Low Levels Of Variation Research Articles

You're Not You When You're Angry: Robust Emotion Features Emerge by Recognizing Speakers

The robustness of an acoustic emotion recognition system hinges on first having access to features that represent an acoustic input signal. These representations should abstract extraneous low-level variations present in acoustic signals and only capture speaker characteristics relevant for emotion recognition. Previous research has demonstrated that, in other classification tasks, when large labeled datasets are available, neural networks trained on these data learn to extract robust features from the input signal. However, the datasets used for developing emotion recognition systems remain significantly smaller than those used for developing other speech systems. Thus, acoustic emotion recognition systems remain in need of robust feature representations. In this work, we study the utility of speaker embeddings, representations extracted from a trained speaker recognition network, as robust features for detecting emotions. We first study the relationship between emotions and speaker embeddings, and demonstrate how speaker embeddings highlight the differences that exist between neutral speech and emotionally expressive speech. We quantify the modulations that variations in emotional expression incur on speaker embeddings, and show how these modulations are greater than those incurred from lexical variations in an utterance. Finally, we demonstrate how speaker embeddings can be used as a replacement for traditional low-level acoustic features for emotion recognition.

Hierarchical optimization of reservoir development strategy based on reinforcement learning

Production optimization is to increase the profitability of reservoir development by adjusting the type of wells and the rate of injection and production. Previous methods put both variables at the same level when optimizing and thus they change at the same frequency. However, these two variables belong to different scales, because well types require time-consuming and labor consuming operations such as borehole operations, while rate adjustment don't. Therefore, in this paper, a hierarchical optimization method is proposed to optimally control well types and corresponding rate in production sequence. Specifically, a hierarchical framework is first employed based on reinforcement learning method, with well types as high-level variables and quantities as low-level variables. Then, two hindsight mechanisms, named action hindsight and goal hindsight, are used to achieve stably joint optimization of two-level variables at different frequencies. Our findings from the case study reveal that our approach is highly practical, as it has successfully reduced operational costs by 90% and increased cumulative oil production by 18% within three years when compared to the single-level reinforcement method.

Remnant habitat patches provide high value for a wide range of insect species in a timber plantation mosaic

Species present in transformed landscapes utilise resources from either the transformed patches, natural patches, or both. Depending on the degree to which species are specialised, species-specific responses to landscape transformation is both varied and dynamic. Understanding species-specific responses across a landscape is therefore essential to make informed conservation decisions. We investigate how spillover of stenotopic, cultural, and ubiquitous insect species respond to the different edge combinations present in a timber plantation mosaic. We use a multi-taxon approach, sampling two distinct insect assemblages (ground and foliage) across four different biotopes in a grassland-forest-plantation landscape in the Midlands of KwaZulu-Natal, South Africa, which forms part of the Maputaland-Pondoland-Albany biodiversity hotspot. We show that in this landscape, ecological networks consisting of natural grassland and forest supported high insect diversity in both ground and foliage assemblages. The foliage assemblage displayed stronger responses to the different biotope combinations than the ground assemblage. There was remarkably little spillover of habitat specialist species of either the ground or foliage assemblages from grassland or forest into the plantation stands. The few species associated with plantation stands (cultural species) had consistent levels of spillover across different types of plantation blocks, but not into the natural areas. Natural biotopes in this landscape conserved many habitat specialists, whereas plantations maintained low levels of diversity. Thus, we recommend that conservation practitioners consider the fidelity and spillover of the species present in the local landscape before designing conservation plans in these dynamic production landscapes.

Gender diversity and publication activity—an analysis of STEM in the UK

Abstract Gender diversity in STEM remains a significant issue, as the field continues to be a male dominated one, despite increased attention on the subject. This article examines the interplay between gender diversity on projects funded by a major UK research council, the Engineering and Physical Sciences Research Council, and the publication activity of a project, as measured by the average journal quality of project publication output, over a 10-year period. The proportion of female representation and leadership on these projects remains very low. For the projects examined as part of this study, over 70% of these projects have no female representation, and less than 15% have a female lead. This study does not find a significant relationship between gender diversity and journal quality output. This study highlights that an important avenue for future work is the development of alternative metrics to assess the performance of research projects in a discipline characterized by very low levels of gender diversity, to fully unpack the impact of project team gender diversity on project output activity.

