Storage Services Research Articles

Unlocking Generation Y’s continuance intentions in personal cloud storage services: an extended expectation confirmation model analysis

PurposeThis study aims to investigate the factors that influence the continuance intention to use personal cloud storage services among Generation Y.Design/methodology/approachA quantitative online survey was carried out to collect data from 271 respondents. Structural equation modelling with SmartPLS 4.0 software was used to run the analysis and examine the hypothesized relationships in the research model.FindingsThe study revealed that both satisfaction and habit exert a significant influence on continuance intention, whereas self-efficacy does not demonstrate a significant effect. In addition, satisfaction was found to be influenced by confirmation, perceived usefulness and perceived security. Furthermore, confirmation and cloud storage service quality were found to impact perceived usefulness, while confirmation also had an effect on perceived security. However, the hypothesized moderating role of perceived privacy risk in the relationship between perceived usefulness, perceived security and satisfaction was not supported.Originality/valueThis study advances the field by adapting an expanded expectation-confirmation model that delineates the nuanced impacts of habit, user satisfaction and self-efficacy on Generation Y’s continuance intention to use personal cloud storage services. It challenges the conventional wisdom regarding self-efficacy’s influence on technology adoption, offering a more intricate portrayal of its role. This research contributes a distinctive theoretical perspective, emphasizing the complex interplay of factors that inform sustained user engagement with cloud technologies.

A Cooperative Game Approach for Optimal Design of Shared Energy Storage System

The energy sector’s long-term sustainability increasingly relies on widespread renewable energy generation. Shared energy storage embodies sharing economy principles within the storage industry. This approach allows storage facilities to monetize unused capacity by offering it to users, generating additional revenue for providers, and supporting renewable energy prosumers’ growth. However, high investment costs and long payback periods often hinder the development of battery storage. To address this challenge, we propose a shared storage investment framework. In this framework, a storage investor virtualizes physical storage equipment, enabling prosumers to access storage services as though they owned the batteries themselves. We adopt a cooperative game approach to incorporate storage sharing into the design phase of energy systems. To ensure a fair distribution of cooperative benefits, we introduce a benefit allocation mechanism based on contributions to energy storage sharing. Utilizing realistic data from three buildings, our simulations demonstrate that the shared storage mechanism creates a win–win situation for all participants. It also enhances the self-sufficiency and self-consumption of renewable energy. This paper provides valuable insights for shared storage investors regarding optimal design and benefit allocation among multiple stakeholders.

Collaborative operation strategy of multiple microenergy grids considering demand‐side energy‐sharing behavior

AbstractIn recent years, as the energy market continues to change, an emerging business model is gradually emerging as an energy provider (EP). The participation of EP as an independent entity in the distribution network investment model not only helps realize a win–win situation for multiple stakeholders but also promotes the innovation and sustainable development of the energy industry. However, in the energy system, EPs, grids, microenergy grids (MEGs), and shared energy storage service providers usually belong to different stakeholders, and their respective pursuit of profit maximization can easily lead to uncontrolled competition, which greatly reduces market efficiency. To address the varying ownership structures within the energy system, a refined bilevel coordinated optimization operation model is introduced, rooted in the master–slave game theory. In this model, the EP assumes the role of the master, while the MEG operator and the shared energy storage operator (SESO) act as slaves. A peer‐to‐peer energy‐sharing mechanism between MEGs and an energy trading mechanism between MEGs and shared energy storage is used to further increase the local consumption rate of new energy. Simulation results demonstrate that the proposed method not only boosts the revenues of an EP, SESO, and MEG operators but also effectively accounts for the MEG's energy storage needs and SESOs' profitability.

