Use Of Principal Component Analysis Research Articles

Sliced inverse regression via natural canonical thresholding

. In a moderate or high-dimensional framework, the sliced inverse regression (SIR) method requires the inversion of the empirical covariance matrix which yields numerical problems in estimating the central subspace. In order to improve SIR, several methods based on the regularization of the covariance matrix or the use of principal component analysis (PCA) were proposed. Yet most of these select the eigen-directions with the largest eigenvalues in an unexplained way. This article circumvents these difficulties by suggesting a new regularization of SIR based on the singular value decomposition (SVD) of the data matrix of the predictors called sliced inverse regression via natural canonical thresholding (SIR-NCT). SIR-NCT makes it possible to relate the vector of new canonical regression coefficients of the reduced dimension to the vector of the initial regression coefficients of large dimension. Moreover, we use thresholding to cut the components associated with insignificant directions. Some theoretical results are presented for SIR-SVD. Experiments with simulated and real data show that SIR-NCT outperforms its competitors.

Pyrolysis of coffee residues, tobacco, and algae biomasses as a source of nitrogen-containing compounds

Agro-industrial residues hold immense potential as sustainable energy and bioproduct sources. Thermoconversion through pyrolysis, which generates bio-oil, is a promising approach that simultaneously reduces environmental impact and adds value to biomass. Notably, biomasses with high nitrogen content yield bio-oils rich in diverse, industrially valuable nitrogen-containing compounds (N-compounds). This study investigates the rapid pyrolysis of five such biomasses: spent coffee grounds (SCG), silverskin (SVK), microalgae (MIA), macroalgae (MAA), and tobacco (TBC). A pretreatment step was employed to reduce fat content and enhance bio-oil quality. Gas chromatography analysis subsequently identified the generated N-compounds. The bio-oils revealed a significant presence of oxygenated compounds and hydrocarbons beside N-compounds. The use of Principal Component Analysis (PCA) effectively allowed to group the samples based on their bio-oil compositions, highlighting microalgae as the richest source of N-compounds, particularly after fat pre-extraction.

Expanding the horizons: Raman probe development and spectra preprocessing evaluation for recognition of large hydroxyapatite‐based samples

AbstractThis paper addresses the challenge posed by the size of certain objects that do not conform to the constraints of microscope‐coupled Raman spectrometers, making sample collection impossible due to their inherent value or nature. Specifically, materials like hydroxyapatite‐based substances used in artistic and ornamental carvings, such as bone or ivory, fall within this problematic category. The focus of this study is the enhancement of analytical capabilities in the context of large objects using a Raman microscope system. The study details the innovation involving a remote probe integrated with fiber optics, elaborating on the design and performance aspects, and emphasizing the appropriateness of chosen components in the analysis of ivory artifacts belonging to private collectors. In order to assess the robustness of our discriminative approaches, an archaeological bone and the exposed dentine in a human tooth were also evaluated and compared. Results showed that using an 805‐nm longpass dichroic mirror successfully directed the near‐infrared laser onto the samples and significantly suppressed the Rayleigh scattering contribution to the spectrum. Regarding the preprocessing methods to spectra evaluation essayed, the most promising approach was the use of principal component analysis for dimension reduction followed by k‐means cluster analysis. By leveraging the complementary strengths of PCA and k‐means clustering, the robustness and interpretability of clustering analyses are enhanced.

Clusterization of Behavioral and Psychological Symptoms of Dementia as Assessed by Neuropsychiatric Inventory: A Case Against the Use of Principal Component Analysis.

