Statistical Clustering Research Articles

“You have to be mentally prepared for that moment”: Attitudes of the adolescent population to death and their educational implications

This article deals with the opinions and attitudes of the adolescent population toward death and its possible treatment in formal education at a public secondary school in Spain. To do so, we use a mixed methodology - DEA-S scale (n = 366) and three focus groups (n = 23), using descriptive statistical analysis, cluster analysis and qualitative thematic analysis. As in other studies, these students show moderately positive attitudes toward the inclusion of death education at their school, but their position is not unanimous, and above all, they admit its inclusion with certain reservations and conditions that reveal a limited conception of the educational potential of death education: they do not consider it appropriate at all educational stages and are critical of the way in which similar topics are usually dealt with at school. The analysis provides important didactic guidelines for the promotion of death awareness in secondary education.

Assessment of the influence of levees along Yangtze River on Oncomelania hupensis, the intermediate host of Schistosoma japonicum

BackgroundOncomelania hupensis is the exclusive intermediate host of Schistosoma japonicum in China. Snail control is an essential component of schistosomiasis elimination programme. With 70 years of continuous efforts, the range of O. hupensis had reduced significantly, but slowed down in last decades. A large number of levees against flooding were constructed along Yangtze River and its affiliated lakes in the middle and lower reaches, which influenced the hydrology and ecology in the alluvial plains. The purpose of this study was to assess the impact of levees on the distribution of O. hupensis in the middle and lower reaches of the Yangtze River.MethodsThe snail habitats were digitalised by hand-held GPS system. The years for discovery and elimination of snail habitats were extracted from historical records. The accumulated snail-infested range for each habitat was calculated on the basis of annual reports. The current distribution of O. hupensis was determined by systematic and environmental sampling. The geographical distribution of levees was obtained from satellite imagery. To assess the impact of levees, the data pertaining to O. hupensis were divided into two parts: inside and outside the Yangtze River. Joinpoint regression was utilised to divide the study time span and further characterise the regression in each period. The 5-year-period moving averages of eliminated area infested by snails were calculated for the habitats inside and outside Yangtze River. The moving routes of corresponding geographical median centres were simulated in ArcGIS. Hotspot analysis was used to determine the areas with statistical significance clustering of O. hupensis density.ResultsThree periods were identified according to Joinpoint regression both inside and outside Yangtze River. The area infested by O. hupensis increased in the first two periods. It decreased rapidly outside Yangtze River year over year after 1970, while that inside the Yangtze River did not change significantly. Furthermore, the latter was significantly higher than the former. It was observed that the present density of O. hupensis inside Yangtze River was lower than outside the Yangtze River. The median centre for eliminated ranges inside Yangtze River wavered between the east (lower reach) and the west (middle reach). In contrast, the median centre for eliminated ranges continuously moved from the east to the west.ConclusionsOur findings indicated that the levees had a considerable negative impact on the distribution of O. hupensis outside Yangtze River. Some hotspots observed in the irrigation areas need a sluice system at the inlet of branch for snail control. The major distribution of O. hupensis located in Hubei might be caused by severe waterlogging. The intensive surveillance should be implemented there. The biggest two freshwater lakes, the major endemic regions historically, were identified as cold spots. The long-term impact of Three Gorges Dam on the distribution of O. hupensis in the lakes should be monitored and evaluated.Graphical

HERisk and statistical clustering integrated for health risk modelling of PTEs in natural water resources for drinking and sanitary uses

Potentially toxic elements (PTEs) are well-known for exposing living organisms and humans to different levels of risk. The present study aimed to evaluate the extent of exposure to health risks sustained by the inhabitants of different suburbs across Southeastern Nigeria as a result of contaminated water sources. There are existing literatures on human health risk assessment in the study region. However, this is the first study to report a detailed breakdown of the risks faced by nine age groups in the study area. This was achieved by integrating a novel code (HERisk), statistical clustering, and water quality data. Laboratory analysis showed that the levels of PTEs (Fe2+, Ni2+, Cr3+, and Pb2+) in 53.6%, 17.8%, 3.5%, and 46% of the samples were found to be above the recommended limits. Aggregated human health risk scores for ingestion and dermal routes revealed that children aged 1 to 2 years are the most vulnerable to health hazards. According to cumulative carcinogenic health risk values (0.00E+00 to 2.41E-04), samples proximal to regions with significant human activities expose the locals to a high risk of developing cancer via ingestion. Q-mode hierarchical cluster analysis successfully validated the classification schemes used for the interpretation of the HERisk code values, captured the patterns in the dataset, and brought forth new perspectives. The comprehensive approach detailed in this study can be adopted as a framework for ongoing monitoring and assessment of water quality.

