Search results for: naturally regenerated acacia forest

Authors: Sakshi Yadav, Sushila Shekhawat

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This study addresses different types of points of view as seen in nature documentary films with the help of three eco documentaries, and it would be significant in understanding the role of the narrative point of view as a tool for showing and telling in documentaries. Narrative analysis of a film forms an essential aspect of the discourse of scholarship in film studies. Narration is the chain of events occurring in time and space. The notion of narrative provides the idea of coherence and wholeness to the story. There are various components that the narration carries, one of which is the perspective or point of view. The narrator plays the role of a mediator between the film and the audience; thus, his perspective influences the way the audience interprets the film. Feature films have been analyzed through narrative points of view; however, this research intends to conduct it from the angle of a nature documentary film. The study will examine narrative viewpoints unique to nature documentary films using three ecological documentary films-The Cove (2009), Tale of a forest (2012), and Before the flood (2016). This research will apply the framework of narrative theory and will investigate the impact of the different types of narrative points of view, as each portrays the human-nature relationship from a different standpoint, and it will also study the effect that the narrative point of view has on the mode of these eco documentaries.

Keywords: ecodocumentary, narrative, human-nature relationship, point of view

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Authors: Ina Dajci

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Biologic Materials presents groundbreaking transdisciplinary research aimed at fostering new collaborative models across the Built Environment, Forestry, and Agriculture sectors. This initiative seeks to establish innovative paradigms for local and global material flows by developing a biocompatible, regenerative material economy. The project focuses on creating materials derived from biowaste and silvicultural practices, ensuring the preservation of endangered indigenous and vernacular techniques through the integration of emerging biosciences. By utilizing biomaterials sourced from agricultural waste and forest byproducts, the initiative incorporates fabrication methods recognized by UNESCO as ‘intangible cultural heritage of humanity,’ which are currently at risk. The structural, mechanical, and environmental properties of these materials are enhanced through advanced CAD-CAM fabrication, along with energy-efficient biochemical and bacterial processes that promote healthy indigo coloration. Furthermore, the integration of AI technologies in species selection facilitates a novel partnership model, enabling designers to collaborate effectively with forest managers and silviculture practitioners. This collaborative approach not only optimizes the use of plant-based materials but also enhances biodiversity and climate resilience in regional ecosystems. Overall, this project embodies a holistic strategy for addressing environmental challenges while revitalizing traditional practices and fostering sustainable innovation.

Keywords: material, architecture, culture, heritage, ecology, environment

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Erol Kam, Z. U. Yümün

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Natural and artificial radionuclides cause radioactive contamination in environments, just as the other non-biodegradable pollutants (heavy metals, etc.) sink to the sea floor and accumulate in sediments. Especially the habitat of benthic foraminifera living on the surface of sediments or in sediments at the seafloor are affected by radioactive pollution in the marine environment. Thus, it is important for pollution analysis to determine the radionuclides. Radioactive pollution accumulates in the lowest level of the food chain and reaches humans at the highest level. The more the accumulation, the more the environment is endangered. This study used gamma spectrometry to investigate the natural and artificial radionuclide distribution of sediment samples taken from living benthic foraminifera habitats in the Western Marmara Sea. The radionuclides, K-40, Cs-137, Ra-226, Mn 54, Zr-95+ and Th-232, were identified in the sediment samples. For this purpose, 18 core samples were taken from depths of about 25-30 meters in the Marmara Sea in 2016. The locations of the core samples were specifically selected exclusively from discharge points for domestic and industrial areas, port locations, and so forth to represent pollution in the study area. Gamma spectrometric analysis was used to determine the radioactive properties of sediments. The radionuclide concentration activity values in the sediment samples obtained were Cs-137=0.9-9.4 Bq/kg, Th-232=18.9-86 Bq/kg, Ra-226=10-50 Bq/kg, K-40=24.4–670 Bq/kg, Mn 54=0.71–0.9 Bq/kg and Zr-95+=0.18–0.19 Bq/kg. These values were compared with the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) data, and an environmental analysis was carried out. The Ra-226 series, the Th-232 series, and the K-40 radionuclides accumulate naturally and are increasing every day due to anthropogenic pollution. Although the Ra-226 values obtained in the study areas remained within normal limits according to the UNSCEAR values, the K-40, and Th-232 series values were found to be high in almost all the locations.

