Amount Of Stock Research Articles

Carbon Stocks Estimation Using the Stock Difference Method of Various Land Use Systems Based on Geospatial in Kualan Watershed

Indonesia controls 75%-80% of the world's carbon stocks, so the amount of carbon stocks must be utilized optimally. This study aims to determine carbon stocks, potential emissions, and economic value of carbon stocks in each land use. The method used is secondary data analysis and field checking. The data collected were Sentinel 2A acquisitions in 2020 and 2022, Digital Elevation Model (DEM), and land use land cover in 2020-2023. Data analysis used SNAP and ArcGIS 10.8. The tool used for data analysis is spatial analysis map algebra. The results showed mixed dryland agriculture has the most extensive carbon stock, at 2,614,178 tons/ha, with potential emissions of 9,585,320 tons/ha. The most minor carbon stock is in mining land use, which is 0 tons/ha with potential emissions of 0 tons/ha. The highest C02 value in USD is the forest land use group. In the Secondary Dryland Forest, Secondary Swamp Forest, and Plantation Forest groups, it is 17,517,400.50 USD, while the lowest is mining land use, which is 0 USD. Overall, the CO2 value of land use in the study area is 34,246,314.45 USD. Integrating remote sensing data analysis and field surveys in geospatial technology is one of the new approaches to studying carbon stocks and CO2 emissions in topsoil from various land uses. By utilizing geospatial technology, efforts to estimate carbon stocks on the surface are easier and faster.

From extraction to end‐uses and waste management: Modeling economy‐wide material cycles and stock dynamics around the world

AbstractMaterial stocks of infrastructure, buildings, and machinery are the biophysical basis of production and consumption. They are a crucial lever for resource efficiency and a sustainable circular economy. While material stock research has proliferated over the last years, most studies investigated specific materials or end‐uses, usually not embedded into an economy‐wide perspective. Herein, we present a novel version of the economy‐wide, dynamic, inflow‐driven model of material inputs, stocks, and outputs (MISO2), and present a global, country‐level application. Currently, MISO2 covers 14 supply chain processes from raw material extraction to processing, trade, recycling, and waste management, as well as 13 end‐uses of stocks. The derived database covers 23 raw materials and 20 stock‐building materials, across 177 countries from 1900 to 2016. We find that total material stocks amount to 1093 Gt in 2016, of which the majority are residential (290 Gt) and non‐residential buildings (234 Gt), as well as civil engineering (243 Gt), and roads (313 Gt). The other nine end‐uses covering stationary and mobile machinery, as well as short‐lived products, amount to 13 Gt. Material stocks per capita are highly unequally distributed around the world, with one order of magnitude difference between low‐ and high‐income countries. Results agree well with similar global country‐level studies. Low data quality for some domains, especially for lower‐income countries and for sand and gravel aggregates, warrant further attention. In conclusion, the MISO2 model and the derived database provide stock‐flow consistent perspectives of the socio‐economic metabolism around the world, enabling multiple novel and policy relevant research opportunities. This article met the requirements for a silver‐gold JIE data openness badge described at http://jie.click/badges.

Production of Oncolytic Measles Virus in Vero Cells: Impact of Culture Medium and Multiplicity of Infection.

Oncolytic measles virus (MeV) is a promising anti-cancer treatment. However, the production of high titers of infectious MeV (typically 107-109 TCID50 per dose) is challenging because the virus is unstable under typical production conditions. The objective of this study was to investigate how the multiplicity of infection (MOI) and different media-a serum-containing medium (SCM), a serum-free medium (SFM) and two chemically defined media (CDM)-affect MeV production. We infected Vero cells at MOIs of 0.02, 0.2 or 2 TCID50 cell-1 and the lowest MOI resulted in the largest number of infected cells towards the end of the production period. However, this did not equate to higher maximum MeV titers, which were similar for all the MOIs. The medium had a moderate effect, generating maximum titers of 0.89-2.17 × 106, 1.08-1.25 × 106 and 4.58-9.90 × 105 TCID50 mL-1 for the SCM, SFM and CDM, respectively. Infection at a low MOI often required longer process times to reach maximum yields. On the other hand, a high MOI requires a large amount of MeV stock. We would therefore recommend a mid-range MOI of 0.2 TCID50 cell-1 for MeV production. Our findings show that SCM, SFM and CDM are equally suitable for MeV production in terms of yield and process time. This will allow MeV production in serum-free conditions, addressing the safety risks and ethical concerns associated with the use of serum.