Quantifying marginal utilities of ecosystem services for sustainable management

Understanding the marginal characteristics of ecosystem services (ESs) is crucial for sustainable management. However, there is limited research on quantifying thresholds for ESs, making it challenging for policymakers to plan effectively. This study established a framework to assess the influencing factors and marginal characteristics of cultural and regulatory ecosystem services (RES) in the Taihu Lake Basin of China and its wetland parks. The framework uses a Structural Equation Model to analyze the impact of visitors' attributes on the perception of different types of cultural ecosystem services (CES). It also employed the InVEST model to quantify the spatial patterns of RES in the basin. Multiple regression analysis and marginal utility theory were used to examine the influencing factors and marginal utilities for different types of cultural and regulatory services. The findings reveal that 1) Education level, income, gender, and age significantly influence the perception of five types of CES provision, with different factors affecting each type. 2) Most park characteristics influence the CES provision score, with their maximum and marginal utility peaks. For instance, as patch density (PD) increases, the score for recreational services initially decreases, then increases, and finally decreases again, reaching a peak of 0.50 at a PD of 325.60. The marginal utility of PD follows a similar pattern, initially increasing and then decreasing, with an increase in utility observed in the range of 0 < PD < 187.80. When PD reaches 187.80, it attains its maximum utility, indicating the point of highest efficiency for PD's impact on recreational services. 3) Heterogeneity exists in the spatial distribution of RES within the basin, with different landscape features providing varying marginal benefits for services like water supply, water purification, carbon storage, and soil erosion prevention. Carbon storage demonstrates an 'increase-then-decrease’ pattern in response to rising FP (Percentage of forest area), reaching a maximum value of 13129.3 t/km2 at FP of 66.4. The marginal utility of FP also exhibits a 'rise-then-fall’ trend, increasing up to FP of 30.4, where it peaks at 230.5. This peak represents the point of maximum efficiency in FP's influence on carbon storage services. 4) The effects of park and landscape characteristics on cultural and regulatory services exhibit marginal utilities that can be used to determine the optimal scale and location of these features to maximize ecological benefits. This research aims to expand the scope of ESs assessment and provide insights from wetland parks for broader environmental planning and optimization.

An improved identity-based public audit protocol for cloud storage

With the rapid development of informatization, a vast amount of data is continuously generated and accumulated, leading to the emergence of cloud storage services. However, data stored in the cloud is beyond the control of users, posing various security risks. Cloud data auditing technology enables the inspection of data integrity in the cloud without the necessity of data downloading. Among these, public auditing schemes have experienced rapid development due to their ability to avoid additional user auditing expenses. However, malicious third-party auditors can compromise data privacy. This paper proposes an improved identity-based cloud auditing scheme that can resist malicious auditors. This scheme is also constructed on an identity-based public auditing scheme using blockchain to prevent malicious auditing. We found the scheme is not secure because a malicious cloud server can forge authentication tags for outsourced data blocks, while our scheme has not these security flaws. Through security proofs and performance analysis, we further demonstrate that our scheme is secure and efficient. Additionally, our scheme has typical application scenarios.

Evolution and Projection of Carbon Storage in Important Ecological Functional Areas of the Minjiang River Basin, 1985–2050

The alteration of land use and cover (LULC) and the landscape ecological risk index (LERI) significantly impact carbon storage. Examining the carbon storage services in ecologically significant places is crucial for achieving a harmonious relationship between economic development in the region, conservation of terrestrial ecosystems, and mitigation of carbon sink depletion. This study aims to provide a complete framework that integrates the PLUS, Fragstats, and InVEST models. This framework will be utilized to optimize LULC and LERI, specifically maximizing carbon storage. The analysis will be carried out over an extended duration and from various viewpoints. The results indicate that the MJRB ecosystem experienced three clearly defined phases: enhancement (1985–1995), degradation (1995–2010), and subsequent enhancement (2010–2020). The LERI of high-level and carbon storage patterns showed similar trends. The degradation of local terrestrial ecosystems can primarily be due to the widespread use of ecological land caused by socio-economic development. The Ecological Preservation Scenario is projected to increase 41.97 Tg and 115.18 Tg in carbon storage. In contrast, the urban development scenario showed a substantial decrease in carbon storage rates, namely 0.89% and 1.34%, primarily evident in the Chengdu urban zone. An analysis of coupling coordination revealed a negative relationship between carbon storage and high LERI, while a positive connection was observed with low LERI. This study established a framework for rapidly assessing and forecasting the trajectory of carbon storage. It aids in optimizing land use patterns, conserving areas with high carbon sequestration, and ensuring the establishment of high-quality ecosystems. This study serves as a guide for achieving regional “dual carbon” objectives.

