Area Monitoring Research Articles

THE CURRENT STATUS AND FUTURE VISION OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN HEALTHCARE

This study examines the impacts of artificial intelligence (AI) technologies in the healthcare sector, particularly in the areas of medical imaging, diagnosis, treatment planning, and patient monitoring. AI's ability to analyze medical images more accurately and quickly, diagnose diseases at an early stage, create personalized treatment plans, and continuously monitor patient conditions has significantly enhanced the quality and accessibility of healthcare services. However, the ethical, privacy, and security issues associated with AI underscore the need for these technologies to be responsibly integrated into the healthcare sector. Moving forward, the role of AI in healthcare services is expected to expand, and realizing this potential in an ethical and secure manner will require ongoing effort.

Spatio-Temporal Characteristics of Land Subsidence and Driving Factors Analysis in Shenzhen

Analyzing land subsidence using Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology holds significant importance for the secure development of urban areas. Shenzhen, being a crucial component of the Pearl River Delta, faces the threat of land subsidence, similar to most deltaic cities. Numerous studies have already indicated the presence of severe land subsidence in certain localities of Shenzhen. However, due to limitations in data scope and research methodologies, the comprehensive spatial-temporal distribution of land subsidence across the entire city of Shenzhen remains unclear. This study initially employed MT-InSAR technology to process a total of 534 Sentinel-1A SAR images from three different frames (P11F71, P113F71, P11F65), covering the entire city of Shenzhen. This processing resulted in the generation of subsidence rate maps and subsidence time series. Subsequently, the temporal evolution patterns of the subsidence were analyzed while significant subsidence regions were identified. By integrating information from optical images reflecting human activities on the Earth’s surface, the study deduced the subsidence mechanisms in various significant subsidence areas. Research findings indicate that land subsidence in Shenzhen is primarily caused by construction activities, with a concentration in the western coastal areas of Shenzhen, reaching a maximum rate of 80 mm/yr, located at the estuary of Dongbao River (113.770385, 22.745305). The cumulative subsidence from March 2017 to June 2023 amounts to 500 mm. The expansion of the Qinglinjing Reservoir has led to an increased demand for water, resulting in a significant rise in formation pressure and subsequent land subsidence. InSAR land subsidence monitoring and analysis in urban areas can address the spatial and temporal resolution limitations of traditional subsidence monitoring methods, providing effective recommendations for widespread subsidence prevention and control.

Incorporating Multi-Temporal Remote Sensing and a Pixel-Based Deep Learning Classification Algorithm to Map Multiple-Crop Cultivated Areas

The accurate monitoring of crop areas is essential for food security and agriculture, but accurately extracting multiple-crop distribution over large areas remains challenging. To solve the above issue, in this study, the Pixel-based One-dimensional convolutional neural network (PB-Conv1D) and Pixel-based Bi-directional Long Short-Term Memory (PB-BiLSTM) were proposed to identify multiple-crop cultivated areas using time-series NaE (a combination of NDVI and EVI) as input for generating a baseline classification. Two approaches, Snapshot and Stochastic weighted averaging (SWA), were used in the base-model to minimize the loss function and improve model accuracy. Using an ensemble algorithm consisting of five PB-Conv1D and seven PB-BiLSTM models, the temporal vegetation index information in the base-model was comprehensively exploited for multiple-crop classification and produced the Pixel-Based Conv1D and BiLSTM Ensemble model (PB-CB), and this was compared with the PB-Transformer model to validate the effectiveness of the proposed method. The multiple-crop cultivated area was extracted from 2005, 2010, 2015, and 2020 in North China by using the PB-Conv1D combine Snapshot (PB-CDST) and PB-CB models, which are a performance-optimized single model and an integrated model, respectively. The results showed that the mapping results of the multiple-crop cultivated area derived by PB-CDST (OA: 81.36%) and PB-BiLSTM combined with Snapshot (PB-BMST) (OA: 79.40%) showed exceptional accuracy compared to PB-Transformer combined with Snapshot and SWA (PB-TRSTSA) (OA: 77.91%). Meanwhile, the PB-CB (OA: 83.43%) had the most accuracy compared to the pixel-based single algorithm. The MODIS-derived PB-CB method accurately identified multiple-crop areas for wheat, corn, and rice, showing a strong correlation with statistical data, exceeding 0.7 at the municipal level and 0.6 at the county level.