Analisis Penentuan Metode Peramalan Persediaan Bahan Baku UKM Tempe Pak Fadli di Desa Parerejo

This study aims to find out the right forecasting method in determining the amount of raw material needs and the amount of safety stock to minimize errors in the purchase of soybean raw materials at Pak Fadli Tempe UKM. The benefit of this research is that it can reduce the excess or shortage of raw materials through forecasting methods, as well as determine the need for the right amount of safety stock. In this study, there are 3 permalan methods used, namely moving average, exponential smoothing and naive method, as well as determining the need for safety stock of raw materials that will be used as a reference for future demand for Pak Fadli Tempe UKM. From the results of this study, it can be seen that of the 3 forecasting methods used, the best method with the smallest error value is the exponential smootihing method a = 0.1, with an error value of MAD of 90, MSE of 10,181, and MAPE of 6.9. Meanwhile, in the calculation of safety stock with a service level value of 95%, the lead time is 3 days, so that the recommended safety stock value can be known as 289 kg.

Carbon Stock Quantities of Gaertn. along Shorea robusta Altitudinal Gradient in Shivalik Hills of Western Himalaya

Carbon Stock Quantities of Gaertn. along Shorea robusta Altitudinal Gradient in Shivalik Hills of Western Himalaya

Soil carbon sequestration potential of different land use systems: evidence from sub-humid southern plains and Aravalli hills of Rajasthan, India.

This study has assessed total soil organic carbon (TOC) fractions, carbon management index (CMI), and carbon sequestration economic value under diverse land use systems (LUSs) of sub-humid Southern plains of Rajasthan, India. The study dealt with six LUSs: barren land (BL), agricultural land (AL), agri-horticulture (AH), horticultural land (HL), grassland (GL), and natural forest land (FL) were selected for the study. FL contained the highest TOC (13.04 ± 0.74gkg-1), particulate organic carbon (POC) (2.2 ± 0.19gkg-1), mineral-associated organic carbon (MAC) (10.84 ± 0.54gkg-1), and CMI (230.36 ± 4.13), whereas BL had the lowest amount of TOC (3.53 ± 0.4gkg-1), POC (0.47 ± 0.06gkg-1), MAC (3.06 ± 0.32gkg-1), and CMI (54.60 ± 4.2). The impact of LUSs on soil carbon lability index (LI) was minimal in all LUSs except FL exhibited statistically insignificant variations in LI. TOC stock showed the highest decline in BL (71.04%), AL (55.29%), AH (44.38%), and HL (25.92%) uses compared with the FL system. Different LUSs result in varying amounts of carbon stocks, representing the relative carbon credit gain. The maximum carbon credit was achieved by FL, which was roughly US $49,303 and 245% higher than BL. These results indicate the reinstatement of BL and AL towards HL and AH systems, and effective recycling of residues could improve the TOC storage in the study region.

Sistem prediksi pengadaan stok bahan baku kayu menggunakan Metode Double Exponential Smoothing (Studi Kasus: CV. Jati Sari)

In managing the supply of wood raw materials at CV. Jati Sari is still carried out based on the personal experience of business entity owners. Because it is based on personal experience without carrying out in-depth analysis or forecasting, it will create a risk of excess or shortage of stock, making stock management inefficient. The software development method used is the Waterfall method. Double Exponential Smoothing is implemented in this research as a forecasting prediction calculation method. This research produces a prediction system that can produce output in the form of a predicted amount of wood needed as inventory or stock in the warehouse in cubic meters. For manual calculations, the DES method has a prediction calculation result for the 21st period of 4.8 m3. Based on the system testing using black box and white box testing that has been carried out, it can be concluded that the prediction system for procurement of wood raw material stock using the website-based Double Exponential Smoothing method has succeeded well in solving the problem of predicting the amount of wood raw material stock for the next period at CV. Jati Sari.

