Search results for: electrochemical sensing

Authors: Linyao Qiu, Zhiqiang Du

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As the rapid development of urbanization, economic developments, and steady population growth in China, the widespread devastation, economic damages, and loss of human lives caused by numerous forms of natural disasters are becoming increasingly serious every year. Disaster management requires available and effective cooperation of different roles and organizations in whole process including mitigation, preparedness, response and recovery. Due to the imbalance of regional development in China, the disaster management capabilities of national and provincial disaster reduction centers are uneven. When an undeveloped area suffers from disaster, neither local reduction department could get first-hand information like high-resolution remote sensing images from satellites and aircrafts independently, nor sharing mechanism is provided for the department to access to data resources deployed in other place directly. Most existing disaster management systems operate in a typical passive data-centric mode and work for single department, where resources cannot be fully shared. The impediment blocks local department and group from quick emergency response and decision-making. In this paper, we introduce a collaborative platform for distributed disaster reduction. To address the issues of imbalance of sharing data sources and technology in the process of disaster reduction, we propose a multi-role oriented collaboration business mechanism, which is capable of scheduling and allocating for optimum utilization of multiple resources, to link various roles for collaborative reduction business in different place. The platform fully considers the difference of equipment conditions in different provinces and provide several service modes to satisfy technology need in disaster reduction. An integrated collaboration system based on focusing services mechanism is designed and implemented for resource scheduling, functional integration, data processing, task management, collaborative mapping, and visualization. Actual applications illustrate that the platform can well support data sharing and business collaboration between national and provincial department. It could significantly improve the capability of disaster reduction in China.

Keywords: business collaboration, data sharing, distributed disaster reduction, focusing service

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Authors: Ji Zhou, Jung Hee Seo, Rajat Mittal

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Fish move in groups for foraging, reproduction, predator protection, and hydrodynamic efficiency. Schooling's predator protection involves the "many eyes" theory, which increases predator detection probability in a group. Reduced visual signature in a group scales with school size, offering per-capita protection. The ‘confusion effect’ makes it hard for predators to target prey in a group. These benefits, however, all focus on vision-based sensing, overlooking sound-based detection. Fish, including predators, possess sophisticated sensory systems for pressure waves and underwater sound. The lateral line system detects acoustic waves, while otolith organs sense infrasound, and sharks use an auditory system for low-frequency sounds. Among sound generation mechanisms of fish, the mechanism of dipole sound relates to hydrodynamic pressure forces on the body surface of the fish and this pressure would be affected by group swimming. Thus, swimming within a group could affect this hydrodynamic noise signature of fish and possibly serve as an additional protection afforded by schooling, but none of the studies to date have explored this effect. BAUVs with fin-like propulsors could reduce acoustic noise without compromising performance, addressing issues of anthropogenic noise pollution in marine environments. Therefore, in this study, we used our in-house immersed-boundary method flow and acoustic solver, ViCar3D, to simulate fish schools consisting of four swimmers in the classic ‘diamond’ configuration and discussed the feasibility of yielding higher swimming efficiency and controlling far-field sound signature of the school. We examine the effects of the relative phase of fin flapping of the swimmers and the simulation results indicate that the phase of the fin flapping is a dominant factor in both thrust enhancement and the total sound radiated into the far-field by a group of swimmers. For fish in the “diamond” configuration, a suitable combination of the relative phase difference between pairs of leading fish and trailing fish can result in better swimming performance with significantly lower hydroacoustic noise.

Keywords: fish schooling, biopropulsion, hydrodynamics, hydroacoustics

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Authors: Akimitsu Kugimiya, Kouhei Iwato, Toru Saito, Jiro Kohda, Yasuhisa Nakano, Yu Takano

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The measurement of amino acid content is reported to be useful for the diagnosis of several types of diseases, including lung cancer, gastric cancer, colorectal cancer, breast cancer, prostate cancer, and diabetes. The conventional detection methods for amino acid are high-performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC-MS), but they have several drawbacks as the equipment is cumbersome and the techniques are costly in terms of time and costs. In contrast, biosensors and biosensing methods provide more rapid and facile detection strategies that use simple equipment. The authors have reported a novel approach for the detection of each amino acid that involved the use of aminoacyl-tRNA synthetase (aaRS) as a molecular recognition element because aaRS is expected to a selective binding ability for corresponding amino acid. The consecutive enzymatic reactions used in this study are as follows: aaRS binds to its cognate amino acid and releases inorganic pyrophosphate. Hydrogen peroxide (H₂O₂) was produced by the enzyme reactions of inorganic pyrophosphatase and pyruvate oxidase. The Trinder’s reagent was added into the reaction mixture, and the absorbance change at 556 nm was measured using a microplate reader. In this study, an amino acid-sensing method using histidyl-tRNA synthetase (HisRS; histidine-specific aaRS) as molecular recognition element in combination with the Trinder’s reagent spectrophotometric method was developed. The quantitative performance and selectivity of the method were evaluated, and the optimal enzyme reaction and detection conditions were determined. The authors developed a simple and rapid method for detecting histidine with a combination of enzymatic reaction and spectrophotometric detection. In this study, HisRS was used to detect histidine, and the reaction and detection conditions were optimized for quantitation of these amino acids in the ranges of 1–100 µM histidine. The detection limits are sufficient to analyze these amino acids in biological fluids. This work was partly supported by Hiroshima City University Grant for Special Academic Research (General Studies).

Keywords: amino acid, aminoacyl-tRNA synthetase, biosensing, enzyme reaction

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Authors: Francisco J. García-de-Quirós, Gianmarco Radice

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When considering small robots working in a cooperative community for Moon surface exploration, navigation and inter-nodes communication aspects become a critical issue for the mission success. For this approach to succeed, it is necessary however to deploy the required infrastructure for the robotic community to achieve efficient self-localization as well as relative positioning and communications between nodes. In this paper, an exploration mission concept in which two cooperative robotic systems co-exist is presented. This paradigm hinges on a community of reference agents that provide support in terms of communication and navigation to a second agent community tasked with exploration goals. The work focuses on the role of the agent community in charge of the overall support and, more specifically, will focus on the positioning and navigation methods implemented in RF microwave bands, which are combined with the communication services. An analysis of the different methods for range and position calculation are presented, as well as the main limiting factors for precision and resolution, such as phase and frequency noise in RF reference carriers and drift mechanisms such as thermal drift and random walk. The effects of carrier frequency instability due to phase noise are categorized in different contributing bands, and the impact of these spectrum regions are considered both in terms of the absolute position and the relative speed. A mission scenario is finally proposed, and key metrics in terms of mass and power consumption for the required payload hardware are also assessed. For this purpose, an application case involving an RF communication network in UHF Band is described, in coexistence with a communications network used for the single agents to communicate within the both the exploring agents as well as the community and with the mission support agents. The proposed approach implements a substantial improvement in planetary navigation since it provides self-localization capabilities for robotic agents characterized by very low mass, volume and power budgets, thus enabling precise navigation capabilities to agents of reduced dimensions. Furthermore, a common and shared localization radiofrequency infrastructure enables new interaction mechanisms such as spatial arrangement of agents over the area of interest for distributed sensing.