The heart of galaxy clusters: Demographics and physical properties of cool-core and non-cool-core halos in the TNG-Cluster simulation

We analyzed the physical properties of the gaseous intracluster medium (ICM) at the center of massive galaxy clusters with TNG-Cluster, a new cosmological magnetohydrodynamical simulation. Our sample contains 352 simulated clusters spanning a halo mass range of 1014 < M500c/M⊙ < 2 × 1015 at z = 0. We focused on the proposed classification of clusters into cool-core (CC) and non-cool-core (NCC) populations, the z = 0 distribution of cluster central ICM properties, and the redshift evolution of the CC cluster population. We analyzed the resolved structure and radial profiles of entropy, temperature, electron number density, and pressure. To distinguish between CC and NCC clusters, we considered several criteria: central cooling time, central entropy, central density, X-ray concentration parameter, and density profile slope. According to TNG-Cluster and with no a priori cluster selection, the distributions of these properties are unimodal, whereby CCs and NCCs represent the two extremes. Across the entire TNG-Cluster sample at z = 0 and based on the central cooling time, the strong CC fraction is fSCC = 24%, compared to fWCC = 60% and fNCC = 16% for weak and NCCs, respectively. However, the fraction of CCs depends strongly on both halo mass and redshift, although the magnitude and even direction of the trends vary with definition. The abundant statistics of simulated high-mass clusters in TNG-Cluster enabled us to match observational samples and make a comparison with data. The CC fractions from z = 0 to z = 2 are in broad agreement with observations, as are the radial profiles of thermodynamical quantities, globally as well as when divided as CC versus NCC halos. TNG-Cluster can therefore be used as a laboratory to study the evolution and transformations of cluster cores due to mergers, AGN feedback, and other physical processes.

Bioavailable fractions of heavy metals in the road dust during infrastructure construction at urban Coimbatore and its potential health implications, India.

Over the past two decades the Global South is witnessing unprecedented economic transformation and Asian Cities in particular have a remarkable upsurge. Coimbatore, an industrial city in Southern India with an estimated population of 2 million (in 2022) is witnessing a rapid transition in terms of infrastructure development. In this context, the present study attempts to assess the particulate matter (PM10 and PM2.5) emissions at road network construction sites and the heavy metal fractionation in the road dust/sediment samples with a core focus to quantify the bioavailable fraction of metals (Fe, Cu, Cr, Cd, Pb and Ni) and its source apportionment in the road side dust/sediment samples. About 60 composite road dust/sediment samples were collected for heavy metal fractionation analysis in the six arterial roads that undergo core developments like construction of road over bridges, additional road incorporation and street expansions. PM monitoring revealed that 24h average PM2.5 (47µg/m3) and PM10 (69µg/m3) concentrations at many construction sites exceeded 24h average recommended by WHO guidelines [PM2.5 (15µg/m3) and PM10 (45µg/m3), respectively]. The bioavailable fractions of Fe, Cu, Cr and Cd are notably higher in the roadside sediment samples at road construction sites. Health Risk assessment, such as carcinogenic risks (Children-4.41 × 10-2, Adult-3.598 × 10-6) and non-carcinogenic risks, inferred substantial risks at high intensity construction sites with statistical analyses, including PCA and cluster analysis, indicating considerable anthropogenic influences in the heavy metal fractions.

