Search results for: plant detection

Authors: Zegrar Ahmed, Ghabi Mohamed

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The degradation of arid and semi-arid ecosystems in Algeria has become a palpable fact that only hinders progress and rural development. In these exceptionally fragile environments, the decline of vegetation is done according to an alarming increase and wind erosion dominates. The ecosystem is subjected to a long hot dry season and low annual average rainfall. The urgency of the fight against desertification is imposed by the very nature of the process that tends to self-accelerate, resulting when human intervention is not forthcoming the irreversibility situations, preventing any possibility of restoration state of these zones. These phenomena have led to different degradation processes, such as the destruction of vegetation, soil erosion, and deterioration of the physical environment. In this study, the work is mainly based on the criteria for classification and identification of physical parameters for spatial analysis and multi-sources to determine the vulnerability of major steppe formations and their impact on desertification. we used Landsat data with two different dates March 2010 and November 2014 in order to determine the changes in land cover, sand moving and land degradation for the diagnosis of the desertification Phenomenon. The application, through specific processes, including the supervised classification was used to characterize the main steppe formations. An analysis of the vulnerability of plant communities was conducted to assign weights and identify areas most susceptible to desertification. Vegetation indices are used to characterize the steppe formations to determine changes in land use.

Keywords: remote sensing, SIG, ecosystem, degradation, desertification

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Authors: Ahmed ElAzab, Amira M. Idrees, Mahmoud A. Mahmoud, Hesham Hefny

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Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different kinds of information during real time events. This popularity in Social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content during life events. This situation can result to a huge damage in the real world to the society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting fake accounts on Twitter. The study determines the minimized set of the main factors that influence the detection of the fake accounts on Twitter, then the determined factors have been applied using different classification techniques, a comparison of the results for these techniques has been performed and the most accurate algorithm is selected according to the accuracy of the results. The study has been compared with different recent research in the same area, this comparison has proved the accuracy of the proposed study. We claim that this study can be continuously applied on Twitter social network to automatically detect the fake accounts, moreover, the study can be applied on different Social network sites such as Facebook with minor changes according to the nature of the social network which are discussed in this paper.

Keywords: fake accounts detection, classification algorithms, twitter accounts analysis, features based techniques

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Authors: Danying Gu, Xiaoyan Li, Yuanlei He

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As a knowledge-intensive industry, nuclear industry highly values the importance of safety and quality. The life cycle of a NPP (Nuclear Power Plant) can last 100 years from the initial research and design to its decommissioning. How to implement the high-quality knowledge management and how to contribute to a more safe, advanced and economic NPP (Nuclear Power Plant) is the most important issue and responsibility for knowledge management. As the lead of nuclear industry, nuclear research and design institute has competitive advantages of its advanced technology, knowledge and information, DKM (Design Knowledge Management) of nuclear research and design institute is the core of the knowledge management in the whole nuclear industry. In this paper, the study and practice on DKM and knowledge transfer across the life cycle of a new-built NPP in China is introduced. For this digital intelligent NPP, the whole design process is based on a digital design platform which includes NPP engineering and design dynamic analyzer, visualization engineering verification platform, digital operation maintenance support platform and digital equipment design, manufacture integrated collaborative platform. In order to make all the design data and information transfer across design, construction, commissioning and operation, the overall architecture of new-built digital NPP should become a modern knowledge management system. So a digital information transfer model across the NPP life cycle is proposed in this paper. The challenges related to design knowledge transfer is also discussed, such as digital information handover, data center and data sorting, unified data coding system. On the other hand, effective delivery of design information during the construction and operation phase will contribute to the comprehensive understanding of design ideas and components and systems for the construction contractor and operation unit, largely increasing the safety, quality and economic benefits during the life cycle. The operation and maintenance records generated from the NPP operation process have great significance for maintaining the operating state of NPP, especially the comprehensiveness, validity and traceability of the records. So the requirements of an online monitoring and smart diagnosis system of NPP is also proposed, to help utility-owners to improve the safety and efficiency.

Keywords: design knowledge management, digital nuclear power plant, knowledge transfer, life cycle

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Authors: Miroslav Hyza, Petr Rulik, Vojtech Bednar, Jan Sury

