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In this work, the effects of wood flour and tetraethyl orthosilicate (TEOS) content on the fusion time, fusion torque, fusion temperature, and fusion energy of polyvinyl chloride/wood flour (PVC/WF) composites were studied. Plasma-assisted surface treatment of WF before modifying with TEOS to form the silica nanoparticles on the surface of wood flour plays a role as a reinforcement of the phase interaction. This modification was confirmed by X-ray photoelectron spectroscopy (XPS) and field emission scanning electron microscopy (FESEM) techniques. Moreover, BET data showed that specific surface area and volume of plasma treated WF and TEOS modified WF (WS) were considerably improved in comparison with original WF. By increasing WF, a remarkable increase in time, temperature, and energy of mixing process led to the enhancement of fusion torque. In the case of composite using WS, the increase of TEOS content resulted in shorter fusion time, whereas the other fusion characteristics of composites increased. The investigation of mechanical and rheological properties such as Young’s modulus and dynamic storage modulus G′ showed the stiffness of the PVC/WF composites has been significantly improved with increasing wood flour and modifier contents. The research showed an application of nanoparticles in the industrial production of polymer composite materials.

This paper is an attempt to investigate the non-stationary process in the global market shares of automotive industry of top 26 automotive producing nations. The time series data of global automotive market shares of top 26 automotive producing nations were collected from data stream Eikon for the period from 2002 to 2014. Augmented Dickey Fuller Unit root test (ADF URT) is used to investigate the non-stationarities or shocks in the outlined series of global automotive market shares. Findings confirmed the presence of shocks or non-stationarity (absence of stationary process) in global market shares of almost all top automotive producing nations except of Australia, India, Malaysia and South Africa for2002 to 2014. Findings further revealed that the non-stationarities got fixed at 2nd difference in all outlined series of global market shares of automotive for the period from 2002 to 2014. It is also revealed that there are the same shocking patterns in the global market shares of all top automotive producing nations with few exception.

The resilience of a community in an extreme event depends mainly on the robustness of the critical infrastructures. Road bridges are a critical link of the road network, which plays a focal role in Australia’s economy, prosperity, social well-being and quality of life. Timber bridges are a weaker link of the Australian road network and they often provide critical access to the rural communities. This research uses a number of bridge inspection reports to develop a method to predict the probability of failure of a timber bridge. The inspected condition states of the elements in the timber bridge are used to develop a Markov chain based model and Gamma process model to predict the deterioration of each element. The probability of condition state movement for each element thus calculated were used in fault tree analysis to estimate likelihood of failure of a bridge in a given time period. Although the developed method is based on limited data and it has several limitations, model can be further refined with the availability of more inspection reports. The method developed is demonstrated using an inspection report for a timber bridge, which was not used in the development of the models.

The work presents a set of significant indicators and a method of detection of sleep apnea episodes based on the cardiovascular and respiratory systems’ state analysis by ECG signal, pulse wave signals and respiratory movements signals. The algorithms of biological signal processing based on wavelet analysis for studying the dynamics of indicators important for diagnosis in time and spectral range, an algorithm for detecting apnea episodes, which allows to differentiate sleep apnea of central and obstructive type, are given. The proposed algorithms are based on the method of smart monitoring of sleep apnea, which allows to identify episodes of sleep apnea during the patient’s stay outside the hospital.

A method and algorithm for intelligent monitoring of heart rhythm for atrial fibrillation episodes detection for the systems of patient’s state continuous remote control in real-time mode was developed. For atrial fibrillation episodes detection in the patient’s vital activity state the complex of diagnostically significant indexes reflecting the dynamics of patient’s atrial rhythm abnormality was formed: heart rhythm frequency, irregularity of heart rhythm, absence of P wave and appearance of f wave in the ECG signal. They are evaluated step by step depending on patient’s state changes. The experimental research of suggested algorithm has shown the accuracy of atrial fibrillation episodes detection no less than 94%. The developed method and algorithm provide the high efficiency of continuous remote control of patient’s health with heart rate abnormality.

