Tool For Real-time Monitoring Research Articles

Machine Learning-based fixed access Network Interface Congestion Prediction

Network interface congestion refers to a situation in which network interfaces or communication channels within a network experience high levels of traffic, leading to performance degradation or potential network failures. In modern networking, this issue remains relevant and can manifest in various contexts due to a surge in data traffic, Internet of Things (IoT) devices can generate substantial data, increased use of VPNs for remote work and when multiple users or applications concurrently access and transfer data in and out of the cloud. Efforts to forecast and address network interface bottlenecks are crucial to sustaining the performance and consistency of modern networks. In the PTCL fixed access network infrastructure, network blocking is a serious problem that necessities to be addressed. The main objective of this study is to overcome network congestion problems that occur in PTCL's fixed access network through the use of machine learning techniques. Despite having real-time monitoring tools such as SolarWinds, there remains a dearth of software capable of predicting imminent bottlenecks in the network interface. The primary aim is to build an analytical model which can predict potential blockages within 30 days and facilitate proactive management strategies for better administration. Top of Form The study classifies the most effective machine learning technique for this purpose and evaluates its performance against current methods. To achieve the goal, a diagnostic dataset was created, leveraging appropriate configurations, software tools, and hardware resources. Data was sourced from SolarWinds, consisting of information from over 23,000 interfaces across 11,000 access network nodes. This data was analyzed in PTCL's Data Warehouse, where a historical aggregated dataset was constructed. Exploratory data analysis was performed using Python libraries, including Matplotlib, NumPy, Pandas, and Seaborn. This investigation sets the foundation for proactive network management and enhanced user experiences within PTCL's fixed access network, addressing the critical essential for advanced predictive tools to combat congestion and enhance network competence.

Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM

Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. This study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM , specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.

Green radiography: Exploring perceptions, practices, and barriers to sustainability

IntroductionPrevious research has delved into the attitudes and behaviors of diverse professions regarding environmental sustainability. However, there needs to be more research specifically targeting radiographers. This study aims to survey radiographers' perceptions, practices, and barriers to change concerning environmental sustainability in radiology. MethodsInstitutional ethical approval was obtained (IRB–COHS–FAC-110-2024) and data collection was conducted using Google Forms (Google Inc., Mountain View, CA). The survey targeted 104 practicing radiographers across several countries. Questions were structured around five domains to gather insights into demographics, training in global warming and climate change, perceptions of sustainability and climate change, sustainability barriers, and current radiology practices on sustainability. Data analysis utilized descriptive and d inferential statistics. ResultsOne hundred and four radiographers completed the study. Females had a significantly higher attendance rate in environmental protection campaigns (P = 0.01). The majority of respondents (68%) believe in climate change's knowledge and impact on the natural world. Our survey findings demonstrate that 74% of respondents believe there's a need to improve sustainability practices. The most commonly used strategies to decrease energy consumption and emissions were low-energy lighting (60%), real-time power monitoring tools (41%), and energy-efficient heating systems (32%). A significant concern regarding sustainability emerges among respondents: time (50%) and lack of leadership (48%) are prevalent concerns among the identified barriers. ConclusionParticipants are recognising the importance of environmental sustainability in radiology, but lack of leadership, support, authority, and facility limitations hinder their adoption. Impact on practiceRadiology must prioritize environmental sustainability by providing resources and training for radiographers and collaborating with healthcare professionals, policymakers, and environmental experts to develop comprehensive strategies for a sustainable healthcare system.

Monitoring bridge bearings based on rubber-based composite materials

To improve the real-time monitoring capability of bridge structure safety, intelligent bridge bearings were prepared based on rubber based composite materials. The study selected T4/3-5 intelligent rubber bearings and conducted a systematic experimental analysis on their response characteristics under different loading speeds and holding states. By measuring the displacement and resistance changes of the intelligent bearings under various load conditions, their sensitivity and stability were evaluated. These experiments confirmed that the T4/3-5 intelligent rubber bearings exhibited a total creep deformation of approximately 1 mm and 1.5 mm under loads of 60 kN and 45 kN, respectively. This suggests that the molecular chains reach a relatively stable configuration under high loads and are not prone to further changes. Moreover, the resistance reduction ratio under a load of 60 kN is approximately 0.57, while under a load of 45 kN it is approximately 0.62, indicating that nickel powder particles in the composite material form a more stable conductive network after molecular rearrangement. At different loading rates, the displacement-load and load-resistance curves of the bearings show consistent trends, and the creep and resistance relaxation behaviors exhibit certain regularity with the concentration of load size. Through the analysis of these data, the study elucidated the resistance change mechanism in the bearings and validated the feasibility of intelligent bearings as a real-time load monitoring tool.