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An early and reliable detection of an increased radioactivity level in the atmosphere is one of the key aspects of atmospheric radiological monitoring. Although the standard laboratory procedures provide detection limits as low as few µBq/m³, their major drawback is the delayed result reporting: typically a few days. This issue is the main objective of the HAMRAD project, which gave rise to a prototype of an autonomous monitoring device. It is based on the idea of sequential aerosol sampling using a carrousel sample changer combined with a gamma-ray spectrometer. In our hardware configuration, the air is drawn through a filter positioned on the carrousel so that it could be rotated into the measuring position after a preset sampling interval. Filter analysis is performed via a 50% HPGe detector inside an 8.5cm lead shielding. The spectrometer output signal is then analyzed using DSP electronics and Gamwin software with preset nuclide libraries and other analysis parameters. After the counting, the filter is placed into a storage bin with a capacity of 250 filters so that the device can run autonomously for several months depending on the preset sampling frequency. The device is connected to a central server via GPRS/GSM where the user can view monitoring data including raw spectra and technological data describing the state of the device. All operating parameters can be remotely adjusted through a simple GUI. The flow rate is continuously adjustable up to 10 m³/h. The main challenge in spectrum analysis is the natural background subtraction. As detection limits are heavily influenced by the deposited activity of radon decay products and the measurement time is fixed, there must exist an optimal sample decay time (delayed spectrum acquisition). To solve this problem, we adopted a simple procedure based on sequential spectrum acquisition and optimal partial spectral sum with respect to the detection limits for a particular radionuclide. The prototyped device proved to be able to detect atmospheric contamination at the level of mBq/m³ per an 8h sampling.

Keywords: aerosols, atmosphere, atmospheric radioactivity monitoring, autonomous sampler

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Authors: Fangyan Li, Lin Min Lee, Hui Zhu Peh, Shoet Harn Chan

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Advantame, a derivative of aspartame, is the latest addition to a family of low caloric and high potent dipeptide sweeteners which include aspartame, neotame and alitame. The use of advantame as a high-intensity sweetener in food was first accepted by Food Standards Australia New Zealand in 2011 and subsequently by US and EU food authorities in 2014, with the results from toxicity and exposure studies showing advantame poses no safety concern to the public at regulated levels. To our knowledge, currently there is barely any detailed information on the analytical method of advantame in food matrix, except for one report published in Japanese, stating a high performance liquid chromatography (HPLC) and liquid chromatography/ mass spectrometry (LC-MS) method with a detection limit at ppm level. However, the use of acid in sample preparation and instrumental analysis in the report raised doubt over the reliability of the method, as there is indication that stability of advantame is compromised under acidic conditions. Besides, the method may not be suitable for analyzing food matrices containing advantame at low ppm or sub-ppm level. In this presentation, a simple, specific and sensitive method for the determination of advantame in food is described. The method involved extraction with water and clean-up via solid phase extraction (SPE) followed by detection using liquid chromatography tandem mass spectrometry (LC-MS/MS) in negative electrospray ionization mode. No acid was used in the entire procedure. Single laboratory validation of the method was performed in terms of linearity, precision and accuracy. A low detection limit at ppb level was achieved. Satisfactory recoveries were obtained using spiked samples at three different concentration levels. This validated method could be used in the routine inspection of the advantame level in food.

Keywords: advantame, food, LC-MS/MS, sweetener

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Authors: Seyed Mohsen Jameii

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Wireless underwater sensor networks are a type of sensor networks that are located in underwater environments and linked together by acoustic waves. The application of these kinds of network includes monitoring of pollutants (chemical, biological, and nuclear), oil fields detection, prediction of the likelihood of a tsunami in coastal areas, the use of wireless sensor nodes to monitor the passing submarines, and determination of appropriate locations for anchoring ships. This paper proposes a boundary coverage algorithm for intrusion detection in underwater sensor networks. In the first phase of the proposed algorithm, optimal deployment of nodes is done in the water. In the second phase, after the employment of nodes at the proper depth, clustering is executed to reduce the exchanges of messages between the sensors. In the third phase, the algorithm of "divide and conquer" is used to save energy and increase network efficiency. The simulation results demonstrate the efficiency of the proposed algorithm.

Keywords: boundary coverage, clustering, divide and ‎conquer, underwater sensor nodes

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Authors: Shariful Islam, Sharun Akter Khushbu, S. M. Shaqib, Shahriar Sultan Ramit

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In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwear. The suggested method precisely locates these safety items by using the YOLO v7 (You Only Look Once) object detection algorithm. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a [email protected] score of 87.7%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research contributes to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry.

Keywords: deep learning, safety equipment detection, YOLOv7, computer vision, workplace safety

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Authors: Rosa M. Gambier, Jennifer L. Carlson

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In order to revitalize the science curriculum for botany courses for non-science majors, we have introduced the use of the medicinal plants into a first-year botany course. We have connected the use of scientific method, scientific inquiry and active learning in the classroom with the study of Western Traditional Medical Botany. The students have researched models of Botanical medicine and have designed a sustainable medicinal plants garden using native medicinal plants from the northeast. Through the semester, the students have researched their chosen species, planted seeds in the college greenhouse, collected germination ratios, growth ratios and have successfully produced a beginners medicinal plant garden. Phase II of the project will be to tie in SCCCs community outreach goals by involving the public in the expanded development of the garden as a way of sharing learning about medicinal plants and traditional medicine outside the classroom.

