Storage Levels Research Articles

The Role of Cooperatives in Improving Smallholder Participation in Agri-Food Value Chains: A Case Study of One Local Municipality in Eastern Cape, South Africa

Increases in demand for food, product development, and agribusiness growth provide new opportunities for smallholder farmers in the developing world. Yet, the smallholder farming sector is still confronted by numerous constraints, including low quality and lower levels of produce. Agricultural cooperatives and other farmer organisations have been identified as crucial vehicles to link smallholder farmers with the evolving food systems. However, little is known about their contribution to enhancing their members’ participation in sustainable agri-food chains. This paper aims to explore the effectiveness of agricultural cooperatives in empowering smallholder farmers to participate in sustainable agri-food chains in Eastern Cape, South Africa. In achieving this, the study adopted a multi-stratified sampling procedure to select 119 cooperative members in the study area. Data were analysed using descriptive statistics and a binary regression model. Our findings revealed that the majority (70%) of cooperatives were participating in crop production. Most (75%) of the cooperative members were old, with a mean average of 52 years. Additionally, the majority (93%) of members reported that agricultural cooperatives contributed positively to their livelihoods. The results of the probit model established that quality, quantity, level of education, and access to information influenced the level of participation in storage, processing, and distribution levels at different levels of significance. Although the findings of this research reported a positive contribution of cooperatives to their members, the study identifies a greater need for cooperatives to be inclusive of young people and improve government support. Also, the study recommends a blended financing model that will cater to smallholder farmers who do not have collateral. Finally, the study recommends that the current extension and advisory services be tailored to cater to the training needs of smallholder farmers.

Analysis on metabolic functions of rhizosphere microbial communities of Pinus massoniana provenances with different carbon storage by Biolog Eco microplates.

Rhizosphere microorganisms are influenced by vegetation. Meanwhile, they respond to vegetation through their own changes, developing an interactive feedback system between microorganisms and vegetation. However, it is still unclear whether the functional diversity of rhizosphere soil microorganisms varies with different carbon storage levels and what factors affect the functional diversity of rhizosphere soil microorganisms. In this study, the Biolog-Eco microplate technique was used to analyze the metabolic diversity of carbon source of rhizosphere soil microorganisms from 6 Pinus massoniana provenances with three levels of high, medium and low carbon storage. The results showed that the average well color development(AWCD) value of rhizosphere microorganisms was significantly positive correlated with carbon storage level of Pinus massoniana (p < 0.05). The AWCD value, Simpson and Shannon diversity of high carbon sequestrance provenances were 1.40 (144h incubation) 0.96 and 3.24, respectively, which were significantly higher (p < 0.05) than those of other P. massoniana provenances. The rhizosphere microbial AWCD, Shannon and Simpson diversity of the 6 provenances showed the same variation trend (SM>AY>QJ>SX>HF>SW). Similarly, microbial biomass carbon (MBC) content was positively correlated with carbon storage level, and there were significant differences among high, medium and low carbon storage provenances. The PCA results showed that the differences in the carbon source metabolism of rhizosphere microorganisms were mainly reflected in the utilization of amino acids, carboxylic acids and carbohydrates. Pearson correlation analysis showed that soil organic carbon (SOC), total nitrogen (TN) and pH were significantly correlated with rhizosphere AWCD (p < 0.05). Soil properties are important factors affecting rhizosphere microbial carbon source metabolism. The study confirmed that the microorganisms of high carbon storage provenances had relatively high carbon metabolic activity. Among them, the carbon metabolic activity of rhizosphere microorganisms of SM provenance was the highest, which was the preferred provenances in effective ecological service function.

The elevator industry is a major link in interaction with agriculture

Elevators purchase and store the commodity part of the gross grain harvest, prepare it for supplying processing industries, providing agriculture with seed grain improved by industrial methods, as well as exchange operations (grain for grain, feed for grain).The problems of economic interaction between grain farming and the elevator industry, determined by the level of production, post-harvest processing, storage and sales, are highlighted. A granary system consisting of three large units is presented. The prospect for the development of the elevator industry in the increasing role of the first and second links in close proximity to grain production in the country has been determined.

