Metal-organic frameworks (MOFs), a novel class of porous crystalline materials, have drawn enormous attention. Due to the inherent porosity and presence of both metal and organic moieties, MOF-based ...materials are naturally suitable as versatile precursors and sacrificial templates for a wide variety of metal/carbon-based nanostructured materials, such as metal oxides, metal carbides, metal sulfides and their composites. Recent developments in MOF-derived hollow nanostructures with well-defined interior voids and low density have revealed their extensive capabilities and thus give enhanced performance for energy storage and conversion. In this review, we summarize the recent progress in the fabrication of MOF-derived hollow materials and their applications for energy storage, particularly for lithium-ion batteries, sodium-ion batteries, lithium-Se batteries, lithium-sulfur batteries and supercapacitors. The superiorities of MOF-derived hollow materials are highlighted, and major challenges or opportunities for future research on them for electrochemical energy storage are also discussed, with prospective solutions in the light of current progress in MOF-derived hollow nanostructures.
The recent progress and major challenges/opportunities of MOF-derived hollow materials for energy storage are summarized in this review, particularly for lithium-ion batteries, sodium-ion batteries, lithium-Se batteries, lithium-sulfur batteries and supercapacitor applications.
Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates ...in each iteration of model learning rather than the raw data. However, model updates can be extremely large if they contain numerous parameters, and many rounds of communication are needed for model training. The huge communication cost in federated learning leads to heavy overheads on clients and high environmental burdens. Here, we present a federated learning method named FedKD that is both communication-efficient and effective, based on adaptive mutual knowledge distillation and dynamic gradient compression techniques. FedKD is validated on three different scenarios that need privacy protection, showing that it maximally can reduce 94.89% of communication cost and achieve competitive results with centralized model learning. FedKD provides a potential to efficiently deploy privacy-preserving intelligent systems in many scenarios, such as intelligent healthcare and personalization.
This paper presents a smart driving direction system leveraging the intelligence of experienced drivers. In this system, GPS-equipped taxis are employed as mobile sensors probing the traffic rhythm ...of a city and taxi drivers' intelligence in choosing driving directions in the physical world. We propose a time-dependent landmark graph to model the dynamic traffic pattern as well as the intelligence of experienced drivers so as to provide a user with the practically fastest route to a given destination at a given departure time. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest and customized route for end users. We build our system based on a real-world trajectory data set generated by over 33,000 taxis in a period of three months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70 percent of the routes suggested by our method are faster than the competing methods, and 20 percent of the routes share the same results. On average, 50 percent of our routes are at least 20 percent faster than the competing approaches.
The exciting development of advanced nanostructured materials has driven the rapid growth of research in the field of electrochemical energy storage (EES) systems which are critical to a variety of ...applications ranging from portable consumer electronics, hybrid electric vehicles, to large industrial scale power and energy management. Owing to their capability to deliver high power performance and extremely long cycle life, electrochemical capacitors (ECs), one of the key EES systems, have attracted increasing attention in the recent years since they can complement or even replace batteries in the energy storage field, especially when high power delivery or uptake is needed. This review article describes the most recent progress in the development of nanostructured electrode materials for EC technology, with a particular focus on hybrid nanostructured materials that combine carbon based materials with pseudocapacitive metal oxides or conducting polymers for achieving high-performance ECs. This review starts with an overview of EES technologies and the comparison between various EES systems, followed by a brief description of energy storage mechanisms for different types of EC materials. This review emphasizes the exciting development of both hybrid nanomaterials and novel support structures for effective electrochemical utilization and high mass loading of active electrode materials, both of which have brought the energy density of ECs closer to that of batteries while still maintaining their characteristic high power density. Last, future research directions and the remaining challenges toward the rational design and synthesis of hybrid nanostructured electrode materials for next-generation ECs are discussed.
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► We review recent progress on hybrid nanostructured electrodes for electrochemical capacitors. ► We focus on hybrid electrodes combining carbon materials with metal oxides or conducting polymers. ► We emphasize novel porous structures for high loading of electroactive nanomaterials.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Large scale energy storage system with low cost, high power, and long cycle life is crucial for addressing the energy problem when connected with renewable energy production. To realize grid-scale ...applications of the energy storage devices, there remain several key issues including the development of low-cost, high-performance materials that are environmentally friendly and compatible with low-temperature and large-scale processing. In this report, we demonstrate that solution-exfoliated graphene nanosheets (∼5 nm thickness) can be conformably coated from solution on three-dimensional, porous textiles support structures for high loading of active electrode materials and to facilitate the access of electrolytes to those materials. With further controlled electrodeposition of pseudocapacitive MnO2 nanomaterials, the hybrid graphene/MnO2-based textile yields high-capacitance performance with specific capacitance up to 315 F/g achieved. Moreover, we have successfully fabricated asymmetric electrochemical capacitors with graphene/MnO2-textile as the positive electrode and single-walled carbon nanotubes (SWNTs)-textile as the negative electrode in an aqueous Na2SO4 electrolyte solution. These devices exhibit promising characteristics with a maximum power density of 110 kW/kg, an energy density of 12.5 Wh/kg, and excellent cycling performance of ∼95% capacitance retention over 5000 cycles. Such low-cost, high-performance energy textiles based on solution-processed graphene/MnO2 hierarchical nanostructures offer great promise in large-scale energy storage device applications.
