Quantitative analysis and prediction of the sound field convergence zone in mesoscale eddy environment based on data mining methods

The mesoscale eddy(ME)has a significant influence on the convergence effect in deep-sea acoustic propagation.This paper use statistical approaches to express quantitative relationships between the ME conditions and convergence zone(CZ)characteristics.Based on the Gaussian vortex model,we construct various sound propagation scenarios under different eddy conditions,and carry out sound propagation experiments to obtain simulation samples.With a large number of samples,we first adopt the unified regression to set up analytic relationships between eddy conditions and CZ parameters.The sensitivity of eddy indicators to the CZ is quantitatively analyzed.Then,we adopt the machine learning(ML)algorithms to establish prediction models of CZ parameters by exploring the nonlinear relationships between multiple ME indicators and CZ parameters.Through the research,we can express the influence of ME on the CZ quantitatively,and achieve the rapid prediction of CZ parameters in ocean eddies.The prediction accuracy(R)of the CZ distance(mean R:0.9815)is obviously better than that of the CZ width(mean R:0.8728).Among the three ML algorithms,Gradient Boosting Decision Tree has the best prediction ability(root mean square error(RMSE):0.136),followed by Random Forest(RMSE:0.441)and Extreme Learning Machine(RMSE:0.518).

Statistical and causes analysis of storm surges along Tianjin coast during the past 20 years

Based on tidal data statistical analysis for 20 years of Tanggu Marine Environmental Monitoring Station from 1991 to 2010, we concluded that an average of nearly 10 days of 100 cm above water increase took place at Tianjin coast every year. The maximum high tide and average tide of Tianjin coast occurred in summer and autumn, and the maximum water increase also occurred in summer and autumn. Days with water increase more than 100 cm mostly occurred in spring, autumn and winter. Then we summarized the causes of coastal storm surge disaster in Tianjin based on astronomical tide factors, meteorological factors, sea level rise, land subsidence, and geographic factors, et al. Finally, we proposed storm surge disaster prevention measures.

A New Scheme of the ARA Transform for Solving Fractional-Order Waves-Like Equations Involving Variable Coefficients

The goal of this research is to develop a new,simplified analytical method known as the ARA-residue power series method for obtaining exact-approximate solutions employing Caputo type fractional partial differential equations(PDEs)with variable coefficient.ARA-transform is a robust and highly flexible generalization that unifies several existing transforms.The key concept behind this method is to create approximate series outcomes by implementing the ARA-transform and Taylor’s expansion.The process of finding approximations for dynamical fractional-order PDEs is challenging,but the ARA-residual power series technique magnifies this challenge by articulating the solution in a series pattern and then determining the series coefficients by employing the residual component and the limit at infinity concepts.This approach is effective and useful for solving a massive class of fractional-order PDEs.Five appealing implementations are taken into consideration to demonstrate the effectiveness of the projected technique in creating solitary series findings for the governing equations with variable coefficients.Additionally,several visualizations are drawn for different fractional-order values.Besides that,the estimated findings by the proposed technique are in close agreement with the exact outcomes.Finally,statistical analyses further validate the efficacy,dependability and steady interconnectivity of the suggested ARA-residue power series approach.

Assessment of Heavy Metals Contamination of Topsoil and Street Dust around Cement Factory in Southern Jordan

Evaluation of assessment of the metal processes governing the metals distribution in soil and dust samples is very significant and protects the health of human and ecological system. Recently, special attention has given to the assessment of metals pollution impact on soil and dust within industrial areas. This study aims to assess the metal contamination levels in the topsoil and street dust around the cement factory in Qadissiya area, southern Jordan. The levels of seven metals (namely Fe, Zn, Cu, Pb, Cr, Cd, and Mn) were analyzed using Flame Atomic Absorption Spec-trophotometer (FAAS) to monitor, evaluate, and to compare topsoil and road dust pollution values of metals of the different types of urban area. The physicochemical parameters which believed to affect the mobility of metals in the soil of the study area were determined such as pH, EC, TOM, CaCO3 and CEC. The levels of metal in soil samples are greater on the surface but decrease in the lower part as a result of the basic nature of soil. The mean values of the metals in soil can be arranged in the following order: Zn > Pb > Mn > Fe > Cu > Cr > Cd. The relatively high concentration of metals in the soil sample was attributed to anthropogenic activities such as traffic emissions, cement factory and agricultural activities. Correlation coefficient analysis and the spatial distribution of indices and the results of statistical analysis indicate three groups of metals: Fe and Mn result by natural origin, Zn, Pb, Cu and Zn result by anthropogenic origin (mainly motor vehicle traffic and abrasion of tires) while Cd is mixed origin. The higher content level values of metals of anthropogenic source in soil samples indicate that it is a source of contamination of air in the studied area. .

