Search results for: forest restoration

Authors: Goudjil kamel, Boukhatem Ghania, Jlailia Djihene

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This paper delves into the development of a sophisticated desktop application designed to calculate soil bearing capacity and predict limit pressure. Drawing from an extensive review of existing methodologies, the study meticulously examines various approaches employed in soil bearing capacity calculations, elucidating their theoretical foundations and practical applications. Furthermore, the study explores the burgeoning intersection of artificial intelligence (AI) and geotechnical engineering, underscoring the transformative potential of AI- driven solutions in enhancing predictive accuracy and efficiency.Central to the research is the utilization of cutting-edge machine learning techniques, including Artificial Neural Networks (ANN), XGBoost, and Random Forest, for predictive modeling. Through comprehensive experimentation and rigorous analysis, the efficacy and performance of each method are rigorously evaluated, with XGBoost emerging as the preeminent algorithm, showcasing superior predictive capabilities compared to its counterparts. The study culminates in a nuanced understanding of the intricate dynamics at play in geotechnical analysis, offering valuable insights into optimizing soil bearing capacity calculations and limit pressure predictions. By harnessing the power of advanced computational techniques and AI-driven algorithms, the paper presents a paradigm shift in the realm of geotechnical engineering, promising enhanced precision and reliability in civil engineering projects.

Keywords: limit pressure of soil, xgboost, random forest, bearing capacity

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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: Hernández Marieta, Aguayo Mauricio, Pedreros María, Llompart Ovidio

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The hydrological cycle is influenced by multiple factors, including climate change, land use changes, and anthropogenic activities, all of which threaten water availability and quality worldwide. In recent decades, numerous investigations have used landscape metrics and hydrological modeling to demonstrate the influence of landscape patterns on the hydrological cycle components' natural dynamics. Many of these investigations have determined the repercussions on the quality and availability of water, sedimentation, and erosion regime, mainly in Asian basins. In fact, there is progress in this branch of science, but there are still unanswered questions for our region. This study examines the hydrological response in Chilean basins under various land use change scenarios (LUCC) and the influence of climate change. The components of the water cycle were modeled using a physically distributed type hydrological and hydraulic simulation model based on and oriented to mountain basins TETIS model. Future climate data were derived from Chilean regional simulations using the WRF-MIROC5 model, forced with the RCP 8.5 scenario, at a 25 km resolution for the periods 2030-2060 and 2061-2091. LUCC scenarios were designed based on nature-based solutions, landscape pattern influences, current national and international water conservation legislation, and extreme scenarios of non-preservation and conservation of native forests. The scenarios that demonstrate greater water availability, even under climate change, are those promoting the restoration of native forests in over 30% of the basins, even alongside agricultural activities. Current legislation promoting the restoration of native forests only in riparian zones (30-60 m or 200 m in steeper areas) will not be resilient enough to address future water shortages. Evapotranspiration, direct runoff, and water availability at basin outlets showed the greatest variations due to LUCC. The relationship between hydrological modeling and landscape configuration is an effective tool for establishing future territorial planning that prioritizes water resource protection.

Keywords: TETIS, landscape pattern, hydrological process, water availability, Chilean basins

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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: D. Sergejevs, A. Tipainis, P. Gavrilovs

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Railway turnout frogs restored with MMA often have such defects as infusions, pores, a.o., which under the influence of dynamic forces cause premature destruction of the restored surfaces. To prolong the operational time of turnout frog, i.e. operational time of the restored surface, turnout frog was restored using FCAW and afterwards matallographic examination was performed. Experimental study revealed that railway turnout frog restored with FCAW had better quality than elements restored with MMA, furthermore it provided considerable time economy.

