Optimal Monitoring Research Articles

Case report of laparoscopic adrenalectomy for a giant pheochromocytoma: medical and surgical challenge

Pheochromocytomas are rare adrenal tumors that can pose significant clinical challenges due to their potential for catecholamine release and hypertensive crises. We present a case of a large pheochromocytoma managed successfully through a laparoscopic adrenalectomy. A 66-year-old male presented with adrenal incidentaloma. Imaging revealed a sizable left adrenal mass (9.5 cm) consistent with a pheochromocytoma. Preoperative alpha- and beta-adrenergic blockade was initiated to optimize hemodynamic control. The patient underwent a laparoscopic adrenalectomy, with meticulous dissection and adrenal vein ligation to minimize catecholamine release during surgery. Post operatively, blood pressure and catecholamine levels normalized. Histopathological evaluation confirmed the diagnosis of pheochromocytoma with high pheochromocytoma of the adrenal gland scaled score (PASS) and grading of adrenal pheochromocytoma and paraganglioma (GAPP) scoring. Laparoscopic adrenalectomy proved to be a safe and effective approach for managing this large pheochromocytoma, resulting in improved blood pressure control and quality of life for the patient. This case underscores the importance of a multidisciplinary approach, including preoperative medical optimization and close postoperative monitoring, in the management of pheochromocytomas, and contributes to the growing body of evidence supporting laparoscopic techniques for these adrenal tumors.

Large language model-based optical network log analysis using LLaMA2 with instruction tuning

The optical network encompasses numerous devices and links, generating a significant volume of logs. Analyzing these logs is significant for network optimization, failure diagnosis, and health monitoring. However, the large-scale and diverse formats of optical network logs present several challenges, including the high cost and difficulty of manual processing, insufficient semantic understanding in existing analysis methods, and the strict requirements for data security and privacy. Generative artificial intelligence (GAI) with powerful language understanding and generation capabilities has the potential to address these challenges. Large language models (LLMs) as a concrete realization of GAI are well-suited for analyzing DCI logs, replacing human experts and enhancing accuracy. Additionally, LLMs enable intelligent interactions with network administrators, automating tasks and improving operational efficiency. Moreover, fine-tuning with open-source LLMs protects data privacy and enhances log analysis accuracy. Therefore, we introduce LLMs and propose a log analysis method with instruction tuning using LLaMA2 for log parsing, anomaly detection and classification, anomaly analysis, and report generation. Real log data extracted from the field-deployed network was used to design and construct instruction tuning datasets. We utilized the dataset for instruction tuning and demonstrated and evaluated the effectiveness of the proposed scheme. The results indicate that this scheme improves the performance of log analysis tasks, especially a 14% improvement in exact match rate for log parsing, a 13% improvement in F1-score for anomaly detection and classification, and a 23% improvement in usability for anomaly analysis, compared with the best baselines.

Development and Application of Drilling Intelligent Assistant Decision System

Abstract In order to systematically harness the substantial volume of data generated during the drilling process and achieve intelligent analysis and auxiliary decision-making for real-time risk monitoring and warnings during drilling, an intelligent drilling auxiliary decision-making system has been developed. This system integrates various technologies, including physical models, intelligent algorithms, and trend analysis. By analyzing the characteristics of logging data under different operating conditions, an intelligent algorithm capable of automatically distinguishing six types of drilling operating conditions has been established. Taking into account the impact of rock cuttings on the stress on the pipe string and annular pressure loss during the drilling process, coupled frictional torque and cyclic pressure loss calculation models were developed under coupled conditions. Building upon this foundation and considering the trend of deviation between predicted and measured parameters, a monitoring algorithm for detecting stuck drilling, wellbore leakage, and overflow incidents was formulated. The development of the drilling intelligent decision-making system was completed based on a C/S three-layer architecture and a data center. This system has been successfully applied in more than 50 exploration wells of various types, including shale oil and gas horizontal wells and deep wells. The static analysis results demonstrated a compliance rate of 91.5% with on-site monitoring, playing a crucial role in drilling parameter optimization and risk monitoring. The practicality and feasibility of the system have been verified, providing essential support for efficient and safe drilling production.

