Wireless Remote Weather Monitoring System Based on MEMS Technologies

This study proposes a wireless remote weather monitoring system based on Micro-Electro-Mechanical Systems (MEMS) and wireless sensor network (WSN) technologies comprising sensors for the measurement of temperature, humidity, pressure, wind speed and direction, integrated on a single chip. The sensing signals are transmitted between the Octopus II-A sensor nodes using WSN technology, following amplification and analog/digital conversion (ADC). Experimental results show that the resistance of the micro temperature sensor increases linearly with input temperature, with an average TCR (temperature coefficient of resistance) value of 8.2 × 10−4 (°C−1). The resistance of the pressure sensor also increases linearly with air pressure, with an average sensitivity value of 3.5 × 10−2 (Ω/kPa). The sensitivity to humidity increases with ambient temperature due to the effect of temperature on the dielectric constant, which was determined to be 16.9, 21.4, 27.0, and 38.2 (pF/%RH) at 27 °C, 30 °C, 40 °C, and 50 °C, respectively. The velocity of airflow is obtained by summing the variations in resistor response as airflow passed over the sensors providing sensitivity of 4.2 × 10−2, 9.2 × 10−2, 9.7 × 10−2 (Ω/ms−1) with power consumption by the heating resistor of 0.2, 0.3, and 0.5 W, respectively. The passage of air across the surface of the flow sensors prompts variations in temperature among each of the sensing resistors. Evaluating these variations in resistance caused by the temperature change enables the measurement of wind direction

A Microcantilever-based Gas Flow Sensor for Flow Rate and Direction Detection

The purpose of this paper is to apply characteristics of residual stress that causes cantilever beams to bend for manufacturing a micro-structured gas flow sensor. This study uses a silicon wafer deposited silicon nitride layers, reassembled the gas flow sensor with four cantilever beams that perpendicular to each other and manufactured piezoresistive structure on each micro-cantilever by MEMS technologies, respectively. When the cantilever beams are formed after etching the silicon wafer, it bends up a little due to the released residual stress induced in the previous fabrication process. As air flows through the sensor upstream and downstream beam deformation was made, thus the airflow direction can be determined through comparing the resistance variation between different cantilever beams. The flow rate can also be measured by calculating the total resistance variations on the four cantilevers.Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/handle/2042/16838

Common and different features of Chinese and Italian hydrogeological mapping guidelines

The definition of common international guidelines for the compilation of high quality hydrogeological maps has been attempted from the second half of the last century for hydrogeologists, to solve the lack of uniformity among national guidelines due to the various geological-hydrogeological and climatic situations of different countries worldwide. With this aim, the China Geological Survey and the Geological Survey of Italy-ISPRA are undertaking cooperative research in implementing 1:50,000 scale hydrogeological survey and mapping at selected sites in both countries. The project intends to develop a new generation of hydrogeological and groundwater resource maps with descriptive effectiveness and consistency with field survey data. The project will promote improvements of technologies in hydrogeological survey and mapping of the two countries and might even be agreed at a wider international level. Chinese and Italian hydrogeological guidelines have similar aspects as well as concerns: 1) the undertaking of field surveys at the 1:50,000 scale and more detailed (1:25000) scale; 2) building of a hydrogeological database; 3) publication of the official map in both paper and electronic form; 4) inclusion of several small scale maps inlayed at the margin of a main map in the hydrogeological map layout; 5) comparable level in required survey quota. Furthermore, more attention will be paid to a 3D map, conceptual model, aquifer structure, groundwater cycle and hydrogeological parameter description.In contrast, the most important difference regards the following. The hydrogeological mapping guidelines of Italy have integrated specifications for both survey and mapping, i.e. they deal with a structural layout characterized by survey contents followed by mapping contents and reflect a technical route of surveying for mapping. In contrast, there are no mapping contents in the current hydrogeological guidelines of China and these then needed to be formulated. The Italian guidelines could provide important references for China in legend organization, mapping rules, survey quota and so on.Finally, the collaboration between China and Italy is of great significance for the two ancient civilized countries sharing the “One Belt and One Road” international initiative. </p

