Prenatal Abdominal Electrocardiogram Monitoring System using IoT in Cloud Environment: A MIMO approach

<p>The application of Internet of Things (IoT) for acquiring, analyzing and transmission of medical data is increasing in recent years. Especially in abdominal ECG processing the need is more. Since the fetal movements are random in the abdomen, a single electrode can’t be able to acquire the fetal ECG. So multi-electrodes are used to record the same. At the same time all electrodes will not provide continuous ECG signal due to the fetal movements. The temperature, pressure and heart rate of the mother also monitored for effective diagnosis. This options makes the design a multi-input structure. In existing methods, Multi-input multi-output options are not available. In addition to that the complexity increases if number of input increases. In conventional methods, the complete machine is available in the patient room. But here in this work the product is divided into three units, bedside unit, doctors unit and main server. The bedside unit is an ECG acquisition device developed using a multi-lead heart rate monitor, sensors and microcontroller. Zigbee is used to transmit the information from the patient bedside to doctors unit which makes it wireless. During the movement of the patient also the data can be viewed. The Multi-output data corresponds to fetal ECG, maternal ECG, heart rate, temperature, pressure. The IoT using raspberry pi module connects the doctors unit with the main server. The machine learning algorithms analyze the ECG data of all electrodes and sensor outputs. The multi-outputs are viewed in a Graphical User Interface (GUI). The integration of the system is conducted to construct a complete IoT-based ECG monitoring system and diagnosis in Cloud environment. </p>

Prenatal Abdominal Electrocardiogram Monitoring System using IoT in Cloud Environment: A MIMO approach

<p>The application of Internet of Things (IoT) for acquiring, analyzing and transmission of medical data is increasing in recent years. Especially in abdominal ECG processing the need is more. Since the fetal movements are random in the abdomen, a single electrode can’t be able to acquire the fetal ECG. So multi-electrodes are used to record the same. At the same time all electrodes will not provide continuous ECG signal due to the fetal movements. The temperature, pressure and heart rate of the mother also monitored for effective diagnosis. This options makes the design a multi-input structure. In existing methods, Multi-input multi-output options are not available. In addition to that the complexity increases if number of input increases. In conventional methods, the complete machine is available in the patient room. But here in this work the product is divided into three units, bedside unit, doctors unit and main server. The bedside unit is an ECG acquisition device developed using a multi-lead heart rate monitor, sensors and microcontroller. Zigbee is used to transmit the information from the patient bedside to doctors unit which makes it wireless. During the movement of the patient also the data can be viewed. The Multi-output data corresponds to fetal ECG, maternal ECG, heart rate, temperature, pressure. The IoT using raspberry pi module connects the doctors unit with the main server. The machine learning algorithms analyze the ECG data of all electrodes and sensor outputs. The multi-outputs are viewed in a Graphical User Interface (GUI). The integration of the system is conducted to construct a complete IoT-based ECG monitoring system and diagnosis in Cloud environment. </p>

Nonderived environment blocking and input-oriented computation

This paper presents a computational account of nonderived environment blocking (NDEB) that indicates the challenges it has posed for phonological theory do not stem from any inherent complexity of the patterns themselves. Specifically, it makes use of input strictly local (ISL) functions, which are among the most restrictive (i.e., lowest computational complexity) classes of functions in the subregular hierarchy (Heinz 2018) and shows that NDEB is ISL provided the derived and nonderived environments correspond to unique substrings in the input structure. Using three classic examples of NDEB from Finnish, Polish, and Turkish, it is shown that the distinction between derived and nonderived sequences is fully determined by the input structure and can be achieved without serial derivation or intermediate representations. This result reveals that such cases of NDEB are computationally unexceptional and lends support to proposals in rule- and constraint-based theories that make use of its input-oriented nature.