Keywords: Ra-226, Th-232, K-40, Cs-137, Mn 54, Zr-95+, radionuclides, Western Marmara Sea

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Shinta Holdsworth, Jan Axmacher, Darren J. Mann

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Beetles represent the most diverse insect taxon, and they contribute significantly to a wide range of vital ecological functions. Examples include decomposition by bark beetles, nitrogen recycling and dung processing by dung beetles or pest control by predatory ground beetles. Nonetheless, research into the distribution patterns, species richness and functional diversity of beetles particularly from tropical regions remains extremely limited. In our research, we aim to investigate the distribution and diversity patterns of beetles and the roles they play in small tropical island ecosystems in the Kepulauan Seribu Marine National Park and on Java. Our research furthermore provides insights into the effects anthropogenic activities have on the assemblage composition and diversity of beetles on the small islands. We recorded a substantial number of highly abundant small island species, including a substantial number of unique small island species across the study area, highlighting these islands’ potential importance for the regional conservation of genetic resources. The highly varied patterns observed in relation to the use of different trapping types - pitfall traps and flight interception traps (FITs) - underscores the need for complementary trapping strategies that combine multiple methods for beetle community surveys in tropical islands. The significant impacts of human activities have on the small island beetle faunas were also highlighted in our research. More island beetle species encountered in settlement than forest areas shows clear trend of positive links between anthropogenic activities and the overall beetle species richness. However, undisturbed forests harboured a high number of unique species, also in comparison to disturbed forests. Finally, our study suggests that, with regards to different feeding guilds, the diversity of herbivorous beetles on islands is strongly affected by the different levels of forest cover encountered.

Keywords: beetle diversity, forest disturbance, island biogeography, island settlement

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Authors: Rikson Gultom

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Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

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Authors: Shashi Mehta, Krishan Kumar Yadav

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This paper presents an overview of issues pertaining to the conservation of the natural environment and factors affecting the coexistence of the forest, wildlife and people. As forests and wildlife together create the basis for economic, cultural and recreational spaces for overall well-being and life-support systems, the adverse impacts of increasing consumerism are only too evident. The IUCN predicts extinction of 41% of all amphibians and 26% of mammals. The major causes behind this threatened extinction are Deforestation, Dysfunctional governance, Climate Change, Pollution and Cataclysmic phenomena. Thus the intrinsic relationship between natural resources and wildlife needs to be understood in totality, not only for the eco-system but for humanity at large. To demonstrate this, forest areas in the Aravalis- the oldest mountain ranges of Asia—falling in the States of Haryana and Rajasthan, have been taken up for study. The Aravalis are characterized by extreme climatic conditions and dry deciduous forest cover on intermittent scattered hills. Extending across the districts of Gurgaon, Faridabad, Mewat, Mahendergarh, Rewari and Bhiwani, these ranges - with village common land on which the entire economy of the rural settlements depends - fall in the state of Haryana. Aravali ranges with diverse fauna and flora near Alwar town of state of Rajasthan also form part of NCR. Once, rich in biodiversity, the Aravalis played an important role in the sustainable co-existence of forest and people. However, with the advent of industrialization and unregulated urbanization, these ranges are facing deforestation, degradation and denudation. The causes are twofold, i.e. the need of the poor and the greed of the rich. People living in and around the Aravalis are mainly poor and eke out a living by rearing live-stock. With shrinking commons, they depend entirely upon these hills for grazing, fuel, NTFP, medicinal plants and even drinking water. But at the same time, the pressure of indiscriminate urbanization and industrialization in these hills fulfils the demands of the rich and powerful in collusion with Government agencies. The functionaries of federal and State Governments play largely a negative role supporting commercial interests. Additionally, planting of a non- indigenous species like prosopis juliflora across the ranges has resulted in the extinction of almost all the indigenous species. The wildlife in the area is also threatened because of the lack of safe corridors and suitable habitat. In this scenario, the participatory role of different stakeholders such as NGOs, civil society and local community in the management of forests becomes crucial not only for conservation but also for the economic wellbeing of the local people. Exclusion of villagers from protection and conservation efforts - be it designing, implementing or monitoring and evaluating could prove counterproductive. A strategy needs to be evolved, wherein Government agencies be made responsible by putting relevant legislation in place along with nurturing and promoting the traditional wisdom and ethics of local communities in the protection and conservation of forests and wild life in the Aravali ranges of States of Haryana and Rajasthan of the National Capital Region, Delhi.

Keywords: deforestation, ecosystem, governance, urbanization

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Authors: Basit Aftab, Feng Zhongke

