Uneven Distribution Research Articles

Research on the clearance evolution of preloading filling spiral case and surrounding concrete

Abstract The embedment of steel spiral cases within concrete under a pressurized condition is a kind of construction approach that widely used in pump-turbines. There will be an initial preloading clearance between the spiral case and surrounding concrete after the construction. The clearance is directly determined by the preloading water head and changes with the variation in operational hydraulic pressure, which will effect the combined load transmission mechanism. This paper investigates the clearance evolution of preloading filling spiral cases and surrounding concrete under different internal pressure. The effect of the preloading water head on the initial clearance, the contact characteristics and the combined load transmission mechanism under different internal pressure are numerically studied. The results show that the initial clearance between the spiral case and concrete increases with the rise of the preloading water head. The maximum clearances are all located at the waist of the spiral case. The clearance during operation presents uneven distribution characteristics in spatial and temporal scales. Before the internal water pressure comes to the preloading water head, the inlet, outer part in the direction of 45° and the sectional area are the first to close, the outer part of the waist follows, while the outer area of the spiral case middle remains open when the pressure reaches the preloading water head. The contact pressure distribution is consistent with the contact status characteristic. The conclusion of this paper aims to improve the understanding of the effect of the clearance between the preloading filling spiral case and concrete, so as to provide theoretical reference for safe and stable operation of pump-turbine units.

ENHANCING QUALITY OF SERVICE IN MANET ENVIRONMENTS WITH CRYPTOGRAPHIC SECURE MECHANISMS TO SECURE SUSTAINABLE SMART CITIES

The utilization of wireless sensor networks to gather, process, and act upon data may be a powerful tool in the development of a city-wide communication network that can accommodate many applications, ultimately leading to smart city solutions. Several challenges might be encountered by a smart city network. These include the need to adapt the network's coverage area to suit various applications, uneven distribution of nodes, a rise in message volume, the use of several communication technologies, and heterogeneity in both nodes and messages. There are many different facets of smart cities that are being studied, and certain applications are being created in each of them. Several options for smart city sensor networks are outlined in this article, which uses a survey format. Applications like ridesharing, emergency medical services, and others that depend on real-time traffic data supplied by the smart traffic domain are used to examine message transmission problems in this study. The versatility and speed with which Mobile Ad Hoc Networks (MANETs) may set up networks have led to their widespread use. Because mobile nodes function best in tandem and have faith in one another, we take a look at MANET routing algorithms and how they handle message transmission for this service.

Anti-symmetric framework for balanced learning of protein-protein interactions.

Protein-protein interactions (PPIs) are essential for the regulation and facilitation of virtually all biological processes. Computational tools, particularly those based on deep learning, are preferred for the efficient prediction of PPIs. Despite recent progress, two challenges remain unresolved: (i) the imbalanced nature of PPI characteristics is often ignored and (ii) there exists a high computational cost associated with capturing long-range dependencies within protein data, typically exhibiting quadratic complexity relative to the length of the protein sequence. Here, we propose an anti-symmetric graph learning model, BaPPI, for the balanced prediction of PPIs and extrapolation of the involved patterns in PPI network. In BaPPI, the contextualized information of protein data is efficiently handled by an attention-free mechanism formed by recurrent convolution operator. The anti-symmetric graph convolutional network is employed to model the uneven distribution within PPI networks, aiming to learn a more robust and balanced representation of the relationships between proteins. Ultimately, the model is updated using asymmetric loss. The experimental results on classical baseline datasets demonstrate that BaPPI outperforms four state-of-the-art PPI prediction methods. In terms of Micro-F1, BaPPI exceeds the second-best method by 6.5% on SHS27K and 5.3% on SHS148K. Further analysis of the generalization ability and patterns of predicted PPIs also demonstrates our model's generalizability and robustness to the imbalanced nature of PPI datasets. The source code of this work is publicly available at https://github.com/ttan6729/BaPPI.

Association between physicians’ maldistribution and core clinical competency of resident physicians: a nationwide cross-sectional study

ObjectivesWith physician maldistribution recognised as a global issue, Japan implemented the physician uneven distribution (PUD) index as a strategic measure. Currently, there is a lack of objective assessment of core...

