Precise Reading Research Articles

A deep learning method for pointer meter reading recognition in inspection robots at refrigeration stations

Abstract To improve the intelligent operation and maintenance of large public building refrigeration stations, various automatic reading methods for pointer meters used in inspection robots have been developed. However, most methods exhibit low detection accuracy for pointer meters in refrigeration stations and poor segmentation of pointers and scale lines, ultimately leading to inaccurate readings. To address the challenges posed by the complex environments of refrigeration stations, this study proposes a deep learning-based image recognition method for precise pointer meter readings. Firstly, the pointer meters in the environment are detected and located by the YOLOV8-CBAM-Wise-IoU model. Secondly, an improved Fast Semantic Segmentation Network (Fast-SCNN) is used for image segmentation of the pointers and scale lines of the pointer meters, and a perspective transformation method is employed to correct images of tilted meters. Then, the image is transformed into a rectangular image using polar coordinate transformation. Finally, the accurate reading is calculated using the distance method, based on the relative distances between the pointers and scale lines. This research conducted experimental tests using an inspection robot in the actual environment of a large public building’s refrigeration station. The results demonstrate that the proposed method for pointer meter reading recognition achieves a fiducial error within 0.41%, which can realize the inspection task of the pointer meter in refrigeration stations.

Improvement of Capacitive Accelerometers: Integrating Modeling, Electrode Design, Signal Processing, and Material Selection

This work offers a thorough method for integrating advanced modeling, electrode design, signal processing, and material selection techniques to maximize the performance of capacitive accelerometers. Key performance measures, including noise reduction, sensitivity, and range, can be systematically analyzed and simulated to forecast how design decisions would affect the overall behavior of the sensor. Advanced electrode designs greatly increase capacitance, which results in more precise and trustworthy sensor readings. These designs include optimizing the shape and using high-permittivity materials. Furthermore, to guarantee that the accelerometer’s output precisely represents actual motion even in noisy surroundings, noise reduction strategies including filtering and digital signal processing methods are essential. Additionally, calibration is emphasized as a critical step in preserving measurement accuracy over time and accounting for environmental changes and sensor drift. The choice of material, with an emphasis on thermally stable and high-permittivity materials, is crucial in determining the capacitance, sensitivity, and durability of the sensor. The study offers a paradigm for the creation of capacitive accelerometers that perform better across a variety of applications by striking a balance between these variables.

GreenhouseGuard: Enabling real-time warning prediction for smart greenhouse management

Greenhouses constitute intricate systems where numerous variables play a pivotal role in enhancing crop yields within the framework of intensive agriculture. Consequently, real-time monitoring and visualization of these variables are imperative to strike a balance between resource efficiency and production maximization. Furthermore, the ability to make predictive assessments regarding these variables is essential to avert potential greenhouse disasters. In this article, we introduce an intelligent alert system designed to efficiently oversee agricultural operations within a functioning greenhouse, ultimately bolstering productivity through the optimization of crop growth and energy consumption. This system comprises a web application, GreenhouseGuard, which improves the graphical and statistical representation of data collected by a network of sensors strategically positioned throughout the greenhouse, as well as the forecasts generated from this data. These sensors are strategically located to provide more precise real-time data readings, thereby minimizing error margins. Moreover, GreenhouseGuard offers diverse data visualization options and forecasts of greenhouse variables to enable in-depth analysis of the acquired information. Consequently, this alert system empowers greenhouse managers to proactively address abnormal situations that may jeopardize their crop yields.

Experimental investigation on wireless integrated smart system for energy and water resource management in Indian smart cities

In the realm of India's smart cities, precise meter readings are essential for effectively managing domestic energy and water systems. Nevertheless, meter reading employing conventional techniques prove to be both costly and time-consuming, especially considering the vast user base and deficit of everyday consumption analysis. To cope with this challenge, the proposed solution introduces a novel unified wireless smart metering system for measuring energy and water usage. This prototype harnesses the smart, advanced metering technology to manage energy and water resources. The presence of data and remote control capabilities in smart distribution transformers offers distribution operators the chance to optimize system operation and control. The innovative system proposed utilizes smart metering technology and incorporates a distribution system self-healing mechanism against power outages, revolutionizing the way utilities manage water and energy consumption. The system not only offers real-time consumption statistics to the utility provider but also provides the flexibility to remotely control the system's turn-on and turn-off functions. The system effectively captures real-time data, transmits it via LoRa to Telegram application. The system automatically re-establishes remote connections in the event of disconnections caused by emergency conditions or non-payment of the bill, once the relevant issue has been resolved. Additionally, the system promptly notifies authorities on overload, over-temperature, and instances of electricity theft. The system proficiently generates bills based on the consumed data and seamlessly transmits the consumed units to the authorities. The innovative system proposed has proven its ability to communicate effectively, yielding results that align with the given circumstances.

