Search results for: forest regeneration

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: 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: Soraya Abtouche, Zeyneb Ghoualem, Syrine Daoudi, Lina Ouldmohamed, Xavier Assfeld

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This work reports density functional theory calculations of the optimized geometries, molecular reactivity, energy gap,and thermodynamic properties of the free base (H2P) and their Zn (II) metallated (ZnP), bearing one, two, or three carboxylic acid groups using the hybrid functional B3LYP, Cam-B3lYP, wb97xd with 6-31G(d,p) basis sets. When donating groups are attached to the molecular dye, the bond lengths are slightly decreased, which is important for the easy transfer of an electron from donating to the accepting group. For all dyes, the highest occupied molecular orbital/lowest occupied molecular orbital analysis results in positive outcomes upon electron injection to the semiconductor and subsequent dye regeneration by the electrolyte. The ionization potential increases with increasing conjugation; therefore, the compound dye attached to one carboxylic acid group has the highest ionization potential. The results show higher efficiencies of those sensitized with ZnP. These results have been explained, taking into account the electronic character of the metal ion, which acts as a mediator in the injection step, and, on the other hand, considering the number of anchoring groups to which it binds to the surface of TiO2.

Keywords: DSSC, porphyrin, TD-DFT, electronic properties, donor-acceptor groups

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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: Mookyada Mankrut, Manit Nithitanakul

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An increase in the amount of atmospheric carbon dioxide (CO2) resulting from anthropogenic CO2 emission has been a concerned problem so far. Adsorption using porous materials is feasible way to reduce the content of CO2 emission into the atmosphere due to several advantages: low energy consumption in regeneration process, low-cost raw materials and, high CO2 adsorption capacity. In this work, the porous poly(divinylbenzene) (poly(DVB)) support was synthesized under high internal phase emulsion (HIPE) polymerization then modified with polyethyleneimine (PEI) by using solution plasma process. These porous polymers were then used as adsorbents for CO2 adsorption study. All samples were characterized by some techniques: Fourier transform infrared spectroscopy (FT-IR), scanning electron spectroscopy (SEM), water contact angle measurement and, surface area analyzer. The results of FT-IR and a decrease in contact angle, pore volume and, surface area of PEI-loaded materials demonstrated that surface of poly(DVB) support was modified. In other words, amine groups were introduced to poly(DVB) surface. In addition, not only the outer surface of poly(DVB) adsorbent was modified, but also the inner structure as shown by FT-IR study. As a result, PEI-loaded materials exhibited higher adsorption capacity, comparing with those of the unmodified poly(DVB) support.

Keywords: polyHIPEs, CO2 adsorption, solution plasma process, high internal phase emulsion

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Authors: M. D. H. Beg, John O. Akindoyo, Suriati Ghazali, Nitthiyah Jeyaratnam

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Over the period of time, several approaches have been undertaken to mitigate the challenges associated with bone regeneration. This includes but not limited to xenografts, allografts, autografts as well as artificial substitutions like bioceramics, synthetic cements and metals. The former three techniques often come along with peculiar limitation and problems such as morbidity, availability, disease transmission, collateral site damage or absolute rejection by the body as the case may be. Synthetic routes remain the only feasible alternative option for treatment of bone defects. Hydroxyapatite (HA) is very compatible and suitable for this application. However, most of the common methods for HA synthesis are either expensive, complicated or environmentally unfriendly. Interestingly, extraction of HA from bio-wastes have been perceived not only to be cost effective, but also environment friendly. In this research, HA was synthesized from bio-waste: namely bovine bones through three different methods which are hydrothermal chemical processes, ultrasound assisted synthesis and ordinary calcination techniques. Structure and property analysis of the HA was carried out through different characterization techniques such as TGA, FTIR, and XRD. All the methods applied were able to produce HA with similar compositional properties to biomaterials found in human calcified tissues. Calcination process was however observed to be more efficient as it eliminated all the organic components from the produced HA. The HA synthesized is unique for its minimal cost and environmental friendliness. It is also perceived to be suitable for tissue and bone engineering applications.

Keywords: hydroxyapatite, bone, calcination, biowaste

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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: Urszula Stachewicz

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Surface properties of materials are the key parameter in many applications, especially in the biomedical field, to control cell-material interactions. In our work, we want to achieve the controllability of surface properties of polymer fibers via a single-step electrospinning process by alternating voltage polarities. Voltage polarity defines the charge accumulated on the surface of the liquid jet and the surface of the fibers. Positive polarity attracts negatively charged groups to fibers’ surface, whereas negative polarity moves the negatively charged functional groups away from the surface. This way, we can control the surface chemistry, wettability, and additionally surface potential of electrospun fibers. Within our research, we characterized surface chemistry using X-ray photoelectron microscopy (XPS) and surface potential with Kelvin probe force microscopy (KPFM) on electrospun fibers of commonly used polymers such as PCL, PVDF, and PMMA, often used as biomaterials. We proved the significant effect of fibers' surface potential on cell integration with the scaffolds and further cells development for the regeneration processes based on the osteoblast and fibroblast culture studies. Acknowledgments: The study was conducted within ‘Nanofiber-based sponges for atopic skin treatment’ project, which is carried out within the First TEAM programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund, project no POIR.04.04.00-00- 4571/18-00.

