Ascendant Hierarchical Clustering Research Articles

A cell-based association data integration method for locating potential drilling targets: A case study at the Dalli Cu–Au porphyry deposit in Iran

The presence of various spatial exploration proxies (predictor patterns) as vectors to distal porphyry mineralization in surrounding areas of a known outcropping mineralized porphyry center at the Dalli Cu–Au deposit in Iran provided evidence for the occurrence of similar porphyry mineralization in the subsurface. Factor analysis was applied to soil geochemical data from the Dalli South Hill area to define favorable geochemical variables and to create surface multi-element anomaly maps reflecting the presence of porphyry Cu–Au mineralization in the subsurface. Reduction-to-pole and analytical signal filters were applied to ground total magnetic field data to define high magnetic signatures that could be associated with potassic alteration and porphyry mineralization in the subsurface. Then, a data-driven cell based association (CBA) data integration method was applied to the favorable spatial proxies to define their association with Cu–Au mineralization and locate subtle high potential targets (cells) for drilling. The conventional CBA algorithms did not consider weights for the spatial proxies, and by using the ascendant hierarchical clustering (AHC) algorithm the results were classified into a binary map to show favorable and unfavorable cells with scores of 1 and 0, respectively. Predicting high potential targets for high-priced drilling, just based on using the binary map, could be very risky. Therefore, an innovative CBA data integration approach was employed to assign proper weights between 0 and 1 to the spatial proxies by calculating the importance of the presence of an exploration proxy in a cell using training (labeled) trenching geochemical data. A final continuous predictor map was generated and it successfully mapped the known outcropping porphyry center and its subsurface extension to the northeast as a high prospectivity class. This map also identified a narrow NE-trending high potential target (a few cells) of 200 m long in the southern part of the known porphyry center for drilling.

Radio Frequency Identification Network Planning Using Ascendant Hierarchical Clustering Algorithm

Radio Frequency Identification (RFID) is a suitable technology for several application scenarios. However, it faces many issues that need to be addressed. RFID Network Planning (RNP) is one of these challenges, which is studied in this paper. It consists in defining the optimal placement of RFID readers and their communication range. In this study, the RNP issue is considered as a clustering problem. Therefore, it is solved using Ascendant Hierarchical Clustering algorithm (AHC) such that each cluster head represents a reader. The advantage of our proposed approach is that, in addition to optimizing the RNP, it is capable to determine the optimal number of readers needed to get optimal performances. Simulation results demonstrate that the proposed AHC algorithm performs better than two efficient RNP algorithms from the literature: Particle Swarm Optimization (PSO) and Leaders and Followers (LaF) algorithms. Hence, it is observed that AHC algorithm is more efficient, scalable and runs faster than PSO and LaF.

Dendrogram-based Heterogeneous Learners for Automatic Modulation Classification in DSTBC-OFDM Systems

Software-Defined Radios (SDRs) have a crucial role in dynamic spectrum sensing and sharing as they can quickly adapt to changes in their environment. Digital Modulation Recognition (DMR) enables this process by giving SDR the ability to recognize the received signal modulation format to make intelligent and autonomous decisions to switch on the optimal modulation scheme. However, with the deployment of the Distributed Space-Time Block Coding Orthogonal Frequency Division Multiplexing (DSTBC-OFDM) signals that support MIMO systems, that provides spatio-temporel multiplexing, new and challenging signal identification problems have emerged.This paper proposes a robust DMR method for relaying Space-Time Block Coding (STBC) Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed classifier is built using heterogeneous base classifiers namely, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RFC), based on a Dendrogram or decision tree structure.The Dendrogram-based Heterogeneous Learners (DHL) classifier were obtained via Ascendant Hierarchical Clustering (AHC) and by employing Higher-Order Moment (HOMs) and Higher-Order Cumulants (HOCs) as features. Moreover, we propose a grid search-based hyperparameter tuning for DHL to produce the best structure and optimize the performance.

