Storage Management Research Articles

The effect of the interactions of water activity, and temperature on OTA, OTB, and OTα produced by Penicillium verrucosum in a mini silo of natural and inoculated wheat using CO2 production as fungal activity sentinel

Ochratoxin A (OTA) is a nephrotoxin that contaminates grains in storage. Moisture and temperature sensors give delayed responses due to their slow kinetic movement within the silo. This study examines if CO2 production could predict OTA contamination and identify storage conditions exceeding the maximum limit (5 μg/kg). The impact of water activity levels (0.70–0.90 aw), temperatures (15 and 20 °C), and storage duration on (a)Penicillium verrucosum population, (b)CO2 respiration rates (RR), and (c)ochratoxins concentrations in stored wheat was investigated. 96 samples were analysed for ochratoxins with LCMS-MS. RR was >7 times higher at wetter conditions than at drier aw levels. A positive correlation between CO2, OTA, OTB, and OTα was observed at the wettest conditions. OTA exceeded the limit at >0.80 aw (16% moisture content) with RR > 0.01 mg CO2 kg−1 h−1. The knowledge of the RR of stored grain would alert grain farmers/managers to improve grain storage management.

Development of the energy hub GEDERlab at Universitat Politècnica de València for encouraging the integration of renewable energy resources

Renewable energies stand out as fundamental solutions to address climate change, providing environmental benefits and promoting energy independence. However, renewable plants face challenges such as intermittency, initial costs, and visual effects on the surroundings. The GEDERlab is an innovative energy hub developed at the Universitat Politècnica de València (UPV) that integrates four renewable energies (solar, wind, biogas, and hydraulic) to tackle these challenges. The main objective is only to optimize complementarity among energies but also to develop strategies for efficient storage and optimal management. This project goes beyond research and it aims to become a replicable model at the municipal level. Therefore, the results are extrapolated to a rural municipality in the Valencian Community, to improve energy efficiency, promote circular economy principles and utilize waste as resources. So, in this paper, the components of the energy hub GEDERlab, their integration, and the control system are described to encourage the integration of renewable energy resources.

Effect of Plant Based Pesticides in Grain Storage Management

The utilization of plant-based pesticides in grain storage management presents a promising avenue for sustainable pest control. This review paper examines the efficacy, mechanisms of action, and challenges associated with the adoption of botanical alternatives in grain storage facilities. Highlighted are the diverse phytochemical compositions of plant-based pesticides, their low toxicity to non-target organisms, and their compatibility with integrated pest management strategies. Despite their potential benefits, obstacles such as formulation complexities, limited chemical data, and regulatory hurdles hinder their widespread use. Addressing these challenges through collaborative research efforts and regulatory support is crucial to realizing the full potential of plant-based pesticides in grain storage management. By integrating these natural solutions into storage practices, we can promote food security while mitigating environmental and health risks associated with synthetic pesticides.

Organization Efficiency Enhancement by DNA Technologies

This research investigates the augmentation of organizational efficiency through the integration of DNA technologies. By leveraging advancements in biotechnology, this study explores novel approaches to enhance processes related to data storage, computation, and information management within organizational frameworks. The utilization of DNA as a storage medium and computational substrate presents opportunities to address scalability challenges and improve overall efficiency. Through case studies and experimental validations, we elucidate the potential impact of DNA technologies on organizational workflows, offering insights into the transformative possibilities for information processing and storage in the evolving landscape of enterprises. Here we evaluate DNA traits by various DNA technologies and create/modify whole space of organizational premises such as office exterior and interior and structures such in such way to accelerate productivity & functioning of overall organisation processes.

A review of control strategies for optimized microgrid operations

AbstractMicrogrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities and remote areas. Integrating diverse renewable energy sources into the grid has further emphasized the need for effective management and sophisticated control strategies. This review explores the crucial role of control strategies in optimizing MG operations and ensuring efficient utilization of distributed energy resources, storage systems, networks, and loads. To maximize energy source utilization and overall system performance, various control strategies are implemented, including demand response, energy storage management, data management, and generation‐load management. Employing artificial intelligence (AI) and optimization techniques further enhances these strategies, leading to improved energy management and performance in MGs. The review delves into the control strategies and their architectures, and highlights the significant contributions of AI and emerging technologies in advancing MG energy management.

Agriculture and Crop Management in the Ninevite V Culture.

