Acquisition Technology Research Articles

<i>In Vitro</i> Assessment of Drug-Induced Liver Injury Using Cell-Based Models: A Review

Drug-induced liver injury (DILI) is the reason for 15–18% of medicinal product recalls from the market. Since interspecies differences often limit the relevance of standard non-clinical tests in vivo, a promising alternative is to develop cell-based in vitro methods.The aim of the study was to review current advances in cell modelling for the in vitro identification of DILI.In vitro mechanistic studies of DILI require cells that exhibit activity specific to hepatic metabolising enzymes and transporters. This article reviews the main cell cultures (primary human hepatocytes, immortal cell lines, stem cell-derived hepatocyte-like cells, co-cultures of hepatocytes and non-parenchymal liver cells) and their configurations. The optimisation of cell systems is directed towards enhancing their viability, functionality, compositional and configurational complexity, thus bringing them closer to in vivo models. Potential DILI causes include chemically reactive metabolites, oxidative stress, mitochondrial damage, intracellular accumulation of toxic bile acids resulting from transporter inhibition, and adaptive immune system activation. Accordingly, DILI studies rely on various methods, including innovative technologies for acquisition, storage, and analysis of large datasets (e.g. high-content screening, transcriptomics, proteomics, and metabolomics). Cell models are applicable to both DILI identification and mechanistic studies. Currently, the most promising technologies are omics, complex co-culture models, and organ-on-a-chip systems.

Where Is It Best to Sit in Class? Description of an Experience Based on STEM Problem Solving in a School Context

This study aims to describe the design and development of a STEM problem-solving activity involving mathematics and physics content. It is an activity whose starting point is a question based on a real problem: to identify the areas of the classroom where the intensity of sound is higher. In this way, the problem involves the physics of sound. However, in addition, by trying to give an answer to the initial question, students have the opportunity to develop progressively more complex mathematical models from already known mathematical concepts and procedures. The analysis of the results of the implementation based on the identification of the learning opportunities and the catalyst elements that generate them, allows us to state that the activity encourages students to enrich their mathematical models by incorporating new mathematical concepts such as the function of two variables. We conclude that the design of the activity and the choice of data acquisition technology are key, but also the interaction between the students and the teacher.

Iterative deblending using unsupervised learning with double-deep neural networks

Simultaneous source acquisition technology can greatly improve seismic acquisition efficiency. However, due to continuous shooting and serious crosstalk noise of the adjacent sources in seismic data, simultaneous source data cannot be directly used in conventional data processing procedures. Therefore, simultaneous source data need to be deblended to obtain the conventional shot record. Under densely sampled sources, we have developed a novel unsupervised deep learning (UDL) method based on the double-deep neural networks for iterative inversion deblending of simultaneous source data. Our UDL, which is mainly composed of the residual neural network (R-net) and the U-net neural network, has excellent nonlinear optimization ability. The total loss function design can optimize our UDL in the correct direction and avoid the problem of overfitting. By minimizing the total loss function, the R-net and U-net branches of the UDL can extract the coherent effective signals of all sources and suppress the crosstalk noise. The most prominent advantage of our UDL method is that it does not require label data, and the training data set does not contain raw unblended data, thus solving the problem of missing training data sets. The examples with two synthetic and one field data set are used to prove the effectiveness of iterative inversion deblending of simultaneous source data based on our UDL method when sources are within a small distance of each other. By comparing our UDL method with the traditional curvelet-based and contourlet-based methods, the superiority of our method in the quality of separation results is demonstrated.

Constructing continuous curves on point-cloud surfaces with Directed Projection Operator

With the development of 3D acquisition technology, the points based representation of complex objects and environment clouds has been widely applied in tool path generation, robot path planning and other fields. A new challenge is to construct continuous curves on point-cloud. We firstly proposed a new energy function to implement directed projection. Most importantly, we transformed the complex energy optimization relative to directed projection into simple numerical iteration named Directed Projection Operator(DPO). Then, by means of the DPO and its approximating surface, we provided two different approaches to construct G2 continuous curves over the point-cloud surface. The constructed curve can not only be embedded into the point-cloud surface, but also be modified locally. A large number of experimental cases have verified the effectiveness and practicability of the proposed algorithms.