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The land use/land cover change (LULCC) is a significant indicator sensitive to an area's environmental changes. As a rapidly developing capital city near the Himalayas Mountains, the city area of Islamabad, Pakistan, has expanded dramatically over the past 20 years. In order to precisely measure the impact of urbanization on the forest and agricultural lands, the Spatio-temporal analysis of LULCC was utilized, which helped us to know the impacts of urbanization, especially on ecosystem processes, biological cycles, and biodiversity. The Islamabad region's Multispectral Satellite Images (MSI) for 2000, 2010, and 2020 were employed as the remote sensing data source. Local documents of city planning, forest inventory and archives in the agriculture management departments were included to verify the image-derived result. The results showed that from 2000 to 2020, the built-up area increased to 48.3% (505.02 Km2). Meanwhile, the forest, agricultural, and barre land decreased to 28.9% (305.64 Km2), 10.04% (104.87 Km2), and 11.61% (121.30 Km2). The overall percentage change in land area between 2000 – 2020 was recorded maximum for the built-up (227.04%). Results revealed that the increase in the built-up area decreased forestland, barren, and agricultural lands (-0.36, -1.00 & -0.34). The association of built-up with respective years was positively linear (R2 = 0.96), whereas forestland, agricultural, and barren lands association with years were recorded as negatively linear (R2 = -0.29, R2 = -0.02, and R2 = -0.96). Large-scale deforestation leads to multiple negative impacts on the local environment, e.g., water degradation and climate change. It would finally affect the environment of the greater Himalayan region in some way. We further analyzed the driving forces of urbanization. It was determined by economic expansion, climate change, and population growth. We hope our study could be utilized to develop efforts to mitigate the consequences of deforestation and agricultural land damage, reducing greenhouse gas emissions while preserving the area's biodiversity.

Keywords: urbanization, Himalaya mountains, landuse landcover change (LULCC), remote sensing., multi-spectral satellite imagery

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Authors: Esmaeil Biazar

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A nanofibrous PHBV nerve conduit has been used to evaluate its efficiency based on the promotion of nerve regeneration in rats. The designed conduits were investigated by physical, mechanical and microscopic analyses. The conduits were implanted into a 30-mm gap in the sciatic nerves of the rats. Four months after surgery, the regenerated nerves were evaluated by macroscopic assessments and histology. This polymeric conduit had sufficiently high mechanical properties to serve as a nerve guide. The results demonstrated that in the nanofibrous graft with cells, the sciatic nerve trunk had been reconstructed with restoration of nerve continuity and formatted nerve fibers with myelination. For the grafts especially the nanofibrous conduits with cells, muscle cells of gastrocnemius on the operated side were uniform in their size and structures. This study proves the feasibility of artificial conduit with Schwann cells for nerve regeneration by bridging a longer defect in a rat model.

Keywords: sciatic regeneration, Schwann cell, artificial conduit, nanofibrous PHBV, histological assessments

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Authors: Titinan Pothong, Prasit Wangpakapattanawong, Stephen Elliott

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Shifting cultivation is an indigenous agricultural practice among upland people and has long been one of the major land-use systems in Southeast Asia. As a result, fallows and secondary forests have come to cover a large part of the region. However, they are increasingly being replaced by monocultures, such as corn cultivation. This is believed to be a main driver of deforestation and forest degradation, and one of the reasons behind the recurring winter smog crisis in Thailand and around Southeast Asia. Accurate biomass estimation of trees is important to quantify valuable carbon stocks and changes to these stocks in case of land use change. However, presently, Thailand lacks proper tools and optimal equations to quantify its carbon stocks, especially for secondary evergreen forests, including fallow areas after shifting cultivation and smaller trees with a diameter at breast height (DBH) of less than 5 cm. Developing new allometric equations to estimate biomass is urgently needed to accurately estimate and manage carbon storage in tropical secondary forests. This study established new equations using a destructive method at three study sites: approximately 50-year-old secondary forest, 4-year-old fallow, and 7-year-old fallow. Tree biomass was collected by harvesting 136 individual trees (including coppiced trees) from 23 species, with a DBH ranging from 1 to 31 cm. Oven-dried samples were sent for carbon analysis. Wood density was calculated from disk samples and samples collected with an increment borer from 79 species, including 35 species currently missing from the Global Wood Densities database. Several models were developed, showing that aboveground biomass (AGB) was strongly related to DBH, height (H), and wood density (WD). Including WD in the model was found to improve the accuracy of the AGB estimation. This study provides insights for reforestation management, and can be used to prepare baseline data for Thailand’s carbon stocks for the REDD+ and other carbon trading schemes. These may provide monetary incentives to stop illegal logging and deforestation for monoculture.

Keywords: aboveground biomass, allometric equation, carbon stock, secondary forest

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Authors: Shehu, B., Muhammad, B. F., Madigawa, I. L., H. A. Alkali

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The study was conducted to assess heifer camel behavior and live weight changes on native range supplemented with different energy levels. A total of nine camels aged between 2 and 3 years were randomly allotted into three groups and supplemented with 3400, 3600 and 3800 Kcal and designated A, B and C, respectively. The data obtained was analyzed for variance in a Completely Randomized Design. The heifers utilized average of 371.70 min/day (64% of daylight time) browsing on native pasture and 2.30 min/day (6%) sand bathing. A significantly higher mean time was spent by heifers on browsing Leptadenia hastata (P<0.001), Dichrostachys cinerea (P<0.01), Acacia nilotica (P<0.001) and Ziziphus spina-christi (P<0.05) in early dry season (January). No significant difference was recorded on browsing time on Tamarindus indica, Adansonia digitata, Piliostigma reticulatum, Parkia biglobosaand Azadirachta indica. No significant (P<0.05) liveweight change was recorded on she-camels due to the three energy levels. It was concluded that nutritive browse species in the study area could meet camel nutrient requirements including energy. Further research on effect of period on camel nutrients requirement in different physiological conditions is recommended.

