Distribution Of Stocks Research Articles

Mapping Forest Carbon Stock Distribution in a Subtropical Region with the Integration of Airborne Lidar and Sentinel-2 Data

Forest carbon stock is an important indicator reflecting a forest ecosystem’s structures and functions. Its spatial distribution is valuable for managing natural resources, protecting ecosystems and biodiversity, and further promoting sustainability, but accurately mapping the forest carbon stock distribution in a large area is a challenging task. This study selected Changting County, Fujian Province, as a case study to explore a method to map the forest carbon stock distribution using the integration of airborne Lidar, Sentinel-2, and ancillary data in 2022. The Bayesian hierarchical modeling approach was used to estimate the local forest carbon stock based on airborne Lidar data and field measurements, and then the random forest approach was used to develop a regional forest carbon stock estimation model based on the Sentinel-2 and ancillary data. The results indicated that the Lidar-based carbon stock distribution effectively provided sample plots with good spatial representativeness for modeling regional carbon stock with a coefficient of determination (R2) of 0.7 and root mean square error (RMSE) of 12.94 t/ha. The average carbon stocks were 48.55 t/ha, 55.51 t/ha, and 57.04 t/ha for Masson pine, Chinese fir, and broadleaf forests, respectively. The carbon stock in non-conservation regions was 15.2–16.1 t/ha higher than that in conservation regions. This study provides a promising method through the use of airborne Lidar data as a linkage between sample plots and Sentinel-2 data to map the regional carbon stock distribution in those subtropical regions where serious soil erosion has led to a relatively sparse forest canopy density. The results are valuable for local government to make scientific decisions for promoting ecosystem restoration due to water and soil erosion.

Spatial distribution of vegetation carbon stock among different organs over the Tibetan Plateau: on an intensive field survey

Spatial distribution of vegetation carbon stock among different organs over the Tibetan Plateau: on an intensive field survey

The Anomalous Behavior of Stock Prices on the Canadian Securities Exchange

This paper examines the conformity of the distribution of stock prices on the Canadian Securities Exchange (CSE) to that theorized by Benford’s Law (BL). BL follows a logarithm law such that the leading digits have a higher probability of lower numbers such as 1, 2, or 3 versus higher numbers like 7, 8, or 9. This analysis can be used to detect fraudulent manipulation of stock prices. Previous research has applied BL to a broad range of data such as cost data, atomic weights, river areas, and populations, as well as stock prices. After collecting stock prices on the CSE, the number count for each digit was compared to the expected number given the frequencies posited by BL. A chi-square test was employed to determine statistical significance. The first digit was found to adhere to BL; however, the second digit was not congruent with that predicted by BL. There is an indication of possible manipulation on this stock exchange. These mixed results are consistent with the empirical evidence of other researchers. This evidence is relevant to auditors, shareholders, financial analysts, investment managers, government, and the CSE.

Assessing the carbon stock in the Alps: Considerations on three different approaches

Alpine protected areas provide a wide range of ecosystem services, with climate regulation being one of the most significant. In line with the European Biodiversity Strategy for 2030, which emphasizes the conservation and enhancement of ecosystem services, there is an urgent need to correctly manage these areas in order to maximize biodiversity conservation and the supply of ecosystem services. To achieve efficient management and decision-making processes, it is crucial to first assess the current supply of ecosystem services and to have a basic reference for monitoring activities. Various approaches can be used to evaluate the carbon storage, a widely used indicator of the climate regulation service. In this study three approaches were compared: fieldwork data collection, the Italian National Inventory and the TESSA toolkit. Discrepancies in the results emerged, in the Aosta Valley, TESSA reported 423 Gg for OC stock in mixed broadleaves, compared to 263 Gg from field data and 210 Gg from the National Inventory. Fieldwork data collection, while the most accurate, was the most time and resource intensive. The national inventory yielded values similar to fieldwork data; for example, in the Adamello spruce forest, the National Inventory reported 1838 Gg, while field data measured 1964 Gg. However, TESSA depicted qualitatively the same organic carbon stock distribution across the habitats compared to the other approaches. Based on the results, we propose different applications for these approaches, considering the advantages and disadvantages of each. Specifically, we suggest using the TESSA toolkit for preliminary and a qualitative screening of a study area to identify potential areas of interest for the carbon stock, while more precise but demanding approaches should be employed for local studies.

