Acquisition Cycle Research Articles

Modbus RTU Protocol Timing Evaluation for Scattered Holding Register Read and ModbusE-Related Implementation

In parallel with the development of data transmission in the telecommunications sector to connect peripheral devices, hardware engineers have defined interfaces for independent communication systems. The basic idea of having standardized interfaces for external devices was quickly extended to control and instrumentation equipment, as the real-time characteristic of distributed systems became a challenge in the field. The proposed Modbus Extension (ModbusE) builds on these new techniques introduced in the UART interface, so that the newly introduced extension retains all the characteristics of the Modbus RTU except for the character bit structure. The validation of the ModbusE protocol allowed the proposal of a new communication message structure, a description of Modbus devices, an acquisition cycle to attain a deterministic temporal response behavior and the definition of an architecture for IIoT integration. In this work, practical research is based on data update times in SMARTConvert software when between 1 and 50 registers are accessed. These registers can, in the most favorable case, be at consecutive addresses and can all be read in a single bus query request, or at addresses 1 to 50, or the registers must be read in separate requests. The contributions of this paper are related to highlighting cases where Modbus modules are incorporated, the addressed registers are not consecutive and the Modbus reads 1 or more holding register records and the ModbusE proposal. As these registers must not be on consecutive addresses, it is necessary to make several requests, defining the function of a Modbus acquisition cycle, adapting the equations to the purpose of the tests carried out, measuring the Modbus communication signals and describing the ModbusE communication concept.

Indoor environmental monitoring based on sensor data acquisition and thermal energy cycle: Design and application of artificial intelligence

Indoor environmental monitoring based on sensor data acquisition and thermal energy cycle: Design and application of artificial intelligence

Machine vision system for automatic defect detection of ultrasound probes

Industry 4.0 conceptualizes the automation of processes through the introduction of technologies such as artificial intelligence and advanced robotics, resulting in a significant production improvement. Detecting defects in the production process, predicting mechanical malfunctions in the assembly line, and identifying defects of the final product are just a few examples of applications of these technologies. In this context, this work focuses on the detection of ultrasound probes’ surface defects, with a focus on Esaote S.p.A.’s production line probes. To date, this control is performed manually and therefore biased by many factors such as surface morphology, color, size of the defect, and by lighting conditions (which can cause reflections preventing detection). To overcome these shortfalls, this work proposes a fully automatic machine vision system for surface acquisition of ultrasound probes coupled with an automated defect detection system that leverage artificial intelligence. The paper addresses two crucial steps: (i) the development of the acquisition system (i.e., selection of the acquisition device, analysis of the illumination system, and design of the camera handling system); (ii) the analysis of neural network models for defect detection and classification by comparing three possible solutions (i.e., MMSD-Net, ResNet, EfficientNet). The results suggest that the developed system has the potential to be used as a defect detection tool in the production line (full image acquisition cycle takes ~ 200 s), with the best detection accuracy obtained with the EfficientNet model being 98.63% and a classification accuracy of 81.90%.

Proposed Modbus Extension Protocol and Real-Time Communication Timing Requirements for Distributed Embedded Systems

The general evolution of fieldbus systems has been variously affected by both computer electrical engineering and science. First, the main contribution undoubtedly originated from network IT systems, when the Open Systems Interconnection model was presented. This reference model with seven layers was and remains the foundation for the development of numerous advanced communication protocols. In this paper, the conducted research resulted in a major contribution; specifically, it describes the mathematical model for the Modbus protocol and defines the acquisition cycle model that corresponds to incompletely defined protocols in order to provide a timestamp and achieve temporal consistency for proposed Modbus Extension. The derived technical contribution of the authors is to exemplify the functionality of a typical industrial protocol that can be decomposed to improve the performance of data acquisition systems. Research results in this area have significant implications for innovations in industrial automation networking because of increasing distributed installations and Industrial Internet of Things (IIoT) applications.

A Zero-Shot Learning Approach for Blockage Detection and Identification Based on the Stacking Ensemble Model.