What are the drivers of eco-innovation? Empirical evidence from French start-ups

The purpose of this paper is to identify the drivers of eco-innovation in start-ups. Firstly, a discriminant analysis (DA) is applied to study what is distinctive about eco-innovative start-ups as compared to non-eco-innovative start-ups. Secondly, a typology of eco-innovative start-ups is developed using a hierarchical ascendant clustering (HAC). Analyses are carried out using original data from a survey of 120 eco-innovative and non-eco-innovative French start-ups.Discriminant analyses reveal that the founders of eco-innovative start-ups are differentiated by characteristics related to their environmental education and professional experience. Furthermore, eco-innovative start-ups are distinguished from the non-eco-innovative start-ups by voluntary environmental practices, such as the adoption of corporate social responsibility policies. Finally, we show that there is a diversity of profiles of eco-innovators. In fact, firms cluster into five main profiles and exhibit different eco-innovation drivers. We highlight that the different types of eco-innovators do not face the same difficulties in accessing funds. These findings have important implications for the implementation of public policy designed to promote eco-innovative activity, and they highlight the need to design policies that take into account the distinctive character of each profile.

An approach to spatio-temporal analysis for climatic data

This work considers a large-scale multisite investigation of the effects of sunshine duration on Algerian territory. It also studies the correlation between sunshine duration and commonly used climatic parameters (temperature, water vapour pressure, evaporation, relative humidity and rainfall). Two data sets have served this purpose. The first one consists of sunshine duration measurements of the whole Algerian weather network over the period 1960-2002 while the second set provides climatic parameters collected in Dar El-Beida, Algiers (36° 41'N, 03°13' E) over the period 1950-2008. This station is the best in terms of fulfilling criteria such as long time data series (at least 50 years) as well as reliable measurements. To achieve the expected goal, we use an appropriate objective clustering method, named Principal Component Analysis (PCA) with a coupling of a Hierarchical Ascending Clustering (HAC) algorithm. PCA is often used not only for reducing the data before the actual clustering is carried out but also because it might help identifying the characteristics of the clusters. In this way, we establish a distribution of weather stations and identify the main homogeneous areas of the country that are more distinguishable and useful to our purpose as well as we classify the months of year. Obtained results divide Algeria into three distinct climate regions which daily monthly means of sunshine duration are also studied. Further information can be drawn through maps established by ‘MapInfo’ Software. According to the correlation matrix, it also turns out that sunshine duration strongly influences climatic parameters.

Determination of four homogeneous subgroups of patients with antiphospholipid syndrome: a cluster analysis based on 509 cases.

APS is a heterogeneous disease with different phenotypes. Using an unsupervised hierarchical cluster analysis, we aimed to determine distinct homogeneous phenotypes among APS patients. We performed an observational, retrospective study of APS patients enrolled in the French multicentre 'APS and SLE' registry who met the Sydney classification criteria. The clustering process involved an unsupervised multiple correspondence analysis followed by a hierarchical ascendant clustering analysis; it used 27 variables selected to cover a broad range of APS clinical and laboratory manifestations. These analyses included 509 patients, mainly women (77.8%). Mean (s.d.) age at APS diagnosis was 36.2 (14.6) years, and mean follow-up since diagnosis 10.3 (8.5) years. This hierarchical classification cluster analysis yielded four homogeneous groups of patients: cluster 1, mostly with venous thromboembolism without any associated autoimmune disease; cluster 2, older, lowest proportion of women, history of arterial events, and/or with migraines, arterial hypertension, diabetes mellitus, or dyslipidaemia; cluster 3, younger, highest proportion of women, associated SLE or other autoimmune diseases, and a history of venous thromboembolism or pregnancy morbidity; and cluster 4, mainly with a history of catastrophic antiphospholipid syndrome, aPL-associated nephropathy, and pregnancy morbidity, with frequent triple positivity and more deaths (16.7%). Our study applied an unsupervised clustering method to distinguish four homogeneous APS patient subgroups that were predominantly venous; arterial; associated with SLE or another autoimmune disease; and arterial microthrombotic. Heterogeneous pathophysiological mechanisms may explain these findings.