Hyposense: An Integrated Sensor Device for Hydroponics Farm Monitoring

In recent years, the monitoring of hydroponics farms has undergone a significant transformation due to the integration of sensors. However, the conventional method of manually observing measurements from multiple sensors has proven to be excessively time-consuming and costly. Additionally, the lack of automatic data recording options in most sensors has been a limiting factor. To address these challenges, we conducted an experimental study to introduce a novel integrated sensor device named “HypoSense”, designed to monitor essential parameters such as temperature, humidity, pH, light intensity, Total Dissolved Solids (TDS), and Electrical Conductivity (EC) in hydroponic systems. HypoSense consists of three microcontroller units (MCUs) incorporated with two controlling circuits based on the Arduino platform: the Arduino UNO, ESP32, and the ESP8266 (NodeMCU) microcontrollers. The device is designed using high-precision sensors, including the DHT11 for temperature and humidity monitoring, an Analog pH sensor kit for pH measurement, BH1750 for light intensity, and an Analog TDS sensor kit for TDS and electrical conductivity measurements. These sensors were chosen for their reliability, accuracy, and compatibility with the Arduino platform, ensuring that HypoSense delivers precise and consistent readings. HypoSense is a cost-effective, portable sensor device specially designed for monitoring growth parameters simultaneously in hydroponic farming, saving growers time and effort. The evaluation was carried out in two phases. Firstly, field sensors were employed to calibrate HypoSense device sensors. Next, a secondary evaluation was conducted to confirm the practicality and user-friendliness of the HypoSense device. To facilitate the evaluation of HypoSense, we set up an indoor hydroponic system for growing tomatoes and lettuce. The results of these evaluations, focusing on the performance and applicability of the HypoSense device, will provide valuable insights for hydroponic growers and future researchers in precision agriculture.

A new multi-function smart electricity measurement device

Algeria's national gas and power provider is called Sonelgaz. It is in charge of producing, distributing, and transmitting power throughout the nation. Sonelgaz has encountered several difficulties in effectively supplying electricity to consumers and keeping track of their usage over the years. One of the primary problems is that client consumption is not accurately metered. Algeria has a large number of old power meters that don't give precise readings of the real electrical usage. Customers receive inaccurate bills as a result of this. It also makes it more challenging for Sonelgaz to monitor usage trends and detect power thieves. Poor metering infrastructure has led to disputes between Sonelgaz and customers over excess bills. In recent years, the idea of a smart power meter has become increasingly popular all around the world. Traditionally, readings must be taken in the meter reading room. As a result, tracking and keeping tabs on your electricity usage is a time-consuming and annoying chore. Furthermore, a lot of us worry about our large electricity bills at the end of the month, therefore, we have to periodically check the energy meter. However, what if we could track how much electricity we consume from anywhere in the world? In this paper, we will develop a new multi-function smart electricity measurement device using an ESP32 microcontroller and the mobile Blynk app. The smart electricity measurement device displays the voltage, current, power factor, and power consumption in real time. In addition, it displays the cost of electricity in dollars and the total energy consumed in kWh, saving you both time and money. The consumer's electricity will be remotely off if the bills are not paid.

Writing with artificial intelligence? Ad-hoc-survey findings raise awareness for critical literacy at the International Literacy Day

ABSTRACT The International Literacy Day 2023 was the first after the launch the text generating artificial intelligence ChatGPT. This was the reason for a Literacy Promptathon that allows users to learn how to handle text and image generation. The International Literacy Day media coverage for the first time touched on the question of AI generated text. One of the difficulties of generative AI is that it does not necessarily produce true information. However, findings show that critical literacy is most difficult for adults with low literacy performance. Research shows that disinformation, algorithmic biases and data collection are among the most relevant dangers of generative AI, while controlling these dangers requires precise reading and builds on high literacy skills. The research question was how users handle the risk of disinformation and whether this varies by experience. Data collection used an ad-hoc online survey with 202 respondents. Findings indicate an overall high awareness to avoid fake news, but not necessarily adequate fact-checking habits, especially among less experienced AI users. While limited to the respondents, the findings give reason to assume that there is a substantial need to improve competences in fact-checking. Adult and continuing education have a pioneering role in this regard.

DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing.

Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach-Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.

An Integrated Smart Metering System Using LORA Technology

Precise meter readings are essential for effectively regulating residential energy systems in India's smart city context. However, due to the high number of users and the absence of daily consumption monitoring, standard meter reading methods are expensive and time-consuming, which causes consumer unhappiness. The suggested approach to address this issue is to put in place an integrated wireless smart energy system that makes use of smart metering technologies. The way utilities manage energy could be completely changed by this method. The integrated system is intended to take the place of the traditional digital energy meters, which are primarily used to record meter readings properly for payment purposes. This allows for automatic meter readings without the need for frequent travels to the location where the meters are located. Low Power Wide Area Networks (LPWAN) technology, which is renowned for its low power consumption, cost-effectiveness, long-range coverage, and efficient penetration, are used in this technology to enable the transfer of accurate and secure data on energy usage in real-time. The experiments that resulted from the trials allowed the energy to successfully communicate and documented reliable readings. By providing users with real-time usage data and guarding against overloaded scenarios, the system also benefits consumers. Additionally, the device reports electricity theft to the utility company. The shortcomings of the conventional systems and design difficulties are addressed by the suggested system.

Artificial intelligence in capsule endoscopy: development status and future expectations

In this review, we aim to illustrate the state-of-the-art artificial intelligence (AI) applications in the field of capsule endoscopy. AI has made significant strides in gastrointestinal imaging, particularly in capsule endoscopy - a non-invasive procedure for capturing gastrointestinal tract images. However, manual analysis of capsule endoscopy videos is labour-intensive and error-prone, prompting the development of automated computational algorithms and AI models. While currently serving as a supplementary observer, AI has the capacity to evolve into an autonomous, integrated reading system, potentially significantly reducing capsule reading time while surpassing human accuracy. We searched Embase, Pubmed, Medline, and Cochrane databases from inception to 06 Jul 2023 for studies investigating the use of AI for capsule endoscopy and screened retrieved records for eligibility. Quantitative and qualitative data were extracted and synthesised to identify current themes. In the search, 824 articles were collected, and 291 duplicates and 31 abstracts were deleted. After a double-screening process and full-text review, 106 publications were included in the review. Themes pertaining to AI for capsule endoscopy included active gastrointestinal bleeding, erosions and ulcers, vascular lesions and angiodysplasias, polyps and tumours, inflammatory bowel disease, coeliac disease, hookworms, bowel prep assessment, and multiple lesion detection. This review provides current insights into the impact of AI on capsule endoscopy as of 2023. AI holds the potential for faster and precise readings and the prospect of autonomous image analysis. However, careful consideration of diagnostic requirements and potential challenges is crucial. The untapped potential within vision transformer technology hints at further evolution and even greater patient benefit.

Streamlining multi-hole probe calibration using artificial neural networks

Accurately measuring the three-dimensional flow field characteristics in complex flow fields, particularly in turbomachines, is of utmost importance and is commonly achieved through experimental methods. Multi-hole pressure probes are a proven measuring technique to provide precise readings of flow characteristics in both two-dimensional (2D) and three-dimensional (3D) flow fields. However, manufacturing imperfections have an impact on their measuring accuracy so each probe has a slightly different measuring behavior. In order to take these specific characteristics into account a thorough calibration process prior to utilization has to be applied. This calibration process involves positioning the probe in various flow conditions in a wind tunnel and collecting all relevant data. A model is then established to correlate the measured pressure distribution across the probe holes with known flow conditions. Unfortunately, this calibration process is time-consuming and requires costly equipment.To address these challenges, this study introduces a novel calibration technique that leverages advanced machine learning methods, specifically artificial neural networks (ANNs), to significantly streamline the process. ANNs are renowned for their ability to model diverse systems and adapt and generalize well, making them a promising solution for simplifying the calibration of multi-hole probes. In this research, an ANN calibration model was developed specifically for a 5-hole probe, and its configuration was optimized through an automated hyperparameter optimization process. The performance of the ANN model was then compared to that of a conventional polynomial model. After confirming its accuracy, the ANN model was successfully re-adapted and applied to new probes, requiring minimal calibration data collection. The results highlight the potential of ANNs in probe calibration, as they enable a simplified process that demands fewer calibration data points.

