Search results for: tropical deciduous forest

Authors: Pamir Gogoi, Ratree Wayland

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This study investigates the acoustic measures of breathiness when coarticulated with nasality. The acoustic correlates of breathiness and nasality that has already been well established after years of empirical research. Some of these acoustic parameters - like low frequency peaks and wider bandwidths- are common for both nasal and breathy voice. Therefore, it is likely that these parameters interact when a sound is coarticulated with breathiness and nasality. This leads to the hypothesis that the acoustic parameters, which usually act as robust cues in differentiating between breathy and modal voice, might not be reliable cues for differentiating between breathy and modal voice when breathiness is coarticulated with nasality. The effect of nasality on the perception of breathiness has been explored in earlier studies using synthesized speech. The results showed that perceptually, nasality and breathiness do interact. The current study investigates if a similar pattern is observed in natural speech. The study is conducted on Marathi, an Indo-Aryan language which has a three-way contrast between nasality and breathiness. That is, there is a phonemic distinction between nasals, breathy voice and breathy-nasals. Voice quality parameters like – H1-H2 (Difference between the amplitude of first and second harmonic), H1-A3 (Difference between the amplitude of first harmonic and third formant, CPP (Cepstral Peak Prominence), HNR (Harmonics to Noise ratio) and B1 (Bandwidth of first formant) were extracted. Statistical models like linear mixed effects regression and Random Forest classifiers show that measures that capture the noise component in the signal- like CPP and HNR- can classify breathy voice from modal voice better than spectral measures when breathy voice is coarticulated with nasality.

Keywords: breathiness, marathi, nasality, voice quality

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Authors: Kavita Bodke

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Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception.

Keywords: SHM, CNN, deep learning, multi-class classification, multi-label classification

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

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Authors: Aree Thongpukdee, Chockpisit Thepsithar, Chuthalak Thammaso

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Use of plants grown in local area for edible has a long tradition in different culture. The indigenous knowledge such as usage of plants as vegetables by local people is risk to disappear when no records are done. In order to conserve and transfer this valuable heritage to the new generation, ethnobotanical study should be investigated and documented. The survey of vegetable plants traditionally used was carried out in the year 2012. Information was accumulated via questionnaires and oral interviewing from 100 people living in 36 villages of 9 districts in Amphoe Huai Mek, Kalasin, Thailand. Local plant names, utilized parts and preparation methods of the plants were recorded. Each mentioned plant species were collected and voucher specimens were prepared. A total of 55 vegetable plant species belonging to 34 families and 54 genera were identified. The plant habits were tree, shrub, herb, climber, and shrubby fern at 21.82%, 18.18%, 38.18%, 20.00% and 1.82% respectively. The most encountered vegetable plant families were Leguminosae (20%), Cucurbitaceae (7.27%), Apiaceae (5.45%), whereas families with 3.64% uses were Araceae, Bignoniaceae, Lamiaceae, Passifloraceae, Piperaceae and Solanaceae. The most common consumptions were fresh or brief boiled young shoot or young leaf as side dishes of ‘jaeo, laab, namprik, pon’ or curries. Most locally known vegetables included 45% of the studied plants which grow along road side, backyard garden, hedgerow, open forest and rice field.

Keywords: vegetable plants, ethnobotanical survey, Kalasin, Thailand

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: SHWETA SRIKUMAR

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Winter stratospheric dynamics is a highly variable and spectacular field of research in middle atmosphere. It is well believed that the interaction of energetic planetary waves with mean flow causes the temperature to increase in the stratosphere and associated circulation reversal. This wave driven sudden disturbances in the polar stratosphere is defined as Sudden Stratospheric Warming. The main objective of the present work is to investigate the mid-winter major stratospheric warming events on equatorial dynamics over Peninsular India. To explore the effect of mid-winter stratospheric warming on Indian region (60oE -100oE), we have selected the winters 2003/04, 2005/06, 2008/09, 2012/13 and 2018/19. This study utilized the data from ERA-Interim Reanalysis, Outgoing Longwave Radiation (OLR) from NOAA and TRMM satellite data from NASA mission. It is observed that a sudden drop in OLR (averaged over Indian Region) occurs during the course of warming for the winters 2005/06, 2008/09 and 2018/19. But in winters 2003/04 and 2012/13, drop in OLR happens prior to the onset of major warming. Significant amplitude of planetary wave activity is observed in equatorial lower stratosphere which indicates the propagation of extra-tropical planetary waves from high latitude to equator. During the course of warming, a strong downward propagation of EP flux convergence is observed from polar to equator region. The polar westward wind reaches upto 20oN and the weak eastward wind dominates the equator during the winters 2003/04, 2005/06 and 2018/19. But in 2012/13 winter, polar westward wind reaches upto equator. The equatorial wind at 2008/09 is dominated by strong westward wind. Further detailed results will be presented in the conference.