Keywords: heifer, camel, grazing, pasture

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Authors: Mahboubeh Davoudi Pahnekolayi

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Begonia rex cv. DS-EYWA is a rare, unique cultivar of begonia rex with curly colorful leaves. Optimization of an in vitro efficient regeneration protocol by focusing on transverse Thin Cell Layer (tTCL) petiole explants for high-scale production of such a beautiful cultivar was considered as our main purpose in this experiment. Thus, various concentrations of Plant Growth Regulators (PGRs) including 6-Benzylaminopurine (BAP), Thidiazuron (TDY), and –Naphthaleneacetic Acid (NAA), were selected in a Completely Randomized Design (CRD) to establish and optimize the direct organogenesis efficiency of this cultivar. Cultivation of 1 mm tTCL petiole explants in noted treatments showed that 1.5 mgl-1 BAP + 0.5 mgl-1 NAA can induce the highest number of direct regenerated shoots and lower concentration of BAP (0.5 mgl-1) can be suggested for shoot elongation before rooting stage. Elongated shoots were successfully rooted in MS free basal medium and acclimatized in 1:1 peat moss: perlite sterilized pot mixture.

Keywords: begonia rare cultivar, direct organogenesis, explant type, regeneration, thin cell layering (TCL)

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Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

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Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

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Authors: Jin-Feng Hu

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Over the past decades, the global biodiversity has continued to decline. The threats to the terrestrial plant species have increased under anthropogenic activities and other massive ecological change impacts. The situation is much more serious in China, the third richest countries regarding plant biodiversity in the world. It was not until 1992 that the first volume of the China Plant Red Data Book was published. Nowadays, a significant number of Chinese endemic plants have been threatened (The IUCN Red List). Nevertheless, plant-originated natural products (NPs) have continued to play a crucial role in the drug discovery and development process. The opportunity for identifying new chemical entities for emerging and malignant diseases depends on a diversity of drug-producing species. Several statistical surveys unveiled that the rare and endangered plants (REPs) have proven to be better sources for drug discovery than other botanic sources. The identification of bioactive NPs from REPs reveals the importance of conservation efforts in preventing species diversity loss and addressing human diseases at the same time. Thus, there is an urgent need to investigate these fragile REPs. Since 2013, our group has initially launched a special program to systematically identify bioactive/novel NPs from REPs native to China. The selected plant species were generally collected from the remote Mountain areas, and have never been chemically or pharmacologically investigated. Due to the difficult collection of the mass-limited samples of REPs, studies on the secondary metabolites of REPs-associated endophytes would provide a promising alternative potential solution. This presentation details the achievements that related to a series of “Phytochemical and biological studies on rare and endangered plants endemic to China”.

Keywords: bioactive naturally-occrring compounds, rare and endengered plants (REPs), plant endophytes, drug discovery

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Ahmed I. Shehab, Sabah M. Abdel Basir, M. A. Abdel Khalek, M. H. Soliman, G. Elgemeie

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Spent bleaching earth (SBE) recycling and utilization as an adsorbent to eliminate dyes from aqueous solution was studied. Organic solvents and subsequent thermal treatment were carried out to recover and reactivate the SBE. The effect of pH, temperature, dye’s initial concentration, and contact time on the dye removal using recycled spent bleaching earth (RSBE) was investigated. Recycled SBE showed better removal affinity of cationic than anionic dyes. The maximum removal was achieved at pH 2 and 8 for anionic and cationic dyes, respectively. Kinetic data matched with the pseudo second-order model. The adsorption phenomenon governing this process was identified by the Langmuir and Freundlich isotherms for anionic dye while Freundlich model represented the sorption process for cationic dye. The changes of Gibbs free energy (ΔG°), enthalpy (ΔH°), and entropy (ΔS°) were computed and compared through thermodynamic study for both dyes.

Keywords: Spent bleaching earth, reactivation, regeneration, thermal treatment, dye removal, thermodynamic

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Authors: Viacheslav Shkuratskyy, Aminu Bello Usman, Michael O’Dea, Saifur Rahman Sabuj

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This paper examines relationships between solar activity and earthquakes; it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth.

