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The article presents a new technique for measuring paper deformation in unidirectional tensile tests, based on recording and analysis of a series of specimen images. The proposed technique differs from the DIC-based deformation measurement in that the cross-correlation of image data has been replaced with linear filtering. For this purpose, a regular grid of markers is printed on the sample. Filtering the image creates local maxima in the places where markers occur. The developed algorithm finds their location with sub-pixel accuracy. Printing a grid of markers on tested paper and use of reference objects visible in the same image as the paper sample, freed from the need to mechanically connect the camera and the universal testing machine and from the necessity to electronically synchronize their work. The obtained deformation distributions and Poisson’s ratios are in accordance with the literature data which confirms the correctness of the developed measurement technique.

Modeling of paper fibrous network is necessary and meaningful. This paper presents a method for modeling and Young’s modulus calculation of wood paper, based on the morphology of fibrous network. Sixteen groups of eucalyptus pulp paper sheets were prepared with different processing parameters. Samples were cut for Scanning Electron Microscope (SEM) images capture, with which structural models of fibrous networks were constructed through digital image processing technology. A reconstructing algorithm is proposed in this paper, which mainly includes “skeleton” extraction and simplification. Two-dimensional line-body fibrous models were established and served as represent volume element models, based on which tensile behavior was simulated in ABAQUS. Young’s modulus of paper sheets was then calculated while tensile test was carried out. Porosity of paper sheets prepared is calculated by hypothetical formula, counted in SEM images and measured by medium saturation method. Porosity of structural models is attained through model-image conversion. Consistency of morphology between reconstructed models and SEM images is assessed. Comparison of properties between paper sheets and models majorly focuses on porosity, Young’s modulus, and their relationship with paper-making process parameters. Patterns of process parameters’ influence on paper’s porosity and elastic modulus are analyzed on the premise that results in simulation and experiments agree to each other.

Microfluidic paper-based analytical devices are emerging as promising options for on-the-spot detection of chemical contaminants in water. The coupling of these devices with digital imaging technology has attracted immense interest in developing portable sensor applications. However, the potential of digital techniques still remains to be fully explored. In this work, we integrate digital imaging with microfluidic paper-based analytical devices to develop a portable colorimetric assay for iron. The experimental conditions are optimised using image analysis and the effects of imaging equipment and colorimetric analysis methods on the readout of devices are studied using numerical, graphical and statistical techniques. The experimental results are obtained for the estimation of iron contamination in water samples using a colorimetric assay based on iron (III)-thiocyanate reaction. The digital image acquisition approach using smartphone as imaging equipment is adopted for on-the-spot colorimetric data collection. Image processing techniques are employed to estimate the iron concentration in water from digital images. Greyscale colour intensity obtained from a digital image is identified as a readout of the analytical device and employed for quantitative assessment. The limit of detection for iron is estimated to be 0.26 ppm. The microfluidic approach offers good recovery and repeatability with relative standard deviation of 1.4%. The maximum percentage difference for intensities between the image analysis methods is 4.94% and that between the imaging equipment is 9.96%. The gradients of regression lines for pairwise equipment and method outputs with zero intercept range from 0.9931 to 1.0162 with high values of coefficients of determination. The correlation coefficient values for various paired comparisons (>0.99) suggest strong agreement. Statistical test results suggest no significant differences between methods and between imaging equipment (significance level 0.05). The findings suggest the efficacy of digital imaging and colorimetric analysis for paper-based analytical devices. [ABSTRACT FROM AUTHOR]

