Search results for: forest bathing

Authors: Deepika Christopher, Garima Anand

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To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

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Authors: Adam J. Bowen

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Abstract— Should robots have rights or legal protections. Often debates concerning whether robots and AI should be afforded rights focus on conditions of personhood and the possibility of future advanced forms of AI satisfying particular intrinsic cognitive and moral attributes of rights-holding persons. Such discussions raise compelling questions about machine consciousness, autonomy, and value alignment with human interests. Although these are important theoretical concerns, especially from a future design perspective, they provide limited guidance for addressing the moral and legal standing of current and near-term AI that operate well below the cognitive and moral agency of human persons. Robots and AI are already being pressed into service in a wide range of roles, especially in healthcare and biomedical contexts. The design and large-scale implementation of robots in the context of core societal institutions like healthcare systems continues to rapidly develop. For example, we bring them into our homes, hospitals, and other care facilities to assist in care for the sick, disabled, elderly, children, or otherwise vulnerable persons. We enlist surgical robotic systems in precision tasks, albeit still human-in-the-loop technology controlled by surgeons. We also entrust them with social roles involving companionship and even assisting in intimate caregiving tasks (e.g., bathing, feeding, turning, medicine administration, monitoring, transporting). There have been advances to enable severely disabled persons to use robots to feed themselves or pilot robot avatars to work in service industries. As the applications for near-term AI increase and the roles of robots in restructuring our biomedical practices expand, we face pressing questions about the normative implications of human-robot interactions and collaborations in our collective worldmaking, as well as the moral and legal status of robots. This paper argues that robots operating in public and private spaces be afforded some protections as either moral patients or legal agents to establish prohibitions on robot abuse, misuse, and mistreatment. We already implement robots and embed them in our practices and institutions, which generates a host of human-to-machine and machine-to-machine relationships. As we interact with machines, whether in service contexts, medical assistance, or home health companions, these robots are first encountered in relationship to us and our respective roles in the encounter (e.g., surgeon, physical or occupational therapist, recipient of care, patient’s family, healthcare professional, stakeholder). This proposal aims to outline a framework for establishing limiting factors and determining the extent of moral or legal protections for robots. In doing so, it advocates for a relational approach that emphasizes the priority of mapping the complex contextually sensitive roles played and the relations in which humans and robots stand to guide policy determinations by relevant institutions and authorities. The relational approach must also be technically informed by the intended uses of the biomedical technologies in question, Design History Files, extensive risk assessments and hazard analyses, as well as use case social impact assessments.

Keywords: biomedical robots, robot ethics, robot laws, human-robot interaction

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Authors: Zachary Blanks, Solomon Sonya

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Poaching presents a serious threat to endangered animal species, environment conservations, and human life. Additionally, some poaching activity has even been linked to supplying funds to support terrorist networks elsewhere around the world. Consequently, agencies dedicated to protecting wildlife habitats have a near intractable task of adequately patrolling an entire area (spanning several thousand kilometers) given limited resources, funds, and personnel at their disposal. Thus, agencies need predictive tools that are both high-performing and easily implementable by the user to help in learning how the significant features (e.g. animal population densities, topography, behavior patterns of the criminals within the area, etc) interact with each other in hopes of abating poaching. This research develops a classification model using machine learning algorithms to aid in forecasting future attacks that is both easy to train and performs well when compared to other models. In this research, we demonstrate how data imputation methods (specifically predictive mean matching, gradient boosting, and random forest multiple imputation) can be applied to analyze data and create significant predictions across a varied data set. Specifically, we apply these methods to improve the accuracy of adopted prediction models (Logistic Regression, Support Vector Machine, etc). Finally, we assess the performance of the model and the accuracy of our data imputation methods by learning on a real-world data set constituting four years of imputed data and testing on one year of non-imputed data. This paper provides three main contributions. First, we extend work done by the Teamcore and CREATE (Center for Risk and Economic Analysis of Terrorism Events) research group at the University of Southern California (USC) working in conjunction with the Department of Homeland Security to apply game theory and machine learning algorithms to develop more efficient ways of reducing poaching. This research introduces ensemble methods (Random Forests and Stochastic Gradient Boosting) and applies it to real-world poaching data gathered from the Ugandan rain forest park rangers. Next, we consider the effect of data imputation on both the performance of various algorithms and the general accuracy of the method itself when applied to a dependent variable where a large number of observations are missing. Third, we provide an alternate approach to predict the probability of observing poaching both by season and by month. The results from this research are very promising. We conclude that by using Stochastic Gradient Boosting to predict observations for non-commercial poaching by season, we are able to produce statistically equivalent results while being orders of magnitude faster in computation time and complexity. Additionally, when predicting potential poaching incidents by individual month vice entire seasons, boosting techniques produce a mean area under the curve increase of approximately 3% relative to previous prediction schedules by entire seasons.