Keywords: Equatorial dynamics, Outgoing Longwave Radiation, Sudden Stratospheric Warming, Planetary Waves

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Authors: Walid Moudani

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The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the coronary heart disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: multi-classifier decisions tree, features reduction, dynamic programming, rough sets

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Authors: Sali Chinsathit, Haruthai Kaenla

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Organic agriculture has become a major policy theme for agricultural development in Thailand since October 2005. Organic farming is enlisted as an important national agenda, to promote safe food and national export, and many government authorities have initiated projects and activities centered on organic farming promotion. Currently, Thailand has the market share of about 32 million US$ a year by exporting organic products of rice, vegetables, tea, fruits and a few medicinal herbs. There is high potential in organic crop production as there is the tropical environment promoting crop growth and leader farmer in organic farming. However, organic sector is relatively small (0.2%) comparing with conventional agricultural area, since there are many factors affecting farmers’ adoption and success in organic farming. The objective of this project was to get the organic production technology for at least 3 organic crops. The treatment and method were complied with Thai Organic Standard, and were mainly concerned on increase plant biodiversity and soil improvement by using organic fertilizer and bio-extract from fish, egg, plant and fruits. The bio-logical control, plant-extracts, and cultural practices were used to control insect pests and diseases of 3 crops including mangosteen (Garcinia mangostana L.), longkong (Aglaia dookoo Griff.) and banana (Musa (AA group)). The experiments were carried out at research centers of Department of Agriculture and farmers’ farms in Rayong and Chanthaburi provinces from 2009 to 2013. We found that both locations, plant biodiversity by intercropping mangosteen or longkong with banana and soil improvement with composts and bio-extract from fish could increased yield and farmers’ income by 6,835 US$/ha/year. Farmers got knowledge from these technologies to produce organic crops. The organic products were sold both in domestic and international countries. The organic production technologies were also environmental friendly and could be used as an alternative way for farmers in Thailand.

Keywords: banana, longkong, mangosteen, organic farming

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Authors: Ekele T. Ochedi, Ahmad H. Taki, Birgit Painter

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The effect of the sun and its path on thermal comfort and energy consumption in residential buildings in tropical climates constitute a serious concern for designers, building owners, and users. Passive design approaches based on the sun and its path have been identified as a means of reducing energy consumption as well as enhancing thermal comfort in buildings worldwide. Hence, a thorough understanding regarding the sun path is key to achieving this. This is necessary due to energy need, poor energy supply, and distribution, energy poverty, and over-dependence on electric generators for power supply in Nigeria. These challenges call for a change in the approach to energy-related issues, especially in terms of buildings. The aim of this study is to explore the influence of building orientation, glazing and the use of shading devices on residential buildings in Nigeria. This is intended to provide data that will guide designers in the design of energy-efficient residential buildings. The paper used EnergyPlus to analyze a typical semi-detached residential building in Lokoja, Nigeria using hourly weather data for a period of 10 years. Building performance was studied as well as possible improvement regarding different orientations, glazing types and shading devices. The simulation results show some reductions in energy consumption in response to changes in building orientation, types of glazing and the use of shading devices. The results indicate 29.45% reduction in solar gains and 1.90% in annual operative temperature using natural ventilation only. This shows a huge potential to reduce energy consumption and improve people’s well-being through the use of proper building orientation, glazing and appropriate shading devices on building envelope. The study concludes that for a significant reduction in total energy consumption by residential buildings, the design should focus on multiple design options rather than concentrating on one or few building elements. Moreover, the investigation confirms that energy performance modeling can be used by building designers to take advantage of the sun and to evaluate various design options.

Keywords: energy consumption, energy-efficient buildings, glazing, thermal comfort, shading devices, solar gains

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Authors: Javier Ascanio Villabona, Brayan Eduardo Tarazona Romero, Camilo Leonardo Sandoval Rodriguez, Arly Dario Rincon, Omar Lengerke Perez

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Efficient energy consumption in the housing sector in relation to refrigeration is a concern in the construction and rehabilitation of houses in tropical areas. Thermal comfort is aggravated by heat gain on the roof surface by heat gains. Thus, in the group of passive cooling techniques, one of the practices and technologies in solar control that provide improvements in comfortable conditions are thermal insulation or geometric changes of the roofs. On the other hand, methods with reflection and radiation are the methods used to decrease heat gain by facilitating the removal of excess heat inside a building to maintain a comfortable environment. Since the potential of these techniques varies in different climatic zones, their application in different zones should be examined. This research is based on the experimental study of a prototype of a roof radiator as a method of passive cooling in homes, which was developed through an experimental research methodology making measurements in a prototype built by means of the paradigm of appropriate technology, with the aim of establishing an initial behavior of the internal temperature resulting from the climate of the external environment. As a starting point, a selection matrix was made to identify the typologies of passive cooling systems to model the system and its subsequent implementation, establishing its constructive characteristics. Step followed by the measurement of the climatic variables (outside the prototype) and microclimatic variables (inside the prototype) to obtain a database to be analyzed. As a final result, the decrease in temperature that occurs inside the chamber with respect to the outside temperature was evidenced. likewise, a linearity in its behavior in relation to the variations of the climatic variables.

