Combination Of Principal Component Analysis Research Articles

Modulation of prey capture kinematics in relation to prey distance helps predict success.

Predators are not perfect, as some of their prey capture attempts result in failure. Successful attempts may be partly due to predators modulating their capture kinematics in relation to variation in the visual cues of the prey to increase the probability of success. In praying mantises, which have been suggested to possess stereoscopic vision, variation in prey distance has been shown to elicit variation in the probability of an attempt. However, it remains to be examined whether variation in prey distance results in mantises modulating their attempt to successfully capture prey. The goals of this study were to examine these relationships using the praying mantis system. Using 11 adult female Sphodromantis lineola, we recorded 192 prey capture attempts at 1000 Hz with two cameras to examine the 3D kinematics of successful and unsuccessful prey capture attempts. Using a combination of principal component analysis (PCA) and logistic regression, our results show that as prey distance increases, mantises adjust through greater and faster expansion of the forelegs and body (PC1), which significantly predicts capture success. However, PC1 only explains 22% of the variation in all prey capture attempts, suggesting that the other components may be related to additional aspects of the prey. Our results suggest that the distances at which mantises prefer to attempt to capture prey may be the result of their greater probability of successfully capturing the prey. These results highlight the range of motions mantises use when attempting to capture prey, suggesting flexibility in their prey capture attempts in relation to prey position.

Exploring Drivers of Wildfires in Spain

Wildfires play a dual role in ecosystems by providing ecological benefits while posing catastrophic events; they also inflict non-catastrophic damage and yield long-term effects on biodiversity, soil quality, and air quality, among other factors, including public health. This study analysed the key determinants of wildland fires in Spain using openly available spatial data from 2008 to 2021, including fire perimeters, bioclimatic variables, topography, and socioeconomic datasets, at a resolution of 1 km2. Our methodology combined principal component analysis (PCA), linear regression analysis, and one-way analysis of variance (ANOVA). Our findings show that scrub/herbaceous vegetation (average 63 ± 1.45% SE) and forests (average 19 ± 0.76% SE) have been highly susceptible to wildfires. The population density exhibited a robust positive correlation with wildfire frequency (R2 = 0.88, p < 0.0001). Although the study provides insights into some fire-related climatic drivers over Spain, it includes only temperature and precipitation-based variables and does not explicitly consider fuel dynamics. Therefore, a more advanced methodology should be applied in the future to understand the local specifics of regional wildfire dynamics. Our study identified that scrub/herbaceous areas and forests near densely populated regions should be prioritised for wildfire management in Spain, particularly under changing climate conditions.

Calculation of power transformer health index using the combination of principal component analysis and mass-spring-damper model

Calculation of power transformer health index using the combination of principal component analysis and mass-spring-damper model

Principal Components and Assortativity-based Assessment of the Similarity of Crime Metrics across Coterminous Wards in the City of Chicago

The City of Chicago (with 50 wards) has one of the highest crime rates in the US. We seek to quantitatively assess the similarity of crime metrics across coterminous wards using a combination of Principal Component Analysis (PCA) and Assortativity analysis. We first build a ward network (nodes are the wards and edges connect coterminous wards) of the city using the ward map. We parse through the 2022 crime dataset for the city and build a matrix whose entries correspond to the number of occurrences of a crime type in a ward. We conduct PCA of this ward-crime type matrix and determine a weighted average PC_crime_score (using the entries in the high-variance principal components and their variances as weights) for each ward. We observe the coterminous wards to exhibit moderate-strong assortativity with respect to three different crime metrics: hotspot classification, PC_crime_scores and the crime counts of the individual crime types.