Characteristics and genesis of groundwater salinization in coastal areas of the Lower Reaches of Oujiang Basin

The coastal areas of the lower reaches of Oujiang River Basin are rich in groundwater resources.However,the unsustainable exploitation and utilization of groundwater have led to significant changes in the groundwater environment.Understanding the characteristics and genesis of groundwater salinization is crucial for preventing its deterioration and ensuring sustainable utilization.In this study,a comprehensive approach combining the ion ratio method,mineral saturation index method and multivariate statistical analysis was employed to investigate the hydrochemical characteristics and main controlling factors in the study area.The findings reveal that:(1)Groundwater samples in study area exhibit a neutral to slightly alkaline pH.The predominant chemical types of unconfined water are HCO_(3)-Ca·Na,HCO_(3)·Cl-Na·Ca and HCO_(3)·SO_(4)-Ca·Na,while confined water mainly exhibits Cl·HCO_(3)-Na and Cl-Na types.(2)Salinity coefficients indicate an increase in salinity from unconfined to confined water.TDS,Na^(+)and Cl^(–)concentrations show an increasing trend from mountainous to coastal areas in unconfined water,while confined water displays variability in TDS,Na^(+)and Cl^(–)concentrations.(3)Groundwater salinity is mainly influenced by water-rock interactions,including the dissolution of halite and gypsum,cation exchange,and seawater intrusion etc.Additionally,human activities and carbonate dissolution contribute to salinity in unconfined water.Seawater intrusion is identified as the primary factor leading to higher salinity in confined water compared to unconfined water,with increasing cation exchange and seawater interaction observed from unconfined to confined water.

The Impact of Nursing Staff’s Work Attitude on the Fear of Patients Recovering from Benign Tumors:Family Support as a Mediating Variable

The perception of nursing staff’s attitude influences patient fear.Understanding this dynamic is crucial for fostering a supportive environment conducive to patient well-being and effective healthcare practices.The purpose of this research is to investigate how the attitudes and behaviours of nursing staff influence the fear and anxiety levels of patients recovering from benign tumors,aiming to improve patient care and recovery outcomes.Data was collected from a sample of 100 participants,comprising 20 nursing staff and 80 patients recovering from benign tumors.Surveys were administered to gather quantitative data on attitudes and fear levels.Participants were selected randomly from hospital records and outpatient clinics.Our analysis encompassed nursing staff attitude,patient fear levels,the influence of family support,progression of tumor recovery,patient-reported satisfaction,and the quality of healthcare services provided.The quantitative aspect utilized PLS-SEM software to perform regression analysis,evaluating both direct and indirect effects.Statistical analysis assessed the relationships between nursing staff attitudes,patient fear during benign tumor recovery,and the mediating role of family support.The findings of the study demonstrate that better nurse attitudes(Hypothesis 1,β=0.45,p<0.001)and stronger family support(Hypothesis 2,β=0.32;p<0.001) are linked to lower levels of patient fear.Partially mediating the relationship between nurse attitudes and patient fear,according to Hypothesis 3(β=0.28,p<0.002),is family support.Patients’perceptions of family support are highly influenced by nursing behaviour,as demonstrated by Hypothesis 4(β=0.38;p<0.001).Our research showed a strong relationship between the attitudes of nursing personnel and patient fear levels.Family support demonstrated a strong mediating effect on patient fear.Patient-reported satisfaction is positively correlated with family support.However,no significant relationship was found between healthcare service quality and patient fear.