Analisis persediaan dengan Economic Order Quantity di UMKM Kacang Atom di Tanah Datar, Sumatera Barat

MSME GDR peanuts are one of the MSMEs in Tanah Datar, West Sumatra. The surge in production and product demand in the market has created challenges for these MSMEs, and they are experiencing difficulties in determining the amount of raw material supply or inventory needed. This research aims to calculate the inventory of GDR Nuts, determine what factors influence inventory planning in GDR Nut MSMEs, and calculate the amount of safety stock and ROP. The method used to overcome this problem is the EOQ (Economic Order Quantity) method to determine economic purchases for Nut MSMEs. The data processing and analysis results show that the ordering of raw material supplies in GDR MSMEs was 1.6 times with order results of 92,156 kg using the EOQ method. The leading cause of this inventory problem is the need for more implementation of effective operational management and inaccuracies in inventory calculations. As a result, there is often a need for more raw materials. To overcome this, GDR MSMEs have a safety stock of 977.3 kg and a reorder point of 13,153 times.

Linear Regression Analysis in Predicting the Amount of Stock of HP Sparepart Goods in GMT

The rapid advancement of the digital era has made smartphones an essential part of daily life, making the availability of high-quality spare parts crucial for their seamless operation. GMT, a store specializing in smartphone spare parts, faces challenges in predicting fluctuating consumer demand, often leading to either stock shortages or excesses. To address this issue, this research develops a stock prediction system based on linear regression, which analyzes sales data to accurately forecast stock needs. The implementation of this method has resulted in improved accuracy in stock management, enabling GMT to optimize inventory, minimize potential losses, and enhance both customer satisfaction and operational efficiency.

A Decision Support Model for Lean Supply Chain Management in City Multifloor Manufacturing Clusters

City manufacturing has once again become one of the priority areas for the sustainable development of smart cities thanks to the use of a wide range of green technologies and, first of all, additive technologies. Shortening the supply chain between producers and consumers has significant effects on economic, social, and environmental dimensions. Zoning of city multifloor manufacturing (CMFM) in areas with a compact population in large cities in the form of clusters with their own city logistics nodes (CLNs) creates favorable conditions for promptly meeting the needs of citizens for goods of everyday demand and for passenger and freight transportation. City multifloor manufacturing clusters (CMFMCs) have been already studied quite a lot for their possible uses; nevertheless, an identified research gap is related to supply chain design efficiency concerning urban CMFMCs. Thus, the main objective of this study was to explore the possibilities of lean supply chain management (LSCM) as the integrated application of lean manufacturing (LM) approaches and I4.0 technologies for customer-centric value stream management based on eliminating all types of waste, reducing the use of natural and energy resources, and continuous improvement of processes related to logistics activities. This paper presents a decision support model for LSCM in CMFMCs, which is a mathematical deterministic model. This model justifies the minimization of the number of road transport transfers within the urban area and the amount of stock that is stored in CMFMC buildings and in CLNs, and also regulating supplier lead time. The model was verified and validated using appropriately selected test data based on the case study, which was designed as a typical CMFM manufacturing system with various parameters of CMFMCs and urban freight transport frameworks. The feasibility of using the proposed model for value stream mapping (VSM) and managing logistics processes and inventories in clusters is discussed. The findings can help decisionmakers and researchers improve the planning and management of logistics processes and inventory in clusters, even in the face of unexpected disruptions.

Organic Carbon Storage in Waterlogging Soils in Ávila, Spain: A Traditional Agrosilvopastoral Region

Soils play a crucial role in the protection, management, and ecological understanding of the La Moraña region, located in Ávila province, Central Spain, which has a moderate population, traditional agriculture, livestock farming, and low industrial activity, resulting in relatively low environmental degradation. The region’s soils often experience prolonged water stagnation, influencing its agronomy, ecology, and economy. This study aimed to estimate and understand the soil’s role in the C sequestration of an agrosilvopastoral system under conditions of temporary water stagnation and different land uses. The results showed that ryegrass-magaza and Pinus pinaster show more content in soil carbon sequestration storage (98.7 and 92.4 Mg per hectare) compared to the adjacent degraded rangeland (75.8 and 63.9 Mg ha−1). Arenosols exhibited a higher total amount of SOC stocks. The soil profile with ryegrass sequestered more nitrogen (9.7 Mg ha−1) than other land uses; moreover, Arenosols have a lower nitrogen sequestration capacity even in low-forest conditions. The study highlights significant differences in carbon accumulation due to the management practices, temporary water layers, and parent material.