Keywords: cooperative robotics, localization, robot navigation, surface exploration

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Authors: Zongmin Ma, Shaowen Zhang, Yueping Fu, Jun Tang, Yunbo Shi, Jun Liu

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Solid-state quantum sensors are attracting wide interest because of their high sensitivity at room temperature. In particular, spin properties of nitrogen–vacancy (NV) color centres in diamond make them outstanding sensors of magnetic fields, electric fields and temperature under ambient conditions. Much of the work on NV magnetic sensing has been done so as to achieve the smallest volume, high sensitivity of NV ensemble-based magnetometry using micro-cavity, light-trapping diamond waveguide (LTDW), nano-cantilevers combined with MEMS (Micro-Electronic-Mechanical System) techniques. Recently, frequency-modulated microwaves with continuous optical excitation method have been proposed to achieve high sensitivity of 6 μT/√Hz using individual NV centres at nanoscale. In this research, we built-up an experiment to measure static magnetic field through continuous wave optical excitation with frequency-modulated microwaves method under continuous illumination with green pump light at 532 nm, and bulk diamond sample with a high density of NV centers (1 ppm). The output of the confocal microscopy was collected by an objective (NA = 0.7) and detected by a high sensitivity photodetector. We design uniform and efficient excitation of the micro strip antenna, which is coupled well with the spin ensembles at 2.87 GHz for zero-field splitting of the NV centers. Output of the PD signal was sent to an LIA (Lock-In Amplifier) modulated signal, generated by the microwave source by IQ mixer. The detected signal is received by the photodetector, and the reference signal enters the lock-in amplifier to realize the open-loop detection of the NV atomic magnetometer. We can plot ODMR spectra under continuous-wave (CW) microwave. Due to the high sensitivity of the lock-in amplifier, the minimum detectable value of the voltage can be measured, and the minimum detectable frequency can be made by the minimum and slope of the voltage. The magnetic field sensitivity can be derived from η = δB√T corresponds to a 10 nT minimum detectable shift in the magnetic field. Further, frequency analysis of the noise in the system indicates that at 10Hz the sensitivity less than 10 nT/√Hz.

Keywords: nitrogen-vacancy (NV) centers, frequency-modulated microwaves, magnetic field sensitivity, noise density

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Authors: Lalit Kumar, Rashid Al Shidi

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Phoenix dactylifera (date palm) is a major crop in many middle-eastern countries, including Oman. The Dubas bug Ommatissus lybicus is the main pest that affects date palm crops. However not all plantations are infested. It is still uncertain why some plantations get infested while others are not. This research investigated whether tree density and the system of planting (random versus systematic) had any relationship with infestation and levels of infestation. Remote Sensing and Geographic Information Systems were used to determine the density of trees (number of trees per unit area) while infestation levels were determined by manual counting of insects on 40 leaflets from two fronds on each tree, with a total of 20-60 trees in each village. The infestation was recorded as the average number of insects per leaflet. For tree density estimation, WorldView-3 scenes, with eight bands and 2m spatial resolution, were used. The Local maxima method, which depends on locating of the pixel of highest brightness inside a certain exploration window, was used to identify the trees in the image and delineating individual trees. This information was then used to determine whether the plantation was random or systematic. The ordinary least square regression (OLS) was used to test the global correlation between tree density and infestation level and the Geographic Weight Regression (GWR) was used to find the local spatial relationship. The accuracy of detecting trees varied from 83–99% in agricultural lands with systematic planting patterns to 50–70% in natural forest areas. Results revealed that the density of the trees in most of the villages was higher than the recommended planting number (120–125 trees/hectare). For infestation correlations, the GWR model showed a good positive significant relationship between infestation and tree density in the spring season with R² = 0.60 and medium positive significant relationship in the autumn season, with R² = 0.30. In contrast, the OLS model results showed a weaker positive significant relationship in the spring season with R² = 0.02, p < 0.05 and insignificant relationship in the autumn season with R² = 0.01, p > 0.05. The results showed a positive correlation between infestation and tree density, which suggests the infestation severity increased as the density of date palm trees increased. The correlation result showed that the density alone was responsible for about 60% of the increase in the infestation. This information can be used by the relevant authorities to better control infestations as well as to manage their pesticide spraying programs.

Keywords: dubas bug, date palm, tree density, infestation levels

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Authors: Valentin Père, Mathieu Milhé, Fabien Baillon, Jean-Louis Dirion

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Microgrids offer potential solutions to meet the need for local grid stability and increase isolated networks autonomy with the integration of intermittent renewable energy production and storage facilities. In such a context, sizing production and storage for a given network is a complex task, highly depending on input data such as power load profile and renewable resource availability. This work aims at developing an operating cost computation methodology for different microgrid designs based on the use of deep reinforcement learning (RL) algorithms to tackle the optimal operation problem in stochastic environments. RL is a data-based sequential decision control method based on Markov decision processes that enable the consideration of random variables for control at a chosen time scale. Agents trained via RL constitute a promising class of Energy Management Systems (EMS) for the operation of microgrids with energy storage. Microgrid sizing (or design) is generally performed by minimizing investment costs and operational costs arising from the EMS behavior. The latter might include economic aspects (power purchase, facilities aging), social aspects (load curtailment), and ecological aspects (carbon emissions). Sizing variables are related to major constraints on the optimal operation of the network by the EMS. In this work, an islanded mode microgrid is considered. Renewable generation is done with photovoltaic panels; an electrochemical battery ensures short-term electricity storage. The controllable unit is a hydrogen tank that is used as a long-term storage unit. The proposed approach focus on the transfer of agent learning for the near-optimal operating cost approximation with deep RL for each microgrid size. Like most data-based algorithms, the training step in RL leads to important computer time. The objective of this work is thus to study the potential of Batch-Constrained Q-learning (BCQ) for the optimal sizing of microgrids and especially to reduce the computation time of operating cost estimation in several microgrid configurations. BCQ is an off-line RL algorithm that is known to be data efficient and can learn better policies than on-line RL algorithms on the same buffer. The general idea is to use the learned policy of agents trained in similar environments to constitute a buffer. The latter is used to train BCQ, and thus the agent learning can be performed without update during interaction sampling. A comparison between online RL and the presented method is performed based on the score by environment and on the computation time.

Keywords: batch-constrained reinforcement learning, control, design, optimal

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Authors: Gema M. Rodado, Jose M. Olavarrieta

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Hydrogen fuel cell technologies have experienced a great boost in the last decades, significantly increasing the production of these devices for both stationary and portable (mainly automotive) applications; these are influenced by two main factors: environmental pollution and energy shortage. A fuel cell is an electrochemical device that converts chemical energy directly into electricity by using hydrogen and oxygen gases as reactive components and obtaining water and heat as byproducts of the chemical reaction. Fuel cells, specifically those of Proton Exchange Membrane (PEM) technology, are considered an alternative to internal combustion engines, mainly because of the low emissions they produce (almost zero), high efficiency and low operating temperatures (< 373 K). The introduction and use of fuel cells in the automotive market requires the development of standardized and validated procedures to test and evaluate their performance in different environmental conditions including vibrations and freeze-thaw cycles. These situations of vibration and extremely low/high temperatures can affect the physical integrity or even the excellent operation or performance of the fuel cell stack placed in a vehicle in circulation or in different climatic conditions. The main objective of this work is the development and validation of vibration and freeze-thaw cycling test procedures for fuel cell stacks that can be used in a vehicle in order to consolidate their safety, performance, and durability. In this context, different experimental tests were carried out at the facilities of the National Hydrogen Centre (CNH2). The experimental equipment used was: A vibration platform (shaker) for vibration test analysis on fuel cells in three axes directions with different vibration profiles. A walk-in climatic chamber to test the starting, operating, and stopping behavior of fuel cells under defined extreme conditions. A test station designed and developed by the CNH2 to test and characterize PEM fuel cell stacks up to 10 kWe. A 5 kWe PEM fuel cell stack in off-operation mode was used to carry out two independent experimental procedures. On the one hand, the fuel cell was subjected to a sinusoidal vibration test on the shaker in the three axes directions. It was defined by acceleration and amplitudes in the frequency range of 7 to 200 Hz for a total of three hours in each direction. On the other hand, the climatic chamber was used to simulate freeze-thaw cycles by defining a temperature range between +313 K and -243 K with an average relative humidity of 50% and a recommended ramp up and rump down of 1 K/min. The polarization curve and gas leakage rate were determined before and after the vibration and freeze-thaw tests at the fuel cell stack test station to evaluate the robustness of the stack. The results were very similar, which indicates that the tests did not affect the fuel cell stack structure and performance. The proposed procedures were verified and can be used as an initial point to perform other tests with different fuel cells.