Artificial neural network modelling of natural gas dehydration process

A multi-input–multi-output artificial neuron network (MIMO-ANN) model has been developed for process monitoring and improvement on a natural gas glycol dehydration process. The MIMO-ANN model was based on a steady-state process simulation model constructed in commercial software Aspen HYSYS. A set of training data was generated with the converged simulation model for the training of the MIMO-ANN model in Python. The process input of the model includes lean glycol recirculation rate and purity along with wet gas inlet pressure. On the other hand, the process output considered includes dehydrated gas water and aromatics content, dehydrated gas hydrate formation temperature and water dew point, stripping gas flow rate as well as reboiler duty. The overall mean squared error (MSE) of the MIMO-ANN model was calculated as 1.79. The best-fit line that highly overlaps with a 45° diagonal line is constructed with a correlation coefficient (R2) score of more than 0.999 for all studied process output testing datasets. The mean absolute percentage error (MAPE) of the predicted outputs is generally less than 1%, except for dehydrated gas water dew point (16.63%) and stripping gas flow rate (11.55%), due to error predictions attempted on pseudo-zero values. This successfully displays an exceptional predictive performance of the MIMO-ANN model developed in this work which can be further deployed as an online dashboard for real-time monitoring tool.

Real-time monitoring of chromatic and phenolic dynamics of vinification employing UV-Vis spectroscopy, Python and chemometrics

Increasing consumer demand for high-quality wines required the introduction of more comprehensive monitoring indicators for quality management, necessitating winemakers to strive for rapid, real-time, and affordable monitoring tools during winemaking. This study investigated the feasibility of cost-efficient and easy-to-implement ultraviolet-visible (UV-Vis) spectroscopy combined with chemometric methods to predictively monitor the evolution of wine color and phenolics throughout commercial fermentation. Fermentations on a winery scale were carried out with a daily analysis of the UV-Vis spectra, color, and phenolics. Furthermore, characteristic wavelengths were established by a self-compiled Python program to reduce redundancy in spectra. The optimized partial least squares (PLS) model predicted well with a residual prediction deviation (RPD) greater than 2.54, and the robustness was further verified by a robust external Bland-Altman method. UV-Vis spectroscopy can achieve objective monitoring of the practical fermentation profile, which can better help winemakers make decisions to improve wine quality and consistency.

Innovative approach to daily carbon dioxide emission forecast based on ensemble of quantile regression and attention BILSTM

Given the increasingly severe challenges of global climate change and sustainable development, accurate prediction of daily carbon dioxide (CO2) emissions has become crucial. However, current research on real-time daily forecasts remains relatively scarce, often limited to annual predictions and point estimates. This paper proposes an innovative integrated model, based on quantile regression and Attention-Bidirectional Long Short-Term Memory (BILSTM), specifically designed for probabilistic prediction of daily carbon emissions. Testing the model on daily carbon emission data from major carbon-emitting countries including China, the United States, India, Russia, and Italy has demonstrated its superior performance across various metrics, including mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R2), and normalized average width of prediction intervals (PINAW), compared to other benchmark models. This achievement not only highlights the model's effectiveness in predicting daily carbon emissions but also provides policymakers with a powerful tool for real-time monitoring and assessment of carbon emissions. Furthermore, the application of probabilistic forecasting methods enables decision-makers to gain a more comprehensive understanding of the potential range of future carbon emissions, providing strong support for the formulation of targeted policies.

Engineering a far-red fluorescent probe for rapid detection of Hg(II) ions in both cells and zebrafish

Abnormal accumulation of mercury ions (Hg2+) in organisms can lead to severe central nervous system and other diseases. Therefore, the monitoring and detection of Hg2+ are of great significance for human health and environmental safety. Herein, we designed and synthesized a novel far-red to NIR emission fluorescent probe (Rho-Hg) based on rhodamine derivative as the fluorophore and thiospirolactone as the recognition site for turn-on detecting of Hg2+ in living cells and zebrafish. The probe Rho-Hg displayed superior sensitivity (detection limit = 17.5 nM), rapid response (<1 min), colorimetric change, high selectivity, and moderate pH stability. Leveraging this probe, we realized the real-time monitoring of Hg2+ in real samples, living cells and zebrafish. By fostering zebrafish embryos and larvae in Hg2+-containing nutrient solution, we noticed that Hg2+ was ingested into the zebrafish liver when zebrafish were grown up to 3 days old, and thus we successfully monitored the accumulation and changes of Hg2+ during zebrafish growth and development. Thus, the probe Rho-Hg could be a powerful tool for sensitive and real-time monitoring of Hg2+ in living systems.