Lab-on-a-paper-based devices are promising alternatives to the existing arduous techniques for point-of-need monitoring. The present work reports an instant and facile method to produce a microfluidic paper-based analytical device (μPAD). The fabricated μPAD has been used to detect hypochlorite (OCl−) by incorporating newly synthesized chromo-fluorogenic ratiometric probes 1 and 2 into the sample reception zone. The probes showed high selectivity and fast response (<10 s) toward OCl− with an excellent linear relationship in the concentration range of 0–100 μM. The concentration-dependent fluorometric change driven by the reaction of 1@μPAD with OCl− has been monitored using gel-doc imaging systems, which is unprecedented. Digitizing the intensity of the colour solution with different mathematical models of colour has developed a straightforward method for monitoring OCl− without any interference from its competitors. 1@μPAD can detect OCl− at ∼10 times lower than the WHO recommended limit. The detection limit of 1@μPAD via a digital camera-based fluorescence technique was found to be better over digital camera-based cuvette assays. Therefore, 1@μPAD has been successfully utilized to monitor OCl− in actual environmental water samples with portability, ease of use, and sensitivity. The analytical RSD was found to be ≤3% based on fluorimetric detection using 1@μPAD. The chemodosimetric reaction between OCl− and the probe was evidenced by UV-vis and fluorescence spectroscopy, 1H NMR, and ESI-MS. The rapid response time, biocompatibility, low cytotoxicity, 100% aqueous solubility, ratiometric feature, and exclusive OCl− selectivity over other competitive ROS/RNS successfully lead to the application of the probes for bioimaging of exogenous as well as endogenous OCl− in normal cells (HEK293) and cancerous cells (HeLa). [ABSTRACT FROM AUTHOR]

In this paper the different salient features of the old and new version of Indian paper currency notes have been studied and compared. The list of security features of Indian currencies have been mentioned and discussed. Various kinds of conventional methods that are used to detect authenticity and to recognize a note has been explained. It has been observed that each technique has its own objective and significance. There are many conventional tools that can be used to recognize different features of a note. To classify different techniques, the detection method comprises of two parts i.e., the first line detection method and the second line detection method. Physical dimensions of a paper note along with its thickness have pointed out a new parameter which is the diagonal of a genuine currency note that has been calculated to obtain a proposed standardization and each value differs from different denominations. The classification of different Indian banknotes giving emphasis on its characteristics, both salient and security features, dimensions and detection methods has been mentioned and briefly explained. For validation of the paper currencies two methods have been discussed emphasizing on fuzzy logic framework by taking into consideration a 3-point likert scale and another validation methodology using digital image processing. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Prostate cancer is the most common malignancy and the third most common cause of death in Canadian men. In light of evolving diagnostic pathways for prostate cancer and the increased use of MRI, which now includes its use in men prior to biopsy, the Canadian Association of Radiologists established a Prostate MRI Working Group to produce a white paper to provide recommendations on establishing and maintaining a Prostate MRI Programme in the context of the Canadian healthcare system. The recommendations, which are based on available scientific evidence and/or expert consensus, are intended to maintain quality in image acquisition, interpretation, reporting and targeted biopsy to ensure optimal patient care. The paper covers technique, reporting, quality assurance and targeted biopsy considerations and includes appendices detailing suggested reporting templates, quality assessment tools and sample image acquisition protocols relevant to the Canadian healthcare context. [ABSTRACT FROM AUTHOR]

In the production of paper cups in industrial factories, it is tried to print high quality cups with less waste loss with the help of sensors and heating resistances mounted on the paper cup machine. In this study, a system that detects faulty products based on image processing and removes it by controlling the machine with servo motors, asynchronous motors and programmable logic controller (PLC) is designed. For fault product detection, classification has been performed using real-time Haarcascade algorithm and You Only Look Once (YOLO) algorithm which is a deep learning methods, and real-time object detection has been carried out using the OpenCv library. With this study, an effective faulty product detection and removing hardware system was realized by adapting artificial intelligence algorithms to a machine used in industry. Based on the results, a whole system can be applied to systems that involve removing a faulty product from a band in any production, packaging etc. facility is proposed. A hardware consisting of servo motors, asynchronous motors and PLC was designed to separate faulty cups from the existing paper cup production machine in this study. Then, a data set composed of 1068 images was created with images taken from the camera for faulty and faultless paper cups. Using this dataset, the effect of different deep learning methods on performance in the real-time system has been examined and successful results have been obtained. The optimal outcome was achieved, yielding a real-time application accuracy rate of 90.8% through the utilization of the Yolov5x architecture. [ABSTRACT FROM AUTHOR]

