Partition Index Research Articles

B-278 Evaluation of serum copper, zinc and selenium reference intervals using The National Health and Nutrition Examination Survey (NHANES) data

Abstract Background Serum copper, zinc and selenium, are commonly ordered for assessment of nutrient status or investigation of deficiency or overload. Review of reference intervals for these analytes is challenging as most laboratories do not have the resources to do a de novo reference interval study and many literature references are not recent. The National Health and Nutrition Examination Survey (NHANES) survey presents a unique opportunity to access a comprehensive database that includes biochemical parameters and health information in order to review reference intervals regularly, especially for more esoteric tests such as trace elements. In this study, copper, zinc and selenium reference intervals were calculated using NHANES data. Methods NHANES data for individuals 18 years of age and older was obtained from three releases between 2012-2016. Copper, selenium and zinc 95% confidence intervals were evaluated by age, sex and body mass index (BMI) partitions using quantile regression. All calculations were performed in R using the quantreg package. Results The calculated reference intervals for serum copper, zinc and selenium are shown in Table 1. Serum copper had the largest changes observed between sexes, with females having a much wider reference interval on the high end. Oral contraceptive use is known to increase serum copper levels which likely explains this trend. BMI over 30 also increased the serum copper reference interval, more clearly in women than in men. Zinc reference intervals were more aligned between women and men, with slightly higher intervals observed in men. BMI did not have much effect in either sex. For selenium, men had slightly higher reference intervals than women. Conclusions NHANES data provides a comprehensive data set for reference interval evaluation, especially for more esoteric analytes such as trace elements.

Correlation between individual phase constitutive properties and plastic heterogeneities in advanced-high strength dual-phase steels

The present study comprehensively investigates the individual phase constitutive properties and plastic heterogeneities in advanced high-strength steels (AHSS), particularly DP590 and DP780 dual-phase (DP) steels. A machine learning-based model is implemented to identify the ferrite and martensite phases in the microstructures of DP590 and DP780. Then, the constitutive properties of ferrite and martensite phases are successfully obtained through a hybrid approach of in-situ neutron diffraction coupled with the crystal plasticity finite element method (CPFEM). The distinct microstructures between DP590 and DP780 result in different macroscopic and microscopic properties among the two materials. Owing to different martensite volume fractions (Vm) in DP590 (Vm = 8.3 %) and DP780 (Vm = 35.4 %), a noticeable dependency of plastic heterogeneities during deformation on martensite fraction and its spatial distribution is revealed. Compared to DP590, the deformed microstructure of DP780 exhibits a more heterogeneous distribution of stress and strain fields, along with significant formation of plastic strain localization leading to a remarkable increase in strain partitioning index. It shows that a lower fraction of martensite with its discrete distribution decreases martensite ability to hinder the ferrite deformation, thus strain localization is primarily concentrated within the ferrite phase as the predominant failure mode in DP590. In contrast, a higher martensite fraction in DP780 causes more pronounced strain localization which occurs in the ferrite and at the ferrite/martensite interface. In addition, interconnect distribution between martensite islands enhances the inhibition of martensite to ferrite deformation, thereby high strain gradient at their interface leads to prevalence of ferrite/martensite interface decohesion in DP780.

Intraspecific and interspecific resource partitioning between bumblebee workers and males related to nectar quantity and quality.

Bumblebees are important pollinators for many natural and agricultural systems in temperate regions. Interspecific and intraspecific variation in floral resource preferences have been proposed to influence bumblebee community structure. In particular, sexual dimorphism is a major source of intraspecific niche variation. Although interspecific resource partitioning is well studied, few studies have explored the intraspecific dynamics between workers and males. Here, we report a study on a total of 11528 workers and 2220 males of 14 bumblebee species recorded over 5years in the Hengduan Mountains of Southwest China. We first compared the potential for interspecific and intraspecific competition between workers and males using visitation records and resource partitioning indices (overlap index). We then evaluated the influence of nectar traits on flower preference, including nectar volume and the levels of hexose, sucrose and 10 essential amino acids (EAAs). We found that the niche overlap between intraspecific workers and males was higher than that between different species, and temporal overlap alone did not strongly determine diet overlap. Males of most species preferred flowers with high levels of EAAs and hexose, whereas workers of some species preferred flowers with high nectar volume and sucrose levels. This study suggests that there is floral resource partitioning among bumblebee species, and between workers and males, which may play a key role in alleviating interspecific and intraspecific competition. These findings also provide a useful guide for which kinds of plants might be most valuable for bumblebees, especially the understudied males, in this biodiversity hotspot.

