Storage Space Usage Research Articles

Sustainable package design for efficient pomegranate cold chain: Enhancing storage space, sea freight efficiency, material sustainability, and energy conservation

This study evaluated the performance of a novel fabricated dual layer ventilated packaging (carton) design for pomegranate fruit. The study argues that higher process throughput, storage space usage (higher cargo density) and packaging material usage can be achieved through systematic design of packaging. The mechanical strength, fruit cooling speeds, and quality preservation characteristics of the new and existing carton designs were evaluated in a controlled laboratory environment. The new carton design achieved 22 % above minimum recommended industry compression strength, confirming its stacking safety during palletization. Fruit packed inside both carton designs attained an average respiration rate of 5.66 ± 1.23 mL CO2 kg−1 h−1 during cold storage. Fruit decay incidence (average 5.5 %), colour changes and sensory attributes were also similar in both carton designs after 12 weeks cold storage and 2 weeks ambient storage period. Finally, fresh pomegranate fruit (cv. Wonderful) were packed in the new and commercial package designs, palletized, containerized and exported from South Africa to Germany. The novel dual layer design had a higher freight density (1720 kg more fruit per reefer) compared to the commercial counterpart. It also improved the precooling process throughput (13 % faster), reduced carton material usage (40 %), and reduced energy usage (13 %). These advantages of the new dual layer ventilated packaging can be implemented with considerable sustainability benefits.

Lossless compression for laser additive manufacturing using continuous rows compressed storage

PurposeThe layer section of laser additive manufacturing (AM) can be rasterized. Subsequently, the rasterized layer section can be converted into sparse matrix. However, large storage space is occupied due to the high manufacturing resolution. In order to reduce the storage space, the purpose of this research is to propose a lossless compression method to compress the sparse matrix.Design/methodology/approachA lossless compression method for additive manufacturing is proposed. According to manifold and irregularity feature of the object of laser AM, a lossless compression method called continuous rows compressed storage (CRCS) based on continuous rows is innovatively proposed. In particular, the better direction strategy of compression method is selected based on the side-projected area per layer.FindingsTake human teeth as an example, compared with compressed sparse row (CSR), the CRCS has advantage up to 98.88% in storage space. Compared with block compressed sparse row (BCSR), the CRCS has advantage up to 60.04% in storage space.Originality/valueThe proposed CRCS could be employed to compress the sparse matrixes of rasterized layer sections of laser AM. Compared with common lossless compression method of sparse matrix, the compression ratio of CRCS is greater. CRCS is propitious to reduce the storage space usage, thereby improving transmission efficiency.

Classification of Resampled Pediatric Epilepsy EEG Data Using Artificial Neural Networks with Discrete Fourier Transforms

Epilepsy is a neurological disorder commonly observed in children. Currently, electroencephalography (EEG) is widely used as the most important diagnostic method for epilepsy in medical practice. The diagnosis of epilepsy in pediatric patients is challenging due to their high level of activity and incomplete brain development. In this study, data sampled at 256 Hz were obtained from patients between the ages of 7–12, collected by Boston Children’s Hospital. First, the image intervals that contain seizure waves were identified in the datasets, and the discrete-time Fourier transform (DFT) was applied. The amplitude-frequency features of the frequency spectrum in seizure and nonseizure states were obtained, and patients were classified for seizure detection using a multilayer perceptron (MLP) based on an artificial neural network (ANN) architecture. In the next step, the EEG signals were resampled at low frequencies, and the same analyses were repeated to minimise the disadvantages of limiting factors such as storage space and processing power, resulting in reduced storage space usage and more efficient performance.

GsNeRF: Fast novel view synthesis of dynamic radiance fields

Synthesis of new views in dynamic 3D scenes is a challenging task in 3D vision. However, most current approaches rely on radiance fields built upon multi-view camera systems for dynamic scenes, which are expensive and time-consuming, and depend on implicit representations based on Neural Radiance Fields . In this study, we introduced a new representation method for dynamic scenes called GsNeRF, which allows for fast reconstruction of objects under motion by moving a single camera in the scene, and it can render high-quality views from arbitrary time frames and camera poses. We utilize five grids to represent the dynamic scene, employing tensor decomposition for each grid to reduce storage space usage. Since the primary task of the entire model is to optimize these planes, we enforce spatio-temporal continuity of these planes through a smoothness loss. GsNeRF is combined with a miniature MLP to regress color outputs and trained using volume rendering. Through testing on a series of synthetic and real datasets, our method reduces the training time by over 100 times compared to implicit methods and achieves better rendering quality compared to explicit methods. Our approach achieves a balance between memory usage, speed, and quality overall.