Keywords: climatic chamber, freeze-thaw cycles, PEM fuel cell, shaker, vibration tests

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Authors: Aayushi Pandey, Manoj Kumar Pandey, Ashutosh Tiwari, Kireet Kumar

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Glaciers play an important role in climate change and are sensitive phenomena of global climate change scenario. Glaciers in Himalayas are unique as they are predominantly valley type and are located in tropical, high altitude regions. These glaciers are often covered with debris which greatly affects ablation rate of glaciers and work as a sensitive indicator of glacier health. The aim of this study is to map high altitude Glacier surface with a focus on glacial lake and debris estimation using different techniques in Nagling glacier of dhauliganga basin in Himalayan region. Different Image Classification techniques i.e. thresholding on different band ratios and supervised classification using maximum likelihood classifier (MLC) have been used on high resolution sentinel 2A level 1c satellite imagery of 14 October 2017.Here Near Infrared (NIR)/Shortwave Infrared (SWIR) ratio image was used to extract the glaciated classes (Snow, Ice, Ice Mixed Debris) from other non-glaciated terrain classes. SWIR/BLUE Ratio Image was used to map valley rock and Debris while Green/NIR ratio image was found most suitable for mapping Glacial Lake. Accuracy assessment was performed using high resolution (3 meters) Planetscope Imagery using 60 stratified random points. The overall accuracy of MLC was 85 % while the accuracy of Band Ratios was 96.66 %. According to Band Ratio technique total areal extent of glaciated classes (Snow, Ice ,IMD) in Nagling glacier was 10.70 km2 nearly 38.07% of study area comprising of 30.87 % Snow covered area, 3.93% Ice and 3.27 % IMD covered area. Non-glaciated classes (vegetation, glacial lake, debris and valley rock) covered 61.93 % of the total area out of which valley rock is dominant with 33.83% coverage followed by debris covering 27.7 % of the area in nagling glacier. Glacial lake and Debris were accurately mapped using Band ratio technique Hence, Band Ratio approach appears to be useful for the mapping of debris covered glacier in Himalayan Region.

Keywords: band ratio, Dhauliganga basin, glacier mapping, Himalayan region, maximum likelihood classifier (MLC), Sentinel-2 satellite image

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Authors: Olga Yu Kurapova, Alexander V. Shorokhov, Vladimir G. Konakov

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Nowadays, due to their exceptional anion conductivity at high temperatures cubic zirconia solid solutions, stabilized by rare-earth and alkaline-earth metal oxides, are widely used as a solid electrolyte (SE) materials in different electrochemical devices such as gas sensors, oxygen pumps, solid oxide fuel cells (SOFC), etc. Nowadays the intensive studies are carried out in a field of novel fully stabilized zirconia based SE development. The use of precursor powders for SE manufacturing allows predetermining the microstructure, electrical and sensor characteristics of zirconia based ceramics used as SE. Thus the goal of the present work was the investigation of the effect of precursor powder size on the electrical and sensor characteristics of fully stabilized zirconia-based solid electrolytes with compositions of 0,08Y2O3∙0,92ZrO2 (YSZ), 0,06Ce2O3∙ 0,06Y2O3∙0,88ZrO2 and 0,09Ce2O3∙0,06Y2O3-0,85ZrO2. The synthesis of precursors powders with different mean particle size was performed by sol-gel synthesis in the form of reversed co-precipitation from aqueous solutions. The cakes were washed until the neutral pH and pan-dried at 110 °С. Also, YSZ ceramics was obtained by conventional solid state synthesis including milling into a planetary mill. Then the powder was cold pressed into the pellets with a diameter of 7.2 and ~4 mm thickness at P ~16 kg/cm2 and then hydrostatically pressed. The pellets were annealed at 1600 °С for 2 hours. The phase composition of as-synthesized SE was investigated by X-Ray photoelectron spectroscopy ESCA (spectrometer ESCA-5400, PHI) X-ray diffraction analysis - XRD (Shimadzu XRD-6000). Following galvanic cell О2 (РО2(1)), Pt | SE | Pt, (РО2(2) = 0.21 atm) was used for SE sensor properties investigation. The value of РО2(1) was set by mixing of O2 and N2 in the defined proportions with the accuracy of  5%. The temperature was measured by Pt/Pt-10% Rh thermocouple, The cell electromotive force (EMF) measurement was carried out with ± 0.1 mV accuracy. During the operation at the constant temperature, reproducibility was better than 5 mV. Asymmetric potential measured for all SE appeared to be negligible. It was shown that the resistivity of YSZ ceramics decreases in about two times upon the mean agglomerates decrease from 200-250 to 40 nm. It is likely due to the both surface and bulk resistivity decrease in grains. So the overall decrease of grain size in ceramic SE results in the significant decrease of the total ceramics resistivity allowing sensor operation at lower temperatures. For the SE manufactured the estimation of oxygen ion transfer number tion was carried out in the range 600-800 °С. YSZ ceramics manufactured from powders with the mean particle size 40-140 nm, shows the highest values i.e. 0.97-0.98. SE manufactured from precursors with the mean particle size 40-140 nm shows higher sensor characteristic i.e. temperature and oxygen concentration EMF dependencies, EMF (ENernst - Ereal), tion, response time, then ceramics, manufactured by conventional solid state synthesis.

Keywords: oxygen sensors, precursor powders, sol-gel synthesis, stabilized zirconia ceramics

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Authors: Tausif A. Ishtiaque, Zarrin T. Tasin, Kazi S. Akter

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Current trend of urban expansion, especially in the developing countries has caused significant changes in land cover, which is generating great concern due to its widespread environmental degradation. Energy consumption of the cities is also increasing with the aggravated heat island effect. Distribution of land surface temperature (LST) is one of the most significant climatic parameters affected by urban land cover change. Recent increasing trend of LST is causing elevated temperature profile of the built up area with less vegetative cover. Gradual change in land cover, especially decrease in vegetative cover is enhancing the Urban Heat Island (UHI) effect in the developing cities around the world. Increase in the amount of urban vegetation cover can be a useful solution for the reduction of UHI intensity. LST and Normalized Difference Vegetation Index (NDVI) have widely been accepted as reliable indicators of UHI and vegetation abundance respectively. Chittagong, the second largest city of Bangladesh, has been a growth center due to rapid urbanization over the last several decades. This study assesses the intensity of UHI in Chittagong city by analyzing the relationship between LST and NDVI based on the type of land use/land cover (LULC) in the study area applying an integrated approach of Geographic Information System (GIS), remote sensing (RS), and regression analysis. Land cover map is prepared through an interactive supervised classification using remotely sensed data from Landsat ETM+ image along with NDVI differencing using ArcGIS. LST and NDVI values are extracted from the same image. The regression analysis between LST and NDVI indicates that within the study area, UHI is directly correlated with LST while negatively correlated with NDVI. It interprets that surface temperature reduces with increase in vegetation cover along with reduction in UHI intensity. Moreover, there are noticeable differences in the relationship between LST and NDVI based on the type of LULC. In other words, depending on the type of land usage, increase in vegetation cover has a varying impact on the UHI intensity. This analysis will contribute to the formulation of sustainable urban land use planning decisions as well as suggesting suitable actions for mitigation of UHI intensity within the study area.

