Personalized day tour design for urban tourists with consideration to CO_(2) emissions

The growing awareness of climate change worldwide has led the urban tourism market to focus on balancing tourist tailored experiences and CO_(2) emissions.Therefore,designing personalized tourist routes with environ‐mental pollution consideration is preferable in this context.This study proposes an evolution algorithm based on reinforcement learning(FSRL-HA)to design a personalized day tour route that simultaneously considers the utility of tourists and the carbon emission.We conducted a case study in Chengdu,Sichuan,China,to evaluate this algorithm's performance.The results indicate that the proposed algorithm outperforms selected baseline methods.Furthermore,the approach can provide more diverse route choices for different tourists,and an experiment was conducted to explore how tourist preferences affect tourist utilities.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Parents'Perceptions of Online Teaching During the Pandemic:A Shanghai Survey Report

●This survey report outlined parents'evaluation of the Shanghai online learning program during the pandemic.●According to the survey results,most parents favorably accepted the Shanghai online learning initiative,while some parents held some concerns about the forms and impacts.●This report reveals nuanced analysis of parents'perception and their education backgrounds,stresses,and expectations.