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IJS, KILJ, NUK, PNG, UL, UM
This paper presents a recommender system for both taxi drivers and people expecting to take a taxi, using the knowledge of 1) passengers' mobility patterns and 2) taxi drivers' ...picking-up/dropping-off behaviors learned from the GPS trajectories of taxicabs. First, this recommender system provides taxi drivers with some locations and the routes to these locations, toward which they are more likely to pick up passengers quickly (during the routes or in these locations) and maximize the profit of the next trip. Second, it recommends people with some locations (within a walking distance) where they can easily find vacant taxis. In our method, we learn the above-mentioned knowledge (represented by probabilities) from GPS trajectories of taxis. Then, we feed the knowledge into a probabilistic model that estimates the profit of the candidate locations for a particular driver based on where and when the driver requests the recommendation. We build our system using historical trajectories generated by over 12,000 taxis during 110 days and validate the system with extensive evaluations including in-the-field user studies.
• The plant hormone ethylene is critical for climacteric fruit ripening, while glucose and anthocyanins determine the fruit quality of climacteric fruits such as apple. Understanding the exact ...molecular mechanism for this process is important for elucidating the interconnection of ethylene and fruit quality.
• Overexpression of apple MdbHLH3 gene, an anthocyanin-related basic helix–loop–helix transcription factor (bHLH TF) gene, promotes ethylene production, and transgenic apple plantlets and trees exhibit ethylene-related root developmental abnormalities, premature leaf senescence, and fruit ripening. Biochemical analyses demonstrate that MdbHLH3 binds to the promoters of three genes that are involved in ethylene biosynthesis, including MdACO1, MdACS1, and MdACS5A, activating their transcriptional expression, thereby promoting ethylene biosynthesis.
• High glucose-inhibited U-box-type E3 ubiquitin ligase MdPUB29, the ortholog of Arabidopsis AtPUB29 in apple, influences the expression of ethylene biosynthetic genes and ethylene production by direct ubiquitination of the MdbHLH3 protein.
• Our findings provide new insights into the ubiquitination of MdbHLH3 by glucose-inhibited ubiquitin E3 ligase MdPUB29 in the regulation of ethylene biosynthesis as well as indicate that the regulatory module MdPUB29-MdbHLH3 connects ethylene biosynthesis with fruit quality in apple.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
The step of urbanization and modern civilization fosters different functional zones in a city, such as residential areas, business districts, and educational areas. In a metropolis, people commute ...between these functional zones every day to engage in different socioeconomic activities, e.g., working, shopping, and entertaining. In this paper, we propose a data-driven framework to discover functional zones in a city. Specifically, we introduce the concept of latent activity trajectory (LAT), which captures socioeconomic activities conducted by citizens at different locations in a chronological order. Later, we segment an urban area into disjointed regions according to major roads, such as highways and urban expressways. We have developed a topic-modeling-based approach to cluster the segmented regions into functional zones leveraging mobility and location semantics mined from LAT. Furthermore, we identify the intensity of each functional zone using Kernel Density Estimation. Extensive experiments are conducted with several urban scale datasets to show that the proposed framework offers a powerful ability to capture city dynamics and provides valuable calibrations to urban planners in terms of functional zones.
The advance of GPS-enabled devices allows people to record their location histories with GPS traces, which imply human behaviors and preferences related to travel. In this article, we perform two ...types of travel recommendations by mining multiple users' GPS traces. The first is a generic one that recommends a user with top interesting locations and travel sequences in a given geospatial region. The second is a personalized recommendation that provides an individual with locations matching her travel preferences. To achieve the first recommendation, we model multiple users' location histories with a tree-based hierarchical graph (
TBHG
). Based on the
TBHG
, we propose a HITS (Hypertext Induced Topic Search)-based model to infer the interest level of a location and a user's travel experience (knowledge). In the personalized recommendation, we first understand the correlation between locations, and then incorporate this correlation into a collaborative filtering (CF)-based model, which predicts a user's interests in an unvisited location based on her locations histories and that of others. We evaluated our system based on a real-world GPS trace dataset collected by 107 users over a period of one year. As a result, our HITS-based inference model outperformed baseline approaches like
rank-by-count
and
rank-by-frequency
. Meanwhile, we achieved a better performance in recommending travel sequences beyond baselines like
rank-by-count
. Regarding the personalized recommendation, our approach is more effective than the weighted Slope One algorithm with a slightly additional computation, and is more efficient than the Pearson correlation-based CF model with the similar effectiveness.