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We estimate the orientation of wood fibers in porous networks like paper, paperboard or fiberboard by computing digital thermal conductivity experiments on micro-computed tomography ( μ CT) images with artificial isotropic thermal conductivity parameters. The accuracy of mechanical and thermal constitutive models for porous wood fiber based materials crucially depends on knowing the wood fiber orientation. Unfortunately, due to the high porosity, the micro-heterogeneity of wood fibers, the high carbon content of organic materials and the unknown additives present in industrial paper, μ CT-scans often exhibit low contrast and strong artifacts. Conventional image processing approaches encounter difficulties, as they rely upon convex fiber cross sections. We propose a solution by circumventing the segmentation of single wood fibers in μ CT images, by performing thermal conductivity simulations on binarized wood fiber structures, where an artificial isotropic thermal conductivity is associated to the fibers and the pore space is considered as isolating. The local and global temperature fluxes are assembled into a fiber orientation tensor. This method overcomes the limitations of the mentioned local image processing approaches, as individual fibers need not be resolved and convergence for increasing resolution is a consequence of abstract mathematical theory. We use our novel method to analyze large three-dimensional μ CT-scans and a synchrotron scan of a paperboard sample, serving as the starting point of an accurate micromechanical modeling of the effective anisotropic mechanical behavior of paper and paperboard. These results are crucial for calculating the mechanical strength of deep-drawn paperboard, which will be accomplished in a subsequent article. [ABSTRACT FROM AUTHOR]

In recent years, with the advent of digital imaging technology, e.g., color printers and color scanners, it has become easier for counterfeiters to produce fake banknotes. The spread of counterfeit money causes loss to everyone involved in financial transactions. Therefore, an effective and reliable verification technique is necessary for successful and reliable financial transactions. This paper presents a cognitive computationbased technique for paper currency verification. In this regard, Scanning Electron Microscopy (SEM) and X-Ray Diffraction (XRD) analyses of counterfeit and genuine banknotes were performed. This experimentation confirmed that the materials used in preparation of genuine and counterfeit banknotes were totally different from each other. Based on these findings, a set of discriminative and robust features was proposed to re ect these differences in currency images. The proposed features represented characteristics of the materials of the banknote, such as printing ink, chemical composition, and surface coarseness. With these robust features, Support Vector Machines (SVMs) were employed for classification. In order to evaluate the performance of the proposed technique, experimentations were performed on a self-constructed dataset of Pakistani banknotes, comprised of 195 currency images, including 35 counterfeit banknotes. The results showed that the proposed system achieved 100% verification ability for properly captured images. [ABSTRACT FROM AUTHOR]

This article explores the applicability of DNA Computing in recyclable waste paper sorting. The primary challenge in the recycling of paper is to obtain raw material with the highest purity. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams w ill facilitate high quality end product, and save processing chemicals and energy. Due to inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. Automated paper sorting systems offer significant advantages over manual paper sorting systems in terms of fatigue, throughput, speed, and accuracy. In this research, a new DNA computing algorithm has been designed. It is based on the template matching technique for segregating recyclable waste paper according to its grades. Using the concepts of replication and massive parallelism operations, the DNA computing algorithm can efficiently reduce the computational time of the template matching method for real time inspection. The algorithm is implemented using silicon-based computer to verify the success rate in paper grade identification. [ABSTRACT FROM AUTHOR]

