Hybrid wideband transceiver design for achievable rate maximization in millimeter-wave MIMO systems

Telecommunication Systems - This paper proposes two algorithms for hybrid (Analog–Digital) beamforming in a single-user millimeter-wave (mm-wave) multi-input multi-output (MIMO) systems under...     

Al2O3/Yttria‐Stabilized Zirconia Hollow‐Fiber Membrane Incorporated with Iron Oxide for Pb(II) Removal

Removal by absorptive ceramic membranes can simultaneously absorb and separate metal ions from water. Alumina/yttria‐stabilized zirconia (Al₂O₃/YSZ) hollow‐fiber membranes, fabricated using phase inversion and sintering process, were deposited with iron oxide by an in‐situ hydrothermal process. The results showed that α‐Fe₂O₃ was produced and incorporated across the membranes. A reduction in flux was recorded with the deposition of α‐Fe₂O_3. However, it improved the adsorption capacity for heavy metal adsorption. The adsorption‐separation test demonstrated that the optimized membrane is able to completely remove Pb(II) ions after two hours.     

Palogue油田复合油藏动态响应及特征描述

以简单的复合油藏模型理论和方法为基础,以Palogue油田的实际测试井为例,介绍了复合油藏模型的试井动态识别与模型拟合,及与地质静态资料相结合的油藏综合描述过程.该描述方法将会对实际试井解释和油藏综合描述工作有所借鉴和帮助.     

Lung nodules detection using semantic segmentation and classification with optimal features

Neural Computing and Applications - Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of...     

Autonomous mapping of HL7 RIM and relational database schema

Healthcare systems need to share information within and across the boundaries in order to provide better care to the patients. For this purpose, they take advantage of the full potential of current state of the art in healthcare standards providing interoperable solutions. HL7 V3 specification is an international message exchange and interoperability standard. HL7 V3 messages exchanged between healthcare applications are ultimately recorded into local healthcare databases, mostly in relational databases. In order to bring these relational databases in compliance with HL7, mappings between HL7 RIM (Reference Information Model) and relational database schema are required. Currently, RIM and database mapping is largely performed manually, therefore it is tedious, time consuming, error prone and expensive process. It is a challenging task to determine all correspondences between RIM and schema automatically because of extreme heterogeneity issues in healthcare databases. To reduce the amount of manual efforts as much as possible, autonomous mapping approaches are required. This paper proposes a technique that addresses the aforementioned mapping issue and aligns healthcare databases to HL7 V3 RIM specifications. Furthermore, the proposed technique has been implemented as a working application and tested on real world healthcare systems. The application loads the target healthcare schema and then identifies the most appropriate match for tables and the associated fields in the schema by using domain knowledge and the matching rules defined in the Mapping Knowledge Repository. These rules are designed to handle the complexity of semantics found in healthcare databases. The GUI allows users to view and edit/re-map the correspondences. Once all the mappings are defined, the application generates Mapping Specification, which contains all the mapping information i.e. database tables and fields with associated RIM classes and attributes. In order to enable the transactions, the application is facilitated with the autonomous code generation from the Mapping Specification. The Code Generator component focuses primarily on generating custom classes and hibernate mapping files against the runtime system to retrieve and parse the data from the data source—thus allows bi-directional HL7 to database communication, with minimum programming required. Our experimental results show 35–65% accuracy on real laboratory systems, thus demonstrating the promise of the approach. The proposed scheme is an effective step in bringing the clinical databases in compliance with RIM, providing ease and flexibility.     

Assessment of Sustainable Yield of Karst Water in Huaibei,China

This paper presents the assessment of sustainable yield in the Huaibei karst water area of Anhui province, China. A review of sustainable yield definition is introduced first in this paper, and sustainable development in karst areas is more difficult due to the complicated hydrogeologic conditions. General hydrogeology of the study area is provided to characterize hydraulic connections between the karst aquifer and an overlying porous aquifer. Groundwater level declines continuously due to over-exploitation of the karst groundwater, and two layers of groundwater dropping funnel were formed in Huaibei. These problems not only threaten the eco-geo-environment, but also compromise the water utilization which depends on the shallow porous water. A “critical water level” is proposed in this study to assess the sustainable yield, and it is determined by the historical exploitation data which represent the relationship between the karst water and the shallow porous water uses. A three layer Artificial Neural Network (ANN) model is used to understand the complex relationship of the karst water level and its influencing factors. Precipitation, exploitation and water level of latest period are chosen as the input nodes, seasonal records of water level are simulated by the ANN model. The sustainable yield is calculated by the trail-and-error adjusting method, and is equal to the pumping rate when the “critical water level” is maintained. The rate of 30.05 MCM/a is the sustainable yield for the Huaibei karst area in 2008, and it is less than the real pumping rate of 35.92 MCM/a. This assessment is meaningful to the management for the Huaibei karst water.     

Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover, TF-IDF vectorizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments. Furthermore, optimal parameter tuning is done for ELMSAE model using MRDA technique. A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced efficiency of MRDA-ELMSAE technique against other recent techniques.     

Fracture toughness of eutectic Al-Si casting alloy with different microstructural features

The static fracture toughness of a series of eutectic Al-Si casting alloy with different microstructural features has been evaluated. The dominant influence of eutectic silicon in controlling the fracture toughness is thus clarified. The relationship between the fracture toughness and the microstructure was established. Fracture toughness was found to be strongly associated with the size and morphology of silicon particles. The other feature which greatly influences the fracture toughness is the ratio (/DE)_Si, i.e. the silicon particle spacing divided by the equivalent particle diameter, rather than the silicon particle spacing, _Si. Fracture toughness also correlates well with the void growth parameter, VGP (=_y (/DE)_Si), proposed by the authors. The results of the present work can be used to develop an understanding of the variation of fracture toughness with the microstructural features of eutectic Al-Si alloys.