User Preference Model of Pollution Source Information Recommendation

2021 ◽  
pp. 543-550
Author(s):  
Lina Wang
Keyword(s):  
Pollution Source ◽  
User Preference ◽  
Source Information ◽  
Preference Model ◽  
Information Recommendation

Analysis of athletes’ stadium stress source based on improved layered K-means algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong
Keyword(s):  
Experimental Data ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Experimental Comparison ◽  
Actual Situation ◽  
Medical Field ◽  
Pressure Source ◽  
Preference Model ◽  
Related Research ◽  
Analysis System

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

Water quality management planning zone development by introducing a GIS tool in Kathmandu valley, Nepal

2001 ◽  
Vol 44 (7) ◽  
pp. 209-221 ◽  
Author(s):  
S.-R. Ha ◽  
D. Pokhrel
Keyword(s):  
Land Use ◽  
Water Quality Management ◽  
River Pollution ◽  
Pollution Source ◽  
Kathmandu Valley ◽  
Delivery Ratio ◽  
Pollution Load ◽  
Source Information ◽  
Priority Areas ◽  
Bagmati River

This research was conducted to identify the critical pollution (BOD, TN, TP) areas and to develop the priority mitigation zone for the Bagmati River pollution in the Kathmandu valley, Nepal. A GIS tool was used to define and identify the critical pollution areas and sources. Pollution source information such as population, livestock, industry and land use were collected on the basis of the individual village boundary. The industrial, land use and living pollution were aggregated by the GIS overlay analysis capability to obtain the combined pollution load within the watershed. Priority areas for the mitigation of the pollution were defined considering the pollution loading rate, distance of stream from pollution source, and political, religious, and touristic values of the area. This research noticed that Kathmandu, Lalitpur and Bhaktapur municipalities are the major polluting areas and living beings are the major factors of Bagmati River pollution. Delivery ratio for the watershed was found to vary from 40-69% for BOD and nitrogen but the delivery of phosphorus was exceptionally high (92% at Gaurighat and 77% at Chovar) due to cremation activity of the Hindu religion on the riverbanks. Thus, the priority areas for the mitigation of the carbonaceous and nutrient source pollution were identified. At present the land use and industry impaired a very low contribution compared to the huge pollution load from the municipalities to the river system.

Towards a user preference model for interior lighting, Part 3: An alternative model

2019 ◽  
Vol 52 (2) ◽  
pp. 189-201
Author(s):  
TQ Khanh ◽  
P Bodrogi ◽  
X Guo
Keyword(s):  
Alternative Model ◽  
User Preference ◽  
Colour Preference ◽  
Type Representation ◽  
Contour Map ◽  
Preference Model ◽  
Colour Temperature ◽  
Independent Variables ◽  
Interior Lighting ◽  
Present Part

In Parts 1 and 2 of this work, an experiment was described in which subjects assessed their visual impressions of scene brightness (B), visual clarity (VC), colour preference (CP) and scene preference (SP) in a real room. In this room, the horizontal illuminance ( Ev), the correlated colour temperature (CCT) and the level of chroma enhancement caused by the spectrum of the light source (Δ C*) were changed systematically. In the present Part 3, these mean subjective B, VC, CP and SP scale values are re-analysed in terms of an alternative model based on a different set of independent variables: CCT, Δ C* and the circadian stimulus (CS). Contour map diagrams resulting from the new modelling equations are shown and compared with the conventional Kruithof-type representation.

scholarly journals Contamination Assessment and Source Apportionment of Metals and Metalloids Pollution in Agricultural Soil: A Comparison of the APCA-MLR and APCA-GWR Models

2022 ◽  
Vol 14 (2) ◽  
pp. 783
Author(s):  
Zhouqiao Ren ◽  
George Christakos ◽  
Zhaohan Lou ◽  
Haitao Xu ◽  
Xiaonan Lv ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Atmospheric Deposition ◽  
Human Health ◽  
Source Apportionment ◽  
Phosphate Fertilizer ◽  
Pollution Source ◽  
Agricultural Activities ◽  
Source Information ◽  
Natural Sources ◽  
Chongming Island

Metals and metalloids accumulate in soil, which not only leads to soil degradation and crop yield reduction but also poses hazards to human health. Commonly, source apportionment methods generate an overall relationship between sources and elements and, thus, lack the ability to capture important geographical variations of pollution sources. The present work uses a dataset collected by intensive sampling (1848 topsoil samples containing the metals Cd, Hg, Cr, Pb, and a metalloid of As) in the Shanghai study area and proposes a synthetic approach to source apportionment in the condition of spatial heterogeneity (non-stationarity) through the integration of absolute principal component scores with geographically weighted regression (APCA-GWR). The results showed that three main sources were detected by the APCA, i.e., natural sources, such as alluvial soil materials; agricultural activities, especially the overuse of phosphate fertilizer; and atmospheric deposition pollution from industry coal combustion and transportation activities. APCA-GWR provided more accurate and site-specific pollution source information than the mainstream APCA-MLR, which was verified by higher R2, lower AIC values, and non-spatial autocorrelation of residuals. According to APCA-GWR, natural sources were responsible for As and Cr accumulation in the northern mainland and Pb accumulation in the southern and northern mainland. Atmospheric deposition was the main source of Hg in the entire study area and Pb in the eastern mainland and Chongming Island. Agricultural activities, especially the overuse of phosphate fertilizer, were the main source of Cd across the study area and of As and Cr in the southern regions of the mainland and the middle of Chongming Island. In summary, this study highlights the use of a synthetic APCA-GWR model to efficiently handle source apportionment issues with spatial heterogeneity, which can provide more accurate and specific pollution source information and better references for pollution prevention and human health protection.

On Design Preference Elicitation With Crowd Implicit Feedback

2012 ◽  
Author(s):  
Yi Ren ◽  
Panos Y. Papalambros
Keyword(s):  
Preference Elicitation ◽  
Black Box ◽  
User Preference ◽  
Implicit Feedback ◽  
Optimization Approach ◽  
Preference Model ◽  
Binary Choices ◽  
Design Preference ◽  
Query Algorithm ◽  
Query Algorithms

We define preference elicitation as an interaction, consisting of a sequence of computer queries and human implicit feedback (binary choices), from which the user’s most preferred design can be elicited. The difficulty of this problem is that, while a human-computer interaction must be short to be effective, query algorithms usually require lengthy interactions to perform well. We address this problem in two steps. A black-box optimization approach is introduced: The query algorithm retrieves and updates a user preference model during the interaction and creates the next query containing designs that are both likely to be preferred and different from existing ones. Next, a heuristic based on accumulated elicitations from previous users is employed to shorten the current elicitation by querying preferred designs from previous users (the “crowd”) who share similar preferences to the current one.

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