Keywords: ensemble methods, imputation, machine learning, random forests, statistical analysis, stochastic gradient boosting, wildlife protection

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Authors: Rosimeire Araújo Silva, Philip Martin Fearnside

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Extreme droughts and floods in the Amazon have serious consequences for natural ecosystems and the human population in the region. The frequency of these events has increased in recent years, and projections of climate change predict greater frequency and intensity of these events. Understanding the links between these extreme events and different patterns of sea surface temperature in the Atlantic and Pacific Oceans is essential, both to improve the modeling of climate change and its consequences and to support efforts of adaptation in the region. The relationship between sea temperatures and events in the Amazon is much more complex than is usually assumed in climatic models. Warming and cooling of different parts of the oceans, as well as the interaction between simultaneous temperature changes in different parts of each ocean and between the two oceans, have specific consequences for the Amazon, with effects on precipitation that vary in different parts of the region. Simplistic generalities, such as the association between El Niño events and droughts in the Amazon, do not capture this complexity. We investigated the variability of Sea Surface Temperature (SST) in the Tropical Pacific Ocean during the period 1950-2022, using Empirical Orthogonal Functions (FOE), spectral analysis coherence and wavelet phase. The two were identified as the main modes of variability, which explain about 53,9% and 13,3%, respectively, of the total variance of the data. The spectral and coherence analysis and wavelets phase showed that the first selected mode represents the warming in the central part of the Pacific Ocean (the “Central El Niño”), while the second mode represents warming in the eastern part of the Pacific (the “Eastern El Niño The effects of the 1982-1983 and 1976-1977 El Niño events in the Amazon, although both events were characterized by an increase in sea surface temperatures in the Equatorial Pacific, the impact on rainfall in the Amazon was distinct. In the rainy season, from December to March, the sub-basins of the Japurá, Jutaí, Jatapu, Tapajós, Trombetas and Xingu rivers were the regions that showed the greatest reductions in rainfall associated with El Niño Central (1982-1983), while the sub-basins of the Javari, Purus, Negro and Madeira rivers had the most pronounced reductions in the year of Eastern El Niño (1976-1977). In the transition to the dry season, in April, the greatest reductions were associated with the Eastern El Niño year for the majority of the study region, with the exception only of the sub-basins of the Madeira, Trombetas and Xingu rivers, which had their associated reductions to Central El Niño. In the dry season from July to September, the sub-basins of the Japurá Jutaí Jatapu Javari Trombetas and Madeira rivers were the rivers that showed the greatest reductions in rainfall associated with El Niño Central, while the sub-basins of the Tapajós Purus Negro and Xingu rivers had the most pronounced reductions. In the Eastern El Niño year this season. In this way, it is possible to conclude that the Central (Eastern) El Niño controlled the reductions in soil moisture in the dry (rainy) season for all sub-basins shown in this study. Extreme drought events associated with these meteorological phenomena can lead to a significant increase in the occurrence of forest fires. These fires have a devastating impact on Amazonian vegetation, resulting in the irreparable loss of biodiversity and the release of large amounts of carbon stored in the forest, contributing to the increase in the greenhouse effect and global climate change.

Keywords: sea surface temperature, variability, climate, Amazon

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Authors: Georgios A. Fragkiadakis, Antonia Psaroudaki, Theodora Mouratidou, Eirini Sfakianaki

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Research: Within the project "Actions for the optimal utilization of the potential of carob in the Region of Crete" which is financed-supervised by the Region, with collaboration of Crete University and Hellenic Mediterranean University, a SWOT (strengths, weaknesses, opportunities, threats) survey was carried out, to evaluate the prospects of carob in human nutrition, in Crete. Results and conclusions: 1). Strengths: There exists a local production of carob for human consumption, based on international reports, and local-product reports. The data on products in the market (over 100 brands of carob food), indicates a sufficiency of carob materials offered in Crete. The variety of carob food products retailed in Crete indicates a strong demand-production-consumption trend. There is a stable number (core) of businesses that invest significantly (Creta carob, Cretan mills, etc.). The great majority of the relevant food stores (bakery, confectionary etc.) do offer carob products. The presence of carob products produced in Crete is strong on the internet (over 20 main professionally designed websites). The promotion of the carob food-products is based on their variety and on a few historical elements connected with the Cretan diet. 2). Weaknesses: The international prices for carob seed affect the sector; the seed had an international price of €20 per kg in 2021-22 and fell to €8 in 2022, causing losses to carob traders. The local producers do not sort the carobs they deliver for processing, causing 30-40% losses of the product in the industry. The occasional high price triggers the collection of degraded raw material; large losses may emerge due to the action of insects. There are many carob trees whose fruits are not collected, e.g. in Apokoronas, Chania. The nutritional and commercial value of the wild carob fruits is very low. Carob trees-production is recorded by Greek statistical services as "other cultures" in combination with prickly pear i.e., creating difficulties in retrieving data. The percentage of carob used for human nutrition, in contrast to animal feeding, is not known. The exact imports of carob are not closely monitored. We have no data on the recycling of carob by-products in Crete. 3). Opportunities: The development of a culture of respect for carob trade may improve professional relations in the sector. Monitoring carob market and connecting production with retailing-industry needs may allow better market-stability. Raw material evaluation procedures may be implemented to maintain carob value-chain. The state agricultural services may be further involved in carob-health protection. The education of farmers on carob cultivation/management, can improve the quality of the product. The selection of local productive varieties, may improve the sustainability of the culture. Connecting the consumption of carob with health-food products, may create added value in the sector. The presence and extent of wild carob threes in Crete, represents, potentially, a target for grafting. 4). Threats: The annual fluctuation of carob yield challenges the programming of local food industry activities. Carob is a forest species also - there is danger of wrong classification of crops as forest areas, where land ownership is not clear.

