Search results for: forest change

Authors: Kaleem Ahmed, Jamal A. Khan

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

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

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Authors: T. Garba, Y. Y. Babanyara, M. Isah, A. K. Muktari, R. Y. Abdullahi

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The increased in demand of fuel wood for heating, cooking and sometimes bakery has continued to exert appreciable impact on natural vegetation. This study focus on the use of multi-temporal data from land sat TM of 1986, land sat EMT of 1999 and lands sat ETM of 2006 to investigate the changes of Natural Vegetation resulting from charcoal production activities. The three images were classified based on bare soil, built up areas, cultivated land, and natural vegetation, Rock out crop and water bodies. From the classified images Land sat TM of 1986 it shows natural vegetation of the study area to be 308,941.48 hectares equivalent to 50% of the area it then reduces to 278,061.21 which is 42.92% in 1999 it again depreciated to 199,647.81 in 2006 equivalent to 30.83% of the area. Consequently cultivated continue increasing from 259,346.80 hectares (42%) in 1986 to 312,966.27 hectares (48.3%) in 1999 and then to 341.719.92 hectares (52.78%). These show that within the span of 20 years (1986 to 2006) the natural vegetation is depreciated by 119,293.81 hectares. This implies that if the menace is not control the natural might likely be lost in another twenty years. This is because forest cleared for charcoal production is normally converted to farmland. The study therefore concluded that there is the need for alternatives source of domestic energy such as the use of biomass which can easily be accessible and affordable to people. In addition, the study recommended that there should be strong policies enforcement for the protection forest reserved.

Keywords: charcoal, classification, data, images, land use, natural vegetation

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Authors: Tesfay Gidey Bezabeh, Tânia Sofia Oliveira, Josep Crous-Duran, João H. N. Palma

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Ethiopia's economy depends strongly on Coffea arabica production. Coffee, like many other crops, is sensitive to climate change. An urgent development and application of strategies against the negative impacts of climate change on coffee production is important. Agroforestry-based system is one of the strategies that may ensure sustainable coffee production amidst the likelihood of future impacts of climate change. This system involves the combination of trees in buffer extremes, thereby modifying microclimate conditions. This paper assessed coffee production under 1) coffee monoculture and 2) coffee grown using an agroforestry system, under a) current climate and b) two different future climate change scenarios. The study focused on two representative coffee-growing regions of Ethiopia under different soil, climate, and elevation conditions. A process-based growth model (Yield-SAFE) was used to simulate coffee production for a time horizon of 40 years. Climate change scenarios considered were representative concentration pathways (RCP) 4.5 and 8.5. The results revealed that in monoculture systems, the current coffee yields are between 1200-1250 kg ha⁻¹ yr⁻¹, with an expected decrease between 4-38% and 20-60% in scenarios RCP 4.5 and 8.5, respectively. However, in agroforestry systems, the current yields are between 1600-2200 kg ha⁻¹ yr⁻¹; the decrease was lower, ranging between 4-13% and 16-25% in RCP 4.5 and 8.5 scenarios, respectively. From the results, it can be concluded that coffee production under agroforestry systems has a higher level of resilience when facing future climate change and reinforces the idea of using this type of management in the near future for adapting climate change's negative impacts on coffee production.

Keywords: Albizia gummifera, CORDEX, Ethiopia, HADCM3 model, process-based model

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Authors: Gamze Fahriye Pehlivan, Tulay Canitez

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The traditional Turkish houses becoming unusable are a result of the deterioration of the balanced interaction between users and house (human and house) continuing during the history. Especially depending upon the change in social structure, the houses becoming neglected do not meet the desires of the users and do not have the meaning but the shelter are becoming unusable and are being destroyed. A conservation policy should be developed and renovations should be made in order to pass the traditional houses carrying the quality of a cultural and historical document presenting the social structure, the lifestyle and the traditions of its own age to the next generations and to keep them alive.