Genotypic Variation Studies in Biomass Partitioning Patterns during Post-flowering Stages under the Late Sown Conditions of Chickpea (Cicer arietinum L.) Germplasm

This study conducted during the 2021-2022 year under field conditions investigates the genotypic variation in biomass partitioning among different chickpea genotypes during post-flowering stages, with a specific emphasis on late sown conditions. The main aim of the study was to elucidate the efficiency of resource allocation toward pod production, measured by the pod harvest index. This research enhances understanding of chickpea performance under delayed sowing conditions by examining genotypic variations in key parameters during critical post-flowering stages, specifically pod filling and seed filling. Biomass partitioning in branches and pods varied considerably during both growth stages, with branches playing a crucial role in pod development during pod filling. The transition from pod filling to seed filling stage resulted in increased biomass partitioning in pods, highlighting their importance as storage organs for seed development. Seed yield exhibited significant variability among genotypes, with some surpassing 2000 kg/ha, while pod harvest index ranged from 35.62% to 70.48%, indicating differences in resource allocation efficiency. Regression analysis showed varying degrees of association between biomass partitioning and pod harvest index, with seed yield and pod harvest index, biomass partitioning in pods exhibiting the highest explanatory power during seed filling. By studying the adaptation of plants towards biphasic and allometric allocation patterns during unfavorable conditions can result in developing the resource use efficient climate resilient varieties. These findings contribute to our understanding of crop productivity under varying environmental conditions and inform strategies for optimizing chickpea yield and resilience.

Revealing spatiotemporal inequalities, hotspots, and determinants in healthcare resource distribution: insights from hospital beds panel data in 2308 Chinese counties

BackgroundEnsuring universal health coverage and equitable access to health services requires a comprehensive understanding of spatiotemporal heterogeneity in healthcare resources, especially in small areas. The absence of a structured spatiotemporal evaluation framework in existing studies inspired us to propose a conceptual framework encompassing three perspectives: spatiotemporal inequalities, hotspots, and determinants.MethodsTo demonstrate our three-perspective conceptual framework, we employed three state-of-the-art methods and analyzed 10 years’ worth of Chinese county-level hospital bed data. First, we depicted spatial inequalities of hospital beds within provinces and their temporal inequalities through the spatial Gini coefficient. Next, we identified different types of spatiotemporal hotspots and coldspots at the county level using the emerging hot spot analysis (Getis-Ord Gi* statistics). Finally, we explored the spatiotemporally heterogeneous impacts of socioeconomic and environmental factors on hospital beds using the Bayesian spatiotemporally varying coefficients (STVC) model and quantified factors’ spatiotemporal explainable percentages with the spatiotemporal variance partitioning index (STVPI).ResultsSpatial inequalities map revealed significant disparities in hospital beds, with gradual improvements observed in 21 provinces over time. Seven types of hot and cold spots among 24.78% counties highlighted the persistent presence of the regional Matthew effect in both high- and low-level hospital bed counties. Socioeconomic factors contributed 36.85% (95% credible intervals [CIs]: 31.84–42.50%) of county-level hospital beds, while environmental factors accounted for 59.12% (53.80–63.83%). Factors’ space-scale variation explained 75.71% (68.94–81.55%), whereas time-scale variation contributed 20.25% (14.14–27.36%). Additionally, six factors (GDP, first industrial output, local general budget revenue, road, river, and slope) were identified as the spatiotemporal determinants, collectively explaining over 84% of the variations.ConclusionsThree-perspective framework enables global policymakers and stakeholders to identify health services disparities at the micro-level, pinpoint regions needing targeted interventions, and create differentiated strategies aligned with their unique spatiotemporal determinants, significantly aiding in achieving sustainable healthcare development.

