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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]

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]

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]

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]

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]

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]

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.

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]

Sensor arrays, which draw inspiration from the mammalian olfactory system, are fundamental concepts in high-throughput analysis based on pattern recognition. Although numerous optical sensor arrays for various targets in aqueous media have demonstrated their diverse applications in a wide range of research fields, practical device platforms for on-site analysis have not been satisfactorily established. The significant limitations of these sensor arrays lie in their solution-based platforms, which require stationary spectrophotometers to record the optical responses in chemical sensing. To address this, this review focuses on paper substrates as device components for solid-state sensor arrays. Paper-based sensor arrays (PSADs) embedded with multiple detection sites having cross-reactivity allow rapid and simultaneous chemical sensing using portable recording apparatuses and powerful data-processing techniques. The applicability of office printing technologies has promoted the realization of PSADs in real-world scenarios, including environmental monitoring, healthcare diagnostics, food safety, and other relevant fields. In this review, we discuss the methodologies of device fabrication and imaging analysis technologies for pattern recognition-driven chemical sensing in aqueous media. [Display omitted] • Cross-reactive sensors are applied to paper-based arrays for simultaneous detection. • Portable digital recorders can be used for detecting various responses. • Imaging analysis techniques can accelerate accurate data processing. [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]

Hydrophobic properties belong to the most important factors which determine functional properties of paper products. A number of methods related to the measurement of the interaction between water and paper are already used but most of them give results after specific period of time whereas many unit operations in modern industrial processes (e.g. printing) last usually less than one second. As a result, most of the existing methods are not corresponding to today’s requirements. The aim of the presented research was to develop the method based on image analysis for measuring the dynamics of penetration and permeation of liquid through paper. Special device named eXtended Liquid Penetration Analyser (XLPA) was built for this purpose. Based on the obtained results, the method developed allows to observe and analyse the dynamics of liquid permeation through paper. The proposed method fully replaces the method implemented in the PLG Sizing Tester, which estimated the time of a 50% decrease in brightness of a tested paper sample permeated by ink. Presented method offers the possibility of quantitative measurement of changes in liquid absorption with time resolution of 1/60 s. At the same time, thanks to the use of a camera and image analysis, it is possible to detect local areas of non-homogeneity in paper structure that differ with regards to their capability to absorb liquid, as well as to determine quantitative measures that characterize the non-homogeneity of the permeation process with reference to paper surface. [ABSTRACT FROM AUTHOR]

Paper used in the printing industry generally contains a relatively thin porous coating covering a thicker fibrous base layer. The three-dimensional pore structure of coatings has a major effect on fluid flow patterns inside the paper medium. Understanding and quantifying the flow properties of thin coating layers is hence crucial. Pore spaces within the coating have an average size of about 180 nm. We used scanning electron microscopy combined with focused ion beam (FIB-SEM) to visualize the nano-scale pore structure of the paper coating layer. Post-processing of the FIB-SEM images allowed us to reconstruct the three-dimensional pore space of the coating. The 3D FIB-SEM images were analyzed in detail to obtain pore size distribution and porosity value. The permeability was estimated using the GeoDict software, based on solutions of the Stokes equation. By determining the porosity and permeability of increasingly larger domain sizes, we estimated the size of a representative elementary volume (REV) for the coating layer to be 60 µm 3 , which is well within the volume analyzed using FIB-SEM. The estimated porosity and permeability of the REV domain were 0.34 and 0.09 mDarcy, respectively. Using the pore morphology method, capillary pressure-saturation ( P c - S ) and relative permeability curves of the REV domain could be constructed next. The P c -S curves showed that the coating had a high air entry suction, which is very favorable for printing in that ink will invade the coating as soon as it is applied to the coating. Our results are essential for macroscale modelling of ink penetration into a coating layer during inkjet printing. Macroscopic models can be valuable tools for optimization of the penetration depth and the spreading of ink on and within paper substrates. [ABSTRACT FROM AUTHOR]

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation. [ABSTRACT FROM AUTHOR]

The formation index of filter paper is one of the most important characteristics used in industrial quality control. Its estimation is often based on subjective comparison chart rating or, even more objective, on the power spectrum of the paper structure observed on a transmission light table. It is shown that paper formation can be modeled as Gaussian random fields with a well defined class of correlation functions, and a formation index can be derived from the density of the Bartlett spectrum estimated from image data. More precisely, the formation index is the mean of the Bessel transform of the correlation taken for wave lengths between 2 and 5 mm.

