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The main purpose of this article is to prove a result of convergence in variation for a family of multidimensional sampling-Kantorovich operators in the case of averaged-type kernels. The setting in which we work is that one of -spaces in the sense of Tonelli.

In this paper, we introduce and study a new multidimensional generalization in the sense of Tonelli of the Riesz–Medvedev φ -variation, proving in particular a multidimensional generalization of the Riesz–Medvedev theorem. We finally discuss an application of such concept of variation to some approximation problems: in particular, we obtain some estimates and convergence results by means of convolution integral operators in the space of functions of bounded φ -variation.

In this paper we obtain estimates, convergence results and rate of approximation for functions belonging to BV–spaces (spaces of functions with bounded variation) by means of nonlinear convolution integral operators. We treat both the periodic and the non-periodic case using, respectively, the classical Jordan variation and the multidimensional variation in the sense of Tonelli.

The growth and development of phytoplankton are influenced by physico-chemical parameters, which can also affect the spatial distribution of phytoplankton community structure. However, it is unclear whether environmental heterogeneity caused by multiple physico-chemical factors can affect the spatial distribution of phytoplankton and its functional groups. In this study, we investigated the seasonal variation and spatial distribution of phytoplankton community structure and its relationships with environmental factors in Lake Chaohu from August 2020 to July 2021. We recorded a total of 190 species from 8 phyla, which were divided into 30 functional groups, including 13 dominating functional groups. The average annual phytoplankton density and biomass were (5.46 ± 7.17) × 10 7 cells/L and 4.80 ± 4.61 mg/L, respectively. The density and biomass of phytoplankton were higher in summer ((14.64 ± 20.34) × 10 7 cells/L, 10.61 ± 13.16 mg/L) and autumn ((6.79 ± 3.97) × 10 7 cells/L, 5.57 ± 2.40 mg/L), with the M and H2 of dominant functional groups. The dominant functional groups were N, C, D, J, MP, H2, and M in spring, whereas functional groups C, N, T, and Y dominated in winter. The distribution of phytoplankton community structure and dominant functional groups exhibited significant spatial heterogeneity in the lake, which was consistent with the environmental heterogeneity of the lake and could be classified into four locations. Location I had higher phytoplankton density and biomass than the other three locations. Additionally, dominant functional groups M, C, and H2 were present throughout the lake, and all 13 dominant functional groups were observed in Location II. Our findings suggest that environmental heterogeneity is a key factor influencing the spatial distribution of phytoplankton functional groups in Lake Chaohu.

The genus Avena consists of approximately 30 wild and cultivated oat species. Cultivated oat is an important food crop, yet the broader genetic diversity within the Avena gene pool remains underexplored and underexploited. Here, we characterize over 9000 wild and cultivated hexaploid oat accessions of global origin using genotyping-by-sequencing and explore population structure using multidimensional scaling and population-based clustering methods. We also conduct analyses to reveal chromosome regions associated with local adaptation, sometimes resulting from large-scale chromosome rearrangements. We report four distinct genetic populations within the wild species A. sterilis , a distinct population of cultivated A. byzantina , and multiple populations within cultivated A. sativa . Some chromosome regions associated with local adaptation are also associated with confirmed structural rearrangements on chromosomes 1A, 1C, 3C, 4C, and 7D. This work provides evidence suggesting multiple polyploid origins, multiple domestications, and/or reproductive barriers amongst Avena populations caused by differential chromosome structure. Oat is an important food crop, but the genetic diversity within the gene pool remains unclear. Here, the authors report the analyses of worldwide diversity and population structure of hexaploid oat, and identify signatures of structural rearrangements within the germplasm collection.

In the realm of oil and gas exploration, accurate identification of lithology is imperative for the assessment of resources and the refinement of extraction strategies. While artificial intelligence techniques have garnered considerable success in lithology identification, existing methodologies encounter difficulties when addressing highly heterogeneous and geologically intricate unconventional oil and gas reservoirs. Specifically, they struggle to account for the dynamic variations in sample characteristics across spatial dimensions and temporal sequences. This separate treatment of spatial and temporal dynamics not only confines the precision of fluid prediction but also significantly attenuates the robustness of the models. To address this challenge, we propose the spatiotemporal network (STNet), a dual-branch deep learning framework that integrates seamlessly spatial feature graph methods with time-sequential prediction methods. By employing a graph structure that accounts for spatial characteristics to capture the complex spatial relationships within logging data, and by utilizing a temporal model to discern the dynamic properties of time series data, this dual-mechanism framework enables a more comprehensive understanding of the multidimensional attributes of subsurface fluids, thereby enhancing the accuracy of lithology identification. Experimental results from multiple wells in different regions of the Tarim and Daqing oilfields demonstrate that STNet not only achieves detection accuracy exceeding 95% but also exhibits strong generalizability. The results indicate a significant improvement in the accuracy of lithology identification compared to seven other advanced models. Integrating both temporal and spatial elements of logging data provides a new perspective for enhancing fluid prediction capabilities.

