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The IEEE International Conference on Plasma Science (ICOPS) is held annually, sponsored by the Plasma Science and Applications Committee (PSAC) of the IEEE Nuclear and Plasma Science Society (NPSS). The 47th IEEE NPSS ICOPS was organized ONLINE from December 06 to 10, 2020, by the National Institute of Education, Nanyang Technological University, Singapore. The ICOPS2020 was co-organized together with the 2nd Asia-Pacific Conference on Plasma and Terahertz Science (APCOPTS) which is envisioned as the future Asian version of the ICOPS series of conferences. The ICOPS2020 was initially planned as an onsite physical conference from May 24 to 28, 2020, at Marina Bay Sands, Singapore. However, due to the COVID-19 pandemic, it was shifted to online virtual conference. The conference’s technical program included a wide range of plasma science and technology topics, which ranged from basic and fundamental research to traditional and emerging technology applications. Since the ICOPS2020 was co-organized with APCOPTS, it also had a very strong coverage of fields related to terahertz science and applications. A total of 627 presentations from scientists and participants from over 41 countries were made at ICOPS2020 with a record total registration of 754. All presentations were recorded and were available to the registered participants for 90 days after the conference was over. The program also included a women-in-engineering talk by Jackie Y. Ying from A* Star Singapore and a young professionals networking symposium on “Plasma Entrepreneurship” with the theme “Plasma entrepreneurship—Active translation of cutting-edge research to commercially viable product.” [ABSTRACT FROM AUTHOR]

The 2018 Asia-Pacific Conference on Plasma and Terahertz Science (APCOPTS), an international conference intended to be held every two years as the Asia-Pacific version of the International Conference on Plasma Science (ICOPS), provides an excellent platform for academic exchange in the fields. The first APCOPTS was held from August 15 to 18, 2018 and the second APCOPTS will be combined with ICOPS in Singapore. [ABSTRACT FROM AUTHOR]

Accurate solar photovoltaic (PV) generation forecast is critical to the reliable and economic operation of a modern power system. In practice, due to various faulty issues in the sensor, communication, or database system, the historical and online measurement data may not be always complete, and the missing data could dramatically degrade the forecasting model's accuracy. To solve this problem, this paper proposes an integrated missing-data tolerant model for probabilistic PV power generation forecasting. Taking historical PV generations as input, this model is based on a recursive long short-term memory network (Rec-LSTM), which can provide multi-step ahead forecasting of the probability distribution of PV generation. The unobserved input data will be imputed recursively based on the model output at the previous time step. During the training process, the imputations and forecasting values are iteratively updated by the negative log-likelihood loss function. As a salient advantage, this method can deal with data missing scenarios at both offline and online stages. Numerical experiments are conducted on two one-year datasets from Australia and Singapore, respectively. Probabilistic forecasting for both large-scale and small-scale building-level PV power generation is tested at the time resolution of 15 mins. Testing results show the proposed method can achieve superior probabilistic prediction accuracy as well as strong robustness under various data missing scenarios, compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Reorganizing bus frequencies to cater for the actual travel demands can significantly save the cost of the public transport system. Many, if not all, previous studies formulate this as a bus frequency optimization problem that tries to minimize passengers’ average waiting time. On the other hand, many investigations have confirmed that the user satisfaction drops faster as the waiting time increases. Consequently, this paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services within the waiting time threshold can be maximized. We propose two variants of the problem, FAST and FASTCO, to cater for different application needs and prove that both are NP-hard. To solve FAST effectively and efficiently, we first present an index-based $(1-1/e)$ (1 - 1 / e) -approximation algorithm. By exploiting the locality property of routes in a bus network, we further propose a partition-based greedy method that achieves a $(1-\rho)(1-1/e)$ (1 - ρ) (1 - 1 / e) approximation ratio. Then we propose a progressive partition-based greedy method to further boost the efficiency while achieving a $(1-\rho)(1-1/e-\varepsilon)$ (1 - ρ) (1 - 1 / e - ɛ) approximation ratio. For the FASTCO problem, two greedy-based heuristic methods are proposed. Experiments on a real city-wide bus dataset in Singapore have been conducted to verify the efficiency, effectiveness, and scalability of our methods in addressing FAST and FASTCO respectively. [ABSTRACT FROM AUTHOR]

