Search results for: CNTs forest

Authors: Yuanjin Liu

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Retirement withdrawal strategies are very important to minimize the probability of ruin in retirement. The ruin probability is modeled as a function of initial withdrawal age, gender, asset allocation, inflation rate, and initial withdrawal rate. The ruin probability is obtained based on the 2019 period life table for the Social Security, IRS Required Minimum Distribution (RMD) Worksheets, US historical bond and equity returns, and inflation rates using simulation. Several popular machine learning algorithms of the generalized additive model, random forest, support vector machine, extreme gradient boosting, and artificial neural network are built. The model validation and selection are based on the test errors using hyperparameter tuning and train-test split. The optimal model is recommended for retirees to monitor the ruin probability. The optimal withdrawal strategy can be obtained based on the optimal predictive model.

Keywords: ruin probability, retirement withdrawal strategies, predictive models, optimal model

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Authors: Vincent Liu

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Machine learning algorithms (ML) can achieve high accuracy in predicting outcomes compared to classical models. Metaheuristic, nature-inspired algorithms can enhance traditional ML algorithms by optimizing them such as by performing feature selection. We compare ten ML algorithms to predict 30-day hospital readmission rates for diabetes patients in the US using a dataset from UCI Machine Learning Repository with feature selection performed by Greywolf nature-inspired algorithm. The baseline accuracy for the initial random forest model was 65%. After performing feature engineering, SMOTE for class balancing, and Greywolf optimization, the machine learning algorithms showed better metrics, including F1 scores, accuracy, and confusion matrix with improvements ranging in 10%-30%, and a best model of XGBoost with an accuracy of 95%. Applying machine learning this way can improve patient outcomes as unnecessary rehospitalizations can be prevented by focusing on patients that are at a higher risk of readmission.

Keywords: diabetes, machine learning, 30-day readmission, metaheuristic

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Authors: N. Moazami, R. Ranjbar, A. Ashori

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Out of hundreds of microalgal strains reported, only very few of them are capable for production of high content of lipid. Therefore, the key technical challenges include identifying the strains with the highest growth rates and oil contents with adequate composition, which were the main aims of this work. From 147 microalgae screened for high biomass and oil productivity, the Nannochloropsis sp. PTCC 6016, which attained 52% lipid content, was selected for large scale cultivation in Persian Gulf Knowledge Island. Nannochloropsis strain PTCC 6016 belongs to Eustigmatophyceae (Phylum heterokontophyta) isolated from Mangrove forest area of Qheshm Island and Persian Gulf (Iran) in 2008. The strain PTCC 6016 had an average biomass productivity of 2.83 g/L/day and 52% lipid content. The biomass productivity and the oil production potential could be projected to be more than 200 tons biomass and 100000 L oil per hectare per year, in an outdoor algal culture (300 day/year) in the Persian Gulf climate.

Keywords: biofuels, microalgae, Nannochloropsis, raceway open pond, bio-jet

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Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

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Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

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Authors: Sudheer Kumar Gundati, Umasankar Patro

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Carbon nanotube (CNT) – polymer composites have been extensively studied due to their exceptional electrical and mechanical properties. In the present study, poly(vinylidene fluoride) (PVDF) – multi-walled CNT composites were prepared by melt-blending technique using pristine (ufCNT) and a modified dilute nitric acid-treated CNTs (fCNT). Due to this dilute acid-treatment, the fCNTs were found to show significantly improved dispersion and retained their electrical property. The fCNT showed an electrical percolation threshold (PT) of 0.15 wt% in the PVDF matrix as against 0.35 wt% for ufCNT. The composites were made into films of thickness ~0.3 mm by compression-molding and the resulting composite films were subjected to various property evaluations. It was found that the water contact angle (WCA) of the films increased with CNT weight content in composites and the composite film surface became hydrophobic (e.g., WCA ~104° for 4 wt% ufCNT and 111.5° for 0.5 wt% fCNT composites) in nature; while the neat PVDF film showed hydrophilic behavior (WCA ~68°). Significant enhancements in the mechanical properties were observed upon CNT incorporation and there is a progressive increase in the tensile strength and modulus with increase in CNT weight fraction in composites. The composite films were tested for strain-sensing applications. For this, a simple and non-destructive method was developed to demonstrate the strain-sensing properties of the composites films. In this method, the change in electrical resistance was measured using a digital multimeter by applying bending strain by oscillation. It was found that by applying dynamic bending strain, there is a systematic change in resistance and the films showed piezo-resistive behavior. Due to the high flexibility of these composite films, the change in resistance was reversible and found to be marginally affected, when large number of tests were performed using a single specimen. It is interesting to note that the composites with CNT content notwithstanding their type near the percolation threshold (PT) showed better strain-sensing properties as compared to the composites with CNT contents well-above the PT. On account of the excellent combination of the various properties, the composite films offer a great promise as strain-sensors for structural health-monitoring.

