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Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional optimization-based approaches do not scale to large networks, and (2) the design of good heuristics or approximation algorithms often requires significant manual trial-and-error. In this work, we argue that data-driven strategies can automate this process and learn efficient algorithms without compromising optimality. To do so, we present network control problems through the lens of reinforcement learning and propose a graph network-based framework to handle a broad class of problems. Instead of naively computing actions over high-dimensional graph elements, e.g., edges, we propose a bi-level formulation where we (1) specify a desired next state via RL, and (2) solve a convex program to best achieve it, leading to drastically improved scalability and performance. We further highlight a collection of desirable features to system designers, investigate design decisions, and present experiments on real-world control problems showing the utility, scalability, and flexibility of our framework., 9 pages, 4 figures

Although Connected Vehicles (CVs) have demonstrated tremendous potential to enhance traffic operations, they can impose privacy risks on individual travelers, e.g., leaking sensitive information about their frequently visited places, routing behavior, etc. Despite the large body of literature that devises various algorithms to exploit CV information, research on privacy-preserving traffic control is still in its infancy. In this paper, we aim to fill this research gap and propose a privacy-preserving adaptive traffic signal control method using CV data. Specifically, we leverage secure Multi-Party Computation and differential privacy to devise a privacy-preserving CV data aggregation mechanism, which can calculate key traffic quantities without any CVs having to reveal their private data. We further develop a linear optimization model for adaptive signal control based on the traffic variables obtained via the data aggregation mechanism. The proposed linear programming problem is further extended to a stochastic programming problem to explicitly handle the noises added by the differentially private mechanism. Evaluation results show that the linear optimization model preserves privacy with a marginal impact on control performance, and the stochastic programming model can significantly reduce residual queues compared to the linear programming model, with almost no increase in vehicle delay. Overall, our methods demonstrate the feasibility of incorporating privacy-preserving mechanisms in CV-based traffic modeling and control, which guarantees both utility and privacy.

Immune checkpoint inhibitors (ICIs) have been an encouraging treatment method in non-small cell lung cancer (NSCLC). However, bone and liver metastases are considered to restrain immunotherapy efficacy. Since serum alkaline phosphatase (ALP) is associated with bone and liver metastases, it was investigated whether serum ALP could be a novel biomarker to predict the efficacy of ICIs treatment. In the present study, 143 patients with NSCLC receiving ICIs treatment were retrospectively analyzed. The objective response rate (ORR) was compared between the ALP high and low groups, bone metastasis and non-bone metastasis groups, and liver metastasis or non-liver metastasis groups. The associations between clinical characteristics, including ALP level, bone or liver metastasis and median progression-free survival (mPFS) time were analyzed by univariate and multivariate Cox regression analysis. It was found that bone metastasis was associated with a lower ORR (24 vs. 43%; P

Using Chinese listed companies as the research setting, this study investigates the impact of climate risk on corporate precautionary cash holdings and further explores possible underlying channels. We first apply the text mining technique to construct the climate risk indicator. The regression results then show that climate risk has a significant and positive impact on corporate precautionary cash holdings. Such positive relationship is stronger for firms with small size and those located in central and eastern China. Further mechanism analysis indicates that risk taking and external financing play a mediating effect between climate risk and corporate precautionary cash holdings. Our findings have important practical implications for companies to make sustainability strategies against potential climate risks.

Supplementary Material 2

Network routing problems are common across many engineering applications. Computing optimal routing policies requires knowledge about network demand, i.e., the origin and destination (OD) of all requests in the network. However, privacy considerations make it challenging to share individual OD data that would be required for computing optimal policies. Privacy can be particularly challenging in standard network routing problems because sources and sinks can be easily identified from flow conservation constraints, making feasibility and privacy mutually exclusive. In this paper, we present a differentially private algorithm for network routing problems. The main ingredient is a reformulation of network routing which moves all user data-dependent parameters out of the constraint set and into the objective function. We then present an algorithm for solving this formulation based on a differentially private variant of stochastic gradient descent. In this algorithm, differential privacy is achieved by injecting noise, and one may wonder if this noise injection compromises solution quality. We prove that our algorithm is both differentially private and asymptotically optimal as the size of the training set goes to infinity. We corroborate the theoretical results with numerical experiments on a road traffic network which show that our algorithm provides differentially private and near-optimal solutions in practice.

Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches. This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles. Despite efforts to provide robust predictions for semi-actuated signal control systems, predicting signal phase timings for fully-actuated controls remains challenging. This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. The performance of a Linear Regression (LR), a Random Forest (RF), and a Long-Short-Term-Memory (LSTM) neural network are assessed against a naive baseline model. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods. Furthermore, tree-based decision models such as the RF perform best with an accuracy that meets requirements for practical applications.

Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches. This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles. Despite efforts to provide robust predictions for semi-actuated signal control systems, predicting signal phase timings for fully-actuated controls remains challenging. This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. The performance of a Linear Regression (LR), a Random Forest (RF), and a Long-Short-Term-Memory (LSTM) neural network are assessed against a naive baseline model. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods. Furthermore, tree-based decision models such as the RF perform best with an accuracy that meets requirements for practical applications.

Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches. This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles. Despite efforts to provide robust predictions for semi-actuated signal control systems, predicting signal phase timings for fully-actuated controls remains challenging. This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. The performance of a Linear Regression (LR), a Random Forest (RF), and a Long-Short-Term-Memory (LSTM) neural network are assessed against a naive baseline model. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods. Furthermore, tree-based decision models such as the RF perform best with an accuracy that meets requirements for practical applications., arXiv

The shock tube can form a good plane wave, The cryogenic shock tube experiment has more phenomena than the room temperature shock tube experiment, evaporation and condensation will occur. The cryogenic experiment of shock tube is very important. Low temperature experiments verified the relationship between the condensing film and shock wave. With the development of aerospace technology, there is a lack of experimental devices for cryogenic shock wave. In this paper, a diaphragm less shock-absorbing tube with higher working efficiency is used. The low-pressure part of the diaphragm less shock tube is designed as a cryogenic device, and the low-temperature characteristics of the shock wave in the shock tube are studied. Finally, fluent is used to numerically simulate the shock wave flow state.

This study proposes a novel flexible bus dispatching system where a fleet of fully automated modular bus units, together with conventional buses, serves the passenger demand. These modular bus units can either operate individually or combined (forming larger modular buses with a higher passenger capacity). This provides enormous flexibility to manage the service frequencies and vehicle allocation, reducing thereby the operating cost and improving passenger mobility. We develop an optimization model used to determine the optimal composition of bus units and the optimal service frequency at which the buses (both conventional and modular) are dispatched across each bus line. We explicitly account for the dynamics of traffic congestion and complex interactions between the modes at the network level based on the recently proposed three-dimensional macroscopic fundamental diagram (3D-MFD). To the best of our knowledge, this is the first application of the 3D-MFD and modular bus units for the frequency setting problem in the domain of bus operations. Numerical results show the improvements in the total system cost made by adapting the number of combined modular bus units and their dispatched frequencies to the evolution of both, the car and the passenger demand. A comparison with the commonly used approach that considers only the bus system (neglecting the complex multimodal interactions and congestion propagation) reveals the value of the proposed modeling framework., SVT Working Papers

The involvement of digital technology has changed the transportation domain significantly in the last decade. The availability of several new data sources (i.e., sensor technology or vehicle technology) postulates for data-driven methodologies that can be incorporated into well-established traffic management systems on a macro- and micro-scopic level. Furthermore, the upcoming developments,such as Vehicle-to-Infrastructure (V2I), open the door for new approaches that allow considering communication between vehicles and infrastructure. Recent evolution in traffic signal control of urban intersections (e.g., actuated signal control, self-control algorithms, etc.) influence the signal phases and result in variable green, red and cycle times. Hence, speed advisory systems would benefit from the information about when the next green phase starts so that vehicles do not have to stop when crossing an intersection. Nevertheless, predictions for residual times of these quantities are not trivial and require a sophisticated modeling approach.