Keywords: house, social structural change, social structural, traditional Turkish houses

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Authors: Bambang Suko Priyono, Tristiana Rijanti

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This study examines the influence of Psychological Capital Dimensions to Organizational Citizenship Behavior. There are four dimensions of Psychological Capital such as hope, optimism, resilience, and self-efficacy. It also tests the moderation effect of Resistance to Change in the relation between Psychological Capital’s dimensions and Organizational Citizenship Behavior, and the influence of Organizational Citizenship Behavior to employees’ performance. The data from the chosen 160 respondents from Public Service Institution is processed using multiple regression and interaction method. The study results in: 1) Hope positively significantly influences Organizational Citizenship Behavior, 2) Optimism positively significantly influences Organizational Citizenship Behavior, 3) Resilience positively significantly influences Organizational Citizenship Behavior, 4) Self-efficacy positively significantly influences Organizational Citizenship Behavior, 5) Resistance to change is moderating variable between hope and Organizational Citizenship Behavior, 6) Resistance to change is moderating variable between self-efficacy and Organizational Citizenship Behavior, 7) Organizational Citizenship Behavior positively significantly influences performance. On the contrary, resistance to change as a moderating variable is proven for hope and resilience.

Keywords: organizational citizenship behavior, performance, psychological capital’s dimensions, and resistance to change

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Authors: Hamid Goharnejad, Amir Hossein Eghbali

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Recent investigations have demonstrated the global sea level rise due to climate change impacts. In this study climate changes study the effects of increasing water level in the strait of Hormuz. The probable changes of sea level rise should be investigated to employ the adaption strategies. The climatic output data of a GCM (General Circulation Model) named CGCM3 under climate change scenario of A1b and A2 were used. Among different variables simulated by this model, those of maximum correlation with sea level changes in the study region and least redundancy among themselves were selected for sea level rise prediction by using stepwise regression. One models of Discrete Wavelet artificial Neural Network (DWNN) was developed to explore the relationship between climatic variables and sea level changes. In these models, wavelet was used to disaggregate the time series of input and output data into different components and then ANN was used to relate the disaggregated components of predictors and predictands to each other. The results showed in the Shahid Rajae Station for scenario A1B sea level rise is among 64 to 75 cm and for the A2 Scenario sea level rise is among 90 to 105 cm. Furthermore the result showed a significant increase of sea level at the study region under climate change impacts, which should be incorporated in coastal areas management.

Keywords: climate change scenarios, sea-level rise, strait of Hormuz, forecasting

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Yosiya Chanta, Jantrararuk Tovaranont

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

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

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Authors: Jongho Park, Taekyun Kim, Jinwoong Choi, Sungnam Hong, Sun-Kyu Park

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Recently, the climate change is the one of the main problems. This abnormal phenomenon is consisted of the scorching heat, heavy rain and snowfall, and cold wave that will be enlarged abnormal climate change repeatedly. Accordingly, the width of temperature change is increased more and more by abnormal climate, and it is the main factor of cracking in the reinforced concrete. The crack of the reinforced concrete will affect corrosion of steel re-bar which can decrease durability of the structure easily. Hence, the elimination of the durability weakening factor (steel re-bar) is needed. Textile which weaves the carbon, AR-glass and aramid fiber has been studied actively for exchanging the steel re-bar in the Europe for about 15 years because of its good durability. To apply textile as the concrete reinforcement, the bond strength between concrete and textile will be investigated closely. Therefore, in this paper, pull-out test was performed with change of development length of textile. Significant load and stress was increasing at D80. But, bond stress decreased by increasing development length.

