Efficient Data Research Articles

Long-Term Monitoring and Analysis of Key Driving Factors in Environmental Quality: A Case Study of Fujian Province

Ecological environment quality reflects the overall condition and health of the environment. Analyzing the spatiotemporal dynamics and driving factors of ecological environment quality across large regions is crucial for environmental protection and policy-making. This study utilized the Google Earth Engine (GEE) platform to efficiently process large-scale remote sensing data and construct a multi-scale Remote Sensing Ecological Index (RSEI) based on Landsat and Sentinel data. This approach overcomes the limitations of traditional single-scale analyses, enabling a comprehensive assessment of ecological environment quality changes across provincial, municipal, and county levels in Fujian Province. Through the Mann–Kendall mutation test and Sen + Mann–Kendall trend analysis, the study identified significant change points in the RSEI for Fujian Province and revealed the temporal dynamics of ecological quality from 1987 to 2023. Additionally, Moran’s I statistic and Geodetector were employed to explore the spatial correlation and driving factors of ecological quality, with a particular focus on the complex interactions between natural factors. The results indicated that: (1) the integration of Landsat and Sentinel data significantly improved the accuracy of RSEI construction; (2) the RSEI showed a consistent upward trend across different scales, validating the effectiveness of the multi-scale analysis approach; (3) the ecological environment quality in Fujian Province experienced significant changes over the past 37 years, showing a trend of initial decline followed by recovery; (4) Moran’s I analysis demonstrated strong spatial clustering of ecological environment quality in Fujian Province, closely linked to human activities; and (5) the interaction between topography and natural factors had a significant impact on the spatial patterns of RSEI, especially in areas with complex terrain. This study not only provides new insights into the dynamic changes in ecological environment quality in Fujian Province over the past 37 years, but also offers a scientific basis for future environmental restoration and management strategies in coastal areas. By leveraging the efficient data processing capabilities of the GEE platform and constructing multi-scale RSEIs, this study significantly enhances the precision and depth of ecological quality assessment, providing robust technical support for long-term monitoring and policy-making in complex ecosystems.

An efficient data compression and storage technique with key management authentication in cloud space

Cloud computing is one of the promising technologies that offers cost-effective choices for processing and storing the huge volumes of data. In today’s world, data is the most important asset that one can have but it needs to be handled and protected properly. Portability of data can be increased by reducing the size of the data to be stored because of the limited storage space. As a result, data compression has arisen significantly. Data compression is a useful technique for reducing data size and increasing the effectiveness of data transit and storage. Data compression reduces the size of a data file while using lossy or lossless compression. One of the newest techniques for data compression is data duplication, which can reduce the amount of data saved while removing unnecessary data and maintaining an exact copy of the data. This analysis presents an Efficient data compression and storage technique with key management authentication in cloud space. This approach uses Regressive probabilistic key encryption (RPKE) to encrypt the cloud data and Lempel-Ziv-77-Huffman coding (LZ77-HM) is used to compress the huge amounts of cloud data. The Performance of presented approach is evaluated in terms of compression ratio and compression rate.

Video Analytic for Human Management and Security and FPGA Accelerated High Concurrency

This paper explores the use of video analytics by leveraging accelerated FPGA technology in combination with high-performance computing, specifically utilizing two Xilinx Alveo U50Lv cards and one U55C card. While many applications exist for motion analysis and detection in videos, the use of FPGAs in this context remains relatively scarce. FPGAs offer significant advantages in terms of energy efficiency and throughput. We present results demonstrating the parallelism capabilities in terms of the number of threads within a single Docker container that shares stack memory, as well as across multiple Docker containers. When operating within a single Docker process, the application shares the same memory space and resources, making it ideal for tasks that require efficient communication or data sharing. In contrast, running in multiple containers isolates processes, each with its own environment, and can significantly increase the number of threads. Our findings show that the combination of these techniques offers optimal performance for video analytics.

Developing Scalable APIs for Data Synchronization in Salesforce Environments

In the modern business landscape, Salesforce has become an essential platform for managing customer relationships, driving sales, and fostering business growth. However, as organizations grow and their data volumes increase, the need for efficient and scalable data synchronization across various systems becomes paramount. Developing scalable APIs for data synchronization in Salesforce environments presents a significant challenge due to the complexity of integrating multiple data sources, ensuring real-time updates, and maintaining data integrity across diverse systems. This paper explores the design and implementation of scalable APIs specifically tailored for data synchronization within Salesforce environments. It delves into the architectural considerations necessary for creating robust, high-performance APIs capable of handling large volumes of data while ensuring consistency and reliability. The discussion begins with an overview of Salesforce’s ecosystem, highlighting the importance of data synchronization in maintaining seamless operations across various departments and systems. A critical component of this study is the examination of different API design patterns that support scalability, such as RESTful services, GraphQL, and event-driven architectures. Each approach is analyzed for its suitability in handling the unique challenges of Salesforce data synchronization, such as managing API limits, optimizing data transfer, and ensuring that data synchronization processes do not disrupt ongoing business activities. The paper also explores the role of middleware solutions, like Enterprise Service Bus (ESB) and API gateways, in facilitating seamless data flow between Salesforce and other systems.

