Emission of Carbon Dioxide (CO2) has adversely affected the environment. One of the major sources of CO2 emission is transportation. In the last few decades, the increase in mobility of people using vehicles has enormously increased the emission of CO2 in the environment. To reduce CO2 emission, sustainable transportation system is required in which smart parking is one of the important measures that need to be established. To contribute to the issue of reducing the amount of CO2 emission, this research proposes a smart parking system. A cloud-based solution is provided to the drivers which automatically searches and recommends the most preferred parking slots. To determine preferences of the parking areas, this methodology exploits a number of unique parking features which ultimately results in the selection of a parking that leads to minimum level of CO2 emission from the current position of the vehicle. To realize the methodology, a scenario-based implementation is considered. During the implementation, a mobile application with GPS signals, vehicles with a number of vehicle features and a list of parking areas with parking features are used by sorting, multi-level filtering, exploratory data analysis (EDA, Analytical Hierarchy Process (AHP)) and weighted sum model (WSM) to rank the parking areas and recommend the drivers with top-k most preferred parking areas. In the EDA process, “2020testcar-2020-03-03”, a freely available dataset is used to estimate CO2 emission of a particular vehicle. To evaluate the system, results of the proposed system are compared with the conventional approach, which reveal that the proposed methodology supersedes the conventional one in reducing the emission of CO2 into the atmosphere.
A growing number of individuals utilize wearable devices on a daily basis. The usage and functionality of these wearable devices vary from user to user. One popular usage of said devices is to track health-related activities that are typically stored on a device’s memory or uploaded to an account in the cloud; based on the current trend, the data accumulated from the wearable device are stored in a standalone location. In many of these cases, this health related datum is not a factor when considering the holistic view of a user’s health lifestyle or record. This health-related data generated from wearable and Internet of Things (IoT) devices can serve as empirical information to a medical provider, as the standalone data can add value to the holistic health record of a patient. This paper proposes a solution to incorporate the data gathered from these wearable and IoT devices, with that a patient’s Personal Health Record (PHR) stored within the confines of a Health Information Exchange (HIE).
Multimodality involves the communication between verbal and visual components in various discourses. A painting represents a form of communication between the artist and the viewer in terms of colors, shades, objects, and the title. This paper aims to present how multimodality can be used to decode the verbal and visual dimensions a painting holds. For that purpose, this study uses Kress and van Leeuwen’s theoretical framework of visual grammar for the analysis of the multimodal semiotic resources of selected paintings of Salvador Dali. This study investigates the visual decoding of the selected paintings of Salvador Dali and analyzing their social and political meanings using Kress and van Leeuwen’s framework of visual grammar. The paper attempts to answer the following questions: 1. How far can multimodality decode the verbal and non-verbal meanings of surrealistic art? 2. How can Kress and van Leeuwen’s theoretical framework of visual grammar be applied to analyze Dali’s paintings? 3. To what extent is Kress and van Leeuwen’s theoretical framework of visual grammar apt to deliver political and social messages of Dali? The paper reached the following findings: the framework’s descriptive tools (representational, interactive, and compositional meanings) can be used to analyze the paintings’ title and their visual elements. Social and political messages were delivered by appropriate usage of color, gesture, vectors, modality, and the way social actors were represented.
Application of health risk assessment methods is important in order to comprehend the risk of human exposure to heavy metals and other dangerous pollutants. Four soil samples were collected at distances of 10, 20, 30 m and the control 100 m away from the dump site at depths of 0.3, 0.6 and 0.9 m. The collected soil samples were examined for Zn, Cu, Pb, Cd and Ni using standard methods. The health risks via the main pathways of human exposure to heavy metal were detected using relevant standard equations. Hazard quotient was calculated to determine non-carcinogenic health risk for each individual heavy metal. Life time cancer risk was calculated to determine the cumulative life cancer rating for each exposure pathway. The estimated health risk values for adults and children were generally lower than the reference dose. The calculated hazard quotient for the ingestion, inhalation and dermal contact pathways were less than unity. This means that there is no detrimental concern to the health on human exposure to heavy metals in contaminated soil. The life time cancer risk 5.4 × 10-2 was higher than the acceptable threshold value of 1 × 10-4 which is reflected to have significant health effects on human exposure to heavy metals in contaminated soil. Good hygienic practices are recommended to ease the potential risk to children and adult who are exposed to contaminated soils. Also, the local authorities should be made aware of such health risks for the purpose of planning the management strategy accordingly.
