scholarly journals Tourism Resource Evaluation and Countermeasures Based on Network Communication and TOPSIS Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weiguang Xie ◽  
Yunchi Ma
Keyword(s):  
Evaluation System ◽  
Principal Component ◽  
Cost Effective ◽  
Network Communication ◽  
Traditional Values ◽  
Resource Evaluation ◽  
Value Evaluation ◽  
Tourism Resource ◽  
Calculation Results ◽  
Tourism Resources

Tourism resources are the basis of tourism activities. Affected by traditional values and the reusable characteristics of tourism resources, tourism resources have been regarded as worthless for a long time. In recent years, people have begun to establish correct values of tourism resources. However, due to the particularity of tourism resources, the issue of their value evaluation has been controversial. Value evaluation is value recognition or evaluative recognition. In order to develop a cost-effective method to obtain tourism resource evaluation and countermeasures, this paper has done a research on tourism resource evaluation and countermeasures based on network communication and the TOPSIS algorithm. For this reason, we selected a tourist attraction to conduct survey experiments. During the experiment, network communication was used to realize the exchange and sharing of their information between different people through specific media. At the same time, the TOPSIS method is used in the selection of the plan, based on the calculation results of the combined weighted TOPSIS, and the SPSS is used for the principal component factor. The suggestion and the exact conditions of the local tourism resources are combined to establish a model of the tourism resource evaluation system in this place. The experimental results show that the relative importance of viewing value is prominent in the evaluation of tourism resource value. Its weight reaches 0.509, and its popularity weight is 0.257. The evaluation and countermeasures of tourism resources based on network communication and TOPSIS algorithm are effective.

scholarly journals Geo-Heritage Specific Visibility as an Important Parameter in Geo-Tourism Resource Evaluation

Geosciences ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 146 ◽  
Author(s):  
Anna Mikhailenko ◽  
Dmitry Ruban
Keyword(s):  
Geological Heritage ◽  
Geochemical Processes ◽  
Western Caucasus ◽  
Resource Evaluation ◽  
Tourist Attractions ◽  
Chemical Type ◽  
Tourism Resource ◽  
The Western Caucasus ◽  
Don River ◽  
Tourism Resources

Geological heritage (geo-heritage) is a resource for geoscience, geo-education, and geo-tourism. Geo-tourist attractions differ in their physical visibility, interpretation (clarity), and aesthetic attractiveness. These three characteristics determine perception of visitors and, thus, the importance of attractions. An integrative parameter, namely specific visibility, is proposed for qualitative geo-tourism resource evaluation. This parameter is examined for all geo-heritage types, and some relevant examples from southwest Russia are considered. The geo-heritage types differ in their specific visibility. For instance, when landforms like the Granite Gorge in the Western Caucasus (geomorphological type) are well-visible, geochemical processes like the heavy metal cycling in the Don River delta and the Pelenkino mud lake (geo-chemical type) are not as visible. Such a difference should be taken into account when geo-tourism resources of any area or a geopark are evaluated. The lower the specific visibility, the higher the costs for professional interpretation and demand for advanced infrastructure solutions.

Monitoring effluent quality in hypertrophic wastewater reservoirs using remote sensing

1996 ◽  
Vol 33 (8) ◽  
pp. 23-29 ◽  
Author(s):  
I. Dor ◽  
N. Ben-Yosef
Keyword(s):  
Water Quality ◽  
Theoretical Model ◽  
Principal Component ◽  
Cost Effective ◽  
Operational Parameters ◽  
Effluent Quality ◽  
Optical Images ◽  
Routine Monitoring ◽  
Cost Effective Method ◽  
Wastewater Quality

About one hundred and fifty wastewater reservoirs store effluents for irrigation in Israel. Effluent qualities differ according to the inflowing wastewater quality, the degree of pretreatment and the operational parameters. Certain aspects of water quality like concentration of organic matter, suspended solids and chlorophyll are significantly correlated with the water column transparency and colour. Accordingly optical images of the reservoirs obtained from the SPOT satellite demonstrate pronounced differences correlated with the water quality. The analysis of satellite multispectral images is based on a theoretical model. The model calculates, using the radiation transfer equation, the volume reflectance of the water body. Satellite images of 99 reservoirs were analyzed in the chromacity space in order to classify them according to water quality. Principal Component Analysis backed by the theoretical model increases the method sensitivity. Further elaboration of this approach will lead to the establishment of a time and cost effective method for the routine monitoring of these hypertrophic wastewater reservoirs.

scholarly journals Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data

Landslides ◽  
2021 ◽  
Author(s):  
Chiara Crippa ◽  
Elena Valbuzzi ◽  
Paolo Frattini ◽  
Giovanni B. Crosta ◽  
Margherita C. Spreafico ◽  
...  
Keyword(s):  
Rock Slope ◽  
Progressive Failure ◽  
Principal Component ◽  
Cost Effective ◽  
Displacement Rate ◽  
Machine Learning Classification ◽  
Management Perspective ◽  
Alpine Environments