Keywords: elements of railway turnout, FCAW, metallographic examination, quality of build-up welding

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Authors: Shu-Yen Wang, Shyh-Huei Hwang

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Designated as an important architectural complex of settlement by the Ministry of Culture, Hua-Zhai Settlement located in Wang-An Township, Peng-Hu County, of Taiwan has been progressively restored year by year and is now at the revitalization and reutilization stage. Over the last 5 years, YunTech has participated in the restoration project while being in compliance with the Bureau of Cultural Heritage’s spirit of 'Living Heritage Conservation'. In this study, reflections have been made to evaluate the contemporariness of traditional settlement development from the aspects of revitalization and reutilization. On the one hand, the connection between settlers’ experiences and emotions have been clarified through the living nodes, collective memory, and social-cultural connotation. On the other hand, activity design has promoted the reconstruction of living nodes and facilitated the reconnection of collective memory, enabling us to explore the contemporariness of living nodes after the reconstruction. With the adoption of literature review, participant observation, and interview analysis methods, this study concludes the following results: 1) The node reconstruction brings back the memories and makes emotional connections: the spatial collective memory is composed of different components. During the reconstruction of node space, villagers participated not only in the narration of the history but also in the restoration of the space. This process enables villagers to bring back their memories and make emotional connections thereto. 2) Villagers’ understanding towards revitalization has been facilitated through node reconstruction: as a medium of this project, activity design has facilitated node reconstruction by offering villagers a natural environment to build up emotional connections to the settlement. This also enables us to better understand the meaning of settlement activation for the local community. 3) New connections are established in life between villagers and the university through the construction of living nodes: through the local implementation of node reconstruction, new connections have been established in life between villagers who participated in the project and the university. In the meantime, the university’s entrance to the community has also been revalued.

Keywords: collective memory, local sense of belonging, reconstruction of living nodes, the important architectural complex of Wang-An Hua-Zhai settlement

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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: Racha Ghariri, Khaldia Belkheir, Assil Ghariri

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In this paper, the researchers present a part of their research on the colors of the city of Bechar (Algeria). It is about a chromatic study of the ancient architecture of the Ksour. Being a subject of intervention, regarding their degradable state, the Ksour are the case of their study, especially that the subject of color does not occupy, virtually, the involved on these heritage sites. This research aims to put the basics for methods which allow to know what to preserve as a color and how to do so, especially during a restoration, and to understand the evolution of the chromatic state of the city.

Keywords: architecture/colours, chromatic identity, geography of colour, regional palette, chromatic architectural analysis

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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: M. Haruna, O. O. Onafowokan, G. Holt, K. Anderson, R. G. Middleton

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Background: Requirements for satisfactory outcomes following total hip arthroplasty (THA) include restoration of femoral offset, version, and leg length. Various techniques have been described for restoring these biomechanical parameters, with leg length restoration being the most predominantly described. We describe a “quartered head technique” (QHT) which uses a stepwise series of femoral head osteotomies to identify and preserve the centre of rotation of the femoral head during THA in order to ensure reconstruction of leg length, offset and stem version, such that hip biomechanics are restored as near to normal as possible. This study aims to identify whether using the QHT during hip arthroplasty effectively restores leg length and femoral offset to within acceptable parameters. Methods: A retrospective review of 206 hips was carried out, leaving 124 hips in the final analysis. Power analysis indicated a minimum of 37 patients required. All operations were performed using an anterolateral approach by a single surgeon. All femoral implants were cemented, collarless, polished double taper CPT® stems (Zimmer, Swindon, UK). Both cemented, and uncemented acetabular components were used (Zimmer, Swindon, UK). Leg length, version, and offset were assessed intra-operatively and reproduced using the QHT. Post-operative leg length and femoral offset were determined and compared with the contralateral native hip, and the difference was then calculated. For the determination of leg length discrepancy (LLD), we used the method described by Williamson & Reckling, which has been shown to be reproducible with a measurement error of ±1mm. As a reference, the inferior margin of the acetabular teardrop and the most prominent point of the lesser trochanter were used. A discrepancy of less than 6mm LLD was chosen as acceptable. All peri-operative radiographs were assessed by two independent observers. Results: The mean absolute post-operative difference in leg length from the contralateral leg was +3.58mm. 84% of patients (104/124) had LLD within ±6mm of the contralateral limb. The mean absolute post-operative difference in offset from contralateral leg was +3.88mm (range -15 to +9mm, median 3mm). 90% of patients (112/124) were within ±6mm offset of the contralateral limb. There was no statistical difference noted between observer measurements. Conclusion: The QHT provides a simple, inexpensive yet effective method of maintaining femoral leg length and offset during total hip arthroplasty. Combining this technique with pre-operative templating or other techniques described may enable surgeons to reduce even further the discrepancies between pre-operative state and post-operative outcome.

