Modelling above-ground live trees biomass and carbon stock estimation of tropical lowland Dipterocarp forest: integration of field-based and remotely sensed estimates

Carbon exists as carbon dioxide (CO₂) which is one of the greenhouse gases (GHG) in the atmosphere that has an enormous influence on the impact of climate change. Therefore, the forest plays an undeniably pivotal role as a carbon sink, which absorbs carbon dioxide from the atmosphere. This research aims to develop allometric equation for above-ground live tree biomass (AGB) by combining field-based, combination of field data observation and technology (WV-3 and light detection and ranging (lidar)) and by using only technology derivation. The independent predictor was induced based on the literature review and theories, and an ordinary least square (OLS) estimator will be used to develop multiple linear regression models. During model selection, the best model fit was selected by calculating statistical parameters such as residual of the coefficient of determination (R²) selection methods, adjusted coefficient of determination (R²_adj), root mean square error, graphical analysis of the residuals, standard error (S_yx), and Akaike information criterion. An allometric equation of this research was developed using carbon stocks as dependent variables, and four of the predictor’s variables: diameter at breast height (DBH); total height observed at field (h_F); total height derived from airborne lidar (h_L); and morphometric variables of the crown projection area (CPA). Based on the statistic indicators, the most suitable model is Model 1, ln (S_c) = – β₀ + β₁ ln (h_L) + β₂ ln (DBH) + β₃ ln (CPA) for the combination of remote sensing and field observation; ln (S_c) = – β₀ + β₁ ln (h_F) + β₂ ln (DBH) for field inventory only; and ln (S_c) = – β₀ + β₁ ln (h_L) + β₂ ln (CPA) for remote sensing only. This model is reliable in forest management to estimate the AGB and carbon stock estimation using a selection of variable sources.     

Wideband microstrip‐based wearable antenna backed with full ground plane

A wideband microstrip‐based wearable antenna with a fractional bandwidth of 51% is designed using textile materials for wearable applications. The antenna operates between 2 and 3 GHz with low back‐radiation to ensure minimum coupling to the body and reduced electromagnetic power absorption in the human tissue. The behavior microstrip antenna topology, which is narrowband in nature, is altered via the combinations of various broadbanding techniques, while maintaining the existence of the full ground plane backing. This ensures that the antenna radiation is directed outward form the body to efficiently propagate wireless signals toward other off‐body nodes and base stations. Simulation and measurement results indicated that the use of this microstrip topology with multiple broadbanding techniques is capable of reducing the back lobe, resulting in a front‐to‐back ratio of about 17 dB and a 3.5 dBi of average gain.     

Adaptive security architecture for protecting RESTful web services in enterprise computing environment

In this modern era of enterprise computing, the enterprise application integration (EAI) is a well-known industry-recognized architectural principle that is built based on loosely coupled application architecture, where service-oriented architecture (SOA) is the architectural pattern for the implementation of EAI, whose computational elements are called as “services.” Though SOA can be implemented in a wide range of technologies, the web services implementation of SOA becomes the current selective choice due to its simplicity that works on basic Internet protocols. Web service technology defines several supporting protocols and specifications such as SOAP and WSDL for communication with client and server for data interchange. A new architectural paradigm has emerged in SOA in recent years called REpresentational State Transfer (REST) that is also used to integrate loosely coupled service components, named RESTful web services, by system integration consortiums. This SOA implementation does not possess adequate security solutions within it, and its security is completely dependent on network/transport layer security that is obsolete owing to latest web technologies such as Web 2.0 and its upgraded version, Web 3.0. Vendor security products have major implementation constraints such as they need secured organizational environment and breach to SOA specifications, hence introducing new vulnerabilities. Herein, we examine the security vulnerabilities of RESTful web services in the view of popular OWASP rating methodologies and analyze the gaps in the existing security solutions. We hence propose an adaptive security solution for REST that uses public key infrastructure techniques to enhance the security architecture. The proposed security architecture is constructed as an adaptive way-forward Internet-of-Things (IoT) friendly security solution that is comprised of three cyclic parts: learn, predict and prevent. A novel security component named “intelligent security engine” is introduced which learns the possible occurrences of security threats on SOA using artificial neural networks learning algorithms, then it predicts the potential attacks on SOA based on obtained results by the developed theoretical security model, and the written algorithms as part of security solution prevent the SOA attacks. This paper is written to present one of such algorithms to prevent SOA attacks on RESTful web services along the discussion on the obtained results of the conducted proof-of-concept on the real-time SOA environment. A comparison of the proposed system with other competing solutions demonstrates its superiority.     

An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach

Supplier evaluation plays a critical role in a successful supply chain management. Hence, the evaluation and selection of the right suppliers have become a central decision of manufacturing business activities around the world. Consequently, numerous individual and integrated methods have been presented to evaluate and select suppliers. The current literature shows that hybrid artificial intelligence (AI)-based models have received much attention for supplier evaluation. Integrated data envelopment analysis–artificial neural network (DEA–ANN) is one of the combined methods that have recently garnered great attention from academics and practitioners. However, DEA–ANN model has some drawbacks, which make some limitation in the evaluation process. In this study, we aim at improving the previous DEA–AI models by integrating the Kourosh and Arash method as a robust model of DEA with a new AI approach namely genetic programming (GP) to overcome the shortcomings of previous DEA–AI models in supplier selection. Indeed, in this paper, GP provides a robust nonlinear mathematical equation for the suppliers’ efficiency using the determined criteria. To validate the model, adaptive neuro-fuzzy inference system as a powerful tool was used to compare the result with GP-based model. In addition, parametric analysis and unseen data set were used to validate the precision of the model.     