Human-Focused Digital Twin Applications for Occupational Safety and Health in Workplaces: A Brief Survey and Research Directions

Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently used to assess risks in workplaces require manual work and are highly subjective. These tools include checklists and work assessments conducted by experts. Modern Industry 4.0 technologies such as a digital twin, a computerized representation in the digital world of a physical asset in the real world, can be used to provide a safe and healthy work environment to human workers and can reduce occupational injuries and accidents. These digital twins should be designed to collect, process, and analyze data about human workers. The problem is that building a human-focused digital twin is quite challenging and requires the integration of various modern hardware and software components. This paper aims to provide a brief survey of recent research papers on digital twins, focusing on occupational safety and health applications, which is considered an emerging research area. The authors focus on enabling technologies for human data acquisition and human representation in a virtual environment, on data processing procedures, and on the objectives of such applications. Additionally, this paper discusses the limitations of existing studies and proposes future research directions.

P‐2.20: Hand Interaction Acquisition Technology Based on Hall Sensor

The design adopts the assistance of Hall sensor and permanent magnet to realize the capture of finger movement, which has the advantages of low cost and accurate recognition. STM32 is used as the main control module and MDK5 development platform in the design. The platform has the advantages of high integration and convenient development, and also has the characteristics of digital signal processing, high performance, real-time function, low voltage and low power consumption. A permanent magnet is designed at the center of the palm of the glove to form a constant magnetic field near the palm. The Hall sensor is a magnetic field sensor made according to the Hall effect, which can convert the magnetic field strength into a voltage signal. Through the AD conversion module built in STM32 chip, the output voltage of Hall sensor can be collected and converted into finger position and posture information through algorithm.

Research on Data Acquisition Technology of Intelligent Laboratory Based on Retinex-Net Image Recognition

The reliability of electric power metering verification data is related to the accuracy and fairness of electric power trade settlement. Most of the existing verification equipment uses traditional pointer-type electrical verification instruments. The verification process requires the manual recording of data and the issuance of verification reports. There are problems such as low data collection efficiency, high error rate, and human tampering, which lead to great uncertainty in the detection results. This paper presents a data acquisition technology of pointer-type electrical verification instruments based on Retinex-Net image recognition for power metering verification data acquisition of intelligent laboratory, which realizes automatic data acquisition of power metering verification, ensures the accuracy and reliability of verification results, and effectively supports the digital transformation and development of power metering verification field.

Research on High-Frequency Data Acquisition Technology in Distributed Photovoltaic Station Areas

With the large-scale development of low-voltage distributed photovoltaic in power systems, the safe and effective operation of the power grid has been put forward. The analysis of the operation of distributed photovoltaics depends on the development of high-frequency data acquisition technology. This paper mainly aims at the problem of high-frequency data acquisition for distributed photovoltaic users and proposes a solution to achieve high-frequency data acquisition using high-speed power line carriers and dual-mode communication technology. The high-speed power line carrier is used to achieve minute-level data acquisition. The dual-mode communication technology further improves communication reliability. This paper provides theoretical support and construction experience for data acquisition and analysis of distributed photovoltaic users.

Recognition Performance Analysis of a Multimodal Biometric System Based on the Fusion of 3D Ultrasound Hand-Geometry and Palmprint

Multimodal biometric systems are often used in a wide variety of applications where high security is required. Such systems show several merits in terms of universality and recognition rate compared to unimodal systems. Among several acquisition technologies, ultrasound bears great potential in high secure access applications because it allows the acquisition of 3D information about the human body and is able to verify liveness of the sample. In this work, recognition performances of a multimodal system obtained by fusing palmprint and hand-geometry 3D features, which are extracted from the same collected volumetric image, are extensively evaluated. Several fusion techniques based on the weighted score sum rule and on a wide variety of possible combinations of palmprint and hand geometry scores are experimented with. Recognition performances of the various methods are evaluated and compared through verification and identification experiments carried out on a homemade database employed in previous works. Verification results demonstrated that the fusion, in most cases, produces a noticeable improvement compared to unimodal systems: an EER value of 0.06% is achieved in at least five cases against values of 1.18% and 0.63% obtained in the best case for unimodal palmprint and hand geometry, respectively. The analysis also revealed that the best fusion results do not include any combination between the best scores of unimodal characteristics. Identification experiments, carried out for the methods that provided the best verification results, consistently demonstrated an identification rate of 100%, against 98% and 91% obtained in the best case for unimodal palmprint and hand geometry, respectively.