Keywords: medicinal plant garden, botany curriculum, active learning, community outreach

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Authors: Dimitrie Marinceu, Alan Murchison

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The Canadian Used Fuel Container (UFC) is a mid-size hemispherical headed copper coated steel container measuring 2.5 meters in length and 0.5 meters in diameter containing 48 used fuel bundles. The contained used fuel produces significant gamma radiation requiring automated assembly processes to complete the assembly. The design throughput of 2,500 UFCs per year places constraints on equipment and hot cell design for repeatability, speed of processing, robustness and recovery from upset conditions. After UFC assembly, the UFC is inserted into a Buffer Box (BB). The BB is made from adequately pre-shaped blocks (lower and upper block) and Highly Compacted Bentonite (HCB) material. The blocks are practically ‘sandwiching’ the UFC between them after assembly. This paper identifies one possible approach for the BB automatic assembly cell and processes. Automation of the BB assembly will have a significant positive impact on nuclear safety, quality, productivity, and reliability.

Keywords: used fuel packing plant, automatic assembly cell, used fuel container, buffer box, deep geological repository

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Authors: Andi Fallahi, Bona Ryan Situmeang

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The Construction Industry is notoriously risky. Much of the preparatory paperwork that precedes construction project can be viewed as the formulation of risk allocation between the owner and the Contractor. The Owner is taking the risk that his project will not get built on the schedule that it will not get built for what he has budgeted and that it will not be of the quality he expected. The Contractor Face a multitude of risk. One of them is an unforeseen condition at the construction site. The Owner usually has the upper hand here if the unforeseen condition occurred. Site data contained in Ground Investigation report is often of significant contractual importance in disputes related to the unforeseen ground condition. A ground investigation can never fully disclose all the details of the underground condition (Risk of an unknown ground condition can never be 100% eliminated). Adipala Coal Fired Steam Power Plant (CSFPP) 1 x 660 project is one of the large CSFPP project in Indonesia based on Engineering, Procurement, and Construction (EPC) Contract. Unforeseen Ground Condition it’s responsible by the Contractor has stipulated in the clausal of Contract. In the implementation, there’s indicated unforeseen ground condition at Circulating Water Pump House (CWPH) area which caused the Contractor should be changed the Method of Work that give big impact against Time of Completion and Cost Project. This paper tries to analyze the best way for allocating the risk between The Owner and The Contractor. All parties that allocating of sharing risk fairly can ultimately save time and money for all parties, and get the job done on schedule for the least overall cost.

Keywords: unforeseen ground condition, coal fired steam power plant, circulating water pump house, Indonesia

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Authors: H. Aldewachi, M. Hines, M. McCulloch, N. Woodroofe, P. Gardiner

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DPP-IV is an enzyme whose expression is affected in a variety of diseases, therefore, has been identified as possible diagnostic or prognostic marker for various tumours, immunological, inflammatory, neuroendocrine, and viral diseases. Recently, DPP-IV enzyme has been identified as a novel target for type II diabetes treatment where the enzyme is involved. There is, therefore, a need to develop sensitive and specific methods that can be easily deployed for the screening of the enzyme either as a tool for drug screening or disease marker in biological samples. A variety of assays have been introduced for the determination of DPP-IV enzyme activity using chromogenic and fluorogenic substrates, nevertheless these assays either lack the required sensitivity especially in inhibited enzyme samples or displays low water solubility implying difficulty for use in vivo samples in addition to labour and time-consuming sample preparation. In this study, novel strategies based on exploiting the high extinction coefficient of gold nanoparticles (GNPs) are investigated in order to develop fast, specific and reliable enzymatic assay by investigating synthetic peptide sequences containing a DPP IV cleavage site and coupling them to GNPs. The DPP IV could be detected by colorimetric response of peptide capped GNPs (P-GNPS) that could be monitored by a UV-visible spectrophotometer or even naked eyes, and the detection limit could reach 0.01 unit/ml. The P-GNPs, when subjected to DPP IV, showed excellent selectivity compared to other proteins (thrombin and human serum albumin) , which led to prominent colour change. This provided a simple and effective colorimetric sensor for on-site and real-time detection of DPP IV.

Keywords: gold nanoparticles, synthetic peptides, colorimetric detection, DPP-IV enzyme

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Authors: Osarobo Omorogieva Ighodaro, Josephus Otejere

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This paper contains the modelling and simulation of GT13E2 combined cycle gas turbine with the aid of the software EBSILON PROFESSIONAL. The design mode was modeled using guaranteed performance data from the power plant, in the off design, temperature variation of ambient air and fogging (spray water at inlet to compressor) was simulated. The fogging was simulated under two different modes; constant fuel consumption and constant turbine exhaust temperature .The model results were validated using actual operating data by applying error percentage analysis. The validation results obtained ranged from -0.0038% to 0% in design condition while the results varied from -0.9202% to 10.24% The model shows that fogging decreases compressor inlet temperature which in turn decreases the power required to drive the compressor hence improving the simple cycle efficiency and hence increasing power generated.

