Co_(3)O_(4)as an efficient passive NO_(x) adsorber for emission control during cold-start of diesel engines

The Co_(3)O_(4)nanoparticles,dominated by a catalytically active(110)lattice plane,were synthesized as a low-temperature NO_(x) adsorbent to control the cold start emissions from vehicles.These nanoparticles boast a substantial quantity of active chemisorbed oxygen and lattice oxygen,which exhibited a NO_(x) uptake capacity commensurate with Pd/SSZ-13 at 100℃.The primary NO_(x) release temperature falls within a temperature range of 200-350℃,making it perfectly suitable for diesel engines.The characterization results demonstrate that chemisorbed oxygen facilitate nitro/nitrites intermediates formation,contributing to the NO_(x) storage at 100℃,while the nitrites begin to decompose within the 150-200℃range.Fortunately,lattice oxygen likely becomes involved in the activation of nitrites into more stable nitrate within this particular temperature range.The concurrent processes of nitrites decomposition and its conversion to nitrates results in a minimal NO_(x) release between the temperatures of 150-200℃.The nitrate formed via lattice oxygen mainly induces the NO_(x) to be released as NO_(2) within a temperature range of 200-350℃,which is advantageous in enhancing the NO_(x) activity of downstream NH_(3)-SCR catalysts,by boosting the fast SCR reaction pathway.Thanks to its low cost,considerable NO_(x) absorption capacity,and optimal release temperature,Co_(3)O_(4)demonstrates potential as an effective material for passive NO_(x) adsorber applications.

Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization

Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks.Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connectedgraph. However, the complexity of the real world makes the complex networksabstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start linkprediction is favored as one of the most valuable subproblems of traditional linkprediction. However, due to the loss of many links in the observation network, thetopological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topologicalinformation from observed network becomes the key point to solve the problemof cold-start link prediction. In this paper, we propose a framework for solving thecold-start link prediction problem, a joint-weighted symmetric nonnegative matrixfactorization model fusing graph regularization information, based on low-rankapproximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designedgraph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain eachother. Finally, a unified framework for implementing cold-start link prediction isconstructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validationon five real networks with attributes shows that the proposed model has very goodpredictive performance when predicting missing edges of isolated nodes.

An Incremental Graph Pattern Matching Based Dynamic Cold-Start Recommendation Method

In order to give accurate recommendations for cold-start user, researchers use social network to find similar users. These efforts assume that cold-start user’s social relationships are static. However social relationships of cold-start user may change as time pass by. In order to give accurate and timely in manner recommendations for cold-start user, it is need to update social relationship continuously. In this paper, we proposed an incremental graph pattern matching based dynamic cold-start recommendation method (IGPMDCR), which updates similar users for cold-start user based on topology of social network, and gives recommendations based on the latest similar users’ records. The experimental results show that, IGPMDCR could give accurate and timely in manner recommendations for cold-start user.

Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before.However,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services.Thus,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service.Most of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality recommendations.Inspired by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF recommendations.The proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in parallel.Next,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item features.Finally,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP networks.We conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation metrics.Ablation experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation performance.Comparative analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rating sparsity and cold-start problems.

A Well-Built Hybrid Recommender System for Agricultural Products in Benue State of Nigeria

Benue State of Nigeria is tagged the Food Basket of the country due to its heavy production of many classes of food. Situated in the North Central Geo-Political area of the country, its food production ranges from root crops, fruits to cereals. Recommender systems (RSs) allow users to access products of interest, given a plethora of interest on the Internet. Recommendation techniques are content-based and collaborative filtering. Recommender systems based on collaborative filtering outshines content-based systems in the quality of their recommendations, but suffers from the cold start problem, i.e., not being able to recommend items that have few or no ratings. On the other hand, content-based recommender systems are able to recommend both old and new items but with low recommendation quality in relation to the user’s preference. This work combines collaborative filtering and content based recommendation into one system and presents experimental results obtained from a web and mobile application used in the simulation. The work solves the problem of serendipity associated with content based (RS) as well as the problem of ramp-up associated with collaborative filtering. The results indicate that the quality of recommendation is promising and is competitive with collaborative technique recommending items that have been seen before and also effective at recommending cold-start products.

Thermal Modeling of a Novel Heated Tip Injector for Otto Cycle Engines Powered by Ethanol

This work presents a thermal modeling of a new cold-start system technology designed for Otto cycle combustion based on the electromagnetic heating principle. Firstly, the paper presents a state-of-the-art review and presents the context of automobile industry where heated injectors are necessary. The novel method of electromagnetic heating principle to solve the cold-start problem is still in the development phase and it enables engine starting at low temperatures in vehicles powered by ethanol or flex-fuel vehicles (FFV). This new system technology should be available as an alternative to replace the existing system. Currently, the cold-start system uses an auxiliary gasoline tank, which brings some inconvenience for the user. Secondly, the aim was also to create a physical model that takes into consideration all the parameters involved on the heating process such as power heating and average heat transfer coefficient. The study is based on the lumped system theory to model the ethanol heating process. From the analysis, two ordinary differential equations arise, which allowed an analytical solution. Particularly, an ethanol heating curve inside the injector was obtained, an important parameter in the process. Comparison with experimental data from other authors is also provided. Finally, a sensitivity analysis of controlling parameters such as heating power and heat transfer coefficient variation. The paper is concluded with suggestions for further studies.

Item Cold-Start Recommendation with Personalized Feature Selection

The problem of recommending new items to users(often referred to as item cold-start recommendation)remains a challenge due to the absence of users’past preferences for these items.Item features from side information are typically leveraged to tackle the problem.Existing methods formulate regression methods,taking item features as input and user ratings as output.These methods are confronted with the issue of overfitting when item features are high-dimensional,which greatly impedes the recommendation experience.Availing of high-dimensional item features,in this work,we opt for feature selection to solve the problem of recommending top-N new items.Existing feature selection methods find a common set of features for all users,which fails to differentiate users1 preferences over item features.To personalize feature selection,we propose to select item features discriminately for different users.We study the personalization of feature selection at the level of the user or user group.We fulfill the task by proposing two embedded feature selection models.The process of personalized feature selection filters out the dimensions that are irrelevant to recommendations or unappealing to users.Experimental results on real-life datasets with high-dimensional side information reveal that the proposed method is effective in singling out features that are crucial to top-N recommendation and hence improving performance.

Application of self-adaptive temperature recognition in cold-start of an air-cooled proton exchange membrane fuel cell stack

The Self-adaptive control of the temperature can achieve the start of fuel cell at different operating temperatures, which is very important for the successful cold-start of the air-cooled PEMFC. The temperature distribution characteristics during the cold-start process were analyzed based on adaptive temperature recognition control in this paper. Preheating model and cold-start model were established and the optimal balance between the hot air flow rate and the temperature required to promote a uniform temperature distribution in the stack was explored in the preheating stage. Finally, the non-equilibrium mass transfer, as well as the temperature rise in the catalyst layer and gas diffusion layer with different current densities, were analyzed in the start-up stage. The results indicate that the air-cooled PEMFC stack can be successfully started up at -40 ◦C within 10 min by means of external gas heating. The current density and air velocity have significant impacts on the temperature of aircooled PEMFC stack. Dynamic analysis of air-cooled PEMFCs and real-time monitoring are suitable for machine learning and self-adaptive control to set the operation parameters to achieve successful cold start. Optimize the matching of load current and cathode inlet speed to achieve thermal management in low temperature environment.