Development and utilisation of fibre optic-based monitoring systems for underground coal mines

The continuous economic growth and depleting shallow reserves have increased the number of deeper mining operations worldwide which has made safety and productivity more challenging due to the higher stresses, heat and increased gas contents. Any major improvements in safety and productivity require a reliable and real-time monitoring system that provides more comprehensive information about various processes. The current monitoring systems suffer from lack of reliability, accuracy and high capital and operating costs. Recent advancements in fibre-optic based sensing technology have introduced unique solutions for various underground coal mine applications such as health and safety, geotechnical, ventilation, borehole, mine environment and condition monitoring. This paper presents recent research, development and utilisation of this technology by a group of researchers at the University of Queensland (UQ) and CRCMining in Australia and Shandong Academy of Science in China

A Study on the Current Situation and Educational Strategies of Parents' Emotional Coping Styles of 3-6 Year Old Children

The pre-school stage is a critical period for young children's emotional development. During this stage, children gradually learn to express their emotions, understand the emotions of others, and acquire the ability toregulate their emotions appropriately. As the first mentors in a child's life, the importance of what parents do to cope with their toddler's mood swings is a form of potential emotional education.In this study, questionnaires and interviews were used to investigate parents' coping styles in dealing with the five emotions of excitement, pride, sadness, fear, and anger in 3-6 year old toddlers and to explore corresponding educational strategies. The results of the study show that young children's emotions are usually in a variety of situations and have many triggering factors. Moreover, parents usually tend to adopt more supportive coping methods in their lives

Marketing strategy of organic agricultural products on e-commerce platforms

With the strong growth of China economy in the dozens of years, there has been increasing demand of products and services of high quality from Chinese consumers with rising salary and disposable income. Despite recent concerns of economy slowdown and softening currency, Chinese consumer confidence still keep upbeat and resilient with strong interest of shifting spending to premium segment. This trend is especially phenomenal when it comes to spending on agricultural products. Chinese consumers tend to choose products with clear and credible indication of Non-GMO and organic label. However, despite of strong demand, it has been a long existing challenge of insufficient supply of organic products in China agricultural market without a well-guarded and reliable system of quality assurance. With E-Commerce platform, a new advent of technology that bridges supply and demand of niche and emerging segment, more opportunities are exposed to both consumers and suppliers in increasing the outreach of organic agricultural products and consumers willing to pay extra. This study analyses pricing, branding and distribution strategy for organic agricultural products and devises a portfolio of integrative marketing strategies and tactics that is applicable in the new era of digital marketing, built from the case of JD.com, one of the largest B2C E-commerce platform in China

MemMap: An Adaptive and Latent Memory Structure for Dynamic Graph Learning

Dynamic graph learning has attracted much attention in recent years due to the fact that most of the real-world graphs are dynamic and evolutionary. As a result, many dynamic learning methods have been proposed to cope with the changes of node states over time. Among these studies, a critical issue is how to update the representations of nodes when new temporal events are observed. In this paper, we provide a novel memory structure - Memory Map (MemMap) for this problem. MemMap is an adaptive and evolutionary latent memory space, where each cell corresponds to an evolving topic of the dynamic graph. Moreover, the representation of a node is generated from its semantically correlated memory cells, rather than linked neighbors of the node. We have conducted experiments on real-world datasets and compared our method with the SOTA ones. It can be concluded that: 1) By constructing an adaptive and evolving memory structure during the dynamic learning process, our method can capture the dynamic graph changes, and the learned MemMap is actually a compact evolving structure organized according to the latent topics of the graph nodes. 2) Our research suggests that it is a more effective and efficient way to generate node representations from a latent semantic space (like MemMap in our method) than from directly connected neighbors (like most of the previous graph learning methods). The reason is that the number of memory cells in latent space could be much smaller than the number of nodes in a real-world graph, and the representation learning process could well balance the global and local message passing by leveraging the semantic similarity of graph nodes via the correlated memory cells

