Monetary Theory from a Chinese Historical Perspective

We discuss monetary thought in ancient China from the perspective of Western monetary theory. It sets out the structure of economic activity in the various dynasties of ancient China and emphasizes the differences in monetary structure from Europe (and later North America). Imperial China was a politically integrated structure with regional segmentation of economic activities and hence with regional money. Monetary policy was one body conducted at regional level, but overseen naturally politically before national integration under the Ming dynasty (14th century). In various regions different forms of money circulated, with gold, silver, copper, and paper all present at various times. Monetary policy was guided by monetary thought, such as later in Europe. Basic concepts such as monetary function, the velocity of circulation, inflation, interest rate parity and the quantity theory were all present. The economics of Imperial China witnessed boom and bust, inflation and deflation and monetary control much like Europe to follow. Monetary thought thus seemingly preceded Western thought, and had remarkable similarities. Whether much of this thought travelled down the silk road remains unknown, but the possibility is intriguing.

Development of an Automatic Contextual Agricultural Metadata Collection App

Data is the base of digital agriculture. Farm activities are the metadata for production data that are often recorded manually, hence erroneous and missing data often occur. A metadata collection app for contextual agricultural activities was developed for recording detailed information on who is doing what in which field, when, and how. It was developed for android smartphones and functions as a geofence responsive field recognizer using the GPS location of the app user. It records the accessed crop fields automatically with time and facilitates a rules-driven chatbot with validated options for collecting detailed metadata about the conducted activities in that accessed field. The app was designed as a multiple-user app for multi-crop and multi-field usage and storing collected data in a cloud database. The app automatically records time, location, and operator\u27s name, which reduces the chance of missing data, and the chatbot with validated options reduces errors in recording

Solar-type Stars Observed by LAMOST and Kepler

Obtaining measurements of chromospheric and photometric activity of stars with near-solar fundamental parameters and rotation periods is important for a better understanding of solar-stellar connection. We select a sample of 2603 stars with near-solar fundamental parameters from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST)-Kepler field and use LAMOST spectra to measure their chromospheric activity and Kepler light curves to measure their photospheric activity (i.e., the amplitude of the photometric variability). While the rotation periods of 1556 of these stars could not be measured due to the low amplitude of the photometric variability and highly irregular temporal profile of light curves, 254 stars were further identified as having near-solar rotation periods. We show that stars with near-solar rotation periods have chromospheric activities that are systematically higher than stars with undetected rotation periods. Furthermore, while the solar level of photospheric and chromospheric activity appears to be typical for stars with undetected rotation periods, the Sun appears to be less active than most stars with near-solar rotation periods (both in terms of photospheric and chromospheric activity).Comment: 7 pages, 6 figure

SQ-Swin: a Pretrained Siamese Quadratic Swin Transformer for Lettuce Browning Prediction

Packaged fresh-cut lettuce is widely consumed as a major component of vegetable salad owing to its high nutrition, freshness, and convenience. However, enzymatic browning discoloration on lettuce cut edges significantly reduces product quality and shelf life. While there are many research and breeding efforts underway to minimize browning, the progress is hindered by the lack of a rapid and reliable methodology to evaluate browning. Current methods to identify and quantify browning are either too subjective, labor intensive, or inaccurate. In this paper, we report a deep learning model for lettuce browning prediction. To the best of our knowledge, it is the first-of-its-kind on deep learning for lettuce browning prediction using a pretrained Siamese Quadratic Swin (SQ-Swin) transformer with several highlights. First, our model includes quadratic features in the transformer model which is more powerful to incorporate real-world representations than the linear transformer. Second, a multi-scale training strategy is proposed to augment the data and explore more of the inherent self-similarity of the lettuce images. Third, the proposed model uses a siamese architecture which learns the inter-relations among the limited training samples. Fourth, the model is pretrained on the ImageNet and then trained with the reptile meta-learning algorithm to learn higher-order gradients than a regular one. Experiment results on the fresh-cut lettuce datasets show that the proposed SQ-Swin outperforms the traditional methods and other deep learning-based backbones

Algorithm and Software for Proactive Pothole Repair

Potholes are a common pavement distress, particularly appearing during the spring freeze-thaw period in northern climates. Potholes reduce ride quality, and if left unrepaired can lead to rapid pavement deterioration. Typically, when a pothole appears a repair crew is dispatched to place patch mixture in the hole with the hope that the patch will last until such time as a more permanent repair can be made. This reactive approach to potholes can often be too late to prevent further pavement damage and also makes it difficult for repairs crews to be scheduled in the most cost effective manner. In this study, the relation between traffic loads combined with weather records, such as temperature, freeze-thaw cycles and the numbers of potholes requiring patching was investigated in an attempt to develop a model to predict pothole formation and distinguish the routes which are prone to pothole formation before the potholes begin to form. If pothole prediction were possible, this proactive approach would enable agencies to plan and schedule maintenance activities more cost and time effectively thus increasing ride safety and mobility. To achieve the objective, four years of maintenance data from Indiana routes were collected and statistically analyzed to develop a model to estimate the probability of occurrence of a pothole due to annual average daily traffic and climate. The model indicates how significant traffic loads combined with weather condition influence the pothole. Also, although traffic loads and weather conditions are the essentials for potholes to form, the effect of pavement condition on the initiation of new potholes cannot be disregarded. Additionally, this study began the development of a basic roadway distress evolution model by employing several standard statistical tools, such as, the empirical cumulative distribution functions (CDF) and the Kolmogorov-Smirnov (KS), to a pavement condition dataset. The goal of the model was to predict and rank areas of probable future concern by likelihood and severity. The resulting analysis showed promise but the data resolution was too low to achieve predictions on the desired fine scale

Iterative point-wise reinforcement learning for highly accurate indoor visible light positioning

Iterative point-wise reinforcement learning (IPWRL) is proposed for highly accurate indoor visible light positioning (VLP). By properly updating the height information in an iterative fashion, the IPWRL not only effectively mitigates the impact of non-deterministic noise but also exhibits excellent tolerance to deterministic errors caused by the inaccurate a priori height information. The principle of the IPWRL is explained, and the performance of the IPWRL is experimentally evaluated in a received signal strength (RSS) based VLP system and compared with other positioning algorithms, including the conventional RSS algorithm, the k-nearest neighbors (KNN) algorithm and the PWRL algorithm where iterations exclude. Unlike the supervised machine learning method, e.g., the KNN, whose performance is highly dependent on the training process, the proposed IPWRL does not require training and demonstrates robust positioning performance for the entire tested area. Experimental results also show that when a large height information mismatch occurs, the IPWRL is able to first correct the height information and then offers robust positioning results with a rather low positioning error, while the positioning errors caused by the other algorithms are significantly higher