Search results for: data harvesting

Authors: Rizka Aisha Rahmi Hariadi, Chao Ou-Yang, Han-Cheng Wang, Rajesri Govindaraju

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As the increasing of medical check-up popularity, there are a huge number of medical check-up data stored in database and have not been useful. These data actually can be very useful for future strategic planning if we mine it correctly. In other side, a lot of patients come with unpredictable coming and also limited available facilities make medical check-up service offered by hospital not maximal. To solve that problem, this study used those medical check-up data to predict patient re-coming. Sequential pattern mining (SPM) and association rules method were chosen because these methods are suitable for predicting patient re-coming using sequential data. First, based on patient personal information the data was grouped into … groups then discriminant analysis was done to check significant of the grouping. Second, for each group some frequent patterns were generated using SPM method. Third, based on frequent patterns of each group, pairs of variable can be extracted using association rules to get general pattern of re-coming patient. Last, discussion and conclusion was done to give some implications of the results.

Keywords: patient re-coming, medical check-up, health examination, data mining, sequential pattern mining, association rules, discriminant analysis

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Authors: Eszter Siposne Nandori

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The paper describes how the subjective interpretation of poverty changed during the COVID-19 pandemic. The results of data collection at the end of 2020 are compared to the results of a similar survey from 2019. The methods of systematic data collection are used to collect data about the beliefs of the population about poverty. The analysis is carried out in Borsod-Abaúj-Zemplén County, one of the most backward areas in Hungary. The paper concludes that poverty is mainly linked to material values, and it did not change from 2019 to 2020. Some slight changes, however, highlight the effect of the pandemic: poverty is increasingly seen as a generational problem in 2020, and another important change is that isolation became more closely related to poverty.

Keywords: Hungary, interpretation of poverty, pandemic, systematic data collection, subjective poverty

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Authors: Eteri Kharaishvili

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Introduction. The paper analyzes the main indicators of export and import of Georgian agri-food products; identifies positive and negative trends under the pandemic; based on the revealed problemssubstantiates the need formodernization ofin agri-food sector. It is argued that low production and productivity rates of food products negatively impact achieving the optimal export-to-import ratio; therefore, it leads toincreaseddependence on other countries andreduces the level of food security. Research objectives. The objective of the research is to identify the key challenges based on the analysis of export-import indicators of Georgian food products during the pandemic period and develop recommendations on the possibilities of post-pandemic perspectives. Research methods. Various theoretical and methodological research tools are used in the paper; in particular, a desk research is carried out on the research topic; endogenous and exogenous variables affecting export and import are determined through factor analysis; SWOT and PESTEL analysis are used to identify development opportunities; selection and groupingof data, identification of similarities and differences is carried outby using analysis, synthesis, sampling, induction and other methods; a qualitative study is conducted based on a survey of agri-food experts and exporters for clarifying the factors that impede export-import flows. Contributions. The factors that impede the export of Georgian agri-food products in the short run under COVID-19 pandemic are identified. These are: reduced income of farmers, delays in the supply of raw materials and supplies to the agri-food sectorfrom the neighboring industries, as well as in harvesting, processing, marketing, transportation, and other sectors; increased indirect costs, etc. The factors that impede the export in the long run areas follows loss of public confidence in the industry, risk of losing positions in traditional markets, etc. Conclusions are made on the problems in the field of export and import of Georgian agri-food products in terms of the pandemic; development opportunities are evaluated based on the analysis of the agri-food sector potential. Recommendations on the development opportunities for export and import of Georgian agri-food products in the post-pandemic period are proposed.

Keywords: agri-food products, export, and import, pandemic period, hindering factor, development potential

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Authors: Nicodemus M. J. Mbwambo, Yu-Shan Sun, Murali Sitaraman, Joan Krone

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This paper presents a generic data abstraction that captures a navigable tree position. The mathematical modeling of the abstraction encapsulates the current tree position, which can be used to navigate and modify the tree. The encapsulation of the tree position in the data abstraction specification avoids the use of explicit references and aliasing, thereby simplifying verification of (imperative) client code that uses the data abstraction. To ease the tasks of such specification and verification, a general tree theory, rich with mathematical notations and results, has been developed. The paper contains an example to illustrate automated verification ramifications. With sufficient tree theory development, automated proving seems plausible even in the absence of a special-purpose tree solver.