Diversity of stored-product beetles at the rice warehouses in Mataram City and Central Lombok Regency, Indonesia

Beetles, the order Coleoptera, are post-harvest rice product pests that cause severe quantitative and qualitative damage. Therefore, the diversity of pest beetles found in rice warehouses is essential because it leads to pest management by detecting and identifying beetles that can be the basis for making decisions in Integrated Pest Management (IPM). The study was conducted at two rice warehouses, Cakranegara I and Ubung, Indonesia, with rice sampling at five points diagonally on each side. Rice was taken using the Nobbe Trier tool inserted into the sack, and 100 g was taken as a rice sample. Rice sampling was conducted from June to July 2019, with five points diagonally on each side of a pile of rice. The results obtained four pest beetle species: Sitophilus oryzae, Cryptolestes ferrugineus, Oryzaephilus surinamensis, and Tribolium castaneum. Based on the Shannon Wiener index (H’) in Cakranegara I warehouse, the pest beetle found was only C. ferrugineus with a low diversity level of 1.60. Meanwhile, four pest beetle species were found in the Ubung warehouse S. oryzae, C. ferrugineus, O. surinamensis, and T. castaneum, with a moderate diversity level of 2.74. The differences in the diversity of pest beetles in storage were influenced by the rice warehouse's temperature and relative humidity (RH).

Association of Gestational Diabetes With the Dynamic Changes of Gut Microbiota in Offspring From 1 to 6 Months of Age.

The present study aimed to prospectively evaluate the influence of gestational diabetes mellitus (GDM) on the gut microbiota in 1- and 6-month-old offspring, as well as the dynamic changes from 1 to 6 months of age. Seventy-three mother-infant dyads (34 GDM vs 39 non-GDM) were included in this longitudinal study. Two fecal samples were collected for each included infant at home by the parents at 1 month of age ("M1 phase") and again at 6 months of age ("M6 phase"). Gut microbiota were profiled by 16S rRNA gene sequencing. Although no significant differences were observed in diversity and composition between GDM and non-GDM groups in the M1 phase, we observed differential structures and composition in the M6 phase between the 2 groups (P < .05), with lower levels of diversity, 6 depleted and 10 enriched gut microbes among infants born to GDM mothers. The dynamic changes in alpha diversity from the M1 to M6 phase were also significantly different according to GDM status (P < .05). Moreover, we found that the altered gut bacteria in the GDM group were correlated with infants' growth. Maternal GDM was associated not only with the community structure and composition in the gut microbiota of offspring at a specific time point, but also with the differential changes from birth to infancy. Altered colonization of the GDM infants' gut microbiota might affect their growth. Our findings underscore the critical impact of GDM on the formation of early-life gut microbiota and on the growth and development of infants.

Population distribution, connectivity and differentiation of Indo‐Pacific humpback dolphins in Chinese waters: Key baselines for improving conservation management

Abstract Humpback dolphins (Sousa spp.), throughout their distribution ranges, are often designated as flagship species for coastal ecosystem conservation. Most of the early research on humpback dolphins was carried out in Chinese waters, and in the last two decades, numerous studies have provided abundant conservation‐valuable findings. In this study, a systematic review was conducted to gain a comprehensive understanding of the distributional ecology of humpback dolphins in Chinese waters, to establish an updated national knowledge base about key conservation baselines on this flagship species. Currently, there are at least eight main distribution areas of humpback dolphins along the coast of southern China, representing a clear picture of place‐based conservation units. However, the overall biogeographical distribution pattern is discontinuous, and existing marine protected areas are far from sufficient to protect these key areas for the survival of humpback dolphins. Based on the photo‐identification technique, cross‐matching of humpback dolphin individuals revealed extremely low inter‐population connectivity among most of the known populations. Genetic studies suggested that several examined populations have low haplotype diversity levels, with limited inter‐population gene flow. Morphological and behavioural evidence also supports population differentiation among geographical locations. These findings provide key baselines for the promotion of conservation management initiatives for humpback dolphins in Chinese waters. All recognizable geographical populations, together with each key habitat, should be considered as independent conservation units. With small distribution areas, low genetic diversity and unfavourable geographical isolation, several populations may be at a high risk of regional extinction. A country‐led scheme with cross‐regional collaboration is required for science‐based, action‐focused and timely conservation efforts. Future conservation management initiatives should aim to increase or at least maintain key habitats, inter‐population connectivity, population‐level genetic diversity and effective population size.