The Flavor Characteristics, Antioxidant Capability, and Storage Year Discrimination Based on Backpropagation Neural Network of Organic Green Tea (Camellia sinensis) during Long-Term Storage.

The storage period of tea is a major factor affecting tea quality. However, the effect of storage years on the non-volatile major functional components and quality of green tea remains largely unknown. In this study, a comparative analysis of organic green teas with varying storage years (1-16 years) was conducted by quantifying 47 functional components, using electronic tongue and chromatic aberration technology, alongside an evaluation of antioxidative capacity. The results indicated a significant negative correlation between the storage years and levels of tea polyphenols, total amino acids, soluble sugars, two phenolic acids, four flavonols, three tea pigments, umami amino acids, and sweet amino acids. The multivariate statistical analysis revealed that 10 functional components were identified as effective in distinguishing organic green teas with different storage years. Electronic tongue technology categorized organic green teas with different storage years into three classes. The backpropagation neural network (BPNN) analysis demonstrated that the classification predictive ability of the model based on the electronic tongue was superior to the one based on color difference values and 10 functional components. The combined analysis of antioxidative activity and functional components suggested that organic green teas with shorter storage periods exhibited stronger abilities to suppress superoxide anion radicals and hydroxyl radicals and reduce iron ions due to the higher content of eight components. Long-term-stored organic green teas, with a higher content of substances like L-serine and theabrownins, demonstrated stronger antioxidative capabilities in clearing both lipid-soluble and water-soluble free radicals. Therefore, this study provided a theoretical basis for the quality assessment of green tea and prediction of green tea storage periods.

Spatio-temporal Evolution and Multi-scenario Simulation of Carbon Storage in Karst Regions of Central Guizhou Province：Taking Puding County as An Example

Regional land use change is the main cause for the change in karst carbon storage. It is important to analyze the spatial and temporal evolution and future spatial distribution trends of carbon storage in typical counties in central Guizhou's karst region， such as Puding County， to improve regional carbon storage， optimize land ecological security patterns， and promote low-carbon sustainable urban development. The PLUS-InVEST model was coupled， based on the interpreted land use data and future land prediction， the spatial and temporal evolution characteristics of land use change and carbon storage in Puding County from 1973 to 2020 were inverted， and the spatial pattern evolution of land use and carbon storage change under different scenarios in 2060 were simulated and predicted. The results showed： ① from 1973 to 2020， the overall carbon storage in Puding County increased by 6.61×105 t， showing an upward trend. The spatial distribution showed a significant increase in the northeastern and southwestern parts of Puding County and a significant decrease in the south-central parts. The change was due to the increase in carbon storage in dryland to shrubland and forest land and the decrease in carbon storage in areas where paddy fields are converted to construction land. ② The land use change in Puding County in the historical period was mainly reflected in the continuous expansion of construction land and the increased fluctuation of the proportion of forest land and shrub forest land， and the change in land use in different scenarios projected in 2060 retained the change characteristics of the historical period. ③ In 2060， the carbon storage in Puding County under the scenarios of natural evolution， ecological protection， and economic development increased by 2.93×105， 5.40×105， and 1.11×105 t， respectively， compared with that in 2020. Of these， the increase in ecological protection scenarios was the most significant， with the transfer of dryland to shrubland being the main reason for the increase in regional carbon sequestration capacity. These results can serve as a scientific reference for land use management decisions and the formulation of emission reduction and sink increase policies in Puding County. The future land use planning of karst areas should be guided by the goal of "carbon neutrality" in 2060， take appropriate ecological protection measures， strictly control the rapid expansion of construction land to paddy fields， optimize the land use structure， and effectively improve the level of regional carbon storage.

