The constitutional relationship between China and Hong Kong: a study of the status of Hong Kong in China’s system of government under the principle of ‘one Country, two systems’

This thesis investigates the sustainability of constitutional review practised in the Hong Kong Special Administrative Region (HKSAR) within a broader political and legal system of the People’s Republic of China (PRC) in post-1997 era. Theoretical questions regarding the compatibility and workability of this type of review have been raised, particularly with respect to the constitutional interpretation of the Hong Kong Basic Law. Setting the scene against the background of thirteen years of implementation of the Hong Kong Basic Law, this thesis examines the challenge presented both to the HKSAR and the Chinese authorities working within the frame of ‘one country, two systems’. It examines practical and theoretical aspects of the interpretation of the Basic Law and of the nature of this unique constitutional relationship between the HKSAR and the PRC. This thesis explores the constitutional relations between the PRC and the HKSAR through the lens of constitutional jurisdiction of the Hong Kong Basic Law, whose interpretation has triggered huge debate in both Hong Kong and mainland China. This thesis finds that the cause for the disparity over the interpretation issue has its origins in the understanding of the fundamental concepts of sovereignty and constitution. The thesis concludes that the Hong Kong Basic Law provides the frame for a new type of constitutional relationship between the PRC and the HKSAR. The Basic Law does not solve the constitutional questions raised but rather serves as a basic framework through which the Central Authorities of the PRC and the HKSAR are enabled to evolve in an on-going process of constitutional norm-formation. My research also aims to contribute to the study on the special constitutional arrangements under the circumstances of Chinese political theory and legal system, and to offer reflections on the road towards constitutionalism in China

The Tuzu Gesar epic : Performance and singers

Different minority groups have different versions of the Gesar epic. Their respective forms differ from the Tibetan version in content, structure, characters, events, and actual performances. This kind of variety is common in Asian oral epic traditions. The Tu people are a unique minority who reside in northwest China with a total population of 200,000. The Gesar epic of this group is found mainly in Tu communities in Gansu and Qinghai provinces. The Tuzu Gesar is performed as a combination of verse and prose. It also shows some differences from the Anduo dialects of Tibetan. As a result of phonetic changes, the Tuzu Gesar has its own structure and follows strict procedures and performance rules. Many native scholars and experts have studied this tradition.Translated by Li Xianting

Developing and applying precision animal farming tools for poultry behavior monitoring

Appropriate measurement of broiler behaviors is critical to optimize broiler production efficiency and improve precision management strategies. However, performance of different precision tools on measuring broiler behaviors of interest remains unclear. This dissertation systematically developed and evaluated radio frequency identification (RFID) system, image processing, and deep learning for automatically detecting and analyzing broiler behaviors. Then different behaviors (i.e., feeding, drinking, stretching, restricted feeding) of broilers under representative management practices were measured using the developed precision tools. The broilers were Ross 708 in weeks 4-8. The major findings show that the RFID system achieved high performance (over 90% accuracy) for continuously tracking feeding and drinking behaviors of individual broilers, after they were customized and modified, such as tag sensitivity test, power adjustment, radio wave shielding, and assessment of interference by add-ons. The image processing algorithms combined with a machine learning model were customized and adjusted based on the experimental conditions and finally achieved 85% sensitivity, specificity, and accuracy for detecting bird number at feeder and at drinkers. After adjusting labeling method and hyperparameter tuning, the faster region-based convolutional neural network (faster R-CNN) had over 86% precision, recall, specificity, and accuracy for detecting broiler stretching behaviors. In comprehensive algorithms, the faster R-CNN showed over 92% precision, recall, and F1 score for detecting feeder, eating birds, and birds around feeder. The bird trackers had a 3.2% error rate to track individual birds around feeder. The support vector machine behavior classifier achieved over 92% performance for classifying walking birds. Image processing model was also developed to detect birds that were restricted to feeder access. Broilers had different behavior responses to different sessions of a day, bird ages, environments, diets, and allocated resources. Reducing stocking density, increasing feeder space, and applying poultry-specific light spectrum and intensity were beneficial for birds to perform behaviors, such as feeding, drinking, and stretching, while using the antibiotics-free diet reduced bird feeding time. In conclusion, the developed tools are useful tools for automated broiler behavior monitoring and the measured behavior responses provide insights into precision management of welfare-oriented broiler production

Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques

The ability to identify individual animals has gained great interest in beef feedlots to allow for animal tracking and all applications for precision management of individuals. This study assessed the feasibility and performance of a total of 59 deep learning models in identifying individual cattle with muzzle images. The best identification accuracy was 98.7%, and the fastest processing speed was 28.3 ms/image. A dataset containing 268 US feedlot cattle and 4923 muzzle images was published along with this article. This study demonstrates the great potential of using deep learning techniques to identify individual cattle using muzzle images and to support precision beef cattle management. Individual feedlot beef cattle identification represents a critical component in cattle traceability in the supply food chain. It also provides insights into tracking disease trajectories, ascertaining ownership, and managing cattle production and distribution. Animal biometric solutions, e.g., identifying cattle muzzle patterns (unique features comparable to human fingerprints), may offer noninvasive and unique methods for cattle identification and tracking, but need validation with advancement in machine learning modeling. The objectives of this research were to (1) collect and publish a high-quality dataset for beef cattle muzzle images, and (2) evaluate and benchmark the performance of recognizing individual beef cattle with a variety of deep learning models. A total of 4923 muzzle images for 268 US feedlot finishing cattle (\u3e12 images per animal on average) were taken with a mirrorless digital camera and processed to form the dataset. A total of 59 deep learning image classification models were comparatively evaluated for identifying individual cattle. The best accuracy for identifying the 268 cattle was 98.7%, and the fastest processing speed was 28.3 ms/image. Weighted cross-entropy loss function and data augmentation can increase the identification accuracy of individual cattle with fewer muzzle images for model development. In conclusion, this study demonstrates the great potential of deep learning applications for individual cattle identification and is favorable for precision livestock management. Scholars are encouraged to utilize the published dataset to develop better models tailored for the beef cattle industry

Uniaxial and Mixed Orientations of Poly(ethylene oxide) in Nanoporous Alumina Studied by X-ray Pole Figure Analysis

The orientation of polymers under confinement is a basic, yet not fully understood phenomenon. In this work, the texture of poly(ethylene oxide) (PEO) infiltrated in nanoporous anodic alumina oxide (AAO) templates was investigated by X-ray pole figures. The influence of geometry and crystallization conditions, such as pore diameter, aspect ratio, and cooling rates, was systematically examined. All the samples exhibited a single, volume-dependent crystallization temperature (Tc) at temperatures much lower than that exhibited by bulk PEO, indicating “clean” microdomains without detectable heterogeneous nucleation. An “orientation diagram” was established to account for the experimental observations. Under very high cooling rates (quenching), crystallization of PEO within AAO was nucleation-controlled, adopting a random distribution of crystallites. Under low cooling rates, growth kinetics played a decisive role on the crystal orientation. A relatively faster cooling rate (10 °C/min) and/or smaller pores lead to the * ║ pore axis (n⃗) mode (uniaxial orientation). When the cooling rate was lower (1 °C/min), and/or the pores were larger, a mixed orientation, with a coexistence of * ║ n⃗ and * ║ n⃗ , was observed. The results favor the kinetic model where the fastest growth direction tends to align parallel to the pore axis.This work is supported by the National Natural Science Foundation of China (NSFC, 21873109, 51820105005, 21274156). G. L. is grateful to the Youth Innovation Promotion Association of the Chinese Academy of Sciences (2015026). G. L., D. W., and A. J. M. also acknowledge European funding by the RISE BIODEST project (H2020-MSCA-RISE-2017-778092). The authors thank Dr. Zhongkai Yang for assistance with pole figure measurement