Automated vehicles (AVs) are expected to be beneficial for Mobility-on-Demand (MoD), thanks to their ability of being globally coordinated. To facilitate the steady transition towards full autonomy, we consider the transition period of AV deployment, whereby an MoD system operates a mixed fleet of automated vehicles (AVs) and human-driven vehicles (HVs). In such systems, AVs are centrally coordinated by the operator, and the HVs might strategically respond to the coordination of AVs. We devise computationally tractable strategies to coordinate mixed fleets in MoD systems. Specifically, we model an MoD system with a mixed fleet using a Stackelberg framework where the MoD operator serves as the leader and human-driven vehicles serve as the followers. We develop two models: 1) a steady-state model to analyze the properties of the problem and determine the planning variables (e.g., compensations, prices, and the fleet size of AVs), and 2) a time-varying model to design a real-time coordination algorithm for AVs. The proposed models are validated using a case study inspired by real operational data of a MoD service in Singapore. Results show that the proposed algorithms can significantly improve system performance.

Kaidi Yang,1,2 Jian Gao,3 Mao Luo1,4,5 1Key Laboratory of Medical Electrophysiology of Ministry of Education, Collaborative Innovation Center for Prevention and Treatment of Cardiovascular Disease of Sichuan Province, Southwest Medical University, Luzhou, China; 2Key Laboratory of Tumor Immunology, The First Affiliated Hospital, Army Medical University, Chongqing, China; 3Department of Life Sciences and Technology, Yangtze Normal University, Chongqing, China; 4Drug Discovery Research Center, Southwest Medical University, Luzhou, Sichuan, China; 5Laboratory for Cardiovascular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, Sichuan, China Background: Basal-like breast cancer (BLBC) is the most aggressive subtype of breast cancer (BC) and links to poor outcomes. As the molecular mechanism of BLBC has not yet been completely discovered, identification of key pathways and hub genes of this disease is an important way for providing new insights into exploring the mechanisms of BLBC initiation and progression.Objective: The aim of this study was to identify potential gene signatures of the development and progression of the BLBC via bioinformatics analysis. Methods and results: The differential expressed genes (DEGs) including 40 up-regulated and 21 down-regulated DEGs were identified between GSE25066 and GSE21422 microarrays, and these DEGs were significantly enriched in the terms related to oncogenic or suppressive roles in BLBC progression. In addition, KEGG pathway and GSEA (Gene Set Enrichment Analysis) enrichment analyses were performed for DEGs between the basal type and non-basal-type breast cancer from GSE25066 microarray. These DEGs were enriched in pathways such as cell cycle, cytokine-cytokine receptor interaction, chemokine signaling pathway, central carbon metabolism signaling and TNF signaling pathway. Moreover, the protein-protein interaction (PPI) network was constructed with those 61 DEGs using the Cytoscape software, and the biological significance of putative modules was established using MCODE. The module 1 was found to be closely related with a term of mitosis regulation and enriched in cell cycle pathway, and thus confirmed the pathological characteristic of BLBC with a high mitotic index. Furthermore, prediction values of the top 10 hub genes such as CCNB2, BUB1, NDC80, CENPE, KIF2C, TOP2A, MELK, TPX2, CKS2 and KIF20A were validated using Oncomine and Kaplan-Meier plotter. Conclusion: Our results suggest the intriguing possibility that the hub genes and modules in the PPI network contributed to in-depth knowledge about the molecular mechanism of BLBC, paving a way for more accurate discovery of potential treatment targets for BLBC patients. Keywords: basal-like breast cancer, bioinformatics, differentially expressed genes, hub genes, molecular mechanism &nbsp

Background oriented schlieren technology is developed from the combination of schlieren technology and PIV technology. It is an emerging non-contact flow field quantitative measurement method. The development and principle of background oriented schlieren technology are introduced in detail. A set of background oriented schlieren imaging system suitable for natural thermal convection field is built. The quantitative measurement research is carried out on the natural thermal convection field above the candle flame, and the density field is calculated. Distorted vector displacement map. The results show that the background oriented schlieren technology can be better applied to the quantitative measurement of natural thermal convection field, and it has broad development prospects.

