Search results for: Random Forest

Authors: John White

Abstract:

This study investigated the effects of practice schedules on learning and performance in golf putting, specifically focusing on the impact of increasing contextual interference (CI). University students (n=7) were randomly assigned to blocked, random, or increasing practice schedules. During acquisition, participants performed 135 putting trials using different weighted golf balls. The blocked group followed a specific sequence of ball weights, while the random group practiced with the balls in a random order. The increasing group started with a blocked schedule, transitioned to a serial schedule, and concluded with a random schedule. Retention and transfer tests were conducted 24 hours later. The results indicated that high levels of CI (random practice) were more beneficial for learning than low levels of CI (blocked practice). The increasing practice schedule, incorporating blocked, serial, and random practice, demonstrated advantages over traditional blocked and random schedules. Additionally, EEG was used to explore the neurophysiological effects of the increasing practice schedule.

Keywords: skill acquisition, motor control, learning, contextual interference

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Authors: Qing Li, Maiko Kobayashi, Shigeyoshi Kumeda, Hiroko Ochiai, Toshiya Ochiai, Takashi Miura, Takahide Kagawa, Michiko Imai, Toshiaki Otsuka, Tomoyuki Kawada

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In the present study, we investigated the effects of a forest bathing program on cardiovascular and metabolic parameters. Nineteen healthy male subjects (mean age: 51.3 ± 8.8 years) were selected after obtaining informed consent. These subjects took day trips to a forest park named Akasawa Shizen Kyuyourin, Agematsu, Nagano Prefecture (situated in central Japan), and to an urban area of Nagano Prefecture as a control in August 2015. On both trips, they walked 2.6 km for 80 min each in the morning and afternoon on Saturdays. Blood and urine were sampled in the morning before and after each trip. Cardiovascular and metabolic parameters were measured. Blood pressure and pulse rate were measured by an ambulatory automatic blood pressure monitor. The Japanese version of the profile of mood states (POMS) test was conducted before, during and after the trips. Ambient temperature and humidity were monitoring during the trips. The forest bathing program significantly reduced pulse rate, and significantly increased the score for vigor and decreased the scores for depression, fatigue, and confusion in the POMS test. The levels of urinary noradrenaline and dopamine after forest bathing were significantly lower than those after urban area walking, suggesting the relaxing effect of the forest bathing program. The level of adiponectin in serum after the forest bathing program was significantly greater than that after urban area walking. There was no significant difference in blood pressure between forest and urban area trips during the trips.

Keywords: ambient temperature, blood pressure, forest bathing, forest therapy, human health, POMS, pulse rate

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Authors: Arzyana Sunkar, Yanto Santosa, Intan Purnamasari, Yohanna Dalimunthe

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A variety of negative allegations have criticized the Indonesian oil palm plantations as being environmentally unfriendly. One of the important allegations thus must be verified is that expansion of Indonesian oil palm plantation has increased the deforestation rate of primary tropical forest. In relation to this, a research was conducted to study the origin or history of the status and land use of 8 private oil palm plantations (with a total of 46,372.38 ha) located in Riau Province. Several methods were employed: (1) conducting analysis of overlay maps between oil palm plantation studied with the 1986 Forest Map Governance Agreement (TGHK) and the 1994 and 2014 Riau Provincial Spatial Plans(RTRWP); (2) studying the Cultivation Right on Land (HGU) documents including the Forestry Ministerial Decree on the release of forest area and (3) interpretation of lands at imagery of bands 542, covering 3 years before and after the oil palm industries operated. In addition, field cross-checked, and interviews were conducted with National Land Agency, Plantation and Forestry Office and community figures. The results indicated that as much as 1.95% of the oil palm plantations under study were converted from production forest, 30.34% from limited production forest and 67.70% from area for other usage /conversion production forest. One year prior to the establishment of the plantations, the land cover types comprised of rubber plantations (49.96%), secondary forest (35.99%), bare land (10.17%), shrubs (3.03%) and mixed dryland farming-shrubs (0.84%), whereas the land use types comprised of 35.99% forest concession areas, 14.04% migrants dryland farms, and 49.96% Cultivation Right on Land of other companies. These results indicated that most of the private oil palm plantations under study, resulted from the conversion of production forests and the previous land use were not primary forest but rubber plantations and secondary forests.

