Search results for: neem tree leave

Authors: Yasuyo Makido, Vivek Shandas, David J. Sailor, M. Salim Ferwati

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Mitigating extreme urban heat is challenging in a desert climate such as Doha, Qatar, since outdoor daytime temperature area often too high for the human body to tolerate. Recent studies demonstrate that cities in arid and semiarid areas can exhibit ‘urban cool islands’ - urban areas that are cooler than the surrounding desert. However, the variation of temperatures as a result of the time of day and factors leading to temperature change remain at the question. To address these questions, we examined the spatial and temporal variation of air temperature in Doha, Qatar by conducting multiple vehicle-base local temperature observations. We also employed three statistical approaches to model surface temperatures using relevant predictors: (1) Ordinary Least Squares, (2) Regression Tree Analysis and (3) Random Forest for three time periods. Although the most important determinant factors varied by day and time, distance to the coast was the significant determinant at midday. A 70%/30% holdout method was used to create a testing dataset to validate the results through Pearson’s correlation coefficient. The Pearson’s analysis suggests that the Random Forest model more accurately predicts the surface temperatures than the other methods. We conclude with recommendations about the types of development patterns that show the greatest potential for reducing extreme heat in air climates.

Keywords: desert cities, tree-structure regression model, urban cool Island, vehicle temperature traverse

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Authors: Abraha Hatsey, Nesibu Yahya, Abeje Eshete

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Ethiopian Orthodox Tewahido Church has a long tradition of conserving the Church vegetation and is an area treated as a refugee camp for many endangered indigenous tree species in Northern Ethiopia. Though around 36,000 churches exist in Ethiopia, only a few churches have been studied so far. Thus, this study assessed the land use land cover change of 3km buffer (1986-2018) and the woody species diversity and regeneration status of Tsimur St. Gebriel monastery in Tigray region, Northern Ethiopia. For vegetation study, systematic sampling was used with 100m spacing between plots and between transects. Plot size was 20m*20m for the main plot and 2 subplots (5m*5m each) for the regeneration study. Tree height, diameter at breast height(DBH) and crown area were measured in the main plot for all trees with DBH ≥ 5cm. In the subplots, all seedlings and saplings were counted with DBH < 5cm. The data was analyzed on excel and Pass biodiversity software for diversity and evenness analysis. The major land cover classes identified include bare land, farmland, forest, shrubland and wetland. The extents of forest and shrubland were declined considerably due to bare land and agricultural land expansions within the 3km buffer, indicating an increasing pressure on the church forest. Regarding the vegetation status, A total of 19 species belonging to 13 families were recorded in the monastery. The diversity (H’) and evenness recorded were 2.4 and 0.5, respectively. The tree density (DBH ≥ 5cm) was 336/ha and a crown cover of 65%. Olea europaea was the dominant (6.4m2/ha out of 10.5m2 total basal area) and a frequent species (100%) with good regeneration in the monastery. The rest of the species are less frequent and are mostly confined to water sources with good site conditions. Juniperus procera (overharvested) and the other indigenous species were with few trees left and with no/very poor regeneration status. The species having poor density, frequency and regeneration (Junperus procera, Nuxia congesta Fersen and Jasminium abyssinica) need prior conservation and enrichment planting. The indigenous species could also serve as a potential seed source for the reproduction and restoration of nearby degraded landscapes. The buffer study also demonstrated expansion of agriculture and bare land, which could be a threat to the forest of the isolated monastery. Hence, restoring the buffer zone is the only guarantee for the healthy existence of the church forest.

Keywords: church forests, regeneration, land use change, vegetation status

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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: Hanadi Mohamed Shawgi Gamal, Ashraf Mohamed Ahmed Abdalla

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Sudan is endowed by a great diversity of tree species; nevertheless, the utilization of wood resources has traditionally concentrated on a few number of species. With the great variation in the climatic zones of Sudan, great variations are expected in the anatomical properties between and within species. This variation needs to be fully explored in order to suggest the best uses for the species. Modern research on wood has substantiated that the climatic condition where the species grow has significant effect on wood properties. Understanding the extent of variability of wood is important because the uses for each kind of wood are related to its characteristics; furthermore, the suitability or quality of wood for a particular purpose is determined by the variability of one or more of these characteristics. The present study demonstrates the effect of rainfall zones in some anatomical properties of Acacia seyal var. seyal growing in Sudan. For this purpose, twenty healthy trees were collected randomly from two zones (ten trees per zone). One zone with relatively low rainfall (273mm annually) which represented by North Kordofan state and White Nile state and the second with relatively high rainfall (701 mm annually) represented by Blue Nile state and South Kordofan state. From each sampled tree, a stem disc (3 cm thick) was cut at 10% from stem height. One radius was obtained in central stem dices. Two representative samples were taken from each disc, one at 10% distance from pith to bark, the second at 90% in order to represent the juvenile and mature wood. The investigated anatomical properties were fibers length, fibers and vessels diameter, lumen diameter, and wall thickness as well as cell proportions. The result of the current study reveals significant differences between zones in mature wood vessels diameter and wall thickness, as well as juvenile wood vessels, wall thickness. The higher values were detected in the drier zone. Significant differences were also observed in juvenile wood fiber length, diameter as well as wall thickness. Contrary to vessels diameter and wall thickness, the fiber length, diameter as well as wall thickness were decreased in the drier zone. No significant differences have been detected in cell proportions of juvenile and mature wood. The significant differences in some fiber and vessels dimension lead to expect significant differences in wood density. From these results, Acacia seyal var. seyal seems to be well adapted with the change in rainfall and may survive in any rainfall zone.