Keywords: k-nearest neighbour, support vector regression, random forest regression, long short-term memory network, earthquakes, solar activity, sunspot number, solar wind, solar flares

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Authors: Ella Sèdé Maforikan

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Accurate land cover mapping is essential for effective environmental monitoring and natural resources management. This study focuses on assessing the classification performance of two satellite datasets and evaluating the impact of different input feature combinations on classification accuracy in the Beterou catchment, situated in the northern part of Benin. Landsat-8 and Sentinel-2 images from June 1, 2020, to March 31, 2021, were utilized. Employing the Random Forest (RF) algorithm on Google Earth Engine (GEE), a supervised classification categorized the land into five classes: forest, savannas, cropland, settlement, and water bodies. GEE was chosen due to its high-performance computing capabilities, mitigating computational burdens associated with traditional land cover classification methods. By eliminating the need for individual satellite image downloads and providing access to an extensive archive of remote sensing data, GEE facilitated efficient model training on remote sensing data. The study achieved commendable overall accuracy (OA), ranging from 84% to 85%, even without incorporating spectral indices and terrain metrics into the model. Notably, the inclusion of additional input sources, specifically terrain features like slope and elevation, enhanced classification accuracy. The highest accuracy was achieved with Sentinel-2 (OA = 91%, Kappa = 0.88), slightly surpassing Landsat-8 (OA = 90%, Kappa = 0.87). This underscores the significance of combining diverse input sources for optimal accuracy in land cover mapping. The methodology presented herein not only enables the creation of precise, expeditious land cover maps but also demonstrates the prowess of cloud computing through GEE for large-scale land cover mapping with remarkable accuracy. The study emphasizes the synergy of different input sources to achieve superior accuracy. As a future recommendation, the application of Light Detection and Ranging (LiDAR) technology is proposed to enhance vegetation type differentiation in the Beterou catchment. Additionally, a cross-comparison between Sentinel-2 and Landsat-8 for assessing long-term land cover changes is suggested.

Keywords: land cover mapping, Google Earth Engine, random forest, Beterou catchment

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Authors: Asmit Neupane, Narayan Prasad Gautam, Prabin Bhusal

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Indian pangolin, locally called as ‘Salak’, ‘Sal machha’, ‘Pakho machha’, is a globally endangered species, nationally categorized as a critically endangered species, protected under the National Parks and Wildlife Conservation (NPWC) Act 1973 and appended in Appendix I of CITES. Indian pangolins occur in the tropical areas of Terai region and Chure foothills of eastern Nepal, and India, Bangladesh, Pakistan, and Sri Lanka. They utilize a wide range of habitats, including primary and secondary tropical forest, limestone forest, bamboo forest, grassland, and agricultural lands. So, in regard to this fact, this research is aimed to provide detailed information regarding the current distribution pattern, status, habitat preference, prevailing threats and attitude of local people towards species conservation in Madhesh Province, Nepal. The study was conducted in four CFs, two from Bara district and two from Dhanusha district. The study area comprised of Churia range and foothills with tropical and sub-tropical vegetation. A total of 24 transects were established, each of 500*50 m2, where indirect signs of Indian pangolin, including active/old burrows, pugmarks and scratches, were found. Altogether 93 burrows were found, where only 20 were active burrows. Similarly, a vegetation survey and social survey was also conducted. The data was analyzed using Stata 16 and SPSS software. Distance from settlement, ground cover, aspect, presence/absence of ants/termites and human disturbance were the important habitat parameters having statistically significant relationship with the distribution of Indian pangolin in the area. The species was found to prefer an elevation of 360 to 540m, 0-15º slope, red soil, North-east aspect, moderate crown and ground cover, without fire and rocks, vicinity of water, roads, settlement, Sal dominated forest and minimum disturbed by human activities. Similarly, the attitude of local people towards Indian pangolin conservation was found to be significantly different with respect to age, sex and education level. The study concludes that majority of active burrows were found in Churia hills, which indicates that Indian pangolin population is gradually moving uphill towards higher elevation as hilly area supports better prey availability and also less human disturbance. Further studies are required to investigate microhabitat preferences, seasonal variability and impacts of climate change on the distribution, habitat and prey availability of Indian pangolin for the sustainable conservation of this species.