A data-driven approach to defect identification requires many labeled samples for model training. Yet new defects tend to appear during data acquisition cycles, which can lead to a lack of labeled samples of these new defects. Aiming at solving this problem, we proposed a zero-shot pipeline blockage detection and identification method based on stacking ensemble learning. The experimental signals were first decomposed using variational modal decomposition (VMD), and then, the information entropy was calculated for each intrinsic modal function (IMF) component to construct the feature sets. Second, the attribute matrix was established according to the attribute descriptions of the defect categories, and the stacking ensemble attribute learner was used for the attribute learning of defect features. Finally, defect identification was accomplished by comparing the similarity within the attribute matrices. The experimental results show that target defects can be identified even without targeted training samples. The model showed better classification performance on the six sets of experimental data, and the average recognition accuracy of the model for unknown defect categories reached 72.5%.

Rapid response to pressure variations in water distribution networks through machine learning-enhanced data acquisition

ABSTRACT This study investigates rapid dynamic pressure variations in water distribution networks due to critical incidents such as pipe bursts and valve operations. We developed and implemented a machine learning (ML)-based methodology that surpasses traditional slow cycles of pressure data acquisition, facilitating the efficient capture of transient phenomena. Employing the Orion ML library, which features advanced algorithms including long short-term memory dynamic threshold, autoencoder with regression, and time series anomaly detection using generative adversarial networks, we engineered a system that dynamically adjusts data acquisition frequencies to enhance the detection and analysis of anomalies indicative of system failures. The system's performance was extensively tested using a pilot-scale water distribution network across diverse operational conditions, yielding significant enhancements in detecting leaks, blockages, and other anomalies. The effectiveness of this approach was further confirmed in real-world settings, demonstrating its operational feasibility and potential for integration into existing water distribution infrastructures. By optimizing data acquisition based on learned data patterns and detected anomalies, our approach introduces a novel solution to the conventionally resource-intensive practice of high-frequency monitoring. This study underscores the critical role of advanced ML techniques in water network management and explores future possibilities for adaptive monitoring systems across various infrastructural applications.

Development of a novel seismic acquisition system based on fully autonomous ocean bottom nodes

It is now established that ocean bottom node (OBN) surveys provide superior seismic data compared to traditional towed streamers, but deployment of the nodes is time-consuming and expensive. As an alternative, we are developing a novel seismic acquisition system that utilises a fleet of fully autonomous underwater vehicles (AUVs) as OBNs. The objective is to eliminate the reliance on remotely operated vehicles (ROVs) or ropes for deployment, making ocean bottom seismic acquisition significantly more efficient and cost-effective. To succeed, several criteria need to be met, including recording of good geophysical data, system affordability, low power consumption, units’ ability to act autonomously (e.g. steer, land, listen, re-position, surface) and to respond to remote commands (e.g. cease activity, return), a failure recovery protocol, automated deployment and retrieval, and system-wide communications. The ability to record good seismic data was assessed in October 2022, when we performed active-source seismic field trials in the UK. Five AUVs were deployed adjacent to commercial nodes (all units carried identical geophysical payloads) and a series of 2D lines were acquired at varying orientations. Analysis of the data confirmed that our units achieved satisfactory coupling with the seabed. The unit’s in-water capabilities were assessed during multiple field trials in 2022 and 2023, offshore UK and Australia. Tests consisted of simulating seismic acquisition cycle (flights, landings, take-offs and repositioning) where it was verified that missions were reliably executed, including landing within 10 m from pre-planned locations and recording good quality passive seismic data.

Industrial Internet of Things Gateway with OPC UA Based on Sitara AM335X with ModbusE Acquisition Cycle Performance Analysis.

This article presents the hardware and software architectures used to implement the Modbus Extension (ModbusE) IIoT gateway, the performance of the acquisition cycle at the PRU real-time programmable core level, the acquisition cycle communication flow-dispatcher-OPC UA server (Linux)-OPC UA client (Windows) as well as the performance analysis of data communications between the IIoT ModbusE gateway and the OPC UA client (Windows). In order to be able to implement both the ModbusE acquisition cycle and the OPC UA server, the BeagleBone Black Board was chosen as the hardware platform. This board uses the Sitara AM335x processor (Texas Instruments (TI), Dallas, TX, USA) from Texas Instruments. Thus, the acquisition cycle was implemented on the PRU0 real-time core, and the OPC UA server, running under the Linux operating system, was implemented on the ARM Cortex A8 processor. From the analysis of the communication speed of the messages between the OPC UA client and the ModbusE servers, it was found that the ModbusE acquisition cycle speed was higher than the acquisition speed of the OPC UA client.