Keywords: automation, data abstraction, maps, specification, tree, verification

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Authors: Z. Ezzouine, A. Nakheli

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This paper presents a high position electromagnetic sensor system (HPESS) that is applicable for moving object detection. The authors have developed a high-performance position sensor prototype dedicated to students’ laboratory. The challenge was to obtain a highly accurate and real-time sensor that is able to calculate position, length or displacement. An electromagnetic solution based on a two coil induction principal was adopted. The HPESS converts mechanical motion to electric energy with direct contact. The output signal can then be fed to an electronic circuit. The voltage output change from the sensor is captured by data acquisition system using LabVIEW software. The displacement of the moving object is determined. The measured data are transmitted to a PC in real-time via a DAQ (NI USB -6281). This paper also describes the data acquisition analysis and the conditioning card developed specially for sensor signal monitoring. The data is then recorded and viewed using a user interface written using National Instrument LabVIEW software. On-line displays of time and voltage of the sensor signal provide a user-friendly data acquisition interface. The sensor provides an uncomplicated, accurate, reliable, inexpensive transducer for highly sophisticated control systems.

Keywords: electromagnetic sensor, accurately, data acquisition, position measurement

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Authors: Gilang Maulana, Dhika Rahma Qomariah, Yasin Fadil

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Purpose: This research meant to measure the magnitude of the influence of the quality of the presentation to the targeted audience perception in catching information presentation. Design/Methodology/Approach: This research uses a quantitative research method. The kind of data that uses in this research is the primary data. The population in this research are students the economics faculty of Semarang State University. The sampling techniques uses in this research is purposive sampling. The retrieving data uses questionnaire on 30 respondents. The data analysis uses descriptive analysis. Result: The quality of presentation influential positive against perception of the audience. This proved that the more qualified presentation will increase the perception of the audience. Limitation: Respondents were limited to only 30 people.

Keywords: quality of presentation, presentation, audience, perception, semarang state university

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Authors: Yuqing Chen, Ying Xu, Renfa Li

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The development of matrix completion theory provides new approaches for data gathering in Wireless Sensor Networks (WSN). The existing matrix completion algorithms for WSN mainly consider how to reduce the sampling number without considering the real-time performance when recovering the data matrix. In order to guarantee the recovery accuracy and reduce the recovery time consumed simultaneously, we propose a new ALM algorithm to recover the weather monitoring data. A lot of experiments have been carried out to investigate the performance of the proposed ALM algorithm by using different parameter settings, different sampling rates and sampling models. In addition, we compare the proposed ALM algorithm with some existing algorithms in the literature. Experimental results show that the ALM algorithm can obtain better overall recovery accuracy with less computing time, which demonstrate that the ALM algorithm is an effective and efficient approach for recovering the real world weather monitoring data in WSN.

Keywords: wireless sensor network, matrix completion, singular value thresholding, augmented Lagrange multiplier

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Authors: Monika Chuchro

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This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain a subgroups of time series data with normal distribution from inflow into waste water treatment plant data which Composed of several groups differing by mean value. Two simple algorithms: K-mean and EM were chosen as a clustering method. The rand index was used to measure the similarity. After simple meta-clustering, regression model was performed for each subgroups. The final model was a sum of subgroups models. The quality of obtained model was compared with the regression model made using the same explanatory variables but with no clustering of data. Results were compared by determination coefficient (R2), measure of prediction accuracy mean absolute percentage error (MAPE) and comparison on linear chart. Preliminary results allows to foresee the potential of the presented technique.

Keywords: clustering, data analysis, data mining, predictive models

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Authors: Xiaogang Li, Jieqiong Miao

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As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines．For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error

Keywords: Grey prediction model, trigonometric functions, support vector machines, genetic algorithms, root mean square error

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Authors: Terungwa Simon Yange, Agana Moses A.