Ninevit V culture is the culture that spread across most of the land of (southern, northern and western of present-day Kurdistan) in the early 3rd millennium BC. and lasted for 500 years. Despite some environmental challenges such as low rainfall, agriculture and administration of its products، the two most important economic sources of Ninevit V community at that time. This article which is part of a doctoral dissertation on Ninevite V culture، seeks to identify these two important aspects of society's life. It divided into two parts، the first being the cultivation and how Ninevite V community was trying to increase their agricultural output. The second part: focuses on the management of agriculture and grain storages. It's about how to build silos and store crops.

Eco‐friendly approach to thermal energy storage: Assessing the thermal and chemical properties of coconut biochar‐enhanced phase change material

AbstractPhase change materials (PCMs) can absorb, store, and release substantial latent heat within a specific temperature range during phase transition and have gained huge attention due to environmental concerns and energy crises. However, PCMs have a significant downside in energy storage due to their relatively lower thermal conductivity, leading to inadequate heat transfer (HT) performance. The foremost aim of the research is to synthesize an eco‐friendly coconut shell biochar (CSB) dispersed with organic A46 PCM in the temperature range of 44°C to 46°C to form a green nanocomposite. A two‐step approach is adopted to formulate the nanocomposites with different weight concentrations (0.2% and 0.8%) of green CSB particles. The developed nanocomposite's thermal conductivity and chemical stability were examined using a thermal properties analyzer and a Fourier transforms infrared spectrometer. The developed biochar composites have excellent thermal conductivity (0.39 W/m K) compared with base PCM (0.22 W/m K). Also, the developed nanocomposites were physically mixed together; there were no additional functional groups formed compared to pristine PCM, and the prepared materials were composite. Furthermore, a numerical analysis was performed using two‐dimensional energy modeling software to ascertain the HT rate of A46 composites. These thermally energized green nanocomposites show great promise for thermal energy storage and thermal management applications like battery thermal management, photovoltaic thermal systems, desalination systems, electronic cooling, building applications, and textiles.

Deduplication on Encrypted Data in Cloud Computing

Cloud storage is a crucial component of cloud computing, allowing users to expand their storage without upgrading their equipment and overcome resource constraints. Cloud users' data is always encrypted before being outsourced to ensure their security and privacy. However, encrypted data may result in a significant waste of cloud resources. Storage complicates data sharing among authorized users. We continue to face issues with encrypted data storage and deduplication. Traditional deduplication strategies are designed for certain application settings where data owners or cloud servers have full control over the process. They cannot meet data owners' varying requests based on data sensitivity. This study proposes a flexible data storage management method that combines deduplication and access control across various Clouds. Service Providers (CSP). We assess its performance through security analyses, comparisons, and implementations. The results demonstrate security, effectiveness, and efficiency for actual applications.

Bi-GRU-APSO: Bi-Directional Gated Recurrent Unit with Adaptive Particle Swarm Optimization Algorithm for Sales Forecasting in Multi-Channel Retail

In the present scenario, retail sales forecasting has a great significance in E-commerce companies. The precise retail sales forecasting enhances the business decision making, storage management, and product sales. Inaccurate retail sales forecasting can decrease customer satisfaction, inventory shortages, product backlog, and unsatisfied customer demands. In order to obtain a better retail sales forecasting, deep learning models are preferred. In this manuscript, an effective Bi-GRU is proposed for accurate sales forecasting related to E-commerce companies. Initially, retail sales data are acquired from two benchmark online datasets: Rossmann dataset and Walmart dataset. From the acquired datasets, the unreliable samples are eliminated by interpolating missing data, outlier’s removal, normalization, and de-normalization. Then, feature engineering is carried out by implementing the Adaptive Particle Swarm Optimization (APSO) algorithm, Recursive Feature Elimination (RFE) technique, and Minimum Redundancy Maximum Relevance (MRMR) technique. Followed by that, the optimized active features from feature engineering are given to the Bi-Directional Gated Recurrent Unit (Bi-GRU) model for precise retail sales forecasting. From the result analysis, it is seen that the proposed Bi-GRU model achieves higher results in terms of an R2 value of 0.98 and 0.99, a Mean Absolute Error (MAE) of 0.05 and 0.07, and a Mean Square Error (MSE) of 0.04 and 0.03 on the Rossmann and Walmart datasets. The proposed method supports the retail sales forecasting by achieving superior results over the conventional models.