Keywords: appropriate technology, enveloping, energy efficiency, passive cooling

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Authors: Gatot Dwi Hendro, Hayyan ul Haq

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This work aims to explore the strategy in embodying the ecological validity in creating the sustainability of public policy, particularly in strengthening the green economy in Indonesia. This green economy plays an important role in supporting the national development in Indonesia, as it is a part of the national policy that posits the primary priority in Indonesian governance. The green economy refers to the national development covering strategic natural resources, such as mining, gold, oil, coal, forest, water, marine, and the other supporting infrastructure for products and distribution, such as fabrics, roads, bridges, and so forth. Thus, all activities in those national development should consider the sustainability. This sustainability requires the strong commitment of the national and regional government, as well as the local governments to put the ecology as the main requirement for issuing any policy, such as licence in mining production, and developing and building new production and supporting infrastructures for optimising the national resources. For that reason this work will focus on the strategy how to embody the ecological values and norms in the public policy. In detail, this work will offer the method, i.e. legal techniques, in visualising and embodying the norms and public policy that valid ecologically. This ecological validity is required in order to maintain and sustain our collective life.

Keywords: ecological validity, sustainable development, coherence, Indonesian Pancasila values, environment, marine

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: Tadesse Menjetta, Daniel Dana, Serkadis Debalke

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Schistosomiasis/Bilharziasis is one of the neglected tropical parasitic diseases caused by different species of genus Schistosoma. Among the species, S. mansoni (causative agents of intestinal schistosomiasis) is one of the causes of severe intestinal parasitic infections with high public and medical importance in Ethiopia. There is a scarcity of information about the status of S. mansoni infection among the fisherman in our study area and in the country at large. Therefore, this study was designed to determine the prevalence and risk factors of S.mansoni infection among fishermen at Lake Hawassa, southern Ethiopia. A cross-sectional study was conducted among the fishermen from April to June 2013 in Hawassa, Southern Ethiopia. A total of 243 fishermen were included by systematic sampling from the lists of the fishermen members in the registration book of fishermen associations in the Hawassa Town. Data on socio-demographic features and risk factors were collected by using semi-structured questionnaires. Stool samples were collected and processed using Kato-Katz thick smear techniques and examined between 30- 40 minute for hookworm and after 24 hours for S. mansoni and other soil-transmitted helminths (STHs). The overall prevalence of S.mansoni among the fishermen was 29.21% (71/243), and the mean intensity of infection was 158.88 egg per gram (EPG). The prevalence of intestinal helminths including S. mansoni was 69.54% (169/243). Moreover, the prevalence of soil-transmitted helminths (STHs) was 40.74% (99/243), 35.80% (87/243) and 5.76% (14/243) for A. lumbricoides, T. trichiura and hookworm species, respectively. Almost similar prevalence of S.mansoni, 31.82%, 31.75%, 31.94% were recorded in age groups of 15-19, 20-24 and 25-29 years, respectively. Fishermen who are swimming always were 2.92 times [95% CI: 1.554, 5.502] more likely to acquire S. mansoni infection than other water contacting habit of the study participants. The results of the current investigation indicated the moderate endemicity of S. mansoni among the fishermen at Lake Hawassa, southern Ethiopia. Fishermen could be the potential risk group for S. mansoni infection and might be responsible for the transmission of S. mansoni to other segments of the communities. Since the high prevalence of STH was recorded among the fishermen, integrated prevention and control strategies from different sectors might be important to tackle the problem.

Keywords: S. mansoni, soil transmitted helminths, fishermen, Lake Hawassa, Ethiopia

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Authors: Abdullahi Jibrin, Aishetu Abdulkadir

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The development of allometric models is crucial to accurate forest biomass/carbon stock assessment. The aim of this study was to develop a set of biomass prediction models that will enable the determination of total tree aboveground biomass for savannah woodland area in Niger State, Nigeria. Based on the data collected through biometric measurements of 1816 trees and destructive sampling of 36 trees, five species specific and one site specific models were developed. The sample size was distributed equally between the five most dominant species in the study site (Vitellaria paradoxa, Irvingia gabonensis, Parkia biglobosa, Anogeissus leiocarpus, Pterocarpus erinaceous). Firstly, the equations were developed for five individual species. Secondly these five species were mixed and were used to develop an allometric equation of mixed species. Overall, there was a strong positive relationship between total tree biomass and the stem diameter. The coefficient of determination (R2 values) ranging from 0.93 to 0.99 P < 0.001 were realised for the models; with considerable low standard error of the estimates (SEE) which confirms that the total tree above ground biomass has a significant relationship with the dbh. The F-test value for the biomass prediction models were also significant at p < 0.001 which indicates that the biomass prediction models are valid. This study recommends that for improved biomass estimates in the study site, the site specific biomass models should preferably be used instead of using generic models.