Keywords: land cover change, land surface temperature, normalized difference vegetation index, urban heat island

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Authors: Rajashree Bothale, Venkateswara Rao

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Indian Himalayas also known as third pole with 0.9 Million SQ km area, contain the largest reserve of ice and snow outside poles and affect global climate and water availability in the perennial rivers. The variations in the extent of snow are indicative of climate change. The snow melt is sensitive to climate change (warming) and also an influencing factor to the climate change. A study of the spatio-temporal dynamics of snow cover and melt/freeze conditions is carried out using space based observations in visible and microwave bands. An analysis period of 2003 to 2015 is selected to identify and map the changes and trend in snow cover using Indian Remote Sensing (IRS) Advanced Wide Field Sensor (AWiFS) and Moderate Resolution Imaging Spectroradiometer(MODIS) data. For mapping of wet snow, microwave data is used, which is sensitive to the presence of liquid water in the snow. The present study uses Ku-band scatterometer data from QuikSCAT and Oceansat satellites. The enhanced resolution images at 2.25 km from the 13.6GHz sensor are used to analyze the backscatter response to dry and wet snow for the period of 2000-2013 using threshold method. The study area is divided into three major river basins namely Brahmaputra, Ganges and Indus which also represent the diversification in Himalayas as the Eastern Himalayas, Central Himalayas and Western Himalayas. Topographic variations across different zones show that a majority of the study area lies in 4000–5500 m elevation range and the maximum percent of high elevated areas (>5500 m) lies in Western Himalayas. The effect of climate change could be seen in the extent of snow cover and also on the melt/freeze status in different parts of Himalayas. Melt onset day increases from east (March11+11) to west (May12+15) with large variation in number of melt days. Western Himalayas has shorter melt duration (120+15) in comparison to Eastern Himalayas (150+16) providing lesser time for melt. Eastern Himalaya glaciers are prone for enhanced melt due to large melt duration. The extent of snow cover coupled with the status of melt/freeze indicating solar radiation can be used as precursor for monsoon prediction.

Keywords: Indian Himalaya, Scatterometer, Snow Melt/Freeze, AWiFS, Cryosphere

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Authors: Anna Drabczyk, Sonia Kudlacik-Kramarczyk, Dagmara Malina, Bozena Tyliszczak, Agnieszka Sobczak-Kupiec

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Nowadays, nano-size materials are very popular group of materials among scientists. What is more, these materials find an application in a wide range of various areas. Therefore constantly increasing demand for nanomaterials including metallic nanoparticles such as silver of gold ones is observed. Therefore, new routes of their preparation are sought. Considering potential application of nanoparticles, it is important to select an adequate methodology of their preparation because it determines their size and shape. Among the most commonly applied methods of preparation of nanoparticles chemical and electrochemical techniques are leading. However, currently growing attention is directed into the biological or biochemical aspects of syntheses of metallic nanoparticles. This is associated with a trend of developing of new routes of preparation of given compounds according to the principles of green chemistry. These principles involve e.g. the reduction of the use of toxic compounds in the synthesis as well as the reduction of the energy demand or minimization of the generated waste. As a result, a growing popularity of the use of such components as natural plant extracts, infusions or essential oils is observed. Such natural substances may be used both as a reducing agent of metal ions and as a stabilizing agent of formed nanoparticles therefore they can replace synthetic compounds previously used for the reduction of metal ions or for the stabilization of obtained nanoparticles suspension. Methods that proceed in the presence of previously mentioned natural compounds are environmentally friendly and proceed without the application of any toxic reagents. Methodology: Presented research involves preparation of silver nanoparticles using selected plant extracts, e.g. artichoke extract. Extracts of natural origin were used as reducing and stabilizing agents at the same time. Furthermore, syntheses were carried out in the presence of additional polymeric stabilizing agent. Next, such features of obtained suspensions of nanoparticles as total antioxidant activity as well as content of phenolic compounds have been characterized. First of the mentioned studies involved the reaction with DPPH (2,2-Diphenyl-1-picrylhydrazyl) radical. The content of phenolic compounds was determined using Folin-Ciocalteu technique. Furthermore, an essential issue was also the determining of the stability of formed suspensions of nanoparticles. Conclusions: In the research it was demonstrated that metallic nanoparticles may be obtained using plant extracts or infusions as stabilizing or reducing agent. The methodology applied, i.e. a type of plant extract used during the synthesis, had an impact on the content of phenolic compounds as well as on the size and polydispersity of obtained nanoparticles. What is more, it is possible to prepare nano-size particles that will be characterized by properties desirable from the viewpoint of their potential application and such an effect may be achieved with the use of non-toxic reagents of natural origin. Furthermore, proposed methodology stays in line with the principles of green chemistry.

Keywords: green chemistry principles, metallic nanoparticles, plant extracts, stabilization of nanoparticles

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Authors: Misha Nedeljkovich

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In the film’s famous scene (Roma città aperta-1945), Pina (Anna Magnani) collapses in the street when machined-gunned by a German soldier. Her son Marcello (Vito Annchiarico) tries to revive her. Her death is signaling not closure, but the cycle of life; Marcello saves Francesco with the shawl taken from his mother’s corpse. One pivotal scene in Brief Encounter (1945) occurs in the apartment of Alec’s (Trevor Howard) friend Stephen (Valentine Dyall), when Stephen returns to catch Alec and Laura (Celia Johnson) together alone. David Lean directs this scene using her shawl as a sign of in flagrante delicto. In La Strada (1954), Gelsomina (Giulietta Masina) was waving good bye when her mother sensing impending doom changed her mind and desperately tried to stop her waving back with her shawl: Don’t go my daughter! Your shawl! Your shawl! Gelsomina refuses to return, waving back: It’s time to go! Stanley Kubrick’s tale of a boxer who crosses a mobster to win the heart of a lady, Killer’s Kiss (1955), reminds us that Times Square used to contain sweaty boxing gyms and dance halls. The film’s longest Times Square interlude is its oddest: the boxer Davie Gordon played by Jamie Smith has his shawl stolen by two playful men in Shriners’ hats who are silent except for one who blows a harmonica, faintly heard over honking cabs and overheard conversations. This long sequence appears to be joining in on directors’ shawl conversations with Kubrick’s own twist. Principle characters will never know why all this happened to them that evening. Love, death, happiness and everlasting misery all of that is caused by Dave’s shawl. Finally, the decade of cinematic shawl conversations conclude in Betolucci’s Before the Revolution (Prima della rivoluzione–1964). One of his character’s lifts up a shawl asking if this was a Rossellini’s shawl. I argue that exploring complimentary allusions in a film where directors are acknowledging their own great debt to another film or filmmaker will further our knowledge of film history adding both depth and resonance to the great works in cinema.

Keywords: allusions, Bertolucci, Fellini, homage, Kubrick, lean, Rossellini

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Authors: Deni̇z Ezgi̇ Gülmez, Mesut Kirca

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Nanoporous (np) metals have attracted considerable attention owing to their cellular morphological features at atomistic scale which yield ultra-high specific surface area awarding a great potential to be employed in diverse applications such as catalytic, electrocatalytic, sensing, mechanical and optical. As one of the carbon based nanostructures, fullerenes are also another type of outstanding nanomaterials that have been extensively investigated due to their remarkable chemical, mechanical and optical properties. In this study, the idea of improving the mechanical behavior of nanoporous metals by inclusion of the fullerenes, which offers a new metal-carbon nanocomposite material, is examined and discussed. With this motivation, tensile mechanical behavior of nanoporous metals reinforced with carbon fullerenes is investigated by classical molecular dynamics (MD) simulations. Atomistic models of the nanoporous metals with ultrathin ligaments are obtained through a stochastic process simply based on the intersection of spherical volumes which has been used previously in literature. According to this technique, the atoms within the ensemble of intersecting spherical volumes is removed from the pristine solid block of the selected metal, which results in porous structures with spherical cells. Following this, fullerene units are added into the cellular voids to obtain final atomistic configurations for the numerical tensile tests. Several numerical specimens are prepared with different number of fullerenes per cell and with varied fullerene sizes. LAMMPS code was used to perform classical MD simulations to conduct uniaxial tension experiments on np models filled by fullerenes. The interactions between the metal atoms are modeled by using embedded atomic method (EAM) while adaptive intermolecular reactive empirical bond order (AIREBO) potential is employed for the interaction of carbon atoms. Furthermore, atomic interactions between the metal and carbon atoms are represented by Lennard-Jones potential with appropriate parameters. In conclusion, the ultimate goal of the study is to present the effects of fullerenes embedded into the cellular structure of np metals on the tensile response of the porous metals. The results are believed to be informative and instructive for the experimentalists to synthesize hybrid nanoporous materials with improved properties and multifunctional characteristics.