[Display omitted] • Assessment of ash from co-combustion of bark and sludges for nutrient recovery. • P amounts indicate that ash from co-combustion can be useful as a soil improver. • Morphology of slag particles was analysed with X-ray micro-tomography. • Overall, a high number of small and discrete pores. • Relatively thick particle walls and few pore openings indicates a need of grounding. Recycling phosphorus from waste streams for fertilization purposes could contribute to a sustainable society. The production in the pulp and paper industry results in several waste streams, among others nutrient-rich sludges in different forms. This study presents a detailed chemical and 3D characterization of ash from co-combustion of bark and two types of sludges from a paper mill; mixed sludge and biosludge. The combustion performance was investigated for these experiments and advanced analysis methods were used to characterise the ashes to correlate chemical and physical properties relevant for nutrient recycling. The elemental composition was determined by energy-dispersive X-ray spectroscopy; dominating crystalline phases by X-ray diffraction; and morphology, porosity, pore size distribution and active surface area of the slag were analysed with synchrotron-based X-ray micro-tomography and image analysis. Slag was formed in all combustion experiments to a large extent with increasing amounts with a higher proportion of sludge. Nutrient amounts indicate that slag particles from co-combustion of both biosludge and mixed sludge can be useful either as a soil improvement directly or for recovery processes. Slag from combustion of 30 wt% biosludge and 70 wt% bark contained the highest amount of phosphorus, 9 at % on a C and O free basis. Evaluation of tomography data showed that discrete and open pores could be distinguished on a micrometre scale. The porosity of the slag varied between the replicates and fuel mixtures, on average between 17 and 23 vol% for the bark and sludge mixtures. Open pore volume displayed large variations, on average 39–56 vol% of the pores were open pores connected to the surrounding volume. For all samples, 90 % of the pores were small, with an equivalent diameter under 30 μm, but the largest pore volume (80–90 %) consists of pores with an equivalent diameter over 75 μm. In soils, pores with a minimum equivalent diameter over 30 μm generally transmit water and the smaller pores store water. The slag particles have relatively thick walls, with few pore openings to the surroundings, indicating that the slag needs to be pre-treated by milling or crushing before application in the soil. [ABSTRACT FROM AUTHOR]

The article presents a new technique for analyzing phenomena occurring during the measurement of the strength properties of paper in the conditions of compression of the tested samples with forces acting in the paper plane. The technique is based on collecting data on the current distance of the clamps holding the tested sample and the force exerted on the sample using a universal testing machine and on the simultaneous recording of image sequence of the sample during the measurement. Next, the resulting images are subjected to processing and analysis, the purpose of which is to extract information about the shape of the sample edge in all phases of the measurement. Its advantage is the ability to determine the deflection arrow of the sample and describe its shape using the selected function given by the analytical parametric formula. It will be helpful in further research on the development of an analytical model describing the phenomena occurring during paper compression, and a method to determine the mechanism of paper destruction and the corresponding maximum force that destroys a paper sample. [ABSTRACT FROM AUTHOR]

In my previous article (Temiz B K 2020 Phys. Educ. 55 027002), I demonstrated that images on a screen covered with paper are seen clearly when the paper is close to the screen but become blurry when the paper is moved away. I wanted you, the readers, to find the reason behind this phenomenon. In this study, I aim to give the answer to this question. [ABSTRACT FROM AUTHOR]