Keywords: Acacia seyal var. seyal, anatomical properties, rainfall zones, variation

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: Bliss Singhal

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Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

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Authors: Jeffrey A. Amelse

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Many corporations aspire to become Net Zero Carbon Carbon Dioxide by 2035-2050. This paper examines what it will take to achieve those goals. Achieving Net Zero CO₂ requires an understanding of where energy is produced and consumed, the magnitude of CO₂ generation, and proper understanding of the Carbon Cycle. The latter leads to the distinction between CO₂ and biomass carbon sequestration. Short reviews are provided for prior technologies proposed for reducing CO₂ emissions from fossil fuels or substitution by renewable energy, to focus on their limitations and to show that none offer a complete solution. Of these, CO₂ sequestration is poised to have the largest impact. It will just cost money, scale-up is a huge challenge, and it will not be a complete solution. CO₂ sequestration is still in the demonstration and semi-commercial scale. Transportation accounts for only about 30% of total U.S. energy demand, and renewables account for only a small fraction of that sector. Yet, bioethanol production consumes 40% of U.S. corn crop, and biodiesel consumes 30% of U.S. soybeans. It is unrealistic to believe that biofuels can completely displace fossil fuels in the transportation market. Bioethanol is traced through its Carbon Cycle and shown to be both energy inefficient and inefficient use of biomass carbon. Both biofuels and CO₂ sequestration reduce future CO₂ emissions from continued use of fossil fuels. They will not remove CO₂ already in the atmosphere. Planting more trees has been proposed as a way to reduce atmospheric CO₂. Trees are a temporary solution. When they complete their Carbon Cycle, they die and release their carbon as CO₂ to the atmosphere. Thus, planting more trees is just 'kicking the can down the road.' The only way to permanently remove CO₂ already in the atmosphere is to break the Carbon Cycle by growing biomass from atmospheric CO₂ and sequestering biomass carbon. Sequestering tree leaves is proposed as a solution. Unlike wood, leaves have a short Carbon Cycle time constant. They renew and decompose every year. Allometric equations from the USDA indicate that theoretically, sequestrating only a fraction of the world’s tree leaves can get the world to Net Zero CO₂ without disturbing the underlying forests. How can tree leaves be permanently sequestered? It may be as simple as rethinking how landfills are designed to discourage instead of encouraging decomposition. In traditional landfills, municipal waste undergoes rapid initial aerobic decomposition to CO₂, followed by slow anaerobic decomposition to methane and CO₂. The latter can take hundreds to thousands of years. The first step in anaerobic decomposition is hydrolysis of cellulose to release sugars, which those who have worked on cellulosic ethanol know is challenging for a number of reasons. The key to permanent leaf sequestration may be keeping the landfills dry and exploiting known inhibitors for anaerobic bacteria.

Keywords: carbon dioxide, net zero, sequestration, biomass, leaves

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Authors: Arlene López-Sampson, Tony Page, Betsy Jackes

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Aquilaria genus produces a highly valuable fragrant oleoresin known as agarwood. Agarwood forms in a few trees in the wild as a response to injure or pathogen attack. The resin is used in perfume and incense industry and medicine. Cultivation of Aquilaria species as a sustainable source of the resin is now a common strategy. Physiological traits are frequently used as a proxy of crop and tree productivity. Aquilaria species growing in Queensland, Australia were studied to investigate relationship between leaf-productivity traits with tree growth. Specifically, 28 trees, representing 12 plus trees and 16 trees from yield plots, were selected to conduct carbon isotope analysis (δ13C) and monitor six leaf attributes. Trees were grouped on four diametric classes (diameter at 150 mm above ground level) ensuring the variability in growth of the whole population was sampled. Model averaging technique based on the Akaike’s information criterion (AIC) was computed to identify whether leaf traits could assist in diameter prediction. Carbon isotope values were correlated with height classes and leaf traits to determine any relationship. In average four leaves per shoot were recorded. Approximately one new leaf per week is produced by a shoot. Rate of leaf expansion was estimated in 1.45 mm day-1. There were no statistical differences between diametric classes and leaf expansion rate and number of new leaves per week (p > 0.05). Range of δ13C values in leaves of Aquilaria species was from -25.5 ‰ to -31 ‰ with an average of -28.4 ‰ (± 1.5 ‰). Only 39% of the variability in height can be explained by δ13C in leaf. Leaf δ13C and nitrogen content values were positively correlated. This relationship implies that leaves with higher photosynthetic capacities also had lower intercellular carbon dioxide concentrations (ci/ca) and less depleted values of 13C. Most of the predictor variables have a weak correlation with diameter (D). However, analysis of the 95% confidence of best-ranked regression models indicated that the predictors that could likely explain growth in Aquilaria species are petiole length (PeLen), values of δ13C (true13C) and δ15N (true15N), leaf area (LA), specific leaf area (SLA) and number of new leaf produced per week (NL.week). The model constructed with PeLen, true13C, true15N, LA, SLA and NL.week could explain 45% (R2 0.4573) of the variability in D. The leaf traits studied gave a better understanding of the leaf attributes that could assist in the selection of high-productivity trees in Aquilaria.

