Landscape Pattern Evolution and Its Response to Human Disturbance in a Newly Metropolitan Area: A Case Study in Jin-Yi Metropolitan Area

The impact of human interference on the ecological environment has attracted a significant amount of attention. In this study, hemeroby index (HI) was constructed to quantify the degree of human disturbance, and the relationship between HI and landscape pattern index was explored in a newly metropolitan area. The main objectives of this study were to analyze the temporal and spatial characteristics of landscape pattern and human disturbance in the process of urbanization of county-level cities in China, and to explore the relationship between the landscape pattern index and human disturbance under different disturbance intensity. The conclusions showed that: (1) the degree of human interference in the new area is on the rise, with a slow increase from 1980 to 2010, but since 2010, human interference has increased significantly. The diffusion of human disturbance intensity has obvious spatial directivity, spreading from east to west. (2) The impact of human activities on landscape pattern is shown as increasing fragmentation and increasing number of landscape types. With the enhancement of human activities, the heterogeneity and fragmentation of landscape types in the region reach their highest points. With the continuous increase of human activities, on a certain scale, the landscape types will gradually tend to be the same, and the same type of landscape patches will become one piece and tend to be integrated. This phenomenon is particularly obvious at the patch type level. (3) There is an inflection point value between human disturbance and landscape pattern index in landscape or patch type. On both sides of the inflection point value, the landscape pattern parameters and human disturbance have obvious opposite trends. (4) In the low interference range (1 ≤ HI < 4), with the increase of human interference, more heterogeneous structures (shown in the increase of SHDI) are brought to the landscape, there is more landscape fragmentation (shown in the decrease of LPI, CONTRACT, and AI), and the patch shape and landscape structure tend to be complex (shown in the increase of LSI, SQP, ED, and other indexes); in the high interference range (4 ≤ HI ≤ 7), due to human intervention, the increase of LPI, contract, and AI indicates that intense human activities turn the landscape into a broken and scattered structure and tend to be consistent and homogeneous. LSI, SQP, and ED were negatively correlated with HI at this stage, indicating that with the enhancement of human activities, the complexity of landscape shape decreased.

Web Intelligent for Forecasting Exchange Rate Currency using Clever Extraction Agent Combine with Financial Data Mining

From the current economic climate results in fluctuations of currency exchange rates in all countries. Since the most countries use USD as the reference exchange rate. The exchange rate will change from day to day so variety of factors which affect the exchange rate forecasting in the exchange rates in advance are critical to evaluate for the impact of the economic system of each country. It is important for investment decisions, exports, and profitability in the money market. It was reported on website (www) in the daily exchange rate changes. We use clever search agent (CSA) gather information from financial website generate to financial data mining. Kohonen Neural Networks is the method to determine similarity of internet documents using pattern index of financial document. And Ontology Structure of Sentence is the method to determine keyword using pattern index of financial content. Both are important components of Financial Data Mining. It is analyzed for exchange rate forecasting about USD/ Pounds. Our experimental forecast exchange rates for currency's USD / Great Britain Pounds by compare three algorithms as fallows GA, Meiosis Genetic Algorithms (MGA). This research propose new algorithm is called Dash Predator Swarm Optimization (DP2SO) which are accurate in prediction than other methods in generation of Genetic algorithm (GA) 35.83-41.52% which it depend on the accuracy of the information in each factor which are important finance dataset. It will present the future trends of exchange rate to the individual website.

