Search results for: random forest classifier

Authors: Jerome Osentowski

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The objective at Central Rocky Mountain Permaculture Institute has been to put in practice a sustainable way of life while growing food, medicine, and providing education. This has been done by applying methods of farming such as agroforestry, forest farming, and perennial polycultures. These methods have been found to be regenerative to the environment through carbon sequestration, soil-building, climate change mitigation, and the provision of food security. After 30 years of implementing carbon farming methods, the results are agro-diversity, self-sustaining systems, and a consistent provision of food and medicine. These results are exhibited through polyculture plantings in an outdoor forest garden spanning roughly an acre containing about 200 varieties of fruits, nuts, nitrogen-fixing trees, and medicinal herbs, and two indoor forest garden greenhouses (one Mediterranean and one Tropical) containing about 50 varieties of tropical fruits, beans, herbaceous plants and more. While the climate zone outside the greenhouse is 6, the tropical forest garden greenhouse retains an indoor climate zone of 11 with near-net-zero energy consumption through the use of a climate battery, allowing the greenhouse to serve as a year-round food producer. The effort to source food from the forest gardens is minimal compared to annual crop production. The findings at Central Rocky Mountain Permaculture Institute conclude that agroecological methods are not only beneficial but necessary in order to revive and regenerate the environment and food security.

Keywords: agroecology, agroforestry, carbon farming, carbon sequestration, climate battery, food security, forest farming, forest garden, greenhouse, near-net-zero, perennial polycultures

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Authors: Yurike, Yonariza, Rudi Febriamansyah, Syafruddin Karimi

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Forest is a natural resource that has an important function as a supporting element of human life. Forest degradation enormous impact on global warming is a reality we have experienced together, that disruption of ecosystems, extreme weather conditions, disruption of water management system watersheds and the threat of natural disasters as floods, landslides and droughts, even disruption food security. Dharmasraya is a district in the province of West Sumatra, which has an area of 92.150 ha of forest, which is largely a former production forest concessions (Forest Management Rights) which is supposed to be a secondary forest. This study answers about the impact of land use in the former concession area Dharmasraya on the environment. The methodology used is the household survey, key informants, and satellite data / GIS. From the results of the study, the former concession area in Dharmasraya experienced a reduction of forest cover over time significantly. Forest concessions should be secondary forests in Dharmasraya, now turned conversion to oil palm plantations. Population pressures and growing economic pressures, resulting in more intensive harvesting. As a result of these forest disturbances caused changes in forest functions. These changes put more emphasis towards economic function by ignoring social functions or ecological function. Society prefers to maximize their benefits today and pay less attention to the protection of natural resources. This causes global warming is increasing and this is not only felt by people around Dharmasraya but also the world. Land clearing by the community through a process in slash and burn. This fire was observed by NOAA satellites and recorded by the Forest Service of West Sumatra. This demonstrates the ability of trees felled trees to absorb carbon dioxide (CO2) to be lost, even with forest fires accounted for carbon dioxide emitted into the air, and this has an impact on global warming. In addition to the change of control of land into oil palm plantations water service has been poor, people began to trouble the water and oil palm plantations are located in the watershed caused the river dried up. Through the findings of this study is expected to contribute ideas to the policy makers to pay more attention to the former concession forest management as the prevention or reduction of global warming.

Keywords: climate change, community, concession forests, environment

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Authors: Raymond Feng, Shadi Ghiasi

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An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy.

Keywords: EEG, electroencephalogram, machine learning, mood, music enjoyment, physiological signals

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Authors: Thierry Ganomanana, Dominique Hervé, Solo Randriamahaleo

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Management of Malagasy renewable natural re-sources allows, in the case of forest, the mobilization of several actors with norms and/or territory. The interaction in this socio-ecosystem is represented by a graph of two different relationships in which most of action chains, from individual activities under the continuous of forest dynamic and discrete interventions by institutional, are also studied. The fuzzy set theory is adapted to graduate the elements of the set Illegal Activities in the space of sanction’s institution by his severity and in the space of degradation of forest by his extent.

Keywords: fuzzy set, graph, institution, renewable resource, system

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Authors: Karima Kimouche

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In this paper, we consider the asymptotic problems in spectral analysis of stationary causal random fields. We impose conditions only involving (conditional) moments, which are easily verifiable for a variety of nonlinear random fields. Limiting distributions of periodograms and smoothed periodogram spectral density estimates are obtained and applications to the spectral domain bootstrap are given.

