Reveal Complex Patterns Research Articles

Prediction of manhole-cover skid-resistance performance using decision trees and regression analysis

This study conducts skid-resistance coefficient tests on 3,600 manhole covers and collects data on various influencing factors, such as surface conditions, pattern types, starting zones, turning zones, usage duration, wheel track locations, and surrounding traffic volume. A preliminary classification analysis of the data is performed using decision trees to identify key factors affecting manhole-cover skid resistance. Subsequently, a multivariate nonlinear regression model is applied to examine the impact of these factors on the target variables. By integrating decision trees and nonlinear regression in a hybrid modeling approach, the study aims to reveal complex patterns in the data and generate reliable predictive results. The decision-tree analysis using non-continuous variables reveals that manhole covers located at turns and starting zones, and those with worn surfaces are more likely to fail to meet safety standards for skid resistance. The multivariate nonlinear regression model, which compares the predicted values against the actual British Pendulum Number (BPN) test results, achieves an accuracy of 82.9%.

Classifying offensive language in Arabic: a novel taxonomy and dataset

Abstract This paper presents a streamlined taxonomy for categorizing offensive language in Arabic, specifically Modern Standard Arabic (MSA) and the Levantine dialect. Addressing a gap in the existing literature, which has mainly focused on Indo-European languages, our taxonomy divides offensive language into seven levels (six explicit and one implicit). We adapted our framework from the simplified offensive language (SOL) taxonomy by (Lewandowska-Tomaszczyk, Barbara, Slavko Žitnik, Anna Bączkowska, Chaya Liebeskind, Jelena Mitrovic & Giedre Valunaite Oleškeviciente. 2021a. Lod-connected offensive language ontology and tagset enrichment. In Shubert R. Carvalho & Renato R. Souza (eds.), Proceedings of the workshops and tutorials held at ldk 2021 co-located with the 3rd language, data and knowledge conference, Vol. 3064, 135–150. CEUR Workshop Proceedings), customizing it to reflect the unique linguistic and cultural nuances of Arabic. To validate this taxonomy, we created a new dataset from various social media platforms, primarily focusing on Twitter. This dataset was manually curated by human annotators and is described in detail within the paper, serving as both a validation tool for our taxonomy and a foundation for future research on offensive language detection in Arabic. Initial analysis of the dataset reveals complex patterns of offensive expressions in MSA and Levantine Arabic, underscoring the need to account for linguistic and cultural variations in studying online abuse. Our taxonomy and dataset are vital for advancing research in Arabic sociocultural studies, natural language processing, and linguistic analysis, and contribute to the study of low-resource languages.

A Multiscale Perspective on Chromatin Architecture through Polymer Physics.

The spatial organization of chromatin within the eukaryotic nucleus is critical in regulating key cellular functions, such as gene expression, and its disruption can lead to disease. Advances in experimental techniques, such as Hi-C and microscopy, have significantly enhanced our understanding of chromatin's intricate and dynamic architecture, revealing complex patterns of interaction at multiple scales. Along with experimental methods, physics-based computational models, including polymer phase separation and loop-extrusion mechanisms, have been developed to explain chromatin structure in a principled manner. Here, we illustrate genome-wide applications of these models, highlighting their ability to predict chromatin contacts across different scales and to spread light on the underlying molecular determinants. Additionally, we discuss how these models provide a framework for understanding alterations in chromosome folding associated with disease states, such as SARS-CoV-2 infection and pathogenic structural variants, providing valuable insights into the role of chromatin architecture in health and disease.