ЕФЕКТИВНІ СХОВИЩА ДАНИХ ДЛЯ РІШЕНЬ МАШИННОГО НАВЧАННЯ

Awareness of the importance of using effective data warehouses to develop machine learning solutions in today's world is becoming increasingly relevant in connection with the growth of data volumes and the growing need for accurate and productive models. The article identifies major vital requirements for data storage systems to ensure effective data management in building machine learning solutions. The study underscores the practical implications of rapid data access and minimal latency, reliable storage systems with robust backup and recovery mechanisms, high-level security, flexibility in handling different data types, and cost-effectiveness. These factors are not just theoretical considerations but directly impact the efficiency and success of machine learning solutions. Together with describing the concepts of block storage and object storage, the article also compares major cloud platforms—AWS (Amazon Web Services), Microsoft Azure, and Google Cloud Platform (GCP)—each providing a comprehensive range of flexible and customizable storage, security, and data processing services. These platforms enable users to manage their information resources flexibly and efficiently, offering unique features that cater to the specific needs of machine learning applications. Understanding the nuances of each provider's offerings empowers engineers and researchers to make informed decisions that align with their unique requirements. The article provides a thorough overview of the key considerations and available options, guiding strategic decisions to support machine learning initiatives effectively. Also the importance of staying up to date with the evolution of major platforms offerings is emphasized. In conclusion, selecting the proper data storage solution is critical for enhancing the performance, security, and cost-efficiency of machine learning models.

Future land use/land cover and its impacts on ecosystem services: case of Aydın, Turkey

AbstractThis paper aims to reveal the impacts of land use/land cover changes on ecosystem services and to guide sustainable development decisions by modelling the future land use/land cover pattern and its ecosystem services in the case of Aydın province, Turkey. In this context, the study examined land use/land cover changes in Aydın province, predicted future land use/land cover patterns with three scenarios (natural development, ecosystem service-based development, and economy-based development) using the PLUS model, and determined the impact of land use/land cover changes on InVEST generated carbon storage and habitat quality ecosystem services. The key drivers of land use/land cover change in Aydın were agricultural expansion, the transformation of different vegetation types into each other, and, even on a small scale, urbanization. The study revealed that changes in the spatial pattern of land use/land cover types, together with the effect of changing vegetation patterns, have a significant impact on carbon storage and habitat quality. While reductions in shrubland and forests were detrimental, transformations from openspaces to them, particularly at their boundaries, enhanced the capacity of carbon storage and habitat quality. On the other hand, even though the scenarios of ecosystem service-based development and economy-based development were based on the economic value of ecosystem services and the value of products/services obtained from different land use/land cover types, respectively, the ecosystem service-based development scenario was characterized by more effective land use/land cover management in terms of maximizing both the economic and ecological benefits. This highlights the significance and emerging need for prioritizing ecological considerations in urban planning.

Optimizing storage on fog computing edge servers: A recent algorithm design with minimal interference.

The burgeoning field of fog computing introduces a transformative computing paradigm with extensive applications across diverse sectors. At the heart of this paradigm lies the pivotal role of edge servers, which are entrusted with critical computing and storage functions. The optimization of these servers' storage capacities emerges as a crucial factor in augmenting the efficacy of fog computing infrastructures. This paper presents a novel storage optimization algorithm, dubbed LIRU (Low Interference Recently Used), which synthesizes the strengths of the LIRS (Low Interference Recency Set) and LRU (Least Recently Used) replacement algorithms. Set against the backdrop of constrained storage resources, this research endeavours to formulate an algorithm that optimizes storage space utilization, elevates data access efficiency, and diminishes access latencies. The investigation initiates a comprehensive analysis of the storage resources available on edge servers, pinpointing the essential considerations for optimization algorithms: storage resource utilization and data access frequency. The study then constructs an optimization model that harmonizes data frequency with cache capacity, employing optimization theory to discern the optimal solution for storage maximization. Subsequent experimental validations of the LIRU algorithm underscore its superiority over conventional replacement algorithms, showcasing significant improvements in storage utilization, data access efficiency, and reduced access delays. Notably, the LIRU algorithm registers a 5% increment in one-hop hit ratio relative to the LFU algorithm, a 66% enhancement over the LRU algorithm, and a 14% elevation in system hit ratio against the LRU algorithm. Moreover, it curtails the average system response time by 2.4% and 16.5% compared to the LRU and LFU algorithms, respectively, particularly in scenarios involving large cache sizes. This research not only sheds light on the intricacies of edge server storage optimization but also significantly propels the performance and efficiency of the broader fog computing ecosystem. Through these insights, the study contributes a valuable framework for enhancing data management strategies within fog computing architectures, marking a noteworthy advancement in the field.

