VR-based digital twin for remote monitoring of mining equipment:Architecture and a case study

Background Traditional methods for monitoring mining equipment rely primarily on visual inspections,which are time-consuming,inefficient,and hazardous.This article introduces a novel approach to monitoring mission-critical systems and services in the mining industry by integrating virtual reality(VR)and digital twin(DT)technologies.VR-based DTs enable remote equipment monitoring,advanced analysis of machine health,enhanced visualization,and improved decision making.Methods This article presents an architecture for VR-based DT development,including the developmental stages,activities,and stakeholders involved.A case study on the condition monitoring of a conveyor belt using real-time synthetic vibration sensor data was conducted using the proposed methodology.The study demonstrated the application of the methodology in remote monitoring and identified the need for further development for implementation in active mining operations.The article also discusses interdisciplinarity,choice of tools,computational resources,time and cost,human involvement,user acceptance,frequency of inspection,multiuser environment,potential risks,and applications beyond the mining industry.Results The findings of this study provide a foundation for future research in the domain of VR-based DTs for remote equipment monitoring and a novel application area for VR in mining.

Advancement in Wide Area Monitoring Protection and Control Using PMU’s Model in MATLAB/SIMULINK

A big step forward to improve power system monitoring and performance, continued load growth without a corresponding increase in transmission resources has resulted in reduced operational margins for many power systems worldwide and has led to operation of power systems closer to their stability limits and to power exchange in new patterns. These issues, as well as the on-going worldwide trend towards deregulation of the entire industry on the one hand and the increased need for accurate and better network monitoring on the other hand, force power utilities exposed to this pressure to demand new solutions for wide area monitoring, protection and control. Wide-area monitoring, protection, and control require communicating the specific-node information to a remote station but all information should be time synchronized so that to neutralize the time difference between information. It gives a complete simultaneous snap shot of the power system. The conventional system is not able to satisfy the time-synchronized requirement of power system. Phasor Measurement Unit (PMU) is enabler of time-synchronized measurement, it communicate the synchronized local information to remote station.

Cathodic Protection Remote Monitoring Based on Wireless Sensor Network

Cathodic Protection system is an efficient system used for protecting the underground metal objects from corrosion. In this paper the use of Cathodic Protection (CP) system and how they can be developed to simulate corrosion control solution was illustrated. The aim of developing a Cathodic Protection system is to provide control over oil pipelines and to reduce the incidence of corrosion. The proposed system integrates the technology of wireless sensor Network (WSN) in order to collect potential data and to realize remote data transmission. In this system each WSN receives the data from the environment and forwards it to a Remote Terminal Unit (RTU). Then each RTU forwards it to its base station (BS). In this work Labview 2010 program was used, due to its high potentials. In addition it contains a Tool Kit that supports the wireless sensor network. In this simulation used many cases study to test and monitoring data and get optimum results, least time delay and high speed to prevent corrosion.

Research on Remote Monitoring and Fault Diagnosis Technology of Numerical Control Machine

Based on the internet technology,it has become possible to complete remote monitoring and fault diagnosis for the numerical control machine.In order to capture the micro-shock signal induced by the incipient fault on the rotating parts,the reso- nance demodulation technology is utilized in the system.As a subsystem of the remote monitoring system,the embedded data acquisi- tion instrument not only integrates the demodulation board but also complete the collection and preprocess of monitoring data from different machines.Furthermore,through connecting to the internet,the data can be transferred to the remote diagnosis center and data reading and writing function can be finished in the database.At the same time,the problem of the IP address floating in the dial-up of web server is solved by the dynamic DNS technology.Finally,the remote diagnosis software developed on the Lab VIEW platform can analyze the monitoring data from manufacturing field.The research results have indicated that the equipment status can be monitored by the system effectively.

