Distribution Of Values Research Articles

Climate interacts with the trait structure of tree communities to influence forest productivity

Abstract Tree functional diversity can increase forest productivity by enhancing species interactions and providing greater growth stability. However, very few studies have examined the influence of tree community trait structure on survivor growth, recruitment and mortality simultaneously, which are the main drivers of forest population dynamics. Here, we explore the interactions among functional diversity, productivity and climate to investigate the role of the trait structure of communities on forest productivity and to determine under what circumstances functional diversity should be promoted to ensure forest adaptive capacity under future climate. Using random‐forest modelling and a network of permanent sample plots covering a broad gradient of climatic conditions, we isolated the effects of functional diversity—described as the distribution of trait values in a community—and climate variables on net forest productivity (NFP), survivor growth, recruitment and mortality. Based on our findings, community‐level trait structure affects forest productivity in different ways. NFP was influenced by three traits from three different plant strategy dimensions, whereas survivor growth and recruitment were strongly correlated with leaf and resource acquisition traits, and tree mortality with a mix of traits reflecting various plant strategies. We also observed climate interactions with the functional trait structure of tree communities. For instance, we observed an interaction between drought tolerance and mean annual temperature: At low temperatures, NFP biomass accumulation increased with the value of the drought tolerance trait; however, at higher temperatures, the opposite pattern was observed. However, we found contrasting patterns of population response to climate variability, depending on their functional diversity. Greater functional diversity does not necessarily increase biomass accumulation under different climatic conditions. Synthesis. As all components of forest productivity contribute to NFP, studies on forest productivity should consider not only survivor growth but also recruitment and mortality. Each component responds differently in terms of biomass changes in climatic variation, according to the trait structure of tree communities. This study provides a framework to identify the trait structure that should be targeted under different climate scenarios to anticipate change and help strengthen forest response capacity to climate change.

Homeownership and the perception of material security in old age

ABSTRACT Homeownership has been shown to be related to various aspects of well-being, although both the causal nature of this relationship and the possible channels behind it have been difficult to identify. We examine an important mechanism which could be responsible for the positive effects of homeownership, namely its role in providing material security in old age. Using data from 15 European countries, we analyse the relationship between homeownership and material security, as measured through subjective evaluation of the likelihood of being better or worse off in the future. We find that homeowners have a higher level of material security than renters, and this association is especially strong for those living in big cities. For this subsample, in comparison to renters, owning a property in the top quartile of the home value distribution is associated with an increase in the probability of expecting to be better off in the future by as much as 43%. We interpret the results as support for the argument that homeownership offers a very particular form of material security, which, through reduction of perceived material risks, may be behind its positive implications for well-being.

Mapping and assessment of karst landscape aesthetic value from a world heritage perspective: a case study of the Huangguoshu Scenic area

Aesthetic value is an essential component of outstanding universal value (OUV) for natural world heritage (NWH) site. However, comparisons of aesthetic value lack a set of effective evaluation systems. In the identification of aesthetic value, there is subjectivity and difficulty in quantifying the methodology suggested by the IUCN in the operation manual, and it is difficult to compare in different NWH sites. This study focused on establishing a universal system to map and assess the aesthetic value of karst NWH sites. The research focused on three dimensions: naturalness, diversity, and uniqueness. The final combination of the three is achieved by geographic information system (GIS)-based spatial map overlay analysis with multisource data. To verify the rationality of the model, the aesthetic value of a case study in the Huangguoshu Scenic Area in China at the WH nominated site was evaluated. The results revealed that the areas with low, relatively low, medium, relatively high, and high values accounted for 12.2%, 20.2%, 32.4%, 21.4% and 13.8%, respectively. The distribution of aesthetic value is basically consistent with the boundary division of the NWH site, and high scores are mostly distributed in areas with high protection levels. Moreover, the impacts of naturalness, uniqueness and diversity on aesthetic value in the research area are in decreasing order. Furthermore, the research analyses the aesthetic characteristics and causes at different levels. The research area combines the quintessential nature of karst, hills and water, caves, fenglin and historic villages; it unifies sturdiness, peculiarity, precipitousness, and peace. Among the 7 nominated NWH sites, the Huangguoshu Waterfall has the highest aesthetic value, while those of Tiantaishan Ancient Temple and Getuhe are relatively low. The rationality of the evaluation system for extracting areas with high aesthetic value was demonstrated. This study compensates for the limitation of the inability of existing studies to quantify the aesthetic value. This approach fills a previous gap in the research on the aesthetic value of WH and can provide a useful reference for better protection and management decisions.