Keywords: 13C, petiole length, specific leaf area, tree growth

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Authors: Arzu K. Harmanci Seren, Feride Eskin Bacaksiz

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The shortage of nursing personnel is considered one of the most important labour force issues in health sector of developed countries since early 1970s. International Labour Organization developed standards for working conditions of nurses in collaboration with World Health Organization with the aim of helping to solve nursing shortage problem all over the world. As a result of this collaboration, ILO Nursing Personnel Convention (C. 149), and the accompanying Recommendation (R. 157) were adopted in 1977. Turkey as a country that has a serious nurse shortage problem, has been a member of ILO since 1932, and has not signed this convention yet. This study was planned to compare some of the working standards in Convention with the present working conditions of nurses in Turkey. The data were collected by an on line survey between 19 January-16 February 2015 for this cross-sectional study. Participants were reached through social network accounts in collaboration with nursing associations. Totally 828 nurses from the 57 provinces of Turkey participated in the study. Survey was consisted of 14 open ended questions related to working conditions of nurses and 34 Likert statements related to nursing policies of the facilities they are working in. The data were analysed using the IBM SPSS 21.0 (licensed to Istanbul University) software. Descriptive and comparative statistics were performed. Most of the participants (81.5%) were staff and 18.5% of them were manager nurses. Most of them had baccalaureate (57.9%) or master (27.4%) degree in nursing. 18.5% of the participants were working in private hospitals, 34.9% of them in university hospitals and 46.6% of them were in Ministry of Health Hospitals. It was found that monthly working schedules were announced mostly 7 days ago (18%), working time of nurses was at least 8 hours (41.5%) and at most 24 hours (22.8%) in a day and had time for lunch or dinner 25.18 (SD=16.66), for resting 21.02 (SD=29.25) minutes. On the other hand, it was determined that 316 (43.2%) nurses did not have time for lunch and 61 (7.9%) of them could not find time for eating anything. It was also explored they were working 15-96 hours in a week (mean=48.28, SD=8.89 hours), 4-29 days in a month (mean=19.29, SD=5.03 days) and 597 (72%) nurses overworked changing form 1 hour to 150 hours (32.80, SD=23.42 hours) before the month in which surveys were filled. Most of the participants did not leave the job due to the sickness (47.5%) even if they felt sick. Also most of them did not leave the job due to any excuse (67.2%) or education (57.3%). This study has significance because of nurses from different provinces participated in and it provides brief information about the working conditions of nurses nationwide. It was explored that nurses in Turkey were working at worse conditions according the International Labour Organization’s recommendations.

Keywords: nurse, international labour organization, recommendations for nurses, working conditions

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Authors: Igor Belykh

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Anti-scatter grids used in radiographic imaging for the contrast enhancement leave specific artifacts. Those artifacts may be visible or may cause Moiré effect when a digital image is resized on a diagnostic monitor. In this paper, we propose an automated grid artifacts detection and suppression algorithm which is still an actual problem. Grid artifacts detection is based on statistical approach in spatial domain. Grid artifacts suppression is based on Kaiser bandstop filter transfer function design and application avoiding ringing artifacts. Experimental results are discussed and concluded with description of advantages over existing approaches.

Keywords: grid, computed radiography, pattern recognition, image processing, filtering

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Authors: Lee Ping Ang, Salvatore Tomasello, Jun Wen, Marc S. Appelhans

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With about 225 species, Zanthoxylum L. is the second most species rich genus in Rutaceae. It is the only genus with a pantropical distribution. Economically, it is used in several Asian countries as traditional medicine and spice. In the past Zanthoxylum was divided into two genera, the temperate Zanthoxylum sensu strictu (s.s.) and the (sub)tropical Fagara, due to the large differences in flower morphology: heterochlamydeous in Fagara and homochlamydeous in Zanthoxylum s.s.. This genus is much under studied and previous phylogenetic studies using Sanger sequencing did not resolve the relationships sufficiently. In this study, we use Hybrid Capture with a specially designed bait set for Zanthoxylum to sequence 347 putatively single-copy genes. The taxon sampling has been largely improved as compared to previous studies and the preliminary results will be based on 371 specimens representing 133 species from all continents and major island groups. Our preliminary results reveal similar tree topology as the previous studies while providing more details to the backbone of the phylogeny. The phylogenetic tree consists of four main clades: A) African/Malagasy clade, B) Z. asiaticum clade - a clade consisting widespread species occurring in (sub)tropical Asia and Africa as well as Madagascar, C) Asian/Pacific clade and D) American clade, which also includes the temperate Asian species. The merging of Fagara and Zanthoxylum is supported by our results and the homochlamydeous flowers of Zanthoxylum s.s. are likely derived from heterochlamydeous flowers. Several of the morphologically defined sections within Zanthoxylum are not monophyletic. The study dissemination will (1) introduce the framework of this project; (2) present preliminary results and (3) the ongoing progress of the study.

