Search results for: Thenzawl Forest Division

Authors: Eleni Stefania Kalapoda

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The current fossil-fuel based urbanization pattern and the estimated human population growth are increasing the environmental footprint on our planet’s precious resources. To mitigate the estimated skyrocketing in greenhouse gas emissions associated with the construction of new cities and infrastructure over the next 50 years, we need a radical rethink in our approach to construction to deliver a net zero built environment. This paper assesses the carbon footprint of a mass-timber, a steel, and a concrete structural alternative for peri-urban densification in the Hudson Valley's urban fringe, along with examining the updated policy and the building code adjustments that support synergies between timber construction in city making and sustainable management of timber forests. By quantifying the carbon footprint of a structural prototype for four different material assemblies—a concrete (post-tensioned), a mass timber, a steel (composite), and a hybrid (timber/steel/concrete) assembly applicable to the three updated building typologies of the IBC 2021 (Type IV-A, Type IV-B, Type IV-C) that range between a nine to eighteen-story structure alternative—and scaling-up that structural prototype to the size of a neighborhood district, the paper presents a quantitative and a qualitative approach for a forest-based construction economy as well as a resilient and a more just supply chain framework that ensures the wellbeing of both the forest and its inhabitants.

Keywords: mass-timber innovation, concrete structure, carbon footprint, densification

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Authors: Guram Esakia, Giorgi Goguadze

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The following paper is written due to overview the concept of R2P, this new dimension in International Relations field. Paper contains the general description of previously mentioned concept, its advantages and disadvantages. We also compare each other R2P and“humanitarian intervention“, trying to make clear division between these two approaches in conflict solution. There is also discussed R2P in real action, successful one in Libya and yet failed in Syria. Essay doesn’t claim to be the part of scientific chain and is based only on personal subjection as well on information gathered from various scholars and UN resolutions.

Keywords: the concept of R2P, humanitarian intervention, Libya, Syria

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Authors: Abu S. M. G. Kibria, Alison M. Behie, Robert Costanza, Colin Groves, Tracy Farrell

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This study is aimed to understand the level of access and the influence of livelihood capitals in maintaining access and control of ecosystem services (ESS) in the Sundarbans, Bangladesh. Besides the villagers, we consider other stakeholders including the forest department, coast guard, police, merchants, pirates and villagers who ‘controlled’ or ‘maintained’ access to ESS (crab catching, shrimp fry, honey, shrimp, mixed fish, fuel wood) in this region. Villagers used human, physical, natural and social capitals to gain access to ESS. The highest level of access was observed in crab catching and the lowest was found in honey collection, both of which were done when balancing the costs and benefits of accessing one ESS against another. The outcomes of these ongoing access negotiations were determined by livelihood capitals of the households. In addition, it was often found that the certain variables could have a positive effect on one ESS and a negative effect on another. For instance, human, social and natural capitals (eldest daughter’s education and No. of livelihood group membership and) had significant positive effects on honey collection while two components of human and social capitals including ‘eldest son’s education’ and ‘severity of pirate problem’ had exactly the opposite impact. These complex interactions were also observed in access to other ESS. It thus seems that access to ESS is not anything which is provided, but rather it is achieved by using livelihood capitals. Protecting any ecosystem from over exploitation and improve wellbeing can be achieved by properly balancing the livelihood capital-access nexus.

Keywords: provisioning services, access level, livelihood capital, interaction, access gain

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Authors: Nien-Ming Hong, Kuei-Fang Huang

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Climate change is an important issue in future, which seriously affects water resources for a long term planning and management. Flood assessment is highly related with climate and land use. Increasing rainfall and urbanization will induce the inundated area in future. For adapting the impacts of climate change, a land use planning is a good strategy for reducing flood damage. The study is to build a trade-off process with different land use types. The Ta-Liao watershed is the study area with three types of land uses that are build-up, farm and forest. The build-up area is concentrated in the downstream of the watershed. Different rainfall amounts are applied for assessing the land use in 1996, 2005 and 2013. The adapted strategies are based on retarding the development of urban and a trade-off process. When a land changes from farm area to built-up area in downstream, this study is to search for a farm area and change it to forest/grass area or building a retention area in the upstream. For assessing the effects of the strategy, the inundation area is simulated by the Flo-2D model with different rainfall conditions and land uses. The results show inundation maps of several cases with land use change planning. The results also show the trade-off strategies and retention areas can decrease the inundated area and divide the inundated area, which are better than retarding urban development. The land use change is usually non-reverse and the planning should be constructed before the climate change.