Keywords: allometriy, biomass, carbon stock , model, regression equation, woodland, inventory

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Authors: Gholba Niranjan Dilip, Anil Kumar

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Conduct of accurate reliable mapping of oil palm plantations and census of individual palm trees is a huge challenge. This study addresses this challenge and developed an optimized solution implemented deep learning techniques on remote sensing data. The oil palm is a very important tropical crop. To improve its productivity and land management, it is imperative to have accurate census over large areas. Since, manual census is costly and prone to approximations, a methodology for automated census using panchromatic images from Cartosat-2, SkySat and World View-3 satellites is demonstrated. It is selected two different study sites in Indonesia. The customized set of training data and ground-truth data are created for this study from Cartosat-2 images. The pre-trained model of Single Shot MultiBox Detector (SSD) Lite MobileNet V2 Convolutional Neural Network (CNN) from the TensorFlow Object Detection API is subjected to transfer learning on this customized dataset. The SSD model is able to generate the bounding boxes for each oil palm and also do the counting of palms with good accuracy on the panchromatic images. The detection yielded an F-Score of 83.16 % on seven different images. The detections are buffered and dissolved to generate polygons demarcating the boundaries of the oil palm plantations. This provided the area under the plantations and also gave maps of their location, thereby completing the automated census, with a fairly high accuracy (≈100%). The trained CNN was found competent enough to detect oil palm crowns from images obtained from multiple satellite sensors and of varying temporal vintage. It helped to estimate the increase in oil palm plantations from 2014 to 2021 in the study area. The study proved that high-resolution panchromatic satellite image can successfully be used to undertake census of oil palm plantations using CNNs.

Keywords: object detection, oil palm tree census, panchromatic images, single shot multibox detector

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Authors: A. Bouzekri, H. Benmassaud

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Aurès region is one of the arid and semi-arid areas that have suffered climate crises and overexploitation of natural resources they have led to significant land degradation. The use of remote sensing data allowed us to analyze the land and its spatiotemporal changes in the Aurès between 1987 and 2013, for this work, we adopted a method of analysis based on the exploitation of the images satellite Landsat TM 1987 and Landsat OLI 2013, from the supervised classification likelihood coupled with field surveys of the mission of May and September of 2013. Using ENVI EX software by the superposition of the ground cover maps from 1987 and 2013, one can extract a spatial map change of different land cover units. The results show that between 1987 and 2013 vegetation has suffered negative changes are the significant degradation of forests and steppe rangelands, and sandy soils and bare land recorded a considerable increase. The spatial change map land cover units between 1987 and 2013 allows us to understand the extensive or regressive orientation of vegetation and soil, this map shows that dense forests give his place to clear forests and steppe vegetation develops from a degraded forest vegetation and bare, sandy soils earn big steppe surfaces that explain its remarkable extension. The analysis of remote sensing data highlights the profound changes in our environment over time and quantitative monitoring of the risk of desertification.

Keywords: remote sensing, spatiotemporal, land use, Aurès

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Authors: S. Niamkaeo, O. Robert, O. Chaowalit

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In this investigation, we focus on discovering spatial relationship of drug smuggling along the northern border of Thailand. Thailand is no longer a drug production site, but Thailand is still one of the major drug trafficking hubs due to its topographic characteristics facilitating drug smuggling from neighboring countries. Our study areas cover three districts (Mae-jan, Mae-fahluang, and Mae-sai) in Chiangrai city and four districts (Chiangdao, Mae-eye, Chaiprakarn, and Wienghang) in Chiangmai city where drug smuggling of methamphetamine crystal and amphetamine occurs mostly. The data on drug smuggling incidents from 2011 to 2017 was collected from several national and local published news. Geo-spatial drug smuggling database was prepared. Decision tree algorithm was applied in order to discover the spatial relationship of factors related to drug smuggling, which was converted into rules using rule-based system. The factors including land use type, smuggling route, season and distance within 500 meters from check points were found that they were related to drug smuggling in terms of rules-based relationship. It was illustrated that drug smuggling was occurred mostly in forest area in winter. Drug smuggling exhibited was discovered mainly along topographic road where check points were not reachable. This spatial relationship of drug smuggling could support the Thai Office of Narcotics Control Board in surveillance drug smuggling.

