Efficient Data Processing Research Articles

The application of multimodal AI large model in the green supply chain of energy industry

With the accelerated advancements in artificial intelligence and the increasing emphasis on sustainable supply chain management, the integration of multimodal artificial intelligence (AI) into green supply chains has emerged as a critical research frontier. This study delves into the synergistic potential and challenges of combining multimodal AI, which leverages diverse data types such as text, images, and numerical data, to enhance decision-making processes in green supply chains. Through the meticulous design of a data strategy and model framework, this research establishes a sophisticated and efficient data processing and model training pipeline. The experimental results reveal that the comprehensive analysis and fusion of multimodal data significantly improve the prediction accuracy of key supply chain metrics, with observed increases in accuracy and recall rates by 12.4% and 9.8%, respectively. Additionally, the model's limitations are critically assessed, and targeted improvement strategies are proposed. The practical implications of this study are profound, offering actionable insights for the application of multimodal AI in real-world energy sector scenarios. The findings underscore the potential of this technology to optimize operations, reduce environmental impact, and drive sustainable growth in the energy industry.

Assessing the health and yield of argan trees in Morocco’s unique ecosystem: a multispectral and machine learning approach

ABSTRACT This research is focused on developing an AI model that utilizes multispectral camera data from the Souss-Massa region. The model aims to estimate the yield and identify a range of diseases in Argan trees through meticulous on-field investigations. Initially, the work is focused on understanding the resistance of Argan plants against different diseases, based on non-irrigated and irrigated Argan trees as well as planted ones. The results show that disease resistance is high in the case of non-irrigated Argan trees and low in the case of irrigated trees. In addition, we conducted a detailed study of the Argan trees to provide a comprehensive view of the plant’s health. Utilizing machine-learning techniques, the yield estimation model suggests that it is possible to achieve up to 97% accuracy in yield estimation, processing data at an impressive rate of 33 images per second. After careful consideration and analysis, we have concluded that machine counting is the most suitable technique for Argan plants. Machine counting and disease detection offer high precision, fast and efficient data processing and cost-effectiveness. Additionally, it is less labour-intensive and can be easily integrated into the existing production process.

Accelerating Linear Congruential Generators with Carbon Nanotube Field-Effect Transistors

Abstract In the era of technological advancements, safeguarding information privacy has become paramount across sectors like defense, finance, healthcare, and more. At the heart of these security measures lies cryptography, a field that necessitates the efficient processing and secure storage of data. Our project focuses on the optimization of Linear Congruential Generators (LCG), pivotal for cryptographic applications and pseudorandom number generation. Here it has been pioneered the implementation of LCG using Carbon Nanotube Field-Effect Transistor (CNTFET) technology, targeting the reduction of leakage power through the innovative application of sleep techniques. This advanced approach not only embodies a leap towards minimizing energy consumption but also enhances the overall performance and reliability of cryptographic systems. By integrating sleep modes into the CNTFET-based LCG design, our methodology significantly curtails leakage power without compromising the generator’s efficiency or output quality. The successful implementation and synthesis of this design, employing cutting-edge CNTFET technology, underscore the potential for substantial improvements in power efficiency while maintaining high standards of security and randomness essential for cryptographic algorithms.

All-optical combinational logical units featuring fifth-order cascade

Modern computational technologies are increasingly encountering significant limitations, driving a shift towards alternative paradigms such as optical computing. In this study, we introduce novel all-optical combinational logic units based on diffractive neural networks (D2NNs), designed to perform high-order logical operations both efficiently and swiftly using only two modulation layers. This innovative design offers increased processing speed, improved energy efficiency, robust environmental stability, and high error tolerance, making it exceptionally well-suited for a broad spectrum of applications in optical computing and communications. By leveraging transfer learning, we successfully developed a fifth-order cascaded combinational logic circuit for practical information transmission system. Furthermore, we reveal a pioneering application of our device in Optical Time Division Multiplexing (OTDM), demonstrating its capability to manage high-speed data transfer seamlessly without the need for electronic conversion. Extensive simulations and experimental validations highlight the potential of our model as a foundational technology for future optical computing architectures, paving the way toward more sustainable and efficient optical data processing platforms.