Keywords: land cover types, land use history, primary forest, private oil palm plantations

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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

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

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

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

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

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

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Authors: Hu Ding, Kai Liu, Guoan Tang

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

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

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Authors: Faiza Babur Khan, Sohail Asghar

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

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

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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

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

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Authors: D. S. Palimkar

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Random fixed point theory has received much attention in recent years, and it is needed for the study of various classes of random equations. The study of random fixed point theorems was initiated by the Prague school of probabilistic in the 1950s. The existence and uniqueness of fixed points for the self-maps of a metric space by altering distances between the points with the use of a control function is an interesting aspect in the classical fixed point theory. In a new category of fixed point problems for a single self-map with the help of a control function that alters the distance between two points in a metric space which they called an altering distance function. In this paper, we prove the results of existence of random common fixed point and its uniqueness for a pair of random mappings under weakly contractive condition for generalizing alter distance function in polish spaces using Random Common Fixed Point Theorem for Generalized Weakly Contractions.

Keywords: Polish space, random common fixed point theorem, weakly contractive mapping, altering function

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

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

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

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Authors: Sudhir Kumar Singh, Debashish Chakravarty

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

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

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Authors: Huong Nguyen Thi Thanh, Van Mai Thi Khanh

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For around the world, payments for environmental services have been implemented since the late 1970s in Europe and North America; then, it was spread to Latin America, Asia, Africa, and finally Oceania in 2008. In Vietnam, payments for environmental services are an interesting issue recently with the forest as the main focus and therefore known as the program on payment for forest environmental services (PFES). PFES was piloted in Lam Dong and Son La in 2008 and has been widely applied in many provinces after 2010. PFES is in the orientation for the socialization of national forest protection in Vietnam and has made great strides in the last decade. By using the primary data and secondary data simultaneously, the paper clarifies two cases of implementing PFES in the Vietnam North Central area with the different mechanisms of payment. In the first case at Phu Loc district (Thua Thien Hue province), PFES is an indirect method by a water supply company via the Forest Protection and Development Fund. In the second one at Phong Nha – Ke Bang National Park (Quang Binh Province), tourism companies are the direct payers to forest owners. The paper describes the PFES implementation process at each site, clarifies the payment mechanism, and models the relationship between stakeholders in PFES implementation. Based on the current status of PFES sites, the paper compares and analyzes the advantages and disadvantages of the two payment methods. Finally, the paper proposes recommendations to improve the existing shortcomings in each payment mechanism.

Keywords: advantages and disadvantages, forest environmental services, forest protection, payment mechanism

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Authors: Ayuko Itsuki, Sachiyo Aburatani

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Soil neutral sugar contents in Kasuga-yama Hill Primeval Forest, which is a World Heritage Site in Nara, Japan consisting of lowland laurel-leaved forest where natural conditions have been preserved for more than 1,000 years, were examined using the two-step hydrolysis to clarify the source of the neutral sugar and relations with the neutral sugar constituted the soil organic matter and the microbial biomass. Samples were selected from the soil (L, F, H and A horizons) surrounding laurel-leaved (BB-1) and Carpinus japonica (BB-2 and PW) trees for analysis. The neutral sugars were one factor of increasing the fungal and bacterial biomass in the laurel-leaved forest soil (BB-1). The more neutral sugar contents in the Cryptomeria japonica forest soil (PW) contributed to the growth of the bacteria and fungi than those of in the Cryptomeria japonica forest soil (BB-2). The neutral sugars had higher correlation with the numbers of bacteria and fungi counted by the dilution plate count method than by the direct microscopic count method. The numbers of fungi had higher correlation with those of bacteria by the dilution plate method.