Grouping Sales Levels Smartphone Of Offline Store Using BIRCH Clustering Algorithm

From 2020 to 2024, TM_Store and Jaya Com exhibited different sales patterns based on cluster analysis using the BIRCH algorithm. The background of this research is to provide strategic insights to both stores for improving their sales performance through data analysis. The sales data used includes brand, type, month, year, stock quantity, quantity sold, unit price, and total sales. The BIRCH method was chosen for its effectiveness in handling large datasets and providing accurate clustering results. The clustering results indicate a significant increase in the "Moderate" category, from 12 sales in 2020 to 354 sales in 2023. Meanwhile, the "Very High" category also saw an increase from 5 sales in 2020 to 97 sales in 2023, with sales in the "Very Low" category remaining high at 70 sales in 2023. On the other hand, Jaya Com was dominated by the "Very High" category, with a sharp increase from 25 sales in 2020 to 597 sales in 2023. The "High" category also showed significant growth, from 6 sales in 2020 to 98 sales in 2023. This data indicates that Jaya Com focuses on high-performance products, while TM_Store shows a more balanced distribution across various sales categories. Based on the analysis, Jaya Com had 1988 data points with 1984 cluster points, whereas TM_Store had 2012 data points with 1811 cluster points. Overall, the study concludes that the BIRCH algorithm can identify significant sales patterns in both stores, aiding in the development of more effective and efficient promotional strategies tailored to each sales category's performance.

Analisis Persediaan Bahan Baku Susu pada Produksi Keju British Cheddar dengan Metode Economic Order Quantity ( EOQ ) di PT. Mazaraat Lokanatura Indonesia Pasuruan

To achieve profit, companies usually process raw materials into finished or semi-finished goods. Therefore, in this company, controlling and managing raw materials becomes an important factor influencing the smooth production process to achieve desired goals. Cow's milk is one of the popular agricultural products, and cheese is a dairy product consumed globally. PT Mazaraat Lokanatura Indonesia is a company that produces local Yogyakarta cheese and creamery products. The instability of organic milk quality from local farmers often negatively impacts the production process and the final quality of the company's British Cheddar Cheese products. This instability also affects raw material inventory planning, so the company must be more careful in maintaining the quality and quantity of organic milk stock to ensure optimal production. To address this issue, an analysis of raw milk inventory using the Economic Order Quantity (EOQ) method was conducted. This research began with observations and identification of problems related to raw material inventory management that could affect production efficiency and operational costs. Then, data collection was conducted regarding the demand amount, order time, ordering cost, and milk storage cost. The research results show that the application of EOQ is proven effective in optimizing inventory costs. Using EOQ allows the company to manage the optimal order quantity, significantly reducing total ordering costs by decreasing the order frequency.

Soil organic carbon and nitrogen in a carboniferous spoil heap as a function of vegetation type and reclamation treatment

AbstractEvaluating the impact of vegetation types and reclamation methods on soil organic carbon and nitrogen in carboniferous spoil heaps is critical for selecting the best vegetation type and reclamation method to improve ecosystem services in a changing climate. This paper presents the relationship between vegetation types (woodland, forbland, and grassland) and reclamation techniques (barren rock, topsoil application, succession, and cultivation) on soil organic carbon (SOC) and total nitrogen (TN) in developing soils on carboniferous rocks in coal mine heaps. Soil samples were collected from the litter layer (Oi + Oe) and the A horizons (0–10 cm). The results revealed that vegetation types and reclamation methods significantly affected SOC and TN stocks. Woodland exhibited higher SOC and TN in the Oi + Oe horizons than other vegetation types. Topsoil application and cultivation resulted in the highest SOC and TN stocks in the A horizons (0–10 cm) under woodland and forbland compared to succession on bare carboniferous rock. In grassland, there was no significant difference in SOC stock under topsoil application and cultivation; however, significantly higher TN stock was observed in the 0–10 cm areas with topsoil application compared to succession on bare carboniferous rock. Based on the results, topsoil application is recommended to improve SOC if the mining site is restored using woodland. Conversely, grassland exhibits a similar amount of SOC stock with or without topsoil application. Considering the difficulty of obtaining topsoil, we suggest that grasses are optimal for SOC stock in the studied mining sites, followed by forbs.

