The prevalence of dengue has been alarming worldwide and most cases are found to reside in Asia. Brunei Darussalam is not excluded from dealing with the dengue spread within the country. There are four circulating serotypes of dengueDengue which are DENV-1, DENV-2, DENV-3 and DENV-4. The predominant serotype in Brunei tends to vary from year to year and according to the research in Thailand, antibodies are responsible for the change of the predominant serotype. In this Chapter, the main focus of the study is to forecast the trend of dengue cases using time series analysisTime series analysis with a multiplicative model and to determine if there is a significant relationshipSignificant relationship, if any, between the number of dengue cases and both rainfallRainfall & average temperatureTemperature in Brunei Darussalam, using multiple regressionMultiple regression. The result shows that the predicted valuesPredicted values of trend for the dengue fever cases are underestimates as well as overestimates. Furthermore, the result also shows that there is a significant relationship between the number of dengue cases and rainfall.

The aim of this research study is to determine suitable creativeCreative and innovativeInnovative teaching methods for secondary school level mathematics during Covid-19 pandemic, based on the findings of interview sessions conducted at nine randomly chosen secondary schoolsRandomly chosen secondary schools in Brunei Darussalam. The interview sessionsInterview sessions were conducted to at least one Mathematics teacherMathematics teacher from each school to find out various teaching methodsTeaching methods and IT toolsIT tools that the teachers used in their teachings. In addition, questionnairesQuestionnaires on teaching methodology were also distributed to the participants. The participants were selected based on their teaching experiencesTeaching experiences of more than 10 years and they must have accurate knowledge of the subject, ability to bring the subject matter to the level of student understanding, self-confidence, ability of expression, knowledge of evolution techniques, ability in questioning and respect for students' opinion (Vijayabarathi et al. in International Journal of Computing Algorithm 2:229–304, 2013). Based on the interview sessions and questionnaires, suitable creative & innovative online teachingOnline teaching methods to help improve the understanding of students in secondary school level mathematics during this Covid-19 pandemicCovid-19 pandemic will be determined.

Solar energySolar energy is one of the most environmentally friendly renewable energyRenewable energy. There are several solar energy systems that have been studied and one of the studies made is to combine solar energy systems, which aims to optimize the energy efficiency. Photovoltaic/thermal solar collector; also known as hybrid solar collector, combines photovoltaic solar cells which convert sunlight into electricity with a solar thermal collector which transfer the wasted heat from the photovoltaic module to a heat transfer fluid. The hybrid technology has shown higher overall energy efficiency than solar photovoltaic or solar thermal alone. The target of this book is to review and present the use of important key properties of mathematical modelsMathematical model that describe the PV/T solar collector system. A review on the current development of new mathematical models to achieve higher energy efficiency as well as challenges and limitations of photovoltaic/thermal solar collector system will also be presented.

Since the first case of novel coronavirus (COVID-19)Novel coronavirus (COVID-19) which was detected on March 9, 2020 in Brunei Darussalam, higher learning institutions and schools have shifted to online based learningOnline based learning instead of face to face (F2F) for teaching and learning (TL). On March 12, 2020, Universiti Brunei Darussalam (UBD) has announced that all TL will be using available online and electronic learning platforms such as CANVASCANVAS, Skype, Zoom meetings, Microsoft Team (MS Team) and Teleconferencing. This study discusses the implementation of CANVAS as one of learning management systemLearning management system (LMS) to teach first year discrete mathematicsDiscrete mathematics to assess the students' performance by giving them online assessments via assignments or quizzes, using built-in features on CANVAS. Based on the feedback from students, it is found that the use of CANVAS has significantly made students' learning more systematic and also improved the understanding of the students on the mathematics course.

The Water Evaluation and Planning (WEAP) model was invented by the Stockholm Environment Institute (SEI) to assist planning and management issues related with the development of water resources. The WEAP model can be applied to agricultural site and other sectors; and it can also tackle a large scope of problems encompassing water demand analyses, water saving, optimizing available water distribution and cost-profit analyses [1]. In this study, the WEAP model is introduced to look at the performance of the effect of different agricultural irrigation scenarios on special type of rice variety, i.e. MRQ76, planted in Wasan padi field, situated at Brunei-Muara District, Brunei Darussalam. Specifically, we apply the WEAP-MABIA model (based on soil-water balance approach) to perform evaluation of irrigation scheduling for three different rice growing seasons. We found that the WEAP model is a good model for providing the best irrigation scheduling strategy for optimum rice yield and efficient water management.

A significant amount of energy is wasted by electrical appliances when they operate inefficiently either due to anomalies and/or incorrect usage. To address this problem, we present SocketWatch - an autonomous appliance monitoring system. SocketWatch is positioned between a wall socket and an appliance. SocketWatch learns the behavioral model of the appliance by analyzing its active and reactive power consumption patterns. It detects appliance malfunctions by observing any marked deviations from these patterns.SocketWatch is inexpensive and is easy to use: it neither requires any enhancement to the appliances nor to the power sockets nor any communication infrastructure. Moreover, the decentralized approach avoids communication latency and costs, and preserves data privacy. Real world experiments with multiple appliances indicate that SocketWatch can be an effective and inexpensive solution for reducing electricity wastage.

Remote sensing is a technique which demands a large amount of analysis on data which may have been captured from a variety of sources. Common sources range from aerial vehicles equipped with scanning devices to sensors attached to satellites in space missions. The data acquisition, however, is commonly subject to the interference of external factors, such as particles in the atmosphere and clouds, which may lead to noise in the data. This paper presents a technique to detect the presence of such artifacts, as observed in some digital elevation model data, and an algorithm to patch them. A case study on the second version of the ASTER GDEM shows that the proposed algorithm is effective in the detection and patching of vertical artifacts and that it can be applied to different data sets in the realm of digital elevation models.