Design, Engineering Analysis, and Fabrication of a Prototype Electromechanical Finger Fixator Control System

This paper investigates a new external fixator system for treating finger contractures. This innovative system is electric and user-friendly, providing precise readings of the angles of diseased finger joints. Accurate measurements of the angles of the diseased finger joints can assist therapists and patients during rehabilitation procedures. The fixator consists of ten parts assembled using polyether-ether-ketone (PEEK) for the plastic components and stainless steel for the metal components. The design model was engineered, drawn, and analyzed using SolidWorks Computer-Aided Design software. The verification process utilizes finite element analysis to demonstrate that the maximum stress was lower than the yield strength of the chosen materials. As a result, the new device design is robust and stable enough to withstand the anticipated loading conditions of human fingers. Subsequently, a prototype was fabricated using advanced additive manufacturing technology, specifically fused deposition modeling (FDM). The proposed fixator is simple to control, reliable, easy to use, and reproducible. It enables device users to exercise their finger joints throughout the day without requiring the assistance of specialists.

Reliable Temperature Measurement in Radiation-Intensive Environments

Radiation-resistant temperature sensors are vital for ensuring reliability in radiation-intensive environments, where the highly energetic and penetrating nature of radiation can significantly impact electronic devices and sensors. In such environments, like those near intense radiation sources or in challenging radiation-rich settings, such as space, gamma radiation can lead to erroneous measurements or equipment failures. Radiation-resistant sensors play a crucial role in maintaining measurement accuracy as they are designed to minimize interference caused by radiation, protecting electronic components and providing precise and reliable temperature readings. Their resilience to radiation-induced effects ensures data durability, reducing the need for frequent replacements, and enhancing the overall reliability of measurements in these demanding conditions. In this paper, we present and analyze two different configurations, aiming to address the challenges posed by radiation in sensitive environments. By exploring these novel approaches, we seek to enhance the robustness and accuracy of temperature sensors in radiation-intensive settings, enabling reliable data collection and facilitating successful operations in challenging radiation-rich conditions. The comparative analysis of these configurations will shed light on their performance and effectiveness in mitigating radiation-induced effects, thereby contributing to the advancement of radiation-resistant temperature sensing technologies.

Organization of correctional work for children with dyslexia at primary school

Relevance. The analysis of disorders connected with reading skills is important because it creates serious obstacles in mastering of the curriculum subjects by children of primary school. Purpose. The use of speech therapy techniques described in the article, along with traditional corrective and pedagogical support, will help to overcome dyslexia of primary school children. Children with a specific reading disorder, which has influence on academic performance, must be analysed through psychological and pedagogical approach to overcome it. Methodology. The method given in this article can be used as a propaedeutic component in the preschool period of literacy training. Proposed exercises can be included into the system of traditional speech therapy classes to overcome reading disorders. Results. In this study, we are focused on experience of specialists, who deal with specific reading disorders providing corrective speech therapy for students. Teachers, psychologists, neurophysiologists and neuropsychologists have carried out the study of reading and writing disorders all over the world, but even now, this problem remains one of the most complex and insufficiently studied one. Conclusions. It is recognized that foundational school education hinges on a child's proficiency in rapid and precise reading, coupled with the capacity to accurately comprehend the core content of the text. Speech therapists, primary school teachers and parents of special individual and subgroup classes can use exercises presented in article.

Evaluation of Soil Water Tension Trigger Levels for Optimizing Peanut (Arachis hypogaea) Yield and Irrigation Water Use Efficiency

Irrigation technologies have provided the ability for growers to effectively manage crops. In recent years, soil moisture sensors have provided more precise soil moisture readings for continued improvement of irrigation decisions. Trigger level irrigation research was first performed on corn (Zea mays L.) and cotton (Gossypium hirsutum L.), but southeastern crop growers often include peanut (Arachis hypogaea L.) in crop rotations. Therefore, research was performed on peanut to determine how differing soil water tension (SWT) trigger levels affected peanut yield. Soil water tension sensors connected to a real-time data logger were used to test irrigation trigger levels in peanut plots during the 2018 and 2019 growing seasons at the University of Georgia (UGA) Stripling Irrigation Research Park (SIRP) in Camilla, Georgia. Nine different treatments were implemented, which included 20, 30, 40, 50, 60 kPa SWT, USDA-ARS Irrigator Pro, UGA Checkbook, 50% UGA Checkbook, and rainfed. Twenty-seven plots were established in a randomized complete block design with three replications. At the conclusion of this study, it was determined that there were no differences on yield within year, but year had an effect on peanut yield (P < 0.05), and treatment within year had a significant effect on irrigation water use efficiency (IWUE) (P < 0.05).

