Dynamic agricultural supply response under economic transformation

China has experienced dramatic economic transformation and is facing the challenge of ensuring steady agricultural growth. This study examines the crop sector by estimating the supply response for major crops in Henan province from 1998 to 2007. We use a Nerlovian adjustment adaptive expectation model. The estimation uses dynamic Generalized Method of Moments (GMM) panel estimation based on pooled data across 108 counties. We estimate acreage and yield response functions and derive the supply response elasticities. This research links supply response to exogenous factors (weather, irrigation, government policy, capital investment, and infrastructure) and endogenous factors (prices). The significant feature of the model specification used in the study is that it addresses the endogeneity problem by capturing different responses to own- and cross-prices. Empirical results illustrate that there is still great potential to increase crop production through improvement of investment priorities and proper government policy. We confirm that farmers respond to price by both reallocating land and more intensively applying non-land inputs to boost yield. Investment in rural infrastructure, human capacity, and technology are highlighted as major drivers for yield increase. Policy incentives such as taxes and subsidies prove to be effective in encouraging grain production.acreage and yield response, dynamic panel model, Generalized Method of Moments (GMM), supply elasticity,

Towards Frame Rate Agnostic Multi-Object Tracking

Multi-Object Tracking (MOT) is one of the most fundamental computer vision tasks which contributes to a variety of video analysis applications. Despite the recent promising progress, current MOT research is still limited to a fixed sampling frame rate of the input stream. In fact, we empirically find that the accuracy of all recent state-of-the-art trackers drops dramatically when the input frame rate changes. For a more intelligent tracking solution, we shift the attention of our research work to the problem of Frame Rate Agnostic MOT (FraMOT). In this paper, we propose a Frame Rate Agnostic MOT framework with Periodic training Scheme (FAPS) to tackle the FraMOT problem for the first time. Specifically, we propose a Frame Rate Agnostic Association Module (FAAM) that infers and encodes the frame rate information to aid identity matching across multi-frame-rate inputs, improving the capability of the learned model in handling complex motion-appearance relations in FraMOT. Besides, the association gap between training and inference is enlarged in FraMOT because those post-processing steps not included in training make a larger difference in lower frame rate scenarios. To address it, we propose Periodic Training Scheme (PTS) to reflect all post-processing steps in training via tracking pattern matching and fusion. Along with the proposed approaches, we make the first attempt to establish an evaluation method for this new task of FraMOT in two different modes, i.e., known frame rate and unknown frame rate, aiming to handle a more complex situation. The quantitative experiments on the challenging MOT datasets (FraMOT version) have clearly demonstrated that the proposed approaches can handle different frame rates better and thus improve the robustness against complicated scenarios.Comment: 21 pages; Author versio

The Application of Driver Models in the Safety Assessment of Autonomous Vehicles: A Survey

Driver models play a vital role in developing and verifying autonomous vehicles (AVs). Previously, they are mainly applied in traffic flow simulation to model realistic driver behavior. With the development of AVs, driver models attract much attention again due to their potential contributions to AV certification. The simulation-based testing method is considered an effective measure to accelerate AV testing due to its safe and efficient characteristics. Nonetheless, realistic driver models are prerequisites for valid simulation results. Additionally, an AV is assumed to be at least as safe as a careful and competent driver. Therefore, driver models are inevitable for AV safety assessment. However, no comparison or discussion of driver models is available regarding their utility to AVs in the last five years despite their necessities in the release of AVs. This motivates us to present a comprehensive survey of driver models in the paper and compare their applicability. Requirements for driver models in terms of their application to AV safety assessment are discussed. A summary of driver models for simulation-based testing and AV certification is provided. Evaluation metrics are defined to compare their strength and weakness. Finally, an architecture for a careful and competent driver model is proposed. Challenges and future work are elaborated. This study gives related researchers especially regulators an overview and helps them to define appropriate driver models for AVs

Thermal behaviour of high amylose cornstarch studied by DSC

The thermal behaviour of high amylose cornstarches (80% amylose content) was studied by DSC using high pressure stainless steel pans in the temperature range between 0-350 degrees C. The number of endotherms and the enthalpy of gelatinization were found to depend on moisture content. Up to four endotherms and one exotherm were determined when the moisture content was above 40%. The meaning of each endotherm has been discussed. The enthalpy of gelatinization was calculated based on the summation of all the gelatinization endotherms and found to increase with increasing water content