Population structure analysis of mangrove mud crab Scylla serrata (Decapoda: Portunidae) from Indian coastal waters

Scylla serrata (Forskal, 1775) is an important crab resource for aquaculture and coastal fisheries in India. Study on population differentiation and population history of this economically important crab species is very much essential for conservation and management. Wild S. serrata samples were collected from 18 different locations in India including Andaman coastal waters. Population structure and demographic history of S. serrata was evaluated using 425bp of cytochrome b and 562bp of 16S rRNA genes. Only nine and three haplotypes were obtained by the analysis of cytochrome b and 16S rRNA gene respectively. Low level of haplotype diversity, nucleotide diversity, non significant F ST values for16S rRNA gene (F ST = 0.0133) and negative non significant F ST values for cytochrome b gene (F ST = −0.0063) were confirming the lack of population structure in Indian coastal waters. The maximum likelihood tree of cytochrome b gene showed that there were only 2 clusters and one cluster had only one representative (Hap8). The haplotypes of 16S rRNA gene were also clustered in to two groups with haplotype 1 (Hap1) as a separate group in maximum likelihood tree. The present study clearly figure out the S. serrata populations in Indian waters can be considered as a single stock during conservation and management programme.

Genetic diversity and structure of Callosobruchus maculatus populations in the different agro-ecological zones of Senegal

The objective of this study is to determine the diversity and genetic structure of cowpea weevil populations in the different agro-ecological zones of Senegal. Thus, to achieve this objective, individuals of Callosobruchus maculatus from localities in each zone were used, after sampling and massive breeding. The sequenced gene is Cyt-B. The results showed high haplotypic diversity (0.901 ±0.00033) and low nucleotide diversity (0.010 ±0.0000003). These diversities studied by agro-ecological zone show that the Casamance zone has the greatest haplotypic diversity (0.901; P&gt;0.05) while the eastern Senegal zone has the lowest value (0.439; P&gt;0.05). The nucleotide diversity is lower in the Sylvo-pastoral zone (0.00112; P&lt;0.05) and higher in the Casamance zone (0.01052; P&lt;0.05). The genetic structuring shows that the Senegal River Valley-eastern Senegal zone couple is more differentiated [FST(Fixation index)]=0.70687; P&lt;0.05) and the Casamance-Groundnut Basin zone couple is less differentiated (FST=0.21248; P&lt;0.05). The greatest genetic distance was found between the Sylvo-pastoral zone and the River Valley and the smallest between the eastern Senegal zone and the River Valley. However, a significant genetic variation within the populations was noted (64.93%). Overall we have a low level of diversity and weakly structured populations except those of Tamba and Fouta. A slight influence of the zones was also noted.

Omicron Waves in Argentina: Dynamics of SARS-CoV-2 Lineages BA.1, BA.2 and the Emerging BA.2.12.1 and BA.4/BA.5.

The COVID-19 pandemic has lately been driven by Omicron. This work aimed to study the dynamics of SARS-CoV-2 Omicron lineages during the third and fourth waves of COVID-19 in Argentina. Molecular surveillance was performed on 3431 samples from Argentina, between EW44/2021 and EW31/2022. Sequencing, phylogenetic and phylodynamic analyses were performed. A differential dynamic between the Omicron waves was found. The third wave was associated with lineage BA.1, characterized by a high number of cases, very fast displacement of Delta, doubling times of 3.3 days and a low level of lineage diversity and clustering. In contrast, the fourth wave was longer but associated with a lower number of cases, initially caused by BA.2, and later by BA.4/BA.5, with doubling times of about 10 days. Several BA.2 and BA.4/BA.5 sublineages and introductions were detected, although very few clusters with a constrained geographical distribution were observed, suggesting limited transmission chains. The differential dynamic could be due to waning immunity and an increase in population gatherings in the BA.1 wave, and a boosted population (for vaccination or recent prior immunity for BA.1 infection) in the wave caused by BA2/BA.4/BA.5, which may have limited the establishment of the new lineages.