Keywords: human nutrition, carob food, SWOT analysis, crete, greece

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Authors: Dwi Purnomo, Anas Bunyamin, Fajar Susilo, Akbar Anugrah

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Beekeeping became a rural economic buffer, especially for people who lived by forest side to diversify their food or sell the honey and bee colony. Aside from the high price of honey and it’s derivative products, there is another revenue stream along beekeeping value chain that could be optimized by the people. There are five of nine honey bee species in the world, exist in Indonesia, such as Apis Cerana, Apis Dorsata, Apis Andreniformis, Apis Koschevnikovi, and Apis Nigrocincta. Indonesian farmer generally developed Apis Cerana and two other honey bees species, like Apis Mellifera and Trigona. This study tried to identify, how beekeeping business practices, challenges and opportunities in four beekeeping business in West Java through the value chain along the business. Data carried out by literature review, interview and focus group discussion with key actors in beekeeping business. There are six revenue stream in beekeeping business in West Java, such as brood hunter, beehives maker, agroforestry, agro-tourism, honey and derivatives products and bee acupuncture. This assesses conclude any criteria that should grasp for developing and sustaining beekeeping business in West Java.

Keywords: beekeeping business, business developing, value chain, West Java

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Authors: Mohammed Taleb

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Morocco is characterized by an important genetic diversity represented by a rich and varied flora with 5211 species and subspecies and many natural ecosystems. Biodiversity and natural ecosystems provide the local population with highly diversified services represented by aromatic and medicinal plants, forage plants, melliferous plants, firewood, lumber, mushrooms, etc. Ecosystem services are currently subject to many pressures: overgrazing and deforestation, climate change, including increased drought, urbanization and forest fire. Conscious of the risks that weigh on biodiversity and ecosystem services, Morocco had made an important effort to reverse the tendencies by developing a consistent biodiversity conservation strategy focused on in-situ and ex-situ conservation. This presentation will be focused on the current state of biodiversity and ecosystem services and their role for the human development and their decline under the action of different pressures (grazing, timber harvest, harvesting of medicinal and aromatic plants, charcoal making...) while emphasizing efforts constructed by Morocco to conserve and sustainably manage biodiversity and ecosystem services.

Keywords: morocco, biodiversity, ecosystem services, local population

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Authors: Joy Chiu, Colin Hemez, Emma Ryan, Jia Sun, Robert Dubrow, Michael Pascucilla

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The popularity of recreational boating is on the rise in the United States, which raises numerous national-level challenges in the management of air and water pollution, aquatic habitat destruction, and waterway access. The need to control sewage discharge from recreational vessels underlies all of these challenges. The release of raw human waste into aquatic environments can lead to eutrophication and algal blooms; can increase human exposure to pathogenic viruses, bacteria, and parasites; can financially impact commercial shellfish harvest/fisheries and marine bathing areas; and can negatively affect access to recreational and/or commercial waterways to the detriment of local economies. Because of the damage that unregulated sewage discharge can do to environments and human health/marine life, recreational vessels in the United States are required by law to 'pump-out' sewage from their holding tanks into sewage treatment systems in all designated 'no discharge areas'. Many pump-out boats, which transfer waste out of recreational vessels, are operated and maintained using funds allocated through the Federal Clean Vessel Act (CVA). The East Shore District Health Department of Branford, Connecticut is protecting this estuary by pioneering the design and construction of the first-in-the-nation zero-emissions, the solar-electric pump-out boat of its size to replace one of its older traditional gasoline-powered models through a Connecticut Department of Energy and Environmental Protection CVA Grant. This study, conducted in collaboration with the East Shore District Health Department, the Connecticut Department of Energy and Environmental Protection, States Organization for Boating Access and Connecticut’s CVA program coordinators, had two aims: (1) To perform a national assessment of pump-out boat programs, supplemented by a limited international assessment, to establish best pump-out boat practices (regardless of how the boat is powered); and (2) to estimate the cost, greenhouse gas emissions, and environmental and public health impacts of solar-electric versus traditional gasoline-powered pump-out boats. A national survey was conducted of all CVA-funded pump-out program managers and selected pump-out boat operators to gauge best practices; costs associated with gasoline-powered pump-out boat operation and management; and the regional, cultural, and policy-related issues that might arise from the adoption of solar-electric pump-out boat technology. We also conducted life-cycle analyses of gasoline-powered and solar-electric pump-out boats to compare their greenhouse gas emissions; production of air, soil and water pollution; and impacts on human health. This work comprises the most comprehensive study into pump-out boating practices in the United States to date, in which information obtained at local, state, national, and international levels is synthesized. This study aims to enable CVA programs to make informed recommendations for sustainable pump-out boating practices and identifies the challenges and opportunities that remain for the wide adoption of solar-electric pump-out boat technology.

