Real-time Domain Research Articles

Real-Time Monitoring of Seawater Quality Parameters in Ayia Napa, Cyprus

Real-time monitoring systems are crucial for the comprehensive management of operations and processes, as well as for assessing the impacts of coastal infrastructures on the marine environment. These systems not only support environmental protection and data-driven decision-making but also enable the early detection of adverse events and the issuance of timely warnings for prompt responses. Although water quality is a critical parameter in this monitoring framework, there are currently limited permanent systems in place dedicated to maintaining these objectives. Even fewer systems leverage their data for research purposes, leading to a gap in the literature regarding effective processing approaches for real-time water quality data. In this context, this study presents a real-time water quality monitoring system integrated into a broader in-field laboratory installed at a coastal area off the coast of Ayia Napa, Cyprus, as well as an initial measured data set of different qualitative quantities. It proposes a holistic approach for post-processing real-time seawater quality data, employing both time and frequency domain analyses, alongside filtering techniques. The study discusses the advantages of each method and emphasizes the importance of their combined use. Utilizing data collected from a three-month operational period, the study assesses the current state of marine seawater quality and examines both temporal and cyclic variations in various seawater quality parameters. The findings reveal that the examined seawater parameters are within reasonable values, indicating that the construction and operation of a nearby marina and the necessary infrastructures (e.g., breakwater) did not affect the seawater quality in the area. Additionally, the study identifies pronounced daily cyclic responses in different seawater quality parameters, including temperature, density, pH, dissolved oxygen, and turbidity. Finally, notable correlations are observed between temperature and dissolved oxygen, temperature and conductivity, oxidation–reduction potential (ORP) and salinity, ORP and dissolved oxygen, and ORP and TDS.

Attosecond electron microscopy and diffraction.

Advances in attosecond spectroscopy have enabled tracing and controlling the electron motion dynamics in matter, although they have yielded insufficient information about the electron dynamic in the space domain. Hence, ultrafast electron and x-ray imaging tools have been developed to image the ultrafast dynamics of matter in real time and space. The cutting-edge temporal resolution of these imaging tools is on the order of a few tens to a hundred femtoseconds, limiting imaging to the atomic dynamics and leaving electron motion imaging out of reach. Here, we obtained the attosecond temporal resolution in the transmission electron microscope, which we coined "attomicroscopy." We demonstrated this resolution by the attosecond diffraction measurements of the field-driven electron dynamics in graphene. This attosecond imaging tool would provide more insights into electron motion and directly connect it to the structural dynamics of matter in real-time and space domains, opening the door for long-anticipated real-life attosecond science applications in quantum physics, chemistry, and biology.

An Accurate Calculation Method on Blasingame Production Decline Model of Horizontal Well with Dumbbell-like Hydraulic Fracture in Tight Gas Reservoirs

Blasingame production decline is an effective method to obtain permeability and single-well controlled reserves. The accurate Blasingame production decline curve needs an accurate wellbore pressure approximate solution of the real-time domain. Therefore, the aim of this study is to present a simple and accurate wellbore pressure approximate solution and Blasingame production decline curves calculation method of a multi-stage fractured horizontal well (MFHW) with complex fractures. A semi-analytical model of MFHWs in circle-closed reservoirs is presented. The wellbore pressure and dimensionless pseudo-steady productivity index JDpss (1/bDpss) are verified with a numerical solution. The comparison result reaches a good match. Wellbore pressure and Blasingame production decline curves are used to analyze parameter sensitivity. Results show that when the crossflow from matrix to natural fracture appears after the pseudo-state flow regime, the value of the inter-porosity coefficient has an obvious influence on the pressure approximate solution of the pseudo-steady flow regime in naturally fractured gas reservoirs. The effects of relevant parameters on wellbore pressure and the Blasingame decline curve are also analyzed. The method of pseudo-steady productivity index JDpss can applied to all well and reservoir models.

