Data Transfer Research Articles

Enhanced Data Transfer Cooperating with Artificial Triplets for Scene Graph Generation

Enhanced Data Transfer Cooperating with Artificial Triplets for Scene Graph Generation

Aerothermal performance of two-pass channel with tilted grater-baffles

Aerothermal performances of four two-pass channels enhanced by tilted 90° or 60° grater-baffles with one- or two-rows of perforation are studied. Aerothermal impacts of baffle attack angle and perforation row-number on Nusselt number (Nu) distribution, friction factor (f), and aerothermal performance index (API) are cross examined. Cold streams from duct core are confluent in oval dimples to eject through inclined grater-baffle as impinging jets that diminish boundary layers at stagnation spots and augment core-to-wall mixings, leading to significant heat transfer enhancements (HTE). Heat transfer data reveals that the HTE impact increases and decreases with perforation-row number and Reynolds number (Re) respectively. With the sectional vortical flows tripped by the 60° grater-baffles, both Nu and f are raised from those with the 90° baffles. The Nusselt numbers and friction factors for the two-pass channel enhanced by the inclined 60° baffles with two perforation rows are elevated to 4.64-4.95and 33.67-34.16 times Dittus-Boelter correlation and Blasius equation levels, giving rise to the API in the range of 1.67 and 1.43 at Re between 5000 and 15,000. Empirical correlations of regional average Nu and channel average f for the four two-pass baffled channels are devised to assist engineering applications.

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.

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.

Ada-LT IP: Functional Discriminant Analysis of Feature Extraction for Adaptive Long-Term Wi-Fi Indoor Localization in Evolving Environments.

Wi-Fi fingerprint-based indoor localization methods are effective in static environments but encounter challenges in dynamic, real-world scenarios due to evolving fingerprint patterns and feature spaces. This study investigates the temporal variations in signal strength over a 25-month period to enhance adaptive long-term Wi-Fi localization. Key aspects explored include the significance of signal features, the effects of sampling fluctuations, and overall accuracy measured by mean absolute error. Techniques such as mean-based feature selection, principal component analysis (PCA), and functional discriminant analysis (FDA) were employed to analyze signal features. The proposed algorithm, Ada-LT IP, which incorporates data reduction and transfer learning, shows improved accuracy compared to state-of-the-art methods evaluated in the study. Additionally, the study addresses multicollinearity through PCA and covariance analysis, revealing a reduction in computational complexity and enhanced accuracy for the proposed method, thereby providing valuable insights for improving adaptive long-term Wi-Fi indoor localization systems.

Edge Integration of Artificial Intelligence into Wireless Smart Sensor Platforms for Railroad Bridge Impact Detection.

Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impact from over-height vehicles. The impact can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts on railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data are transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events, like impact detection, that require a rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine-learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications.

Parallel implementation for real-time visual SLAM systems based on heterogeneous computing

Simultaneous localization and mapping has become rapidly developed and plays an indispensable role in intelligent vehicles. However, many state-of-the-art visual simultaneous localization and mapping (VSLAM) frameworks are very time-consuming both in front-end and back-end, especially for large-scale scenes. Nowadays, the increasingly popular use of graphics processors for general-purpose computing, and the progressively mature high-performance programming theory based on compute unified device architecture (CUDA) have given the possibility for large-scale VSLAM to solve the conflict between limited computing power and excessive computing tasks. The paper proposes a full-flow optimal parallelization scheme based on heterogeneous computing to speed up the time-consuming modules in VSLAM. Firstly, a parallel strategy for feature extraction and matching is designed to reduce the time consumption arising from multiple data transfers between devices. Secondly, a bundle adjustment method based solely on CUDA is developed. By fully optimizing memory scheduling and task allocation, a large increase in speed is achieved while maintaining accuracy. Besides, CUDA heterogeneous acceleration is fully utilized for tasks such as error computation and linear system construction in the VSLAM back-end to enhance the operation speed. Our proposed method is tested on numerous public datasets on both computer and embedded sides, respectively. A number of qualitative and quantitative experiments are performed to verify its superiority in terms of speed compared to other states-of-the-art.