Exploring black hole-neutron star binary merger by detecting gravitational waves

Unlike black hole binary merger, the merger between a neutron star and a black hole will produce an abundant number of gravitational waves and electromagnetic waves. Using this information, scientists can easily find many properties of the universe and test the general relativity and some other gravitational theories. The detection of the gravitational wave from source is essential to develop the current knowledge of the gravitational force. From last century, scientists were trying to detect the gravitational waves, and as the time passes, the method of detection has already developed from on land detector to space detector in order to take more precise readings. This paper provides some basic information of the neutron star and the black hole, together with the formation of the binary neutron star-black hole system. The relationship between the neutron star and black hole is explained in this paper. The knowledge of the current methods of detecting gravitational waves is also provided and the paper specifically elaborated the space laser interferometry.

IOT-based solution for detection of air quality using ESP32

Air pollution, a growing public health crisis worldwide, has become one of the most imperative concerns challenging human health, the environment, and the economy. The detrimental effects of air pollution on individuals' well-being and the ecological system necessitate implementing advanced solutions to accurately monitor and manage air quality. In response to this global challenge, a comprehensive, real-time IoT-based air quality monitoring system is proposed using ESP32 and multiple pollutant sensors. This versatile system endeavors to measure and report various air quality parameters and pollutants, such as temperature, humidity, carbon dioxide (CO2), particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), and other noxious gases, including nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2). The innovative solution showcased in this proposal combines the functionalities of an ESP32 microcontroller, a highly integrated and low-power device, with diverse pollutant sensors chosen for their high sensitivity and ability to offer precise and reliable readings. The various sensors integrated into the system include the MQ135, CCS811, PMS5003, and BME280, each responsible for detecting specific pollutants and air quality parameters. As a leading component of the air quality monitoring system, the ESP32 microcontroller warrants seamless wireless data transmission to cloud servers for real-time monitoring, allowing for remote access and analysis of the air quality data through secure networks. The modular design and ease of deployment of the IoT-based air quality monitoring systems make them installable in various scenarios, including residential, commercial, and industrial settings. Users can make informed decisions about their environment by analyzing extensive real-time data, promoting timely exposure warnings, proactive pollution management, and environmental sustainability. This portability and versatility of deployment ensure substantial coverage of areas that would benefit from real-time air quality monitoring. The air quality monitoring system is further enhanced by incorporating a user-friendly mobile application or web interface, empowering users to access real-time sensor data directly, configure threshold-based alerts and notifications, and visualize trends in air quality parameters over time. This hands-on approach to accessing real-time air quality data encourages individuals to adopt environmentally conscious habits and fosters a better understanding of the impact of pollution on their health and surroundings. To ensure the optimum performance and efficacy of the air quality monitoring system, regular maintenance and calibration services are advocated. By undertaking timely sensor calibration, replacing worn-out components, and updating software when required, the life span, accuracy, and reliability of the system are enhanced, promising continued effectiveness in the real-time monitoring and management of air quality

A CNN based multifaceted signal processing framework for heart rate proctoring using Millimeter wave radar ballistocardiography

The recent pandemic has refocused the medical world’s attention on the diagnostic techniques associated with cardiovascular disease. Heart rate provides a real-time snapshot of cardiovascular health. A more precise heart rate reading enables a better understanding of cardiac muscle activity. Although many existing diagnostic techniques are approaching the limits of perfection, there remains potential for further development. In this paper, we propose MIBINET, a novel multifaceted approach for real-time proctoring of heart rate from Millimeter wave (mm-wave) radar ballistocardiography signals via inter-beat-interval (IBI) using a convolutional neural NETwork (CNN). The central theme of our approach is to synergize the feature extraction capabilities of CNN with novel signal processing techniques, resulting in enhanced estimation accuracy while simultaneously reducing computational complexity. This proposed network can be used in hospitals, homes, and passenger vehicles due to its lightweight and contactless properties. It employs classical signal processing prior to fitting the data into the network. Although MIBINET is primarily designed to work on mm-wave signals, it is found equally effective on signals of various modalities such as PCG, ECG, and PPG. Our approach outperforms state-of-the-art techniques by more than 5% in inter-beat-interval (IBI) estimation accuracy. The architecture achieves a 98.73% correlation coefficient and a 20.69 ms Root-Mean-Square Error (RMSE) over 11 different test subjects. The paper contributes by being the first to apply CNN-based feature extraction in concert with unique signal processing strategies to mm-wave radar data for heart rate monitoring. Our methodology also introduces a synthetic IBI augmentation technique, custom loss function, and novel post-processing methods, all contributing to the robust performance of the model in various settings and modalities.