A Simple Framework for Multi-mode Spatial-Temporal Data Modeling

Spatial-temporal data modeling aims to mine the underlying spatial relationships and temporal dependencies of objects in a system. However, most existing methods focus on the modeling of spatial-temporal data in a single mode, lacking the understanding of multiple modes. Though very few methods have been presented to learn the multi-mode relationships recently, they are built on complicated components with higher model complexities. In this paper, we propose a simple framework for multi-mode spatial-temporal data modeling to bring both effectiveness and efficiency together. Specifically, we design a general cross-mode spatial relationships learning component to adaptively establish connections between multiple modes and propagate information along the learned connections. Moreover, we employ multi-layer perceptrons to capture the temporal dependencies and channel correlations, which are conceptually and technically succinct. Experiments on three real-world datasets show that our model can consistently outperform the baselines with lower space and time complexity, opening up a promising direction for modeling spatial-temporal data. The generalizability of the cross-mode spatial relationships learning module is also validated

A Single‐Cell Perspective of the Mammalian Liver in Health and Disease

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154943/1/hep31149_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154943/2/hep31149.pd

Optical sensor based on hybrid LPG/FBG in D-fiber for simultaneous refractive index and temperature measurement

A dual-parameter optical sensor has been realized by UV-writing a long-period and a Bragg grating structure in D-fiber. The hybrid configuration permits the detection of the temperature from the latter and measuring the external refractive index from the former responses, respectively. The employment of the D-fiber allows as effective modification and enhancement of the device sensitivity by cladding etching. The grating sensor has been used to measure the concentrations of aqueous sugar solutions, demonstrating the potential capability to detect concentration changes as small as 0.01%

Coffee: Cost-Effective Edge Caching for 360 Degree Live Video Streaming

While live 360 degree video streaming delivers immersive viewing experience, it poses significant bandwidth and latency challenges for content delivery networks. Edge servers are expected to play an important role in facilitating live streaming of 360 degree videos. In this paper, we propose a novel predictive edge caching algorithm (Coffee) for live 360 degree video that employ collaborative FoV prediction and predictive tile prefetching to reduce bandwidth consumption, streaming cost and improve the streaming quality and robustness. Our light-weight caching algorithms exploit the unique tile consumption patterns of live 360 degree video streaming to achieve high tile caching gains. Through extensive experiments driven by real 360 degree video streaming traces, we demonstrate that edge caching algorithms specifically designed for live 360 degree video streaming can achieve high streaming cost savings with small edge cache space consumption. Coffee, guided by viewer FoV predictions, significantly reduces back-haul traffic up to 76% compared to state-of-the-art edge caching algorithms. Furthermore, we develop a transcoding-aware variant (TransCoffee) and evaluate it using comprehensive experiments, which demonstrate that TransCoffee can achieve 63\% lower cost compared to state-of-the-art transcoding-aware approaches

Ada-Tracker: Soft Tissue Tracking via Inter-Frame and Adaptive-Template Matching

Soft tissue tracking is crucial for computer-assisted interventions. Existing approaches mainly rely on extracting discriminative features from the template and videos to recover corresponding matches. However, it is difficult to adopt these techniques in surgical scenes, where tissues are changing in shape and appearance throughout the surgery. To address this problem, we exploit optical flow to naturally capture the pixel-wise tissue deformations and adaptively correct the tracked template. Specifically, we first implement an inter-frame matching mechanism to extract a coarse region of interest based on optical flow from consecutive frames. To accommodate appearance change and alleviate drift, we then propose an adaptive-template matching method, which updates the tracked template based on the reliability of the estimates. Our approach, Ada-Tracker, enjoys both short-term dynamics modeling by capturing local deformations and long-term dynamics modeling by introducing global temporal compensation. We evaluate our approach on the public SurgT benchmark, which is generated from Hamlyn, SCARED, and Kidney boundary datasets. The experimental results show that Ada-Tracker achieves superior accuracy and performs more robustly against prior works. Code is available at https://github.com/wrld/Ada-Tracker.Comment: IEEE International Conference on Robotics and Automation (ICRA) 202