The purpose of the paper is to develop an analytical investigation on free vibration of a simply supported functionally graded (FG) doubly curved shell panels resting on elastic foundation in thermal environment. Heat conduction and temperature-dependent material properties are both taken into account. The temperature field considered is assumed to be a uniform distribution over the shell surface and varied in the thickness direction only. Material properties are assumed to be temperature dependent and graded in the thickness direction according to a simple power law distribution in terms of the volume fractions of the constituents. Based on the first-order shear deformation theory and applying the Hamilton’s principle, governing equations of motion are derived. The results of the study are compared with the available published literature. The numerical results obtained reveal that the material volume fraction index, geometrical parameters and temperature change have significant effects on natural frequencies of the FG doubly curved shell panels.

Soil-transmitted helminth (STH) infections caused 4.98 million years live with disability globally in 2014, mostly affecting the poor. When farmers handle excreta for reuse in agriculture, involunt...

This study aims to identify and measure the impact on the Competitive capability of frozen seafood processing enterprises in BR-VT province. Research access tocompetitiveness is based on competency theory, different from previous domestic studies primarily approaching competitiveness based on traditional competition theory and value chain theory. The research model consists of 9 factors. The study was conducted with 356 survey samples of 76 frozen seafood processing enterprises, surveyed through a questionnaire.Data analysis through SPSS 24.0. The test results have 8 accepted hypothesis, the highest impact factor is the ability to access and innovate technology with β = 0.3328 (p = 0.000) and the lowest is local infrastructure factorwith β = 0.067 (p = 0.042), and Policy and Legal Factor with β = - 0.038 (p = 0.249), there is not statistical significance should not be accepted.

In the health-care system, parameters of patients (EEG, ECG, EMG, Glucose, Blood pressure, motion, ...) are monitored in real-time that assist doctors to timely detect and treat unexpected risks of patients. Thus, it is critical for collected sensor data seamlessly roaming between various types of wireless transmission networks such as PAN, WiFi, LTE to a data center. Moreover, carrier frequency congestion between multiple close-by PANs can cause high packet drop rates and useless energy consumption of sensors. From above reasons, we proposed a protocol for PAN gateway to integrate many wireless communication standards (typically ZigBee, WiFi, LTE) and an optimal control network algorithm for Quality of Service (QoS). Flows of sensor data are seamlessly transmitted through many wireless interface standards in order to achieve the best average data rate; sensor energy are used effectively and fairness/priority between many users.

The production dysregulation of reactive oxygen species (ROS) and nitric oxide (NO) in ischemic tissues results in endothelial dysfunction, hyperinflammation and poor blood circulation. Here, we report a hybrid molecule, SA-10 with both NO donating and ROS scavenging abilities that demonstrated potent cytoprotection and tube formation activity in endothelial cells under H2O2-induced oxidative stress. SA-10 loaded poly(lactic-co-glycolic acid) (PLGA) nanoparticles (SA-10 NPs) were delivered intramuscularly (IM) to two murine hindlimb ischemia models. In the acute mode ischemia/reperfusion (I/R), the muscle damage, hyperinflammation, and lung edema were significantly reduced 3 days post-dose while in the chronic ischemia model, significant improvement of blood perfusion and physical endurance was observed over 30 days (P

Wind energy has become increasingly important in the total electrical energy supply mix in Vietnam over the last few years. Small, kW turbines were installed in isolated areas a decade ago, while wind farms of several MW to few hundred MW are now being connected directly to national grid, with many additional projects in planning or under construction to fulfill an objective of 6% of the total installed capacity by 2030 (approximately 6200 MW of wind energy component). The increase in wind farm generation results in increased damage from lightning. In this paper, the annual frequency of lightning strikes to wind turbines in Vietnam is calculated using electrogeometric model. Reported lightning incidents to three major wind farms in Vietnam are summarized. Possible causes of failure are discussed, and an EMTP simulation for each incident was performed accordingly. The simulations suggest the failure mechanisms as well the potential of improved grounding to reduce lightning induced damage in future ...