Prediction of technological properties of wheat flour by combination of UV-VIS-NIR spectroscopy and multivariate analysis methods

Over the last decades, optical spectroscopy methods that do not require complex sample preparation have been widely used to identify and control the composition of food products. In the present study, the possibility of using UV-VIS-NIR spectroscopy combined with multivariate analysis for grading wheat flour into groups differing in technological properties was analyzed. UV-VIS-NIR spectra contain information on the combination and intensity of absorption bands assigned to functional groups of the composition components and determining the technological properties of wheat flour. The database of UV-VIS-NIR spectra of wheat flour samples differing by technological properties was formed into three groups: the first group — wheat flour samples with good baking properties, the second group — with reduced baking properties, the third group — with low baking properties. The visible range of UV-VIS-NIR diffuse reflectance spectrum was used to calculate the color coordinates in the CIE colorimetric system L*a*b*. The greatest difference among the groups in the color coordinates of the samples was found for the coordinate b*, which is associated with the different content of coloring pigments. The spectra database was used to build a classification model for grading wheat flour into quality groups using a combination of principal component analysis and linear discriminant analysis (PCA-LDA) methods. The achieved results indicate that the classification model built on the training sample is able to distinguish wheat flour spectra by quality groups with an accuracy of 96.49%. The effective- ness of the model is verified using a test set of spectra of wheat flour samples. The present study confirms that the combina- tion of UV-VIS-NIR spectroscopy in conjunction with the PCA-LDA method has significant potential for determining a quality group of wheat flour based on technological properties.

Determinants of Household Extreme Poverty Among Female-Headed Households in Indonesia: Does Disability Matter?

ABSTRACT This paper investigates factors contributing to extreme poverty among female-headed households in Indonesia, with a specific focus on the impact of disabilities. Analyzing data from the 2021 National Socio-Economic Survey, this research combined both principal component and path analysis to uncover the link between poverty dimensions (isolation, physical weakness, powerlessness, and vulnerability) and extreme poverty. The study revealed that various dimensions influence extreme poverty, and the presence of disabilities within a household exacerbates the risk of extreme poverty and can alter the significance of initially insignificant variables. These findings underscore the critical role of disabilities in shaping extreme poverty outcomes.

How different can smart cities be? A typology of smart cities in China

China, renowned for being one of the world's largest smart city testing fields, has witnessed the emergence of numerous smart cities. Existing publications highlight the diversity in smart city development, wherein cities with different contexts and institutional factors exhibit distinct characteristics. To delve deeper into this subject, we employed a mixed methods approach to classify 49 Chinese smart cities and subsequently developed a typology for Chinese smart cities in this study. Our methodology involved three steps. Initially, we devised a classification framework based on the input-throughput-output model and conducted content analysis to identify the characteristics of the 49 cities. Subsequently, we utilized a combination of principal component analysis (PCA) and K-means clustering analysis to categorize these cases. Finally, we formulated a typology consisting of five types of Chinese smart cities: knowledge-technocratic smart cities, holistic smart cities, green smart cities, equipment-technocratic smart cities, and emerging smart cities. The findings reveal that different smart city types are characterized by distinct features and priorities in input, throughput, and output. The development of smart cities should comprehensively consider and respect the local urban contexts and the challenges they present. The insights from this study hold relevance for both policymakers and academic researchers.

Automated classification pipeline for real-time in vivo examination of colorectal tissue using Raman spectroscopy

Colorectal cancer is the third most common malignancy worldwide and one of the leading causes of death in oncological patients with its diagnosis typically involving confirmation by tissue biopsy. In vivo Raman spectroscopy, an experimental diagnostic method less invasive than a biopsy, has shown great potential to discriminate between normal and cancerous tissue. However, the complex and often manual processing of Raman spectra along with the absence of a suitable instant classifier are the main obstacles to its adoption in clinical practice. This study aims to address these issues by developing a real-time automated classification pipeline coupled with a user-friendly application tailored for non-spectroscopists. First, in addition to routine colonoscopy, 377 subjects underwent in vivo acquisitions of Raman spectra of healthy tissue, adenomatous polyps, or cancerous tissue, which were conducted using a custom-made microprobe. The spectra were then loaded into the pipeline and pre-processed in several steps, including standard normal variate transformation and finite impulse response filtration. The quality of the pre-processed spectral data was checked based on their signal-to-noise ratio before the suitable spectra were decomposed and classified using a combination of principal component analysis and a support vector machine, respectively. After five-fold cross-validation, the developed classifier exhibited 100% sensitivity toward adenocarcinoma and adenomatous polyps. The overall accuracy was 96.9% and 79.2% for adenocarcinoma and adenomatous polyps respectively. In addition, an application with a graphical user interface was developed to facilitate the use of our data pipeline by medical professionals in a clinical environment. Overall, the combination of supervised and unsupervised machine learning with algorithmic pre-processing of in vivo Raman spectra appears to be a viable way of reducing the relatively large number of biopsies currently needed to definitively diagnose colorectal cancer.

