Real-time Monitoring Research Articles

Real-time detection of coke particles in blast furnace operations using machine learning: Case of a steel plant in India

The efficiency of blast furnace operations in a steel plant relies on the quality of raw materials, including coke. The particle size distribution (PSD) of coke plays a vital role in ensuring the efficiency of the blast furnace. Conventional methods of measuring average particle size are time-consuming and labour-intensive leading to delayed operations. To address the issue, various studies have used different techniques for real-time measurement of coke particle size, including laser diffraction, acoustic sounding, X-ray computed tomography, and computer vision. While these methods have shown successful applications, there have been limitations in their lower prediction accuracy and detection speed. Therefore, our study proposes a practical approach that leverages the integration of computer vision algorithms to enhance accuracy and detection speed. The proposed methodology is compared with the conventional method of measuring particle size in the lab at a blast furnace. Our results reveal that the YOLOv8 algorithm outperforms the conventional method, providing efficient detection of 5.419 frames per second and with a mean absolute error of [Formula: see text]1.77 mm within the lab results. YOLOv8 can perform object segmentation providing much more precise polygon masks of the coke particles instead of simply providing rectangular dimensions using traditional object detection, as explored in previous studies. This approach enables real-time monitoring of PSD and timely alerts to the onsite team, significantly improving the efficiency of blast furnace operations.

The FLASHFIT Interactive Device in Healthcare: Revolutionizing Patient Engagement and Monitoring

The study investigates the effectiveness of the FLASHFIT interactive device in enhancing patient engagement and monitoring within healthcare settings. As digital health solutions become increasingly vital, FLASHFIT aims to bridge the gap between patients and healthcare providers by promoting active participation in health management. This research employs a mixed-methods approach, combining quantitative data from patient usage metrics with qualitative feedback from users and healthcare professionals. Preliminary findings indicate that FLASHFIT significantly improves patient adherence to treatment plans and fosters a sense of autonomy in health management. Moreover, real-time monitoring capabilities facilitate timely interventions, enhancing overall patient outcomes. The study concludes that integrating interactive devices like FLASHFIT into healthcare practices empowers patients and streamlines communication between patients and providers, suggesting a transformative potential for future healthcare delivery models. Keywords: FLASHFIT interactive device, patient engagement and monitoring

Implementation of Solar PV Protection System in Indonesia: A Review

Conventional energy sources will run out if used continuously and damage the environment. Therefore, it is necessary to develop other energy sources that are safe, environmentally friendly, and inexhaustible known as renewable energy sources to meet energy needs in Indonesia. This article presents a review that discusses the protection of solar panels and PLTS. Solar panels are vulnerable to lightning strikes and other electrical disturbances, so an effective protection system is needed. This study uses a qualitative method through a literature study, reviewing various protection methods such as conventional and electrostatic lightning rods, good grounding systems, and overcurrent detection devices such as Solar Charge Controller (SCC) and Maximum Power Point Tracking (MPPT). In addition, battery protection with Automatic Transfer Switch (ATS) and Low Voltage Disconnected (LVD) is also discussed. Using Arduino Uno, ESP 32, and PLC, automation approaches offer real-time monitoring and control solutions for solar power plant performance. Innovations in the lightning protection zone concept and minimal quantity measurement schemes enable more cost-effective design of protection systems without reducing the level of protection. The results show that these various protection methods can provide effective and efficient protection for solar power plants, enabling optimization of system function and safety. In conclusion, the protection systems reviewed in this study offer a sustainable solution to Indonesia's energy challenges by utilizing the maximum potential of solar energy. The potential for further research is presented in this article to develop more efficient and effective protection methods.

Tumor microenvironment-based smart beacon drug delivery system for in situ visualization of tumor therapy

Real-time monitoring of the effectiveness of tumor-targeted drugs is crucial for assessing their impact. To address this need, we developed a beacon drug delivery system, MDB, which incorporates tumor targeting, anti-tumor effects, and near-infrared fluorescence emission for on-site visualization of tumor therapy. In non-cancerous cells, MDB remains inactive as a prodrug but is selectively activated within tumor cells when the borate ester group reacts with H2O2 in the tumor microenvironment (TME), releasing the beacon drug MDBRe. MDBRe can down-regulate the expression of the protein heme oxygenase 1 (HO-1), ultimately disrupting the mitochondrial energy supply system and leading to tumor cell death. It also emits near-infrared fluorescence for drug tracking under structured illumination microscopy (SIM). Both in vitro and in vivo experiments confirm the effective delivery of MDBRe by MDB to H2O2-enriched tumor tissues, resulting in a potent anti-tumor effect, visualization of drug distribution in tumors, and monitoring of therapy progress. In conclusion, our approach presents a novel method for targeted drug delivery to tumors and enables real-time visual monitoring of the treatment process.