Diffusion Analysis of Intracranial Epidermoid, Head and Neck Epidermal Inclusion Cyst, and Temporal Bone Cholesteatoma.

Intracranial epidermoid tumors, temporal bone cholesteatomas, and head and neck epidermoid inclusion cysts are typically slow-growing, benign conditions arising from ectodermal tissue. They exhibit increased signal on DWI. While much of the imaging literature describes these lesions as showing diffusion restriction, we aimed to investigate these qualitative signal intensities and interpretations of restricted diffusion with respect to normal brain structures. This study aimed to quantitatively evaluate the ADC values and histogram features of these lesions. This retrospective study included children with histologically confirmed diagnoses of intracranial epidermoid tumors, temporal bone cholesteatomas, or head and neck epidermoid inclusion cysts. Lesions were segmented, and voxelwise calculation of ADC values was performed along with histogram analysis. ADC calculations were validated with a second analysis software to ensure accuracy. Normal brain ROIs-including the cerebellum, white matter, and thalamus-served as normal comparators. Correlational analysis and Bland-Altman plots assessed agreement among software tools for ADC calculations. Differences in the distribution of values between the lesions and normal brain tissues were assessed using the Wilcoxon rank sum and Kruskal-Wallis tests. Forty-eight pathology-proved cases were included in this study. Among them, 13 (27.1%) patients had intracranial epidermoid tumors, 14 (29.2%) had head and neck epidermoid inclusion cysts, and 21 (43.7%) had temporal bone cholesteatomas. The mean age was 8.67 (SD, 5.30) years, and 27 (52.9%) were female. The intraclass correlation for absolute agreement for lesional ADC between the 2 software tools was 0.997 (95% CI, 0.995-0.998). The intracranial epidermoid tumor, head and neck epidermoid inclusion cyst, and temporal bone cholesteatoma median ADC values were not significantly different (973.7 versus 875.7 versus 933.2 ×10-6 mm2/s, P = .265). However, the ADCs of the 3 types of lesions were higher than those of 3 normal brain tissue types (933 versus 766, × 10-6 mm2/s, P < .0001). The ADC values of intracranial epidermoid tumors, temporal bone cholesteatomas, and head and neck epidermoid inclusion cysts are higher than those of normal brain regions. It is not accurate to simply classify these lesions as exhibiting restricted diffusion or reduced diffusivity without considering the tissue used for comparison. The observed hyperintensity on DWI compared with the brain is likely attributable to a relatively higher contribution of the T2 shinethrough effect.

Research on the Value Distribution of China's Emerging Industries under the Background of "Double Carbon"

China's emerging industries have developed rapidly in recent years, still have significant growth, high added value, is the economic low-carbon environmental protection development of the positive driving force. Sanger algorithm is used to analyze the examples of emerging industries in China, explore efficient and scientific value distribution methods of emerging industries, promote rapid economic development, and summarize the specific construction paths of transforming traditional economic industries and developing emerging industries in various places, so as to provide reference for relevant departments to formulate and improve the healthy development of value distribution of developing emerging industries.

Generalized weighted permutation entropy analysis of SPEI index in Serbia as a proxy of corn production

AbstractIn this work we analyse yearly standardized precipitation evapotranspiration index for accumulation timescale of 6 months (SPEI‐6) for August which has been identified as a strong proxy for corn production in Serbia. By applying recently proposed method generalized weighted permutation entropy (GWPE), which provides the information about predictability of large/small fluctuations in nonstationary time series, on SPEI index calculated from high‐resolution gridded dataset for the period 1951–2022, we found that the small fluctuation sequences of consecutive 4‐year SPEI values are the most predictable (indicated by lowest entropy values). The order of large fluctuation sequences is less predictable, while the order of sequences considering all magnitudes of fluctuations are the least predictable (indicated by highest entropy values). We also analysed 4‐month SPEI‐6 sequences for May, June, July and August (during the growing season of corn) along the years under study and found that they display lower GWPE values and thus are more predictable than yearly SPEI‐6 August sequences (small fluctuations displaying lower entropy values than large fluctuations). Regarding spatial distributions, in both cases SPEI‐6 sequences show similar pattern for large fluctuations: higher predictability (lower entropy values) in northern and eastern part of Serbia and lower predictability (higher entropy values) in southern and western part. For small fluctuations spatial distribution of GWPE values indicates that yearly SPEI‐6 August series are more predictable in the western part of the country while May to August monthly SPEI‐6 sequences are more predictable in the eastern part.