The term Behavioral and Psychological Symptoms of Dementia (BPSD) covers a group of phenomenologically and medically distinct symptoms that rarely occur in isolation. Their therapy represents a major unmet medical need across dementias of different types, including Alzheimer's disease. Understanding of the symptom occurrence and their clusterization can inform clinical drug development and use of existing and future BPSD treatments. The primary aim of the present study was to investigate the ability of a commonly used principal component analysis to identify BPSD patterns as assessed by Neuropsychiatric Inventory (NPI). NPI scores from the Aging, Demographics, and Memory Study (ADAMS) were used to characterize reported occurrence of individual symptoms and their combinations. Based on this information, we have designed and conducted a simulation experiment to compare Principal Component analysis (PCA) and zero-inflated PCA (ZI PCA) by their ability to reveal true symptom associations. Exploratory analysis of the ADAMS database revealed overlapping multivariate distributions of NPI symptom scores. Simulation experiments have indicated that PCA and ZI PCA cannot handle data with multiple overlapping patterns. Although the principal component analysis approach is commonly applied to NPI scores, it is at risk to reveal BPSD clusters that are a statistical phenomenon rather than symptom associations occurring in clinical practice. We recommend the thorough characterization of multivariate distributions before subjecting any dataset to Principal Component Analysis.

A framework for selecting and assessing soil quality indicators for sustainable soil management in waste dumps

The primary objective of this study was to develop soil quality indexes (SQIs) to reveal the changes in SQ during the restoration of vegetation in the reclaimed waste dumps of the Hequ open-pit coal mine. The study built an SQI evaluation model for waste dumps based on the soil management assessment framework. The total data set (TDS) consisted of nine physicochemical property indicators. The selection of the minimum data set (MDS) involved the utilization of principal component analysis (PCA) and Norm values. The SQ was comprehensively evaluated for nine indicators, taking into account the non-linear membership function and the improved Nemerow index. The findings suggested a notable disparity in the SQ between the reclaimed area and the unreclaimed area, yet the overall SQ fell short. In the TDS index system, the organic matter has the highest weight and a greater contribution to the soil quality of the waste dumps. In the MDS indicator system, the weights of organic matter and total nitrogen are both 0.5. According to Nemerow index method, the average SQIN of 5 plots is calculated to be 0.4352 ± 0.194. The average value obtained from TDS is 0.581 ± 0.236, and the average value obtained from MDS is 0.602 ± 0.351. The weighted additive method was employed to compute three SQIs, all of which yielded satisfactory outcomes. And the above evaluation methods indicate that the overall soil quality level of the waste dumps is at a moderate level. The sequence of SQ in various waste dumps was as follows: No.4lower > No.1 > No.2 > No.3 > No.4upper. Specifically, the non-linear membership function indicated that pH, available nitrogen (AN), available phosphorus (AP), surface moisture content (SMC), and bulk density (BD) were crucial in limiting SQIs in total waste dumps. The crucial limiting SQIs in unreclaimed areas were total phosphorus (TP) and total nitrogen (TN). This analysis demonstrates its efficacy in formulating strategies for the SQ evaluation and targeted soil reclamation plans of waste dumps.

Elucidating the Polymorphism of Xanthone: A Crystallization and Characterization Study.

The aim of this work is to shed light on the polymorphism of xanthones, a class of oxygenated molecules well known for their bioactivity, including antioxidant, anticancer, and anti-inflammatory effects. Understanding the polymorphism of xanthones can enable the design of novel solid products for pharmaceutical, nutraceutical, and agrochemical applications. Prior to this work, two entries accounting for different space groups were deposited for 9-xanthone in the Cambridge Structure Database (CSD): an orthorhombic P212121 and a monoclinic P21 structure solved at room and low temperatures, respectively. However, the very high similarity between these two structures and the lack of clear differences in their physical properties (e.g., thermal behavior) suggested the possibility of the existence of only one crystal structure. In fact, the differences shown in the literature data might be related to the chosen operating parameters, as well as the instrumental resolution of the single-crystal X-ray diffraction experiments. In the work presented here, the ambiguity in the polymorphism of xanthone is investigated using thermal analysis, powder and synchrotron single-crystal XRD, and optical microscopy. Additionally, a workflow for the correct identification of twinned crystal structures, which can be applied to other polymorphic systems, is presented. Such workflow combines the collection of a large data set of high-resolution diffraction patterns using synchrotron radiation with the use of principal component analysis, a dimensionality reduction technique, for a quick and effective identification of phase transitions happening during the data collection. Crystallization experiments were designed to promote the formation of different crystal structures of xanthone that were recrystallized based on past literature and beyond.