A Comprehensive Review of Sustainable Assessment and Innovation in Jet Grouting Technologies

A Comprehensive Review of Sustainable Assessment and Innovation in Jet Grouting Technologies

Real-Time Particle Emission Monitoring for the Non-Invasive Prediction of Lung Deposition via a Dry Powder Inhaler

Although inhalation therapy represents a promising drug delivery route for the treatment of respiratory diseases, the real-time evaluation of lung drug deposition remains an area yet to be fully explored. To evaluate the utility of the photo reflection method (PRM) as a real-time non-invasive monitoring of pulmonary drug delivery, the relationship between particle emission signals measured by the PRM and in vitro inhalation performance was evaluated in this study. Symbicort® Turbuhaler® was used as a model dry powder inhaler. In vitro aerodynamic particle deposition was evaluated using a twin-stage liquid impinger (TSLI). Four different inhalation patterns were defined based on the slope of increased flow rate (4.9–9.8 L/s2) and peak flow rate (30 L/min and 60 L/min). The inhalation flow rate and particle emission profile were measured using an inhalation flow meter and a PRM drug release detector, respectively. The inhalation performance was characterized by output efficiency (OE, %) and stage 2 deposition of TSLI (an index of the deagglomerating efficiency, St2, %). The OE × St2 is defined as the amount delivered to the lungs. The particle emissions generated by four different inhalation patterns were completed within 0.4 s after the start of inhalation, and were observed as a sharper and larger peak under conditions of a higher flow increase rate. These were significantly correlated between the OE or OE × St2 and the photo reflection signal (p < 0.001). The particle emission signal by PRM could be a useful non-invasive real-time monitoring tool for dry powder inhalers.Graphical

Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors.

Precision medicine, characterized by the personalized integration of a patient's genetic blueprint and clinical history, represents a dynamic paradigm in healthcare evolution. The emerging field of personalized anesthesia is at the intersection of genetics and anesthesiology, where anesthetic care will be tailored to an individual's genetic make-up, comorbidities and patient-specific factors. Genomics and biomarkers can provide more accurate anesthetic protocols, while artificial intelligence can simplify anesthetic procedures and reduce anesthetic risks, and real-time monitoring tools can improve perioperative safety and efficacy. The aim of this paper is to present and summarize the applications of these related fields in anesthesiology by reviewing them, exploring the potential of advanced technologies in the implementation and development of personalized anesthesia, realizing the future integration of new technologies into clinical practice, and promoting multidisciplinary collaboration between anesthesiology and disciplines such as genomics and artificial intelligence.

Remote Sensing Crop Water Stress Determination Using CNN-ViT Architecture

Efficiently determining crop water stress is vital for optimising irrigation practices and enhancing agricultural productivity. In this realm, the synergy of deep learning with remote sensing technologies offers a significant opportunity. This study introduces an innovative end-to-end deep learning pipeline for within-field crop water determination. This involves the following: (1) creating an annotated dataset for crop water stress using Landsat 8 imagery, (2) deploying a standalone vision transformer model ViT, and (3) the implementation of a proposed CNN-ViT model. This approach allows for a comparative analysis between the two architectures, ViT and CNN-ViT, in accurately determining crop water stress. The results of our study demonstrate the effectiveness of the CNN-ViT framework compared to the standalone vision transformer model. The CNN-ViT approach exhibits superior performance, highlighting its enhanced accuracy and generalisation capabilities. The findings underscore the significance of an integrated deep learning pipeline combined with remote sensing data in the determination of crop water stress, providing a reliable and scalable tool for real-time monitoring and resource management contributing to sustainable agricultural practices.

Heart rate variability as a preictal marker for determining the laterality of seizure onset zone in frontal lobe epilepsy.