Keywords: Zanthoxylum, phylogenomic, hybrid capture, pantropical

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Authors: Chenchieh Chang

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Objectives: This study had two objectives. First, it used affective events theory to investigate the relationship between workplace bullying and the intention to resign in nurses, a topic rarely explored in previous studies. Second, according to the conservation of resource theory, individuals encountering work incidents will utilize resources that are at their disposal to strengthen or weaken the effects of the incidents on them. Such resources include social support that comes from their bosses, colleagues, family, and friends. To answer the question of whether different social supports exert distinct effects on alleviating stress experienced by nurses, this study examined the moderating effects of different social supports on the relationship between workplace bullying and nurses’ intention to resign. Method: This study was approved by an institutional review board (code number: 105070) and adopted purposive sampling to survey 911, full-time nurses. Results: Work-related bullying exerted a significant and positive effect on the intention to resign, whereas bullying pertaining to interpersonal relationships and body-related bullying nonsignificantly affected intention to resign. Support from supervisors enhanced the effect of work-related bullying on an intention to resign, whereas support from colleagues and family did not moderate said effect. Research Limitations/Implications: The self-reporting method and cross-sectional research　design adopted in this study might have resulted in common method variance and limited the ability to make causal inferences. This study suggests future studies to obtain measures of predictor and criterion variables from different sources or ensure a temporal, proximal, or psychological separation between predictor and criterion in the collection of data to avoid the common method bias. Practical Implications: First, businesses should establish a friendly work environment and prevent employees from encountering workplace bullying. Second, because social support cannot diminish the effect of workplace bullying on employees’ intention to resign, businesses should offer other means of assistance. For example, business managers may introduce confidential systems for employees to report workplace bullying; or they may establish consultation centers where employees can properly express their thoughts and feelings when encountering workplace bullying.

Keywords: workplace bullying, intention to occupation leave, social supports, nurses

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Authors: Jahede Tekeykhah, Seyed Mohsen Hossini, Gholamali Jalali

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Industrial and traffic activities cause large amounts of heavy metals enter into the atmosphere and the use of plant species can be effective in assessing and reducing air pollution by metals. This study aimed to investigate the adsorption level of heavy metals in leaves of Fraxinus rotundifolia, Robinia, Platanus orientalis, Platycladus orientalis and Pinus eldarica trees in Abidar forest park. For this purpose, samples leaves of the trees were prepared from the contaminated and control areas in each region in 3 stations with 3 replicates in mid-August and finally 90 samples were sent to the laboratory. Then, the concentrations of heavy metals were measured by graphite furnace. To do this, factorial experiment based on a completely randomized design with two factors of location on two levels (contaminated area and control area) and the factor of species on five levels (Fraxinus rotundifolia, Robinia, Platanus orientalis, Platycladus orientalis and Pinus eldarica) with three replications was used. The analysis of collected data was performed by SPSS software and Duncan's multiple range test was used to compare the means. The results showed that the accumulation of all metals in the leaves of most species in the infected area with a significant difference at 95% level was higher than the control area. In the contaminated area, with a significant difference at 5% level, the highest accumulations of metals were observed as the following: lead, cadmium, zinc and manganese in Platanus orientalis, nickel in Fraxinus rotundifolia and copper in Platycladus orientalis.

Keywords: airborne, tree species, heavy metals, absorption, Abidar Forest Park

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Authors: Fanny Soulard

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Today work environment is more multicultural than ever: spatial limits have been blown out, encouraging people and ideas mobility all around the globe. Indeed, opportunities to design with culturally diverse team workers, clients, or end-users, have become within everyone's reach. We enjoy traveling to discover other civilizations, but when it comes to business, we often take for granted that our own work methodology will be generic enough to federate each party and cover the project needs. This paper aims to explore why, by skipping cultural awareness, we often create misunderstandings, frustration, and even counterproductive design. Tree lighting projects successively developed by a French lighting studio, a Vietnamese lighting studio, and an Australian Engineering company will be assessed from their concept stage to completion. All these study cases are based in Vietnam, where the construction market is equally led by local and international consultants. Core criteria such as lighting standard reference, service scope, communication tools, internal team organization, delivery package content, key priorities, and client relationship will help to spot and list when and how cultural diversity has impacted the design output and effectiveness. On the second hand, we will demonstrate through the same selected projects how intercultural intelligence tools and mindset can not only respond positively to previous situations and avoid major clashes but also turn cultural differences into a key added value to generate significant benefits for individuals, teams, and companies. By understanding the major importance of including a cultural factor within any design, intercultural intelligence will quickly turn out as a “must have” skill to be developed and acquired by any designer.

