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Curl propensity is a critical-to-quality (CTQ) property of paper, as it causes severe problems during printing and other final conversion operations. The main papermaking factor causing the curl phenomenon is the existence of a fiber orientation (FO) gradient across the thickness direction (or ZD), also known as two-sidedness. Therefore, a methodology that characterizes the FO across the ZD is fundamental for papermakers. In this work, we propose and validate an efficient and cost-effective protocol based on sheet splitting and image analysis. Besides assessing the level of FO two-sidedness, the methodology also provides insights into the flow dynamics in the draining zone of the forming section of the paper machine and the drying stresses built into the paper. This information is relevant for monitoring, optimizing, and troubleshooting activities in the paper industry. [ABSTRACT FROM AUTHOR]

One of the most popular and well-established forms of payment in use today is paper money. Handling paper money might be challenging for those with vision impairments. Assistive technology has been reinventing itself throughout time to better serve the elderly and disabled people. To detect paper currency and extract other useful information from them, image processing techniques and other advanced technologies, such as Artificial Intelligence, Deep Learning, etc., can be used. In this paper, we present a meticulously curated and comprehensive dataset named ‘NSTU-BDTAKA’ tailored for the simultaneous detection and recognition of a specific object of cultural significance - the Bangladeshi paper currency (in Bengali it is called ‘Taka’). This research aims to facilitate the development and evaluation of models for both taka detection and recognition tasks, offering a rich resource for researchers and practitioners alike. The dataset is divided into two distinct components: (i) taka detection, and (ii) taka recognition. The taka detection subset comprises 3,111 high-resolution images, each meticulously annotated with rectangular bounding boxes that encompass instances of the taka. These annotations serve as ground truth for training and validating object detection models, and we adopt the state-of-the-art YOLOv5 architecture for this purpose. In the taka recognition subset, the dataset has been extended to include a vast collection of 28,875 images, each showcasing various instances of the taka captured in diverse contexts and environments. The recognition dataset is designed to address the nuanced task of taka recognition providing researchers with a comprehensive set of images to train, validate, and test recognition models. This subset encompasses challenges such as variations in lighting, scale, orientation, and occlusion, further enhancing the robustness of developed recognition algorithms. The dataset NSTU-BDTAKA not only serves as a benchmark for taka detection and recognition but also fosters advancements in object detection and recognition methods that can be extrapolated to other cultural artifacts and objects. We envision that the dataset will catalyze research efforts in the field of computer vision, enabling the development of more accurate, robust, and efficient models for both detection and recognition tasks.

Forensic document examination has been considered one of the most important fields in the forensic science. For this, a quick, effective, and cheap techniques for evaluation of forensic evidence is highly required. This study explores the comparison of A4 document paper by chemical and physical analysis. 16 of white A4 paper from different brands were analyzed by FTIR for investigation of structural characteristics. Video Spectral Comparator 8000 has been used to compare the A4 papers under different light source. The inorganic filler composition in the paper is also explored by ICP‐OES. The calcium carbonate content in document papers play a discriminant role which found to be between 16.02 % and 35.54 %. Also, water contact angle has been utilized in order to examine the surface of paper which is generally related to the inorganic filler and other additives. The WCA results ranged between 83° and 111° that enable quick and easy way to compare A4 document papers. The experimental results have demonstrated that besides FTIR methods, other methods such as ICP‐OES for elemental composition, WCA for wetting test, VSC for visualization under different light of source could have been applied for comparison of document papers in case of criminal cases. [ABSTRACT FROM AUTHOR]

This study proposes a method to determine the two‐dimensional absorption capacity of paper towels. Individual plies were tested to minimize the effects of transverse wicking on wetting. The method involves arbitrary point source injection of liquid into the towel surface while recording microscopic images of the wetted areas. The samples were selected from the main manufacturing processes: conventional wet pressing and through‐air drying. Wetting graphs that illustrate the dynamic and static wetting behaviors were generated. The driving force was found to have a negligible effect on the maximum wetted area. The maximum wetted area and the paper grammage were used to determine the two‐dimensional absorption capacity. The results were validated by the basket‐immersion test method. [ABSTRACT FROM AUTHOR]