Abstract 3800: Next generation of spatial biology: High-throughput multiplexed Imaging Mass Cytometry™ with whole slide modes

Abstract Gaining spatial insights into the cellular composition of tumor tissue has tremendous potential to inform clinical and translational researchers about mechanisms behind spatial predictors of immunotherapy success, disease etiology and progression. Imaging Mass Cytometry™ (IMC™) is a high-plex spatial biology imaging technique that enables deep characterization of the diversity and complexity of the tumor microenvironment (TME). IMC supports detailed assessment of cell phenotype and function using 40-plus metal-tagged antibodies simultaneously on a single slide without issues associated with fluorescence-based spectral overlap, tissue autofluorescence or implementation of multiple washing and acquisition cycles. Currently, IMC enables user-defined regions of interest (ROI) in tissues to evaluate cellular and structural composition. To enhance the IMC user experience, we developed two new whole slide imaging (WSI) modes which enable streamlined workflows using ultrafast preview mode (PM) and high-throughput tissue mode (TM). PM samples the entire tissue at predefined spacing to rapidly capture a low-resolution image of all expressed markers in the antibody panel. PM generates an image in minutes to enable informed ROI placements while leaving the stained tissue intact for higher-resolution imaging. PM and TM are designed so acquisitions can easily occur on the same slide without additional processing steps. TM rapidly acquires images of the whole tissue at lower resolution (7 μm pixel size) at a quality that can be used for quantitative analysis of the tissue spatial biology. Specifically designed for high-throughput applications, TM in combination with a newly available 40-slide loader for the Hyperion XTi™ Imaging System permits automated and continuous imaging of more than 40 large tissue samples (400 mm2 per tissue) per week. We showcase the application of WSI modes using the newly developed Maxpar® Human Immuno-Oncology IMC Panel Kit. The 31-marker panel was combined with catalog antibodies to create a 40-plus-marker panel that expands the ability to conduct comprehensive high-plex tumor and immune cell profiling. Tumor tissue microarrays (TMA) and whole tumor tissue sections were stained with the expanded panel. Single-cell analysis of selected ROIs, on whole tumor sections and TMAs, guided by PM data successfully provided quantitative analyses of spatial biology at single-cell resolution. In parallel, TM on whole tumor sections followed by pixel-based analysis provided a spatially resolved quantitative assessment of specific tumor and immune components of the TME. This work demonstrates the expanded capabilities of IMC and establishes it as a reliable high-plex spatial biology imaging platform with high-throughput imaging capabilities ideally suited for translational and clinical applications. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Qanber Raza, Thomas D. Pfister, Jyh Yun Chwee, Liang Lim, David Howell, Nikesh Parsotam, David King, Christina Loh. Next generation of spatial biology: High-throughput multiplexed Imaging Mass Cytometry™ with whole slide modes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3800.

Integrated Sustainability Assessment of Electric Bus Adoption: A Comprehensive Model for Ecological, Economic, and Operational Impact

The spread of electric stations in cities around the world is an important step towards sustainable urban transportation. In Task 1,We developed the FE-VC model to determine carbon emission indicators based on the BWM approach, and demonstrated the ecological consequences over the life cycle through quantitative models. In the quantitative calculation of urban data, compared with diesel buses, the carbon emission reduction rate of pure electric buses is 28.68%, mainly from the fuel cycle, in which the carbon emission reduction rate of the fuel cycle stage is as high as 40.11%, and it takes about 3.5 years to achieve the relative carbon emission reduction. In Task 2, we construct an economic impact consequence model based on the time value of capital with the theory of time value of money as the core, consider the full life cycle of acquisition, operation and vehicle maintenance, quantify and visualize the impact mechanism of electric buses on the economy, and provide a method to understand the economic impact for metropolitan areas. For task 3, based on the optimal enterprise operating cost and the optimized economic impact model, we construct the initial replacement model of the electric bus. The initial replacement model was optimized by simplifying the social cost of carbon emission, and an electric bus replacement model based on the optimal enterprise operating cost was obtained. In city-specific applications, we find that vehicle choice has a significant impact on fleet replacement plans and operating costs, and that using multiple vehicles can significantly reduce operating costs as electrification rates increase.