Regional Strain Pattern Index—A Novel Technique to Predict CRT Response

Background: Cardiac resynchronization therapy (CRT) improves outcome in patients with heart failure (HF) however approximately 30% of patients still remain non-responsive. We propose a novel index—Regional Strain Pattern Index (RSPI)—to prospectively evaluate response to CRT. Methods: Echocardiography was performed in 49 patients with HF (66.5 ± 10 years, LVEF 24.9 ± 6.4%, QRS width 173.1 ± 19.1 ms) two times: before CRT implantation and 15 ± 7 months after. At baseline, dyssynchrony was assessed including RSPI and strain pattern. RSPI was calculated from all three apical views across 12 segments as the sum of dyssynchronous components. From every apical view, presence of four components were assessed: (1) contraction of the early-activated wall; (2) prestretching of the late activated wall; (3) contraction of the early-activated wall in the first 70% of the systolic ejection phase; (4) peak contraction of the late-activated wall after aortic valve closure. Each component scored 1 point, thus the maximum was 12 points. Results: Responders reached higher mean RSPI values than non-responders (5.86 ± 2.9 vs. 4.08 ± 2.4; p = 0.044). In logistic regression analysis value of RSPI ≥ 7 points was a predictor of favorable CRT effect (OR: 12; 95% CI = 1.33–108.17; p = 0.004). Conclusions: RSPI could be a valuable predictor of positive outcome in HF patients treated with CRT.

Indicators for Cooperative, Online-Based Learning and Their Role in Quality Management of Online Learning

Learning is a constructive and social process that works best in interaction with others. From this perspective, interaction and cooperation are seen as essential for learning especially in online-based learning environments. The objective of this chapter is to propose and test indicators for cooperative online-based learning. The indicators focus on three areas: presence of participants (indicators: access index, access pattern index), participation of participants (reading index, contribution index, completion index), and interaction of participants (answer contribution index, connectivity index, reciprocity index). The indicators can be applied both to students and instructors. The indicators were applied to three online-based courses in higher education. Log data from the learning management system was used. Also, success rates, student evaluations, and workload analysis were conducted. Results show that the indicators can be calculated automatically and can provide meaningful information for students' and instructors' dashboards. The presented indicators are tailored to cooperative online-based learning environments, where interaction and cooperation are means of fostering higher levels of learning.

Cultivated Land Change, Driving Forces and Its Impact on Landscape Pattern Changes in the Dongting Lake Basin

Comprehending the dynamic change characteristics of land use/cover and the driving factors causing the change are prerequisites for protecting land resources. This paper analyzes changes in cultivated land, the driving factors that cause them, and their tremendous impact on landscape pattern changes in the Dongting Lake Basin. For this purpose, we used mathematical statistics, buffer analysis, trend analysis, landscape pattern index, and logistic regression model to analyze the land use data of the study area from 1980 to 2018. The results show that the cultivated land showed a decreasing trend, with the total area decreased by 4.76% (or 716.13 km2) from 1980 to 2018, and the activity of mutual transformation with other land use types decreased. The spatial distribution pattern of cultivated land and landscape shows the change characteristics gradually from Dongting Lake to the surroundings. Among the driving factors of cultivated land changes, the influence of human activities was gradually increasing, while the natural factors were decreasing. The cultivated land landscape pattern index and the overall landscape pattern index have a significant positive correlation, showing relatively consistent change trend and spatial distribution characteristics. We believe that the decrease of cultivated land area has a certain relationship with the increase of landscape fragmentation in the Dongting Lake Basin. Our research is expected to provide a reference for strengthening regional cultivated land management and rational development and utilization of regional land resources.

Refining Urban Built-Up Area via Multi-Source Data Fusion for the Analysis of Dongting Lake Eco-Economic Zone Spatiotemporal Expansion

Rapid urbanization has given rise to serious urban problems. It is crucial to understand the urbanization process to accurately and quickly identify boundary changes in urban built-up areas and implement planning schemes and adjustments in scientific and effective ways. This study proposes a new method to automate and refine the extraction of urban built-up areas by using Landsat and nighttime light (NTL) imagery. The urban agglomeration of Dongting Lake Ecological Economic Zone (UADLEEZ) Landsat data are mapped to NTL data using resampling, superpixel segmentation, and assigning the blank part with the Euclidean distance method. We then compared our findings with those produced via traditional threshold extraction methods. In total, 33 built-up areas of UADLEEZ boundary maps were produced between 1992 and 2018. Thus, we reached the following conclusions: (1) the urban built-up areas obtained via our proposed method are finer than those obtained via other threshold extraction methods; (2) we applied the extraction method to UADLEEZ, and analyzed the expansion of the urban agglomeration based on expansion scale, gravity center offset, and landscape pattern index, the analysis of expansion process is consistent with the actual situation; (3) the proposed method can be used to draw long-term dynamic maps of urban extents in units of years, and the results can be used to update the existing products. This study can serve as a reference for future urban planning, and provide both adjustment programs for relevant departments, and an objective basis for governmental decision-making.

