Comparative Analysis of Differences among Northern,Jiangnan,and Lingnan Classical Private Gardens Using Principal Component Cluster Method

This paper investigates the design essence of Chinese classical private gardens,integrating their design elements and fundamental principles.It systematically analyzes the unique characteristics and differences among classical private gardens in the Northern,Jiangnan,and Lingnan regions.The study examines nine classical private gardens from Northern China,Jiangnan,and Lingnan by utilizing the advanced tool of principal component cluster analysis.Based on literature analysis and field research,273 variables were selected for principal component analysis,from which four components with higher contribution rates were chosen for further study.Subsequently,we employed clustering analysis techniques to compare the differences among the three types of gardens.The results reveal that the first principal component effectively highlights the differences between Jiangnan and Lingnan private gardens.The second principal component serves as the key to defining the types of Northern private gardens and distinguishing them from the other two types,and the third principal component indicates that Lingnan private gardens can be categorized into two distinct types as well.

Comparative assessment of the frying efficiency of standard and low linolenic rapeseed oils: Principal Component Analysis (PCA)

In this research,the performance of regular rapeseed oil(RSO)and modified low-linolenic rapeseed oil(LLRO)during frying was assessed using a frying procedure that commonly found in fast-food restaurants.Key physicochemical attributes of these oils were investigated.RSO and LLRO differed for initial linolenic acid(12.21%vs.2.59%),linoleic acid(19.15%vs.24.73%).After 6 successive days frying period of French fries,the ratio of linoleic acid to palmitic acid dropped by 54.49%in RSO,higher than that in LLRO(51.54%).The increment in total oxidation value for LLRO(40.46 unit)was observed to be significantly lower than those of RSO(42.58 unit).The changes in carbonyl group value and iodine value throughout the frying trial were also lower in LLRO compared to RSO.The formation rate in total polar compounds for LLRO was 1.08%per frying day,lower than that of RSO(1.31%).In addition,the formation in color component and degradation in tocopherols were proportional to the frying time for two frying oils.Besides,a longer induction period was also observed in LLRO(8.87 h)compared to RSO(7.68 h)after frying period.Overall,LLRO exhibited the better frying stability,which was confirmed by principal component analysis(PCA).

Construction of Anti-breaking Models of the Main Veins of Flue-cured Tobacco Leaves and Principal Component Analysis

[Objective] This study aimed to explore the related mechanisms of the breaking of flue-cured tobacco leaves. [Method] Anti-breaking models of the main veins of flue-cured tobacco leaves were constructed for principal component analysis on the anti-breaking index, leaf traits and cellulose contents. [Result] The results showed that the growth traits had certain relevance with the cellulose contents while the leaf weight assumed a significant negative correlation with the anti-breaking index, indicating that the heavier the leaf weight was, the weaker the anti-breaking capacity of flue-cured tobacco would be; the cross-sectional area of main veins and the cellulose contents had shown a positive correlation with the anti-breaking index, indicating that the thicker the main vein of flue-cured tobacco was, the higher the cellulose contents would be, and the stronger the anti-breaking capacity of flue-cured tobacco leaves would be. [Conclusion] This study provided theoretical basis and reference to improve tobacco production and enhance the quality of flue-cured tobacco.

Estimation of the Number of Collapsed Houses Damaged by Typhoon Based on Principal Components Analysis and Support Vector Machine

The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang Province.The factor leading to disaster,the environment fostering disaster and the exposure of buildings were processed by Principal Component Analysis.The key factor was extracted to support input of vector machine model and to build an evaluation model;the historical fitting result kept in line with the fact.In the real evaluation of two typhoons landed in Zhejiang Province in 2008 and 2009,the coincidence of evaluating result and actual value proved the feasibility of this model.

A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss

We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.

Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning

The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and healthcare.To address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering algorithms.Although various conventional studies have aimed to enhance the lifespan of a network through robust systems,they do not always provide optimal efficiency for real-time applications.This paper presents an approach based on state-of-the-art machine-learning methods.In this study,we employed a novel approach that combines an extended version of principal component analysis(PCA)and a reinforcement learning algorithm to achieve efficient clustering and data reduction.The primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation efficiency.We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring.Our proposed approach(PQL)was compared to previous studies that utilized adaptive Q-learning(AQL)and regional energy-aware clustering(REAC).Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.

Assessment of the Water Quality in the Ariake Sea Using Principal Component Analysis

The aim of this study is to investigate the condition of the Ariake Sea, Japan, which has been suffering from severe environmental issues for the past few decades. Water quality data have been generated from several points in this area for over 30 years by the Fukuoka, Saga, Kumamoto, and Nagasaki prefectures. In order to understand the characteristics of this sea, principal component analysis (PCA) was utilized using 11 water quality parameters;transparency, temperature, salinity, dissolved oxygen (DO), chemical oxygen demand (COD), dissolved inorganic nitrogen (DIN), ammonium-nitrogen (NH4<sup style='margin-left:-7px;'>+-N), nitrate-nitrogen (NO3<sup style='margin-left:-7px;'>--N), nitrite-nitrogen (NO2<sup style='margin-left:-7px;'>--N), phosphate-phosphorus, (PO4<sup style='margin-left:-7px;'>3--P) and silica. PCA conveyed the amount of nutrients originating from the river, the organic pollution level, and seasonal changes. Subsequently, principal component scores were calculated for each point. It was concluded that the Ariake Sea environment has been affected by two main factors, which are the nutrients from the Chikugo River and anticlockwise tidal residual flow. These two factors must be considered for the environmental restoration of the Ariake Sea.

Extracting the core indicators of pulverized coal for blast furnace injection based on principal component analysis

An updated approach to refining the core indicators of pulverized coal used for blast furnace injection based on principal component analysis is proposed in view of the disadvantages of the existing performance indicator system of pulverized coal used in blast furnaces. This presented method takes into account all the performance indicators of pulverized coal injection, including calorific value, igniting point, combustibility, reactivity, flowability, grindability, etc. Four core indicators of pulverized coal injection are selected and studied by using principal component analysis, namely, comprehensive combustibility, comprehensive reactivity, comprehensive flowability, and comprehensive grindability. The newly established core index system is not only beneficial to narrowing down current evaluation indices but also effective to avoid previous overlapping problems among indicators by mutually independent index design. Furthermore, a comprehensive property indicator is introduced on the basis of the four core indicators, and the injection properties of pulverized coal can be overall evaluated.

Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

Laser-induced breakdown spectroscopy（LIBS） is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis（PCA） and support vector machine（SVM） is applied to rock analysis.Fourteen emission lines including Fe,Mg,Ca,Al,Si,and Ti are selected as analysis lines.A good accuracy（91.38% for the real rock） is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA.It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program,but also solve the problem of linear inseparability by combining PCA and SVM.By this method,the ability of LIBS to classify rock is validated.

Prioritization of Semi-Arid Agricultural Watershed Using Morphometric and Principal Component Analysis, Remote Sensing, and GIS Techniques, the Zerqa River Watershed, Northern Jordan