Keywords: fullerene, intersecting spheres, molecular dynamic, nanoporous metals

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Authors: Jhoga Parth, T. Nasar, K. V. Anand

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An idealized definition of shoreline is that it is the zone of coincidence of three spheres such as atmosphere, lithosphere, and hydrosphere. Despite its apparent simplicity, this definition in practice a challenge to apply. In reality, the shoreline location deviates continually through time, because of various dynamic factors such as wave characteristics, currents, coastal orientation and the bathymetry, which makes the shoreline volatile. This necessitates us to monitor the shoreline in a temporal basis. If shoreline’s nature is understood at particular coastal stretch, it need not be the same trend at the other location, though belonging to the same sea front. Shoreline change is hence a local phenomenon and has to be studied with great intensity considering as many factors involved as possible. Erosion and accretion of sediment are such natures of a shoreline, which needs to be quantified by comparing with its predeceasing variations and understood before implementing any coastal projects. In recent years, advent of Global Positioning System (GPS) and Geographic Information System (GIS) acts as an emerging tool to quantify the intra and inter annual sediment rate getting accreted or deposited compared to other conventional methods in regards with time was taken and man power. Remote sensing data, on the other hand, paves way to acquire historical sets of data where field data is unavailable with a higher resolution. Short term and long term period shoreline change can be accurately tracked and monitored using a software residing in GIS - Digital Shoreline Analysis System (DSAS) developed by United States Geological Survey (USGS). In the present study, using DSAS, End Point Rate (EPR) is calculated analyze the intra-annual changes, and Linear Rate Regression (LRR) is adopted to study inter annual changes of shoreline. The shoreline changes are quantified for the scenario during the construction of breakwater in Ponnani river inlet along Kerala coast, India. Ponnani is a major fishing and landing center located 10°47’12.81”N and 75°54’38.62”E in Malappuram district of Kerala, India. The rate of erosion and accretion is explored using satellite and field data. The full paper contains the rate of change of shoreline, and its analysis would provide us understanding the behavior of the inlet at the study area during the construction of the training walls.

Keywords: DSAS, end point rate, field measurements, geo-informatics, shoreline variation

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Authors: Meng Miao, Jeongwoo Han, Cam Nguyen

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Ultra-wideband (UWB) time-domain impulse communication and radar systems use ultra-short duration pulses in the sub-nanosecond regime, instead of continuous sinusoidal waves, to transmit information. The pulse directly generates a very wide-band instantaneous signal with various duty cycles depending on specific usages. In UWB systems, the total transmitted power is spread over an extremely wide range of frequencies; the power spectral density is extremely low. This effectively results in extremely small interference to other radio signals while maintains excellent immunity to interference from these signals. UWB devices can therefore work within frequencies already allocated for other radio services, thus helping to maximize this dwindling resource. Therefore, impulse UWB technique is attractive for realizing high-data-rate, short-range communications, ground penetrating radar (GPR), and military radar with relatively low emission power levels. UWB antennas are the key element dictating the transmitted and received pulse shape and amplitude in both time and frequency domain. They should have good impulse response with minimal distortion. To facilitate integration with transmitters and receivers employing microwave integrated circuits, UWB antennas enabling direct integration are preferred. We present the development of two UWB antennas operating from 3.1 to 10.6 GHz and 0.3-6 GHz for UWB systems that provide direct integration with microwave integrated circuits. The operation of these antennas is based on the principle of wave propagation on a non-uniform transmission line. Time-domain EM simulation is conducted to optimize the antenna structures to minimize reflections occurring at the open-end transition. Calculated and measured results of these UWB antennas are presented in both frequency and time domains. The antennas have good time-domain responses. They can transmit and receive pulses effectively with minimum distortion, little ringing, and small reflection, clearly demonstrating the signal fidelity of the antennas in reproducing the waveform of UWB signals which is critical for UWB sensors and communication systems. Good performance together with seamless microwave integrated-circuit integration makes these antennas good candidates not only for UWB applications but also for integration with printed-circuit UWB transmitters and receivers.

Keywords: antennas, ultra-wideband, UWB, UWB communication systems, UWB radar systems

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Authors: Shelley Harrington

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As a result of increasingly dynamic business environments, the creation and fostering of dynamic capabilities, [those capabilities that enable sustained competitive success despite of dynamism through the awareness and reconfiguration of internal and external competencies], supported by organisational learning [a dynamic capability] has gained increased and prevalent momentum in the research arena. Presenting findings funded by the Economic Social Research Council, this paper investigates the extent to which Senior Management Team (SMT) personality (at the trait and facet level) is associated with the creation of dynamic managerial capabilities at the team level, and effective organisational learning/knowledge sharing within the firm. In doing so, this research highlights the importance of micro-foundations in organisational psychology and specifically dynamic capabilities, a field which to date has largely ignored the importance of psychology in understanding these important and necessary capabilities. Using a direct measure of personality (NEO PI-3) at the trait and facet level across 32 high technology and finance firms in the UK, their CEOs (N=32) and their complete SMTs [N=212], a new measure of dynamic managerial capabilities at the team level was created and statistically validated for use within the work. A quantitative methodology was employed with regression and gap analysis being used to show the empirical foundations of personality being positioned as a micro-foundation of dynamic capabilities. The results of this study found that personality homogeneity within the SMT was required to strengthen the dynamic managerial capabilities of sensing, seizing and transforming, something which was required to reflect strong organisational learning at middle management level [N=533]. In particular, it was found that the greater the difference [t-score gaps] between the personality profiles of a Chief Executive Officer (CEO) and their complete, collective SMT, the lower the resulting self-reported nature of dynamic managerial capabilities. For example; the larger the difference between a CEOs level of dutifulness, a facet contributing to the definition of conscientiousness, and their SMT’s level of dutifulness, the lower the reported level of transforming, a capability fundamental to strategic change in a dynamic business environment. This in turn directly questions recent trends, particularly in upper echelons research highlighting the need for heterogeneity within teams. In doing so, it successfully positions personality as a micro-foundation of dynamic capabilities, thus contributing to recent discussions from within the strategic management field calling for the need to empirically explore dynamic capabilities at such a level.

Keywords: dynamic managerial capabilities, senior management teams, personality, dynamism

Procedia PDF Downloads 254

Authors: Laura C. Loaiza, Elodie Salager, Nicolas Louvain, Athmane Boulaoued, Antonella Iadecola, Patrik Johansson, Lorenzo Stievano, Vincent Seznec, Laure Monconduit