Keywords: forest soil, neutral sugars, soil organic matter, two-step hydrolysis

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Authors: Maffo Maffo Nicole Liliane, Mounmemi Kpoumie Hubert, Libalah Moses, Ouandji Angele, Zapfack Louis

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Forest degradation causes biodiversity and carbon loss and thus indirectly contributes to climate change. In order to assess the contribution of forests to climate change mitigation, the present study was conducted in the Ngambe-Ndom-Nyanon Communal Forest with the main objective of assessing the floristic diversity and estimating the carbon stock in the different reservoirs of the said forest. Nine plots of 2000 m² each were installed in 3 TOSs of the forest (young secondary forests, gallery forests and fallow lands) with a total area of 18,000 m² or 1,8 ha. All trees with a Diameter at Breast Height (DBH) ≥ 5 cm were inventoried at 1.30 m from the ground in each plot. Species richness, floristic diversity indices, and structural parameters were studied. 1542 trees divided into 162 species, 122 genera and 44 families were identified. The most important families were listed: Myristicaceae (30.22%), Apocynaceae (25.20%), Fabaceae (24.41%), Euphorbiaceae (22.91%) and Phyllanthaceae (20.23%). The richest genera are: Cola, Macaranga, Oncoba (4 species each); the genera Diospyros, Trichilia, Vitex and Zanthoxylum (3 species each). The ecologically important species within the forest studied are: Funtumia africana (26.14%), Coelocaryon preussii (18.46%), Pycnanthus angolensis (15.57%), Tabernaemontana crassa (14.85%) and Olax subscorpioidea (13.04%). Assessment of carbon stocks in the six forest reservoirs studied (living trees and roots, understorey, dead wood, litter and rootlets) shows that they vary according to the land-use types. It is 119.41 t.C.ha-¹ in gallery forest, 115.2 t.C.ha-¹ in young secondary forest and 90.56 t.C.ha-¹ in fallow. The Wilcoxon statistical test shows that the carbon in the young secondary forest is identical to that in the fallow, which is identical to the carbon in the gallery forest. At the individual species level, the largest diameter class [25-35[ sequesters the most carbon (232.94 tC/ha). This work shows that the quantity of carbon sequestered by a biotope is a function of the age of the stand.

Keywords: floristic diversity, carbon stocks, evergreen forests, communal forest, Ngambé-Ndom-Nyanon

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Authors: Zohreh Mohammadi, Jan Kaspar, Peter Lohmander, Robert Marusak, Harald Vacik, Ljusk Ola Eriksson

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Storms are important damaging agents in European forest ecosystems. In the latest decades, significant economic losses in European forestry occurred due to storms. This study investigates the problem of optimal harvest planning when forest stands risk to be felled by storms. One of the most applicable mathematical methods which are being used to optimize forest management is stochastic dynamic programming (SDP). This method belongs to the adaptive optimization class. Sequential decisions, such as harvest decisions, can be optimized based on sequential information about events that cannot be perfectly predicted, such as the future storms and the future states of wind protection from other forest stands. In this paper, stochastic dynamic programming is used to maximize the expected present value of the profits from an area consisting of several forest stands. The region of analysis is the Czech Republic. The harvest decisions, in a particular time period, should be simultaneously taken in all neighbor stands. The reason is that different stands protect each other from possible winds. The optimal harvest age of a particular stand is a function of wind speed and different wind protection effects. The optimal harvest age often decreases with wind speed, but it cannot be determined for one stand at a time. When we consider a particular stand, this stand also protects other stands. Furthermore, the particular stand is protected by neighbor stands. In some forest stands, it may even be rational to increase the harvest age under the influence of stronger winds, in order to protect more valuable stands in the neighborhood. It is important to integrate wind risk in forestry decision-making.