A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities

The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively.

Experimental data analysis and dimensionless exergy levels in a commercial stratified thermal storage

The continuously rising demand for renewable energy sources within the energy system has highlighted the need for technologies capable of mitigating the impact of renewable energy intermittency and addressing the generation-demand mismatch in the system. Thermal energy storage (TES) emerges as a versatile storage medium with a wide range of applications, spanning from solar energy utilization and power peak shaving to the storage of industrial waste heat. In this study, data collected from an operating commercial stratified tank are used to validate a 2-D axisymmetric CFD model. Temperature profiles are collected throughout one month with a one-minute refresh rate. The model replicating the tank is generated in COMSOL Multiphysics® and validated by emulating the registered charging phases of the real storage. The model is then employed to optimize the stratification capability of the tank, by varying the logics applied to pinpoint optimal values of both inlet water temperature and velocity. The study aims to maximize the exergetic efficiency of the system using dimensionless exergy, a parameter often utilized in literature to identify the ability of the storage to generate and preserve optimal temperature stratification. Finally, the experimental dimensionless exergy has been evaluated for the aforementioned temperature profiles.

In situ electrochemical Mn vacancies in CoMnHCF for a high level of zinc storage.

CoMnHCF is utilized in aqueous sodium/zinc mixed ion batteries and exhibits a high reversible capacity with good rate and cycle performances. At 0.05 A g-1 current density, the CoMnHCF can deliver a specific capacity for 180.4 mA h g-1, and have 99.3% capacity retention after 300 cycles at 0.3 A g-1. Such high reversible capacity profits from Mn vacancies that generate in situ during the first cycle, which provides more active sites for Zn storage. The de-intercalation of Na+ further elevates this good electrochemical performance. Co atoms in the framework are not only involved in the redox reactions, but help to support the structure, thus achieving better cycle stabilities.

Data needs for sea otter bioenergetics modeling.

Sea otters are keystone predators whose recovery and expansion from historical exploitation throughout their range can serve to enhance local biodiversity, promote community stability, and buffer against habitat loss in nearshore marine systems. Bioenergetics models have become a useful tool in conservation and management efforts of marine mammals generally, yet no bioenergetics model exists for sea otters. Previous research provides abundant data that can be used to develop bioenergetics models for this species, yet important data gaps remain. Here we review the available data that could inform a bioenergetics model, and point to specific open questions that could be answered to more fully inform such an effort. These data gaps include quantifying energy intake through foraging by females with different aged pups in different quality habitats, the influence of body size on energy intake through foraging, and determining the level of fat storage that is possible in sea otters of different body sizes. The more completely we fill these data gaps, the more confidence we can have in the results and predictions produced by future bioenergetics modeling efforts for this species.

Exploring the optimization of spatial patterns for carbon sequestration services based on multi-scenario land use/cover changes in the changchun-Jilin-Tumen region, China

Optimizing the spatial pattern of carbon sequestration services is essential for enhancing regional ecosystem carbon sink capacity and promoting carbon budget equilibrium. This study leveraged land use/cover changes (LUCC), meteorological, ecological, and socio-economic data, integrating the InVEST and PLUS models to quantitatively evaluate changes in carbon sequestration services within three 2035 scenarios: economic development (ED), ecological protection (EP), and natural development (ND). Furthermore, it incorporated a Bayesian belief network (BBN) with decision-making optimization capabilities to pinpoint areas necessitating the optimization of spatial patterns for carbon sequestration services. The research findings reveal: (1) Substantial impacts of land use/cover changes on carbon sequestration services, with the spatial distribution of carbon sequestration capacity closely linked to land use patterns. (2) Considerable variation in carbon storage levels across different Changchun-Jilin-Tumen scenarios, while their spatial distribution patterns remain generally consistent. Over time, a notable declining trend in carbon storage levels by 2035 compared to previous years is observed. Spatially, the eastern and central regions of the study area emerge as high-value carbon storage zones, with the northwestern region exhibiting lower carbon storage values. (3) The three scenarios categorize CSS into ecological conservation zones, ecological buffer zones, construction development zones, and arable land protection areas. Notably, the construction development zones in the western and northwestern parts of the study area urgently require optimization management. Undertaking spatial pattern optimization studies based on the current state of CSS will sustain their sustainable development and assist in future spatial planning, carbon neutrality goals, and ecological restoration in the Changchun-Jilin-Tumen region.