The promotion of sustainable land use planning for the enhancement of ecosystem service capacity: Based on the FLUS-INVEST-RUSLE-CASA model.

Land Use/Land Cover (LULC) is one of the most significant human variables influencing the efficiency of Ecosystem Services (ESs) in terrestrial ecosystems. Theoretical and technical assistance for regional sustainable land use planning and management, as well as ecosystem conservation and restoration, is provided by investigating the influence of changes in the LULC pattern on the efficiency of ESs. This research focuses on the interactions between socioeconomic activities and natural ecological processes in the Three Gorges Reservoir Area (TGRA). We use LULC data from the TGRA for the years 1990, 2000, 2010, and 2020. The study includes the analysis and calculation of the spatiotemporal evolution features of the current LULC pattern and the efficiency of ESs, including their spatiotemporal distribution. Considering the TGRA's national development orientation and guidance, three potential LULC patterns are constructed under various develop-ment scenarios. To calculate the efficiency of ESs, the GeoSOS-FLUS future LULC simulation model is linked, and several methodologies such as INVEST, RUSLE, and CASA are used. The goal is to investigate the influence of future changes in LULC patterns on ESs efficiency. The findings show the following: (1) From 1990 to 2020, the values of water conservation services in the TGRA decreased and subsequently increased. High-value areas are primarily located in the reservoir's centre and eastern sections, whereas low-value areas are mostly found in the western section. Soil conservation service values initially declined and later climbed. The TGRA's carbon storage services have in-creased yearly, from 552.64 g/m2 in 2000 to 615.92 g/m2 in 2020. (2) In the ecological protection scenario, carbon storage and soil erosion increased compared to the ecosystem services in 2020. The ecological system service benefits are greater when compared to the natural development scenario. (3) The four ESs show positive spatial correlations across all three scenarios, and local spatial au-tocorrelation analysis findings demonstrate that carbon storage, water yield, and habitat quality have comparable spatial distributions across all three scenarios. To some extent, high-value areas for water conservation, soil retention, carbon storage, and habitat quality overlap.

Cloud-Based Remote Real-Time Monitoring and Control System for Spring Fatigue Testing Machine

In recent years, the utilization of cloud technology has witnessed a surge, particularly in the domains of industrial automation and intelligent scenarios. However, the prevailing spring fatigue testing machine is still in the traditional single-machine working mode. In this mode, there are many problems such as low automation of testing equipment, poor experimental site environment, and the need for experimenters to be on duty for a long time. In order to solve the above problems, this paper builds a cloud-based remote monitoring and control system based on the high-temperature constant-force spring fatigue testing machine. The system is based on Browser/Server architecture, and clients can access it anytime and anywhere using a browser in a public network environment. The server is hosted on a public cloud platform and includes website service, data storage service, WebSocket real-time communication service, and remote video monitoring service. Clients can remotely monitor and control the testing machine in real time through the cloud. After experimental verification, the real-time monitoring and control messages delay is 11 ms, and the video monitoring delay is 291 ms, which can meet the actual needs of remote spring fatigue testing. This remote monitoring and control system improves the automation of the spring fatigue testing machine and improves the working environment of the experimenters. In addition, it can be applied to other reliability testing machines in the laboratory, and can further help build a workshop-level remote monitoring and control platform.