Managing Digital Transformation for Nusantara New Capital City Project Construction

Abstract The Indonesian government targeted a radical change in how to develop the Nusantara New Capital City development through the digital transformation vision. Thus, this article presents a construction transformation framework to achieve the long-term vision. Despite the construction activities still in the same basic roles, the efficient operation, data processing, and entity role involvement are continuously affected by technology adoption. Meanwhile, along with the change it also required some supporting infrastructure for improvement that will be as well as the new investment for authority. The role of regulatory, supervisory, and performing entities will change to achieve effective operation integration. The state-of-the-art literature review and gap identification are used through various studies related to the research objectives. The study found that it requires 3 aspects that need to be widely improved to achieve the transformation vision for construction; the data-based activities, the efficient data management, and the selected integrative approaches used for the ecosystem. A regulation-based delivery is also required to effectively maintain the transformation objective. The study contributes to the framework of digital twin for aerial construction.

LEVERAGING DATA-DRIVEN TECHNIQUES FOR EFFICIENT DATA MINING IN CLOUD COMPUTING ENVIRONMENTS

The capacity to efficiently use big data and analytics is becoming a critical differentiator for company growth in today's data-driven environment. Using important trends, obstacles, and best practices as a framework, this article investigates how to promote company growth via the use of big data and analytics. An important issue in cloud computing is deciding on an acceptable amount and location of data. Decisions about resource management are based on data aspects and operations in data-driven infrastructure management (DDIM), a novel solution to this problem. It is critical to have a unified system that can manage various forms of big data and the analysis of that data, as well as common knowledge management functions. The approach stated in this research is DD-DM-CCE, or Data-Driven Methods for Efficient Data Mining in Cloud Computing Environments. Improving data using derived information from maximum frequent correlated pattern mining is the main focus of the work. By considering the centrality factor, the DD-DM-CCE method may help choose the best locations to store data in order to reduce access latency. In order to gain a competitive edge, this study offers a cloud-based conceptual framework that can analyze large data in real time and improve decision making. Efficient big data processing is possible with cloud computing infrastructures that can store and analyze massive amounts of data, as this reduces the upfront cost of the massively parallel computer infrastructure needed for big data analytics. According to simulations run on cloud computing, the DD-DM-CCE approach does better than the status quo regarding hit ratio, effective network utilization, and average response time. According to this study, data mining methods are valuable and successful in predicting how consumers will utilize cloud services.

Recent trends in neuromorphic systems for non-von Neumann in materia computing and cognitive functionalities

In the era of artificial intelligence and smart automated systems, the quest for efficient data processing has driven exploration into neuromorphic systems, aiming to replicate brain functionality and complex cognitive actions. This review assesses, based on recent literature, the challenges and progress in developing basic neuromorphic systems, focusing on “material-neuron” concepts, that integrate structural similarities, analog memory, retention, and Hebbian learning of the brain, contrasting with conventional von Neumann architecture and spiking circuits. We categorize these devices into filamentary and non-filamentary types, highlighting their ability to mimic synaptic plasticity through external stimuli manipulation. Additionally, we emphasize the importance of heterogeneous neural content to support conductance linearity, plasticity, and volatility, enabling effective processing and storage of various types of information. Our comprehensive approach categorizes fundamentally different devices under a generalized pattern dictated by the driving parameters, namely, the pulse number, amplitude, duration, interval, as well as the current compliance employed to contain the conducting pathways. We also discuss the importance of hybridization protocols in fabricating neuromorphic systems making use of existing complementary metal oxide semiconductor technologies being practiced in the silicon foundries, which perhaps ensures a smooth translation and user interfacing of these new generation devices. The review concludes by outlining insights into developing cognitive systems, current challenges, and future directions in realizing deployable neuromorphic systems in the field of artificial intelligence.

Power data analysis and mining technology in smart grid

This study proposes a smart grid model named “GridOptiPredict”, which aims to achieve efficient analysis and processing of power system data through deep fusion of deep learning and graph neural network, so as to improve the intelligent level and overall efficiency of power grid operation. The model integrates three core functions of load forecasting, power grid state sensing and resource optimization into one, forming a closely connected and complementary framework. Through carefully designed experimental scheme, the practical value and effectiveness of “Grid OptiPredict” model are fully verified from three aspects: accuracy of load forecasting, sensitivity of power grid state sensing and efficiency of resource allocation strategy. Experimental results show that the model has significant advantages in prediction accuracy, model stability and robustness, resource optimization, security, information security, social and economic benefits and user experience.