Keywords: spatial nonlinear processes, spectral estimators, GMC condition, bootstrap method

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Authors: Dionisios Panagiotaras, Ioannis P. Kokkoris, Dionysios Koulougliotis, Dimitra Lekka, Alexandra Skalioti

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In the summer of 2021, Greece was affected by devastating forest fires in various regions of the country, resulting in human losses, destruction or degradation of the natural environment, infrastructure, livestock and cultivations. The present study concerns a pilot assessment of natural vegetation regeneration in the second, in terms of area, fire-affected region for 2021, at Ancient Olympia area, located in West Peloponnisos (Ilia Prefecture), Greece. A standardised field sampling protocol for assessing natural regeneration was implemented at selected sites where the forest fire had occurred previously (in 2007), and the vegetation (Pinus halepensis forest) had regenerated naturally. The results of the study indicate the loss of the established natural regeneration of Pinus halepensis forest, as well as of the tree-layer in total. Post-fire succession species are recorded to the shrub and the herb layer, with a varying cover. Present findings correspond to the results of field work and analysis one year after the fire, which will form the basis for further research and conclusions on taking action for restoration schemes in areas that have been affected by fire more than once within a 20-year period.

Keywords: forest, pinus halepensis, ancient olympia, post fire vegetation

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Authors: Carmen Avilés Palacios, Camilo Muñoz Arenas, Joaquín García Alfonso, Jesús González Arteaga, Alberto Alcalde Calonge

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There is a need for review of the consumption and production models of industrialized states in accordance with the Paris Agreement and the Sustainable Development Goals (1) (OECD, 2016). This constitutes the basis of the bioeconomy (2) that is focused on striking a balance between economic development, social development and environmental protection. Bioeconomy promotes the adequate use and consumption of renewable natural resources (3) and involves developing new products and services adapted to the principles of circular economy: more sustainable (reusable, biodegradable, renewable and recyclable) and with a lower carbon footprint (4). In this context, Urban Forest Innovation Lab (UFIL) grows, an Urban Laboratory for experimentation focused on promoting entrepreneurship in forest bioeconomy (www.uiacuenca.es). UFIL generates local wellness taking sustainable advantage of an endogenous asset, forests. UFIL boosts forest bioeconomy opening its doors of knowledge to pioneers in this field, giving the opportunity to be an active part of a change in the way of understanding the exploitation of natural resources, discovering business, learning strategies and techniques and incubating business ideas So far now, 100 entrepreneurs are incubating their nearly 30 new business plans. UFIL has promoted an ecosystem to connect the rural-urban world that promotes sustainable rural development around the forest.

Keywords: bioeconomy, forestry, innovation, entrepreneurship

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Authors: Ambali Alade Odebowale, Andargachew Mekonnen Berhe, Haroldo T. Hattori, Andrey E. Miroshnichenko

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Solar thermophotovoltaic systems (STPV) have emerged as a promising solution to overcome the Shockley-Queisser limit, a significant impediment in the direct conversion of solar radiation into electricity using conventional solar cells. The STPV system comprises essential components such as an optical concentrator, selective emitter, and a thermophotovoltaic (TPV) cell. The pivotal element in achieving high efficiency in an STPV system lies in the design of a spectrally selective emitter or absorber. Traditional methods for designing and optimizing selective emitters are often time-consuming and may not yield highly selective emitters, posing a challenge to the overall system performance. In recent years, the application of machine learning techniques in various scientific disciplines has demonstrated significant advantages. This paper proposes a novel nanostructure composed of four-layered materials (SiC/W/SiO2/W) to function as a selective emitter in the energy conversion process of an STPV system. Unlike conventional approaches widely adopted by researchers, this study employs a machine learning-based approach for the design and optimization of the selective emitter. Specifically, a random forest algorithm (RFA) is employed for the design of the selective emitter, while the optimization process is executed using genetic algorithms. This innovative methodology holds promise in addressing the challenges posed by traditional methods, offering a more efficient and streamlined approach to selective emitter design. The utilization of a machine learning approach brings several advantages to the design and optimization of a selective emitter within the STPV system. Machine learning algorithms, such as the random forest algorithm, have the capability to analyze complex datasets and identify intricate patterns that may not be apparent through traditional methods. This allows for a more comprehensive exploration of the design space, potentially leading to highly efficient emitter configurations. Moreover, the application of genetic algorithms in the optimization process enhances the adaptability and efficiency of the overall system. Genetic algorithms mimic the principles of natural selection, enabling the exploration of a diverse range of emitter configurations and facilitating the identification of optimal solutions. This not only accelerates the design and optimization process but also increases the likelihood of discovering configurations that exhibit superior performance compared to traditional methods. In conclusion, the integration of machine learning techniques in the design and optimization of a selective emitter for solar thermophotovoltaic systems represents a groundbreaking approach. This innovative methodology not only addresses the limitations of traditional methods but also holds the potential to significantly improve the overall performance of STPV systems, paving the way for enhanced solar energy conversion efficiency.