Potential environmental implications of sandbar afforestation: Insights from ecosystem restoration initiatives in a sandbar of Brahmaputra River Assam, India

Plantation in riverine sandbars offers an excellent opportunity for restoration of ecosystem with a vast potential for enhancing carbon stock. Afforestation on barren islands is challenging task; however, Padmashree Jadav Payeng has single-handedly transformed a sandbar of the river Brahmaputra into a forested landscape in India’s northeastern state of Assam. Such inspiring initiative needs more attention in terms of scientific assessment to quantify ecosystem value and services that can be enriched through such activities. This study uses remote sensing data viz., Landsat4–5TM and Sentinel 2 A data to provide a detailed information on spatio-temporal variability of land use land cover of study region from 1990 to 2021. It aims to conduct a scientific and systematic assessment of biophysical changes that have occurred in the sandbars, understand the status of afforestation, and evaluate the current levels of aboveground biomass (AGB), belowground biomass (BGB), and carbon stock in the selected sandbars. The spatial distribution of AGB, BGB and carbon stock is derived using linear regression model between SAR backscatter and field-based AGB. The results demonstrate that forest cover increased by 31.92 % and sandbar area is reduced by 26.87 % from 1990 to 2021. Spatially derived AGB ranges from 9 to 1295.89 Mg per ha, BGB from 2.35 to 290.50 Mg per ha and carbon stock from 6.18 to 763.34 Mg per ha. Ficus religiosa exhibits a high biomass, ranging from 564.7 Mg per ha to 994.7 Mg per ha, and a high carbon stock, ranging from 8 to 557.30 Mg per ha, attributed to its larger diameter at breast height (DBH). Moreover, phytosociological assessment was conducted for the studied forest, which reveal a total tree species richness of 79. The forest exhibits a total tree density of 395 individuals per hectare. Different biodiversity indices provide a comprehensive understanding of species composition; where results show a Shannon diversity index of 2.81, a Simpson's index of 0.08, a Menhinick's richness index of 2.7, and a Margalef's richness index of 5.26. The article provides detailed information on change in forest cover and present status of AGB, BGB and Carbon stock, providing evidence-based narratives on ecosystem restoration. The initiative by a single person has created a forested landscape and habitat for a large number of wild animals, contributing to better carbon stock and a healthier ecosystem. With regard to the current debate of carbon market, the study suggests the need of instrumentalization of carbon credits for such restoration activities to encourage increase in carbon stock. It also concludes that in-depth research should be taken up on long-term effectiveness of such plantation activities and the potential for scaling up these initiatives for ecosystem restoration in view of climate change mitigation and sustainable forest development.

Enhancing Agricultural Commodity Price Forecasting Using Generative Models: A Deep Learning Approach

Predicting stock market prices is a critical yet challenging task in finance. Technical analysis, a widely used methodology in investment theory, involves forecasting price movements by analyzing historical market data. Recently, deep learning has gained prominence for its exceptional ability to process complex data, making it a popular tool for financial applications such as stock prediction, portfolio optimization, financial information analysis, and trade execution strategies. In this study, we propose a novel deep learning architecture that integrates a Generative Adversarial Network (GAN) with a Convolutional Neural Network (CNN) as the discriminator and Gated Recurrent Units (GRU) as the generator. This framework generates distributions of daily stock prices through adversarial learning to predict stock closing prices. Using daily trading data from Ruchi Soya Industries Limited, an empirical analysis was performed across a broad time frame. Results show that the proposed GAN-based architecture significantly outperforms traditional deep learning models, including GRU, LSTM, and Bi-LSTM, in predicting stock closing prices. These findings demonstrate the potential of this novel approach for improving stock price forecasting accuracy.

