Complex Region Research Articles

Aridification and major geotectonic landscape change shaped an extraordinary species radiation across a world's extreme elevational gradient.

Understanding the profound influence of climatic and tectonic histories on adaptation and speciation is a crucial focus in biology research. While voyages like Humboldt's expedition shaped our understanding of adaptation, the origin of current biodiversity remains unclear - whether it arose in situ or through dispersal from analogous habitats. Situated in the geologically complex Australopacific region, our study focuses on Limbodessus diving beetles (Dytiscidae), a diverse genus distributed from underground aquifers in Western Australia to alpine meadows in New Guinea. Using low-coverage whole-genome sequencing, we established a time-calibrated phylogenetic tree, elucidating Limbodessus' origin in the mid-late Miocene, most likely in the Sahul continent (i.e., Australia and New Guinea) and western Pacific archipelagos. Our results provide evidence for parallel colonization and speciation at extreme altitudinal ends, driven by aridification in Australia, influencing subterranean colonization, and in situ diversification of alpine taxa by passive-uplifting of local biota in New Guinea. Furthermore, our findings highlight instances of subterranean speciation in isolated underground aquifers, marked by recurrent independent colonizations of this habitat.

Feature Intensification Using Perception-Guided Regional Classification for Remote Sensing Image Super-Resolution

In recent years, super-resolution technology has gained widespread attention in the field of remote sensing. Despite advancements, current methods often employ uniform reconstruction techniques across entire remote sensing images, neglecting the inherent variability in spatial frequency distributions, particularly the distinction between high-frequency texture regions and smoother areas, leading to computational inefficiency, which introduces redundant computations and fails to optimize the reconstruction process for regions of higher complexity. To address these issues, we propose the Perception-guided Classification Feature Intensification (PCFI) network. PCFI integrates two key components: a compressed sensing classifier that optimizes speed and performance, and a deep texture interaction fusion module that enhances content interaction and detail extraction. This network mitigates the tendency of Transformers to favor global information over local details, achieving improved image information integration through residual connections across windows. Furthermore, a classifier is employed to segment sub-image blocks prior to super-resolution, enabling efficient large-scale processing. The experimental results on the AID dataset indicate that PCFI achieves state-of-the-art performance, with a PSNR of 30.87 dB and an SSIM of 0.8131, while also delivering a 4.33% improvement in processing speed compared to the second-best method.

Genome-wide methylation and transcriptome differential analysis of skeletal muscle in broilers with valgus-varus deformity

ABSTRACT 1. Valgus-varus deformity (VVD) is a disease that severely affects leg function in broilers and for which there is no effective control method current available. Although DNA methylation has an important impact on most physiological and pathological processes, its involvement in skeletal muscle growth and development in VVD broilers is unknown. In this study, genome-wide DNA methylation was analysed in VVD-affected and normal broilers using whole genome resulphite sequencing. 2. The results showed that in the cytosine-phosphoric acid-guanine (CG) sequence environment there was a methylation rate of about 55% and 4,265 differentially methylated regions (DMRs) were found in the CG. Of these, 550 were located in the promoter, 547 in the exon region, and 1,718 in the intron region. 3. All differentially methylated genes (DMGs) were analysed for enrichment of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The GO was enriched in pathways related to protein degradation such as proteasome complex, endopeptidase complex and extracellular region. The KEGG pathways were enriched in signalling pathways related to protein degradation and catabolism such as proteasome, nitrogen metabolism, adherens junction and alanine. 4. Protein interactions analysis revealed that FOS, MYL9, and FRAS1 had a high degree of interactions, in which the DNA methylation level of the MYL9 promoter region was negatively correlated with mRNA expression level. Further studies showed that 5-azacytidine (5-AzaC) inhibited DNMT1 and DNMT3A gene expression and promoted MYL9 expression. 5. This study systematically investigated overall DNA methylation patterns in the leg muscle of VVD and normal broilers. It screened common differential genes in conjunction with transcriptomic data to further identify genes associated with muscle growth and development. This study provides new insights to better understand the pathogenesis of VVD from an epigenetic perspective.