Keywords: emitter, genetic algorithm, radiation, random forest, thermophotovoltaic

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Authors: Samipraj Mishra

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Despite the successful implementation of community forestry program, a number of pros and cons have been raised on Terai community forestry in the case of lowland locally called Terai region of Nepal, which is climatically belongs to tropical humid and possessed high quality forests in terms of ecology and economy. The study aims to investigate the local pricing strategy of forest products and its impacts on equitable forest benefit sharing, collection of community fund and carrying out livelihood improvement activities. The study was carried out on six community forests revealed that local people have substantially benefited from the community forests. However, being the region is heterogeneous by socio-economic conditions and forest resources have higher economical potential, the decision of low pricing strategy made by the local people have created inequality problems while sharing the forest benefits, and poorly contributed to community fund collection and consequently carrying out limited activities of livelihood improvement. The paper argued that the decision of low pricing strategy of forest products is counter-productive to promote the equitable benefit sharing in the areas of heterogeneous socio-economic conditions with high value forests. The low pricing strategy has been increasing accessibility of better off households at higher rate than poor; as such households always have higher affording capacity. It is also defective to increase the community fund and carry out activities of livelihood improvement effectively. The study concluded that unilateral decentralized forest policy and decision-making autonomy to the local people seems questionable unless their decision-making capacities are enriched sufficiently. Therefore, it is recommended that empowerment of decision-making capacity of local people and their respective institutions together with policy and program formulation are prerequisite for efficient and equitable community forest management and its long-term sustainability.

Keywords: community forest, livelihood, socio-economy, pricing system, Nepal

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Authors: Meilisha Putri Pertiwi, Mufti Petala Patria

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Mangrove forest often categorized as a productive ecosystem in trophic water and the highest carbon storage among all the forest types. Mangrove-derived organic matter determines the food web of fish and invertebrates. In Indonesia trophic water ecosystem, 80% commersial fish caught in coastal area are high related to food web in mangrove forest ecosystem. Based on the previous research in Panjang Island, Bojonegara, Banten, Indonesia, removed mangrove litterfall to the sea water were 9,023 g/m³/s for two stations (west station–5,169 g/m³/s and north station-3,854 g/m³/s). The vegetation were dominated from Rhizophora apiculata and Rhizopora stylosa. C element is the highest content (27,303% and 30,373%) than N element (0,427% and 0,35%) and P element (0,19% and 0,143%). The aim of research also to know the diversity of fish inhabit in mangrove forest. Fish sampling is by push net. Fish caught are collected into plastics, total length measured, weigh measured, and individual and total counted. Meanwhile, the 3 modified pipes (1 m long, 5 inches diameter, and a closed one hole part facing the river by using a nylon cloth) set in the water channel connecting mangrove forest and sea water for each stasiun. They placed for 1 hour at low tide. Then calculate the speed of water flow and volume of modified pipes. The fish and mangrove litter will be weigh for wet weight, dry weight, and analyze the C, N, and P element content. The sampling data will be conduct 3 times of month in full moon. The salinity, temperature, turbidity, pH, DO, and the sediment of mangrove forest will be measure too. This research will give information about the fish diversity in mangrove forest, the removed mangrove litterfall to the sea water, the composition of sediment, the total element content (C, N, P) of fish and mangrove litter, and the correlation of element content absorption between fish and mangrove litter. The data will be use for the fish and mangrove ecosystem conservation.

Keywords: fish diversity, mangrove forest, mangrove litter, carbon element, nitrogen element, P element, conservation

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Authors: Mehrnoosh Omati, Mahmod Reza Sahebi

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The information on land use/land cover changing plays an essential role for environmental assessment, planning and management in regional development. Remotely sensed imagery is widely used for providing information in many change detection applications. Polarimetric Synthetic aperture radar (PolSAR) image, with the discrimination capability between different scattering mechanisms, is a powerful tool for environmental monitoring applications. This paper proposes a new boundary-based segmentation algorithm as a fundamental step for land cover change detection. In this method, first, two PolSAR images are segmented using integration of marker-controlled watershed algorithm and coupled Markov random field (MRF). Then, object-based classification is performed to determine changed/no changed image objects. Compared with pixel-based support vector machine (SVM) classifier, this novel segmentation algorithm significantly reduces the speckle effect in PolSAR images and improves the accuracy of binary classification in object-based level. The experimental results on Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric images show a 3% and 6% improvement in overall accuracy and kappa coefficient, respectively. Also, the proposed method can correctly distinguish homogeneous image parcels.

Keywords: coupled Markov random field (MRF), environment, object-based analysis, polarimetric SAR (PolSAR) images

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Authors: Isaac Kwaku Adu, Jacob Doku Tetteh, John Joseph Puthenkalam, Kwabena Effah Antwi

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The continuous increase in population implies increase in food demand. There is, therefore, the need to increase agricultural production and other forest products to ensure food security and economic development. This paper employs the three-level intensity analysis to assess the total change of land-use in two-time intervals (1986-2002 and 2002-2017), the net change and swap as well as gross gains and losses in the two intervals. The results revealed that the overall change in the 31-year period was greater in the second period (2002-2017). Agriculture and forest categories lost in the first period while the other land class gained. However, in the second period agriculture and built-up increased greatly while forest, water bodies and thick bushes/shrubland experienced loss. An assessment revealed a reduction of forest in both periods but was greater in the second period and expansion of agricultural land was recorded as population increases. The pixels gaining built-up targeted agricultural land in both intervals, it also targeted thick bushes/shrubland and waterbody in the second period only. Built-up avoided forest in both intervals as well as waterbody and thick bushes/shrubland. To help in developing the best land-use strategies/policies, a further validation of the social factors is necessary.

