Search results for: non timber forest product
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 4622

Search results for: non timber forest product

4412 Reduce the Impact of Wildfires by Identifying Them Early from Space and Sending Location Directly to Closest First Responders

Authors: Gregory Sullivan

Abstract:

The evolution of global warming has escalated the number and complexity of forest fires around the world. As an example, the United States and Brazil combined generated more than 30,000 forest fires last year. The impact to our environment, structures and individuals is incalculable. The world has learned to try to take this in stride, trying multiple ways to contain fires. Some countries are trying to use cameras in limited areas. There are discussions of using hundreds of low earth orbit satellites and linking them together, and, interfacing them through ground networks. These are all truly noble attempts to defeat the forest fire phenomenon. But there is a better, simpler answer. A bigger piece of the solutions puzzle is to see the fires while they are small, soon after initiation. The approach is to see the fires while they are very small and report their location (latitude and longitude) to local first responders. This is done by placing a sensor at geostationary orbit (GEO: 26,000 miles above the earth). By placing this small satellite in GEO, we can “stare” at the earth, and sense temperature changes. We do not “see” fires, but “measure” temperature changes. This has already been demonstrated on an experimental scale. Fires were seen at close to initiation, and info forwarded to first responders. it were the first to identify the fires 7 out of 8 times. The goal is to have a small independent satellite at GEO orbit focused only on forest fire initiation. Thus, with one small satellite, focused only on forest fire initiation, we hope to greatly decrease the impact to persons, property and the environment.

Keywords: space detection, wildfire early warning, demonstration wildfire detection and action from space, space detection to first responders

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4411 Mechanical Properties of Aspen Wood of Structural Dimensions

Authors: Barbora Herdová, Rastislav Lagaňa

Abstract:

The paper investigates the mechanical properties of European aspen (Populus tremula L.) as a potential replacement for load-bearing elements in historical structures. One of the main aims of the research has been the quantification of mechanical properties via destructive testing and the subsequent calculation of characteristic values of these properties. The research encompasses experimental testing of wood specimens for the determination of dynamic modulus of elasticity (MOEdyn), modulus of elasticity (MOE), modulus of rupture (MOR), and density. The results were analyzed and compared to established standards for structural timber. The results confirmed statistically significant dependence between MOR and MOEdyn. The correlation between the MOR and the dynamic MOEdyn enabled non-destructive strength grading using the Sylvatest Duo® system. The findings of this research contribute to the potential use of European aspen as a structural timber, which could have implications for the sustainable use of this abundant and renewable resource in the construction industry. They also show the usability of European aspen in the reconstruction of historical buildings.

Keywords: populus tremula, MOE, MOR, sylvatest Duo®.

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4410 Forecasting the Fluctuation of Currency Exchange Rate Using Random Forest

Authors: Lule Basha, Eralda Gjika

Abstract:

The exchange rate is one of the most important economic variables, especially for a small, open economy such as Albania. Its effect is noticeable in one country's competitiveness, trade and current account, inflation, wages, domestic economic activity, and bank stability. This study investigates the fluctuation of Albania’s exchange rates using monthly average foreign currency, Euro (Eur) to Albanian Lek (ALL) exchange rate with a time span from January 2008 to June 2021, and the macroeconomic factors that have a significant effect on the exchange rate. Initially, the Random Forest Regression algorithm is constructed to understand the impact of economic variables on the behavior of monthly average foreign currencies exchange rates. Then the forecast of macro-economic indicators for 12 months was performed using time series models. The predicted values received are placed in the random forest model in order to obtain the average monthly forecast of the Euro to Albanian Lek (ALL) exchange rate for the period July 2021 to June 2022.

Keywords: exchange rate, random forest, time series, machine learning, prediction

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4409 Application of Random Forest Model in The Prediction of River Water Quality

Authors: Turuganti Venkateswarlu, Jagadeesh Anmala

Abstract:

Excessive runoffs from various non-point source land uses, and other point sources are rapidly contaminating the water quality of streams in the Upper Green River watershed, Kentucky, USA. It is essential to maintain the stream water quality as the river basin is one of the major freshwater sources in this province. It is also important to understand the water quality parameters (WQPs) quantitatively and qualitatively along with their important features as stream water is sensitive to climatic events and land-use practices. In this paper, a model was developed for predicting one of the significant WQPs, Fecal Coliform (FC) from precipitation, temperature, urban land use factor (ULUF), agricultural land use factor (ALUF), and forest land-use factor (FLUF) using Random Forest (RF) algorithm. The RF model, a novel ensemble learning algorithm, can even find out advanced feature importance characteristics from the given model inputs for different combinations. This model’s outcomes showed a good correlation between FC and climate events and land use factors (R2 = 0.94) and precipitation and temperature are the primary influencing factors for FC.

