of glycemic control and insulin resistance in older adults.

Construction of E-Commerce Entrepreneurship Curriculum System for College Students: An Information Entrepreneurship Perspective

Yan Hou,Dawei Liu,Shuling Yang 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.11

With the rapid development of Electronic Commerce, the demand for the quantity and professional knowledge of the professional talents in electronic commerce is continuously improving. The development of network market provides an opportunity for college students to start their own business, and the ultimate goal of e-commerce business entrepreneurship is to cultivate students' ability to use e-commerce to start a business. In this paper, the authors analyze the construction of e-commerce entrepreneurship curriculum system for college students based on information technology perspective. In the curriculum system construction, the course is divided into three levels; the first level is mainly about the basic knowledge of e-business, second level is mainly about business development course, and the third levels of curriculum mainly about laws and regulations. Establish and perfect the innovation and entrepreneurship curriculum system, and cultivate the students' awareness of online entrepreneurship is a development trend in the future.

Genetic Algorithm-Based Coordinated Replenishment in Multi-Item Inventory Control

Keisuke Nagasawa,Takashi Irohara,Yosuke Matoba,Shuling Liu 대한산업공학회 2013 Industrial Engineeering & Management Systems Vol.12 No.3

We herein consider a stochastic multi-item inventory management problem in which a warehouse sells multiple items with stochastic demand and periodic replenishment from a supplier. Inventory management requires the timing and amounts of orders to be determined. For inventory replenishment, trucks of finite capacity are available. Most inventory management models consider either a single item or assume that multiple items are ordered independently, and whether there is sufficient space in trucks. The order cost is commonly calculated based on the number of carriers and the usage fees of carriers. In this situation, we can reduce future shipments by supplementing items to an order, even if the item is not scheduled to be ordered. On the other hand, we can reduce the average number of items in storage by reducing the order volume and at the risk of running out of stock. The primary variables of interest in the present research are the average number of items in storage, the stock-out volume, and the number of carriers used. We formulate this problem as a multi-objective optimization problem. In a numerical experiment based on actual shipment data, we consider the item shipping characteristics and simulate the warehouse replenishing items coordinately. The results of the simulation indicate that applying a conventional ordering policy individually will not provide effective inventory management.

Applying Genetic Algorithm for Can-Order Policies in the Joint Replenishment Problem

Keisuke Nagasawa,Takashi Irohara,Yosuke Matoba,Shuling Liu 대한산업공학회 2015 Industrial Engineeering & Management Systems Vol.14 No.1

In this paper, we consider multi-item inventory management. When managing a multi-item inventory, we coordinate replenishment orders of items supplied by the same supplier. The associated problem is called the joint replenishment problem (JRP). One often-used approach to the JRP is to apply a can-order policy. Under a can-order policy, some items are re-ordered when their inventory level drops to or below their re-order level, and any other item with an inventory level at or below its can-order level can be included in this order. In the present paper, we propose a method for finding the optimal parameter of a can-order policy, the can-order level, for each item in a lost-sales model. The main objectives in our model are minimizing the number of ordering, inventory, and shortage (i.e., lost-sales) respectively, compared with the conventional JRP, in which the objective is to minimize total cost. In order to solve this multi-objective optimization problem, we apply a genetic algorithm. In a numerical experiment using actual shipment data, we simulate the proposed model and compare the results with those of other methods.

Selecting Ordering Policy and Items Classification Based on Canonical Correlation and Cluster Analysis

Keisuke Nagasawa,Takashi Irohara,Yosuke Matoba,Shuling Liu 대한산업공학회 2012 Industrial Engineeering & Management Systems Vol.11 No.2

It is difficult to find an appropriate ordering policy for a many types of items. One of the reasons for this difficulty is that each item has a different demand trend. We will classify items by shipment trend and then decide the ordering policy for each item category. In this study, we indicate that categorizing items from their statistical characteristics leads to an ordering policy suitable for that category. We analyze the ordering policy and shipment trend and propose a new method for selecting the ordering policy which is based on finding the strongest relation between the classification of the items and the ordering policy. In our numerical experiment, from actual shipment data of about 5,000 items over the past year, we calculated many statistics that represent the trend of each item. Next, we applied the canonical correlation analysis between the evaluations of ordering policies and the various statistics. Furthermore, we applied the cluster analysis on the statistics concerning the performance of ordering policies. Finally, we separate items into several categories and show that the appropriate ordering policies are different for each category.