Parallel and scalable AI in HPC systems for CFD applications and beyond

This manuscript presents the library AI4HPC with its architecture and components. The library enables large-scale trainings of AI models on High-Performance Computing systems. It addresses challenges in handling non-uniform datasets through data manipulation routines, model complexity through specialized ML architectures, scalability through extensive code optimizations that augment performance, HyperParameter Optimization (HPO), and performance monitoring. The scalability of the library is demonstrated by strong scaling experiments on up to 3,664 Graphical Processing Units (GPUs) resulting in a scaling efficiency of 96%, using the performance on 1 node as baseline. Furthermore, code optimizations and communication/computation bottlenecks are discussed for training a neural network on an actuated Turbulent Boundary Layer (TBL) simulation dataset (8.3 TB) on the HPC system JURECA at the Jülich Supercomputing Centre. The distributed training approach significantly influences the accuracy, which can be drastically compromised by varying mini-batch sizes. Therefore, AI4HPC implements learning rate scaling and adaptive summation algorithms, which are tested and evaluated in this work. For the TBL use case, results scaled up to 64 workers are shown. A further increase in the number of workers causes an additional overhead due to too small dataset samples per worker. Finally, the library is applied for the reconstruction of TBL flows with a convolutional autoencoder-based architecture and a diffusion model. In case of the autoencoder, a modal decomposition shows that the network provides accurate reconstructions of the underlying field and achieves a mean drag prediction error of ≈5%. With the diffusion model, a reconstruction error of ≈4% is achieved when super-resolution is applied to 5-fold coarsened velocity fields. The AI4HPC library is agnostic to the underlying network and can be adapted across various scientific and technical disciplines.

Pharmacokinetics of anti-TB drugs in children and adolescents with drug-resistant TB: a multicentre observational study from India.

Drug-resistant tuberculosis (DR-TB) is one of the challenging forms of TB to treat, not only in adults but also in children and adolescents. Further, there is a void in the treatment strategy exclusively for children due to various reasons, including paucity of pharmacokinetic (PK) data on anti-TB drugs across the globe. In this context, the present study aimed at assessing the PK of some of the anti-TB drugs used in DR-TB treatment regimens. A multicentre observational study was conducted among DR-TB children and adolescents (n = 200) aged 1-18 years (median: 12 years; IQR: 9-14) treated under programmatic settings in India. Steady-state PK (intensive: n = 89; and sparse: n = 111) evaluation of moxifloxacin, levofloxacin, cycloserine, ethionamide, rifampicin, isoniazid and pyrazinamide was carried out by measuring plasma levels using HPLC methods. In the study population, the frequency of achieving peak plasma concentrations ranged between 13% (for rifampicin) to 82% (for pyrazinamide), whereas the frequency of suboptimal peak concentration for pyrazinamide, cycloserine, moxifloxacin, levofloxacin and rifampicin was 15%, 19%, 29%, 41% and 74%, respectively. Further, the frequency of supratherapeutic levels among patients varied between 3% for pyrazinamide and 60% for isoniazid. In the below-12 years age category, the median plasma maximum concentration and 12 h exposure of moxifloxacin were significantly lower than that of the above-12 years category despite similar weight-adjusted dosing. Age significantly impacted the plasma concentration and exposure of moxifloxacin. The observed frequencies of suboptimal and supratherapeutic concentrations underscore the necessity for dose optimization and therapeutic drug monitoring in children and adolescents undergoing DR-TB treatment.

Possibility of the optimum monitoring and evaluation (M&E) production frontier for risk-informed health governance in disaster-prone districts of West Bengal, India

An efficient M&E system in public healthcare is crucial for achieving universal health coverage in low- and middle-income countries, especially when the need for service remains unmet due to the exposure of the population to disaster risks and uncertainties. Current research has conducted exploratory and predictive analyses to estimate the determinants of sustainable M&E solutions for ensuring uninterrupted access during and after disasters. The aim was to estimate the efficiency of reaching a higher M&E production frontier via the Cobb‒Douglas model and stochastic frontier model as the basic theoretical and empirical frameworks. The research followed a deductive approach and used a stratified purposive sampling method to collect data from different layers of health and disaster governance in a flood-prone rural setting in the Malda, South 24 Parganas and Purulia districts in West Bengal, India. The present mixed-method study revealed multiple challenges in healthcare seeking during disasters and how a well-structured M&E system can increase system readiness to combat these challenges. The stochastic frontier model estimated the highest M&E frontier producing the most attainable M&E effectiveness through horizontal convergence between departments, enhanced coordination, the availability of frontline health workers at health centers, the adoption of learned innovation and the outsourcing of the evaluation component to external evaluators to improve M&E process quality. Although the study has several limitations, it shows the potential to increase technical and allocative efficiency through building skills in innovative techniques and applying them in process implementation. In the future, research on strategy improvement followed by real-world evidence-based policy advocacy is needed to increase the impact of M&E on access to healthcare services.