A stochastic modeling method for three-dimensional corrosion pits of bridge cable wires and its application

ABSTRACT Cables have usually served as critical and vulnerable structural components in long span cable-supported bridges. Cable inspections revealed that corrosion, fatigue or coupled corrosion-fatigue were the ones of the main failure mechanisms. This paper proposed a stochastic modelling method for three-dimensional (3-D) corrosion pits of high-strength bridge wires, which can be applied to rapid fatigue life evaluation according to mass loss caused by surface corrosion pits of bridge wires nondestructively. High-strength steel wire specimens dismantled from the cable-stayed bridge served for 15 years were scanned to obtain the original surface corrosion data. The spatial position coordinate of corrosion pits were considered as random variable and can be well fitted by uniform distribution. While the number of corrosion pits can be fitted with generalized extreme value (GEV) distribution. The uniform corrosion depth du, which can be equivalent to mass loss rate, was calculated as the input corrosion parameter for 3-D corrosion pit modelling. The maximum pitting depth dmax for the steel wire was found to be associated with du. The geometric parameters for individual corrosion pits were recognized as pit depth d, depth-to-width ratio d/b, and aspect ratio b/a, which were fitted with different probability distributions. What follows is 3-D spatial corrosion pits simulation based on the individual corrosion parameter that were sampled and combined from the corresponding probabilistic distributions. Hereafter, fatigue life evaluation of corroded wires was conducted based on equivalent surface defect method and compared with the experimental results, verifying the effectiveness of the proposed modelling approaches.

W0_sample = np.random.normal(0,1)?

In this note, we explore the distribution of vacuum expectation values of the superpotential W0 in explicit Type IIB flux compactifications. We show that the distribution can be approximated universally across geometries by a two-dimensional Gaussian with a model-dependent standard deviation. We identify this behaviour in 20 Calabi-Yau orientifold compactifications with between two and five complex structure moduli by constructing a total of O(107) flux vacua. We observe a characteristic scaling behaviour of the width σ of our distributions with respect to the D3-charge contributions Nflux from fluxes which can be approximated by σ∼Nflux. This W0 distribution implies that locating small values of |W0| as a preferred regime associated with classes of string theory solutions typically featuring hierarchies, simplifies to the basic statement of finding small Euclidean norms of normally distributed values. We also identify small modifications to this Gaussian behaviour in our samples, which might be seen as indications for the breakdown of the continuous flux approximation commonly used in the context of statistical analyses of the flux landscape.

Analysis of Regional Differences in Cadmium Ecological Risk in Surface Waters of the Yangtze River Basin

This study primarily focused on the regional disparities in both water quality criteria and ecological risks attributed to cadmium presence within the surface waters of the Yangtze River Basin. In the initial phase, the long-term water quality criteria for cadmium were recalibrated in accordance with the guidelines outlined in China's "Water Quality Criteria for Freshwater Aquatic Organisms-Cadmium," accounting for the prevalent hardness distribution within the Yangtze River Basin's surface water. Subsequently, a more refined revision was undertaken considering the specific characteristics of the species residing within the Yangtze River Basin. This undertaking led to a comprehensive interpretation of the regional variations in both the distribution of long-term water quality criteria values and the risk quotient distribution of cadmium throughout the Yangtze River Basin. The incorporation of hardness and species-specific attributes resulted in a revised range of long-term water quality criteria for cadmium across different urban locales within the Yangtze River Basin. Notably, the recalibrated values ranged from 0.08 μg·L-1 as the lowest threshold to 0.75 μg·L-1 as the upper limit, signifying a tenfold differentiation. Correspondingly, the urban average annual risk quotient associated with cadmium exposure demonstrated a variation from 0.035 to 1.12, marking a significant 32-fold discrepancy between the lowest and highest values. It is essential to highlight that regions of paramount importance, such as the confluence area connecting the upper and middle stretches of the Yangtze River Basin and the intricate Dongting Lake system, exhibited noteworthy ecological risks attributed to cadmium presence. Consequently, further in-depth investigations into these critical regions are imperative for a comprehensive understanding of the associated risks.