Optimizing High-Speed Serial Links for Multicore Processors and Network Interfaces

In modern computing environments, high-speed serial links have become a critical component for ensuring efficient data transfer between multicore processors and network interfaces. These links, characterized by their ability to transmit data at very high rates over single or multiple lanes, are essential for meeting the increasing bandwidth demands of contemporary applications. The optimization of these serial links is crucial for maintaining performance, reliability, and energy efficiency in systems that leverage multicore processors and advanced network interfaces. This paper explores the key strategies for optimizing high-speed serial links in the context of multicore processors and network interfaces. We begin by examining the architectural considerations and design principles that influence the performance of serial links, including signal integrity, power consumption, and thermal management. We also discuss the impact of link speed and data encoding techniques on overall system efficiency. One of the central challenges in optimizing high-speed serial links is managing signal integrity across different operating conditions. Techniques such as equalization and adaptive filtering are essential for mitigating the effects of signal degradation due to channel impairments and crosstalk. The paper provides a detailed analysis of these techniques, including their implementation in both hardware and software.

Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains

In the evolving landscape of the Internet of Things (IoT), ensuring the security and integrity of data transmission remains a paramount challenge. Routing Protocol for Low-Power and Lossy Networks (RPL) is commonly utilized in IoT networks to facilitate efficient data routing. However, RPL networks are susceptible to various security threats, with Sybil and flood attacks being particularly detrimental. Sybil attacks involve malicious nodes generating multiple fake identities to disrupt network operations, while flood attacks overwhelm network resources by inundating them with excessive traffic. This paper proposes a novel mitigation strategy leveraging Bloom filters and hash chains to enhance the security of RPL-based IoT networks against sybil and flood attacks. Extensive simulation and performance analysis demonstrate that this solution significantly reduces the impact of sybil and flood attacks while maintaining a low power consumption profile and low computational overhead.

Optimizing High-Speed Serial Links for Multicore Processors and Network Interfaces

In modern computing environments, high-speed serial links have become a critical component for ensuring efficient data transfer between multicore processors and network interfaces. These links, characterized by their ability to transmit data at very high rates over single or multiple lanes, are essential for meeting the increasing bandwidth demands of contemporary applications. The optimization of these serial links is crucial for maintaining performance, reliability, and energy efficiency in systems that leverage multicore processors and advanced network interfaces. This paper explores the key strategies for optimizing high-speed serial links in the context of multicore processors and network interfaces. We begin by examining the architectural considerations and design principles that influence the performance of serial links, including signal integrity, power consumption, and thermal management. We also discuss the impact of link speed and data encoding techniques on overall system efficiency.

Benchmarking Performance Indices of Electrochemical CO2 Reduction to Ethylene Based on Prospective Life Cycle Assessment for Negative Emissions.

To mitigate global warming to the most ambitious targets, it is necessary to remove CO2 from the atmosphere and reduce fossil fuels use. The electrochemical conversion of CO2 to ethylene (C2H4) as a basic chemical is a promising technology that meets both requirements; however, its life cycle CO2 emissions remain inconclusive because of varying assumptions in the performance indices. This study aimed to set benchmarks for the four most sensitive indices to achieve -0.5 t-CO2/t-C2H4 by calculating net greenhouse gas (GHG) emissions through a prospective life cycle assessment of a model system including CO2 capture, CO2 enrichment, electrochemical conversion, CO2 recycling, and cryogenic separation. As a result, the electrochemical conversion process was the hotspot of life cycle emissions, and representative benchmarks were determined as follows: cell voltage, 3.5 V; C2H4 Faraday efficiency, 70%; conversion rate, 20%; and electrochemical CO2 recycling energy, 2.2 GJ/t-CO2. The gaps between the benchmarks and current top data of cell voltage and Faraday efficiency were <10%, and suppressing the performance degradation for up to one year was found to be a critical requirement. These results can direct research towards the development of a year-round stable system, rather than further improving the performance indices.