Keywords: decision tree, drug smuggling, Geographic Information System, GIS knowledge discovery, rule-based system

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Authors: Scott Nielsen, Luca Longanesi, Chris Chuck

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Palm oil is obtained from the flesh and kernel of the fruit of oil palms and is the most productive and inexpensive oil crop. The global demand for palm oil is approximately 75 million metric tonnes, a 29% increase in global production of palm oil since 2016. This expansion of oil palm cultivation has resulted in mass deforestation, vast biodiversity destruction and increasing net greenhouse gas emissions. One possible alternative is to produce a saturated oil, similar to palm, from microbes such as oleaginous yeast. The yeasts can be cultured on sugars derived from second-generation sources and do not compete with tropical forests for land. One highly promising oleaginous yeast for this application is Metschnikowia pulcherrima. However, recent techno-economic modeling has shown that cell lysis and standard lipid extraction are major contributors to the cost of the oil. Typical cell disruption techniques to extract either single cell oils or proteins have been based around bead-beating, homogenization and acid lysis. However, these can have a detrimental effect on lipid quality and are energy-intensive. In this study, a vortex separator, which produces high sheer with minimal energy input, was investigated as a potential low energy method of lysing cells. This was compared to four more traditional methods (thermal lysis, acid lysis, alkaline lysis, and osmotic lysis). For each method, the yeast loading was also examined at 1 g/L, 10 g/L and 100 g/L. The quality of the cell disruption was measured by optical cell density, cell counting and the particle size distribution profile comparison over a 2-hour period. This study demonstrates that the vortex separator is highly effective at lysing the cells and could potentially be used as a simple apparatus for lipid recovery in an oleaginous yeast process. The further development of this technology could potentially reduce the overall cost of microbial lipids in the future.

Keywords: palm oil substitute, metschnikowia pulcherrima, cell disruption, cell lysis

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Authors: Abdelmoniem A. Attaelmanan, Ahmed A. H. Siddig

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This study was conducted in light of continual global climate changes that projected increasing aridity, changes in soil fertility, and pollution. Plant growth and development largely depend on the combination of availing water and nutrients in the soil. Changes in the climate and atmospheric chemistry can cause serious effects on these growth factors. Plant carbon (C), nitrogen (N), and hydrogen (H) play a fundamental role in the maintenance of ecosystem structure and function. Hashab (Acacia senegal), which produces gum Arabic, supports dryland ecosystems in tropical zones by its potentiality to restore degraded soils; hence it is ecologically and economically important for the dry areas of sub-Saharan Africa. The study aims at investigating the effects of water stress (simulated drought) and poor soil type on Acacia senegal C, N, and H contents. Seven days old seedlings were assigned to the treatments in Split- plot design for four weeks. The main plot is irrigation interval (well-watered and water-stressed), and the subplot is soil types (silt and sand soils). Seedling's C%, N%, and H% were measured using CHNS-O Analyzer and applying Standard Test Method. Irrigation intervals and soil types had no effects on seedlings and leaves C%, N%, and H%, irrigation interval had affected stem C and H%, both irrigation intervals and soil types had affected root N% and interaction effect of water and soil was found on leaves and root's N%. Synthesis application of well-watered irrigation with soil that is rich in N and other nutrients would result in the greatest seedling C, N, and H content which will enhance growth and biomass accumulation and can play a crucial role in ecosystem productivity and services in the dryland regions.

Keywords: Acacia senegal, Africa, climate change, drylands, nutrients biomass, Sub-Saharan, Sudan

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Authors: Vishaal Subramanian, Annuatha Vinod Kumar, Santosh Kumar Budankayala, M. Senthil Kumar

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This paper discusses the aerodynamic and structural design, simulation and optimization of a mini-High Altitude Long Endurance (HALE) UAV. The applications of this mini HALE UAV vary from aerial topological surveys, quick first aid supply, emergency medical blood transport, search and relief activates to border patrol, surveillance and estimation of forest fire progression. Although classified as a mini UAV according to UVS International, our design is an amalgamation of the features of ‘mini’ and ‘HALE’ categories, combining the light weight of the ‘mini’ and the high altitude ceiling and endurance of the HALE. Designed with the idea of implementation in India, it is in strict compliance with the UAS rules proposed by the office of the Director General of Civil Aviation. The plane can be completely automated or have partial override control and is equipped with an Infra-Red camera and a multi coloured camera with on-board storage or live telemetry, GPS system with Geo Fencing and fail safe measures. An additional of 1.5 kg payload can be attached to three major hard points on the aircraft and can comprise of delicate equipment or releasable payloads. The paper details the design, optimization process and the simulations performed using various software such as Design Foil, XFLR5, Solidworks and Ansys.