Keywords: climate change, land use change, flood risk assessment, land use planning

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Authors: Said Alshukri, Mazhar Hussain Malik

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Green hydrogen is hydrogen produced using renewable energy sources. In the last few years, Oman aimed to reduce its dependency on fossil fuels. Recently, the hydrogen economy has become a global trend, and many countries have started to investigate the feasibility of implementing this sector. Oman created an alliance to establish the policy and rules for this sector. With motivation coming from both global and local interest in green hydrogen, this paper investigates the potential of producing hydrogen from wind and solar energies in three different locations in Oman, namely Duqm, Salalah, and Sohar. By using machine learning-based software “WEKA” and local metrological data, the project was designed to figure out which location has the highest wind and solar energy potential. First, various supervised models were tested to obtain their prediction accuracy, and it was found that the Random Forest (RF) model has the best prediction performance. The RF model was applied to 2021 metrological data for each location, and the results indicated that Duqm has the highest wind and solar energy potential. The system of one wind turbine in Duqm can produce 8335 MWh/year, which could be utilized in the water electrolysis process to produce 88847 kg of hydrogen mass, while a solar system consisting of 2820 solar cells is estimated to produce 1666.223 MWh/ year which is capable of producing 177591 kg of hydrogen mass.

Keywords: green hydrogen, machine learning, wind and solar energies, WEKA, supervised models, random forest

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Authors: Nubwimana Rachel, Mugabowindekwe Maurice

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Trees play a crucial role in maintaining biodiversity and providing various ecological services. Traditional methods of counting trees are time-consuming, and there is a need for more efficient techniques. However, deep learning makes it feasible to identify the multi-scale elements hidden in aerial imagery. This research focuses on the application of deep learning techniques for tree detection and counting in both forest and non-forest areas through the exploration of the deep learning application for automated tree detection and counting using satellite imagery. The objective is to identify the most effective model for automated tree counting. We used different deep learning models such as YOLOV7, SSD, and UNET, along with Generative Adversarial Networks to generate synthetic samples for training and other augmentation techniques, including Random Resized Crop, AutoAugment, and Linear Contrast Enhancement. These models were trained and fine-tuned using satellite imagery to identify and count trees. The performance of the models was assessed through multiple trials; after training and fine-tuning the models, UNET demonstrated the best performance with a validation loss of 0.1211, validation accuracy of 0.9509, and validation precision of 0.9799. This research showcases the success of deep learning in accurate tree counting through remote sensing, particularly with the UNET model. It represents a significant contribution to the field by offering an efficient and precise alternative to conventional tree-counting methods.

Keywords: remote sensing, deep learning, tree counting, image segmentation, object detection, visualization

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Authors: Saba Gachpaz, Hamid Reza Heidari

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The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. To achieve this, more resources should be consumed and, besides other environmental concerns, highlight sustainable agricultural development. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for ten different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: land suitability, machine learning, random forest, sustainable agriculture

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Authors: Rosa Suryantini, Reine S. Wulandari, Slamet Rifanjani

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Acacia (Acacia mangium) is a forest plant species which is produced to pulp and paper. The high demand for pulp and paper increase the acacia plantation forest area. However, the outbreak of Ganoderma (root rot pathogen) infection becomes obstacles for the development of acacia plantations. This is due to the extent of host range and species of Ganoderma. Ganoderma has also the ability to survive the long-term without hosts. The diversity of the host and Ganoderma species affects its virulence. Therefore, this study aimed to determine the virulence of Ganoderma from different hosts (acacia, palm oil (Elaeis guineensis) and rubber (Hevea brasiliensis)). The methods were isolation and morphology identification of Ganoderma, and inoculation of Ganoderma isolates on acacia seedlings. The results showed that the three isolates of Ganoderma from different hosts had a morphological similarity with G. Lucidum (according to Ganoderma isolated from acacia or G1), G. boninense (according to Ganoderma isolated from palm oil or G2) and G. applanatum (according to Ganoderma isolated from rubber or G3). Symptoms of infection in acacia were seen at 3 months of age. The symptoms were begun with chlorosis, necrosis and death of seedlings (such as burning). Necrosis was started from the tip of the leaf. Based on this visible symptoms, G1 was moderate virulence isolate and G2 was low virulence isolate while G3 was avirulen isolate. The symptoms were still growing in accordance with the development of plant so it affected the value of diseases severity index. Ganoderma infection decreased the dry weight of seedlings, ie. 3.82 g (seedlings that were inoculated by G1), 4.01 g (seedlings that were inoculated by G2); and 5.02 g (seedlings that were inoculated by G3) when the dry weight of seedlings control was 10,02 g. These results provide information for early control of Ganoderma diseases on acacia especially those planted near rubber and oil palm crops.