Assessment of Genetic Diversity of Bambara Groundnut Accessions [Vigna subterranea (L) Verdcourt] in Sahelian Zone of Niger

The agro-morphological characterization of plant genetic resources is crucial to understand the phenotypic and genotypic relationships between different crop varieties. Bambara groundnut (Vigna subterranea) is a grain legume grown worldwide due to its high nutritional values, nitrogen-fixing ability, and drought tolerance. To assess the existing genotypic and phenotypic diversity within Bambara groundnut accessions and to estimate the genetic parameters guiding future varietal improvement programs, 22 Bambara groundnut accessions were characterized in the Sahelian zone of Niger. Thus, the coefficient of variation ranged from 2.42% to 48.25%, and 7 of the 22 parameters measured presented high coefficients of variation (CV˃20%). Yield had positive high correlation with shell weight (r=0.56), 100-seed weight (r=0.61), and seed weight (r=0.99). The highest GCV and PCV were observed in number of seeds per plant (PVC=35.05% and GCV=22.65%), hull weight (PVC=85.91% and GCV=25.51%) and dry biomass weight (PVC=36. 61% and GCV=23.66%), for a respective heritability and genetic gain of: (h2b=41.58%; GA=30.03%), (h2b=8.82%; GA=15.60%) and (h2b=41.76%; GA=31.49%). The highest heritability is observed in the number of leaves (92.40%) and the date of maturity (92.08%). Emergence date revealed high heritability and genetic gain (h2b=78.77% and GA=20%). Parameters with high heritability and genetic gain (h2b≥60% and GA≥20%), are the least influenced by the environment. So, direct selection for improvement of these traits may be beneficial. To authenticate genetic inequality, principal component analysis (PCA) and hierarchical ascending clustering (HAC) were performed. The results showed a great agro-morphological diversity divided into four different groups, for the studied characters. Group 3 (4 accessions) and group 1 (3 accessions) are the high yielding accessions, while group 4 (8 accessions) and group 2 (7 accessions) are the lower yielding accessions. ...................

Predicting dynamic shear wave slowness from well logs using machine learning methods in the Mishrif Reservoir, Iraq

Shear wave slowness is needed in reservoir characterization for seismic modeling, amplitude variation analysis and determination of rock elastic properties. Conventional methods such as multi-array polarization acoustic measurements as well as Dipole Shear Sonic Imager DSI are employed for measuring the shear slowness directly. However, data from these tools aren't available in all wells, particularly older ones, and in some cases, due to high cost and technical difficulties (for instance, in cased hole). Consequently, statistical techniques and machine learning applications are quick and can be applied with the appropriate algorithm to obtain this acoustic parameter. This provides reasonable results, saves time and cost of running experiments and well loggings. This research, therefore, provides an improved methodology via Geolog software's Facimage module to predict the dynamic shear slowness for the Mishrif formation, Iraq. Five different techniques were employed: self-organizing map (SOM), ascendant hierarchical clustering (AHC), dynamic clustering (DYN), artificial neural networks (ANNs), and multi-resolution graph-based clustering (MRGC). The predictions yielded root mean square error (RMSE) of 5.778, 6.914, 6.011, 4.845 and 2.897, while the R – squared (R2) values are 0.838, 0.766, 0.824, 0.888, and 0.9591 for SOM, AHC, DYN, ANN and MRGC methods, respectively. Implying that the MRGC yielded a predicted shear wave slowness with best match. The application of this advanced statistical approach in reservoir characterization will help to exploit the huge well log data available, consequently, saving time for reservoir description and production cost as well as improved ultimate recovery.