Design and Implementation of Optimized PCI Express Physical Layer for High-Speed and Low-Latency Data Transfer

The PCIe (Peripheral Component Interconnect Express) Protocol is crucial for establishing high-speed data communication between computer peripherals, such as graphics cards and network cards etc. This communication protocol is transmitting data in packet format, with each packet containing both data and destination address and other essential information for accurate data delivery. This paper focused on Physical Layer to achieve High-Speed Data Transmission by reducing delay parameter. To minimize disturbances and enhance reliability, the physical layer uses scrambling technique and an 8b – 10b encoding technique is used for synchronization and error detection. Additionally, SIPO and PISO converts data format to improve efficiency and accuracy. The design is implemented using the Cadence compiler targeting 45nm process technology. This design is delay efficient resulting in path delay of 5.0ns and the operating frequency of 200MHz, power consumption of 1.2mw and an area of 1999µm².

Comparative case study on NAMs: towards enhancing specific target organ toxicity analysis.

Traditional risk assessment methodologies in toxicology have relied upon animal testing, despite concerns regarding interspecies consistency, reproducibility, costs, and ethics. New Approach Methodologies (NAMs), including cell culture and multi-level omics analyses, hold promise by providing mechanistic information rather than assessing organ pathology. However, NAMs face limitations, like lacking a whole organism and restricted toxicokinetic interactions. This is an inherent challenge when it comes to the use of omics data from in vitro studies for the prediction of organ toxicity in vivo. One solution in this context are comparative in vitro-in vivo studies as they allow for a more detailed assessment of the transferability of the respective NAM data. Hence, hepatotoxic and nephrotoxic pesticide active substances were tested in human cell lines and the results subsequently related to the biology underlying established effects in vivo. To this end, substances were tested in HepaRG and RPTEC/tERT1 cells at non-cytotoxic concentrations and analyzed for effects on the transcriptome and parts of the proteome using quantitative real-time PCR arrays and multiplexed microsphere-based sandwich immunoassays, respectively. Transcriptomics data were analyzed using three bioinformatics tools. Where possible, in vitro endpoints were connected to in vivo observations. Targeted protein analysis revealed various affected pathways, with generally fewer effects present in RPTEC/tERT1. The strongest transcriptional impact was observed for Chlorotoluron in HepaRG cells (increased CYP1A1 and CYP1A2 expression). A comprehensive comparison of early cellular responses with data from in vivo studies revealed that transcriptomics outperformed targeted protein analysis, correctly predicting up to 50% of in vivo effects.

DEVELOPMENT OF LOW-LATENCY WIRELESS COMMUNICATION SYSTEMS FOR AUTONOMOUS VEHICLES

This research aims to explore the effectiveness of the L3 protocol in improving the V2X communication systems in key performance areas such as low communication delay, high-speed protocols and the effectiveness under real-life conditions. According to the L3 protocol simulation of the research, which employs platforms including COOJA and NS-3, the latency of the networks is lowered compared to that of the conventional DSRC/LTE system at below one millisecond levels adopting the time slots and the capture effect. These challenges observed on other systems such as delays experienced due to high-speed data transfer are addressed in the protocol. The performed measurements prove that the L3 protocol of the system exhibits reliability and scalability depending on the number of vehicles on the road as well as the network load, and retains its workability even with the number of 225 automobiles. These results conform with prior works that underlined the necessity of using sophisticated and low-latency relative communication systems for the usage of an autonomous vehicle network. In doing so, the study shows a future of L3 protocol in enhancing the current V2X communication systems in aspects of low latency, fast data transfer and competency in various conditions. It is suggested that future research include extensive field trials, the increase of security measures, the analysis of the scalability and integration of the new technologies, and the solution of the interoperability issues for the further investigation of the possibilities of the application of the L3 protocol.

When Digitalization Meets Regulation across Borders: An Explainer with Actionable Insights

A flurry of regulations has emerged across the world to govern the use, transfer and storage of data, affecting digital and traditional firms, of all sizes. To guide business practitioners and educators, this article provides insights into key components of digital laws and what they mean for different types of firms. We take the European Union as starting point, given its prominent and extra-territorial rule-setting, but also reflect on other cross-border (future) digital regulations. Our analysis of relevant details reveals a need for practitioners to be prepared and forward-looking, considering firms’ specifics, internationally converging ethical principles and (future) digitally-aware generations.