Statistical and density-based clustering of geographical flows for crowd movement patterns recognition

With the rapid development of sensors and communication technologies, it has become easy to collect large-scale and long-term crowd movement positioning data, which brings new opportunities for studying crowd movement patterns. This paper proposes a novel Statistical and Density-Based Clustering algorithm (SDBC) to identify implicit significant spatial aggregation patterns in geographical flow data. Unlike existing flow clustering algorithms, this method identifies the hot spots of origin-destination (OD) flows based on local spatial statistics and density-growing clustering. It also evaluates the significance of the identified geographic flow clusters, effectively reducing the identification of spurious clusters generated by chance in data. In our method, the spatial neighborhood of each flow is first obtained based on spatial proximity, temporal similarity, and directional similarity. Then, the number of flows in the spatial neighborhood of each flow is calculated and used as the density measure. Based on this, high-density flows are automatically detected using local spatial aggregation statistics, and a hierarchical density-based clustering strategy is developed to merge adjacent high-density flows to generate candidate flow clusters. Finally, we perform permutation tests to infer the statistical significance of each flow cluster and eliminate the candidate clusters generated by chance. Experiments on synthetic data and real-world taxi trajectory data were conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method can accurately identify the statistically significant flow clusters of different shapes and densities and performs better than the available state-of-the-art flow clustering algorithms.

In Situ Lung Dust Analysis by Automated Field Emission Scanning Electron Microscopy With Energy Dispersive X-ray Spectroscopy: A Method for Assessing Inorganic Particles in Lung Tissue From Coal Miners.

Overexposure to respirable coal mine dust can cause severe lung disease including progressive massive fibrosis (PMF). Field emission scanning electron microscopy with energy dispersive x-ray spectroscopy (FESEM-EDS) has been used for in situ lung dust particle analysis for evaluation of disease etiology. Automating such work can reduce time, costs, and user bias. To develop and test an automated FESEM-EDS method for in situ analysis of inorganic particles in coal miner lung tissue. We programmed an automated FESEM-EDS procedure to collect particle size and elemental data, using lung tissue from 10 underground coal miners with PMF and 4 control cases. A statistical clustering approach was used to establish classification criteria based on particle chemistry. Data were correlated to PMF/non-PMF areas of the tissue, using corresponding brightfield microscopy images. Results for each miner case were compared with a separate corresponding analysis of particles recovered following tissue digestion. In situ analysis of miner tissues showed higher particle number densities than controls and densities were generally higher in PMF than non-PMF areas. Particle counts were typically dominated by aluminum silicates with varying percentages of silica. Compared to digestion results for the miner tissues, in situ results indicated lower density of particles (number per tissue volume), larger size, and a lower ratio of silica to total silicates-probably due to frequent particle clustering in situ. Automated FESEM-EDS analysis of lung dust is feasible in situ and could be applied to a larger set of mineral dust-exposed lung tissues to investigate specific histologic features of PMF and other dust-related occupational diseases.

A High-Precision Earthquake Catalog for Nevada

Abstract The state of Nevada is home to one of the most seismically active regions in the world, with crustal deformation associated with the Walker Lane transitioning into Basin and Range tectonics as one traverses from west to east across the state. Despite hosting numerous prominent earthquake sequences over the past century and beyond, at present, there exists no unified research-quality earthquake catalog for the state and its surrounding region. Here, we present a newly compiled, high-precision catalog of more than 180,000 earthquakes occurring around Nevada from 2008 to 2023. The data processing workflow to create this catalog includes an absolute location step that accounts for topography and 3D variations in subsurface wavespeed, and a relative relocation step that refines event positions using differential times measured from waveform cross-correlation. We also provide an update to the local magnitude scale that better accounts for the observed distance attenuation of waveform amplitudes as well as local site effects. We describe some fundamental insights that can be derived from the new catalog, including regional variations in event depth distributions and sequence clustering statistics, and publish the catalog to the wider community to facilitate future research efforts.