Lightweight Geometric Compression Encoding for Additive Manufacturing

In additive manufacturing (AM), 3D geometric models often contain some repetitive structures. To represent model topology more effectively and reduce storage space, this paper introduces a data structure using the repetition encoding framework and the hash encoding framework. First, we identify repeated meshes within the model and establish transformation groups. Then, we cluster the identical transformation groups to optimize storage space usage by utilizing the geometric relationships among facets. Finally, efficient data storage is achieved using the hash encoding framework. We devise three compatible encodings for prevalent additive manufacturing file formats based on these frameworks. We compare the encoding process with three commercial software solutions regarding the reconstruction efficiency and the storage space requirement. Experimental results exhibit that our proposed methods reduce file sizes by over 70% while preserving model topology information.

Distributed storage optimization using multi-agent systems in Hadoop

Understanding data and extracting information from it are the main objectives of data science, especially when it comes to big data. To achieve these goals, it is necessary to collect and process massive data sets, arriving at the system in different formats at great velocity. The Big Data era has brought us new challenges in data storage and management, and existing state-ofthe-art data storage and processing tools are poised to meet the challenges while posing challenges to the next generation of data. Big Data storage optimization is essential for improving the overall efficiency of Big Data systems by maximizing the use of storage resources. It also reduces the energy consumption of Big Data systems, resulting in financial savings, environmental protection, and improved system performance. Hadoop provides a solution for storing and analysing large quantities of data. However, Hadoop can encounter storage management problems due to its distributed nature and the management of large volumes of data. In order to meet future challenges, the system needs to intelligently manage its storage system. The use of a multi-agent system presents a promising approach for efficiently managing hot and cold data in HDFS. These systems offer a flexible, distributed solution for solving complex problems. This work proposes an approach based on a multi-agent system capable of gathering information on data access activity in the HDFS cluster. Using this information, it classifies data according to its temperature (hot or cold) and makes decisions about data replication based on its classification. In addition, it compresses unused data to manage resources efficiently and reduce storage space usage.

Data storage model in low-cost mobile applications

<span lang="EN-US">Mobile applications that have data transactions between users require a database relational database management system (RDBMS) and RESTful API operating on the hosting service so that all users can access the data. Renting a hosting service is not cheap and creating a RESTful API takes plenty of time. As an alternative to hosting, a free version of the Cloud Firestore service gives full access rights to the database and has an application programming interface (API) to manage data or access data. However, the free version of Cloud Firestore has limitations in terms of storage capacity, read, write, and delete processes. Therefore, redesigning process of the database was carried out into a low-cost version of the database model consisting of SQLite database and a low-cost version of the NoSQL database to overcome this problem. The goal is to reduce storage space usage and read, write, and delete processing on Cloud Firestore. The low-cost version of the database was tested with 6,030 data. The results obtained were savings of 47.27% storage usage, 83.08% write usage, 91.26% read process usage, and 83.19% delete process usage compared to the test results of the relational database model.</span>

A Comprehensive Study of Different Types of Deduplication Technique in Various Dimensions

In the current digital era, the growth of digital data is highly exceptional. There are various sources available for these digital data. The quantity of digital data being produced rose exponentially with time because of organizations and even by individuals, finally end up in the need of huge storage space. Cloud storage provides the storage space for such requirement. Since the storage space is utilized by many different users, having the duplicate data cannot be avoided. So it is necessary to make use of some storage optimization technique to handle such duplicate contents. Deduplication is a technique which is used to evade redundant data get stored. Among the various digital data, the possibility of having duplicate copies is high for data. In this research work, we review the benefits of having deduplication in optimizing the usage of storage space and study about the various types of deduplication techniques in different dimensions which can be used for data. It helps to select the appropriate data deduplication technique to increase their effective storage utilization and reduce the wastage of memory space because of duplicate data.