Keywords: Acacia, Ganoderma, infection, virulence

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Authors: Peifei Zhu, Zisheng Li, Yasuki Kakishita, Mayumi Suzuki, Tomoaki Chono

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Segmentation of left ventricle (LV) from cardiac ultrasound images provides a quantitative functional analysis of the heart to diagnose disease. Active Shape Model (ASM) is a widely used approach for LV segmentation but suffers from the drawback that initialization of the shape model is not sufficiently close to the target, especially when dealing with abnormal shapes in disease. In this work, a two-step framework is proposed to improve the accuracy and speed of the model-based segmentation. Firstly, a robust and efficient detector based on Hough forest is proposed to localize cardiac feature points, and such points are used to predict the initial fitting of the LV shape model. Secondly, to achieve more accurate and detailed segmentation, ASM is applied to further fit the LV shape model to the cardiac ultrasound image. The performance of the proposed method is evaluated on a dataset of 800 cardiac ultrasound images that are mostly of abnormal shapes. The proposed method is compared to several combinations of ASM and existing initialization methods. The experiment results demonstrate that the accuracy of feature point detection for initialization was improved by 40% compared to the existing methods. Moreover, the proposed method significantly reduces the number of necessary ASM fitting loops, thus speeding up the whole segmentation process. Therefore, the proposed method is able to achieve more accurate and efficient segmentation results and is applicable to unusual shapes of heart with cardiac diseases, such as left atrial enlargement.

Keywords: hough forest, active shape model, segmentation, cardiac left ventricle

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Authors: Songul Cinaroglu

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Regional planning in health has gained speed for developing countries in recent years. In Turkey, 89 different Public Hospital Unions (PHUs) were conducted based on provincial levels. In this study technical efficiencies of 89 PHUs were examined by using Data Envelopment Analysis (DEA) and machine learning techniques by dividing them into two clusters in terms of similarities of input and output indicators. Number of beds, physicians and nurses determined as input variables and number of outpatients, inpatients and surgical operations determined as output indicators. Before performing DEA, PHUs were grouped into two clusters. It is seen that the first cluster represents PHUs which have higher population, demand and service density than the others. The difference between clusters was statistically significant in terms of all study variables (p ˂ 0.001). After clustering, DEA was performed for general and for two clusters separately. It was found that 11% of PHUs were efficient in general, additionally 21% and 17% of them were efficient for the first and second clusters respectively. It is seen that PHUs, which are representing urban parts of the country and have higher population and service density, are more efficient than others. Random forest decision tree graph shows that number of inpatients is a determinative factor of efficiency of PHUs, which is a measure of service density. It is advisable for public health policy makers to use statistical learning methods in resource planning decisions to improve efficiency in health care.

Keywords: public hospital unions, efficiency, data envelopment analysis, random forest

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Authors: Kever Bruno Paradelo Gomes, Rosana Carvalho Cristo Martins

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Payment for Environmental Services (PSA) has been a strategy used since the late 1990s by Latin American countries to finance environmental conservation. Payment for Environmental Services has been absorbing a growing amount of time in the discussions around environmentally sustainable development strategies in the world. In Brazil, this theme has permeated the discussions since the publication of the new Forest Code. The objective of this work was to verify the perception of the resident farmers in the region of Ponte Alta, Gama, Federal District, Brazil, on environmental legislation and Payments for Environmental Services. The work was carried out in 99 rural properties of the family farmers of the Rural Nucleus Ponte Alta, Administrative Region of Gama, in the city of Brasília, Federal District, Brazil. The present research is characterized methodologically as a quantitative, exploratory, and descriptive nature. The data treatment was performed through descriptive statistical analysis and hypothesis testing. The perceptions about environmental legislation in the rural area of Ponte Alta, Gama, DF respondents were positive. Although most of the family farmers interviewed have some knowledge about environmental legislation, it is perceived that in practice, the environmental adequacy of property is ineffective given the current situation of sustainable rural development; there is an abyss between what is envisaged by legislation and reality in the field. Thus, as in the reports of other researchers, it is verified that the majority of respondents are not aware of PSA (62.62%). Among those interviewed who were aware of the subject, two learned through the course, three through the university, two through TV and five through other people. The planting of native forest species on the rural property was the most informed practice by farmers if they received some Environmental Service Payment (PSA). Reflections on the environment allow us to infer that the effectiveness and fulfillment of the incentives and rewards in the scope of public policies to encourage the maintenance of environmental services, already existing in all spheres of government, are of great relevance to the process of environmental sustainability of rural properties. The relevance of the present research is an important tool to promote the discussion and formulation of public policies focused on sustainable rural development, especially on payments for environmental services; it is a space of great interest for the strengthening of the social group dedicated to production. Public policies that are efficient and accessible to the small rural producers become decisive elements for the promotion of changes in behavior in the field, be it economic, social, or environmental.