Improving the Efficiency of Photovoltaic Systems through Hybrid Energy Storage Management for DC Applications

Improving the Efficiency of Photovoltaic Systems through Hybrid Energy Storage Management for DC Applications

Evaluation of the Contribution of Cemeteries to Urban Ecosystem Services: A Case Study of Edirne

Although green spaces in cities play a critical role in providing ecosystem services, cemeteries are often consideredan overlooked part of urban green spaces. However, in recent years, these areas have come to the forefront due totheir potential to provide a range of important ecosystem services such as conservation of biological diversity,habitat provision, air quality regulation, carbon storage, and water management. This study aims to concretelydetermine the contribution of four cemeteries -Acıçeşme, Buçuktepe, Bademlik and Karaağaç- located within thecentral district boundaries of Edirne, to urban ecosystem services. Using the i-Tree Canopy model, the land coverdistribution, air pollution impacts, and carbon sequestration/storage amounts of these cemeteries were analysed.The results indicate that Buçuktepe cemetery provides the highest ecosystem services due to its large area and densetree canopy. It is estimated that the 64.2% tree canopy cover in Buçuktepe Cemetery removes 365.44 kg of gasesand particulate matter from the air annually, sequesters 16.02 tonnes of carbon and stores 402.21 tonnes of carbon.These results show that cemeteries make a significant contribution to regulating ecosystem services in urban areas.The study highlights the potential for transforming cemeteries into multifunctional landscapes that contribute tourban ecosystem services.

Comprehensive exploration of riboflavin solid forms and preparation mechanism of stable forms

Riboflavin is a water-soluble vitamin regarded as an essential nutrient participating in biological oxidation in vivo. The strong hygroscopicity and crystalline phase impurity of riboflavin in the solid state are obstacles to its industrial production and storage. Riboflavin is polymorphic and is usually produced and marketed in crystalline form. However, the phase transformation among riboflavin polymorphs and preparation mechanism of stable crystal form have not been clarified, hindering the production optimization and storage management of products. In this work, the relationship of phase transformation among the riboflavin polymorphs was elucidated, and their thermal stability as well as hygroscopicity were evaluated. Anhydrate I is the most stable form with the best thermal stability and lowest hygroscopicity could not be prepared in antisolvent crystallization process, where monohydrate is the most frequent form. Consequently, a solution-mediated phase transformation (SMPT) process was proposed to prepare anhydrate I from monohydrate. In-situ Raman, UV–VIS spectrum and Powder X-ray Diffraction were utilized to monitoring the SMPT process. It was found that the nucleation and growth of anhydrate I was the rate-determining step. Elevated temperatures, reduced solid loads and addition of seeds can accelerate the conversion procedure. This work provides guiding significance for the product development and quality control of Riboflavin.

Mixed-Integer Nonlinear Programming Model for Optimal Field Management for Carbon Capture and Storage

Mixed-Integer Nonlinear Programming Model for Optimal Field Management for Carbon Capture and Storage

The Hygroscopic Properties and Sorption Isosteric Heat of Coix Seeds

Coix seeds are one of the most difficult-to-store cereal foods, and in order to maintain their quality during storage, the present study was conducted to determine the moisture adsorption and desorption isotherms of five varieties of coix seeds in the ranges of 10-35 °C and 11.3%-96.0% equilibrium relative humidity (ERH) using a static gravimetric method. The isotherms showed sigmoidal curves, and there was a hysteresis between adsorption and desorption isotherms. Six equilibrium moisture equations were used to fit the isotherms, and the Modified Chung-Pfost (MCPE), Modified Guggenheim-Anderson-deBoer (MGAB), Modified Henderson (MHE), Modified Oswin (MOE) and our developed modified Caurie (MCaurie) equations were suitable for fitting the data, among which MCPE was the best fitting equation. The monolayer water content decreased with increasing temperature, and the content of monolayer water, the density and layer number of adsorbed water, and the content of bound water for desorption were significantly higher than those of adsorption. The absolutely (ERH 65%) and relatively (ERH 70%) safe moisture contents (MC, wet basis, w.b.) of coix seeds were respectively 12.45%-12.82% and 13.15%-13.69% at 25 ℃. At equilibrium moisture content (EMC) less than 14.9% w.b., the desorption isosteric heats of coix seed were greater than the adsorption isosteric heats, and both decreased sharply in a hyperbolic form, approaching the latent heat of pure water at 14.9% w.b. The sorption isosteric heat of large-grain coix seed was slightly less than that of small-grain seed due to more cracks in the kernel back surface and more micropores in starch granular surface and resultantly more moisture sorption capacity. These results suggest that the desorptive and adsorptive behaviors of coix seeds should be differentiated for cooldown aeration, demoisturizing and remoisturizing operation, and the average isotherm can be used for common storage management.

