In this paper, the effect of utilizing crumb rubber (CR) and recycled coarse aggregate (RCA) as partial replacements for fine aggregate (FA) and coarse aggregate (CA), respectively in concrete on the mechanical properties of concrete was experimentally investigated. The experimental work was carried out on 84 concrete cylinder (150x300 mm in size) at different levels of replacement of FA and CA with CR and RCA, respectively. The mechanical properties of concrete were measured at 28 days. The utilization of CR and RCA showed a marked influence on the mechanical properties of concrete especially in the case of FA replacement with CR. Eight effective predictive models utilizing multivariate regression to estimate the compressive strength and splitting tensile strength of concrete containing CR and RCA as partial replacement of FA and CA, respectively at 28 days were proposed. The models were compared to find the best one. There are two input parameters: a) level of replacement of CA with RCA, and b) Level of replacement of FA with CR. The output parameters were compressive and splitting tensile strengths. As compared to other proposed models, the full quadratic model had the highest correlation in both cases of compressive and splitting tensile strength models.

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A fatty acid amide has been made from diethanolamine and glucosamine using Novozyme 435 lipase in organic solvents. Fatty acid amide is a surfactant that is widely used in the pharmaceutical and cosmetic fields. As a biosurfactant, fatty acid amides have the advantage of being a surfactant that is renewable and environmentally friendly. This study aimed to obtain biosurfactants by applying the Response Surface Method and determining the effect of adding lipase, amine, and the yield of fatty acid amide. The results showed that the addition of amines and temperature would increase the yield of the two amides produced. However, the effect of temperature is more significant than the addition of amines. The optimal results obtained in the synthesis of diethanolamide were obtained when using enzyme 11-12%, diethanolamine 2-2.5 molar, and temperature 45-49°C, and in the synthesis of glucosamide, optimal glucosamide yield was obtained when using enzyme 9-10%, glucosamine 2-3 molar, and a temperature of 40-45°C.

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The purpose of this article is to conceptualize the use of Drones and Remote Sensors technology in pavement evaluation, which have allowed to advance in the development of different methods to detect deterioration with precision and reliability. Thus, through the use of images, data collection with high precision and the ease in traveling long distances have made the drone-remote sensor integration a tool that can undoubtedly be used today in road projects such as improvement, rehabilitation and conservation. The methodology consisted in the interpretation and explanation of the relationship between Drone, Remote Sensors and their application in the visual evaluation of pavements. For this purpose, several normative documents, academic and research works at national and international level have been consulted. As results and discussion, the importance and conceptualization of the subject in question is shown. Finally, it was concluded by showing the need to implement these technologies in the development of road projects and the possible advantages in the immediate future.

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Now-a-days, the voltage and current wave-shapes of distribution system is deviated due to power-electronic controlled non-linear load devices causing severe power-quality issues. Among the various custom-compensation devices, the DSTATCOM is most suitable for eradication of harmonic currents, reactive-power control, voltage balancing, power-factor correction, etc. The DSTATCOM utilizes the inverter for mitigation, but it is un-popular because of high rated switching devices, absence of current-sharing principle, high size and low efficiency, so on. A parallel-connected dual-inverter based DSTATCOM is also available; it reduces the rated capacity of DSTATCOM. But, un-popular due to complex control circuitry and dual DC capacitors, need of more switches which increases the switching losses, size, cost, complex design and low efficiency. In this paper, a novel reduced-switch isolated-VSIs DSTATCOM has been developed for PQ enhancement in distribution system using proposed MRE-PWM control scheme. The performance evaluation of proposed reduced-switch isolated-VSIs DSTATCOM is investigated in balanced, un-balanced and variable non-linear load conditions by using Matlab-Simulink computational tool, simulation results are presented.