The potential mechanism and clinical application value of remote ischemic conditioning in stroke

Some studies have confirmed the neuroprotective effect of remote ischemic conditioning against stroke. Although numerous animal researches have shown that the neuroprotective effect of remote ischemic conditioning may be related to neuroinflammation, cellular immunity, apoptosis, and autophagy, the exact underlying molecular mechanisms are unclear. This review summarizes the current status of different types of remote ischemic conditioning methods in animal and clinical studies and analyzes their commonalities and differences in neuroprotective mechanisms and signaling pathways. Remote ischemic conditioning has emerged as a potential therapeutic approach for improving stroke-induced brain injury owing to its simplicity, non-invasiveness, safety, and patient tolerability. Different forms of remote ischemic conditioning exhibit distinct intervention patterns, timing, and application range. Mechanistically, remote ischemic conditioning can exert neuroprotective effects by activating the Notch1/phosphatidylinositol 3-kinase/Akt signaling pathway, improving cerebral perfusion, suppressing neuroinflammation, inhibiting cell apoptosis, activating autophagy, and promoting neural regeneration. While remote ischemic conditioning has shown potential in improving stroke outcomes, its full clinical translation has not yet been achieved.

Comparison of CWSI and T_(s)-T_(a)-VIs in moisture monitoring of dryland crops(sorghum and maize)based on UAV remote sensing

Monitoring agricultural drought using remote sensing data is crucial for precision irrigation in modern agriculture.Utilizing unmanned aerial vehicle(UAV)remote sensing,we explored the applicability of an empirical crop water stress index(CWSI)based on canopy temperature and three-dimensional drought indices(TDDI)constructed from surface temperature(T_(s)),air temperature(T_(a))and five vegetation indices(VIs)for monitoring the moisture status of dryland crops.Three machine learning algorithms(random forest regression(RFR),support vector regression,and partial least squares regression)were used to compare the performance of the drought indices for vegetation moisture content(VMC)estimation in sorghum and maize.The main results of the study were as follows:(1)Comparative analysis of the drought indices revealed that T_(s)-T_(a)-normalized difference vegetation index(TDDIn)and T_(s)-T_(a)-enhanced vegetation index(TDDIe)were more strongly correlated with VMC compared with the other indices.The indices exhibited varying sensitivities to VMC under different irrigation regimes;the strongest correlation observed was for the TDDIe index with maize under the fully irrigated treatment(r=-0.93).(2)Regarding spatial and temporal characteristics,the TDDIn,TDDIe and CWSI indices showed minimal differences Over the experimental period,with coefficients of variation were 0.25,0.18 and 0.24,respectively.All three indices were capable of effectively characterizing the moisture distribution in dryland maize and sorghum crops,but the TDDI indices more accurately monitored the spatial distribution of crop moisture after a rainfall or irrigation event.(3)For prediction of the moisture content of single crops,RFR models based on TDDIn and TDDIe estimated VMC most accurately(R^(2)>0.7),and the TDDIn-based model predicted VMC with the highest accuracy when considering multiple-crop samples,with R^(2)and RMSE of 0.62 and 14.26%,respectively.Thus,TDDI proved more effective than the CWSI in estimating crop water content.

Interpreting the Shortwave Infrared &Thermal Infrared Regions of Remote Sensed Electromagnetic Spectrum with Application for Mineral-Deposits Exploration

The ASTER (Advanced Space-borne Thermal Emission and Reflection radiometer) data, including all the 3 parts: VNIR (Visible and Near-Infrared), SWIR (Short Wave Infrared), TIR (Thermal Infrared), were applied for extraction of mineral deposits, such as the Ni-Cu deposit in eastern Tianshan, the gypsum in western Tianshan, and the borax in Tibetan. This paper discusses the extraction methodology using the ASTER remote sensing data and reveals the good extraction results. This paper bravely represents the summary of the main achievement for this field by the scientists in other countries and gives a comparison with the works by others. The new achievements, described in this paper, comprise the extraction of anomalies for Ni-Cu deposit, gypsum, and borax.