Keywords: inlet fogging, off design, combined cycle, modelling

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Authors: Slimani Halima

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One of the main problems of degradation of arid steppe rangelands in the southern Mediterranean is the loss of plant diversity and changes in soil properties. During the last decades, these rangelands faced two main driving forces: climate through more or less lasting and recurrent droughts and overgrazing by sheep. In the present work, the preexisting system was an arid steppe with alfa grass (Stipa tenacissima L.) as the dominant plant, which was considered to be the "keystone" species toward the whole ecosystem structure and functioning. Vegetation and soil change was monitored for 45 years along a grazing intensity gradient. Changes in species richness and diversity, in the vegetation and in the soil, enabled to better understand climate fluctuations effects in comparison to overgrazing ones. The aim is to assess the impacts of grazing and climatic variability and change on biodiversity,vegetation and soil over a period of 45 years, based on data from seven reference years.

Keywords: biodiversity, desertification, droughts, el bayadh, overgrazing, soil, steppe

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Authors: Hyun-Woo Cho

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Automatic process monitoring schemes are designed to give early warnings for unusual process events or abnormalities as soon as possible. For this end, various techniques have been developed and utilized in various industrial processes. It includes multivariate statistical methods, representation skills in reduced spaces, kernel-based nonlinear techniques, etc. This work presents a nonlinear empirical monitoring scheme for batch type production processes with incomplete process measurement data. While normal operation data are easy to get, unusual fault data occurs infrequently and thus are difficult to collect. In this work, noise filtering steps are added in order to enhance monitoring performance by eliminating irrelevant information of the data. The performance of the monitoring scheme was demonstrated using batch process data. The results showed that the monitoring performance was improved significantly in terms of detection success rate of process fault.

Keywords: batch process, monitoring, measurement, kernel method

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Authors: Faezeh Aghazadeh, Mohammad Javad Sharifi

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The application of neural networks to model a full-scale industrial acetylene hydrogenation in olefin plant has been studied. The operating variables studied are the, input-temperature of the reactor, output-temperature of the reactor, hydrogen ratio of the reactor, [C₂H₂]input, and [C₂H₆]input. The studied operating variables were used as the input to the constructed neural network to predict the [C₂H₆]output at any time as the output or the target. The constructed neural network was found to be highly precise in predicting the quantity of [C₂H₆]output for the new input data, which are kept unaware of the trained neural network showing its applicability to determine the [C₂H₆]output for any operating conditions. The enhancement of [C₂H₆]output as compared with [C₂H₆]input was a consequence of low selective acetylene hydrogenation to ethylene.

Keywords: acetylene hydrogenation, Pd-Ag/Al₂O₃, artificial neural network, modeling, optimal design

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Authors: Walaa M.Abd-Elhafiez, Wajeb Gharibi

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In this paper a novel color image compression technique for efficient storage and delivery of data is proposed. The proposed compression technique started by RGB to YCbCr color transformation process. Secondly, the canny edge detection method is used to classify the blocks into edge and non-edge blocks. Each color component Y, Cb, and Cr compressed by discrete cosine transform (DCT) process, quantizing and coding step by step using adaptive arithmetic coding. Our technique is concerned with the compression ratio, bits per pixel and peak signal to noise ratio, and produce better results than JPEG and more recent published schemes (like, CBDCT-CABS and MHC). The provided experimental results illustrate the proposed technique which is efficient and feasible in terms of compression ratio, bits per pixel and peak signal to noise ratio.

Keywords: image compression, color image, q-coder, quantization, edge-detection

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Authors: Sunil Kumar

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Background: The present study was performed to investigate the antidiabetic effect of the constituents isolated from Sarca asoca by enzyme inhibitory activity. Methods: The dried leaves of Sarca asoca were defatted with petroleum ether and further the same amount plant materials were extracted with methanol. The dried methanol extract was subjected to fractionation and chromatographic separation, which led to the isolation of kaemferol, β-sitosterol and quercetin stigmasterol. Their structures were elucidated on the basis of spectroscopic studies as well as by comparison with the data available in the literature. The compounds were evaluated for in vitro enzyme inhibition effect. Results: The isolated compounds kaemferol, β-sitosterol and stigmasterol showed 45.32, 40.5 and 41.23% α-amylase inhibition respectively and 43.45, 39.29 and 32.43% α-glucosidase inhibition respectively at the conc. of 50 µg/kg. Conclusion: The compounds isolated from Sarca asoca showed in vitro and in vivo antidiabetic activity. So, Euphorbia hirta is a beneficial plant for management of diabetic disorders.