Keywords: forest code, public policy, rural development, sustainable agriculture

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Authors: Khasim Munir Bhasha Shaik

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Veligonda and adjoining hill range spread along about 170 Km North to South in Kadapa and Nellore Districts stretching a little further into Prakasam District. The latitude in general ranges up to 1000m. The forests are generally dry deciduous type. Veligonda and adjoining hill ranges comprise of Palakonda, Seshachalam, Lankamala and the terminal part of Nallamalais from mid-region of Southern Eastern Ghats. The Veligonda range which separates the Nellore district from Kadapa and Kurnool is the backbone of the Eastern Ghats, starting from Nagari promontory in Chittoor district. It runs in a northerly direction along the western border of the Nellore district, with a raising elevation of 3,626 ft at Penchalakona in Raipur thaluk. Veligonda hill ranges are high in altitude and have deep valleys. Among the Veligondas range of hills the Durgam in Venkatagiri range and Penchalakona are the most prominent and are situated 914 meters above mean sea level. It has more than 3000 species of plants along with 500 animal species. The unique specialty of this region is the presence of Pterocarpus santalinus(endangered) and Santalum album (vulnerable). In the present study, an attempt is made to assess the efforts that are going on to conserve the biodiversity of flora and fauna of this region. Various conservation strategies were suggested to protect the biodiversity and richness of Veligonda forest, hill region of Eastern Ghats of Andhra Pradesh. The major threats and the reasons for the dwindling species richness are poor rainfall, adverse climatic conditions, robbery of Red sanders and poaching of animals by the local tribals. Efforts are to be made to conserve some of the animals by both in situ and ex-situ methods. More awareness is to be developed among the local communities who are dwelling in the vicinity and importance of conservation is to be emphasized to them. Anthropogenic attachments are to be made by introducing more numbers of sacred groves. Gross enforcement of law is to be made to protect the various forest resources in this area. The important species with the medicinal values are to be identified. It was found that two important wildlife sanctuaries named Sri Lankamalleswarawildlife sanctuary and Sripenusila Narasimha wildlife sanctuary are working for the comprehensive conservation of the environment in this area. Apart from this more than 38 important sacred grooves are there where the plants and animals are protected by local Yanadi and other communities.

Keywords: biodiversity, wild life sanctuary, habitat destruction, eastern Ghats

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Authors: Nazneen Zehra

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Gir Protected Area (PA) is home to two sympatric large carnivores, the Asiatic lion and the common leopard, which share the same habitat. Understanding their interactions and coexistence is crucial for effective conservation management. From 2009 to 2012, we studied the availability and consumption of prey by these two carnivores to understand the dynamics of their interactions and coexistence. Ungulates provided approximately 3634.45 kg/km² of prey biomass, primarily composed of chital (ca. 2711.25 kg/km²), sambar (ca. 411.78 kg/km²), and nilgai (ca. 511.52 kg/km²). Other prey included peafowl (75.76 kg/km²) and langur (ca. 158.72 kg/km²). Both carnivores prioritized chital as their key prey species. The diet of Asiatic lions was predominantly composed of ungulates, with biomass contributions of chital (301.14 kg), sambar (378.75 kg), and nilgai (291.42 kg). Other prey species, such as peafowl and langur, contributed 1.36 kg and 2.40 kg, respectively, to the lions' diet. For leopards, the diet also heavily relied on chital (311.49 kg), followed by sambar (44.03 kg) and nilgai (172.78 kg). The biomass of other prey species in the leopards' diet included peafowl (2.08 kg) and langur (36.07 kg). Both species were found to primarily utilize teak-mixed forest, followed by riverine forest and teak-acacia-zizyphus habitats. The similarities in diet composition and habitat use indicate competition between these sympatric species. This competition may require one predator species to bear certain costs for the benefit of the other, which can influence conservation and management strategies. Effective conservation strategies are necessary to ensure the long-term survival of both the Asiatic lion and the common leopard equally and to maintain ecological balance in Gir PA.

Keywords: large carnivores, Gir PA, coexistence, resource utilization

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Authors: Mohana Basu, Snehamanju Basu

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Urbanization has an immense impact on the holistic development of a region. In that same context, the level of women empowerment plays a significant role in the development of any region, particularly a region belonging to a developing country. The present study investigates the status of women empowerment in the Asansol Durgapur Planning Area of the state of West Bengal, India by investigating the status of women and their access to various facilities and awareness about the various governmental and non-governmental schemes meant for their elevation. Through this study, an attempt has been to made to understand the perception of the respondents on the context of women's empowerment. The study integrates multiple sources of qualitative and quantitative data collected from various reports, field-based measurements, questionnaire survey and community based participatory appraisals. Results reveal that women of the rural parts of the region are relatively disempowered due to the various restrictions imposed on them and enjoy lower socioeconomic clout than their male counterparts in spite of the several remedial efforts taken by the government and NGOs to elevate their position in the society. A considerable gender gap still exists regarding access to education, employment and decision-making power in the family and significant differences in attitude towards women are observable in the rural and urban areas. Freedom of women primarily vary according to their age group, educational level, employment and income status and also on the degree of urbanization. Asansol Durgapur Planning Area is primarily an industrial region where huge employment generation scope exists. But these disparities are quite alarming and indicate that economic development does not always usher in socially justifiable rights and access to resources for both men and women alike in its awake. In this backdrop, this study will attempt to forward relevant suggestions which can be followed for betterment of the status of women.