Keywords: agricultural land, forest, Ghana, land-use, intensity analysis, remote sensing

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Authors: Maher J. Tadros, Yaseen Ananbeh

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The study area is located in Ajluon Forest Reserve northern part of Jordan. It consists of Mediterranean hills dominated by open woodlands of oak and pistachio. The aims of the study were to investigate the positive and negative relationships between the locals and the protected area and how it can affect the long-term forest conservation. The main research objectives are to review the impact of establishing Ajloun Forest Reserve on nature conservation and on the livelihood level of local communities around the reserve. The Ajloun forest reserve plays a fundamental role in Ajloun area development. The existence of initiatives of nature conservation in the area supports various socio-economic activities around the reserve that contribute towards the development of local communities in Ajloun area. A part of this research was to conduct a survey to study the impact of Ajloun forest reserve on biodiversity composition. Also, studying the biodiversity content especially for vegetation to determine the economic impacts of Ajloun forest reserve on its surroundings was studied. In this study, several methods were used to fill the objectives including point-centered quarter method which involves selecting randomly 50 plots at the study site. The collected data from the field showed that the absolute density was (1031.24 plant per hectare). Density was recorded and found to be the highest for Quecus coccifera, and relative density of (73.7%), this was followed by Arbutus andrachne and relative density (7.1%), Pistacia palaestina and relative density (10.5%) and Crataegus azarulus (82.5 p/ha) and relative density (5.1%),

Keywords: composition, density, frequency, importance value, point-centered quarter, structure, tree cover

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Authors: Samuel O. Akande

Abstract:

The human-induced climate change is likely to have severe consequences on forest ecosystems in Nigeria. Recent discussions and emphasis on issues concerning the environment justify the need for this research which examined deforestation monitoring in Oban Forest, Nigeria using Remote Sensing techniques. The Landsat images from TM (1986), ETM+ (2001) and OLI (2015) sensors were obtained from Landsat online archive and processed using Erdas Imagine 2014 and ArcGIS 10.3 to obtain the land use/land cover and Normalized Differential Vegetative Index (NDVI) values. Ground control points of deforested areas were collected for validation. It was observed that the forest cover decreased in area by about 689.14 km² between 1986 and 2015. The NDVI was used to determine the vegetation health of the forest and its implications on agricultural sustainability. The result showed that the total percentage of the healthy forest cover has reduced to about 45.9% from 1986 to 2015. The results obtained from analysed questionnaires shown that there was a positive correlation between the causes and effects of deforestation in the study area. The coefficient of determination value was calculated as R² ≥ 0.7, to ascertain the level of anthropogenic activities, such as fuelwood harvesting, intensive farming, and logging, urbanization, and engineering construction activities, responsible for deforestation in the study area. Similarly, temperature and rainfall data were obtained from Nigerian Meteorological Agency (NIMET) for the period of 1986 to 2015 in the study area. It was observed that there was a significant increase in temperature while rainfall decreased over the study area. Responses from the administered questionnaires also showed that futile destruction of forest ecosystem in Oban forest could be reduced to its barest minimum if fuelwood harvesting is disallowed. Thus, the projected impacts of climate change on Nigeria’s forest ecosystems and environmental stability is better imagined than experienced.

Keywords: deforestation, ecosystems, normalized differential vegetative index, sustainability

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Authors: Pennapa Palapin

Abstract:

This study aimed to examine the management and development of forest tourism Kamchanoad. Ban Dung, Udon Thani sustainability. Data were collected by means of qualitative research including in-depth interviews, semi-structured, and then the data were summarized and discussed in accordance with the objectives. And make a presentation in the form of lectures. The target population for the study consisted of 16 people, including representatives from government agencies, community leaders and the community. The results showed that Guidelines for the Management and Development of Forest Tourism Kamchanoad include management of buildings and infrastructure such as roads, water, electricity, toilets. Other developments are the establishment of a service center that provides information and resources to facilitate tourists.; nature trails and informative signage to educate visitors on the path to the jungle Kamchanoad; forest activities for tourists who are interested only in occasional educational activities such as vegetation, etc.; disseminating information on various aspects of tourism through various channels in both Thailand and English, as well as a website to encourage community involvement in the planning and management of tourism together with the care and preservation of natural resources and preserving the local cultural tourist area of Kamchanoad.