Keywords: leg length discrepancy, technical tip, total hip arthroplasty, operative technique

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Authors: Boris Melnikov, Dmitrii Chaikovskii, Elena Melnikova

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The traveling salesman problem (TSP) is a well-known optimization problem that seeks to find the shortest possible route that visits a set of points and returns to the starting point. In this paper, we apply some heuristics of the TSP for the task of restoring the DNA matrix. This restoration problem is often considered in biocybernetics. For it, we must recover the matrix of distances between DNA sequences if not all the elements of the matrix under consideration are known at the input. We consider the possibility of using this method in the testing of distance calculation algorithms between a pair of DNAs to restore the partially filled matrix.

Keywords: optimization problems, DNA matrix, partially filled matrix, traveling salesman problem, heuristic algorithms

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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: Lauren B. Birney

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The BOP-CCERS (Billion Oyster Project- Curriculum and Community Enterprise for Restoration Science) Near-Peer Mentoring Program provides the long-term (five-year) support network to motivate and guide students toward restoration science-based CTE pathways. Students are selected from middle schools with actively participating BOP-CCERS teachers. Teachers will nominate students from grades 6-8 to join cohorts of between 10 and 15 students each. Cohorts are comprised primarily of students from the same school in order to facilitate mentors' travel logistics as well as to sustain connections with students and their families. Each cohort is matched with an exceptional undergraduate or graduate student, either a BOP research associate or STEM mentor recruited from collaborating City University of New York (CUNY) partner programs. In rare cases, an exceptional high school junior or senior may be matched with a cohort in addition to a research associate or graduate student. In no case is a high school student or minor be placed individually with a cohort. Mentors meet with students at least once per month and provide at least one offsite field visit per month, either to a local STEM Hub or research lab. Keeping with its five-year trajectory, the near-peer mentoring program will seek to retain students in the same cohort with the same mentor for the full duration of middle school and for at least two additional years of high school. Upon reaching the final quarter of 8th grade, the mentor will develop a meeting plan for each individual mentee. The mentee and the mentor will be required to meet individually or in small groups once per month. Once per quarter, individual meetings will be substituted for full cohort professional outings. The mentor will organize the entire cohort on a field visit or educational workshop with a museum or aquarium partner. In addition to the mentor-mentee relationship, each participating student will also be asked to conduct and present his or her own BOP field research. This research is ideally carried out with the support of the students’ regular high school STEM subject teacher; however, in cases where the teacher or school does not permit independent study, the student will be asked to conduct the research on an extracurricular basis. Near-peer mentoring affects students’ social identities and helps them to connect to role models from similar groups, ultimately giving them a sense of belonging. Qualitative and quantitative analytics were performed throughout the study. Interviews and focus groups also ensued. Additionally, an external evaluator was utilized to ensure project efficacy, efficiency, and effectiveness throughout the entire project. The BOP-CCERS Near Peer Mentoring program is a peer support network in which high school students with interest or experience in BOP (Billion Oyster Project) topics and activities (such as classroom oyster tanks, STEM Hubs, or digital platform research) provide mentorship and support for middle school or high school freshmen mentees. Peer mentoring not only empowers those students being taught but also increases the content knowledge and engagement of mentors. This support provides the necessary resources, structure, and tools to assist students in finding success.