Edge Computing and Cloud Computing for Internet of Things: A Review

The rapid expansion of the Internet of Things ecosystem has created an urgent need for efficient data processing and analysis technologies. This review aims to systematically examine and compare edge computing, cloud computing, and hybrid architectures, focusing on their applications within IoT environments. The methodology involved a comprehensive search and analysis of peer-reviewed journals, conference proceedings, and industry reports, highlighting recent advancements in computing technologies for IoT. Key findings reveal that edge computing excels in reducing latency and enhancing data privacy through localized processing, while cloud computing offers superior scalability and flexibility. Hybrid approaches, such as fog and mist computing, present a promising solution by combining the strengths of both edge and cloud systems. These hybrid models optimize bandwidth use and support low-latency, privacy-sensitive applications in IoT ecosystems. Hybrid architectures are identified as particularly effective for scenarios requiring efficient bandwidth management and low-latency processing. These models represent a significant step forward in addressing the limitations of both edge and cloud computing for IoT, offering a balanced approach to data analysis and resource management.

Using the Ethereum Blockchain in the Internet of Things Network for IT Diagnostics

The article discusses the use of Ethereum blockchain technology in the Internet of Things (IoT) network for IT diagnostics of patients, which increases data security and user privacy. This integration is proving effective for storing and managing sensitive data of patients with neurological diseases. An integrated system architecture has been developed that combines the IoT network, the IPFS (InterPlanetary File System) file structure with the Ethereum blockchain to create a reliable data storage model. This system ensures efficient, secure and transparent data processing, optimizing the processes of data registration, authorization and verification. Using IPFS for decentralized file storage, along with the Ethereum blockchain to create tamper-proof medical records, provides increased efficiency, scalability and privacy. During the experiments, the process of creating and testing the system was implemented, including setting up the environment, connecting an IPFS node, programming Ethereum smart contracts, sampling voice data and storing their hashes.

A three-tier approach to Internet of Things software architecture design

The Internet of Things (IoT) rapidly evolves, presenting challenges and opportunities. This study emphasises the critical role of software in advancing IoT technologies, focusing on machine learning (ML), fog computing, and process optimisation for security and resiliency. ML is pivotal in IoT for predicting equipment failures, evaluating process efficiency, and enabling informed decision- making through real-time data analysis. By integrating ML models directly into IoT devices (edge computing), latency is reduced, and data transmission needs are minimised. Fog computing and cloud computing address latency issues by moving computational resources closer to IoT devices, enhancing scalability and optimising network resource usage. Security remains a paramount concern due to the increasing number of connected devices and their vulnerabilities. IoT software must balance efficiency, security, and performance, employing deep learning for anomaly detection, blockchain for data transparency, and optimised encryption protocols. The trend towards distributed architectures like Edge Computing enhances system resilience by reducing latency and increasing fault tolerance. The proposed IoT system architecture is a three-tier structure consisting of Edge, Fog, and Cloud levels. At the Edge level, initial data processing occurs directly on IoT devices, reducing latency and network load. The Fog level processes data within the local network, utilising more powerful computational resources for complex tasks and ensuring security through advanced machine learning and encryption. The Cloud level serves as a central repository for long-term storage and global data analysis, leveraging containerisation and orchestration technologies for scalability and reliability. This multi-layered architecture ensures efficient data processing, high security, and adaptability, making it suitable for real-time applications. The study highlights the importance of software in optimising data processing across these levels, ensuring the IoT system’s resilience, scalability, and long-term sustainability.

Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information

In this study, a 4D trajectory weather (4DT-Wx) prototype system was developed and evaluated for effective weather information integration in trajectory-based operation (TBO) environments. The system has two key distinguishing features: multi-model-based trajectory services and buffer zone information provision. We constructed a distributed processing system using Apache Spark, enabling the efficient processing of large-scale weather data. The performance evaluation demonstrated excellent scalability and efficiency in processing large-scale data. An analysis of the buffer configurations highlighted that buffer zone information is valuable in decision-making processes and has the potential to enhance the system performance. The system’s practical applicability is presented through visualizations of the extracted weather information. This system is expected to enhance aviation safety and operational efficiency, providing a foundation for addressing increasingly complex weather conditions and flight scenarios in the future. The approach presented in this study marks a significant step toward effective TBO implementation and the advancement of future air traffic management. The evaluation of the 4DT-Wx system analyzed the accuracy of weather data processing and the performance of distributed processing, finding that the temperature (T) estimation had the highest accuracy, and that the parallel processing using Apache Spark was most effectively modeled by Ahmed et al.’s model. The findings suggest the potential for further optimization in integrating various weather models and developing algorithms to enhance their utilization.