Tracking the Domestic Carbon Emission Intensity of China’s Construction Industry: A Global Value Chain Perspective

China is the largest producer of carbon in the world. China’s construction industry has received widespread attention in recent years due to its environmental issues. However, little research has been conducted to investigate the environmental efficiency of the domestic part of this industry. As the foreign contribution is beyond China’s control, identification of domestic carbon emissions is necessary to formulate effective policy interventions. Based on a multi-regional input‐output model, this study attempts to reduce the statistical bias associated with international trade, thereby obtaining a more accurate indicator of domestic carbon emission intensity. This study aims to reveal the change in the domestic carbon emission intensity of China’s construction industry during 2000–2014 and analyze the reason behind it. The results show that, first, both the constructed intensity indicator and commonly used measures of carbon emission intensity have exhibited a decreasing trend over the study period. However, the former has been consistently larger than the latter. Moreover, this difference first increased and then suddenly decreased after a particular year. Second, although the domestic carbon emission intensity shows a gradually declining trend, it has moved from second to first in global rankings, implying that China’s domestic construction industry’s carbon emission efficiency, while falling, lags behind other major economies. Third, the structural decomposition results reveal that changes in direct production emission intensity are the leading causes of the decline in domestic carbon emission intensity. In contrast, a change in the intermediate input structure led to an increase in the emission intensity in China’s construction industry. In addition, the enormous gaps of domestic carbon emission intensity in the construction industry between China and the selected countries are mainly attributable to the difference in the intermediate input structure. The study suggests that China’s construction industry needs to promote high value-added output, optimize intermediate input structure, and improve energy and emission efficiency.

NARX-based water quality index model of Air Busuk River using chemical parameter measurements

<p>Water quality plays a major role in issues related to public health and marine life. Hence, monitoring river for contaminations is vital for ensuring safe and sustainable water resources. Conventional method for assessing water quality index is costly as it requires considerable amount of time and laboratory resources. Therefore, this study proposes a water quality index model based on artificial neural network. A six-year data for Air Busuk River is obtained from the Department of Environment. Dissolved oxygen, biological oxygen demand, and ammoniacal nitrogen has shown high correlation with water quality index. The water quality index model is then developed based on these parameters, employing the non-linear autoregressive with exogeneous input structure. Generally, the model which is based on three chemical parameters has shown satisfactory performance with overall regression of 0.8767 and passed the correlation function tests. The model offers a potentially efficient method for assessing water quality with cost-saving benefits for government agencies and monitoring authorities.</p>

Input Structure Effect of Total Factor Productivity Growth of Animal Husbandry

This paper uses the input-output panel data of China's animal husbandry industry from 1997 to 2017, based on the total factor decomposition framework of total factor productivity (TFP), and uses the Hicks-Moorsteen index completely decompose the growth of animal husbandry TFP. By measuring the effect of mixed efficiency on the development of TFP in animal husbandry and then evaluating the input structure effect of TFP growth in animal husbandry. The results show that the impact of input structure on the TFP growth of animal husbandry has also changed from negative to positive. From 1997 to 2007, the input structure of the Huanghuaihai region alone contributed to the growth of TFP in animal husbandry, and the rest of the region was the opposite. From 2008 to 2017, the input structure of the Mengxin Plateau region hindered the growth of TFP in animal husbandry, while the rest of the region was the opposite.

ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold

<div>Local chemical space exploration of an experimentally synthesized material can be done by making slight structural</div><div>variations of the synthesized material. This generation of many molecular structures with reasonable quality,</div><div>that resemble an existing (chemical) purposeful material, is needed for high-throughput screening purposes in</div><div>material design. Large databases of geometry and chemical properties of transition metal complexes are not</div><div>readily available, although these complexes are widely used in homogeneous catalysis. A Python-based workflow,</div><div>ChemSpaX, that is aimed at automating local chemical space exploration for any type of molecule, is introduced.</div><div>The overall computational workflow of ChemSpaX is explained in more detail. ChemSpaX uses 3D information,</div><div>to place functional groups on an input structure. For example, the input structure can be a catalyst for which one</div><div>wants to use high-throughput screening to investigate if the catalytic activity can be improved. The newly placed</div><div>substituents are optimized using a computationally cheap force-field optimization method. After placement of</div><div>new substituents, higher level optimizations using xTB or DFT instead of force-field optimization are also possible</div><div>in the current workflow. In representative applications of ChemSpaX, it is shown that the structures generated by</div><div>ChemSpaX have a reasonable quality for usage in high-throughput screening applications. Representative applications</div><div>of ChemSpaX are shown by investigating various adducts on functionalized Mn-based pincer complexes,</div><div>hydrogenation of Ru-based pincer complexes, functionalization of cobalt porphyrin complexes and functionalization</div><div>of a bipyridyl functionalized cobalt-porphyrin trapped in a M2L4 type cage complex. Descriptors such as</div><div>the Gibbs free energy of reaction and HOMO-LUMO gap, that can be used in data-driven design and discovery</div><div>of catalysts, were selected and studied in more detail for the selected use cases. The relatively fast GFN2-xTB</div><div>method was used to calculate these descriptors and a comparison was done against DFT calculated descriptors.</div><div>ChemSpaX is open-source and aims to bolster the efforts of the scientific community towards data-driven material</div><div>discovery.</div>

ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold

<div>Local chemical space exploration of an experimentally synthesized material can be done by making slight structural</div><div>variations of the synthesized material. This generation of many molecular structures with reasonable quality,</div><div>that resemble an existing (chemical) purposeful material, is needed for high-throughput screening purposes in</div><div>material design. Large databases of geometry and chemical properties of transition metal complexes are not</div><div>readily available, although these complexes are widely used in homogeneous catalysis. A Python-based workflow,</div><div>ChemSpaX, that is aimed at automating local chemical space exploration for any type of molecule, is introduced.</div><div>The overall computational workflow of ChemSpaX is explained in more detail. ChemSpaX uses 3D information,</div><div>to place functional groups on an input structure. For example, the input structure can be a catalyst for which one</div><div>wants to use high-throughput screening to investigate if the catalytic activity can be improved. The newly placed</div><div>substituents are optimized using a computationally cheap force-field optimization method. After placement of</div><div>new substituents, higher level optimizations using xTB or DFT instead of force-field optimization are also possible</div><div>in the current workflow. In representative applications of ChemSpaX, it is shown that the structures generated by</div><div>ChemSpaX have a reasonable quality for usage in high-throughput screening applications. Representative applications</div><div>of ChemSpaX are shown by investigating various adducts on functionalized Mn-based pincer complexes,</div><div>hydrogenation of Ru-based pincer complexes, functionalization of cobalt porphyrin complexes and functionalization</div><div>of a bipyridyl functionalized cobalt-porphyrin trapped in a M2L4 type cage complex. Descriptors such as</div><div>the Gibbs free energy of reaction and HOMO-LUMO gap, that can be used in data-driven design and discovery</div><div>of catalysts, were selected and studied in more detail for the selected use cases. The relatively fast GFN2-xTB</div><div>method was used to calculate these descriptors and a comparison was done against DFT calculated descriptors.</div><div>ChemSpaX is open-source and aims to bolster the efforts of the scientific community towards data-driven material</div><div>discovery.</div>

ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold

<div>Local chemical space exploration of an experimentally synthesized material can be done by making slight structural</div><div>variations of the synthesized material. This generation of many molecular structures with reasonable quality,</div><div>that resemble an existing (chemical) purposeful material, is needed for high-throughput screening purposes in</div><div>material design. Large databases of geometry and chemical properties of transition metal complexes are not</div><div>readily available, although these complexes are widely used in homogeneous catalysis. A Python-based workflow,</div><div>ChemSpaX, that is aimed at automating local chemical space exploration for any type of molecule, is introduced.</div><div>The overall computational workflow of ChemSpaX is explained in more detail. ChemSpaX uses 3D information,</div><div>to place functional groups on an input structure. For example, the input structure can be a catalyst for which one</div><div>wants to use high-throughput screening to investigate if the catalytic activity can be improved. The newly placed</div><div>substituents are optimized using a computationally cheap force-field optimization method. After placement of</div><div>new substituents, higher level optimizations using xTB or DFT instead of force-field optimization are also possible</div><div>in the current workflow. In representative applications of ChemSpaX, it is shown that the structures generated by</div><div>ChemSpaX have a reasonable quality for usage in high-throughput screening applications. Representative applications</div><div>of ChemSpaX are shown by investigating various adducts on functionalized Mn-based pincer complexes,</div><div>hydrogenation of Ru-based pincer complexes, functionalization of cobalt porphyrin complexes and functionalization</div><div>of a bipyridyl functionalized cobalt-porphyrin trapped in a M2L4 type cage complex. Descriptors such as</div><div>the Gibbs free energy of reaction and HOMO-LUMO gap, that can be used in data-driven design and discovery</div><div>of catalysts, were selected and studied in more detail for the selected use cases. The relatively fast GFN2-xTB</div><div>method was used to calculate these descriptors and a comparison was done against DFT calculated descriptors.</div><div>ChemSpaX is open-source and aims to bolster the efforts of the scientific community towards data-driven material</div><div>discovery.</div>