NIMG-60. SUBTYPING GLIOBLASTOMA FROM JOINT CLUSTERING OF IMAGING AND GENOMIC DATA

Abstract PURPOSE To comprehensively unravel the heterogeneity of glioblastoma, we developed a subtyping method using integrated imaging and genomic data. METHODS We assembled a retrospective cohort of 571 IDH-wildtype glioblastoma patients, obtaining pre-operative multi-parametric MRI (T1, T1-GD, T2, T2-FLAIR, DSC, DTI) scans (available for 462 patients) and targeted next-generation sequencing (NGS) data (available for 355 patients). We extracted 971 radiomic features from these MRI scans, selecting 12 significant dimensions through L21-norm minimization guided by 13 key driver genes from the five most frequently altered pathways in glioblastoma compared to healthy controls (RB1, P53, MAPK, PI3K, and RTK). Subtypes were identified using a joint clustering approach that integrated radiomics and genomics. RESULTS Our method identified three glioblastoma subtypes with distinct risk levels: high-risk (subtype 1), medium-risk (subtype 2), and low-risk (subtype 3), each characterized by their differential overall survival outcomes (Kaplan-Meier analysis, p < 0.05). The intensities of axial diffusivity in enhancing tumor regions and radial diffusivity in non-enhancing cores increased across three subtypes. All subtypes shared a common tendency for more frequent mutation co-occurrence pattern in [TP53,RB1] but exhibited unique molecular characteristics. For example, subtype 1 displayed a relatively high frequency of co-occurring mutations in [TP53,KDR] and [NOTCH2,MDM4]; subtype 2 had six co-occurring pairs [TP53,KDR], [PTPN11,NF1], [RB1,FUBP1], [PTEN,CIC], [PTEN,KRAS], and [NOTCH2,CDKN2A]; subtype 3 showed six co-occurring pairs [PTPN11,NF1], [PDGFRA,NF1], [NTRK1,NOTCH2], [MET,ATRX], [KRAS,KDR], and [FGFR2,DNMT3A] plus four significant patterns of mutual exclusivity in [TP53,PTPN11], [TP53,EGFR], [EGFR,RB1], and [EGFR,NF1]. CONCLUSION Our study discovered distinct glioblastoma subtypes, revealing complex patterns and structures across multiple data modalities without relying on prior clinical assumptions. This is promising to help enhance the precision of diagnosis and treatment of glioblastoma.

Subsurface basement structures of the Usangu basin, East African rift system, with implications for basin structural configuration and hydrocarbon potential

The Usangu basin is a rift basin developed along the Eastern arm of the East African rift system trending in the NE-SW direction. Although the general structures of the basin have been well studied, the structural configuration of the basin and the spatial and depth variations of sediment thickness are still not well known. This study investigates the structures related basement configuration and the variation of sediment thickness within the basin using the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), aeromagnetic and gravity data. Results from DEM data indicate a few lineaments on the basin flanks representing the Usangu and Chimala border faults with no structures in the central part of the basin. The aeromagnetic and gravity data highlight three sets of normal and strike-slip faults, most of which trend NE-SW and others NNE-SSW, while a few trends WNW-ESE or NW-SE. Structures on the southwest of the basin reveal complex patterns attributed to the concentration of important tectonic and seismic activities in the study area. The Euler deconvolution and gravity models used to calculate the depth to the basement show that the basement is shallow in the north and south to southwest, and the basin deepens in the northeastern, northwestern and western parts. The findings also reveal that the basin has two grabens, troughs, depression and intrabasinal basement trending in the same direction as the basin configuration. The general thickness of sediments filling the basin ranges from 3 to 4.5 km, with the maximum accumulation of sediments reaching up to 4.8 km in the two depocenters at the south and southwest of the basin. The depth range of the sediments obtained implies that the basin has potential for hydrocarbon exploration with the possibility of natural gas occurrence.

Enhancing the Predictive Power of Machine Learning Models through a Chemical Space Complementary DEL Screening Strategy.

DNA-encoded library (DEL) technology is an effective method for small molecule drug discovery, enabling high-throughput screening against target proteins. While DEL screening produces extensive data, it can reveal complex patterns not easily recognized by human analysis. Lead compounds from DEL screens often have higher molecular weights, posing challenges for drug development. This study refines traditional DELs by integrating alternative techniques like photocross-linking screening to enhance chemical diversity. Combining these methods improved predictive performance for small molecule identification models. Using this approach, we predicted active small molecules for BRD4 and p300, achieving hit rates of 26.7 and 35.7%. Notably, the identified compounds exhibit smaller molecular weights and better modification potential compared to traditional DEL molecules. This research demonstrates the synergy between DEL and AI technologies, enhancing drug discovery.