Eğitim Kurumlarında İnsan Kaynakları Yönetimi Bağlamında Eğitim İhtiyaçlarının Önemi ve Amaçları

Educational needs are the starting point in the cycle of education and skill acquisition, and this cycle forms a continuous cycle that begins with identifying the learning needs of the target community. In addition, if the evaluation ignored the opinions of stakeholders or posed the wrong questions or contained incorrect information, the training to be provided will not coincide with the actual needs of the organization or its employees. It is possible for education to be effective by applying an effective educational needs analysis. The effectiveness of educational needs analysis, on the other hand, requires good planning, including its execution and clear returns. Learning objectives, designing education provides a high-quality tool to the organization in revealing the results of the education provided. Similarly, some researchers have stated that designing an education and development program involves a number of stages that can be grouped into five stages: educational needs analysis, educational objectives, design, implementation and evaluation. Long before a real education takes place, the education manager should determine down to the finest details in advance which methods and tools the education will be conducted in which educational institutions under the supervision of which experts. For this purpose, it is essential to determine the purpose of the educational needs analysis if the purpose of education is in the real sense. The purpose of the training needs analysis is to determine the performance requirements in the organization in order to direct those who are closely related to the areas where they are most needed, to fulfill the goals and objectives of the institution, to increase productivity, to provide quality products and services. Accordingly, educational needs are used as the basis for determining educational goals, choosing and designing educational programs, implementing programs, and evaluating the provided education as the first step in establishing an educational and development program. As a result of data collection and analysis, the need for education is determined, it becomes clear which education is required, and the type and scope of resources required to support an educational program can be examined. Key Words: Educational Institutions, Human Resources Management, the Importance and Purpose of Educational Needs

From High Discipline Referrals to High-Engagement Classrooms: A Design-Based Approach to Creating a Campus-Wide Culture of Positive Learning Support for At-Risk Student Populations through Transforming Educators’ Instructional Teaming Practices

Instructional leaders in many school districts throughout the United States are increasingly confronted with spiraling numbers of discipline referrals from teachers who are unable to manage the off-task behaviors and discipline infractions of the growing percentages of diverse, academically at-risk students populating their classrooms. Many of these at-risk students, who come from families that are economically disadvantaged and have culturally rich and linguistically diverse backgrounds, have become stigmatized and minoritized by an education system whose curricula are often woefully inadequate in being able to meet the unique social and academic support needs of these diverse learners. This article profiles how one high school principal and his instructional turnaround leadership task force colleagues working in a high diversity urban school district utilized multi-leveled data analysis investigative thinking and innovative professional learning program design strategies derived from the education improvement science literature to implement a comprehensive intervention program for ninth through twelfth grade teams of core content teachers and their instructional supervisors. The specific goals of the intervention program were to: 1) change educators’ entrenched pedagogical thinking and instructional planning mindsets regarding the motivation levels and learning capabilities of the high discipline referral students who regularly engage in off-task disruptive behaviors in their classrooms; and 2) transform teachers’ instructional data teaming practices to better serve the academic learning support needs of the campus’s minoritized students. The design-based approach to instructional improvement employed in the high school case study leveraged the use of multiple cycles of new knowledge and skills acquisition, critical self-reflection, and immersive professional learning to assist educators in exploring how to engage together in authentic data teaming and data analysis–informed differentiated instructional planning to craft high-engagement classroom learning environments and interactive instructional units to effectively address the academic development and support needs of the campus’s diverse, at-risk learners.