A polydiacetylene (PDA)-based colorimetric sensor for on-site cyanide anion monitoring integrated with a lateral flow assay platform

An advanced sensing platform for the on-site and real-time monitoring of cyanide (CN−) pollution was developed, addressing the key challenges of enhancing the selectivity and sensitivity, whilst simplifying the sensing process. This platform features a novel colorimetric sensor (PDA-BMN) created by incorporating a receptor for CN− recognition within a polydiacetylene (PDA) conjugated polymer system. The interaction of CN− with the receptor distorted the π-conjugated backbone, causing a distinct color change from blue to orange, with a limit of detection (LOD) of 0.55 µM, thereby highlighting its high sensitivity. The PDA-BMN system was integrated into a lateral flow assay (LFA) strip to facilitate on-site monitoring. The LFA strip was strategically designed with a partially pressed zone to reduce the fluid flow rate, enhance the reaction time, and increase the probability of interaction with the PDA-BMN sensor. This innovation resulted in a significant colorimetric transition, enabling the rapid and sensitive detection of CN− without the requirement for analytical instruments. This study demonstrates the effectiveness of combining PDA and LFA systems for real-time environmental monitoring, offering a robust, efficient, and user-friendly solution for detecting CN− pollution.

Enhanced visualization of quench dynamics of HTS coils through embedded and distributed fiber optical sensing

The development of high-temperature superconducting (HTS) coils and magnets marks a significant advancement across various fields. To ensure their safety and reliability, thorough performance testing under extreme multi-field conditions is essential. This study introduces an innovative method that utilizes embedded and distributed fiber optical sensing (ED-FOS) in HTS pancake coils for precise real-time thermal monitoring and quench visualization. Incorporated during the manufacturing process, the ED-FOS sensors offer comprehensive and distributed measurements of thermal behavior during quenches. Our experiments demonstrate the ED-FOS effectively detects quenches caused by overcurrent and thermal issues. Unlike conventional techniques that rely on global voltage or magnetic field assessments, ED-FOS provides high spatiotemporal resolution and enhances temperature sensitivity. This capability allows for accurate, real-time identification and tracking of quench locations, which is essential for evaluating reliability and safety, as well as for improving performance. This advancement in HTS coil technology paves the way for wider application in critical fields and significantly enhances the design and management of superconducting coils.

Structures of a DNA Polymerase Caught while Incorporating Responsive Dual-Functional Nucleotide Probes.

Functionalizing nucleic acids using DNA polymerases is essential in biophysical and biotechnology applications. This study focuses on understanding how DNA polymerases recognize and incorporate nucleotides with diverse chemical modifications, aiming to develop advanced nucleotide probes. We present the crystal structures of ternary complexes of Thermus aquaticus DNA polymerase (KlenTaq) with C5-heterocycle-modified environment-sensitive 2'-deoxyuridine-5'-triphosphate (dUTP) probes. These nucleotides include SedUTP, BFdUTP and FBFdUTP, which bear selenophene, benzofuran and fluorobenzofuran, respectively, at the C5 position of uracil, and exhibit high conformational sensitivity. SedUTP and FBFdUTP serve as dual-app probes, combining a fluorophore with X-ray anomalous scattering Se or 19F NMR labels. Our study reveals that the size of the heterocycle influences how DNA polymerase families A and B incorporate these modified nucleotides during single nucleotide incorporation and primer extension reactions. Remarkably, FBFdUTP's responsiveness enabled real-time monitoring of the binary complex formation and polymerase activity through fluorescence and 19F NMR. Comparative analysis of incorporation profiles, fluorescence, 19F NMR data, and crystal structures of ternary complexes highlights the enzyme's plasticity. Key insights are provided into the role of gatekeeper amino acids (Arg660 and Arg587) in accommodating and processing these modified substrates, offering a structural basis for next-generation nucleotide probe development.