Keywords: water quality, land use factors, random forest, fecal coliform

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4408 Conception of a Predictive Maintenance System for Forest Harvesters from Multiple Data Sources

Authors: Lazlo Fauth, Andreas Ligocki

Abstract:

For cost-effective use of harvesters, expensive repairs and unplanned downtimes must be reduced as far as possible. The predictive detection of failing systems and the calculation of intelligent service intervals, necessary to avoid these factors, require in-depth knowledge of the machines' behavior. Such know-how needs permanent monitoring of the machine state from different technical perspectives. In this paper, three approaches will be presented as they are currently pursued in the publicly funded project PreForst at Ostfalia University of Applied Sciences. These include the intelligent linking of workshop and service data, sensors on the harvester, and a special online hydraulic oil condition monitoring system. Furthermore the paper shows potentials as well as challenges for the use of these data in the conception of a predictive maintenance system.

Keywords: predictive maintenance, condition monitoring, forest harvesting, forest engineering, oil data, hydraulic data

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4407 Current Status of Nitrogen Saturation in the Upper Reaches of the Kanna River, Japan

Authors: Sakura Yoshii, Masakazu Abe, Akihiro Iijima

Abstract:

Nitrogen saturation has become one of the serious issues in the field of forest environment. The watershed protection forests located in the downwind hinterland of Tokyo Metropolitan Area are believed to be facing nitrogen saturation. In this study, we carefully focus on the balance of nitrogen between load and runoff. Annual nitrogen load via atmospheric deposition was estimated to 461.1 t-N/year in the upper reaches of the Kanna River. Annual nitrogen runoff to the forested headwater stream of the Kanna River was determined to 184.9 t-N/year, corresponding to 40.1% of the total nitrogen load. Clear seasonal change in NO3-N concentration was still observed. Therefore, watershed protection forest of the Kanna River is most likely to be in Stage-1 on the status of nitrogen saturation.

Keywords: atmospheric deposition, nitrogen accumulation, denitrification, forest ecosystems

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4406 Classification of Forest Types Using Remote Sensing and Self-Organizing Maps

Authors: Wanderson Goncalves e Goncalves, José Alberto Silva de Sá

Abstract:

Human actions are a threat to the balance and conservation of the Amazon forest. Therefore the environmental monitoring services play an important role as the preservation and maintenance of this environment. This study classified forest types using data from a forest inventory provided by the 'Florestal e da Biodiversidade do Estado do Pará' (IDEFLOR-BIO), located between the municipalities of Santarém, Juruti and Aveiro, in the state of Pará, Brazil, covering an area approximately of 600,000 hectares, Bands 3, 4 and 5 of the TM-Landsat satellite image, and Self - Organizing Maps. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training the neural network. The midpoints of each sample of forest inventory have been linked to images. Later the Digital Numbers of the pixels have been extracted, composing the database that fed the training process and testing of the classifier. The neural network was trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and the number of examples in the training data set was 400, 200 examples for each class (Dbe and Dbe + Abp), and the size of the test data set was 100, with 50 examples for each class (Dbe and Dbe + Abp). Therefore, total mass of data consisted of 500 examples. The classifier was compiled in Orange Data Mining 2.7 Software and was evaluated in terms of the confusion matrix indicators. The results of the classifier were considered satisfactory, and being obtained values of the global accuracy equal to 89% and Kappa coefficient equal to 78% and F1 score equal to 0,88. It evaluated also the efficiency of the classifier by the ROC plot (receiver operating characteristics), obtaining results close to ideal ratings, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon.

Keywords: artificial neural network, computational intelligence, pattern recognition, unsupervised learning

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4405 Properties of Rhizophora Charcoal for Product Design

Authors: Tanutpong Phriwanrat

Abstract:

This research investigated the properties of Rhizophora charcoal for product design on 3 aspects: electrical conductor, impurity absorption, and fresh fruit shelf life. After the study, the properties of Rhizophora charcoal were applied to produce local product model at Ban Yisarn, Ampawa District, Samudsongkram Province which can add value to the Rhizophora charcoal as one of the OTOP (One-Tambon-One product). The results showed that the Rhizophora charcoal is not an electrical conductor but good liquid impurity absorber and it can extend fresh fruit shelf life.