Optimizing government strategy for deep decarbonization in towns: A game theory approach incorporating energy storage services

Renewable district energy systems present a promising solution for decarbonizing the energy sector. However, optimal strategies to mitigate the financial challenges of deep decarbonization are still underexplored. This study aims to optimize government subsidy strategies and users' system designs to facilitate cost-effective deep decarbonization using game theory. Two indicators, the renewable generation rate (RGR) and the self-sufficiency rate (SSR), are formulated to understand the principles of achieving deep decarbonization. Additionally, the study introduces a new strategy called subsidy plus energy storage service (SUB + ESS), contrasting it with the traditional subsidy-only strategy. Key findings indicate that merely increasing the RGR requirements does not achieve deep decarbonization due to energy mismatches. Restricting the SSR requirements proves effective in achieving deep decarbonization but imposes a significant economic burden on the government. When the SSR requirement is set at 100%, carbon emissions are reduced by 93%, but the government's subsidy expenditure increases nearly fivefold. In contrast to the subsidy-only strategy, the novel SUB + ESS strategy, with a 100% SSR requirement, reduces government expenditure by approximately 52% and lowers total expenditure by 19.7%. A two-stage decarbonization strategy is proposed: initially, offering subsidies is sufficient, but as deeper decarbonization is pursued, establishing energy storage service becomes essential.

PD-DETECTOR: A sustainable and computationally intelligent mobile application model for Parkinson's disease severity assessment

This paper introduces a mobile cloud-based predictive model for assisting Parkinson's disease (PD) patients. PD, a chronic neurodegenerative disorder, impairs motor functions and daily tasks due to the degeneration of dopamine-producing neurons in the brain. The model utilizes smartphones to aid patients in collecting voice samples, which are then sent to a cloud service for storage and processing. A hybrid deep learning model, trained using the UCI Parkinson's Telemonitoring Voice dataset, analyzes this data to estimate the severity of PD symptoms. The model's performance is noteworthy, with accuracy, sensitivity, and specificity metrics of 96.2 %, 94.15 %, and 96.15 %, respectively. Additionally, it boasts a rapid response time of just 13 s. Results are delivered to users via smartphone alert notifications, coupled with a knowledge base feature that educates them about PD. This system provides reliable home-based assessment and monitoring of PD and enables prompt medical intervention, significantly enhancing the quality of life for patients with Parkinson's disease.

Exploring AWS S3: In-Depth Analysis of Bucket Management

Amazon Simple Storage Service (S3) is a cornerstone in modern data management strategies with in Amazon Web Services (AWS). It is pivotal in providing scalable, durable, and accessible object storage infrastructure. A key advantage of S3 is its scalability, enabling seamless storage capacity expansion without upfront investments or provisioning delays. This flexibility empowers organizations to accommodate dynamic data growth while maintaining operational agility, which is crucial in today's rapidly evolving digital landscape. S3's exceptional durability, boasting an impressive 99.999999999% reliability, ensures steadfast data integrity and resilience against potential failures, mitigating concerns about data loss and bolstering confidence in managing mission-critical information effectively. Accessibility is another crucial aspect, with robust access controls and APIs facilitating secure data retrieval, storage, and management from any location. This accessibility fosters collaboration and innovation by enabling seamless data interaction and utilization across geographical boundaries.S3's integration capabilities with other AWS services enhance operational efficiency and optimize data analytics workflows. It is a foundational element in data analytics, facilitating data ingestion, storage, and processing to derive actionable insights from diverse datasets. Furthermore, S3's versatility extends beyond storage, supporting applications such as content delivery, backup and archiving, disaster recovery, and machine learning model training. This versatility drives innovation across industries and underscores S3's role in driving digital transformation and fostering a culture rooted in data-driven decision-making. In conclusion, Amazon S3's unparalleled scalability, durability, and accessibility make it indispensable for organizations navigating the data-centric landscape of modern business environments, underlining the imperative of harnessing its full potential for organizational success.