Remote sensing and GIS techniques were employed for prioritization of the Zerqa River watershed. Forty-three 4th order sub-watersheds were prioritized based on morphometric and Principal Component Analysis (PCA), in order to examine the effectiveness of morphometric parameters in watershed prioritization. A comparison has been carried out between the results achieved through applying the two methods of analysis (morphometric and PCA). Afterwards, suitable measures are proposed for soil and water conservation. Topo sheets and ASTER DEM have been employed to demarcate the 43 sub-watersheds, to extract the drainage networks, and to compile the required thematic maps such as slope categories and elevation. LANDSAT 8 image (April-2015) is employed to generate land use/cover maps using ENVI (v 5.1) software. The soil map of the watershed has been digitized using Arc GIS software. Prioritization of the 43 sub-watersheds was performed using ten linear and shape parameters, and three parameters which are highly correlated with components 1 and 2. Subsequently, different sub-watersheds were prioritized by ascribing ranks based on the calculated compound parameters (Cp) using the two approaches. Comparison of the results revealed that prioritization of watersheds based on morphometric analysis is more consistent and serves for better decision making in conservation planning as compared with the PCA approach. The recommended soil conservation measures are prescribed in accordance with the specified priority, in order to avoid undesirable effects on land and environment. Sub-watersheds classified under high priority class are subjected to high erosion risk, thus, creating an urgent need for applying soil and water conservation measures. It is expected that decision makers will pay sufficient attention to the present results/information, activate programs encouraging soil conservation, integrated watershed management, and will continue working on the afforestation of the government-owned sloping lands. Such a viable approach can be applied at different parts of the rainfed highland areas to minimize soil erosion loss, and to increase infiltration and soil moisture in the soil profile, thus, reducing the impact of recurrent droughts and the possibility of flooding hazards.

Ecological Safety Evaluation of Land Use in Ji'an City Based on the Principal Component Analysis

According to the ecological safety evaluation index data of land-use change in Ji'an City from 1999 to 2008,positive treatment on selected reverse indices is conducted by Reciprocal Method.Meanwhile,Index Method is used to standardize the selected indices,and Principal Component Analysis is applied by using year as a unit.FB is obtained,which is related with the ecological safety of land-use change from 1999 to 2008.According to the scientific,integrative,hierarchical,practical and dynamic principles,ecological safety evaluation index system of land-use change in Ji'an City is established.Principal Component Analysis and evaluation model are used to calculate four parameters,including the natural resources safety index of land use,the socio-economic safety indicators of land use,the eco-environmental safety index of land use,and the ecological safety degree of land use in Ji'an City.Result indicates that the ecological safety degree of land use in Ji'an City shows a slow upward trend as a whole.At the same time,ecological safety degree of land-use change is relatively low in Ji'an City with the safety value of 0.645,which is at a weak safety zone and needs further monitoring and maintenance.

Application of Principal Component Analysis(PCA)to the Evaluation and Screening of Multiactivity Fungi

Continued innovation in screening methodologies remains important for the discovery of high-quality multiactive fungi,which have been of great significance to the development of new drugs.Mangrove-derived fungi,which are well recognized as prolific sources of natural products,are worth sustained attention and further study.In this study,118 fungi,which mainly included Aspergillus spp.(34.62%)and Penicillium spp.(15.38%),were isolated from the mangrove ecosystem of the Maowei Sea,and 83.1%of the cultured fungi showed at least one bioactivity in four antibacterial and three antioxidant assays.To accurately evaluate the fungal bioactivities,the fungi with multiple bioactivities were successfully evaluated and screened by principal component analysis(PCA),and this analysis provided a dataset for comparing and selecting multibioactive fungi.Among the 118 mangrove-derived fungi tested in this study,Aspergillus spp.showed the best comprehensive activity.Fungi such as A.clavatonanicus,A.flavipes and A.citrinoterreus,which exhibited high comprehensive bioactivity as determined by the PCA,have great potential in the exploitation of natural products and the development of new drugs.This study demonstrated the first use of PCA as a time-saving,scientific method with a strong ability to evaluate and screen multiactive fungi,which indicated that this method can affect the discovery and development of new drugs.

A Study of the Comprehensive Urban Competitiveness Based On Principal Component Analysis

This paper studies the comprehensive urban competitiveness and performs the principal component analysis. The results show that the comprehensive evaluation of urban competitiveness is not entirely dependent on the city's economic strength or GDP,and it is necessary to consider from resource allocation capacity,openness and public service capacity. By selecting various data concerning 11 prefecture-level cities in Jiangxi Province in 2006,2009 and 2012,this paper gets the ranking results and analyzes trends,to provide a basis for making future economic policy.