Keywords: aircraft, endurance, HALE, high altitude, long range, UAV, unmanned aerial vehicle

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Authors: Nuria Vargas, Frances Rodriguez

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Neotropical medium cities have opportunities to develop in a good manner. Xalapa City (Veracruz capital, Mexico) and its metropolitan region, near to the Gulf of Mexico, has already <1 million inhabitants, a medium city size, but it’s growing rapidly as several cities in Latin America. Inside a landscape where it had been a forest cloud and coffee land, emerges the city with an irregular topography. The rapid grow of the urbanization and the loss of vegetation has result in a change on the climate parameters. Frequently warms spells, floods and landslides had been impacted last 2 decades, also a higher incidence of dengue and diarrhea is mentioned in the region. Therefore, the analysis of hydrometeorological events is crucial to understand the role they play in its problem. The urbanization and others radiative forces has created a modulation that can explain the decadal climate changes on the Xalapa region. The Atlantic Multidecadal Oscillation directly influences the temperature and precipitation of the region, even more than climate change does. The total effect of these drivers can create a significant context that origin more risk. However, the most policies frequently consider only the climate change as a principal factor, but other drivers are important to consider and evaluate for the implementation of actions that improve our ambient and cities, in a context of climate change. Medium-sized cities could create better conditions for future citizens, preventing with urban planning that considers possible risks associated with weather and climate.

Keywords: natural variability, urbanization, atlantic multidecadal oscillation, land use changes

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Authors: Thamer Alshuwaili, Yonglin Ren, Bob Du, Manjree Agarwal

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The warehouse beetle, Trogoderma variabile Ballion (Coleoptera: Dermestidae), is a major pest of packaged and processed stored products. Warehouse beetle is the common name which was given by Okumura (1972). This pest has been reported to infest 119 different commodities, and it is distributed throughout the tropical and subtropical parts of the world. Also, it is difficult to control because of the insect's ability to stay without food for long times, and it can survive for years under dry conditions and low-moisture food, and it has also developed resistance to many insecticides. The young larvae of these insects can cause damage to seeds, but older larvae prefer to feed on whole grains. The percentage of damage caused by these insects range between 30-70% in the storage. T. variabile is the species most responsible for causing significant damage in grain stores worldwide. Trogoderma spp. is a huge problem for cereal grains, and there are many countries, such as the USA, Australia, China, Kenya, Uganda and Tanzania who have specific quarantine regulations against possible importation. Also, grain stocks can be almost completely destroyed because of the massive populations the insect may develop. However, the purpose of the current research was to optimize conditions to collect volatile organic compound from Trogoderma variabile at different life stages by using headspace solid phase microextraction (SPME) coupled with gas chromatography-mass spectrometry (GC-MS) and flame ionization detection (FID). Using SPME technique to extract volatile from insects is an efficient, straightforward and nondestructive method. Result of the study shows that 15 insects were optimal number for larvae and adults. Selection of the number of insects depend on the height of the peak area and the number of peaks. Sixteen hours were optimized as the best extraction time for larvae and 8 hours was the optimal number of adults.

Keywords: Trogoderma variabile, warehouse beetle , GC-MS, Solid phase microextraction

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Authors: Vu H. N. Khue, Marian Pavelka, Georg Jocher, Jiří Dušek, Le T. Son, Bui T. An, Ho Q. Bang, Pham Q. Huong

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Agriculture is an important economic sector of Vietnam, the most popular of which is wet rice cultivation. These activities are also known as the main contributor to the national greenhouse gas. In order to understand more about greenhouse gas exchange in these activities and to investigate the factors influencing carbon cycling and sequestration in these types of ecosystems, since 2019, the first eddy covariance station has been installed in a paddy field in Long An province, Mekong Delta. The station was equipped with state-of-the-art equipment for CO₂ and CH₄ gas exchange and micrometeorology measurements. In this study, data from the station was processed following the ICOS recommendations (Integrated Carbon Observation System) standards for CO₂, while CH₄ was manually processed and gap-filled using a random forest model from methane-gapfill-ml, a machine learning package, as there is no standard method for CH₄ flux gap-filling yet. Finally, the carbon equivalent (Ce) balance based on CO₂ and CH₄ fluxes was estimated. The results show that in 2020, even though a new water management practice - alternate wetting and drying - was applied to reduce methane emissions, the paddy field released 928 g Cₑ.m⁻².yr⁻¹, and in 2021, it was reduced to 707 g Cₑ.m⁻².yr⁻¹. On a provincial level, rice cultivation activities in Long An, with a total area of 498,293 ha, released 4.6 million tons of Cₑ in 2020 and 3.5 million tons of Cₑ in 2021.

Keywords: eddy covariance, greenhouse gas, methane, rice cultivation, Mekong Delta

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Authors: Abdulrahman M. Alajlan, Saichao Dang, Qiaoqiang Gan

