What Development for Bioenergy in Asia: A Long-term Analysis of the Effects of Policy Instruments using TIAM-FR model

International audienceAsian countries have sharply increased their energy consumption with economic growth, inducing an extremely high dependency on energy fossil due to a low level of energy resources. In 2011, Asia’s share in global energy consumption reached 40%, of which China, India, Japan and South Korea represented 70%. Furthermore, these countries are ranked among the world’s largest CO2 emitters. Hence, bioenergy is currently being highlighted to diversify the energy mix and to mitigate climate change in these countries. Along with the direct bioenergy promotion policies, a variety of economic policy instruments are introduced as emission trading scheme, carbon and energy taxes. In this study, we estimated the evolution of bioenergy deployment from current energy and climate policies through scenarios analysis using bottom-up energy system optimization model, TIAM-FR, a TIMES family model from ETSAP/IEA. Different scenarios were developed with basis on political instruments in these countries: (1) BAU, (2) Global climate change (50% of world GHG emissions reduction), (3) National GHG mitigation targets from INDCs, (4) National GHG mitigation targets + Bioenergy policy, (5) Carbon tax system. According to the results, the higher is climate constraints, the higher is bioenergy development. Hence, carbon tax system derived the most bioenergy consumption by 2030 followed by global climate change scenario. Current INDCs target is evaluated as not sufficient to promote bioenergy development without renewable energy targets and biofuel blending mandates

CoMon: Cooperative Ambience Monitoring Platform with Continuity and Benefit Awareness

Mobile applications that sense continuously, such as location monitoring, are emerging. Despite their usefulness, their adoption in real-world deployment situations has been extremely slow. Many smartphone users are turned away by the drastic battery drain caused by continuous sensing and processing. Also, the extractable contexts from the phone are quite limited due to its position and sensing modalities. In this paper, we propose CoMon, a novel cooperative ambience monitoring platform, which newly addresses the energy problem through opportunistic cooperation among nearby mobile users. To maximize the benefit of cooperation, we develop two key techniques, (1) continuity-aware cooperator detection and (2) benefit-aware negotiation. The former employs heuristics to detect cooperators who will remain in the vicinity for a long period of time, while the latter automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Through continuity- and benefit-aware operation, CoMon enables applications to monitor the environment at much lower energy consumption. We implement and deploy a CoMon prototype and show that it provides significant benefit for mobile sensing applications

Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials

The discovery of new multicomponent inorganic compounds can provide direct solutions to many scientific and engineering challenges, yet the vast size of the uncharted material space dwarfs current synthesis throughput. While the computational crystal structure prediction is expected to mitigate this frustration, the NP-hardness and steep costs of density functional theory (DFT) calculations prohibit material exploration at scale. Herein, we introduce SPINNER, a highly efficient and reliable structure-prediction framework based on exhaustive random searches and evolutionary algorithms, which is completely free from empiricism. Empowered by accurate neural network potentials, the program can navigate the configuration space faster than DFT by more than 10$^{2}$-fold. In blind tests on 60 ternary compositions diversely selected from the experimental database, SPINNER successfully identifies experimental (or theoretically more stable) phases for ~80% of materials within 5000 generations, entailing up to half a million structure evaluations for each composition. When benchmarked against previous data mining or DFT-based evolutionary predictions, SPINNER identifies more stable phases in the majority of cases. By developing a reliable and fast structure-prediction framework, this work opens the door to large-scale, unbounded computational exploration of undiscovered inorganic crystals.Comment: 3 figure

Disorder-dependent Li diffusion in Li6PS5Cl\mathrm{Li_6PS_5Cl} investigated by machine learning potential

Solid-state electrolytes with argyrodite structures, such as $\mathrm{Li_6PS_5Cl}$, have attracted considerable attention due to their superior safety compared to liquid electrolytes and higher ionic conductivity than other solid electrolytes. Although experimental efforts have been made to enhance conductivity by controlling the degree of disorder, the underlying diffusion mechanism is not yet fully understood. Moreover, existing theoretical analyses based on ab initio MD simulations have limitations in addressing various types of disorder at room temperature. In this study, we directly investigate Li-ion diffusion in $\mathrm{Li_6PS_5Cl}$ at 300 K using large-scale, long-term MD simulations empowered by machine learning potentials (MLPs). To ensure the convergence of conductivity values within an error range of 10%, we employ a 25 ns simulation using a $5\times5\times5$ supercell containing 6500 atoms. The computed Li-ion conductivity, activation energies, and equilibrium site occupancies align well with experimental observations. Notably, Li-ion conductivity peaks when Cl ions occupy 25% of the 4c sites, rather than at 50% where the disorder is maximized. This phenomenon is explained by the interplay between inter-cage and intra-cage jumps. By elucidating the key factors affecting Li-ion diffusion in $\mathrm{Li_6PS_5Cl}$, this work paves the way for optimizing ionic conductivity in the argyrodite family.Comment: 34 pages, 6 figure

PADA: Power-aware development assistant for mobile sensing applications

� 2016 ACM. We propose PADA, a new power evaluation tool to measure and optimize power use of mobile sensing applications. Our motivational study with 53 professional developers shows they face huge challenges in meeting power requirements. The key challenges are from the significant time and effort for repetitive power measurements since the power use of sensing applications needs to be evaluated under various real-world usage scenarios and sensing parameters. PADA enables developers to obtain enriched power information under diverse usage scenarios in development environments without deploying and testing applications on real phones in real-life situations. We conducted two user studies with 19 developers to evaluate the usability of PADA. We show that developers benefit from using PADA in the implementation and power tuning of mobile sensing applications.N

A survey on state-of-the-art experimental simulations for privacy-preserving federated learning in intelligent networking

Federated learning (FL) provides a collaborative framework that enables intelligent networking devices to train a shared model without the need to share local data. FL has been applied in communication networks, which offers the dual advantage of preserving user privacy and reducing communication overhead. Networking systems and FL are highly complementary. Networking environments provide critical support for data acquisition, edge computing capabilities, round communication/connectivity, and scalable topologies. In turn, FL can leverage capabilities to achieve learning adaptation, low-latency operation, edge intelligence, personalization, and, notably, privacy preservation. In our review, we gather relevant literature and open-source platforms that point out the feasibility of conducting experiments at the confluence of FL and intelligent networking. Our review is structured around key sections, including the introduction of FL concepts, the background of FL applied in networking, and experimental simulations covering networking for FL and FL for networking. Additionally, we delved into case studies showcasing FL potential in optimizing state-of-the-art network optimization objectives, such as learning performance, quality of service, energy, and cost. We also addressed the challenges and outlined future research directions that provide valuable guidance to researchers and practitioners in this trending field

PowerForecaster: Predicting power impact of mobile sensing applications at pre-installation time

This article presents a hierarchical context monitoring and composition framework that effectively supports next-generation context-aware services. The upcoming ubiquitous space will be covered with innumerable sensors and tiny devices, which ceaselessly pump out a huge volume of data. This data gives us an opportunity for numerous proactive and intelligent services. The services require extensive understanding of rich and comprehensive contexts in real time. The framework provides three hierarchical abstractions: PocketMon (personal), HiperMon (regional), and EGI (global). The framework provides effective approaches to combining context from each level, thereby allowing us to create a rich set of applications, not possible otherwise. It deals with an extensively broad spectrum of contexts, from personal to worldwide in terms of scale, and from crude to highly processed in terms of complexity. It also facilitates efficient context monitoring and addresses the performance issues, achieving a high level of scalability. We have prototyped the proposed framework and several applications running on top of it in order to demonstrate its effectiveness.11Nothe