The article presents an ongoing work in research projects such as SCAN4RECO or ARCH, both funded by the European Commission under Horizon 2020 program. The former one concerns multimodal and multispectral scanning of Cultural Heritage assets for their digitization and conservation via spatiotemporal reconstruction and 3D printing, while the latter one aims to better preserve areas of cultural heritage from hazards and risks. It co-creates tools that would help pilot cities to save cultural heritage from the effects of climate change. It develops a disaster risk management framework for assessing and improving the resilience of historic areas to climate change and natural hazards. Tools and methodologies are designed for local authorities and practitioners, urban population, as well as national and international expert communities, aiding authorities in knowledge-aware decision making. In this article we focus on 3D modelling of object geometry using primarily photogrammetric methods to achieve very high model accuracy using consumer types of devices, attractive both to professions and hobbyists alike.
Healthy and viable herbal supplement were prepared from wheat by a green route. This organic biomaterial was named Nutrition Bio-shield Superfood (NBS). The NBS supplement had various vitamins, macro and micro molecules, and ingredients. In this study, 20 small Balb/C labile specimens were used in a weighing 30 ± 5 range. The samples were randomly divided into different groups, then the groups were divided into 5 groups. According to the results of this study, the mean number of white blood cells and neutrophil percentage in the experimental group receiving healthy and live dietary supplement showed a significant increase at the 5% probability level in all three groups received 50, 100 and 150 mg/ kg body weight of the mouse compared to the control group. In general, the dietary supplement increases the level of immunity.
Telfairia occidentalis is a leafy vegetable commonly grown in the tropics for nutritional benefits. The use of enzymes and probiotics is becoming prominent due to the ban on antibiotics as growth promoters in many parts of the world. It is conceived that with enzymes and probiotics additives, fibrous leafy vegetables can be incorporated into poultry feeds as protein source. However, certain antinutrients were also found in the leaves of Telfairia occidentalis. Four broiler starter and finisher diets were formulated for the two phases of the broiler experiments. A mixture of fiber degrading enzymes, Roxazyme G2 (combination of cellulase, glucanase and xylanase) and probiotics (Turbotox), a growth promoter, were used in broiler diets at 1:1. The Roxazyme G2/Turbotox mixtures were used in diets containing four varying levels of Telfairia occidentalis leaf meal (TOLM) at 0, 10, 20 and 30%. Diets 1 were standard broiler diets without TOLM and Roxazyme G2 and Turbotox additives. Diets 2, 3 and 4 had enzymes and probiotics additives. Certain mineral elements such as Ca, P, K, Na, Mg, Fe, Mn, Cu and Zn were found in notable quantities viz. 2.6 g/100 g, 1.2 g/100 g, 6.2 g/100 g, 5.1 g/100 g, 4.7 g/100 g, 5875 ppm, 182 ppm, 136 ppm and 1036 ppm, respectively. Phytin, phytin-P, oxalate, tannin and HCN were also found in ample quantities viz. 189.2 mg/100 g, 120.1 mg/100 g, 80.7 mg/100 g, 43.1 mg/100 g and 61.2 mg/100 g, respectively. The average weight gain was highest at 46.3 g/bird/day for birds on 10% TOLM diet but similar (P > 0.05) to 46.2 g/bird/day for birds on 20% TOLM. The feed conversion ratio (FCR) of 2.27 was the lowest and optimum for birds on 10% TOLM although similar (P > 0.05) to 2.29 obtained for birds on 20% TOLM. FCR of 2.61 was the highest at 2.61 for birds on 30% TOLM diet. The lowest FCR of 2.27 was obtained for birds on 10% TOLM diet although similar (P > 0.05) to 2.29 for birds on 20% TOLM diet. Most carcass characteristics and organ weights were similar (P > 0.05) for the experimental birds on the different diets except for kidney, gizzard and intestinal length. The values for kidney, gizzard and intestinal length were significantly higher (P < 0.05) for birds on the TOLM diets. The nitrogen retention had the highest value of 72.37 ± 0.10% for birds on 10% TOLM diet although similar (P > 0.05) to 71.54 ± 1.89 obtained for birds on the control diet without TOLM and enzymes/probiotics mixture. There was evidence of a better utilization of TOLM as a plant protein source. The carcass characteristics and organ weights all showed evidence of uniform tissue buildup and muscles development particularly in diets containing 10% of TOLM level. There was also better nitrogen utilization in birds on the 10% TOLM diet. Considering the cheap cost of TOLM, it is envisaged that its introduction into poultry feeds as a plant protein source will ultimately reduce the cost of poultry feeds.