Geochemical Signatures of Mafic Volcanic Rocks in Modern Oceanic Settings and Implications for Archean Mafic Magmatism

Abstract Greenstone belts are dominated by mafic volcanic rocks with geochemical characteristics that indicate a range of possible geodynamic influences. Many analogies with modern tectonic settings have been suggested. Increasing exploration of the modern oceans and comprehensive sampling of volcanic rocks from the sea floor are now providing unique opportunities to characterize different melt sources and petrogenesis that can be more closely compared to greenstone belts. In this study, we have compiled high-quality geochemical analyses of more than 2,850 unique samples of submarine mafic volcanic rocks (<60 wt % SiO2) from a wide range of settings, including mid-ocean ridges, ridge-hotspot intersections, intraoceanic arc and back-arc spreading centers, and ocean islands. The compiled data show significant geochemical variability spanning the full range of compositions of basalts found in greenstone belts. This diversity is interpreted to be due to variable crustal thickness, dry melting versus wet melting conditions, mantle mixing, and contamination. In particular, different melting conditions have been linked to mantle heterogeneity, complex mantle flow regimes, and short-lived tectonic domains, such as those associated with diffuse spreading, overlapping spreading centers, and triple junctions. These are well documented in the microplate mosaics of the Western Pacific. Systematic differences in mafic volcanic rock compositions in modern oceanic settings are revealed by a combination of principal components analysis and unsupervised hierarchical clustering of the compiled data. Mafic volcanic rocks from most arc-back arc systems have strongly depleted mantle signatures and well-known subduction-related chemistry such as large ion lithophile element (LILE) enrichment in combination with strong negative Nb-Ta anomalies and low heavy rare earth elements (HREEs). This contrasts with mafic volcanic rocks in Archean greenstone belts, which show no, or at least weaker, subduction-related chemistry, a less depleted mantle, less wet melting, and variable crustal contamination. The differences are interpreted to be the result of the lower mantle temperatures, thinner crust, and subduction-related processes of present-day settings. However, mafic rocks that are geochemically identical to those in Archean greenstone belts occur in many modern back-arc basins, including the Lau basin, East Scotia ridge, Bransfield Strait, and Manus basin, which are characterized by fertile mantle sources, high heat flow, and complex spreading regimes typical of small-scale microplate mosaics. These types of settings are recognized as favorable for volcanogenic massive sulfide (VMS) deposits in modern and ancient greenstone belts, and therefore the particular geochemical signatures of the mafic volcanic rocks are potentially important for area selection in base metal exploration.

Tracking element-mineral associations with unsupervised learning and dimensionality reduction in chemical and optical image stacks of thin sections