Improving the accuracy of halo mass based statistics for fast approximate N-body simulations

ABSTRACT Approximate N-body methods, such as fastpm and cola, have been successful in modelling halo and galaxy clustering statistics, but their low resolution on small scales is a limitation for applications that require high precision. Full N-body simulations can provide better accuracy but are too computationally expensive for a quick exploration of cosmological parameters. This paper presents a method for correcting distinct haloes identified in fast N-body simulations, so that various halo statistics improve to a percent level accuracy. The scheme seeks to find empirical corrections to halo properties such that the virial mass is the same as that of a corresponding halo in a full N-body simulation. The modified outer density contour of the corrected halo is determined on the basis of the fastpm settings and the number of particles inside the halo. This method only changes some parameters of the halo finder, and does not require any extra CPU-cost. We demonstrate that the adjusted halo catalogues of fastpm simulations significantly improve the precision of halo mass-based statistics from redshifts $z=0.0$ to 1.0, and that our calibration can be applied to different cosmologies without needing to be recalibrated.

Chestnut as a culinary ingredient: Analysis of recipes and proposed innovative thematic menu

Chestnuts are natural dried fruits grown in many regions of the globe and have been used in different gastronomic preparations for centuries. Chestnuts are part of the local gastronomic cultures in several regions and are also being used for culinary innovations. This work analysed 266 recipes containing chestnuts obtained from different sources, such as cookbooks or the internet. The recipes were classified according to different criteria, and data analysis included basic statistical tools, significance tests, factor analysis and cluster analysis.The results showed that the utilisation of chestnuts was particularly frequent in main dishes containing meat of some sort (26.3%), as well as in desserts (24.4%). The chestnuts were usually added to the recipes mostly fresh, whole (24.1%) or cut (26.7%). The nature of the recipes was divided into classical or modern, being significantly associated with the form of incorporation of the chestnut, but not significantly associated with most of the variables used to classify the recipes (dish type, cooking, cooking method, preparation time, or moment of incorporation of the chestnut). Factor analysis indicated three factors, clearly differentiated, that in globally explained 58.1% of the variance. Cluster analysis allowed the establishment of four clusters of recipes, which were found significantly different according to all the variables used to classify the recipes. Ingredient analysis showed that the most frequent categories were spices, followed by vegetables & legumes and that the ingredients used in lower quantities were most frequently listed. Word clouds based on frequency indicated, in each of the categories, the most relevant ingredients, which were flour, butter, egg, pork, bacon, shrimp, onion, garlic, lemon, almond, salt, pepper, oil, and sugar. Lastly, a successful innovative thematic menu was developed and composed of recipes containing chestnuts, including one starter, one main dish, and one dessert.This work allows to conclude about the diverse utilisation of chestnut as a gastronomic ingredient and not only as a dried fruit.

Geo-constrained clustering of resistivity data revealing the heterogeneous lithological architectures and the distinctive geoelectrical signature of shallow deposits

For all applications, subsurface models should be consistent with all available geological and geophysical knowledge. Current practices for synergistic interpretation of geological and geophysical approaches often rely on purely qualitative comparisons, resulting sometimes in inconsistent findings. This study introduces a procedure for a statistical and geo-constrained clustering of electrical resistivity data derived from Electrical Resistivity Tomography (ERT) to address this gap, providing a quantitative parameterization for site-specific geoelectrical signatures of litho-stratigraphic architectures. Seventeen boreholes and three ERT surface profiles were employed to link geophysical inversion results to geological criteria. Core samples allowed grain size analyses, while geological-statistical clustering of electrical resistivity, driven by the observation of stratigraphic contacts in drilled boreholes, established a parametric relationship between geology and geophysics. The iterative clustering procedure, utilizing a classification algorithm, geological boundary constraints, and granulometric analyses, discriminated six distinct lithological clusters, capturing the lateral and vertical heterogeneity of shallow deposits. Subsequent spatial grouping of anthropogenic materials delineated lithological structures and facilitated the classification and identification of filling materials, silty sands, clayey sands, and clays and silts, each exhibiting distinct resistivity variations. The geo-driven geophysical clustering revealed complex lithological structures, especially paleo-channels, capturing their unique geoelectric footprints. The iterative clustering of geo-constrained resistivity data emerges as a powerful tool for subsurface exploration, contributing significantly to understanding lithological heterogeneity, quantifying statistically-based geoelectrical parametrization of shallow sediments, and evaluating the resistivity signature of different deposits. By bridging the gap between geology and geophysics, this data-driven approach establishes a benchmark for future applications. For instance, in the context of contaminated sites, it can be applied to identify pollutants versus geological heterogeneities.