Survey and Molecular Characterization of Echinococcus granulosus sensu stricto from Livestock and Humans in the Altai Region of Xinjiang, China

Cystic echinococcosis (CE), caused by the metacestode Echinococcus granulosus sensu stricto (s.s.), is an important zoonotic parasite, endemic in the Altai region of China. It is a serious human health risk and causes livestock losses. To evaluate the prevalence, genetic variation, and population structure of CE, 2898 sheep and 703 cattle were examined from October 2019 to mid-February 2020 in the Altai region (Altai, Habahe, Fuhai, and Buerjin). Sheep had an infection rate of 4.52% (131/2898) and cattle had an infection rate of 4.84% (34/703). In total, 180 cyst isolates were obtained, including 131 sheep, 34 cattle, and 15 from CE human patients. The cysts were investigated using mitochondrial cytochrome C oxidase subunit 1 (cox1). Polymerase Chain Reaction (PCR) results showed that, among the two genotypes of E. granulosus s.s., there were 22 different haplotypes (Haps). Phylogenetic analysis and parsimony network indicated that seventeen (77.27%) Haps belonged to the sheep strain (G1 genotype) and five Haps (22.73%) belonged to the buffalo strain (G3 genotype). Hap3 was the most common haplotype (65.00%, 112/180), which belongs to the G1 genotype. Hap18−Hap22 were found in human samples, indicating that sheep and cattle reservoirs of human CE. Molecular diversity indices revealed the high levels of haplotype diversity and relatively low levels of nucleotide diversity. Tajima’s D and Fu’s Fs tests displayed that the Altai population had a significant deviation from neutrality. Based on pairwise fixation index (Fst) values, a low level of genetic differentiation was found between the populations of E. granulosus s.s. isolated from different regions. The present survey findings represent an epidemiological survey of CE in the Altai region where there were two genotypes simultaneously and will provide more information on the genetic structure of E. granulosus s.s. within this region.

A Survey of the Interpretability Aspect of Deep Learning Models

Deep neural networks have attained near-human degree of quality in images, textual, audio, and video recording categorization and predictions tasks. The networks, on the other hand, are still typically thought of as black-box functional probabilistic models that transfer an input data to a trained classifier. Integrating these systems into mission-critical activities like clinical diagnosis, scheduling, and management is the next stage in this human-machine evolutionary change, and it necessitates a degree of confidence in the technology output. Statistical measures are often employed to estimate an output's volatility. The idea of trust, on the other hand, is dependent on a human's sight into a machine's inner workings. To put it another way, the neural networks must justify its outputs in a way that is intelligible to humans, leading to new insights into its internal workings. "Interpretable deep networks" is the name we give to such networks. The concept of interpretability is not one-dimensional. Indeed, the variability of an interpretation owing to varying degrees of human comprehension necessitates the existence of a plethora of characteristics that together define interpretability. Furthermore, the model's interpretations may be expressed in terms of low-level network variables or input properties. We describe several of the variables that are helpful for model interpretability in this study, as well as previous work on those dimensions. We do a gap analysis to determine what remains to be improved to increase models interpretability as step of the procedure.

WAVE FUNCTION COLLAPSE. APPLICATION OF COLLAPSE METHOD IN GAME SPACE GENERATION

The article explores a method for creating game content using procedural generation based on the wave function collapse. It has been determined that using this method with the basic approach yields relatively acceptable results. In some cases, anomalies are encountered, or spaces with a low level of diversity are generated. At the initial stages of generation, zones where generation is impossible using the basic method have been identified. One way to address this issue is through repeated generation, which requires additional resources. A modification of the method has been proposed using repeated passes. In some cases, this method has proven to be quite effective. To improve the method's efficiency, the space has been divided into zones to verify the correctness of the generated area. The most effective application of the basic method has been determined – traversing the space with another algorithm and post-processing after generation. One such algorithm allows for the preparation of samples to be substituted into closed zones that appear. Preparing generation materials significantly speeds up the operation of the wave function collapse. Preliminary traversal of space to identify probable problem areas occurs separately from the main generation. The algorithm has been modified for the automatic recognition of compatible samples, significantly reducing the time for generation preparation and the number of problem areas created. Enclosed spaces that restrict access to parts of the map have been identified. Depending on the size of the restricted area, several solutions have been proposed. For small areas, replacement with decorative content, for large areas – regeneration with samples designed to create internal content, or replacement of certain blocks with passable ones. Based on the research results, it is concluded that the generation of game space using the wave function collapse method is more efficient when auxiliary algorithms and constraints are applied at the initial and final stages of generation. Also, the idea of dividing generation into visible and invisible parts is proposed: the visible part is created during scene preparation, and the invisible part is generated during level traversal.