Keywords: pump-out boat, marine water, solar-electric, zero emissions

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Authors: Shafiq Alam, Yen Ning Lee

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Conventional hydrometallurgical processing of metals involves the use of large quantities of toxic chemicals. Realizing a need to develop sustainable technologies, extensive research studies are being carried out to recover and recycle base, precious and rare earth metals from their pregnant leach solutions (PLS) using green chemicals/biomaterials prepared from biomass wastes derived from agriculture, marine and forest resources. Our innovative research showed that bio-adsorbents prepared from such biomass wastes can effectively adsorb precious metals, especially gold after conversion of their functional groups in a very simple process. The highly effective ‘Adsorption-coupled-Reduction’ phenomenon witnessed appears promising for the potential use of this gold biosorption process in the mining industry. Proper management and effective use of biomass wastes as value added green chemicals will not only reduce the volume of wastes being generated every day in our society, but will also have a high-end value to the mining and mineral processing industries as those biomaterials would be cheap, but very selective for gold recovery/recycling from low grade ore, leach residue or e-wastes.

Keywords: biosorption, hydrometallurgy, gold, adsorption, reduction, biomass, sustainability

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Authors: Hexiong Liu

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Powder metallurgy is the optimal method for the consolidation and preparation of W(Mo) alloys, which exhibit excellent application prospects at high temperatures. The properties of W(Mo) alloys are closely related to the sintered density. However, controlling the sintered density and porosity of these alloys is still challenging. In the past, the regulation methods mainly focused on time-consuming and costly trial-and-error experiments. In this study, the sintering data for more than a dozen W(Mo) alloys constituted a small-scale dataset, including both solid and liquid phases of sintering. Furthermore, simple descriptors were used to predict the sintered density of W(Mo) alloys based on the descriptor selection strategy and machine learning method (ML), where the ML algorithm included the least absolute shrinkage and selection operator (Lasso) regression, k-nearest neighbor (k-NN), random forest (RF), and multi-layer perceptron (MLP). The results showed that the interpretable descriptors extracted by our proposed selection strategy and the MLP neural network achieved a high prediction accuracy (R>0.950). By further predicting the sintered density of W(Mo) alloys using different sintering processes, the error between the predicted and experimental values was less than 0.063, confirming the application potential of the model.

Keywords: sintered density, machine learning, interpretable descriptors, W(Mo) alloy

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Authors: Yakubu Bala Mohammed, Nadire Chavus, Mohammed Bulama

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Generative Pre-trained Transformer (ChatGPT) represents an artificial intelligence (AI) tool capable of swiftly generating comprehensive responses to prompts and follow-up inquiries. This emerging AI tool was introduced in November 2022 by OpenAI firm, an American AI research laboratory, utilizing substantial language models. This present study aims to delve into the potential future consequences of ChatGPT usage in education using AI-based algorithms. The paper will bring forth the likely potential risks of ChatGBT utilization, such as academic integrity concerns, unfair learning assessments, excessive reliance on AI, and dissemination of inaccurate information using four machine learning algorithms: eXtreme-Gradient Boosting (XGBoost), Support vector machine (SVM), Emotional artificial neural network (EANN), and Random forest (RF) would be used to analyze the study collected data due to their robustness. Finally, the findings of the study will assist education stakeholders in understanding the future implications of ChatGPT usage in education and propose solutions and directions for upcoming studies.