In-situ chemical analysis on thin sections is a cornerstone of geochemical research. Despite massive advances in digital image analysis, the interpretation of such data within the optical petrological context of the thin section has largely remained an analogue task in geochemistry. In this contribution, we registered optical and micro-chemical images from large thin section areas. Chemical datasets from scanning electron microscopy energy dispersive spectrometry (SEM-EDX) and Synchrotron X-ray fluorescence microscopy (S-XFM) were exported from proprietary software. We then evaluated two dimensionality reduction techniques against false-colour images and phase maps to test whether previously unnoticed features became discoverable. Principal component analysis (PCA) and deep sparse autoencoder (DSA) neural networks were used to summarise the multi-channel SEM-EDX and S-XFM into one single red-green-blue (RGB) representation each.Applied to an oceanic gabbro, a cratonic peridotite, and a pelagic limestone, the PCA-based dimensionality reduction was found to produce crisp RGB maps of phases with less noise than false-colour three-element RGB maps. They were registered with optical images to form a simple multi-channel input for pixel-based classification into classes (i.e., semantic segmentation). This was fast and worked well for typical igneous and metamorphic rocks (1–3 h routine). In the chemically quite homogenous limestone, the combined optical and S-XFM PCA input was successfully classified into many phases that were unclassifiable on the conventional SEM-EDX phase map. From the segmented optical and S-XFM PCA map, we exported pixel locations of classified accessory phases back into the original proprietary S-XFM software. This allowed quantification of trace elements whose X-ray peaks were invisible in the original S-XFM quantification.The DSA-based approach yielded phase maps with greater noise, but the technique was very strong at detecting subtle features. In the gabbro sample, the DSA image identified cryptic relict cores in clinopyroxene more clearly than in the previously used Cr concentration map. A DSA training set can be obtained on a small ROI to generate a consistent large area DSA image from which more fragmental cores were observed. For a ROI on the gabbro thin section, the DSA representation map and the blended optical image were segmented and inspected with a loupe tool. This allowed us to generate interactive histograms and bin-scatter concentration plots of selected phases and to search for co-variations of element concentrations. This approach can be expanded to include data from any in situ geochemical acquisition tools and may help discover previously unseen features in complex elemental imagery.

Land use and land cover mapping using Landsat-8 and Sentinel-2 data in Al Qunfudhah coast, western Saudi Arabia: A comparative study

Al Qunfudhah Governorate has assumed a tourist status in the strip of Saudi coastal cities on the Red Sea coast. Agriculture, food industries, and engineering industries are among the most important economic activities in the city. As a result, Land use and land cover (LULC) detection is essential for several reasons: urban planning, sustainability, resource management and environmental preservation. This research aims to identify the LULC of the Al Qunfudhah area in western Saudi Arabia by comparing Landsat-8 and Sentinel-2 data outputs and evaluating their accuracy results. Various methods are used to process satellite image data to enhance the contrast between surface features. Some of these techniques are true colour composite (TCC) images, enhanced true colour composite (ETCC) images, false colour composite (FCC) images, principal component analysis (PCA), band ratios and Maximum Likelihood Algorithm (MLA) Supervised Classification. By utilising band combination methods, PCA methods, and band ratio methods, we were able to map a variety of features, including vegetation cover, mangroves, Sabkha and non-Sabkha deposits, wadi basins, shallow and deep shelf areas, islands, sand and mud sediments, rocky reefs and urban areas. As part of the study, ArcGIS 10.8's supervised classification tool was employed to categorise the study region's features and calculate the percentage of each class. In both Landat-8 and Sentinel-2 classification images, the same classes were identified (i.e. sand and mud sediments, sabkhas, wadi basins, aeolian sand and loess sediments, islands, urban areas, vegetation, and mangroves). The accuracy of the classification results was determined using a reliable statistical approach. The accuracy assessments of Landsat-8 and Sentinel-2 revealed accuracy rates of 87% and 89.1% respectively, along with kappa coefficients of 84.9% and 87.3%. For the investigated area, the Landsat-8 and Sentinel-2 satellites were effective in producing LULC maps.

Hierarchical fusion based high precision SLAM for solid-state lidar

Abstract Solid-state LiDARs have become an important perceptual device for simultaneous localization and mapping (SLAM) due to its low-cost and high-reliability compared to mechanical LiDARs. Nevertheless, existing solid-state LiDARs-based SLAM methods face challenges, including drift and mapping inconsistency, when operating in dynamic environments over extended periods and long distances. To this end, this paper proposes a robust, high-precision, real-time LiDAR-inertial SLAM method for solid-state LiDARs. At the front-end, the raw point cloud is segmented to filter dynamic points in preprocessing process. Subsequently, features are extracted using a combination of Principal Component Analysis (PCA) and Mean Clustering to reduce redundant points and improve data processing efficiency. At the back-end, a hierarchical fusion method is proposed to improve the accuracy of the system by fusing the feature information to iteratively optimize the LiDAR frames, and then adaptively selecting the LiDAR keyframes to be fused with the IMU. The proposed method is extensively evaluated using a Livox Avia solid-state LiDAR collecting datasets on two different platforms. In experiments, the end-to-end error is reduced by 35% and the single-frame operational efficiency is improved by 12% compared to LiLi-OM.