Efficient convex PCA with applications to Wasserstein GPCA and ranked data

Convex PCA, which was introduced in Bigot et al. (2017), modifies Euclidean PCA by restricting the data and the principal components to lie in a given convex subset of a Hilbert space. This setting arises naturally in many applications, including distributional data in the Wasserstein space of an interval, and ranked compositional data under the Aitchison geometry. Our contribution in this paper is threefold. First, we present several new theoretical results including consistency as well as continuity and differentiability of the objective function in the finite dimensional case. Second, we develop a numerical implementation of finite dimensional convex PCA when the convex set is polyhedral, and show that this provides a natural approximation of Wasserstein GPCA. Third, we illustrate our results with two financial applications, namely distributions of stock returns ranked by size and the capital distribution curve, both of which are of independent interest in stochastic portfolio theory. Supplementary materials for this article are available online.

Soil carbon stocks and nutrient stratification in a volcanically active coffee-dominated landscape in south-central Guatemala

The volcanoclastic mountains of Central America offer elevations ideal for coffee production. Both vulcanism and land use can have significant impacts on soil formation and properties. We evaluated how the interactive impacts of soil formation and coffee-dominated land use modulate soil storage and vertical distribution of organic carbon and nutrient elements in a volcanically active landscape dominated by coffee agriculture in south-central Guatemala. Thirty-seven pedons under coffee production (n = 29), forest cover (n = 5) and pasture (n = 3) were characterized and classified, and concentrations and stocks of organic carbon and macronutrient and micronutrient elements were quantified across genetic horizons. Additionally, soil organic carbon (SOC) stock values calculated using the fixed depth (FD) and equivalent soil mass (ESM) methodologies were compared. The active stratovolcano in the region had a strong effect on the development of andic properties and soil horizon burial, with thirty-one pedons classified as Andisols, three as Inceptisols and three as Entisols. Land use management practices and soil horizon burial by deposition of volcanic material were partly reflected in the vertical distribution of SOC concentrations and stocks. The vertical distribution of macronutrients was generally more sensitive to land use than the vertical distribution micronutrients, which could potentially reflect differences in inputs via fertilizers and vegetation cover and in outputs with biomass harvest. Soil organic carbon stocks were similar when calculated by FD and ESM, reflecting relatively consistent depth-wise bulk density. These results demonstrate that in volcanically active landscapes, land use should be considered in concert with soil-forming factors to comprehensively understand organic carbon and nutrient element storage in soils.

Soil Carbon and Nitrogen Stocks Across Hillslopes Underlain by Continuous Permafrost in the Northern Arctic Foothills, Alaska, United States

ABSTRACTConstraining the variability of soil organic carbon (SOC) and total nitrogen (TN) stocks across hillslopes in Low Arctic permafrost‐affected landscapes remains a significant challenge for improving global estimates of permafrost SOC stocks. We investigated SOC and TN stocks across hillslopes at two sites in the Arctic Foothills of Alaska, United States (Happy Valley and Sagwon Hills). Average SOC and TN stocks for the 0–1‐m depth interval were high (52.0 ± 15.1 kg C m−2 and 2.74 ± 0.82 kg N m−2) and linearly related (R2 = 0.74, p < 0.0001). Unlike soils of other permafrost and nonpermafrost landscapes, variability was greatest within rather than between hillslope positions. Furthermore, SOC and TN stocks in the surface 1 m did not exhibit strong patterns by hillslope position and were only weakly associated with major geomorphic parameters that typically predict SOC and TN stocks well in other landscapes. Although sampling at upper hillslope positions was largely limited to depths of less than 1.5 m due to the presence of coarse fragments in reworked glacial till, deeper observations at lower hillslope positions (footslopes, toeslopes, and basins) revealed significantly larger SOC stocks (92.0 ± 18.0 kg C m−2 at 2 m; 117.1 ± 10.4 kg C m−2 at 3 m). The unique small‐scale variability in ice content, cryoturbation, patterned ground, and organic layer thickness on these broad, Low Arctic sites contributes to the relatively homogeneous distribution of SOC and TN stocks across hillslope positions in the top 1 m, but a future focus on deeper sampling may reveal greater differences in SOC and TN stocks.