Prevalence and influencing factors of anemia among pregnant women across first, second and third trimesters of pregnancy in monitoring areas, from 2016 to 2020: a population-based multi-center cohort study.

To assess the prevalence of anemia among pregnant women across their entire pregnancy and the factors affecting it in the monitoring areas. A total of 108,351 pregnant women who received antenatal health care and delivered from January 1, 2016 to December 31, 2020 in 15 monitoring counties of 8 provinces in the Maternal and Newborn Health Monitoring Program (MNHMP) of National Center for Women and Children's Health (NCWCH) were selected as the study subjects. The anemia status among the subjects across their first, second and third trimester of pregnancy and the influencing factors were analyzed. From 2016 to 2020, the prevalence of anemia at any stage during pregnancy in the monitoring areas was 43.59%. The prevalence of anemia among pregnant women across all three trimesters was 3.95%, and the prevalence of mild and moderate-to-severe anemia was 1.04% and 2.90%, respectively. Protective factors were living in the northern area (OR = 0.395) and being a member of an ethnic minority (OR = 0.632). The risk factors were residing in rural areas (OR = 1.207), with no more than junior high school education (OR = 1.203), having ≥ 3 gravidities (OR = 1.195) and multiple fetuses (OR = 1.478). Although the prevalence of anemia among pregnant women across all trimesters in the monitoring area was low, the severity of anemia was high. Since the prevalence of anemia among pregnant women across their entire pregnancy in the monitoring area is affected by many different factors, more attention should be paid to pregnant women living in rural areas, with low literacy, ≥ 3 gravidities and multiple fetuses for early intervention.

Deformation Monitoring and Primary Driving Factor Analysis in the Coastal Area of Liaohe Oilfield Utilizing MT-InSAR and PCA

Deformation Monitoring and Primary Driving Factor Analysis in the Coastal Area of Liaohe Oilfield Utilizing MT-InSAR and PCA

Internet of Things assisted Unmanned Aerial Vehicle for Pest Detection with Optimized Deep Learning Model

IoT technologies & UAVs are frequently utilized in ecological monitoring areas. Unmanned Aerial Vehicles (UAVs) & IoT in farming technology can evaluate crop disease & pest incidence from the ground’s micro & macro aspects, correspondingly. UAVs could capture images of farms using a spectral camera system, and these images are been used to examine the presence of agricultural pests and diseases. In this research work, a novel IoT- assisted UAV- based pest detection with Arithmetic Crossover based Black Widow Optimization-Convolutional Neural Network (ACBWO-CNN) model is developed in the field of agriculture. Cloud computing mechanism is used for monitoring and discovering the pest during crop production by using UAVs. The need for this method is to provide data centers, so there is a necessary amount of memory storage in addition to the processing of several images. Initially, the collected input image by the UAV is assumed on handling the via-IoT-cloud server, from which the pest identification takes place. The pest detection unit will be designed with three major phases: (a) background &foreground Segmentation, (b) Feature Extraction & (c) Classification. In the foreground and background Segmentation phase, the K-means clustering will be utilized for segmenting the pest images. From the segmented images, it extracts the features including Local Binary Pattern (LBP) &improved Local Vector Pattern (LVP) features. With these features, the optimized CNN classifier in the classification phase will be trained for the identification of pests in crops. Since the final detection outcome is from the Convolutional Neural Network (CNN); its weights are fine-tuned through the ACBWO approach. Thus, the output from optimized CNN will portray the type of pest identified in the field. This method’s performance is compared to other existing methods concerning a few measures.

Dynamic monitoring of surface area and water volume of reservoirs using satellite imagery, computer vision and deep learning

The development of surface area and estimated volume measurements for water reservoirs using advanced computational techniques has become an increasingly important task for reservoir operation. Thus, the objective of this study is to determine the surface area and estimate the volume of water reservoirs through the segmentation of multispectral satellite images using a Fully Convolutional Neural Network (FCN). This paper introduces a universally applicable framework that utilizes deep convolutional neural networks for segmenting water bodies, particularly focusing on areas in Paraíba State, Northeast Brazil, susceptible to severe droughts. The study utilized Sentinel-2 satellite data obtained from Google Earth Engine for the period between January 2019 and September 2023. The extraction of water surface area from satellite images was achieved using the U-Net model, a deep learning framework based on FCN. The accuracy of water surface area extraction was analyzed using the Intersection-Over-Union (IoU) metric, also known as Jaccard Index. Subsequently, reservoir volumes were calculated using elevation-area-volume curves. The evaluation metrics for the results included root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Pearson correlation coefficient (r). The network demonstrated high accuracy, achieving a 95% IoU on the test set. This methodology was applied to four reservoirs, yielding promising results that closely aligned the monitored reservoir curves derived from satellite imagery with observed field data. The findings of this study are significant for research on semiarid region changes, extending their relevance to a broader understanding of global environmental dynamics. This work not only contributes to the field of remote sensing in hydrology but also offers practical insights for effective water resource management in drought-prone regions.