Keywords: cell attachment, fibers, fibroblasts, osteoblast, proliferation, surface potential

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Authors: Hassan Mohammadi Khujin

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Tissue engineering is the science of tissues and complex organs creation using scaffolds, cells and biologically active components. Most cells require scaffolds to grow and proliferate. These temporary support structures for tissue regeneration are later replaced with extracellular matrix produced inside the body. Recent advances in additive manufacturing methods allow production of highly porous, complex three dimensional scaffolds suitable for cell growth and proliferation. The current paper investigates the mechanical properties, including elastic modulus and compressive strength, as well as fluid flow dynamics, including permeability and flow-induced shear stress of scaffolds with four triply periodic minimal surface (TPMS) configurations, namely the Schwarz primitive, the Schwarz diamond, the gyroid, and the Neovius structures. Higher porosity in all scaffold types resulted in lower mechanical properties. The permeability of the scaffolds was determined using Darcy's law with reference to geometrical parameters and the pressure drop derived from the computational fluid dynamics (CFD) analysis. Higher porosity enhanced permeability and reduced wall shear stress in all scaffold designs.

Keywords: highly porous scaffolds, tissue engineering, finite elements analysis, CFD analysis

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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

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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: 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: Tuong Thi Van Thuy, Dao Van Toan, Nguyen Duc Phuc

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Mesenchymal stem cells (MSCs) were discovered in the 1970s with their unique properties of differentiation, immunomodulation, multiple secreting, and homing factors to injured organs. MSC-based therapies have emerged as a promising strategy for various diseases such as cancer, tissue regeneration, or immunologic/inflammatory-related diseases. This study evaluated the clinical application of MSCs for cancer therapy in trials registered on Clinical Trial as of July 2022. The results showed 40 clinical trials used MSCs in various cancer conditions. 62% of trials used MSCs for therapeutic purposes to minimize the side effects of cancer treatment. Besides, 38% of trials were focused on using MSCs as a therapeutic agent to treat cancer directly. Most trials (38/40) are ongoing phase I/II, and 2 are entering phase III. 84% of trials used allogeneic MSCs compared with 13% using autologous sources and 3% using both. 25/40 trials showed participants received a single dose of MSCs, while the most times were 12 times in a pancreatic cancer treatment trial. Conclusion: MSC-based therapy for cancer in clinical trials should be applied to (1) minimize the side effects of oncological treatments and (2) directly affect the tumor via selectively delivering anti-cancer payloads to tumor cells. Allogeneic MSCs are a priority selected in clinical cancer therapy.

Keywords: mesenchymal stem cells, MSC-based therapy, cancer condition, cancer treatment, clinical trials

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Authors: Sara Mercedes Barroso Pinzón, Álvaro Jesús Caicedo Castro, Antonio Javer Sánchez Herencia

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In recent years there has been increased academic and industrial research into the development of orthopaedic implants with structural properties and functionality similar to mechanical strength, osseointegration, thermal stability and antibacterial capacity similar to bone structure. Hydroxyapatite has been considered for decades as an ideal biomaterial for bone regeneration due to its chemical and crystallographic similarity to the mineral structure bioapatites. However, the lack of trace elements in the hydroxyapatite structure confers very low mechanical and biological properties. Under this scenario, the objective of the research is the synthesis of hydroxyapatite with Mg from the francolite mineral present in phosphate rock from the central-eastern region of Colombia, taking advantage of the extraction of mineral species as natural precursors of Ca, P and Mg. The minerals present were studied, fluorapatite as the mineral of interest associated with magnesium carbonates and quartz. The chemical and mineralogical composition was determined by X-ray fluorescence (XRF) and X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX); the optimum conditions were established using the acid leaching mechanism in the wet concentration process. From the products obtained and characterised by XRD, XRF, SEM, FTIR, RAMAN, HAp-Mg biocomposite scaffolds are fabricated and the influence of Mg on morphometric parameters, mechanical and biological properties in the formed materials is evaluated.