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This paper aims at applying neural networks to the prediction of the output power of a photovoltaic installation in the framework of the valorisation of renewable energies in the Benin Republic. This prediction was made by using a hybrid neural network (NARX network and feedforward neural network) that we trained with MATLAB's Neural Network Start (NNS) tool where we used 12 years (01-01-2005 to 12-31-2016) meteorological data uploaded in the PVGIS database and Levenberg-Marquardt’s algorithm. We used the NARX network (Nonlinear Auto-Regressive with eXogenous Input) network for the prediction of meteorological data such as the beam radiation (direct) on the inclined plane, the diffuse irradiance on the inclined plane, the position of the sun, the air temperature at 2m from the ground and the total wind speed at 10m from the ground at a 3-hour horizon. The feedforward network was then used to forecast PV power from the predicted meteorological data at a 72-hour horizon. At the end of the prediction, we were able to obtain a mean square error of 0.46 and a regression coefficient of 0.99.

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In the present study, an instrumented central tube’s wear analysis was carried out in a tube bundle with triangular configuration having parallel tubes. The pitch to diameter ratio (P/D) is 1.45. The fluid i.e. water’s velocity in cross flow increases from 0.18 to 0.55 m/s in equal increments of 0.09 m/s. Tests were carried out using five distinct flow velocities along with three unique time intervals (i.e. 60, 90 and 120 minutes). A Scanning Electron Microscope (SEM) is utilized during experimentation to examine the scars and gauge the wear scar dimensions developed on the surface of tubes. It can be seen during vibrations analysis that the amplitude of transverse vibrations increases considerably when we move past the velocity of 0.27 m/s and monotonically increases till the maximum velocity i.e. 0.55 m/s is reached. This analysis concludes that the considerable wear is caused in the transverse direction at higher velocities. Examining the wear area points out that there is no unusual change in it against all the time durations up till 0.37 m/s, but once this value is surpassed the wear area inclines towards appreciable increase with an increase the in cross flow velocity. The analysis of wear volume suggests that wear volume with increasing cross flow velocity shows an increasing trend. There is not any considerable change in pattern of wear volume against 60 and 90 minutes time intervals even at higher velocities. The change is observed against the 120 minutes interval; as the behavior becomes quite distinct and displays a notable increase in the wear volume.

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This study analyses the feasibility of using embedded processing device that will be used as edge deep learning device for real-time short-term load forecast for home energy management system (HEMS). We trained and implemented two well-known artificial neural network architecture, Multi-layer Perceptron (MLP) and Long Short-Term Memory (LSTM), into the Raspberry Pi as embedded computer and measure its prediction accuracy and processing time. The results revealed that MLP performs better than LSTM in terms of prediction accuracy and both models able to complete the forecasting process in less than a second for one-minute interval forecast. The embedded computing device has shown its potential in home energy management system field for reliable short-term load forecast which can significantly reduce the cost of the system for machine learning computation and thus reduce reliance on cloud computer for artificial intelligence inference.

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An electromechanical residential water flow meter was built, with IOT functions for reporting and control of consumption by the user, the system works with long-life rechargeable batteries. Reporting its consumption in real time to a non-relational database, a web application was also developed for monitoring and configuration. The system is configurable so that it can consult periodic reports and can suspend the flow of water if the user requires it.

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Over the years, mobile device has become the most preferred way to communication, sharing information and even sensitive data. In fact, many security measurements such as 2-step verification, OTP based transaction are also dependent on the authentication of mobile user. A lot of research initiatives have tried to develop both explicit and implicit authentication mechanism for the devices. But implicit authentication measures have gained much attention over the year, as they are more secure continuous measure that can be made undetectable. Majority of the previous studies on implicit authentication of mobile device are mainly focused on analyzing different data attributes in retrieving behavioral traits and using linear techniques to describe an individuals behavioral pattern. On top of that, almost all the previous studies ignores the consideration of analyzing how a user interact with a mobile environment as they are more focused on finding pattern within the retrieved data features. To our knowledge for the first time, this study analyze the user interaction with the mobile phone by mapping them into the Markov Decision Process (MDP) environment and later proposes a RNN agent based technique to extract user’s unique policy that could be used as biometric traits. The proposed technique is also able to extract a continually evolving user’s policy by incorporating data aggregation while ensuring authenticity. The comparative analysis of the proposed technique clearly indicates the suitability of this approach, as the proposed technique is able to outperform some of the well-known implicit authentication techniques and attain a recognition rate of around 94%.