An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases

Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However, there is no standard guideline for data transformation from relational database to NoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processing time when compared to the relational database query processing time. However, these approaches produced redundant tables in the resulted schema that in turn consume large unnecessary storage size and produce high query processing time due to the generated schema with redundant column families in the transformed column-oriented database. In this paper, an efficient data transformation technique from relational database to column-oriented database is proposed. The proposed schema transformation technique is based on the combination of denormalization approach, data access pattern and multiple-nested schema. In order to validate the proposed work, the proposed technique is implemented by transforming data from MySQL database to MongoDB database. A benchmark transformation technique is also performed in which the query processing time and the storage size are compared. Based on the experimental results, the proposed transformation technique showed significant improvement in terms query processing time and storage space usage due to the reduced number of column families in the column-oriented database.

Managing Spare Parts Inventory by Incorporating Holding Costs and Storage Constraints

Abstract A key factor for motivating intending buyers of raw materials is vendor responsiveness. Therefore, to meet demand, a pre-approved level of stocks is often maintained. In contrast, the decision to keep an uncontrolled amount of stock could be counter-productive with cost components associated with holding often ignored unintentionally. In this study, the objective is to develop a spare parts inventory model that incorporates ignored holding costs with a storage constraint for a motorcycle assembly plant (MAP). The inventory policy, structure of holding costs, and spare parts sales reports were consulted for relevant data. The spare parts were categorized and selected using ABC analysis. A spare parts inventory model, which considers ignored holding cost, was formulated. The model was executed using Lingo optimisation software release 18.0.56 to determine the pair of the order quantity (Ɋ) and reorder point (Ɍ). 177 spare part items were identified using ABC analysis. The parts categorisation revealed that 21, 31, 125 part items belong to categories A, B, and C with 81, 15 and 4% of annual sales value, respectively. From category A, nine items contributed significantly to overall sales. The demand pattern for these items was probabilistic based on their coefficient of variation. The pair (Ɋ, Ɍ) for items N, Z, AY, K, AM, J, P, AL and AZ are (174,688), (71,147), (78,150), (86,163), (18,15), (88,170), (128,118), (33,43) and (87,152), respectively. These pairs yielded a total inventory cost of ₦2,177,363 when compared to the current total inventory investment of ₦6,800,000 resulting in a 67.9% cost reduction. A model to manage spare parts inventory with relevant holding cost components was developed for MAP to ensure the availability of items, maximize usage of storage space, and minimize total inventory cost.

Inline Deduplication Checking in cloud environment

Inline deduplication is a technique that can reduce the amount of storage space used in a cloud environment. It can also improve restore and backup times. However, it is not always effective due to the various factors that affect the effectiveness of this process. For instance, the type of data that is stored and the method that is used to perform deduplication are all important factors that affect the effectiveness of this solution. The goal of this paper is to provide an overview of the various aspects of inline deduplication and its effectiveness in improving the performance and reducing the cost of storage in a cloud-based environment. We then conduct a case study to demonstrate the efficiency of this process in an Amazon Web Services instance. Experiments were able to demonstrate that in cloud environments, the level of deduplication that is achieved can improve performance and reduce the amount of storage space that is used. In case study, the use of inline deduplication on Amazon Web Services led to a 60% reduction in the storage space usage. After conducting a comprehensive analysis of the various aspects of inline deduplication, we concluded that it is a standard feature that can be used by cloud storage providers to reduce their costs and improve the performance of their customers.

A Lightweight Mutual Authentication for Smart Grid Neighborhood Area Network Communications Based on Physically Unclonable Function

Smart grid as the future of power system improves efficiency, flexibility, and reliability by employing the information and communication technologies. However, these new technologies cause some challenges in security issues in the smart grid. In addition to the active and passive attacks in the communication link, such as tampering and eavesdropping, the physical attacks can also be performed on smart meters. In the absence of hardware protection, physical attacks could easily disrupt the major performance of the system. On the other hand, the security schemes must be designed as lightweight as possible according to the constrained resource of the smart meters. To solve the mentioned problems simultaneously, this article proposes a secure and lightweight protocol based on physically unclonable function for communication between the neighborhood gateways and smart meters. The security analysis shows that the proposed scheme not only resists against the all possible cyber and physical attacks, but also provides confidentiality, integrity, and mutual authentication. Moreover, the comparative performance evaluation demonstrates that the proposed scheme is efficient in terms of usage of storage space, communication overhead, and computational cost.