Donor–π bridge-acceptor dyes featuring dual chromophoric groups for enhanced sensitivity in wearable sweat sensor

The pH value of human sweat serves as a crucial biomarker for disease diagnosis, rendering wearable sweat sensors an innovative tool for monitoring human health. However, the commercialization of these devices is seriously impeded by their low sensitivity, poor wearing comfort, and limited reusability. In this work, a reactive dye capable of color change under pH change has been synthesized. Due to the unique molecular structure, the dye molecule exhibits remarkable color change recognition and high sensitivity. To fabricate a wearable pH sensor, screen printing technology was employed for depositing the color-changing dye onto cotton fabric. The resulting sensor demonstrated excellent performance attributes including high color sensitivity, robust cyclic stability in response to pH changes, and exceptional resistance against washing, friction, and sunlight-induced fading. Therefore, this study presents a highly promising approach for achieving real-time monitoring of human sweat pH levels.

Background-Free Imaging of Food Freshness Using Curcumin-Functionalized Upconversion Reversible Hydrogel Patch.

Functionalized upconversion nanomaterials can overcome the drawbacks faced of strong background interference, photodamage, and spectral overlap by conventional optical labeling. Here, curcumin-functionalized upconversion hydrogel patch is designed with background-free and reversible for food freshness monitoring by ultra-sensitive response to biogenic amines. By loading the probes onto hydrogel patch, utilizing the good ductility to solve the problem of non-smooth surface coverage, thus accurately capturing biogenic amines. The presence of biogenic amines leads to the conversion of the diketone group on the probe to enolate ions, which triggers fluorescence resonance energy transfer (FRET) and ultimately causes the upconverted fluorescence to gradually change from green to red. The probe exhibits good detection capability for biogenic amines with a low limit of detection (LOD) of 2.73 µm. Interestingly, the patch can be restored to its initial state after water rinsing, realizing reversible detection of biogenic amines. Additionally, combining the color recognition system of smartphone can convert the imaging signal into a data signal to achieve quantitative analysis and show a reliable assessment comparable to the results of high performance liquid chromatography (HPLC). This study demonstrates the practical applicability in real-time monitoring of freshness, suggests great potential in developing optical nano-sensing strategy to ensure food safety.

Enhancing cotton whitefly (Bemisia tabaci) detection and counting with a cost-effective deep learning approach on the Raspberry Pi.

The cotton whitefly (Bemisia tabaci) is a major global pest, causing significant crop damage through viral infestation and feeding. Traditional B. tabaci recognition relies on human eyes, which requires a large amount of work and high labor costs. The pests overlapping generations, high reproductive capacity, small size, and migratory behavior present challenges for the real-time monitoring and early warning systems. This study aims to develop an efficient, high-throughput automated system for detection of the cotton whiteflies. In this work, a novel tool for cotton whitefly fast identification and quantification was developed based on deep learning-based model. This approach enhances the effectiveness of B. tabaci control by facilitating earlier detection of its establishment in cotton, thereby allowing for a quicker implementation of management strategies. We compiled a dataset of 1200 annotated images of whiteflies on cotton leaves, augmented using techniques like flipping and rotation. We modified the YOLO v8s model by replacing the C2f module with the Swin-Transformer and introducing a P2 structure in the Head, achieving a precision of 0.87, mAP50 of 0.92, and F1 score of 0.88 through ablation studies. Additionally, we employed SAHI for image preprocessing and integrated the whitefly detection algorithm on a Raspberry Pi, and developed a GUI-based visual interface. Our preliminary analysis revealed a higher density of whiteflies on cotton leaves in the afternoon and the middle-top, middle, and middle-down plant sections. Utilizing the enhanced YOLO v8s deep learning model, we have achieved precise detection and counting of whiteflies, enabling its application on hardware devices like the Raspberry Pi. This approach is highly suitable for research requiring accurate quantification of cotton whiteflies, including phenotypic analyses. Future work will focus on deploying such equipment in large fields to manage whitefly infestations.

An Adaptive Parameter Optimization Deep Learning Model for Energetic Liquid Vision Recognition Based on Feedback Mechanism.

The precise detection of liquid flow and viscosity is a crucial challenge in industrial processes and environmental monitoring due to the variety of liquid samples and the complex reflective properties of energetic liquids. Traditional methods often struggle to maintain accuracy under such conditions. This study addresses the complexity arising from sample diversity and the reflective properties of energetic liquids by introducing a novel model based on computer vision and deep learning. We propose the DBN-AGS-FLSS, an integrated deep learning model for high-precision, real-time liquid surface pointer detection. The model combines Deep Belief Networks (DBN), Feedback Least-Squares SVM classifiers (FLSS), and Adaptive Genetic Selectors (AGS). Enhanced by bilateral filtering and adaptive contrast enhancement algorithms, the model significantly improves image clarity and detection accuracy. The use of a feedback mechanism for reverse judgment dynamically optimizes model parameters, enhancing system accuracy and robustness. The model achieved an accuracy, precision, F1 score, and recall of 99.37%, 99.36%, 99.16%, and 99.36%, respectively, with an inference speed of only 1.5 ms/frame. Experimental results demonstrate the model's superior performance across various complex detection scenarios, validating its practicality and reliability. This study opens new avenues for industrial applications, especially in real-time monitoring and automated systems, and provides valuable reference for future advancements in computer vision-based detection technologies.