Current perspectives on genotype classification and individualized drug targeting in triple-negative breast cancer

Triple negative breast cancer (TNBC), a special subset of breast cancer, refers to negative expressions of estrogen receptors (ER), progesterone receptors (PR) and human epidermal growth receptor 2 (HER2). It is associated with extreme local recurrence and distant metastasis with highly invasive character. With advances in genomics, the bases of molecular classification of TNBC now include the heterogeneity of its expression at the molecular level and clinical pathology, apart from classical immunohistochemistry. Every subtype of TNBC has different individualized target drugs, which include epidermal growth factor receptor (EGFR) inhibitor, poly-AD-ribose polymerase (PARP) inhibitor, anthracycline or paclitaxel, immunotherapy and vascular endothelial growth factor receptor (VEGFR) inhibitor. Combinations of target drugs are also used. Thus, there are no widely recognized standards of genotype classification and individualized drug targeting in TNBC. In this review, relevant studies and latest developments on TNBC are presented.Keywords: Triple-negative breast cancer, Genotype classification, Individualized drug targeting, Breast cance

catena-Poly[silver(I)-μ-acridine-9-carboxyl­ato-κ3 N:O,O′]

In the title coordination polymer, [Ag(C14H8NO2)]n, the AgI cation is coordinated by two O atoms and one N atom from two symmetry-related acridine-9-carboxyl­ate ligands in a distorted trigonal-planar geometry. The metal atoms are connected by the ligands to form chains running parallel to the b axis. π–π stacking inter­actions [centroid-to-centroid distances 3.757 (2)–3.820 (2) Å] and weak Ag⋯O inter­actions further link the chains to form a layer network parallel to the ab plane. The AgI cation is disordered over two positions, with refined site-occupancy factors of 0.73 (3):0.27 (3)

Stationary Response of a Class of Nonlinear Stochastic Systems Undergoing Markovian Jumps

Systems whose specifications change abruptly and statistically, referred to as Markovianjump systems, are considered in this paper. An approximate method is presented to assess the stationary response of multidegree, nonlinear, Markovian-jump, quasi-nonintegrable Hamiltonian systems subjected to stochastic excitation. Using stochastic averaging, the quasi-nonintegrable Hamiltonian equations are first reduced to a one-dimensional Itô equation governing the energy envelope. The associated Fokker-Planck-Kolmogorov equation is then set up, from which approximate stationary probabilities of the original system are obtained for different jump rules. The validity of this technique is demonstrated by using a nonlinear two-degree oscillator that is stochastically driven and capable of Markovian jumps

A framework for cardiac arrhythmia detection from IoT-based ECGs

Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12% of all deaths globally. The development of Internet-of-Things has spawned novel ways for heart monitoring but also presented new challenges for manual arrhythmia detection. An automated method is highly demanded to provide support for physicians. Current attempts for automatic arrhythmia detection can roughly be divided as feature-engineering based and deep-learning based methods. Most of the feature-engineering based methods are suffering from adopting single classifier and use fixed features for classifying all five types of heartbeats. This introduces difficulties in identification of the problematic heartbeats and limits the overall classification performance. The deep-learning based methods are usually not evaluated in a realistic manner and report overoptimistic results which may hide potential limitations of the models. Moreover, the lack of consideration of frequency patterns and the heart rhythms can also limit the model performance. To fill in the gaps, we propose a framework for arrhythmia detection from IoT-based ECGs. The framework consists of two modules: a data cleaning module and a heartbeat classification module. Specifically, we propose two solutions for the heartbeat classification task, namely Dynamic Heartbeat Classification with Adjusted Features (DHCAF) and Multi-channel Heartbeat Convolution Neural Network (MCHCNN). DHCAF is a feature-engineering based approach, in which we introduce dynamic ensemble selection (DES) technique and develop a result regulator to improve classification performance. MCHCNN is deep-learning based solution that performs multi-channel convolutions to capture both temporal and frequency patterns from heartbeat to assist the classification. We evaluate the proposed framework with DHCAF and with MCHCNN on the well-known MIT-BIH-AR database, respectively. The results reported in this paper have proven the effectiveness of our framework