Keywords: design, product design, properties of rhizophora, rhizophora charcoal

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4404 Wildland Fire in Terai Arc Landscape of Lesser Himalayas Threatning the Tiger Habitat

Authors: Amit Kumar Verma

Abstract:

The present study deals with fire prediction model in Terai Arc Landscape, one of the most dramatic ecosystems in Asia where large, wide-ranging species such as tiger, rhinos, and elephant will thrive while bringing economic benefits to the local people. Forest fires cause huge economic and ecological losses and release considerable quantities of carbon into the air and is an important factor inflating the global burden of carbon emissions. Forest fire is an important factor of behavioral cum ecological habit of tiger in wild. Post fire changes i.e. micro and macro habitat directly affect the tiger habitat or land. Vulnerability of fire depicts the changes in microhabitat (humus, soil profile, litter, vegetation, grassland ecosystem). Microorganism like spider, annelids, arthropods and other favorable microorganism directly affect by the forest fire and indirectly these entire microorganisms are responsible for the development of tiger (Panthera tigris) habitat. On the other hand, fire brings depletion in prey species and negative movement of tiger from wild to human- dominated areas, which may leads the conflict i.e. dangerous for both tiger & human beings. Early forest fire prediction through mapping the risk zones can help minimize the fire frequency and manage forest fires thereby minimizing losses. Satellite data plays a vital role in identifying and mapping forest fire and recording the frequency with which different vegetation types are affected. Thematic hazard maps have been generated by using IDW technique. A prediction model for fire occurrence is developed for TAL. The fire occurrence records were collected from state forest department from 2000 to 2014. Disciminant function models was used for developing a prediction model for forest fires in TAL, random points for non-occurrence of fire have been generated. Based on the attributes of points of occurrence and non-occurrence, the model developed predicts the fire occurrence. The map of predicted probabilities classified the study area into five classes very high (12.94%), high (23.63%), moderate (25.87%), low(27.46%) and no fire (10.1%) based upon the intensity of hazard. model is able to classify 78.73 percent of points correctly and hence can be used for the purpose with confidence. Overall, also the model works correctly with almost 69% of points. This study exemplifies the usefulness of prediction model of forest fire and offers a more effective way for management of forest fire. Overall, this study depicts the model for conservation of tiger’s natural habitat and forest conservation which is beneficial for the wild and human beings for future prospective.

Keywords: fire prediction model, forest fire hazard, GIS, landsat, MODIS, TAL

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4403 A Comprehensive Survey of Artificial Intelligence and Machine Learning Approaches across Distinct Phases of Wildland Fire Management

Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

Abstract:

Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

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4402 Supply, Trade-offs, and Synergies Estimation for Regulating Ecosystem Services of a Local Forest

Authors: Jang-Hwan Jo

Abstract:

The supply management of ecosystem services of local forests is an essential issue as it is linked to the ecological welfare of local residents. This study aims to estimate the supply, trade-offs, and synergies of local forest regulating ecosystem services using a land cover classification map (LCCM) and a forest types map (FTM). Rigorous literature reviews and Expert Delphi analysis were conducted using the detailed variables of 1:5,000 LCCM and FTM. Land-use scoring method and Getis-Ord Gi* Analysis were utilized on detailed variables to propose a method for estimating supply, trade-offs, and synergies of the local forest regulating ecosystem services. The analysis revealed that the rank order (1st to 5th) of supply of regulating ecosystem services was Erosion prevention, Air quality regulation, Heat island mitigation, Water quality regulation, and Carbon storage. When analyzing the correlation between defined services of the entire city, almost all services showed a synergistic effect. However, when analyzing locally, trade-off effects (Heat island mitigation – Air quality regulation, Water quality regulation – Air quality regulation) appeared in the eastern and northwestern forest areas. This suggests the need to consider not only the synergy and trade-offs of the entire forest between specific ecosystem services but also the synergy and trade-offs of local areas in managing the regulating ecosystem services of local forests. The study result can provide primary data for the stakeholders to determine the initial conditions of the planning stage when discussing the establishment of policies related to the adjustment of the supply of regulating ecosystem services of the forests with limited access. Moreover, the study result can also help refine the estimation of the supply of the regulating ecosystem services with the availability of other forms of data.