Jet centrifugal pump internal flow numerical simulation and structural optimization

Abstract Jet centrifugal pump is essential equipment in multiple systems such as agricultural irrigation and energy production. This paper investigates the internal flow characteristics of jet centrifugal pumps at different flow rates and the cavitation behavior under high flow conditions by numerical simulation methods. Additionally, two performance optimization strategies are proposed based on simulation results. The findings indicate that high-velocity regions and low-pressure zones exist within the nozzle. As flow rate increases, the outlet velocity of the nozzle gradually decreases, and large areas of low pressure are observed near the inlet of the impeller. Cavitation occurs to varying degrees at flow rates of 4.056 m3/h and 3.840 m3/h. The turbulent kinetic energy contours for these conditions reveal uneven distribution within the jet nozzle, with higher values near the jet mixing layer, inner wall of the jet nozzle and impeller inlet. Structural optimization using hemispherical bolts significantly reduces the turbulent kinetic energy around the bolts, resulting in an increase in head ranging from 1.68% to 6.70% across various flow rates. Shortening the clearance between the impeller and the rear cover reduces energy losses, leading to an increase in head ranging from 2.72% to 5.86%.

Single-image SVBRDF estimation with auto-adaptive high-frequency feature extraction

In this paper, we address the task of estimating spatially-varying bi-directional reflectance distribution functions (SVBRDF) of a near-planar surface from a single flash-lit image. Disentangling SVBRDF from the material appearance by deep learning has proven a formidable challenge. This difficulty is particularly pronounced when dealing with images lit by a point light source because the uneven distribution of irradiance in the scene interacts with the surface, leading to significant global luminance variations across the image. These variations may be overemphasized by the network and wrongly baked into the material property space. To tackle this issue, we propose a high-frequency path that contains an auto-adaptive subband “knob”. This path aims to extract crucial image textures and details while eliminating global luminance variations present in the original image. Furthermore, recognizing that color information is ignored in this path, we design a two-path strategy to jointly estimate material reflectance from both the high-frequency path and the original image. Extensive experiments on a substantial dataset have confirmed the effectiveness of our method. Our method outperforms state-of-the-art methods across a wide range of materials.

The redrawing of oval shells for the use of a connector part

Abstract Oval-shell parts have been widely used in consumer electronics such as the receptacle of the lightning connector. The current stamped part, based on the design of rectangular redrawing, exhibits uneven distribution of wall thickness and cannot meet the required tolerance. The wall thickness tends to be smaller near the entrance of the flat wall than that of the semi-cylindrical wall while it tends to be larger near the bottom of the flat wall than that of the semi-cylindrical wall. This paper proposes an elliptical scheme for the redrawing of oval shells for the progressive stamping process. The variation of the wall thickness between the flat wall and the semi-cylindrical wall has been improved.

Application of GRACE-based satellite estimates in the assessment of flood potential: A case study of Gangetic-Brahmaputra basin, India.

The high frequency of flood occurrences and the uneven distribution of hydrological stations make it difficult to monitor large-scale floods. Emergence of the Gravity Recovery and Climate Experiment (GRACE) satellite system sets up a new era of large-scale flood monitoring without much reliance on in situ hydrological observations. The GRACE-derived flood potential index (FPI) exhibits its ability to monitor major events of 2003, 2004, 2007, and 2008 over the Indo-Gangetic-Brahmaputra Basin (IGBB). Precipitation and soil moisture are the major influencing factors of flood. However, the response of potential flooding to such parameters is little known. Pearson's lag correlation analysis is used to examine the response of the GRACE-based FPI to precipitation and soil moisture over the study region comparing seasonal time series of the variables. Results exhibited a 2-month lagged response of FPI to precipitation in the Upper Gangetic Yamuna Chambal Basin (UGYCB) and the Lower Gangetic Basin (LGB) and 1-month lagged response in the Lower Brahmaputra Basin (LBB). With context to soil moisture, a 1-month lag is observed in the Gangetic basins, and no lag is observed in the LBB. Event wise analysis of the lags portrays slightly varying lags for different events; however, it provides a picture on the interaction between these variables. This study also assesses the agreement between FPI and satellite-based river discharge, i.e. Dartmouth Flood Observatory (DFO) discharge. A good correlation (> 0.60) between the two is observed. Threshold values of FPI are determined for the LBB due to its annual flood frequency. The nearly similar accuracy of threshold FPI, determined using DFO discharge, in monitoring floods and the predictive skill measure of FPI for LBB to the previous studies demonstrates the utility of satellite-based discharge in the quantification of threshold FPI values for different percentile floods.