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This paper studied steganography and designed a simplistic approach to a steganographic tool for hiding information in image files with the view of addressing the security challenges with data by hiding data from unauthorized users to improve its security. The Structured Systems Analysis and Design Method (SSADM) was used in this work. The system was developed using Java Development Kit (JDK) 1.7.0_10 and MySQL Server as its backend. The system was tested with some hypothetical health records which proved the possibility of protecting data from unauthorized users by making it secret so that its existence cannot be easily recognized by fraudulent users. It further strengthens the confidentiality of patient records kept by medical practitioners in the health setting. In conclusion, this work was able to produce a user friendly steganography software that is very fast to install and easy to operate to ensure privacy and secrecy of sensitive data. It also produced an exact copy of the original image and the one carrying the secret message when compared with each.

Keywords: steganography, cryptography, encryption, decryption, secrecy

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Authors: Linnea Stenliden

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The aim of this paper is to understand emerging learning conditions, when a visual analytics is implemented and used in K 12 (education). To date, little attention has been paid to the role visual analytics (digital media and technology that highlight visual data communication in order to support analytical tasks) can play in education, and to the extent to which these tools can process actionable data for young students. This study was conducted in three public K 12 schools, in four social science classes with students aged 10 to 13 years, over a period of two to four weeks at each school. Empirical data were generated using video observations and analyzed with help of metaphors by Latour. The learning conditions are found to be distinguished by broad complexity characterized by four dimensions. These emerge from the actors’ deeply intertwined relations in the activities. The paper argues in relation to the found dimensions that novel approaches to teaching and learning could benefit students’ knowledge building as they work with visual analytics, analyzing visualized data.

Keywords: analytical reasoning, complexity, data use, problem space, visual analytics, visual storytelling, translation

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Authors: Mahmoud Ahmed Farrag, Ali Zain Elabdeen Heikal, Mohamed Shawky Ahmed, Ahmed Osama Amer

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Crowdsourced data refers to data that is collected and shared by a large number of individuals or organizations, often through the use of digital technologies such as mobile devices and social media. The shortage in crash data collection in developing countries makes it difficult to fully understand and address road safety issues in these regions. In developing countries, crowdsourced data can be a valuable tool for improving road safety, particularly in urban areas where the majority of road crashes occur. This study is the first to develop safety performance functions using crowdsourced data by adopting a negative binomial structure model and Full Bayes model to investigate traffic safety for urban road networks and provide insights into the impact of roadway characteristics. Furthermore, as a part of the safety management process, network screening has been undergone through applying two different methods to rank the most hazardous road segments: PCR method (adopted in the Highway Capacity Manual HCM) as well as a graphical method using GIS tools to compare and validate. Lastly, recommendations were suggested for policymakers to ensure safer roads.

Keywords: crowdsourced data, road crashes, safety performance functions, Full Bayes models, network screening

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Authors: Apurva Saxena, Sunita Gond

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Demand of computation, data storage in large amount are rapidly increases day by day. Cloud computing technology fulfill the demand of today’s computation but this will lead to high power consumption in cloud data centers. Initiative for Green IT try to reduce power consumption and its adverse environmental impacts. Paper also focus on various green computing techniques, proposed models and efficient way to make cloud greener.

Keywords: virtualization, cloud computing, green computing, data center

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Authors: Dmitry Yu. Korulkin, Raissa A. Muzychkina, Evgenii N. Kojaev

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In review the generalized data about biological activity of anthraquinone-containing plants and specimens on their basis is presented. Data of traditional medicine, results of bioscreening and clinical researches of specimens are analyzed.

Keywords: anthraquinones, physiologically active substances, phytopreparation, Ramon

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Authors: Lars Laußat, Manfred Helmus, Kamil Szczesny, Markus König

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As one result of the project “Reactive Construction Project Scheduling using Real Time Construction Logistic Data and Simulation”, a procedure for using data about uncertain resource availability assumptions in reactive scheduling processes has been developed. Prediction data about resource availability is generated in a formalized way using real-time monitoring data e.g. from auto-ID systems on the construction site and in the supply chains. The paper focuses on the formalization of the procedure for monitoring construction logistic processes, for the detection of disturbance and for generating of new and uncertain scheduling assumptions for the reactive resource constrained simulation procedure that is and will be further described in other papers.