Keywords: diabetes, quercetin, sitosterol, stigmasterol

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Authors: S. N. T. I. Sampath, J. M. S. Jayasinghe, A. P. Attanayake, V. Karunaratne

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Diabetes mellitus is one of the major public health posers throughout the world today that incidence and associated with increasing mortality. Insufficient regulation of the blood glucose level might be serious effects for health and its necessity to identify new therapeutics that have ability to reduce hyperglycaemic condition in the human body. Even though synthetic antidiabetic drugs are more effective to control diabetes mellitus, there are considerable side effects have been reported. Thus, there is an increasing demand for searching new natural products having high antidiabetic activity with lesser side effects. The purposes of the present study were to evaluate different physical parameters and in-vitro and in-vivo antidiabetic potential of the selected combined medicinal plant extracts mixture composed of leaves of Murraya koenigii, cloves of Allium sativum, fruits of Garcinia queasita and seeds of Piper nigrum. The selected plants parts were mixed and ground together and extracted sequentially into the hexane, ethyl acetate and methanol. Solvents were evaporated and they were further dried by freeze-drying to obtain a fine powder of each extract. Various physical parameters such as moisture, total ash, acid insoluble ash and water soluble ash were evaluated using standard test procedures. In-vitro antidiabetic activity of combined plant extracts mixture was screened using enzyme assays such as α-amylase inhibition assay and α-glucosidase inhibition assay. The acute anti-hyperglycaemic activity was performed using oral glucose tolerance test for the streptozotocin induced diabetic Wistar rats to find out in-vivo antidiabetic activity of combined plant extracts mixture and it was assessed through total oral glucose tolerance curve (TAUC) values. The percentage of moisture content, total ash content, acid insoluble ash content and water soluble ash content were ranged of 7.6-17.8, 8.1-11.78, 0.019-0.134 and 6.2-9.2 respectively for the plant extracts and those values were less than standard values except the methanol extract. The hexane and ethyl acetate extracts exhibited highest α-amylase (IC50 = 25.7 ±0.6; 27.1 ±1.2 ppm) and α-glucosidase (IC50 = 22.4 ±0.1; 33.7 ±0.2 ppm) inhibitory activities than methanol extract (IC50 = 360.2 ±0.6; 179.6 ±0.9 ppm) when compared with the acarbose positive control (IC50 = 5.7 ±0.4; 17.1 ±0.6 ppm). The TAUC values for hexane, ethyl acetate, and methanol extracts and glibenclamide (positive control) treated rats were 8.01 ±0.66; 8.05 ±1.07; 8.40±0.50; 5.87 ±0.93 mmol/L.h respectively, whereas in diabetic control rats the TAUC value was 13.22 ±1.07 mmol/L.h. Administration of plant extracts treated rats significantly suppressed (p<0.05) the rise in plasma blood glucose levels compared to control rats but less significant than glibenclamide. The obtained results from in-vivo and in-vitro antidiabetic study showed that the hexane and ethyl acetate extracts of selected combined plant mixture might be considered as a potential source to isolate natural antidiabetic agents and physical parameters of hexane and ethyl acetate extracts will helpful to develop antidiabetic drug with further standardize properties.

Keywords: diabetes mellitus, in-vitro antidiabetic assays, medicinal plants, standardization

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Authors: Asiya Rezzouq, Mehdi El Bouchti, Omar Cherkaoui, Sanaa Majid, Souad Zyade

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In recent years, there has been a steady increase in the exploration of new renewable and non-conventional sources for the production of biodegradable nanomaterials. Nature harbours valuable cellulose-rich materials that have so far been under-exploited and can be used to create cellulose derivatives such as cellulose microfibres (CMFs) and cellulose nanocrystals (CNCs). These unconventional sources have considerable potential as alternatives to conventional sources such as wood and cotton. By using agricultural waste to produce these cellulose derivatives, we are responding to the global call for sustainable solutions to environmental and economic challenges. Responsible management of agricultural waste is increasingly crucial to reducing the environmental consequences of its disposal, including soil and water pollution, while making efficient use of these untapped resources. In this study, the main objective was to extract cellulose nanocrystals (CNC) from melon plant residues using methods that are both efficient and sustainable. To achieve this high-quality extraction, we followed a well-defined protocol involving several key steps: pre-treatment of the residues by grinding, filtration and chemical purification to obtain high-quality (CMF) with a yield of 52% relative to the initial mass of the melon plant residue. Acid hydrolysis was then carried out using phosphoric acid and sulphuric acid to convert (CMF) into cellulose nanocrystals. The extracted cellulose nanocrystals were subjected to in-depth characterization using advanced techniques such as transmission electron microscopy (TEM), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction. The resulting cellulose nanocrystals have exceptional properties, including a large specific surface area, high thermal stability and high mechanical strength, making them suitable for a variety of applications, including as reinforcements for composite materials. In summary, the study highlights the potential for recovering agricultural melon waste to produce high-quality cellulose nanocrystals with promising applications in industry, nanotechnology, and biotechnology, thereby contributing to environmental and economic sustainability.