Determining the laterality of the seizure onset zone is challenging in frontal lobe epilepsy (FLE) due to the rapid propagation of epileptic discharges to the contralateral hemisphere. There is hemispheric lateralization of autonomic control, and heart rate is modulated by interactions between the sympathetic and parasympathetic nervous systems. Based on this notion, the laterality of seizure foci in FLE might be determined using heart rate variability (HRV) parameters. We explored preictal markers for differentiating the laterality of seizure foci in FLE using HRV parameters. Twelve patients with FLE (6 right FLE and 6 left FLE) were included in the analyzes. A total of 551 (460 left FLE and 91 right FLE) 1-min epoch electrocardiography data were used for HRV analysis. We found that most HRV parameters differed between the left and right FLE groups. Among the machine learning algorithms applied in this study, the light gradient boosting machine was the most accurate, with an AUC value of 0.983 and a classification accuracy of 0.961. Our findings suggest that HRV parameter-based laterality determination models can be convenient and effective tools in clinical settings. Considering that heart rate can be easily measured in real time with a wearable device, our proposed method can be applied to a closed-loop device as a real-time monitoring tool for determining the side of stimulation.

Cross-modal zero-sample diagnosis framework utilizing non-contact sensing data fusion

Gearboxes, fundamental components in the domains of manufacturing, transportation, and aerospace apparatus, are highly susceptible to impairments. The emerging technique of non-contact sensing measurement holds promise as a valuable tool for real-time monitoring and diagnosis of gearboxes, especially in challenging, dynamic environments. However, historical non-contact diagnostic techniques often face difficulties due to load-variation-induced interference, which leads to inconsistent diagnostic results. Moreover, acquiring sufficient non-contact sensing data for specific rare or severe gearbox faults can be exceedingly challenging and, at times, unattainable. This data scarcity limits the application of existing supervised and semi-supervised non-contact diagnostic methods. To address these limitations, this paper develops a novel diagnostic model, called Non-Contact Sensing Data Fusion Driven Cross-Modal Zero-sample Diagnostic Network. Specifically, we first develop a novel central fusion module to facilitate feature-level fusion at both global and local scales utilizing infrared thermal and acoustic data, thereby establishing a rich and comprehensive fault representation. Following that, we construct an end-to-end cross-modal zero-sample diagnostic architecture to mine and fuse complementary fault information. This approach enhances zero-sample diagnostic performance through efficient information utilization and cross-modal feature fusion. Afterward, we adopt a cross-modal self-enhancing fusion strategy during the training phase, which improves the information fusion performance of intra-class features from various modalities. This strategy effectively reduces misclassification risks and augments the robustness of the proposed zero-sample diagnostic network against interference from load variations. Comprehensive experimental results confirm that our proposed NCFZD sets the new state-of-the-art in multiple non-contact, zero-sample diagnostic scenarios, reaching HM scores of 86.06 %, 81.53 %, and 62.56 % respectively. By incorporating the information fusion theory, this model advances gearbox diagnostics in non-contact sensing and contributes to the broader field of information fusion theory by demonstrating its practical application in real-world problem-solving scenarios.

Exhalation metabolomics: A new force in revealing the impact of ozone pollution on respiratory health

Near-surface ozone pollution poses a significant threat to respiratory health, with the lung micro-environment playing a crucial role in its effects. This article highlights the potential of exhalation metabolomics as a novel approach to investigate the respiratory health impacts of ozone pollution. By analyzing exhaled breath metabolites, we can gain insights into the biological responses triggered by ozone exposure. This strategy offers a non-invasive, real-time monitoring tool to assess individual susceptibility and health outcomes. Furthermore, exhalation metabolomics has the potential to inform the development of concentration limits for ozone pollution control, providing a scientific basis for effective policy-making and public health interventions.

Broadband backscattering by Atlantic herring (Clupea harengus L.) differs when measured from a research vessel vs. a silent uncrewed surface vehicle

Abstract Broadband frequency-modulated signals are believed to improve acoustic spectral-based target classification. Efficient use of uncrewed surface vehicles (USV) for fisheries science applications, with no possibility for biological sampling, is believed to be facilitated by use of broadband signals with methods for target classification. If the broadband frequency response used to train automated target classifiers are obtained from conventional research vessels (RVs), due to potential vessel avoidance, the swimming angle distribution may be different than for USVs. This may have consequences for target classification if the model is trained with RV data. The aim of this study was to assess whether the frequency response differs between platforms due to avoidance. Broadband acoustic data were collected with a conventional RV and a small USV. The broadband frequency response of Norwegian spring spawning herring obtained with the USV and RV was found to be significantly different for shallow herring layers in the 200 kHz band. This indicates that broadband frequency response has potential as a tool for real-time monitoring of behaviour reactions to vessels and to provide insight into fish behaviour in general. When using broadband frequency response for target classification, the potential platform-dependent broadband frequency response should be considered.

Real-Time Monitoring of Tyrosine Hydroxylase Activity with a Ratiometric Fluorescent Probe.