Keywords: machine learning, ChatGPT, education, learning, implications

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Authors: Seyed Armin Hashemi, Somayeh Rahimzadeh

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The urban green space and environment should be considered to be among the most fundamental elements of the sustainability of natural and human life in the new citizenship. The present research is intended to evaluate the impact of irrigation using urban wastewater of Cadmium (Cd) in the soil and leaves of the pine trees of Rasht in the forest territories of Rasht. For this purpose, following the exact specification of the geographical and topographical attributes of under treatment area, 100 sample trees were implemented randomly –systematically in each compound studied. Approaching the end of growth season, five trees were selected randomly in each of the plats and samples of leaves were collected from the parts near to the end of the crown and the part which was adjacent to the light. At the foot of each of the trees selected, a soil profile was dug and samples of soil were extracted from three depths of 0-20, centimeters. The measurements done in the laboratory showed that the density of nutritious elements of the samples of leaf and soil in the compound irrigated with wastewater .The results of the present research suggest that urban can be used as a source of irrigation whereas muck can be employed in forestation and irrigation with precise and particular supervision and control.

Keywords: irrigation, forestation, urban waste water, pine, wastewater

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Authors: Jelena Popović, Andrijana Mićanović

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Officially, 60% of Montenegrin territory is covered in forests, but they are continually being destroyed by illegal cutting, concession politics and wildfires. Montenegrin Ecologists Society started the first dendrochronology lab in Montenegro, and data collection began in the Summer of 2021. The cores were taken from 3 localities around the peak Lisac on the mt. Prekornica, where biggest P.heldreichii forests existed until recent huge wildfires. This research is the first step towards comprehensive dendrochronology research in Montenegro. It will show how old certain forest stands of Pinus heldreichii on mountain Prekornica are, that were not destroyed in huge wildfires from the recent years. It will also show how do they correlate between each other. Per locality 15 trees were sampled. Electric sanders (150 - 2000) were used for preparation. Cores were scanned, then measured in CooRecorder. Analysis is done in Cofecha. Process will be repeated with Lintab 6 and TSAP (Time Series Analysis and Presentation for Dendrochronology and Related Applications) - Win Scientific software by Rinntech. Since this is the first dendrochronology research entirely done in Montenegro it is a foundation for the dendroclimatology research. Besides, it’ll contribute to the understanding of the life of these forests in this part of its areal, and in designing good management practices.

Keywords: dendrochronology, bosnian pine, pinus heldreichii, montenegro, forests

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Authors: Guru Prakash, Revanth Dugalam

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A road’s condition often deteriorates due to harsh loading such as overload due to trucks, and severe environmental conditions such as heavy rain, snow load, and cyclic loading. In absence of proper maintenance planning, this results in potholes, wide cracks, bumps, and increased roughness of roads. In this paper, a data-driven model will be developed to detect these damages using vibration and image signals. The key idea of the proposed methodology is that the road anomaly manifests in these signals, which can be detected by training a machine learning algorithm. The use of various machine learning techniques such as the support vector machine and Radom Forest method will be investigated. The proposed model will first be trained and tested with artificially simulated data, and the model architecture will be finalized by comparing the accuracies of various models. Once a model is fixed, the field study will be performed, and data will be collected. The field data will be used to validate the proposed model and to predict the future road’s health condition. The proposed will help to automate the road condition monitoring process, repair cost estimation, and maintenance planning process.

Keywords: SVM, data-driven, road health monitoring, pot-hole

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Authors: Emmanuel Maiyanga

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Access to sufficiently good quality freshwater has been a global challenge, but more notably in low-income countries, particularly in the Sub-Saharan countries, which Nigeria is one. Urban population is soaring, especially in many low-income countries, the existing centralised water supply infrastructures are ageing and inadequate, moreover in households peoples’ lifestyles have become more water-demanding. So, people mostly device coping strategies where municipal supply is perceived to have failed. This development threatens the futures of groundwater and calls for a review of management strategy and research approach. The various issues associated with water demand management in low-income countries and Nigeria, in particular, are well documented in the literature. However, the way people use water daily in households and the reasons they do so, and how the situation is constructing demand among the middle-class population in Abuja Nigeria is poorly understood. This is what this research aims to unpack. This is achieved by using the social practices research approach (which is based on the Theory of Practices) to understand how this situation impacts on the shared groundwater resource. A qualitative method was used for data gathering. This involved audio-recorded interviews of householders and water professionals in the private and public sectors. It also involved observation, note-taking, and document study. The data were analysed thematically using NVIVO software. The research reveals the major household practices that draw on the water on a domestic scale, and they include water sourcing, body hygiene and sanitation, laundry, kitchen, and outdoor practices (car washing, domestic livestock farming, and gardening). Among all the practices, water sourcing, body hygiene, kitchen, and laundry practices, are identified to impact most on groundwater, with impact scale varying with household peculiarities. Water sourcing practices involve people sourcing mostly from personal boreholes because the municipal water supply is perceived inadequate and unreliable in terms of service delivery and water quality, and people prefer easier and unlimited access and control using boreholes. Body hygiene practices reveal that every respondent prefers bucket bathing at least once daily, and the majority bathe twice or more every day. Frequency is determined by the feeling of hotness and dirt on the skin. Thus, people bathe to cool down, stay clean, and satisfy perceived social, religious, and hygiene demand. Kitchen practice consumes water significantly as people run the tap for vegetable washing in daily food preparation and dishwashing after each meal. Laundry practice reveals that most people wash clothes most frequently (twice in a week) during hot and dusty weather, and washing with hands in basins and buckets is the most prevalent and water wasting due to soap overdose. The research also reveals poor water governance as a major cause of current inadequate municipal water delivery. The implication poor governance and widespread use of boreholes is an uncontrolled abstraction of groundwater to satisfy desired household practices, thereby putting the future of the shared aquifer at great risk of total depletion with attendant multiplying effects on the people and the environment and population continues to soar.