Craftify: An Online Marketplace for Handcrafted Treasures

Abstract : THIS is the project about the cloud services provider to any of site and application and cloud storage and databases services to client Shirley Rodriguez, a researcher at the nonprofit organization, developed the website. She thought it would be valuable to share the data that she had gathered with other researchers. Shirley stores the data in a MySQL database, and the data is available through a PHP website that she built. She initially published the site through a commercial hosting company that provides limited support for technical issues security.Over the past year, Shirley’s website has grown in popularity. As a result of increased traffic, she started receiving complaints that the site is not as responsive as it used to be. She also experienced an attempted ransomware security breach. The security breach was unsuccessful, but her supervisor, Mateo Jackson, suggested that Shirley investigate new ways to host the website.Shirley heard about AWS and initially moved her website and database to an EC2 instance that runs in a public subnet. She also runs an instance of MySQL on the same EC2 instance.Shirley approached your team to make sure that her current design follows architectural best practices. She wants to make sure that she has a robust and secure website. One of your colleagues started the process of migrating the site to a more secure implementation, but they were reassigned to another project. Your tasks are to complete the implementation, make sure that the website is secure, and confirm that the website returns data from the query page. IndexTerms – E-commerce, Online Marketplace, Handcrafted Products, Artisan Empowerment, Cultural Preservation.

Secure data sharing scheme with privacy-preserving and certificateless integrity auditing in cloud storage

With cloud storage services becoming wildspread and low-cost, individuals and organizations select outsourcing large amounts of document to cloud. Cloud storage applications provide data sharing services with varying functionality. In some cloud storage systems that provide data sharing, such as e-health systems, hiding sensitive information fields is a necessity. Meanwhile, in sharing process, unauthorized entities may have access to these privacy fields. The resource-constrained computation capability for data owner (DO) is universal. To tackle above problems, we propose a sanitizer-based secure data sharing scheme where sanitizer performs most computations of encryption and signature after receiving the partially blinded data from DO. It also checks the legitimacy of visitors before CSP returns stored data file. Besides, adopting an attribute-based access policy guarantees further privacy and fine-grained access authorization. After verification, cloud will return the required data to the legitimate visitors. Moreover, integrity auditing to shared data file is based on certificateless authentication technique, which removes certificates issue of traditional cryptographic technique, avoids key-escrow attack risk caused by identity-based cryptography. Finally, the performance analysis and experimental results show that our proposed scheme is competitive in practical applications since it reduces the authenticator generation overhead by up to 21.6% and proof generation overhead by up to 12% compared with related schemes.

Cloud-based serverless computing enables accelerated monte carlo simulations for nuclear medicine imaging

Objective. This study investigates the potential of cloud-based serverless computing to accelerate Monte Carlo (MC) simulations for nuclear medicine imaging tasks. MC simulations can pose a high computational burden—even when executed on modern multi-core computing servers. Cloud computing allows simulation tasks to be highly parallelized and considerably accelerated. Approach. We investigate the computational performance of a cloud-based serverless MC simulation of radioactive decays for positron emission tomography imaging using Amazon Web Service (AWS) Lambda serverless computing platform for the first time in scientific literature. We provide a comparison of the computational performance of AWS to a modern on-premises multi-thread reconstruction server by measuring the execution times of the processes using between 105 and 2·1010 simulated decays. We deployed two popular MC simulation frameworks—SimSET and GATE—within the AWS computing environment. Containerized application images were used as a basis for an AWS Lambda function, and local (non-cloud) scripts were used to orchestrate the deployment of simulations. The task was broken down into smaller parallel runs, and launched on concurrently running AWS Lambda instances, and the results were postprocessed and downloaded via the Simple Storage Service. Main results. Our implementation of cloud-based MC simulations with SimSET outperforms local server-based computations by more than an order of magnitude. However, the GATE implementation creates more and larger output file sizes and reveals that the internet connection speed can become the primary bottleneck for data transfers. Simulating 109 decays using SimSET is possible within 5 min and accrues computation costs of about $10 on AWS, whereas GATE would have to run in batches for more than 100 min at considerably higher costs. Significance. Adopting cloud-based serverless computing architecture in medical imaging research facilities can considerably improve processing times and overall workflow efficiency, with future research exploring additional enhancements through optimized configurations and computational methods.