Keywords: ecosystem service, getis ord gi* analysis, land use scoring method, regional forest, regulating service, synergies, trade-offs

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4401 Land Cover Classification Using Sentinel-2 Image Data and Random Forest Algorithm

Authors: Thanh Noi Phan, Martin Kappas, Jan Degener

Abstract:

The currently launched Sentinel 2 (S2) satellite (June, 2015) bring a great potential and opportunities for land use/cover map applications, due to its fine spatial resolution multispectral as well as high temporal resolutions. So far, there are handful studies using S2 real data for land cover classification. Especially in northern Vietnam, to our best knowledge, there exist no studies using S2 data for land cover map application. The aim of this study is to provide the preliminary result of land cover classification using Sentinel -2 data with a rising state – of – art classifier, Random Forest. A case study with heterogeneous land use/cover in the eastern of Hanoi Capital – Vietnam was chosen for this study. All 10 spectral bands of 10 and 20 m pixel size of S2 images were used, the 10 m bands were resampled to 20 m. Among several classified algorithms, supervised Random Forest classifier (RF) was applied because it was reported as one of the most accuracy methods of satellite image classification. The results showed that the red-edge and shortwave infrared (SWIR) bands play an important role in land cover classified results. A very high overall accuracy above 90% of classification results was achieved.

Keywords: classify algorithm, classification, land cover, random forest, sentinel 2, Vietnam

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4400 The Impact of Agricultural Product Export on Income and Employment in Thai Economy

Authors: Anucha Wittayakorn-Puripunpinyoo

Abstract:

The research objectives were 1) to study the situation and its trend of agricultural product export of Thailand 2) to study the impact of agricultural product export on income of Thai economy 3) the impact of agricultural product export on employment of Thai economy and 4) to find out the recommendations of agricultural product export policy of Thailand. In this research, secondary data were collected as yearly time series data from 1990 to 2016 accounted for 27 years. Data were collected from the Bank of Thailand database. Primary data were collected from the steakholders of agricultural product export policy of Thailand. Data analysis was applied descriptive statistics such as arithmetic mean, standard deviation. The forecasting of agricultural product was applied Mote Carlo Simulation technique as well as time trend analysis. In addition, the impact of agricultural product export on income and employment by applying econometric model while the estimated parameters were utilized the ordinary least square technique. The research results revealed that 1) agricultural product export value of Thailand from 1990 to 2016 was 338,959.5 Million Thai baht with its growth rate of 4.984 percent yearly, in addition, the forecasting of agricultural product export value of Thailand has increased but its growth rate has been declined 2) the impact of agricultural product export has positive impact on income in Thai economy, increasing in agricultural product export of Thailand by 1 percent would lead income increased by 0.0051 percent 3) the impact of agricultural product export has positive impact on employment in Thai economy, increasing in agricultural product export of Thailand by 1 percent would lead income increased by 0.079 percent and 4) in the future, agricultural product export policy would focused on finished or semi-finished agricultural product instead of raw material by applying technology and innovation in to make value added of agricultural product export. The public agricultural product export policy would support exporters in private sector in order to encourage them as agricultural exporters in Thailand.

Keywords: agricultural product export, income, employment, Thai economy

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4399 Using Machine Learning to Enhance Win Ratio for College Ice Hockey Teams

Authors: Sadixa Sanjel, Ahmed Sadek, Naseef Mansoor, Zelalem Denekew

Abstract:

Collegiate ice hockey (NCAA) sports analytics is different from the national level hockey (NHL). We apply and compare multiple machine learning models such as Linear Regression, Random Forest, and Neural Networks to predict the win ratio for a team based on their statistics. Data exploration helps determine which statistics are most useful in increasing the win ratio, which would be beneficial to coaches and team managers. We ran experiments to select the best model and chose Random Forest as the best performing. We conclude with how to bridge the gap between the college and national levels of sports analytics and the use of machine learning to enhance team performance despite not having a lot of metrics or budget for automatic tracking.

Keywords: NCAA, NHL, sports analytics, random forest, regression, neural networks, game predictions

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4398 Impact of Climate Change on Forest Ecosystem Services: In situ Biodiversity Conservation and Sustainable Management of Forest Resources in Tropical Forests

Authors: Rajendra Kumar Pandey

Abstract:

Forest genetic resources not only represent regional biodiversity but also have immense value as the wealth for securing livelihood of poor people. These are vulnerable to ecological due to depletion/deforestation and /or impact of climate change. These resources of various plant categories are vulnerable on the floor of natural tropical forests, and leading to the threat on the growth and development of future forests. More than 170 species, including NTFPs, are in critical condition for their survival in natural tropical forests of Central India. Forest degradation, commensurate with biodiversity loss, is now pervasive, disproportionately affecting the rural poor who directly depend on forests for their subsistence. Looking ahead the interaction between forest and water, soil, precipitation, climate change, etc. and its impact on biodiversity of tropical forests, it is inevitable to develop co-operation policies and programmes to address new emerging realities. Forests ecosystem also known as the 'wealth of poor' providing goods and ecosystem services on a sustainable basis, are now recognized as a stepping stone to move poor people beyond subsistence. Poverty alleviation is the prime objective of the Millennium Development Goals (MDGs). However, environmental sustainability including other MDGs, is essential to ensure successful elimination of poverty and well being of human society. Loss and degradation of ecosystem are the most serious threats to achieving development goals worldwide. Millennium Ecosystem Assessment (MEA, 2005) was an attempt to identify provisioning and regulating cultural and supporting ecosystem services to provide livelihood security of human beings. Climate change may have a substantial impact on ecological structure and function of forests, provisioning, regulations and management of resources which can affect sustainable flow of ecosystem services. To overcome these limitations, policy guidelines with respect to planning and consistent research strategy need to be framed for conservation and sustainable development of forest genetic resources.

Keywords: climate change, forest ecosystem services, sustainable forest management, biodiversity conservation

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4397 Biodiversity And Ecosystem Services In Morocco: Current State And Human Development

Authors: Mohammed Taleb

Abstract:

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

Keywords: morocco, biodiversity, ecosystem services, local population

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4396 Using Combination of Sets of Features of Molecules for Aqueous Solubility Prediction: A Random Forest Model

Authors: Muhammet Baldan, Emel Timuçin

Abstract:

Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, random forest, molecular descriptors, maccs keys

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4395 Optimization of Machine Learning Regression Results: An Application on Health Expenditures

Authors: Songul Cinaroglu

Abstract:

Machine learning regression methods are recommended as an alternative to classical regression methods in the existence of variables which are difficult to model. Data for health expenditure is typically non-normal and have a heavily skewed distribution. This study aims to compare machine learning regression methods by hyperparameter tuning to predict health expenditure per capita. A multiple regression model was conducted and performance results of Lasso Regression, Random Forest Regression and Support Vector Machine Regression recorded when different hyperparameters are assigned. Lambda (λ) value for Lasso Regression, number of trees for Random Forest Regression, epsilon (ε) value for Support Vector Regression was determined as hyperparameters. Study results performed by using 'k' fold cross validation changed from 5 to 50, indicate the difference between machine learning regression results in terms of R², RMSE and MAE values that are statistically significant (p < 0.001). Study results reveal that Random Forest Regression (R² ˃ 0.7500, RMSE ≤ 0.6000 ve MAE ≤ 0.4000) outperforms other machine learning regression methods. It is highly advisable to use machine learning regression methods for modelling health expenditures.

Keywords: machine learning, lasso regression, random forest regression, support vector regression, hyperparameter tuning, health expenditure

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4394 Product Modularity, Collaboration and the Impact on Innovation Performance in Intra-Organizational R&D Networks

Authors: Daniel Martinez, Tim de Leeuw, Stefan Haefliger

Abstract:

The challenges of managing a large and geographically dispersed R&D organization have been further increasing during the past years, concentrating on the leverage of a geo-graphically dispersed body of knowledge in an efficient and effective manner. In order to reduce complexity and improve performance, firms introduce product modularity as one key element for global R&D network teams to develop their products and projects in collaboration. However, empirical studies on the effects of product modularity on innovation performance are really scant. Furthermore, some researchers have suggested that product modularity promotes innovation performance, while others argue that it inhibits innovation performance. This research fills this gap by investigating the impact of product modularity on various dimensions of innovation performance, i.e. effectiveness and efficiency. By constructing the theoretical framework, this study suggests that that there is an inverted U-shaped relationship between product modularity and innovation performance. Moreover, this research work suggests that the optimum of innovation performance efficiency will be at a higher level than innovation performance effectiveness at a given product modularity level.

Keywords: modularity, innovation performance, networks, R&D, collaboration

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4393 Understand and Redefine Lean Product Development

Authors: Alemu Moges Belay, Torgeir Welo, Jan Ola Strandhagen

Abstract:

Lean has long been linked with manufacturing, but its application claimed also by other functions such as product development and services. However, there is a challenge on understanding and defining lean in each function context. This paper aims to investigate the literature that focus mainly on PD process improvement, obtain better understanding and redefine LPD in systematic way. In addition to that, the paper attempts to summarize various proposed transformation strategies, definitions, identifying features of manufacturing and product development that would help to redefining lean in product development context. Finally we redefine LPD in organized way that encompasses different steps such as stage gate, communication and information, events, learning, innovation, knowledge and value creation.