Keywords: auto-ID, construction logistic, fuzzy, monitoring, RFID, scheduling

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Authors: Ajish Sreedharan

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With the fast evolution of digital data exchange, security information becomes much important in data storage and transmission. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access. As encryption process is applied to the whole image in AES ,it is difficult to improve the efficiency. In this paper, wavelet decomposition is used to concentrate the main information of image to the low frequency part. Then, AES encryption is applied to the low frequency part. The high frequency parts are XORed with the encrypted low frequency part and a wavelet reconstruction is applied. Theoretical analysis and experimental results show that the proposed algorithm has high efficiency, and satisfied security suits for image data transmission.

Keywords: discrete wavelet transforms, AES, dynamic SBox

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Authors: Taras Agryzkov, Almudena Nolasco-Cirugeda, Jose L. Oliver, Leticia Serrano-Estrada, Leandro Tortosa, Jose F. Vicent

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This paper aims to represent the commercial activity of a city taking as source data the social network Foursquare. The city of Murcia is selected as case study, and the location-based social network Foursquare is the main source of information. After carrying out a reorganisation of the user-generated data extracted from Foursquare, it is possible to graphically display on a map the various city spaces and venues –especially those related to commercial, food and entertainment sector businesses. The obtained visualisation provides information about activity patterns in the city of Murcia according to the people`s interests and preferences and, moreover, interesting facts about certain characteristics of the town itself.

Keywords: social networks, spatial analysis, data visualization, geocomputation, Foursquare

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

Procedia PDF Downloads 275

Authors: T. Thulare, M. Herselman, A. Botha

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Poor system use, including inappropriate design of health information systems, causes difficulties in communication with patients and increased time spent by healthcare professionals in recording the necessary health information for medical records. System features like pop-up reminders, complex menus, and poor user interfaces can make medical records far more time consuming than paper cards as well as affect decision-making processes. Although errors associated with health information and their real and likely effect on the quality of care and patient safety have been documented for many years, more research is needed to measure the occurrence of these errors and determine the causes to implement solutions. Therefore, the purpose of this paper is to identify data integrity challenges in hospital information systems through a scoping review and based on the results provide recommendations on how to manage these. Only 34 papers were found to be most suitable out of 297 publications initially identified in the field. The results indicated that human and computerized systems are the most common challenges associated with data integrity and factors such as policy, environment, health workforce, and lack of awareness attribute to these challenges but if measures are taken the data integrity challenges can be managed.

Keywords: data integrity, data integrity challenges, hospital information systems, South Africa

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Authors: Shoujia Fang, Guoqing Ding, Xin Chen

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The quality of press-fit assembly is closely related to reliability and safety of product. The paper proposed a keypoint detection method based on convolutional neural network to improve the accuracy of keypoint detection in press-fit curve. It would provide an auxiliary basis for judging quality of press-fit assembly. The press-fit curve is a curve of press-fit force and displacement. Both force data and distance data are time-series data. Therefore, one-dimensional convolutional neural network is used to process the press-fit curve. After the obtained press-fit data is filtered, the multi-layer one-dimensional convolutional neural network is used to perform the automatic learning of press-fit curve features, and then sent to the multi-layer perceptron to finally output keypoint of the curve. We used the data of press-fit assembly equipment in the actual production process to train CNN model, and we used different data from the same equipment to evaluate the performance of detection. Compared with the existing research result, the performance of detection was significantly improved. This method can provide a reliable basis for the judgment of press-fit quality.

Keywords: keypoint detection, curve feature, convolutional neural network, press-fit assembly

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Authors: Sen Tanmoy, Jain Sarika, Panda Jagabandhu

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The studies regarding the impact of urbanization using the WRF-ARW model rely heavily on the static geographical information selected, including domain configuration and land use land cover (LULC) data. Accurate representation of LULC data provides essential information for understanding urban growth and simulating meteorological parameters such as temperature, precipitation etc. Researchers are using different LULC data as per availability and their requirements. As far as India is concerned, we have very limited resources and data availability. So, it is important to understand how we can optimize our results using limited LULC data. In this review article, we explored how a LULC map is generated from different sources in the Indian context and what its significance is in WRF-ARW modeling to study urbanization/Climate change or any other meteorological parameters. Bibliometric analyses were also performed in this review article based on countries of study and indexed keywords. Finally, some key points are marked out for selecting the most suitable LULC map for any urbanization-related study.