Evaluating Intra-Field Spatial Variability for Nutrient Management Zone Delineation through Geospatial Techniques and Multivariate Analysis

This research study underscores the importance of effectively managing soil nutrients in a site-specific manner to enhance crop productivity while considering the spatial variability of the soil. The objective is to identify subfields with similar soil characteristics, referred to as management zones (MZs), to promote sustainable land utilization. This study was conducted in two central pivot fields located in Southern Idaho, USA, where barley and sugar beets were grown. Soil samples were collected from each field in a grid pattern and analyzed for various chemical properties. These properties included soil pH, organic matter, cation exchange capacity, excess lime, electrical conductivity, total inorganic nitrogen, phosphorus, potassium, calcium, magnesium, zinc, iron, manganese, copper, and boron. Descriptive statistics and normality assessments were performed, and the coefficient of variation was calculated to assess the heterogeneity of soil properties, revealing significant variability. To determine the spatial variability of soil properties, ordinary kriging was used revealing diverse spatial patterns for each location and soil variable examined with moderate to strong spatial dependence. To develop the MZs, a combination of principal component analysis and fuzzy k-means clustering was utilized, and specific parameters that represented the overall variability of soil properties in each field were identified. Based on the identified parameters, two clusters were created in each field. The first management zone (MZ1) exhibited lower values of soil pH, excess lime content, and electrical conductivity compared to the MZ2. Consequently, higher crop productivity was observed in MZ1 in both fields. The biomass yields of barley and sugar beets in MZ1 surpassed those in MZ2. This study highlights the effectiveness of the methodology employed to delineate MZs, which can be instrumental in precise soil nutrient management and maximizing crop productivity.

The Modeling of the Probable Behaviour of Insider Cyber Fraudsters in Banks

Insider cyber fraud in the banking sector is a serious and complex issue for financial institutions. This form of cyber fraud is particularly insidious due to insiders’ inherent access and knowledge, necessitating banks to implement comprehensive strategies for detecting, preventing, and responding to these internal threats. The aim of this study is to develop a scientific and methodological approach to model the probable behaviour of insider cyber fraudsters in banks based on a complex combination of principal component analysis, k-means clustering, and associative analysis. During the analysis of current challenges in the financial sector regarding the evolution of cyber fraud and its implications, the systematization of existing theoretical approaches concerning the examination of cyber fraud in banks was performed. Its result revealed a positive trend in the dynamics of the number of published materials in conferences and articles using keywords “cyber” and “frauds” in the Scopus database from 2000 to 2023. Additionally, utilizing the VOSviewer software facilitated the systematization of keyword combinations used in scholarly publications on the chosen topic, forming clusters to visualize and organize vectors of scientific research. Analytical data from Google Trends on critical issues related to cyber fraud were chosen as input data. Twenty variables were formed, which are the results of search queries, characterizing cyberattacks and decreased trust in financial institutions. The principal components method was used to reduce the dimensionality of the input data array, making it possible to select the nine most significant for the study. Conducting a cluster analysis using the k-means method made it possible to form 3 main groups of search queries, which included 12 of the selected variables. The results of the performed procedures contributed to the implementation of associative analysis for three sets of variables. It has been found that what intrigues potential insider cybercriminals in banks the most is the personal financial information of the client, access to the client’s profile in online banking and gaining access to his phone data. The obtained results can be utilized by commercial banks for identifying potential insider cyber fraudsters and ensuring a higher level of client protection against the actions of insider cyber fraudsters, by bank clients for analysing and mitigating potential threats from insider cyber fraudsters, and by law enforcement agencies for prompt responses to potential threats posed by insider cyber fraudsters in banks.