Keywords: carbon nanotubes, electrical percolation threshold, mechanical properties, poly(vinylidene fluoride), strain-sensor, water contact angle

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Authors: Rana Parween, Rob Marchant

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With the continued loss of global biodiversity despite the application of modern conservation techniques, it has become crucial to investigate non-conventional methods. Accelerated destruction of ecosystems due to altered land use, climate change, cultural and social change, necessitates the exploration of society-biodiversity attitudes and links. While the loss of species and their extinction is a well-known and well-documented process that attracts much-needed attention from researchers, academics, government and non-governmental organizations, the loss of traditional ecological knowledge and practices is more insidious and goes unnoticed. The growing availability of 'indirect experiences' such as the internet and media are leading to a disaffection towards nature and the 'Extinction of Experience'. Exacerbated by the lack of documentation of traditional practices and skills, there is the possibility for the 'extinction' of traditional practices and skills before they are fully recognized and captured. India, as a mega-biodiverse country, is also known for its historical conservation strategies entwined in traditional beliefs. Indigenous communities hold skillsets, knowledge, and traditions that have accumulated over multiple generations and may play an important role in conserving biodiversity today. This study explores the differences in knowledge and attitudes towards conserving biodiversity, of three different stakeholder groups living around Jim Corbett National Park, based on their age, traditions, and association with the protected area. A triangulation designed multi-strategy investigation collected qualitative and quantitative data through a questionnaire survey of village elders, the general public, and forest officers. Following an inductive approach to analyzing qualitative data, the thematic content analysis was followed. All coding and analysis were completed using NVivo 11. Although the village elders and some general public had vast amounts of traditional knowledge, most of it was related to animal husbandry and the medicinal value of plants. Village elders were unfamiliar with the concept of the term ‘biodiversity’ albeit their way of life and attitudes ensured that they care for the ecosystem without having the scientific basis underpinning biodiversity conservation. Inherently, village elders were keen to conserve nature; the superimposition of governmental policies without any tangible benefit or consultation was seen as detrimental. Alienating villagers and consequently the village elders who are the reservoirs of traditional knowledge would not only be damaging to the social network of the area but would also disdain years of tried and tested techniques held by the elders. Forest officers advocated for biodiversity and conservation education for women and children. Women, across all groups, when questioned about nature conservation, showed more interest in learning and participation. Biodiversity not only has an ethical and cultural value, but also plays a role in ecosystem function and, thus, provides ecosystem services and supports livelihoods. Therefore, underpinning and using traditional knowledge and incorporating them into programs of biodiversity conservation should be explored with a sense of urgency.

Keywords: biological diversity, mega-biodiverse countries, traditional ecological knowledge, society-biodiversity links

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Authors: Raghad A. Altowairgi

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With the wide spread of social media platforms, young people tend to use them extensively as the first means of communication due to their ease and modernity. But these platforms often create a fertile ground for bullies to practice their aggressive behavior against their victims. Platform usage cannot be reduced, but intelligent mechanisms can be implemented to reduce the abuse. This is where machine learning comes in. Understanding and classifying text can be helpful in order to minimize the act of cyberbullying. Artificial intelligence techniques have expanded to formulate an applied tool to address the phenomenon of cyberbullying. In this research, machine learning models are built to classify text into two classes; cyberbullying and non-cyberbullying. After preprocessing the data in 4 stages; removing characters that do not provide meaningful information to the models, tokenization, removing stop words, and lowering text. BoW and TF-IDF are used as the main features for the five classifiers, which are; logistic regression, Naïve Bayes, Random Forest, XGboost, and Catboost classifiers. Each of them scores 92%, 90%, 92%, 91%, 86% respectively.

Keywords: cyberbullying, machine learning, Bag-of-Words, term frequency-inverse document frequency, natural language processing, Catboost

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Authors: Hemant Kumar, R. N. K. Sharma, A. P. Krishna

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The study has been carried out on the Saranda in Jharkhand and a part of Odisha state. Geospatial data of Hyperion, a remote sensing satellite, have been used. This study has used a wide variety of patterns related to image processing to enhance and extract the mining class of Fe and Mn ores.Landsat-8, OLI sensor data have also been used to correctly explore related minerals. In this way, various processes have been applied to increase the mineralogy class and comparative evaluation with related frequency done. The Hyperion dataset for hyperspectral remote sensing has been specifically verified as an effective tool for mineral or rock information extraction within the band range of shortwave infrared used. The abundant spatial and spectral information contained in hyperspectral images enables the differentiation of different objects of any object into targeted applications for exploration such as exploration detection, mining.

Keywords: Hyperion, hyperspectral, sensor, Landsat-8

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Authors: Norah Mohammed Alshahrani, Abdulaziz Almaleh

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Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD and they are Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then Feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by the Support Vector Machine (SVM), achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: autism spectrum disorder, machine learning, feature selection, support vector machine

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Authors: Samrat Paul, Satyajit Pahari, Krishnendu Basak, Amitava Roy

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Fishing is one of the pivotal livelihood activities, especially in developing countries. Today it is considered an important occupation for human society from the era of human settlement began. In simple terms, non-target catches of any species during fishing can be considered as ‘bycatch,’ and fishing bycatch is neither a new fishery management issue nor a new problem. Sundarban is one of the world’s largest mangrove land expanding up to 10,200 sq. km in India and Bangladesh. This largest mangrove biome resource is used by the local inhabitants commercially to run their livelihood, especially by forest fringe villagers (FFVs). In Sundarban, over-fishing, especially post larvae collection of wild Penaeus monodon, is one of the major concerns, as during the collection of P. monodon, different aquatic species are destroyed as a result of bycatch mortality which changes in productivity and may negatively impact entire biodiversity, of the ecosystem. Wild prawn seed collection gear like a small mesh sized net poses a serious threat to aquatic stocks, where the collection isn’t only limited to prawn seed larvae. As prawn seed collection processes are inexpensive, require less monetary investment, and are lucrative; people are easily engaged here as their source of income. Wildlife Trust of India’s (WTI) intervention in selected forest fringe villages of Sundarban Tiger Reserve (STR) was to estimate and reduce the mortality of aquatic bycatches by involving local communities in newly developed release method and their time engagement in prawn seed collection (PSC) by involving them in Alternate Income Generation (AIG). The study was conducted for their taxonomic identification during the period of March to October 2019. Collected samples were preserved in 70% ethyl alcohol for identification, and all the preserved bycatch samples were identified morphologically by the expertise of the Zoological Survey of India (ZSI), Kolkata. Around 74 different aquatic species, where 11 different species are molluscs, 41 fish species, out of which 31 species were identified, and 22 species of crustacean collected, out of which 18 species were identified. Around 13 different species belong to a different order, and families were unable to identify them morphologically as they were collected in the juvenile stage. The study reveals that for collecting one single prawn seed, eight individual life of associated faunas are being lost. Zero bycatch mortality is not practical; rather, collectors should focus on bycatch reduction by avoiding capturing, allowing escaping, and mortality reduction, and must make changes in their fishing method by increasing net mesh size, which will avoid non-target captures. But as the prawns are small in size (generally 1-1.5 inches in length), thus increase net size making economically less or no profit for collectors if they do so. In this case, returning bycatches is considered one of the best ways to a reduction in bycatch mortality which is a more sustainable practice.