Keywords: lean, lean manufacturing, lean product development, transformation, strategies

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4392 Neutral Sugar Contents of Laurel-leaved and Cryptomeria japonica Forests

Authors: Ayuko Itsuki, Sachiyo Aburatani

Abstract:

Soil neutral sugar contents in Kasuga-yama Hill Primeval Forest (Nara, Japan) were examined using the Waksman’s approximation analysis to clarify relations with the neutral sugar constituted the soil organic matter and the microbial biomass. Samples were selected from the soil surrounding laurel-leaved (BB-1) and Carpinus japonica (BB-2) trees for analysis. The water and HCl soluble neutral sugars increased microbial biomass of the laurel-leaved forest soil. Arabinose, xylose, and galactose of the HCl soluble fraction were used immediately in comparison with other neutral sugars. Rhamnose, glucose, and fructose of the HCl soluble fraction were re-composed by the microbes.

Keywords: forest soil, neutral sugaras, soil organic matter, Waksman’s approximation analysis

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4391 Multi-Spectral Deep Learning Models for Forest Fire Detection

Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

Abstract:

Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

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4390 Seasons and Saproxylic Beetles Biodiversity in an Urban Park in Tunisia

Authors: Zina Nasr, Faiek Errouissi

Abstract:

Forest ecosystems are known for its ability to contain a large diversity of fauna especially insects that represent a huge taxonomic group. A portion of forest insects are recognized as saproxylic including the group of organisms that ‘depend on dead or dying wood’ about them, 20% are beetles. We focused our study on saproxylic beetles in an old urban park ‘the park of Belvedere’, located in the north west of Tunis (36° 49'21’ N 10°10'24’ W). The vegetation is dominated by old trees (Eucalyptus, Olea, Aberia, Pinus) and many fallen wood exist. Saproxylic beetles were collected using three interception traps set in the park over one year (from June 2014 to May 2015) and recovered monthly. In total, we collected 189 beetles belonging to 20 families and 57 species. Several saproxylic families (Bostrichidae, Cerambycidae, Curculionidae, Melyridae, Nitidulidae, Staphylinidae), and well known genus (Rhizopertha, Thrychoplerus, Otiorhychus, Dolichosoma, Epuraea, Anotylus) are recorded. We have retained the largest activity of beetles in spring and a very low richness in winter with zero insect per traps. This result was certainly caused by the variation of meteorological factors that mainly influenced the activity of these organisms. Therefore, we were interested on the saproxylic diversity in an urban ‘forest’, and these results will be more interesting when they are compared in the future with other works from natural forest.

Keywords: saproxylic beetles, seasons, urban park, wood

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4389 Soil Respiration Rate of Laurel-Leaved and Cryptomeria japonica Forests

Authors: Ayuko Itsuki, Sachiyo Aburatani

Abstract:

We assessed the ecology of the organic and mineral soil layers of laurel-leaved (BB-1) and Cryptomeria japonica (BB-2 and Pw) forests in the Kasugayama Hill Primeval Forest (Nara, Japan). The soil respiration rate was higher in the deeper horizons (F and H) of organic layers than in those of mineral soil layers, suggesting organic layers may be where active microbial metabolism occurs. Respiration rates in the soil of BB-1, BB-2 and Pw forests were closely similar at 5 and 10°C. However, the soil respiration rate increased in proportion to temperatures of 15°C or above. We therefore consider the activity of soil microorganisms to markedly decrease at temperatures below 10°C. At a temperature of 15°C or above, the soil respiration rate in the BB-1 organic layers was higher than in those of the BB-2 and Pw organic layers, due to differences in forest vegetation that appeared to influence several salient soil properties, particularly pH and the carbon (C) and nitrogen (N) content of the F and H horizons.

