Averting biodiversity collapse in tropical forest protected areas

The rapid disruption of tropical forests probably imperils global biodiversity more than any other contemporary phenomenon¹⁻³. With deforestation advancing quickly, protected areas are increasingly becoming final refuges for threatened species and natural ecosystem processes. However, many protected areas in the tropics are themselves vulnerable to human encroachment and other environmental stresses⁴⁻⁹. As pressures mount, it is vital to know whether existing reserves can sustain their biodiversity. A critical constraint in addressing this question has been that data describing a broad array of biodiversity groups have been unavailable for a sufficiently large and representative sample of reserves. Here we present a uniquely comprehensive data set on changes over the past 20 to 30 years in 31 functional groups of species and 21 potential drivers of environmental change, for 60 protected areas stratified across the world’s major tropical regions. Our analysis reveals great variation in reserve ‘health’: about half of all reserves have been effective or performed passably, but the rest are experiencing an erosion of biodiversity that is often alarmingly widespread taxonomically and functionally. Habitat disruption, hunting and forest-product exploitation were the strongest predictors of declining reserve health. Crucially, environmental changes immediately outside reserves seemed nearly as important as those inside in determining their ecological fate, with changes inside reserves strongly mirroring those occurring around them. These findings suggest that tropical protected areas are often intimately linked ecologically to their surrounding habitats, and that a failure to stem broad-scale loss and degradation of such habitats could sharply increase the likelihood of serious biodiversity declines.Keywords: Ecology, Environmental scienc

HPC simulations of information propagation over complex networks

Simulation provides a flexible and valuable method to study the behavior of information propagation over complex networks. High Performance Computing (HPC) is a technology that may be used to apply optimized algorithms on powerful new hardware resources for accelerating execution performance. With the increased computing resources required for simulation of large-scale networks, it is attractive to apply HPC techniques to promote the simulation performance both in the temporal and spatial domains. This thesis describes a simulation methodology that can facilitate simulations of information propagation in HPC environments. The novelty of our approach comes from the way we provide optimized simulation strategies and integrate such strategies with emerging computing architectures, including both Multi-core CPU and Many-core GPGPU for parallel performance acceleration. As a motivational example, an agent-based infectious disease simulation is used to illustrate the detailed performance modeling and runtime algorithmic adaptation during simulation, both in a serial processing environment and in a Multi-core CPU parallel processing environment. Our observations indicate that a significant performance gain can be achieved based on algorithmic adaptation at runtime. However, the experimental results show that the number of CPU cores limits the scalability of parallel processing, and the OpenMP overhead in creating and destroying threads is a major factor in the bottleneck of parallel execution. Inspired by the motivational example, we can construct a general simulation methodology with optimized execution strategies. We introduce two general simulation algorithms derived from two widely used information propagation models. In addition, two types of data structure can be defined for a given network, named the Vertex-Oriented Structure, and the Edge-Oriented Structure. According to these two data structures, we can design two types of execution approaches: Vertex-Oriented Processing and Edge-Oriented Processing. With vertex-oriented processing, we can implement an adaptive simulation algorithm which enables different algorithms to be interchanged at runtime in order to obtain execution performance benefits. Using performance modeling and prediction, we are able to derive accurate rules for the runtime algorithmic adaptation. According to the proposed simulation strategies, we implement the general simulation algorithms for information propagation on both Multi-core CPU and Many-core GPGPU. We adopt identical data structures and the same random number generation library on GPU and CPU platforms in order to compare and analyze the performance. Both vertex-oriented processing and edge-oriented processing are comprehensively investigated on both Random and Scale-free networks. The performance benefits of vertex-oriented and edge-oriented processing are compared and analyzed. Because of the limited size of GPU memory, the single GPU execution algorithms are further extended to multiple GPU devices. The system design is illustrated in detail, including the network partitioning and replication strategy, and the data synchronization scheme. Furthermore, the performance of distributed simulation across multiple GPUs is also investigated in detail. Finally, as a case study, this thesis describes studies of viral advertisement diffusion using our simulation strategies on CPU and multiple GPUs. The advertisement diffusion behaviors influenced by varying thresholds and different initial nodes selection policies are investigated. According to the experimental results, we can observe that the largest degree selection policy and a large number of initial selected nodes can help in maximizing the diffusion effect in the beginning and early stages of advertisement diffusion. However, the influence of a good node selection policy in advertisement diffusion is not that crucial compared to reducing the threshold. The conclusions given by the simulation studies are comparable with real cases in marketing studies.DOCTOR OF PHILOSOPHY (SCE

Interior Sound Field Subjective Evaluation Based on the 3D Distribution of Sound Quality Objective Parameters and Sound Source Localization

Controlling low frequency noise in an interior sound field is always a challenge in engineering, because it is hard to accurately localize the sound source. Spherical acoustic holography can reconstruct the 3D distributions of acoustic quantities in the interior sound field, and identify low-frequency sound sources, but the ultimate goal of controlling the interior noise is to improve the sound quality in the interior sound field. It is essential to know the contributions of sound sources to the sound quality objective parameters. This paper presents the mapping methodology from sound pressure to sound quality objective parameters, where sound quality objective parameters are calculated from sound pressure at each specific point. The 3D distributions of the loudness and sharpness are obtained by calculating each point in the entire interior sound field. The reconstruction errors of those quantities varying with reconstruction distance, sound frequency, and intersection angle are analyzed in numerical simulation for one- and two-monopole source sound fields. Verification experiments have been conducted in an anechoic chamber. Simulation and experimental results demonstrate that the sound source localization results based on 3D distributions of sound quality objective parameters are different from those based on sound pressure