Evaluation of satellite precipitation products for real-time extreme river flow modeling in data scarce regions

Abstract. Inadequacy of spatio-temporal hydro-climatic data limits the efficacy of hazard monitoring and disaster risk reduction activities in disaster-prone areas. Various satellite missions are recently providing climate data, but prior evaluation and enhancement of these data are necessary for a reliable application. In this study, we conducted performance evaluation and enhancement of three real-time satellite precipitation products (SPPs) (GSMaP, GPM-IMERG, and PERSIANN) for flood modeling in the Blue Nile basin. The bias correction improved the original SPPs, with the largest improvement being for factors generated from 10 d mean data. Flood event hydrograph indicated satisfactory results of error metrics on the devastating flood event of 2012. Employing reliable physical–based distributed hydrologic models provided longer lead time and high-accuracy flood simulation. Furthermore, the results indicate that integrating available initial observed precipitation data improved the efficiency of SPPs simulation, and hence are applicable in operational flood monitoring.

Mercury monitoring in the coastal areas of the Republic of Korea using the black-tailed gull egg as an indicator

In the southeast and east coasts of the Republic of Korea, it is essential to monitor mercury accumulation in coastal organisms in view of the higher mercury distribution in sediments and human samples. However, mercury pollution monitoring in organisms, especially higher trophic-level organisms that can exhibit high mercury accumulation, is limited. Here, we examined the applicability of the eggs of the black-tailed gull (Larus crassirostris), which belongs to a high trophic level, for mercury monitoring in coastal areas. Breeding sites were selected in West, Southeast, and East Seas with different mercury concentrations in other matrices (sediment and biological samples of residents). The 5-year mean total mercury concentration in eggs collected during the breeding seasons from 2016 to 2020 was lower in Baengnyeongdo (705 ± 81 ng/g dry weight (dry), West Sea) than in Hongdo (1,207 ± 214 ng/g dry, Southeast Sea) and Ulleungdo (1,095 ± 95 ng/g dry, East Sea). The different patterns of mercury concentration in gull eggs among the breeding sites was consistent with those in the other matrices among the coastal areas. These results support the applicability of the black-tailed gull egg as an indicator for establishing a monitoring framework in the coastal areas of the Republic of Korea.

Dynamic monitoring and restorability evaluation of alpine wetland in the eastern edge of Qinghai–Tibet Plateau

Accurate monitoring of alpine wetland areas and scientific evaluation of alpine wetland restorability play important roles in revealing the changing mechanism of the ecological environment. However, because of the complexity and lack of spatial data, few studies on evaluation index system of restorability of alpine wetland considered wild animal habitat, such as habitat suitability index of black necked crane. Based on Landsat images of seven periods from 1990 to 2020, this study adopts the methods of spectral mixture analysis and change vector analysis to quantitatively demonstrate the variations and distribution patterns of alpine wetland ecosystem in the eastern edge of Qinghai–Tibet Plateau. The study focuses on determining the abundance of water, vegetation and soil, which the interannual variations represent as the shapes of ‘√’, mirror ‘N’ and normal ‘N’, respectively. In terms of natural ecological condition and human activity interference, we construct a comprehensive evaluation model of restorability of alpine wetland, separate the restorability district and construct the Bayesian network model to discuss the causal relationship between the restorability value and evaluation factor. Results show that the regions of alpine wetland in the study area changed most drastically from 1995 to 2000 and from 2000 to 2005. In addition, the total areas classified as suitable, relatively suitable and suitable for restoration account for 63.69 % of the study area, with a large potential for wetland restorability. Moreover, the prioritisation scheme of wetland restoration action includes the valley swamps of Heihe River in the middle, followed by the flat wetlands and grasslands in the south and lastly by uneven wetlands in the north.