Geographical variations of cancer incidence in Guadeloupe, French West Indies

BackgroundGeographical disparities in cancer incidence are observed at different scales and may highlight areas of high risk that need special attention to improve health policies. In Guadeloupe, a French archipelago in the Caribbean, environmental and socioeconomic factors are potential factors associated with cancer incidence. Our objective was to describe geographical variations of cancer incidence in Guadeloupe at a small-area level, in order to identify potential clusters.MethodsWe conducted spatial analyses for the 18 most frequent cancer sites, using data collected by the population-based cancer registry of Guadeloupe over the period 2008–2017. For each cancer sites, we used the Besag, York and Mollié model to estimate smoothed standardized incidence ratios (SIRs) at a sub-municipality level. In addition, we performed ascendant hierarchical clustering of these smoothed SIRs to describe the relationship between the different cancer sites and to identify geographical clusters.ResultsWe observed geographical disparities with a spatial pattern that varied across cancer sites. Clustering of the smoothed SIRs showed aggregations between breast cancer and multiple myeloma, thyroid and stomach cancer, cervical and head and neck cancers, lung and rectal cancers, ovarian and endometrial cancers. Cluster analysis also identified six geographical clusters. Features of these clusters suggest alcohol consumption, exposure to pesticides, pollution generated by open landfills, and ethnicity as possible explanatory factors.Discussion/conclusionOur study provided for the first time an extensive description of geographical disparities in cancer incidence in Guadeloupe, in a region where socioeconomic and environmental issues are major concerns. Although the identification of underlying factors was out of the scope of the present study, we highlighted areas of special interest and put forward some hypotheses that warrant to be further investigated in more in-depth analyses.

Preventive planning against mercury over-exposure among Moroccan dentists using multidimensional statistical methods.

Mercury used in dental amalgams constitutes a significant source of chronic exposure to this heavy metal among dentists. Thus, the safety of dental amalgam remains a controversial issue despite its long history of use. In Morocco, most studies about dental mercury were mainly focused on the environmental risk related to the management of mercury-contaminated waste. In order to evaluate the occupational exposure to mercury among liberal dentists practicing in two Moroccan regions, a multidimensional statistical approach was used to analyze the collected data. The main objective was to help establishing a targeted prevention plan aiming to reduce the mercury exposure among Moroccan dentists. Fifteen variables from 146 dentists were elected for a three-step classification procedure: a multiple correspondence analysis followed by a hierarchical ascendant clustering consolidated by the k-Means algorithm. Three homogenous clusters were identified. The most important one includes 57.5% of the population as well as the majority of the risky factors. The characterization of these clusters allows proposing concise guidelines for a targeted preventive plan. A real mercurial risk has been observed in the studied population. However, its impact on health as well as the efficiency of simple preventive recommendations remains to be unveiled.

A Schema-Free Instance Matching Algorithm Based on Virtual Document Similarity

With the continuous development of semantic web, especially of the web of data, several knowledge bases expressed by ontologies are independently created and added to the Linked Open Data (LOD) cloud, on a daily basis. A major challenge for the LOD paradigm is to discover resources that refer to the same real-world object, in order to interlink web resources and hold large scale data integration and sharing. In this context, instance matching is a promising solution. It aims to link co-referent instances belonging to heterogeneous knowledge bases with owl:sameAs links. Several state-of-the-art existing approaches addressing this issue are based on the prior schema-level matchings, which does not avoid the limitation of heterogeneity at the property-level. In this paper, we propose a schema-free, scalable and efficient instance matching approach that is independent from matching results at the schema-level. We transform the instance matching problem to a document similarity problem and we solve it by a Clustering technique that uses an Ascendant Hierarchical Clustering algorithm to group similar instances in the same clusters. Furthermore, we design multiple validating patterns that use some structural information to validate obtained mappings and eliminate wrong ones. Experiments on instance matching track from Ontology Alignment Evaluation Initiative (OAEI) show that our approach gets prominent results compared to several participating systems in OAEI’2019, OAEI’2020 and OAEI’2021.

Environmental flow assessment in the context of climate change: a case study in Southern Quebec (Canada)

Abstract Through a case study in Southern Quebec (Canada), the assessment of environmental flows in light of the effects of climate change is investigated. Currently, the 7Q2 flow metric (7-day average flow with a 2-year return period) is used for water abstraction management. Several flow metrics were calculated using flow time series simulated by a deterministic hydrological model (HYDROTEL) and climate change scenarios as inputs. Results were compared within homogeneous low flow regions defined using ascendant hierarchical clustering, for the 1990, 2020 and 2050 horizons and annual, summer and winter periods. The impact of each flow metric on the potential availability of physical habitat was analyzed using the wetted perimeter as a proxy. Results indicated that: (1) the increasing non-stationarity of simulated flow data sets over time will complicate the use of frequency analysis to calculate the 7Q2 flow metric; (2) summer low flow values are expected to be lower than winter low flows; and (3) flow-duration curve metrics like the LQ50 (median discharge value of the month with the lowest flow) may become relevant environmental flow metrics by 2050. Results question current water abstraction management tools and permit us to anticipate future local and regional issues during low flow periods.