Keywords: intercultural intelligence, lighting design, work methodology, multicultural diversity

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Krzysztof Buczak, Sonia Lachowska, Pawel Kucharski, Agnieszka Antonczyk

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Tiletamine - zolazepam combination is increasingly used in veterinary anaesthesiology in wild animals, including snakes. The available literature shows a lack of information about anesthesia in this mixture in ovoviviparous snakes. The studies show the possibility of using the combination at a dose of 20 mg/kg or more for snake immobilization. This paper presents an anesthetic protocol with the use of a combination of tiletamine - zolazepam at the dose of 10 mg/kg intramuscularly and maintenance with inhalant anesthesia with isoflurane in pure oxygen. The objective of this study was to evaluate the usefulness of the anesthetic protocol to proceed with coeliotomy in Amazon Tree Boa. The patient was a five years old bicolor female Amazon Tree Boa (Corallus hortulanus) with dystocia. The clinical examination reveals significant emaciation (bodyweight 520g), high degree of dehydration, heart rate (HR = 60 / min), pale mucous membranes and poor reactivity. Meloxicam (1 mg/kg) and tramadol (10 mg/kg) were administered subcutaneously and the patient was placed in an incubator with access to fresh oxygen. Four hours later, the combination of tiletamine - zolazepam (10 mg/kg) was administered intramuscularly for induction of anesthesia. The snake was intubated and connected to inhalant anesthesia equipment. For maintenance, the anesthesia isoflurane in pure oxygen was used due to apnea, which occurs 30 minutes after the induction semi-closed system was attached and the ventilator was turned on (PCV system, four breaths per minute, 8 cm of H2O). Cardiopulmonary parameters (HR, RR, SPO2, ETCO2, ETISO) were assessed throughout the procedure. During the entire procedure, the operating room was heated to a temperature of 26 degrees Celsius. Additionally, the hose was placed on a heating mat, which maintained a temperature of 30 degrees Celsius. For 15 minutes after induction, the loss of muscle tone was observed from the head to the tail. Induction of general anesthesia was scored as good because of the possibility of intubation. During the whole procedure, the heart rate was at the rate of 58 beats per minute (bpm). Ventilation parameters were stable throughout the procedure. The recovery period lasts for about 4 hours after the end of general anesthesia. The muscle tension returned from tail to head. The snake started to breathe spontaneously within 1,5 hours after the end of general anesthesia. The protocol of general anesthesia with the combination of tiletamine- zolazepam with a dose of 10 mg/kg is useful for proceeding with the emergency coeliotomy in maintenance with isoflurane in oxygen. Further study about the impact of the combination of tiletamine- zolazepam for the recovery period is needed.

Keywords: anesthesia, corallus hortulanus, ovoviparous, snake, tiletamine, zolazepam

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Authors: Juang R. Matangaran, Qi Adlan

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Industrial plantation forest in Indonesia was developed in 1983, and since then, several companies have been successfully planted a total area of concessionaire approximately 10 million hectares. Currently, these plantation forests have their annual harvesting period. In the timber harvesting process, amount part of the trees generally become logging residue. Tree parts such as branches, twigs, defected stem and leaves are unused section of tree on the ground after timber harvesting. The use of heavy machines in timber harvesting area has caused damage to the forest soil. The negative impact of such machines includes loss of topsoil, soil erosion, and soil compaction. Forest soil compaction caused reduction of forest water infiltration, increase runoff and causes difficulty for root penetration. In this study, we used logging residue as soil covers on the passages passed by skidding machines in order to observe the reduction soil compaction. Bulk density of soil was measured and analyzed after several times of skidding machines passage on skid trail. The objective of the research was to analyze the effect of logging residue on reducing soil compaction. The research was taken place at one of the industrial plantation forest area of South Sumatra Indonesia. The result of the study showed that percentage increase of soil compaction bare soil was larger than soil surface covered by logging residue. The maximum soil compaction occurred after 4 to 5 passes on soil without logging residue or bare soil and after 7 to 8 passes on soil cover by logging residue. The use of logging residue coverings could reduce soil compaction from 45% to 60%. The logging residue was effective in decreasing soil disturbance of timber harvesting at the plantation forest area.

Keywords: bulk density, logging residue, plantation forest, soil compaction, timber harvesting

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Authors: Jesús Ulloa

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The creation, rise, and consolidation of far right-wing, ultranationalist, and eurosceptic parties in Europe after the Second World War pose a real threat towards the disintegration of the European Union. Starting more than thirty years ago with Jean-Marie Le Pen's FN and Margaret Thatcher's policies, to Marine Le Pen's current FN and anti-immigration proposals along with Nigel Farage's UKIP and their intentions to leave the European Union, the progress of right-wing parties should be noted, taking into account that they may have very important differences within their postures but that they also reach common ground in certain areas. The actual disintegration of the EU would represent an enormous failure of the new liberal world order. Through this essay, the roots of this political parties will be analyzed and the conclusion of whether the disintegration may become a reality or if the principles of cooperation and unity will prevail will be answered.

Keywords: eurosceptic, ultarnationalist, right-wing, European Union

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Authors: Nene Pearl Eluchie

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Globally, metal ion, particularly those generated from oil and gas effluents, form environmental pollution, particularly in developing regions like Nigeria, where water borne disease is fatal. This is clear evidence for metal ion contamination within the environment. Ecofriendly and cost effective biomaterials are the best ways of reducing metal ion contamination, thus reducing the need for chemical treatment of oil and gas effluent. Despite this, research efforts to understand the mechanism of adsorption and possible bio-adsorptive remediation interventions are limited. The study combined biomaterial and adsorption techniques: A. Indica, UV-Visible spectroscopy, SEM, FTIR in a progressive manner to provide insight. The biosorption efficiency of Azadirachta Indica silver nanoparticle AI-AgNPs was within the range of 63-95%. The study demonstrates that AI-AgNPs can be a promising agent, cheap, efficient, and biodegradable bio-sorbent for lowering oil and gas effluents. This is one of the studies to show that Azadirachta Indica is just one of the many biomaterials to synthesize silver nanoparticles through the reduction of active constituents (Nimbidin) present in them to ensure stability and surface properties, which are critical for their performance in effluent treatment. Therefore, leveraging the knowledge from this study to raise awareness through public health initiatives and community engagement will help. The prevalence of metal ions observed in the visible region in the study indicates the need for bio-adsorptive remediation interventions, not only in social settings but also in the immediate environment. There is, thus, an urgent need for targeted interventions in vulnerable communities.