Numerical calculation of seepage field and stability analysis of dam body of closed tailings pond

Abstract In this study, taking a certain iron ore tailings pond as the research object, a closed pond engineering design was conducted to ensure its sustained and stable operation. Firstly, installation of a flood drainage system (spillway) and closure of the existing overflow tower and drainage pipe are suggested. Secondly, numerical analysis is conducted on the seepage field of the dam body to determine the position of the saturation line of the dam body under normal water storage level and design flood level. Subsequently, the stability of the dam body under various working conditions is assessed using the limit equilibrium method.

Thermal storage performance analysis of building envelope based on big data sensor network

In order to understand the thermal storage performance analysis of building envelope, the author proposes a research on thermal storage performance analysis of building envelope based on Big data sensor network. The author first compares and analyzes the thermal storage performance parameter systems of different theoretical methods, and determines the key characterization parameters of thermal storage performance under different working conditions based on the analysis of the thermal storage process. Second, in order to compare and verify the measured and simulated indoor temperature of the building, two groups of buildings with identical exterior wall insulation performance and distinct thermal storage performance were chosen. Three distinct thermal storage levels of the building?s exterior walls were used to simulate and analyze the internal surface temperature, heat flux density, and building cooling and heating load. Summer night ventilation was used to investigate the effect of ventilation volume on building cooling loads. In the end, a provincial office building was chosen as the research subject. The structure comprises of two stories, one underground and two over the ground, with a north hub point of 180?. The investigation object is a two-story room. The trial results demonstrate that the deliberate outcomes are in great concurrence with the mimicked results. For the district of the region, under a similar protection execution of the nook structure, the inner surface temperature of the weighty construction outside wall is higher in winter, lower in summer, with a higher intensity load in winter and a lower cooling load in summer. The proper air changes each hour of building night ventilation is 16 ach, and the effect on the cooling heap of weighty structures is huge. Demonstrated the precision of the reproduction technique and model.

Geostatistical approach in estimating the capacity volume of the mudflow reservoir

Mudflow reservoirs play a crucial role in mitigating flood risks triggered by natural events like heavy rains and snowmelt, safeguarding surrounding areas from potential inundation. However, sedimentation poses a significant challenge by reducing the capacity and effectiveness of these mudflow reservoirs over time. This study focused on estimating the capacity of the Kalkama mudflow reservoir, constructed in 1987, using a geostatistical approach. Bathymetric survey data were analyzed using various interpolation methods. Kriging (Ordinary Kriging) provided the best performance with the lowest RMSE (0.28) and a high R² (0.99), indicating it is the most accurate method for this dataset. Based on this method, a spatial model of the mudflow reservoir was developed to assess its current capacity. Findings indicate a capacity loss of 2.33 million m³ (23.6%) over 36 years, alongside a 22% reduction in surface area at Full Storage Level, and the dead volume was completely filled with sediment.

A time-multiplexed self-erasing nanopaper for water induced information transmission

The progressive presentation of multilevel information enhances the security level of information storage and transmission. Here, a time-multiplexed self-erasing nanopaper was developed by integrating cellulose nanofiber (CNF)-stabilized gold nanoclusters and CNF-modified long afterglow materials. The orange fluorescence of gold nanoclusters on nanopaper was regulated by the reversible swelling and shrinking of CNF induced by water solution, while the cyan fluorescence of micron-long afterglow remained stable and acted as the background signal. It was noteworthy that the fluorescence colour and intensity of the nanopaper could be freely adjusted between orange and cyan on the time scale. Therefore, the array information on the nanopaper could be encoded by a water solution, iterated variation as the step-by-step solvent volatilized on the time scale measured by the time of the afterglow duration. This work provides a new approach for constructing time-multiplexed self-erasing nanopaper for confidential information storage and transmission.