Morphological variability within the indigenous sheep population of Benin.

Knowledge of both the genetic diversity and geographical distribution of animal genetic resources is a prerequisite for their sustainable utilization, improvement and conservation. The present study was undertaken to explore the current morphological variability within the sheep population in Benin as a prelude for their molecular characterization. From November 2018 to February 2020, 25 quantitative linear body measurements and 5 qualitative physical traits were recorded on 1240 adult ewes from the 10 phytogeographic zones that comprise the three vegetation zones of Benin. Fourteen morphological indices were calculated based on the linear body measurements. The collected data were first analyzed using multiple comparisons of least-square means (LSmeans), followed by generalized linear model (GLM) procedures, to explore the relationships among the measured morphometric traits and the 10 phytogeographic zones. Next, the presence of any genetic sub-populations was examined using multivariate analytical methods, including canonical discriminant analysis (CDA) and ascending hierarchical clustering (AHC). Univariate analyses indicated that all quantitative linear body measurements varied significantly (P<0.05) across the phytogeographic zones. The highest values (LSmean± standard error) of withers height (68.3±0.47 cm), sternum height (46.0±0.35 cm), and rump height (68.8±0.47 cm) were recorded in the Mekrou-Pendjari zone, the drier phytogeographic zone in the North, whereas the lowest values, 49.2±0.34, 25.9±0.26, and 52.0±0.35 cm, respectively, were recorded in the Pobe zone in the South. Multivariate analyses revealed the prevalence of four distinct sheep sub-populations in Benin. The sub-population from the South could be assimilated to the short-legged and that from the North to the West African long-legged sheep. The two other sub-populations were intermediate and closer to the crossbreeds or another short-legged sub-breed. The proportion of individuals correctly classified in their group of origin was approximately 74%. These results uncovered a spatial morphological variation in the Beninese sheep population along a South-North phytogeographic gradient.

Impacts of the invasive species Caulerpa cylindracea Sonder, 1845 on the algae flora of the west coast of Algeria.

The assessment of the impacts of the expansion of the invasive species on taxonomic diversity, the abundance and dominance of groups of algae, the presence and/or absence of species of ecological interest that may or may not be indicative of water quality well mentioned, through the installation of a 20 × 20 cm quadrat representing the minimum area. The observation stations were visited monthly, during a repetitive three-year cycle, during the spring, summer and autumn seasons, periods of maximum growth and development of the algal flora and the results suggest the following facts. The invasive alga Caulerpa cylindracea Sonder, 1845 tends to colonise disturbed ecosystems reflecting a reduction in native algal diversity; in fact, we note a drastic impoverishment of the invaded algal community, represented by a limited number of Macrophyte algae accompanying the invasive taxon in phytosociological surveys and a Shannon-Weaver Diversity Index (H’) and Equitability reduced by 4.49 and 0.77 n the heavily affected station. The number of macroalgal species accompanying the invasive species has dropped by 52% in Salamandre. In addition, the multidimensional analysis, represented by the Hierarchical Ascendant Clustering applied to this case, confirms our results.