Keywords: bond strength, climate change, pull-out test, substitution of reinforcement material, textile

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Authors: Zafer Yüce, Paşa Yayla, Alev Taşkın

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There are heaps of analytical methods to estimate the crack growth life of a component. Soft computing methods have an increasing trend in predicting fatigue life. Their ability to build complex relationships and capability to handle huge amounts of data are motivating researchers and industry professionals to employ them for challenging problems. This study focuses on soft computing methods, especially random forest regressors and artificial neural networks with hyperparameter optimization algorithms such as grid search and random grid search, to estimate the crack growth life of an aircraft wing splice joint under variable amplitude loading. TensorFlow and Scikit-learn libraries of Python are used to build the machine learning models for this study. The material considered in this work is 7050-T7451 aluminum, which is commonly preferred as a structural element in the aerospace industry, and regarding the crack type; corner crack is used. A finite element model is built for the joint to calculate fastener loads and stresses on the structure. Since finite element model results are validated with analytical calculations, findings of the finite element model are fed to AFGROW software to calculate analytical crack growth lives. Based on Fighter Aircraft Loading Standard for Fatigue (FALSTAFF), 90 unique fatigue loading spectra are developed for various load levels, and then, these spectrums are utilized as inputs to the artificial neural network and random forest regression models for predicting crack growth life. Finally, the crack growth life predictions of the machine learning models are compared with analytical calculations. According to the findings, a good correlation is observed between analytical and predicted crack growth lives.

Keywords: aircraft, fatigue, joint, life, optimization, prediction.

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Authors: Jagannath Singh, Durga Prasad Mohapatra

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Software refactoring is very essential for maintaining the software quality. It is an usual practice that we first design the software and then go for coding. But after coding is completed, if the requirement changes slightly or our expected output is not achieved, then we change the codes. For each small code change, we cannot change the design. In course of time, due to these small changes made to the code, the software design decays. Software refactoring is used to restructure the code in order to improve the design and quality of the software. In this paper, we propose an approach for performing code refactoring. We use slice-based cohesion metrics to identify the target methods which requires refactoring. After identifying the target methods, we use program slicing to divide the target method into two parts. Finally, we have used the concepts of Aspects to adjust the code structure so that the external behaviour of the original module does not change.

Keywords: software refactoring, program slicing, AOP, cohesion metrics, code restructure, AspectJ

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Authors: Soheila Sadeghi

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In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

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Authors: Ana Cristina A. P. Carvalho, Solange Teles Da Silva

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In April, 174 countries signed the Paris Agreement, a multilateral agreement on climate change which deals with greenhouse gas emissions, mitigation, adaptation, finance, access to information, transparency, among other subjects related to the environment. Since then, Parties shall cooperate in taking measures, as appropriate, to enhance climate change education, training, public awareness, public participation and public access to information, recognizing the importance of these steps with respect to enhancing actions under this Agreement. This paper aims to analyze the consequences of this new rule in terms of the implementation of the Agreement, collecting data from Brazilian and Canadian legislations in order to identify if these countries have rules complying with the Treaty, the steps that have been already taken and if they could be used as examples for other countries. The analysis will take into consideration the different kinds of climate change information, means of transparency, reliability of the data and how to spread the information. The methodology comprehends a comparative legal research based on both the Paris Agreement and domestic laws of Brazil and Canada, as well as on doctrine and Court decisions. The findings can contribute to the implementation of the Paris Agreement through compliance with this Treaty at countries’ domestic and policy level.

Keywords: climate change information, domestic legislation, Paris Agreement, public policy

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Authors: Muhammad Rasyid Angkotasan