Computational Study of Pump Turbine Performance Operating at Off-Design Condition-Part I: Vortex Rope Dynamic Effects

As global power demand increases, hydropower plants often must operate beyond their optimal efficiency to meet grid requirements, leading to unstable, high-swirling flows under various load conditions that can significantly shorten the lifespan of turbine components. This paper presents an in-depth computational study on the performance and dynamics of a pump-turbine operating under 80% partial load, focusing on the formation and impact of vortex ropes. Large Eddy Simulation (LES) was utilized to model the turbulent flow, revealing complex patterns and significant pressure fluctuations. A pronounced straight vortex rope was identified in the draft tube, maintaining its trajectory and core size consistently, profoundly affecting flow characteristics. Pressure fluctuations were observed at various cross-sectional planes, with peaks and troughs primarily near the runner, indicating areas prone to instability. The standard deviation of pressure fluctuations ranged from 4.51 to 5.26 along the draft tube wall and 4.27 to 4.97 along the axial center, highlighting significant unsteady flow. Moreover, the frequency corresponding to the highest amplitude in pressure coefficient spectrographs remained consistent at approximately 9.93 to 9.95, emphasizing the persistent influence of vortex rope dynamics. These dynamics affected power generation, which was approximately 29.1 kW, with fluctuations accounting for about 3% of the total generated power, underscoring the critical impact of vortex rope formation on the performance and operational stability of pump-turbines under off-design conditions. This study provides essential insights vital for enhancing the design and operational strategies of these turbines, ensuring more efficient and reliable energy production in the face of increasing power demands.

Integrated physiological response by four species of Rhodophyta to submarine groundwater discharge reveals complex patterns among closely-related species

Algal physiological ecology on submarine groundwater discharge (SGD) influenced reefs is likely shaped by intermittent, tidally-driven estuarine conditions that occur with SGD fluxes of fresh-to-brackish groundwater from the subterranean estuary to reef ecosystems. SGD is a common inconspicuous feature worldwide on reefs of basaltic high islands and continental margins. Yet, SGD-driven dynamics of algal physiology are not well understood. To understand how invasive species have physiologically outcompeted native species on many SGD-influenced reefs, physiology in tissue water potential (TWP) regulation, photosynthesis, nitrogen storage, and cellular anatomy were measured across a gradient of SGD-influence, for four Rhodophyte species. Compared with non-SGD conditions, SGD was associated with higher TWP, larger medulla cells with thinner walls, and thinner cortical cell walls for two invasives, Gracilaria salicornia and Acanthophora spicifera, higher photosynthetic rates in G. salicornia, greater nitrogen concentration for A. spicifera and G. salicornia, and increased δ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta$$\\end{document}15N ratios for A. spicifera, G. salicornia, and native Laurencia dendroidea. Distinct physiological strategies were measured for the two invasive species across the gradient of SGD-influence, and for L. dendroidea and Gracilaria perplexa offshore. This study illuminates species-specific physiological response, and how introduced opportunistic species may outcompete native species under conditions of SGD.

Deep embedded clustering: Delineating multivariate geochemical anomalies in the Feizabad region

The objective of this research was to identify geochemical anomalies associated with mineralization in the Feizabad region using deep embedded clustering (DEC). Two approaches were employed: using all geochemical elements (non-selective) and using factor scores (selective) to construct input data. Results showed that the non-selective approach was highly successful in identifying anomalies, not only confirming known mineral occurrences (KMO) but also introducing new exploration targets. Additionally, model evaluation using the success rate curve (SRC) confirmed its high predictive power. This method is particularly useful in green field. The DEC model, by combining deep neural networks (DNN) and clustering algorithms, reveals complex patterns in geochemical data. This algorithm increases clustering accuracy through automatic data clustering, gradual input of large data, and compatibility with imbalanced data. Furthermore, by simultaneously integrating feature learning and clustering, it provides higher optimization and accuracy. The anomalies identified by the DEC model are consistent with granodiorite units in the region and may indicate the potential for mineralization in the study area. Additionally, the identified targets are associated with granitoids, hydrothermal alteration zones, and intrusive rocks.