Attenuating majority attack class bias using hybrid deep learning based IDS framework

In real-time application domains, like finance, healthcare and defence, delay in service or stealing information may lead to unrecoverable consequences. So, early detection of intrusion is important to prevent security breaches. In recent days, anomaly-based intrusion detection using Hybrid Deep Learning approaches are becoming more popular. The most used benchmark datasets in the literature are NSL-KDD and UNSW-NB15, and these datasets are imbalanced. The models built on imbalanced datasets may lead to biased results towards majority classes by neglecting the minority class, even though they are equally important. In many cases, high accuracy is achieved for majority classes in the imbalanced datasets. But, the class-level performances are poor with respect to the minority class. The class balancing will also play an important role in attenuating the bias in prediction for imbalanced datasets. In this paper, a Hybrid Deep Learning Based Intrusion Detection (HDLBID) framework is proposed with CNN-BiLSTM combination. The four techniques, namely, Random Oversampling (ROS), ADASYN, SMOTE, and SMOTE-Tomek, are used for class balancing in the proposed HDLBID framework. The proposed HDLBID with SMOTE-Tomek achieves an overall accuracy of 99.6% with NSL-KDD and 89.02% for UNSW-NB15. It results in an improvement of 13.67% for NSL-KDD and 10.62% for UNSW-NB15 over the existing recent related models. In the proposed HDLBID, in addition to overall accuracy, the class-level F1 score is also calculated. A comparative study is presented to show the effectiveness of balancing dataset compared to imbalanced dataset, and observed that the SMOTE-Tomek class balancing comparatively performed well. An improvement of 37.43% is observed in the U2R class of the NSL-KDD dataset and 61.65% improvement is seen in the Worms class of the UNSW-NB15 dataset, both with SMOTE-Tomek class balancing. Therefore, the proposed HDLBID with SMOTE-Tomek class balancing reports the best results in terms of overall accuracy compared to existing recent related approaches. Also, in terms of class-level analysis, HDLBID reports best results with SMOTE-Tomek over imbalanced version of datasets.

SADGFeat: Learning local features with layer spatial attention and domain generalization

Local feature plays a pivotal role in various robotic tasks, including 3D reconstruction and visual localization. Although deep learning-based local features have proven superior to their traditional counterparts, they still face challenges in practical applications due to matching failures. These challenges primarily stem from inaccuracies in keypoint localization and the limited robustness of descriptors particularly with significant appearance changes. In this study, we introduce a novel method for local feature learning based on layer spatial attention and domain generalization. Firstly, a keypoint extraction strategy driven by layer spatial attention from high-level feature is proposed to enhance the accuracy of keypoint localization, progressing from coarse to fine granularity. Secondly, a new learning paradigm based on domain generalization is developed to extract local features with resilience against variations in illumination. To enrich the domain diversity within the training dataset, a real-time and lossless Fourier transform-based domain augmentation method is introduced. This method seamlessly integrates into the training process, enhancing the model's adaptability to varying domains. Additionally, explicit feature alignment-based representation learning is performed, further reinforcing the extraction of domain-invariant local features. Experimental results on public datasets demonstrate that the proposed method achieves state-of-the-art performance across various downstream tasks reliant on local feature matching, such as image matching, 3D reconstruction, and long-term visual localization.

Experimental investigation of the oscillation threshold to limit state operation of a single branch pulsating heat pipe

The study investigates the characteristics of oscillations and associated thermo-hydrodynamic phenomena for startup, limit state, and intermediate transitional behavior of a pulsating heat pipe (PHP) with the help of its single branch. Experiments are performed on a transparent single branch PHP (SBPHP) at different orientations, evaporator and condenser section temperatures using n-pentane as the working fluid. The entire two-phase flow behavior is recorded with the help of shadowgraph based high speed imaging synchronized in real-time domain with pressure data acquisition at evaporator end. Instability threshold is determined in terms of evaporator temperature at different tube orientations and condenser temperatures. The oscillation startup mechanism along with the associated intermittency is studied in detail. The SBPHP is found to have characteristic variations in oscillation amplitude and frequency as it undergoes a gradual transition from its oscillation threshold to limit state. The mechanism by which the limit state creeps-in and intumesces further is an intrinsic function of liquid-vapor distribution in SBPHP, which in turn depends on the applied boundary conditions. The operational limit is governed by evaporator dryout exceeding permissible limits marked by the steep rise in the dry length of evaporator. As SBPHP approaches or operates at its limit state, evaporator is found to have different regimes across its length, viz., mild convective evaporation combined with subcooled nucleate boiling, saturated nucleate boiling, forced convective boiling, film breakdown and permanent dryout. The liquid film distribution, pressure fluctuations, and heat transfer components in evaporator at the limit state have also been studied.