Migration of On-Premises Database to Cloud and Perform Explanatory Analytics on Sales Data

This paper explores the migration of an industrial company's sales database from its servers to the cloud using Microsoft Azure, emphasizing three unique aspects of the process. First, it integrates the Delta Lake format within Azure Data Lake Storage Gen2, which is critical for maintaining multiple versions of data securely and ensuring ACID compliance. Second, the paper addresses significant adoption challenges such as data security, recovery, and vendor lock-in. It provides practical advice and strategic insights to help organizations navigate the complexities of cloud migration. Third, it offers a comparative analysis of two cloud storage methods—serverless and dedicated pool storage. This analysis evaluates their performance, cost-effectiveness, and suitability for different workload sizes, providing valuable insights for selecting the optimal storage strategy. Overall, this study contributes to a deeper understanding of cloud migrations, emphasizing practical applications and strategic decision-making necessary for enhancing operational efficiency and effective data management in cloud environments.

Enhancing greenhouse strawberry irrigation: integrating IoT technologies and low-cost moisture sensors in substrate

The integration of Internet of Things (IoT) technologies, particularly sensor-based systems, is revolutionizing agriculture, including strawberry cultivation in greenhouses. Furthermore, the use of wireless communication technologies, alongside efficient data storage and visualization platforms, can enhance monitoring and decision-making capabilities. This study aimed to calibrate and verify the real-time operation of the low-cost SKU SEN0193 V2.0 sensor and the SHT20 in strawberry cultivation substrate, evaluating IoT technologies for data transmission, including LoRa, InfluxDB for storage, and Grafana for visualization and monitoring. The adaptability and accuracy of the SHT20 and SKU SEN0193 V2.0 sensors were tested in laboratory and greenhouse strawberry crops. Water retention curves in the substrate were determined using the Haines Funnel, while recording sensor measurements simultaneously for calibration. For real-time monitoring, sensors were placed in two slabs with strawberry plants in a greenhouse. InfluxDB stored the data, and Grafana visualized it. While the SHT20 sensor showed limited suitability for substrates due to its lack of response to moisture variations, SKU SEN0193 V2.0 produced excellent results, detecting changes in moisture content in both laboratory and greenhouse tests. Sensor-specific calibration for greenhouse slabs is crucial. The utilization of data storage and visualization tools like InfluxDB and Grafana facilitated real-time data manipulation, demonstrating their potential for enhancing irrigation management in greenhouse strawberry production.

Digital health interventions for cardiovascular disease: A review of recent developments

Digital healthcare is revolutionizing cardiovascular care by enhancing diagnostic accuracy, treatment precision, and patient management. This review highlights key advancements, including the integration of smartphones and tablets with echocardiogram transducers for remote diagnosis, particularly in underserved regions. Emerging technologies such as 3-D printing are expected to make cardiac valve replacements more affordable, while artificial intelligence (AI) is transforming diagnostic practices by improving image analysis and risk prediction. Wearable devices, equipped with sensors for monitoring heart rate, rhythm, and other vital signs, offer real-time data that aids in early detection and personalized treatment. Mobile health applications facilitate immediate cardiac care and long-term management by leveraging the portability of smartphones. The Internet of Things (IOT) further enhances cardiovascular care by enabling comprehensive, remote monitoring and efficient data management. Collectively, these innovations are not only advancing the accuracy and accessibility of cardiovascular diagnostics but also empowering patients through more personalized and proactive healthcare approaches. This review explores the impact of these technologies on improving cardiovascular health outcomes and highlights future directions for further advancements.

Uncertainty Calculation as a Service: Integrating Cloud-Based Microservices for Enhanced Calibration and DCC Generation.

The calibration industry is renowned for its diverse and sophisticated equipment and complex processes, which necessitate innovative solutions to keep pace with rapidly advancing technology. This paper introduces an enhancement to an existing microservice-based cloud architecture, aimed at effectively managing the inherent complexity within this field. The enhanced architecture seamlessly integrates various equipment types and communication technologies, aligning diverse stakeholder expectations into a unified system that ensures efficient and accurate calibration processes. It highlights the integration of microservices to facilitate various methods of uncertainty calculation and the generation of digital calibration certificates (DCCs). A case study on RF power measurement illustrates the practical application and benefits of the enhanced architecture. Although initially focused on RF power measurement, the flexible architecture allows for future expansions to accommodate new standards and measurement techniques. The enhanced system offers a comprehensive approach to managing data flow from calibration equipment to the final generation of DCCs, utilizing cloud-based services for efficient data processing. As a future direction, this extension sets the groundwork for broader applicability across multiple measurement types, ensuring readiness for upcoming advancements in metrology.