Keywords: Czech republic, forest stands, stochastic dynamic programming, wind risk

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Authors: Muhammad Nur Haniff Mohd Noor, Rokiah Kadir, Suriyani Muhamad

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The National Forestry Act (NFA) 1984 is the primary forest law that regulates forest-related activities in Peninsular Malaysia. In the 1990s, abundance of illegal logging cases have called for legislative reform of the NFA 1984. As a result, NFA 1984 was amended in 1993 with the principal goal of controlling illegal forest encroachment in the forms of illegal logging, unauthorized harvesting, unlicensed forest settlement and other forms of unlawful activities. At a conceptual level, this paper discusses the policies and strategies implemented under the NFA 1984 (Amendment 1993) that are dedicated to overcome illegal logging. Then, the policies and strategies employed are reviewed and evaluated. Next, this paper conceptually discusses the loopholes of NFA 1984 (Amendment 1993) in relation to aspects where the regulation is considered insufficient to curb illegal logging. In the final section, vital actions and suggested improvements to improve the overall effectiveness of NFA 1984 (Amendment 1993) are examined.

Keywords: forest law and regulation, illegal logging, National Forestry Act 1984, NFA 1984, Amendment 1993, Peninsular Malaysia

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Authors: Medani Prasad Rijal

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The study outlines the impacts of decentralized forest policy on natural Sal (shorea robusta) forests in the Terai region of Nepal. The government has implemented community forestry program to manage the forest resources and improve the livelihood of local people collectively. The forest management authorities such as conserve, manage, develop and use of forest resources were shifted to the local communities, however, the ownership right of the forestland retained by the government. Local communities took the decision on harvesting, distribution, and sell of forest products by fixing the prices independently. The local communities were putting the low value of forest products and distributed among the user households on the name of collective decision. The decision of low valuation is devaluating the worth of forest products. Therefore, the study hypothesized that decision-making capacities are equally prominent next to the decentralized policy and program formulation. To accomplish the study, individual to group level discussions and questionnaire survey methods were applied with executive committee members and user households. The study revealed that the local intuition called Community Forest User Group (CFUG) committee normally took the decisions on consensus basis. Considering to the access and affording capacity of user households having poor economic backgrounds, low pricing mechanism of forest products has been practiced, even though the Sal timber is far expensive in the local market. The local communities thought that low pricing mechanism is accessible to all user households from poor to better off households. However, the analysis of forest products distribution opposed the assumption as most of the Sal timber, which is the most valuable forest product of community forest only purchased by the limited households of better economic conditions. Since the Terai region is heterogeneous by socio-economic conditions, better off households always have higher affording capacity and possibility of taking higher timber benefits because of low price mechanism. On the other hand, the minimum price rate of forest products has poor contribution in community fund collection. Consequently, it has poor support to carry out poverty alleviation activities to poor people. The local communities have been fixed Sal timber price rate around three times cheaper than normal market price, which is a strong evidence of forest product devaluation itself. Finally, the study concluded that the capacity building of local executives as the decision-makers of natural Sal forests is equally indispensable next to the policy and program formulation for effective decentralized forest management. Unilateral decentralized forest policy may devaluate the forest products rather than devolve of power to the local communities and empower to them.

Keywords: community forestry program, decentralized forest policy, Nepal, Sal forests, Terai

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Authors: István Fekete, Imre Berki, Áron Béni, Katalin Juhos, Marianna Makádi, Zsolt Kotroczó