Keywords: Azadirachta indica, bioadsorption, biosynthesis, effluent, nimbidin, silver nanoparticle

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Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

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Authors: P. S. Dhananjaya Swamy, B. Chinnappa, G. B. Ramesh, Naveen P. Kumar

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Kodagu district is one of the most densely forested districts in the India as around sixty five per cent of geographical areas under tree cover. Nearly 53 per cent of the flora of Kodagu is endemic. The district is also a hotspot of endemic orchids found mainly in the Thadiandamol. Shade grown, eco-friendly coffee farms are perhaps a selected few places on this planet where nature runs wild. The Kodagu accounts for more than 8.8 per cent of floral diversity of Karnataka state. Estimation of unit cost of cultivation plays a vital role in determining the governmental program their market intervention policies. On an average, planters incurred around Rs. 17041 per acre. The extent of production risk was highest among small category of planters (66 %) compared to other two exhibiting production instability. The result shows that, the coffee productivity in medium plantations was 1051.2 kg per acre as against 758.5 and 789.2 kg in the case of small and large plantations. An annual net return per acre was highest in the case of medium planters (Rs. 26109.3) as against Rs. 20566.7 and Rs. 18572.7 in the case of small and large planters. Cost of production was lowest in the case of small planters (Rs. 18.9 per kg of output) followed by medium planters (Rs. 21.2 per kg of output) and large planters (Rs. 22.5 per kg of output). The productivity of coffee is less whenever it is grown under high shade and native tree cover; it is around 6 quintals per acre when compared with low shade conditions, which is around 8.9 quintals per acre, without a significant difference in the amount invested for growing coffee. Net gain was lower by Rs. 15.5 per kg for the planters growing under high shade and native trees cover when compared with low shade and exotic trees cover.

Keywords: coffee, cultivation, economics, Kodagu

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Authors: Ashwinder K. Dhaliwal, G. P. S. Dhillon

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Eucalyptus is one of the most important forest tree species that can tolerate and grow well on degraded and unfertile soils which are not suitable for other tree species. Besides this, these trees have a short rotation and good economic value. However, the gall inducing wasp Leptocybe invasa Fisher and La Salle has been reported from many countries throughout the world. The spread of L. invasa is of huge economic concern as more than 20,000 ha of young Eucalyptus trees have already been affected in southern states of India. The host plant resistance being the first line of defense against insect pests demands the screening of different germplasm source against L. invasa. Keeping this in view, fourteen different clones of Eucalyptus spp. were evaluated for their susceptibility to L. invasa from a replicated clonal trial planted at Punjab Agricultural University, Ludhiana. The degree of gall infestation was recorded from three plants of each clone in each replication. Three branches selected from the lower, middle and upper canopy of the trees were selected for recording the total number of galls induced by L. invasa. The statistical analysis was done as per the procedure laid down for completely randomised block design (CRBD), analysis of variance (ANOVA), critical difference (CD) and variance components using Proc GLM (SAS software 9.3, SAS Institute Ltd. U.S.A). All possible treatment means were compared with Duncan’s multiple range test (DMRT) at 1 % probability level. The results showed that the clones C-9, C-45 and C-42 were completely free from the infestation of L. invasa. However, there was minor infestation of L. invasa on C-2135, C-413, C-407, C-35, C-72 and C-37 clones. The clone C-6 was severely infested by L. invasa followed by C-11, C-12, F-316 and C-25 clones. The information generated by this study will be helpful for future breeding and use in afforestation programmes.

Keywords: eucalyptus clones, gall wasp, Leptocybe invasa, screening, susceptibility

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Authors: Kai-Fen Tu, Jenn-Wen Huang, Yao-Tung Lin

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Cucumber is a major economic crop in the world. The global production of cucumber in 2017 was more than 71 million tonnes. Nonetheless, downy mildew, caused by Pseudoperonospora cubensis, is a devastating and common disease on cucumber in around 80 countries and causes severe economic losses. The long-term usage of fungicide also leads to the occurrence of fungicide resistance and decreases host resistance. In this study, six types of oil (neem oil, moringa oil, soybean oil, cinnamon oil, clove oil, and camellia oil) were selected to synthesize micro/nano-sized emulsions, and the disease control efficacy of micro/nano-sized emulsions were evaluated. Moreover, oil concentrations (0.125% - 1%) and droplet size of emulsion were studied. Results showed cinnamon-type emulsion had the best efficacy among these oils. The disease control efficacy of these emulsions increased as the oil concentration increased. Both disease incidence and disease severity were measured by detached leaf and pot experiment, respectively. For the droplet size effect, results showed that the 114 nm of droplet size synthesized by 0.25% cinnamon oil emulsion had the lowest disease incidence (6.67%) and lowest disease severity (33.33%). The release of zoospore was inhibited (5.33%), and the sporangia germination was damaged. These results suggest that cinnamon oil emulsion will be a valuable and environmentally friendly alternative to control cucumber downy mildew. The economic loss caused by plant disease could also be reduced.