Metabonomic studies of pancreatic cancer response to radiotherapy in a mouse xenograft model using magnetic resonance spectroscopy and principal components analysis

AIM: To investigate the metabolic profiles of xenograft pancreatic cancer before and after radiotherapy by high-resolution magic angle spinning proton magnetic resonance spectroscopy (HRMAS 1H NMR) combined with principal components analysis (PCA) and evaluate the radiotherapeutic effect. METHODS: The nude mouse xenograft model of human pancreatic cancer was established by injecting human pancreatic cancer cell SW1990 subcutaneously into the nude mice. When the tumors volume reached 800 mm3 , the mice received various radiation doses. Two weeks later, tumor tissue sections were prepared for running the NMR measurements. 1H NMR and PCA were used to determine the changes in the metabolic profiles of tumor tissues after radiotherapy. Metabolic profiles of normal pancreas, pancreatic tumor tissues, and radiationtreated pancreatic tumor tissues were compared. RESULTS: Compared with 1H NMR spectra of the normal nude mouse pancreas, the levels of choline, taurine, alanine, isoleucine, leucine, valine, lactate, and glutamic acid of the pancreatic cancer group were increased, whereas an opposite trend for phosphocholine, glycerophosphocholine, and betaine was observed. The ratio of phosphocholine to creatine, and glycerophosphocholine to creatine showed noticeable decrease in the pancreatic cancer group. After further evaluation of the tissue metabolic profile after treatment with three different radiation doses, no significant change in metabolites was observed in the 1H NMR spectra, while the inhibition of tumor growth was in proportion to the radiation doses. However, PCA results showed that the levels of choline and betaine were decreased with the increased radiation dose, and conversely, the level of acetic acid was dramatically increased. CONCLUSION: The combined methods were demonstrated to have the potential for allowing early diagnosis and assessment of pancreatic cancer response to radiotherapy.

Assessment of depth of anesthesia using principal component analysis

A new approach to estimating level of uncon-sciousness based on Principal Component Analysis (PCA) is proposed. The Electroen-cephalogram (EEG) data was captured in both Intensive Care Unit (ICU) and operating room, using different anesthetic drugs. Assuming the central nervous system as a 20-tuple source, window length of 20 seconds is applied to EEG. The mentioned window is considered as 20 nonoverlapping mixed-signals (epoch). PCA algorithm is applied to these epochs, and larg-est remaining eigenvalue (LRE) and smallest remaining eigenvalue (SRE) were extracted. Correlation between extracted parameters (LRE and SRE) and depth of anesthesia (DOA) was measured using Prediction probability (PK). The results show the superiority of SRE than LRE in predicting DOA in the case of ICU and isoflurane, and the slight superiority of LRE than SRE in propofol induction. Finally, a mixture model containing both LRE and SRE could predict DOA as well as Relative Beta Ratio (RBR), which expresses the high capability of the proposed PCA based method in estimating DOA.

Selection of tree species by principal component analysis for abandoned farmland in southeastern Horqin Sandy Land,China

With changes in global climate and land use,the area of desertified farmland in southeastern Horqin Sandy Land(HSL)has increased in recent years,and farmlands are being abandoned.These abandoned farmlands(AFs)nega-tively impact the local ecology.Therefore,the aim of the present study was to select suitable trees and shrubs for those AFs to prevent and control the desertification tendency.In this study,three AFs were fenced for 2 years,then 37 arbor and shrub species or varieties of 21 families were planted in the fenced AFs and grown for 10 years.The ecological adaptability of the species was evaluated and ranked using a principal component analysis.The results showed that the biodiversity of the AFs significantly improved after 2 years of fencing;the Shannon-Wiener index and species rich-ness of perennial grasses and forbs were 1.45 and 3.6 times higher,respectively,than for the unfenced AF.Among all species planted in fenced AFs,nine tree species had posi-tive comprehensive F(CF)values;Pinus sylvestris(Russian Shira steppe provenance),Populus alba‘Berolinensis’and Gleditsia triacanthos had CF greater than 1,and the first(PC1),second(PC2)and third(PC3)principal component values(F_(1),F_(2),F_(3))were all positive.Among the shrubs,only Lespedeza bicolor and Rosa xanthina f.normalis had CF greater than 0.All these results suggest that fencing improves biodiversity and that planting trees and shrubs that have higher CF values on the basis of fencing is an effective way to green and beautify AFs in HSL.