Keywords: conservation, IUCN red list, local participation, small mammal, status, threats

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Authors: Munruchi Kaur

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Agarics are the fruiting bodies of the fungi falling under Phylum Basidiomycota of class Agaricomycetes. North Western parts of India which comprises of mighty Himalayas decorated with snow cap mountains, forested areas, grassland and the Gangetic plains with the altitude varying between 196m to 3600m have a huge potential of naturally growing wild agarics. These mushrooms lavishly grow in wet humid weather conditions that prevail in these parts of India during the monsoon which hits in the early June and continue up to mid-October. In this area, a diverse form of mixed vegetation is available which is represented by coniferous and angiospermic trees, shrubs, herbs, epiphytes, parasites, climbers etc. The vegetation, topography and climate of this area is quite favorable for the growth of agarics. Cedrus deodara, Pinus longifolia, P. roxburghii, P. wallichiana, Abies pindrow, A. spectabilis, Picea smithiana, Taxus sp., Rhododendron sp. and Quercus sp. occur in pure formations or as scattered patches or as mixed forests, whereas the Gangetic plains are dominated by the angiospermic trees and shrubs, they commonly occur along roadsides or in conserved areas or are the avenues plantations, common amongst these are Shorea robusta, Dalbergia sissoo, Melia azadirachta, Acacia sp., Ficus benghalensis, Eucalyptus sp. and Butea monosperma. These agarics can be categorized on the basis of the habitat in which they grow they are usually foliocolous, lignicolous, humicolous, coprophilous or termitophilous. A number of fungal forays were undertaken to different parts of North West India from time to time during the monsoon season with an aim to decipher the agarics diversity of this part of India. Along with collecting the various agarics from diverse habitat, the ethnomycological data was also collected along with by interacting with the local inhabitants of those areas. Based upon the ethnomycological data collected over the years, cataloging of the edible and inedible agarics has been done and cultures of such potential edible agarics were raised with an aim to domesticate these selected taxa. With an aim to reduce the local pressure on these natural resources, a low-cost technology was developed to make it available to the public for cultivation. As a result, 104 taxa were found edible such as Amanita hemibapha var. ochracea, A. chepangiana, A. banningiana, A. vaginata, Agrocybe parasitica, Author: Professor & Dean Faculty of Life Sciences Punjabi University, Patiala. Punjab, India [email protected] Agaricus bisporus, A. andrewii, A. campestris var. campestris, A. silvicola, A. subrutilescens, A. bernardii, A. abruptibulbus, A. fuscovelatus, A. brunnescens, A. augustus, A. silvaticus, A. arvensis, Volvariella bakeri, V. terastia, V. bombycina, V. diplasia, Psathyrella candolleana, Volvopluteus gloiocephalus, Russula cyanoxantha, R. atropurpurea, R. aurea, Clitocybe gibba,Lentinus transitus, L. kashmirinus, L. crinitus, L. ligrinus, Lactarius rubrilacteus, Pleurotus sapidus, Pluteus subcervinus, Macrocybe gigantea, etc. Cultures of various taxa viz. Pleurotus sajor-caju, Macrocybe gigantea, Pluteus petasatus and Lentinus tigrinus were raised and a proper protocol for the domestication of Pleurotus sajor-caju, Macrocybe gigantea, and Lentinus tigrinus has been developed using the locally available agro-wastes.

Keywords: Agaric, culture, domestication, edible

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Authors: Magda Gorczyca, Zofia Nocoń

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Past and current studies from different countries showed that river engineering leads to environmental degradation and extinction of many species - often those protected by local and international wildlife conservation laws. Through the years, the main focus of rivers utilization has shifted from industrial applications to recreation and wildlife preservation with a focus on keeping the biodiversity which plays a significant role in preventing climate changes. Thus an opportunity appeared to recreate flooding areas and natural habitats, which are very rare in the scale of Europe. Additionally, river restoration helps to avoid floodings and periodic droughts, which are usually very damaging to the economy. In this research, the biodiversity of dragonflies and ground beetles was analyzed in the context of plant communities and forest stands structure. Results were enriched with data from past and current literature. A comparison was made between two parts of the Odra river. A part where oxbow lake and riparian forest were separated from the river bed by embankment and a part of the river with floodplains left intact. Validity assessment of embankments relocation was made based on the research results. In the period between May and September, insects were collected, phytosociological analysis were taken, and forest stand structure properties were specified. In the part of the river not separated by the embankments, rare and protected species of plants were spotted (e.g., Trapanatans, Salvinianatans) as well as greater species and quantitive diversity of dragonfly. Ground beetles fauna, though, was richer in the area separated by the embankment. Even though the research was done during only one season and in a limited area, the results can be a starting point for further extended research and may contribute to acquiring legal wildlife protection and restoration of the researched area. During the research, the presence of invasive species Impatiens parviflora, Echinocystislobata, and Procyonlotor were observed, which may lead to loss of the natural values of the researched areas.