Few‐shot object detection based on global context and implicit knowledge decoupled head

AbstractThe acquisition cycle of remote sensing images is slow, and the labelling process encounters challenges, which have become prominent with the rapid development of remote sensing image object detection research. Therefore, this article provides a way to make the model better capture the diversity and contextual relationships in the data, and solve this problem by more than just data augmentation. Specifically, this method is a few‐shot object detection method for remote sensing based on global context combined with implicit knowledge decoupled head (GC‐IKDH). This method first uses a segmentation strategy to convert high‐resolution images into low‐resolution images and expands the sample size through a generative model. Secondly, GC attention is introduced to generate a GC vector by weighting and averaging the information of each position in the input sequence, which helps the model better understand the semantics of the input sequence. Finally, an IKDH is added to improve the model head, which is used to learn specific features in the data so that the model can better handle the diversity in the data. Experimental results show that GC attention and IKDH boosting provide a good performance boost to the baseline model. Compared with other few‐shot samples, this method achieves state‐of‐the‐art performance under different shot settings and highly competitive results on two benchmark datasets (NWPU VHR‐10 and DIOR).

The Gordian Knot of C. auris: If You Cannot Cut It, Prevent It.

Since its first description in 2009, Candida auris has, so far, resulted in large hospital outbreaks worldwide and is considered an emerging global public health threat. Exceptionally for yeast, it is gifted with a profoundly worrying invasive potential and high inter-patient transmissibility. At the same time, it is capable of colonizing and persisting in both patients and hospital settings for prolonged periods of time, thus creating a vicious cycle of acquisition, spreading, and infection. It exhibits various virulence qualities and thermotolerance, osmotolerance, filamentation, biofilm formation and hydrolytic enzyme production, which are mainly implicated in its pathogenesis. Owing to its unfavorable profile of resistance to diverse antifungal agents and the lack of effective treatment options, the implementation of robust infection prevention and control (IPC) practices is crucial for controlling and minimizing intra-hospital transmission of C. auris. Rapid and accurate microbiological identification, adherence to hand hygiene, use of adequate personal protective equipment (PPE), proper handling of catheters and implantable devices, contact isolation, periodical environmental decontamination, targeted screening, implementation of antimicrobial stewardship (AMS) programs and communication between healthcare facilities about residents' C. auris colonization status are recognized as coherent strategies for preventing its spread. Current knowledge on C. auris epidemiology, clinical characteristics, and its mechanisms of pathogenicity are summarized in the present review and a comprehensive overview of IPC practices ensuring yeast prevention is also provided.

Developing an ethical framework-guided instrument for assessing bias in EHR-based Big Data studies: a research protocol

IntroductionThe emergence of Big Data health research has exponentially advanced the fields of medicine and public health but has also faced many ethical challenges. One of most worrying but still...

Isomers Recognition in HPLC-MS/MS Analysis of Human Plasma Samples by Using an Ion Trap Supported by a Linear Equations-Based Algorithm.

The tandem mass spectrometry (MS/MS) approach employing an ion trap mass analyzer (IT) was evaluated in isomers recognition. The proposed approach consists of sole, simple, and rapid liquid chromatographic separation (HPLC) without requiring resolution between the analytes. Then, the MS/MS properties were optimized to solve the signal assignment using post-processing data elaboration (LEDA). The IT-MS/MS experiment uses the same site, helium as collision gas, and different time steps to modify the applied conditions on the studied ions. Nevertheless, helium cannot ensure the quick energization of the precursor ion due to its small cross-section. Then, different combinations between excitation amplitude (ExA) and excitation time (ExT) were tested to achieve the activation of the fragmentation channels and the formation of the MS/MS spectrum. Usually, the IT-MS/MS acquisition cycle is longer for other multistage instruments, decreasing the frequency of sample data collection and influencing the chromatographic profile. To solve these problems, two time segments were set up, and the elution conditions were optimized with a compromise between peaks distinction and run time reduction. The developed HPLC-MS/MS method was checked and applied to analyze a series of human plasma samples spiked with an equimolar mixture of pair of isomers.