The study was conducted at the University of Nebraska Pesticide Application and Technology Laboratory in North Platte, Nebraska in July 2015. Two application volume rates (100 and 200 l · ha-1) and three nozzle types (XR, AIXR, TTI) were selected at two flow rates (0.8 and 1.6 l · min-1) and at a single application speed of 7.7 km · h-1. Each collector type [Mylar washed (MW), Mylar image analysis (MIA), water-sensitive paper (WSP), and Kromekote (KK)] was arranged in a randomized complete block design. Each nozzle treatment was replicated twice, providing six cards of each collector type for each nozzle treatment. A water + 0.4% v/v Rhodamine WT spray solution was applied, given the fluorescent and visible qualities of Rhodamine, which allows it to be applied over all the collector types. MW had the highest coverage at 18.3% across nozzle type, followed by WSP at 18%, KK at 12% and lastly by MIA at 4%. MW resulted in a 58% increase in coverage, WSP in a 56% increase, and KK only an increase of 39% when the volume rate was doubled from 100 l · ha-1 to 200 l · ha-1 across nozzle type. MW coverage was similar to KK for half of the nozzles (XR 11002, XR 11004, AIXR 11002). Droplet number density fixed effects were all significant for nozzle type and collector type (p < 0.001) as was the interaction of nozzle type and collector type (p < 0.001). Results from this study suggest a strong correlation to data produced with WSP and MW collectors, as there was full agreement between both types except for the TTI 11004. Using both collector types in the same study would allow for a visual understanding of the distribution of the spray, while also giving an idea of the concentration of that distribution. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate a simple and low-cost colorimetric method based on Fe(III) chloride and potassium hexacyanoferrate(III) for determination of acetaminophen, ascorbic acid and phenylephrine, which can be used in small laboratories using restricted materials and resources. A paper based sensor using the reagent was introduced. Significance of different variables in determination of drugs were studied by Placket-Burman design and optimal values for concentration of hydrochloric acid, Fe(III) chloride and potassium hexacyano ferrate(III), 0.01 M, 0.004 M and 0.004 M, respectively were obtained. Without using any sophisticated instrument and only by image analysis the concentration of acetaminophen, ascorbic acid and phenylephrine was determined in pharmaceutical samples. In this way we were able to determine by analyzing the final color of the reactions. The linear range in ppm for all the analytes was obtained as 0.00–50.00 and limit of detection of 2.52, 2.48 and 2.48 were obtained for acetaminophen, ascorbic acid and phenylephrine, respectively. Percent relative standard deviation (RSD %) and percent relative error (RE %) of the proposed method for analysis of real samples of acetaminophen tablet were 4.77 and 7.64, ascorbic acid tablet, 4.73 and 1.41, and phenylephrine syrup, 2.44 and − 1.61, respectively. [ABSTRACT FROM AUTHOR]

A novel green fabrication approach is being proposed based on radiation assisted modification of Whatman filter paper 1 (WFP) for development of Acid Free-Paper based Analytical Devices (A f -PADs). A f -PADs exude immense potential as handy tools for on-site detection of toxic pollutants such as, Cr(VI), boron, etc., which have established detection protocols involving acid mediated colorimetric reactions that necessitate external acid addition. The proposed A f -PAD fabrication protocol asserts its novelty through elimination of external acid addition step, making the detection process safer and simpler. To achieve this, poly(acrylic acid) (PAA) was grafted onto WFP via a single step, room temperature process of gamma radiation induced simultaneous irradiation grafting, introducing acidic -COOH groups in the paper thereon. Grafting parameters namely, absorbed dose and concentrations of monomer, homopolymer inhibitor and acid were optimized. The -COOH groups incorporated in PAA-grafted-WFP (PAA- g -WFP) provide localized acidic conditions for colorimetric reactions between pollutants and their sensing agents, anchored on the PAA- g -WFP. A f -PADs loaded with 1,5-diphenylcarbazide (DPC) have been ably demonstrated for visual detection and quantitative estimation of Cr(VI) in water samples using RGB image analysis, with LOD value of 1.2 mg.L−1 and a measurement range comparable to that of commercially available PADs based Cr(VI) visual detection kits. [Display omitted] [ABSTRACT FROM AUTHOR]

The authors present a comprehensive overview of image processing and analysis work done to support research into the model flowering plant Arabidopsis thaliana. Beside the plant's importance in biological research, using image analysis to obtain experimental measurements of it is an interesting vision problem in its own right, involving the segmentation and analysis of sequences of images of objects whose shape varies between individual specimens and also changes over time. While useful measurements can be obtained by segmenting a whole plant from the background, they suggest that the increased range and precision of measurements made available by leaf‐level segmentation makes this a problem well worth solving. A variety of approaches have been tried by biologists as well as computer vision researchers. This is an interdisciplinary area and the computer vision community has an important contribution to make. They suggest that there is a need for publicly available datasets with ground truth annotations to enable the evaluation of new approaches and to support the building of training data for modern data‐driven computer vision approaches, which are those most likely to result in the kind of fully automated systems that will be of use to biologists.

Plant root system architecture (RSA) has an important role in crop production, particularly for water and nutrient uptake under limiting conditions. In the last few years, several root phenotyping methods have been developed. Here we present a new technique which has been developed for non-destructive, inexpensive and high-throughput root growth studies and RSA analyses. To illustrate the potential applications, this method was tested in an experiment with nitrogen and phosphorous deficiencies in a nutrient solution, affecting RSA parameters of two spring barley varieties (Bojos and Barke). This technique is based on root growth on vertically positioned black filter paper (30 × 60 cm) placed between two black plastic (PVC-P) foils and micro-irrigation systems providing the recirculation of nutrient solution. The pre-germinated seeds were placed in the slit between two plastic bars which carry the filter paper and plastic sheets and fix the plant in the vertical position. This system allows easy repeated non-invasive access to roots for their measuring and sampling. Eighteen days after transplanting the root imaging was done using an RGB digital camera. To evaluate the root architecture parameters the "SmartRoot" software was used. The results revealed that the system is able to detect changes in RSA which are caused mainly by P deficiency (particularly changes in lateral root length and total root area). It can be concluded that this technique has a great potential for non-destructive root growth studies, RSA measurement and root sampling.

