An energy-saving control strategy for VRF and VAV combined air conditioning system in heating mode

Although variable refrigerant flow (VRF) systems have become attractive due to good energy performances in part load conditions, the shortcoming of no outdoor air intake has not been solved thoroughly. A VRF and VAV combined air conditioning system is proposed to solve this problem. VAV part of the combined system consists of an outdoor air processing (OAP) unit and VAV boxes. Generally the VRF unit operates to maintain indoor temperature and the OAP unit operates to process the outdoor air. A control strategy for the combined system aiming at reducing energy consumption is presented in this paper. When both VRF unit and OAP unit are operating, a load allocation optimization module is executed to find the best load allocation between them to minimize the energy consumption of the combined system. When the allocated load of the OAP unit is very small, the proposed control strategy stops the OAP unit, leaving only the VRF unit to operate to improve the overall energy efficiency of the combined system. When load requirements are met, the OAP unit is restarted and the load allocation optimization module is executed again. The proposed control strategy is evaluated based on the developed simulation platform. Results show that the proposed control strategy can effectively decrease the energy consumption of the combined system

Prognostic value of routine laboratory variables in prediction of breast cancer recurrence.

The prognostic value of routine laboratory variables in breast cancer has been largely overlooked. Based on laboratory tests commonly performed in clinical practice, we aimed to develop a new model to predict disease free survival (DFS) after surgical removal of primary breast cancer. In a cohort of 1,596 breast cancer patients, we analyzed the associations of 33 laboratory variables with patient DFS. Based on 3 significant laboratory variables (hemoglobin, alkaline phosphatase, and international normalized ratio), together with important demographic and clinical variables, we developed a prognostic model, achieving the area under the curve of 0.79. We categorized patients into 3 risk groups according to the prognostic index developed from the final model. Compared with the patients in the low-risk group, those in the medium- and high-risk group had a significantly increased risk of recurrence with a hazard ratio (HR) of 1.75 (95% confidence interval [CI] 1.30-2.38) and 4.66 (95% CI 3.54-6.14), respectively. The results from the training set were validated in the testing set. Overall, our prognostic model incorporating readily available routine laboratory tests is powerful in identifying breast cancer patients who are at high risk of recurrence. Further study is warranted to validate its clinical application

Supporting Blockchain-Based Cryptocurrency Mobile Payment With Smart Devices

The smart device owning rate such as smart phone and smart watch is higher than ever before and mobile payment has become one of the major payment methods in many different areas. At the same time, blockchain-based cryptocurrency is becoming a nonnegligible type of currency and the total value of all types of cryptocurrency has reached USD 200 billion. Therefore, it is a natural demand to support cryptocurrency payment on mobile devices. Considering the poor infrastructure and low penetration of financial service in developing countries, this combination is especially attractive. The high storage cost and payment processing latency are the two main obstacles for mobile payment using cryptocurrency. We propose two different schemes for cryptocurrency mobile payment, one involves a centralized bank and the other one does not require any centralized party. We also provide a solution for the bank to meet KYC (know your customer)/AML (antimoney laundering) compliance requirements when it is involved in cryptocurrency mobile payment processing

DIoTA: Decentralized Ledger based Framework for Data Authenticity Protection in IoT Systems

It is predicted that more than 20 billion IoT devices will be deployed worldwide by 2020. These devices form the critical infrastructure to support a variety of important applications such as smart city, smart grid, and industrial internet. To guarantee that these applications work properly, it is imperative to authenticate these devices and data generated from them. Although digital signatures can be applied for these purposes, the scale of the overall system and the limited computation capability of IoT devices pose two big challenges. In order to overcome these obstacles, we propose DIoTA, a novel decentralized ledger-based authentication framework for IoT devices. DIoTA uses a two-layer decentralized ledger architecture together with a lightweight data authentication mechanism to facilitate IoT devices and data management. We also analyze the performance and security of DIoTA, and explicitly give the major parameters an administrator can choose to achieve a desirable balance between different metrics