Woody species’ carbon sequestration and soil seed bank conservation potentials of traditional rangeland management strategies in Western Guji, Southern Ethiopia

Rangelands are important for mitigating climate change and rehabilitating land by storing carbon from woody plants and seeds in the soil. However, there is a lack of empirical data on how traditional rangeland management practices (such as enclosure, communal grazing and prescribed fire) affect the carbon storage of woody species and the conservation potential of soil seed banks. This study evaluates the carbon sequestration and soil seed bank conservation potential of traditional rangeland management practices in the Western Guji zone, Ethiopia. A total of 32 sample plots were established using systematic random sampling, with 12 in each management practice, to measure woody biomass and estimate carbon stock using a nondestructive method. Additionally, 72 soil samples were collected for soil seed bank analysis, with 24 samples taken from each type of rangeland management practice. The study found that enclosures had the highest amount of carbon stocks 63.1 tons per hectare (t ha−1), with a corresponding carbon dioxide equivalent (CO2e) sequestration of 231.58 t ha-1, followed by communal grazing areas that stored 27.05 t ha−1 of carbon with a CO2e sequestration of 99.27 t ha−1. Prescribed fire had the lowest carbon stocks at 19.39 t ha−1, with a CO2e sequestration of 71.16 t ha−1. The study also found that different traditional rangeland management practices significantly influenced the diversity, richness, and density of woody species in soil seed banks. The highest Shannon diversity was recorded in enclosures (3.56 ± 0.09), followed by communal grazing (2.87 ± 0.14) and prescribed fire (1.27 ± 0.08). As a result, enclosure promotes the storage of carbon in woody plants and increases the diversity, density, and richness of woody species seeds in the soil, while prescribed fire reduces it. Therefore, it recommend for pastoralists to expand the use of enclosures as it improves the potential for conserving carbon stocks and soil seed banks of woody species.

Bamboo’s Role in Climate Change Adaptation and Mitigation: An Analysis of Biomass, Carbon Stock, and Economic Potential in Tanzania

Bamboo, a rapidly growing member of the grass family, thrives across diverse climates worldwide and offers vital ecosystem services essential for human well-being and economic development. Notably, bamboo releases 35% more oxygen than other trees and sequesters an average of 12 tons of carbon dioxide per hectare, highlighting its significant ecological benefits. This study analyses the contribution of bamboo to climate change adaptation and mitigation, focusing on Tanzania as a case study. Specifically, the study aims to determine the amount of biomass and carbon stock of bamboo in Tanzania, and estimate the financial benefits from including bamboo in carbon payment projects like REDD+. Utilizing adapted allometric models and data from the National Forest Resources Monitoring and Assessment (NAFORMA) of 2015, the study reveals that Tanzania possesses 4.04 billion tons of stored bamboo biomass and 1.9 billion tons of stored bamboo carbon, translating to an estimated 7.6 billion US dollars in unutilized conservation profit from carbon trading. The findings indicate that 51.4% of the stored bamboo biomass, carbon, and potential conservation profit are concentrated in the genus Bambusa, with nearly half of these resources located in the southern regions of Lindi and Mtwara. These results underscore the substantial ecological and economic benefits of bamboo ecosystem services, emphasizing the need for stakeholders to formulate strategies for the sustainable production, conservation, and management of bamboo in Tanzania. The study provides a benchmark for developing policies and plans that leverage bamboo's potential in climate change adaptation and mitigation, promoting environmental sustainability and economic growth.