Aim Surf zones are crucial nursery habitats for the early life stages of fish species associated with typical coastal ecosystems. However, little is known about the temporal patterns and drivers of fish assemblages in tropical surf zones. This study aimed to assess the (1) main changes in fish community structure throughout 1 year, (2) seasonal dynamic patterns in fish assemblages, and (3) key factors influencing fish assemblages in the tropical surf zones. Location Gaolong Bay, Wenchang City, Hainan Island, China. Methods Fish sampling was conducted monthly from June 2021 to May 2022 using a beach seine net. Fish species were identified using both morphological and molecular analyses. Kruskal–Wallis test, analysis of similarity, non‐metric multidimensional scaling analysis, and similarity percentage analysis were used to investigate the temporal fish assemblage patterns. Generalised additive models and canonical correspondence analysis were used to assess how environmental variables influence fish assemblages. Results We identified 83 fish species, which were grouped into three ecotypes based on their primary habitat: coral reef‐seagrass‐associated species (CS) (35), mangrove‐estuarine‐associated species (ME) (30), and common coastal‐estuarine‐associated species (CE) (18). Most captured individuals were juveniles, and fish abundance and diversity were highest in May. Most CS species were abundant between March and May. ME and most CE species were dominant from June to August, and Mugilidae (CE) was abundant between October and February. Furthermore, surf fish assemblages were substantially influenced by tidal level, water temperature, conductivity, pH, turbidity, and dissolved oxygen. Conclusions Juvenile fish were abundant in May and fish species with three ecotypes alternate in the surf zones throughout the year. Counter to much current thinking, March maybe the spawn peak of most fish species in the studied area, and we suggest that the fishing ban period could start from March instead of May in the inshore areas of Hainan Island.

Salinity is one of the most important abiotic stresses that negatively affects plant growth and development around the world. It has been reported that approximately 19.5% of all irrigated land and 2.1% of dry land is affected by salt stress, and these percentages continue to increase. Sorghum, a C4 plant, is the fifth most important cereal in the world. Numerous studies reported that there are high genetic variations in sorghum. These genetic variations can be monitored to search for the most salt-tolerant genotypes. Therefore, the aim of our study was to investigate the responses of ten sorghum genotypes to different levels of salinity. We focused on germination and seedling growth as the most critical stages of plant development. In our research we included germination percentage, germination index, mean germination time, seedling vigor index, seedlings’ shoot and root lengths, fresh and dry seedling weight, and salinity tolerance indices. For data assessment we applied two-way ANOVA, non-metric multidimensional scaling, and hierarchical agglomerative classification. Our results demonstrate that salinity was responsible for 98% of the variation in assessed parameters, whereas genotype effect accounted for only 2% of the documented variation. It can be concluded that seedling traits can be used as a valid criterion for the selection of genotypes with a better tolerance to salinity stress.

Increasing agricultural production while reducing the reliance on synthetic inputs such as antibiotics is an important challenge. For cattle breeding, this implies better understanding the genetics underlying meat production and the immune response. Here, we use systems immunology to investigate the genetic and environmental drivers of immune variation in Belgian White Blue male cattle, a breed historically bred for meat production. While seasonality and other non-genetic factors account for much of the immune variation observed, genome-wide association studies identify loci with major effects on specific immunophenotypes. Genetics also emerges as the primary driver of cytokine production. Finally, we develop a predictive model linking genetic data to cytokine responses. Our findings support the selection of cattle with improved immunity and advance our understanding of mammalian immune variation. The links between meat production and immunity in cattle can affect agricultural production. Here, Li et al. identify genetic and environmental factors that shape immune responses in cattle, which can guide breeding for improved health.

The health of populations living in extreme poverty has been a long-standing focus of global development efforts, and continues to be a priority during the Sustainable Development Goal era. However, there has not been a systematic attempt to quantify the magnitude and causes of the burden in this specific population for almost two decades. We estimated disease rates by cause for the world's poorest billion and compared these rates to those in high-income populations. We defined the population in extreme poverty using a multidimensional poverty index. We used national-level disease burden estimates from the 2017 Global Burden of Disease Study and adjusted these to account for within-country variation in rates. To adjust for within-country variation, we looked to the relationship between rates of extreme poverty and disease rates across countries. In our main modeling approach, we used these relationships when there was consistency with expert opinion from a survey we conducted of disease experts regarding the associations between household poverty and the incidence and fatality of conditions. Otherwise, no within-country variation was assumed. We compared results across multiple approaches for estimating the burden in the poorest billion, including aggregating national-level burden from the countries with the highest poverty rates. We examined the composition of the estimated disease burden among the poorest billion and made comparisons with estimates for high-income countries. The composition of disease burden among the poorest billion, as measured by disability-adjusted life years (DALYs), was 65% communicable, maternal, neonatal, and nutritional (CMNN) diseases, 29% non-communicable diseases (NCDs), and 6% injuries. Age-standardized DALY rates from NCDs were 44% higher in the poorest billion (23,583 DALYs per 100,000) compared to high-income regions (16,344 DALYs per 100,000). Age-standardized DALY rates were 2,147% higher for CMNN conditions (32,334 DALYs per 100,000) and 86% higher for injuries (4,182 DALYs per 100,000) in the poorest billion, compared to high-income regions. The disease burden among the poorest people globally compared to that in high income countries is highly influenced by demographics as well as large disparities in burden from many conditions. The comparisons show that the largest disparities remain in communicable, maternal, neonatal, and nutritional diseases, though NCDs and injuries are an important part of the \"unfinished agenda\" of poor health among those living in extreme poverty.