Изменчивость вишни войлочной (Prunus tomentosa Thunb .) в Республике Алтай

Felted cherry is a valuable fruit crop due to its high adaptability and nutritional value of the fruit. In the territory of Russia, felted cherry is a cultivated plant. All natural populations are feral plants that entered the European part of Russia more than 150 years ago through the Primorsky Krai. The aim of the study is to assess the variability and conjugacy of fruit and leaf characters in a population of felted cherry. The subjects of the study were the wild felted cherry trees growing near the island of Patmos, Chemalsky district, Republic of Altai. Fruits and leaves were collected from 8–10-year-old plants in the first decade of July. For the research, 30 fruits and leaves each were randomly selected from the middle part of the shoots and their characteristics were estimated: fruit length (mm), fruit diameter (mm), fruit weight (g), sugar (°Bx), leaf length (mm), leaf width (mm). Fruit length and width were found to have a very low level of variability and were within 5–6%, mainly fruit length from 11.12 to 12.79 mm and fruit diameter from 10.90 to 11.39 mm. For the research, 30 fruits and leaves each were randomly selected from the middle part of the shoots and their characteristics were estimated. This population can be recommended for further research on resistance to moniliosis and possible introduction into industrial production.

Chloroplast DNA Markers on the Phylogeography Study of Roburoid Oaks (&lt;i&gt;Quercus&lt;/i&gt; L. sect. &lt;i&gt;Quercus&lt;/i&gt;, Fagaceae) in the Crimean-Caucasian Region

The variability of five fragments of chloroplast DNA (ASq, CDq, TFq, trnH-psbA and trnK-matK) with a total length of more than 10,000 bp in three species of roburoid oaks of the Crimean-Caucasian region (Q. robur, Q. petraea, Q. pubescens) was studied. A total of 11 haplotypes were found in 290 individuals from 20 populations, 10 of which differed from the haplotypes of the main range. The obtained results show a pronounced structure of the geographical distribution of haplotypes of roburoid oaks in the Crimea and the Caucasus, and the chloroplast haplotypes of the region belong to several divergent phylogenetic lines. A comparison with data from other studies was carried out, with an assessment of the degree of effectiveness of the use of various chloroplast fragments and markers. The isolated phylogenetic position of the haplotypes of the Crimean-Caucasian populations and deep differences from the haplotypes of the main area allows us to conclude that robouroid oaks have historically been isolated for a long time in the Caucasus and Crimea. At the same time, the presence of common haplotypes indicates closer historical ties between the Crimean-Caucasian populations with Asia Minor and the Balkans. All haplotypes are identified by three fragments (ASq, CDq and TFq), with a sufficient level of variability and suitable for solving problems of oak phylogeography in the Crimean-Caucasian region. The trnH-psbA and trnK-matK markers had a low level of variability and did not reveal additional haplotypes. Optimized genotyping options have been proposed, including both step by step sequencing, and a combination of analysis of chloroplast DNA microsatellite loci (cpSSR), restriction analysis (PCR-RFLP), and sequencing. To assess the variability of markers in a larger taxonomic range, representatives of other sections of the genus Quercus were included in the analysis. It was shown that the oak-specific primers proposed for ASq, CDq, and TFq fragments are suitable for species of other sections.