Adaptive Responses of Biofortified Common Bean Lines to Acidic Soil and High Temperatures in the Colombian Amazon Region

One of the strategies to combat micronutrient malnutrition is by developing biofortified common bean lines (Phaseolus vulgaris L.) capable of tolerating different stress conditions. In this study, the adaptive responses of different biofortified bean lines grown under combined stress of acidic soil and high-temperatures were evaluated in the Colombian Amazon. A total of 247 common bean lines from the Mesoamerican gene pool were used to determine the adaptive response in terms of phenological, physiological, and agronomic behavior under combined stress conditions. The lines tested were obtained from different single crosses, double crosses, and backcrosses between different bean materials, of which 146 were obtained from F4 families with high iron (Fe) content in seed and 99 common bean lines from F5 families. Different bean lines had grain yields (GY) higher than 1400 kg ha−1 from the F5 (lines: 859, 805, 865, and 657) and F4 (lines: 2853 and 2796) families. The superior performance of these lines was related to a higher photosynthate partitioning that has allowed an increase in pod formation (pod partitioning index, PPI) from the canopy biomass (CB) and grain filling (pod harvest index, PHI; harvest index, HI), resulting in higher values of GY. Values of GY were correlated with CB (r = 0.36), PPI (r = 0.6), PHI (r = 0.68), and HI (r = 0.8, p < 0.001). This increase in agronomic performance is due to a greater allocation of energy to the photosynthetic machinery (ΦII) and its dissipation in the form of heat (ΦNPQ), with increases in the leaf temperature difference (LTD). Based on the results obtained, six biofortified lines of common bean (lines F5: 859, 805, 865, and 657; lines F4: 2853 and 2796) showed traits of tolerance to combined stress and can serve as progenitors to increase Fe and Zn concentration in the seeds of lines that tolerate the combined stress from acidic soil and high temperature in the Colombian Amazon region.

Seismic response prediction of a damped structure based on data-driven machine learning methods

Damping technology has been widely used because of its good vibration control effect. However, due to the strong nonlinearity of the added dampers, accurately predicting the seismic response of damped structures remains a challenge. This study investigates the application of interpretable machine learning (ML)-based and deep learning-based approaches to the prediction of the maximum inter-storey displacement of a damped structure. A comprehensive database consisting of 13,855 structural responses to ground motions was collected. Seven traditional interpretable ML algorithms including random forest and extreme gradient boosting (XGBoost), a convolutional neural network based on large receptive field, and seismic wave transformer (SWT) model based on a transformer network were developed. The predictions show that the error of the SWT based on unsupervised feature extraction is reduced by 50.90% compared with that of the optimal XGBoost in ensemble learning. Although the SWT has the highest global accuracy, XGBoost is found to have a smaller error when the structure is in a linear state with peak ground acceleration as partition index, so an aggregation model (AM)-based structural response prediction method was also proposed. The accuracy of the AM improved by 27.95% compared with that of the SWT. In contrast with other ML models, the proposed AM is more advantageous in terms of computational efficiency and accuracy.

The Interaction Effects of Drought–Flood Abrupt Alternation on Rice Yield and Dry Matter Partitioning

The frequent occurrence of drought–flood abrupt alternation (DFAA) seriously affects crop yield. It is particularly important to explore the dynamics of material accumulation and distribution under DFAA stress to analyze the mechanism of yield formation. In this study, a bucket experiment with DFAA stress groups, drought control (DC) groups, flood control (FC) groups, and normal irrigation (CK) groups was set up from the jointing to the heading stage of rice to analyze the interaction effects of DFAA stress on rice yield and dry matter partitioning. The results showed that compared with the CK group, the average yield reduction rate of rice in the DFAA groups was 23.03%, and the number of grains per panicle, total grain number, thousand-seed mass, and seed setting rate decreased. Compared with the DC groups, the DFAA groups had a significant reduction in yield and its components during the flooding period. Compared with the FC groups, the DFAA groups showed a compensation phenomenon in the yield and its components during the drought period. From the end of DFAA stress to the harvest period, the root partitioning index (PI) of the DFAA groups decreased, the stem PI increased first and then decreased, the leaf PI decreased, and the panicle PI increased. The results showed that the rice leaves increased and thickened, and the stems thickened under DFAA conditions to enhance the ability to resist drought and flooding stress, but the panicle rate was reduced, the growth period of rice was delayed, and the redundant growth of stems and leaves was increased. It is suggested that the depth and duration of stagnant water storage during the flood period of DFAA should be controlled, and the transfer and supply of photosynthetic products to grains should be increased to avoid serious yield reductions. The research results provide a theoretical basis for the rational development of farmland DFAA mitigation measures.