SUNBIRD: a simulation-based model for full-shape density-split clustering

ABSTRACT Combining galaxy clustering information from regions of different environmental densities can help break cosmological parameter degeneracies and access non-Gaussian information from the density field that is not readily captured by the standard two-point correlation function (2PCF) analyses. However, modelling these density-dependent statistics down to the non-linear regime has so far remained challenging. We present a simulation-based model that is able to capture the cosmological dependence of the full shape of the density-split clustering (DSC) statistics down to intra-halo scales. Our models are based on neural-network emulators that are trained on high-fidelity mock galaxy catalogues within an extended-ΛCDM framework, incorporating the effects of redshift-space, Alcock–Paczynski distortions, and models of the halo–galaxy connection. Our models reach sub-per cent level accuracy down to $1 \, h^{-1}\text{Mpc}$ and are robust against different choices of galaxy–halo connection modelling. When combined with the galaxy 2PCF, DSC can tighten the constraints on ωcdm, σ8, and ns by factors of 2.9, 1.9, and 2.1, respectively, compared to a 2PCF-only analysis. DSC additionally puts strong constraints on environment-based assembly bias parameters.

Disentangling the relationship between social cognition, executive functions and behaviour changes in amyotrophic lateral sclerosis

BackgroundSocial cognition (SC) deficits are included in the amyotrophic lateral sclerosis-frontotemporal spectrum disorder (ALS-FTDS) revised diagnostic criteria. However, the impact of SC assessment on cognitive classification and the cognitive–behavioural correlates...

Concentration, Pollution Level, and Sources of Heavy Metals from Household Dust in Ramadi City, Iraq

Researchers and environmental managers have been interested in household dust pollution because exposure to heavy metals has a detrimental effect on health. This study aims to determine heavy metals concentrations (Cd, Cr, Cu, Ni, Pb and Zn), pollution level, and their potential sources in the household dust of Ramadi City, Iraq. The dust was collected from 35 houses. The atomic absorption spectrophotometry method was used to measure concentrations of heavy metals in the dust. The following was the order of the heavy metal mean concentration: Zn > Ni > Cu > Pb > Cr > Cd. These metals’ mean concentrations are below background guidelines. The descending order of the EF values for the measured metals is as follows: Ni (6.35) > Cu (5.40) > Pb (1.53 > Cr (1.24) > Zn (1.14) > Cd (0.84). Results of the calculation of CF for metals measured in house dust in Ramadi City showed values of less than 1 (CF < 1 indicating a minimum contamination level. Based on the results of multivariate statistics (correlation matrix, cluster and principle components analysis), sources of Cr, Cu, Ni and Zn are indoor anthropogenic sources while the sources of Cd and Pb are outdoor anthropogenic.

Some results on $I_2$-deferred statistically convergent double sequences in fuzzy normed spaces

The primary objective of this study is to introduce the concepts of $I_2$-deferred Cesàro summability and $I_2-$ deferred statistical convergence for double sequences in fuzzy normed spaces (FNS). Furthermore, the aim is to explore the connections between these concepts and subsequently establish several theorems pertaining to the notion of $I_2$-deferred statistical convergence in FNS for double sequences. We further define $I_2$-deferred statistical limit points and $I_2$-deferred statistical cluster points of a sequence within FNS and explore the relationships among these concepts.