A method for purifying and preparing rebaudioside A (RA) from steviol glycosides at preparative scale was developed with resin based on hydrophilic interaction liquid chromatography (HILIC). In pure water and acetone-water system, the adsorption capacity and selectivity of five anion resins for RA, rebaudioside C (RC) and stevioside (ST) were examined and discussed. Strongly basic IRA458 with the quaternary amine group was chosen as the resin used for separating and preparing RA. The hydroxide form of IRA458 (IRA458-OH) showed the best results in terms of the adsorption behaviors for RA, RC and ST. The retentions of RA, RC and ST on IRA458-OH resin at high concentration of acetone solution followed HILIC mode, in which retention is probably based on surface adsorption of the resin. Under optimized chromatographic conditions, the pooled purity and yield of RA were up to 98.18 and 98.62%, the relative standard deviations (n = 3) for these two parameters were 1.2 and 5.7%, respectively. The present method has the characteristics of simple, low cost, high purity and high yield. The study will be a promising tool for RA industrialized production and also provides a possible mode for purifying and preparing polar components from their analogs.

The simultaneous determination of two binding parameters for metal ions on an immobilized metal affinity chromatography column was performed by frontal chromatography. In this study, the binding parameters of Cu(2+) to l-glutamic acid were measured, the metal ion-binding characteristics of the complex ligand were evaluated. The linear correlation coefficients were all greater than 99%, and the relative standard deviations of two binding parameters were 0.58 and 0.059%, respectively. The experiments proved that the frontal chromatography method was accurate, reproducible, and could be used to determine the metal-binding parameters of the affinity column. The effects of buffer pH, type, and concentration on binding parameters were explored by uniform design experiment. Regression, matching and residual analyses of the models were performed. Meanwhile, the optimum-binding conditions of Cu(2+) on the l-glutamic acid-silica column were obtained. Under these binding conditions, observations and regression values of two parameters were similar, and the observation values were the best. The results demonstrated that high intensity metal affinity column could be effectively prepared by measuring and evaluating binding parameters using frontal chromatography combined with a uniform design experiment. The present work provided a new mode for evaluating and preparing immobilized metal affinity column with good metal-binding behaviors.

Shock wave is an important physical phenomenon in supersonic airflow. The shock tube is an experimental device that generates shock wave. It generates shock wave by generating a pressure difference between its high-pressure section and low-pressure section. Most researchers in the world use diaphragm shock tubes to generate shock wave, such as single diaphragm structure and double diaphragms structure type shock tubes. Although the type of shock tube can generate an incident shock wave with a wide range of Mach numbers, in the process of producing shock wave and breaking film, there will be broken film fragments interfering with the flow field. And every time the researcher needs to replace the diaphragm after the experiment, the efficiency of the experiment is low. In this paper, a diaphragmless shock tube is designed. The high-speed reciprocating motion of two pistons in the high-pressure section is used to replace the process of membrane rupture. And the normal temperature characteristics of the shock tube are researched.

Verflechtungszonen, in denen ein Ein- und Ausfädelungsbereich unmittelbar aufeinanderfolgen, gelten als entscheidende Engpässe im Autobahnnetz. In diesem Artikel wird ein Modellansatz beschrieben, mit welchem der Einfluss automatisierten Fahrens auf die Kapazität von Verflechtungsbereichen untersucht wird. Das anhand empirischer Daten validierte Modell wird in einer Simulationsstudie eingesetzt, um mögliche Kapazitätssteigerungen abschätzen zu können. Die Ergebnisse zeigen das Potenzial automatisierten Fahrens zur Erhöhung der Kapazität von besagten Autobahnabschnitten auf., Sur le réseau autoroutier, les zones de cisaillement, dans lesquelles les zones d’entrecroisement des véhicules se suivent directement, sont considérées comme des goulots d’étranglement majeurs. Le présent article décrit un modèle permettant d’étudier l’impact de la conduite automatisée sur la capacité des zones de cisaillement. Ce modèle, validé au moyen de données empiriques, est utilisé dans le cadre d’une étude de simulation dont le but est d’estimer les augmentations possibles de la capacité. Les résultats montrent le potentiel d’amélioration de la capacité des dites sections d’autoroute grâce à la conduite automatisée., Strasse und Verkehr, 104 (11), ISSN:0039-2189

Current evidence shows that commercialization and deployment of connected and autonomous vehicles will most likely first happen in the public transport sector, before their general adoption by private cars. This paper examines such a deployment scenario for a bi-modal highway system with an automated highway lane, which is originally dedicated to autonomous buses. We develop a control framework to allow additional autonomous cars to share this automated lane while not affecting the operations of the buses. Our proposed control strategy can optimize the benefits offered by the autonomous vehicles at wide range of penetration rates, while significantly increase the road space efficiency of the bi-modal system.