Human–animal relationship influences husbandry practices, animal welfare and productivity in pig farming

The nature of the relationship between humans and farm animals has multiple repercussions on the animals and the farmers and varies with farmers attitudes towards their animals. In particular, this relationship influences animal welfare and human working conditions. The present study, part of a larger research project investigating human–animal relationship (HAR) in pig farming and ways to improve it, had two objectives: 1) to investigate the HAR in a diversity of pig farming situations and to evaluate the possible correlation between farmer attitudes, pigs' reactions to humans, husbandry practices, animal health, welfare and productivity and 2) to find a way to rapidly assign a farmer to a profile, in order to better adapt course content during training sessions on HAR. The study focused on 52 farrow-to-finish farms and consisted of a semi-structured interview with the farmer, observations of the farmer in contact with his/her livestock, two human approach tests conducted on sows and growers and productivity data. Finally, a questionnaire was left at the farm to be filled out by all stockpersons on the farm. Interviews, analyzed using a thematic analysis followed by multiple correspondence analysis and ascendant hierarchical clustering, showed that some farmers develop husbandry practices to improve their HARs and identified three farmer profiles that have been named in relation to the place of the HAR in their profession: Profile 1 farmers for whom HAR is secondary in their work with their pigs, Profile 2 for whom the HAR is useful in their work with their pigs and Profile 3 for whom HAR is central in their work with their pigs. Logistic regression models of the relationships between behavioral tests and productivity data showed that confident sows produce and wean more piglets than fearful sows and that sows of Profile 3 farmers trust more humans than those of Profile 1 or 2 farmers. Farmers' responses to the questionnaire did not predict their profile assignment. Our results confirm the main role of the farmer's attitude towards animals on their farming practices, animal welfare and productivity. The farmers who have more confident pigs and better productivity are those for whom the farm animal is central to their profession, express the most pleasure in working with them and convey empathy for them.

Untargeted UHPLC-Q-TOF-HRMS based determination of discrimating compounds for oak species Quercus robur L. and Quercus petraea Liebl. identification.

Two species of oak are dominant in French forests: pedunculate oak (Quercus robur L.) and sessile oak (Quercus petraea Liebl.). Differentiating oak species is difficult, since features such as morphological characters, geographical origin and grain are not always relevant. Even if the former is generally richer in tannin compounds while the latter is often richer in aromatic compounds, the intra-species variability is high. The characterisation of the oak species remains a suitable indicator of the molecular composition and quality of the wood. The aim of this study was to determine differentiating molecules allowing oak species identification in order to assist in a suitable wood selection for a better oak tree valorisation since the selection of the oak wood to be used in the production of barrels plays an essential role in wine ageing. Oak wood samples were collected both in forests and in cooperage timber yards. An untargeted metabolomic approach using ultra-high-pressure liquid chromatography qualitative time-of-flight high-resolution mass spectrometry (UHPLC-Q-TOF-HRMS) associated to multivariate statistical analyses (hierarchical ascendant clustering and partial least squares discriminant analysis) was implemented to determine molecular markers of oak species. Heartwood was identified as the suitable wood part to distinguish oak species. Discriminating molecules did not depend on the sample set. The pedunculate species showed overexpression of bartogenic derivatives while sessile oak presented a higher content in oak lactone precursors and in quercotriterpenosids. The developed method allowed the identification of relevant compounds for oak species identification to a better wood valorisation and selection.

Machine Learning-Based Probabilistic Lithofacies Prediction from Conventional Well Logs: A Case from the Umiat Oil Field of Alaska

A good understanding of different rock types and their distribution is critical to locate oil and gas accumulations in the subsurface. Traditionally, rock core samples are used to directly determine the exact rock facies and what geological environments might be present. Core samples are often expensive to recover and, therefore, not always available for each well. Wireline logs provide a cheaper alternative to core samples, but they do not distinguish between various rock facies alone. This problem can be overcome by integrating limited core data with largely available wireline log data with machine learning. Here, we presented an application of machine learning in rock facies predictions based on limited core data from the Umiat Oil Field of Alaska. First, we identified five sandstone reservoir facies within the Lower Grandstand Member using core samples and mineralogical data available for the Umiat 18 well. Next, we applied machine learning algorithms (ascendant hierarchical clustering, self-organizing maps, artificial neural network, and multi-resolution graph-based clustering) to available wireline log data to build our models trained with core-driven information. We found that self-organizing maps provided the best result among other techniques for facies predictions. We used the best self-organizing maps scheme for predicting similar reservoir facies in nearby uncored wells—Umiat 23H and SeaBee-1. We validated our facies prediction results for these wells with observed seismic data.