A fast-partitioning decision method for demand side resources based on grid resilience assessment

With the large-scale renewable energy integrated into the distribution grid, the grid’s regulating ability and disturbance tolerance are weakening. When partitioning demand-side resources, it is necessary to enhance resilience to ensure the reliability of electric power. This paper proposes a fast-partitioning method that considers resilience, structure, and functionality to adapt to the evolving requirements of the distribution system. Specifically, the comprehensive partition index system is constructed with the resilience assessment index reflecting the ability of partitions to withstand and mitigate the effects of faults, the modularity index based on electrical distance, and regional power balance indexes. Meanwhile, a modified genetic algorithm is proposed to calculate the comprehensive partition index. The modified algorithm first uses a sensitivity matrix to perform initial partitions and construct initial populations. Then, it utilizes a triangular network adjacency matrix for chromosome encoding, significantly reducing the algorithm’s search space and enhancing partitioning efficiency. Finally, the applicability and effectiveness of the proposed method are verified through simulation analysis of the IEEE 28-node system.

An entropy-based membership approach on type-II fuzzy set (EMT2FCM) for biomedical image segmentation

Medical image segmentation is a challenging task owing to its aliasing artifacts, existence of mixed pixels, interference of noise and blurring effect, etc. The brain and the spinal cord together constitute the Central Nervous System (CNS), with the brain being the most complex component. Brain segmentation is crucial for surgical planning, disease detection, and therapeutic interventions. A unique entropy based soft clustering approach (EMT2FCM) for the brain MRI imaging on both real and simulated datasets is proposed in this paper. This paper presents a refined version of the type-2 Fuzzy C-Means (FCM) technique (EMT2FCM) for isolating diverse tissues in Magnetic Resonance (MR) brain images, achieved through an enhanced entropy-based membership function. Difficulty in edge detection occurs due to the vagueness caused by mixed pixels. To overcome the problem with mixed pixels a new membership function proposal has been given. EMT2FCM, i.e., fuzzy membership function with Shannon's entropy-based variation, is able to handle uncertainty and vagueness in images offered by mixed pixels, by implementing new distance criteria modification for the calculation of optimum cluster. The approach has been validated using datasets from BRAINWEB (simulated) and IBSR (real-life). Following the deployment of our new algorithm, we assessed its performance using six observation criteria. The results demonstrated significant improvements in clustering performance, as evidenced by enhanced Mean Squared Error (MSE), Dice Index, and Jaccard Index, Accuracy, Sensitivity and Partition Indices validation measures. The evaluation of EMT2FCM in comparison to other state-of-the-art methods is established to reduce segmentation errors significantly.

3DT-CM: A Low-complexity Cross-matching Algorithm for Large Astronomical Catalogues Using 3d-tree Approach

Location-based cross-matching is a preprocessing step in astronomy that aims to identify records belonging to the same celestial body based on the angular distance formula. The traditional approach involves comparing each record in one catalog with every record in the other catalog, resulting in a one-to-one comparison with high computational complexity. To reduce the computational time, index partitioning methods are used to divide the sky into regions and perform local cross-matching. In addition, cross-matching algorithms have been adopted on high-performance architectures to improve their efficiency. But the index partitioning methods and computation architectures only increase the degree of parallelism, and cannot decrease the complexity of pairwise-based cross-matching algorithm itself. A better algorithm is needed to further improve the performance of cross-matching algorithm. In this paper, we propose a 3d-tree-based cross-matching algorithm that converts the angular distance formula into an equivalent 3d Euclidean distance and uses 3d-tree method to reduce the overall computational complexity and to avoid boundary issues. Furthermore, we demonstrate the superiority of the 3d-tree approach over the 2d-tree method and implement it using a multi-threading technique during both the construction and querying phases. We have experimentally evaluated the proposed 3d-tree cross-matching algorithm using publicly available catalog data. The results show that our algorithm applied on two 32-core CPUs achieves equivalent performance than previous experiments conducted on a six-node CPU-GPU cluster.