Factors controlling peat soil thickness and carbon storage in temperate peatlands based on UAV high-resolution remote sensing

Peatlands store a large amount of carbon. However, peatlands are complex ecosystems, and acquiring reliable estimates of how much carbon is stored underneath the Earth’s surface is inherently challenging, even at small scales. Here, we aim to establish links between the above- and below-ground factors that control soil carbon status, identify the key environmental variables associated with carbon storage, as well as to explore the potential for using Unmanned Aerial Vehicle (UAV) remote sensing for spatial mapping of peatlands. We combine UAVs equipped with Red-Green-Blue (RGB), multispectral, thermal infrared, and light detection and ranging (LiDAR) sensors with ground-penetrating radar (GPR) technology and traditional field surveys to provide a comprehensive, 3-dimensional mapping of a peatland hillslope-floodplain landscape in the Belgian Hautes Fagnes. Our results indicate that both peat thickness and soil organic carbon (SOC) stock (top 1 m) are spatially heterogeneous and that the contributions from the surface topography to peat thickness and SOC stock varied from micro- to macro-scales. Peat thickness was more strongly controlled by macro-topography (R2 = 0.46) than SOC stock, which was more influenced by micro-topography (R2 = 0.21). Current vegetation had little predictive power for explaining their spatial variability. Additionally, the UAV data provided accurate estimates of both peat thickness and SOC stock, with RMSE and R2 values of 0.16 m and 0.85 for the peat thickness, and 59.25 t/ha and 0.85 for the SOC stock. However, similar performance can already be achieved by using only topographical data from the LiDAR sensor (for peat thickness) and a combination of peat thickness and topography (for SOC stock) as predictor variables. Our study bridges the gap between surface observations and the hidden carbon reservoir below. This not only allows us to improve our ability to assess the spatial distribution of SOC stocks, but also contributes to our understanding of the environmental factors associated with SOC storage in these highly heterogeneous landscapes, providing insights for environmental science and climate projections.

Changes in Wuhan’s Carbon Stocks and Their Spatial Distributions in 2050 under Multiple Projection Scenarios

Urbanization in the 21st century has reshaped carbon stock distributions through the expansion of cities. By using the PLUS and InVEST models, this study predicts land use and carbon stocks in Wuhan in 2050 using three future scenarios. Employing local Moran’s I, we analyze carbon stock clustering under these scenarios, and the Getis–Ord Gi* statistic identifies regions with significantly higher and lower carbon-stock changes between 2020 and 2050. The results reveal a 2.5 Tg decline in Wuhan’s carbon stock from 2000 to 2020, concentrated from the central to the outer city areas along the Yangtze River. By 2050, the ecological conservation scenario produced the highest carbon stock prediction, 77.48 Tg, while the economic development scenario produced the lowest, 76.4 Tg. High-carbon stock-change areas cluster in the north and south, contrasting with low-change area concentrations in the center. This research provides practical insights that support Wuhan’s sustainable development and carbon neutrality goals.

Connectedness at extremes between real estate tokens and real estate stocks

We test the interconnectedness between returns on real estate (RE) crypto tokens and on RE stocks using a quantile vector autoregression (QVAR) methodology. Our main findings show that there is a strong dependence between RE tokens and RE stocks in the distributions of returns in the top and bottom quantiles, and a rather weaker correlation in the median quantile. From an investment perspective, the findings demonstrate that, contrary to the usual focus on the center of the general distribution of returns, RE tokens and RE stocks correlate most strongly at the tails of the distributions. For this reason, diversification benefits may be limited, and careful risk management and investment strategies should be employed to navigate this strong tail connection. Considering the limited diversification benefits at the extremes, investors should exercise caution and allocate resources to mitigate potential losses when RE crypto tokens and RE stocks experience extreme market movements.