SERS detection of foodborne pathogens in beverage with Au nanostars.

The aim of this study is to develop a simple but rapid method for the determination of foodborne pathogens in complex matrices (beverages) by surface enhanced Raman spectroscopy (SERS) combined with Au nanostar solid-phase substrates. The star-shaped singlet Au nanostructure was formed on the surface of astainless steel sheetby chemical replacement reaction. Rhodamine 6G verified the sensitivity and reproducibility of this substrate, and the relative standard deviations of the SERS intensity at 1312 cm-1, 1364 cm-1, and 1510 cm-1 displacements were 3.40%, 5.64%, and 3.48%, respectively. By detecting four pathogens in beverage samples on Au nanostar substrates, the utility of the SERS assay was demonstrated, while the combination of principal component analysis (PCA) and hierarchical cluster analysis (HCA) further enabled the isolation and identification of pathogens. The results of spiked beverages were validated in conventional culture identification and Vitek 2 Compact biochemical identification system experiments. Thus, this research demonstrated that Au nanostar substrates can be effectively utilized for the recognition of pathogenic bacteria and have immense promise to advance the progress of quick detection of foodborne pathogens and food safety.

Label-free screening of common urinary system tumors from blood plasma based on surface-enhanced Raman spectroscopy

BackgroundThe incidence of common urinary system tumors has been rising rapidly in recent years, and most urinary system-derived tumors lack specific biomarkers. ObjectivesTo explore the efficacy of surface-enhanced Raman spectroscopy (SERS) of blood plasma in screening three common urinary system tumors, including bladder cancer (BC), prostate cancer (PCa), and renal cell carcinoma (RCC). MethodsSERS plasma spectra from 125 plasma samples, including 25 PCa, 38 RCC, 24 BC patients, and 38 normal volunteers, were collected. All candidates had no other comorbidities. The Diagnosis was based on the combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and the effectiveness of the diagnostic algorithms was verified using the Receiver Operating Characteristic Curve (ROC). ResultsThere are significant differences in SERS signals between PCa, BC, RCC, and normal plasma, especially at 639, 889, 1010, 1136, and 1205 cm−1. The PCA-LDA results show that high sensitivity (100 %), specificity (100 %), and accuracy (100 %) could be achieved for screening the PCa, RCC, BC group vs. the normal group, the PCa group vs. the BC and RCC group, respectively. The diagnostic sensitivity, specificity, and accuracy for the BC group vs. the RCC group are 79.2 %, 71.1 %, and 75.15 %, respectively. The integrated area under the ROC curve (AUC) is 1.0, 1.0, and 1.0 for the PCa, RCC, and BC group vs. the normal group, respectively. The AUC of the PCa group vs. the BC group and RCC group and the BC group vs. the RCC group are 1.0, 1.0, and 0.842, respectively. ConclusionsLabel-free plasma-SERS technology with PCA-LDA analysis could be a useful screening method for detecting urinary system tumors (PCa, RCC, and BC) in this exploratory study.

Research on Collaborative Innovation Evaluation of Intelligent Logistics Park

The collaborative innovation capability of intelligent logistics parks plays a crucial role in promoting their development, but still encounters various problems in terms of progress. On the basis of summarizing the evaluation indexes and methods of collaborative innovation capability in logistics systems, this article constructs an evaluation index system from three dimensions of intelligence, collaboration capabilities, and innovation capabilities using the combined principal component analysis and data envelopment analysis (DEA). In this empirical article, 18 intelligent logistics parks in the upper, middle, and lower reaches of the Yangtze River Economic Belt are taken as the research objects to analyze the key factors influencing collaborative innovation of practical intelligent logistics parks. Results show that, on the one hand, the collaborative innovation process of many intelligent logistics parks have redundant inputs and outputs, which leads to failure of realizing DEA effectively; on the other hand, gaps in the collaborative innovation level leads to failure of realizing the overall coordinated development. The level of economic development cannot completely determine the level of collaborative innovation of the intelligent logistics park.