Keywords: STEM education, environmental science, citizen science, near peer mentoring

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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: Siavash Ostovar

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The Iranian national Law on the Ramsar Convention (officially known as the Convention of International Wetlands and Aquatic Birds' Habitat Wetlands) was approved by the Senate and became a law in 1974 after the ratification of the National Council. There are other national laws with the aim of preservation of environment in the country. However, Lake Urmia which is declared a wetland of international importance by the Ramsar Convention in 1971 and designated a UNESCO Biosphere Reserve in 1976 is now at the brink of total disappearance due mainly to the climate change, water mismanagement, dam construction, and agricultural deficiencies. Lake Urmia is located in the north western corner of Iran. It is the third largest salt water lake in the world and the largest lake in the Middle East. Locally, it is designated as a National Park. It is, indeed, a unique lake both nationally and internationally. This study investigated how effective the national legal regulation of the ecosystem of Lake Urmia is in Iran. To do so, the Iranian national laws as Enforcement of Ramsar Convention in the country including three nationally established laws of (i) Five sets of laws for the programme of economic, social and cultural development of Islamic Republic of Iran, (ii) The Iranian Penal Code, (iii) law of conservation, restoration and management of the country were investigated. Using black letter law methods, it was revealed that (i) regarding the national five sets of laws; the benchmark to force the implementation of the legislations and policies is not set clearly. In other words, there is no clear guarantee to enforce these legislations and policies at the time of deviation and violation; (ii) regarding the Penal Code, there is lack of determining the environmental crimes, determining appropriate penalties for the environmental crimes, implementing those penalties appropriately, monitoring and training programmes precisely; (iii) regarding the law of conservation, restoration and management, implementation of this regulation is adjourned to preparation, announcement and approval of several categories of enactments and guidelines. In fact, this study used a national environmental catastrophe caused by drying up of Lake Urmia as an excuse to direct the attention to the weaknesses of the existing national rules and regulations. Finally, as we all depend on the natural world for our survival, this study recommended further research on every environmental issue including the Lake Urmia.

Keywords: conservation, environmental law, Lake Urmia, national laws, Ramsar Convention, water management, wetlands

Procedia PDF Downloads 196

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: 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: 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

Procedia PDF Downloads 239

Authors: Viviane A. O. Silva, Angela M. Costa, Glaucia N. M. Hajj, Ana Preto, Aline Tansini, Martin Roffé, Peter Jordan, Rui M. Reis

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Autophagy is a recycling and degradative system suggested to be a major cell death pathway in cancer cells. Autophagy pathway is interconnected with the endocytosis pathways sharing the same ultimate lysosomal destination. Lysosomes are crucial regulators of cell homeostasis, responsible to downregulate receptor signalling and turnover. It seems highly likely that derailed endocytosis can make major contributions to several hallmarks of cancer. WNK2, a member of the WNK (with-no-lysine [K]) subfamily of protein kinases, had been found downregulated by its promoter hypermethylation, and has been proposed to act as a specific tumour-suppressor gene in brain tumors. Although some contradictory studies indicated WNK2 as an autophagy modulator, its role in cancer cell death is largely unknown. There is also growing evidence for additional roles of WNK kinases in vesicular traffic. Aim: To evaluate the role of WNK2 in autophagy and endocytosis on glioma context. Methods: Wild-type (wt) A172 cells (WNK2 promoter-methylated), and A172 transfected either with an empty vector (Ev) or with a WNK2 expression vector, were used to assess the cellular basal capacities to promote autophagy, through western blot and flow-cytometry analysis. Additionally, we evaluated the effect of WNK2 on general endocytosis trafficking routes by immunofluorescence. Results: The re-expression of ectopic WNK2 did not interfere with autophagy-related protein light chain 3 (LC3-II) expression levels as well as did not promote mTOR signaling pathway alteration when compared with Ev or wt A172 cells. However, the restoration of WNK2 resulted in a marked increase (8 to 92,4%) of Acidic Vesicular Organelles formation (AVOs). Moreover, our results also suggest that WNK2 cells promotes delay in uptake and internalization rate of cholera toxin B and transferrin ligands. Conclusions: The restoration of WNK2 interferes in vesicular traffic during endocytosis pathway and increase AVOs formation. This results also suggest the role of WNK2 in growth factor receptor turnover related to cell growth and homeostasis and associates one more time, WNK2 silencing contribution in genesis of gliomas.

Keywords: autophagy, endocytosis, glioma, WNK2

Procedia PDF Downloads 368