The article presents a new technique for measuring paper deformation in unidirectional tensile tests, based on recording and analysis of a series of specimen images. The proposed technique differs from the DIC-based deformation measurement in that the cross-correlation of image data has been replaced with linear filtering. For this purpose, a regular grid of markers is printed on the sample. Filtering the image creates local maxima in the places where markers occur. The developed algorithm finds their location with sub-pixel accuracy. Printing a grid of markers on tested paper and use of reference objects visible in the same image as the paper sample, freed from the need to mechanically connect the camera and the universal testing machine and from the necessity to electronically synchronize their work. The obtained deformation distributions and Poisson's ratios are in accordance with the literature data which confirms the correctness of the developed measurement technique. [ABSTRACT FROM AUTHOR]

History paper seismograms are very important information for earthquake monitoring and prediction, and the vectorization of paper seismograms is a very import problem to be resolved. In this paper, a new waveform mosaic algorithm in the vectorization of paper seismograms is presented. We also give out the technological process to waveform mosaic, and a waveform mosaic system used to vectorize analog seismic record has been accomplished independently. Using it, we can precisely and speedy accomplish waveform mosaic for vectorizing analog seismic records.

In this paper, a Shearing Invariant Texture Descriptor (SITD) is proposed, which is a theoretically and computationally simple method based on the Rotation invariant Local Binary Pattern (Rot-LBP) descriptor. In real-world applications using flatbed scanners, such as paper texture fingerprinting, it's common for a sheet of paper to rotate during the image acquisition process. Because the rotation is usually not based on the paper's geometrical centre pivot, the produced image is deformed with irregular rotation resulting in shearing transforms. To tackle the shearing problem, the proposed SITD selects a few patterns from the conventional Rot-LBP to achieve either horizontal or vertical invariance. This paper presents the construction of the SITD operators and their performance in recognizing self-developed and standard image datasets, including real paper texture and Outex images, as well as those with distinctive shapes. The images were distorted with only a shearing transform. The self-developed images were distorted manually, while the standard images were distorted by software. The proposed description method achieved up to 100% correctly recognition rate in all the tested datasets based on the horizontal shear invariant operator. In addition to the accurate performance in all the conducted experiments, the operator significantly outperformed the Rot-LBP and another benchmark method, the Shearing Moment Invariant (SMI). The superiority of the descriptor in recognizing different types of patterns demonstrate its ability to be used in applications where the shearing transform is present. [ABSTRACT FROM AUTHOR]

Perchlorate, a hazardous pollutant, is mainly found in untreated wastewater from urban and industrial sites and unregulated surface and groundwater sources. Effective monitoring of perchlorate in water is essential to mitigate its potential harmful effects. Microfluidic systems are evolving as promising technologies for detecting chemical contaminants in water due to their ability to enable rapid analysis with minimal consumption of reagents and samples. The integration of paper-based microfluidic devices with digital imaging has garnered enormous attention from the perspective of developing portable analytical techniques. Nevertheless, there is a need for further exploration to fully realize the potential of these systems. This study aimed to develop and evaluate the performance of a microfluidic paper-based device for measuring perchlorate levels in water samples. Smartphone-based digital imaging was integrated with microfluidic paper-based analytical device to establish a reliable colorimetric method for detecting perchlorate contamination. The results demonstrated successful quantitative estimation of perchlorate levels in water samples using a colorimetric assay based on the methylene blue-perchlorate reaction. Real-time, on-site colorimetric data were collected using a digital smartphone, and image processing methods were used to detect the occurrence of perchlorate in water samples from digital images. The developed approach yielded a broad linear response ranging from 4 to 12 µg/L (R2 = 0.97) for perchlorate detection, with a limit of detection of 3.41 µg/L and a limit of quantification of 10.34 µg/L. The findings underscore the effectiveness of colorimetric analysis and digital imaging for paper-based analytical devices. The limitations of this method include the capability to detect only a single analyte and the requirement for additional steps in image processing to obtain analytical results. Future developments should focus on designing devices for simultaneous detection of multiple contaminants and exploring automated methods of image analysis. [ABSTRACT FROM AUTHOR]