A turn-on fluorescent aptasensor based on a paper-based microfluidic chip was developed to detect arsenite via aptamer competition strategy and smartphone imaging. The chip was prepared by wax-printing hydrophilic channels on filter paper. It is portable, low-cost, and environmentally friendly. Double-stranded DNA consisting of aptamer and fluorescence-labeled complementary strands was immobilized on the reaction zone of the paper chip. Due to the specific strong binding between aptamer and arsenite, the fluorescent complementary strand was squeezed out and driven by capillary force to the detection area of the paper chip, so that the fluorescent signal arose in the detection area under the excitation wavelength of 488 nm. Arsenite can be quantified by using smartphone imaging and RGB image analysis. Under the optimal conditions, the paper-based microfluidic aptasensor exhibited excellent linear response over a wide range of 1 to 1000 nM, with a detection limit as low as 0.96 nM (3σ). [ABSTRACT FROM AUTHOR]

BACKGROUND: This work proposes the use of a simple, inexpensive method for the hydrothermal synthesis of fluorescent carbon dot nanoparticles (CDs) from rice starch aimed at the determination of Hg2+ in water. The proposed method involved using a paper‐based analytical device coupled to a 3D plate, with a UV‐LED chamber and a smartphone for the acquisition and analysis of the fluorescence digital images of the CDs. RESULTS: The size of the carbon dots ranged from 0.5 to 3 nm, with an average particle size of ~1 nm. The functionalization of carbon dots with methimazole allowed a high selective for Hg2+ determination. The results obtained showed a linear response R2 of 0.997 and Hg2+ concentration in the range of 0.5–45.0 μmol L−1 with a limit of detection and limit of quantitation of 0.23 and 0.62 μmol L−1, respectively. The results of the study show that there were no significant differences, at 95% confidence level, between the data obtained from the application of the proposed method and the reference method. CONCLUSION: The proposed method is in line with the principles of green chemistry, as it involves the use of renewable sources for starch extraction and a hydrothermal synthesis process that does not employ toxic reagents. In addition, the method employs only 15 μL reagent/sample. © 2024 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

We have achieved a successful synthesis of a label-free, flexible, portable, and colour-indicating biosensor using cholesteric nanostructure liquid crystals (LCs), commonly referred to as cholesteric LCs (CLCs), on paper fibres. This innovative approach holds significant promise for rapid screening in precision medicine and other applications. We incorporated optically active nematic LCs in various configurations within CLCs, allowing them to rotate along different axes. When exposed to light, these LCs reflected light at specific wavelengths. By employing polarised optical microscopy and a charged couple device (CCD), we conducted titration experiments with bovine serum albumin (BSA) on the paper substrate and quantified protein concentrations based on the observed colour shifts. Utilising internal Python pixel analysis software, we evaluated the red, green, and blue colour components, which served as biomarkers for different BSA concentrations. Our results demonstrated a linear correlation between BSA concentration and colour variations. This CLC-based paper-substrate biosensor exhibits the potential to efficiently screen a large number of samples, thereby enhancing the diagnostic accuracy in clinical settings. Combining the colour-indicating and quantifiable attributes of CLCs with the affordability and portability of paper substrates, we have developed a versatile biosensor. Its ease of use, rapid response, and high sensitivity make it a promising tool for future disease detection endeavours. [ABSTRACT FROM AUTHOR]

A colorimetric sensing method based on a paper-based vapor-test kit was successfully developed for the selective and sensitive real-time monitoring of formalin in food samples. The device was specifically designed to efficiently extract and detect formalin simultaneously. A microcentrifuge tube was used as the sample solution container, with the inner cap serving as the reaction and detection zone. Formalin was converted into gaseous formaldehyde through controlled heating, which was then extracted and collected on a filter paper coated with Nash's reagent. The color change on paper was used for formalin quantification using a smartphone for detection and image analysis. Under optimal conditions, our method provided a linear range of 0.5–75 mg L−1 with a detection limit of 0.11 mg L−1. This method effectively determined formalin in fresh food and vegetable samples, with recoveries ranging from 92 to 111%, demonstrating comparable accuracy to the standard method for practical food quality control and safety. [Display omitted] • A sensitive paper-based vapor-test kit was successfully developed for formalin. • This innovative approach combined headspace extraction, PADs, and image analysis. • An efficient sensing test kit represents a significant advancement for rapid, on-site detection of formalin in food products. • The sensor is cost-effective by utilizing commercially available materials. [ABSTRACT FROM AUTHOR]