Integration between Genomic and Computational Statistical Surveys for the Screening of SNP Genetic Variants in Inflammatory Bowel Disease (IBD) Pediatric Patients*

Inflammatory bowel diseases (IBD) are complex multifactorial disorders that include Crohn’s disease (CD) and ulcerative colitis (UC). Considering that IBD is a genetic and multifactorial disease, we screened for the distribution dynamism of IBD pathogenic genetic variants (single nucleotide polymorphisms;SNPs) and risk factors in four (4) IBD pediatric patients, by integrating both clinical exome sequencing and computational statistical approaches, aiming to categorize IBD patients in CD and UC phenotype. To this end, we first aligned genomic read sequences of these IBD patients to hg19 human genome by using bowtie 2 package. Next, we performed genetic variant calling analysis in terms of single nucleotide polymorphism (SNP) for genes covered by at least 20 read genomic sequences. Finally, we checked for biological and genomic functions of genes exhibiting statistically significant genetic variant (SNPs) by introducing Fitcon genomic parameter. Findings showed Fitcon parameter as normalizing IBD patient’s population variability, as well as inducing a relative good clustering between IBD patients in terms of CD and UC phenotypes. Genomic analysis revealed a random distribution of risk factors and as well pathogenic SNPs genetic variants in the four IBD patient’s genome, claiming to be involved in: i) Metabolic disorders, ii) Autoimmune deficiencies;iii) Crohn’s disease pathways. Integration of genomic and computational statistical analysis supported a relative genetic variability regarding IBD patient population by processing IBD pathogenic SNP genetic variants as opposite to IBD risk factor variants. Interestingly, findings clearly allowed categorizing IBD patients in CD and UC phenotypes by applying Fitcon parameter in selecting IBD pathogenic genetic variants. Considering as a whole, the study suggested the efficiency of integrating clinical exome sequencing and computational statistical tools as a right approach in discriminating IBD phenotypes as well as improving inflammatory bowel disease (IBD) molecular diagnostic process.

The Performance of Small Diameter Aluminum Light Support Structures Containing Handholes under Cyclic Fatigue

Aluminum light poles play a pivotal role in modern infrastructure, ensuring proper illumination along highways and in populated areas during nighttime. These poles typically feature handholes near their bases, providing access to electrical wiring for installation and maintenance. While essential for functionality, these handholes introduce a vulnerability to the overall structure, making them a potential failure point. Although prior research and analyses on aluminum light poles have been conducted, the behavior of smaller diameter poles containing handholes remains unexplored. Recognizing this need, a research team at the University of Akron undertook a comprehensive experimental program involving aluminum light poles with handholes containing welded inserts in order to gain a better understanding of their fatigue life, mechanical behavior, and failure mechanisms. The research involved testing seven large-scale aluminum light poles each 8-inch diameter, with two separate handholes. These handholes included a reinforcement that was welded to the poles. Finite Element Analysis (FEA), statistical analysis, and comparison analysis with their large counterparts (10-inch diameter) were used to augment the experimental results. The results revealed two distinct failure modes: progressive crack propagation leading to ultimate failure, and rupture of the pole near the weld initiation/termination site around the handhole. The comparison analysis indicated that the 8-inch diameter specimens exhibited an average fatigue life exceeding that of their 10-inch counterparts by an average of 30.7%. The experimental results were plotted alongside the fatigue detail classifications outlined in the Aluminum Design Manual (ADM), enhancing understanding of the fatigue detail category of the respective poles/handholes.

Research on the Reliability Tolerance Analysis Method of Electromagnetic Relay in Aerospace

Electromagnetic relay in aerospace is one of the main electronic components in aerospace electronic systems for information transfer, control and power distribution, and its reliability will influence the reliability of the whole aerospace electronic systems. Reliability design is the key technique of electromagnetic relay reliability engineering. This paper synthetically analyzes the present reliability design methods, and presents the reliability tolerance analyzing mathematic models of electromagnetic force basing on orthogonal design, mechanical spring force basing on probability statistics theory, and matching characteristics of electromagnetic force and mechanical spring force basing on method of stressstrength interference. Some instructive conclusions are draw by researching on the reliability tolerance of some type electromagnetic relay in aerospace.

Interval Analysis Method on the Buckling of Composite Laminate

A method named interval analysis method, which solves the buckling load of composite laminate with uncertainties, is presented. Based on interval mathematics and Taylor series expansion, the interval analysis method is used to deal with uncertainties. Not necessarily knowing the probabilistic statistics characteristics of the uncertain variables, only little information on physical properties of material is needed in the interval analysis method, that is, the upper bound and lower bound of the uncertain variable. So the interval of response of the structure can be gotten through less computational efforts. The interval analysis method is efficient under the condition that probability approach cannot work well because of small samples and deficient statistics characteristics. For buckling load of a special cross-ply laminates and antisymmetric angle-ply laminates with all edges simply supported, calculations and comparisons between interval analysis method and probability method are performed.

