Singapore (1°21'07.6"N 103°49'11.3"E) is a tropical island-state situated south of Peninsular Malaysia, and north of the Riau Archipelago. Similar to tropical areas elsewhere, Singapore's climate is characterised by abundant rainfall (mean annual: 2165.9 mm), and generally high temperature (diurnal range: 23-33°C) and humidity (mean annual relative: 83.9%) throughout the year (Meteorological Service Singapore, 2018). Singapore's terrain is generally low-lying, with about three quarters of the island below 15 m. The highest point is the summit of Bukit Timah, which is at 163.63 m elevation (Wong, 2011). In 1963, the land area was 581.5 km 2 , and by 2018 had increased to 724.2 km 2 through land reclamation (Singapore Land Authority, 2019). There are 62 offshore islands-the two largest (Pulau Ubin and Pulau Tekong) are located northeast of the main island, and the rest are mostly found south of the main island. Before the arrival of the British in 1819, Singapore was likely covered mostly by lowland mixed dipterocarp forest (Fig. 1), similar to the original forest that was dominant in this part of Southeast Asia (Corlett, 1991, 1992). Mangrove forest likely occurred near river mouths with the vegetation transiting into freshwater swamp forest further inland. Stretches of sandy beach and rocky shore would have lined parts of the coast. Early human settlements (fourteenth to eighteenth centuries) have been recorded at the mouths or estuaries of the Singapore River, Kallang River, Seletar River and Punggol River. Singapore underwent tremendous landscape and ecological transformation after British colonisation (see Corlett (1992) and O'Dempsey (2014) for detailed accounts). Large tracts of lowland dipterocarp rain forest were cleared for timber, then converted to plantations of various crops, including black pepper, gambier, and pineapple. By the early 1900s, most lowland dipterocarp rain forest had been cleared and the remaining few patches were mostly gazetted as forest reserves. Extensive black pepper and gambier plantations had rendered the soil infertile in many areas. After H.N. Ridley's arrival in Singapore in 1888 and his experiments with the excision method to tap rubber more effectively and sustainably, large areas were converted into rubber plantations (Wycherley, 1959; Wee & Corlett, 1986). Kampungs (traditional villages) also increased in number as the island became more developed. Mangrove habitats were also affected by over-exploitation although later, in 1939, Pandan and Kranji mangrove areas were gazetted as forest reserves to allow regeneration (Wee & Corlett, 1986). After Singapore became independent from Malaysia in 1965, the new island nation began to focus on its industrial infrastructure resulting in the discontinuation or down-scaling of many agriculture-based activities. The growth of public housing estates to cater to the basic needs of a growing population saw most residents of the rural kampungs eventually resettled to high-rise public housing by the 1990s. Plantations and kampungs were grad...

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To support robust plan execution of autonomous robots in dynamic environments, autonomous robot software should include adaptive and reactive capabilities to cope with the dynamics and uncertainties of the evolving states of real-world environments. However, conventional software architectures such as sense-model-plan-act and behaviour-based paradigms are inadequate for meeting the requirements. A lack of sensing during acting in the sense-model-plan-act paradigm makes the software slow to react to run-time contingencies, whereas the behaviour-based architectures typically fall short in planning of long-range steps and making optimized plan adaptations. This article proposes a hybrid software architecture that maintains both adaptivity and reactivity of robot behaviours in dynamic environments. To implement this architecture, we further present the multi-agent development framework known as AutoRobot, which views the robot software as a multi-agent system in which diverse agent roles collaborate to achieve software functionalities. To demonstrate the applicability and validity of our concrete framework and software architecture, we conduct an experiment to implement a typical case, for example, a robot that autonomously picks up and drops off dishes for remote guests, which requires the robot to plan and navigate in a highly dynamic environment and can adapt its behaviours to unexpected situations.

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Mountain farming systems rely on both empirical and academic knowledge. Their sustainability depends on how effectively diverse knowledge is used for solution-oriented decision making. For mountains, decisions must be conducive to rural farmers whose livelihoods depend on agriculture and related activities. Adopting transdisciplinary research approach, we define a composite Sustainability Space indicator that will help decision makers better understand the ingredients for sustainability, and formulate policy and management decisions to reinforce on-the-ground sustainability. Sustainability Space was derived through analysis of the positive and negative impact factors co-defined by community and disciplinary experts, and visualized through a radar diagram. We used Principal Component Analysis to understand relationships between factors. The results on Sustainability Spaces for eight cases of farming systems from the far-Eastern Himalayas indicated that the sustainability of farming systems is strengthened if decisions holistically cater to (i) geophysical pre-requisites, (ii) ecological foundations, (iii) integrated processes and practices, (iv) resources, knowledge, and value systems, (v) stakeholders’ development and economic aspirations, (vi) well-being of farming communities, and (vii) government support mechanisms. More equitable the attention to these seven components, the higher the sustainability of farming systems in this region could be.

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