The Internet of Things (IoT) represents the best option for smart vineyard applications, even if it is necessary to integrate the technologies required for the development. This article is based on the research and the results obtained in the DISAVIT project. For Smart Agriculture, the project aims to provide a trustworthy, intelligent, integrated vineyard management solution that is based on the IoT. To have interoperability through the use of a multiprotocol technology (being the future connected wireless IoT) it is necessary to adopt an agnostic approach, providing a reliable environment to address cyber security, IoT-based threats and traceability through blockchain-based design, but also creating a concept for long-term implementations (modular, scalable). The ones described above represent the main innovative technical aspects of this project. The DISAVIT project studies and promotes the incorporation of better management tools based on objective data-based decisions, which are necessary for agriculture adapted and more resistant to climate change. It also exploits the opportunities generated by the digital services market for smart agriculture management stakeholders. The project's final result aims to improve decision-making, performance, and viticulturally infrastructure and increase real-time data accuracy and interoperability. Innovative aspects such as end-to-end solutions, adaptability, scalability, security and traceability, place our product in a favorable situation over competitors. None of the solutions in the market meet every one of these requirements by a unique product being innovative.
Pseudomonas aeruginosa is an opportunistic pathogen that forms surface-associated microbial communities (biofilms) on artificial implant devices and on human tissue. Biofilm infections are difficult to treat with antibiotics, in part, because the bacteria in biofilms are physiologically heterogeneous. One measure of biological heterogeneity in a population of cells is to quantify the cellular concentrations of ribosomes, which can be probed with fluorescently labeled nucleic acids. The fluorescent signal intensity following fluorescence in situ hybridization (FISH) analysis correlates to the cellular level of ribosomes. The goals here are to provide computationally and statistically robust approaches to automatically quantify cellular heterogeneity in biofilms from a large library of epifluorescent microscopy FISH images. In this work, the initial steps were developed toward these goals by developing an automated biofilm detection approach for use with FISH images. The approach allows rapid identification of biofilm regions from FISH images that are counterstained with fluorescent dyes. This methodology provides advances over other computational methods, allowing subtraction of spurious signals and non-biological fluorescent substrata. This method will be a robust and user-friendly approach which will enable users to semi-automatically detect biofilm boundaries and extract intensity values from fluorescent images for quantitative analysis of biofilm heterogeneity.
This paper focuses on the formulation of phytobiotic designated for further implantation in poultry farming. Composition was meant to be water-soluble powder containing antibacterial essential oils. The development process involved Thyme, Monarda and Clary sage essential oils. The antimicrobial activity of essential oils composite was meant to be tested against gram-negative and gram-positive bacterial strains. The results are processed using the statistical program Sigma STAT. To make essential oils composition water soluble surfactants were added to them. At the first stage of the study, nine options for the optimal composition of essential oils and surfactants were developed. The effect of the amount of surfactants on the essential oils composition solubility in water has been investigated. On the basis of biopharmaceutical studies, the formulation of phytobiotic has been determined: Thyme, monarda and clary sage essential oils 2:1:1 - 100 parts; Licorice extract 5.25 parts and inhalation lactose 300 parts. A technology for the preparation of phytobiotic has been developed and a technological scheme for the preparation of phytobiotic has been made up. The research was performed within the framework of the grant project CARYS-19-363 funded be the Shota Rustaveli National Science Foundation of Georgia.
The dynamic facility layout problem is a really critical issue in the competitive industrial market; thus, solving this problem requires robust design and effective simulation systems. The sustainable simulation requires inputting reliable and accurate data into the system. So this paper describes an automated system integrated into the real environment to measure the duration of the material handling operations, collect the data in real-time, and determine the variances between the actual and estimated time schedule of the operations in order to update the simulation software and redesign the facility layout periodically. The automated method- time measurement system collects the real data through using Radio Frequency-Identification (RFID) and Internet of Things (IoT) technologies. Hence, attaching RFID- antenna reader and RFID tags enables the system to identify the location of the objects and gathering the time data. The real duration gathered will be manipulated by calculating the moving average duration of the material handling operations, choosing the shortest material handling path, and then updating the simulation software to redesign the facility layout accommodating with the shortest/real operation schedule. The periodic simulation in real-time is more sustainable and reliable than the simulation system relying on an analysis of historical data. The case study of this methodology is in cooperation with a workshop team for producing mechanical parts. Although there are some technical limitations, this methodology is promising, and it can be significantly useful in the redesigning of the manufacturing layout.