AI-powered Strategies for Optimizing Waste Management in Smart Cities in Beijing

The study investigates the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies into Beijing's waste management system, emphasizing their effects on operational efficiency, environmental sustainability, and economic feasibility. The deployment of AI-driven route optimization and IoT-enabled real-time monitoring resulted in a 25% reduction in waste collection trips and a 30% decrease in waste overflow incidents. These advancements led to notable reductions in fuel consumption and environmental impact, while an economic analysis projected a Net Present Value (NPV) of $3.5 million over a 10-year period, affirming the financial benefits of these technologies. The findings highlight the pivotal role of AI and IoT in optimizing urban waste management practices. The study offers policy recommendations for the phased and strategic adoption of these technologies within Beijing, with the potential to enhance efficiency and contribute to the city’s sustainability objectives. Future research is advised to examine the long-term sustainability of AI-driven waste management strategies and assess the applicability of these technologies in diverse urban environments.

Prescription Precision: A Comprehensive Review of Intelligent Prescription Systems.

Intelligent Prescription Systems (IPS) represent a promising frontier in healthcare, offering the potential to optimize medication selection, dosing, and monitoring tailored to individual patient needs. This comprehensive review explores the current landscape of IPS, encompassing various technological approaches, applications, benefits, and challenges. IPS leverages advanced computational algorithms, machine learning techniques, and big data analytics to analyze patient-specific factors, such as medical history, genetic makeup, biomarkers, and lifestyle variables. By integrating this information with evidence-based guidelines, clinical decision support systems, and real-time patient data, IPS generates personalized treatment recommendations that enhance therapeutic outcomes while minimizing adverse effects and drug interactions. Key components of IPS include predictive modeling, drug-drug interaction detection, adverse event prediction, dose optimization, and medication adherence monitoring. These systems offer clinicians invaluable decision-support tools to navigate the complexities of medication management, particularly in the context of polypharmacy and chronic disease management. While IPS holds immense promise for improving patient care and reducing healthcare costs, several challenges must be addressed. These include data privacy and security concerns, interoperability issues, integration with existing electronic health record systems, and clinician adoption barriers. Additionally, the regulatory landscape surrounding IPS requires clarification to ensure compliance with evolving healthcare regulations. Despite these challenges, the rapid advancements in artificial intelligence, data analytics, and digital health technologies are driving the continued evolution and adoption of IPS. As precision medicine gains momentum, IPS is poised to play a central role in revolutionizing medication management, ultimately leading to more effective, personalized, and patient-centric healthcare delivery.

Cost-Effective Scalability in Cloud Monitoring Systems: A Comparative Stud

This research article explores strategies for cost-effective scalability in cloud monitoring systems. As the complexity and scale of IT-based infrastructure are growing, an efficient monitoring system becomes vital for maintaining performance and optimizing resources while managing costs. The current study aims to shed light on various approaches to cost-effective scalability by considering factors such as data collection methods, storage optimization, and adaptive monitoring techniques. A comparison between cloud monitoring tools, Amazon Cloud Watch and Datadog, has been made to gain a better understanding of the monitoring tools. The research indicates that a multi-faceted approach is necessary for cost-effective scalability in cloud monitoring systems, and there must be a holistic approach in the selection of cloud monitoring tools, depending on the organization's requirements. In the later sections, strategies, like distributed data collection, hierarchical aggregation, adaptive sampling, and machine learning- based predictive scaling, can significantly improve monitoring system scalability while optimizing resource utilization.

Machine Learning for Additive Manufacturing of Functionally Graded Materials.