Autoregressive time series forecasting is common in different areas within water resources, which include hydrology, ecology, and the environment. Simple forecasting models such as linear regression have the advantage of fast runtime, which is attractive for real-time forecasting. However, their forecasting performance might not be acceptable when a non-linear relationship exists between model inputs and output, which necessitates the use of more sophisticated forecasting models such as artificial neural networks. This study investigates the performance and potential of a hybrid pre-processing technique to enhance the forecasting accuracy of two commonly used neural network models (feed-forward and layered recurrent neural network models) and a multiple linear regression model. The hybrid technique is a combination of significant input variable selection (using partial linear correlation) for reducing the dimensionality of the input data and input data transformation using discrete wavelet transform for decomposing the input time series into low and high frequency components. Two case study forecasting applications, namely, monthly inflow forecasting for a lake in Victoria (Australia) and weekly algal bloom predictions at a bay in Hong Kong were used to assess the forecasting ability of the models when used in conjunction with the hybrid technique. Results demonstrated that the hybrid technique can significantly improve the forecasting performance of all the models used.

The article substantiates the choice of biomedical signals and significant indicators used to diagnose sleep apnea. According to the task given, a block diagram of a multi-level telemedicine system for the diagnosis of apnea has been designed and studied. In the developed system, the “patient terminal device” is a special equipment for the receiving and recording of biomedical signals. It consists of two parts — an inductive sensor-converter that converts the mechanical movements of the chest during breathing, and an analog signal processing unit. The design of an inductive sensor-converter, which provides high sensitivity of transformation of mechanical movements of the chest during breathing, is proposed. Algorithms for processing and analyzing biomedical signals for automatic diagnosis of sleep apnea episodes in the context of limited energy resources of the telemedicine system have been developed.

generation of virtual models for the purpose of conducting static-dynamic research is a complex task that requires a systematic approach. The work presents a technique for creating biomechanical computer models of human organs from multiplanar reconstructions of MDCT images with complex geometry and their subsequent investigation in CAD/CAE packages.

This research aims to better comprehend & dissect the supply & demand in automotive industry of Pakistan. The primary objective of this study is to delve into casing-works which empowers us to better understand the biggest piece of the overall automotive (car) industry of Pakistan by utilizing interest & expense parameters of automotive items in business sector. The research aims to determine market shares of automotive (car) brands in Pakistani automotive industry. Finding confirms that in Pakistan in automotive or automobile (cars) industry, the Suzuki brand is enjoying the highest market share among the selected/ outlined brands and leading the industry with 29% of market shares. Toyota is enjoying 26% market shares while Honda brand is the third most winning brands in automotive industry in Pakistan with 25% market shares. Further, Nissan, Kia and Daihatsu have 10%, 6% and 4% market shares respectively for the years 2000 to 2019. In developing countries like Pakistan individuals have relatively lower wages, thus a vehicle-purchase decision requires more due-diligence, whereby the accessibility of car-parts in neighborhood markets, how pricy the car parts are, & the resale estimation of brand, are the primary decision-marking factors. The highest market shares which is being enjoyed by Suzuki brand confirms that people of Pakistan buy Suzuki brand mostly due to the above mentioned factors. Therefore automotive businesses need not only focus upon bringing vehicles into the Pakistani market, but also need to focus upon rating high in the purchase decision-making factors as that of car-parts availability, credit/installment payments, and a worth-while resale price.