InSAR phase gradient reveals fault‐zone controls on the spatial distribution of slow‐moving landslides in the active orogenic region of Hazara‐Kashmir, Pakistan

AbstractSlow‐moving landslides play important roles in the landscape evolution and hazards planning. Studies along some strike‐slip faults have shown that the geological structures and bed‐rock lithology significantly contribute the distribution of slow‐moving landslides. However, controls on the distribution of slow‐moving landslides are poorly constrained in active orogenic regions, hindering our understanding of its role in the rapid orogenic process. The Hazara Kashmir Syntaxis in Pakistan is such a prominent geological structure of lesser Himalaya, where the inventory of slow‐moving landslides is scarce. Here, we attempt the interferometric synthetic aperture radar phase‐gradient stacking coupled with a deep‐learning system to provide the first slow‐moving landslides inventory (1066 presently active landslides, 2016–2023) in the Hazara‐Kashmir region. Along with optical imagery and field investigations, we analyse the impacts of fault structures, bed‐rock lithology, topography along with spatial distribution of earthquake and precipitation on the distribution of these slow‐moving landslides. We find that 33% of the detected slow‐moving landslides are distributed within 1000 m to active faults, and show a decreasing trend moving away from fault zones. This pattern strongly suggests that the active thrusting faults in this region significantly controls the distribution of slow‐moving landslides, while topography and precipitation show less impacts. Our study reveals the spatial distribution of slow‐moving landslides in a tectonic complex region with rapid orogenic process, and thus shows potential implications in geomorphology modelling and hazards evaluation for many less‐monitored, contemporary uplifting high‐mountain regions.

Solar active region evolution and imminent flaring activity through color-coded visualization of photospheric vector magnetograms

Context. The emergence of magnetic flux, its transition to complex configurations, and the pre-eruptive state of active regions are probed using photospheric magnetograms. Aims. Our aim is to pinpoint different evolutionary stages in emerging active regions, explore their differences, and produce parameters that could advance flare prediction using color-coded maps of the photospheric magnetic field. Methods. The three components of the photospheric magnetic field vector are combined to create color-combined magnetograms (COCOMAGs). From these, the areas occupied by different color hues are extracted, creating appropriate time series (color curves). These COCOMAGs and color curves are used as proxies of the active region evolution and its complexity. Results. The morphology of COCOMAGs showcases typical features of active regions, such as sunspots, plages, and sheared polarity inversion lines. The color curves represent the area occupied by photospheric magnetic field of different orientation and contain information pertaining to the evolutionary stages of active regions. During emergence, most of the region area is dominated by horizontal or highly inclined magnetic field, which is gradually replaced by more vertical magnetic field. In complex regions, large parts are covered by highly inclined magnetic fields, appearing as abrupt color changes in COCOMAGs. The decay of a region is signified by a domination of vertical magnetic field, indicating a gradual relaxation of the magnetic field configuration. The color curves exhibit a varying degree of correlation with active region complexity. Particularly the red and magenta color curves, which represent strong, purely horizontal magnetic field, are good indicators of future flaring activity. Conclusions. Color-combined magnetograms facilitate a comprehensive view of the evolution of active regions and their complexity. They offer a framework for the treatment of complex observations and can be used in pattern recognition, feature extraction, and flare-prediction schemes.

Spatial distribution and migration characteristics of heavy metals at an abandoned industrial site in the Southwest of China

The rapid acceleration of global industrialization has rendered heavy metal contamination at abandoned industrial sites a severe challenge, particularly in geologically complex and fragile karst regions of Southwest China, posing significant threats to ecosystems and public health. However, existing research lacks a comprehensive understanding of the spatial distribution and migration mechanisms of heavy metals in this region. In this study, 523 soil samples and 30 groundwater samples were collected, and the pollution levels were systematically assessed using the Geo-Accumulation Index, Single Pollution Index, and Nemerow Integrated Pollution Index. Horizontal and vertical spatial heterogeneity was explored through Moran's I and Voronoi polygon analysis. Furthermore, 3D geological modeling and groundwater flow simulations were employed to investigate the influence of hydrogeological conditions on contaminant migration. The results indicate elevated concentrations of Cd, Hg, Pb, and As in the surface layer, with concentrations initially decreasing and then increasing with depth, likely due to the presence of discontinuous clay layers. Moran's I revealed significant clustering effects at depths of 0.2 meters and 4 meters, while Voronoi analysis confirmed vertical heterogeneity. This study provides a scientific basis for pollution assessment and targeted remediation in karst regions.