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The soil is a dense granules and arrangement of the pores that are related to each other, so that the water can flow from one point which has higher energy to a point that has lower energy. The flow of water through the pores of the porous ground is urgently needed in water seepage estimates in ground water pumping problems, investigate for underground construction, as well as analyzing the stability of the construction of Weirs. Climate change resulted in long-term changes in the distribution of weather patterns are statistically throughout the period start time of decades to millions of years. In other words, changes in the average weather circumstances or a change in the distribution of weather events, on average, for example, the number of extreme weather events that increasingly a lot or a little. Climate change is limited to a particular regional or can occur in all regions of the Earth. Geographical location between two continents and two oceans and is located around the equator is klimatologis factor is the cause of flooding and drought in Indonesia. This caused Indonesia' geographical position is on a hemisphere with a tropical monsoon climate is very sensitive to climatic anomaly El Nino Southern Oscillation (ENSO). ENSO causes drought occurrence in sea surface temperature conditions in the Pacific Equator warms up to the middle part of the East (El Nino). Based on the analysis of the climate of the last 30 years show that there is a tendency, the formation of a new pattern of climate causes the onset of climate change. The impact of climate change on the occurrence of the agricultural sector is the bergesernya beginning of the dry season which led to the above-mentioned pattern planting due to drought. The impact of climate change (drought) which is very extreme in Indonesia affect the shrinkage dimensions grain land and reduced the value of a percentage of the soil Plasticity Index caused by climate change.

Keywords: climate change, soil shrinkage, plasticity index, shrinking dimensions

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Authors: Mohamed Haffar, Khalil Al-Hyari, Mohammed Khair Abu Zaid, Ramadane Djbarni, Mohammed Hamdan

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While employee readiness for change (ERFC) is recognised as critical for total quality management (TQM) implementation, there is a lack of systematic and empirical studies regarding the relationship between ERFC dimensions and TQM. Therefore, this study proposes to fill this gap by providing empirical evidence leading to advancement in the understanding of the influences of ERFC components on TQM implementation. The empirical data for this study was drawn from a survey of 400 middle and senior managers of Jordanian firms. The analysis of the collected data, which was conducted using Structural Equation Modeling technique, revealed that three of the ERFC components, namely personally beneficial, change self-efficacy and management support are the most supportive ERFC dimensions for TQM implementation. Therefore, this paper makes a novel contribution by providing a refined and deeper comprehension of the relationships between ERFCs and TQM implementation.

Keywords: total quality management, employee readiness for change, manufacturing organisations, Jordan

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Authors: U. Datta

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The main objective of this study is to find a suitable approach to monitor the land infrastructure growth over a period of time using multispectral satellite images. Bi-temporal change detection method is unable to indicate the continuous change occurring over a long period of time. To achieve this objective, the approach used here estimates a statistical model from series of multispectral image data over a long period of time, assuming there is no considerable change during that time period and then compare it with the multispectral image data obtained at a later time. The change is estimated pixel-wise. Statistical composite hypothesis technique is used for estimating pixel based change detection in a defined region. The generalized likelihood ratio test (GLRT) is used to detect the changed pixel from probabilistic estimated model of the corresponding pixel. The changed pixel is detected assuming that the images have been co-registered prior to estimation. To minimize error due to co-registration, 8-neighborhood pixels around the pixel under test are also considered. The multispectral images from Sentinel-2 and Landsat-8 from 2015 to 2018 are used for this purpose. There are different challenges in this method. First and foremost challenge is to get quite a large number of datasets for multivariate distribution modelling. A large number of images are always discarded due to cloud coverage. Due to imperfect modelling there will be high probability of false alarm. Overall conclusion that can be drawn from this work is that the probabilistic method described in this paper has given some promising results, which need to be pursued further.

Keywords: co-registration, GLRT, infrastructure growth, multispectral, multitemporal, pixel-based change detection

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Authors: Chia-Ling Chang, Hao-Bo Chang

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Global climate change alters rainfall characteristics, while the variation of these characteristics further influences environmental conditions, such as hydrologic responses, landslide areas, and the amounts of diffuse pollution. The variations of environmental conditions may impact the stability of water resource systems. The objective of this study is to assess the present conditions of major water resource systems in Taiwan. The impact of climate change on each system is also discussed herein. Compared to the water resource systems in northern Taiwan, the ratio of the precipitation during the rainy season to that during the dry season has a larger increase in southern Taiwan. This variation of hydrologic condition impacts the stability of water resource systems and increases the risk of normal water supply. The findings in this work can be important references for water resource management.