Iot and AI-Based MPPT Techniques for Hybrid Solar and Fuel Cell

Rising electricity demands necessitate advancements in energy generation efficiency and reliability, prompting increased interest in hybrid systems integrating solar and fuel cell renewable sources. This study proposes the MPPTs (maximum power point tracking) approach utilizing Internet of Things (IoT) for actual-time data collections from diverse sensors in a hybrid system. Sensors measuring solar irradiance, temperature, fuel cell performance, and energy storage levels feed data into AI algorithms, dynamically optimizing operational settings for enhanced energy extraction. The AI algorithms adapt to environmental changes, component deterioration, and energy consumption fluctuations, resulting in a continuous efficiency improvement. The proposed technique demonstrates superior tracking accuracy, boosting energy conversion efficiency from 88% to 94%, with a reduced system response time from 4.5 to 2.25 min. Performance metrics validate the effectiveness, suggesting potential for advanced control strategies in future renewable energy systems. Further research avenues include scalability, robustness, and cost-effectiveness for broader real-world applications.

PREDICTING THE IMPACT OF RAPID URBANISATION ON URBAN GROUNDWATER LEVEL VARIATIONS AND ANALYZING CLIMATE DYNAMICS IN PUNJAB'S CITIES, INDIA: AN APPROACH TOWARDS SUSTAINABLE CITIES

Abstract. In the past few decades, India has witnessed an unparallel population growth across its urban centres. The significant rise in urban population has led to acute demands for resources to sustain them, contributing to anthropogenic activities that impact urban resources and climate. This study was carried out to analyse the impact of rapid urbanisation on the groundwater level variation and climate dynamics. Focus was laid on Ludhiana and Patiala urban centres of Punjab state, India. Multi-temporal analysis of LULC was computed using Landsat-4, 5, 7 &amp;amp; 8 datasets to analyse urban sprawl during 1990–2023 for both the cities. A significant increase of 30% urban area was observed during 1990–2023. Further, to analyse the impact of urbanisation on ground water levels, the groundwater water storage variation was analysed using the GRACE and GRACE-FO mission datasets. It was found that the groundwater withdrawal was greater than the amount of total groundwater recharge during 1990–2023. Both these cities Ludhiana and Patiala has experienced an extreme decline in groundwater storage levels. Further, the time-series statistical relationships were explored to understand the relation between urban sprawl and annual temperature, rainfall patterns and total water storage for these cities during the years 2000-2022 for analysing the effect of rapid urbanization.

Dynamic reservoir rule curves – Their creation and utilization

This paper presents a methodology for the creation of dynamic reservoir rule curves on the basis of the results of implicit stochastic optimization coupled with optimized demand hedging embedded as constraints to optimization. The novelty of the method is a dynamic rule curve that always starts from the current storage level and projects a range of anticipated target levels in the immediate future based on the statistical analyses of the results of implicit stochastic optimization. The method is particularly useful in dry years when storage is not completely filled at the end of wet seasons. Such situations cannot be addressed with standard traditional rule curves, thus causing reservoir operators to base their decisions on mere judgment. The proposed method can be helpful in such situations. The method has been demonstrated on the Tawa reservoir in the Narmada River Basin in India.