INTEGRATING ADVANCED TECHNOLOGIES FOR ENHANCED HSE MANAGEMENT IN THE FMCG SECTOR

The Fast-Moving Consumer Goods (FMCG) sector operates in a dynamic environment, facing numerous challenges in maintaining Health, Safety, and Environment (HSE) standards while meeting the demands of a rapidly evolving market. To address these challenges, integrating advanced technologies has emerged as a strategic approach for enhancing HSE management practices within the FMCG sector. This review explores the integration of various advanced technologies and their impact on improving HSE management in the FMCG industry. The utilization of technologies such as Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics has revolutionized HSE management practices in the FMCG sector. IoT sensors embedded in production machinery and equipment enable real-time monitoring of environmental conditions, equipment performance, and worker safety. AI-driven predictive analytics algorithms analyze vast amounts of data to identify potential safety hazards, predict equipment failures, and optimize HSE protocols. Furthermore, the adoption of wearable devices equipped with biometric sensors provides continuous health monitoring for employees, ensuring early detection of fatigue, stress, or other health-related issues. Virtual Reality (VR) and Augmented Reality (AR) technologies are utilized for immersive HSE training simulations, enabling employees to practice safety procedures in realistic virtual environments, thus enhancing their preparedness for real-life scenarios. Moreover, the integration of drone technology facilitates remote monitoring of vast operational areas, enabling quick identification of potential hazards and swift response to emergencies. Additionally, blockchain technology ensures the transparency and traceability of HSE data across the supply chain, enhancing accountability and compliance with regulatory standards. The integration of advanced technologies holds significant promise for enhancing HSE management practices in the FMCG sector, fostering a safer and more sustainable operational environment while addressing the evolving challenges of the industry.
 Keywords: HSE, Management, FMCG, Technologies, Technology, Review, Innovation.

Spatial downscaling analysis of GPM IMERG precipitation dataset based on multiscale geographically weighted regression model: a case study of the Inner Mongolia Reach of the Yellow River basin

The Inner Mongolia Reach of the Yellow River Basin is characterized by a relative scarcity of meteorological stations. While satellite precipitation products can complement observations from meteorological stations, their limited spatial resolution restricts their efficacy in regional studies. This study utilizes the GPM IMERG precipitation dataset, considering various factors that influence the spatial distribution of precipitation, such as the Normalized Difference Vegetation Index (NDVI), elevation, slope, aspect, and topographical relief, to construct a multiscale geographically weighted regression (MGWR) model. A spatial downscaling method for the GPM IMERG precipitation dataset is proposed, and its reliability is validated through an accuracy assessment. Moreover, the scale differences in the impact of different factors on the spatial pattern of precipitation in the Inner Mongolia Reach of the Yellow River Basin are scrutinized. The results indicate that: 1) The downscaled GPM IMERG precipitation data (1 km × 1 km) exhibit enhanced accuracy compared to the pre-downscaled data (approximately 11 km × 11 km). The correlation coefficient, Bias, and RMSE of the annual precipitation data after downscaling of GPM IMERG are 0.865, 6.05%, and 68.50 mm/year, respectively. For the monthly downscaled precipitation data, the correlation coefficient, Bias, and RMSE are 0.895, 6.09%, and 16.25 mm/month, respectively. The downscaled GPM IMERG precipitation dataset exhibit high accuracy on both annual and monthly temporal scales. 2) Different factors demonstrate localized effects on precipitation in both dry and wet years. Elevation is the dominant factor influencing the spatial heterogeneity of annual precipitation. The findings from this study can provide technical support for hydrological modeling, drought monitoring, and water resource management in data-scarce areas of the Inner Mongolia Reach of the Yellow River Basin.

Speciation, mobility, and risks of heavy metals in sediment and water of a freshwater Lake in Fujian Province, China

ABSTRACT This study investigated the level of dissolved heavy metals in the water of Longhu Lake and focused on the speciation, mobility, and risks of heavy metals (Cr, Mn, Ni, Cu, Zn, As, Cd, and Pb) in sediment. The levels of heavy metals in the water were found to be below the thresholds set by the Chinese safe drinking water standard. The highest average percentages of Cr, Ni, Zn, and As were bound with residual fractions, while Pb and Cu were bound with reducible fractions. Additionally, the highest percentage of Cd and Mn was bound to the acid-soluble fraction in the sediment. These findings suggest that Cd, Mn, Pb, and Cu likely originated from anthropogenic sources in the lake. There was a strong correlation between total organic carbon (TOC) and metal fractions in the sediment, indicating that TOC may play a role in transporting heavy metal fractions. Risk assessment code values for Cd and Mn indicated higher risks and mobility, while Ni, Cu, and Zn showed medium risks and mobility for aquatic biota. To mitigate heavy metal contamination, it is recommended to improve monitoring and regulation of urban runoff and inlet areas.