Keywords: basin management, climate change, water resource system, water resource management

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Authors: Mahmoud Mohamed Emam, Yasser Fathy Hendawy Al-Mahdy

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Higher education institutions (HEIs) in Arab countries have witnessed large scope transformations as a result of embracing globalised practices. The introduction of program academic accreditation in HEIs in the Arab context has been regarded as a change enterprise that has proponents and opponents. In essence, introducing new systems or practices trigger changes that may shatter employees at a given organization. Therefore, it is argued that the interaction between organizational, contextual, and individual-related variables are likely to determine how the organization succeeds in facing resistance to change. This study investigated a mediated-effects model of organizational support and citizenship behavior. The model proposes organizational support as an antecedent of citizenship behavior and commitment to change as a mediator in the organizational support–citizenship behavior relationship. Survey data were collected and analyzed from university faculty (n=221) using structural equation modeling. Findings showed that organizational support significantly contributes to increasedcitizenshipbehaviour and the commitment of university faculty to program accreditation as a change enterprise, which has a significant and direct impact on their citizenship behaviour. We conclude that university-level organizational support shapes faculty’s commitment to change both directly and indirectly. The findings have significant practical implications for HEIs in Arab countries when they introduce new practices that aim at improving institutional effectiveness.

Keywords: organizational support, accreditation, commitment, citizenship behaviour

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Authors: Sakshi Yadav, Sushila Shekhawat

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

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

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Authors: Nothando Gwazani, K. R. Marembo

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An increase in the atmospheric concentration of carbon dioxide (CO₂) from fossil fuel and land use change necessitates identification of strategies for mitigating threats associated with global warming. Oceans are insufficient to offset the accelerating rate of carbon emission. However, the challenges of oceans as a source of reducing carbon footprint can be effectively overcome by the storage of carbon in terrestrial carbon sinks. The gases with special optical properties that are responsible for climate warming include carbon dioxide (CO₂), water vapors, methane (CH₄), nitrous oxide (N₂O), nitrogen oxides (NOₓ), stratospheric ozone (O₃), carbon monoxide (CO) and chlorofluorocarbons (CFC’s). Amongst these, CO₂ plays a crucial role as it contributes to 50% of the total greenhouse effect and has been linked to climate change. Because plants act as carbon sinks, interest in terrestrial carbon sequestration has increased in an effort to explore opportunities for climate change mitigation. Removal of carbon from the atmosphere is a topical issue that addresses one important aspect of an overall strategy for carbon management namely to help mitigate the increasing emissions of CO₂. Thus, terrestrial ecosystems have gained importance for their potential to sequester carbon and reduce carbon sink in oceans, which have a substantial impact on the ocean species. Field data and electromagnetic spectrum bands were analyzed using ArcGIS 10.2, QGIS 2.8 and ERDAS IMAGINE 2015 to examine the vegetation distribution. Satellite remote sensing data coupled with Normalized Difference Vegetation Index (NDVI) was employed to assess future potential changes in vegetation distributions in Eastern Cape Province of South Africa. The observed 5-year interval analysis examines the amount of carbon absorbed using vegetation distribution. In 2015, the numerical results showed low vegetation distribution, therefore increased the acidity of the oceans and gravely affected fish species and corals. The outcomes suggest that the study area could be effectively utilized for carbon sequestration so as to mitigate ocean acidification. The vegetation changes measured through this investigation suggest an environmental shift and reduced vegetation carbon sink, and that threatens biodiversity and ecosystem. In order to sustain the amount of carbon in the terrestrial ecosystems, the identified ecological factors should be enhanced through the application of good land and forest management practices. This will increase the carbon stock of terrestrial ecosystems thereby reducing direct loss to the atmosphere.