Sistem E-Presensi Berbasis Web Dengan Kode QR di SMK Negeri 2 Cimahi

The web-based e-Attendance system with QR Code at SMK Negeri 2 Cimahi enhances student attendance monitoring digitally. By integrating QR Code technology, students easily record their attendance by scanning a QR Code on the attendance page. The system’s key advantage is automatic attendance recording, providing real-time access for teachers and administrative staff. The user-friendly interface ensures accurate monitoring and efficient data management, minimizing errors and offering a precise understanding of student attendance. The implementation of QR Code technology has improved administrative efficiency and reduced errors, leading to better insights into student attendance. Data analysis enables teachers to take appropriate actions to enhance student engagement, fostering a more responsive learning environment. Overall, the e-Attendance system significantly optimizes student attendance management in the digital era.

Improving Database Performance with SQL Server Optimization Techniques

Database performance is critical for ensuring efficient data management and retrieval, particularly in environments that utilize SQL Server. As the complexity and size of databases increase, performance optimization becomes essential for maintaining responsiveness and reliability. This paper examines a range of SQL Server optimization techniques that can significantly enhance database performance. Key strategies include effective indexing, which improves query execution speed; query optimization, which refines SQL statements for better efficiency; and proper database configuration, which ensures that SQL Server is tuned for optimal resource utilization. Additionally, we explore the importance of regular maintenance tasks such as updating statistics and monitoring performance metrics to identify bottlenecks. By employing these techniques, database administrators can minimize latency, reduce resource consumption, and enhance overall system performance. Furthermore, this paper highlights the significance of ongoing performance assessments to adapt to evolving application demands and data growth. Ultimately, implementing these optimization strategies not only boosts SQL Server performance but also contributes to improved user experiences and organizational productivity.

The Frontier Exploration of the Development Trend of Key Technologies in Digital Healthcare in Medical Big Data Analysis

With the rapid development of medical big data, digital healthcare as an interdisciplinary field is gradually transforming the traditional healthcare model, continuously advancing the realization of personalized medical services and remote health monitoring. This paper systematically reviews 138 research articles related to the development trends of key technologies in digital healthcare, exploring various critical areas including digital health and privacy security, the application of artificial intelligence and machine learning in health, remote monitoring and mobile health, medical data analysis and electronic health records, and the ethical and policy challenges in digital health. Studies indicate that the improvement of privacy security measures and efficient medical data processing technologies are the foundation for the safe promotion of digital healthcare; Artificial intelligence and machine learning technologies play an important role in enhancing the accuracy of diagnosis and disease prediction. Meanwhile, mobile health and remote monitoring technologies show great potential in improving the accessibility and continuity of medical services. In addition, this paper discusses the challenges of ethics and policies in digital health practice, such as data sharing, conflicts of interest, and respect for patient privacy. Future research must focus on refining technological solutions to address these challenges and fully integrate digital healthcare technologies into clinical practice.

MACSFeD-a database of mosquito acoustic communication and swarming features.

Acoustic communication plays an important role during the courtship of many mosquito species. Male mosquitoes show strong attraction to female wing beat frequencies, mediated via spectral matching between female wing beat frequency and male ear mechanical tuning frequency. Such acoustic communication typically occurs within swarms, male-dominated aggregations with species-specific properties. Despite hundreds of relevant publications being available, the lack of a central platform hosting all associated data hinders research efforts and limits cross-species comparisons. Here, we introduce MACSFeD (Mosquito Acoustic Communication and Swarming Features Database), an interactive platform for the exploration of our comprehensive database containing 251 unique reports focusing on different aspects of mosquito acoustic communication, including hearing function, wing beat frequency and phonotaxis, as well as male swarming parameters. MACSFeD serves as an easily accessible, efficient, and robust data visualization tool for mosquito acoustic communication research. We envision that further in-depth studies could arise following the use of this new platform. Database URL: https://minmatt.shinyapps.io/MACSFeD/.