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The average annual precipitation significantly influences the moisture content of the soils and, through this, the decomposition of the organic substances in the soils, the leaching of nutrients from the soils, and the pH of the soils. Climate change, together with the lengthening of the vegetation period and the increasing CO₂ level, can increase the amount of biomass that is formed. Degradation processes, which accelerate as the temperature increases and slow down due to the drying climate, and the change in the degree of leaching can cancel out or strengthen each other's effects. In the course of our research, we looked for oak forests with climate-zonal soils where the geological, geographical and ecological background conditions are as similar as possible, apart from the different annual precipitation averages and the differences that can arise from them. We examined 5 dry and 5 humid Hungarian oak soils. Climate change affects the soils of drier and wetter forests differently. The aim of our research was to compare the content of carbon, nitrogen and some other nutrients, as well as the pH of the soils of humid and dry forests. Showing the effects of the drier climate on the tested soil parameters. In the case of the examined forest soils, we found a significant difference between the soils of dry and humid forests: in the case of the annual average precipitation values (p≥ 0.0001, for dry forest soils: 564±5.2 mm; for humid forest soils: 716±3.8 mm) for pH (p= 0.0004, for dry forest soils: 5.49±0.16; for wet forest soils: 5.36±0.21); for C content (p= 0.0054, for dry forest soils: 6.92%±0.59; for humid forest soils 3.09%±0.24), for N content (p= 0.0022, dry forest in the case of soils: 0.44%±0.047; in the case of humid forest soils: 0.23%±0.013), for the K content (p=0.0017, in the case of dry forest soils: 5684±732 (mg/kg); in the case of humid forest soils 2169±196 (mg/kg)), for the Ca content (p= 0.0096, for dry forest soils: 8207±2118 (mg/kg); for wet forest soils 957±320 (mg/kg)). No significant difference was found in the case of Mg. In a wetter environment, especially if the moisture content of the soil is also optimal for the decomposing organisms during the growing season, the decomposition of organic residues accelerates, and the processes of leaching from the soil are also intensified. The different intensity of the leaching processes is also well reflected in the quantitative differences of Ca and K, and in connection with these, it is also reflected in the difference in pH values. The differences in the C and N content can be explained by differences in the intensity of the decomposition processes. In addition to warming, drying is expected in a significant part of Hungary due to climate change. Thus, the comparison of the soils of dry and humid forests allows us to predict the subsequent changes in the case of the examined parameters.

Keywords: soil nutrients, precipitation difference, climate change, organic matter decomposition, leaching

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Authors: Anil Kumar Kursenge

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In the State of united Andhra Pradesh, many Adivasi People live in areas rich in living and non-living resources, including forests that contain abundant biodiversity, water and minerals. Of united Andhra Pradesh 76.2m population, over five million are Adivasi population of forest landscape. They depend on forests for a substantial part of their livelihoods and close cultural affinity with forests. However, they are the most impoverished population of the State, and the high levels of poverty in Andhra Pradesh forest landscapes are largely an outcome of historically-rooted institutionalised marginalisation. As the State appropriated forests and forest land for itself, it deprived local people of their customary rights in the forest. The local realities of the forest rights deprivations are extremely complex, reflecting a century and a half of compounded processes. With growing population pressure and ever-increasing demands for natural and mineral resources, Adivasi Peoples' lands, which are often relatively rich in resources, become more and more attractive to 'developers.' The development projects and institutionalised marginalisation have been deprived Adivasi people's rights over natural resources has resulted in serious negative effects on Adivasi people and on their lands. Historically, the desire for development for such resources has resulted in the removal, decimation, or extermination of many tribal communities. These deprivations have led to highly conflictual relations between the State and the Adivasi people and forest areas in Andhra Pradesh. Today, the survival of the Adivasi Peoples requires recognition of their rights to the forest resources found in their lands and territories on which they depend for their economic, cultural, survival, spiritual and physical well-being. In this context, this paper attempts to discuss the issues of deprivation with regard to access to forest resources and development projects where many Adivasis in State uprooted from their homes and lands.

Keywords: tribal people, forest rights, livelihoods, deprivation, marginalisation, Andhra Pradesh

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Authors: Brindha Senthil Kumar, Payel Chakraborty, Senthil Kumar Nachimuthu, Arindam Maitra, Prem Nath

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Gastric cancer is predominantly caused by demographic and diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (ECG) from diet and lifestyle factors using supervised machine learning algorithms. For this study, 160 healthy individual and 80 cases were selected who had been followed for 3 years (2016-2019), at Civil Hospital, Aizawl, Mizoram. A dataset containing 11 features that are core risk factors for the gastric cancer were extracted. Supervised machine algorithms: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Multilayer perceptron, and Random Forest were used to analyze the dataset using Python Jupyter Notebook Version 3. The obtained classified results had been evaluated using metrics parameters: minimum_false_positives, brier_score, accuracy, precision, recall, F1_score, and Receiver Operating Characteristics (ROC) curve. Data analysis results showed Naive Bayes - 88, 0.11; Random Forest - 83, 0.16; SVM - 77, 0.22; Logistic Regression - 75, 0.25 and Multilayer perceptron - 72, 0.27 with respect to accuracy and brier_score in percent. Naive Bayes algorithm out performs with very low false positive rates as well as brier_score and good accuracy. Naive Bayes algorithm classification results in predicting ECG showed very satisfactory results using only diet cum lifestyle factors which will be very helpful for the physicians to educate the patients and public, thereby mortality of gastric cancer can be reduced/avoided with this knowledge mining work.