Keywords: bycatch mortality, biodiversity, mangrove biome resource, sustainable practice, Alternate Income Generation (AIG)

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Authors: Kamyar Tolouei, Ehsan Moosavi

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In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework.

Keywords: grade uncertainty, metaheuristic algorithms, open-pit mine, production scheduling optimization

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Authors: Wei Wang, Yuan Hu

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Epoxy resins (EP), one of the most important thermosetting polymers, is widely applied in various fields due to its desirable properties, such as excellent electrical insulation, low shrinkage, outstanding mechanical stiffness, satisfactory adhesion and solvent resistance. However, like most of the polymeric materials, EP has the fatal drawbacks including inherent flammability and high yield of toxic smoke, which restricts its application in the fields requiring fire safety. So, it is still a challenge and an interesting subject to develop new flame retardants which can not only remarkably improve the flame retardancy, but also render modified resins low toxic gases generation. In recent work, polymer nanocomposites based on nanohybrids that contain two or more kinds of nanofillers have drawn intensive interest, which can realize performance enhancements. The realization of previous hybrids of carbon nanotubes (CNTs) and molybdenum disulfide provides us a novel route to decorate layered double hydroxide (LDH) nanosheets on the surface of β-FeOOH nanorods; the deposited LDH nanosheets can fill the network and promote the work efficiency of β-FeOOH nanorods. Moreover, the synergistic effects between LDH and β-FeOOH can be anticipated to have potential applications in reducing fire hazards of EP composites for the combination of condense-phase and gas-phase mechanism. As reported, β-FeOOH nanorods can act as a core to prepare hybrid nanostructures combining with other nanoparticles through electrostatic attraction through layer-by-layer assembly technique. In this work, LDH nanosheets wrapped β-FeOOH nanorods (LDH-β-FeOOH) hybrids was synthesized by a facile method, with the purpose of combining the characteristics of one dimension (1D) and two dimension (2D), to improve the fire resistance of epoxy resin. The hybrids showed a well dispersion in EP matrix and had no obvious aggregation. Thermogravimetric analysis and cone calorimeter tests confirmed that LDH-β-FeOOH hybrids into EP matrix with a loading of 3% could obviously improve the fire safety of EP composites. The plausible flame retardancy mechanism was explored by thermogravimetric infrared (TG-IR) and X-ray photoelectron spectroscopy. The reasons were concluded: condense-phase and gas-phase. Nanofillers were transferred to the surface of matrix during combustion, which could not only shield EP matrix from external radiation and heat feedback from the fire zone, but also efficiently retard transport of oxygen and flammable pyrolysis.

Keywords: fire hazards, toxic gases, self-assembly, epoxy

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Authors: E. Marei, A. Hammad, S. Ismail, A. El-Gindy

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This study aims to investigate the ability of different formula of mixed bacteria as a biological treatments of wastewater after primary treatment as a bio-treatment and bio-removal and bio-adsorbent of different heavy metals in natural circumstances. The wastewater was collected from Sarpium forest site-Ismailia Governorate, Egypt. These treatments were mixture of free cells and mixture of immobilized cells of different bacteria. These different formulas of mixed bacteria were prepared under Lab. condition. The obtained data indicated that, as a result of wastewater bio-treatment, the removal rate was found to be 76.92 and 76.70% for biological oxygen demand, 79.78 and 71.07% for chemical oxygen demand, 32.45 and 36.84 % for ammonia nitrogen as well as 91.67 and 50.0% for phosphate after 24 and 28 hrs with mixed free cells and mixed immobilized cells, respectively. Moreover, the bio-removals of different heavy metals were found to reach 90.0 and 50. 0% for Cu ion, 98.0 and 98.5% for Fe ion, 97.0 and 99.3% for Mn ion, 90.0 and 90.0% Pb, 80.0% and 75.0% for Zn ion after 24 and 28 hrs with mixed free cells and mixed immobilized cells, respectively. The results indicated that 13.86 and 17.43% of removal efficiency and reduction of total dissolved solids were achieved after 24 and 28 hrs with mixed free cells and mixed immobilized cells, respectively.