Keywords: guidelines for the management and development, forest tourism, Kamchanoad, sustainability

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Authors: Mohan Bikram Thapa

Abstract:

Despite the successful implementation of community forestry program, a number of pros and cons have been raised on Terai community forestry in the case of lowland locally called Terai region of Nepal, which climatically belongs to tropical humid and possessed high-quality forests in terms of ecology and economy. The study aims to investigate the local pricing strategy of forest products and its impacts on equitable forest benefits sharing, the collection of community fund and carrying out livelihood improvement activities. The study was carried out on six community forests revealed that local people have substantially benefited from the community forests. However, being the region is heterogeneous by socio-economic conditions and forest resources have higher economic potential, the decision of low pricing strategy made by the local people have created inequality problems while sharing the forest benefits, and poorly contributed to community fund collection and consequently carrying out limited activities of livelihood improvement. The paper argued that the decision of low pricing strategy of forest products is counterproductive to promote the equitable benefit-sharing in the areas of heterogeneous socio-economic conditions with high-value forests. The low pricing strategy has been increasing accessibility of better off households at a higher rate than poor, as such households always have the higher affording capacity. It is also defective to increase the community fund and carry out activities of livelihood improvement effectively. The study concluded that unilateral decentralized forest policy and decision-making autonomy to the local people seems questionable unless their decision-making capacities are enriched sufficiently. Therefore, it is recommended that empowerments of decision-making capacity of local people and their respective institutions together with policy and program formulation are prerequisite for efficient and equitable community forest management and its long-term sustainability.

Keywords: benefit sharing, community forest, livelihood, pricing mechanism, Nepal

Procedia PDF Downloads 338

Authors: Stephen Dankwa, Zheng Wenfeng, Xiaolu Li

Abstract:

Climate change is recently gaining the attention of many countries across the world. Climate change, which is also known as global warming, referring to the increasing in average surface temperature has been a concern to the Environmental Protection Agency of Ghana. Recently, Ghana has become vulnerable to the effect of the climate change as a result of the dependence of the majority of the population on agriculture. The clearing down of trees to grow crops and burning of charcoal in the country has been a contributing factor to the rise in temperature nowadays in the country as a result of releasing of carbon dioxide and greenhouse gases into the air. Recently, petroleum stations across the cities have been on fire due to this climate changes and which have position Ghana in a way not able to withstand this climate event. As a result, the significant of this research paper is to project how the rise in the average surface temperature will be like at the end of the mid-21st century when agriculture and deforestation are allowed to continue for some time in the country. This study uses the Coupled Modeled Intercomparison Project phase 5 (CMIP5) experiment RCP 8.5 model output data to monitor the future climate changes from 2041-2050, at the end of the mid-21st century over the ten (10) major cities (Accra, Bolgatanga, Cape Coast, Koforidua, Kumasi, Sekondi-Takoradi, Sunyani, Ho, Tamale, Wa) in Ghana. In the models, Support Vector Machine and Random forest, where the cities as a function of heat wave metrics (minimum temperature, maximum temperature, mean temperature, heat wave duration and number of heat waves) assisted to provide more than 50% accuracy to predict and monitor the pattern of the surface air temperature. The findings identified were that the near-surface air temperature will rise between 1°C-2°C (degrees Celsius) over the coastal cities (Accra, Cape Coast, Sekondi-Takoradi). The temperature over Kumasi, Ho and Sunyani by the end of 2050 will rise by 1°C. In Koforidua, it will rise between 1°C-2°C. The temperature will rise in Bolgatanga, Tamale and Wa by 0.5°C by 2050. This indicates how the coastal and the southern part of the country are becoming hotter compared with the north, even though the northern part is the hottest. During heat waves from 2041-2050, Bolgatanga, Tamale, and Wa will experience the highest mean daily air temperature between 34°C-36°C. Kumasi, Koforidua, and Sunyani will experience about 34°C. The coastal cities (Accra, Cape Coast, Sekondi-Takoradi) will experience below 32°C. Even though, the coastal cities will experience the lowest mean temperature, they will have the highest number of heat waves about 62. Majority of the heat waves will last between 2 to 10 days with the maximum 30 days. The surface temperature will continue to rise by the end of the mid-21st century (2041-2050) over the major cities in Ghana and so needs to be addressed to the Environmental Protection Agency in Ghana in order to mitigate this problem.

Keywords: climate changes, CMIP5, Ghana, heat waves, random forest, SVM

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Authors: Xiaoying Zhang

Abstract:

Forest school – an outdoor educational experience that is undertaken in an outdoor environment with trees – becomes an emerging field of early childhood education recently. In China, the benefits of natural outdoor education to children and young people’s wellness have raised attention. Although different types of outdoor-based activities have been involved in some pre-school of China, few study and practice have been conducted in terms of the notion of forest school. To comprehend the impact of forest school for children and young people, this study aims to systematically review articles on the topic of forest school in preschool education from an ecological perspective, i.e. from individual level (e.g., behavior and mental health) to microsystem level (e.g., the relationship between teachers and children) to ecosystem level. Based on PRISMA framework flow, using the key words of “Forest School” and “Early Childhood Education” for searching in Web-of-science database, a total of 33 articles were identified. Sample participants of 13 studies were not preschool children, five studies were not on forest school theme, and two literature review articles were excluded for further analysis. Finally, 13 articles were eligible for thematic analysis. According to Bronfenbrenner's ecological systems theory, there are some fingdings, on the individual level, current forest school studies are concerned about the children behavioral experience in forest school, how these experience may relate to their achievement or to develop children’s wellbeing/wellness, and how this type of learning experience may enhance children’s self-awareness on risk and safety issues. On the microsystem/mesosystem level, this review indicated that pedagogical development for forest school, risk perception from teachers and parents, social development between peers, and adult’s role in the participation of forest school were concerned, explored and discussed most frequently. On the macrosystem, the conceptualization of forest school is the key theme. Different forms of presentation in various countries with diverse cultures could provide various models of forest school education. However, there was no study investigating forest school on an ecosystem level. As for the potential benefits of physical health and mental wellness that results from forest school, it informs us to reflect the system of preschool education from the ecological perspective for Chinese children. For instance, most Chinese kindergartens ignored the significance of natural outdoor activities for children. Preschool education in China is strongly oriented by primary school system, which means pre-school children are expected to be trained as primary school students to do different subjects, such as math. Hardly any kindergarteners provide the opportunities for children and young people to take risks in a natural environment like forest school does. However, merely copying forest school model for a Chinese preschool education system will be less effective. This review of different level concerns could inform us that the localization the idea of forest school to adapt to a Chinese political, educational and cultural background. More detailed results and profound discussions will be presented in the full paper.

Keywords: early childhood education, ecological system, education development prospects in China, forest school

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Authors: Dharmpal Singh

Abstract:

The objective of this proposed project is to relocation and resettlement of carnivores tribal communities who are currently residing in the protected forest land in all over the world just like resettlement project of the carnivores tribal families of Mongia who at past were residing in Ranthambhore Tiger Reserve (RTR) and had caused excess damage of endangered species of wildlife including Tigers. At present several tribal communities are residing in the another national parks and they not only consuming the wild animals but also involved in illegal trading of vital organs, skin and bones with National and international traders. Tribal are ideally suited for the job because they are highly skilled game trackers and due to having had a definite source of income over the years, they easily drawn in to the illegal wildlife trade and slaughter of wild animals. Their income is increasing but wild animals are on the brink of extinction. For the conservation of flora and fauna the rehabilitation process should be thought out according to the RTR project (which not only totally change the quality of life of mongia tribal community but also increased the conopy cover of forest and grass due to reduced the biotic pressure on protected land of forest in Rajasthan state) with appropriate understanding of the sociology of the people involved, their culture, education standard and the need of different skills to be acquired by them for sustenance such as agriculture, dairy, poultry, social forestry, job as forest guard and others eco-development programmes. Perhaps, the dimensions presented by me may generate discussion among the international wild life lovers and conservationists and remedies may be result oriented in the field of management of forest and conservation of wildlife on this planet.

Keywords: strategies, rehablety of tribals, conservation of forest, eco-development Programmes, wildlife

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Authors: Danica Maljkovic, Igor Balen, Bojana Dalbelo Basic

Abstract:

Following the regulation of EU Directive on Energy Efficiency, specifically Article 9, individual metering in district heating systems has to be introduced by the end of 2016. These directions have been implemented in member state’s legal framework, Croatia is one of these states. The directive allows installation of both heat metering devices and heat cost allocators. Mainly due to bad communication and PR, the general public false image was created that the heat cost allocators are devices that save energy. Although this notion is wrong, the aim of this work is to develop a model that would precisely express the influence of installation heat cost allocators on potential energy savings in each unit within multifamily buildings. At the same time, in recent years, a science of machine learning has gain larger application in various fields, as it is proven to give good results in cases where large amounts of data are to be processed with an aim to recognize a pattern and correlation of each of the relevant parameter as well as in the cases where the problem is too complex for a human intelligence to solve. A special method of machine learning, decision tree method, has proven an accuracy of over 92% in prediction general building consumption. In this paper, a machine learning algorithms will be used to isolate the sole impact of installation of heat cost allocators on a single building in multifamily houses connected to district heating systems. Special emphasises will be given regression analysis, logistic regression, support vector machines, decision trees and random forest method.

Keywords: district heating, heat cost allocator, energy efficiency, machine learning, decision tree model, regression analysis, logistic regression, support vector machines, decision trees and random forest method

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Authors: Arnab Roy, P. S. Anil Kumar

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We discuss angle dependent changes to the Barkhausen noise signatures of thin epitaxial Fe films upon altering the angle of the applied field. We observe a sub-critical to critical phase transition in the hysteresis loop of the sample upon increasing the out-of-plane component of the applied field. The observations are discussed in the light of simulations of a 2D Gaussian Random Field Ising Model with references to a reducible form of the Random Anisotropy Ising Model.