Keywords: forest soil, mineralization rate, heterotroph, soil respiration rate

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4388 Soil Moisture Control System: A Product Development Approach

Authors: Swapneel U. Naphade, Dushyant A. Patil, Satyabodh M. Kulkarni

Abstract:

In this work, we propose the concept and geometrical design of a soil moisture control system (SMCS) module by following the product development approach to develop an inexpensive, easy to use and quick to install product targeted towards agriculture practitioners. The module delivers water to the agricultural land efficiently by sensing the soil moisture and activating the delivery valve. We start with identifying the general needs of the potential customer. Then, based on customer needs we establish product specifications and identify important measuring quantities to evaluate our product. Keeping in mind the specifications, we develop various conceptual solutions of the product and select the best solution through concept screening and selection matrices. Then, we develop the product architecture by integrating the systems into the final product. In the end, the geometric design is done using human factors engineering concepts like heuristic analysis, task analysis, and human error reduction analysis. The result of human factors analysis reveals the remedies which should be applied while designing the geometry and software components of the product. We find that to design the best grip in terms of comfort and applied force, for a power-type grip, a grip-diameter of 35 mm is the most ideal.

Keywords: agriculture, human factors, product design, soil moisture control

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4387 Finite Element Simulation of Four Point Bending of Laminated Veneer Lumber (LVL) Arch

Authors: Eliska Smidova, Petr Kabele

Abstract:

This paper describes non-linear finite element simulation of laminated veneer lumber (LVL) under tensile and shear loads that induce cracking along fibers. For this purpose, we use 2D homogeneous orthotropic constitutive model of tensile and shear fracture in timber that has been recently developed and implemented into ATENA® finite element software by the authors. The model captures (i) material orthotropy for small deformations in both linear and non-linear range, (ii) elastic behavior until anisotropic failure criterion is fulfilled, (iii) inelastic behavior after failure criterion is satisfied, (iv) different post-failure response for cracks along and across the grain, (v) unloading/reloading behavior. The post-cracking response is treated by fixed smeared crack model where Reinhardt-Hordijk function is used. The model requires in total 14 input parameters that can be obtained from standard tests, off-axis test results and iterative numerical simulation of compact tension (CT) or compact tension-shear (CTS) test. New engineered timber composites, such as laminated veneer lumber (LVL), offer improved structural parameters compared to sawn timber. LVL is manufactured by laminating 3 mm thick wood veneers aligned in one direction using water-resistant adhesives (e.g. polyurethane). Thus, 3 main grain directions, namely longitudinal (L), tangential (T), and radial (R), are observed within the layered LVL product. The core of this work consists in 3 numerical simulations of experiments where Radiata Pine LVL and Yellow Poplar LVL were involved. The first analysis deals with calibration and validation of the proposed model through off-axis tensile test (at a load-grain angle of 0°, 10°, 45°, and 90°) and CTS test (at a load-grain angle of 30°, 60°, and 90°), both of which were conducted for Radiata Pine LVL. The second finite element simulation reproduces load-CMOD curve of compact tension (CT) test of Yellow Poplar with the aim of obtaining cohesive law parameters to be used as an input in the third finite element analysis. That is four point bending test of small-size arch of 780 mm span that is made of Yellow Poplar LVL. The arch is designed with a through crack between two middle layers in the crown. Curved laminated beams are exposed to high radial tensile stress compared to timber strength in radial tension in the crown area. Let us note that in this case the latter parameter stands for tensile strength in perpendicular direction with respect to the grain. Standard tests deliver most of the relevant input data whereas traction-separation law for crack along the grain can be obtained partly by inverse analysis of compact tension (CT) test or compact tension-shear test (CTS). The initial crack was modeled as a narrow gap separating two layers in the middle the arch crown. Calculated load-deflection curve is in good agreement with the experimental ones. Furthermore, crack pattern given by numerical simulation coincides with the most important observed crack paths.

Keywords: compact tension (CT) test, compact tension shear (CTS) test, fixed smeared crack model, four point bending test, laminated arch, laminated veneer lumber LVL, off-axis test, orthotropic elasticity, orthotropic fracture criterion, Radiata Pine LVL, traction-separation law, yellow poplar LVL, 2D constitutive model

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4386 Carbon Sequestration Modeling in the Implementation of REDD+ Programmes in Nigeria

Authors: Oluwafemi Samuel Oyamakin

Abstract:

The forest in Nigeria is currently estimated to extend to around 9.6 million hectares, but used to expand over central and southern Nigeria decades ago. The forest estate is shrinking due to long-term human exploitation for agricultural development, fuel wood demand, uncontrolled forest harvesting and urbanization, amongst other factors, compounded by population growth in rural areas. Nigeria has lost more than 50% of its forest cover since 1990 and currently less than 10% of the country is forested. The current deforestation rate is estimated at 3.7%, which is one of the highest in the world. Reducing Emissions from Deforestation and forest Degradation plus conservation, sustainable management of forests and enhancement of forest carbon stocks constituted what is referred to as REDD+. This study evaluated some of the existing way of computing carbon stocks using eight indigenous tree species like Mansonia, Shorea, Bombax, Terminalia superba, Khaya grandifolia, Khaya senegalenses, Pines and Gmelina arborea. While these components are the essential elements of REDD+ programme, they can be brought under a broader framework of systems analysis designed to arrive at optimal solutions for future predictions through statistical distribution pattern of carbon sequestrated by various species of tree. Available data on height and diameter of trees in Ibadan were studied and their respective potentials of carbon sequestration level were assessed and subjected to tests so as to determine the best statistical distribution that would describe the carbon sequestration pattern of trees. The result of this study suggests a reasonable statistical distribution for carbons sequestered in simulation studies and hence, allow planners and government in determining resources forecast for sustainable development especially where experiments with real-life systems are infeasible. Sustainable management of forest can then be achieved by projecting future condition of forests under different management regimes thereby supporting conservation and REDD+ programmes in Nigeria.

Keywords: REDD+, carbon, climate change, height and diameter

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4385 Development of Pasta Production by Using of Hard and Soft Domestic Sorts of Wheat

Authors: A.N. Zhilkaidarov, G.K. Iskakova, V.Y. Chernyh

Abstract:

High-qualified and not-expensive products of daily usage have a big demand on food products’ market. Moreover, it is about independent and irreplaceable product as pasta. Pasta is a product, which represents itself the conserved dough from wheat flour made through special milling process. A wide assortment of the product and its pleasant taste properties allow to use pasta products in very different combinations with other food products. Pasta industry of Kazakhstan has large perspectives of development. There are many premises for it, which includes first an importance of pasta as a social product. Due to for its nutritional and energetically value pasta is the part of must have food. Besides that, the pasta production in Kazakhstan has traditional bases, and nowadays the market of this product develops rapidly as in quantity as well as in quality aspects. Moreover, one of the advantages of this branch is an economical aspect – pasta is the product of secondary processing, and therefore price for sailing is much higher as its own costs.

Keywords: pasta, new wheat sorts, domesic sorts of wheat, macaronic flour

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4384 Ex-Post Export Data for Differentiated Products Revealing the Existence of Productcycles

Authors: Ranajoy Bhattcharyya

Abstract:

We estimate international product cycles as shifting product spaces by using 1976 to 2010 UN Comtrade data on all differentiated tradable products in all countries. We use a product space approach to identify the representative product baskets of high-, middle and low-income countries and then use these baskets to identify the patterns of change in comparative advantage of countries over time. We find evidence of a product cycle in two senses: First, high-, middle- and low-income countries differ in comparative advantage, and high-income products migrate to the middle-income basket. A similar pattern is observed for middle- and low-income countries. Our estimation of the lag shows that middle-income countries tend to quickly take up the products of high-income countries, but low-income countries take a longer time absorbing these products. Thus, the gap between low- and middle-income countries is considerably higher than that between middle- and high-income nations.

Keywords: product cycle, comparative advantage, representative product basket, ex-post data

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4383 Impact Factor Analysis for Spatially Varying Aerosol Optical Depth in Wuhan Agglomeration

Authors: Wenting Zhang, Shishi Liu, Peihong Fu

Abstract:

As an indicator of air quality and directly related to concentration of ground PM2.5, the spatial-temporal variation and impact factor analysis of Aerosol Optical Depth (AOD) have been a hot spot in air pollution. This paper concerns the non-stationarity and the autocorrelation (with Moran’s I index of 0.75) of the AOD in Wuhan agglomeration (WHA), in central China, uses the geographically weighted regression (GRW) to identify the spatial relationship of AOD and its impact factors. The 3 km AOD product of Moderate Resolution Imaging Spectrometer (MODIS) is used in this study. Beyond the economic-social factor, land use density factors, vegetable cover, and elevation, the landscape metric is also considered as one factor. The results suggest that the GWR model is capable of dealing with spatial varying relationship, with R square, corrected Akaike Information Criterion (AICc) and standard residual better than that of ordinary least square (OLS) model. The results of GWR suggest that the urban developing, forest, landscape metric, and elevation are the major driving factors of AOD. Generally, the higher AOD trends to located in the place with higher urban developing, less forest, and flat area.

Keywords: aerosol optical depth, geographically weighted regression, land use change, Wuhan agglomeration

Procedia PDF Downloads 347