The promising development towards vehicle connectivity and autonomy will impose substantial societal and economic impact not only on the mobility systems but also on the entire cities. This dissertation will, from the perspective of the traffic operators, design effective traffic estimation and control strategies to maximize the benefits of these technologies for the urban traffic systems. We will propose both methodological frameworks and pragmatic guidelines to address this timely and relevant research topic. The main contributions of this dissertation are three-fold. First, this dissertation is among the first to handle the challenging issues in the transition period where vehicles with various technologies coexist in the traffic system (i.e. conventional, connected, and automated vehicles). We propose more accurate queue estimation methods that better exploit the limited information provided by connected vehicles, and develop efficient and robust control strategies to handle the uncertainties due to low penetration rates. Second, this dissertation develops a novel control framework for large-scale urban traffic networks at both the local and network levels. At the local level, we integrate traffic control at signalized intersections with trajectory design of automated vehicles. At the network level, we develop a multi-scale perimeter control strategy that fills two important research gaps: i) how the macroscopic control decisions can be translated into microscopic variables, and ii) how the control objectives at different levels can be synthesized. Third, this dissertation explicitly integrates priority schemes into the proposed control strategies, considering the interactions between different groups of vehicles (i.e. vehicles corresponding to different transportation modes, or with different occupancies, values of time, priority levels, etc.). Part I addresses traffic estimation in a connected vehicle environment, with a particular focus on queue estimation at signalized intersections. The queue estimation results facilitate trajectory reconstruction and thus provide a holistic picture of the urban traffic system. These results also serve as essential inputs to traffic control and management strategies. In this part, we propose a computationally efficient methodology based on a convex optimization to exploit the information provided by connected vehicles. We fill the research gaps in two aspects. First, we relax the widely adopted assumption of uniform demand in a signal cycle. Second, we further reuse the information provided by upstream intersections in order to improve the performance of the algorithm. Simulation results show that the proposed strategy significantly improves the estimation accuracy with a reasonable solution time (0.8s), sufficient for most real-time applications. Results further show that the proposed algorithm is able to handle scenarios with penetration rates as low as 0.1 and is also robust to measurement noises. Part II and Part III investigate advanced traffic control strategies exploiting the information and flexibility provided by connected and automated vehicles at both the local (Part II) and network (Part III) levels, and develop a multi-scale, multi-modal, and multi-technological control framework to maximize the potential of these technologies in urban traffic systems. In each part, we address the challenging issues in the transition period and study how the priority schemes can be integrated into the control strategies to differentiate different vehicle types. In part II, we develop a bi-level optimization based strategy to integrate traffic signal timing and the trajectory planning of automated vehicles at local intersections. We further develop heuristics to switch between different signal control algorithms as the technology evolves. Simulation results show an evident decrease in the total number of stops and delay even when the penetration rate of the connected vehicles is lower than 50%. We further extend the proposed strategy to account for transit signal priority, considering bus stops and bus schedule. Simulation results show that such extension successfully improves passenger mobility over the original strategy. Results also demonstrate that it is valuable to consider bus stops, especially when they are near-side. In part III, we develop a multi-scale perimeter control strategy based on a Model Predictive Control (MPC) algorithm where the network-level decision can be optimally distributed to local-level perimeter intersections to synthesize the competing objectives of both levels. Connected vehicles are assumed to be the only source of information. Simulation results show that the proposed strategy optimizes the performance at both the network and the perimeter intersections, providing much better outputs than the classical controllers. The multi-scale perimeter control strategy is further extended to a stochastic MPC that explicitly handles the uncertainties due to low penetration rates of connected and automated vehicles. It is shown that the total travel is significantly reduced by applying such stochastic MPC. This work is then integrated with priority lanes to prioritize certain groups of vehicles to maximize social welfare. Results show that by introducing the priority scheme, the social welfare can be improved. The proposed estimation and control strategies in this dissertation provide insights on how the emerging technologies can be employed to build a more efficient and flexible urban multimodal urban transportation system. The findings serve as the cornerstone for some promising directions of future research, including the integration with special infrastructure, new mobility systems, heterogeneous data sources, multiple control strategies (e.g. routing), cyber-security, and interactions with other systems (e.g. logistics). This dissertation can be beneficial for traffic managers, local authorities, practitioners, and the automotive industry.