Keywords: Barkhausen noise, Planar Hall effect, Random Field Ising Model, Random Anisotropy Ising Model

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Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

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Authors: Juang R. Matangaran, Qi Adlan

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Industrial plantation forest in Indonesia was developed in 1983, and since then, several companies have been successfully planted a total area of concessionaire approximately 10 million hectares. Currently, these plantation forests have their annual harvesting period. In the timber harvesting process, amount part of the trees generally become logging residue. Tree parts such as branches, twigs, defected stem and leaves are unused section of tree on the ground after timber harvesting. The use of heavy machines in timber harvesting area has caused damage to the forest soil. The negative impact of such machines includes loss of topsoil, soil erosion, and soil compaction. Forest soil compaction caused reduction of forest water infiltration, increase runoff and causes difficulty for root penetration. In this study, we used logging residue as soil covers on the passages passed by skidding machines in order to observe the reduction soil compaction. Bulk density of soil was measured and analyzed after several times of skidding machines passage on skid trail. The objective of the research was to analyze the effect of logging residue on reducing soil compaction. The research was taken place at one of the industrial plantation forest area of South Sumatra Indonesia. The result of the study showed that percentage increase of soil compaction bare soil was larger than soil surface covered by logging residue. The maximum soil compaction occurred after 4 to 5 passes on soil without logging residue or bare soil and after 7 to 8 passes on soil cover by logging residue. The use of logging residue coverings could reduce soil compaction from 45% to 60%. The logging residue was effective in decreasing soil disturbance of timber harvesting at the plantation forest area.

Keywords: bulk density, logging residue, plantation forest, soil compaction, timber harvesting

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Authors: Andrey V. Timofeev, Dmitry V. Egorov

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This paper introduces an original method of parametric optimization of the structure for multimodal decision-level fusion scheme which combines the results of the partial solution of the classification task obtained from assembly of the mono-modal classifiers. As a result, a multimodal fusion classifier which has the minimum value of the total error rate has been obtained.

Keywords: classification accuracy, fusion solution, total error rate, multimodal fusion classifier

Procedia PDF Downloads 434

Authors: Syed Asif Hassan, Syed Atif Hassan

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Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.

Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction

Procedia PDF Downloads 356

Authors: Todor Nickolov Stoyanov

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Cooperatives are present in all countries and in almost all sectors, including agriculture, forestry, food, finance, health, marketing, insurance and credit. Strong cooperatives are able to overcome many of the difficulties faced by private owners. Cooperatives use seven principles, including the 'Community Concern" principle, which enables cooperatives to work for the sustainable development of the community. The members of cooperatives may use different systems for generating year-round employment and for receiving sustainable income through performing different forestry activities. Various methods are used during the preparation of the report. These include literature reviews, statistics, secondary data and expert interviews. The members of the cooperatives are benefits exclusively from increasing the efficiency of the various products and from the overall yield of the harvest, and ultimately from achieving better profit through cooperative efforts. Cooperatives also use other types of activities that are an additional opportunity for cooperative income. There are many heterogeneous activities in the production and service sectors of the forest cooperatives under consideration. Some cooperatives serve dairies, distilleries, woodworking enterprises, tourist homes, hotels and motels, shops, ski slopes, sheep breeding, etc. Through the revenue generated by the activity, cooperatives have the opportunity to carry out various environmental and protective activities - recreation, water protection, protection of endangered and endemic species, etc., which in the case of small-scale forests cannot be achieved and the management is not sustainable. The conclusions indicate the results received in the analysis. Cooperative management of forests and forest lands gives higher incomes to individual owners. The management of forests and forest lands through cooperatives helps to carry out different environmental and protective activities. Cooperative forest management provides additional means of subsistence to the owners of poor forest lands. Cooperative management of forests and forest lands support owners to implement the forest management plans and to apply sustainable management of these territories.