Utilizing the GARCH Model for Analysis and Prediction of Stock Market Trends

This study utilizes the Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) model to analyze the Shanghai Stock Exchange Composite Index and the Shenzhen Composite Index. It computes the returns and logarithmic returns, fits the GARCH model with a student-t distribution, and calculates the standardized residuals of the stock. Tests for normality and heteroskedasticity of residuals are conducted, follows by the creation of auto-correlation and partial auto-correlation plots to aid in volatility forecasting. The findings indicate that both returns of stock distributions exhibit multiple peaks and significant fluctuations, revealing that there exists pronounced volatility clustering and strong abrupt changes. Historical fluctuations significantly affect current fluctuations, showing a clustering effect. Volatility in returns has been very persistent, reflecting strong speculation and elevated overall risk. Histograms and skewness assessments show a left-skewed distribution for both indices, with a kurtosis greater than three, indicating a "fat-tailed" distribution. The positive mean values imply that China's economy is growing constantly, while the low standard deviation implies the immaturity of China's stock market. These insights highlight the dynamic behaviors and risk profiles in Chinas major stock indices, offering a deeper understanding of their speculative nature and economic implications. The Advantages and disadvantages of GARCH models and limitations of a single model are acknowledged in the report.

Remote sensing of depth-induced variations in soil organic carbon stocks distribution within different vegetated landscapes

The preservation and augmentation of soil organic carbon (SOC) stocks is critical to designing climate change mitigation strategies and alleviating global warming. However, due to the susceptibility of SOC stocks to environmental and topo-climatic variability and changes, it is essential to obtain a comprehensive understanding of the state of current SOC stocks both spatially and vertically. Consequently, to effectively assess SOC storage and sequestration capacity, precise evaluations at multiple soil depths are required. Hence, this study implemented an advanced Deep Neural Network (DNN) model incorporating Sentinel-1 Synthetic Aperture Radar (SAR) data, topo-climatic features, and soil physical properties to predict SOC stocks at multiple depths (0–30 cm, 30–60 cm, 60–100 cm, and 100–200 cm) across diverse land-use categories in the KwaZulu-Natal province, South Africa. There was a general decline in the accuracy of the DNN model’s prediction with increasing soil depth, with the root mean square error (RMSE) ranging from 8.34 t/h to 11.97 t/h for the four depths. These findings imply that the link between environmental covariates and SOC stocks weakens with soil depth. Additionally, distinct factors driving SOC stocks were discovered in both topsoil and deep-soil, with vegetation having the strongest effect in topsoil, and topo-climate factors and soil physical properties becoming more important as depth increases. This underscores the importance of incorporating depth-related soil properties in SOC modelling. Grasslands had the largest SOC stocks, while commercial forests have the highest SOC sequestration rates per unit area. This study offers valuable insights to policymakers and provides a basis for devising regional management strategies that can be used to effectively mitigate climate change.

Global turnover of soil mineral-associated and particulate organic carbon

Soil organic carbon (SOC) persistence is predominantly governed by mineral protection, consequently, soil mineral-associated (MAOC) and particulate organic carbon (POC) turnovers have different impacts on the vulnerability of SOC to climate change. Here, we generate the global MAOC and POC maps using 8341 observations and then infer the turnover times of MAOC and POC by a data-model integration approach. Global MAOC and POC storages are 975964987\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${975}_{964}^{987}$$\\end{document} Pg C (mean with 5% and 95% quantiles) and 330323337\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${330}_{323}^{337}$$\\end{document} Pg C, while global mean MAOC and POC turnover times are 12945383\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${129}_{45}^{383}$$\\end{document} yr and 23582\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${23}_{5}^{82}$$\\end{document} yr in the top meter, respectively. Climate warming-induced acceleration of MAOC and POC decomposition is greater in subsoil than that in topsoil. Overall, the global atlas of MAOC and POC turnover, together with the global distributions of MAOC and POC stocks, provide a benchmark for Earth system models to diagnose SOC-climate change feedback.