Chaotic dynamics of flexible graphene electronic membranes with variable density in motion

With advancements in artificial intelligence and wearable technology, flexible electronic devices characterized by their flexibility and extensibility have found widespread applications in fields such as information technology, healthcare, and military. Printing technology can accurately print a circuit diagram onto a flexible membrane substrate by the pressure transfer of a conductive ink, which makes the large-scale printing of flexible graphene electronic membranes possible. However, during the roll-to-roll printing process used to prepare flexible graphene electron membranes, the density of electron membranes is variable due to the uneven distribution of inkjet-printed circuits, which limits the printing speed of flexible graphene electron membranes. Hence, investigating the dynamic properties of flexible graphene electron materials with different densities is of paramount importance to improve the production efficiency and quality of flexible graphene electron membranes. This paper takes roll-to-roll intelligent graphene electronic membranes as the research object. According to Hamilton’s principle, nonlinear vibration partial differential equations for the motion of flexible graphene electron membranes with varying densities were established and subsequently discretized using the assumed displacement function and the Bubnov–Galerkin method. Through numerical calculations, the simulation results obtained based on the fourth-order Runge–Kutta method and the multiscale algorithm were compared, and the multiscale algorithm was verified to be more correct and effective. The primary resonance amplitude–parameter characteristic curve, along with phase-plane portraits, Poincaré maps, power spectrum, time history plots, and bifurcation diagrams, for the nonlinear behavior of the membrane was obtained. The impacts of the density coefficient, velocity, damping ratio, excitation force, and detuning parameters on the nonlinear primary resonance and chaotic behavior of the moving graphene electron membrane were determined, and the stable operational region was identified, laying a theoretical foundation for the development of flexible graphene electronic membranes.

Advancing digital health in China: Aligning challenges, opportunities, and solutions with the Global Initiative on Digital Health (GIDH).

We summarized the unique challenges that China faced in digital health due to its large population, regional disparities, and uneven distribution of medical resources. Under the guidance of the Global Initiative on Digital Health (GIDH) released by WHO, we proposed corresponding solutions that address infrastructure, data, terminology, technology and security.

Adaptive prototype few-shot image classification method based on feature pyramid

Few-shot learning aims to enable machines to recognize unseen novel classes using limited samples akin to human capabilities. Metric learning is a crucial approach to addressing this challenge, with its performance primarily dependent on the effectiveness of feature extraction and prototype computation. This article introduces an Adaptive Prototype few-shot image classification method based on Feature Pyramid (APFP). APFP employs a novel feature extraction method called FResNet, which builds upon the ResNet architecture and leverages a feature pyramid structure to retain finer details. In the 5-shot scenario, traditional methods for computing average prototypes exhibit limitations due to the typically diverse and uneven distribution of samples, where simple means may inadequately reflect such diversity. To address this issue, APFP proposes an Adaptive Prototype method (AP) that dynamically computes class prototypes of the support set based on the similarity between support set samples and query samples. Experimental results demonstrate that APFP achieves 67.98% and 85.32% accuracy in the 5-way 1-shot and 5-way 5-shot scenarios on the MiniImageNet dataset, respectively, and 84.02% and 94.44% accuracy on the CUB dataset. These results indicate that the proposed APFP method addresses the few-shot learning problem.

Addressing multiple facets of bias and uncertainty in continental scale biodiversity databases

The availability of biodiversity databases is expanding at unprecedented rates. Nevertheless, species occurrence data can be intrinsically biased and contain uncertainties that impact the accuracy and reliability of biodiversity estimates. In this study, we developed a reproducible framework to assess three dimensions of bias—taxonomic, spatial, and temporal—as well as temporal uncertainty associated with data collections. We utilized the vegetation plot data located in Europe, from sPlotOpen, an open-access database, as a case study. The metrics proposed for estimating bias include completeness of the species richness for taxonomic bias, Nearest Neighbor Index for spatial bias, and Pielou’s index for temporal bias. Additionally, we introduced a new method based on a negative exponential curve to model the temporal decay in biodiversity data, aiming to quantify temporal uncertainty. Finally, we assessed the sampling bias considering the influence of various spatial variables (i.e, road density, human population count, Natura 2000 network and topographic roughness). We discovered that the facets of bias and the temporal uncertainty varied throughout Europe, as did the different roles played by spatial variables in determining biases. sPlotOpen showed a clustered distribution of the vegetation plots, and an uneven distribution in sampling completeness, year of sampling and temporal uncertainty. The facets of bias were significantly explained mainly by the presence of Natura 2000 network and marginally by the human population count. These results suggest that employing an efficient procedure to examine biases and uncertainties in data collections can enhance data quality and provide more reliable biodiversity estimates.