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Lithium-ion batteries (LIBs) have an important place among energy storage devices due to their high capacity and good cyclability. However, the advancements in portable and transportation applications have extended the research towards new horizons, and today the development is hampered, e.g., by the capacity of the electrodes employed. Silicon and germanium are among the considered modern anode materials as they can undergo alloying reactions with lithium while delivering high capacities. It has been demonstrated that silicon in its highest lithiated state can deliver up to ten times more capacity than graphite (372 mAh/g): 4200 mAh/g for Li₂₂Si₅ and 3579 mAh/g for Li₁₅Si₄, respectively. On the other hand, germanium presents a capacity of 1384 mAh/g for Li₁₅Ge₄, and a better electronic conductivity and Li ion diffusivity as compared to Si. Nonetheless, the commercialization potential of Ge is limited by its cost. The synergetic effect of Si₁₋ₓGeₓ alloys has been proven, the capacity is increased compared to Ge-rich electrodes and the capacity retention is increased compared to Si-rich electrodes, but the exact performance of this type of electrodes will depend on factors like specific capacity, C-rates, cost, etc. There are several reports on various formulations of Si₁₋ₓGeₓ alloys with promising LIB anode performance with most work performed on complex nanostructures resulting from synthesis efforts implying high cost. In the present work, we studied the electrochemical mechanism of the Si₀.₅Ge₀.₅ alloy as a realistic micron-sized electrode formulation using carboxymethyl cellulose (CMC) as the binder. A combination of a large set of in situ and operando techniques were employed to investigate the structural evolution of Si₀.₅Ge₀.₅ during lithiation and delithiation processes: powder X-ray diffraction (XRD), X-ray absorption spectroscopy (XAS), Raman spectroscopy, and 7Li solid state nuclear magnetic resonance spectroscopy (NMR). The results have presented a whole view of the structural modifications induced by the lithiation/delithiation processes. The Si₀.₅Ge₀.₅ amorphization was observed at the beginning of discharge. Further lithiation induces the formation of a-Liₓ(Si/Ge) intermediates and the crystallization of Li₁₅(Si₀.₅Ge₀.₅)₄ at the end of the discharge. At really low voltages a reversible process of overlithiation and formation of Li₁₅₊δ(Si₀.₅Ge₀.₅)₄ was identified and related with a structural evolution of Li₁₅(Si₀.₅Ge₀.₅)₄. Upon charge, the c-Li₁₅(Si₀.₅Ge₀.₅)₄ was transformed into a-Liₓ(Si/Ge) intermediates. At the end of the process an amorphous phase assigned to a-SiₓGey was recovered. Thereby, it was demonstrated that Si and Ge are collectively active along the cycling process, upon discharge with the formation of a ternary Li₁₅(Si₀.₅Ge₀.₅)₄ phase (with a step of overlithiation) and upon charge with the rebuilding of the a-Si-Ge phase. This process is undoubtedly behind the enhanced performance of Si₀.₅Ge₀.₅ compared to a physical mixture of Si and Ge.

Keywords: lithium ion battery, silicon germanium anode, in situ characterization, X-Ray diffraction

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Authors: Vinicius G. Goecks, Gregory M. Gremillion, William D. Nothwang

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Modern approaches to train intelligent agents rely on prolonged training sessions, high amounts of input data, and multiple interactions with the environment. This restricts the application of these learning algorithms in robotics and real-world applications, in which there is low tolerance to inadequate actions, interactions are expensive, and real-time processing and action are required. This paper addresses this issue introducing CyberSteer, a novel approach to efficiently design intrinsic reward functions based on human intention to guide deep reinforcement learning agents with no environment-dependent rewards. CyberSteer uses non-expert human operators for initial demonstration of a given task or desired behavior. The trajectories collected are used to train a behavior cloning deep neural network that asynchronously runs in the background and suggests actions to the deep reinforcement learning module. An intrinsic reward is computed based on the similarity between actions suggested and taken by the deep reinforcement learning algorithm commanding the agent. This intrinsic reward can also be reshaped through additional human demonstration or critique. This approach removes the need for environment-dependent or hand-engineered rewards while still being able to safely shape the behavior of autonomous robotic agents, in this case, based on human intention. CyberSteer is tested in a high-fidelity unmanned aerial vehicle simulation environment, the Microsoft AirSim. The simulated aerial robot performs collision avoidance through a clustered forest environment using forward-looking depth sensing and roll, pitch, and yaw references angle commands to the flight controller. This approach shows that the behavior of robotic systems can be shaped in a reduced amount of time when guided by a non-expert human, who is only aware of the high-level goals of the task. Decreasing the amount of training time required and increasing safety during training maneuvers will allow for faster deployment of intelligent robotic agents in dynamic real-world applications.

Keywords: human-robot interaction, intelligent robots, robot learning, semisupervised learning, unmanned aerial vehicles

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Authors: Coriolano Salvini

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The increasing utilization of renewable energy sources for electric power production calls for the introduction of energy storage systems to match the electric demand along the time. Although many countries are pursuing as a final goal a “decarbonized” electrical system, in the next decades the traditional fossil fuel fed power plant still will play a relevant role in fulfilling the electric demand. Presently, such plants provide grid ancillary services (frequency control, grid balance, reserve, etc.) by adapting the output power to the grid requirements. An interesting option is represented by the possibility to use traditional plants to improve the grid storage capabilities. The present paper is addressed to small-medium size systems suited for distributed energy storage. The proposed Energy Storage System (ESS) is based on a Compressed Air Energy Storage (CAES) integrated into a Gas-Steam Combined Cycle (GSCC) or a Gas Turbine based CHP plants. The systems can be incorporated in an ex novo built plant or added to an already existing one. To avoid any geological restriction related to the availability of natural compressed air reservoirs, artificial storage is addressed. During the charging phase, electric power is absorbed from the grid by an electric driven intercooled/aftercooled compressor. In the course of the discharge phase, the compressed stored air is sent to a heat transfer device fed by hot gas taken upstream the Heat Recovery Steam Generator (HRSG) and subsequently expanded for power production. To maximize the output power, a staged reheated expansion process is adopted. The specific power production related to the kilogram per second of exhaust gas used to heat the stored air is two/three times larger than that achieved if the gas were used to produce steam in the HRSG. As a result, a relevant power augmentation is attained with respect to normal GSCC plant operations without additional use of fuel. Therefore, the excess of output power can be considered “fuel free” and the storage system can be compared to “pure” ESSs such as electrochemical, pumped hydro or adiabatic CAES. Representative cases featured by different power absorption, production capability, and storage capacity have been taken into consideration. For each case, a technical optimization aimed at maximizing the storage efficiency has been carried out. On the basis of the resulting storage pressure and volume, number of compression and expansion stages, air heater arrangement and process quantities found for each case, a cost estimation of the storage systems has been performed. Storage efficiencies from 0.6 to 0.7 have been assessed. Capital costs in the range of 400-800 €/kW and 500-1000 €/kWh have been estimated. Such figures are similar or lower to those featuring alternative storage technologies.

Keywords: artificial air storage reservoir, compressed air energy storage (CAES), gas steam combined cycle (GSCC), techno-economic analysis

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Authors: Kumar Shashvat, Amol P. Bhondekar

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In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset.

Keywords: odor classification, generative models, naive bayes, linear discriminant analysis

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Authors: H. Arzani, Z. Arzani

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Every piece of rangeland has a different set of physical and biological characteristics. This requires the manager to synthesis various information for regular monitoring to define changes trend to get wright decision for sustainable management. So range managers need to use computerized technologies to monitor rangeland, and select. The best management practices. There are four examples of computerized technologies that can benefit sustainable management: (1) Photographic method for cover measurement: The method was tested in different vegetation communities in semi humid and arid regions. Interpretation of pictures of quadrats was done using Arc View software. Data analysis was done by SPSS software using paired t test. Based on the results, generally, photographic method can be used to measure ground cover in most vegetation communities. (2) GPS application for corresponding ground samples and satellite pixels: In two provinces of Tehran and Markazi, six reference points were selected and in each point, eight GPS models were tested. Significant relation among GPS model, time and location with accuracy of estimated coordinates was found. After selection of suitable method, in Markazi province coordinates of plots along four transects in each 6 sites of rangelands was recorded. The best time of GPS application was in the morning hours, Etrex Vista had less error than other models, and a significant relation among GPS model, time and location with accuracy of estimated coordinates was found. (3) Application of satellite data for rangeland monitoring: Focusing on the long term variation of vegetation parameters such as vegetation cover and production is essential. Our study in grass and shrub lands showed that there were significant correlations between quantitative vegetation characteristics and satellite data. So it is possible to monitor rangeland vegetation using digital data for sustainable utilization. (4) Rangeland suitability classification with GIS: Range suitability assessment can facilitate sustainable management planning. Three sub-models of sensitivity to erosion, water suitability and forage production out puts were entered to final range suitability classification model. GIS was facilitate classification of range suitability and produced suitability maps for sheep grazing. Generally digital computers assist range managers to interpret, modify, calibrate or integrating information for correct management.