How do we match technology transfer sources to recipients? In this paper, we approach this question by addressing gaps in two distinct streams of the literature. In the technology-transfer literature, the integrated pretransfer matching framework is in need of empirical validation, which calls for an empirical approach that can cope with its configurational, equifinal, causally asymmetric, and multinomial features. In the qualitative comparative analysis (QCA) literature, the emerging variant multivalue QCA (mvQCA), one of the few techniques to tackle the above four features is in need of multivalue theory applications. Thus, these two seemingly unrelated literature hold the key to fill each other's gap. To address the gaps, we leveraged the unique public research institute (PRI) to small and medium-sized enterprise (SME) transfer context in Singapore. We collected 188 responses from three stakeholders [i.e., research scientists and engineers, public research institutes (PRIs), and small and medium-sized enterprises (SMEs)] who are involved in technology transfer. Our analysis suggested one necessary and six sufficient PRI-SME configurations that enhance the commercialization potential of the transferred technology. Furthermore, we identified three sufficient PRI-SME configurations that hinder the commercialization of technology transfer activities. Not only does our paper establishes the validity of pretransfer matching framework, but also introduces the mvQCA method to management scholars. [ABSTRACT FROM AUTHOR]

Smart buildings form an integral part of the smart grid that lowers electricity cost. This objective can be achieved by using suitable enhanced algorithms and techniques. The motivation of this paper is the request for an economical decision-making system from a commercial company in Singapore. This paper presents an economical building management system (EBMS) concept inspired by the building management system and machine learning. EBMS improves the decision-making process through rule-based algorithm within the system. Machine learning algorithm is included as part of EBMS to enhance the computational calculation for the electricity distribution. It includes the function of self-making decision for purchasing electricity based on forecasting system for electricity price or real-time electricity price data when facility managers are not present. EBMS uses multiagent system to communicate, interact, and negotiate with multiple agents for energy supply and demand in the building. Simulation studies have shown the potential of EBMS concept to provide a cost reduction solution up to 61.42% savings for smart buildings. Better electricity purchasing contract option can be decided based on the forecasting system for building power consumption. [ABSTRACT FROM AUTHOR]

With the recent technological innovation, unmanned aerial vehicles, known as drones, have found numerous applications including package and parcel delivery for shippers. Drone delivery offers benefits over conventional ground-based vehicle delivery in terms of faster speed, lower cost, more environment-friendly, and less manpower needed. However, most of existing studies on drone delivery planning and scheduling focus on a single shipper and ignore uncertainty factors. As such, in this paper, we consider a scenario that multiple shippers can cooperate to minimize their drone delivery cost. We propose the Bayesian Shipper Cooperation in Stochastic Drone Delivery (BCoSDD) framework. The framework is composed of three functions, i.e., package assignment, shipper cooperation formation and cost management. The uncertainties of drone breakdown and misbehavior of cooperative shippers are taken into account by using multistage stochastic programming optimization and dynamic Bayesian coalition formation game. We conduct extensive performance evaluation of the BCoSDD framework by using customer locations from Solomon benchmark suite and a real Singapore logistics industry. As a result, the framework can help the shippers plan and schedule their drone delivery effectively. [ABSTRACT FROM AUTHOR]

Optimal scheduling and robust sequence control of a central chiller system with thermal energy storage may not be a straightforward solution to reduce building cooling load demand (CLD) due to unpredictable thermal comfort requirements and the stochastic nature of occupancy-dependent CLD. One potential way to address this challenge is to design and develop an energy management strategy (EMS) for a zone-variable air volume unit. This paper proposes an EMS to reduce CLD by deciding the zone reference setpoint temperature based on fuzzy logic strategy (FLS) and zone thermal models. The objective is to reduce zone CLD while maintaining zone temperature and zone thermal comfort margin within the threshold limits. In this context, first, we modeled the zone thermal behavior using an artificial neural network (ANN) approach to predict zone temperature for next time slot, modeled the zone cooling energy behavior using a regression approach to predict zone CLD for the next time slot, and estimated the zone occupancy status using noise data. Second, we implemented zone thermal feedback model and comfort margin based on zone thermal conditions and predicted mean vote index, respectively. Finally, we implemented an EMS that includes both demand supply strategy and cooling load demand reduction strategy based on one time-slot ahead prediction. The novelty of the proposed EMS lies within the zone CLD reduction using FLS, predicting zone thermal behaviors using an ANN and a regression approach, estimating zone occupancy using noise data, and maintaining zone thermal comfort margin within the threshold limits. Historical data from a commercial building in Singapore are used to demonstrate the effectiveness of the proposed EMS. [ABSTRACT FROM AUTHOR]