Developing Multi-Label Breast Cancer Screening using Efficientnetb3 Feature Extractor

Breast cancer is the first new case occurring and the fifth death-causing cancer disease in 2020, which collapses the families as women are the pillar of the family members. 3D image acquisition technology increases treatment methods and decreases the rate of breast cancer death. Furthermore, single-label breast cancer classifications are deployed to solve the problem. However, it cannot convey more information about mammogram images. In the area where the shortage of datasets is there as in the medical image, data augmentation and transfer learning are the best solution. A Multi-Label Breast Cancer Screening is a deep learning model using a transfer learning model that gives information on finding, density, and pathology of patients' breast mammograms to the radiologists, aiming for life-saving and supporting the family. The existing model shows low accuracy on feature extraction and more layers with a very large number of nodes in a layer in the classification part. In this study, the best feature extractor for this particular work is investigated and identified as efficientnetb3, and ONN is used for the classification part. The proposed model outperforms the previous work in all evaluation metrics with a 13.25% f1_score, 53% hamming loss, 36.7% coverage error, and 12.5 an exact match. And , the number of parameters were decreased from 134M to 20M which was resulted from the optimizing of classification part of the model. The developed model with ONN in the classification part has made the best improvement over the existing model in terms of evaluation metrics and network performances.

Development of Geospatial Data Acquisition, Modeling, and Service Technology for Digital Twin Implementation of Underground Utility Tunnel

In the maintenance domain of the construction industry, digital twins have been actively introduced based on the technologies of the Fourth Industrial Revolution with growing interest in three-dimensional spatial information facility management and disaster response service using digital twin technology. In particular, disasters or abnormal situations in an underground utility tunnel (UTU) can cause serious casualties and property damage since key elements of a city such as power, communications, water supply, and heating facilities are collectively accommodated in a certain underground space. This study established the methodology for implementing an underground utility tunnel through a digital twin. The novelty of this study is demonstrating a sequential procedure of implementing digital twin technology by configuring major layers such as data acquisition, modeling, and service. This methodology can serve as reference material or a training aid for implementing a digital twin of underground facilities or underground utility tunnels in the future; the proposed methodology will be verified when an actual digital twin service is provided.

Development of Mobile Information Collection System Based on IServer

Data is the "blood" of GIS. GIS focuses on data input, analysis and output, so data collection is particularly important. Traditional surveying and mapping has some shortcomings, such as backward equipment, low accuracy of data acquisition, low acquisition efficiency, and high cost of manpower and material resources. In the information collection of modern urban planning, traditional surveying and mapping has been difficult to meet the needs of data collection. At the same time, the emergence of various mobile intelligent devices and the increasingly mature development of related software and hardware make mobile terminal information acquisition technology gradually replace traditional surveying and mapping in field operations. This paper combines traditional surveying and mapping and modern information technology to develop the mobile terminal information acquisition system based on iServer. First, learn about the development of information collection technology in mobile GIS at home and abroad by consulting a large number of relevant literature, and define the value and significance of developing mobile terminal information collection system. Then, analyze the shortcomings of traditional surveying and mapping in field survey and the problems existing in mobile GIS collection information at present, combine available resource tools to form a project demand analysis, and conceive a project outline design according to the demand analysis, Finally, the information collection system based on iServer and Android is designed in detail, including software functional framework, database design and server side functional design. Theoretical analysis and practical results show that the system can effectively improve the accuracy and speed of data acquisition while reducing the cost of data acquisition.

Artificial intelligence applied to potential assessment and talent identification in an organisational context

Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive synthesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increasingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and recommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.