Some recent advances in remote sensing-based monitoring of changes in the Greenland Ice Sheet

The mass balance of the Greenland Ice Sheet(GrIS)plays a crucial role in global sea level change.Since the 1960s,remote sensing missions have been providing extensive and continuous observation data for change monitoring of the GrIS.In this paper,we present our recent research results from remote sensing-based GrIS change monitoring.First,historical satellite data are processed and used to fill data gaps and are combined with existing partial maps,completing an ice velocity map of the GrIS from the 1960s to 1980s.This map provides valuable data for estimating the historical mass balance of Greenland.Second,the monthly gravimetry-based mass balance of the GrIS from 2002 to 2020 is estimated by combining Gravity Recovery and Climate Experiment(GRACE)and GRACE Follow On(GRACE-FO)data.It is found that the GrIS has lost a total mass of approximately 4443±75 Gt during this period.Third,based on Global Land Ice Measurements from Space(GLIMS),an updated Greenland glacier inventory is achieved utilizing data collected between 2006 and 2020.This inventory provides more detailed and up-to-data glacier boundaries of Greenland.Overall,these advances provide essential data support for estimating the mass balance of the GrIS,contributing to the advancement of research on global sea level change.

Design and Construction of Automatic Monitoring System for Open-pit Coal Mine Slopes

[Objectives]To monitor the stability of open-pit coal mine slopes in real time and ensure the safety of coal mine production.[Methods]The automatic monitoring system of coal mine slope was explored in depth,and the core functions of the system were designed comprehensively.According to the design function of the automatic monitoring system,the slope automatic monitoring system was constructed.Besides,in accordance with the actual situation of the slope,the monitoring frequency of slopes was set scientifically,and the key indicators such as rainfall,deep displacement and surface displacement of the slopes were monitored in an all-round and multi-angle way.[Results]During the monitoring period,the overall condition of the slope remained good,and no landslides or other geological disasters occurred.At the same time,the overall rainfall in the slope area remained low.In terms of monitoring data,the horizontal displacement and settlement of the slopes increased first and then tended to be stable.Specifically,the maximum horizontal displacement during the monitoring period was 22.74 mm,while the maximum settlement was 18.65 mm.[Conclusions]The automatic slope monitoring system has obtained remarkable achievements in practical application.It not only improves the accuracy and efficiency of slope stability monitoring,but also provides valuable reference experience for similar projects.

ZiLOG的Remote on a Chip全包解决方案为通用红外遥控市场确立新标准

ZiLOG的通用红外遥控解决方案是由Crimzon微控制器系列产品支持的，包括一次性可编程(OTP)和ROM两种版本。该系列产品都是基于Z8架构而生产的。凭借237个字节的多用途RAM(随机存取存储器)、2．0～3．6V的工作电压范围、高低压电池检测以及高达32KB的一次性可编程存储容量，ZiLOG的Crimzon系列红外微控制器可以实现快速执行、有效存储、完善的中断能力、输入和输出二进制处理能力、自动化的脉冲发生／接收以及内部键控扫描上拉晶体管。

Application of Remote Sensing and GIS in Mineral Alteration Mapping and Lineament Extraction Case of Oudiane Elkharoub (Requibat Shield, Northern of Mauritania)