Magnetically- regulated close contact melting for high-power-density latent heat energy storage

Rapid melting of solid-liquid phase change materials (PCMs) is critical to the high-power-density latent heat energy storage and efficient thermal management. The pressure-enhanced close contact melting is seen as a promising method because of the suppressed heat transfer resistance between the heating surface and the melt front during thermal charging, but it remains a key challenge how to tactfully apply external pressure for a practical device. Herein, we present a magnetically-regulated close contact melting strategy for rapid melting of PCMs from the perspective of the practical application. In this method, a PCM and a permanent magnet are contained inside a highly thermally conductive vessel, and the magnet regulated by another one can push the solid PCM toward the heating surface. Consequently, the superheated molten PCM can be instantly removed from the heating surface due to the magnetic force so as to achieve the suppressed thermal resistance. The temperature-control performance of paraffin wax is evaluated under various applied external forces and heat loads. Experimental results indicate that the heating surface temperature can be controlled near the melting temperature of PCM even under a relatively high heating flux. With applying the external pressure of 900 Pa, the average heat transfer resistance is reduced to lower than 4.5 × 10−4 K·m2·W−1. Finally, a closed thermal management device is fabricated and measured by using a graphite vessel to encapsulate the magnet and the PCMs such as paraffin and low-melting-point alloy. This device demonstrates good recyclability for repeated thermal charging/discharging. Our work provides a new method to develop the practical thermal management device based on pressure-enhanced close contact melting.

Fully Physically Crosslinked PNIPAM Ionogels with High Mechanical Properties and Temperature‐Managed Adhesion Achieved by H2O/Ionic Liquid Binary Solvents

AbstractPoly(N‐isopropylacrylamide) (PNIPAM) gels change their volume and hydrophilicity in response to temperature stimulus in flexible actuators, flexible sensors, and energy storage management. However, traditional PNIPAM gels exhibit low mechanical properties and poor self‐recovery ability due to weak intermolecular (chain) interactions and necessary covalent crosslinking. Herein, a water/ionic liquid (H2O/IL) binary solvent system that provides additional hydrogen bonding and hydrophobic interactions as well as can significantly increase the solubility (up to 60 wt.%) of NIPAM is developed. The fully physically crosslinked PNIPAM ionogel is prepared by utilizing the H2O/IL binary solvent and exhibited excellent mechanical performances, rapid self‐recovery properties, and strong adhesion strength. In addition, the lower critical solution temperature of the PNIPAM ionogel can also be tuned over a wide range (37‐70 °C) by adjusting the IL ratio in the binary solvent. Furthermore, based on the temperature sensitivity and ionic conductivity of the PNIPAM ionogel, applications of PNIPAM ionogels in smart devices, temperature‐managed adhesion, and flexible sensing are demonstrated, respectively. This work improves various performances of PNIPAM gels by using the H2O/IL binary solvent to adjust non‐covalent interactions and provided new ideas for developing high‐performance temperature‐sensitive gels.

The implementation of an optimized neural network in a hybrid system for energy management

In the face of increasing global energy demand and volatile energy prices, many countries are searching for solutions to ensure their energy independence. One of the most popular solutions is to incorporate renewable energy sources in their energy systems. While there are many advantages to integrating renewable energy sources, it is important to note that their intermittent operation can present challenges. Energy storage and smart grid management systems are key solutions to overcome these challenges and ensure sustainable, reliable use of renewable energy sources. In this article, we present an intelligent electrical energy management system for hybrid energy systems. This management system is based on a multi-layer neural network that has undergone an architecture optimization phase to improve the accuracy of real-time energy management and simplify its implementation. The management model that was built demonstrated highly good performance across a range of test circumstances.