Keywords: boreholes, groundwater, household water practices, self-supply

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Authors: Laxmi Prasad Bhattarai

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Climate change is regarded as one of the most fundamental threats to sustainable livelihood and global development. There is a growing global concern in linking community-managed forests as potential climate change mitigation projects. This study was conducted to explore local people’s perception on climate change and the role of community forestry (CF) to combat climate change impacts. Two active community forest user groups (CFUGs) from Kaski and Syangja Districts in Nepal were selected as study sites, and various participatory tools were applied to collect primary data. Although most of the respondents were unaware about the words “Climate Change” in study sites, they were quite familiar with the irregularities in rainfall season and other weather extremities. 60% of the respondents had the idea that, due to increase in precipitation, there is a frequent occurrence of erosion, floods, and landslide. Around 85% of the people agreed that community forests help in stabilizing soil, reducing the natural hazards like erosion, landslide. Biogas as an alternative source of cooking energy, and changes in crops and their varieties are the common adaptation measures that local people start practicing in both CFUGs in Nepal.

Keywords: community forestry, climate change, global warming, adaptation, Nepal

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Authors: Shahid-ul-Islam, Faqeer Mohammad

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The present study was conducted to investigate the effect of annatto, teak and flame of the forest natural dyes on color, fastness, and antimicrobial property of protein based textile substrate. The color strength (K/S) of wool samples at various concentrations of dyes were analysed using a Reflective Spectrophotometer. The antimicrobial activity of natural dyes before and after application on wool was tested against common human pathogens Escherichia coli, Staphylococcus aureus, and Candida albicans, by using micro-broth dilution method, disc diffusion assay and growth curve studies. The structural morphology of natural protein fibre (wool) was investigated by Scanning Electron Microscopy (SEM). Annatto and teak natural dyes proved very effective in inhibiting the microbial growth in solution phase and after application on wool and resulted in a broad beautiful spectrum of colors with exceptional fastness properties. The results encourage the search and exploitation of new plant species as source of dyes to replace toxic synthetic antimicrobial agents currently used in textile industry.

Keywords: annatto, antimicrobial agents, natural dyes, green textiles

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Authors: M. C. Paliwal, A. K. Jain, S. K. Katiyar

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Classification of satellite imagery is very important for the assessment of its accuracy. In order to determine the accuracy of the classified image, usually the assumed-true data are derived from ground truth data using Global Positioning System. The data collected from satellite imagery and ground truth data is then compared to find out the accuracy of data and error matrices are prepared. Overall and individual accuracies are calculated using different methods. The study illustrates advanced classification and accuracy assessment of land use/land cover mapping using satellite imagery. IRS-1C-LISS IV data were used for classification of satellite imagery. The satellite image was classified using the software in fourteen classes namely water bodies, agricultural fields, forest land, urban settlement, barren land and unclassified area etc. Classification of satellite imagery and calculation of accuracy was done by using ERDAS-Imagine software to find out the best method. This study is based on the data collected for Bhopal city boundaries of Madhya Pradesh State of India.

Keywords: resolution, accuracy assessment, land use mapping, satellite imagery, ground truth data, error matrices

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Authors: Juliana C. Alencar, Monica Ferreira do Amaral Porto

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Watercourses in Brazilian urban areas are constantly being degraded due to the unplanned use of the urban space; however, due to the different contexts of land use and occupation in the river watersheds, different intervention strategies are required to requalify them. When it comes to requalifying watercourses, we can list three main techniques to fulfill this purpose: restoration, revitalization and recovery; each one being indicated for specific contexts of land use and occupation in the basin. In this study, it was demonstrated that the application of these three techniques to three small basins in São Paulo city, listing the aspects involved in each of the contexts and techniques of requalification. For a protected watercourse within a forest park, renaturalization was proposed, where the watercourse is preserved in a state closer to the natural one. For a watercourse in an urban context that still preserves open spaces for its maintenance as a landscape element, an intervention was proposed following the principles of revitalization, integrating the watercourse with the landscape and the population. In the case of a watercourse in a harder context, only recovery was proposed, since the watercourse is found under the road system, which makes it difficult to integrate it into the landscape.