B-032 Total lab Automation Validation/Verification Protocol: First Step of Process Excellence Journey in Smart Core Lab in Kokilaben Dhirubhai Ambani Hospital & Medical Research Institute, Mumbai, India

Abstract Background The total laboratory automation (TLA) involves the integration, automation, consolidation and digitalization of pre-examination, examination, and post-examination processes. It is far simpler to introduce TLA in a lean, highly standardized smart core lab setting than it is to replicate complex procedures based on manual workflows, therefore harmonization/standardization is a requirement. In this era of modern clinical laboratory, a very important step with respect to the quality of the entire process is method validation/ verification. Hence, it is fundamental to verify/validate TLA process flow, new techniques, new methods and new test parameters.During laboratory accreditation process, the most frequently cited deficiency involves inappropriate PQ (Performance Qualification) and verification protocol. The main principles to the PQ laboratory test validation or verification protocol are Accuracy, Precision, Reference Interval, Detection Limit (LOD), Analytical Measurement Range (AMR) and Clinically Reportable Range (CRR), etc. Hence a clinically appropriate turnaround time is guaranteed via validation, which also provides accurate and precise readings. Methods Around 250 parameters, of biochemical and immunoassay for samples like serum/plasma/cerebrospinal fluid/urine have been validated/verified on this automated system. Clinical Laboratory Standards Institute (CLSI) and best practise checklist as per international and national accreditation bodies served as the foundation for verification. Following are the steps for verification. Analytical Accuracy: Run samples by both methods to evaluate bias. Verify Precision: Run 5 replicates per run for 5 days, at all levels of control sera. Check Reference Intervals: Representative 20 healthy individuals are selected. Verify LOB and LOD Values: Run 20 blank (zero calibrator) or low patient samples. Pneumatic System Verification Protocol: 20 samples carried manually by health care assistants and 20 samples transported via the pneumatic system. We have done sigma metrics of 250 parameters in the lab. Results With particular reference to Thyroid Stimulating Hormone (TSH), we have obtained the following results: For the Reference interval study, out of 353 volunteers recruited, 8 volunteer data were excluded (7 outliers, 1 abnormal TPO values). We obtained the reference interval of 0.53–4.489 μIU/mL, which is within the range specified by the manufacturer's reference range. A good level of analytical accuracy is demonstrated between the two instruments with r2 = 0.9938 having a coefficient of variation of 4.32%. The coefficient of variation values of precision for inter laboratory method comparison for lower and high-level values is 1.39% and 1.76% respectively, which is comparable with the manufacturers claims values. The coefficient of repeatability is 1.3442 is higher than 0.7 and hence considered as the two instruments having a good retest reliability. We observed more than six sigma performance of TSH. Based on Operational Specification (OPSpecs) Chart and Method Decision Chart sigma values of all the parameters are evaluated and quality control strategies have been defined. Conclusion Thus, validation of all biochemistry & immunoassay parameters in TLA set up of the smart core laboratory promotes an environment, emphasizing patient safety and quality patient care.

Elemental Analysis of Printing Ink with the Application of Laser-Induced Breakdown Spectroscopy (LIBS)- A Review

Forgery cases involving printed documents have recently increased due to advanced printing technologies. As a result, various analytical instruments are currently applied in forensic document examination to analyse printing ink. Analyses of printing ink are primarily focused on the ink’s chemical components, namely colourants, vehicles, and additives. Previous studies on ink analysis show an increasing trend in the use of spectroscopy methods including the application of Laser-Induced Breakdown Spectroscopy (LIBS). This instrument is favoured by document examiners in analysing printing ink due to its versatility, simple preparation, multi-elemental detection, minimal destruction, and precise readings. The examination of printing ink using LIBS is reviewed in this publication in addition to findings from previous research. The strengths and drawbacks of this technique, as well as contemporary approaches, are also explored in this article.