Estimating the economic effects of US state and local fiscal policy: A synthetic control method matching‐regression approach

AbstractIn this paper, we advance the empirical literature on US state and local fiscal policymaking by using the synthetic control method (SCM) to create pairwise matches for states in subsequent regression analysis of the relationships between state and local fiscal policies and several state economic outcomes. Additional contributions include the use of principal component analysis to construct broader narratives of state economic performance and to reduce the dimensionality of the characteristics used in SCM matching, while the regressions also include variables to control for post‐matching economic shocks. Compared to conventional regression analysis, the SCM matching‐regression approach better addresses potential endogeneity, reduces interpolation bias, and creates fiscal policy measures that better reflect policy differences. The SCM‐matched regressions produce more statistically significant relationships between state and local fiscal variables and economic outcomes than do the conventional unmatched regressions, suggesting improved identification of state and local fiscal policy effects on economic outcomes. Robust relationships found include negative economic effects of the own‐source revenue burden and property taxes. Consistent with the existing literature, the estimated fiscal policy effects are quantitatively small and unlikely to drive differences in state economic performance.

Revision of the genus Robbea (Stilbonematinae: Desmodoridae), worldwide abundant marine nematodes with chromophoric Fe–Br inclusions and the description of a new stilbonematine genus

Abstract Nematodes are one of the most widespread and abundant animal taxa across aquatic and terrestrial environments. In marine shallow-water, porous sediments, members of the subfamily Stilbonematinae may be found in high numbers. Stilbonematinae are characterized by their coat of symbiotic bacteria, which give the nematodes a white appearance, while the nematodes themselves are usually colourless. We identified several species of the genus Robbea (Desmodoridae: Stilbonematinae) in which live specimens had a conspicuous dark purple coloration of the glandular sense organs (GSOs), highly specialized epidermal glands that are prominent in the Stilbonematinae. The dark inclusions in the GSOs of Robbea contained high concentrations of iron and bromide, regardless of their habitat. Morphological and phylogenetic analyses show that the genus Robbea is paraphyletic and we define the new genus Cyathorobbea gen. nov. for species with sucker-shaped post-pharyngeal supplements in males. Furthermore, we describe five new Robbea species from around the world. Some Robbea species are morphologically very similar and required the use of principal component analysis of morphometric data to untangle the diversity in this taxon. Our work emphasizes the value of live animal observations that enable the identification of important characters and highlights the overlooked diversity within the Stilbonematinae.

Identification of geotechnical units in soil exploration through principal component analysis and clustering

AbstractObtaining accurate information on soil characteristics is essential for large‐scale construction projects. Geotechnical exploration methods are commonly used to obtain information on soil characteristics. However, integrating the characteristics obtained through different exploration methods can be challenging. To address this problem, this study proposes the use of Principal Component Analysis (PCA) to combine geotechnical variables obtained from cone penetration tests (CPT), standard penetration tests (SPT) with soil sampling, and Suspension PS Logging tests. PCA and clustering analysis were applied to the data collected from a study area, and the results were highly effective in identifying the stratigraphy and soil type, leading to the definition of geotechnical units. This approach provides valuable information for geotechnical engineering projects, enhancing the accuracy of stratigraphic investigations and improving our understanding of soil characteristics and their influence on construction projects. Moreover, the proposed technique can be applied in other areas and different types of soils, making it a valuable tool for evaluating soil characteristics.

Influence of cultivation substrate on antioxidant activities and triterpenoid profiles of the fruiting body of Ganoderma lucidum.