MirrorKV: An Efficient Key-Value Store on Hybrid Cloud Storage with Balanced Performance of Compaction and Querying

LSM-based key-value stores have been leveraged in many state-of-the-art data-intensive applications as storage engines. As data volume scales up, a cost-efficient approach is to deploy these applications on hybrid cloud storage with hot/cold separation, which splits the LSM-tree into two parts and thus brings new challenges on how to split and how to close the significant performance gap between these two parts. Existing LSM-tree key-value stores mainly focus on the optimizations of local storage, which incurs sub-optimal performance when directly applied to hybrid storage. In this paper, we present MirrorKV for efficient compaction and querying on hybrid cloud storage. First, based on the capacities of fast and slow cloud storage, MirrorKV vertically separates hot/cold data of different levels stored in different cloud storage with different compaction mechanisms. To avoid compaction in slow storage being the bottleneck of the write path, MirrorKV proposes a novel virtual split to only compact the metadata during the compaction, which postpones the actual compaction until it reaches deep enough levels. Second, to reduce accessing slow storage during querying, MirrorKV horizontally separates keys and values into two mirrored LSM-trees to differentiate caching priorities; the maintained tree structures preserve the data locality for efficient sequential reading without incurring the overhead of the traditional key-value separation solutions. Finally, MirrorKV leverages cached data to guide the compaction where the hot data is retained in the fast storage while the cold data is compacted to deeper levels in slow storage. Compared with RocksDB-cloud, MirrorKV achieves 2.4× higher random insertion throughput, 29% higher random read throughput, and 99% less compaction time.

PREVENTION OF RODENT ATTACK ON TERTIARY PACKAGE AT STORAGE LEVEL

Packaging is the science, art and technology of enclosing or protecting products for distribution, storage, sale, and use. Theses packaging are done at three levels they are namely primary, secondary and tertiary. Tertiary packaging is a type of wrapping which protects the manufactured products. It is used to smooth the progress of the transport and safe handling of goods. The synthetic polymer is best suitable for packaging as they are having good strength properties. It is selected for packaging application based on their grade of suitability and recyclability towards product. The production of film for tertiary pack at storage level is mainly focused on availability and process capabilities. The problem mostly arises in the case of tertiary packaging sector where the goods are packed and stored at warehouse. The synthesis of film was carried out by blending linear low density polyethylene and essential oil as a plasticizer through solvent casting method. The synthesis is accomplished by formulating the elements certain percentage toproduce film suitable for packaging. Thereafter testing was carried out and found that elongation has improvedfor LLDPE blends at a rate of 65.6% than LLDPE. The tensile strength was found to be reduced in LLDPE blends with value of 1.60Mpa. The contact angle was measured and it was found that the LLDPE film produce hydrophobicity than LLDPE with an increase of 3%. The UV spectroscopy was also measured and it was found that LLDPE blend film gave a translucent effect. The rodent test was also carried out and LLDPE film tends to repel them. Thus the LLDPE blends are made suitable for tertiary package.

Evaluating the Stormwater Management Model for hydrological simulation of infiltration swales in cold climates

Abstract The Stormwater Management Model (SWMM) is a widely used tool for assessing the hydrological performance of infiltration swales. However, validating the accuracy of SWMM simulation against observed data has been challenging, primarily because well-functioning infiltration swales rarely produce surface runoff, especially over short monitoring periods. This study addresses this challenge by using measured subsurface water storage levels for calibration and validation. The study evaluated three SWMM modules, namely, the snowpack, aquifer, and low-impact development (LID) modules, to simulate subsurface water storage levels of an infiltration swale located in a cold climate region during snow and snow-free periods. Global sensitivity analysis was used to identify influential parameters within these modules. The findings revealed that only a few parameters significantly influenced model outputs. Moreover, the aquifer module outperformed the LID module in simulating subsurface water storage due to limitations in setting the initial saturation of the LID module. Furthermore, simulation accuracy was better during snow-free periods due to challenges in simulating snow dynamics during snow periods with the snowpack module. The calibrated models offer valuable insights into the long-term hydrological performance of infiltration swales, enabling practitioners to identify events that trigger flooding in these systems.