A user-friendly, cost-effectively, portable, and environmentally friendly colorimetric sensor for the quantitative determination of formaldehyde was developed based on the combining of microfluidic paper-based analytical device (μPAD), headspace microextraction (HSME), and digital image colorimetry. Coupling HSME and μPAD led to enhancements in selectivity and sensitivity of the sensor through sample cleanup and analyte enrichment. To construct the μPAD-HSME device, two pieces of paper as the sample and detection zone were placed facing each other so that a small common and sealed space was created between them. The color change occurred when the analyte in the gaseous form crossed this gap and reached the detection zone. Colorimetric sensing in the detection zone was performed based on the Hantzsch reaction. The color change in the detection zone was recorded by a smartphone and digital images were processed using image analysis software based on the RGB model. The influence of some key variables on the sensitivity of the method including derivatization reagent composition, sample volume, extraction temperature, and extraction time was studied and optimized. The linear dynamic range of the method was obtained in two ranges of 0.10–0.75 and 0.75–5.0 mg L−1 with a limit of detection of 0.03 mg L−1. The recoveries were in the range 80–126% for the quantification of formaldehyde in textile, milk, and wastewater samples. [ABSTRACT FROM AUTHOR]

During the storage of celluloid pictures for animation films over half a century, an interleave paper adhered to acrylic paint. The purpose of this study is to establish a methodology to cleanly remove the paper from the paint. A layered film, a replica of the celluloid pictures, adhered with paper was prepared and immersed in water or ethanol. The effect of these solvents on the peeling behavior was investigated using a peel test. The maximum peel force for the dry layered film in was distributed at ~0.5 N, independently of the peel speed. The peel force was significantly reduced after the layered film was immersed in pure water or ethanol. A morphological observation revealed that the dry paper was peeled off via the cohesive failure of the paper. After the layered film was immersed in pure water, the paper was also peeled off via cohesive failure. The layered film immersed in ethanol was peeled off at the paper/paint interface. To clear the effect of the volume change in the paint on peel behavior, the relative volume was determined via image analysis. The relative volume of paint was 1.56 in pure water and 1.37 in ethanol. It can be considered that the large difference in the volume of paint induces a large shear stress at the paint/paper interface. [ABSTRACT FROM AUTHOR]

We studied the feasibility of image-based quantitative analysis of foxing stains on collections of old (16th−20th century) European books stored in the Rare Book Library of the Seoul National University in Korea. We were able to quantitatively determine the foxing affected areas on books from their photographs using a newly developed image processing software (PicMan) including cultural property characterization applications, specifically. Dimensional and color analysis of photographs were successfully done quantitatively. Histograms of RGB (red, green, blue) pixels of photographs clearly showed the change in color distribution of foxing stains compared to the other areas of the photographs. Several sample images of quantitative measurement of foxing stains and virtually restored images were generated to provide easy visual inspection and comparison between restored images and the original photographs. Image quality, resolution, and digital file format requirements for quantitative analysis are described. Image-based quantitative analysis of foxing stains on paper documents are found to be very promising towards automation for objective characterization of photographs of cultural properties. This technique can be used to create a cultural property digital database. Quantitative and statistical analysis techniques can be introduced to monitor the effect of storage and conservation environment on the cultural properties.

Flow and spontaneous imbibition phenomena in porous media are important for various industrial applications, including printing and medical lateral flow assays. Their quantitative characterization is important to better understand and select the appropriate raw materials. However, standard methods often require time-consuming tests, and/or expensive equipment. Different time scales must be considered, limiting the range of possible characterization tools. A novel experimental approach based on image analysis for characterizing spontaneous imbibition processes is presented. Hence, ultra-fast diffusion may be quantitatively characterized. Models are issued from the literature to consider physical phenomena at small (milliseconds) and medium range (seconds) of time scales. The obtained experimental data fit with theoretical results, providing valuable insights into the understanding of fluid flow behavior at different time scales. Furthermore, the identification of some physical properties for either the fluid, or the substrate, based on the theoretical models are possible, as the contact angle, which remains to be otherwise challenging. This study contributes to bridging the gap between spontaneous imbibition and capillary phenomena at different time scales, their modeling, and a characterization of material and/or fluid properties paving the way for enhanced understanding and control of fluid behavior in porous media. Different papers are considered to illustrate the method. [ABSTRACT FROM AUTHOR]