MAP-IO: an atmospheric and marine observatory program on board Marion Dufresne over the Southern Ocean

Abstract. This article is devoted to the presentation of the MAP-IO observation program. This program, launched in early 2021, has enabled the observation of nearly 700 d of measurements over the Indian and Southern Ocean with the equipment of 17 meteorological and oceanographic scientific instruments on board the ship Marion Dufresne. Several observational techniques have been developed to respond to the difficulties of observations on board the ship, in particular for passive remote sensing data, as well as for quasi-autonomous data acquisition and transfer. The first measurements made it possible to draw up unprecedented climatological data of the Southern Ocean regarding the size distribution and optical thickness of aerosols, the concentration of trace gases and greenhouse gases, UV, and integrated water vapor. High-resolution observations of phytoplankton in surface waters have also shown a great variability in latitude in terms of abundance and community structure (diversity). The operational success of this program and these unique scientific results together establish a proof of concept and underline the need to transform this program into a permanent observatory. The multi-year rotations over the Indian Ocean will enable us to assess the trends and seasonal variability of phytoplankton, greenhouse gases, ozone, and marine aerosols in a sensitive and poorly documented climatic region. Without being exhaustive, MAP-IO should make it possible to better understand and assess the biological carbon pump, to study the variability of gases and aerosols in a region that is remote in relation to the main anthropogenic sources, and to monitor the transport of stratospheric ozone by the Brewer–Dobson circulation. The meteorological MAP-IO data set is publicly available at https://www.aeris-data.fr/catalogue-map-io/ (last access: 26 August 2024) (atmospheric data) and at https://doi.org/10.17882/89505 (Thyssen et al., 2022a) (phytoplankton data).

The anatomy of a data transfer agreement for health research.

In a data-driven era, the exchange and safeguarding of personal information has become paramount. Data transfer agreements (DTAs) serve to guard privacy, defining the rules for sharing and protecting sensitive data. Yet, the complexities surrounding issues such as data privacy, intellectual property, and dispute resolution within these agreements pose challenges that demand careful consideration. Through a scoping review of twenty-four publicly available, English language DTAs relevant to health research, this article undertakes a comprehensive analysis, examining common clauses, their vital components, and charting a course for responsible data sharing through the provision of insights and practical guidance for drafting DTAs. The article underscores the need for attention to detail and an understanding of data protection legislation in order to ensure that DTAs align with the law and maximize legal certainty.

Twin data multimode collaborative transfer learning for bearing failure diagnosis

To tackle the challenges of gathering and labeling data in practical engineering applications, a multimode collaborative transfer learning method is proposed to bridge the reality gap between labeled twin fault data and unlabeled real-world data. A bearing fault digital twin model is built to produce failure twin data of the test bearing under different health conditions. Three simulation source domains are constructed in accordance with the twin data's transferable modes. The iterative joint geometric-statistical alignment is used to perform the collaborative transfer of twin data's modes, which suppresses the negative transfer brought on by insufficient transferable information and the large inter-domain discrepancy. The fuzzy integral decision fusion is optimized and used to automatically label the real-world samples, which increases the cross-domain category alignment feasibility. The experimental results of extensive diagnosis tasks verified that the proposed method significantly outperforms the state-of-the-art domain adaption methods and can achieve an average classification accuracy of more than 91% in the bearing fault diagnosis with the support of fault twin data alone, without the supervision and guidance of historical labeling data.

Occlusion-guided multi-modal fusion for vehicle-infrastructure cooperative 3D object detection

In autonomous driving, leveraging sensor data (e.g. camera, LiDAR data) from both the vehicle and the infrastructure significantly improves perception capabilities. However, this integration traditionally results in increased demands on communication bandwidth. To address these challenges, we introduce Fusion2comm, an occlusion-guided feature fusion approach designed to optimize vehicle-infrastructure cooperative 3D object detection. Our innovative strategy employs an intelligent fusion of camera and LiDAR data to enhance the expressiveness of features. Subsequently, it leverages a segmentation model to extract foreground features and utilizes an occlusion-based selection of communication content, effectively easing bandwidth constraints. We propose a multimodal foreground feature fusion architecture that selectively processes and transmits critical information, substantially reducing irrelevant background data transfer. An innovative occlusion confidence-aware communication technique dynamically adjusts communication regions based on occlusion levels, ensuring efficient data exchange. Fusion2comm sets a new benchmark in the DAIR-V2X dataset, achieving an average precision of 71.25% with minimal bandwidth usage of 221.04 bytes. Our comprehensive experimental evaluations confirm that Fusion2comm substantially advances detection precision while simultaneously improving communication efficiency.