Keywords: Early Gastric cancer, Machine Learning, Diet, Lifestyle Characteristics

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Authors: Alemayehu Muluneh, Saskia Keesstra, Leo Stroosnijder, Woldeamlak Bewket, Ashenafi Burka

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There are some scientific evidences and a belief by many that forests attract rain and deforestation contributes to a decline of rainfall. However, there is still a lack of concrete scientific evidence on the role of forests in rainfall amount. In this paper, we investigate the forest-rainfall relationships in the environmentally hot spot area of the Central Rift Valley (CRV) of Ethiopia. Specifically, we evaluate long term (1970-2009) rainfall variability and its relationship with historical forest cover and the relationship between existing forest cover and topographical variables and rainfall distribution. The study used 16 long term and 15 short term rainfall stations. The Mann-Kendall test, bi variate and multiple regression models were used. The results show forest and wood land cover continuously declined over the 40 years period (1970-2009), but annual rainfall in the rift valley floor increased by 6.42 mm/year. But, on the escarpment and highlands, annual rainfall decreased by 2.48 mm/year. The increase in annual rainfall in the rift valley floor is partly attributable to the increase in evaporation as a result of increasing temperatures from the 4 existing lakes in the rift valley floor. Though, annual rainfall is decreasing on the escarpment and highlands, there was no significant correlation between this rainfall decrease and forest and wood land decline and also rainfall variability in the region was not explained by forest cover. Hence, the decrease in annual rainfall on the escarpment and highlands is likely related to the global warming of the atmosphere and the surface waters of the Indian Ocean. Spatial variability of number of rainy days from systematically observed two-year’s rainfall data (2012-2013) was significantly (R2=-0.63) explained by forest cover (distance from forest). But, forest cover was not a significant variable (R2=-0.40) in explaining annual rainfall amount. Generally, past deforestation and existing forest cover showed very little effect on long term and short term rainfall distribution, but a significant effect on number of rainy days in the CRV of Ethiopia.

Keywords: elevation, forest cover, rainfall, slope

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Authors: Roman Snytsar

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Pseudo-random numbers are often used in scientific computing such as the Monte Carlo Simulations or the Quantum Inspired Optimization. Requirements for a parallel random number generator running in the modern multi-core vector environment are more stringent than those for sequential random number generators. As well as passing the usual quality tests, the output of the parallel random number generator must be verifiable and reproducible throughout the concurrent execution. We propose a family of vectorized Permuted Congruential Generators. Implementations are available for multiple modern vector modern computer architectures. Besides demonstrating good single core performance, the generators scale easily across many processor cores and multiple distributed nodes. We provide performance and parallel speedup analysis and comparisons between the implementations.

Keywords: pseudo-random numbers, quantum optimization, SIMD, parallel computing

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Authors: Timothy L. Porter, T. Randy Dillingham

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Vegetation growth and decomposition, along with soil microbial activity play a complex role in the production of greenhouse gases originating in forest soils. The absorption or emission (respiration) of these gases is a function of many factors relating to the soils themselves, the plants, and the environment in which the plants are growing. For this study, we have constructed a battery-powered, portable field mass spectrometer for use in analyzing gases in the soils surrounding trees, plants, and other areas. We have used the instrument to sample in real-time the greenhouse gases carbon dioxide and methane in soils where plant life may be contributing to the production of gases such as methane. Gases such as isoprene, which may help correlate gas respiration to microbial activity have also been measured. The instrument is composed of a quadrupole mass spectrometer with part per billion or better sensitivity, coupled to battery-powered turbo and diaphragm pumps. A unique ambient air pressure differentially pumped intake apparatus allows for the real-time sampling of gases in the soils from the surface to several inches below the surface. Results show that this instrument is capable of instant, part-per-billion sensitivity measurement of carbon dioxide and methane in the near surface region of various forest soils. We have measured differences in soil respiration resulting from forest thinning, forest burning, and forest logging as compared to pristine, untouched forests. Further studies will include measurements of greenhouse gas respiration as a function of temperature, microbial activity as measured by isoprene production, and forest restoration after fire.