Os efeitos do aquecimento global na ampliação das áreas desertificadas: um estudo acerca da geografia econômica dos municípios do sertão da Bahia e possíveis mitigações da teoria da tríplice hélice

Global warming, caused by the burning of fossil fuels and deforestation, is triggering significant changes in the Earth's environment. Phenomena like El Niño and La Niña, analyzed through modeling techniques, reveal complex patterns affecting various time scales. In regions with fragile soils, the trend is rapid progression towards scenarios of desertification and abandonment in the coming years. The migration of rural populations from these areas to urban centers, abandoning subsistence economies, may increase poverty in cities, a movement comparable to the Industrial Revolution's exodus, already visible in economic and social data. This article theoretically reviews topics related to desertification resulting from climate change, aiming to identify the social and economic consequences of this phenomenon in the region. Additionally, it explores possible actions to mitigate problems caused by global warming. In conclusion, a solution based on the Triple Helix Theory, which promotes interaction between government, universities, and businesses, will be presented. This approach could be crucial in mitigating the impacts of desertification, fostering sustainable development and innovation in the affected region.

Causal discovery reveals complex patterns of drought-induced displacement

The increasing frequency and severity of droughts present a significant risk to vulnerable regions of the globe, potentially leading to substantial human displacement in extreme situations. Drought-induced displacement is a complex and multifaceted issue that can perpetuate cycles of poverty, exacerbate food and water scarcity, and reinforce socio-economic inequalities. However, our understanding of human mobility in drought scenarios is currently limited, inhibiting accurate predictions and effective policy responses. Drought-induced displacement is driven by numerous factors and identifying its key drivers, causal-effect lags, and consequential effects is often challenging, typically relying on mechanistic models and qualitative assumptions. This paper presents a novel, data-driven methodology, grounded in causal discovery, to retrieve the drivers of drought-induced displacement within Somalia from 2016 to 2023. Our model exposes the intertwined vulnerabilities and the leading times that connect drought impacts, water and food security systems along with episodes of violent conflict, emphasizing that causal mechanisms change across districts. These findings pave the way for the development of algorithms with the ability to learn from human mobility data, enhancing anticipatory action, policy formulation, and humanitarian aid.

Linking Multi-Dimensional Religiosity in Childhood and Later Adulthood: Implications for Later Life Health.

This study examines religiosity patterns across childhood and later adulthood and their associations with later-life health using an experimental module from the 2016 Health and Retirement Study (N = 1649; Mean Age = 64.0). Latent class analysis is used to categorize individuals by commonalities in religious attendance, religious identity, and spiritual identity. Cross-sectional and longitudinal associations are then explored using probable depression, disability, and mortality as health indicators. Results reveal complex patterns, often characterized by declining attendance and fluctuating identity. Relationships with health appear stronger in cross-sectional analyses, suggesting that some associations may be non-causal. Individuals with consistently strong religiosity show significantly better psychological health compared to their relatively non-religious counterparts. Moreover, the absence of religiosity in later adulthood is associated with an increased risk of mortality. Overall, the findings support the promotion of religiosity whilst acknowledging individual variations and highlighting the need for more individualistic approaches to the study of religion and health.

Moderating the Impact of E-Commerce Challenges on Adoption: Examining the Moderating Role of General Business Experience in Western, Sierra Leone SMES

Purposes The study seeks to examine how SMEs in the Western Area are impacted by e-commerce adoption challenges. Moderating role of general business experience. Research methodology Descriptive and inferential statistics were used in the study's quantitative research methodology to reveal complex patterns and relationships. Respondents to a web-based survey using respondent-driven sampling included consultants, IT professionals, and business owners. Underlying patterns and model structure were identified using the Partial Least Square (SEM PLS) method of structural equation modeling in conjunction with path analysis and exploratory factor analysis. Conclusion The study reveals a strong positive relationship between e-commerce challenges and adoption rates, with higher challenge levels leading to higher adoption rates. Improved business experiences moderate the impact of e-commerce obstacles. The study emphasizes the importance of addressing e-commerce challenges and utilizing business experience to promote adoption among SMEs in Western Sierra Leone. Policy implication This paper highlights the need for improved infrastructure, technology accessibility, and digital literacy for SMEs in Western Sierra Leone, recommending specialized training, evidence-based policies, and a comprehensive strategy.