Predicting soil phosphorus and studying the effect of texture on the prediction accuracy using machine learning combined with near-infrared spectroscopy.

The estimation of soil phosphorus is essential for agricultural activity. The laboratory chemical analysis techniques are expensive and labor-intensive. In the last decade, near-infrared spectroscopy has been become used as an alternative for soil attributes analysis. It is a rapid technique, and inexpensive relatively. However, this technique requires a calibration step using different machine learning and chemometrics tools. This study aims to develop predictive models for total soil phosphorus and extractable phosphorus by the Olson method (P-Olson) using three regression methods, namely partial least squares (PLS), regression support vector machine (RSVM) and backward propagation neural network (BPNN), combined with a proposed variable selection algorithm (PARtest) and a genetic algorithm PLS (GA-PAS). Also, it aims to investigate the effect of the texture on the accuracy of the prediction. The results show that PARtest combined with PBNN outperform the other used algorithms with an R2t = 0.86, RMSEt = 1104 mg kg-1, and RPD = 3.23 for the TP. For P-Olson the RSVM coupled with GA-PLS outperforms all other methods with an R2t = 0.77, RMSEt = 20.09 mg kg-1, and RPD = 1.90. The use of hierarchical ascendant clustering (HAC) helps to reduce the heterogeneity of soil and helps to increase the quality of prediction. The obtained results show that the models for clayey and loamy soils yielded an excellent prediction quality with an R2t = 0.88, RMSEt = 857.33 mg kg-1, and RPD = 4.10 using BPNN with PARtest for TP. Furthermore, an R2 = 0.83 RMSE = 8.30 mg kg-1, RPD = 11.00 3.11using RSVM with GA-PLS for P-Olson. Thus, the texture has a significant effect on the prediction accuracy.

Adaptive multi-resolution graph-based clustering algorithm for electrofacies analysis

Logging facies analysis is a significant aspect of reservoir description. In particular, as a commonly used method for logging facies identification, Multi-Resolution Graph-based Clustering (MRGC) can perform depth analysis on multidimensional logging curves to predict logging facies. However, this method is very time-consuming and highly dependent on the initial parameters in the propagation process, which limits the practical application effect of the method. In this paper, an Adaptive Multi-Resolution Graph-based Clustering (AMRGC) is proposed, which is capable of both improving the efficiency of calculation process and achieving a stable propagation result. More specifically, the proposed method, 1) presents a light kernel representative index (LKRI) algorithm which is proved to need less calculation resource than those kernel selection methods in the literature by exclusively considering those “free attractor” points; 2) builds a Multi-Layer Perceptron (MLP) network with back propagation algorithm (BP) so as to avoid the uncertain results brought by uncertain parameter initializations which often happened by only using the K nearest neighbors (KNN) method. Compared with those clustering methods often used in image-based sedimentary phase analysis, such as Self Organizing Map (SOM), Dynamic Clustering (DYN) and Ascendant Hierarchical Clustering (AHC), etc., the AMRGC performs much better without the prior knowledge of data structure. Eventually, the experimental results illustrate that the proposed method also outperformed the original MRGC method on the task of clustering and propagation prediction, with a higher efficiency and stability.

Tourist satisfaction with the Municipality of Ponta Delgada (Azores)

The combination of unique and differentiating aspects and the concentration of a substantial proportion of accommodation and tourism-related companies has made Ponta Delgada the most developed tourism destination in the Azores. A questionnaire was structured to evaluate tourist satisfaction with the municipality as a tourism destination. The data was analyzed using measures of descriptive statistics, nonparametric hypothesis tests, algorithms of Ascendant Hierarchical Cluster Analysis and the Nonlinear Principal Component Analysis. It reveals that Ponta Delgada has earned competitive tourist satisfaction, has competitive attractions and attracts different tourist profiles, with cruise passengers being more satisfied and more likely to stay in the city and engage in softly activities while tourists travelling by airplane, who are more critical, tend to engage in more dynamic, nature-based activities. This paper contributes to research on destination competitiveness, tourism performance and strategic tourism planning at the local level through statistical analysis of tourism data.