Terminal removal at first square enhances vegetative branching to increase seedcotton yield at low plant density

ContextCotton has a complex branching pattern including sympodial and vegetative branching. Traditionally managed cotton mainly depends on sympodial branching for formation of yield-contributing fruit. Whether seedcotton yield based solely on vegetative branching is comparable with that based solely on sympodial or both types of branching is unclear. ObjectiveThe study determined how terminal removal at first square to avoid formation of sympodial branches (SBs) regulated vegetative branching and yield under different plant densities. MethodsA two-year split-plot design field experiment had main plots with plant density (4.5 and 9.0 plants m−2) and subplots with plant pruning mode. Pruning modes were terminal removal at first square (TRS) to avoid formation of sympodial branches but retain only vegetative branches (VBs), removal of VBs (RVB) to retain only SBs, and no pruning (CK), with both branch types remaining intact. Canopy photosynthesis, dry matter accumulation and partitioning, and seedcotton yield and yield components were examined. ResultsAt low plant density (4.5 plants m−2), compared with other pruning modes, TRS increased seedcotton yield and biological yield but did not affect harvest index. At high plant density (9.0 plants m−2), compared with other pruning modes, TRS decreased seedcotton yield and harvest index, although biological yield increased. The yield increase with TRS at low plant density was attributed to the increase in biological yield, which was due to significantly higher canopy photosynthesis with TRS than with other pruning modes. At high plant density, photosynthesis with TRS was higher than that with no pruning at peak boll-setting and boll-opening stages and higher than that with RVB from peak squaring to boll-opening. Compared with other pruning modes, TRS increased dry matter partitioning to vegetative organs but decreased partitioning to reproductive organs. Pearson correlation analysis revealed a significant positive correlation between canopy apparent photosynthesis and biological yield, biomass partitioning, and harvest index, irrespective of plant pruning and plant density. ConclusionPlant pruning and plant density interacted to affect seedcotton yield. With TRS, cotton yield at low plant density was higher than that with other pruning modes, which was attributed to an increase in biological yield associated with an increase in canopy photosynthesis. At high plant density, the yield decrease with TRS was attributed to low harvest index associated with reduced reproductive partitioning. ImplicationsCotton cultivation relying solely on vegetative branching can be an alternative to obtain moderate yields and economic benefits under conditions of low plant density.

Nano-zinc and plant growth-promoting bacteria is a sustainable alternative for improving productivity and agronomic biofortification of common bean

Background and aimsNano-zinc (Zn) fertilizer is an easily adaptable and environmentally safe alternative option that can effectively improve growth, yield and biofortification of common bean. Plant growth-promoting bacteria (PGPBs) could promote plant growth and nutrients availability in sustainable manner. Therefore, this study aimed to investigate the influence of foliar nano-Zn application in association with seed co-inoculations of PGPBs on growth, yield, biofortification and Zn use efficiencies in common bean cultivation. Two field experiments were performed with seven co-inoculations of PGPBs and three foliar nano-Zn doses applied 50% at R5 and 50% at R8 stages of common bean to determine plant height, shoot dry matter, grain yield, Zn concentration and uptake in shoot and grains, Zn partitioning index, daily Zn intake and Zn use efficiencies for agronomic biofortification.ResultsThe combined foliar nano-Zn application and co-inoculation of R. tropici + B. subtilis enhance grain yield, leaf chlorophyll index, total protein content, grain Zn concentration and uptake, daily Zn intake, Zn use efficiency, applied Zn recovery and Zn utilization efficiency in common beans in 2019 and 2020 cropping seasons. Foliar nano-Zn application at a dose of 1.5 kg ha−1 increased plant height, shoot dry matter, shoot Zn uptake, Zn partitioning and agro-physiological efficiency under co-inoculation with R. tropici + B. subtilis in both cropping years.ConclusionsThe treatments with foliar nano-Zn application at a dose of 1.5 ha−1 and co-inoculation with R. tropici + B. subtilis improved performance, chlorophyll index, protein content, grain yield, and Zn efficiencies that can lead to better biofortification of common bean in tropical savannah. Therefore, it is recommended that applying nano-Zn via foliar along with co-inoculation of PGPBs could be the better option for productivity and biofortification of common bean.Graphical