As the output from the solar PV systems varies significantly with technologies, designs and prevailing weather parameters, their evaluation under actual field conditions is important in identifying their real performance characteristics. In this paper, comparative performances of six different PV systems connected to a 1.2 MWp grid integrated solar farm are presented. The solar technologies considered are the single crystalline (sc-Si), poly crystalline (mc-Si), micro crystalline (nc-Si/a-Si), amorphous silicon (a-Si), Copper Indium Selenium (CIS) and Heterojunction with Intrinsic Thin Layer (HIT). Following the IEA guidelines, the array yield, capture losses, array efficiency ratio, and performance ratio were taken as the criteria for the performance comparison. Among the six different module types, the systems based on amorphous silicon and HIT offer the best performance under the tropical environment considered.

Growing fuel costs, environmental awareness, government directives, an aggressive push to deploy Electric Vehicles (EVs) (a single EV consumes the equivalent of 3 to 10 homes) have led to a severe strain on a grid already on the brink. Maintaining the stability of the grid requires automatic agent based control of these loads and rapid coordination between them. In the literature, a number of iterative pricing, signaling and tâtonnement (or bargaining) approaches have been proposed to allow smart homes, storage devices and the autonomous agents that control them to be responsive to the state of the grid in a distributed manner. These existing approaches are not scalable due to slow convergence and moreover the approaches are not incentive compatible. In this paper, we present a tâtonnement framework for resource allocation among intelligent agents in the smart grid, that non-trivially generalizes past work in this area. Our approach based on the work in server load balancing involves communicating carefully chosen, centrally verifiable constraints on the set of actions available to agents and cost functions, leading to distributed, incentive compatible protocols that converge in a constant number of iterations, independent of the number of users. These protocols can work on the top of prior approaches and result in a substantial speed-up, while ensuring that it is in the best interests of the agents to be truthful. We demonstrate this theoretically and through extensive simulations for three important scenarios that have been discussed in the literature. We extend the techniques to account for capacity limits in each time slot, the EV charging problem and the distributed storage control problem. We establish the generality and usefulness of this technique and making the case that it should be incorporated into future smart grid protocols.

The Indian electricity sector, despite having the world's fifth largest installed capacity, suffers from a 12.9% peaking shortage. This shortage could be alleviated, if a large number of deferrable loads, particularly the high powered ones, could be moved from on-peak to off-peak times. However, conventional Demand Side Management (DSM) strategies may not be suitable for India as the local conditions usually favor inexpensive solutions with minimal dependence on the pre-existing infrastructure. In this work, we present a completely autonomous DSM controller called the nPlug. nPlug is positioned between the wall socket and deferrable load(s) such as water heaters, washing machines, and electric vehicles. nPlugs combine local sensing and analytics to infer peak periods as well as supply-demand imbalance conditions. They schedule attached appliances in a decentralized manner to alleviate peaks whenever possible without violating the requirements of consumers. nPlugs do not require any manual intervention by the end consumer nor any communication infrastructure nor any enhancements to the appliances or the power grids. Some of nPlug's capabilities are demonstrated using experiments on a combination of synthetic and real data collected from plug-level energy monitors. Our results indicate that nPlug can be an effective and inexpensive technology to address the peaking shortage. This technology could potentially be integrated into millions of future deferrable loads: appliances, electric vehicle (EV) chargers, heat pumps, water heaters, etc.

Weather models with high spatial and temporal resolutions are required for accurate prediction of meso-micro scale weather phenomena. Using these models for operational purposes requires forecasts with sufficient lead time, which in turn calls for large computational power. There exists a lot of prior studies on the performance of weather models on single domain simulations with a uniform horizontal resolution. However, there has not been much work on high resolution nested domains that are essential for high-fidelity weather forecasts.In this paper, we focus on improving and analyzing the performance of nested domain simulations using WRF on IBM Blue Gene/P. We demonstrate a significant reduction (up to 29%) in runtime via a combination of compiler optimizations, mapping of process topology to the physical torus topology, overlapping communication with computation, and parallel communications along torus dimensions. We also conduct a detailed performance evaluation using four nested domain configurations to assess the benefits of the different optimizations as well as the scalability of different WRF operations. Our analysis indicates that the choice of nesting configuration is critical for good performance. To aid WRF practitioners in making this choice, we describe a performance modeling approach that can predict the total simulation time in terms of the domain and processor configurations with a very high accuracy (< 8%) using a regression-based model learned from empirical timing data.

The Indian electricity sector, despite having the world's fifth largest installed capacity, suffers from a 12.9% peaking shortage. This shortage could be alleviated, if a large number of deferrable loads, particularly the high powered ones, could be moved from on-peak to off-peak times. However, conventional DSM strategies may not be suitable for India as the local conditions usually favor only inexpensive solutions with minimal dependence on the pre-existing infrastructure. In this work, we present nPlug, a smart plug that sits between the wall socket and deferrable loads such as water heaters, washing machines, and electric vehicles. nPlugs combine real-time sensing and analytics to infer peak periods as well as supply-demand imbalance and reschedule attached appliances in a decentralized manner to alleviate peaks whenever possible. They do not require any manual intervention by the end consumer nor any enhancements to the appliances or existing infrastructure. Some of nPlug's capabilities are demonstrated using experiments on a combination of synthetic and real data collected from plug-level energy monitors. Our results indicate that nPlug can be an effective and inexpensive technology to address the peaking shortage.