The influence of electrical/thermal fields on the microstructure and mechanical properties of Ti2AlNb alloy from the view of molecular dynamics

Abstract The mechanism of the electrical non-thermal effects on metals is still unclear. Simulations at the atomic level are used to obtain some causes of non-thermal electroplasticity. Molecular dynamics simulations are used to study the change of defects including vacancies, edge dislocations and screw dislocations in B2, α 2 and O phases of Ti2AlNb alloy in static or dynamic situations under pure thermal field and continuous/pulsed electric fields. External energy fields can restore most of these defects. Moreover, different energy input methods have the same restoration effect on defects in the same phase. Thus, the restoration of defects in Ti2AlNb alloy by an electric field is mainly based on the thermal effect. However, the uneven distribution of electro-induced atomic kinetic energy in uniaxial tension simultaneously reduces its deformation resistance. Non-thermal effects in the electrically-assisted processing of industrial-grade materials consist of the instantaneous atomic kinetic distribution imbalance induced by electrical pulses.

How generalizable is the relationship between feedback and reading literacy across different competence levels, multilingual learners, and countries? A meta-analytic approach

Contrary to theoretical assumptions, only weak relationships have emerged between feedback and reading literacy in non-experimental settings. Our study contributes to a better understanding of those weak relationships by shedding light on generalizability across specific student groups (different competence levels, multilingual learners) and contexts (countries). Using a meta-analytic approach, we analyzed the PISA 2018 dataset with data from 505,906 students from 75 countries. Our results indicate a weak overall relationship between feedback and reading literacy, an uneven distribution of feedback across competence levels, and heterogeneity across countries. In contrast, multilingual learners do not profit differently from feedback compared to their monolingual peers. Future research should focus on the mechanisms behind these differences.

Research and Analysis on Regional Heterogeneity of Educational Development Level and Economic Growth

China's rapid economic development over the past 40 years has improved the overall quality of life of the population but has also brought about uneven distribution of resources. The problem of educational inequality under the conditions of uneven distribution of economic development is one of the main problems that China needs to solve. Solving the problem of educational inequality plays a positive role in social fairness, justice, and long-term stability. Based on the above background, this paper focuses on the development of education policies in recent decades, the gains and losses of education poverty alleviation, the causes of the "involutionary" competition in urban education, and the effectiveness of relevant policies. It is recommended to improve the allocation of educational resources further and invest appropriate resources in the construction of vocational and technical schools to match the current development vision of China's economy. This paper suggests that students from poor areas be allowed to study in economically developed areas to improve educational resources in poor areas in the short term. This paper also calls on the government to establish a long-term mechanism to promote the growth of the education industry and promote a positive cycle of educational development and economic development.

Evolution of phosphate scouting in the terrestrial biosphere.

Chemistry assigns phosphorus and its most oxidized form, inorganic phosphate, unique roles for propelling bioenergetics and metabolism in all domains of life, possibly since its very origin on prebiotic Earth. For plants, access to the vital mineral nutrient profoundly affects growth, development and vigour, thus constraining net primary productivity in natural ecosystems and crop production in modern agriculture. Unlike other major biogenic elements, the low abundance and uneven distribution of phosphate in Earth's crust result from the peculiarities of phosphorus cosmochemistry and geochemistry. Here, we trace the chemical evolution of the element, the geochemical phosphorus cycle and its acceleration during Earth's history until the present (Anthropocene) as well as during the evolution and rise of terrestrial plants. We highlight the chemical and biological processes of phosphate mobilization and acquisition, first evolved in bacteria, refined in fungi and algae and expanded into powerful phosphate-prospecting strategies during land plant colonization. Furthermore, we review the evolution of the genetic and molecular networks from bacteria to terrestrial plants, which monitor intracellular and extracellular phosphate availabilities and coordinate the appropriate responses and adjustments to fluctuating phosphate supply. Lastly, we discuss the modern global phosphorus cycle deranged by human activity and the challenges imposed ahead. This article is part of the theme issue 'Evolution and diversity of plant metabolism'.