Keywords: forest, soil, greenhouse, quadrupole

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Authors: Dhiraj Neupane

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Decentralized policy and practices have largely concentrated on the transformation of decision-making authorities from central to local institutions (or people) in the developing world. Such policy and practices always aimed for the equitable and efficient management of resources in the line of poverty reduction. The transformation of forest decision-making autonomy has also glorified as the best forest management alternatives to maximize the forest benefits and improve the livelihood of local people living nearby the forests. However, social heterogeneity and poor decision-making capacity of local institutions (or people) pose a nexus while managing the resources and sharing the forest benefits among the user households despite the policy objectives. The situation is severe in the lowland of Nepal, where forest resources have higher economic potential and user households have heterogeneous socio-economic conditions. The study discovered that utilizing the power of decision-making autonomy, user households were putting low values of timber considering the equitable access of timber to all user households as it is the most valuable product of community forest. Being the society is heterogeneous by socio-economic conditions, households of better economic conditions were always taking higher amount of forest benefits. The low valuation of timber has negative consequences on equitable benefit sharing and poor support to livelihood improvement of user households. Moreover, low valuation has possibility to increase the local demands of timber and increase the human pressure on forests.

Keywords: decentralized forest policy, Nepal, poverty, social heterogeneity, Terai

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Authors: Ayuko Itsuki, Sachiyo Aburatani

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Soil enzyme activities in Kasuga-yama Hill Primeval Forest (Nara, Japan) were examined to determine levels of mineralization and metabolism. Samples were selected from the soil surrounding laurel-leaved (B_B-1) and Carpinus japonica (B_B-2 and P_w) trees for analysis. Cellulase, β-xylosidase, and protease activities were higher in B_B-1 samples those in B_B-2 samples. These activity levels corresponded to the distribution of cellulose and hemicellulose in the soil horizons. Cellulase, β-xylosidase, and chymotrypsin activities were higher in soil from the P_w forest than in that from the B_B-2 forest. The relationships between the soil enzymes calculated by Spearman’s rank correlation indicate that the interactions between enzymes in B_B-2 samples were more complex than those in P_w samples.

Keywords: comparative analysis, enzyme activities, forest soil, Spearman's rank correlation

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Authors: Kazuma Okada, Tomoaki Hashimoto, Hirokazu Tahara

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Recently, the effectiveness of random dither quantization method for linear feedback control systems has been shown in several papers. However, the random dither quantization method has not yet been applied to nonlinear feedback control systems. The objective of this paper is to verify the effectiveness of random dither quantization method for nonlinear feedback control systems. For this purpose, we consider the attitude stabilization problem of satellites using discrete-level actuators. Namely, this paper provides a control method based on the random dither quantization method for stabilizing the attitude of satellites using discrete-level actuators.

Keywords: quantized control, nonlinear systems, random dither quantization

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Authors: Bikram Jung Kunwar

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Forests have central roles in climate change. The conservation of forests sequestrates the carbon from the atmosphere and also regulates the carbon cycle. However, knowingly and unknowingly the world’s forests were deforested and degraded annually at the rate of 0.18% and emitted the carbon to the atmosphere. The IPCC reports claimed that the deforestation and forest degradation accounts 1/5th of total carbon emission, which is second position after fossil fuels. Since 1.6 billion people depend on varying degree on forests for their daily livelihood, not all deforestation are undesirable. Therefore, to conserve the forests and find the livelihood opportunities for forest surrounding people is prerequisites to address the climate change problems especially in developing countries, and also a growing concern to the forestry sector researchers, planners and policy makers. The study examines the role of community based forest management in carbon mitigation and adaptation taking the examples of Nepal’s community forestry program. In the program, the government hands over a part of national forests to the local communities with sole forest management authorities. However, the government itself retained the ownership rights of forestland. Local communities organized through a local institution called Community Forest User Group (CFUG) managed the forests. They also formed an operational plan with technical prescriptions and a constitution with forest management rules and regulations. The implementation results showed that the CFUGs are not only found effective to organize the local people and construct a local institution to forest conservation and management activities, but also they are able to collect a community fund from the sale of forest products and carried out various community development activities. These development activities have decisive roles to improve the livelihood of forest surrounding people and eventually to address the climate change problems.