The fruiting body of Ganoderma lucidum has been believed to possess a wide range of therapeutic effects. There are two main methods for artificial cultivation of G. lucidum to produce the fruiting body, namely wood log cultivation and substitute cultivation. The impact of cultivation substrates on the composition of bioactive compounds remains largely unexplored. This study aims to compare the antioxidant activities and triterpenoid profiles of the fruiting bodies of G. lucidum that cultivated through wood log cultivation (WGL) and substitute cultivation (SGL) methods. The antioxidant activities, including the DPPH radical scavenging, hydroxyl radical scavenging, superoxide radical scavenging, and total antioxidant activities, were assessed in both WGL and SGL samples. Furthermore, the UHPLC-Q-Orbitrap-MS technique was employed to compare their phytochemical profiles, with a specific emphasis on triterpenoid constituents. It was found that WGL samples exhibited significantly higher total triterpenoid content, DPPH radical scavenging activity, and total antioxidant activity. Furthermore, an untargeted metabolomics approach employing UHPLC-Q-Orbitrap-MS tentatively identified a total of 96 triterpenoids. Distinguishingly different triterpenoid profiles between the two types of G. lucidum samples were revealed via the utilization of principal component analysis (PCA) and hierarchical cluster analysis (HCA). Specifically, 17 triterpenoids showed significant differences. Of these triterpenoids, 6 compounds, such as ganosporelactone B, ganoderol A, ganoderic acid A, ganoderic acid alpha, were significantly higher in SGL samples; 11 compounds, such as lucidenic acid A, lucidenic acid D1, lucidenic acid F, lucidenic acid G, lucidenic acid J, ganoderic acid E, and ganoderic acid O, were significantly higher in WGL samples. These findings expand our knowledge regarding the impact of cultivation substrate on the antioxidant activities and triterpenoid profiles of G. lucidum, and offer practical implications for its cultivation.

A hierarchical approach for evaluating athlete performance with an application in elite basketball

In this paper, we present the ON score for evaluating the performance of athletes and teams that includes a season-long evaluation system, a single-game evaluation, and an evaluation of an athlete’s overall contribution to their team. The approach used to calculate the ON score is based on mixed-effects regression models that take into account the hierarchical structure of the data and a principal component analysis to calculate athlete rating. We apply our methodology to a large dataset of National Basketball Association (NBA) games spanning four seasons from 2015–2016 to 2018–2019. Our model is validated using two systematic approaches, and our results demonstrate the reliability of our approach to calculate an athlete’s performance. This provides coaches, General Managers and player agents with a powerful tool to gain deeper insights into their players’ performance, make more informed decisions and ultimately improve team performance. Our methodology has several key advantages. First, by incorporating the hierarchical structure of the data, we can obtain valuable information about an athlete’s contribution within their team. Second, the use of principal component analysis allows us to calculate a single score, the ON score, that captures the overall performance of an athlete. Third, our approach is based on classical restricted likelihood methods, which makes the calculation faster than Bayesian methods typically requiring 1000 posterior samples. With our approach, coaches and managers can evaluate athletes’ performance throughout the season, compare athletes and teams over a year, and assess an athlete’s performance during a single game. Our methodology can also complement other ratings and box score metrics to provide a more comprehensive assessment of an athlete’s performance as our method uses the hierarchical nature of performance data (i.e. player nested within team over season) which is typically ignored in player rating systems. In summary, our methodology represents a significant contribution to the field of sports analytics and provides the foundation for future developments.