Keywords: computer, GPS, GIS, remote sensing, photographic method, monitoring, rangeland ecosystem, management, suitability, sheep grazing

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Authors: Huinan Zhang, Wenjie Jiang

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Accurate estimation of tropical cyclone intensity is of great importance for disaster prevention and mitigation. Existing techniques are largely based on satellite imagery data, and research and utilization of the inner thermal core structure characteristics of tropical cyclones still pose challenges. This paper presents a two-stage tropical cyclone intensity estimation network based on the fusion of coarse and fine-grained features from microwave brightness temperature data. The data used in this network are obtained from the thermal core structure of tropical cyclones through the Advanced Technology Microwave Sounder (ATMS) inversion. Firstly, the thermal core information in the pressure direction is comprehensively expressed through the maximal intensity projection (MIP) method, constructing coarse-grained thermal core images that represent the tropical cyclone. These images provide a coarse-grained feature range wind speed estimation result in the first stage. Then, based on this result, fine-grained features are extracted by combining thermal core information from multiple view profiles with a distributed network and fused with coarse-grained features from the first stage to obtain the final two-stage network wind speed estimation. Furthermore, to better capture the long-tail distribution characteristics of tropical cyclones, focal loss is used in the coarse-grained loss function of the first stage, and ordinal regression loss is adopted in the second stage to replace traditional single-value regression. The selection of tropical cyclones spans from 2012 to 2021, distributed in the North Atlantic (NA) regions. The training set includes 2012 to 2017, the validation set includes 2018 to 2019, and the test set includes 2020 to 2021. Based on the Saffir-Simpson Hurricane Wind Scale (SSHS), this paper categorizes tropical cyclone levels into three major categories: pre-hurricane, minor hurricane, and major hurricane, with a classification accuracy rate of 86.18% and an intensity estimation error of 4.01m/s for NA based on this accuracy. The results indicate that thermal core data can effectively represent the level and intensity of tropical cyclones, warranting further exploration of tropical cyclone attributes under this data.

Keywords: Artificial intelligence, deep learning, data mining, remote sensing

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Authors: Sulemana Ibrahim

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Food security remains a paramount global challenge, with vulnerable regions grappling with issues of hunger and malnutrition. This study embarks on a comprehensive exploration of data-driven strategies aimed at ameliorating food security in such regions. Our research employs a multifaceted approach, integrating data analytics to predict crop yields, optimizing supply chains, and enhancing food distribution networks. The study unfolds as a multi-dimensional analysis, commencing with the development of robust machine learning models harnessing remote sensing data, historical crop yield records, and meteorological data to foresee crop yields. These predictive models, underpinned by convolutional and recurrent neural networks, furnish critical insights into anticipated harvests, empowering proactive measures to confront food insecurity. Subsequently, the research scrutinizes supply chain optimization to address food security challenges, capitalizing on linear programming and network optimization techniques. These strategies intend to mitigate loss and wastage while streamlining the distribution of agricultural produce from field to fork. In conjunction, the study investigates food distribution networks with a particular focus on network efficiency, accessibility, and equitable food resource allocation. Network analysis tools, complemented by data-driven simulation methodologies, unveil opportunities for augmenting the efficacy of these critical lifelines. This study also considers the ethical implications and privacy concerns associated with the extensive use of data in the realm of food security. The proposed methodology outlines guidelines for responsible data acquisition, storage, and usage. The ultimate aspiration of this research is to forge a nexus between data science and food security policy, bestowing actionable insights to mitigate the ordeal of food insecurity. The holistic approach converging data-driven crop yield forecasts, optimized supply chains, and improved distribution networks aspire to revitalize food security in the most vulnerable regions, elevating the quality of life for millions worldwide.

Keywords: data-driven strategies, crop yield prediction, supply chain optimization, food distribution networks

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Authors: Melanie Francois, Gilles Caboche, Frederic Demoisson, Francois Maeght, Maria Paola Carpanese, Lionel Combemale, Pascal Briois

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Finding new clean, and efficient way for energy production is one of the actual global challenges. Advances in fuel cell technology have shown that, for few years, Protonic Ceramic Fuel Cell (PCFC) has attracted much attention in the field of new hydrogen energy thanks to their lower working temperature, possible higher efficiency, and better durability than classical SOFC. On the contrary of SOFC, where O²⁻ oxygen ion is the charge carrier, PCFC works with H⁺ proton as a charge carrier. Consequently, the lower activation energy of proton diffusion compared to the one of oxygen ion explains those benefits and allows PCFC to work in the 400-600°C temperature range. Doped-BaCeO₃ is currently the most chosen material for this application because of its high protonic conductivity; for example, BaCe₀.₉Y₀.₁O₃ δ exhibits a total conductivity of 1.5×10⁻² S.cm⁻¹ at 600°C in wet H₂. However, BaCeO₃ based perovskite has low stability in H₂O and/or CO₂ containing atmosphere, which limits their practical application. On the contrary, BaZrO₃ based perovskite exhibits good chemical stability but lower total conductivity than BaCeO₃ due to its larger grain boundary resistance. By substituting zirconium with 20% of yttrium, it is possible to achieve a total conductivity of 2.5×10⁻² S.cm⁻¹ at 600°C in wet H₂. However, the high refractory property of BaZr₀.₈Y₀.₂O₃-δ (noted BZY20) causes problems to obtain a dense membrane with large grains. Thereby, using a synthesis process that gives fine particles could allow better sinterability and thus decrease the number of grain boundaries leading to a higher total conductivity. In this work, BaZr₀.₈Y₀.₂O₃-δ have been synthesized by classical batch hydrothermal device and by a continuous hydrothermal device developed at ICB laboratory. The two variants of this process are able to work in supercritical conditions, leading to the formation of nanoparticles, which could be sintered at a lower temperature. The as-synthesized powder exhibits the right composition for the perovskite phase, impurities such as BaCO₃ and YO-OH were detected at very low concentration. Microstructural investigation and densification rate measurement showed that the addition of 1 wt% of ZnO as sintering aid and a sintering at 1550°C for 5 hours give high densified electrolyte material. Furthermore, it is necessary to heat the synthesized powder prior to the sintering to prevent the formation of secondary phases. It is assumed that this thermal treatment homogenizes the crystal structure of the powder and reduces the number of defects into the bulk grains. Electrochemical impedance spectroscopy investigations in various atmospheres and a large range of temperature (200-700°C) were then performed on sintered samples, and the protonic conductivity of BZY20 has been highlighted. Further experiments on half-cell, NiO-BZY20 as anode and BZY20 as electrolyte, are in progress.