The role of mobile muds in basin form and fill in the tectonic and sedimentologically complex region of the Southeastern Margin of the Caribbean Plate, Trinidad and Tobago, West Indies

The southern portions of the Barbados Accretionary Prism and the eastern deepwater region of Trinidad and Tobago are a seafloor terrain marked by scattered mud volcanoes, mud walls, and mud canopies that rise several hundred meters above the modern seafloor. We use 3D seismic and recently drilled wells to examine this area, dividing it into three tectonomorphic provinces based on the nature of the mud architecture, the structuration, basin types developed, and sedimentary systems occupying the basins. Mud architectures become more massive as one moves northward toward the active tectonic prism. The mud wall in the far northern Mud Wall Province is classified as a mud canopy system, similar to those documented by workers in the North Sabah Pagasa Wedge, Borneo. The Trinidad mud canopy exhibits sutures in the mud mass margins (akin to salt sutures) that separate submasses likely composed of different materials and exhibiting different pressures and properties. These submasses and associated sutures are imageable using the sweetness attributes on the near-stack seismic volume. Anticlines in the isolated mud and distributive provinces, similar to those that are the dominant hydrocarbon-producing structures onshore and offshore Trinidad, show mobile mud cores and are associated with extensional and compressional faulting that provide pathways for hydrocarbon migration into the surrounding lutokinetic stratigraphy. A bottom simulating reflector associated with the base of the gas hydrate zone, extensive across the study area, may be a good indicator of fluid flux activity and subsurface pressure states in mud masses. It is important to use all the seismic data volumes (full, near, mid, and far seismic data stacks) to assess different aspects of these mud diapiric systems.

Evaluating the productivity of basement rock aquifers for sustainable groundwater development in sub-Sahara Africa using borehole pumping experiments and geophysical data

Ensuring all-year-round groundwater availability in the basement complex regions of sub-Sahara Africa requires careful combinations of strategies for exploration, development, and management of groundwater resources. The productivity of the basement aquifer of sub-Sahara Africa, SSA, was evaluated in terms of the volume of water produced per unit time, the aquifer transmissivity, hydraulic conductivity, specific capacity, and the thickness of the water-bearing zones using a suit of data from borehole pumping experiments, geophysical surveys, and lithologic logs from an area measuring 85.56 square kilometers in sub-Sahara Africa. Findings from the study have been used to classify the basement complex aquifers of sub-Sahara Africa into three categories: marginally productive, low productive, and moderately productive. Weathered and fractured zones were found to be bright spots for groundwater storage, and aquifer productivity correlates with topography and the thickness of the weather zones. Most of the boreholes located on the low topographic heights were found to have the highest groundwater discharge, transmissivity, and hydraulic conductivity. Further, the study showed that the popular models used for estimating aquifer properties are unsuitable for basement complex aquifers. The results of the models are exaggerated when compared to the pumping test results. Cases of dry holes or poorly performing wells in the basement complex terrain may be attributed to the exaggeration. Consequently, two new and novel quantitative models were proposed and tested for evaluating the productivity parameters of basement aquifers of SSA for optimal selection of sites for groundwater development. The aquifer productivity maps computed with the new quantitative models show striking similarities to those computed from the pumping tests results and confirmed the appropriateness of the new quantitative models. The appropriate pumping device, pump operation time, and installation depth were recommended for the boreholes producing groundwater from the three categories of aquifers identified in this study.

Two-Sample Mendelian Randomization Study Identifies Tissue-Dependent Risk Genes in Autoimmune Diseases