Keywords: development, disempowered, economic development, urbanization, women empowerment

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Authors: Manal Suleiman, George Abu-Aqil, Uraib Sharaha, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman, Mahmoud Huleihel

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Klebsiella pneumoniae is one of the most aggressive multidrug-resistant bacteria associated with human infections resulting in high mortality and morbidity. Thus, for an effective treatment, it is important to diagnose both the species of infecting bacteria and their susceptibility to antibiotics. Current used methods for diagnosing the bacterial susceptibility to antibiotics are time-consuming (about 24h following the first culture). Thus, there is a clear need for rapid methods to determine the bacterial susceptibility to antibiotics. Infrared spectroscopy is a well-known method that is known as sensitive and simple which is able to detect minor biomolecular changes in biological samples associated with developing abnormalities. The main goal of this study is to evaluate the potential of infrared spectroscopy in tandem with Random Forest and XGBoost machine learning algorithms to diagnose the susceptibility of Klebsiella pneumoniae to antibiotics within approximately 20 minutes following the first culture. In this study, 1190 Klebsiella pneumoniae isolates were obtained from different patients with urinary tract infections. The isolates were measured by the infrared spectrometer, and the spectra were analyzed by machine learning algorithms Random Forest and XGBoost to determine their susceptibility regarding nine specific antibiotics. Our results confirm that it was possible to classify the isolates into sensitive and resistant to specific antibiotics with a success rate range of 80%-85% for the different tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful diagnostic method for determining the Klebsiella pneumoniae susceptibility to antibiotics.

Keywords: urinary tract infection (UTI), Klebsiella pneumoniae, bacterial susceptibility, infrared spectroscopy, machine learning

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

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

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

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Authors: D. T. Chabalala, J. M. Ndambuki, M. F. Ilunga

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Sedimentation includes the processes of erosion, transportation, deposition, and the compaction of sediment. Sedimentation in reservoir results in a decrease in water storage capacity, downstream problems involving aggregation and degradation, blockage of the intake, and change in water quality. A study was conducted in Caledon River catchment in the upstream of Welbedacht Reservoir located in the South Eastern part of Free State province, South Africa. The aim of this research was to investigate and develop a model for an Integrated Catchment Modelling of Sedimentation processes and management for the Welbedacht reservoir. Revised Universal Soil Loss Equation (RUSLE) was applied to determine sources of sediment at different areas of the catchment. The model has been also used to determine the impact of changes from management practice on erosion generation. The results revealed that the main sources of sediment in the watershed are cultivated land (273 ton per hectare), built up and forest (103.3 ton per hectare), and grassland, degraded land, mining and quarry (3.9, 9.8 and 5.3 ton per hectare) respectively. After application of soil conservation practices to developed Revised Universal Soil Loss Equation model, the results revealed that the total average annual soil loss in the catchment decreased by 76% and sediment yield from cultivated land decreased by 75%, while the built up and forest area decreased by 42% and 99% respectively. Thus, results of this study will be used by government departments in order to develop sustainable policies.

Keywords: Welbedacht reservoir, sedimentation, RUSLE, Caledon River

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Authors: Hu Ding, Kai Liu, Guoan Tang

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The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest

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Authors: Faiza Babur Khan, Sohail Asghar

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With an emergence of distributed environment, cloud computing is proving to be the most stimulating computing paradigm shift in computer technology, resulting in spectacular expansion in IT industry. Many companies have augmented their technical infrastructure by adopting cloud resource sharing architecture. Cloud computing has opened doors to unlimited opportunities from application to platform availability, expandable storage and provision of computing environment. However, from a security viewpoint, an added risk level is introduced from clouds, weakening the protection mechanisms, and hardening the availability of privacy, data security and on demand service. Issues of trust, confidentiality, and integrity are elevated due to multitenant resource sharing architecture of cloud. Trust or reliability of cloud refers to its capability of providing the needed services precisely and unfailingly. Confidentiality is the ability of the architecture to ensure authorization of the relevant party to access its private data. It also guarantees integrity to protect the data from being fabricated by an unauthorized user. So in order to assure provision of secured cloud, a roadmap or model is obligatory to analyze a security problem, design mitigation strategies, and evaluate solutions. The aim of the paper is twofold; first to enlighten the factors which make cloud security critical along with alleviation strategies and secondly to propose an intrusion detection model that identifies the attackers in a preventive way using machine learning Random Forest classifier with an accuracy of 99.8%. This model uses less number of features. A comparison with other classifiers is also presented.