Modeling of paper fibrous network is necessary and meaningful. This paper presents a method for modeling and Young's modulus calculation of wood paper, based on the morphology of fibrous network. Sixteen groups of eucalyptus pulp paper sheets were prepared with different processing parameters. Samples were cut for Scanning Electron Microscope (SEM) images capture, with which structural models of fibrous networks were constructed through digital image processing technology. A reconstructing algorithm is proposed in this paper, which mainly includes "skeleton" extraction and simplification. Two-dimensional line-body fibrous models were established and served as represent volume element models, based on which tensile behavior was simulated in ABAQUS. Young's modulus of paper sheets was then calculated while tensile test was carried out. Porosity of paper sheets prepared is calculated by hypothetical formula, counted in SEM images and measured by medium saturation method. Porosity of structural models is attained through model-image conversion. Consistency of morphology between reconstructed models and SEM images is assessed. Comparison of properties between paper sheets and models majorly focuses on porosity, Young's modulus, and their relationship with paper-making process parameters. Patterns of process parameters' influence on paper's porosity and elastic modulus are analyzed on the premise that results in simulation and experiments agree to each other. [ABSTRACT FROM AUTHOR]

Simple Summary: Automatic segmentation of organs and other regions of interest is a promising approach for reducing the workload of doctors in radiotherapeutic planning, but it can be hard for doctors and researchers to keep up with current developments. This review evaluates 807 papers and reveals trends, commonalities, and gaps in the existing corpus. A set of recommendations for conducting effective segmentation studies is also provided. This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: "What should researchers think about when starting a segmentation study?", "How can research practices in medical image segmentation be improved?", "What is missing from the current corpus?", and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today's competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information. [ABSTRACT FROM AUTHOR]

Although microfluidic paper-based analytical devices (μPADs) get a lot of attention in the scientific literature, they rarely reach the level of commercialization. One possible reason for this is a lack of application of machine learning techniques supporting the design, optimization and fabrication of such devices. This work demonstrates the potential of two chemometric techniques including design of experiments (DoE) and digital image processing to support the production of μPADs. On the example of a simple colorimetric assay for isoniazid relying on the protonation equilibrium of methyl orange, the experimental conditions were optimized using a D-optimal design (DO) and the impact of multiple factors on the μPAD response was investigated. In addition, this work demonstrates the impact of automatic image processing on accelerating color value analysis and on minimizing errors caused by manual detection area selection. The employed algorithm is based on morphological recognition and allows the analysis of RGB (red, green, and blue) values in a repeatable way. In our belief, DoE and digital image processing methodologies are keys to overcome some of the remaining weaknesses in μPAD development to facilitate their future market entry. Image 1 • Optimization of device geometry and amount of assay reagent using D-optimal design. • Investigation of seven factors at three levels by only performing 44 trials. • MATLAB-based morphological recognition algorithm for signal readout. [ABSTRACT FROM AUTHOR]

The current work was aimed to develop the paper microzone plates (PµZP) as a scanometric system for determining the total antioxidant capacity of plant extracts. The PµZP was constructed by immobilizing CUPRAC reagent onto 70-well of patterned paper as a sensing zone for the scanometric detection system. The light blue-sensing zone showed a sensitive response to standard antioxidant (rutin) by forming light yellow color adducts which can be scanned and quantitatively measured by image processing program as a scanometric method. The sensor gave a notable signal at 8 min after rutin addition and presented a linear response in the concentration span of 1–10 mM. The developed method was shown to be reproducible (RSD < 3%) and accurate (98–101% recovery) for determining the TAC of plant extracts (as mM rutin equivalents) and showed strong correlation (r = 0.9887) with the standard CUPRAC method, suggesting that it can be applied for the simple and rapid method for TAC determination. [ABSTRACT FROM AUTHOR]