Autoimmune diseases are among the most prevalent diseases across the world with genetic and environmental factors that contribute to their etiology. Because the exact causes of autoimmune diseases are largely unknown, a Mendelian randomization (MR) approach is used here to examine the potential causal association between gene expression levels and disease risk across various tissues. Specifically, this study focuses on six autoimmune diseases including Crohn’s disease, ulcerative colitis, rheumatoid arthritis, multiple sclerosis, type 1 diabetes mellitus, and systemic lupus erythematosus. Several of these diseases are currently treatable with immunosuppressants that target specific genes, such as TNF-alpha, IL-23, CD20, and more. In this study, a two-sample MR analysis is performed with multitissue expression quantitative trait loci (eQTLs) and large-scale genome-wide association studies to investigate how gene expression can influence the risk of developing these diseases. Our results show that genes HLA-DQA1/2, HLA-DRB1/6, HLA-DQB2, C4A, CYP21A2, and HLA-DQB1-AS1 have a high causal effect across several diseases and tissues, and almost all of these findings originate from the major histocompatibility complex (MHC) region on Chromosome 6. Our findings support the current knowledge of genes associated with these diseases while also revealing novel genes that can be used for drug therapies in the future. Although several drug therapies currently exist to treat this selection of autoimmune diseases, we provide further insights into the main, common pathways responsible for autoimmune disease pathogenesis and discuss novel genes that lack research focus.

Dual-path aggregation transformer network for super-resolution with images occlusions and variability

While Transformer-based approaches have recently achieved notable success in super-resolution, their extensive computational requirements impede widespread practical adoption. High-resolution meteorological satellite cloud imagery is essential for weather analysis and forecasting. Enhancing image resolution through super-resolution techniques facilitates the accurate identification and localization of geographic features by meteorological systems. However, current super-resolution methods fail to restore the intricacies of cloud formations and complex regions fully. This research introduces a novel dual-path aggregation Transformer network (DPAT) tailored to enhance the super-resolution of meteorological satellite cloud images. The DPAT network adeptly captures cloud imagery's subtle details and textures, effectively addressing occlusions and the variability inherent in satellite imagery. It bolsters the model's ability to manage the complex attributes of cloud images through the introduction of the Dual-path Aggregation Self-Attention (DASA) mechanism and the Multi-scale Feature Aggregation Block (MFAB), thereby enhancing performance in processing intricate cloud features. The DASA mechanism synthesizes features across spatial, depth, and channel dimensions via a dual-path approach, thoroughly exploiting feature correlations. The MFAB, designed to supplant the multilayer perceptron, incorporates shift convolution and a multi-scale interaction block to augment feature information, compensating for the deficiency in local information absorption due to fixed receptive fields. Experimental outcomes indicate that DPAT delivers superior super-resolution outcomes. With a parameter count of only 32% of the Enhanced Deep Residual Network (EDSR) or 77% of the Image Restoration using Shift Window Transformer (SwinIR), DPAT matches SwinIR's performance on the satellite cloud dataset. Moreover, DPAT balances accuracy and parameter economy across various datasets. This technology is expected to improve image super-resolution capabilities in multiple fields such as human action recognition and industrial recognition, and indirectly improve the accuracy of image perception tasks.

Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection‐Permitting Regional Climate Model Simulations in a Complex Topographical Region

ABSTRACTIn mountainous areas, accurately estimating the long‐term climatology of seasonal precipitations is challenging due to the lack of high‐altitude rain gauges and the complexity of the topography. This study addresses these challenges by interpolating seasonal precipitation data from 3189 rain gauges across France over the 1982–2018 period, using geographical coordinates, and altitude. In this study, an additional predictor is provided from simulations of a Convection‐Permitting Regional Climate Model (CP‐RCM). The simulations are averaged to obtain seasonal precipitation climatology, which helps capture the relationship between topography and long‐term seasonal precipitation. Geostatistical and machine learning models are evaluated within a cross‐validation framework to determine the most appropriate approach to generate seasonal precipitation reference fields. Results indicate that the best model uses a machine learning approach to interpolate the ratio between long‐term seasonal precipitation from observations and CP‐RCM simulations. This method successfully reproduces both the mean and variance of observed data, and slightly outperforms the best geostatistical model. Moreover, incorporating the CP‐RCM outputs as an explanatory variable significantly improves interpolation accuracy and altitude extrapolation, especially when the rain gauge density is low. These results imply that the commonly used altitude‐precipitation relationship may be insufficient to derive seasonal precipitation fields. The CP‐RCM simulations, increasingly available worldwide, present an opportunity for improving precipitation interpolation, especially in sparse and complex topographical regions.

Genotyping single nucleotide polymorphisms in homologous regions using multiplex kb level amplicon capture sequencing.