Nonstandard employment and support for older parents in urban China

The recent rise of nonstandard employment worldwide has significant implications for intergenerational transfers but has been neglected in existing research on the relationships between adult children's employment experiences and parental support. Utilizing data from the urban sample of the China Family Panel Studies (CFPS), we examine how nonstandard employment is related to adult children's money (financial) and time (instrumental and emotional) support for parents. Our analyses among four groups of adult children reveal complex patterns in the nonstandard employment–parental support relationship by children's gender, coresidence status, and educational attainment. Results suggest nonstandard employment primarily undermines the financial ability to support parents among non-coresident daughters, but nonstandard jobs have little to do with daughters’ time caring for parents regardless of their coresident status. Meanwhile, nonstandard employment appears to be weakly related to help for parents in the short term among all adult sons. Disadvantages associated with nonstandard employment in upstream parental support, such as a “less money, less time” situation, are concentrated among less-educated adult children (mainly daughters) who live apart from their parents.

Bayesian smoothing for time-varying extremal dependence

Abstract We propose a Bayesian time-varying model that learns about the dynamics governing joint extreme values over time. Our model relies on dual measures of time-varying extremal dependence, that are modelled via a suitable class of generalized linear models conditional on a large threshold. The simulation study indicates that the proposed methods perform well in a variety of scenarios. The application of the proposed methods to some of the world’s most important stock markets reveals complex patterns of extremal dependence over the last 30 years, including passages from asymptotic dependence to asymptotic independence.

Review on Panoptic Segmentation of Images with Text-to-Image and Image-to-Image Diffusion Models

The strategic integration of panoptic segmentation and diffusion models within a comprehensive project is explored in detail in this review study, with a focus on the respective contributions and usefulness of each approach. Selected due to their capacity for dynamic process analysis, the diffusion models are expected to reveal complex patterns and behaviors in a variety of applications, providing fine-grained understanding of the temporal evolution of things. In addition, the project's analytical depth is enhanced by the intentional inclusion of panoptic segmentation, which highlights its critical role in obtaining total scene interpretation. Results are expected that will demonstrate the project's usefulness in reconstructing distorted images and classifying them into labels in order to improve comprehension for average people interacting with complex visual data. A comprehensive analysis of the methodology, applications, difficulties, and potential applications is included in the paper's conclusion, providing insight into the strategic combination of diffusion models with panoptic segmentation. The integration of perspectives from many scholarly articles enhances our comprehension and offers significant insights on the intentional use of these models. This collaborative method promotes continual learning and improvement in the use of diffusion models and panoptic segmentation across multiple disciplines, while also advancing grasp of dynamic processes and scene interpretation. We also in the end tend to provide a detailed and systematic review on all the current models and their papers that are published to provide a clear observation on where we stand on image generation and segmentation

Tracing the Origin and Migration of Humans— Significant Progress in Paleoanthropological and Paleolithic Fields Made by IVPP

Over the past 15 years, the Paleoanthropological and Paleolithic team from the Institute of Vertebrate Paleontology and Paleoanthropology (IVPP), Chinese Academy of Sciences, has made significant progress in understanding modern human origins and evolution through extensive fieldwork, fossil and artifact discoveries, and cutting-edge ancient molecular analysis. The new fossil findings from Hualongdong, Xuchang, the first report on Harbin, and the reevaluations of previously found fossils like Xujiayao revealed high population diversity in East Asia during the late Middle Pleistocene, implying multiple evolutionary lineages linked to modern humans, Neanderthals, or Denisovans. Different species names were proposed for these unique fossils. Discoveries from Zhiren Cave and Fuyan Cave refreshed our knowledge of modern human emergence in China, sparking debates between the “Recent African Origin” and “Multiregional Evolution” hypothesis. Additionally, new archaeological evidence from Nwya Devu and Xiamabei highlighted human adaptations to extreme climates and technological innovations. Genomic studies further revealed complex patterns of modern human dispersal, admixture, and adaptation in China, especially in the past 40,000 years. The first mitochondrial DNA of Denisovan outside Denisova Cave was successfully extracted from the sediments on the Plateau, suggesting the long-term occupation of this population in this area.