Machine Learning-Driven RUL Prediction and Uncertainty Quantification for Ball Screw Drives in a Cloud-Ready Maintenance Framework

In today's rapidly evolving industrial landscape, efficient predictive maintenance solutions are essential for minimizing downtime and enhancing productivity. This research introduces an adaptive cloud-based model pipeline for predicting the Remaining Useful Life (RUL) of machine components, specifically ball screws. The pipeline integrates local preprocessing, edge computing, and cloud-based adaptive model training, ensuring data privacy and reducing data transmission volumes. The system classifies wear states using various machine learning mod-els and predicts RUL through regression analysis, incorporating uncertainty quantification for robust maintenance scheduling. The experimental setup includes accelerated degradation of ball screws, with data collected via a three-dimensional accelerometer. Feature extraction and data augmentation techniques are employed to enhance prediction accuracy. Random Forest and Gradient Boosting models demonstrate superior performance, with Random Forest selected for its robustness and uncertainty quantification capabilities. Empirical results indicate high prediction accuracy, with Random Forest achieving up to 91% accuracy in Phase 2. This cloud-ready predictive maintenance framework leverages scalable cloud infrastructure for efficient data processing and real-time updates, offering a practical solution for industrial applications. The proposed approach significantly advances the adoption of digital business models within the manufacturing industry, providing a reliable and efficient tool for predictive maintenance.

Security, QoS and energy aware optimization of cloud-edge data centers using game theory and homomorphic encryption: Modeling and formal verification

Privacy and efficient data processing are crucial research areas in the field of outsourcing computing. Thus, the contribution of the paper is twofold. First of all, we develop a secure offloading technique that can process data while preserving privacy. Although RSA and Paillier cryptosystems are highly beneficial for mathematical operations on basic encrypted data due to their homomorphic properties, images are processed using permutation-ordered binary (POB) and Shamir's secret sharing (SSS). Second, a non-cooperative game theory model was formulated to examine the interactions between service providers, characterizing each edge as an M/M/1 system with an infinite queue. Through formal verification using rigorous mathematical reasoning, the suggested strategies for task offloading and resource allocation are shown to be effective in terms of security, quality of service (QoS), and energy consumption. The best-response dynamics approach is used to calculate the final strategy for each participant and assess how fairly resources are allocated. Finally, the potential of our approach is demonstrated numerically, revealing a faster convergence towards attaining Nash equilibrium in a duopoly cloud-edge data center market, granted that the necessary and sufficient conditions are met.

Clustering scRNA-seq data with the cross-view collaborative information fusion strategy.

Single-cell RNA sequencing (scRNA-seq) technology has revolutionized biological research by enabling high-throughput, cellular-resolution gene expression profiling. A critical step in scRNA-seq data analysis is cell clustering, which supports downstream analyses. However, the high-dimensional and sparse nature of scRNA-seq data poses significant challenges to existing clustering methods. Furthermore, integrating gene expression information with potential cell structure data remains largely unexplored. Here, we present scCFIB, a novel information bottleneck (IB)-based clustering algorithm that leverages the power of IB for efficient processing of high-dimensional sparse data and incorporates a cross-view fusion strategy to achieve robust cell clustering. scCFIB constructs a multi-feature space by establishing two distinct views from the original features. We then formulate the cell clustering problem as a target loss function within the IB framework, employing a collaborative information fusion strategy. To further optimize scCFIB's performance, we introduce a novel sequential optimization approach through an iterative process. Benchmarking against established methods on diverse scRNA-seq datasets demonstrates that scCFIB achieves superior performance in scRNA-seq data clustering tasks. Availability: the source code is publicly available on GitHub: https://github.com/weixiaojiao/scCFIB.

Disjoint paths construction algorithm in the data center network DPCell

Abstract With the development of the fourth industrial revolution, the importance of data centers has significantly increased. Data centers are widely used in many fields due to their ability to provide efficient, secure, and reliable data storage and processing services. However, with the increasing amount of data, traditional data center networks (DCNs) are currently facing various challenges, prompting academia and industry to propose new DCN architectures. As a dual-port server-based DCN, DPCell has excellent scalability and bisection width, enabling it to meet the demands of large-scale data storage, processing, and computation in the digital revolution. In order to ensure the secure and reliable data communication in the DPCell, this paper designs a disjoint paths communication scheme based on the actual DCN routing requirements. This scheme constructs the optimal number of disjoint paths in DPCell, with a maximum path length of $2^{k}+3$, where $k$ represents the dimension of the DPCell. Furthermore, experiments have verified that the time complexity of this scheme is sublinear, making it more efficient than the current optimal maximum flow algorithm. To a certain extent, this scheme provides DPCell with the required high bandwidth, fault tolerance, and security for data communication.