Nowadays, the raising in agriculture productions is required to meet the demand for growing population. Besides macronutrients (nitrogen, phosphorus and potassium, and to a lesser degree, calcium, sulfur and magnesium) requirements, plants need micronutrients (copper, iron, zinc, boron...) to ensure productivity as well as high agricultural quality. The activity of these elements is greatly affected by very small changes in environmental factors such as pH, mechanical composition and organic matter in the soil. Therefore, there must be another way to effectively bring these elements to the crop. This leads to research and development of a new fertilizer to provide nutrients more effectively. This study reports the synthesis of a new nanofertilizer incorporating micronutrients with hydroxyapatite (nHA) and its impacts on Asparagus officinalis germination. FESEM images show that the nanofertilizer particles were in rod-shaped with diameters of 20–25 nm and length of approximately 86 nm, while EDX image reveals micronutrient compositions: Ag (696 mg kg−1), Zn (619 mg kg−1), Cu (427 mg kg−1), Co (519 mg kg−1g) and Fe (751 mg kg−1). The 10-day-long Asparagus officinalis seeds test proved a faster germination rate (46.4 ± 1.2 mm/10 days) compared to that of normal treatment (35.3 ± 0.8 mm/10 days). When asparagus was used the micronutrients nano system, they had the ability to grow faster than unused. These results suggest promising application of nanoferitilizer in agriculture.

After many years of service, constructed infrastructure facilities including drainage pipes, bridges, and roads show sign of deterioration. To ensure public safety and efficient management of these crucial assets, condition monitoring and mathematical predictive models have been widely used. Despite the advance in condition-monitoring techniques, closed-circuit television (CCTV) and expert-based inspection technique are still commonly used owing to their ease of use, productivity, and lower cost. Utilizing those condition data, Markov models have been widely used as a predictive tool for asset management of constructed infrastructure facilities. However, the sensitivity of the Markov model to different sampling sizes of condition data has not been investigated. This has a practical implication as more owners of infrastructure facilities start to collect condition data and are interested in understanding current and future deterioration of their infrastructure assets. This study addresses this know...

This study aims to assess the reliability of hydraulic performance of concrete pipes at the design stage. The reliability assessment is on the basis of the use of Markov chain theory for modeling pipe deterioration during service life and coefficient of variation (COV) for representing uncertainty in design, installation, and operational parameters. The results of a case study in this study show that increasing pipe diameter from 600 mm (as per current design) to 750 mm (as per reliability assessment) can significantly reduce the risk of hydraulic failure during service life and delay inspection and hydraulic rehabilitation by 10 years.

Advances in modern computing technology have enabled a wide variety of applications in many areas. Realizing that mobile devices such as smartphones and tablets are great learning platforms for educating young children, the authors create an interactive system that helps young kids learn new concepts via videos. By allowing kids to choose objects in the video that they are watching, the system provides relevant information about those objects. It has the effect of enhancing young kids’ understanding about new concepts in the video. The system employs Cross-based Local Multipoint Filtering (CLMF) for object selection and Support Vector Machine (SVM) for recognition. Experimental results show that our system has achieved 95 % recognition accuracy.

This work mentions the effect of polyethylene oxide (PEO) content on some characteristics and properties of polylactic acid (PLA)/chitosan (CS) films loading nifedipine (NIF). The water contact angle, droplet size values and Field Emission Scanning Electron Microscopy (FESEM) images of the films are used to investigate their morphology and hydrophobicity. The obtained results show that the hydrophobic property of the PLA/CS/NIF films is improved by the presence of PEO. Besides, the PLA/CS/NIF films containing PEO have tighter structure and water absorbed ability less than those of the PLA/CS/NIF films

Condition monitoring of storm-water pipe systems are carried-out regularly using semi-automated processors. Semi-automated inspection is time consuming, expensive and produces varying and relatively unreliable results due to operators fatigue and novicity. This paper propose an innovative method to automate the storm-water pipe inspection and condition assessment process which employs a computer vision algorithm based on deep-neural network architecture to classify the defect types automatically. With the proposed method, the operator only needs to guide the robot through each pipe and no longer needs to be an expert. The results obtained on a CCTV video dataset of storm-water pipes shows that the deep neural network architectures trained with data augmentation and transfer learning is capable of achieving high accuracies in identifying the defect types.