Keywords: cloud security, threats, machine learning, random forest, classification

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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

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

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Authors: Yanqun Li, Hong Geng

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The rapid urbanization process taking big cities as the main body leads to the unequal configuration of urban and rural areas in the aspects of land supply, industrial division of labor, service supply and space allocation, and induces the shrinking characterization of service energy, industrial system and population vitality in small towns. As an important spatial unit in the spectrum of urbanization that serves, connects and couples urban and rural areas, the shrinking phenomenon faced by small towns has an important influence on the healthy development of urbanization. Based on the census of small towns in Wuhan metropolitan area, we have found that the shrinking of small towns is a passive contraction of elastic tension under the squeeze in cities. Once affected by the external forces such as policy regulation, planning guidance, and population return, small towns will achieve expansion and growth. Based on the theory of complex adaptive systems, this paper comprehensively constructs the development index evaluation system of small towns from five aspects of population, economy, space, society and ecology, measures the shrinking level of small towns, further analyzes the shrinking characteristics of small towns, and identifies whether the shrinking is elastic or not. And then this paper measures the resilience ability index of small town contract from the above-mentioned five aspects. Finally, this paper proposes an adaptive mechanism of urban-rural interaction evolution under fine division of labor to response the passive shrinking in small towns of Wuhan. Based on the above, the paper creatively puts forward the planning response measures of the small towns on the aspects of spatial layout, function orientation and service support, which can provide reference for other regions.

Keywords: complex adaptive systems, resiliency shrinking, adaptation mechanism, planning response

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

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Authors: Nazli Kazanoglu

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Reconciliation of work and family life has been an area of interest within the academic as well as in the political debate for more than three decades. With the dramatic changes in the extent to which women and men contribute to unpaid domestic work and paid employment, the reconciliation of work and family life issues have become more prominent than ever before. And they have begun to enjoy an increased attention of policy makers both at the EU and national levels. Over the last three decades the EU has initiated numerous equality programs and strategies and roadmaps regarding reconciliation of work and family life, though particularly because of the crisis and increasing willingness of achieving the EUs target of seventy five per cent of men and women in employment by 2020, those programs, strategies and roadmaps emphasized on eradicating womens familial burdens while entering labor market and providing them as equal opportunities as their male counterparts have. Reconciliation of work and family life policies thus bit by bit moved away from the objectives with a strong commitment to ensuring gender equality towards employment objectives. This paper is thus an endeavor to look at the nature of EU reconciliation of work and family life policies from the angle of gender equality. More precisely relying on the feminist literature, this paper rests on the assumption that reconciliation of work and family policies should provide the sufficient measures indeed with a more emphasis on endorsing gender equality rather than economic concerns and prioritizes two inter-related aspects while evaluating the gender equality of reconciliation of work and family life policies. First providing free choice to women in terms of their family and work lives and second challenge the unequal division of labor at home. In that sense, it investigates the nature of the changing uses and meanings of gender equality in reconciliation of work and family life policies in different stages of the EU social policy development particularly after the introduction of European Employment Strategy which gave a tremendous importance to reconciliation of work and family life during their collaborations with other issues on the EU agenda as well as the major rationale behind their development and implementation and locates them in terms of two inter-related parameters mentioned above.

Keywords: European Union, division of unpaid work, gender equality, rhetoric of free choice

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Authors: Sudhir Kumar Singh, Debashish Chakravarty

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Slope stability analysis is an important aspect in the field of geotechnical engineering. It is also important from safety, and economic point of view as any slope failure leads to loss of valuable lives and damage to property worth millions. This paper aims at mitigating the risk of slope failure by studying the effect of different joint sets on the stability of mine pit slopes under the influence of various external factors, namely degree of saturation, rainfall intensity, and seismic coefficients. Supervised machine learning approach has been utilized for making accurate and reliable predictions regarding the stability of slopes based on the value of Factor of Safety. Numerous cases have been studied for analyzing the stability of slopes using the popular Finite Element Method, and the data thus obtained has been used as training data for the supervised machine learning models. The input data has been trained on different supervised machine learning models, namely Random Forest, Decision Tree, Support vector Machine, and XGBoost. Distinct test data that is not present in training data has been used for measuring the performance and accuracy of different models. Although all models have performed well on the test dataset but Random Forest stands out from others due to its high accuracy of greater than 95%, thus helping us by providing a valuable tool at our disposition which is neither computationally expensive nor time consuming and in good accordance with the numerical analysis result.