Development of a competitive array for discriminative determination of amphenicols in egg based on ribosomal protein L16

Abstract Object The amphenicols (chloramphenicol, thiamphenicol and florfenicol) can cause aplastic anaemia and other severe side effects to the consumers, so it is very necessary to inspect their residues in foods of animal origin. However, there has been no paper reporting the use of amphenicols receptor for the determination of their residues, and all the previously reported immunoassays for amphenicols can not differentiate the specific species. Materials and Methods In this study, the ribosomal protein L16 of Escherichia coli was first expressed, and its intermolecular interaction mechanisms with the three amphenicols were studied by using molecular docking technique. Then the protein was combined with three enzyme labeled conjugates to develop a direct competitive array on microplate for determination of the three drugs in egg. Results Due to the use of principal component analysis to analyze the data, this method could discriminate the three drugs in the range of 0.1-10 ng/mL, and the limits of detection for the three drugs were in the range of 0.0002-0.0009 ng/mL. The analysis results for the unknown egg samples were consistent with a liquid chromatography tandem mass spectrometry method, and the method performances were superior to the previous immunoassays for amphenicols. Conclusion This is the first paper reporting the use of ribosomal protein L16 to develop a competitive array for discriminative determination of amphenicols in food sample.

A Survey on Factors Preventing the Adoption of Automated Software Testing: A Principal Component Analysis Approach

Automated software testing is a crucial yet resource-intensive aspect of software development. This burden on resources affects widespread adoption, with expertise and cost being the primary challenges preventing adoption. This paper focuses on automated testing driven by manually created test cases, acknowledging its advantages while critically analysing its implications across various development stages that are affecting its adoption. Additionally, it analyses the differences in perception between those in nontechnical and technical roles, where nontechnical roles (e.g., management) predominantly strive to reduce costs and delivery time, whereas technical roles are often driven by quality and completeness. This study investigates the difference in attitudes toward automated testing (AtAT), specifically focusing on why it is not adopted. This article presents a survey conducted among software industry professionals that spans various roles to determine common trends and draw conclusions. A two-stage approach is presented, comprising a comprehensive descriptive analysis and the use of Principal Component Analysis. In total, 81 participants received a series of 22 questions, and their responses were compared against job role types and experience levels. In summary, six key findings are presented that cover expertise, time, cost, tools and techniques, utilisation, organisation, and capacity.

Application of Decision Support Expert Systems for Improved gasoline yield in Refinery Catalytic Cracking

The Catalytic Cracking Unit (CCU) stands out as the primary factor responsible for enhancing gasoline production in the refinery. This paper provides a deep insight on the complexity of the factors within this unit, explore their broader implications, and offer a rationale for their technical significance. A survey approach involving the use of Principal Component Analysis (PCA) facilitated by StatistiXL software package and Kendall Coefficient of Concordance (KCC) to measure agreement among the respondents was employed. A total of 32 variables were identified, and questionnaires were developed using Rensis Likert's 5-point scale. These questionnaires were then administered to 100 respondents. Prior to this step, the Kendall Coefficient of Concordance was utilized to establish the sequential order of importance among these identified factors. The results obtained showed an index of agreement among the judges in ranking the variables W, as 0.54, the null hypothesis of disconcordance among the judges was rejected at a p-value of 0.01. The study identified 14 significant variables based on the KCC results, including Catalytic Cracking Charge, Mid Riser Temperature, Riser Inlet Temperature, Riser Outlet Temperature, and Stripper Level. Additionally, Principal Component Analysis (PCA) effectively condensed the set of 32 variables into four manageable clusters. The integration of data analytics techniques, such as KCC and PCA, alongside expert opinions and collected data enabled the assessment of the influence of independent variables on gasoline yield and the categorization of these variables. This study offers valuable insights for both academia and the industry, especially in the context of the Catalytic Cracking Unit.

A Study on Improving Tactical Strategies in Sports Games Using Optimization Algorithms