Starch gelatinization under shearless and shear conditions

This article reviews the development of studying starch gelatinization under shear and shearless conditions, in particular the technologies used to detect the degree of gelatinization. Advantages and disadvantages of each technology were discussed and then some examples were presented to demonstrate their application. A new technology RheoScope, an instrument that can measure viscosity under shear stress and simultaneously observes variation of starch particles using a microscope, was also introduced. It was found the definition of "gelatinization" could be different for different detection technologies. Under shearless condition full gelatinization of starch needs about ratio of water 3/starch 1, while the gelatinization under shear condition requires less water content since shear stress enhances the processing. The number of endotherm and enthalpy of gelatinization depends on amylose/amylopectin, moisture and lipid content

Morphology and properties of thermal/cooling-gel bi-phasic systems based on hydroxypropyl methylcellulose and hydroxypropyl starch

The miscibility between two gels with largely different gelation behaviors is an interesting topic both scientifically and practically. This paper reports a novel bi-phasic system based on two natural polymers, hydroxypropyl methylcellulose (HPMC) which has a thermal gelation behavior, and hydroxypropyl starch (HPS) which has a cooling gelation property. While both biopolymers have the same glucose unit grafted with propylene oxide, and are compatible to a certain degree, they were observed immiscible because of their different gelation behaviors. The immiscibility of these two compatible polymers could result in special structures leading to different blend film properties. Regarding this, the morphology, thermal transition, mechanical properties and oxygen barrier property could be well tailored by the ratio of two biopolymers and the environmental conditions. The knowledge obtained from this work could be useful for understanding other similar systems with desirable structure and properties

One-step method to prepare starch-based superabsorbent polymer for slow release of fertilizer

Here we report the use of a one-step process of reactive melt mixing to prepare starch-based superabsorbent polymers (SBSAPs) for the slow release of urea as a fertilizer. A modified twin-rotor mixer, with improved sealing to establish an oxygen-free environment, was used to study the chemical and physical reactions during the melt-processing through monitoring the temperature and torque. The effects of the initiator (ceric ammonium nitrate, or CAN), crosslinker (N,N′-methylene-bisacrylamide, or N,N′-MBA) and saponification agent (NaOH) under different reaction conditions (time, temperature, and shear intensity) were systematically studied. Also investigated was the effect of starch with different amylose content. Fourier-transform infrared (FTIR) spectroscopy and thermogravimetric analysis (TGA) confirmed that using this simple technique, SBSAPs were successfully prepared from either high-amylopectin starch (waxy corn starch) or high-amylose starch (Gelose 50) grafted with AM and crosslinked by N,N′-MBA. Gel strength was evaluated by rheometry, which revealed a significant increase in storage modulus (G′) obtained in the crosslinked high-amylose SBSAP gels. Also, scanning electron microscopy (SEM) images showed a more sophisticated structural network with a smaller pore size in the crosslinked high-amylose gels. Urea as a fertilizer was embedded in the SBSAP gel network, and this network controlled the urea release in water. The release rate of urea depended on the gel strength, gel microstructure and water absorption capacity (WAC) of SAP, which was affected by the reaction conditions and degree of saponification

Forward and Backward Information Retention for Accurate Binary Neural Networks

Weight and activation binarization is an effective approach to deep neural network compression and can accelerate the inference by leveraging bitwise operations. Although many binarization methods have improved the accuracy of the model by minimizing the quantization error in forward propagation, there remains a noticeable performance gap between the binarized model and the full-precision one. Our empirical study indicates that the quantization brings information loss in both forward and backward propagation, which is the bottleneck of training accurate binary neural networks. To address these issues, we propose an Information Retention Network (IR-Net) to retain the information that consists in the forward activations and backward gradients. IR-Net mainly relies on two technical contributions: (1) Libra Parameter Binarization (Libra-PB): simultaneously minimizing both quantization error and information loss of parameters by balanced and standardized weights in forward propagation; (2) Error Decay Estimator (EDE): minimizing the information loss of gradients by gradually approximating the sign function in backward propagation, jointly considering the updating ability and accurate gradients. We are the first to investigate both forward and backward processes of binary networks from the unified information perspective, which provides new insight into the mechanism of network binarization. Comprehensive experiments with various network structures on CIFAR-10 and ImageNet datasets manifest that the proposed IR-Net can consistently outperform state-of-the-art quantization methods