Keywords: climate change, community forestry, local institution, Nepal

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Authors: Amri Yusoff, Shahrizuan Shafiril, Ashardi Abas, Norma Che Yusoff

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Nowadays, illegal logging has been causing much effect to our forest. Some of it causes a flash flood, avalanche, global warming, and etc. This comprehensibly makes us wonder why, what, and who has made it happened. Often, it already has been too late after we have known the cause of it. Even the Malaysian Royal Belum forest has not been spared from land clearing or illegal activity by the natives although this area has been gazetted as a protected area preserved for future generations. Furthermore, because of its sizeable and wide area, these illegal activities are difficult to monitor and to maintain. A critical action must be called upon to prevent all of these unhealthy activities from recurrence. Therefore, a remote monitoring device must be developed in order to capture critical real-time data such as temperature, humidity, gaseous, fire, and rain detection which indicates the current and preserved natural state and habitat in the forest. Besides, this device location can be detected via GPS by showing the latitudes and longitudes of its current location and then to be transmitted by SMS via GSM system. All of its readings will be sent in real-time for data management and analysis. This result will be benefited to the monitoring bodies or relevant authority in keeping the forest in the natural habitat. Furthermore, this research is to gather a unified data and then will be analysed for its comparison with an existing method.

Keywords: remote monitoring system, forest data, GSM, GPS, wireless sensor

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Authors: Susan Mwai, Mary Muchane, Peter Wachira, Sheila Okoth, Muchai Muchane, Halima Saado

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Tropical forests harbor a wide range of biodiversity and rich macro-fungi diversity compared to the temperate regions in the World. However, biodiversity is facing the threat of extinction following the rate of forest loss taking place before proper study and documentation of macrofungi is achieved. The present study was undertaken to determine the effect of converting indigenous habitat to plantation forest on macrofungi diversity. To achieve the objective of this study, an inventory focusing on macro-fungi diversity was conducted within Kereita block in Kikuyu Escarpment forest which is on the southern side of Aberdare mountain range. The macrofungi diversity was conducted in the indigenous forest and in more than 15 year old Patula plantation forest , during the wet (long rain season, December 2014) and dry (Short rain season, May, 2015). In each forest type, 15 permanent (20m x 20m) sampling plots distributed across three (3) forest blocks were used. Both field and laboratory methods involved recording abundance of fruiting bodies, taxonomic identity of species and analysis of diversity indices and measures in terms of species richness, density and diversity. R statistical program was used to analyze for species diversity and Canoco 4.5 software for species composition. A total number of 76 genera in 28 families and 224 species were encountered in both forest types. The most represented taxa belonged to the Agaricaceae (16%), Polyporaceae (12%), Marasmiaceae, Mycenaceae (7%) families respectively. Most of the recorded macro-fungi were saprophytic, mostly colonizing the litter 38% and wood 34% based substrates, which was followed by soil organic dwelling species (17%). Ecto-mycorrhiza fungi (5%) and parasitic fungi (2%) were the least encountered. The data established that indigenous forests (native ecosystems) hosts a wide range of macrofungi assemblage in terms of density (2.6 individual fruit bodies / m2), species richness (8.3 species / plot) and species diversity (1.49/ plot level) compared to the plantation forest. The Conversion of native forest to plantation forest also interfered with species composition though did not alter species diversity. Seasonality was also shown to significantly affect the diversity of macro-fungi and 61% of the total species being present during the wet season. Based on the present findings, forested ecosystems in Kenya hold diverse macro-fungi community which warrants conservation measures.

Keywords: diversity, Indigenous forest, macro-fungi, plantation forest, season

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