This paper proposes a convex optimization based algorithm for queue profile estimation in a connected vehicle environment, which can also be used for trajectory reconstruction, delay evaluation, etc. This algorithm generalizes the widely-adopted assumption of a linear back of queue (BoQ) curve to a piecewise linear BoQ curve to consider more practical scenarios. The piecewise linear BoQ curve is estimated via a convex optimization model, ensuring efficient computation. Moreover, this paper explicitly handles cases with low penetration rates and low sampling rates, as well as measurement noises. Additionally, the proposed methodology is extended to an urban arterial, reusing the estimated departure information from the upstream intersections to further improve the estimation accuracy. Finally, two online implementation approaches are presented to perform real-time queue estimation. The proposed methodology is tested with two datasets: the Lankershim dataset in the NGSIM project and the simulated dataset of Wehntalerstrasse, Zurich, Switzerland. Results show that the error is less than 1.5 cars in undersaturated scenarios and 5.2 cars in oversaturated scenarios if the penetration rates are larger than 0.1 and sampling rates are higher than 0.05s-1. It is demonstrated that by considering a piecewise linear BoQ curve, the estimation accuracy can be improved by up to 16%. Incorporating flow successfully can also reduce the estimation error by up to 16\%. Results further show that the proposed methodology is robust to measurement errors. It is finally shown that the proposed framework can be solved within a reasonable time (0.8s), which is sufficient for most real-time applications., SVT Working Paper, 352

The emerging vehicle technologies, i.e. connected vehicle technology and autonomous driving technology, can be beneficial for traffic control and operations. They not only serve as new source of information, but also enable us to control and modify the trajectory of vehicles. Reinforcement learning is widely used to design intelligent control algorithms in various disciplines. This paper provides preliminary results on how the reinforcement learning methods perform in a connected vehicle environment.

During pretreatment process for oil sample, charring and ashing processes of oil in AOCS Official Method Ca 12-55 were improved. Oil was pretreated with concentrated sulfuric acid and potassium hydroxide instead of zinc oxide. As a result, soluble phosphate was obtained in a short time, the subsequent steps were also simplified. Firstly, phosphorus contents were measured, then the equivalent phosphatide contents in oil samples were calculated, RSD (relative SD) was only 1.03%. According to the spiking experiments at low, middle and high concentration levels, recoveries were between 97.03 and 100.99%, RSD were all less than 1.57% (n = 5). The method was applied for determining the equivalent phosphatide content of different types of oils. Compared with AOCS Official Method Ca 12-55, the improved method can provide a more effective means for detecting and analyzing the phosphorus or the equivalent phosphatide content in vegetable oils. Practical applications: In theory, the applicable scope of the improved method should be consistent with that of AOCS Official Method Ca 12-55, that is, the method is also suitable for detecting phosphorus or the equivalent phosphatide content in crude, degummed and refined vegetable oils. However, the promotion of the method should be based on more experimental data from various vegetable oils.

Starting from the actual demand of logistics enterprise information management, vehicle scheduling problem model was established and the solving method of model was found based on the ant colony algorithm. At the same time, based on Android and MySQL database development environment, the paper carried on the system requirements and functional analysis, database design, interface design, etc. The system overcomes the limitations of other management information systems, improves the management efficiency, realizes the flexibility, intelligence and digital of information management, and provides a new platform for enterprise logistics information management.

We report on the hydrogen storage material of ternary and tetra systems based on MNi5 in which rich-La and rich-Ce mischmetal (La)M, (Ce)M are used, and the nickel is replaced partially by Mn, Fe or by Fe and Mn simultaneously. The characteristic curve of the hydrogen storage material is measured by the method of PCT. The difference of the plateau pressures resulted by various compositions of mischmetal sire observed. The desorption pressure increases as the content of Ce in mischmetal increases. The plateau can be decreased by partially substituting Mn for Ni. The amount of Mn is weighed up by the content of Ce, and the dissociation pressure that are needed can obtained. Ni can be partially substituted by Fe. The function of Fe is contrary to Mn, it makes the plateau pressure increasing greatly. When Ni be partially substituted with Fe and Mn at the same time, the dependence of plateau pressure versus temperature is reduced.