수중시공현장 적용을 위한 위치 획득 신호 주기와 인식 방법에 관한 연구

With the increase in the research on marine IoT in recent years, the demand for enhanced mission performance in marine construction and civil engineering is increasing. Accordingly, various equipment have been introduced in the field. However, recognizing the location of the equipment is difficult because of the underwater environmental characteristics (e.g., radiated noise and turbidity). As a result, studies have been conducted on underwater positioning methods that combine technologies such as GPS (Global Positioning System)and IMU (Inertial Measurement Unit).However, the problem of intermittent disconnection persists due to radiated noise and turbidity generated in offshore construction environments. To solve this problem, we proposed the method of using a KF(Kalman Filter) in underwater construction environments. To verify the proposed method, we conducted an experiment on land at the speed used in offshore construction. In the experiment, the signal acquisition cycle was varied, and the usable cycle in the field was checked. The experimental results demonstrated the applicability of the proposed underwater positioning system.

Improved Label-Free Quantification of Intact Proteoforms Using Field Asymmetric Ion Mobility Spectrometry.

The high-throughput quantification of intact proteoforms using a label-free approach is typically performed on proteins in the 0-30 kDa mass range extracted from whole cell or tissue lysates. Unfortunately, even when high-resolution separation of proteoforms is achieved by either high-performance liquid chromatography or capillary electrophoresis, the number of proteoforms that can be identified and quantified is inevitably limited by the inherent sample complexity. Here, we benchmark label-free quantification of proteoforms of Escherichia coli by applying gas-phase fractionation (GPF) via field asymmetric ion mobility spectrometry (FAIMS). Recent advances in Orbitrap instrumentation have enabled the acquisition of high-quality intact and fragmentation mass spectra without the need for averaging time-domain transients prior to Fourier transform. The resulting speed improvements allowed for the application of multiple FAIMS compensation voltages in the same liquid chromatography-tandem mass spectrometry experiment without increasing the overall data acquisition cycle. As a result, the application of FAIMS to label-free quantification based on intact mass spectra substantially increases the number of both identified and quantified proteoforms without penalizing quantification accuracy in comparison to traditional label-free experiments that do not adopt GPF.

Repeat-Enhancing Featured Ion-Guided Stoichiometry for Identification and Quantification of Direct Infusion Proteome.

The direct infusion-shotgun proteome analysis (DI-SPA) alongside data-independent acquisition mass spectrometry achieved fast proteome identification and quantification without chromatographic separation. However, robust peptide identification and quantification (label and label-free) for the DI-SPA data is still insufficient. We find that in the absence of chromatography, the identification of DI-SPA can be boosted by extending acquisition cycles repeatedly and maximizing the utilization of the featured repetition characteristics, combined with the machine learning-based automatic peptide scoring strategy. Here, we present the repeat-enhancing featured ion-guided stoichiometry (RE-FIGS), a complete and compact solution to (repeated) DI-SPA data. Using our strategy, the peptide identification can be improved above 30% with high reproducibility (70.0%). Notably, the label-free quantification of repeated DI-SPA can be successfully obtained with high accuracy (mean median error, 0.108) and high reproducibility (median error, 0.001). We believe our RE-FIGS method could boost the broad application of the (repeated) DI-SPA method and offer a new choice for proteomic analysis.

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

Modern data-driven science often consists of iterative cycles of data discovery, acquisition, preparation, analysis, model building and validation leading to knowledge discovery as well as dissemination at scale. The unique challenges of building and simulating the whole rodent brain in the Swiss EPFL Blue Brain Project (BBP) required a solution to managing large-scale highly heterogeneous data, and tracking their provenance to ensure quality, reproducibility and attribution throughout these iterative cycles. Here, we describe Blue Brain Nexus (BBN), an ecosystem of open source, domain agnostic, scalable, extensible data and knowledge graph management systems built by BBP to address these challenges. BBN builds on open standards and interoperable semantic web technologies to enable the creation and management of secure RDF-based knowledge graphs validated by W3C SHACL. BBN supports a spectrum of (meta)data modeling and representation formats including JSON and JSON-LD as well as more formally specified SHACL-based schemas enabling domain model-driven runtime API. With its streaming event-based architecture, BBN supports asynchronous building and maintenance of multiple extensible indices to ensure high performance search capabilities and enable analytics. We present four use cases and applications of BBN to large-scale data integration and dissemination challenges in computational modeling, neuroscience, psychiatry and open linked data.