Keywords: cellulose, melon plant residues, cellulose nanocrystals, properties, applications, composite materials

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Authors: Ayanda Ndokwana, Stanley Fore

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Industrial prosperity and rapid expansion of human population in South Africa over the past two decades, have increased the use of conventional fossil fuels such as crude oil, coal and natural gas to meet the country’s energy demands. However, the inevitable depletion of fossil fuel reserves, global volatile oil price and large carbon footprint are some of the crucial reasons the South African Government needs to make a considerable investment in the development of the biofuel industry. In South Africa, this industry is still at the introductory stage with no large scale manufacturing plant that has been commissioned yet. Bioethanol is a potential replacement of gasoline which is a fossil fuel that is used in motor vehicles. Using bioethanol for the transport sector as a source of fuel will help Government to save heavy foreign exchange incurred during importation of oil and create many job opportunities in rural farming. In 2007, the South African Government developed the National Biofuels Industrial Strategy in an effort to make provision for support and attract investment in bioethanol production. However, capital investment in the production of bioethanol on a large scale, depends on the sound economic assessment of the available manufacturing technologies. The aim of this study is to evaluate the profitability of an advanced bioethanol manufacturing technology which uses maize as a feedstock in South Africa. The impact of fiber or bran fractionation in this technology causes it to possess a number of merits such as energy efficiency, low capital expenditure, and profitability compared to a conventional dry-mill bioethanol technology. Quantitative techniques will be used to collect and analyze numerical data from suitable organisations in South Africa. The dependence of three profitability indicators such as the Discounted Payback Period (DPP), Net Present Value (NPV) and Return On Investment (ROI) on plant capacity will be evaluated. Profitability analysis will be done on the following plant capacities: 100 000 ton/year, 150 000 ton/year and 200 000 ton/year. The plant capacity with the shortest Discounted Payback Period, positive Net Present Value and highest Return On Investment implies that a further consideration in terms of capital investment is warranted.

Keywords: bioethanol, economic evaluation, maize, profitability indicators

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Authors: M. Imran Hamid, Muzammil Hussain, Yunpeng Wu, Meichun Xiang, Xingzhong Liu

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The rhizosphere microbiome elucidate multiple functioning in the soil suppressiveness against plant pathogens. Soybean rhizosphere microbial communities may involve in the natural suppression of soybean cyst nematode (SCN) populations in disease suppressive soils. To explore these ecological mechanisms of microbes, a long term monoculture suppressive soil were taken into account for further investigation to test the disease suppressive ability by using different treatments. The designed treatments are as, i) suppressive soil (S), ii) conducive soil (C), iii) conducive soil mixed with 10% (w/w) suppressive soil (CS), iv) suppressive soil treated at 80°C for 1 hr (S80), and v) suppressive soil treated with formalin (SF). By using an ultra-high-throughput sequencing approach, we identified the key bacterial and fungal taxa involved in SCN suppression. The Phylum-level investigation of bacteria revealed that Actinobacteria, Bacteroidetes, and Proteobacteria in the rhizosphere soil of soybean seedlings were more abundant in the suppressive soil than in the conducive soil. The phylum-level analysis of fungi in rhizosphere soil indicated that relative abundance of Ascomycota was higher in suppressive soil than in the conducive soil, where Basidiomycota was more abundant. Transferring suppressive soil to conducive soil increased the population of Ascomycota in the conducive soil by lowering the populations of Basidiomycota. The genera, such as, Pochonia, Purpureocillium, Fusarium, Stachybotrys that have been well documented as bio-control agents of plant nematodes were far more in the disease suppressive soils. Our results suggested that the plants engage a subset of functional microbial groups in the rhizosphere for initial defense upon nematode attack and protect the plant roots later on by nematodes to response for suppression of SCN in disease-suppressive soils.

Keywords: disease suppressive soil, high-throughput sequencing, rhizosphere microbiome, soybean cyst nematode

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Authors: Hu Ding, Kai Liu, Guoan Tang

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The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest

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Authors: Animut Meseret Simachew

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Parallel Code Phase Search Acquisition (PCSA) Algorithm has been considered as a promising method in GPS software receivers for detection and estimation of the accurate correlation peak between the received Global Positioning System (GPS) signal and locally generated replicas. GPS signal acquisition in highly dense multipath environments is the main research challenge. In this work, we proposed a robust variable step-size (RVSS) PCSA algorithm based on fast frequency transform (FFT) filtering technique to mitigate short time delay multipath signals. Simulation results reveal the effectiveness of the proposed algorithm over the conventional PCSA algorithm. The proposed RVSS-PCSA algorithm equalizes the received carrier wiped-off signal with locally generated C/A code.