Comparative Performance of Non-Stationary Intensity-Duration-Frequency (NS-IDF) Models for Selected Gauge Stations in the Niger Delta

This study focused on a comparative analysis of developed Non-stationary rainfall intensity-duration-frequency (NS-IDF) models with existing IDF models for the Niger Delta with Uyo, Benin, Port Harcourt, and Warri as selected stations. Applied was 24-hourly (daily) annual maximum series (AMS) data with downscaling models also used to downscale the time series data. Uyo and Benin had statistically significant trends with Port Harcourt and Warri showing mild trends. The best linear behavioural parameter extremes model integrating time as co-variate was selected for each station for computation of the General extreme value (GEV) distribution fitted NS-IDF models with the open-access R-studio software. The Non-stationary intensity values were higher than computed stationary ones, with significant differences at a 5% significance level for a given return period. For example, for 2 and 10-year return periods for 1-hour storms the differences of 22.71% &amp; 17.0%, 15.24% &amp; 9.40%, 5.09% &amp; 4.04%, and 6.15% &amp; 4.43% for Uyo, Benin, Port Harcourt and Warri, respectively were recorded. While, the percentage difference in intensities was very high between the Non-stationary and existing, Stationary IDF models. For a return period of 2 years at 15 and 60 min durations, the differences were 97.9 &amp; 3.2%, 240.6 &amp; 67.2%, 78.2 &amp; 0%, and 121.6 &amp; 50.1% for Uyo, Benin, Port Harcourt and Warri, respectively. Such extreme value difference in intensity underestimates the peak flood and exagerate the flood risk. The general NS-IDF calibrated models showed very good match and fit with R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; = 0.977, 0.999, 0.999 &amp; 0.999, and MSE accuracy = 193.5, 1.011, 4.1552 &amp; 1.011 for Uyo, Benin, Port Harcourt, and Warri, respectively. Erosion and flood control facilities in the Niger Delta require upgrading using the calibrated general NS-IDF models to accommodate extra-value rainfall intensities due to climate change.

Sustainable Value-Sharing Mechanisms of the Industrial Internet of Things Platforms: A Case Study of Haier’s Service-Oriented Transformation

Ensuring fairness and equity in value distribution is crucial for the sustainability of platform ecosystems. However, existing approaches to distributing benefits among cooperative entities often find it difficult to accurately assess each stakeholder’s contributions. This paper tackles this challenge through a case study of the Haier COSMOPlat IIoT platform. By analyzing its value creation and value distribution processes, the research uncovers how platform enterprises can overcome existing limitations by quantifying and revealing intangible customer relationships alongside financial metrics. This revised value-sharing mechanism encourages a shift from “post-event value-sharing” to “mid-event adjustment”, promoting a fair and equitable profit distribution framework that motivates stakeholders toward sustainable value co-creation.

Electrical current-assisted reactive crucible melting technique: Case study of the Fe-Sn system

Modern materials science relies heavily on the advancement of combinatorial high-throughput experimental and computational methods to discover new advanced materials. Among these methods, the reactive crucible melting (RCM) technique stands out as particularly promising. However, a significant challenge in RCM combinatorial analysis is the occurrence of "missing" phases. In this study, we successfully addressed this limitation through the application of electrical current stressing. Furthermore, we investigate the influence of electric current stressing on diffusion rates and the phase formation processes within the reactive crucible. We employed Comsol Multiphysics modeling to analyze the experimental results in the framework of electromigration theory, focusing on the distribution of electrical field lines, temperature gradients, and current density values. Additionally, through the integration of finite element calculations with analysis of Electron Backscatter Diffraction (EBSD) and Magneto-Optical Kerr Effect (MOKE) data, we estimated the gradient of internal stresses along the boundaries of the crucible's reaction volume induced by the diffusion of electrically-induced vacancies and adatoms. Due to modest values of the current density in our experiment and the disparity between the rates of crucible body dissolution and vacancy diffusion, no compressive or tensile stresses were observed. Nonetheless, we propose that the developed electrical current-assisted reactive crucible melting technique holds promise for synthesizing metastable phases existing under high positive or negative pressures.