Keywords: finite element method, geotechnical engineering, machine learning, slope stability

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Authors: Muhammet Baldan, Emel Timuçin

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Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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Authors: Josiah O. Carter, Charlotte Hayden, Elizabeth Arthurs

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Background: During the COVID-19 pandemic, hospital management policies have changed frequently and rapidly. This has created novel challenges in keeping the workforce abreast of these changes to enable them to deliver safe and effective care. Traditional communication methods, e.g. email, do not keep pace with the rapidly changing environment in the hospital, resulting in inaccurate, irrelevant, or outdated information being communicated, resulting in inefficiencies in patient care. Methods: The creation of a WhatsApp messaging group within the medical division at the Bristol Royal Infirmary has enabled senior clinicians and the hospital management team to update the medical workforce in real-time. It has two primary functions: (1) To enable dissemination of a concise, important operational summary. This comprises information on bed status and infection control procedural changes. It is fed directly from a daily critical incident briefing (2) To facilitate a monthly scheduled question and answer (Q&A) session for junior doctors to clarify issues with clinical directors, rota, and management staff. Additional ad-hoc updates are sent out for time-critical information; otherwise, it mainly functions as a broadcast-only group to prevent important information from being lost amongst other communication. All junior doctors within the medical division were invited to join the group. At present, the group comprises 131 participants, of which 10 are administrative staff (rota coordinators, management staff & clinical directors); the remaining 121 are junior clinicians working within the medical division. An electronic survey via Microsoft forms was sent out to junior doctors via the WhatsApp group and via email to assess its utilisation and effectiveness with the aim of quality improvements. Results: Of the 121 group participants, 19 completed the questionnaire (response rate 15.7%). Of these, 16/19 (84.2%) used it regularly, and 12/19 (63.2%) rated it as the most useful source for reliable updates relating to the hospital response to the COVID-19 pandemic, whereas only 2/19 (10.5%) found the trust intranet and the trust COVID-19 operational email update most useful. Respondents rated the WhatsApp group more useful as an information source (mean score 7.7/10) than as a means of providing feedback to management staff (mean score 6.3/10). Qualitative feedback suggested information around ward closures and changes to COVID cohorting, along with updates on staffing issues, were most useful. Respondents also noted the Q&A sessions were an efficient way of relaying feedback about management decisions but that it would be preferable if these sessions could be delivered more frequently. Discussion: During the current global COVID-19 pandemic, there is an increased need for rapid dissemination of critical information within NHS trusts; this includes communication between junior doctors, managers, and senior clinicians. The versatility of WhatsApp permits a variety of functions allowing for regular updates, the dissemination of time-critical information, and enables conversing and feedback. The project has demonstrated that reserved and well-managed use of a WhatsApp group is a welcome, efficient and practical means of communication between the senior management team and the junior medical workforce.

Keywords: communication, COVID-19, hospital management, WhatsApp

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Authors: Tong-Hsien Chow

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Background: Plantar pressure measurement is an effective method for assessing plantar loading and can be applied to evaluating movement performance of the foot. The purpose of this study is to explore the sprint athletes’ plantar loading characteristics and pain profiles in static standing. Methods: Experiments were undertaken on 80 first-division college sprint athletes and 85 healthy non-sprinters. ‘JC Mat’, the optical plantar pressure measurement was applied to examining the differences between both groups in the arch index (AI), three regional and six distinct sub-regional plantar pressure distributions (PPD), and footprint characteristics. Pain assessment and self-reported health status in sprint athletes were examined for evaluating their common pain areas. Results: Findings from the control group, the males’ AI fell into the normal range. Yet, the females’ AI was classified as the high-arch type. AI values of the sprint group were found to be significantly lower than the control group. PPD were higher at the medial metatarsal bone of both feet and the lateral heel of the right foot in the sprint group, the males in particular, whereas lower at the medial and lateral longitudinal arches of both feet. Footprint characteristics tended to support the results of the AI and PPD, and this reflected the corresponding pressure profiles. For the sprint athletes, the lateral knee joint and biceps femoris were the most common musculoskeletal pains. Conclusions: The sprint athletes’ AI were generally classified as high arches, and that their PPD were categorized between the features of runners and high-arched runners. These findings also correspond to the profiles of patellofemoral pain syndrome (PFPS)-related plantar pressure. The pain profiles appeared to correspond to the symptoms of high-arched runners and PFPS. The findings reflected upon the possible link between high arches and PFPS. The correlation between high-arched runners and PFPS development is worth further studies.