Keywords: hydrothermal synthesis, impedance measurement, Y-doped BaZrO₃, proton conductor

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Authors: Marita Heyns

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Job insecurity is a distressing phenomenon which has far reaching consequences for both employees and their organizations. Previously, much attention has been given to the link between job insecurity and individual level performance outcomes, while less is known about how subjectively perceived job insecurity might transfer beyond the individual level to affect performance of the organization on an aggregated level. Research focusing on how employees’ fear of job loss might affect the organization’s ability to respond proactively to volatility and drastic change through applying its capabilities of sensing, seizing, and reconfiguring, appears to be practically non-existent. Equally little is known about the potential underlying mechanisms through which job insecurity might affect the dynamic capabilities of an organization. This study examines how job insecurity might affect dynamic organizational capability through trust as an underling process. More specifically, it considered the simultaneous roles of trust at an impersonal (organizational) level as well as trust at an interpersonal level (in leaders and co-workers) as potential underlying mechanisms through which job insecurity might affect the organization’s dynamic capability to respond to opportunities and imminent, drastic change. A quantitative research approach and a stratified random sampling technique enabled the collection of data among 314 managers at four different plant sites of a large South African steel manufacturing organization undergoing dramatic changes. To assess the study hypotheses, the following statistical procedures were employed: Structural equation modelling was performed in Mplus to evaluate the measurement and structural models. The Chi-square values test for absolute fit as well as alternative fit indexes such as the Comparative Fit Index and the Tucker-Lewis Index, the Root Mean Square Error of Approximation and the Standardized Root Mean Square Residual were used as indicators of model fit. Composite reliabilities were calculated to evaluate the reliability of the factors. Finally, interaction effects were tested by using PROCESS and the construction of two-sided 95% confidence intervals. The findings indicate that job insecurity had a lower-than-expected detrimental effect on evaluations of the organization’s dynamic capability through the conducive buffering effects of trust in the organization and in its leaders respectively. In contrast, trust in colleagues did not seem to have any noticeable facilitative effect. The study proposes that both job insecurity and dynamic capability can be managed more effectively by also paying attention to factors that could promote trust in the organization and its leaders; some practical recommendations are given in this regard.

Keywords: dynamic organizational capability, impersonal trust, interpersonal trust, job insecurity

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Authors: Masauso Moses Phiri

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Frequent glucose monitoring is essential to the management of diabetes. Plasmonic enzyme-based glucose biosensors have the advantages of greater specificity, simplicity and rapidity. The aim of this study was to develop a rapid plasmonic colorimetric glucose biosensor based on biocatalytic enlargement of AuNS guided by GOx. Gold nanoparticles of 18 nm in diameter were synthesized using the citrate method. Using these as seeds, a modified seeded method for the synthesis of monodispersed gold nanostars was followed. Both the spherical and star-shaped nanoparticles were characterized using ultra-violet visible spectroscopy, agarose gel electrophoresis, dynamic light scattering, high-resolution transmission electron microscopy and energy-dispersive X-ray spectroscopy. The feasibility of a plasmonic colorimetric assay through growth of AuNS by silver coating in the presence of hydrogen peroxide was investigated by several control and optimization experiments. Conditions for excellent sensing such as the concentration of the detection solution in the presence of 20 µL AuNS, 10 mM of 2-(N-morpholino) ethanesulfonic acid (MES), ammonia and hydrogen peroxide were optimized. Using the optimized conditions, the glucose assay was developed by adding 5mM of GOx to the solution and varying concentrations of glucose to it. Kinetic readings, as well as color changes, were observed. The results showed that the absorbance values of the AuNS were blue shifting and increasing as the concentration of glucose was elevated. Control experiments indicated no growth of AuNS in the absence of GOx, glucose or molecular O₂. Increased glucose concentration led to an enhanced growth of AuNS. The detection of glucose was also done by naked-eye. The color development was near complete in ± 10 minutes. The kinetic readings which were monitored at 450 and 560 nm showed that the assay could discriminate between different concentrations of glucose by ± 50 seconds and near complete at ± 120 seconds. A calibration curve for the qualitative measurement of glucose was derived. The magnitude of wavelength shifts and absorbance values increased concomitantly with glucose concentrations until 90 µg/mL. Beyond that, it leveled off. The lowest amount of glucose that could produce a blue shift in the localized surface plasmon resonance (LSPR) absorption maxima was found to be 10 – 90 µg/mL. The limit of detection was 0.12 µg/mL. This enabled the construction of a direct sensitivity plasmonic colorimetric detection of glucose using AuNS that was rapid, sensitive and cost-effective with naked-eye detection. It has great potential for transfer of technology for point-of-care devices.

Keywords: colorimetric, gold nanostars, glucose, glucose oxidase, plasmonic

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Authors: Mari A. Virtanen, Elina Haavisto, Eeva Liikanen, Maria Kääriäinen

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Rapid technological development and digitalization has affected also on higher education. During last twenty years multiple of electronic and mobile learning (e-learning, m-learning) platforms have been developed and have become prevalent in many universities and in the all fields of education. Ubiquitous learning (u-learning) is not that widely known or used. Ubiquitous learning environments (ULE) are the new era of computer-assisted learning. They are based on ubiquitous technology and computing that fuses the learner seamlessly into learning process by using sensing technology as tags, badges or barcodes and smart devices like smartphones and tablets. ULE combines real-life learning situations into virtual aspects and can be flexible used in anytime and anyplace. The aim of this study was to assess the effects of ubiquitous 360 o learning environment on higher education students’ clinical histotechnology competence. A quasi-experimental study design was used. 57 students in biomedical laboratory science degree program was assigned voluntarily to experiment (n=29) and to control group (n=28). Experimental group studied via ubiquitous 360o learning environment and control group via traditional web-based learning environment (WLE) in a 8-week educational intervention. Ubiquitous 360o learning environment (ULE) combined authentic learning environment (histotechnology laboratory), digital environment (virtual laboratory), virtual microscope, multimedia learning content, interactive communication tools, electronic library and quick response barcodes placed into authentic laboratory. Web-based learning environment contained equal content and components with the exception of the use of mobile device, interactive communication tools and quick response barcodes. Competence of clinical histotechnology was assessed by using knowledge test and self-report developed for this study. Data was collected electronically before and after clinical histotechnology course and analysed by using descriptive statistics. Differences among groups were identified by using Wilcoxon test and differences between groups by using Mann-Whitney U-test. Statistically significant differences among groups were identified in both groups (p<0.001). Competence scores in post-test were higher in both groups, than in pre-test. Differences between groups were very small and not statistically significant. In this study the learning environment have developed based on 360o technology and successfully implemented into higher education context. And students’ competence increases when ubiquitous learning environment were used. In the future, ULE can be used as a learning management system for any learning situation in health sciences. More studies are needed to show differences between ULE and WLE.

Keywords: competence, higher education, histotechnology, ubiquitous learning, u-learning, 360o

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Authors: Krisala Joshi, Sayanta Ghosh, Renu Lata, Jagdish C. Kuniyal

Abstract:

Throughout the world, monitoring and estimating the changing pattern of forests across diverse landscapes through remote sensing is instrumental in understanding the interactions of human activities and the ecological environment with the changing climate. Forest change detection using satellite imageries has emerged as an important means to gather information on a regional scale. Kullu valley in Himachal Pradesh, India is situated in a transitional zone between the lesser and the greater Himalayas. Thus, it presents a typical rugged mountainous terrain with moderate to high altitude which varies from 1200 meters to over 6000 meters. Due to changes in agricultural cropping patterns, urbanization, industrialization, hydropower generation, climate change, tourism, and anthropogenic forest fire, it has undergone a tremendous transformation in forest cover in the past three decades. The loss and degradation of forest cover results in soil erosion, loss of biodiversity including damage to wildlife habitats, and degradation of watershed areas, and deterioration of the overall quality of nature and life. The supervised classification of LANDSAT satellite data was performed to assess the changes in forest cover in Kullu valley over the years 2000 to 2020. Normalized Burn Ratio (NBR) was calculated to discriminate between burned and unburned areas of the forest. Our study reveals that in Kullu valley, the increasing number of forest fire incidents specifically, those due to anthropogenic activities has been on a rise, each subsequent year. The main objective of the present study is, therefore, to estimate the change in the forest cover of Kullu valley and to address the various social aspects responsible for the anthropogenic forest fires. Also, to assess its impact on the significant changes in the regional climatic factors, specifically, temperature, humidity, and precipitation over three decades, with the help of satellite imageries and ground data. The main outcome of the paper, we believe, will be helpful for the administration for making a quantitative assessment of the forest cover area changes due to anthropogenic activities and devising long-term measures for creating awareness among the local people of the area.

Keywords: Anthropogenic Activities, Forest Change Detection, Normalized Burn Ratio (NBR), Supervised Classification

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