Dynamic Multiple-Replica Provable Data Possession in Cloud Storage System

In cloud storage scenarios, data security has received considerably more attention than before. To ensure the reliability and availability of outsourced data and improve disaster resilience and data recovery ability, important data files possessed by users must be stored on multiple cloud service providers (CSPs). However, we know that CSP is invariably not reliable. In this situation, to verify the integrity of replica files stored by users on multiple CSPs simultaneously, a new dynamic multiple-replica provable data possession (DMR-PDP) scheme is proposed. In addition, due to the importance of the tag set, we utilize vector dot products instead of using the modular power calculation in the traditional PDP scheme, which greatly reduces the calculation time and storage space usage. Moreover, a novel dynamic data structure, the divided address-version mapping table (DAVMT), is presented and used to solve the problem of data dynamic operation. A practical experiment validates the effectiveness of our proposed scheme in the end.

Flexible replica placement for enhancing the availability in edge computing environment

As edge devices that make up the Internet become more powerful, these devices in edge computing environment will even perform better in improving the computing and storage capabilities of system. In this paper, to enhancing data availability and improve cloud storage performance in edge computing environment, an adaptive replica placement strategy in edge computing environment is proposed to study the relationship among user access characteristics, the number of replicas, and the replicas location. To deal with the volatility of user access, the strategy in this paper takes data block as the data unit and the dynamic replica creation algorithm (DRC-GM) is proposed. DRC-GM uses the relationship between access frequency of data blocks and the number of replicas to dynamically adjust the number of replicas to ensure data availability requirements. To find the optimal location for replica distributed placement, in this paper, the replica placement algorithm (RP-FNSG) is proposed. And replica number, node performance and replica consistency are considered. Experiment results show that DRC-GM and RP-FNSG algorithms in edge computing environment can greatly improve system performance in terms of better prediction accuracy, shorter access response, higher effective network and storage space usage, which enhancing the data availability.

Authentication and Delegation for Operating a Multi-Drone System.

As the era of IoT comes, drones are in the spotlight as a mobile medium of Internet of Things (IoT) devices and services. However, drones appear to be vulnerable to physical capture attacks since they usually operate far from operators. If a drone is illegally captured, some important data will be exposed to the attacker. In this paper, we propose a saveless-based key management and delegation system for a multi-drone control system. The proposed system enables a multi-drone control system to highly resist physical capture by minimizing exposure of confidential data. In addition, when the drone leaves the formation for performing another mission or by a natural environment, the system can allow the drone to securely re-participate in the formation with the help of the ground control station (GCS) when it comes back. The analysis result shows that the proposed system can reduce storage space usage and require less computational overhead. From the result, we expect that the system can guarantee the resistance of physical capture and secure key management to the drones as well as many mobile IoT devices.

Synthetic zeolites market worth $5.9 bn by 2023

The Monte Carlo (MC) method is the most common technique used for uncertainty quantification, due to its simplicity and good statistical results. However, its computational cost is extremely high, and, in many cases, prohibitive. Fortunately, the MC algorithm is easily parallelizable, which allows its use in simulations where the computation of a single realization is very costly. This work presents a methodology for the parallelization of the MC method, in the context of cloud computing. This strategy is based on the MapReduce paradigm, and allows an efficient distribution of tasks in the cloud. This methodology is illustrated on a problem of structural dynamics that is subject to uncertainties. The results show that the technique is capable of producing good results concerning statistical moments of low order. It is shown that even a simple problem may require many realizations for convergence of histograms, which makes the cloud computing strategy very attractive (due to its high scalability capacity and low-cost). Additionally, the results regarding the time of processing and storage space usage allow one to qualify this new methodology as a solution for simulations that require a number of MC realizations beyond the standard.

FCloSM, FGenSM: two efficient algorithms for mining frequent closed and generator sequences using the local pruning strategy