Keywords: carabidae, floodplains, middle Odra river, Odonata, oxbow lakes, riparian forests

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Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

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Authors: Farooq A. Al-Sheikh, Ali Elkamel, William A. Anderson

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A fluidized catalytic cracking unit (FCCU) is one of the effective units in many refineries. Modeling and optimization of FCCU were done by many researchers in past decades, but in this research, comparison between post- and oxy-combustion was studied in the regenerator-FCCU. Therefore, a simplified mathematical model was derived by doing mass/heat balances around both reactor and regenerator. A state space analysis was employed to show effects of the flow rates variables such as air, feed, spent catalyst, regenerated catalyst and flue gas on the output variables. The main aim of studying dynamic responses is to figure out the most influencing variables that affect both reactor/regenerator temperatures; also, finding the upper/lower limits of the influencing variables to ensure that temperatures of the reactors and regenerator work within normal operating conditions. Therefore, those values will be used as side constraints in the optimization technique to find appropriate operating regimes. The objective functions were modeled to be maximizing the energy in the reactor while minimizing the energy consumption in the regenerator. In conclusion, an oxy-combustion process can be used instead of a post-combustion one.

Keywords: FCCU modeling, optimization, oxy-combustion, post-combustion

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Authors: York Neudel

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The phenomenon of tourism has been considered as tool to overcome cultural frontiers, to comprehend the other and to cope with mutual mistrust and suspicion. Well, that has been a myth, at least when it comes to mass-tourism. Other approaches, like community-based tourism, still are based on the idea of embracing the other in order to help or to understand the cultural difference. In 1997, two American NGOs incentivized a tourism-project in a community in the highlands of Ecuador, in order to protect the cloud forest from destructive exploitation of its own inhabitants. Nineteen years after that, I analyze in this investigation the interactions between the Ecuadorian hosts in the mestizo-community of Yunguilla and the foreign tourist in the quest for “authentic life” in the Ecuadorian cloud forest. As a sort of “contemporary pilgrim” the traveller tries to find authenticity in other times and places far away from their everyday life in Europe or North America. Therefore, tourists are guided by stereotypes and expectations that are produced by the touristic industry. The host, on the other hand, has to negotiate this pre-established imaginary. That generates a kind of theatre-play with front- and backstage in organic gardens, little fabrics and even private housing, since this alternative project offers to share the private space of the host with the tourist in the setting the community-based tourism. In order to protect their privacy, the community creates new hybrid spaces that oscillate between front- and backstages that culminates in a game of hide and seek – a phenomenon that promises interesting frictions for an anthropological case-study.

Keywords: Tourism, Authenticity, Community-based tourism, Ecuador, Yunguilla

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Authors: Priti Rani, Rajni Johar, P. S. Jassal

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The magnetic chitosan beads (MCB) were synthesized using co-precipitation method and made to react with epichlorohydrin (ECH) to get the cross-linked derivative (ECH-MCB). The beads were characterized by FTIR, SEM, EDX, and TGA. It is found that zinc metal ion sorption efficiency of ECH-MCB is significantly higher than MCB. Various factors affecting the uptake behavior of metal ions, such as pH, adsorbent dosage, contact time, and temperature effects, were investigated. The adsorption parameters fitted well with Langmuir and Freundlich isotherms. The equilibrium parameter RL values support that the adsorption (0 < RL < 1) is favorable and spontaneous process. The thermodynamic parameters confirm that it is an endothermic reaction, which results in an increase in the randomness of adsorption process. The beads were regenerated using ethylene diamine tetraacetic acid (EDTA) for further use. These beads prove as promising materials for the removal of pollutants from industrial wastewater. Water samples from Yamuna and Hindon rivers were analysed for the detection of Zn (II) ions.

Keywords: chitosan magnetic beads, EDTA, epichlorohydrin, removal efficiency

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Authors: Hyppolite Aignon, Souleymane Yorou, Martin Ryberg, Anneli Svanholm

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Climate change and human actions cause the extinction of wild mushrooms. In Benin, the diversity of fungi is large and may still contain species new to science but the inventory effort remains low and focuses on particularly edible species (Russula, Lactarius, Lactifluus, and also Amanita). In addition, inventories have started recently and some groups of fungi are not sufficiently sampled, however, the degradation of fungal habitat continues to increase and some species are already disappearing. (Yorou and De Kesel, 2011), however, the degradation of fungi habitat continues to increase and some species may disappear without being known. This genus (Inocybe) overlooked has a worldwide distribution and includes more than 700 species with many undiscovered or poorly known species worldwide and particularly in tropical Africa. It is therefore important to orient the inventory to other genera or important families such as Inocybe (Fungi, Agaricales) in order to highlight their diversity and also to know their phylogenetic positions with a combined approach of gene regions. This study aims to evaluate the species richness and phylogenetic position of Inocybe species and affiliated taxa in West Africa. Thus, in North Benin, we visited the Forest Reserve of Ouémé Supérieur, the Okpara forest and the Alibori Supérieur Forest Reserve. In the center, we targeted the Forest Reserve of Toui-Kilibo. The surveys have been carried during the raining season in the study area meaning from June to October. A total of 24 taxa were collected, photographed and described. The DNA was extracted, the Polymerase Chain Reaction was carried out using primers (ITS1-F, ITS4-B) for Internal transcribed spacer (ITS), (LROR, LWRB, LR7, LR5) for nuclear ribosomal (LSU), (RPB2-f5F, RPB2-b6F, RPB2- b6R2, RPB2-b7R) for RNA polymerase II gene (RPB2) and sequenced. The ITS sequences of the 24 collections of Inocybaceae were edited in Staden and all the sequences were aligned and edited with Aliview v1.17. The sequences were examined by eye for sufficient similarity to be considered the same species. 13 different species were present in the collections. In addition, sequences similar to the ITS sequences of the thirteen final species were searched using BLAST. The nLSU and RPB2 markers for these species have been inserted in a complete alignment, where species from all major Inocybaceae clades as well as from all continents except Antarctica are present. Our new sequences for nLSU and RPB2 have been manually aligned in this dataset. Phylogenetic analysis was performed using the RAxML v7.2.6 maximum likelihood software. Bootstrap replications have been set to 100 and no partitioning of the dataset has been performed. The resulting tree was viewed and edited with FigTree v1.4.3. The preliminary tree resulting from the analysis of maximum likelihood shows us that these species coming from Benin are much diversified and are distributed in four different clades (Inosperma, Inocybe, Mallocybe and Pseudosperma) on the seven clades of Inocybaceae but the phylogeny position of 7 is currently known. This study marks the diversity of Inocybe in Benin and the investigations will continue and a protection plan will be developed in the coming years.