Building Depth Maps Using an Active-Pulse Television Measuring System in Real Time Domain

The paper presents the results of software development for building depth maps based on video data from a television camera of an active-pulse television measuring system (AP TMS) in real time domain. The development of such software is required to conduct various scientific studies, as well as to improve the methods and techniques for building depth maps and remote measurement of the characteristics of objects of interest. The software was implemented using the Python programming language with additional libraries installed. According to the results of testing the implemented algorithm, it was found that the calculation speed using the graphics processing unit (GPU) is on average 3.5 times higher than the speed of the algorithm using only the central processing unit (CPU). It has been established that with the help of CUDA cores it is possible to build depth maps in real time domain at the maximum possible resolution of video frames of the system (1544x2064 pixels), while when using the central processor, real-time operation is possible only at a reduced resolution of video frames (772x1032 pixels).

Effects of Soliton Creation on Transient Transport through a Polymer Chain.

By using a nonadiabatic molecular dynamics method combined with the hierarchical equations of motion, we have investigated the nonequilibrium transient transport through a conjugated polymer chain. The polymer chain is described by the Su-Schrieffer-Heeger model, and its two ends are coupled with metal electrodes of different chemical potentials. In order to present the evolutions of the electronic injection and transport in the real-time domain, we have mainly discussed the dynamic relaxation processes of the excited states and transient transport currents. It is found that due to the existence of electron-phonon couplings in the conjugated polymers, creation of solitons not only affects the time of the system achieving the steady state but also leads to periodical oscillations of the steady-state transport currents with time in our simulations. Furthermore, with increasing applied bias voltage, the steady-state transport electronic current increases, which proved that the creation of the solitons can assist the electronic transport. These results have shown that the creation of the excited states is important in understanding the transport properties in organic nanostructures.

Power Source Converter Based on a Variable-Domain Fuzzy PI Control

The inadequacy of conventional control strategies for multi-phase interleaved parallel circuits in terms of adaptive adjustment makes it difficult to meet the power source design requirements for transient electromagnetic detection systems. This paper introduces a novel approach, the variable-domain fuzzy proportional–integral (PI) adaptive control strategy. This strategy dynamically adjusts the fuzzy domain in real time based on the input error magnitude, ensuring improved control effectiveness. By leveraging the benefits of both the functional scaling factor and the fuzzy reasoning scaling factor, we design scaling factors for the input and output domains to enhance control precision. The focus of this study is on a four-phase interleaved parallel converter, emphasizing the design of the variable-domain fuzzy PI control strategy for the voltage outer loop within the traditional dual closed-loop structure. An experimental prototype with a 220 Vac input, 380 V output, and a power rating of 1000 W is constructed. A comparative analysis between fuzzy control and variable-domain fuzzy PI control is conducted in the voltage outer loop of the dual closed-loop control. Results reveal that dual closed-loop control with variable-domain fuzzy PI control for the voltage outer loop significantly enhances system stability. The startup time to reach the steady state is reduced to 0.632 s, with an overshoot of 28.8 V. Transitioning from 25% load to full load takes only 0.096 s, resulting in a minimal drop of 21.4 V and an overshoot of 13.4 V. Similarly, switching from full load to 25% load in 0.167 s exhibits an overshoot of only 19.6 V. The adaptive regulation capability of the converter is markedly improved, showcasing smaller overshoots and higher-level controlling effectiveness.