Medieval paper, a handmade product, is made with a mould which leaves an indelible imprint on the sheet of paper. This imprint includes chain lines, laid lines and watermarks which are often visible on the sheet. Extracting these features allows the identification of the paper stock and gives information about the chronology, localisation and movement of manuscripts and people. Most computational work for feature extraction of paper analysis has so far focused on radiography or transmitted light images. While these imaging methods provide clear visualisation of the features of interest, they are expensive and time consuming in their acquisition and not feasible for smaller institutions. However, reflected light images of medieval paper manuscripts are abundant and possibly cheaper in their acquisition. In this paper, we propose algorithms to detect and extract the laid and chain lines from reflected light images. We tackle the main drawback of reflected light images, that is, the low contrast attenuation of chain and laid lines and intensity jumps due to noise and degradation, by employing the spectral total variation decomposition and develop methods for subsequent chain and laid line extraction. Our results clearly demonstrate the feasibility of using reflected light images in paper analysis. This work enables feature extraction for paper manuscripts that have otherwise not been analysed due to a lack of appropriate images. We also open the door for paper stock identification at scale. [ABSTRACT FROM AUTHOR]

We believe the switch to a digital pathology (DP) workflow is imminent and it is essential to understand the economic implications of conversion. Many aspects of the adoption of DP will be disruptive and have a direct financial impact, both in short term costs, such as investment in equipment and personnel, and long term revenue potential, such as improved productivity and novel tests. The focus of this whitepaper is to educate pathologists, laboratorians and other stakeholders about the business and monetary considerations of converting to a digital pathology workflow. The components of a DP business plan will be thoroughly summarized, and guidance will be provided on how to build a case for adoption and implementation as well as a roadmap for transitioning from an analog to a digital pathology workflow in various laboratory settings. It is important to clarify that this publication is not intended to list prices although some financials will be mentioned as examples. The authors encourage readers who are evaluating conversion to a DP workflow to use this paper as a foundational guide for conducting a thorough and complete assessment while incorporating in current market pricing. Contributors to this paper analyzed peer-reviewed literature and data collected from various institutions, some of which are mentioned. Digital pathology will change the way we practice through facilitating patient access to expert pathology services and enabling image analysis tools and assays to aid in diagnosis, prognosis, risk stratification and therapeutic selection. Together, they will result in the delivery of valuable information from which to make better decisions and improve the health of patients. [ABSTRACT FROM AUTHOR]

We propose a printed 384‐well microtiter paper‐based fluorescent chemosensor array device (384‐well microtiter PCAD) to simultaneously categorize and discriminate saccharides and sulfur‐containing amino acids for food analysis. The 384‐well microtiter PCAD requiring 1 μL/4 mm2 of each well can allow high‐throughput sensing. The device embedded with self‐assembled fluorescence chemosensors displayed a fingerprint‐like response pattern for targets, the image of which was rapidly captured by a portable digital camera. Indeed, the paper‐based chemosensor array system combined with imaging analysis and pattern recognition techniques not only successfully categorized saccharides and sulfur‐containing amino acids but also classified mono‐ and disaccharide groups. Furthermore, the quantitative detectability of the printed device was revealed by a spike and recovery test for fructose and glutathione in a diluted freshly made tomato juice. We believe that the 384‐well microtiter PCAD using the imaging analysis system will be a powerful sensor for multi‐analytes at several categorized groups in real samples. [ABSTRACT FROM AUTHOR]

Especially in the field of paper and print post-processing on an industrial scale, continuous cutting operations generate paper dust. Despite preventive measures like dedusting units, a high paper web velocity induces the release of particulate dust. As a result, the particles settle and accumulate on machine components and in the surrounding area. Resuspended particles around a potential ignition source represent a fire hazard. The prevention of such fire incidents requires frequent servicing and maintenance. However, the shutdown time on industrial scale applications remains cost-intensive. In this research, a cutting plotter executes reproducible knife edge cutting sequences to identify significant parameters affecting the paper dust formation on a macro and micro scale. For this purpose, an extraction system collects the exposed particles along the cutting line. The applied methods include gravimetric determination of the dust mass and dynamic image analysis to characterise particle size and shape. Final results indicate a significant influence of the blade wear condition. [ABSTRACT FROM AUTHOR]

Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep convolutional neural networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the work exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pretrained models and general adversarial networks that aid in improving convolutional networks' performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on COVID-19 detection and child bone age prediction. [ABSTRACT FROM AUTHOR]