The use of hydrogeochemical analyses and multivariate statistics for the characterization of thermal springs in the Constantine area, Northeastern Algeria

This paper deals with the results of a hydrogeochemistry study on the thermal waters of the Constantine area, Northeastern Algeria, using geochemical and statistical tools. The samples were collected in December2016 from twelve hot springs and were analyzed for physicochemical parameters(electric conductivity, p H,total dissolved solids, temperature, Ca, Mg, Na, K, HCO_3,Cl, SO_4, and SiO_2). The waters of the thermal springs have temperatures varying from 28 to 51 °C and electric conductivity values ranging from 853 to 5630 l S/cm. Q-mode Cluster analysis resulted in the determination of two major water types: a Ca–HCO_3–SO_4 type with a moderate salinity and a Na–K–Cl type with high salinity. The plot of the major ions versus the saturation indices suggested that the hydrogeochemistry of thermal groundwater is mainly controlled by dissolution/precipitation of carbonate minerals, dissolution of evaporite minerals(halite and gypsum), and ion exchange of Ca(and/or Mg) by Na. The Gibbs diagram shows that evaporation is another factor playing a minor role. Principal Component Analysis produced three significant factors which have 88.2% of totalvariance that illustrate the main processes controlling the chemistry of groundwaters, which are respectively: the dissolution of evaporite minerals(halite and gypsum), ion exchange, and dissolution/precipitation of carbonate minerals. The subsurface reservoir temperatures were calculated using different cation and silica geothermometers and gave temperatures ranging between 17 and 279 °C. The Na–K and Na–K-Ca geothermometers provided high temperatures(up to 279 °C), whereas, estimated geotemperatures from K/Mg geothermometers were the lowest(17–53 °C). Silica geothermometers gave the most reasonable temperature estimate of the subsurface waters overlap between 20 and 58 °C, which indicate possible mixing with cooler Mg groundwaters indicated by the Na–K–Mg plot in the immature water field and in silica and chloride mixing models. The results of stable isotope analyses(δ^(18) O and δ~2 H) suggest that the origin of thermal water recharge is precipitation, which recharged from a higher altitude(600–1200 m) and infiltrated through deep faults and fractures in carbonate formations. They circulate at an estimated depth that does not exceed 2 km and are heated by a high conductive heat flow before rising to the surface through faults that acted as hydrothermal conduits.During their ascent to the surface, they are subjected to various physical and chemical changes such as cooling by conduction and change in their chemical constituents due to the mixing with cold groundwaters.

Statistic Analysis on Quantitative Characteristics for Developing the DUS Test Guideline of Ranunculus asiaticus L.

Selection of quantitative characteristics, division of their expression ranges, and selection of example varieties are key issues on developing DUS Test Guidelines, which are more crucial for quantitative characteristics since their expressions vary in different degrees. Taking the development of DUS Test Guideline of Ranunculus asiaticus L. as an example, this paper applied statistic-based approaches for the analyses of quantitative characteristics. We selected 9 quantitative characteristics from 18 pre-selected characteristics, based on within-variety uniformity, stability between different growing cycles, and correlation among characteristics, by the analyses of coefficient of variation, paired-samples t-test and partial correlation. The expression ranges of the 9 selected quantitative characteristics were divided into different states using descriptive statistics and distribution frequency of varieties. Eight of the 9 selected quantitative characteristics were categorized as standard characteristics as they showed one peak in distribution frequency of 120 varieties in various expressions of the characteristics, whereas, plant height can be categorized as grouping characteristic since it gave two peaks, and can group the varieties into pot and cut varieties. Finally, box-plot was applied to visually select the example varieties, and varieties 7, 12, and 28 were determined as the example varieties for plant height. The methods described in this paper are effective for the selection of quantitative characteristics, division of expression ranges, and selection of example varieties in Ranunculus asiaticus L. for DUS test, and may also be interest for other plant genera.