Keywords: sprint athletes, arch index, plantar pressure distributions, high arches, patellofemoral pain syndrome

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Authors: Subhankar Kumar Sarkar

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Scarab beetles composing the family Scarabaeidae is one of the largest families in the order Coleoptera. The family is comprised of 11 subfamilies. Of these, the subfamily Scarabaeinae includes 13 tribes globally. Indian species are however considered within 2 tribes Scarabaeini and Coprini. Scarab beetles under this subfamily also known as Coprophagus beetles play an indispensable role in forestry and agriculture. Both adults and larvae of these beetles do a remarkable job of carrying excrement into the soil thus enriching the soil to a great extent. Eastern and North Eastern states of India are heavily rich in diversity of organisms as this region exhibits the tropical rain forests of the eastern Himalayas, which exhibits one of the 18 biodiversity hotspots of the world and one of the three of India. Buxa Tiger Reserve located in Dooars between latitudes 26°30” to 26°55” North & longitudes 89°20” to 89°35” East is one such fine example of rain forests of the eastern Himalayas. Despite this, the subfamily is poorly known, particularly from this part of the globe and demands serious revisionary studies. It is with this background; the attempt is being made to assess the Scarabaeinae fauna of the forest. Both extensive and intensive surveys were conducted in different beats under different ranges of Buxa Tiger Reserve. For collection sweep nets, bush beating and collection in inverted umbrella, hand picking techniques were used. Several pit fall traps were laid in the collection localities of the Reserve to trap ground dwelling scarabs. Dung of various animals was also examined to make collections. In the evening hours UV light, trap was used to collect nocturnal beetles. The collected samples were studied under Stereozoom Binocular Microscopes Zeiss SV6, SV11 and Olympus SZ 30. The faunistic investigation of the forest revealed in the recognition of 19 species under 6 genera distributed over 2 tribes. Of these Heliocopris tyrannus Thomson, 1859 was recorded new from the Country, while Catharsius javanus Lansberge, 1886, Copris corpulentus Gillet, 1910, C. doriae Harold, 1877 and C. sarpedon Harold, 1868 from the state. 4 species are recorded as endemic to India. The forest is dominated by the members of the Genus Onthophagus, of which Onthophagus (Colobonthophagus) dama (Fabricius, 1798) is represented by highest number of individuals. Their seasonal distribution is most during Premonsoon followed by Monsoon and Postmonsoon. Zoogeographically all the recorded species are of oriental distribution.

Keywords: buxa tiger reserve, diversity, India, new records, scarabaeinae, scarabaeidae

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Authors: Yogendra K. Karna, Lauren T. Bennett

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Fire-tolerant eucalypt forests are one of the major forest ecosystems of south-eastern Australia and thought to be highly resistant to frequent high severity wildfires. However, the impact of different severity wildfires on the canopy structure of fire-tolerant forest type is under-studied, and there are significant knowledge gaps in relation to the assessment of tree and stand level canopy structural dynamics and recovery after fire. Assessment of canopy structure is a complex task involving accurate measurements of the horizontal and vertical arrangement of the canopy in space and time. This study examined the utility of multitemporal, small-footprint lidar data to describe the changes in the horizontal and vertical canopy structure of fire-tolerant eucalypt forests seven years after wildfire of different severities from the tree to stand level. Extensive ground measurements were carried out in four severity classes to describe and validate canopy cover and height metrics as they change after wildfire. Several metrics such as crown height and width, crown base height and clumpiness of crown were assessed at tree and stand level using several individual tree top detection and measurement algorithm. Persistent effects of high severity fire 8 years after both on tree crowns and stand canopy were observed. High severity fire increased the crown depth but decreased the crown projective cover leading to more open canopy.

Keywords: canopy gaps, canopy structure, crown architecture, crown projective cover, multi-temporal lidar, wildfire severity

Procedia PDF Downloads 161

Authors: R. T. Aggangan

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Woodfuels continue to be a major component of the energy supply mix of the Philippines due to increasing demand for energy that are not adequately met by decreasing supply and increasing prices of fuel oil such as liquefied petroleum gas (LPG) and kerosene. The Development Academy of the Philippines projects the demand of woodfuels in 2016 as 28.3 million metric tons in the household sector and about 105.4 million metric tons combined supply potentials of both forest and non-forest lands. However, the Revised Master Plan for Forestry Development projects a demand of about 50 million cu meters of fuelwood in 2016 but the capability to supply from local sources is only about 28 million cu meters indicating a 44 % deficiency. Household demand constitutes 82% while industries demand is 18%. Domestic household demand for energy is for cooking needs while the industrial demand is for steam power generation, curing barns of tobacco: brick, ceramics and pot making; bakery; lime production; and small scale food processing. Factors that favour increased use of wood-based energy include the relatively low prices (increasing oil-based fuel prices), availability of efficient wood-based energy utilization technology, increasing supply, and increasing population that cannot afford conventional fuels. Moreover, innovations in combustion technology and cogeneration of heat and power from biomass for modern applications favour biomass energy development. This paper recommends policies and strategic directions for the development of the woodfuel industry with the twin goals of sustainably supplying the energy requirements of households and industry.

Keywords: biomass energy development, fuelwood, households and industry, innovations in combustion technology, supply and demand

Procedia PDF Downloads 317