Improving urban resilience and habitability by an effective regeneration of the streets: A comprehensive approach step-by-step validated in a real case

The world's average temperature has increased by 1 °C, leading to more frequent and intense extreme weather events. Attention has turned to the Urban Heat Island (UHI), a warming phenomenon in cities resulting from human activities and the urban design. In this way, urban adaptation of streets and the development of new urban spaces focus on achieving thermal comfort. While the research community proposed surface energy balances (SEB), a limitation arises from SEB's ability to identify UHI due to its exclusion of anthropogenic heat. This study highlights the need to address anthropogenic variables and the importance of continuous monitoring in urban areas undergoing adaptation. A calculation method called co-simulation is suggested to incorporate anthropogenic heat using ENVI-met software as the basis. Adaptation justification involves monitoring and assessing the COMFA comfort index in an avenue. The calculation process proposes improvements for a comfortable environment, evaluating the microclimate impact on urban adaptation. Implementation, ongoing monitoring, and a comprehensive sensitivity analysis of the COMFA comfort index ensure a 60% improvement in the thermal sensation from June to September. This approach promotes the adoption of innovative solutions for climate change, emphasizing energy efficiency and well-being in urban spaces.

Seismic monitoring using the telecom fiber network

Laser interferometry enables to remotely measure microscopical length changes of deployed telecommunication cables originating from earthquakes. Long reach and compatibility with data transmission make it attractive for the exploration of both remote regions and highly-populated areas where optical networks are pervasive. However, interpretation of its response still suffers from a limited number of available datasets. We systematically analyze 1.5 years of acquisitions on a land-based telecommunication cable in comparison to co-located seismometers, with successful detection of events in a broad magnitude range, including very weak ones. We determine relations between a cable’s detection probability and the events magnitude and distance, introducing spectral analysis of fiber data as a tool to investigate earthquake dynamics. Our results reveal that quantitative analysis is possible, confirming applicability of this technique both for the global monitoring of our planet and the daily seismicity monitoring of populated areas, in perspective exploitable for civilian protection.

Efektivitas Hukum Kawasan Konservasi Perairan Maluku Utara: Studi Kasus Pulau Rao

The use of prohibited fishing gear continues to pose a serious threat to aquatic ecosystems. The Regional Water Conservation Area Agency (Balai Kawasan Konservasi Perairan Daerah or KKPD) in North Maluku plays a crucial role in educating the public on responsible fishing practices for the sustainability of the region. However, insufficient socialization and monitoring in the conservation area of Pulau Mare have led to illegal fishing activities that harm the ecosystem. Lack of transparency in information regarding the performance of the KKPD becomes a challenge, leaving the community unaware of the progress in surveillance, rehabilitation, and utilization by the agency. This research analyzes the legal effectiveness of water conservation area management in North Maluku Province, focusing on the Pulau Rao-Tanjung Dehegila Conservation Area. The research employs a juridical-empirical method with primary data obtained through interviews and secondary data from literature studies. Ministerial Decree No. 67/KEPMEN-KP/2020 and Minister of Marine Affairs and Fisheries Regulation No. 31/PERMEN-KP/2020 mandate proper management. However, the implementation of these regulations has yet to be effective. In conclusion, water conservation area management in North Maluku requires increased socialization to raise awareness and garner public support for established programs. Further efforts are needed to ensure the effectiveness of these programs.

Coating effects of a strain sensor on durability and sensitivity using the fiber loop ringdown spectroscopy technique

A sensor region in a single-mode optical fiber loop was created and utilized in order to study the coating effect on sensor durability and system sensitivity by the Fiber Loop Ringdown Spectroscopy (FLRDS) technique. The sensor system was simply designed without any additional optical components. The bending loss theory in the single-mode fiber (SMF) was taken into account in data calculation. After stretching was performed on 10.0 cm long coated and noncoated sensorheads from the mid-points, the strain detection limits were determined as 5.3345 με and 6.7497 με with bare and coated sensorheads, respectively. The purpose of this study is to analyze the effect of N,N-Diethyl-p-phenylenediamine (NDPD) coating of the sensorhead on the sensor durability and sensitivity. The baseline stability of the system was obtained as 1.18% by considering a hundred consecutive data. Regarding to obtained results, the difference between calculated total optical losses of FLRDS systems with noncoated and NDPD coated sensorheads shows that coating sensorhead enhanced the sensor durability and the system sensitivity. An FLRDS system with high sensitivity, simple design and easy setup offers real-time measurement with continuous monitoring and provides advantages on durability by modification the sensorhead such as NDPD coating. Due to its attractive features such as low cost, simplicity, easy setup, high sensitivity, increased durability and continuous monitoring, an FLRDS system has a wide range of application areas in structural health monitoring, transportation, early detection, biomedical, chemical trace elements, rail and asphalt applications for continuous monitoring in a real-time merit.