Keywords: adaptive PCSA, detection and estimation, GPS signal acquisition, GPS software receiver

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Authors: Patrick Cormier, Cesar Meylan, Matt Jensen, Dana Agar-Newman, Chloe Werle, Ming-Chang Tsai, Marc Klimstra

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Sprint-derived horizontal force-velocity-power (FVP) profiles can be developed with adequate validity and reliability with satellite (GPS/GNSS) systems. However, FVP metrics are sensitive to small nuances in data processing procedures such that minor differences in defining the onset and end of the sprint could result in different FVP metric outcomes. Furthermore, in team-sports, there is a requirement for rapid analysis and feedback of results from multiple athletes, therefore developing standardized and automated methods to improve the speed, efficiency and reliability of this process are warranted. Thus, the purpose of this study was to compare different methods of sprint end detection on the development of FVP profiles from 10Hz GPS/GNSS data through goodness-of-fit and intertrial reliability statistics. Seventeen national team female soccer players participated in the FVP protocol which consisted of 2x40m maximal sprints performed towards the end of a soccer specific warm-up in a training session (1020 hPa, wind = 0, temperature = 30°C) on an open grass field. Each player wore a 10Hz Catapult system unit (Vector S7, Catapult Innovations) inserted in a vest in a pouch between the scapulae. All data were analyzed following common procedures. Variables computed and assessed were the model parameters, estimated maximal sprint speed (MSS) and the acceleration constant τ, in addition to horizontal relative force (F₀), velocity at zero (V₀), and relative mechanical power (Pmax). The onset of the sprints was standardized with an acceleration threshold of 0.1 m/s². The sprint end detection methods were: 1. Time when peak velocity (MSS) was achieved (zero acceleration), 2. Time after peak velocity drops by -0.4 m/s, 3. Time after peak velocity drops by -0.6 m/s, and 4. When the integrated distance from the GPS/GNSS signal achieves 40-m. Goodness-of-fit of each sprint end detection method was determined using the residual sum of squares (RSS) to demonstrate the error of the FVP modeling with the sprint data from the GPS/GNSS system. Inter-trial reliability (from 2 trials) was assessed utilizing intraclass correlation coefficients (ICC). For goodness-of-fit results, the end detection technique that used the time when peak velocity was achieved (zero acceleration) had the lowest RSS values, followed by -0.4 and -0.6 velocity decay, and 40-m end had the highest RSS values. For intertrial reliability, the end of sprint detection techniques that were defined as the time at (method 1) or shortly after (method 2 and 3) when MSS was achieved had very large to near perfect ICC and the time at the 40 m integrated distance (method 4) had large to very large ICCs. Peak velocity was reached at 29.52 ± 4.02-m. Therefore, sport scientists should implement end of sprint detection either when peak velocity is determined or shortly after to improve goodness of fit to achieve reliable between trial FVP profile metrics. Although, more robust processing and modeling procedures should be developed in future research to improve sprint model fitting. This protocol was seamlessly integrated into the usual training which shows promise for sprint monitoring in the field with this technology.

Keywords: automated, biomechanics, team-sports, sprint

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Authors: Birmohan Singh, V.K.Jain

Abstract:

Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Masses and microcalcifications, architectural distortions are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support Vector Machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and accuracy of 96% for the detection of abnormalities with mammogram images collected from Digital Database for Screening Mammography (DDSM) database.

Keywords: architecture distortion, mammograms, GLCM texture features, GLRLM texture features, support vector machine classifier

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Authors: Ayaka A. Matsuo, Gweyneth Victoria I. Maranan, Shawn Mikel Hobayan

Abstract:

In this study, we delve into the application of mahogany fruits as biochar for soil amendment, aiming to evaluate their effectiveness in improving soil quality and influencing the growth parameters of okra plants through a comprehensive analysis employing various multivariate tests. In a more straightforward approach, our results show that biochar derived from isn't just a minor player but emerges as a key contributor to our study. This finding holds profound implications, as it highlights the material significance of biochar derived from Mahogany (Swietenia macrophylla) fruits, leaves, and branches in shaping the outcomes. The importance of this discovery lies in its contribution to an enhanced comprehension of the overall effects of biochar on the variables explored in our investigation. Notably, the positive changes observed in height, number of leaves, and width of leaves in okra plants further support the premise that the incorporation of biochar improves soil quality. These findings provide valuable insights for agricultural practices, suggesting that biochar derived from Mahogany (Swietenia macrophylla) fruits, leaves, and branches holds promise as a sustainable soil amendment with positive implications for plant growth. The statistical results from multivariate tests serve to solidify the conclusion that biochar plays a pivotal role in driving the observed outcomes in our study. In essence, this research not only sheds light on the potential of mahogany fruit-derived biochar but also emphasizes its significance in fostering healthier soil conditions and, consequently, enhanced plant growth.

Keywords: soil amendment, biochar, mahogany, soil health

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Authors: Assma Azeroual, Karim Afdel, Mohamed El Hajji, Hassan Douzi

Abstract:

Key frame extraction methods select the most representative frames of a video, which can be used in different areas of video processing such as video retrieval, video summary, and video indexing. In this paper we present a novel approach for extracting key frames from video sequences. The frame is characterized uniquely by his contours which are represented by the dominant blocks. These dominant blocks are located on the contours and its near textures. When the video frames have a noticeable changement, its dominant blocks changed, then we can extracte a key frame. The dominant blocks of every frame is computed, and then feature vectors are extracted from the dominant blocks image of each frame and arranged in a feature matrix. Singular Value Decomposition is used to calculate sliding windows ranks of those matrices. Finally the computed ranks are traced and then we are able to extract key frames of a video. Experimental results show that the proposed approach is robust against a large range of digital effects used during shot transition.