AbstractLarge slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

An Exploration of Provincial Low-Carbon Economy Evaluation System Based on Principal Component-Variable Fuzzy Evaluation

2013 ◽  
Vol 291-294 ◽  
pp. 1562-1567
Author(s):  
Ji Min Hu ◽  
Jian Long Gu ◽  
Chang Cui Hu ◽  
Hai Feng Wang
Keyword(s):  
Evaluation System ◽  
Comprehensive Evaluation ◽  
Principal Component ◽  
Fuzzy Comprehensive Evaluation ◽  
Fuzzy Evaluation ◽  
Low Carbon ◽  
Low Carbon Economy ◽  
Component Variable ◽  
The Status ◽  
Carbon Economy

According to indicators’ information repetition and subjectivity of the indicators’ weight set during the variable fuzzy comprehensive evaluation, Principal Component analysis can help solve the weight of the relative indicators and reduce comprehensive evaluation dimensions of the variable fussy comprehensive evaluation. This paper has made a comprehensive evaluation of the status quo of Yunnan’s low carbon economy development(2005-2009), which turns out to be more practical compared with the mere variable fussy theory analysis, thus, principal component-variable fuzzy evaluation is a kind of feasible way to analyze the regional low carbon development status.

Piper nigrum Oil – Determination of Selected Terpenes for Quality Evaluation

Planta Medica ◽  
2018 ◽  
Vol 85 (03) ◽  
pp. 185-194 ◽  
Author(s):  
Mei Wang ◽  
Amar Chittiboyina ◽  
Jon Parcher ◽  
Zulfiqar Ali ◽  
Paul Ford ◽  
...  
Keyword(s):  
Quality Evaluation ◽  
Geographical Location ◽  
Chemical Characterization ◽  
Principal Component ◽  
Cost Effective ◽  
Black Pepper ◽  
Piper Nigrum ◽  
Ms Analysis ◽  
Chemical Profiles ◽  
High Concentrations

AbstractThe growing demand and commercial value of black pepper (Piper nigrum) has resulted in considerable interest in developing suitable and cost-effective methods for chemical characterization and quality evaluation purposes. In the current study, an extensive set of oil samples (n = 23) that were extracted by steam distillation from black pepper seeds was investigated to compare the chemical profiles of samples originating from nine major producing countries, as well as to identify potential chemical markers for quality evaluation. The twenty-two most abundant volatile compounds, mainly terpenes, in these oils were determined by conventional GC/MS analysis. Principal component analysis with this set of data revealed distinct clusters for samples that originated from China and Malaysia. Relatively low concentrations of sabinene (< 0.2%) and high concentrations of 3-carene (10.9 – 21.1%) were observed in these samples, respectively, compared to oil samples from other countries. The enantiomeric distributions of key terpene markers, viz., β-pinene, sabinene, limonene, and terpinen-4-ol, were determined by chiral GC/MS analysis. Interestingly, for these four monoterpenes, levo-isomers were found to be predominant, emphasizing the highly conserved enzymatic processes occurring in P. nigrum. Moreover, consistent enantiomeric ratios ((−) isomer/(+) isomer) of 92.2 ± 3.0% for β-pinene, 94.8 ± 2.8% for sabinene, 60.7 ± 1.1% for limonene, and 78.3 ± 1.3% for terpinen-4-ol were observed, independent of geographical location. These results demonstrate the potential of using stereospecific compositions as chiral signatures for establishing the authenticity and quality of black pepper oil.

scholarly journals A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Sahar S. Tabrizi ◽  
Saeid Pashazadeh ◽  
Vajiheh Javani
Keyword(s):  
Evaluation System ◽  
Low Cost ◽  
Estimation Error ◽  
Weighted Average ◽  
Principal Component ◽  
Training System ◽  
Table Tennis ◽  
Sports Activity ◽  
Nonlinear Regression Models ◽  
Shadow Play

Psychological and behavioral evidence suggests that home sports activity reduces negative moods and anxiety during lockdown days of COVID-19. Low-cost, nonintrusive, and privacy-preserving smart virtual-coach Table Tennis training assistance could help to stay active and healthy at home. In this paper, a study was performed to develop a Forehand stroke’ performance evaluation system as the second principal component of the virtual-coach Table Tennis shadow-play training system. This study was conducted to show the effectiveness of the proposed LSTM model, compared with 2DCNN and RBF-SVR time-series analysis and machine learning methods, in evaluating the Table Tennis Forehand shadow-play sensory data provided by the authors. The data was generated, comprising 16 players’ Forehand strokes racket’s movement and orientation measurements; besides, the strokes’ evaluation scores were assigned by the three coaches. The authors investigated the ML models’ behaviors changed by the hyperparameters values. The experimental results of the weighted average of RMSE revealed that the modified LSTM models achieved 33.79% and 4.24% estimation error lower than 2DCNN and RBF-SVR, respectively. However, the R ¯ 2 results show that all nonlinear regression models are fit enough on the observed data. The modified LSTM is the most powerful regression method among all the three Forehand types in the current study.