Innovations in Drug Delivery: From Microtechnology to Targeted Therapeutics

Drug delivery systems are developed to maximize drug efficacy and minimize side effects. As drug delivery technologies improve, the drug becomes safer and more comfortable for patients to use. The study on advanced drug delivery systems aims to increase patient compliance, decrease adverse effects, and improve treatment efficacy. Furthermore, the goal of these systems is to deliver drugs to particular body tissues or cells in a targeted manner, which should enhance treatment outcomes. During the last seven decades, extraordinary progress has been made in drug delivery technologies, such as systems for long-term delivery for months and years, localized delivery, and targeted delivery. In this review, we explain the general principles of several advanced drug delivery systems, their introduction objective, components, mechanism, functioning principle, and application. The implantable polymeric drug delivery system, cardiac pacemaker, prefilled dual chamber device, closed-loop insulin delivery system, hepatic infusion pumps, MEMS devices, inhaled insulin devices, and hydrogel-forming microneedles are among the medical devices that are being discussed in this article as being at the forefront of technological innovation in medicine. For a variety of medical diseases, these devices provide minimally invasive procedures, real-time monitoring, and more accurate drug delivery. The scope of medical treatment and monitoring procedures is changing due to the integration of cutting-edge materials and technology. By increasing convenience, decreasing adverse effects, and increasing efficacy, these cutting-edge solutions directly help patients. Furthermore, the adoption of advanced drug delivery systems has demonstrated promising results in enhancing patient compliance and treatment outcomes. By increasing the bioavailability and pharmacokinetics of pharmaceuticals, these systems have the potential to transform the fields of drug delivery and personalized medicine. However, the field also faces challenges in overcoming physicochemical barriers and biological unknowns. Challenges facing the advanced drug delivery systems industry include complicated formulation requirements, regulatory obstacles, and the need for new technologies to enhance patient outcomes and therapeutic efficacy. Strict intellectual property rules and competition from generic drug manufacturers can also directly hinder the expansion and financial success of businesses in this field. To progress, the field should focus on translatable research ideas, develop realistic goals, and most importantly, diversify technologies. This emphasis on diversity will inspire new ideas and approaches, leading to the development of more effective, patient-friendly drug delivery systems.

Superior filaments-based fabric with thermal-mechanical-electrical coupling properties for remote temperature alarm

The safeguarding fabrics featuring good thermal insulation, flame retardancy, and force dissipation simultaneously remain challenging due to the current filaments with limited properties. Herein, a novel and flame-retardant filament (F-filament) is developed by hot-extruding the mixture of shear stiffening gel/thermoplastic polyurethane (SSG/TPU) and microencapsulated ammonium polyphosphate (MHAPP), which is conducive to knitting protective fabrics. The conductive F-filament (cF-filament) is further prepared by incorporating with MXene. The F-filament-based fabrics present better anti-impact properties than commercial fabrics and can dissipate 6.32 kN impacting force to 1.89 kN. The patterned knitted fabric also facilitates heat exchange, reducing the contact temperature of the body surface to approximately 37 °C when exposed to an 80 °C heat source, effectively avoiding thermal damage through its porous structure. Moreover, the F-filament-based fabrics are intact even after 19 s of continuous burning. Notably, the cF-filament is demonstrated for use as a sensor for real-time multimodal monitoring, such as detecting external mechanical loadings and temperature stimuli. The cF-filament sensor can be integrated into the F-filament-based knitted fabrics, which accurately realizes environmental monitoring and explores the best-escaping route through a remote temperature alarm system.

Real-time seepage health monitoring using spatial autocorrelation of distributed temperature data from fiber optic sensor

Real-time seepage health monitoring using spatial autocorrelation of distributed temperature data from fiber optic sensor

CONTEMPORARY CHALLENGES IN CIVIL PROTECTION AND DEFENSE

The addressed problem is the need to enhance public policies and regulations in disaster management in Brazil, due to the increasing frequency and severity of these events. The hypothesis posits that integrating public policies with emerging technologies and community participation can significantly improve disaster management. The methodology involves a literature review of existing legislation, case study analysis, and the implementation of real-time hydrometeorological monitoring systems. Key results indicate that, despite advancements in prevention and mitigation policies, significant gaps remain in inter-institutional coordination and practical application of regulations. The main conclusion is that effective integration of public policies and emerging technologies is essential for risk reduction and the effective protection of vulnerable communities.

Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts.

Calixarene films on QCM via electrospin coating for real-time monitoring of phenolic species in aqueous media

Calixarene films on QCM via electrospin coating for real-time monitoring of phenolic species in aqueous media

Research on Through-Flame Imaging Using Mid-Wave Infrared Camera Based on Flame Filter.

High-temperature furnaces and coal-fired boilers are widely employed in the petrochemical and metal-smelting sectors. Over time, the deterioration, corrosion, and wear of pipelines can lead to equipment malfunctions and safety incidents. Nevertheless, effective real-time monitoring of equipment conditions remains insufficient, primarily due to the interference caused by flames generated from fuel combustion. To address this issue, in this study, a through-flame infrared imager is developed based on the mid-wave infrared (MWIR) radiation characteristics of the flame. The imager incorporates a narrowband filter that operates within the wavelength range of 3.80 μm to 4.05 μm, which is integrated into conventional thermal imagers to perform flame filtering. This configuration enables the radiation from the background to pass through the flame and reach the detector, thereby allowing the infrared imager to visualize objects obscured by the flame and measure their temperatures directly. Our experimental findings indicate that the imager is capable of through-flame imaging; specifically, when the temperature of the target exceeds 50 °C, the imager can effectively penetrate the outer flame of an alcohol lamp and distinctly capture the target's outline. Importantly, as the temperature of the target increases, the clarity of the target's contour in the images improves. The MWIR through-flame imager presents considerable potential for the real-time monitoring and preventive maintenance of high-temperature furnaces and similar equipment, such as detecting the degradation of refractory materials and damage to pipelines.

Analysis of the Application and Prospects of Intelligent Technology in Sustainable Railway Operations

With the rapid development of technology, intelligent technology plays an increasingly important role in the sustainable operation of railways. This paper explores the application and prospects of intelligent technology in sustainable railway operations, emphasizing its key roles in enhancing operational efficiency, strengthening safety assurance, optimizing passenger experience, and promoting environmental protection and energy conservation. Through intelligent dispatching, real-time monitoring, big data prediction, personalized services, and mobile ticketing technologies, the railway system achieves efficient resource allocation and service innovation. The intelligent transformation of railways demonstrates significant economic and social benefits, contributing to emission reduction targets and sustainable development. Future efforts should focus on strengthening top-level design, technological innovation, standard construction, and network security to advance the intelligent railway process and co-create a new era of smart, green, and efficient railway transportation.

A New Self-Sensing Fiber Optic Anchor to Monitor Bolt Axial Force and Identify Loose Zones in the Surrounding Rock of Open TBM Tunnels.

TBM has been widely used in underground engineering and construction, but there is no precedent for the application of open TBM in the inclined shafts of coal mines, which brings new challenges to the support system. The distribution of the axial forces on anchors and the range of loosening of the surrounding rock are crucial considerations in tunnel support design. Existing methods for measuring the axial forces in anchors and determining the extent of loosening in the surrounding rock typically remain at the inspection level, lacking long-term and real-time monitoring capabilities. This paper presents a new self-sensing anchor with embedded optical fibers (made using an improved stirrer) and proposes an intelligent tunnel rock monitoring system. The paper also outlines a method for identifying loosening zones in surrounding rock based on monitoring data and theoretical analysis. Installing self-sensing anchors in the deep sections of the rock surrounding a tunnel provides three-dimensional, round-the-clock real-time monitoring of the axial forces acting on the anchors, using new technology and methods to recognize the deformation characteristics of loosening zones within the surrounding rock. This new self-sensing fiber optic anchor was first applied to an open TBM tunneling project in an inclined shaft in the Kekegai coal mine, and monitoring data indicate that self-sensing optical fiber anchors can accurately reflect stress patterns in real time. The axial force curve can be divided into four segments: the borehole area, the loosening zone, the stable zone, and the anchoring zone. Consequently, it accurately identifies the thickness of loosening zones at different positions within the tunnel's surrounding rock. This information is compared and verified against results obtained from bolt dynamometers and borehole inspection. On this basis, an intelligent monitoring system was established to provide a basis for making engineering construction decisions, which makes tunnel construction smarter and helps technicians timely adjust TBM driving and support parameters.