Fourier-Transform Infrared micro-spectroscopy (µFT-IR) was used for an in vitro investigation on spermatozoa (SPZ) samples separately collected from caput and cauda of mouse epididymis. SPZ are characterized by deep biochemical changes during the transit along the epididymis and they can constitute ideal candidates for a µFT-IR investigation thanks to the ability of this technique in analyzing cells at a molecular level. For FT-IR spectral analysis SPZ pellets were resuspended in 0.9% NaCl solution and drops of few microliters were used for spectra acquisition. For this purpose, a Perkin Elmer Spectrum One FT-IR spectrometer equipped with a Perkin Elmer Multiscope system infrared microscope (Mercury Cadmium Telluride detector) was used. Spectral acquisitions were performed in specular-reflection mode with thin layers of the sample (2 ml) put on metallic IR-reflective surface. Infrared spectra were obtained for the two different kinds of samples in the wavenumber range 4000-500 nm. The use of a univariate analysis allowed us to use average spectra. Differences were evidenced in the infrared spectra from caput and cauda SPZ and biochemical changes in protein, nucleic acid, lipid, and carbohydrate content of cells were observed. In order to have quantitative information about the changes occurring in the infrared spectra related to different experimental conditions, the ratios between the area of selected peaks were evaluated. This ratiometric approach can be useful for assessing changes in protein, nucleic acid, lipid, and carbohydrate components.

The present investigation indicates that µFT-IR can constitute a valuable tool for monitoring in an easy and fast way the changes suffered by SPZ during the transit along the epididymis.

Pattern recognition is known as one of the applicable technologies for structural health monitoring (SHM) based on statistical characteristics extracted from raw vibration data. Structural condition assessment is an important step in SHM since either linear or nonlinear changes in the relevant properties may adversely alter the behavior of any structure. It looks therefore necessary to adopt efficient and robust approaches for the classification of different structural conditions using features extracted from the said raw vibration data. To achieve this goal, it is essential to correctly distinguish a normal or undamaged state of the structure, from an abnormal or damaged one. The primary aim of this work is to present and compare efficient classification methods using feature selection techniques to classify the structural conditions, even characterized by different levels of damage severity. All of the utilized classifiers need a training set pertinent to the undamaged and (foreseen) damaged conditions of the structure, as well as known class labels to be adopted in a supervised learning strategy. Autoregressive (AR) modeling and principal component analysis (PCA) are used in an effort to extract the features for the classification process. The performance and accuracy of the considered classification methods are assessed through a numerical benchmark concrete beam and an experimental benchmark laboratory frame.

Deep learning techniques are being widely applied to Human Activity Recognition (HAR). This paper describes the implementation and evaluation of a HAR system for daily life activities using the accelerometer of an Iphone-6. This system is based on a deep neural network including convolutional layers for feature extraction from accelerations and fully-connected layers for classification between activities. Different transformations have been applied to the acceleration signals in order to find the appropriate input data to the deep neural network. This study has used acceleration recordings from the MotionSense dataset, where 24 subjects performed 6 activities: walking downstairs, walking upstairs, sitting, standing, walking and jogging. The evaluation has been performed using a subject-wise cross validation: recordings from the same subject do not appear in training and testing sets at the same time. The proposed system has obtained a 9% improvement in accuracy compared to the baseline system based on Support Vector Machines. The best results was obtained using raw data as input to a deep neural network composed of 2 convolutional and 2 max-pooling layers with decreasing kernel sizes. Results suggest that using the module of the Fourier transform as inputs provides better results when classifying only between dynamic activities.

In some parts of the world, climate change has led to periods of drought that require to manage efficiently the scarce water and energy resources.
This paper proposes an IoT smart irrigation system specifically designed for urban areas where remote IoT devices have no direct access to the Internet or to the electrical grid, and where wireless communications are difficult due to the existence of long distances and multiple obstacles.
To tackle such issues, this paper proposes a LoRaWAN based architecture that provides long distance and communications with reduced power consumption. Specifically, the proposed system consists of IoT nodes that collect sensor data and send them to local fog computing nodes or a remote cloud, which determine an irrigation schedule that considers factors like the weather forecast or the moist detected by nearby nodes.
It is essential to deploy the IoT nodes in locations within the provided coverage range and that guarantee good speed rates and reduced energy consumption. Due to this reason, this paper describes the use of an in-house 3D-ray launching radio-planning tool to determine the best locations for IoT nodes on a real medium-scale scenario (a university campus) that was modelled with precision, including obstacles like buildings, vegetation or vehicles.
The obtained simulation results are compared with empirical measurements in order to assess the operating conditions and the radio planning tool accuracy. Thus, it is possible to optimize the wireless network topology and the overall performance of the network in terms of coverage, cost and energy consumption.

Remote radiation sensing technologies are important for radiation safety and environmental security applications. A dual mode Cs₂LiYCl₆:Ce³⁺ (CLYC) sensor was developed for simultaneous neutron measurements and gamma-ray spectroscopy. To keep users away from the hazardous areas, an unmanned aerial system (UAS) was used as a mobile sensor platform. The sensor package was integrated into the multicopter platform as a 'plug-and-fly' component allowing supports easy plugging and unplugging of the radiation sensor into the robotic platform in the field conditions. The measured photon energy resolution of the CLYC sensor is less than 5% at 662 keV. The detection of neutrons was achieved via ⁶Li(n,alpha)³H reaction. The sensor's signal communication and data fusion was designed using Robot Operating System (ROS) framework. The on-board signal analysis suite programmed as ROS functions in this sensor package includes the neutron-photon pulse shape discrimination (PSD) and the identification of photopeaks in the gamma spectrum. The time stamp and the GPS data were added to the resulting data of the automated analysis of sensor's signals. This data were reported to the user for real time awareness of the monitored area and for further analysis in temporal and spatial domains, and also in radiation mapping and source search tasks.