Heterogeneity analysis of food supply-demand patterns and land carbon loss under different dietary structures in China

China’s diets are undergoing a profound transformation, and carbon storage loss generated by dietary structure change is increasingly serious, accompanied by concerns about sustainable diets and food supply–demand conflicts. This paper designed a dual ‘material–environmental’ framework based on the dietary structure classification, around which we conducted the heterogeneity analysis of food supply–demand patterns and land carbon loss, and then tracked hotspot diets of trade-generated carbon loss through the multi-regional input–output (MRIO) model. The research found that 31 provinces could be classified into five clusters with obvious spatial agglomeration patterns, most of which still had a high proportion of staches, but also showed an evolutionary trend towards diversified compositions. Driven by dietary structure, the national food supply–demand match under the material accessibility dimension showed barely balanced overall, while in terms of environmental effects, ecosystems lost significant amounts of carbon stocks, with losses of 331 and 950 Tg11Tg refers to teragram, a weight unit, 1 Tg = 106 t = 1012 g of vegetation carbon and soil organic carbon, respectively, which together accounted for 14 percent of the total carbon stocks in the vegetation and topsoil carbon pools. The clusters to which Henan, Jiangsu, Zhejiang and Guangdong belong were tracked as hot dietary clusters exhibiting hotspots in inter-regional trade, contributing to carbon loss in external provinces through trade linkages. Heilongjiang and Inner Mongolia were at a low level of supply–demand balance but played the role of a carbon storage loser, while Guangdong, Zhejiang, and Jiangsu not only possessed higher balance levels but also pulled more external carbon loss. China's food system should optimise internal cropping structures and monitor external land use conversion. It is equally important to track food consumption side rights and responsibilities.

Investment allocation in an adjustment-cost production technology framework for two-stage network structures

Effective resource allocation can assist organizations in utilizing infinite resources, thereby improving efficiency and productivity. For an organization, investment is an essential production resource, and how to appropriately allocate investment is one of the most important decisions they need to make. This study aims to rationally allocate investment across each stage of two-stage network structure under two scenarios. We first propose general investment allocation model with adjustment costs to efficiently allocate investments across each stage. Then, given a fixed total investment, we use the DEA-based fixed cost allocation method to achieve the optimal allocation across stages, and we also incorporate two cooperative and noncooperative relationships between two stages. Additionally, capital stock efficiency is defined to analyze the relationship between optimal and actual capital stocks, clarifying the degree to which the optimal capital stock is reached. Finally, the proposed approach is applied to 14 A-share listed companies in China, and suggestions on the optimal amount of investment allocation and capital stock required are provided to facilitate organizational production.

A Fuzzy Optimal Base Stock System for Tolerant Clients

Base stock is an amount of stock that a company requires to maintain in order to handle an unexpected huge demand. To lerant clients are the clients who are tolerant to the company’s delayed supply, who come back to the same company even if their demands are not met within their expected time. This implies that the clients are solely relied on that company. Marketing manager requires to maintain on hand to satisfy such tolerant clients’ demands with some delay, no longer than expected by that tolerant client.This paper diagnosis the performance measures of optimal base stock system for tolerant clients in fuzzy environment. The fuzzy numbers can be modified to crisp number with the help of fuzzy ranking methods. Here the used fuzzy ranking method is prominent for de-fuzzification. The famous Triangular fuzzy number and Trapezoidal fuzzy number methods are played a major role for de-fuzzification. At last, the optimization of base stock is verified with numerical examples in fuzzy environment.

Stocks and Options Portfolio Optimisation With Reinforcement Learning

We present a model-free reinforcement learning (RL) framework for portfolio optimization across multiple assets and option prices. We directly model the relationship between the portfolio weights and the predictors with a network and maximize performance metrics for portfolio construction by using RL. We construct a portfolio, such that for any quantity of option that we buy or sell, we have a quasi-replicating portfolio made of a quantity of stock and a quantity of cash. We propose Cross-Asset-Option Transformers (CAOTs) to recover the interrelationships among options and their corresponding assets, which we use to construct a long-short or a bottom-up portfolio. We then maximize performance metrics of our portfolio with RL.