P4354Regional Strain Pattern Index - A novel technique to predict CRT response

Background Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure individuals. Despite many efforts 30–40% patients with CRT still do not respond to therapy. Purpose The purpose was to propose and evaluate a novel dyssynchrony scoring system – Regional Strain Pattern Index (RSPI) in prediction of response to CRT. Methods Forty-nine HF patients (age: 66.5±10 years, LV ejection fraction: 24.9±6.4%, QRS: 173.1±19.1 ms, NYHA class II/III: 34.7%/63.3%, ischemic aetiology: 57.1%) underwent CRT implantation. Transthoracic echocardiography was performed before and 15±7 months after CRT implantation. Baseline dyssynchrony was studied by standard indices, strain pattern analysis and novel, author's index- RSPI. RSPI constitutes a method of quantification of LBBB-related strain curve pattern analysis. RSPI was calculated using longitudinal strain by 2D speckle tracking as the sum of dyssynchronous components across 12 segments from all three apical views. In one view, four components were assessed: 1) the contraction of the early-activated wall, 2) the prestretching of the late activated wall, 3) the contraction of the early-activated wall in the first 70% of the systolic ejection phase and 4) the peak contraction of the late-activated wall after aortic valve closure. One point was attributed to each component, thus the maximum was 12 points. RSPI was prospectively evaluated in predicting response to CRT. The response to CRT was defined as ≥15% reduction in the left ventricular end-systolic volume after 15 month follow-up. Results Thirty-six (73.5%) patients were responders. There were no significant differences in the baseline demographics, clinical, echocardiographic parameters between responders and non-responders. The mean RSPI in the overall population was 5.39±2.9 and higher RSPI values were observed in responders (5.86±2.9) than in non-responders (4.08±2.4, p=0.044). The ROC curve indicated that RSPI significantly predicted CRT response (area under curve, AUC=0.691, p=0.014) with the cut-off value at 7 points. Therefore, study population was divided into two groups according to RSPI score: ≥7 points (19 patients, 38.8% of population) and <7 points (30 patients, 61.2%). In univariate logistic regression analysis, among all of the dyssynchrony indices only RSPI≥7 points predicted the positive response to CRT (OR: 12; 95% CI=1.33–108.17; p=0.0036) with specificity of 92.3%, sensitivity of 50%, positive of 64.7% and negative of 40% predictive value for CRT response. ROC curve for RSPI Conclusion RSPI constitutes a novel, valuable predictor for CRT response. Acknowledgement/Funding Grant for scientific research in the field of CRT efficacy funded by Medical University of Silesia.

Instruction on rhythmic structure during learning enhances the breaststroke kick efficiency

Motor learning can be fostered by visual or auditory instruction conveying information on different features of the skill, like spatial and rhythmic characteristics. For swimming skills like the breaststroke kick, manuals predominantly emphasise spatial features, neglecting rhythmic aspects – even though motor learning considers movement rhythm crucial for distinguishing one skill from another. We aimed to analyse the impact of instruction containing or not the adequate rhythmic pattern information, conveyed by visual or auditory stimulus, on learning the breaststroke kick. Fifty university students, assigned to four experimental groups (auditory, visual, with and without rhythmic information), performed 400 acquisition plus 50 retention and 50 transfer trials during which stroke index, rhythmic and spatial pattern indices and instruction request frequency were mapped. Results showed a marginal difference (p= .075) between higher indices in the retention test of participants receiving information about adequate rhythm, as well as a strong correlation between stroke index and rhythm pattern index, but not with spatial pattern index. No difference between auditory and visual groups was found. This result supports earlier research on the impact of rhythmic information on the learning of complex motor skills, and emphasises the role rhythm plays in skill efficiency and consequently in motor learning.