A dynamic analysis of UAV -based IoT networks for efficient communication using falcon optimization approach

Unmanned Aerial Vehicles (UAVs) play a pivotal role in Internet of Things (IoT) applications by sensing data, conducting continuous surveillance, and maintaining connectivity among users. However, their high mobility and continuous surveillance for data transmission result in high energy consumption, possibly resulting in lesser network performance, including link failure, node depletion, data loss, and high delays. To address these challenges, a novel scheme termed as 'On-Demand Cross-Layered Falcon Optimization Approach (OCLFO)' has been proposed to enhance connectivity and optimize network performance in UAV-based IoT systems. The on- demand Cross-Layer Optimization (CLO) scheme facilitates data transmission across each layer of the OSI model. CLO is applied to the design of UAVs with the integration of on-demand, based on the queuing size. Falcon Optimization Approach (FOA) has been integrated with the CLO to overcome the energy consumption drawback, and provide successful data transmission between UAV users with the help of fitness value. The fitness value depends maximum on the energy and minimum on the queuing size, and this provides the energy efficient routing path for an efficient data transmission. The proposed OCLFO aims to optimize energy consumption while ensuring successful and expedited data transmission among UAV users. Simulations have been conducted using a network simulator to analyse Quality of Service (QoS) parameters, considering high mobility scenarios by varying energy levels such as 100 J and 200 J. Compared to the existing optimization algorithms, the proposed OCLFO achieves high delivery ratio, high throughput, less delay, lesser energy consumption and enhanced link efficiency.

HfAlOx-based ferroelectric memristor for nociceptor and synapse functions.

Efficient data processing is heavily reliant on prioritizing specific stimuli and categorizing incoming information. Within human biological systems, dorsal root ganglions (particularly nociceptors situated in the skin) perform a pivotal role in detecting external stimuli. These neurons send warnings to our brain, priming it to anticipate potential harm and prevent injury. In this study, we explore the potential of using a ferroelectric memristor device structured as a metal-ferroelectric-insulator-semiconductor as an artificial nociceptor. The aim of this device is to electrically receive external damage and interpret signals of danger. The TiN/HfAlOx (HAO)/HfSiOx (HSO)/n+ Si configuration of this device replicates the key functions of a biological nociceptor. The emulation includes crucial aspects, such as threshold reactivity, relaxation, no adaptation, and sensitization phenomena known as "allodynia" and "hyperalgesia." Moreover, we propose establishing a connection between nociceptors and synapses by training the Hebbian learning rule. This involves exposing the device to injurious stimuli and using this experience to enhance its responsiveness, replicating synaptic plasticity.

An accessible digital imaging workflow for multiplexed quantitative analysis of adult eye phenotypes in Drosophila melanogaster.

The compound eye of Drosophila melanogaster has long been a model for studying genetics, development, neurodegeneration, and heterochromatin. Imaging and morphometry of adult Drosophila and other insects is hampered by the low throughput, narrow focal plane, and small image sensors typical of stereomicroscope cameras. When data collection is distributed among many individuals or extended time periods, these limitations are compounded by inter-operator variability in lighting, sample positioning, focus, and post-acquisition processing. To address these limitations we developed a method for multiplexed quantitative analysis of adult Drosophila melanogaster phenotypes. Efficient data collection and analysis of up to 60 adult flies in a single image with standardized conditions eliminates inter-operator variability and enables precise quantitative comparison of morphology. Semi-automated data analysis using ImageJ and R reduces image manipulations, facilitates reproducibility, and supports emerging automated segmentation methods, as well as a wide range of graphical and statistical tools. These methods also serve as a low-cost hands-on introduction to imaging, data visualization, and statistical analysis for students and trainees.

Automated data collection tool for real-world cohort studies of chronic hepatitis B: Leveraging OCR and NLP technologies for improved efficiency

BackgroundCollecting and standardizing clinical research data is a very tedious task. This study is to develop an intelligent data collection tool, named CHB-EDC, for real-world cohort studies of chronic hepatitis B (CHB), which can assist in standardized and efficient data collection. MethodsCHB_EDC is capable of automatically processing various formats of data, including raw data in image format, using internationally recognized data standards, OCR, and NLP models. It can automatically populate the data into eCRFs designed in the REDCap system, supporting the integration of patient data from electronic medical record systems through commonly used web application interfaces. This tool enables intelligent extraction and aggregation of data, as well as secure and anonymous data sharing. ResultsFor non-electronic data collection, the average accuracy of manual collection was 98.65 %, with an average time of 63.64 min to collect information for one patient. The average accuracy CHB-EDC was 98.66 %, with an average time of 3.57 min to collect information for one patient. In the same data collection task, CHB-EDC achieved a comparable average accuracy to manual collection. However, in terms of time, CHB-EDC significantly outperformed manual collection (p < 0.05). Our research has significantly reduced the required collection time and lowered the cost of data collection while ensuring accuracy. ConclusionThe tool has significantly improved the efficiency of data collection while ensuring accuracy, enabling standardized collection of real-world data.