Keywords: sustainable drainage, river restoration, river revitalization, river recovery

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Authors: Chukwuma Anya, Mekwa Eme

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Foundation is popularly defined as the lowest load-bearing part of a building, typically below the ground level. It serves as an underlying base which acts as the principle on which every building stands. There are various types of foundations in practice, which includes the strip, pile, pad, and raft foundations, and each of these have their various applications in building construction. However due to lack of professional knowledge, cost, or scheduled time frame to complete a certain project, some of these foundation types are some times neglected or used interchangeably, resulting to misuse or abuse of the building materials man, power, and some times altering the stability, balance and aesthetics of most buildings. This research work is aimed at educating the academic community on the proper application of the various foundation types to suit different environments such as the rain forest, desert, swampy area, rocky area etc. A proper application of the foundation will ensure the safety of the building from acid grounds, damping and weakening of foundation, even building settlement and stability. In addition to those, it will improve aesthetics, maintain cost effectiveness both construction cost and maintenance cost. Finally it will ensure the safety of the building and its inhabitants. At the end of this research work we will be able to differentiate the various foundation types and there proper application in the design and construction of buildings.

Keywords: foundation, application, stability, aesthetics

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

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Authors: Yosiya Chanta, Jantrararuk Tovaranont

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Species distribution model (SDMs) is one of the powerful tools used to create a suitability map used to predict and address ecology and conservation approaches. MaxEnt is a tool used among SDMs that is highly popular because it only uses presence data. Zanthoxylum rhetsa has more than 200 species distributed in the tropics. Most commonly found in cooler forest environments, there are 8-9 species found in Thailand. In northern Thailand, 3 varieties are commonly grown: Zanthoxylum myriacanthum, Zanthoxylum rhetsa and Zanthoxylum armatum. In the northern regions, these varieties are mainly used as a spice and as a cooking ingredient. MaxEnt has been used in this study to predict potential habitats for these Zanthoxylums in current and future times (2041and 2060). Suitable habitats are predicted using data from the EC-Earth3-Veg general circulation model with 19 climatic variables. The results indicate that the suitability of future habitats of Zanthoxylum rhetsa may expand into the lower northern part of Thailand. The habitat suitability map obtained from the MaxEnt tool shows that the Precipitation of Wettest Quarter (Bio16) is the most important climatic variable influencing the current and future spread of Zanthoxylum rhetsa.

Keywords: MaxEnt, Zanthoxylum rhets, species distribution modelling, climate change

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Authors: Ravi Kumar Pandit

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Climate change is regarded as one of the most fundamental threats to sustainable livelihood and global development. There is growing a global concern in linking community-managed forests as potential climate change mitigation projects. This study was conducted to explore the local people’s perception on climate change and the role of community forestry (CF) to combat climate change impacts. Two active community forest user groups (CFUGs) from Kaski and Syangja Districts in Nepal were selected as study sites, and various participatory tools were applied to collect primary data. Although most of the respondents were unaware about the words “Climate Change” in study sites, they were quite familiar with the irregularities in rainfall season and other weather extremities. 60% of the respondents had the idea that, due to increase in precipitation, there is a frequent occurrence of erosion, floods and landslide. Around 85% of the people agreed that community forests help in stabilizing soil, reducing the natural hazards like erosion, landslide. Biogas as an alternative source of cooking energy, and changes in crops and their varieties are the common adaptation measures that local people start practicing in both CFUGs in Nepal.

Keywords: climate change, community forestry, global warming, adaptation in Nepal

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Authors: George D. Ranis, Valasia Iakovoglou, George N. Zaimes

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Fires are one of the main types of disturbances that shape ecosystems in the Mediterranean region. However nowadays, climate alterations towards higher temperature regimes results on the increased levels of the intensity, frequency and the spread of fires inducing obstacles for the natural regeneration. Thasos Island is one of the Greek islands that have experienced those problems. Since 1984, a series of wildfires led to the reduction of forest cover from 61.6% to almost 20%. The negative impacts were devastating in many different aspects for the island. The absence of plant cover, post-wildfire precipitation and steep slopes were the major factors that induced severe soil erosion and intense flooding events. That also resulted to serious economic problems to the local communities and the ability of the burnt areas to regenerate naturally. Despite the substantial amount of published work regarding Thasos wildfires, there is no information related to post-wildfire effects on the hydrology and soil erosion. More research related to post-fire effects should help to an overall assessment of the negative impacts of wildfires on land degradation through processes such as soil erosion and flooding.