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Photovoltaic (PV) cells, while pivotal for solar energy harnessing, confront a challenge due to the presence of persistent residual heat. This thermal energy poses significant obstacles to the performance and longevity of PV cells. Mitigating this thermal issue is imperative, particularly in tropical regions where solar abundance coexists with elevated ambient temperatures. In response, a sustainable and economically viable solution has been devised, incorporating water-passive cooling within a Photovoltaic-Thermoelectric (PV-TEG) hybrid system to address PV cell overheating. The implemented system has significantly reduced the operating temperatures of PV cells, achieving a notable reduction of up to 15 °C below the temperature observed in standalone PV systems. In addition, a thermoelectric generator (TEG) integrated into the system significantly enhances power generation, particularly during nighttime operation. The developed hybrid system demonstrates its capability to generate power at a density of 0.5 Wm⁻² during nighttime, which is sufficient to concurrently power multiple light-emitting diodes, demonstrating practical applications for nighttime power generation. Key findings from this research include a consistent temperature reduction exceeding 10 °C for PV cells, translating to a 5% average enhancement in PV output power compared to standalone PV systems. Experimental demonstrations underscore nighttime power generation of 0.5 Wm⁻², with the potential to achieve 0.8 Wm⁻² through simple geometric optimizations. The optimal cooling of PV cells is determined by the volume of water in the heat storage unit, exhibiting an inverse relationship with the optimal performance for nighttime power generation. Furthermore, the TEG output effectively powers a lighting system with up to 5 LEDs during the night. This research not only proposes a practical solution for maximizing solar radiation utilization but also charts a course for future advancements in energy harvesting technologies.

Keywords: photovoltaic-thermoelectric systems, nighttime power generation, PV thermal management, PV cooling

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Authors: Diane H. Dotson, Shari L. Rodriguez

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Habitat for predators is on the decline worldwide, which often brings humans and predators into conflict over remaining shared space and common resources. While the direct impacts of human predator conflict on humans (i.e., attacks on livestock or humans resulting in injury or death) are well documented, the indirect impacts of conflict on humans (i.e., downstream effects such as fear, stress, opportunity costs, PTSD) have not been addressed. We interviewed 437 people living in 54 villages on the periphery of Kanha National Park, India, to assess the amount and severity of direct and indirect impacts of predator conflict. ​While 58% of livestock owners believed that predator attacks on livestock guards occurred frequently and 62% of those who collect forest products believed that predator attacks on those collecting occurred frequently, less than 20% of all participants knew of someone who had experienced an attack. Data related to indirect impacts suggest that such impacts are common; 76% of participants indicated they were afraid a predator will physically injure them. Livestock owners reported that livestock guarding took time away from their primary job (61%) and getting enough sleep (73%), and believed that it increased their vulnerability to illnesses (80%). These results suggest that the perceptions of risk of predator attack are likely inflated, yet the costs of human predator impacts may be substantially higher than previously estimated, particularly related to human well-being, making the implementation of appropriate and effective conservation and conflict mitigation strategies and policies increasingly urgent.

Keywords: direct impacts, indirect impacts, human-predator conflict, India

Procedia PDF Downloads 156

Authors: G. Vishnu, T. V. Bharat

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Rainfall plays a major role in the stability of natural slopes in tropical and subtropical regions. These slopes usually have high slope angles and are stable during the dry season. The critical rainfall intensity that might trigger a landslide may not be the highest rainfall. In addition to geological discontinuities and anthropogenic factors, water content, suction, and hydraulic conductivity also play a role. A thorough geotechnical investigation with the principles of unsaturated soil mechanics is required to predict the failures in these cases. The study discusses three landslide events that had occurred in residual hills of Guwahati, India. Rainfall data analysis, history image analysis, land use, and slope maps of the region were analyzed and discussed. The landslide occurred on June (24, 26, and 28) 2020, on the respective sites, but the highest rainfall was on June (6 and 17) 2020. The factors that lead to the landslide occurrence is the combination of critical events initiated with rainfall, causing a reduction in suction. The sites consist of a mixture of rocks and soil. The slope failure occurs due to the saturation of the soil layer leading to loss of soil strength resulting in the flow of the entire soil rock mass. The land-use change, construction activities, other human and natural activities that lead to faster disintegration of rock mass may accelerate the landslide events. Landslides in these slopes are inevitable, and the development of an early warning system (EWS) to save human lives and resources is a feasible way. The actual time of failure of a slope can be better predicted by considering all these factors rather than depending solely on the rainfall intensities. An effective EWS is required with less false alarms in these regions by proper instrumentation of slope and appropriate climatic downscaling.

Keywords: early warning system, historic image analysis, slope instrumentation, unsaturated soil mechanics

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Authors: Jesryl B. Paulite, Eixer Niel V. Enesco