Keywords: downy mildew, emulsion, oil droplet size, plant protectant

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Authors: Armand Okende, Joëlle De Weerdt, Esther Fichtler, Maaike De Ridder, Hans Beeckman

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The rainforest plays a crucial role in regulating the climate balance. The biodiversity of tropical rainforests is undeniable, but many aspects remain poorly known, which directly influences its management. Despite the efforts of sustainable forest management, human pressure in terms of exploitation and smuggling of timber forms a problem compared to exploited species whose status is considered "vulnerable" on the IUCN red list compiled by. Commercial species in Class III of the Democratic Republic of Congo are the least known in the market operating, and their biology is unknown or non-existent. Identification of wood in terms of descriptions and anatomical measurements of the wood is in great demand for various stakeholders such as scientists, customs, IUCN, etc. The objective of this study is the qualitative and quantitative description of the anatomical characteristics of commercial species in Class III of DR Congo. The site of the Luki Biosphere Reserve was chosen because of its high tree species richness. This study focuses on the wood anatomy of 14 commercial species of Class III of DR Congo. Thirty-four wooden discs were collected for these species. The following parameters were measured in the field: Diameter at breast height (DBH), total height and geographic coordinates. Microtomy, identification of vessel parameters (diameter, density and grouping) and photograph of the microscopic sections and determining age were performed in this study. The results obtained are detailed anatomical descriptions of species in Class III of the Democratic Republic of Congo.

Keywords: sustainable management of forest, rainforest, commercial species of class iii, vessel diameter, vessel density, grouping vessel

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Authors: F. Ghazalnaz Sharifonnasabi, Iman Makhdoom

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Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It’s important to be aware of the risk factors for breast cancer and to get regular check- ups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We also considered the five-machine learning algorithm titled: Decision Tree (C4.5), Naïve Bayesian (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Algorithm and XGBoost (eXtreme Gradient Boosting) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms and also discovering of the most effective with respect to confusion matrix, accuracy and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the Anaconda environment based on Python programing language.

Keywords: breast cancer, multi-layer perceptron, Naïve Bayesian, SVM, decision tree, convolutional neural network, XGBoost, KNN

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Authors: Jinan Fiaidhi, Sabah Mohammed, Petros Zezos

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Capsule endoscopy (CE) is an established noninvasive diagnostic modality in investigating small bowel disease. CE has a pivotal role in assessing patients with suspected bleeding or identifying evidence of active Crohn's disease in the small bowel. However, CE produces lengthy videos with at least eighty thousand frames, with a frequency rate of 2 frames per second. Gastroenterologists cannot dedicate 8 to 15 hours to reading the CE video frames to arrive at a diagnosis. This is why the issue of analyzing CE videos based on modern artificial intelligence techniques becomes a necessity. However, machine learning, including deep learning, has failed to report robust results because of the lack of large samples to train its neural nets. In this paper, we are describing a thick data approach that learns from a few anchor images. We are using sound datasets like KVASIR and CrohnIPI to filter candidate frames that include interesting anomalies in any CE video. We are identifying candidate frames based on feature extraction to provide representative measures of the anomaly, like the size of the anomaly and the color contrast compared to the image background, and later feed these features to a decision tree that can classify the candidate frames as having a condition like the Crohn's Disease. Our thick data approach reported accuracy of detecting Crohn's Disease based on the availability of ulcer areas at the candidate frames for KVASIR was 89.9% and for the CrohnIPI was 83.3%. We are continuing our research to fine-tune our approach by adding more thick data methods for enhancing diagnosis accuracy.

Keywords: thick data analytics, capsule endoscopy, Crohn’s disease, siamese neural network, decision tree

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Authors: Adesuyi Ayodeji Steve, Zahn Munch

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This study investigates the use of MODIS NDVI to identify agricultural land cover change areas on an annual time step (2007 - 2012) and characterize the trend in the study area. An ISODATA classification was performed on the MODIS imagery to select only the agricultural class producing 3 class groups namely: agriculture, agriculture/semi-natural, and semi-natural. NDVI signatures were created for the time series to identify areas dominated by cereals and vineyards with the aid of ancillary, pictometry and field sample data. The NDVI signature curve and training samples aided in creating a decision tree model in WEKA 3.6.9. From the training samples two classification models were built in WEKA using decision tree classifier (J48) algorithm; Model 1 included ISODATA classification and Model 2 without, both having accuracies of 90.7% and 88.3% respectively. The two models were used to classify the whole study area, thus producing two land cover maps with Model 1 and 2 having classification accuracies of 77% and 80% respectively. Model 2 was used to create change detection maps for all the other years. Subtle changes and areas of consistency (unchanged) were observed in the agricultural classes and crop practices over the years as predicted by the land cover classification. 41% of the catchment comprises of cereals with 35% possibly following a crop rotation system. Vineyard largely remained constant over the years, with some conversion to vineyard (1%) from other land cover classes. Some of the changes might be as a result of misclassification and crop rotation system.