Alternate intercropping of cotton and peanut increases productivity by increasing canopy photosynthesis and nutrient uptake under the influence of rhizobacteria

ContextAlternate intercropping of cotton and peanut is an emerging cropping practice that involves the intercropping of two crops in a wide strip, with annual switching of planting positions. The productivity achieved through alternate intercropping, which combines intercropping and rotation, surpasses that of either mode used alone. However, the underlying mechanisms responsible for the observed increase in crop productivity remain elusive. We put forth the hypothesis that alternate intercropping enhances productivity by promoting canopy photosynthesis and nutrient uptake, facilitated by the influence of rhizosphere bacteria. MethodsA two-year field experiment was conducted employing a randomized block design to investigate the effects of monoculture, traditional intercropping, and alternate intercropping on yield, yield components, canopy photosynthesis, 13C photoassimilate partitioning, uptake of major nutrients, and diversity of rhizosphere bacterial communities. ResultsAlternate intercropping increased biological by 9.3% and seed cotton yield by 20.0% compared to monocultures without sacrificing peanut yields. In comparison to traditional intercropping, alternate intercropping also increased seed cotton yield by 3.9%, peanut pod yield by 6.7%, and total productivity by 10.6%. The cotton harvest index in alternate intercropping was similar to that in traditional intercropping but 9.9% higher than in monoculture. However, the peanut harvest index did not differ between alternate intercropping, traditional intercropping, and monoculture. Net ecosystem CO2 exchange (NEE) under alternate intercropping was higher in cotton than that under traditional intercropping and monoculture, and 13C partitioning to reproductive organs in cotton was higher in alternate intercropping than in monoculture, although there was no difference between alternate and traditional intercropping. In peanut, NEE was higher under alternate intercropping than under traditional intercropping at all stages. At peak pod setting, 13C partitioning to reproductive organs was higher under both alternate intercropping and monoculture than under traditional intercropping. Alternate intercropping resulted in a 7.2% increase in cotton nitrogen uptake compared to traditional intercropping, and a 8.8% increase in nitrogen uptake, 10.9% increase in phosphorus uptake, and 8.5% increase in potassium uptake compared to monoculture cotton. Peanut uptake of nitrogen, phosphorus, and potassium was greater in alternate intercropping than in traditional intercropping and was approximately the same as that in monoculture. Alternate intercropping increased the relative abundances of plant growth promoting rhizobacteria (PGPR). Significant correlations were detected between NEE and biological yield, 13C partitioning and harvest index, as well as the relative abundance of PGPR and uptake of major nutrients. ConclusionThe enhanced crop productivity observed in alternate intercropping can be attributed to improved shoot-root coordination, as evidenced by the increased canopy photosynthesis and nutrient uptake associated with altered rhizosphere bacterial communities.

Late nitrogen fertilization improves cotton yield through optimizing dry matter accumulation and partitioning

Cotton (Gossypium hirsutum L.) production efficiency is constrained by issues such as a low nitrogen (N) utilization rate (30–35 %) and high N application rate (300–350 kg ha−1) in particular arid climates, such as Xinjiang, the largest irrigated cotton-producing region in China. These issues could be alleviated by allocating more N to late application to satisfy plant needs for growth and development. Over a three-year (2019–2021) field experiment, 240 kg ha−1 N was applied in 3 periods (squaring, flowering to peak boll, and late peak boll). The amount of N applied in the second period was fixed at 60 %, and the remaining 40 % was split between the first and the third periods, with five ratios (treatments), i.e., 0:6:4 (N064), 1:6:3 (N163), 2:6:2 (N262), 3:6:1 (N361) and 4:6:0 (N460), to investigate cotton response in terms of growth, biomass accumulation, and yield. Compared with the conventional treatment (N262), the N064 treatment narrowed the plant width by 11.7–12.0 %, increased canopy light transmittance by 6.1–56.9 %, extended the boll growth period by 8.8–9.4 %, improved defoliation by 9.3–11.7 % and increased both seed cotton yield and N partial factor productivity by 7.1–8.1 %, depending on the year. N064 accumulated 4.3–39.5 % more biomass (K) than N262, with the average and maximum growth rates of the reproductive organs (Vt and Vm, respectively) increasing by 8.6–89.0 % and 6.9–125.7 %, respectively, while the fast growth duration (∆t) shortened by 9.9–31.6 %. Again, N064 partitioned 1.5–35.8 % more biomass to bolls (higher partitioning index, PIboll). Seed cotton yield was significantly positively correlated with K, t1, Vt, Vm and PIboll and negatively correlated with ∆t in reproductive organs. Collectively, the data suggest that allocating more N to late application could be an efficient N fertilizer management strategy in arid areas under N-reduced cultivation and fertigation.