Euclid preparation. XLIV. Modelling spectroscopic clustering on mildly nonlinear scales in beyond-LCDM models

The space satellite mission will measure the large-scale clustering of galaxies at an unprecedented precision, providing a unique probe of modifications to the Lambda CDM model. We investigated the approximations needed to efficiently predict the large-scale clustering of matter and dark matter halos in the context of modified gravity and exotic dark energy scenarios. We examined the normal branch of the Dvali--Gabadadze--Porrati model, the Hu--Sawicki $f(R)$ model, a slowly evolving dark energy model, an interacting dark energy model, and massive neutrinos. For each, we tested approximations for the perturbative kernel calculations, including the omission of screening terms and the use of perturbative kernels based on the Einstein--de Sitter universe; we explored different infrared-resummation schemes, tracer bias models and a linear treatment of massive neutrinos; we investigated various approaches for dealing with redshift-space distortions and modelling the mildly nonlinear scales, namely the Taruya--Nishimishi--Saito prescription and the effective field theory of large-scale structure. This work provides a first validation of the various codes being considered by for the spectroscopic clustering probe in beyond-Lambda CDM scenarios. We calculated and compared the $ statistic to assess the different modelling choices. This was done by fitting the spectroscopic clustering predictions to measurements from numerical simulations and perturbation theory-based mock data. We compared the behaviour of this statistic in the beyond-Lambda CDM cases, as a function of the maximum scale included in the fit, to the baseline Lambda CDM case. We find that the Einstein--de Sitter approximation without screening is surprisingly accurate for the modified gravity cases when comparing to the halo clustering monopole and quadrupole obtained from simulations and mock data. Further, we find the same goodness-of-fit for both cases -- the one including and the one omitting non-standard physics in the predictions. Our results suggest that the inclusion of multiple redshift bins, higher-order multipoles, higher-order clustering statistics (such as the bispectrum), and photometric probes such as weak lensing, will be essential to extract information on massive neutrinos, modified gravity and dark energy. Additionally, we show that the three codes used in our analysis, namely PBJ Pybird and MG-Copter exhibit sub-percent agreement for $k Mpc $ across all the models. This consistency underscores their value as reliable tools.

Palynostratigraphy of the Mississippian-Pennsylvanian boundary in the Tyler Formation, Williston Basin, USA: Implications for organic matter-rich source rocks and paleoenvironmental reconstruction

The Carboniferous was a period of intense environmental perturbations, climate changes between greenhouse and icehouse, eustatic sea level change, and accumulation of organic carbon-rich sediments. At this time, the Tyler Formation was deposited in the midcontinent USA. A detailed palynological analysis of the Tyler Formation revealed a highly diverse assemblage of spores with minor pollen content, represented by 100 species belonging to 51 genera. Stratigraphically constrained spores and pollen grains were used to construct three interval zones of middle-late Chesterian (late Viséan-Serpukhovian) to early Morrowan (middle Bashkirian) ages. The stratigraphic position of the Mississippian-Pennsylvanian boundary was determined in the lower Tyler Formation based on the last appearance of typical forms of the late Chesterian, including Tripartites vetustus, Knoxisporites triradiatus, Knoxisporites stephanephorus, Densosporites diatretus, and Schopfipollenites acadiensis, compared to the first appearance of early Morrowan Crassispora kosankei, Cirratriradites saturnii, Radiizonates aligerens, and Raistrickia saetosa. Palynofacies analysis and statistical clustering of the Tyler Formation showed three palynofacies assemblages. PFA-1 showed moderate relative abundances of phytoclasts and AOM, suggesting deposition close to fluvio-deltaic and shallow marine environments, while PFA-2 exhibited high abundances of phytoclasts, mostly of opaque wood, reflecting deposition in active river-dominated delta plains. PFA-3 showed the highest abundances of AOM, suggesting deposition in a shallow marine environment. Organic petrography and geochemistry data indicate that the Tyler Formation is one of the best source rock intervals throughout the midcontinent USA. Based on organic matter richness, the Tyler Formation is subdivided into three groups. The first group has TOC contents higher than 10 wt% of kerogen Types III, mixed II/III, and II with excellent hydrocarbon generation potential. The second group has TOC content in the range of 2–10 wt% of kerogen Types III, mixed II/III, and II, and fair to excellent hydrocarbon generation potential. The third group shows organic matter richness with TOC content below 2 wt% with good organic matter richness and kerogen Types III to IV, and poor to fair hydrocarbon generation potential. The organic matter thermal maturity is evaluated based on Tmax and VRo% values, suggesting that all samples of Tyler Formation are in the early to late stages of the oil window. However, care should be considered when assessing a mature source rock because the kerogen typing and generation is based on present-day TOC, S2, and HI rather than their original values.