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Lettuce is not well known in the lowland locality in the tropical countries like Philippines. Farmers thought that this crop is not adaptable to the climate that we have in lowland. But some new varieties can tolerate warmer conditions. The massive use of pesticides in lettuce production might chronically affect human health and environment. The move of the Philippine government is toward organic. One of the organic material is vermicompost. It is an organic fertilizer that serves as soil conditioner and enhances soil fertility and promotes vigorous and healthy crop growth and Supplementation of 19-19-19+M.E. will make it better since it contains N-P-K and selected microelements to meet the nutritive requirements of the crop. The experiment was conducted at Purok 3, Brgy. Tiburcia, Kapalong, Davao del Norte from February 6, 2014 to March 4, 2014. The study was conducted to determine the effect of varying frequency application of vermicast as supplemented with 19-19-19+M.E. in lettuce. Specifically, this aimed to 1.) Identify the agronomic performance of lettuce as affected by varying frequency application of vermicast as supplemented with 19-19-19+M.E.; 2.) Assess the economic profitability of lettuce as applied with vermicast as supplemented with 19-19-19+M.E. The study was laid out in Randomized Complete Block Design (RCBD) with four treatments and three replications. The treatments were as follow: T1 – Untreated, T2 - Weekly Application, T3- Bi-weekly Application, and T4- Monthly Application. The data on percent (%) mortality were transformed using square root of transformation before Analysis of Variance (ANOVA). Results revealed not significant in terms of percent mortality in weekly and monthly application of the treatment having a mean of 1.76 % and 3.09 %. However, Significant differences were observed in agronomic performances such as; plant height with a mean of 10.63 cm in weekly application and 6.40 cm for the untreated, leaf width with a mean of 10.80 cm for the weekly application and 6.03 for the untreated, fresh weight with a mean of 25.67 g for the weekly application and 6.83 g for the untreated, and yield with a mean of 1,208.33 kg/ha for the weekly application and 327.08 kg/ha for the untreated, respectively. Results further exposed that profitability of lettuce in terms of Return of Production Cost (RPC) were; bi-weekly with 91.01 %, monthly with 68.20 %, weekly with 25.34 % and untreated (control) with 16.69 %.

Keywords: agronomic performance, economic profitability, vermicast, percent mortality, 19-19-19+ME

Procedia PDF Downloads 449

Authors: M. Devaki, K. B. Jayanthi

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The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%.

Keywords: water body, Deep learning, satellite images, convolution neural network

Procedia PDF Downloads 90

Authors: F. O. Oboite, P. G. Comeau

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Advance regeneration is present in many lodgepole pine stands in Alberta. When the overstory pine canopy is killed by Mountain Pine Beetle (MPB) the growth of this advance is likely to increase. Understanding the growth response of these understory tree species is needed to improve mid-term timber supply projections and management decisions. To quantify the growth (diameter, height, height/diameter ratio) responses of black spruce and white spruce to lodgepole pine mortality, sample trees of black and white spruce advance regeneration were selected from 7 lodgepole pine dominated stands (5 attacked; 2 control) in the Foothills Region of western Alberta. Measurements were collected 7-8 years after MPB attack across a wide range of spruce height and stand densities. Analysis was done using mixed model linear regression. Result indicates that there was an increase in both diameter and height growth after MPB attack; however, this increase in growth was delayed for about four years. Both spruce species had similar height response and their height/diameter ratio decreased after release, partly as a result of increased understory light associated with loss of needles in the pine canopy. In addition, the diameter and height growth responses of both spruce species were strongly related to density, prerelease growth and initial size.

Keywords: mountain pine beetle, forest regeneration, lodgepole pine, growth response

Procedia PDF Downloads 376

Authors: Mohit Wadhawan, Sushma Rathaur

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Lymphatic filariasis, also called elephantiasis is a tropical disease afflicting over 120 million people in 81 countries worldwide. Existing anti filarial drugs are effective against the larval stages of filarial parasites which call for an urgent need of drugs which are macrofilaricidal. Identification of molecular targets crucial for survival of filarial parasites is a prerequisite for drug designing. Prolyl oligopeptidase (POP) is one such crucial enzyme involved in the maturation and degradation of neuropeptides and peptide hormones. We have identified this peptidase in the bovine filarial parasite, Setaria cervi. Effect of inhibition of POP on the proteome profile of filarial parasite has been discussed in this study. Filarial parasites were exposed to Z-pro-prolinal (ZPP), a specific POP inhibitor for 8 h and the motility and viability of the parasites was observed. It significantly reduced the motility and viability of the parasites. To study the proteome profile, the cytosolic, endoplasmic reticulum (ER) and mitochondrial extracts of the adult female parasites were subjected to 2-dimensional electrophoresis. As analyzed by the PD-Quest software, the ZPP caused the alteration in the different subcellular proteins, and the significantly altered proteins were identified using MALDI-MS/MS spectrometry. The major proteins identified were found to play important role in diverse biological functions like signaling, redox regulation, energy metabolism, stress response, and cytoskeleton formation. Moreover, we found upregulation in the calcium binding proteins such as calreticulin, calponin, and calpain-6 suggesting that POP inhibition regulates calcium release. This relates to earlier reports that POP plays non-catalytic role in inositol 1,4,5-trisphosphate (IP3) signaling inducing release of calcium from ER. Taken together, the data demonstrated that inhibition of prolyl oligopeptidase alter the overall proteome signifying its role in survival of the filarial parasites. Thus this study provides a basis for the use of POP as a chemotherapeutic target for the treatment of lymphatic filariasis.

Keywords: lymphatic filariasis, setaria cervi, prolyl oligopeptidase, proteomics

Procedia PDF Downloads 285