In Liberia, many of the agricultural households cultivate cassava for either sustenance purposes, or to generate farm income. Many of the concentrated cassava farmers reside in Nimba, a north-eastern County that borders two other economies: the Republics of Cote D’Ivoire and Guinea. With a high demand for cassava output and products in emerging Asian markets coupled with an objective of the Liberia agriculture policies to increase the competitiveness of valued agriculture crops; there is a need to examine the level of resource-use efficiency for many agriculture crops. However, there is a scarcity of information on the efficiency of many agriculture crops, including cassava. Hence the study applying an output-oriented method seeks to assess the economic efficiency of cassava farmers in Nimba County, Liberia. A multi-stage sampling technique was employed to generate a sample for the study. From 216 cassava farmers, data related to on-farm attributes, socio-economic and institutional factors were collected. The stochastic frontier models, using the Translog functional forms, of production and revenue, were used to determine the level of revenue efficiency and its determinants. The result showed that most of the cassava farmers are male (60%). Many of the farmers are either married, engaged or living together with a spouse (83%), with a mean household size of nine persons. Farmland is prevalently obtained by inheritance (95%), average farm size is 1.34 hectares, and most cassava farmers did not access agriculture credits (76%) and extension services (91%). The mean cassava output per hectare is 1,506.02 kg, which estimates average revenue of L$23,551.16 (Liberian dollars). Empirical results showed that the revenue efficiency of cassava farmers varies from 0.1% to 73.5%; with the mean revenue efficiency of 12.9%. This indicates that on average, there is a vast potential of 87.1% to increase the economic efficiency of cassava farmers in Nimba by improving technical and allocative efficiencies. For the significant determinants of revenue efficiency, age and group membership had negative effects on revenue efficiency of cassava production; while farming experience, access to extension, formal education, and average wage rate have positive effects. The study recommends the setting-up and incentivizing of farmer field schools for cassava farmers to primarily share their farming experiences with others and to learn robust cultivation techniques of sustainable agriculture. Also, farm managers and farmers should consider a fix wage rate in labor contracts for all stages of cassava farming.
Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.
The technique called as Generalized frequency division multiplexing (GFDM) used in the free space optical channel can be a good option for implementation free space optical communication systems. This technique has several strengths e.g. good spectral efficiency, low peak-to-average power ratio (PAPR), adaptability and low co-channel interference. In this paper, the impact of weather conditions such as haze, rain and fog on GFDM over the gamma-gamma channel model is discussed. A Trade off between link distance and system performance under intense weather conditions is also analysed. The symbol error probability (SEP) of GFDM over the gamma-gamma turbulence channel is derived and verified with the computer simulations.
Authentication protocols based on public key infrastructure (PKI) and trusted third party (TTP) are no longer adequate for industrial scale IoT networks thanks to issues such as low compute and power availability, the use of widely distributed and commercial off-the-shelf (COTS) systems, and the increasingly sophisticated attackers and attacks we now have to counter. For example, there is increasing concern about nation-state-based interference and future quantum computing capability. We have examined this space from first principles and have developed several approaches to group and point-to-point authentication for IoT that do not depend on the use of a centralised client-server model. We emphasise the use of quantum resistant primitives such as strong cryptographic hashing and the use multi-factor authentication.
The accumulation of heavy metals in food is a constant problem in many parts of the world. Vegetable oils are widely used, both for cooking and for processing in the food industry, meeting the main dietary requirements. One of the main chemical pollutants, heavy metals, is usually found in vegetable oils. These chemical pollutants are carcinogenic, teratogenic and immunotoxic, harmful to consumption and have a negative effect on human health even in trace amounts. Residues of these substances can easily accumulate in vegetable oil during cultivation, processing and storage. In this article, the content of the concentration of heavy metal ions in vegetable oils of Kazakhstan production is studied: sunflower, rapeseed, safflower and linseed oil. Heavy metals: arsenic, cadmium, lead and nickel, were determined in three repetitions by the method of flame atomic absorption. Analysis of vegetable oil samples revealed that the largest lead contamination (Pb) was determined to be 0.065 mg/kg in linseed oil. The content of cadmium (Cd) in the largest amount of 0.009 mg/kg was found in safflower oil. Arsenic (As) content was determined in rapeseed and safflower oils at 0.003 mg/kg, and arsenic (As) was not detected in linseed and sunflower oil. The nickel (Ni) content in the largest amount of 0.433 mg/kg was in linseed oil. The heavy metal contents in the test samples complied with the requirements of regulatory documents for vegetable oils. An assessment of the health risk of vegetable oils with a daily consumption of 36 g per day shows that all samples of vegetable oils produced in Kazakhstan are safe for consumption. But further monitoring is needed, since all these metals are toxic and their harmful effects become apparent only after several years of exposure.