Applying Genetic Algorithm for Can-Order Policies in the Joint Replenishment Problem

Nagasawa, Keisuke,Irohara, Takashi,Matoba, Yosuke,Liu, Shuling Korean Institute of Industrial Engineers 2015 Industrial Engineeering & Management Systems Vol.14 No.1

In this paper, we consider multi-item inventory management. When managing a multi-item inventory, we coordinate replenishment orders of items supplied by the same supplier. The associated problem is called the joint replenishment problem (JRP). One often-used approach to the JRP is to apply a can-order policy. Under a can-order policy, some items are re-ordered when their inventory level drops to or below their re-order level, and any other item with an inventory level at or below its can-order level can be included in this order. In the present paper, we propose a method for finding the optimal parameter of a can-order policy, the can-order level, for each item in a lost-sales model. The main objectives in our model are minimizing the number of ordering, inventory, and shortage (i.e., lost-sales) respectively, compared with the conventional JRP, in which the objective is to minimize total cost. In order to solve this multi-objective optimization problem, we apply a genetic algorithm. In a numerical experiment using actual shipment data, we simulate the proposed model and compare the results with those of other methods.

Genetic Algorithm-Based Coordinated Replenishment in Multi-Item Inventory Control

Nagasawa, Keisuke,Irohara, Takashi,Matoba, Yosuke,Liu, Shuling Korean Institute of Industrial Engineers 2013 Industrial Engineeering & Management Systems Vol.12 No.3

We herein consider a stochastic multi-item inventory management problem in which a warehouse sells multiple items with stochastic demand and periodic replenishment from a supplier. Inventory management requires the timing and amounts of orders to be determined. For inventory replenishment, trucks of finite capacity are available. Most inventory management models consider either a single item or assume that multiple items are ordered independently, and whether there is sufficient space in trucks. The order cost is commonly calculated based on the number of carriers and the usage fees of carriers. In this situation, we can reduce future shipments by supplementing items to an order, even if the item is not scheduled to be ordered. On the other hand, we can reduce the average number of items in storage by reducing the order volume and at the risk of running out of stock. The primary variables of interest in the present research are the average number of items in storage, the stock-out volume, and the number of carriers used. We formulate this problem as a multi-objective optimization problem. In a numerical experiment based on actual shipment data, we consider the item shipping characteristics and simulate the warehouse replenishing items coordinately. The results of the simulation indicate that applying a conventional ordering policy individually will not provide effective inventory management.

Selecting Ordering Policy and Items Classification Based on Canonical Correlation and Cluster Analysis

Nagasawa, Keisuke,Irohara, Takashi,Matoba, Yosuke,Liu, Shuling Korean Institute of Industrial Engineers 2012 Industrial Engineeering & Management Systems Vol.11 No.2

It is difficult to find an appropriate ordering policy for a many types of items. One of the reasons for this difficulty is that each item has a different demand trend. We will classify items by shipment trend and then decide the ordering policy for each item category. In this study, we indicate that categorizing items from their statistical characteristics leads to an ordering policy suitable for that category. We analyze the ordering policy and shipment trend and propose a new method for selecting the ordering policy which is based on finding the strongest relation between the classification of the items and the ordering policy. In our numerical experiment, from actual shipment data of about 5,000 items over the past year, we calculated many statistics that represent the trend of each item. Next, we applied the canonical correlation analysis between the evaluations of ordering policies and the various statistics. Furthermore, we applied the cluster analysis on the statistics concerning the performance of ordering policies. Finally, we separate items into several categories and show that the appropriate ordering policies are different for each category.