The integration of remotely sensed data allowed the successful characterization of the mineral alteration zones of the Oudiane Elkharoub area in the Northeastern part of Reguibat Shield using image transformation techniques. As both chemical and geochemical analyses showed significant Au, Ag, Cu, Pb, Mn, Cr, Ni, Th and Y anomalies, it’s very interesting to apply the remote sensing and GIS in mineral resources mapping. The remote sensing is a direct adjunct to the field, lithologic and structural mapping, and more recently, GIS has played an important role in the study of mineralization areas. The integration of several evidential maps highlighted the plausible areas with high concentrations of chlorite, epidote, kaolinite, calcite, alunite, hematite, illite and sulfur among other key mineral alterations that reflect the intensity of hydrothermal effects and the probable sites of ore bodies. The methodological approach integrates geological information acquired from Aster and Landsat 8 OLI/TIRS (Operational Land Imager/Thermal InfraRed Sensor) images and a multi-criteria GIS analysis. The superimposition of various lineament and hydrothermal alteration maps and the consideration of precious and base metal indicators allowed the zoning of sites likely to contain mineral concentrations. Remote sensing becomes an important tool for locating mineral deposits in its own right, when the primary and secondary processes of mineralization result in the formation of spectral anomalies. Reconnaissance lithological mapping is usually the first step of mineral resource mapping. This is complimented with structural mapping, as mineral deposits usually occur along or adjacent to geologic structures, and alteration mapping, as mineral deposits are commonly associated with hydrothermal alteration of the surrounding rocks. Ground truthing and laboratory studies including XRD analysis were utilized to verify the results.

Extensive identification of landslide boundaries using remote sensing images and deep learning method

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.

Doppler Radar Fault Judgment System and Remote Monitoring Platform

The Doppler weather radar fault judging system and remote monitoring platform were introduced.Through the real-time scanning of radar alarm information coding,the platform can realize dynamic monitoring and real-time alarm of Doppler radar equipment components,so as to improve the reliability of equipment operation,and truly realize"unattended"remote monitoring.

Visual Analysis of Remote Sensing Monitoring of Soil Salinization

Soil salinization seriously restricts the development of agricultural production,the sustainable use of land resources,and the stability of the ecological environment.In order to objectively reveal the research status of soil salinization,CiteSpace software was used to conduct data mining and quantitative analysis on research papers on soil salinization from 2008 to 2023 in China National Knowledge Infrastructure(CNKI)and Web of science databases.The data sources were transformed into visual graphs by reproducing clustering statistics from aspects such as publication volume,authors,keywords,and publishing institutions.In addition,this paper also combined the actual needs and cutting-edge hotspots in relevant research in China,and proposed and analyzed the limitations and future development trends of soil salinity monitoring research in China.This has important practical significance for comprehensively grasping the current research status of salinization,further clarifying and sorting out the research ideas of salinization monitoring,enriching the remote sensing monitoring methods of saline soil,and solving the actual problems of soil salinization in China.

Study of Remote Monitoring and Maintenance Guiding Technique Based on LabVIEW for Machining Centers

The virtual instruments （VIs）, as a new type of instrument based on computer, has many advanced attractive characteristics. This research is based on Vls, and brings condition monitoring and knowledge-based maintenance support together through an integrated （including hate.met, ASP. NET, XML tochnique, Vls） network environme~. Within the enviromnent, machining centers operators, engineers or managers can share real-time data through the browser-based interface and minimize machining centers downtime by providing status monitoring and remote maintenance guiding from service centers.

Development of Spectral Features for Monitoring Rice Bacterial Leaf Blight Disease Using Broad-Band Remote Sensing Systems

As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv.oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result ofthe disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remotesensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutionsoffer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapiddispersal under suitable conditions, making it difficult to track the disease at a regional scale with a single sensorin practice. Therefore, it is necessary to identify or construct features that are effective across different sensors formonitoring RBLB. To achieve this goal, the spectral response of RBLB was first analyzed based on the canopyhyperspectral data. Using the relative spectral response (RSR) functions of four representative satellite or UAVsensors (i.e., Sentinel-2, GF-6, Planet, and Rededge-M) and the hyperspectral data, the corresponding broad-bandspectral data was simulated. According to a thorough band combination and sensitivity analysis, two novel spectralindices for monitoring RBLB that can be effective across multiple sensors (i.e., RBBRI and RBBDI) weredeveloped. An optimal feature set that includes the two novel indices and a classical vegetation index was formed.The capability of such a feature set in monitoring RBLB was assessed via FLDA and SVM algorithms. The resultdemonstrated that both constructed novel indices exhibited high sensitivity to the disease across multiple sensors.Meanwhile, the feature set yielded an overall accuracy above 90% for all sensors, which indicates its cross-sensorgenerality in monitoring RBLB. The outcome of this research permits disease monitoring with different remotesensing data over a large scale.