Keywords: wastewater bio-treatment , bio-sorption heavy metals, biological desalination, immobilized bacteria, free cell bacteria

Procedia PDF Downloads 178

Authors: Anudeep Appe, Bhanu Poluparthi, Lakshmi Kasivajjula, Udai Mv, Sobha Bagadi, Punya Modi, Aditya Singh, Hemanth Gunupudi, Spenser Troiano, Jeff Paul, Justin Stovall, Justin Yamamoto

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The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data is considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since its data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for ex. quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP (SHapley Additive exPlanations), a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: competition, DAGs, facility, healthcare, machine learning, market share, random forest, SHAP

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Authors: Glykeria A. Visvini, George N. Mathioudakis, Amaia Soto Beobide, Aris E. Giannakas, George A. Voyiatzis

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Polymeric nanocomposites have generated considerable interest in both academic research and industry because their properties can be tailored by adjusting the type & concentration of nano-inclusions, resulting in complementary and adaptable characteristics. The exceptional and/or unique properties of the nanocomposites, including the high mechanical strength and stiffness, the ease of processing, and their lightweight nature, are attributed to the high surface area, the electrical and/or thermal conductivity of the nano-fillers, which make them appealing materials for a wide range of engineering applications. Polymeric «breathable» membranes enabling water vapor permeability (WVP) can be designed either by using micro/nano-fillers with the ability to interrupt the continuity of the polymer phase generating micro/nano-porous structures or/and by creating micro/nano-pores into the composite material by uniaxial/biaxial stretching. Among the nanofillers, carbon nanotubes (CNTs) exhibit particular high WVP and for this reason, they have already been proposed for gas separation membranes. In a similar context, they could prove to be promising alternative/complementary filler nano-materials, for the development of "breathable" products. Polypropylene (PP) is a commonly utilized thermoplastic polymer matrix in the development of composite films, due to its easy processability and low price, combined with its good chemical & physical properties. PP is known to present several crystalline phases (α, β and γ), depending on the applied treatment process, which have a significant impact on its final properties, particularly in terms of WVP. Specifically, the development of the β-phase in PP in combination with stretching is anticipated to modify the crystalline behavior and extend the microporosity of the polymer matrix exhibiting enhanced WVP. The primary objective of this study is to develop breathable nano-carbon based (functionalized MWCNTs) PP composite membranes, potentially also avoiding the stretching process. This proposed alternative is expected to have a better performance/cost ratio over current stretched PP/CaCO3 composite benchmark membranes. The focus is to investigate the impact of both β-nucleator(s) and nano-carbon fillers on water vapor transmission rate properties of relevant PP nanocomposites.

Keywords: carbon nanotubes, nanocomposites, nucleating agents, polypropylene, water vapor permeability

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Authors: Jing Jiang, Wenhuan XU, Lei Zhang, Shiyi Zhang, Tongli Wang

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Climate data quality significantly affects the reliability of ecological modeling. In the Asia Pacific (AP) region, low-quality climate data hinders ecological modeling. ClimateAP, a software developed in 2017, generates high-quality climate data for the AP region, benefiting researchers in forestry and agriculture. However, its adoption remains limited. This study aims to confirm the validity of biologically relevant variable data generated by ClimateAP during the normal climate period through comparison with the currently available gridded data. Climate data from 2,366 weather stations were used to evaluate the prediction accuracy of ClimateAP in comparison with the commonly used gridded data from WorldClim1.4. Univariate regressions were applied to 48 monthly biologically relevant variables, and the relationship between the observational data and the predictions made by ClimateAP and WorldClim was evaluated using Adjusted R-Squared and Root Mean Squared Error (RMSE). Locations were categorized into mountainous and flat landforms, considering elevation, slope, ruggedness, and Topographic Position Index. Univariate regressions were then applied to all biologically relevant variables for each landform category. Random Forest (RF) models were implemented for the climatic niche modeling of Cunninghamia lanceolata. A comparative analysis of the prediction accuracies of RF models constructed with distinct climate data sources was conducted to evaluate their relative effectiveness. Biologically relevant variables were obtained from three unpublished Chinese meteorological datasets. ClimateAPv3.0 and WorldClim predictions were obtained from weather station coordinates and WorldClim1.4 rasters, respectively, for the normal climate period of 1961-1990. Occurrence data for Cunninghamia lanceolata came from integrated biodiversity databases with 3,745 unique points. ClimateAP explains a minimum of 94.74%, 97.77%, 96.89%, and 94.40% of monthly maximum, minimum, average temperature, and precipitation variances, respectively. It outperforms WorldClim in 37 biologically relevant variables with lower RMSE values. ClimateAP achieves higher R-squared values for the 12 monthly minimum temperature variables and consistently higher Adjusted R-squared values across all landforms for precipitation. ClimateAP's temperature data yields lower Adjusted R-squared values than gridded data in high-elevation, rugged, and mountainous areas but achieves higher values in mid-slope drainages, plains, open slopes, and upper slopes. Using ClimateAP improves the prediction accuracy of tree occurrence from 77.90% to 82.77%. The biologically relevant climate data produced by ClimateAP is validated based on evaluations using observations from weather stations. The use of ClimateAP leads to an improvement in data quality, especially in non-mountainous regions. The results also suggest that using biologically relevant variables generated by ClimateAP can slightly enhance climatic niche modeling for tree species, offering a better understanding of tree species adaptation and resilience compared to using gridded data.

Keywords: climate data validation, data quality, Asia pacific climate, climatic niche modeling, random forest models, tree species

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Authors: M. Elanchezhian, C. Gunasekaran, A. Agnes Deepa, M. Salahudeen

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In recent years climate change is one of the most emerging threats facing by biodiversity both the animals and plants species. Elevated carbon dioxide and ozone concentrations, extreme temperature, changes in rainfall patterns, insects-plant interaction are the main criteria that affect biodiversity. In the present study, which emphasis the climate change and its effects on terrestrial insect diversity in Mukuruthi National Park a protected areas of Western Ghats in India. Sampling was done seasonally at the three areas using pitfall traps, over the period of January to December 2013. The statistical findings were done by Shannon wiener diversity index (H). A significant seasonal variation pattern was detected for total insect’s diversity at the different study areas. Totally nine orders of insects were recorded. Diversity and abundance of terrestrial insects shows much difference between the Natural, Shoal forest and the Grasslands.