Well Production Decline Models Considering Mechanical Skin Factor

Production decline analysis is an essential tool in predicting reservoir performance and in estimating reservoir properties. In its most basic form, decline curve analysis is to a large extent based on Arps’ empirical models that have little theoretical basis. But the mechanical skin factor due to formation damage is not taken into account in the production decline models available in the literature. The conventional models presented in the literature are mostly empirical and not grounded in fundamental in theory. New theoretical models are developed in this paper to forecast the production decline of a well producing in a closed circular or rectangular reservoir at constant bottomhole pressure, and mechanical skin factor due to formation damage is taken into account. Both the transient flow behavior and boundary‐dominated flow behavior are incorporated; the effects of reservoir size, well location, and mechanical skin factor on transient flow rate and pressure drop in the reservoir are studied. Analytical solutions are obtained through combinations of Green’s function and inverse Laplace transform. Stehfest’s algorithm is used to convert the obtained solution from the Laplace transform domain into the real time domain. The proposed models in this paper are based on analytical solutions to diffusivity equations in porous media, and the solutions are fast and practical tools to forecast the production performance of a well producing at constant flowing bottomhole pressure.

Analysis of Block Chain Applications in Asset Backed Securitization System

Blockchain is an emerging technology penetrating various real-time and sensitive domains including financial products. Block chain can deal with complex system of financial transactions with focus to provide security and integrity to both the parties with reliable flow of transactions and asset securitization. Block chain is a distributed technology providing a lot of benefits such as cryptic transactions, smart contracts, distributed ledger system along with decentralization, authentication, authorization, and immutability to enhance the efficiency of the financial system. In this paper, the existing solutions for asset-backed securitization in blockchain are analysed and reviewed to highlight the requirements for change in design and ideas for enterprise applicability of blockchain for asset securitization. Also, the benefits of blockchain to reduce the risk.

Progress in polymerase chain reaction technology and its contribution to the tuberculosis diagnostic ecosystem in India: A mini review

The advent of Polymerase Chain Reaction (PCR) has revolutionized the human molecular diagnostic scenario. Its ability to exponentially generate desired stretch of DNA from chromosomal or complimentary DNA has laid the foundation for sensitive and specific detection of a pathogen or an aberrant stretch of DNA. Importantly, PCR technology has helped generate diagnostic immunity from phenotypic variation of pathogenic targets. The technological evolution of conventional end-point PCR to its real time version further added clinically important features such as enhanced sensitivity and quantification of target DNA molecules. Two important chemistries became prominent in the real-time PCR domain. The intercalating dye-based chemistry comes with convenience, economy and feature such as melt curve analysis but also carries the disadvantage of overestimation of signal due to unwanted signal from primer dimers. Dual labelled probe on the other hand provides unprecedented sensitivity and specificity. Isothermal method of genome nucleic acid amplification has also evolved as a parallel and convenient method of amplification-based detection of nucleic acid targets. Tuberculosis diagnostics has immensely benefitted from these technologies. However, unmet need exists in sputum handling methods and in its adaptation for molecular diagnostics in resource limited settings. Significant dependence for diagnostic needs on the western world is a matter of concern in India. The country will benefit from rapid and indigenous research and development of molecular diagnostic solutions by way of formulating kits for rapid extraction of DNA and RNA from various clinical sources and developing detection technologies that can compete international products in its category. A solution to address sputum transportation, storage and DNA extraction that has enhanced advantages compared to current methods will also be helpful in the efforts to make India free from tuberculosis.

A novel method for real-time object-based copy-move tampering localization in videos using fine-tuned YOLO V8

The research community faces challenges for video forgery detection techniques as advancements in multimedia technology have made it easy to alter the original video content and share it on electronic and social media with false propaganda. The copy-move attack is the most commonly practiced type of attack in videos/images, where an object is copied and moved into the current frame or any other frame of the video. Hence an illusion of recreation can be created to forge the content. It is very difficult to differentiate to uncover the forgery traces by the naked eye. Hence, a passive method-based algorithm is proposed to scientifically investigate the statistical properties of the video by normalizing the median difference of the frames at the pixel level, and graphical analysis successfully shows the clear peak in the forged region. After that, a new deep learning approach, “You Only Look at Once”, the latest eighth version of YOLO, is tuned and trained for the localization of forged objects in the real-time domain. The validation and testing results obtained from the trained YOLO V8 are successfully able to detect and localize the forged objects in the videos with mean average precision (mAP) of 0.99, recall is 0.99, precision is 0.99, and highest confidence score. The proposed YOLO V8 is fine-tuned in three different ways, and the performance of the proposed method outperforms existing state-of-the-art techniques in terms of inference speed, accuracy, precision, recall, testing, and training time.