Single nucleotide polymorphisms (SNPs) in homologous regions play a critical role in the field of genetics. However, genotyping these SNPs is challenging due to the presence of repetitive sequences within genome, which demand specific method. We introduce a new, mid-throughput method that simplifies SNP genotyping in homologous DNA sequences by utilizing a combination of multiplex kb level PCR (PCR size 2.5k-3.5kb) for capturing targeted regions and multiplex nested PCR library construction for next-generation sequencing (Multi-kb level capture-seq). First of all, we randomly selected 7 SNPs in homologous regions and successfully captured 6-plex kb level amplicons (one of segments contains 2 SNPs, while the remaining segments each have only one SNP) in a single tube. And then, the amplification products were subjected to multiplex nested PCR for library construction and sequenced on Illumina platform. We tested this strategy using 600 amplicons from 100 samples and accurately genotyped 96.8% of target SNPs with a coverage depth of ≥ 15×. For the uniformity within the samples, over 66.7% (4/6) of the amplicons had a coverage depth above 0.2-fold of average sequencing depth. To validate the accuracy of this approach, we performed Ligase detection reaction PCR for genotyping the 100 samples, and found that the genotyping data was 97.71% consistent with our NGS results. In conclusion, we have developed a highly efficient and accurate method for SNP genotyping in homologous regions, which offers researchers a new strategy to explore the complex regions of genome.

Integrating Multimodal Deep Learning with Multipoint Statistics for 3D Crustal Modeling: A Case Study of the South China Sea

Constructing an accurate three-dimensional (3D) geological model is crucial for advancing our understanding of subsurface structures and their evolution, particularly in complex regions such as the South China Sea (SCS). This study introduces a novel approach that integrates multimodal deep learning with multipoint statistics (MPS) to develop a high-resolution 3D crustal P-wave velocity structure model of the SCS. Our method addresses the limitations of traditional algorithms in capturing non-stationary geological features and effectively incorporates heterogeneous data from multiple geophysical sources, including 44 wide-angle seismic crustal structure profiles obtained by ocean bottom seismometers (OBSs), gravity anomalies, magnetic anomalies, and topographic data. The proposed model is rigorously validated against existing methods such as Kriging interpolation and MPS alone, demonstrating superior performance in reconstructing both global and local spatial features of the crustal structure. The integration of diverse datasets significantly enhances the model’s accuracy, reducing errors and improving the alignment with known geological information. The resulting 3D model provides a detailed and reliable representation of the SCS crust, offering critical insights for studies on tectonic evolution, resource exploration, and geodynamic processes. This work highlights the potential of combining deep learning with geostatistical methods for geological modeling, providing a robust framework for future applications in geosciences. The flexibility of our approach also suggests its applicability to other regions and geological attributes, paving the way for more comprehensive and data-driven investigations of Earth’s subsurface.

Unravelling the architecture of major histocompatibility complex class II haplotypes in rhesus macaques.

The regions in the genome that encode components of the immune system are often featured by polymorphism, copy number variation, and segmental duplications. There is a need to thoroughly characterize these complex regions to gain insight into the impact of genomic diversity on health and disease. Here we resolve the organization of complete major histocompatibility complex (MHC) class II regions in rhesus macaques by using a long-read sequencing strategy (Oxford Nanopore Technologies) in concert with adaptive sampling. In particular, the expansion and contraction of the primate DRB-region appear to be a dynamic process that involves the rearrangement of different cassettes of paralogous genes. These chromosomal recombination events are propagated by a conserved pseudogene, DRB6, which features the integration of two retroviral elements. In contrast, the DRA locus appears to be protected from rearrangements, which may be owing to the presence of an adjacently located truncated gene segment, DRB9 With our sequencing strategy, the annotation, evolutionary conservation, and potential function of pseudogenes can be reassessed, an aspect that was neglected by most genome studies in primates. Furthermore, our approach facilitates the characterization and refinement of an animal model essential to study human biology and disease.