Keywords: biodiversity, climate change, mukuruthi national park, terrestrial invertebrates

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Authors: Medjadj Tarek, Ghribi Hayet

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This study aims to develop a flood sensitivity assessment tool using machine learning (ML) techniques and geographic information system (GIS). The importance of this study is integrating the geographic information systems (GIS) and machine learning (ML) techniques for mapping flood risks, which help decision-makers to identify the most vulnerable areas and take the necessary precautions to face this type of natural disaster. To reach this goal, we will study the case of the city of M'sila, which is among the areas most vulnerable to floods. This study drew a map of flood-prone areas based on the methodology where we have made a comparison between 3 machine learning algorithms: the xGboost model, the Random Forest algorithm and the K Nearest Neighbour algorithm. Each of them gave an accuracy respectively of 97.92 - 95 - 93.75. In the process of mapping flood-prone areas, the first model was relied upon, which gave the greatest accuracy (xGboost).

Keywords: Geographic information systems (GIS), machine learning (ML), emergency mapping, flood disaster management

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Authors: K. Sangeeth, P. Rekha, P. Preeja, P. Divya, R. Arya, R. Maneesha

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Wireless sensor networks (WSN) are small or tiny devices that consists of different sensors to sense physical parameters like air pressure, temperature, vibrations, movement etc., process these data and sends it to the central data center to take decisions. The WSN domain, has wide range of applications such as monitoring and detecting natural hazards like landslides, forest fire, avalanche, flood monitoring and also in healthcare applications. With such different applications, it is being taught in undergraduate/post graduate level in many universities under department of computer science. But the cost and infrastructure required to purchase WSN nodes for having the students getting hands on expertise on these devices is expensive. This paper gives overview about the remote triggered lab that consists of more than 100 WSN nodes that helps the students to remotely login from anywhere in the world using the World Wide Web, configure the nodes and learn the WSN concepts in intuitive way. It proposes new way called over the air programming (OTAP) and its internals that program the 100 nodes simultaneously and view the results without the nodes being physical connected to the computer system, thereby allowing for sparse deployment.

Keywords: WSN, over the air programming, virtual lab, AT45DB

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Authors: Hairin Taha, Aditya Arya, M. A. Hapipah, A. M. Mustafa

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Plants have been an important source of medicine since ancient times. Pseuduvaria monticola is a rare montane forest species from the Annonaceae family. Traditionally, the plant was used to cure symptoms of fever, inflammation, stomach-ache and also to reduce the elevated levels of blood glucose. Scientifically, we have evaluated the antidiabetic potential of the Pseuduvaria monticola bark methanolic extract on certain in vitro cell based assays, followed by in vivo study. Results from in vitro models displayed PMm upregulated glucose uptake and insulin secretion in mouse pancreatic β-cells. In vivo study demonstrated the PMm down-regulated hyperglycaemia, oxidative stress and elevated levels of pro-inflammatory cytokines in type 2 diabetic rat models. Altogether, the study revealed that Pseuduvaria monticola might be used as a potential candidate for the management of type 2 diabetes and its related complications.

Keywords: type 2 diabetes, Pseuduvaria monticola, insulin secretion, glucose uptake

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Authors: S. O. Oyamakin, A. U. Chukwu,

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We proposed a Hyperbolic Gompertz Growth Model (HGGM), which was developed by introducing a stabilizing parameter called θ using hyperbolic sine function into the classical gompertz growth equation. The resulting integral solution obtained deterministically was reprogrammed into a statistical model and used in modeling the height and diameter of Pines (Pinus caribaea). Its ability in model prediction was compared with the classical gompertz growth model, an approach which mimicked the natural variability of height/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using goodness of fit tests and model selection criteria. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the compliance of the error term to normality assumptions while using testing the independence of the error term using the runs test. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic gompertz growth models better than the source model (classical gompertz growth model) while the results of R2, Adj. R2, MSE, and AIC confirmed the predictive power of the Hyperbolic Monomolecular growth models over its source model.

Keywords: height, Dbh, forest, Pinus caribaea, hyperbolic, gompertz

Procedia PDF Downloads 422

Authors: Jalil Badamfirooz

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Soil wind erosion has a negative impact on the environment. Mulching is one of the most efficient soil protection techniques. Bitumen emulsion has recently been utilized as a soil cover that is sprayed directly over the soil and forms a thin film. The thin coating of bitumen emulsion prevents soil erosion and keeps moisture in the soil. Besides, some compounds release into the soil and cause environmental problems. In the present study, the effect of bitumen emulsion on the release of polycyclic aromatic hydrocarbons (PAHs) into the soil is studied in an arid land located in the central part of Iran. The soil was Loamy-Sand and saline with a pH of 8.03. Bitumen emulsion was used in this study as mulch at a rate of 4 L m2. The effect of this mulch on soil properties was investigated after 6 months of mulch application. Then PAHs concentrations were determined in samples collected from different depths in bitumen emulsion sprayed and control soils. In general, bitumen emulsion application on soil led to a significant increase in some PAHs, which was higher than soil pollution standards critical level of pollution for commerce, groundwater protection, pasture forest, and park and residence uses.