Keywords: Benin, diversity, Inocybe, phylogeny placement

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Authors: Iahtasham Khan, Vania Lucia De Assis Santana, Marcilia Maria Alves De Souza, Mabel Hanna Vance Harrop, Fernando Leandro Dos Santos, Cecília Maria Souza Leão E. Silva, Pedro Paulo Silveira, Marcelo Brasil, Marcus Vinícius, Hélio Cordeiro Manso Filho, Muhammad Younus, Aman Ullah Khan

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Glanders ranks high on clinical lists in some regions of Brazil as a cause of respiratory and lymphatic disease in equids. Glanders is caused by Burkolderia mallei (B. mallei) Gram-negative bacterium. B. mallei was first biological agent used in World War I in 20th century. The complement fixation test (CFT) is a serodiagnostic tool prescribed by the World Organization for Animal Health (OIE)for the diagnosis of glanders in the international trade of equids. The aim of the present study was to monitor the serological responses in equines naturally infected with B. mallei using the CFT. A total of 574 equids were tested with CFT, 30 days apart in a total of 12 samplings. One hundred thirty-four sera tested negative in all samplings; 192 sera tested positive in one sampling and 125 sera tested positive in two or more samplings. Remaining 123 samples showed uncertain results. Thus, CFT results can vary over a period of time. These variations could be the consequence of the effects of the natural immune response in each animal. The findings of the present study demonstrate difficulties regarding the simultaneous implementation of CFT and test and slaughter policies to eradicate glanders. Another constraint to control this disease is the presence of carrier/transitory CFT-negative animals, which are a potential source of disease in glanders-free areas. Serodiagnostic tests of higher sensitivity and specificity like immunobloat should be implemented to achieve success in the eradication of glanders.

Keywords: glanders, equids, horses, immunological, mules

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Authors: Naduni Ranasinghe

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E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

Procedia PDF Downloads 157

Authors: Neha Gupta

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Landslide vulnerability is considered as the crucial parameter for the assessment of landslide risk. The term vulnerability defined as the damage or degree of elements at risk of different dimensions, i.e., physical, social, economic, and environmental dimensions. Himalaya region is very prone to multi-hazard such as floods, forest fires, earthquakes, and landslides. With the increases in fatalities rates, loss of infrastructure, and economy due to landslide in the Himalaya region, leads to the assessment of vulnerability. In this study, a methodology to measure the combination of vulnerability dimension, i.e., social vulnerability, physical vulnerability, and environmental vulnerability in one framework. A combined result of these vulnerabilities has rarely been carried out. But no such approach was applied in the Indian Scenario. The methodology was applied in an area of east Sikkim Himalaya, India. The physical vulnerability comprises of building footprint layer extracted from remote sensing data and Google Earth imaginary. The social vulnerability was assessed by using population density based on land use. The land use map was derived from a high-resolution satellite image, and for environment vulnerability assessment NDVI, forest, agriculture land, distance from the river were assessed from remote sensing and DEM. The classes of social vulnerability, physical vulnerability, and environment vulnerability were normalized at the scale of 0 (no loss) to 1 (loss) to get the homogenous dataset. Then the Multi-Criteria Analysis (MCA) was used to assign individual weights to each dimension and then integrate it into one frame. The final vulnerability was further classified into four classes from very low to very high.

Keywords: landslide, multi-criteria analysis, MCA, physical vulnerability, social vulnerability

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