scholarly journals Characterization of a haplotype- reference panel for genotyping by low-pass sequencing in Swiss Large White pigs

2021 ◽  
Author(s):  
Adéla Nosková ◽  
Meenu Bhati ◽  
Naveen Kumar Kadri ◽  
Danang Crysnanto ◽  
Stefan Neuenschwander ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Principal Component ◽  
Cost Effective ◽  
Reference Panel ◽  
Effective Population ◽  
Large White ◽  
Breeding Populations ◽  
Low Pass ◽  
Signatures Of Selection ◽  
Genomic Inbreeding

Abstract Background The key-ancestor approach has been frequently applied to prioritize individuals for whole-genome sequencing based on their marginal genetic contribution to current populations. Using this approach, we selected 70 key ancestors from two lines of the Swiss Large White breed that have been selected divergently for fertility and fattening traits and sequenced their genomes with short paired-end reads. Results Using pedigree records, we estimated the effective population size of the dam and sire line to 72 and 44, respectively. In order to assess sequence variation in both lines, we sequenced the genomes of 70 boars at an average coverage of 16.69-fold. The boars explained 87.95 and 95.35% of the genetic diversity of the breeding populations of the dam and sire line, respectively. Reference-guided variant discovery using the GATK revealed 26,862,369 polymorphic sites. Principal component, admixture and FST analyses indicated considerable genetic differentiation between the lines. Genomic inbreeding quantified using runs of homozygosity was higher in the sire than dam line (0.28 vs 0.26). Using two complementary approaches (CLR and iHS), we detected 51 signatures of selection. However, only six signatures of selection overlapped between both lines. We used the sequenced haplotypes of the 70 key ancestors as a reference panel to call 22,618,811 genotypes in 175 pigs that had been sequenced at very low coverage (1.11-fold) using GLIMPSE. The genotype concordance, non-reference sensitivity and non-reference discrepancy between thus inferred and Illumina PorcineSNP60 BeadChip-called genotypes was 97.60, 98.73 and 3.24%, respectively. The low-pass sequencing-derived genomic relationship coefficients were highly correlated (r > 0.99) with those obtained from microarray genotyping. Conclusions We assessed genetic diversity within and between two lines of the Swiss Large White pig breed. Our analyses revealed considerable differentiation, even though the split into two populations occurred only few generations ago. The sequenced haplotypes of the key ancestor animals enabled us to implement genotyping by low-pass sequencing which offers an intriguing cost-effective approach to increase the variant density over current array-based genotyping by more than 350-fold.

scholarly journals Student Attendance Management System using Principal Component Analysis Method

2020 ◽  
Vol 9 (4S2) ◽  
pp. 9-15
Keyword(s):  
Management System ◽  
Iris Recognition ◽  
Principal Component ◽  
Cost Effective ◽  
Analysis Method ◽  
Academic Institutions ◽  
Student Attendance ◽  
Eigen Values ◽  
Cost Effective Technique ◽  
Day By Day

Attendance taking and maintaining is a tedious job in the academic institutions where the time of class is restricted. The manual attendance i.e., roll call or paper-based signature systems usually consumes more time and error prone and also possibility of recording proxy attendance is more. Attendance is one of the criteria in considering students’ eligibility for attending the external examinations and also for the promotion to the next semester / year, where these kinds of problems may cause severe effect on the academic institutions. As the strength of students in a class is increasing day by day; monitoring, awarding and maintenance of attendance has becoming a challenge for the academic institutions. As a solution, attendance can be recorded using anyone of the existing biometric techniques like fingerprinting, iris recognition, signature, face recognition etc. Face identification is the best method among all the earlier mentioned methods for implementing in the academic institutions as it does not require human intervention and it is a cost-effective technique. A novel student attendance recording and management system using a MATLAB application, LabVIEW, Camera interface and GSM is proposed in this paper. Students’ faces will be captured with the help of a camera connected to a computer and Eigen values of the captured images will be detected with the help of MATLAB executed by LabVIEW Mathscript node. LabVIEW, a graphical programming environment is adopted for acquiring face, processing and authenticating the student once the match is found. Authenticated student attendance will be updated, and a message will be sent with the help of GSM module interface to myRIO. Proposed system replaces the manual attendance system which improves the performance of existing system.

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