A Comparison between 2D and 3D Rescaling Masks of Initial Condition Perturbation in a 3-km Storm-Scale Ensemble Prediction System

Abstract Using a 3-km regional ensemble prediction system (EPS), this study tested a three-dimensional (3D) rescaling mask for initial condition (IC) perturbation. Whether the 3D mask-based EPS improves ensemble forecasts over current two-dimensional (2D) mask-based EPS has been evaluated in three aspects: ensemble mean, spread, and probability. The forecasts of wind, temperature, geopotential height, sea level pressure, and precipitation were examined for a summer month (1–28 July 2018) and a winter month (1–27 February 2019) over a region in North China. The EPS was run twice per day (initiated at 0000 and 1200 UTC) to 36 h in forecast length, providing 56 warm-season forecast cases and 54 cold-season cases for verification. The warm and cold seasons are verified separately for comparison. The study found the following: 1) The vertical profile of IC perturbation becomes closer to that of analysis uncertainty with the 3D rescaling mask. 2) Ensemble performance is significantly improved in all three aspects. The biggest improvement is in the ensemble spread, followed by the probabilistic forecast, and the least improvement is in the ensemble mean forecast. Larger improvements are seen in the warm season than in the cold season. 3) More improvement is in the shorter time range (&lt;24 h) than in the longer range. 4) Surface and lower-level variables are improved more than upper-level ones. 5) The underlying mechanism for the improvement has been investigated. Convective instability is found to be responsible for the spread increment and, thus, overall ensemble forecast improvement. Therefore, using a 3D rescaling mask is recommended for an EPS to increase its utility especially for shorter time range and surface weather elements. Significant Statement A weather prediction model is a complex system that consists of nonlinear differential equations. Small errors in either its inputs or model itself will grow with time during model integration, which will contaminate a forecast. To quantify such contamination (“uncertainty”) of a forecast, the ensemble forecasting technique is used. An ensemble of forecasts is a multiple of model runs at the same time but with slightly “perturbed” inputs or model versions. These small perturbations are supposed to represent true “uncertainty” in inputs or model representation. This study proposed a technique that makes a perturbation’s vertical structure more resemble real uncertainty (intrinsic error) in input data and confirmed that it can significantly improve ensemble forecast quality especially for a shorter time range and lower-level weather elements. It is found that convective instability is responsible for the improvement.

Phylogeography and Genetic Structure of the Bush Cricket Decma fissa (Orthoptera, Tettigoniidae) in Southern China.

Decma fissa is the most widely distributed species of the genus Decma occuring in southern China. This study presents the first phylogeographic work of D. fissa based on COI, Cytb and ITS sequence. We examined genetic diversity with ITS and mitochondrial sequence respectively, and phylogenetic work was based on the mitochondrial data. A high-level genetic diversity was revealed based on mitochondrial data but a low-level diversity was shown with ITS sequence. For the mitochondrial data, divergence time analysis displayed five lineages. Based on the Mantel test, geographic and genetic distances among D. fissa populations revealed a significant positive correlation. Bayesian skyline plot (BSP) analyses implied that none of three major lineages of D. fissa was seemingly affected by the last glacial maximum (LGM, 0.015-0.025 Mya). Ecological niche modeling was used to predict the distribution of D. fissa in four periods (LGM, Mid-Holocene, current and 2070) in China. Analysis of the ancestral area reconstruction indicated that D. fissa occurred in the South China area.

Spatiotemporal dynamics and geo-environmental factors influencing mangrove gross primary productivity during 2000–2020 in Gaoqiao Mangrove Reserve, China

BackgroundMangrove forests are a significant contributor to the global carbon cycle, and the accurate estimation of their gross primary productivity (GPP) is essential for understanding the carbon budget within blue carbon ecosystems. Little attention has been given to the investigation of spatiotemporal patterns and ecological variations within mangrove ecosystems, as well as the quantitative analysis of the influence of geo-environmental factors on time-series estimations of mangrove GPP. MethodsThis study explored the spatiotemporal dynamics of mangrove GPP from 2000 to 2020 in Gaoqiao Mangrove Reserve, China. A leaf area index (LAI)-based light-use efficiency (LUE) model was combined with Landsat data on Google Earth Engine (GEE) to reveal the variations in mangrove GPP using the Mann-Kendall (MK) test and Theil-Sen median trend. Moreover, the spatiotemporal patterns and ecological variations in mangrove ecosystems across regions were explored using four landscape indicators. Furthermore, the effects of six geo-environmental factors (species distribution, offshore distance, elevation, slope, planar curvature and profile curvature) on GPP were investigated using Geodetector and multi-scale geo-weighted regression (MGWR). ResultsThe results showed that the mangrove forest in the study area experienced an area loss from 766.26 ​ha in 2000 to 718.29 ​ha in 2020, mainly due to the conversion to farming, terrestrial forest and aquaculture zones. Landscape patterns indicated high levels of vegetation aggregation near water bodies and aquaculture zones, and low levels of aggregation but high species diversity and distribution density near building zone. The mean value of mangrove GPP continuously increased from 6.35 ​g ​C·m−2·d−1 in 2000 to 8.33 ​g ​C·m−2·d−1 in 2020, with 23.21% of areas showing a highly and significantly increasing trend (trend value ​> ​0.50). The Geodetector and MGWR analyses showed that species distribution, offshore distance and elevation contributed most to the GPP variations. ConclusionsThese results provide guidelines for selecting GPP products, and the combination of Geodetector and MGWR based on multiple geo-environmental factors could quantitatively capture the mode, direction, pathway and intensity of the influencing factors on mangrove GPP variation. The findings provide a foundation for understanding the spatiotemporal dynamics of mangrove GPP at the landscape or regional scale.