This Special Issue is associated with the 10th International Conference on Materials for Advanced Technologies (ICMAT), Symposium C: Semiconductor Photonics held on 23–28 June, 2019 at Marina Bay Sands in Singapore. Organised through the Materials Research Society of Singapore (MRS-S), ICMAT has been held since 2001 and has attracted strong interest amongst the global materials science and engineering research communities. The 10th conference in the ICMAT series attracted over 3,500 delegates with a number of keynote and plenary presentations notably including four Nobel Laureates in Physics and Chemistry. The ICMAT conference consists of a number of topical symposia. Symposium C was organized jointly with Australian MRS and co-chaired by Professors Dao-Hua Zhang (NTU, Singapore), Chennupati Jagadish (ANU, Australia), Stephen J. Sweeney (University of Surrey, UK), Boon S. Ooi (KAUST, Saudi Arabia), Hao Gong (NUS, Singapore) and Weijun Fan (NTU, Singapore). The Symposium focused on recent developments in materials for applications in photonic devices covering systems including III-V and II-VI semiconductors, group IV alloys for silicon compatibility, oxides and perovskites for solar cell and emitter applications, quantum dot based devices and in general a wide variety of organic and inorganic materials spanning applications from the ultra-violet through to the mid-infrared. In total, 20 invited and 53 oral and 19 poster papers were presented over the four days of technical sessions. [ABSTRACT FROM AUTHOR]

This Mini-Special Issue of the IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES (TMTT) is devoted to the 2019 Asia-Pacific Microwave Conference (APMC 2019), Singapore, December 10–13, 2019. APMC is one of the premier international conferences in the microwave community. The conference brought in a unique mix of high-quality keynote, invited, and contributed papers by world-class speakers from academia and industry, providing an opportunity for microwave family members to exchange their visions, insights, challenges, and progress in microwaves. It received a total submission of 1064 papers covering microwave to terahertz spectrum with a variety of applications by the authors from 56 countries/regions and finally accepted 720 papers after careful and rigorous peer review. The conference consisted of 71 technical oral and poster sessions from 53 technical tracks, eight workshops/short courses, 29 invited talks, and two plenary talks. In particular, 17 special sessions are convened by session organizers to discuss emerging technologies. [ABSTRACT FROM AUTHOR]

Gamma model is fitted using the second, fourth, and sixth moments to model the rain drop size distribution (DSD) of Singapore. As the Joss distrometer measures the number of rain drops between the drop diameters from 0.3 to 5 mm, the truncated moment fitting between these drop diameter ranges is also used for modeling the DSD. Gamma DSD requires three-parameter estimation: N0, the intercept parameter; \mu, the shape parameter; and \Lambda, the slope parameter. The aim of this paper is to find a suitable fixed \mu and derive an appropriate \mu-\Lambda relation for the tropical region in order to form a two-parameter gamma model. To find an appropriate \mu value, observed DSDs are fitted with different \mu values to estimate the rain rates, which are assessed by rain rate observations of the distrometer. Shape–slope relationships are fitted for different categories according to the rain rate and the number of drops. The derived \mu-\Lambda relationships for the Singapore region are compared to the published results from two other regions, and the analysis is presented. Two-parameter gamma models are compared by retrieving the rain rate using the polarimetric radar variables. The effect of truncation on rain rate retrieval is also studied, and the use of the \mu-\Lambda relationship for rain retrieval is recommended for the tropical region. The \mu-\Lambda relation using the truncated moment method for the rain category R \geq 5\ \mm/hr and rain\ counts \geq 1000\ \drops retrieves the rain rates well compared to other \mu-\Lambda relations. [ABSTRACT FROM AUTHOR]

Knowledge repository systems (KRSs) have gained popularity for enabling knowledge codification and reuse. This paper develops and tests a model of KRS success, including perceived KRS searchability, perceived KRS output quality, perceived usefulness, and user satisfaction, and examines how three aspects of social context (extrinsic rewards, intrinsic rewards, and organizational trust) affect these dimensions of KRS success. Empirical results from a survey of 141 KRS users in China and Singapore indicate that perceived KRS output quality depends on KRS searchability and the social context, perceived usefulness depends on perceived KRS output quality, and user satisfaction depends on perceived KRS output quality and perceived KRS searchability. However, the study provides some surprises: perceived KRS searchability only indirectly (through perceived KRS output quality) affects perceived usefulness, and the social context directly affects only perceived KRS output quality and not perceived usefulness. Our results suggest a four-pronged approach toward enhancing knowledge contribution and reuse in organizations: 1) developing organizational trust; 2) facilitating intrinsic rewards for knowledge contribution, partly through organizational trust; 3) the exercise of caution in the use of extrinsic rewards; and 4) the design of a KRS with a high level of searchability. [ABSTRACT FROM AUTHOR]

The nine papers in this special section were presented at the 2013 IMWS-Bio Conference that was held in Singapore, December 9?11, 2013. [ABSTRACT FROM AUTHOR]