Additive Manufacturing (AM) is a transformative manufacturing technology enabling direct fabrication of complex parts layer-by-layer from 3D modeling data. Among AM applications, the fabrication of Functionally Graded Materials (FGMs) has significant importance due to the potential to enhance component performance across several industries. FGMs are manufactured with a gradient composition transition between dissimilar materials, enabling the design of new materials with location-dependent mechanical and physical properties. This study presents a comprehensive review of published literature pertaining to the implementation of Machine Learning (ML) techniques in AM, with an emphasis on ML-based methods for optimizing FGMs fabrication processes. Through an extensive survey of the literature, this review article explores the role of ML in addressing the inherent challenges in FGMs fabrication and encompasses parameter optimization, defect detection, and real-time monitoring. The article also provides a discussion of future research directions and challenges in employing ML-based methods in the AM fabrication of FGMs.

Research on Optimization of Communication Network Security and Intelligent Supervision Model in Urban Community Aging Adaptation Renovation

In the process of increasing the number of elderly people, their care and attention have received greater attention, which has made traditional community environments face insufficient data encryption performance and data mining services in the communication network security of aging communities. This has made communication security optimization and intelligent monitoring models in urban community aging adaptation and transformation a focus of research in the field of data communication security. This study delves into the improvement and enhancement of intelligent monitoring models for communication network security in urban communities. Its goal is to improve the security and intelligent monitoring mode of data communication networks during the transformation process through technological innovation and progress in management practices, and further improve the security performance of data communication. This research has solved the communication network security problems existing in intelligent devices and Internet services in the community aging transformation. Through the analysis of improvement strategies, this article compares the basic performance indicators of data encryption, firewall performance, data analysis and mining, and intelligent recommendation in communication network security and intelligent recommendation between different models. The optimization of communication network security in urban community aging adaptation can ensure the data security of terminal devices and further improve and enhance intelligent recommendation services for aging, providing more references and suggestions for communication network security and intelligent monitoring.

Real-time bus arrival delays analysis using seemingly unrelated regression model

AbstractTo effectively manage and control public transport operations, understanding the various factors that impact bus arrival delays is crucial. However, limited research has focused on a comprehensive analysis of bus delay factors, often relying on single-step delay prediction models that are unable to account for the heterogeneous impacts of spatiotemporal factors along the bus route. To analyze the heterogeneous impact of bus arrival delay factors, the paper proposes a set of regression equations conditional on the bus location. A seemingly unrelated regression equation (SURE) model is developed to estimate the regression coefficients, accounting for potential correlations between regression residuals caused by shared unobserved factors among equations. The model is validated using bus operations data from Stockholm, Sweden. The results highlight the importance of developing stop-specific bus arrival delay models to understand the heterogeneous impact of explanatory variables. The significant factors impacting bus arrival delays are primarily associated with bus operations, such as delays at consecutive upstream stops, dwell time, scheduled travel time, recurrent congestion, and current traffic conditions. Factors like the calendar and weather have significant but marginal impacts on arrival delays. The study suggests that different bus operating management strategies, such as schedule adjustments, route optimization, and real-time monitoring and control, should be tailored to the characteristics of stop sections since the impacts of these factors vary depending on the stop location.

Methodology for Researching the Synthesis of Transport and Logistics Systems of the Kyrgyz Republic

The article discusses the research methodology for the synthesis of transport and logistics systems, adapted to the specific conditions of the Kyrgyz Republic. The relevance of the study is due to the need to increase the competitiveness of the national economy and realize the country’s transit potential through the creation of an effective logistics infrastructure. The proposed methodology is based on the integrated application of modern theories and concepts from the field of transport logistics, modeling, geographic information systems, intelligent transport systems, as well as the principles of green logistics and sustainable development. Particular attention is paid to taking into account the features of the mountainous terrain of Kyrgyzstan and the need to integrate various modes of transport into multimodal transportation. The methodology provides for the creation of an optimal network of logistics centers and transport hubs that concentrate cargo flows and ensure their efficient processing. The implementation of information technologies and systems is considered as a key factor in the integration of supply chain participants, monitoring of transport flows, modeling and optimization of logistics processes. Successful implementation will create a competitive transport and logistics system, integrated into global supply chains and meeting modern requirements for efficiency and sustainable development.