PROTECTION OF MINORITY SHAREHOLDERS FROM FRAUD IN LIMITED LIABILITY COMPANY DIVIDEND DISTRIBUTION

<p>This research investigates the potential fraudulent actions carried out by majority shareholders and other corporate organs in the context of stock distribution. The primary focus is on the continuity of practices that may disadvantage minority shareholders and undermine the legal protections they should receive. Majority shareholders, often wielding significant influence in the General Meeting of Shareholders (RUPS), may exploit their power to manipulate decisions related to stock distribution. The presence of a quorum that can be achieved without the participation of minority shareholders increases the risk of fraud in this process. The aim of this research is to identify various fraudulent actions that may occur in stock distribution by majority shareholders and related corporate organs. Using a legal analysis approach and case studies, this research also explores ways in which minority shareholders might prevent or respond to fraudulent actions. Furthermore, the research examines the extent to which Law No. 40 of 2007 concerning Limited Liability Companies (UU PT) provides a legal basis to prevent and respond to fraudulent actions in stock distribution. Practical implications of potential fraud are analyzed to provide recommendations that can strengthen legal protections for minority shareholders. This research is expected to contribute to a deeper understanding of the dynamics of fraud in stock distribution, outline the legal challenges faced by minority shareholders, and formulate concrete steps to prevent and respond to such fraud in companies.<em></em></p>

Investigating the effects of climate change and anthropogenic activities on SOC storage and cumulative CO2 emissions in forest soils across altitudinal gradients using the century model

This study investigated the impact of climate change, grazing, manure application, and liming on soil organic carbon (SOC) stock and cumulative carbon dioxide (CO2) emissions in forest soils across different altitudes. Despite similar soil texture, acidity, and salinity across elevations, SOC stock significantly increased with altitude due to cooler temperatures and higher precipitation. The highest SOC stock (97.46 t ha−1) was observed at 2000–2500 m, compared to the lowest (44.23 t ha−1) at 500–1000 m. The Century C Model accurately predicted SOC stock, with correlation and determination coefficients exceeding 0.98. A climate change scenario projecting decreased precipitation (2.15 mm per decade) and increased temperature (0.4 °C) revealed potential SOC stock losses ranging from 28.36 to 36.35 %, particularly at higher altitudes. Grazing further decreased SOC stock, with a more pronounced effect at higher elevations. However, manure application (40 t ha−1 every four years) and liming (7–10 t ha−1 every three years) had positive effects on SOC stock, again amplified at higher altitudes and with an increase in lime application rate. In scenarios combining climate change with manure application and climate change with liming, manure application and liming mitigated some negative impacts of climate change, but could not fully offset them, resulting in 1.49–5.42 % and 0.39–4.07 % decreases respectively. Simulations of cumulative CO2 emissions mirrored the distribution of SOC stock, with higher emissions observed at higher altitudes and with management practices that increased SOC stock. This study emphasizes the critical role of conserving high-altitude forest soils and implementing optimal forest management strategies to combat climate change by minimizing SOC losses.

Leveraging digital footprints data for accurate estimation of the residential housing stock in the United Kingdom, 1997–2022

ABSTRACT This paper explores the use of digital footprints data to improve on the limited information available on the size and distribution of the residential housing stock in the United Kingdom over the past two decades. We use a subset of a large dataset consisting of the names and addresses of more than one billion individuals dating back to 1997 (LCRs: Linked Consumer Registers) to calibrate property lifecycle information within an authoritative geolocated address and property dataset. We validate the results of our novel calibration method against official estimates by the UK government covering the same period. We further show that our calibration method, based on individual level data, captures a much larger share of the residential housing stock since 1997 than would have been possible with other available data sources. We argue that these housing stock estimates are fundamental to the development of a comprehensive digital footprints data infrastructure that has the potential to significantly enhance population, migration, and other social statistics in the UK. More generally, this study highlights the benefits of using digital footprints data in refining, calibrating, and extending existing datasets.