Towards Mine 4.0: A Proposed Multi-Layered Architecture for Real-Time Surveillance and Anomaly Detection in an Open-Pit Phosphate Mine

Given the global importance and limited availability of phosphate, optimizing the use of this critical resource and minimizing its wastage are of paramount importance. In this context, this paper proposes an innovative architecture for the implementation of an intelligent video surveillance system specifically designed for open-pit phosphate mines. The proposed architecture is designed to meet the overall functional requirements of a surveillance system in the challenging environment of open-pit mining, while aligning with the guidelines of the Mine 4.0 revolution. It incorporates advanced technologies that address the critical challenges of latency, data security, and transparency commonly encountered in traditional monitoring systems. By adopting a multi-layered approach that leverages edge, fog, and cloud computing, coupled with blockchain technology and expert collaboration, our architecture offers a comprehensive framework for efficient data processing at every stage, from initial data acquisition to real-time anomaly detection and decision-making.

Interstellar computation their challenges and approaches in interplanetary cloud systems and data transmission

With the advancement of human civilisation beyond the confines of Earth's atmosphere, there has been an increasing need for efficient data processing and transmission in extraterrestrial environments. The article explores innovative strategies for resolving intricate computational challenges encountered in the context of interplanetary distances. The purpose of this study is to investigate the impact of issues such as latency, data corruption, and power consumption on classical computing paradigms in space. We examine the concept of "interplanetary cloud systems", which use distributed computation and data storage to tackle these issues. The article also covers advanced techniques for sending data, including communication systems using quantum entanglement and deep space networks. The article integrates the disciplines of astronomy, computer science, and space exploration to provide a holistic perspective on the issue. It is crucial to emphasise that the implementation of fault-tolerant systems, adaptive algorithms, and robust networking protocols is essential for the success of extrasolar computing programs. The article thoroughly explores the potential and risks that arise when space travel, astronomy, and computer science intersect. We explore the potential future of interplanetary data processing and communication technologies via the examination of fault tolerance, adaptive algorithms, and resilient networking. The importance of resolving computational issues in space increases as humanity extends its presence in the cosmos. The study results have significant ramifications for the development of forthcoming technology enabling interplanetary data transfer, hence facilitating human colonisation and scientific exploration beyond Earth.

A Machine Learning Model for Training Your AI

Artificial Intelligence is playing an increasing role in solving some of the world’s biggest problems. Machine Learning Models, within the context of reinforcement learning, define and structure a problem in a format that can be used to learn about an environment in order to find an optimal solution. This includes the states, actions, rewards, and other elements in a learning environment. This also includes the logic and policies that guide learning agents to an optimal or nearly optimal solution to the problem. This paper outlines a process for developing machine learning models. The process is extensible and can be applied to solve various problems. This includes a process for implementing data models using multi-dimensional arrays for efficient data processing. We include an evaluation of learning policies, assessing their performance relative to manual and automated approaches.

Penerapan Teknologi Sidik Jari dalam Sistem Hukum: Tinjauan terhadap Prinsip – Prinsip Pembuktian

The application of fingerprint technology in the legal system has brought significant changes to the evidentiary process. This technology enables the identification of individuals with high accuracy, which is an important element in law enforcement and dispute resolution. This study explores how fingerprint technology is applied in the Indonesian legal context, reviews relevant evidentiary principles, and analyzes the implications of its use in various legal cases. This research is descriptive analytical, meaning that this research aims to describe certain circumstances or situations of the objects or events being studied without intending to draw conclusions that can be applied generally. The approach used in this research is the normative juridical method. The results show that, although fingerprint technology makes a significant contribution to the effectiveness of evidentiary systems, fingerprint technology has long been used in the legal system as a tool for individual identification. Fingerprints, with their unique characteristics in each person, provide a guarantee of highly accurate identification. This technology is not only used in crime but also in various aspects of government and public services, such as population registration and securing access. The application of fingerprint technology in a modern legal context is becoming increasingly relevant with advances in digital technology that enable fast and efficient data processing. There are challenges related to privacy, data reliability and the integrity of legal processes. This research also proposes recommendations to increase the integration of fingerprint technology in the legal system in accordance with the principles of justice and human rights.