Analyzing workload trends for boosting triple stores performance

The Resource Description Framework (RDF) is widely used to model web data. The scale and complexity of the modeled data emphasized performance challenges on the RDF-triple stores. Workload adaption is one important strategy to deal with those challenges on the storage level. Current workload-adaption approaches lack the necessary generalization of the problem and only optimize part of the storage layer with the workload (mostly the replication). This creates a big performance gap within other data structures (e.g. indexes and cache) that could heavily benefit from the same workload adaption strategy. Moreover, the workload statistics are built collectively in most of the current approaches. Thus, the analysis process is unaware of whether workloads’ items are old or recent. However, that does not simulate the temporal trends that exist naturally in user queries which causes the analysis process to lag behind the rapid workload development. We present a novel universal adaption approach to the storage management of a distributed RDF store. The system aims to find optimal data assignments to the different indexes, replications, and join cache within the limited storage space. We present a cost model based on the workload that often contains frequent patterns. The workload is dynamically and continuously analyzed to evaluate predefined rules considering the benefits and costs of all options of assigning data to the storage structures. The objective is to reduce query execution time by letting different data containers compete on the limited storage space. By modeling the workload statistics as time series, we can apply well-known smoothing techniques allowing the importance of the workload to decay over time. That allows the universal adaption to stay tuned with potential changes in the workload trends.

FACTORS ANALYSIS USE ELECTRICAL ENERGY SOURCES IN MICROGRIDS FROM ENERGY MANAGEMENT SYSTEM POSITION

The analysis of the factors of the use of electrical energy sources in microgrids from the point of view of the energy management system is a comprehensive study aimed at studying and evaluating various aspects of the use of electrical energy in small energy systems that can function independently of centralized energy networks. The main purpose of such an analysis is to identify key factors , which affect the efficiency and stability of the microgrid, as well as in the development of strategies to optimize their operation from the point of view of energy management. To achieve this goal, research is conducted covering the following aspects: technical characteristics of electrical sources, economic aspects, environmental consequences, energy management, social aspects. This is an important stage in the development of energy systems, aimed at creating efficient and sustainable energy solutions for the future. The analysis of the efficiency, reliability and power of various energy sources, such as solar batteries, wind turbines, batteries, etc., was considered. The cost of installation and operation of electric energy sources was calculated. , including the costs of maintenance, maintenance and replacement of equipment. The method of analyzing the impact of the use of various energy sources on the environment and the development of strategies for reducing emissions of greenhouse gases and other harmful substances are given. The essence of energy management systems that allow efficient management of production, storage and distribution of electricity in microgrids to ensure maximum efficiency and stability of system operation is considered. The question of the development of alternative energy sources has become particularly relevant, therefore, considering the existing structures of microgrids, it is advisable to notice the peculiarities of the construction of each microgrid, for example, the structure of a microgrid based on a wind generator can be built in several variants, and in a solar power plant, for efficient operation, the MPPT control algorithm is used, which aims to control the step-up converter in that point of the current and voltage characteristics of the photovoltaic battery, where the maximum output power can be obtained. In such microgrid structures, storage batteries are used for reserve, which are charged through step-up converters. Inverters are used to power the AC load. It is advisable to combine the solar power plant and the wind generator system into one microgrid structure, and to ensure the operation of the microgrid in conditions of low generation of one or both generating units, a diesel-generator system is used. In the built hybrid structure of the microgrid, there are operational costs that must be minimized.

CLOUD-BASED DATA INTRUSION DETECTION SYSTEM USING ML TECHNIQUES

Cloud computing (CC) stands as a groundbreaking technology, streamlining access to network and computer resources while offering a plethora of services, such as storage and data management, all with the aim of optimizing system functionality. Despite its array of advantages, cloud providers grapple with notable security challenges, particularly concerning the safeguarding of resources and services. Addressing these concerns and bolstering security measures necessitates vigilant monitoring of resources, services, and networks to promptly detect and respond to potential attacks. Central to this effort is the implementation of an advanced mechanism known as an intrusion detection system (IDS), which plays a pivotal role in regulating network traffic and identifying anomalous activities. This paper introduces an innovative cloud-based intrusion detection model that harnesses the power of the random forest (RF) algorithm alongside cutting-edge feature engineering techniques. Specifically, the integration of the RF classifier aims to enhance the accuracy (ACC) of the detection model significantly. The efficacy of the proposed model is rigorously evaluated using the NSL-KDD dataset, demonstrating a remarkable 99.99% ACC. This performance surpasses that of existing methodologies, underscoring the effectiveness and robustness of the proposed approach in addressing security challenges within cloud environments. Keywords —Accuracy, Anomaly detection, Cloud security, Feature Engineering, Intrusion Detection System (IDS), Random forest (RF)