A Study on the Acquisition Technology for Weak Seismic Signals from Deep Geothermal Reservoirs

There are rich geothermal resources in China and they are widely distributed. After years of continuous exploration and production, most of the shallow geothermal resources have been explored and the current exploration mainly focuses on deep ones. Among a great many geophysical exploration methods, seismic survey is the most effective means of geothermal resource exploration and production, but its weak seismic reflection signal, low S/N ratio and poor imaging of thermal reservoirs due to the seismic geological conditions restrict the production and utilization of deep geothermal resources. It considers through the analysis of the geophysical characteristics of the thermal reservoir that the main causes for the weak seismic reflection signals from deep thermal reservoirs are (1) the shielding effect of the strong wave impedance interface between the thermal reservoir and the caprocks; (2) the small reflection coefficient inside the thermal reservoir; and (3) the serious absorption and attenuation of high-temperature fluids such as hydrothermal fluids and steam in the thermal reservoirs by seismic waves. Accordingly, a series of seismic data acquisition technologies are proposed based on the high-precision vibroseis low-frequency shooting, high-sensitivity geophones long-spread receiving and small bin size highfold technologies, and their application in the seismic acquisition of dry-hot rocks (HDR) in Gonghe Basin, Qinghai, shows a very good application effect.

Using deep learning in an embedded system for real-time target detection based on images from an unmanned aerial vehicle: vehicle detection as a case study

ABSTRACT For a majority of remote sensing applications of unmanned aerial vehicles (UAVs), the data need to be downloaded to ground devices for processing, but this procedure cannot satisfy the demands of real-time target detection. Our objective in this study is to develop a real-time system based on an embedded technology for image acquisition, target detection, the transmission and display of the results, and user interaction while providing support for the interactions between multiple UAVs and users. This work is divided into three parts: (1) We design the technical procedure and the framework for the implementation of a real-time target detection system according to application requirements. (2) We develop an efficient and reliable data transmission module to realize real-time cross-platform communication between airborne embedded devices and ground-side servers. (3) We optimize the YOLOv4 algorithm by using the K-Means algorithm and TensorRT inference to improve the accuracy and speed of the NVIDIA Jetson TX2. In experiments involving static detection, it had an overall confidence of 89.6% and a rate of missed detection of 3.8%; in experiments involving dynamic detection, it had an overall confidence and a rate of missed detection of 88.2% and 4.6%, respectively.

Industrial Internet Data Collection and Processing Technology Research

With the rapid development of the Industrial Internet of Things, data acquisition and processing technology has become a research hotspot. This paper focuses on the industrial Internet data acquisition and processing technology. In view of the current situation and existing problems of industrial Internet data collection technology, such as slow transmission speed, low data quality, poor security, etc., from the three aspects of data transmission, data processing and data security. Specifically, the transmission mode based on the Internet of Things, distributed data processing technology and encryption algorithm and other technical means are adopted to improve the speed and quality of data transmission and ensure the data security. The research results of this paper provide reference and reference for the further development of industrial Internet data acquisition and processing technology, which has certain theoretical and practical significance.

DIA-based technology explores hub pathways and biomarkers of neurological recovery in ischemic stroke after rehabilitation.

Ischemic stroke (IS) is a common disease that causes severe and long-term neurological disability in people worldwide. Although rehabilitation is indispensable to promote neurological recovery in ischemic stroke, it is limited to providing a timely and efficient reference for developing and adjusting treatment strategies because neurological assessment after stroke treatment is mostly performed using scales and imaging. Therefore, there is an urgent need to find biomarkers that can help us evaluate and optimize the treatment plan. We used data-independent acquisition (DIA) technology to screen differentially expressed proteins (DEPs) before and after ischemic stroke rehabilitation treatment, and then performed Gene Ontology (GO) and pathway enrichment analysis of DEPs using bioinformatics tools such as KEGG pathway and Reactome. In addition, the protein-protein interaction (PPI) network and modularity analysis of DEPs were integrated to identify the hub proteins (genes) and hub signaling pathways for neurological recovery in ischemic stroke. PRM-targeted proteomics was also used to validate some of the screened proteins of interest. Analyzing the serum protein expression profiles before and after rehabilitation, we identified 22 DEPs that were upregulated and downregulated each. Through GO and pathway enrichment analysis and subsequent PPI network analysis constructed using STRING data and subsequent Cytoscape MCODE analysis, we identified that complement-related pathways, lipoprotein-related functions and effects, thrombosis and hemostasis, coronavirus disease (COVID-19), and inflammatory and immune pathways are the major pathways involved in the improvement of neurological function after stroke rehabilitation. Complement-related pathways, lipoprotein-related functions and effects, thrombosis and hemostasis, coronavirus disease (COVID-19), and inflammation and immunity pathways are not only key pathways in the pathogenesis of ischemic stroke but also the main pathways of action of rehabilitation therapy. In addition, IGHA1, LRG1, IGHV3-64D, and CP are upregulated in patients with ischemic stroke and downregulated after rehabilitation, which may be used as biomarkers to monitor neurological impairment and recovery after stroke.