In this study, a multivariate analysis of potato spectroscopic data was presented to detect the presence of brown rot disease or not. Near-Infrared (NIR) spectroscopy (1,350-2,500 nm) combined with multivariate analysis was used as a rapid, non-destructive technique for the detection of brown rot disease in potatoes. Spectral measurements were performed in 565 samples, which were chosen randomly at the infection place in the potato slice. In this study, 254 infected and 311 uninfected (brown rot-free) samples were analyzed using different advanced statistical analysis techniques. The discrimination performance of different multivariate analysis techniques, including classification, pre-processing, and dimension reduction, were compared. Applying a random forest algorithm classifier with different pre-processing techniques to raw spectra had the best performance as the total classification accuracy of 98.7% was achieved in discriminating infected potatoes from control.
Microalgae-based photobioreactors (PBR) for Life Support Systems (LSS) are currently being investigated for future space missions such as a crewed base on planets or moons. Biological components may help reducing resupply masses by closing material mass flows with the help of regenerative components. Via photosynthesis, the microalgae use CO2, water, light and nutrients to provide oxygen and biomass for the astronauts. These capabilities could have synergies with Earth applications that tackle current problems and the developed technologies can be transferred. For example, a current worldwide discussed issue is the increased nitrate and phosphate pollution of ground water from agricultural waste waters. To investigate the potential use of a biological system based on the ability of the microalgae to extract and use nitrate and phosphate for the treatment of polluted ground water from agricultural applications, a scalable test stand is being developed. This test stand investigates the maximization of intake rates of nitrate and quantifies the produced biomass and oxygen. To minimize the required energy, for the uptake of nitrate from artificial waste water (AWW) the Flashing Light Effect (FLE) and the adaption of the illumination spectrum were realized. This paper describes the composition of the AWW, the development of the illumination unit and the possibility of non-invasive process optimization and control via the adaption of the illumination spectrum and illumination cycles. The findings were a doubling of the energy related growth rate by adapting the illumination setting.
The efficacy of two essential oils applied as a bean seed dressing followed by seedlings foliar spray with four commercial disinfectants against root rot and wilt incidence was evaluated under field conditions. The essential oils, grape seed and peppermint oils and the disinfectants, Gold, Revarest, Klenva, Malva were applied. Chitosan and the fungicide Topsin-M were used as comparison treatment. Essential oils individually or combined with disinfectants were applied as a bean seed dressing. Furthermore, emerged bean plants were sprayed with the same treatments. Under laboratory conditions, growth inhibition effect was observed for the isolated, tested fungi R. solani and F. oxysporum when exposed to essential oils individually or combined with disinfectants. A high inhibitor effect was recorded for peppermint followed by grape seed oils. Concentrations of 1% and 2% of chitosan as well as Topsin M at 400 ppm showed complete reduction (100%) in the two fungal growths. Under field conditions, the obtained results showed that the applied treatments of chitosan had a superior effect on root rot and wilt disease incidence compared with other tested treatments. It was found that seed coating treatment provides good protection of emerged green bean seeds against the root pathogens attack compared with the fungicide and control treatments. Also, the application of seed dressing with essential oils accompanied by seedling spray demonstrated similar results. It was observed that essential oils had an enhancing effect against disease incidence when combined with disinfectants compared with their application. The obvious yield increase was significantly higher in all applied treatments than in fungicide and control.
Higher Education is resisting the pull of technology, especially as this concerns the issuance and verification of degrees and certificates. It is widely known that education certificates are largely produced in paper form making them vulnerable to damage while holders of such certificates are dependent on the universities and other issuing organisations. QualiChain is an EU Horizon 2020 (H2020) research project aiming to transform and revolutionise the domain of public education and its ties with the job market by leveraging blockchain, analytics and decision support to develop a platform for the verification and sharing of education certificates. Blockchain plays an integral part in the QualiChain solution in providing a trustworthy environment to store, share and manage such accreditations. Under the context of this paper, three prominent blockchain platforms (Ethereum, Hyperledger Fabric, Hyperledger Iroha) were considered as a means of experimentation for creating a system with the basic functionalities that will be needed for trustworthy degree verification. The methodology and respective system developed and presented in this paper used Hyperledger Iroha and proved that this specific platform can be used to easily develop decentralize applications. Future papers will attempt to further experiment with other blockchain platforms and assess which has the best potential.