Diabetic cheiroarthropathy in uncontrolled type 2 diabetes with positive anti-nuclear antibodies: a case report from Sudan

Background: Diabetic cheiroarthropathy, also known as limited joint mobility, is one of the long-standing complications of type 2 diabetes mellitus (DM). It affects 8–50% of patients with type 1 diabetes and is also seen in type 2 diabetic patients. Consequently, it can mimic many rheumatological diseases and is often underdiagnosed. The authors present a case of a long-standing poorly controlled diabetes with diabetic cheiroarthropathy and diabetic neuropathy, along with positive ANA in the absence of any correlated autoimmune or rheumatological diseases. Case presentation: A 52-year-old female patient with poorly controlled diabetes (her last HbA1c reading was 9.5%) presented to the Rheumatology clinic with flexion deformities of the fingers. The patient has impaired vibration, two-point discrimination, and pinprick sensation in gloves and stock distribution, indicating peripheral neuropathy, entrapment neuropathy in the forms of bilateral carpal tunnel syndrome, and the diagnosis of diabetic cheiroarthropathy was made. Additionally, she has a positive prayer sign and a tabletop sign. Despite the absence of symptoms and signs of autoimmune disorders, this patient has positive anti-nuclear antibodies global (ANA positive by indirect immuno-fluorescence (IIF) 1\320 nucleolar pattern) with a negative: ANA profile, rheumatoid factor (RF) and anticyclic citrullinated peptide antibody (ACPA). Conclusion: Regular and careful hand examination should be part of clinical assessment for diabetic patients as it could be a very simple and useful screening tool for diabetic cheiroarthropathy. Physicians can use this condition as a mirror for microvascular complications of diabetes. This allows for early detection and appropriate interventions to prevent further progression of diabetes-related complications. It is also essential to consider the presence of positive ANA in diabetic cheiroarthropathy despite the absence of any rheumatological and autoimmune diseases.

Pattern of plant biomass and carbon stock along different elevational forests in eastern Nepal

The primary aim of this investigation was to determine the biomass and carbon stock distribution pattern among different forest stands of diverse elevations in the Morang district of East Nepal. It is noteworthy to estimate carbon stock and biomass of relatively least underexplored forests in east Nepal. The data for estimating the biomass and carbon stocks of the five different forest sites, viz. Bhaunne, Raja-Rani, Murchungi, Adheri, and Sagma located between 100-1300m above the mean sea level, were acquired through the measurement of inventory plots selected randomly. Altogether, 50 sample plots were established within five forest stands located on different elevational zone; within each forest site, 10 sample plots of 20m × 20m size, were laid out for the measurement of trees. In the case of shrubs and herbs, nested plots of 5m × 5m and 1m ×1m, respectively were established. Calculation of the biomass of trees and shrubs was facilitated through the application of an allometric equation, while the biomass of herbs was determined by the harvest method. The carbon concentration in the plant materials was estimated using ash content method. The comprehensive analysis of the stand biomass in the Bhaunne, Raja-Rani, Murchungi, Adheri, and Sagma forest sites were: 815.86 Mg ha-1, 414.19 Mg ha-1, 606.81 Mg ha-1, 519.20 Mg ha-1, and 299.96 Mg ha-1, respectively, with minimum at the Sagma site (high-altitude forest) and maximum at the Bhaunne site (low-altitude forest). As per the variation in stand biomass, the carbon stocks in the forest sites also showed the same trend, but the values ranged from 140.19 Mg C ha-1 to 333.63 Mg C ha-1, with the minimum in the Sagma site and the maximum in the Bhaunne site. The application of the Friedman Test revealed statistically significant variation in the tree biomass between the Murchungi and Sagma sites and also in the shrub biomass between the Adheri and Sagma sites. Similarly, noteworthy variations were observed in the herb biomass of the Bhaunne, Raja-Rani, Murchungi, and Adheri sites as compared to that of the Sagma site. The present study contributes to the understanding of forest ecosystems in context to carbon management.