Projecting the response of carbon sink potential to land use/land cover change in ecologically fragile regions

Introduction: The carbon storage service of ecosystems in ecologically fragile areas is highly sensitive to regional land use/land cover (LULC) changes. Predicting changes in regional carbon storage under different LULC scenarios is crucial for land use management decisions and exploring carbon sink potential. This study focuses on the Luan River Basin, a typical ecologically fragile area, to analyze the impact of LULC changes on carbon storage.Methods: The PLUS-InVEST model was employed to simulate LULC patterns for the year 2030 under three scenarios: natural development, cropland protection and urban development, and ecological protection. The model projected the future carbon sink potential of the basin under these scenarios.Results: From 2000 to 2020, carbon storage showed a trend of decrease followed by an increase. By 2030, compared to 2020, carbon storage is projected to increase by 16.97% under the ecological protection scenario and decrease by 22.14% under the cropland protection and urban development scenario. The increase in carbon storage was primarily due to the conversion of cropland and grassland to forestland, while the decrease was mainly associated with the conversion of forestland to grassland and cropland, and the transformation of grassland to cropland and construction land. In the potential LULC scenarios of 2030, certain regions within the basin exhibited unstable carbon sink potential, strongly influenced by LULC changes. These areas were predominantly characterized by artificially cultivated forests, shrubs, and agricultural land. Implementing appropriate forest management measures and optimizing agricultural land management practices are essential to enhance carbon sink potential in these regions. Population density, annual average temperature, and DEM (Digital Elevation Model) were the dominant factors driving the spatial variation of carbon sink potential in the Luan River Basin.Discussion: The research results provide a theoretical basis for rational planning of land use and the enhancement of carbon sink potential in ecologically fragile regions.

Caching or not: An online cost optimization algorithm for geodistributed data analysis in cloud environments

With the wide application of big data technology, a large number of data geographically stored in data centers across various regions are generated everyday, waiting to be analyzed by big data tasks. Examples of such data analysis tasks include weather prediction and intelligent healthcare applications. Clouds are being used by more and more enterprises due to their nearly infinite resources, ease of scaling, and other characteristics. Organizations often rent StaaS (Storage-as-a-Service) storage products offered by cloud providers, such as OSS (Object Storage Service), for massive data storage, while building a big data cluster in cloud environments to analyze the collected data from various regions. However, when the data to be analyzed are not located in the rented cluster, how to efficiently and economically process the distributed input data stored in clouds becomes an urgent problem to be solved. A simple approach is to only cache frequently accessed data rather than all data from other regions into the cluster to reduce total traffic costs. However, it is generally very hard to predict future data access curves. Thus, a rash caching decision may incur more costs. To address this problem, in this paper we propose an online algorithm for guiding cloud users to make cost-effective caching decisions properly, while not requiring any future information. We prove theoretically that the competitive ratio of our online algorithm is less than 2. Finally we verify the effectiveness of our proposed algorithm through extensive experiments based on the real price of Alibaba’s public IaaS cloud products using both real-world Yahoo S2 data and synthesized datasets.

Forensic Approaches for End-to-End Encryption Cloud Storage Services: MEGA as a Case Study

The advancement of cloud-based data storage technology allows users to conveniently access and manage files using endpoint devices without being constrained by their environment. While cloud storage services have improved the efficiency of performing our daily tasks, they have also become a medium for criminals to distribute illegal materials. Services that support end-to-end encryption (E2EE), cannot decrypt data even when it's stored on their servers, attracting users who require high security. There are some existing studies related to cloud-based services using E2EE, but they only deal with local artifacts, which makes it difficult to analyze when local devices cannot be found or when there are changes to local artifacts. This study identifies the mechanisms by which MEGA, a cloud-based file hosting service, operates to obtain user authentication, explore metadata, and collect files while applying end-to-end encryption. Furthermore, we propose a forensic investigation methodology to explore various metadata and selectively acquire cloud resources relevant to an incident through an understanding of E2EE algorithms. Also, we apply MEGA to the existing framework to suggest improving the framework that encompasses E2EE cloud-based services. The findings of this study serve as a valuable reference for dealing with cloud-based services with E2EE from the perspectives of computer security and digital forensics.