A deep learning-based super-resolution method for building height estimation at 2.5 m spatial resolution in the Northern Hemisphere

Building height is an important indicator for assessing the level of urban development along the vertical dimension. Existing large-scale building height estimation studies focus on coarse spatial resolution (e.g., 10, 500, and 1000 m), which cannot reveal height variations across buildings in urban areas. High-resolution images (e.g., < 5 m resolution) can support building-scale height estimation, but they usually have small spatial coverage and are not openly accessible. In this context, we proposed a deep learning-based super-resolution method to generate building height maps at a spatial resolution of 2.5 m using Sentinel-1/2 images. The proposed method consisted of two parts: 1) a super-resolution module (SR) for learning high-resolution features; and 2) a height stratification estimation module (HS) for guiding the network to learn different height levels to mitigate the imbalanced distribution of height values. We created an open building height dataset with 45,000 samples covering multiple urban areas in the Northern Hemisphere, including China, the conterminous United States (CONUS), and Europe. Experimental results showed that for height estimation at the pixel level, the proposed method obtained a root mean square error of 10.318 m in China, 5.654 m in CONUS, and 4.113 m in Europe, respectively. Predicted results provided rich spatial details, due to the inclusion of the super-resolution module, which was heavily missed by existing large-scale studies. Moreover, we calculated the mean and standard deviation of building height in 301 urban centers, each having at least a population of 500,000, and found that the buildings in China were the highest (11.353 m ± 9.543 m), followed by CONUS (8.487 m ± 6.202 m) and Europe (8.136 m ± 5.020 m). Ablation studies indicated that the joint use of Sentinel-1/2 images and the proposed modules (SR and HS) can effectively improve the performance of building height estimation. The building dataset we generated provides great potential in high-resolution database updating, urban planning, and natural disaster assessment, and indeed, a new perspective of how we can utilize cutting-edge satellite imaging technology in urban observation, measurement, monitoring, and management. The dataset and code of this study will be available at: https://github.com/lauraset/Super-resolution-building-height-estimation.

Threat and Vulnerability of Thermal Discomfort in Yogyakarta City

The world’s attention to climate issues is increasing as the issue of global warming and climate change emerges. Several studies with the scope of cities and urban areas concluded that urban surface temperatures have an increasing trend. This phenomenon will have an impact on the thermal comfort of urban population. Yogyakarta City as one of the National Activity Centers (PKN), is experiencing rapid urban development and leads to become a metropolitan city so it needs to be studied regarding its thermal comfort. This research is intended to identify the potential and vulnerability of thermal discomfort in Yogyakarta City. The identification of potential discomfort is carried out using the Discomfort Index (DI) approach and the Land Surface Temperature (LST) value. Meanwhile, vulnerability is identified in the social aspects of population. Furthermore, the potential and vulnerability scores are assessed to get an overview of thermal discomfort mitigation priorities. Based on DI analysis, thermal discomfort is felt by more than 50% of the Yogyakarta’s residents and it is estimated that in the future will get worse. By looking at the distribution of LST values, the high potential for discomfort is in the city center and has expanded to the north and northeast. Meanwhile, areas with high vulnerability are located in the center and south side of Yogyakarta City. From the combined assessment of threat and vulnerability, the areas that are the top priority for mitigation are in the center of Yogyakarta City, which include 7 districts, namely Danurejan, Gedongtengen, Gondomanan, Jetis, Kraton, Ngampilan, and Pakualaman. The medium priority districts are Gondokusuman and Wirobrajan.

Modeling Extreme Values of Non-Stationary Precipitation Data with Effects of Covariates

Background/Objectives: Climate change is one of the most challenging problems of recent decades as it is highly volatile and needs a very effective scientific approach to find a solution. Further, the changes in the climate especially extreme cases have a more negative influence on day to day affairs of society. Hence, developed countries pay much attention to climate change and make policies at a global level. To find the scientific solution for these kinds of climate change, Extreme value theory offers effective methods for estimating and quantifying these types of natural hazards associated with climate. Methods: Block (Annual) maxima and peak over threshold are two strategies employed in this theory. The data observed on precipitation are mostly having non-stationary characteristics along with covariates. The generalized extreme value distribution and generalized Pareto distribution are used to model this type of non-stationary stochastic process. Findings: This study proposes a pragmatic automated dual-phase threshold selection technique that employs the entropy method to combine the results from various goodness of fit tests into a single unified measure known as the evaluation indicator, resulting in an efficient threshold for capturing extreme values. This allows for a more comprehensive examination of various thresholds using evaluation indicators and avoids assessing each test criterion individually. Novelty: In contrast to the subjective results of threshold stability plots, the dual-phase technique is based on numerical computations, which reduce bias and improve decision-making objectivity. We illustrate the applicability of the proposed technique by analyzing a precipitation dataset that includes time and wind speed as covariates. The results of the comparative analysis reveal that the proposed automated dual-phase threshold approach outperforms the peaks over threshold and annual maxima methods. Keywords: Non-stationary, Extreme values, Annual maxima, Threshold selection, Evaluation indicator