An optimised silicon piezoresistive microcantilever sensor for surface stress studies

Surface stress is a versatile and efficient means to study various physical, chemical, biochemical and biological processes. This work focuses on developing high sensitive piezoresistive microcantilever designs to study surface stress. The cantilevers are made of silicon with rectangular holes at their base that also circumscribe a piezoresistor sensing element. To find the optimum design, the effects of change in cantilever width, rectangular hole length and type of dopant on mechanical properties like deflection, frequency and maximum stress are characterised using finite element analysis software. The surface stress sensitivity characteristics of the different cantilever designs is ascertained by applying a surface stress on their top surfaces. Results show that the sensitivity is increased by increasing the cantilever width as well as the length of the hole and the sensitivity of p-type designs is more than two times the n-type.     

Enhancement of metal coil quality on selective p-silicon based planar electromagnetic coil by thermal annealing process

In this paper we report an optimization of metal quality of planar MEMS electromagnetic coil through thermal annealing process. The study aims to see the effects of annealing process on the quality of metal layer deposited on localized p-type silicon regions. Two annealing process parameters namely isothermal (annealing under time variations in constant temperature) and isochronal (annealing under temperature variations at constant time) were performed on metal contact on highly doped Si substrate and characterized using transfer length method method by measuring the specific contact resistance ρ _C of the metal traces. The measurement results showed that the annealing process have significant influence on physical and electrical characteristics of the metal layer. Analysis showed that the quality of metal layer was significantly improved through the annealing process after treatment at temperature variations between 425–550 °C. An optimum annealing at 525 °C for 15 min was observed and the contact resistance can be reduced significantly up to 400 %. The results also showed that the surface roughness improves while metal contact resistance decreases 40 times when the metal is annealed for more than 10 min. The planar coil structure was designed to reduce the device density of a compact magnetic micro-sensor system.     

Software SMEs’ unofficial readiness for CMMI<Superscript>®</Superscript>-based software process improvement

The goal of software process improvement (SPI) is to improve software processes and produce high-quality software, but the results of SPI efforts in small- and medium-sized enterprises (SMEs) that develop software have been unsatisfactory. The objective of this study is to support the prolific and successful CMMI-based implementation of SPI in SMEs by presenting the facts related to the unofficial adoption of CMMI level 2 process area-specific practices by software SMEs. Two questionnaire surveys were performed, and 42 questionnaires were selected for data analysis. The questionnaires were filled out by experts from 42 non-CMMI-certified software SMEs based in Malaysia and Pakistan. In the case of each process area of CMMI level 2, the respondents were asked to choose from three categories, namely ‘below 50 %,’ ‘50–75 %,’ and ‘above 75 %’. The percentages indicated the extent to which process area-specific practices are routinely followed in the respondents’ respective organizations. To deal with differing standards for defining SMEs, the notion of the common range standard has been introduced. The results of the study show that a large segment of software development SMEs informally follows the specific practices of CMMI level 2 process areas and thus has true potential for rapid and effective CMMI-based SPI. The results further indicate that, in the case of four process areas of CMMI level 2, there are statistically significant differences between the readiness of small and medium software enterprises to adopt the specific practices of those process areas, and between trends on their part to do so unofficially. The findings, manifesting various degrees of unofficial readiness for CMMI-based SPI among SMEs, can be used to define criteria for the selection of SMEs that would be included in SPI initiatives funded by relevant authorities. In the interests of developing fruitful CMMI-based SPI and to enhance the success rate of CMMI-based SPI initiatives, the study suggests that ‘ready’ or ‘potential’ SMEs should be given priority for SPI initiatives.     

Dynamical features of GPS PWV variation associated with lightning activity

Water vapour is one component that causes spatial and temporal fluctuations in the lower atmosphere, which, in turn, introduce time delays into the global positioning system (GPS) signal. These delays can be exploited to estimate the precipitable water vapour (PWV), which is beneficial for meteorological applications. Because the vertical transfer of warm air to the troposphere triggers instability and atmospheric charges, lightning phenomena can fundamentally affect the GPS signals through changes in water vapour. From this hypothesis, GPS-derived PWV data have been proposed as a tool for monitoring lightning activity. In this paper, the variation of PWV in days with lightning at four selected stations in Peninsular Malaysia during the past two inter-monsoon events in May and November 2009 was observed. To verify the response, a peak alignment method was proposed with regard to the lightning with more than 100 discharge events and lightning days from 07:00 to 20:00 LT. A total of 66 lightning series were assessed, and PWV was observed to increase by approximately 2.46 mm and decrease by 4.16 mm before and after the peak point, respectively, when compared to fair-weather conditions. Approximately 62% of the lightning start times were concentrated within a period of less than 1 h before or after the PWV reached a maximum peak, 24% were observed between 1–2 h, and 14% started after 2 h. This trend implies that the use of GPS PWV data was more consistent and robust for the detection of lightning activity compared to the use of surface temperature and relative humidity data.     

Application of Complex Networks Theory in Urban Traffic Network Researches

Networks and Spatial Economics - Complex network theory is a multidisciplinary research direction of complexity science which has experienced a rapid surge of interest over the last two decades....     

Nonnegative matrix factorization and metamorphic malware detection

Metamorphic malware change their internal code structure by adopting code obfuscation technique while maintaining their malicious functionality during each infection. This causes change of their signature pattern across each infection and makes signature based detection particularly difficult. In this paper, through static analysis, we use similarity score from matrix factorization technique called Nonnegative Matrix Factorization for detecting challenging metamorphic malware. We apply this technique using structural compression ratio and entropy features and compare our results with previous eigenvector-based techniques. Experimental results from three malware datasets show this is a promising technique as the accuracy detection is more than 95%.