Interior Sound Field Subjective Evaluation Based on the 3D Distribution of Sound Quality Objective Parameters and Sound Source Localization

Controlling low frequency noise in an interior sound field is always a challenge in engineering, because it is hard to accurately localize the sound source. Spherical acoustic holography can reconstruct the 3D distributions of acoustic quantities in the interior sound field, and identify low-frequency sound sources, but the ultimate goal of controlling the interior noise is to improve the sound quality in the interior sound field. It is essential to know the contributions of sound sources to the sound quality objective parameters. This paper presents the mapping methodology from sound pressure to sound quality objective parameters, where sound quality objective parameters are calculated from sound pressure at each specific point. The 3D distributions of the loudness and sharpness are obtained by calculating each point in the entire interior sound field. The reconstruction errors of those quantities varying with reconstruction distance, sound frequency, and intersection angle are analyzed in numerical simulation for one- and two-monopole source sound fields. Verification experiments have been conducted in an anechoic chamber. Simulation and experimental results demonstrate that the sound source localization results based on 3D distributions of sound quality objective parameters are different from those based on sound pressure

Morphological Structure, Rheological Behavior, Mechanical Properties and Sound Insulation Performance of Thermoplastic Rubber Composites Reinforced by Different Inorganic Fillers

The application area of a sound insulation material is highly dependent on the technology adopted for its processing. In this study, thermoplastic rubber (TPR, polypropylene/ethylene propylene diene monomer) composites were simply prepared via an extrusion method. Two microscale particles, CaCO3 and hollow glass microspheres (HGW) were chosen to not only enhance the sound insulation but also reinforced the mechanical properties. Meanwhile, the processing capability of composites was confirmed. SEM images showed that the CaCO3 was uniformly dispersed in TPR matrix with ~3 μm scale aggregates, while the HGM was slightly aggregated to ~13 μm scale. The heterogeneous dispersion of micro-scale fillers strongly affected the sound transmission loss (STL) value of composites. The STL values of TPR composites with 40 wt % CaCO3 and 20 wt % HGM composites were about 12 dB and 7 dB higher than that of pure TPR sample, respectively. The improved sound insulation performances of the composites have been attributed to the enhanced reflection and dissipate sound energy in the heterogeneous composite. Moreover, the mechanical properties were also enhanced. The discontinued sound impedance and reinforced stiffness were considered as crucial for the sound insulation

Extrusion Foaming of Lightweight Polystyrene Composite Foams with Controllable Cellular Structure for Sound Absorption Application

Polymer foams are promising for sound absorption applications. In order to process an industrial product, a series of polystyrene (PS) composite foams were prepared by continuous extrusion foaming assisted by supercritical CO2. Because the cell size and cell density were the key to determine the sound absorption coefficient at normal incidence, the bio-resource lignin was employed for the first time to control the cellular structure on basis of hetero-nucleation effect. The sound absorption range of the PS/lignin composite foams was corresponding to the cellular structure and lignin content. As a result, the maximum sound absorption coefficient at normal incidence was higher than 0.90. For a comparison, multiwall carbon nanotube (MWCNT) and micro graphite (mGr) particles were also used as the nucleation agent during the foaming process, respectively, which were more effective on the hetero-nucleation effect. The mechanical property and thermal stability of various foams were measured as well. Lignin showed a fire retardant effect in PS composite foam

Decentralized edge intelligence : a dynamic resource allocation framework for hierarchical federated learning

To enable the large scale and efficient deployment of Artificial Intelligence (AI), the confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages on the computation and communication capabilities of end devices and edge servers to process data closer to where it is produced. One of the enabling technologies of Edge Intelligence is the privacy preserving machine learning paradigm known as Federated Learning (FL), which enables data owners to conduct model training without having to transmit their raw data to third-party servers. However, the FL network is envisioned to involve thousands of heterogeneous distributed devices. As a result, communication inefficiency remains a key bottleneck. To reduce node failures and device dropouts, the Hierarchical Federated Learning (HFL) framework has been proposed whereby cluster heads are designated to support the data owners through intermediate model aggregation. This decentralized learning approach reduces the reliance on a central controller, e.g., the model owner. However, the issues of resource allocation and incentive design are not well-studied in the HFL framework. In this article, we consider a two-level resource allocation and incentive mechanism design problem. In the lower level, the cluster heads offer rewards in exchange for the data owners' participation, and the data owners are free to choose which cluster to join. Specifically, we apply the evolutionary game theory to model the dynamics of the cluster selection process. In the upper level, each cluster head can choose to serve a model owner, whereas the model owners have to compete amongst each other for the services of the cluster heads. As such, we propose a deep learning based auction mechanism to derive the valuation of each cluster head's services. The performance evaluation shows the uniqueness and stability of our proposed evolutionary game, as well as the revenue maximizing properties of the deep learning based auction.AI SingaporeMinistry of Education (MOE)Nanyang Technological UniversityNational Research Foundation (NRF)Submitted/Accepted versionThis work was supported in part by Alibaba Group through Alibaba Innovative Research (AIR) Program and AlibabaNTU Singapore Joint Research Institute (JRI), by National Research Foundation, Singapore, under its AI Singapore Programme under AISG awards AISG2-RP-2020-019 and AISGGC-2019-003, in part by WASP/NTU under Grant M4082187 (4080), in part by the Singapore Ministry of Education (MOE) Tier 1 (RG16/20), in part by the National Natural Science Foundation of China under Grant 62071343, and in part by SUTD under Grant SRG-ISTD-2021-165