EVALUATION THE MICROELEMENTS AND HEAVY METALS CONTENT IN THE CONDITIONS OF SOME AGRICULTURAL AND FOREST LANDS

The study analyzed and evaluated the content of some microelements and heavy metals in the conditions of two categories of agricultural and forest land. The study location was in the "Padurea Cenad" Protected Area and the bordering agricultural area, Timis County, Romania. Among microelements, the content of iron (Fe, ppm), manganese (Mn, ppm), copper (Cu, ppm) and zinc (Zn, ppm) was analyzed, and among heavy metals, the content of chromium (Cr, ppm), nickel (Ni, ppm) and lead (Pb, ppm) was analysed. The ANOVA test was used to quantify the statistical reliability of the data, and to evaluate the presence of variance in the data set (Alpha=0.001). The correlation level of the determined microelements and heavy metals was evaluated by correlation analysis (Pearson), and the statistical safety was evaluated based on p parameters (p &lt;0.05; p &lt;0.01; p &lt;0.001). Correlations of different intensity levels were recorded between the analyzed elements (eg r=0.914 between Fe and Cu; r=0.916 between Fe and Ni; r=0.935 between Mn and Zn; r=0.933 between Mn and Cr; r= 0.920 between Zn and Cr; r=0.990 between Ni and Pb; r=0.851 between Fe and Pb; r=-0.732 between Mn and Ni; r=-0.731 between Mn and Pb). A high level of variability was recorded in the case of Pb content (CVPb=43.8455), and a low level of variability in the case of Mn content (CVMn=8.1807). Intermediate values was recorded in the other cases, CVCr=25.4583, CVNi=23.9941, CVCu=23.0055, and CVFe=21.0282 respectively. According to PCA, PC1 explained 70.240% of variance, and PC2 explained 22.469% of variance. Separate positioning in relation to the content of microelements and heavy metals (as biplot) was found at CP1-A and CP2-F. Associated with the content of Mn, Zn and Cr (as biplot) CP2-A was positioned, and associated with Fe, Cu, Ni and Pb (as biplot) CP1-F was associated.

Application of an Empirical Best Linear Unbiased Prediction Fay–Herriot (EBLUP-FH) Multivariate Method with Cluster Information to Estimate Average Household Expenditure

Data at a smaller regional level has now become a necessity for local governments. The average data on household expenditure on food and non-food is designed for provincial and district/city estimation levels. Subdistrict-level statistics are not currently available. Small area estimation (SAE) is one method to address the problem. The Empirical Best Linear Unbiased Prediction (EBLUP)—Fay Herriot Multivariate method estimates the average household expenditure on food and non-food at the sub-district level in Central Java Province in 2020. Meanwhile, for the sub-districts that are not sampled, the estimation of average household expenditure is done by adding cluster information to the EBLUP Multivariate modeling. The K-Medoids Cluster method is used to classify sub-districts based on their characteristics. Small area estimation using the EBLUP-FH Multivariate method can enhance the parameter estimations obtained using the direct estimation method because it results in a lower level of variation (RSE). For sub-districts that are not sampled, the Residual Standard Error (RSE) value from the estimated results using the EBLUP-FH Multivariate method with cluster information is lower than 25%, indicating that the estimate is accurate.