Identification of distant co-evolving residues in antigen 85C from Mycobacterium tuberculosis using statistical coupling analysis of the esterase family proteins

A fundamental goal in cellular signaling is to understand allosteric communication, the process by which sig-nals originating at one site in a protein propagate reliably to affect distant functional sites. The general principles of protein structure that underlie this process remain unknown. Statistical coupling analysis （SCA） is a statistical technique that uses evolutionary data of a protein family to measure correlation between distant functional sites and suggests allosteric communication. In proteins, very distant and small interactions between collections of amino acids provide the communication which can be important for signaling process. In this paper, we present the SCA of protein alignment of the esterase family （pfam ID： PF00756） containing the sequence of antigen 85C secreted by Mycobacterium tuberculosis to identify a subset of interacting residues. Clustering analysis of the pairwise correlation highlighted seven important residue positions in the esterase family alignments. These resi-dues were then mapped on the crystal structure of antigen 85C （PDB ID： 1DQZ）. The mapping revealed corre-lation between 3 distant residues （Asp38, Leu123 and Met125） and suggests allosteric communication between them. This information can be used for a new drug against this fatal disease.

Analysis of field coupling to transmission lines with random rotation over the ground

In this paper we analyze plane wave coupling to transmission lines rotating randomly over an infinite and perfectly conducting ground and present an efficient method to calculate average voltage. Under the assumption of small rotation quantity, the factors affecting the induced voltage and their effects are analyzed and then an efficient method to calculate the average voltage is presented when the distribution of the random rotation angles is uniform in [-π, π]. The results show that voltage variation is mainly due to the change of the source term. The effects of the source term increase linearly with the magnitude of the incident wave, change periodically with the rotation angle, and are larger in the high frequency range than in the low frequency range. The results show that the average voltages obtained by the proposed method agree well with those via the Monte Carlo method and the proposed method is much more efficient. The results also imply that the effect of random rotation is more important than that of random translation.

Statistical Characteristics and Mechanistic Analysis of Suddenly Reversed Tropical Cyclones over the Western North Pacific Ocean

Based on best track data of tropical cyclones（TCs） from the Japan Meteorological Agency, the characteristics of suddenly reversed TCs（SRTCs）, which have turning angles usually approaching 180°, are statistically analyzed from 1949 to 2011 over the western North Pacific Ocean. The typical large-scale circulation patterns of SRTCs are investigated using reanalysis data and dynamical composite analysis. Results show that turnings mainly occur in low latitudes between 10°N and 20°N,and mainly west of 135°E. The majority of SRTCs reach their peak intensity at, or slightly before, the turning time and subsequently decrease at some variable rate. Specifically, SRTCs are divided into four types, each containing two groups（i.e.eight groups in total） in terms of the moving-direction changes. The moving speed of all SRTC types except the south–north type decreases to its lowest during the 24 h, corresponding to a significant reduction in the primary steering components.According to the analysis of the 13 typical flow patterns found in this study, we suggest that sudden track changes are caused by the reversal steering flow. The original balance of the background flow patterns are broken up by new systems, e.g. binary TCs or dispersion-induced anticyclones. Additionally, sudden track changes are often due to double ridge variations of the subtropical high or weakened/strengthened high pressure in the east and west, respectively.

Metabolomics-based comparative analysis of the effects of host and environment on Viscum coloratum metabolites and antioxidative activities

Viscum coloratum(Kom.)Nakai is a well-known medicinal hemiparasite widely distributed in Asia.The synthesis and accumulation of its metabolites are affected by both environmental factors and the host plants,while the latter of which is usually overlooked.The purpose of this study was to comprehensively evaluate the effects of host and habitat on the metabolites in V.coloratum through multiple chemical and biological approaches.The metabolite profile of V.coloratum harvested from three different host plants in two habitats were determined by multiple chemical methods including high-performance liquid chromatography-ultraviolet(HPLC-UV),gas chromatography-flame ionization detector(GC-FID)and ultra-performance liquid chromatography quadrupole time of flight mass spectrometry(UPLC-QTOF/MS).The differences in antioxidant efficacy of V.coloratum were determined based on multiple in vitro models.The multivariate statistical analysis and data fusion strategy were applied to analyze the differences in metabolite profile and antioxidant activity of V.coloratum.Results indicated that the metabolite profile obtained by various chemical approaches was simultaneously affected by host and environment factors,and the environment plays a key role.Meanwhile,three main differential metabolites between two environment groups were identified.The results of antioxidant assay indicated that the environment has greater effects on the biological activity of V.coloratum than the host.Therefore,we conclude that the integration of various chemical and biological approaches combined with multivariate statistical and data fusion analysis,which can determine the influences of host plant and habitat on the metabolites,is a powerful strategy to control the quality of semi-parasitic herbal medicine.