Keywords: remote sensing, vegetation dynamics, carbon sequestration, terrestrial carbon sink

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Authors: Isa Muhammad Zumo

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

Keywords: monitoring, green wall, Landsat 8, Nigeria

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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

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

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Authors: Sarra Khelifi, Youcef Guerabli, Ahcene Boumaiza

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Advances in techniques for measuring individual crystallographic orientations have made it possible to investigate the role of local crystallography during the plastic deformation of materials. In this study, the change in crystallographic orientation distribution during deformation by deep drawing in carbon steel has been investigated in order to understand their role in propagation and arrest of crack. The results show that the change of grain orientation from initial recrystallization texture components of {111}<112> to deformation orientation {111}<110> incites the initiation and propagation of cracks in the region of {111}<112> small grains. Moreover, the misorientation profile and local orientation are analyzed in detail to discuss the change from {111}<112> to {111}<110>. The deformation of the grain with {111}<110> orientation is discussed in terms of stops of the crack in carbon steel during drawing. The SEM-EBSD technique was used to reveal the change of orientation; XRD was performed for the characterization of the global evolution of texture for deformed samples.

Keywords: fracture, heterogeneity, misorientation profile, stored energy

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Authors: L. Miranda

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The requirements of the engineering education are nowadays very broad and demand a set of skills which demands not only technical knowledge but also the ability to lead and innovate and personal and interpersonal skills. A framework for the assessment of a potential curricular change is necessary to guide the analysis of the program with respect to the stakeholders and the legislation of the country, in order to develop appropriate learning outcomes. A Conceive-Design-Implement-Operate (CDIO) approach was chosen for an evaluation conducted in a mechanical engineering degree in Brazil. The work consisted in the application of a survey with students and professors and a literature review of the legislation and studies that raised the required competences and skills for the modern engineer. The results show a great potential for a CDIO set of skills in engineering degrees in Brazil and reveal the frequent demands of stakeholders before a curricular change.

Keywords: curriculum change, conceive design implement operate, accreditation, personal and interpersonal skills

Procedia PDF Downloads 344

Authors: Vathanachannbo Veth, Ilan Ich, Sophea Rom Phy, Ty Sok, Layheang Song, Sophal Try, Chantha Oeurng

Abstract:

Climate change is one of the major global challenges inducing disaster risks and threatening livelihoods and communities through adverse impacts on food and water security, ecosystems, and services. Prek Thnot River Basin of Cambodia is one of the largest tributaries in the Lower Mekong that has been exposed to hazards and disasters, particularly floods and is said to be the effect of climate change. Therefore, the assessment of precipitation and streamflow changes under the effect of climate change was proposed in this river basin using Soil Water Assessment Tool (SWAT) model and different flow indices under baseline (1997 to 2011) and climate change scenarios (RCP2.6 and RCP8.5 with three General Circulation Models (GCMs): GFDL, GISS, and IPSL) in two time-horizons: near future (the 2030s: 2021 to 2040) and medium future (2060s: 2051 to 2070). Both intensity and frequency indices compared with the historical extreme rainfall indices significantly change in the GFDL under the RCP8.5 for both 2030s and 2060s. The average rate change of Rx1day, Rx10day, SDII, and R20mm in the 2030s and 2060s of both RCP2.6 and RCP8.5 was found to increase in GFDL and decrease in both GISS and IPSL. The mean percentage change of the flow analyzed in the IHA tool (Group1) indicated that the flow in the Prek Thnot River increased in GFDL for both RCP2.6 and RCP8.5 in both 2030s and 2060s, oppositely in GISS, the flow decreases. Moreover, the IPSL affected the flow by increasing in five months (January, February, October, November, and December), and in the other seven months, the flow decreased accordingly. This study provides water resources managers and policymakers with a wide range of precipitation and water flow projections within the Prek Thnot River Basin in the context of plausible climate change scenarios.