Keywords: change detection, land cover, modis, NDVI

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Authors: Muhammed Rabiu, Moses S. Oluyomi, Joshua Olorundare

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Quarry activities are important to modern day life and the socio-economic development of local communities. Unfortunately, this industry is usually associated with air pollution. To assess the impact of quarry dust on plant biodiversity and agriculture, PM2.5, PM10 and some meteorological parameters were measured using Gas analyzer, handheld thermometer and Multifunction Anemometer (PCE-EM 888) as well as taking a social survey. High amount of particulate matters that exceeded the international standard were recorded at the study locations which include the Julius Berger Quarry and 1km away from the quarry site which serve as the base for the farmlands. The correlation coefficient between the particulate matters with the meteorological parameters of the locations all show a strong relationship with temperature recording a stronger value of 0.952 and 0.931 for PM2.5 and PM10 respectively. Similarly, the coefficient of determination 0.906 and 0.866 shows that temperature has the highest meteorological percentage variation on PM2.5 and PM10. Furthermore, a notable negative impact of quarrying on plant biodiversity and local farm crops are also revealed based on respondents’ results where wide range of local plants were affected with Maize and Azadiracta indica (Neem) been the most with respondent of 31.5% and 27.5%. According to the obtained results, it is highly recommended to develop green belt surrounding the quarrying using pollutant-tolerant trees (usually with broad leaves) in order to restrict spreading of quarrying dust via intercepting, filtering and absorbing pollutants.

Keywords: agriculture, air pollution, biodiversity, quarry

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Authors: Kaleem Ahmed, Jamal A. Khan

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Grouping patterns, habitat use, and overlap studies were conducted on five sympatric ungulate species sambar (Cervus unicolor), chital (Axis axis), muntjac (Muntiacus muntjac), goral (Nemorhaedus goral), and serow (Capricornis sumatraensis) in the Dabka watershed area within Indian West Himalayan range. Data on age, sex composition, group size, and various ecological and topographical factors governing the presence/absence of species within the study area were collected using a 250 km of a trail walk, 95 permanent circular plots of 10 m radius, and 3 vantage points with 58 scannings. The highest mean group size was recorded for chital (6.35 ± 0.50), followed by sambar (1.35 ± 0.10), goral (1.25 ±0.63), muntjac (1.12 ± 0.05), and serow (1.00 ± 0.00). Grouping pattern significantly varied among sympatric species (F = 85.10, df. = 6, P = 0.000). The highest mean pellet group density (/ha ± SE) was recorded for sambar (41.56 ± 3.51), followed by goral (23.31 ± 3.45), chital (19.21 ± 3.51), muntjac (7.43 ± 1.21), and serow (1.02 ± 0.10). Two-way variance analysis showed a significant difference in the density of the pellet group of all ungulate species across different study area habitats (F = 6.38, df = 4, P = 0.027). The availability-utilization (AU) analysis reveals that goral was mostly sighted in steep slopes, preferred > 2100 m altitudinal range with low shrub understory, avoided dense forest, and relatively more southern aspects were used. Chital had used a wide range of tree and shrub coverings with a preference towards moderate cover range (26-50%), preferred areas with low slope category ( < 25), avoided areas of high altitude > 900 m. Sambar avoided less tree cover (0-25), preferred slope category (26-500), altitudes between 1600-2100 m, and preferred dense forest with northern aspects. Muntjac used all elevation ranges in the study area with a preference towards the dense forest and northern aspects. Serow preferred high tree cover > 75%, avoided low shrub cover (0-25%), preferred high shrub cover 51-75%, utilized higher elevation > 2100 m, avoided low elevation range and northern aspects. All species occupied similar habitat types, forest or scrub, except for the goral, which preferred open spaces. Between muntjac and sambar, the highest overlap was found (65%), and there was no overlap between chital and serow, chital and goral. Aspect, slope, altitude, and vegetation characteristics were found to be important factors for the overlap of ungulate sympatric species. One major reason for their ecological separation at the fine-scale level is the differential use of altitude by ungulates in the present study. This is confirmed by the avoidance by chital of altitudes > 900 m and serow of < 2100 m.

Keywords: altitude, grouping pattern, Himalayas, overlap, ungulates

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Authors: Pshtiwan Shafiq Ahmed

Abstract:

The invasion of Iraq was a turning point in Iraq, destroying the economic infrastructure of several important strategic and historic cities, including Mosul, Anbar and Diyala, which will take decades to rebuild It left 18,805 people dead and 37,000 injured, destroying hundreds of villages and cities, displacing 2.3 million people, and increasing the number of orphans The increase in the number of windows and the destruction of society and the structure of the population so that the number of children, women and the elderly has increased. Religious clashes have increased and religious cleansing has begun, trying to eradicate Christianity, Yazidis and Kakais from the whole of Iraq, causing the largest number of Christians, Yazidis and Kakais to leave Iraq and many of them went missing.

Keywords: ISIS, population change, geographical change, Iraq

Procedia PDF Downloads 90