Citizen science data reveal altitudinal movement and seasonal ecosystem use by hummingbirds in the Andes Mountains

Ensuring connectivity is crucial to protect landscapes but it requires knowledge about how animals use ecosystems throughout the year. However, animal movements remain largely unknown in biodiversity hotspots, even for species that fulfill key ecological roles, as is the case of hummingbirds in the Andes. In the complex topography of mountain slopes, movement of these avian pollinators may occur either between habitat patches with asynchronous plant blooms or across ecosystems that are located within the same elevation bands or along altitudinal gradients. Here, we used two decades (2000–2020) of records from citizen science data and boosted regression trees to predict monthly distributions for 55 hummingbird species in the Andes. We identified shifts in altitudinal distribution between contiguous months and calculated changes in the proportion of predicted distributions occupied by ecosystem types. Our findings reveal substantial altitudinal movement and differences in the proportion of ecosystem types utilized throughout the year that had not been previously reported for several species. Yet the magnitude of altitudinal and ecosystem shifts varies between hummingbird clades, and in some cases changes in the proportion of ecosystem types within estimated distributions occurs with little variation in altitude. All ecosystems across the Andes show temporal changes in hummingbird occurrence, but these are higher in natural landscapes compared to croplands or urban areas. Finally, we used phylogenetic logistic regression to test whether altitudinal and ecosystem shifts affect population trends. We found that higher ecosystem seasonality is more strongly associated with decreasing populations in comparison to altitudinal shifts. Altogether, our study reveals complex patterns of movement in hummingbirds and highlights the importance of ecological connectivity across different ecosystem types. More generally, it demonstrates the opportunity of using citizen science data to increase understanding about species' seasonal occurrences, so that landscapes can be better managed to protect animal movement.Keywords: boosted regression trees, eBird, ecological connectivity, species distribution models

Environmental Consequences in the Neighbourhood of Rapid Unplanned Urbanisation in Bangalore City

The knowledge of landscape dynamics aids in evolving strategies for the prudent management of natural resources to sustain ecosystem services. The availability of spatiotemporal remote sensing data with advancements in artificial intelligence (AI) and machine learning (ML) algorithms has aided in assessing the ecological status in urban environments, markedly revealing complex patterns and interactions. The current communication presents landscape dynamics in the Bengaluru Urban district from 1973 to 2022 using a supervised machine learning technique based on the Random Forest algorithm with temporal Landsat data, which showed a 51.86% increase in the built-up area and a 26.28% decrease in the green cover. Rapid unplanned urbanization after globalization and the opening up of Indian markets (in Bengaluru city) has witnessed erosion in the natural surface (waterbodies and green cover) in the neighborhood, which has been impacting the health of the environment and people. Computation of fragmentation indices showed a decline of the native green cover by 177.2 sq. km. in the southern part of the district. Likely land use changes are predicted using the Cellular Automata Markov model considering the base case scenario. The analyses revealed a further possible increase in built-up to 1536.08 sq. km, a decrease in green cover by 14.32 sq. km by 2038, and the disappearance of water bodies, which highlights the need to mitigate the adverse impacts of land use changes through planned urbanization considering the environment and livelihood of local communities. The decline of heat sinks such as water bodies and green cover would contribute to an increase in the land surface temperature (LST), which would affect the microclimate of Bengaluru, highlighting the need to sustain ecosystem services to support the livelihood of local communities. Understanding the ecological significance of diverse habitat characteristics of the urban region and the prediction of likely changes in a high degree of spatial heterogeneity would assist the decision-makers in framing appropriate policies.

Einkorn genomics sheds light on history of the oldest domesticated wheat

Einkorn (Triticum monococcum) was the first domesticated wheat species, and was central to the birth of agriculture and the Neolithic Revolution in the Fertile Crescent around 10,000 years ago1,2. Here we generate and analyse 5.2-Gb genome assemblies for wild and domesticated einkorn, including completely assembled centromeres. Einkorn centromeres are highly dynamic, showing evidence of ancient and recent centromere shifts caused by structural rearrangements. Whole-genome sequencing analysis of a diversity panel uncovered the population structure and evolutionary history of einkorn, revealing complex patterns of hybridizations and introgressions after the dispersal of domesticated einkorn from the Fertile Crescent. We also show that around 1% of the modern bread wheat (Triticum aestivum) A subgenome originates from einkorn. These resources and findings highlight the history of einkorn evolution and provide a basis to accelerate the genomics-assisted improvement of einkorn and bread wheat.