Elemental analysis of wild Eriocheir sinensis: Determining the geographic origin and human health risk assessment

Identification of composition characteristics of major and trace elements in wild mitten crabs (Eriocheir sinensis) from different water systems is important for protected geographical indication as well as food quality control and safety. In this study, inductively coupled plasma-mass spectrometry was employed to measure the contents of 23 elements in the muscle samples of wild Eriocheir sinensis from three water systems (Suifenhe, Nanliujiang, and Liaohe). The results of Kruskal–Wallis one-way ANOVA test comparisons revealed that most elements were significantly different in the samples collected from three water systems (P < 0.05). Geographic origin discrimination was achieved using principal component analysis in combination with hierarchical cluster analysis (HCA) and stepwise linear discriminant analysis (S-LDA). The HCA results showed the potential of eight characteristic elements for exploratory hierarchical clustering of the samples from three water systems; however, the clustering effect was unsatisfactory. The discrimination accuracy of S-LDA model for the samples from three water systems and that in cross-validation reached 100%. The health risk assessment further revealed that the normal level consumption of wild Eriocheir sinensis did not pose an appreciable health risk to consumers.

Detection of driver drowsiness level using a hybrid learning model based on ECG signals.

Fatigue has a considerable impact on the driver's vehicle and even the driver's own operating ability. An intelligent algorithm is proposed for the problem that it is difficult to classify the degree of drowsiness generated by the driver during the driving process. By studying the driver's electrocardiogram (ECG) during driving, two models were established to jointly classify the ECG signals as awake, stress, and fatigue or drowsiness states for drowsiness levels. Firstly, the deep learning method was used to establish the model_1 to predict the drowsiness of the original ECG, and model_2 was developed using the combination of principal component analysis (PCA) and weighted K-nearest neighbor (WKNN) algorithm to classify the heart rate variability characteristics. Then, the drowsiness prediction results of the two models were weighted according to certain rules, and the hybrid learning model combining dilated convolution and bidirectional long short-term memory network with PCA and WKNN algorithm was established, and the mixed model was denoted as DiCNN-BiLSTM and PCA-WKNN (DBPW). Finally, the validity of the DBPW model was verified by simulation of the public database. The experimental results show that the average accuracy, sensitivity and F1 score of the test model in the dataset containing multiple drivers are 98.79, 98.81, and 98.79 % respectively, and the recognition accuracy for drowsiness or drowsiness state is 99.33 %. Using the proposed algorithm, it is possible to identify driver anomalies and provide new ideas for the development of intelligent vehicles.

Re-defining Transport for London’s strategic neighbourhoods from spatial and social perspectives

Neighbourhoods are fundamental spatial units to present social phenomena in urban studies. Many studies use administrative boundaries such as census tracts as representations of neighbourhoods, but such boundaries may poorly represent the underlying social structures and physical attributes which might help define more vernacular conceptions and dialectical evolution of these zones. In this paper, using the goal of creating a new set of ‘Strategic Neighbourhoods’ for Transport for London (TfL) as vehicle for analysis, we evaluate two contrasting spatially and socially focused methodologies of neighbourhood generation. In comparing the outputs of a tertiary-communities (T-Communities) method and a combined Principal Component Analysis (PCA) and Minimum Spanning Tree (MST) cluster analysis method with an earlier iteration of Strategic Neighbourhoods defined by TfL, indices including neighbourhood size, intra-class correlation coefficient (ICC), and the number of community centres are calculated to evaluate their relative performance which demonstrate that both methods create neighbourhood boundaries that can better capture intra-group social homogeneity and are more suitable for analysis than the original SNA boundaries. These results are discussed in the context of the dialectic relationship between neighbourhood outcomes, spatial structures, and social characteristics, leading to more widely relevant conclusions that neighbourhood boundary delineation should combine spatial structure, social attributes, and experimental knowledge to effectively sub-divide urban activity.