Keywords: mulch, bitumen emulsion, arid land, PAH

Procedia PDF Downloads 63

Authors: Behrang Ekrami, Amin Tamadon

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Caspian red deer or maral (Cervus elaphus maral) is a ruminant from the family of Cervidae. Maintenance and protection of maral requires knowing the behavioral, physiological, environmental characteristics and factors harmful to this species. In this article, reproductive and behavioral traits of this species in both sexes are presented based on observations and the available records of protected deer in Wildlife Refuge of Semeskande, Sari (one of the sites that preserve the maral in the Free Zones of Hyrcanian forest) from 2006 to 2011. Hart characteristics including sexual behavior, apparent changes during reproductive season and reproductive physiology; and hind characteristics including of ovulation, reproductive cycle, mating, pregnancy and parturition, have been evaluated. Identification of maral reproductive characteristics in Wildlife Refuge of Semeskande, Sari is one of the most important information requirements to preserve and breed this species and will open up new routes for performing new methods of reproduction of this species in Iran wildlife parks or other refuge areas.

Keywords: caspian red deer, reproduction, behavior, Iran

Procedia PDF Downloads 453

Authors: Wei Ji

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This study was conducted in the Kansas City metropolitan area of the United States, which has experienced significant urban sprawling in recent decades. The remote sensing of land cover changes in this area spanned over four decades from 1972 through 2010. The project was implemented in two stages: the first stage focused on detection of long-term trends of urban land cover change, while the second one examined how to detect the coupled effects of human impact and climate change on urban landscapes. For the first-stage study, six Landsat images were used with a time interval of about five years for the period from 1972 through 2001. Four major land cover types, built-up land, forestland, non-forest vegetation land, and surface water, were mapped using supervised image classification techniques. The study found that over the three decades the built-up lands in the study area were more than doubled, which was mainly at the expense of non-forest vegetation lands. Surprisingly and interestingly, the area also saw a significant gain in surface water coverage. This observation raised questions: How have human activities and precipitation variation jointly impacted surface water cover during recent decades? How can we detect such coupled impacts through remote sensing analysis? These questions led to the second stage of the study, in which we designed and developed approaches to detecting fine-scale surface waters and analyzing coupled effects of human impact and precipitation variation on the waters. To effectively detect urban landscape changes that might be jointly shaped by precipitation variation, our study proposed “urban wetscapes” (loosely-defined urban wetlands) as a new indicator for remote sensing detection. The study examined whether urban wetscape dynamics was a sensitive indicator of the coupled effects of the two driving forces. To better detect this indicator, a rule-based classification algorithm was developed to identify fine-scale, hidden wetlands that could not be appropriately detected based on their spectral differentiability by a traditional image classification. Three SPOT images for years 1992, 2008, and 2010, respectively were classified with this technique to generate the four types of land cover as described above. The spatial analyses of remotely-sensed wetscape changes were implemented at the scales of metropolitan, watershed, and sub-watershed, as well as based on the size of surface water bodies in order to accurately reveal urban wetscape change trends in relation to the driving forces. The study identified that urban wetscape dynamics varied in trend and magnitude from the metropolitan, watersheds, to sub-watersheds in response to human impacts at different scales. The study also found that increased precipitation in the region in the past decades swelled larger wetlands in particular while generally smaller wetlands decreased mainly due to human development activities. These results confirm that wetscape dynamics can effectively reveal the coupled effects of human impact and climate change on urban landscapes. As such, remote sensing of this indicator provides new insights into the relationships between urban land cover changes and driving forces.

Keywords: urban land cover, human impact, climate change, rule-based classification, across-scale analysis

Procedia PDF Downloads 292

Authors: Syed Jamil H. Kazmi, Faiza Sarwar

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The Indus delta of Sindh Province forms the largest creeks zone of Pakistan. The Sindh coast starts from the mouth of Hab River and terminates at Sir Creek area. In this paper, we have considered the major creeks from the site of Bin Qasim Port in Karachi to Jetty of Keti Bunder in Thatta District. A general decline in the mangrove forest has been observed that within a span of last 25 years. The unprecedented human interventions damage the creeks habitat badly which includes haphazard urban development, industrial and sewage disposal, illegal cutting of mangroves forest, reduced and inconsistent fresh water flow mainly from Jhang and Indus rivers. These activities not only harm the creeks habitat but affected the fish stock substantially. Fishing is the main livelihood of coastal people but with the above-mentioned threats, it is also under enormous pressure by fish catches resulted in unchecked overutilization of the fish resources. This pressure is almost unbearable when it joins with deleterious fishing methods, uncontrolled fleet size, increase trash and by-catch of juvenile and illegal mesh size. Along with these anthropogenic interventions study area is under the red zone of tropical cyclones and active seismicity causing floods, sea intrusion, damage mangroves forests and devastation of fish stock. In order to sustain the natural resources of the Indus Creeks, this study was initiated with the support of FAO, WWF and NIO, the main purpose was to develop a Geo-Spatial dataset for fish stock assessment. The study has been spread over a year (2013-14) on monthly basis which mainly includes detailed fish stock survey, water analysis and few other environmental analyses. Environmental analysis also includes the habitat classification of study area which has done through remote sensing techniques for 22 years’ time series (1992-2014). Furthermore, out of 252 species collected, fifteen species from estuarine and marine groups were short-listed to measure the weight, health and growth of fish species at each creek under GIS data through SPSS system. Furthermore, habitat suitability analysis has been conducted by assessing the surface topographic and aspect derivation through different GIS techniques. The output variables then overlaid in GIS system to measure the creeks productivity. Which provided the results in terms of subsequent classes: extremely productive, highly productive, productive, moderately productive and less productive. This study has revealed the Geospatial tools utilization along with the evaluation of the fisheries resources and creeks habitat risk zone mapping. It has also been identified that the geo-spatial technologies are highly beneficial to identify the areas of high environmental risk in Sindh Creeks. This has been clearly discovered from this study that creeks with high rugosity are more productive than the creeks with low levels of rugosity. The study area has the immense potential to boost the economy of Pakistan in terms of fish export, if geo-spatial techniques are implemented instead of conventional techniques.