Experimental investigation on magneto-convective flows around two differentially heated horizontal cylinders

Liquid metal buoyant flow around two differentially heated horizontal cylinders in the presence of a uniform vertical magnetic field is investigated experimentally. While magneto-convection in pipes or ducts has been studied theoretically and experimentally in recent years, data for heat transfer at immersed obstacles are rare and, to our knowledge, detailed experimental investigations on this fundamental magnetohydrodynamic problem do not exist. In the present work, two horizontal cylinders inserted into an adiabatic rectangular cavity filled with gallium–indium–tin are kept at constant temperatures to establish a driving temperature gradient in the surrounding liquid metal. The buoyancy-driven flow, quantified by the Grashof number $Gr$ , is varied in the range ${10^{6} \leq Gr \leq ~5\times 10^{7}}$ . With increasing magnetic field, expressed via the Hartmann number $Ha$ , different flow regimes are identified from measurements for $0 \leq Ha \leq ~3000$ . The effect of the electromagnetic force primarily consists in suppressing turbulence and damping the convective flow. The heat transfer is quantified in terms of the non-dimensional Nusselt number $Nu$ , and its dependence on $Gr/{Ha}^{2}$ , which is identified as the important group governing the flow, is discussed.

Cross-modal Transfer Learning Based on an Improved CycleGAN Model for Accurate Kidney Segmentation in Ultrasound Images

ObjectiveDeep-learning algorithms have been widely applied in the field of automatic kidney ultrasound (US) image segmentation. However, obtaining a large number of accurate kidney labels clinically is very difficult and time-consuming. To solve this problem, we have proposed an efficient cross-modal transfer learning method to improve the performance of the segmentation network on a limited labeled kidney US dataset. MethodsWe aim to implement an improved image-to-image translation network called Seg-CycleGAN to generate accurate annotated kidney US data from labeled abdomen computed tomography images. The Seg-CycleGAN framework primarily consists of two structures: (i) a standard CycleGAN network to visually simulate kidney US from a publicly available labeled abdomen computed tomography dataset; (ii) and a segmentation network to ensure accurate kidney anatomical structures in US images. Based on the large number of simulated kidney US images and small number of real annotated kidney US images, we then aimed to employ a fine-tuning strategy to obtain better segmentation results. ResultsTo validate the effectiveness of the proposed method, we tested this method on both normal and abnormal kidney US images. The experimental results showed that the proposed method achieved a segmentation accuracy of 0.8548 in dice similarity coefficient on all testing datasets and 0.7622 on the abnormal testing dataset. ConclusionsCompared with existing data augmentation and transfer learning methods, the proposed method improved the accuracy and generalization of the kidney US image segmentation network on a limited number of training datasets. It therefore has the potential to significantly reduce annotation costs in clinical settings.

STOP-HCV-HCC Program: Privacy-Preserving Innovation for Remote Data Access Analytics at Federally Qualified Health Centers in South Texas.

STOP-HCV-HCC program to screen and treat hepatitis C, vaccinate for hepatitis B, and prevent hepatocellular carcinoma is implementing a cloud-based privacy-preserving platform to overcome electronic health record barriers to reporting, without data transfer, at four federally qualified health centers in South Texas, USA.

Optimized intelligent 3D localization in wireless sensor networks for better data sharing

WSNs are networks of small, autonomous devices called sensors or nodes that are equipped with sensors, processing capabilities, and wireless communication capabilities. These networks are designed to monitor and collect data from their surrounding environments and transmit that data wirelessly to a central location for analysis. However, challenges such as packet loss and limited throughput have impeded their efficiency. To address these issues, this research presents an innovative approach known as Squirrel-based Elman Neural Localization (SbENL) to optimize 3D localization in WSNs. Squirrel fitness tracks the node's location in the sensor network environment. This study initially configures WSN nodes with constrained energy resources, and data rates are continually monitored and predicted. The outcome is improved data sharing with higher throughput rates. The research assesses the performance of SbENL against existing localization techniques, demonstrating its effectiveness in minimizing data loss, reducing energy consumption, and maximizing data transfer rates. This optimized intelligent 3D localization approach holds promise for enhancing data sharing and communication efficiency in WSNs, benefiting a wide range of applications. Finally, the communication metrics were measured and valued with recent existing approaches. A SbENL has minimized the data loss energy consumption and maximized the data transferring rate.