On a New Method of Quasi-static and Dynamic Analysis of Viscoelastic Plate on Elastic Foundation

In the present work, an alternative solution technique based on mixed finite element (MFE) formulation in the Laplace-Carson domain is proposed for quasi-static and dynamic analyses of viscoelastic plate (VEP) resting on an elastic foundation (EF). This work contributed a numerical solution to the problem of a viscoelastic Kirchhoff plate supported on a Winkler foundation. VEP-EF interaction problems are taken into account under different wave-type loadings. The viscoelastic material behavior of the plate is modeled by the Zener rheological solid model. A four-nodded linear isoparametric element containing sixteen degrees of freedom is used to model the VEP. Developed functional in the Laplace-Carson domain based on the Gâteaux differential method is transformed to the real time domain by utilizing the Dubner and Abate (D&A) inverse Laplace transform technique (ILTT). To evaluate the applicability of the results, five numerical samples are considered. Further analyzes are performed on different wave type loadings to offer a new perspective on the time-dependent behavior of VEP on EF.

Molecular characterization of the Plasmodium falciparum k13 gene helix domain in samples from native communities of Condorcanqui, Amazonas, Perú

Introduction. Resistance of Plasmodium falciparum to different antimalarial drugs is an obstacle to disease elimination. The artemisinin-resistant genotype of P. falciparum can be assessed by examining polymorphisms in the helix domain of the Pfk13 gene. The World Health Organization recommends these mutations as molecular markers to detect artemisinin-resistant in countries where P. falciparum malaria is endemic. Objective. To identify artemisinin resistance-related mutations present in the helix domain of the P. falciparum k13 gene. Materials and methods. We collected a total of 51 samples through passive case detection, positive for Plasmodium by microscopy, from six communities in the district of Río Santiago in Condorcanqui, Amazonas. Molecular species confirmation was performed by real-time PCR and Pfk13 helix domain was amplified and sequenced by capillary electrophoresis. The obtained sequences were compared with the wild type 3D7 reference strain. Results. A total of 51 positive samples were confirmed for P. falciparum from the communities of Ayambis, Chapiza, Palometa, Muchinguis, Alianza Progreso and Caterpiza. DNA sequences alignment showed the absence of resistance-associated mutations in the k13 gene of the collected samples. Discussion. The obtained results are consistent with similar studies conducted in other South American countries, including Perú, so these data provide a baseline for artemisinin-resistance molecular surveillance in the Amazon region and reinforce the efficacy of artemisinin-based combination therapy in this area.

Real-time online unsupervised domain adaptation for real-world person re-identification

Real-time online unsupervised domain adaptation for real-world person re-identification

Robust Scheduling of Thermostatically Controlled Loads With Statistically Feasible Guarantees

Flexible resources are increasingly significant for the reliable operation of power grids due to the high penetration of renewable energy. Thermostatically controlled loads (TCLs) are one of the common flexible resources, whose control has been extensively studied. Yet, much can be improved. We investigate the scheduling of TCLs facing uncertain temperatures and dynamic prices. Classical approaches often employ the chance-constrained program or robust optimization to handle such uncertainties. However, these approaches either require specific distribution knowledge or yield too conservative solutions. The distribution knowledge can be rather challenging to obtain especially when the uncertainties from different sources are correlated. To this end, we adopt the notion of statistical feasibility and propose a robust sample-based scheduling scheme for TCLs. Such a sample-based scheme relieves the reliance on the distribution knowledge and is able to characterize the coupling effects of the two uncertainty sources. Besides, through integrating the real-time domain knowledge into uncertainty set reconstruction, we relax the solutions’ conservation by exploring the consumers’ tolerance to the room temperature. Numerical studies highlight the remarkable performance of our proposed scheme. Specifically, our approach is able to simultaneously effectively reduce the electricity bills of consumers and satisfy the consumers’ tolerance to the room temperature.