Keywords: fish stock, geo-spatial, productivity analysis, risk

Procedia PDF Downloads 224

Authors: Jennifer Buz, Alvin Spivey

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The most common greenhouse gas emitted through human activities, carbon dioxide (CO2), is naturally consumed by plants during photosynthesis. This process is actively being monetized by companies wishing to offset their carbon dioxide emissions. For example, companies are now able to purchase protections for vegetated land due-to-be clear cut or purchase barren land for reforestation. Therefore, by actively preventing the destruction/decay of plant matter or by introducing more plant matter (reforestation), a company can theoretically offset some of their emissions. One of the biggest issues in the carbon credit market is validating and verifying carbon offsets. There is a need for a system that can accurately and frequently ensure that the areas sold for carbon credits have the vegetation mass (and therefore for carbon offset capability) they claim. Traditional techniques for measuring vegetation mass and determining health are costly and require many person-hours. Orbital Sidekick offers an alternative approach that accurately quantifies carbon mass and assesses vegetation health through satellite hyperspectral imagery, a technique which enables us to remotely identify material composition (including plant species) and condition (e.g., health and growth stage). How much carbon a plant is capable of storing ultimately is tied to many factors, including material density (primarily species-dependent), plant size, and health (trees that are actively decaying are not effectively storing carbon). All of these factors are capable of being observed through satellite hyperspectral imagery. This abstract focuses on speciation. To build a species classification model, we matched pixels in our remote sensing imagery to plants on the ground for which we know the species. To accomplish this, we collaborated with the researchers at the Teakettle Experimental Forest. Our remote sensing data comes from our airborne “Kato” sensor, which flew over the study area and acquired hyperspectral imagery (400-2500 nm, 472 bands) at ~0.5 m/pixel resolution. Coverage of the entire teakettle experimental forest required capturing dozens of individual hyperspectral images. In order to combine these images into a mosaic, we accounted for potential variations of atmospheric conditions throughout the data collection. To do this, we ran an open source atmospheric correction routine called ISOFIT1 (Imaging Spectrometer Optiman FITting), which converted all of our remote sensing data from radiance to reflectance. A database of reflectance spectra for each of the tree species within the study area was acquired using the Teakettle stem map and the geo-referenced hyperspectral images. We found that a wide variety of machine learning classifiers were able to identify the species within our images with high (>95%) accuracy. For the most robust quantification of carbon mass and the best assessment of the health of a vegetated area, speciation is critical. Through the use of high resolution hyperspectral data, ground-truth databases, and complex analytical techniques, we are able to determine the species present within a pixel to a high degree of accuracy. These species identifications will feed directly into our carbon mass model.

Keywords: hyperspectral, satellite, carbon, imagery, python, machine learning, speciation

Procedia PDF Downloads 101

Authors: Jawad Ul Haq, Evan Mazur, Ahmed Qureshi, Mohamed Al-Hussein

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The uses of wood in furniture, building, bridges and aluminum in transportation and construction, make aluminum and forest economy a prominent matter in North America. Machines available to date to cut the aforementioned materials are mostly industry oriented with complex structure and operations which require special training and skill. Furthermore, requirements such as pneumatics, 3-phase supply are associated with cost, maintenance, and safety hazards. Power saws are very useful tools used to cut and shape materials; however, they can cause serious hand injuries. Operator’s hands in table saw are vulnerable as they are used to guide pieces into the saw. Apart from hands, saw operator is also prone to material being kicked back out of the saw or sustain eye or respiratory injuries due to rapidly flying sawdust and other debris. In this paper, design of an automatic saw cutting machine has been proposed to ensure safety, portability, usage at domestic level and capability to cut both aluminum and wood. This paper demonstrates detailed Mechanical design in SOLIDWORKS and Control Systems using Programmable Logic Controller (PLC), based on the aforementioned design objectives.

Keywords: programmable logic controller, saw cutting, control, automation

Procedia PDF Downloads 253

Authors: Anjali Vanpariya, Priyanka Marathey, Sakshum Khanna, Roma Patel, Indrajit Mukhopadhyay