Abstract Sports are dynamic, unpredictable battles that require teams to develop strategic plans for making use of their advantages and adjust to changing conditions during play. In this study, we use optimization algorithms to improve the tactical methods which are used in sports games. Tactics are the intentional, premeditated moves, choices, and configurations made by groups or individuals to accomplish particular goals and outplay their rivals. The number of corresponding solutions addresses the problem of designing security strategies optimally using a game theory simulation. In this simulation, players choose to attack the structure based on certain unpredictable attack scenarios, while the attacker selects the way to defend the system by managing the available options for strategic solutions. The unpredictable nature of human performance is a barrier to the advancement of sports strategy tactics, since players may depart from pre-planned plans as a result of weariness, injuries, or emotional states. We suggested the self-regulated immune cat swarm optimization (SRICSO) strategy as a solution to these issues to improve tactical strategies in sports. A National Basketball Association (NBA) dataset was gathered for this study’s evaluation. The collected dataset underwent preprocessing using the minmax normalization technique, and feature extraction was accomplished through the use of principal component analysis. We compare our method’s precision (87%), accuracy (97%), F2-score (95%), and recall (92%) with more traditional methods such as the optimized convolutional neural network (OCNN), naïve Bayes (NB), and random forest (RF). According to the findings of an assessment conducted with a dataset from the NBA, the SRICSO approach was shown to have higher performance in terms of improving tactical strategies in sports.

Applications of Rasch modeling in chemometrics: Binary data analysis and analytical platform selection

Often researchers make use of Principal Component Analysis and Partial Least Squares Regression, an unsupervised and a supervised method, respectively, to extract the chemical information in the shape of one or more latent variables. However, when the research question is qualitative and requires a figure of merit, these two models will primarily focus on the quantitative and continuous information present in the data. In these cases, a valid approach may be to dichotomize the data and to analyze the resulting non-linear data via Non-Linear Principal Component Analysis. However, the results of the method are not always easy to interpret due to the possible multidimensionality of the solution. Here we introduce an alternative framework, composed of Rasch modeling and Generalized Linear Mixed Effect Models, to extract information from multivariate binary chemical data with an underlying design of experiment. The model obtained by this framework provides information in a unidimensional representation that can be easily translated into one-dimensional action and control. Furthermore, we show that, through Generalized Linear Mixed Effect Models, it is possible to extend the Rasch model to its multilevel form, which enables the consideration of each random factor possibly present.

The implementation of soundscape attributes in Indonesian: A case study of soundwalk

The ISO 12913-3 method A of part 2 is a standardized method to calculate the score of pleasantness and eventfulness according to the rating of standard soundscape attributes mentioned in ISO 12913-2. However, implementing the attributes might result in invalid measurement if the attributes are used directly for soundscape studies in non-English speaking countries. The Soundscape Attributes Translation Project (SATP) is an initiation to develop the standard soundscape attributes in different languages. This study employs a soundwalk to validate soundscape attributes in Indonesian and English for soundscape study in Indonesia. The validation process involves the utilization of Principal Component Analysis (PCA), Multidimensional Scaling (MDS), Reliability Analysis, and Confirmatory Factor Analysis (CFA). The analysis shows that implementing soundscape attributes in Indonesian gives a more similar result to the standard circumplex model than the standard attributes in English. Further analysis of the soundwalk data shows that the circumplex model, which implemented Indonesian attributes, is not ideal. As a result, the circumplex model might need correction. The model developed using CFA from the soundwalk experiment with Indonesian attributes demonstrates that the application of the CFA model results in average differences in pleasantness score of −1.76 and eventfulness score of −0.06 compared to the result from standard ISO 12913-3 model calculation.

Comparing machine learning algorithms for non-invasive detection and classification of failure in piezoresistive bone cement via electrical impedance tomography