Future of neurocritical care:Integrating neurophysics,multimodal monitoring,and machine learning

Multimodal monitoring(MMM)in the intensive care unit(ICU)has become increasingly sophisticated with the integration of neurophysical principles.However,the challenge remains to select and interpret the most appropriate combination of neuromonitoring modalities to optimize patient outcomes.This manuscript reviewed current neuromonitoring tools,focusing on intracranial pressure,cerebral electrical activity,metabolism,and invasive and noninvasive autoregulation moni-toring.In addition,the integration of advanced machine learning and data science tools within the ICU were discussed.Invasive monitoring includes analysis of intracranial pressure waveforms,jugular venous oximetry,monitoring of brain tissue oxygenation,thermal diffusion flowmetry,electrocorticography,depth electroencephalography,and cerebral microdialysis.Noninvasive measures include transcranial Doppler,tympanic membrane displacement,near-infrared spectroscopy,optic nerve sheath diameter,positron emission tomography,and systemic hemodynamic monitoring including heart rate variability analysis.The neurophysical basis and clinical relevance of each method within the ICU setting were examined.Machine learning algorithms have shown promise by helping to analyze and interpret data in real time from continuous MMM tools,helping clinicians make more accurate and timely decisions.These algorithms can integrate diverse data streams to generate predictive models for patient outcomes and optimize treatment strategies.MMM,grounded in neurophysics,offers a more nuanced understanding of cerebral physiology and disease in the ICU.Although each modality has its strengths and limitations,its integrated use,especially in combination with machine learning algorithms,can offer invaluable information for individualized patient care.

Contribution of the MERISE-Type Conceptual Data Model to the Construction of Monitoring and Evaluation Indicators of the Effectiveness of Training in Relation to the Needs of the Labor Market in the Republic of Congo

This study proposes the use of the MERISE conceptual data model to create indicators for monitoring and evaluating the effectiveness of vocational training in the Republic of Congo. The importance of MERISE for structuring and analyzing data is underlined, as it enables the measurement of the adequacy between training and the needs of the labor market. The innovation of the study lies in the adaptation of the MERISE model to the local context, the development of innovative indicators, and the integration of a participatory approach including all relevant stakeholders. Contextual adaptation and local innovation: The study suggests adapting MERISE to the specific context of the Republic of Congo, considering the local particularities of the labor market. Development of innovative indicators and new measurement tools: It proposes creating indicators to assess skills matching and employer satisfaction, which are crucial for evaluating the effectiveness of vocational training. Participatory approach and inclusion of stakeholders: The study emphasizes actively involving training centers, employers, and recruitment agencies in the evaluation process. This participatory approach ensures that the perspectives of all stakeholders are considered, leading to more relevant and practical outcomes. Using the MERISE model allows for: • Rigorous data structuring, organization, and standardization: Clearly defining entities and relationships facilitates data organization and standardization, crucial for effective data analysis. • Facilitation of monitoring, analysis, and relevant indicators: Developing both quantitative and qualitative indicators helps measure the effectiveness of training in relation to the labor market, allowing for a comprehensive evaluation. • Improved communication and common language: By providing a common language for different stakeholders, MERISE enhances communication and collaboration, ensuring that all parties have a shared understanding. The study’s approach and contribution to existing research lie in: • Structured theoretical and practical framework and holistic approach: The study offers a structured framework for data collection and analysis, covering both quantitative and qualitative aspects, thus providing a comprehensive view of the training system. • Reproducible methodology and international comparison: The proposed methodology can be replicated in other contexts, facilitating international comparison and the adoption of best practices. • Extension of knowledge and new perspective: By integrating a participatory approach and developing indicators adapted to local needs, the study extends existing research and offers new perspectives on vocational training evaluation.