Application of synthetic principal component analysis model to mine area farmland heavy metal pollution assessment

Referring to GB5618-1995 about heavy metal pollution,and using statistical analysis SPSS,the major pollutants of mine area farmland heavy metal pollution were identified by variable clustering analysis.Assessment and classification were done to the mine area farmland heavy metal pollution situation by synthetic principal components analysis (PCA).The results show that variable clustering analysis is efficient to identify the principal components of mine area farmland heavy metal pollution.Sort and clustering were done to the synthetic principal components scores of soil sample,which is given by synthetic principal components analysis.Data structure of soil heavy metal contaminations relationships and pollution level of different soil samples are discovered.The results of mine area farmland heavy metal pollution quality assessed and classified with synthetic component scores reflect the influence of both the major and compound heavy metal pol- lutants.Identification and assessment results of mine area farmland heavy metal pollution can provide reference and guide to propose control measures of mine area farmland heavy metal pollution and focus on the key treatment region.

Comprehensive Evaluation on the Level of Agricultural Economic Development in Hubei Province Based on Principal Component Analysis

[Objectives]Hubei Province has a superior geographical location,and is located in the middle and lower reaches of the Yangtze River,with pleasant climate and abundant natural resources.It is an important province of population,agriculture and resources in China.[Methods]Based on the data of Statistical Yearbook of Hubei 2018,the agricultural economic indicators of the cities and prefectures in Hubei Province were analyzed with principal component analysis method by using SPSS19.0.[Results]The comprehensive scores and rankings of the agricultural economic development level of the 17 cities and prefectures in Hubei Province were obtained.They were divided into four agricultural development levels.[Conclusions]According to the analysis results,corresponding policy recommendations were put forward to promote the development of agricultural economy in Hubei Province.

The Formation Mechanism of Hydrogeochemical Features in a Karst System During Storm Events as Revealed by Principal Component Analysis

The hydrogeochemical parameters of Jiangjia Spring,the outlet of Qingrnuguan underground river system(QURS) in Chongqing,were found responding rapidly to storm events in late April,2008.A total of 20 kinds of hydrogeochemical parameters,including discharge,specific conductance,pH,water tempera-

Effects of granulation process variables on the physical properties of dosage forms by combination of experimental design and principal component analysis

The current study was to understand how process variables of high shear wet granulations affect physical properties of granules and tablets. The knowledge gained was intended to be used for Quality-by-Design based process design and optimization. The variables were selected based on the risk assessment as impeller speed, liquid addition rate, and wet massing time. Formulation compositions were kept constant to minimize their influence on granules properties. Multiple linear regression models were built providing understanding of the impact of each variable on granule hardness, Carr’s index, tablet tensile strength, surface mean diameter of granules, and compression behavior. The experimental results showed that the impact of impeller speed was more dominant compared to wet massing time and water addition rate. The results also revealed that quality of granules and tablets could be optimized by adjusting specific process variables(impeller speed 1193 rpm, water spray rate 3.7 ml/min, and wet massing time 2.84 min). Overall desirability was 0.84 suggesting that the response values were closer to the target one. The SEM image of granules showed that spherical and smooth granules produced at higher impeller speed, whereas rough and irregular shape granules at lower speed. Moreover, multivariate data analysis demonstrated that impeller speed and massing time had strong correlation with the granule and tablet properties. In overall, the combined experimental design and principal component analysis approach allowed to better understand the correlation between process variables and granules and tablet attributes.