Energy and delay aware routing model for smart crop monitoring application using internet of things

The adoption of modern technology into farming has seen significant expansion to enhance the quality and quantity of crop production. In this research work, wireless sensor networks (WSNs) and the Internet of Things (IoTs) are integrated to provide a real-time crop monitoring system to farmers. Usually, battery-powered IoT devices equipped with sensors like temperature, humidity, motion sensors, etc. are placed across agriculture areas for real-time monitoring of crops and cattle. In improving energy efficiency and reducing delay clustering multi-hop routing has been used with good effect; However, the existing methods failed to enhance network lifetime and target coverage due to failure in addressing load-balancing and hotspot problems. In addressing research problems this paper presents an Energy and Delay Aware Routing (EDAR) scheme for heterogenous WSN-IoTs leveraging unequal grouping of networks and adoption of multipath routing for provisioning IoT applications. The simulation outcome shows the EDAR model achieves very good performance in terms of throughput and lifetime performance with minimal delay and communication overhead.

Wireless sensor networks-based adaptive differential evolution for multimodal optimization problems

In wireless sensor networks (WSN), we often detect the monitoring areas among different sensors so that the sensors can be switched on and off adaptively to save energy and extend their lifetime. Inspired by the principle of WSN, a WSN-based adaptive differential evolution (WSNADE) algorithm is proposed in this paper, together with a WSN-based adaptive niching technique (WANT) and two novel strategies called protection-based dual-scale mutation (PDM) strategy and multi-level reset (MLR) strategy, for solving multimodal optimization problems (MMOPs). In WANT, each individual is considered as a sensor with its monitoring area. If the monitoring areas of two individuals intersect, which means these two individuals monitor the similar area and should be partitioned into the same niche. In this way, WANT can adaptively form a niche for each individual, avoiding the sensitivity of niching parameters. Based on WANT, the PDM strategy is designed to select the appropriate mutation strategy for each individual. Besides, to save fitness evaluations (FEs) for exploring more promising areas, the MLR strategy is developed to store the promising individuals and reset the stagnant individuals. The experimental results on 20 multimodal benchmark test functions in CEC2015 multimodal competition show that the proposed WSNADE algorithm generally performs better than or at least comparable with other state-of-the-art multimodal algorithms, including the winner of the CEC2015 competition. Finally, WSNADE is applied to a real-world multimodal application in multiple competitive facilities location design (MCFLD) problem to illustrate its practical applicability.

Electrochemical sensors based on modified graphene electrode for real-time monitoring of cancer drug concentrations in biological fluids

This work reports on the creation of an electrochemical sensor for the real-time monitoring of paclitaxel (PCX) concentrations in biological fluids. The sensor is based on a poly-melamine-reduced graphene oxide nanocomposite-modified glassy carbon electrode (p-Melamine/rGO/GCE). The electrochemical sensor revealed a linear relationship between the peak current of the DPVs and PCX concentrations between 1 and 1180 μM, with a detection limit of 12 nM. The Ip value of PCX dropped to 98.15% of the starting value after 25 days, indicating excellent stability and repeatability of the p-Melamine/rGO/GCE sensors, which were assessed for long-term stability. The sensor's peak currents of PCX did not exhibit any significant variation when 50 μM organic compounds and inorganic ions were present, suggesting that it could find application in clinical samples. Relative standard deviation values for recovery tests on prepared urine samples ranged from 3.36% to 4.72%. In the area of chemotherapeutic agent monitoring, this work presents a novel method that shows remarkable sensitivity and selectivity in the real-time monitoring of PCX concentrations in biological fluids. The suggested sensor offers a practical and affordable means of tracking PCX concentrations in biological fluids in real time, with potential uses in therapeutic medication monitoring and clinical diagnostics.