Water-energy-food nexus of local bioeconomy hub and future climate change impact implication

Policies on bioeconomy are some of the key emerging ones around the world to support sustainability in terms of using renewable resources. However, these may lead to significant demand for biomass feedstock which relies on limited resources, viz., water, land, and energy. The impact of climate change on water availability makes it more challenging. This paper demonstrates the analysis of the water, energy, and food resource nexus of a bioeconomy-based industrial complex and future climate change impact. The model structure consists of cultivation, crop processing, and urban sectors. Sugarcane is the major crop in the case study area and is the main bioeconomy crop.The results indicate that the average sugarcane yield will be lower than the minimum industrial goal for the 2020s and 2030s decades. Consequently, sugarcane feedstock shortage may often occur. The drought situation tends to be increased, so groundwater resources can be the main reserve source of water. In all improvement scenarios on water supply management, nexus energy used for water acquisition was mainly for groundwater acquisition. Direct energy use in the cultivation process is high and fossil fuels based, while most of the energy used in processing came from biomass waste. This implies that technologies for water acquisition will be more important in the future and should be developed in terms of energy efficiency and the aspect of promoting the usage of renewable energy to reduce greenhouse gases and fossil fuels use. The impact of climate change will cause significant fluctuations in the annual rainfall characteristics of the study area. An additional irrigation water supply of about 250 mm and expansion to cover more than 90% of the cultivation area should be considered to secure the minimum supply of sugarcane for the local biorefinery complex in the following decades.

Improving Seaport Wharf Maintenance and Safety with Structural Health Monitoring System in High Salt and Humidity Environments

Due to the harsh working conditions, the durability of the seaport wharf structure is poor compared with similar hydraulic structures. According to the structural characteristics of coastal port wharf and the particularity of a high salt and high humidity environment, the stress features of the coastal wharf structure are analyzed, and the health inspection indicators of the wharf structure are proposed. A fiber grating sensor-based structural health monitoring system for coastal high-pile piers is established. A corresponding system for detecting structural health is designed according to the standard structural section of the wharf’s front platform. The corresponding monitoring implementation scheme, sensor selection, and performance parameters are proposed. Finally, the realization technology and related indicators of data acquisition and transmission subsystem are given. The experimental results indicate that the waveform of the structural response to the wave load has a consistent sine wave pattern with the actual wave load. The maximum strain of the berthing pier appears at 4.35 m and 6.14 m, and the elevations reach 4.66 με and 5.31 με, respectively. The strain at other positions also has an obvious change trend. The experimental results provide some help for the research of the wharf health monitoring system.

Using image analysis to quantify defects and prioritize repairs in built-up roofs

PurposeThis paper aims to use convolutional neural networks (CNNs) to provide an objective approach to classify deteriorated building assets according to the type and extent of damage. This research supports automated inspection of buildings and focuses on roofing elements as one of the most critical and externally distressed elements in buildings.Design/methodology/approachIn this paper, 5,000+ images of deteriorated roofs from several buildings were collected to design a CNN system that automatically identifies and sizes roofing defects. Experimenting with different CNN formulations, the best accuracy is achieved using two-stage CNNs. The first-stage CNN classifies images into defect/no defect, while the second stage classifies the defected images according to the damage type. Based on the image classification, optimization is used to prioritize roof repairs by maximizing the return from limited rehabilitation funds.FindingsThe developed CNNs reached 95% and 97% accuracy for the first and second phases, respectively, which is higher than achieved in previous literature efforts. Using the proposed model to automate inspection and condition assessment activities proved to be faster than conventional methods. Repair/replace strategy for a case study of 21 campus buildings based on their condition and budgetary constraints was suggested.Research limitations/implicationsFuture research includes testing different data acquisition technologies (e.g. infrared imaging), performing severity-based classification and integrating with BIM for defect localization.Originality/valueThis study provides an objective approach to automate asset condition assessment and improve funding decisions using a combination of image analysis and optimization techniques. The proposed approach is applicable toward other asset types and components.