At an estimated cost of $8 billion annually in the United States, revision surgeries to total joint replacements represent a substantial financial burden to the health care system and a tremendous mental and physical burden on patients and their caretakers. Fixation failures, such as implant loosening, wear, and mechanical instability of the poly(methyl methacrylate) (PMMA) cement, which bonds the implant to the bone, are the main causes of long-term implant failure. Early and accurate diagnosis of cement failure is critical for developing novel therapeutic strategies and reducing the high risk of a misjudged revision. Unfortunately, prevailing imaging modalities, notably plain radiographs, struggle to detect the precursors of implant failure and are often interpreted incorrectly. Our prior work has shown that the modification of PMMA bone cement with low concentrations of conductive fillers makes it piezoresistive and therefore self-sensing. When combined with a conductivity imaging modality such as electrical impedance tomography (EIT), it is possible to monitor load transfer across the PMMA using cost-effective, physiologically benign, non-contact, and real-time electrical measurements. Despite the ability of EIT for monitoring load transfer across self-sensing PMMA bone cement, it is unable to accurately characterize failure mechanisms. Overcoming this challenge is critical to the success of this technology in practice. Therefore, we herein expand upon our previous results by integrating machine learning techniques with EIT for cement condition characterization with the goal of establishing the feasibility of even off-the-shelf machine learning algorithms to address this important problem. We survey a wide variety of different machine learning algorithms for application to this problem, including neural networks on voltage readings of an EIT phantom for tracking the spatial position of a sample, specifying defect orientation within a sample, and classifying defect types, including cracks and delaminations. In addition, we explore the utilization of principal component analysis (PCA) for pre-treating impedance signals in each of these problems. Within the tested algorithms, our results show clear advantages of neural networks, support vector machines, and K-nearest neighbor algorithms for interpreting EIT signals. We also show that PCA is an effective addition to machine learning. These preliminary results demonstrate that the combination of smart materials, EIT, and machine learning may be a powerful instrumentation tool for diagnosing the origin and evolution of mechanical failure in joint replacements.

Research on feature vector construction and classification recognition of typical mine microseismic waveforms

In the realm of mining seismology, the identification of microseismic waveforms is a critical step for enhancing the precision of microseismic event localization. Addressing the complexities and diverse sources of noise in subterranean mining environments, this study has developed a novel methodology for the automated categorization and recognition of characteristic microseismic waveforms. Initially, a comprehensive database for mining-induced microseismic waveforms was established, catering to their non-stationary and nonlinear attributes. Based on the distinct features of different microseismic waveforms, this study introduces an approach for classification and identification that relies on waveform characteristics. A feature selection model, KNN-LOO (K-Nearest Neighbor - Leave-One-Out), was employed to optimize the feature vectors, in addition to the utilization of Principal Component Analysis (PCA) for dimensionality reduction of the feature vectors. Ultimately, an enhanced BT-SVM (Binary Tree-Support Vector Machine) model was developed to accomplish the automated classification and identification of mining-induced microseismic waveforms. Experimental results indicate that, compared to conventional SVM classification methods, the proposed methodology not only exhibits theoretical and mechanistic advancements but also significantly boosts performance in practical applications. Specifically, the average misclassification rate for each category of waveforms is reduced to less than 5%, and the overall accuracy has increased by 20%. This considerably elevates both the accuracy and speed of microseismic waveform classification and recognition, thereby providing valuable insights for monitoring and analyzing microseismic activities within mining operations as well as enhancing the safety protocols for mineral extraction.

Detecting Air Pollution Clusters in Japan: A Spatial Analysis Approach

Abstract We rely on satellite data to study the spatial distribution of air pollutants and economic activity for 1,650 municipalities of all four main islands of Japan: Honshu, Kyushu, Hokkaido, and Shikoku. Specifically, we analyze atmospheric particulate matter and ozone concentrations, as well as population density, accessibility to cities, and night lights for the above islands. We then make use of principal component analysis, spatial dependence analysis, and methods of regionalization to endogenously classify the municipalities based on their similarity in attributes and geographic location. The spatial dependence analysis provides results which show us the specific sites where the high-value clusters (hot spots) and low-value clusters (cold spots) are located. These show a high positive correlation between economic activity and air pollution. Additionally, we perform a regionalization analysis of the variables under consideration, which specifies how the four main islands can be regionalized into six to nine geographical regions or structures, each. The regionalization takes into consideration both pollution levels and economic activity. We then conclude by discussing how these different analyses can complement each other, and how they contribute in finding the locations where policies related to air quality can help in improving the quality of life of the population.