Mining frequent sequences in sequential databases are highly valuable for many real-life applications. However, in several cases, especially when databases are huge and when low minimum support thresholds are used, the cardinality of the result set can be enormous. Consequently, algorithms for discovering frequent sequences exhibit poor performance, showing an important increase in execution time, memory consumption and storage space usage. To address this issue, researchers have studied the tasks of mining frequent closed and generator sequences, as they provide several benefits when compared to the set of frequent sequences. One of the most important benefits is that the cardinalities of frequent closed and generator sequences are generally much less than the cardinality of frequent sequences. Hence, humans find it more convenient to analyze the information provided by closed and generator sequences. Moreover, it was shown that frequent closed sequences have the advantage of being lossless, and they thus preserve information about the frequency of all frequent subsequences, while generator sequences can provide higher accuracy for sequence classification tasks since they are the smallest patterns that characterize groups of sequences. Besides, frequent closed sequences can be combined with generators to produce non-redundant sequential rules and recover the complete set of frequent sequences and their frequencies. This paper proposes two novel algorithms named FCloSM and FGenSM to mine frequent closed and generator sequences efficiently. These algorithms are based on new pruning conditions called extended early elimination (3E) and early pruning techniques named EPCLO and EPGEN, designed to identify non-closed and non-generator patterns early. Based on these techniques, two local pruning strategies called LPCLO and LPGEN are proposed to eliminate non-closed and non-generator patterns more efficiently at two successive levels of the prefix search tree without performing subsequence relation checking. These theoretical results, which are the basis of FCloSM and FGenSM, are mathematically proved and are shown to be more general than those presented in previous work. Extensive experiments show that FCloSM and FGenSM are one to two orders of magnitude faster than the state-of-the-art algorithms for discovering frequent closed sequences (CloSpan, BIDE, ClaSP and CM-ClaSP) and for mining frequent generators (FEAT, FSGP and VGEN), and that FCloSM and FGenSM consume much less memory.

Establishing an Intelligent Transportation System With a Network Security Mechanism in an Internet of Vehicle Environment

The Internet of Vehicle (IoV) utilizes networks to conduct message exchange and related services or application. In recent years, smart cities and IoVs have become areas of interest in the new generation Internet of Things development, especially since the development of intelligent transportation system has focused on bettering traffic conditions. This paper proposes establishing an intelligent transportation system with a network security mechanism in an IoV environment, with emphasis on the following aspects: 1) this paper integrates intelligent transportation systems in traffic signal control to aid emergency vehicles in more promptly arriving at its destination; 2) in the case of traffic incidents, this paper’s approach allows regular vehicles to obtain proof of incident from pertaining authorities and learn about nearby vehicles global positioning system information, such as position and speed, and utilize their car camcorder data for proving purposes; and 3) this paper combines roadside units (RSUs) with traffic signal control and transmits important information to the certificate authority (CA) for storage. Given that RSUs are limited in computation ability and storage space, we can assess and filter the information before sending it to the CA, reducing RSUs computational burden and storage space usage. This paper satisfies IoVs network security requirements of authentication, non-repudiation, conditional anonymity, and conditional untraceability, and, as seen from experiment results, the proposed method is superior to that of other studies.

Toward cost‐effective replica placements in cloud storage systems with QoS‐awareness

SummaryIn this paper, we propose a simulation model to study real‐world replication workflows for cloud storage systems. With this model, we present three new methods to maximize the storage space usage during replica creation, and two novel QoS aware greedy algorithms for replica placement optimization. By using a simulation method, our algorithms are evaluated, through a comparison with the existing placement algorithms, to show that (i) a more evenly distributed replicas for a data set can be achieved by using round‐robin methods in replica creation phase and (ii) the two proposed greedy algorithms, named GS_QoS and GS_QoS_C1, not only have more economical results than those from Chen et al., but also guarantee the QoS for clients. Copyright © 2016 John Wiley & Sons, Ltd.

A low-complexity method based on compressed sensing for long term field measurement of insulator leakage current

Field measurements of insulator leakage current are necessary for long term data storage for research purposes and condition-based maintenance. Long term monitoring and storage of actual waveform are required since the electrical activity on the insulator surface cannot be predicted in advance. This leads to the accumulation of large amount of data. One solution is to optimize the local storage space usage by implementing mathematical tools to eliminate noise and data with low information content. However, the method is computationally intensive and large scale deployment of devices based on such methods is costly. Proper condition-based maintenance cannot be guaranteed without large-scale use of monitoring devices in the field. Thus, the authors propose the use of an alternate low-complexity approach based on compressed sensing that addresses the issue of data size in field measurements. The compressed sensing based technique is perfectly suitable for large-scale deployment of leakage current measurement devices in the context of field monitoring in insulators. The method is structured to deploy low computational burden in the monitoring devices by shifting the entire computational load from the monitoring device in the field to the decoder at the central monitoring station. Different categories of field measured waveforms have been used to demonstrate the performance of the proposed method. It is expected that this paper will contribute to the development of efficient field monitoring devices in insulators.