47‐4: A Novel Demura Compensation Data Compression Algorithm based on JPEG‐LS

JPEG‐LS is a lossless/near‐lossless image compression algorithm based on context modeling, which has the advantages of easy implementation, low resource consumption and high compression rate. It is widely used in the compression field of continuous‐tone still images. However, single image compression has significant latency and high hardware resource consumption issues, as the JPEG‐LS algorithm requires pixel by pixel prediction and real‐time context updates during the compression process, which is not conducive to algorithm IP implementation. Based on Demura compensation data compression requirements, this article has made changes to the pixel prediction method of the JPEG‐LS algorithm and delayed the update of the context to solve the compression latency and resource consumption issues. After algorithm optimization (NJPEGLS), the linebuffer resource occupation was reduced by 8/9, the clock frequency reached above 140 MHz, and the comprehensive compression rate loss was within 4.5%, meeting the demand for Demura compensation data compression/decompression. In this paper, we also conducted statistics and analysis on the distribution of values, and found that the distribution of values was very regular, and we propose an adaptive value scheme (KNJPEG‐LS) to further simplify the hardware circuit.

Spatial variation of earthworm communities in the motorway proximity

Background. The spatial features of the structure of earthworm communities in the area of influence of motor vehicles were analyzed. Five species of lumbricides belonging to three families were found in the studied biocenosis located near the M06 Kyiv–Chop motorway (Ukraine): Aporrectodea caliginosa (Savigny, 1826), A. rosea (Savigny, 1826), A. trapezoidеs (Dugesi, 1828), Lumbricus terrestris (Linnaeus, 1758) and Dendrobaena octaedra (Savigny, 1826). Materials and Methods. Earthworms were collected during 2021–2022 in the biocenosis near the M06 Kyiv–Chop motorway (Berezyna village, Zhytomyr region). The material was collected by excavation and layer-by-layer analysis of soil samples. The thickness of each layer was 10 cm. The maximum depth – 0.5 m. Samples were taken every 10 m from the road to a distance of 210 m. The distance between the rows of samples along the road was 30 m. STATISTICA software package was used for statistical analysis of the data. Biodiversity assessments were calculated using the PAST software package. SAGA and Q-GIS software packages were used for spatial analysis and mapping of the data. Results and Discussion. The key factor that influences the structure of earthworm communities in the area of road transport impact is the distance from the source of impact. The maximum values of the dominance, Margalef and Berger–Parker indexes and the number of species are observed in areas near the motorway, while the values of the Shannon, Simpson, Menhinik and Brillouin indexes have the opposite trend. There is a correlation between the spatial variability of the structure of earthworm communities and the values of reflectance in the bands B3, B5, B11 of the Sentinel-2 satellite image. It allowed us to apply a geographically weighted regression algorithm with several predictors that indirectly reflect environmental parameters to the data. Conclusion. The results obtained show that the use of predictors allows us to obtain a more mosaic model of the distribution of indicator values compared to interpolation by kriging, which can be used to predict the values of earthworm biodiversity indicators within the study area.

Radial Undersampled MRI Reconstruction Using Deep Learning With Mutual Constraints Between Real and Imaginary Components of K-Space.

The deep learning method is an efficient solution for improving the quality of undersampled magnetic resonance (MR) image reconstruction while reducing lengthy data acquisition. Most deep learning methods neglect the mutual constraints between the real and imaginary components of complex-valued k-space data. In this paper, a new complex-valued convolutional neural network, namely, Dense-U-Dense Net (DUD-Net), is proposed to interpolate the undersampled k-space data and reconstruct MR images. The proposed network comprises dense layers, U-Net, and other dense layers in sequence. The dense layers are used to simulate the mutual constraints between real and imaginary components, and U-Net performs feature sparsity and interpolation estimation for k-space data. Two MRI datasets were used to evaluate the proposed method: brain magnitude-only MR images and knee complex-valued k-space data. Several operations were conducted for data preprocessing. First, the complex-valued MR images were synthesized by phase modulation on magnitude-only images. Second, a radial trajectory based on the golden angle was used for k-space undersampling, whereby a reversible normalization method was proposed to balance the distribution of positive and negative values in k-space data. The optimal performance of DUD-Net was demonstrated based on a quantitative evaluation of inter-method and intra-method comparisons. When compared with other methods, significant improvements were achieved, PSNRs were increased by 10.78 and 5.74dB, whereas RMSEs were decreased by 71.53% and 30.31% for magnitude and phase image, respectively. It is concluded that DUD-Net significantly improves the performance of MR image reconstruction.