A porous silica gel plate impregnated with a colorimetric reagent, 4-amino-3-penten-2-one (Fluoral-P), has been fabricated for the first time to determinate formaldehyde. The reaction of formaldehyde and Fluoral-P produced a yellow product 3,5-diacetyl-1,4-dihydrolutidine (DDL) which was further photographed by a digital camera. A good linear relationship has been found between the intensity of blue component from the digital image and formaldehyde concentration in the range of 0-50 mg L^-1 with low detection limit of 2.2 0.1 mg L^-1. A good precision in the range of 0.59-7.75%RSD and an accuracy with the relative error of +3.7% from control samples are also obtained. These results demonstrate that our developed low cost sensor together with digital image colorimetry is potential in sensitively and quickly measuring formaldehyde.

This paper presents the design and implementation of an internet of thing (IoT) based smart framework for human heartbeat rate monitoring and control system. A comprehensive study of various techniques and technologies that are used in controlling the heartbeat rate is explored. The proposed system was designed and implemented on a breadboard with the various system components that are assembled, connected and tasted. Experimental results obtained from implemented prototype were found to be accurate as the system was able to sense and read the heartbeat rate of its user and transmits the sensed data through the internet. The system components were soldered on Vero board and cased inside a plastic container with the heart pulse sensor stretched so as to be clipped on the finger tip of the system user. From the results obtained, it was found that the resting heartbeat rate of children below the age of 17 is between 65-115 BMP and the resting heartbeat rate of an adult between the age 17-60 is 60-100 BPM. In addition, the resting heartbeat rate of old people who are sixty years old and above, their heartbeat rate is between 65-120 BPM respectively. These findings are in agreement with the state-of-the-art in the medical field. Furthermore, this paper presents an approach that is flexible, reliable, and confidential for heart beat rate monitoring and control system using sensor network and IoT technology that can be deployed to the medical field to assist the medical doctors in doing their work.

Alcohols have been widely employed in different applications from solvents to fuels but today the main concern is their energy value as fuel. This work reports a graphite-epoxy composite electrode with nickel oxyhydroxide nanoparticles anchored in reduced graphene oxide for the electrooxidation of methanol, ethanol and glycerol, aiming for future application in electronic tongues for biofuel quality control. The electrode was formed by a graphite-epoxy graphite compound with cyclic voltammetry electrodeposited reduced graphene oxide and nickel oxyhydroxide nanoparticles formed by the decomposition in NaOH alkaline solution of cyclic voltammetry electrodeposited nickel hexacyanoferrate. FE-SEM studies were performed to confirm NiOOH nanoparticle morphology; EDX was applied to analyze chemical composition and ImageJ software was applied to size nanoparticles: the average size of the NiOOH nanoparticles was 61±16 nm. To verify the performance of the prepared electrode, it was applied in the electrooxidation of alcohols in alkaline medium by cyclic voltammetry. By performing different calibration experiments of methanol, ethanol and glycerol it was possible to extract some information about the electrode in the presence of alcohols. The LOD for methanol, ethanol and glycerol were 2.16 mM, 2.73 mM and 0.09 mM, respectively, with sensitivity values of 1.32 µA mM^-1, 1.80 µA mM^-1 and 24.60 µA mM^-1, also for methanol, ethanol and glycerol. Multivariate inspection of the data using Principal Component Analysis (performed with use of the ClustVis online tool) demonstrated the potential ability to discriminate between the different alcohols, whereas the explained variance with the first two components was as high as 89.7%.

In Nepal, most of the farmers depend upon traditional agricultural practices. Adapting modern agricultural technology plays an important role in improving overall efficiency as well as the productivity of their yields. In modern agriculture, the Internet of Things (IoT) connects farmers to their farm via the sensors so that they could easily monitor the real-time conditions of their farm from anywhere. White Button Mushroom is a widely cultivated crop among Nepalese farmers. Although being the most consumed and cultivated crop, it is still overshadowed by the traditional cultivation approach which is resulting in low productivity, high manpower efficiency, more effort, and cost. This work aims to develop a monitoring system to monitor the environmental conditions of a mushroom farm. It enables a user to monitor crucial factors such as temperature, humidity, moisture, the light intensity on a mushroom farm through the end devices. White Button Mushroom requires optimum temperature ranging from 22°C to 25°C and humidity from 70% to 90%. Sensors are placed on fixed locations and spots of the farm. Then, the sensors measure the status of parameters that are transmitted to the remote monitoring station via a low power Node MCU. The codes for the controller are written in the Arduino programming language, debugged, compiled, and burnt into the microcontroller using the Arduino integrated development environment. The result shows successful monitoring of environmental conditions accessing the internet from anywhere. It minimizes human efforts and automates production, which could be beneficial to Nepalese farmers.

Colloids are metastable suspensions of particles dispersed in a base fluid, with high scientific and industrial importance, but the monitoring of these systems still demands expensive and large instrumentation. In this research, the measurement of concentration gradients in colloidal silica samples using an optical fiber sensor is reported. (189 nm)-silica nanoparticles were sedimented in test tubes for creating environments with different concentrations. The fiber probe was immersed in the assessed liquid, resulting in an increase in the dispersion of the reflected light intensity, which is caused by the particles Brownian motion. Therefore, the quasi-elastic light scattering phenomenon related to the diffusivity can be analyzed, providing information about the concentration gradients of the nanosystem with a straightforward, in-situ, and non-destructive approach.