Keywords: phosphate rock, hydroxyapatite, magnesium, biomaterials

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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

Abstract:

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

Procedia PDF Downloads 422

Authors: Mustafa Abu Ghalia, Yaser Dahman

Abstract:

A new class of polyurethane (PU) reinforced with green bacterial cellulose nanofibers (BC) were prepared using a solvent casting method, with the goal of fabricating green nanocomposites. Four series classes of BC (1, 2.5, 5, and 10 wt%) were reinforced into PU matrices via BC surface modification and subsequently BC-grafted into PU throughout silane coupling agent to improve BC dispersion and its interfacial interaction. The experiment results from the tensile tester were evaluated according to the response surface method (RSM) for optimizing the impacts of variable parameters, pore size, porosity, and BC contents on the mechanical properties. The compressive strength for PU-5 BC wt% was about 9.8 MPa, and decrease when being generated prosperity to recorded at 4.9 MPa. Nielson model was applied to investigate the BC stress concentration on the PU matrices. Likewise, krenche and Hapli-Tasi model were employed to evaluate the BC nanofiber reinforcement potential and BC orientation into PU matrices. The analysis of variance (ANOVA) demonstrated that only BC loading has a significant effect in increases tensile strength, young’s modulus, and a flexural modulus of the PU-BC nanocomposites. The optimal factors of the variables experiment confirmed to be 5 wt% for BC, 230 for pore size, and 80 % for porosity. Scanning electron microscopy (SEM) micrographs showed that the uniform distribution of nanofibers in the PU matrices with the addition of BC 5 wt %. Hydrolytic degradation revealed that the weight loss in PU-BC scaffold is higher than PU-BC wt %.

Keywords: polyurethane scaffold, mechanical properties, tissue engineering, polyurethane

Procedia PDF Downloads 194

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

Abstract:

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 427

Authors: Jalil Badamfirooz

Abstract:

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 71

Authors: Behrang Ekrami, Amin Tamadon

Abstract:

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 457

Authors: Kiomars Habibi, Mostafa Behzadfar, Airin Jaberi

Abstract:

Designing appropriate places for the comfort of pedestrians is one of the most important aspects of modern urbanization and renovation and rehabilitation stimulus of urban old fabrics. So, that special cities designed for pedestrians with a complete network of streets without cars, can be considered as one of the best habitations in the world. The number of these cities with a network of streets and squares in which beauty, enjoyment and comfort are mostly concerned for the pedestrians designed regions is increasing around the world, such as Stockholm, Copenhagen, Munich, Frankfurt, Venice, Rome, etc. In this paper, we are going to explain the influential factors regarding the efficiency of these cities by identifying one of the most important pedestrian ways of the world; Strøget is a car free zone in Copenhagen, Denmark. This popular tourist attraction in the center of town is the longest pedestrian shopping area in Europe. Analyses indicate that world-wide experience concerning the renovation and rehabilitation of old fabrics has many advantages in exploiting the idea of pedestrian way for regeneration of old fabrics. Transforming the streets to appropriate places for the comfort of pedestrians, expanding the public spaces such as city squares, and decreasing the masses of building alongside the brought comfort and peace is the main reason in the success of Strøget pedestrian street in urban old fabrics of Copenhagen. Hypothesis: The Strøget pedestrian street has been the development stimulus in Copenhagen and the urban old fabrics development as a result

Keywords: development, stimulus, pedestrian street, urban landscape, Stroget

Procedia PDF Downloads 86

Authors: Wei Ji

Abstract:

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 295

Authors: Gehan El-Sayed Sharaf El-Deen

Abstract:

In this study, super paramagnetic iron-oxide nano- materials (SPMIN) were investigated for removal of toxic heavy metals from aqueous solution. The magnetic nanoparticles of 12 nm were synthesized using a co-precipitation method and characterized by transmission electron microscopy (TEM), transform infrared spectroscopy (FTIR), x-ray diffraction (XRD) and vibrating sample magnetometer (VSM). Batch experiments carried out to investigate the influence of different parameters such as contact time, initial concentration of metal ions, the dosage of SPMIN, desorption,pH value of solutions. The adsorption process was found to be highly pH dependent, which made the nanoparticles selectively adsorb these three metals from wastewater. Maximum sorption for all the studies cations obtained at the first half hour and reached equilibrium at one hour. The adsorption data of heavy metals studied were well fitted with the Langmuir isotherm and the equilibrium data show the percent removal of Ni2+, Zn2+ and Cd2+ were 96.5%, 80% and 75%, respectively. Desorption studies in acidic medium indicate that Zn2+, Ni2+ and Cd2+ were removed by 89%, 2% and 18% from the first cycle. Regeneration studies indicated that SPMIN nanoparticles undergoing successive adsorption–desorption processes for Zn2+ ions retained original metal removal capacity. The results revealed that the most prominent advantage of the prepared SPMIN adsorbent consisted in their separation convenience compared to the other adsorbents and SPMIN has high efficiency for removal the investigated metals from aqueous solution.

Keywords: heavy metals, magnetic nanoparticles, removal efficiency, Batch technique

Procedia PDF Downloads 233