Improving tuberculosis control: assessing the value of medical masks and case detection-a multi-country study with cost-effectiveness analysis.

Tuberculosis (TB) remains a significant global health concern, necessitating effective control strategies. This article presents a mathematical model to evaluate the comparative effectiveness of medical mask usage and case detection in TB control. The model is constructed as a system of ordinary differential equations and incorporates crucial aspects of TB dynamics, including slow-fast progression, medical mask use, case detection, treatment interventions and differentiation between symptomatic and asymptomatic cases. A key objective of TB control is to ensure that the reproduction number, , remains below unity to achieve TB elimination or persistence if exceeds 1. Our mathematical analysis reveals the presence of a transcritical bifurcation when the signifies a critical juncture in TB control strategies. These results confirm that the effectiveness of case detection in diminishing the endemic population of symptomatic individuals within a TB-endemic equilibrium depends on exceeding a critical threshold value. Furthermore, our model is calibrated using TB yearly case incidence data per 100 000 population from Indonesia, India, Lesotho and Angola. We employed the bootstrap resampling residual approach to assess the uncertainty inherent in our parameter estimates which provides a comprehensive distribution of the parameter values. Despite a declining trend in new incidence, these four countries exhibit a reproduction number greater than 1, indicating persistent TB cases in the presence of ongoing TB control programmes. We employ the partial rank correlation coefficient in conjunction with the Latin hypercube sampling method to conduct a global sensitivity analysis of the parameter for each fitted parameter in every country. We find that the medical mask use is more sensitive to reduce compared with the case detection implementation. To further gain insight into the necessary control strategy, we formulated an optimal control and studied the cost-effectiveness analysis of our model to investigate the impact of case detection and medical mask use as control measures in TB spread. Cost-effectiveness analysis demonstrates that combining these interventions emerges as the most cost-effective strategy for TB control. Our findings highlight the critical importance of medical masks and their efficacy coupled with case detection in shaping TB control dynamics, elucidating the primary parameter of concern for managing the control reproduction number. We envisage our findings to have implications and be vital for TB control if implemented by policymakers and healthcare practitioners involved in TB control efforts.

Groundwater health risk assessment of North China Plain based on Monte Carlo model sensitivity analysis and morphological analysis: A case study of Shijiazhuang City.

The rapid development of the social economy and the influence of human activities can lead to aggravated groundwater pollution. Groundwater safety is the premise of residents' health. Therefore, studying the sustainable utilization and health risks of groundwater quality is important. The groundwater quality and potential health risks were evaluated in the Shijiazhuang area, which is located in the North China Plain in this paper. Based on 159 groundwater samples collected in the study area, the potential health risks of As, Cr6+, Ni, Pb, F-, and NO3 - to humans were evaluated from oral and skin contact. Results of the human health risk assessment showed that the average carcinogenic risk and non-carcinogenic risk of children are higher than those of adults. According to the spatial distribution of the total risk value, adults and children in the southwest of the study area face higher risks. Because of the uncertainty of USEPA, Monte Carlo simulation was used to calculate the probability of health risk assessment and prioritization of contaminant treatment. The results of the Monte Carlo simulation showed that the acceptable range for children is 6.82%, and the acceptable range for adults is 18.07%. According to the HRWM model, carcinogenic pollutants mainly include As, Cr6+, and Ni. The most important chemical species of As is HAsO4 2-, followed by H2AsO4 -. Similarly, CrO4 2- and Ni2+ are the main forms of Cr6+ and Ni. The results of this study can provide data support for the protection and management of groundwater quality in the North China Plain. PRACTITIONER POINTS: Children are more susceptible to carcinogenic risk than adults. After calculation, the main influencing elements are Ni and Cr. Metal morphology analysis was carried out, and the results showed that HAsO4 2-, CrO4 2-, and Ni2+ were the main types.