Adaptive Indexing in High-Dimensional Metric Spaces

Similarity search in high-dimensional metric spaces is routinely used in many applications including content-based image retrieval, bioinformatics, data mining, and recommender systems. Search can be accelerated by the use of an index. However, constructing a high-dimensional index can be quite expensive and may not pay off if the number of queries against the data is not large. In these circumstances, it is beneficial to construct an index adaptively , while responding to a query workload. Existing work on multidimensional adaptive indexing partitions space into orthotopes (i.e., hyperrectangular units). This approach, however, is highly ineffective in high-dimensional spaces. In this paper, we propose AV-tree: an alternative method for adaptive high-dimensional indexing that exploits previously computed distances, using query centers as vantage points. Our experimental study shows that AV-tree yields cumulative cost for the first several hundred or even thousand queries much lower than that of pre-built indices. After thousands of queries, the per-query performance of the AV-tree converges or even surpasses that of the state-of-the-art MVP-tree. Arguably, our approach is commendable in environments where the expected number of queries is not large while there is a need to start answering queries as soon as possible, such as applications where data are updated frequently and past data soon become obsolete.

Integrated use of plant growth-promoting bacteria and nano-zinc foliar spray is a sustainable approach for wheat biofortification, yield, and zinc use efficiency.

The intensive cropping system and imbalance use of chemical fertilizers to pursue high grain production and feed the fast-growing global population has disturbed agricultural sustainability and nutritional security. Understanding micronutrient fertilizer management especially zinc (Zn) through foliar application is a crucial agronomic approach that could improve agronomic biofortification of staple grain crops. The use of plant growth-promoting bacteria (PGPBs) is considered as one of the sustainable and safe strategies that could improve nutrient acquisition and uptake in edible tissues of wheat to combat Zn malnutrition and hidden hunger in humans. Therefore, the objective of this study was to evaluate the best-performing PGPB inoculants in combination with nano-Zn foliar application on the growth, grain yield, and concentration of Zn in shoots and grains, Zn use efficiencies, and estimated Zn intake under wheat cultivation in the tropical savannah of Brazil. The treatments consisted of four PGPB inoculations (without inoculation, Azospirillum brasilense, Bacillus subtilis, and Pseudomonas fluorescens, applied by seeds) and five Zn doses (0, 0.75, 1.5, 3, and 6 kg ha-1, applied from nano ZnO in two splits by leaf). Inoculation of B. subtilis and P. fluorescens in combination with 1.5 kg ha-1 foliar nano-Zn fertilization increased the concentration of Zn, nitrogen, and phosphorus in the shoot and grain of wheat in the 2019 and 2020 cropping seasons. Shoot dry matter was increased by 5.3% and 5.4% with the inoculation of P. fluorescens, which was statistically not different from the treatments with inoculation of B. subtilis as compared to control. The grain yield of wheat was increased with increasing nano-Zn foliar application up to 5 kg Zn ha-1 with the inoculation of A. brasilense in 2019, and foliar nano-Zn up to a dose of 1.5 kg ha-1 along with the inoculation of P. fluorescens in the 2020 cropping season. The zinc partitioning index was increased with increasing nano Zn application up to 3 kg ha-1 along with the inoculation of P. fluorescens. Zinc use efficiency and applied Zn recovery were improved at low doses of nano-Zn application in combination with the inoculation of A. brasilense, B. subtilis, and P. fluorescens, respectively, as compared to control. Therefore, inoculation with B. subtilis and P. fluorescens along with foliar nano-Zn application is considered a sustainable and environmentally safe strategy to increase nutrition, growth, productivity, and Zn biofortification of wheat in tropical savannah.