Exploring Topography Downscaling Methods for Hyper‐Resolution Land Surface Modeling

AbstractHyper‐resolution land surface modeling provides an unprecedented opportunity to simulate locally relevant water and energy cycles. However, the available meteorological forcing data is often insufficient to fulfill the requirements of hyper‐resolution modeling. Here, we developed a comprehensive downscaling framework based on topography‐adjusted methods and automated machine learning (AutoML). With this framework, a 90 m and hourly atmospheric forcing data set was developed from ERA5 data at a 0.25° resolution, and the Common Land Model (CoLM) was then forced with the developed forcing data over two complex terrain regions (the Heihe River Basin and Upper Colorado River Basin). We systematically evaluated the downscaled forcing and the CoLM outputs against both in situ observations and gridded data. The ground‐based validation results suggested consistent improvements for all downscaled forcing variables with mean RMSE improved by 6.362%–95.86%. The downscaled forcings, which incorporated detailed topographic features, offered improved magnitude estimates, achieving a comparable level of performance to that of regional reanalysis forcing data. The downscaled forcing driving the CoLM model showed comparable or better skills in simulating water and energy fluxes, as verified by in situ validations. The hyper‐resolution simulations provided a detailed and more reasonable description of land surface processes and attained similar spatial patterns and magnitudes with high‐resolution land surface data, especially over highly elevated areas. Additionally, this study highlighted the benefits of using mountain radiation theory‐based shortwave radiation downscaling models and AutoML‐assisted precipitation downscaling models. These findings emphasized the significance of integrating topography‐based downscaling methods for hillslope‐scale simulations.

The 2021 and 2022 North Coast California Earthquake Sequences and Fault Complexity in the Vicinity of the Mendocino Triple Junction

ABSTRACT The Mendocino Triple Junction (MTJ), one of the most tectonically active and complex regions of California, has damaging earthquakes on the San Andreas and Mendocino faults, within the oceanic and subducting regions of the Gorda section of the Juan de Fuca plate, and within the overriding North American plate (NAP). Two recent earthquake sequences in the MTJ region, starting on 20 December 2021 and 20 December 2022, highlight the complex interactions of regional faults. We explore these sequences to better define the deep faults in the MTJ region, and their rupture modes. Our finite-source analysis shows the 2021 sequence began with two M ∼6.0 earthquakes separated by ∼11 s in time and 30 km in distance. The first earthquake occurred offshore on the Mendocino fault at a depth of 16.5 km. Its S waves triggered an “onshore” intraplate Gorda event at a depth of 27 km, which ruptured a vertical fault toward the northeast. Finite-source analysis of the mainshock of the 2022 sequence, M 6.4, indicates the rupture started offshore north of Cape Mendocino within the Gorda plate and propagated east-northeast, toward populated areas. Damage to towns and infrastructure was exacerbated by directivity and the sediment-filled valleys, as well as by a large aftershock (M 5.4) centered 20 km south-southeast of the mainshock rupture plane. The depths and mechanisms of the onshore 2021 and the 2022 earthquakes and their aftershock sequences indicate that they occurred on different strike-slip faults within the subducted portion of the Gorda plate. The faults active in these earthquakes are unrelated to mapped surface faults in the overriding NAP and are oblique to the tectonic trends seen at the surface. The 2021 and 2022 earthquakes are close to the boundary between two distinct regions of the Gorda plate, where offshore north–south horizontal compression transitions to east–west downslab tension.

Ecological and Human Health Risk Assessment Based on Stream Sediments from Coastal Oecusse (Timor)

Timor Island is located in a geologically complex region strongly affected by the collision of the Australian margin with the Banda volcanic arc. In Oecusse, an enclave of East Timor in the western part of Timor, crop out several lithological units of the Banda Terrane that are associated with the obduction of oceanic crust and upper mantle on the Australian continental crust. This study reports the geochemistry of stream sediments from the coastal region of the Oecusse enclave, where the Banda Terrane is best represented, employing statistical analyses to discern the sources of metal(oid)s and assessing ecological and health risks. Arsenic, Cr, and Ni are the elements with higher potential ecological risk factors. The potential ecological risk index (PERI), which combines single indexes of ecological risk factors for multiple elements, is very high in a stream sourced by the Lolotoi-Mutis Complex. Significant risks for human health were found for As (sourced by the Lolotoi-Multis Metamorphic Complex and basalts of the Barique Formation), Mn (sourced by the Maubisse and Barique formations), and V (sourced by the Manamas Formation). The highest values of hazard index (HI), however, were determined with Cr, in particular for children (HI higher than 10 in 12 sediments sourced by ultramafic units and their covering sedimentary units). This investigation shows that high geogenic concentrations of several elements, particularly those derived from the oceanic crust and the upper mantle, raise significant cancer and non-carcinogenic risks.