Mangrove Restoration and Regeneration Monitoring in Gulf of Kachchh, Gujarat State, India, Using Remote Sensing and Geo-Informatics

Indian coast harbors richly diverse and critical coastal habitats like coral reefs and mangroves. Mangroves form one of the most important ecosystems of coastal and marine areas. It safeguards the ecology of the coastal areas and provides livelihood opportunities to the fishermen and pastoral families living in these areas. In real sense, mangrove is the Kalpvriksh (divine tree which fulfills all the desires) for the coastal communities. The restoration and plantation of mangroves have received a lot of attentions worldwide. To assess the impact of mangrove plantation activities and to monitor the mangrove regeneration and restoration in various villages, a joint study under the Integrated Coastal Zone Management Project (ICZMP) was taken up by Gujarat Ecology Commission (GEC) and Bhaskaracharya Institute for Space Applications and Geo-Informatics (BISAG) in the Gulf of Kachchh, Gujarat State.?The major objective of this study was to monitor the increase in mangrove cover in coastal areas of Gulf of Kachchh using the Indian Remote Sensing Satellite data of 2005, 2011 and 2014. The mangrove regeneration was monitored using multi-temporal Indian Remote Sensing Satellite (IRS) LISS-III and LISS-IV digital data covering Gulf of Kachchh region. The multi-temporal IRS LISS-III data covering Gulf of Kachchh of October-2005, November-2011 and LISS-IV data of April-2014 was analyzed. The mangrove density and mangrove area in different talukas was estimated based on the analysis of IRS LISS-III digital data. The mangroves have been delineated based on the pink colour observed on satellite images and the area was estimated in the Geographic Information System (GIS) environment. The taluka or block-level mangrove areas were estimated and changes in the areas were monitored during the period of six years from 2005 to 2011. It was observed that the areas where mangrove regeneration activities were carried out with active participation of Community Based Organizations (CBOs), mangrove density as well as mangrove area have substantially increased in the Gulf of Kachchh region.

IoT-Enabled Plant Monitoring System with Power Optimization and Secure Authentication

Global food security is a pressing issue that affects the stability and well-being of communities worldwide.While existing Internet of Things(IoT)enabled plant monitoring systems have made significant strides in agricultural monitoring,they often face limitations such as high power consumption,restricted mobility,complex deployment requirements,and inadequate security measures for data access.This paper introduces an enhanced IoT application for agricultural monitoring systems that address these critical shortcomings.Our system strategically combines power efficiency,portability,and secure access capabilities,assisting farmers in monitoring and tracking crop environmental conditions.The proposed system includes a remote camera that captures images of surrounding plants and a sensor module that regularly monitors various environmental factors,including temperature,humidity,and soil moisture.We implement power management strategies to minimize energy consumption compared to existing solutions.Unlike conventional systems,our implementation utilizes the Amazon Web Services(AWS)cloud platform for reliable data storage and processing while incorporating comprehensive security measures,including Two-Factor Authentication(2FA)and JSON Web Tokens(JWT),features often overlooked in current agricultural IoT solutions.Users can access this secure monitoring system via a developed Android application,providing convenient mobile access to the gathered plant data.We validate our system’s advantages by implementing it with two potted garlic plants on Okayama University’s rooftop.Our evaluation demonstrates high sensor reliabil-ity,with strong correlations between sensor readings and reference data,achieving determination coefficients(R2)of 0.979 for temperature and 0.750 for humidity measurements.The implemented power management strategies extend battery life to 10 days on a single charge,significantly outperforming existing systems that typically require daily recharging.Furthermore,our dual-layer security implementation utilizing 2FA and JWT successfully protects sensitive agricultural data from unauthorized access.