A distributed PageRank-based dynamic partition algorithm to improve distributed energy storages participation in frequency regulation

In high renewable energy sources penetrated power systems, a large number of distributed small-capacity energy storages (ESs) are expected to form virtual power plants (VPPs) and provide comparable frequency regulation capacity to conventional power plants. In this paper, a dynamic and distributed partition method is proposed to cluster ESs into VPPs by selecting the electrical distance and local power demands as the index for partitioning. Utilizing the partition indices, the Personalized PageRank (PPR) scores are used to measure the similarity of all ESs with respect to other ESs in parallel. Then Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to partition the ESs into different VPPs. Within the VPPs, the secondary frequency control strategy is adopted to achieve the frequency regulation and state of charge (SoC) balancing. Finally, the performance comparison of the proposed partition method with several classic clustering algorithms is conducted to validate the efficiency and the quality of the partition. And numerical studies are performed in an IEEE 39-bus system to verify that the proposed control method can eliminate the frequency deviation and reduce power losses.

A dataset of spatial distribution of highland barley planting area on the Qinghai-Tibet Plateau (2019)

Highland barley is the dominant crop that can best adapt to the natural environment of the Qinghai-Tibet Plateau characterized by Alpine low temperature, hypoxia and strong radiation. In order to obtain the spatial distribution of the highland barley planting areas on the Qinghai-Tibet Plateau, we adopted a highland barley extraction method based on multi-element fusion of partition classification. First, we impose restrictions on the range of highland barley map spots of different agricultural partitions in terms of altitude, slope, precipitation and hydrological factors. Second, we optimized the optimal band for highland barley extraction through the OIF index partition. Finally, we used the object-oriented classification method to extract the planting areas of highland barley on the Qinghai-Tibet Plateau. The accuracy test of confusion matrix shows that the overall accuracy is 91.74% and Kappa coefficient is 0.83. According to the extraction results of highland barley on the Qinghai-Tibet Plateau, the total planting area of highland barley is about 2.74×105 hm2. The dataset improves the understanding of the existing highland barley spatial distribution pattern from the administrative unit scale to the patch scale. And it can provide data reference for optimizing the spatial distribution pattern of highland barley planting in the future.

Grain Yield Formation and Nitrogen Utilization Efficiency of Different Winter Wheat Varieties under Rainfed Conditions in the Huang-Huai-Hai Plain

Winter wheat production is threatened by drought stress under rainfed conditions; thus, screening high- and stable-yielding wheat varieties to ensure the sustainable development of wheat production and food security in the Huang-Huai-Hai Plain (HHHP) is vital. In this research, four-year field experiments with twelve winter wheat varieties were conducted during the winter wheat-growing seasons between 2016 and 2020 in order to: (1) screen high- and stable-yielding winter wheat varieties under rainfed conditions, (2) investigate the mechanism of high-yielding wheat yield formation and the relationships among grain-yield formation traits, and (3) investigate the nitrogen utilization efficiency (NUtE) of high-yielding wheat. The results showed that high-yield level wheat varieties (HL; Yannong999, Taimai1918 and Yannong173) obtained a higher average grain yield than medium-yield level wheat varieties (ML) and low-yield level wheat varieties (LL) by 10.1% and 29.0%, respectively. Compared with ML and LL, HL had a higher biomass at anthesis, higher spike dry matter at anthesis, higher spike partitioning index and fruiting efficiency (grain set per unit of spike dry weight at anthesis), and the highest grain number per square meter (24.2 × 103 m−2). Simultaneously, HL maintained a higher leaf area index (LAI) at anthesis and a higher net photosynthesis rate (Pn) of flag leaves after anthesis, which contributed to a higher post-anthesis biomass; HL also had higher maturity biomass, harvest index (HI), and biomass remobilization in comparison to ML and LL. The above results demonstrated that HL improved grain yield by increasing grain number per square meter, post-anthesis biomass, biomass remobilization, maturity biomass, and HI. Additionally, HL also obtained higher NUtE by improving grain yield. Therefore, screening winter wheat varieties with traits such as HL can help achieve high and stable yields and high NUtE under rainfed conditions.