Keywords: LULC, LULC mapping, LANDSAT, WRF-ARW, ISRO, bibliometric Analysis.

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Authors: B. Y. Danjuma, A. Archibong-Eso, Aliyu M. Aliyu, H. Yeung

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In this experimental investigation, a new data for slugging frequency for high viscosity oil-gas flow are reported. Scale experiments were carried out using a mixture of air and mineral oil as the liquid phase in a 17 m long horizontal pipe with 0.0762 ID. The data set was acquired using two high-speed Gamma Densitometers at a data acquisition frequency of 250 Hz over a time interval of 30 seconds. For the range of flow conditions investigated, increase in liquid oil viscosity was observed to strongly influence the slug frequency. A comparison of the present data with prediction models available in the literature revealed huge discrepancies. A new correlation incorporating the effect of viscosity on slug frequency has been proposed for the horizontal flow, which represents the main contribution of this work.

Keywords: gamma densitometer, flow pattern, pressure gradient, slug frequency

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Authors: Tolga Hayit, Ucman Ergun, Ugur Fidan

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Being healthy is undoubtedly an indispensable necessity for human life. With technological improvements, in the literature, various health monitoring and imaging systems have been developed to satisfy your health needs. In this context, the work of monitoring and recording the data of individual health monitoring data via wireless technology is also being part of these studies. Nowadays, mobile devices which are located in almost every house and which become indispensable of our life and have wireless technology infrastructure have an important place of making follow-up health everywhere and every time because these devices were using in the health monitoring systems. In this study, Arduino an open-source microcontroller card was used in which a sample sugar measuring device was connected in series. In this way, the glucose data (glucose ratio, time) obtained with the glucometer is transferred to the mobile device based on the Android operating system with the Bluetooth technology channel. A mobile application was developed using the Apache Cordova framework for listing data, presenting graphically and reading data over Arduino. Apache Cordova, HTML, Javascript and CSS are used in coding section. The data received from the glucometer is stored in the local database of the mobile device. It is intended that people can transfer their measurements to their mobile device by using wireless technology and access the graphical representations of their data. In this context, the aim of the study is to be able to perform health monitoring by using different wireless technologies in mobile devices that can respond to different wireless technologies at present. Thus, that will contribute the other works done in this area.

Keywords: Arduino, Bluetooth, glucose measurement, mobile health monitoring

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Authors: Ahmed Rashad Harb Riad Ismail

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The purpose of this research is to study the potential of Dynamic Pricing if deployed by mobile operators and analyse its effects from both operators and consumers side. Furthermore, to conclude, throughout the research study, the recommended conditions for successful Dynamic Pricing deployment, recommended factors identifying the type of markets where Dynamic Pricing can be effective, and proposal for a Dynamic Pricing stakeholders’ framework were presented. Currently, the mobile telecommunications industry is witnessing a dramatic growth rate in the data consumption, being fostered mainly by higher data speed technology as the 4G LTE and by the smart devices penetration rates. However, operators’ revenue from data services lags behind and is decupled from this data consumption growth. Pricing strategy is a key factor affecting this ecosystem. Since the introduction of the 4G LTE technology will increase the pace of data growth in multiples, consequently, if pricing strategies remain constant, then the revenue and usage gap will grow wider, risking the sustainability of the ecosystem. Therefore, this research study is focused on Dynamic Pricing for 4G LTE data services, researching the drivers, benefits and challenges of 4G LTE Dynamic Pricing and the feasibility of its deployment in practice from different perspectives including operators, regulators, consumers, and telecommunications equipment manufacturers point of views.

Keywords: LTE, dynamic pricing, EPC, research

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Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

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Authors: Thomas L. McCullough, John D. Hague, Marylesa M. Howard, Matthew K. Kiser, Michael A. Mazur, Lance K. McLean, Johanna L. Turk

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Radiological search and mapping depends on the successful recognition of anomalies in large data sets which contain varied and dynamic backgrounds. We present a new algorithmic approach for real-time anomaly detection which is resistant to common detector imperfections, avoids the limitations of a source template library and provides immediate, and easily interpretable, user feedback. This algorithm is based on a continuous wavelet transform for variance reduction and evaluates the deviation between a foreground measurement and a local background expectation using methods from linear algebra. We also present a technique for recognizing and visualizing spectrally similar clusters of data. This technique uses Laplacian Eigenmap Manifold Learning to perform dimensional reduction which preserves the geometric "closeness" of the data while maintaining sensitivity to outlying data. We illustrate the utility of both techniques on real-world data sets.

