A Successful Experience of Traceability System for the Case of Yin Chuan Organic Farm in Taiwan

In Taiwan, the traceability system (TS) of whole food chain is in infantile time. Yin Chuan Organic Farm, constituted by the team of rice production and marketing (TRPM), is the first brand in Taiwan rice market. The leader of TRPM is not only to produce the premising rice quality, but also to implement the (TS) that was introduced by Taiwan government with a great result and could be a successful experience passed to other proprietors. The findings of this paper are listed as following: 1. The TS executed by the case is only emphasized on the production side that needs to build more detailed marketing side. 2. The key successful factor to get sound TS model is to have obvious purposes and system harmony.traceability system, food safety, organic food, analytic hierarchy process (AHP), Agribusiness, Food Consumption/Nutrition/Food Safety,

Communication in a Wired World: Understanding Receiver Perceptions

The ability to communicate effectively is a key skill for all employees. While extensive research exists regarding the characteristics of competent oral communicators, there has yet to be systematic research on the characteristics of competent electronic communicators. A new set of rules has emerged in this electronic arena, and whether those rules have transferred to the corporate environment or not remains to be seen. This paper discusses an exploratory study to identify the characteristics of email communication and the evaluations those characteristics engender. The study employs an on-line survey in which subjects are asked to read and respond to a series of four emails from the same general context. The messages are designed to be consistent with established “norms” of electronic communication, and semantic differential scales are used to assess the degree to which the different forms evoke personality evaluations in the receiver

Optimal Trade Execution under Endogenous Order Flow

We consider an optimal liquidation model in which an investor is required to execute meta-orders during intraday trading periods, and his trading activity triggers child orders and endogenously affects future order flow, both instantaneously and permanently. Under the assumptions of risk neutrality and deterministic constants of the impact parameters, we provide closed-form solutions and illustrate the relationship between trading strategies and feedback effects. The optimal trading strategy is of hyperbolic form if the feedback effect of current trading on future order flow is not too strong. If the feedback effect becomes too dominating, a cyclic strategy with possible beneficial round-trips may emerge. We set up an estimation framework so that parameter estimates can be made directly from public data and are consistent with the theoretical model. When implementing our model on 110 NASDAQ stocks, the empirical analysis shows that as the level of endogeneity increases, our strategy provides increasingly better performance than the commonly adopted trading strategy. The empirical analysis also shows that too strong feedback effects do not exist in practice, thus ruling out statistical arbitrage

Joint Transaction Transmission and Channel Selection in Cognitive Radio Based Blockchain Networks: A Deep Reinforcement Learning Approach

To ensure that the data aggregation, data storage, and data processing are all performed in a decentralized but trusted manner, we propose to use the blockchain with the mining pool to support IoT services based on cognitive radio networks. As such, the secondary user can send its sensing data, i.e., transactions, to the mining pools. After being verified by miners, the transactions are added to the blocks. However, under the dynamics of the primary channel and the uncertainty of the mempool state of the mining pool, it is challenging for the secondary user to determine an optimal transaction transmission policy. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal transaction transmission policy for the secondary user. Specifically, we adopt a Double Deep-Q Network (DDQN) that allows the secondary user to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms the conventional Q-learning scheme in terms of reward and learning speed

SolarFormer: Multi-scale Transformer for Solar PV Profiling

As climate change intensifies, the global imperative to shift towards sustainable energy sources becomes more pronounced. Photovoltaic (PV) energy is a favored choice due to its reliability and ease of installation. Accurate mapping of PV installations is crucial for understanding their adoption and informing energy policy. To meet this need, we introduce the SolarFormer, designed to segment solar panels from aerial imagery, offering insights into their location and size. However, solar panel identification in Computer Vision is intricate due to various factors like weather conditions, roof conditions, and Ground Sampling Distance (GSD) variations. To tackle these complexities, we present the SolarFormer, featuring a multi-scale Transformer encoder and a masked-attention Transformer decoder. Our model leverages low-level features and incorporates an instance query mechanism to enhance the localization of solar PV installations. We rigorously evaluated our SolarFormer using diverse datasets, including GGE (France), IGN (France), and USGS (California, USA), across different GSDs. Our extensive experiments consistently demonstrate that our model either matches or surpasses state-of-the-art models, promising enhanced solar panel segmentation for global sustainable energy initiatives.Comment: Pre-prin

Angiotensin II Type II Receptor Deficiency Accelerates the Development of Nephropathy in Type I Diabetes via Oxidative Stress and ACE2

Since the functional role(s) of angiotensin II (Ang II) type II receptor (AT2R) in type I diabetes is unknown, we hypothesized that AT2R is involved in decreasing the effects of type I diabetes on the kidneys. We induced diabetes with low-dose streptozotocin (STZ) in both AT2R knockout (AT2RKO) and wild-type (WT) male mice aged 12 weeks and followed them for 4 weeks. Three subgroups nondiabetic, diabetic, and insulin-treated diabetic (Rx insulin implant) were studied. Systolic blood pressure (SBP), physiological parameters, glomerular filtration rate (GFR), renal morphology, gene expression, and apoptosis were assessed. After 4 weeks of diabetes, compared to WT controls, AT2RKO mice clearly developed features of early diabetic nephropathy (DN), such as renal hypertrophy, tubular apoptosis, and progressive extracellular matrix (ECM) protein accumulation as well as increased GFR. AT2RKO mice presented hypertension unaffected by diabetes. Renal oxidative stress (measured as heme oxygenase 1 (HO-1) gene expression and reactive oxygen species (ROS) generation) and intrarenal renin angiotensin system components, such as angiotensinogen (Agt), AT1R, and angiotensin-converting enzyme (ACE) gene expression, were augmented whereas angiotensin-converting enzyme2 (ACE2) gene expression was decreased in renal proximal tubules (RPTs) of AT2RKO mice. The renal changes noted above were significantly enhanced in diabetic AT2RKO mice but partially attenuated in insulin-treated diabetic WT and AT2RKO mice. In conclusion, AT2R deficiency accelerates the development of DN, which appears to be mediated, at least in part, via heightened oxidative stress and ACE/ACE2 ratio in RPTs

Fast Hybrid Algorithms For High Frequency Scattering

This paper deals with numerical methods for high frequency wave scattering. It introduces a new hybrid technique that couples a directional fast multipole method for a subsection of it scattering surface to an asymptotic formulation over the rest of the scattering domain. The directional fast multipole method is new and highly efficient for the solution of the boundary integral formulation of a general scattering problem but it requires at least a few unknowns per wavelength on the boundary. The asymptotic method that was introduced by Bruno and collaborators requires much fewer unknowns. Oil the other hand the scattered field must have a simple structure. Hybridization of these two methods retains their best properties for the solution of the full problem, Numerical examples are given for the solution of the Helmholtz equation in two space dimensions.Mathematic