Abstract A lka l ine p hosphatase (ALP) is a metalloprotein found naturally in raw milk samples and is considered as an important biomarker in quality control of milk. Its easy, personalized, as well as instrument-less detection is important to ensure the pasteurization and its differentiation form raw milk. In view of such importance of APL, we have developed an office punching machine crafted paper biosensor for naked eye detection of ALP in milk samples. The quantitative estimation is done by d igital i mage c olorimetry (DIC) based technique integrated with smartphone. The sensor-probe was developed by the covalent immobilization of ALP antibody (anti-ALP) onto the functionalized paper surface. The fabrication of the biosensing probe was characterized using DIC, F ourier t ransform i nfrared s pectroscopy (FTIR), and a tomic f orce m icroscopy (AFM). The detection was based on immunocomplexation between the sensor-probe and ALP, which generates blue-green precipitate as an analytical signal by exploiting the catalytic activity of ALP towards 5- b romo-4- c hloro 3- i ndolyl p hosphate (BCIP). The dose dependent appearance of the blue-green complex was captured using smartphone camera and DIC was employed using R ed, G reen, and B lue (RGB) profiling system, where the maximum sensitivity was obtained for the red color channel. Based on the DIC analysis, a wide dynamic range for the ALP detection is obtained from 10 to 1000 U/mL with the detection limit of 0.87 (± 0.07) U/mL. The designed paper-based biosensor is successfully applied to detect ALP in commercial and raw milk samples. Interferences due to components present in the milk samples was evaluated and the long-term stability of the designed biosensor was examined. Based on the detection principle, a miniaturized kit [20.0 mm (L) × 20.0 mm (W) × 2.15 mm (H)] was developed and applied for the ALP detection to demonstrate the instrument-free direct in-kitchen applicability. Graphical abstract fx1 Highlights • DIC based novel biosensing kit for ALP detection in milk has been developed. • Requires merely 13 min to detect ALP, so it can be applied for quick onsite analysis. • A quantitative personalized analysis approach using smart phone. • Successfully tested the milk obtained from villages and commercially available milk samples. [ABSTRACT FROM AUTHOR]

A simple, cost-effective, instrumental free and user-friendly analytical method based on the combination of paper-based headspace extraction and digital scanning image analysis was developed for the sensitive detection of cyanide in water and wastewater samples. The method relied on chloramine-T/pyridine-barbituric acid reaction, which is a well-known process for measuring cyanide in standard analytical methods. During the reaction, cyanide was converted to cyanogen chloride, which was extracted and collected on a paper impregnated with pyridine-barbituric acid. The color appeared on the paper was used to quantify the analyte using a scanning-assisted image processing software. Experimental parameters affecting the sensitivity of the method such as stirring rate, paper size, chloramine-T and barbituric acid concentration, equilibrium and extraction time were studied. Under optimal conditions, the calibration curve was linear in the range of 3.0–100 μg L −1 with the detection limit of 0.7 μg L −1 . The intra- and inter- day relative standard deviations of the method at 10 and 70 μg L −1 concentration levels were less than 6%. Analyte recovery in real samples was in the range of 69–111%. [ABSTRACT FROM AUTHOR]

The trade business license certificate (SIUP) is a paper-based license to conduct trade businesses in Indonesia issued by the government. Until today, there is no mechanism for verifying the validity of document unless to verify it manually. The current paper presents a design that allows paperbased verification of the printed trade business license. It aims to provide the verification mechanism and ensure the document validity. Our design implemented digital signature with QR Code image that placed into the document and the digital certificate issued by a certification authority (CA). The proposed scheme generated 442 Bytes of data and version 14 of QR Code to scan easily by a reader device. The experimental result indicates that our scheme is easy and feasible to implement in Indonesia with guaranteed the document integrity, authentication, and nonrepudiation. [ABSTRACT FROM AUTHOR]

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 1 of this article will inform CAR members on principles of de-identification, pseudonymization, encryption, direct and indirect identifiers, k-anonymization, risks of reidentification, implementations, data set release models, and validation of AI algorithms, with a view to developing appropriate standards to safeguard patient information effectively. [ABSTRACT FROM AUTHOR]