An Analytical Model Coupled with Orthogonal Experimental Design Is Used to Analyze the Main Controlling Factors of Multi-Layer Commingled Gas Reservoirs

The majority of China’s multi-layer low permeability tight gas reservoirs are currently being extracted through the method of multi-layer co-production. However, due to the significant disparity in physical properties and varying degrees of pressure depletion among the production layers, elucidating the primary factors influencing the productivity contribution of each gas layer remains challenging. A multi-factor analytical model is proposed for commingled gas wells with multiple layers. An unstable model is established for the production of commingled layers, and the problem of flow distribution is addressed using the Duhamel convolution principle. The Laplace transform is subsequently employed to derive the solution in the Laplace domain, which can be inverted utilizing the Stehfest inversion algorithm to obtain a real-time domain solution. The influence of reservoir factors on the stratification contribution rate has been comprehensively analyzed, encompassing permeability, porosity, initial pressure, drainage radius, and layer thickness. The orthogonal test design was employed to conduct range analysis and variance analysis separately, yielding the primary and secondary order as well as influence weight of the five factors. The findings demonstrate that, within this gas reservoir, the discharge radius, thickness, and porosity are identified as the primary factors influencing gas well productivity. Furthermore, seven horizontal flow charts illustrating the double-layer gas reservoir and five horizontal flow charts depicting single-factor variations in the double-layer gas reservoir were constructed. These charts provide a clear visualization of the impact of each reservoir factor on stratification’s contribution rate. In contrast to previous studies, this novel approach presents a comprehensive optimization framework that ranks the influence weights of individual factors and identifies the most significant factors impacting multi-layer gas reservoirs. The presented method also serves as a foundation for the subsequent selection of multi-layer gas reservoirs, formulation of gas well stimulation measures, and efficient development.

An integrating computing framework based on edge-fog-cloud for internet of healthcare things applications

History has demonstrated that healthcare and medical systems play a crucial role in enforcing the development of science and technology. Humans have been seeing an explosive growth of e-health applications, where cloud computing has dominated e-healthcare systems and all domains in the past decades. However, one primary barrier to cloud-based e-health systems is their high response time. In emergencies, data aggregation and handling and decision-making regarding treatment need to be performed in seconds and directly affect the life of patients. These issues relate to resource constraints, such as medical equipment, ambulances, medical staff, medical facilities, and other conditions. To address these problems, computing architectures have been introduced, such as edge, fog, and cloud computing methods. In this work, we propose an all-in-one computing framework based on combining different computing solutions in real-time smart healthcare domains. Then, considering the efficiency of the proposed solution by the queue network model tool, we give recommendations for the optimal network infrastructure deployment depending on the scale-patients of hospitals. We believe that the results of this work will play an important role in the network infrastructure optimization of medical facilities while ensuring the provision of real-time healthcare services.

Dynamic monitoring of saturation profiles during two phase immiscible displacements in confined sand packs using time-domain analysis of the frequency domain electromagnetic sweeps

Water saturation 1D spatial profiles were monitored using real-time domain analysis of the measured frequency domain complex reflection coefficients of electromagnetic sweeps during oil and deionized water flood experiments at ambient conditions. Gravity and viscous induced fingering and piston-like displacement scenarios were explored. A patented coaxial transmission line core holder was designed to host saturated sand packs compressed in their axial and radial dimensions. An inverse chirp z-transform algorithm was used to convert the measured responses from frequency to time domain. An optimization routine was developed to convert the resulting time domain step responses to water saturation profiles at every pore volume fraction of fluids injected. Advancing and trailing fronts and their breakthrough times were detected. Darcy's flow analysis was performed to extract 1D pressure distribution spatial profiles and dynamic capillary pressure curves for 3 drainage floods. The oil ganglia flow and gravity fluid separation were detected in water floods.