Assessing the impact of climate change on reproductive health: A content analysis of climate journalism coverage.

complications, infectious diseases, maternal and infant health in disasters, gender-based violence, healthcare access inequities, mental health impacts, and food security issues. Findings reveal an uneven distribution of coverage across continents, with potential language bias in English-dominated sources. Acknowledging limitations, future research directions emphasize a more inclusive approach, incorporating diverse linguistic perspectives and qualitative exploration of community experiences. The study underscores the imperative for global representation in media discourse This research explores the intricate relationship between climate change and reproductive health through an analysis of 1000 global news articles from 2018 onwards. Utilizing Latent Dirichlet Allocation (LDA), key topics in media coverage are identified, encompassing fertility challenges, pregnancy and advocates for comprehensive, evidence-based strategies to address the complex interplay between climate change and reproductive health.

Feasibility and application of novel electrical curing method for constant-temperature hardening of concrete at cold regions

To explore the feasibility and strategies of applying electric curing technology to reinforced concrete components, this study introduces a method of electric curing for concrete constant temperature hardening (EC-CCTH). Concrete specimens were placed in an environment with a temperature of −10 °C, and an EC-CCTH system was used to maintain the internal temperature of the specimens at 20 °C. The analysis focused on the specimens' strength, temperature, microstructure, and energy consumption. Under winter conditions with ambient temperatures ranging from 1 °C to 7 °C, EC-CCTH was applied to concrete slabs, and their strength and temperature were monitored. Additionally, numerical simulations were conducted to further examine the current density and temperature distributions within the slabs. The results showed that the 28-day strength values of electrically cured specimens were comparable to those of standard-cured specimens. The hydration process and pore structure of the concrete specimens remained unaffected by the application of alternating current. Although the presence of steel bars led to an uneven distribution of current density and non-uniform heat generation within the concrete, this was mitigated by thermal conduction effects. Due to the constant temperature effect of the electric field, the concrete slab reached a strength of 13.9 MPa at 3 days, indicating the practical feasibility of EC-CCTH in engineering. Furthermore, the energy consumption for EC-CCTH of the slab was only 20.1 kW h, highlighting its effectiveness in reducing energy consumption and carbon emissions.

Experimental study of shielding gas concentration on mechanical properties of stainless steel alloy parts using wire and arc additive manufacturing

Wire Arc Additive Manufacturing (WAAM) is an emerging three-dimensional fabrication technology that enables higher deposition rate, material savings, and near net-shape. In this study, various shielding gas concentration (SGC) of argon (Ar) and carbon-di-oxide (CO2) was employed to fabricate 316L using Cold Metal Transfer (CMT) WAAM process. Microstructure characteristics and mechanical properties of thick-walled parts were studied. Microstructure studies show a combination of columnar and equiaxed grains in various regions. Microhardness results exhibit the uneven distribution of hardness value from bottom to top region along the building direction and the values are ranging between185 and 240 HV. Tensile studies indicates the both horizontal and vertical direction samples secures the higher ultimate tensile strength (UTS), yield strength (YS) and elongation (EL) values and confirmed the isotropic properties. Fractography studies prove ductile fracture with the evidence of dimples and enough plastic deformation. From the experimental results, the SGC-2 possesses finer solidification structure and higher mechanical properties than the other samples. The as-fabricated WAAM parts possess higher mechanical properties than the 316L parts fabricated via casting and wrought parts.

Sewage leakage challenges urban wastewater management as evidenced by the Yangtze River basin of China

Sewage leakage, including uncollected, collected but exfiltrated, and collected but discharged with stormwater in combined sewer overflows, becomes a key challenge to clean water around the world. This study proposed a mass balance model to quantify sewage leakage and analyzed its influencing factors. Approximately 56% (53%–59%) of domestic total nitrogen loads in the Yangtze River basin of China leak out without treatment, two-thirds are exfiltrated or overflowed, significantly impacting receiving water quality. Cities with higher precipitation have greater leakage, indicating overflows are important. Pipe density has limited impact, showing higher importance of pipe quality over quantity. The per capita leakage rate first increases with city size and then decreases, showing the economies of scale. Yet uneven distribution of population and economy within megacities leads to greater leakage. Hence, separated sewage systems of rainwater and wastewater, regular maintenance and adapting centralized and decentralized systems are recommended, tailored to city-specific leakage characteristics.