Keywords: IHA, climate change, disaster risk, Prek Thnot River Basin, Cambodia

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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

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

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Authors: T. Davitashvili

Abstract:

At present impact of global climate change on the territory of Georgia is evident at least on the background of the Caucasus glaciers melting which during the last century have decreased to half their size. Glaciers are early indicators of ongoing global and regional climate change. Knowledge of the Caucasus glaciers fluctuation (melting) is an extremely necessary tool for planning hydro-electric stations and water reservoir, for development tourism and agriculture, for provision of population with drinking water and for prediction of water supplies in more arid regions of Georgia. Otherwise, the activity of anthropogenic factors has resulted in decreasing of the mowing, arable, unused lands, water resources, shrubs and forests, owing to increasing the production and building. Transformation of one type structural unit into another one has resulted in local climate change and its directly or indirectly impacts on different components of water resources on the territory of Georgia. In the present paper, some hydrological specifications of Georgian water resources and its potential pollutants on the background of regional climate change are presented. Some results of Georgian’s glaciers pollution and its melting process are given. The possibility of surface and subsurface water pollution owing to accidents at oil pipelines or railway routes are discussed. The specific properties of regional climate warming process in the eastern Georgia are studied by statistical methods. The effect of the eastern Georgian climate change upon water resources is investigated.

Keywords: climate, droughts, pollution, water resources

Procedia PDF Downloads 464

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

Abstract:

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

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

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Authors: Aman Guru, Chiron Singhi

Abstract:

The Earth’s climate is being changed at an unrivalled rate since beginning of the evolution of the Earth, 4–5 billion years back, but presently it gained pace due to unintentional anthropogenic disturbances and also increased global warming since the mid-20th century, and these incessant changes in the climatic pattern may bring unpropitious effect on global health and security. Today, however, it is not only the air, or water that are polluted, but the whole atmosphere is prone to pollution and this resulted in other cascading ramification in the form of change in the pattern of rainfall, melting of ice, the rise in the sea level etc. Human activities like production, transport, burning of fuels are adding umpteen dangerous pollutants to the atmosphere which in turn gives rise to global warming. Agriculture plays an imperative part in India's economy. Agriculture, along with fisheries and forestry, is one of the largest contributors to the Gross Domestic Product in India. Research on the effect of climate change and vulnerability of agriculture is a high need in India. A steady increase of CO2 is a primary cause of climate change and global warming and which in turn have a great impact on Indian agriculture. The research focuses on the effect of climate change on Indian agriculture and the proceedings and legal control of legislative measures on such issues and the ways to implement such laws which can help to provide a solution to these problems which can prove beneficial to Indian farmers and their agricultural produce.

Keywords: agriculture, climate change, global warming, India laws, legislative measures

Procedia PDF Downloads 293

Authors: Mehtab Alam

Abstract:

Sustainable tourism practices require to improve the decreased performance levels in phases of change management for image branding. This paper addresses the innovative approach of using sustainable tourism for image branding as a contemporary tool of foreign policy. The sustainable tourism-based foreign policy promotes cultural values, green tourism, economy, and image management for the avoidance of rising global conflict. The mixed-method approach (quantitative 382 surveys, qualitative 11 interviews at saturation point) implied for the data analysis. The research finding provides the potential of using sustainable tourism by implying skills and knowledge, capacity, and personal factors of change management in improving tourism-based performance levels. It includes the valuable tourism performance role for the success of a foreign policy through sustainable tourism. Change management in tourism-based foreign policy provides the destination readiness for international engagement and curbing of climate issues through green tourism. The research recommends the impact of change management in improving the tourism-based performance levels of image branding for a coercive foreign policy. The paper’s future direction for the immediate implementation of tourism-based foreign policy is to overcome the contemporary issues of travel marketing management, green infrastructure, and cross-border regulation.

Keywords: decrease performance levels, change management, sustainable tourism, image branding, foreign policy

Procedia PDF Downloads 105