Spectral Galerkin Approximation and Error Analysis Based on a Mixed Scheme for Fourth‐Order Problems in Complex Regions

ABSTRACTIn this article, an efficient spectral Galerkin method, which is based on a mixed scheme, is proposed and studied for solving fourth‐order problems in complex regions. The fundamental idea behind this approach is to transform the initial problem into an equivalent form in cylindrical coordinates and to reshape the computational domain into a product‐type rectangular one, which facilitates the utilization of spectral methods. However, when considering the equivalent fourth‐order form directly in cylindrical coordinates, it introduces intricate pole conditions and variable coefficients, posing challenges to both theoretical analysis and algorithm implementation. To address this, we employ the orthogonality of Fourier series to further decompose it into a sequence of decoupled two‐dimensional fourth‐order eigenvalue problems. For each such problem, we introduce an auxiliary function to transform it into an equivalent second‐order coupled system. Building on this, we formulate a mixed variational formulation and discrete scheme, and prove the error estimates for eigenvalue and eigenfunction approximations. Furthermore, we extend this algorithm to the two‐dimensional complex domains. Finally, a series of numerical examples are presented, and the numerical results validate the effectiveness of the algorithm and the correctness of the theoretical results.

Control of a Directional Downhole Drilling System Using a State Barrier Avoidance Based Method

Abstract Vibrations such as bit-bouncing, stick–slip, and whirl motion can significantly reduce downhole drilling's performance and efficiency. These phenomena are likely to arise once the dynamical states of drilling fall into undesired operating regimes, as verified by both theoretical analyses and experimental studies. To effectively avoid such undesired operating conditions of a downhole drilling process, this paper introduces an advanced nonlinear state-constrained control method to formulate a setpoint tracking problem of high-order directional drilling dynamics under state constraints. These constraints are carefully defined using the field test results, and their shapes are in complex state-space regions, causing additional complexity to the control design. Moreover, unlike vertical drilling, the directional drilling system requires modeling of coupled axial and torsional dynamics with a higher degree-of-freedom (DOF). In this study, the proposed method will tackle these challenges by converting the original high-order constrained control problem into a standard nonlinear control problem. Leveraging on the linear parameter varying (LPV) method that is applied to the converted system, we can actively prevent drilling from falling into the undesired regimes, to achieve the constrained control objective.

Heatwave Intensifications in Armenia: Evidence From Temporal and Spatial Analysis of Observational Data Over the Last Decades

ABSTRACTIncreasing temperatures cause the weather to become more severe. Studying how heatwaves (HWs) impact individual heat exposure and susceptibility is vital for building climate change mitigation and adaptation measures. So, this study explores the impact of HWs on individual heat exposure and susceptibility. The assessment was based on the highly complex topographic region contribution to HWs in the Republic of Armenia (RA) using data from 16 meteorological stations for the extended warm season (May–September). We developed a regional HW catchment program to estimate the HW indices' responses to climatic change and to present them in terms of topography changes. The Mann–Kendall (MK) test was used to determine the statistical significance of the trends for 10 distinct HW indicators using linear and exponential trends along with graphical interpretations. This study reveals a large‐scale, significant increasing trend in annual maximum temperatures (Tmax) and observed HWs over the period 1955–2019. The rising temperature is accompanied by an increase in HW indices, particularly at low altitudes up to 1250 m above sea level (ASL), where the main population centres and national crop production are concentrated. According to our classification, the above‐mentioned areas have faced extreme and severe increases in HW intensity, covering more than 60% of the country's territory. Moreover, not only the intensity and frequency have been identified but the HW period extension as well. This extreme increase has been found in the low‐lying highly populated and intensively cultivated areas, such as in the Ararat valley. These results have implications for future climate assessments, adaptation strategies, agriculture and public health in Armenia. The development of targeted mitigation measures and adaptation strategies informed by these findings is essential for addressing the escalating challenges posed by HWs in the region.