Keywords: erosion, land degradation, Mediterranean islands, regeneration, Thasos, wildfires

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Bo Wang

Abstract:

As the first high-resolution civil optical satellite, ZY-3 satellite is able to obtain high-resolution multi-view images with three linear array sensors. The images can be used to generate Digital Elevation Models (DEM) through dense matching of stereo images. However, due to the clouds, forest, water and buildings covered on the images, there are some problems in the dense matching results such as outliers and areas failed to be matched (matching holes). This paper introduced an algorithm to verify the accuracy of DEM that generated by ZY-3 satellite with Shuttle Radar Topography Mission (SRTM). Since the accuracy of SRTM (Internal accuracy: 5 m; External accuracy: 15 m) is relatively uniform in the worldwide, it may be used to improve the accuracy of ZY-3 DEM. Based on the analysis of mass DEM and SRTM data, the processing can be divided into two aspects. The registration of ZY-3 DEM and SRTM can be firstly performed using the conjugate line features and area features matched between these two datasets. Then the ZY-3 DEM can be refined by eliminating the matching outliers and filling the matching holes. The matching outliers can be eliminated based on the statistics on Local Vector Binning (LVB). The matching holes can be filled by the elevation interpolated from SRTM. Some works are also conducted for the accuracy statistics of the ZY-3 DEM.

Keywords: ZY-3 satellite imagery, DEM, SRTM, refinement

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Authors: Nuraeni, Suryanti, Saida, Annas Boceng

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Farm forestry is a forest where farmers or landowners do cultivation and farming activities on their land. This study aims to determine the dimensions of sustainable development of farm forestry and to analyze the leverage factors to improve the sustainability status of farm forestry management in Bulukumba Regency. This research was conducted in Kajang District, Bulukumba Regency. The analysis of the sustainability of farm forestry management applied Multi-Dimensional Scaling (MDS), a modification of the Rapid Appraisal of The Status of Farming (RAPFARM). The index value of farm forestry sustainability was by 62.01% for ecological dimension, 51.54% for economic dimension, 61.00% for the social and cultural dimension, and 63.24% for legal and institutional dimension with sustainable enough category status. Meanwhile, the index value for the technology and infrastructure was by 47.16% of less sustainable category status. The result of leverage analysis of attributes for the dimensions of ecological, economic, social and cultural, legal and institutional as well as infrastructure and technology afforded twenty-two (22) leverage sensitive factors that influence the sustainability of farm forestry.

Keywords: farm forestry, South Sulawesi, management, sustainability

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Authors: Prasanna Ramachandran, Kareem Graham, Helena Kiefel, Sunit Jain, Todd DeSantis

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Microbes that reside inside the mammalian GI tract, commonly referred to as the gut microbiome, have been shown to have therapeutic effects in animal models of disease. We hypothesize that specific proteins produced by these microbes are responsible for this activity and may be used directly as therapeutics. To speed up the discovery of these key proteins from the big-data metagenomics, we have applied machine learning techniques. Using amino acid sequences of known epitopes and their corresponding binding partners, protein interaction descriptors (PID) were calculated, making a positive interaction set. A negative interaction dataset was calculated using sequences of proteins known not to interact with these same binding partners. Using Random Forest and positive and negative PID, a machine learning model was trained and used to predict interacting versus non-interacting proteins. Furthermore, the continuous variable, cosine similarity in the interaction descriptors was used to rank bacterial therapeutic candidates. Laboratory binding assays were conducted to test the candidates for their potential as therapeutics. Results from binding assays reveal the accuracy of the machine learning prediction and are subsequently used to further improve the model.

Keywords: protein-interactions, machine-learning, metagenomics, microbiome

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Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

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Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

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Authors: Isa Muhammad Zumo

Abstract:

The African Great Green Wall (GGW) is a significant initiative in northern Nigeria because it promotes land restoration and conservation utilizing both commercial and species of forest trees while also helping to mitigate desertification and hazards from the sand dunes and shifting Sahara deserts. Conflicts and weather, however, pose a significant danger to the achievement of these goals. The scientific method for monitoring the vegetation dynamics since inception has not received the required attention, despite the African Development Bank (ADB)'s help in funding the project and its integration into the state's development plans for GGW initiatives. This study will monitor the changes in the vegetation cover of the great green wall within Yobe State Nigeria from 2014 to 2023. The vegetation dynamics will be monitored using Landsat 8 Operational Land Imager (OLI) for 6 years at 2 years intervals. The result will show the fluctuations in the vegetation cover density within the period of study. This will guide the design and implementation of policies of the GGW in achieving its objectives. The result can also contribute to the realization of Sustainable Development Goal (SDG) Target 13.2: Integrate climate change measures into national policies, strategies, and planning.

Keywords: monitoring, green wall, Landsat 8, Nigeria

Procedia PDF Downloads 55