AI and machine learning tools in plantation mapping: potentials of high-resolution satellite data

Abstract. Plantation mapping plays a vital role in agriculture, forestry, and land management. The integration of Artificial intelligence and Machine learning techniques with high-resolution satellite data has revolutionized the accuracy and efficiency of plantation mapping. Utilizing AI and machine learning tools for plantation mapping offers a transformative approach to efficient and accurate land management. These technologies enable automated analysis of satellite imagery and other geospatial data, facilitating rapid and precise identification of plantations, crop health assessment, and yield predictions. The integration of AI enhances the mapping process, providing valuable insights for sustainable agriculture, resource optimization, and environmental monitoring. The application of these advanced tools in plantation mapping represents a significant leap towards data-driven and environmentally conscious land management practices. It presents a promising advancement in agricultural practices. By leveraging these technologies for automated analysis of satellite imagery and geospatial data, accurate and timely mapping of plantations becomes feasible. The use of AI and ML tools in Plantation mapping, challenges in integration, the possible solutions and its future prospects are reviewed in this paper not only to enhance efficiency but also to offer insights into crop health, aiding in precision agriculture and resource optimization.

A mean flow velocity estimation scheme based on flow field characteristics and distribution point optimization for carbon monitoring in coal-fired power plants: A numerical study

CO2 online monitoring provides a method to obtain carbon emissions by directly quantifying the flue gas flow rate and CO2 concentration. However, significant flow rate measurement errors arise due to the complexity of turbulence in large-diameter stacks. To address the issue, this study utilizes turbulence numerical simulation combined with mean velocity estimation error analysis to investigate the flow field of typical stacks in coal-fired power plants. The simulation results demonstrated that the evolution of turbulence exaggerates velocities. The flow field exhibits stable turbulence characteristics in a single-introduced stack, while in the double-introduced stack, the flow field dynamically evolves due to asymmetric collisions and abrupt expansions. Furthermore, the effects of load and temperature on the estimation error of flow velocity are established. The optimum monitoring height, the ideal chord angle and the stepped number of deployment points based on the log-linear method are determined. Within the single-introduced stack, a correction factor is introduced and a corresponding formula is established that links estimation errors with monitoring heights. This resulted in a dramatic reduction of the maximum estimation error from 14% to 0.3%. Finally, the systematic and simplified optimization scheme for estimating mean flow velocity is summarised, which provides a powerful reference for flow rate measurement for online CO2 monitoring in coal-fired power plants.

Beyond oncology: Selinexor's journey into anti-inflammatory treatment and long-term management.

Selinexor, a selective inhibitor of nuclear export (SINE), is gaining recognition beyond oncology for its potential in anti-inflammatory therapy. This review elucidates Selinexor's dual action, highlighting its anti-tumor efficacy in various cancers including hematologic malignancies and solid tumors, and its promising anti-inflammatory effects. In cancer treatment, Selinexor has demonstrated benefits as monotherapy and in combination with other therapeutics, particularly in drug-resistant cases. Its role in enhancing the effectiveness of bone marrow transplants has also been noted. Importantly, the drug's impact on key inflammatory pathways provides a new avenue for the management of conditions like sepsis, viral infections including COVID-19, and chronic inflammatory diseases such as Duchenne Muscular Dystrophy and Parkinson's Disease. The review emphasizes the criticality of managing Selinexor's side effects through diligent dose optimization and patient monitoring. Given the complexities of its broader applications, extensive research is called upon to validate Selinexor's long-term safety and effectiveness, with a keen focus on its integration into clinical practice for a diverse spectrum of disorders.

Simulation of thermal-fluid coupling in silicon single crystal growth based on gradient normalized physics-informed neural network

The thermal-fluid coupling phenomenon of silicon melt is significant in the growth process of silicon single crystals. Complex convection affects the temperature and concentration distribution of the silicon melt. Therefore, establishing and solving the thermal-fluid coupling model of silicon melts is crucial to optimizing the crystal growth process and improving crystal quality. Traditional numerical simulation methods have limitations in regard to optimization, control, and real-time monitoring. Physics-Informed Neural Network (PINN) does not require model discretization, after training, it can make predictions quickly, showing potential for industrial applications. However, when solving practical industrial coupling models, PINN often struggles to converge due to large parameter values and significant gaps between solution variables. Moreover, solving the thermal-fluid coupling model with PINN can be treated as a multitask problem, where the gradients of different equations interfere with each other, leading to gradient confusion, slow convergence, or even divergence. Therefore, this paper proposes a gradient normalized PINN (GNPINN) for solving the thermal-fluid coupling model of silicon melt. GNPINN balances the contribution of each task, ensuring a more equitable training speed between different tasks to stabilize the training process of the coupling model. This paper considers the thermal-fluid coupling model of silicon melt under different rotation conditions. GNPINN can accurately and comprehensively capture the complex temperature, velocity, and pressure distribution of silicon melt compared with other methods. Additionally, the experimental results uncover the flow and heat transfer properties of silicon melt, validating the effectiveness and industrial applicability of GNPINN.

