The upper bound revisited

Owing to the increasing emissions of carbon dioxide (CO2), human life and the ecological environment have been affected by global warming and climate changes. To mitigate the concentration of CO2 in the atmosphere various strategies have been implemented such as separation, storage, and utilization of CO2. Although it has been explored for many years, hydrogenation reaction, an important representative among chemical conversions of CO2, offers challenging opportunities for sustainable development in energy and the environment. Indeed, the hydrogenation of CO2 not only reduces the increasing CO2 buildup but also produces fuels and chemicals. In this critical review we discuss recent developments in this area, with emphases on catalytic reactivity, reactor innovation, and reaction mechanism. We also provide an overview regarding the challenges and opportunities for future research in the field (319 references).

Recent advances in catalytic hydrogenation of carbon dioxide

While the study of psychology has offered little in the way of explaining the creative process, Koestler examines the idea that we are at our most creative when rational thought is suspended--for example, in dreams and trancelike states. All who read The Act of Creation will find it a compelling and illuminating book.

The Act of Creation

Nanotechnology is being used to enhance conventional ceramic and polymeric water treatment membrane materials through various avenues. Among the numerous concepts proposed, the most promising to date include zeolitic and catalytic nanoparticle coated ceramic membranes, hybrid inorganic–organic nanocomposite membranes, and bio-inspired membranes such as hybrid protein–polymer biomimetic membranes, aligned nanotube membranes, and isoporous block copolymer membranes. A semi-quantitative ranking system was proposed considering projected performance enhancement (over state-of-the-art analogs) and state of commercial readiness. Performance enhancement was based on water permeability, solute selectivity, and operational robustness, while commercial readiness was based on known or anticipated material costs, scalability (for large scale water treatment applications), and compatibility with existing manufacturing infrastructure. Overall, bio-inspired membranes are farthest from commercial reality, but offer the most promise for performance enhancements; however, nanocomposite membranes offering significant performance enhancements are already commercially available. Zeolitic and catalytic membranes appear reasonably far from commercial reality and offer small to moderate performance enhancements. The ranking of each membrane nanotechnology is discussed along with the key commercialization hurdles for each membrane nanotechnology.

/pdf/a-review-of-water-treatment-membrane-nanotechnologies-1np07if946.pdf

A review of water treatment membrane nanotechnologies

https://purehost.bath.ac.uk/ws/files/9234686/JMS_2011.pdf

A review of reverse osmosis membrane materials for desalination—Development to date and future potential

The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to recommend places where users have not visited before. Several techniques have been recently proposed for the recommendation service. However, no existing work has considered the temporal information for POI recommendations in LBSNs. We believe that time plays an important role in POI recommendations because most users tend to visit different places at different time in a day, \eg visiting a restaurant at noon and visiting a bar at night. In this paper, we define a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day. To solve the problem, we develop a collaborative recommendation model that is able to incorporate temporal information. Moreover, based on the observation that users tend to visit nearby POIs, we further enhance the recommendation model by considering geographical information. Our experimental results on two real-world datasets show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.

/pdf/time-aware-point-of-interest-recommendation-1v6rz29ngs.pdf

Time-aware point-of-interest recommendation

The rapidly growing of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which enables many services, e.g., point-of-interest (POI) recommendation. In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current location are to be recommended. The challenge lies in the difficulty in precisely learning users' sequential information and personalizing the recommendation model. To this end, we resort to the Metric Embedding method for the recommendation, which avoids drawbacks of the Matrix Factorization technique. We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences. We further develop a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance. Experiments on two real-world LBSN datasets demonstrate that our new algorithm outperforms the state-of-the-art next POI recommendation methods.

/pdf/personalized-ranking-metric-embedding-for-next-new-poi-54hr0tycco.pdf

Personalized ranking metric embedding for next new POI recommendation

Study on hypochlorite degradation of aromatic polyamide reverse osmosis membrane

Recommender system is one of the most popular data mining topics that keep drawing extensive attention from both academia and industry. Among them, POI (point of interest) recommendation is extremely practical but challenging: it greatly benefits both users and businesses in real-world life, but it is hard due to data scarcity and various context. While a number of algorithms attempt to tackle the problem w.r.t. specific data and problem settings, they often fail when the scenarios change. In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs. To enable such a framework, we develop PACE (Preference And Context Embedding), a deep neural architecture that jointly learns the embeddings of users and POIs to predict both user preference over POIs and various context associated with users and POIs. We show that PACE successfully bridges CF (collaborative filtering) and SSL by generalizing the de facto methods matrix factorization of CF and graph Laplacian regularization of SSL. Extensive experiments on two real location-based social network datasets demonstrate the effectiveness of PACE.

Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation

Point-of-interest (POI) recommendation is an important service to Location-Based Social Networks (LBSNs) that can benefit both users and businesses. In recent years, a number of POI recommender systems have been proposed, but there is still a lack of systematical comparison thereof. In this paper, we provide an all-around evaluation of 12 state-of-the-art POI recommendation models. From the evaluation, we obtain several important findings, based on which we can better understand and utilize POI recommendation models in various scenarios. We anticipate this work to provide readers with an overall picture of the cutting-edge research on POI recommendation.

/pdf/an-experimental-evaluation-of-point-of-interest-2f3ur58tr4.pdf

Quan Yuan

Papers

Time-aware point-of-interest recommendation

Personalized ranking metric embedding for next new POI recommendation

Study on hypochlorite degradation of aromatic polyamide reverse osmosis membrane

Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation

An experimental evaluation of point-of-interest recommendation in location-based social networks