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In this article the design and implementation of a digital level controller for a coupled tank system is presented. The CE105 MV tank system model is developed. Due to the system is not linear, mathematical modeling is performed using the equations that describe the dynamic behavior of the system through its physical characteristics. The controller is implemented using microcontrollers, visualization hardware and instructions introduction is also performed. The relative gains array (RGA) is used to determine the interaction between the inputs and outputs of the system, in order to choose the best way to control the variables. Finally, a graphical interface in LabVIEW software is performed in order to observe the system behavior in real time. As a result, good monitoring of set points is achieved by the control system implemented.

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Modern processes of agricultural production are characterized by a high level of mechanization and automation and are aimed at creating favorable growth and development of plants as well as soil microorganisms. The machinery used to mechanize these processes is science-intensive, its cost is constantly growing. The high level of agricultural production technologies, technical excellence and high cost of machinery require a high level of management of the technical base, finding ways to reduce the share of material and energy costs in the cost of agricultural products. The search for rational solutions in the acquisition and use of machine-tractor fleet (MTF) of agricultural enterprises is one of the most difficult tasks, because in its solution it is necessary to take into account a large number of factors, most of which are probabilistic and unmanageable. The article presents the possibilities of using application software for planning mechanized works in agricultural enterprises allows to form a rational composition of MTF, determining the need for machinery based on the structure of sown areas, technologies for growing crops and operating conditions of machine-tractor units (MTU) by scheduling their use. taking into account the value of direct operating costs. The use of the proposed algorithm in the application software allows you to choose the appropriate MTU, the use of which ensures the implementation of technological operations in the optimal agricultural terms with minimal energy consumption, which in turn reduces the cost of cultivated products.

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The present study was carried out to analyze the water quality of Udumalpet Taluk, Tamil Nadu, India by Water Quality Index. Spatial variation maps of physico-chemical parameter were produced using ArcGIS 10.8 software. 40 samples were collected from different sampling points and investigated for 17 physico-chemical parameters. The MNSF-WQI reveals that 72% of the water sample falls under good quality and 28% belong to the medium category. According to spatial distribution maps of WQI, water quality of the study area is fit for drinking and irrigation purpose but in urban areas it needs of prior treatment before usage because of improper drainage from domestic, paper and textile industries.

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Previous research shown that men seminal fertility rates significantly decreased in the last twenty years due to health status, life habit and environment. Thus, seminal quality prediction in men fertility has become very demanding decision support systems in the biomedical engineering field. Existing solutions focused on producing the accuracy of the prediction model. They also acknowledged that there were problems of imbalance class, ambiguity and noise of the sample dataset. However, the real issues of the domain were still vague. A framework consists of Resample-Smote Balance with the state-of-art Random Forest (RF) method is proposed to overcome the real-world problem and tested on the Fertility dataset. This method is introduced to alleviate the ambiguity and noise of the sample set. Subsequently, the Smote method is applied to balance the size of the dataset before the classification phase using RF is taken place. The performance of the proposed model is compared with other state-of-art classifiers such as MLP, SVM and DT. Consequently, this work manages to produce the best accuracy model with 98.2% that can improve the ambiguity and noise from the sample set at the same time competent to handle the imbalance class of the dataset. The RF also boost the accuracy model compare with other classifiers due to its capability to produce the most probable class from its majority-voting task as output.

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This paper addresses a Bianchi type-I Cosmological model with time-varying Gravitational constant, G and Cosmological constant, ? for perfect fluid in a homogeneous anisotropic space. For constant deceleration parameter q, a set of realistic solutions are obtained. The physical and geometrical properties of the model have been discussed in detail.

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This paper describes a scheme and implementation of a mirror turned intelligence that results in interactive mirror planned via a Raspberry Pi as the key controller. The most important principle of graphical representation of this prototype is that it can display customized information such as clock, calendar, current weather, food menu and even for news feed based on two-way voice communication. In other words, the Smart Mirror can function as a virtual assistant that responds to the user's request and questions delivered automatically. Thus, user can easily get information of various places near the hotel, or any information that the user wishes for by using voice commands and even smartly user can control appliances that are powered by Google Home. The guest would enjoy a wide range of interesting functionality through the mirror featured innovations that could certainly plays a significant part of technology in the human daily life.

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