Classifying Ingestive Behavior of Dairy Cows via Automatic Sound Recognition

Determining ingestive behaviors of dairy cows is critical to evaluate their productivity and health status. The objectives of this research were to (1) develop the relationship between forage species/heights and sound characteristics of three different ingestive behaviors (bites, chews, and chew-bites); (2) comparatively evaluate three deep learning models and optimization strategies for classifying the three behaviors; and (3) examine the ability of deep learning modeling for classifying the three ingestive behaviors under various forage characteristics. The results show that the amplitude and duration of the bite, chew, and chew-bite sounds were mostly larger for tall forages (tall fescue and alfalfa) compared to their counterparts. The long short-term memory network using a filtered dataset with balanced duration and imbalanced audio files offered better performance than its counterparts. The best classification performance was over 0.93, and the best and poorest performance difference was 0.4–0.5 under different forage species and heights. In conclusion, the deep learning technique could classify the dairy cow ingestive behaviors but was unable to differentiate between them under some forage characteristics using acoustic signals. Thus, while the developed tool is useful to support precision dairy cow management, it requires further improvement

RESEARCH ON DATA MANAGEMENT MODEL OF NATIONAL DEFENSE MOBILIZATION POTENTIAL BASED ON GEO-SPATIAL FRAMEWORK

At present, the national defense mobilization potential data is mainly unstructured data composed of text, images, report forms, lacking space attribute and location information. Therefore, a large study of national defense mobilization potential database has focused on data collection, reporting and information system construction, etc. To solve national defense mobilization potential data application problems in the construction of informatization, taking advantage of the characteristics of geographical spatial framework, this paper discusses national defense mobilization potential data management model based on geographical spatial framework

Numerical simulation for optimizing the nozzle of moist-mix shotcrete based on orthogonal test

The nozzles of moist-mix shotcrete are the key parts of forming a steady jet flow field and ensuring the uniform mixing of water and other ingredients. In this paper, for optimizing the nozzle of moist-mix shotcrete, both the internal and external field of a variety of spray nozzles were simulated and analyzed by adopting orthogonal test method with Fluent simulation software combined. Then the phase volume fraction and single-phase velocity of the outlet section of flow field inside the nozzles and cloud pictures including single-phase velocity and volume of different sections lengthways in the external flow field of nozzle were obtained. The results demonstrated that the change of different factors and different levels of the same factor affected the shotcreting performance of spray nozzle, but the effect degree is different. Additionally, compared with the traditional nozzle, the rationality of new-type nozzle structure was verified, which provided a basis for the improvement and optimization of the nozzle structure in the future

Role of DNA Methylation and Adenosine in Ketogenic Diet for Pharmacoresistant Epilepsy: Focus on Epileptogenesis and Associated Comorbidities

Epilepsy is a neurological disorder characterized by a long term propensity to produce unprovoked seizures and by the associated comorbidities including neurological, cognitive, psychiatric, and impairment the quality of life. Despite the clinic availability of several novel antiepileptic drugs (AEDs) with different mechanisms of action, more than one-third of patients with epilepsy suffer with pharmacoresistant epilepsy. Until now, no AEDs have been proven to confer the efficacy in alteration of disease progression or inhibition of the development of epilepsy. The ketogenic diet, the high-fat, low-carbohydrate composition is an alternative metabolic therapy for epilepsy, especially for children with drug-resistant epilepsy. Recently clinical and experimental results demonstrate its efficacy in ameliorating both seizures and comorbidities associated with epilepsy, such as cognitive/psychiatric concerns for the patients with refractory epilepsy. Of importance, ketogenic diet demonstrates to be a promising disease-modifying or partial antiepileptogenesis therapy for epilepsy. The mechanisms of action of ketogenic diet in epilepsy have been revealed recently, such as epigenetic mechanism for increase the adenosine level in the brain and inhibition of DNA methylation. In the present review, we will focus on the mechanisms of ketogenic diet therapies underlying adenosine system in the prevention of epileptogenesis and disease modification. In addition, we will review the role of ketogenic diet therapy in comorbidities associated epilepsy and the underlying mechanisms of adenosine