Parkinson's disease (PD) represents the second most widespread neurodegenerative disease, and early monitoring and diagnosis are urgent at present. Tyrosine hydroxylase (TH) is a key enzyme for producing dopamine, the levels of which can serve as an indicator for assessing the severity and progression of PD. This renders the specific detection and visualization of TH a strategically vital way to meet the above demands. However, a fluorescent probe for TH monitoring is still missing. Herein, three rationally designed wash-free ratiometric fluorescent probes were proposed. Among them, TH-1 exhibited ideal photophysical properties and specific dual-channel bioimaging of TH activity in SH-SY5Y nerve cells. Moreover, the probe allowed for in vivo imaging of TH activity in zebrafish brain and living striatal slices of mice. Overall, the ratiometric fluorescent probe TH-1 could serve as a potential tool for real-time monitoring of PD in complex biosystems.

Wearable electrochemical sensors for real-time monitoring in diabetes mellitus and associated complications

This comprehensive review underscores the pivotal role wearable electrochemical sensors play in the proactive management and prevention of diabetes mellitus (DM) and its associated complications. Acknowledging the substantial impact of DM on individuals and the urgency for effective monitoring strategies, wearable sensors have emerged as a pragmatic solution. These sensors can detect analytical signals from biofluids, including sweat, tears, saliva, and interstitial fluid (ISF), employing minimally invasive techniques facilitated by technological advancements. The seamless integration of these sensors with computational platforms such as smartphones enhances their practicality for routine use. The review systematically explores diverse methodologies, encompassing both enzymatic and non-enzymatic principles, employed for the surveillance of analytes within biofluids. These foundational principles are meticulously applied to wearable devices, affording point-of-care solutions catering to the detection of individual analytes or simultaneous multiplexed analyte detection. The integration of wireless systems and the incorporation of machine learning algorithms introduce a layer of sophistication, elevating the capability of these sensors for the nuanced monitoring of DM and its complications. Through an in-depth analysis of these advancements, this review describes the significant potential of wearable electrochemical sensors as an essential tool for real-time monitoring and managing DM. The diverse approaches presented underscore the adaptability, versatility, and inherent efficacy of these sensors in addressing the multifaceted challenges intrinsic to DM and its associated complications within academic discourse.

A Dual-Signal Sensing for the Visual and Luminescent Detection of p-Phenylenediamine Based on Cerium-Nitrogen-Co-Doped Carbon Dots.

Herein, a visual and luminescent dual-mode (colorimetric and fluorometric) method for the detection of P-phenylenediamine (PPD) in hair dye was successfully established based on cerium-nitrogen co-doped carbon dots (Ce, N-CDs) that displayed remarkable luminescence and peroxidase activity. Ce, N-CDs catalyzed H2O2 to produce superoxide anion, which then oxidized the colorless 3,3,5,5-tetramethylbenzidine (TMB) into blue oxidized TMB (oxTMB), capable of quenching the fluorescence through fluorescence resonance energy transfer (FRET) between Ce, N-CDs and oxTMB. The reducing properties of PPD could reduce oxTMB back to TMB, leading to a decrease in the absorption intensity of oxTMB and a fluorescence recovery of Ce, N-CDs. As a result, the quantitative detection of PPD could be achieved by measuring the absorption values of oxTMB and the fluorescence signal of Ce, N-CDs. The detection limits for PPD were calculated as 0.36 µM and 0.10 µM for colorimetry and fluorimetry, respectively. Furthermore, smartphone application (ColorPicker) capable of measuring the RGB value of the color was utilized in the detection system, facilitating on-site quantitative detection. This approach effectively shortens the detection time and simplifies the operation, offering a powerful and convenient tool for real-time monitoring of PPD.

ServiceNet: resource-efficient architecture for topology discovery in large-scale multi-tenant clouds

AbstractModern computing infrastructures are evolving due to virtualisation, especially with the advent of 5G and future technologies. While this transition offers numerous benefits, it also presents challenges. Consequently, understanding these complex systems, including networks, services, and their interconnections, is crucial. This paper introduces ServiceNet, a groundbreaking architecture that accurately performs the important task of providing understanding of a multi-tenant architecture by discovering the complete topology, crucial in the realm of high-performance distributed computing. Experimental results have been carried out in different scenarios in order to validate our approach, demonstrating the effectiveness of our approach in comprehensive multi-tenant topology discovery. The experiments, involving up to forty tenant, highlight the adaptability of ServiceNet as a valuable tool for real-time monitoring in topology discovery purposes, even in challenging scenarios.