Keywords: cooperative, forestry, forest owners, principles of cooperation

Procedia PDF Downloads 206

Authors: G. Singer, M. Golan

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Providing students with learning disabilities (LD) with extra time to grant them equal access to the exam is a necessary but insufficient condition to compensate for their LD; there should also be a clear indication that the additional time was actually used. For example, if students with LD use more time than students without LD and yet receive lower grades, this may indicate that a different accommodation is required. If they achieve higher grades but use the same amount of time, then the effectiveness of the accommodation has not been demonstrated. The main goal of this study is to evaluate the effect of including parameters related to LD and extended exam time, along with other commonly-used characteristics (e.g., student background and ability measures such as high-school grades), on the ability of ordinal decision-tree algorithms to predict exam performance. We use naturally-occurring data collected from hundreds of undergraduate engineering students. The sub-goals are i) to examine the improvement in prediction accuracy when the indicator of exam performance includes 'actual time used' in addition to the conventional indicator (exam grade) employed in most research; ii) to explore the effectiveness of extended exam time on exam performance for different courses and for LD students with different profiles (i.e., sets of characteristics). This is achieved by using the patterns (i.e., subgroups) generated by the algorithms to identify pairs of subgroups that differ in just one characteristic (e.g., course or type of LD) but have different outcomes in terms of exam performance (grade and time used). Since grade and time used to exhibit an ordering form, we propose a method based on ordinal decision-trees, which applies a weighted information-gain ratio (WIGR) measure for selecting the classifying attributes. Unlike other known ordinal algorithms, our method does not assume monotonicity in the data. The proposed WIGR is an extension of an information-theoretic measure, in the sense that it adjusts to the case of an ordinal target and takes into account the error severity between two different target classes. Specifically, we use ordinal C4.5, random-forest, and AdaBoost algorithms, as well as an ensemble technique composed of ordinal and non-ordinal classifiers. Firstly, we find that the inclusion of LD and extended exam-time parameters improves prediction of exam performance (compared to specifications of the algorithms that do not include these variables). Secondly, when the indicator of exam performance includes 'actual time used' together with grade (as opposed to grade only), the prediction accuracy improves. Thirdly, our subgroup analyses show clear differences in the effect of extended exam time on exam performance among different courses and different student profiles. From a methodological perspective, we find that the ordinal decision-tree based algorithms outperform their conventional, non-ordinal counterparts. Further, we demonstrate that the ensemble-based approach leverages the strengths of each type of classifier (ordinal and non-ordinal) and yields better performance than each classifier individually.

Keywords: actual exam time usage, ensemble learning, learning disabilities, ordinal classification, time extension

Procedia PDF Downloads 68

Authors: Agnieszka Dudzik

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The problem of correlation between random variables in the structural reliability analysis has been extensively discussed in literature on the subject. The cases taken under consideration were usually related to correlation between random variables from one side of ultimate limit state: correlation between particular loads applied on structure or correlation between resistance of particular members of a structure as a system. It has been proved that positive correlation between these random variables reduces the reliability of structure and increases the probability of failure. In the paper, the problem of correlation between random variables from both side of the limit state equation will be taken under consideration. The simplest case where these random variables are of the normal distributions will be concerned. The case when a degree of that correlation is described by the covariance or the coefficient of correlation will be used. Special attention will be paid on questions: how much that correlation changes the reliability level and can it be ignored. In reliability analysis will be used well-known methods for assessment of the failure probability: based on the Hasofer-Lind reliability index and Monte Carlo method adapted to the problem of correlation. The main purpose of this work will be a presentation how correlation of random variables influence on reliability index of steel bar structures. Structural design parameters will be defined as deterministic values and random variables. The latter will be correlated. The criterion of structural failure will be expressed by limit functions related to the ultimate and serviceability limit state. In the description of random variables will be used only for the normal distribution. Sensitivity of reliability index to the random variables will be defined. If the reliability index sensitivity due to the random variable X will be low when compared with other variables, it can be stated that the impact of this variable on failure probability is small. Therefore, in successive computations, it can be treated as a deterministic parameter. Sensitivity analysis leads to simplify the description of the mathematical model, determine the new limit functions and values of the Hasofer-Lind reliability index. In the examples, the NUMPRESS software will be used in the reliability analysis.

Keywords: correlation of random variables, reliability index, sensitivity of reliability index, steel structure

Procedia PDF Downloads 206

Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

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The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

Procedia PDF Downloads 106

Authors: Livia Herle

Abstract:

A large part of the Principality of Liechtenstein is covered by forest. Three-quarters of this forest is defined as protective due to the alpine landscape of the country, which is deteriorating the quality of the wood. Nevertheless, the forest is one of the most important sources of raw material. However, out of the wood harvested annually in Liechtenstein, about two-thirds are used directly as an energy source, drastically shortening up the carbon storage cycle of wood. Furthermore, due to climate change, forest structures are changing. Predictions for the forest in Liechtenstein have stated that the spruce will mostly vanish in low altitudes, only being able to survive in the higher regions. In contrast, hardwood species will experience a rise, resulting in a more mixed forest. Thus, the main research focus will be put upon the potential of hardwood as well as prolonging the lifespan of a timber log before ending up as an energy source. An analysis of the local occurrence of hardwood species and their quality will serve as a tool to implement this knowledge upon constructional solutions. As a system that works with short spam timber and thus qualifies for the regional conditions of hardwood, reciprocal frame systems will be further investigated. These can be defined as load-bearing structures with only two beams connecting at a time, avoiding complex joining situations. Furthermore, every beam is mutually supporting. This allows the usage of short pieces of preferably massive wood. As a result, the system permits for an easy assembly but also disassembly. To promote a more circular application of wood, possible cascading scenarios of the structural solutions will be added. In a workshop at the School of Architecture of the University of Liechtenstein in the Sommer Semester 2024, prototypes in 1:1 of reciprocal frame systems using only local hardwood will help as a tool to further test the theoretical analyses.

Keywords: hardwood, cascading wood, reciprocal frames, crooked wood, forest structures, climate change

Procedia PDF Downloads 49