Atomistic insights into argon clusters and nucleation dynamics

Accurate predictions of nucleation and atomic-level estimates of cluster properties in gas-phase chemical physics have proven challenging. These challenges arise from two primary sources: finite-size effects associated with nanoscopic particles and the emergence of non-standard thermodynamics, particularly at elevated temperatures. This study reexamines the formation of argon clusters using established methodologies such as atomistic simulations, configurational sampling, and statistical thermochemistry. To enhance the representation of condensed-phase argon, we employ an ab initio-based two-body potential, complemented by a three-body Axilrod–Teller potential. Additionally, we address the impact of anharmonicities on cluster stabilities using a recently developed extension to the standard statistical cluster model. The employed anharmonic model is rigorously benchmarked against molecular dynamics simulations. The subsequent analysis demonstrates a robust and consistent agreement between our model and experimental data. Our analysis covers nearly every experimental data point collected between 1971 and 2010, offering valuable insights into the predictive capabilities of the model. Moreover, in contrast to previous studies, our findings indicate that individual measurements are consistently in alignment with each other.

Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors

Maintaining optimal temperatures in the critical parts of an induction traction motor is crucial for railway propulsion systems. A reduced-order lumped-parameter thermal network (LPTN) model enables computably inexpensive, accurate temperature estimation; however, it requires empirically based parameter estimation exercises. The calibration process is typically performed in labs in a controlled experimental setting, which is associated with a lot of supervised human efforts. However, the exploration of machine learning (ML) techniques in varied domains has enabled the model parameterization in the drive system outside the laboratory settings. This paper presents an innovative use of a multi-agent reinforcement learning (MARL) approach for the parametrization of an LPTN model. First, a set of reinforcement learning agents are trained to estimate the optimized thermal parameters using the simulated data in several driving cycles (DCs). The selection of a reinforcement learning agent and the level of neurons in the RL model is made based on variability of the driving cycle data. Furthermore, transfer learning is performed on a new driving cycle data collected on the measurement setup. Statistical analysis and clustering techniques are proposed for the selection of an RL agent that has been pre-trained on the historical data. It is established that by synergizing within reinforcement learning techniques, it is possible to refine and adjust the RL learning models to effectively capture the complexities of thermal dynamics. The proposed MARL framework shows its capability to accurately reflect the motor’s thermal behavior under various driving conditions. The transfer learning usage in the proposed approach could yield significant improvement in the accuracy of temperature prediction in the new driving cycles data. This approach is proposed with the aim of developing more adaptive and efficient thermal management strategies for railway propulsion systems.

Layout Characteristics and Design Principles of Atrium Integrated Space

With the development of China's cruise tourism industry, it is necessary to study the design and construction of cruise ships, especially the central atrium, which is an important part of public area design and represents the quality and characteristics of the entire ship's design. However, compared with Europe, China's research foundation for large-scale cruise ship design is still lacking. Therefore, studying the spatial layout characteristics of the central atrium is of great significance for improving China's cruise industry. This article focuses on the design principles and characteristics of integrated space layout in the central atrium of cruise ships. By applying basic theories from architecture, shipbuilding engineering, aesthetics, international maritime safety conventions and technology to analyzing representative central atria from 25 typical domestic and foreign cruise brands through data collection and research methods including mathematical statistics methodology, clustering methodology and cases research methodology. Through comparison and generalization of the cases, the study eventually summarizes six overall principles and four structural principles for designing integrated space in the central atrium of a cruise ship. The six overall principles include safety, rationality, applicability, artistry, coordination and technique-economics. The four structural principles include clear space circulation, natural spatial connection, theme coordination & unification and prominent key levels. This study provides insights into designing integrated space in the central atriums of cruise ships that hopefully can be applied in improving China's cruise design work while also supporting guidance for future related fields. Keywords: central atrium of cruise ship, integrated space, design principles, functional layout, decoration style