Statistical Analysis and Prediction of the Concentration of Harmful Algae in Bohai Bay

Based on the data of phytoplankton concentration and environmental factors in Bohai Bay from May to September in 2003. the relationship between environmental factors and phytoplankton biomass was analyzed. By analysis of variance, the weather condition was found to have no direct relation with phytoplankton biomass. Correlation coefficients showed that temperature, pH value,the concentrations of silicate and nitrate exhibited linear relationship with phytoplankton biomass.With principal component analysis, pollution types which affected the abundance of phytoplankton included point sources such as municipal and industrial effluents, agricultural runoff and earth＇s surface water. Using multivariate stepwise regression method and taking the correlation analysis results into consideration, a multi-step regression equation was developed to predict the concentration of phytoplankton in September 2003. Combined results show that temperature, pH value, the concentrations of silicate and nitrate are the critical ecological factors affecting the phytoplankton biomass in Bohai Bay.

Application of an Error Statistics Estimation Method to the PSAS Forecast Error Covariance Model

In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the paralneters required by a forecast error covariance model are difficult to obtain due to the absence of the truth. This study applies an error statistics estimation method to the Pfiysical-space Statistical Analysis System （PSAS） height-wind forecast error covariance model. This method consists of two components： the first component computes the error statistics by using the National Meteorological Center （NMC） method, which is a lagged-forecast difference approach, within the framework of the PSAS height-wind forecast error covariance model; the second obtains a calibration formula to rescale the error standard deviations provided by the NMC method. The calibration is against the error statistics estimated by using a maximum-likelihood estimation （MLE） with rawindsonde height observed-minus-forecast residuals. A complete set of formulas for estimating the error statistics and for the calibration is applied to a one-month-long dataset generated by a general circulation model of the Global Model and Assimilation Office （GMAO）, NASA. There is a clear constant relationship between the error statistics estimates of the NMC-method and MLE. The final product provides a full set of 6-hour error statistics required by the PSAS height-wind forecast error covariance model over the globe. The features of these error statistics are examined and discussed.

Water Quality Analysis of the Songhua River Basin Using Multivariate Techniques

Multivariate statistical techniques, including cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were used to evaluate temporal and spatial variations and to interpret a large and complex water quality data sets collected from the Songhua River Basin. The data sets, which contained 14 parameters, were generated during the 7-year (1998-2004) monitoring program at 14 different sites along the rivers. Three significant sampling locations (less polluted sites, moderately polluted sites and highly polluted sites) were detected by CA method, and five latent factors (organic, inor-ganic, petrochemical, physiochemical, and heavy metals) were identified by PCA and FA methods. The re-sults of DA showed only five parameters (temperature, pH, dissolved oxygen, ammonia nitrogen, and nitrate nitrogen) and eight parameters (temperature, pH, dissolved oxygen, biochemical oxygen demand, ammonia nitrogen, nitrate nitrogen, volatile phenols and total arsenic) were necessarily in temporal and spatial varia-tions analysis, respectively. Furthermore, this study revealed the major causes of water quality deterioration were related to inflow of effluent from domestic and industrial wastewater disposal.

Spatial autocorrelation analysis of 13 leading malignant neoplasms in Taiwan: a comparison between the 1995-1998 and 2005-2008 periods

Spatial autocorrelation methodologies, including Global Moran’s I and Local Indicators of Spatial Association statistic (LISA), were used to describe and map spatial clusters of 13 leading malignant neoplasms in Taiwan. A logistic regression fit model was also used to identify similar characteristics over time. Two time periods (1995-1998 and 2005-2008) were compared in an attempt to formulate common spatio-temporal risks. Spatial cluster patterns were identified using local spatial autocorrelation analysis. We found a significant spatio-temporal variation between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, cancer of the oral cavity in males was found to be clustered in locations in central Taiwan, with distinct differences between the two time periods. Stomach cancer morbidity clustered in aboriginal townships, where the prevalence of Helicobacter pylori is high and even quite marked differences between the two time periods were found. A method which combines LISA statistics and logistic regression is an effective tool for the detection of space-time patterns with discontinuous data. Spatio-temporal mapping comparison helps to clarify issues such as the spatial aspects of both two time periods for leading malignant neoplasms. This helps planners to assess spatio-temporal risk factors, and to ascertain what would be the most advantageous types of health care policies for the planning and implementation of health care services. These issues can greatly affect the performance and effectiveness of health care services and also provide a clear outline for helping us to better understand the results in depth.