Keywords: radiological search, radiological mapping, radioactivity, radiation protection

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Authors: Tero Huhtala, Minna Pikkarainen, Saila Saraniemi

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Information technology has long been used as an enabler of exchange for goods and services. Services are evolving from generic to personalized, and the reverse use of customer data has been discussed in both academia and industry for the past few years. This article presents the results of an empirical case study in the area of preventive health care services. The primary data were gathered in workshops, in which future personal data-based services were conceptualized by analyzing future scenarios from a business perspective. The aim of this study is to understand business model transformation in emerging personal data ecosystems. The work was done as a case study in the context of occupational healthcare. The results have implications to theory and practice, indicating that adopting personal data management principles requires transformation of the business model, which, if successfully managed, may provide access to more resources, potential to offer better value, and additional customer channels. These advantages correlate with the broadening of the business ecosystem. Expanding the scope of this study to include more actors would improve the validity of the research. The results draw from existing literature and are based on findings from a case study and the economic properties of the healthcare industry in Finland.

Keywords: ecosystem, business model, personal data, preventive healthcare

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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: Khoi A. Nguyen, Rodney A. Stewart, Hong Zhang

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‘Water-related energy’ is energy use which is directly or indirectly influenced by changes to water use. Informatics applying a range of mathematical, statistical and rule-based approaches can be used to reveal important information on demand from the available data provided at second, minute or hourly intervals. This study aims to combine these two concepts to improve the current water end use disaggregation problem through applying a wide range of most advanced pattern recognition techniques to analyse the concurrent high-resolution water-energy consumption data. The obtained results have shown that recognition accuracies of all end-uses have significantly increased, especially for mechanised categories, including clothes washer, dishwasher and evaporative air cooler where over 95% of events were correctly classified.

Keywords: deep learning network, smart metering, water end use, water-energy data

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Authors: Titinan Pothong, Prasit Wangpakapattanawong, Stephen Elliott

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Shifting cultivation is an indigenous agricultural practice among upland people and has long been one of the major land-use systems in Southeast Asia. As a result, fallows and secondary forests have come to cover a large part of the region. However, they are increasingly being replaced by monocultures, such as corn cultivation. This is believed to be a main driver of deforestation and forest degradation, and one of the reasons behind the recurring winter smog crisis in Thailand and around Southeast Asia. Accurate biomass estimation of trees is important to quantify valuable carbon stocks and changes to these stocks in case of land use change. However, presently, Thailand lacks proper tools and optimal equations to quantify its carbon stocks, especially for secondary evergreen forests, including fallow areas after shifting cultivation and smaller trees with a diameter at breast height (DBH) of less than 5 cm. Developing new allometric equations to estimate biomass is urgently needed to accurately estimate and manage carbon storage in tropical secondary forests. This study established new equations using a destructive method at three study sites: approximately 50-year-old secondary forest, 4-year-old fallow, and 7-year-old fallow. Tree biomass was collected by harvesting 136 individual trees (including coppiced trees) from 23 species, with a DBH ranging from 1 to 31 cm. Oven-dried samples were sent for carbon analysis. Wood density was calculated from disk samples and samples collected with an increment borer from 79 species, including 35 species currently missing from the Global Wood Densities database. Several models were developed, showing that aboveground biomass (AGB) was strongly related to DBH, height (H), and wood density (WD). Including WD in the model was found to improve the accuracy of the AGB estimation. This study provides insights for reforestation management, and can be used to prepare baseline data for Thailand’s carbon stocks for the REDD+ and other carbon trading schemes. These may provide monetary incentives to stop illegal logging and deforestation for monoculture.

Keywords: aboveground biomass, allometric equation, carbon stock, secondary forest

Procedia PDF Downloads 270