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Silicon (Si) is a promising negative electrode material for lithium-ion batteries (LiBs) due to its low cost, non-toxicity, and a high theoretical capacity of 4200 mAhg⁻¹. The primary challenge of the application of Si-based LiBs is large volume expansion (~ 300%) during the charge-discharge process. Incorporation of graphene, carbon nanotubes (CNTs), morphological control, and nanoparticles was utilized as effective strategies to tackle volume expansion issues. However, molten salt methods can resolve the issue, but high-temperature requirement limits its application. For sustainable and practical approach, room temperature (RT) based methods are essentially required. Use of ionic liquids (ILs) for electrodeposition of Si nanostructures can possibly resolve the issue of temperature as well as greener media. In this work, electrodeposition of Si nanoparticles on gold substrate was successfully carried out in the presence of ILs media, 1-butyl-3-methylimidazolium-bis (trifluoromethyl sulfonyl) imide (BMImTf₂N) at room temperature. Cyclic voltammetry (CV) suggests the sequential reduction of Si⁴⁺ to Si²⁺ and then Si nanoparticles (SiNs). The structure and morphology of the electrodeposited SiNs were investigated by FE-SEM and observed interconnected Si nanoparticles of average particle size ⁓100-200 nm. XRD and XPS data confirm the deposition of Si on Au (111). The first discharge-charge capacity of Si anode material has been found to be 1857 and 422 mAhg⁻¹, respectively, at current density 7.8 Ag⁻¹. The irreversible capacity of the first discharge-charge process can be attributed to the solid electrolyte interface (SEI) formation via electrolyte decomposition, and trapped Li⁺ inserted into the inner pores of Si. Pulverization of SiNs results in the creation of a new active site, which facilitates the formation of new SEI in the subsequent cycles leading to fading in a specific capacity. After 20 cycles, charge-discharge profiles have been stabilized, and a reversible capacity of 150 mAhg⁻¹ is retained. Electrochemical impedance spectroscopy (EIS) data shows the decrease in Rct value from 94.7 to 47.6 kΩ after 50 cycles of charge-discharge, which demonstrates the improvements of the interfacial charge transfer kinetics. The decrease in the Warburg impedance after 50 cycles of charge-discharge measurements indicates facile diffusion in fragmented and smaller Si nanoparticles. In summary, Si nanoparticles deposited on gold substrate using ILs as media and characterized well with different analytical techniques. Synthesized material was successfully utilized for LiBs application, which is well supported by CV and EIS data.

Keywords: silicon nanoparticles, ionic liquid, electrodeposition, cyclic voltammetry, Li-ion battery

Procedia PDF Downloads 110

Authors: D. Madhushanka, Y. Liu, H. C. Fernando

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Fires are one of the foremost factors of land surface disturbance in diverse ecosystems, causing soil erosion and land-cover changes and atmospheric effects affecting people's lives and properties. Generally, the severity of the fire is calculated as the Normalized Burn Ratio (NBR) index. This is performed manually by comparing two images obtained afterward. Then by using the bitemporal difference of the preprocessed satellite images, the dNBR is calculated. The burnt area is then classified as either unburnt (dNBR<0.1) or burnt (dNBR>= 0.1). Furthermore, Wildfire Severity Assessment (WSA) classifies burnt areas and unburnt areas using classification levels proposed by USGS and comprises seven classes. This procedure generates a burn severity report for the area chosen by the user manually. This study is carried out with the objective of producing an automated tool for the above-mentioned process, namely the World Wildfire Severity Assessment Tool (WWSAT). It is implemented in Google Earth Engine (GEE), which is a free cloud-computing platform for satellite data processing, with several data catalogs at different resolutions (notably Landsat, Sentinel-2, and MODIS) and planetary-scale analysis capabilities. Sentinel-2 MSI is chosen to obtain regular processes related to burnt area severity mapping using a medium spatial resolution sensor (15m). This tool uses machine learning classification techniques to identify burnt areas using NBR and to classify their severity over the user-selected extent and period automatically. Cloud coverage is one of the biggest concerns when fire severity mapping is performed. In WWSAT based on GEE, we present a fully automatic workflow to aggregate cloud-free Sentinel-2 images for both pre-fire and post-fire image compositing. The parallel processing capabilities and preloaded geospatial datasets of GEE facilitated the production of this tool. This tool consists of a Graphical User Interface (GUI) to make it user-friendly. The advantage of this tool is the ability to obtain burn area severity over a large extent and more extended temporal periods. Two case studies were carried out to demonstrate the performance of this tool. The Blue Mountain national park forest affected by the Australian fire season between 2019 and 2020 is used to describe the workflow of the WWSAT. This site detected more than 7809 km2, using Sentinel-2 data, giving an error below 6.5% when compared with the area detected on the field. Furthermore, 86.77% of the detected area was recognized as fully burnt out, of which high severity (17.29%), moderate-high severity (19.63%), moderate-low severity (22.35%), and low severity (27.51%). The Arapaho and Roosevelt National Forest Park, California, the USA, which is affected by the Cameron peak fire in 2020, is chosen for the second case study. It was found that around 983 km2 had burned out, of which high severity (2.73%), moderate-high severity (1.57%), moderate-low severity (1.18%), and low severity (5.45%). These spots also can be detected through the visual inspection made possible by cloud-free images generated by WWSAT. This tool is cost-effective in calculating the burnt area since satellite images are free and the cost of field surveys is avoided.

Keywords: burnt area, burnt severity, fires, google earth engine (GEE), sentinel-2

Procedia PDF Downloads 207

Authors: Mukesh Singh Boori, Vít Voženílek

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Olomouc is a unique and complex landmark with widespread forestation and land use. This research work was conducted to assess important and complex land use change trajectories in Olomouc region. Multi-temporal satellite data from 1991, 2001 and 2013 were used to extract land use/cover types by object oriented classification method. To achieve the objectives, three different aspects were used: (1) Calculate the quantity of each transition; (2) Allocate location based landscape pattern (3) Compare land use/cover evaluation procedure. Land cover change trajectories shows that 16.69% agriculture, 54.33% forest and 21.98% other areas (settlement, pasture and water-body) were stable in all three decade. Approximately 30% of the study area maintained as a same land cove type from 1991 to 2013. Here broad scale of political and socio-economic factors was also affect the rate and direction of landscape changes. Distance from the settlements was the most important predictor of land cover change trajectories. This showed that most of landscape trajectories were caused by socio-economic activities and mainly led to virtuous change on the ecological environment.

Keywords: remote sensing, land use/cover, change trajectories, image classification

Procedia PDF Downloads 387