Statistical parameterized physics-based machine learning digital shadow models for laser powder bed fusion process

This paper presents a statistical physics-based machine learning model for predicting defects, such as surface roughness and lack-of-fusion porosity, in the laser powder bed fusion of metals (PBF-LB/M) additive manufacturing process. The statistical physics-based model is calibrated and validated against controlled single-track experiments and used for statistical prediction for multi-layer and multi-track cases for PBF-LB/M defects. A mechanistic reduced-order-based stochastic calibration process is introduced to capture the stochastic nature of the melt pool. The calibrated physics-based digital shadow model is demonstrated for predicting the surface roughness of the National Institute of Standards and Technology (NIST) overhang part X4, with a difference of 9.3% compared to the experimental results. By leveraging data obtained from both the physics-based model and experiments, a machine learning model has been trained for fast predictions (inference time of 0.4 ms) with high accuracy (error bound of 6.7%). This model can predict melt pool geometries under various processing conditions, offering a control strategy for the PBF-LB/M process. Further, the trained machine learning model is showcased to demonstrate a control application of melt pool geometries (width and depth) for specific processing parameters. These developed models (physics-based and machine learning) serve as a digital shadow of the PBF-LB/M process, offering predictive capabilities to build a digital twin model for process control, optimization, and online monitoring.

An Estimation Method of High-Order LC Circuits in Power Electronic Converters

This paper proposes a parameter estimation method for high-order LC circuits conducted during the power-OFF of power electronic converters. It is based on a battery-powered compact signal injection unit and a circuit modeling method to estimate the LC circuit parameters. The proposed method aims for LC component condition monitoring in applications with routine-based maintenance or frequent ON/OFF operation (e.g., wind power, photovoltaic, traction systems). Compared to LCR meters and impedance analyzers, the proposed method has a significantly lower implementation cost and does not require disassembly of components. Moreover, it is a converter-level method in which the signal injection unit only needs to connect the converter inputs or outputs. Compared to power-ON LC parameter estimation methods, the proposed one is non-invasive without adding any hardware or software algorithm that may introduce new risks to converter operation. This method can handle the converters with high-order LC structures (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{nd}$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4^{th}$</tex-math></inline-formula> -order) in converter power-OFF state, which has not been achieved by existing power-OFF solutions. Therefore, the proposed method is promising for the specific applications of interest mentioned above. The parameter estimation algorithm and signal injection unit design are presented. The experimental results indicate that the estimation errors of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{nd}$</tex-math></inline-formula> -order equivalent circuit, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3^{rd}$</tex-math></inline-formula> -order equivalent circuit, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4^{th}$</tex-math></inline-formula> -order equivalent circuit are lower than 1.6%, 2.5%, and 3%, respectively. The estimated LC parameters can be further used as inputs for operation optimization or condition monitoring of the converters.

Estimation of 241Am and 239Pu activity embedded in the tissue using portable planar HPGe detector.

This paper describes a procedure for the estimation of 241Am and 239Pu activity present in the human tissue by measuring the depth of contaminant using a portable Planar High Purity Germanium detector (HPGe). The ratios of photopeak counts of X-rays or gammas obtained with the detector coupled to collimator are calculated for the estimation of depth of the contaminant and the optimum one is determined. Since Minimum Detectable Activities (MDA) for the detector coupled to a collimator are higher than that of bare detector, activity must be estimated using bare detector, after locating the contaminant. Two methods are described for the estimation of plutonium coexisting with 241Am: (i) Abundance and isotopic correction for 239Pu (ii) and 239Pu:241Am ratio. The procedure to estimate 239Pu when plutonium isotopes alone are present is also established. An optimum monitoring period to detect the minimum value of intake for both radionuclides corresponding to chelation therapy and excision is also derived.