Keywords: FSDWT, key frame extraction, shot detection, singular value decomposition

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Authors: Prodipta Bhowmik, Poulami Saha, Preety Mehra, Yogesh Soni, Triloki Nath Jha

Abstract:

In India, the smart city concept is growing day by day. So, for smart city development, a better traffic management and monitoring system is a very important requirement. Nowadays, road accidents increase due to more vehicles on the road. Reckless driving is mainly responsible for a huge number of accidents. So, an efficient traffic management system is required for all kinds of roads to control the traffic speed. The speed limit varies from road to road basis. Previously, there was a radar system but due to high cost and less precision, the radar system is unable to become favorable in a traffic management system. Traffic management system faces different types of problems every day and it has become a researchable topic on how to solve this problem. This paper proposed a computer vision and machine learning-based automated system for multiple vehicle detection, tracking, and speed estimation of vehicles using image processing. Detection of vehicles and estimating their speed from a real-time video is tough work to do. The objective of this paper is to detect vehicles and estimate their speed as accurately as possible. So for this, a real-time video is first captured, then the frames are extracted from that video, then from that frames, the vehicles are detected, and thereafter, the tracking of vehicles starts, and finally, the speed of the moving vehicles is estimated. The goal of this method is to develop a cost-friendly system that can able to detect multiple types of vehicles at the same time.

Keywords: OpenCV, Haar Cascade classifier, DLIB, YOLOV3, centroid tracker, vehicle detection, vehicle tracking, vehicle speed estimation, computer vision

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Authors: Aleya Ferdausi, Meriel Jones, Anthony Halls

Abstract:

The Amaryllidaceae genus Narcissus contains secondary metabolites, which are important sources of bioactive compounds such as pharmaceuticals indicating that their biological activity extends from the native plant to humans. Transcriptome analysis (RNA-seq) is an effective platform for the identification and functional characterization of candidate genes as well as to identify genes encoding uncharacterized enzymes. The biotechnological production of secondary metabolites in plant cell or organ cultures has become a tempting alternative to the extraction of whole plant material. The biochemical pathways for the production of secondary metabolites require primary metabolites to undergo a series of modifications catalyzed by enzymes such as cytochrome P450s, methyltransferases, glycosyltransferases, and acyltransferases. Differential gene expression analysis of Narcissus was obtained from two conditions, i.e. field and in vitro callus. Callus was obtained from modified MS (Murashige and Skoog) media supplemented with growth regulators and twin-scale explants from Narcissus cv. Carlton bulb. A total of 2153 differentially expressed transcripts were detected in Narcissus bulb and in vitro callus, and 78.95% of those were annotated. It showed the expression of genes involved in the biosynthesis of alkaloids were present in both conditions i.e. cytochrome P450s, O-methyltransferase (OMTs), NADP/NADPH dehydrogenases or reductases, SAM-synthetases or decarboxylases, 3-ketoacyl-CoA, acyl-CoA, cinnamoyl-CoA, cinnamate 4-hydroxylase, alcohol dehydrogenase, caffeic acid, N-methyltransferase, and NADPH-cytochrome P450s. However, cytochrome P450s and OMTs involved in the later stage of Amaryllidaceae alkaloids biosynthesis were mainly up-regulated in field samples. Whereas, the enzymes involved in initial biosynthetic pathways i.e. fructose biphosphate adolase, aminotransferases, dehydrogenases, hydroxyl methyl glutarate and glutamate synthase leading to the biosynthesis of precursors; tyrosine, phenylalanine and tryptophan for secondary metabolites were up-regulated in callus. The knowledge of probable genes involved in secondary metabolism and their regulation in different tissues will provide insight into the Narcissus plant biology related to alkaloid production.

Keywords: narcissus, callus, transcriptomics, secondary metabolites

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Authors: Abdullah S. El-Ammari, Omar M. El-Sanousi, Fathi S. El-Mesmari

Abstract:

One distinct virus namely bean yellow mosaic potyvirus (BYMV) was isolated from naturally infected faba bean plants and identified through the serological reaction, mechanical transmission, host range and symptomology. To study the effect of BYMV on faba bean crop productivity, the experiment was carried out in naturally infected field in a completely randomized design with two treatments (the early infected plants and the lately infected plants). T- test was used to analyze the data. plants of each treatment were harvested when the pods were fully ripened. Early infection significantly reduced the yield of broad bean crop leading to 85.04% yield loss in productivity of seeds per plant, 72.42% yield loss in number of pods per plants, 31.58% yield loss in number of seeds per pod and 18.2% yield loss in weight of seeds per plant.

Keywords: bean yellow mosaic potyvirus, faba bean, productivity, libya

Procedia PDF Downloads 310