This article introduces a novel approach by coupling paper points with hollow fiber membrane for electroextraction (PP–HF-EE). The method was innovatively applied to extract methylene blue (MB) from large water volumes (up to 580 mL). A comprehensive study of six key parameters – organic filter, acceptor and donor phase composition, extraction time, applied voltage, and sample volume – was conducted using conventional flatbed scanning and digital image analysis. Our results revealed that extraction performance was primarily influenced by time, with low voltages (50 V) and low-conductivity organic filters (1-decanol) yielding comparable results to higher settings (300 V or 1-pentanol). Under optimized conditions (50 V, 60 min, 1-decanol as the organic filter), analytical performance parameters were assessed, demonstrating acceptable precision (RSD <18% for intra- and inter-day measurements) within a linear range of 5–100 μg L−1 (r = 0.98). PP–HF–EE demonstrated reliability through stable and reproducible electric current measurements during all extraction studies. Utilizing an extremely cost-effective detection system, PP–HF–EE achieved detection limits in the low ppb range, highlighting its potential as a promising variation of electromembrane extraction for environmental sample analysis. [Display omitted] • A new electroextraction approach for environmental samples was presented. • Paper point after extraction was subsequently examined by digital image analysis. • Quantification of methylene blue in tap water was achieved in the low ppb range. • The new electroextraction approach might be promising for environmental analysis. [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]

Development and application of a new, simple, inexpensive, and eco-friendly method is presented for the quantification of bumetanide in human urine samples for the detection of doping in sports. The method involves the use of a paper platform and digital image detection and is based on the reaction between bumetanide, p -dimethylaminocinnamaldehyde (p -DAC), and HCl in a methanolic medium, yielding a colored compound in a delimited area on a qualitative filter paper. The concentrations of p- DAC and HCl were optimized by a chemometric experimental design. After the addition of the reagents to the paper, digital images were obtained by scanning in a multifunctional printer and analyzed using the RGB pattern. The linear bumetanide concentration range was 6.90 × 10−5–1.37 × 10−3 mol L−1 with R 2 = 0.993 and the limits of detection (LOD) and quantification (LOQ) were 3.60 × 10−4 and 1.09 × 10−4 mol L−1, respectively. A SPE clean-up step for the samples were required to eliminate the interference of urinary urea. The recoveries obtained varied from 94% to 105%, indicating the absence of significant matrix effects or interferences in urine samples after clean-up step. The proposed method was successfully applied to the analysis of bumetanide in human urine samples. • Spot test employing a paper platform delimited with hydrophobic barriers • μPAD associated with digital image detection for bumetanide determination • Determination of bumetanide in urine samples for doping sports • The method represents an advantageous alternative to most of the reported methods. [ABSTRACT FROM AUTHOR]

In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products and save processing chemicals and energy. Automated paper sorting systems offer significant advantages over human inspection in terms of worker fatigue, throughput, speed, and accuracy. As a consequence, many automated mechanical and optical paper sorting methods have been developed to fill the paper sorting demand during 1932 to 2009. Because of inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. The implementation of the previous methods, while being a step forward in the large-volume automated sorting technology, is still complex, expensive and sometimes offers limited reliability. This research attempts to develop a smart vision sensing system that is able to separate the different grades of paper using chromaticity. For constructing template database, hue and saturation of the paper object image in a selected area are considered. The paper grade is identified based on the maximum occurrence of a specific template in the paper object image. The classification success rates .for white paper, old newsprint paper and old corrugated cardboard are 95%, 92% and 90%, respectively. Finally, the best result of the proposed method is compared with the results published in literature where waste paper grade identification systems were developed using other methods. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using chromaticity, which is the best among the prevailing techniques of optical or electronic image based systems. [ABSTRACT FROM AUTHOR]

This article analyses Italian illustrated editions of Little Women published between 1908 and 1945. After an overview of the publishing history of the novel in Italy, the paper will examine Italian illustrations as hermeneutic tools in order to examine their ideological function in the representation of gender. The belated reception of Alcott's novel in Italy, caused by its representation of a nonconformist educational model for girls, is mirrored in the tension between the nationalistic drive and the influence of foreign models that can be pinpointed in the illustrated editions. By analyzing adaptations of American illustrations, original Italian creations, and omissions of iconic illustrations, this paper will unveil how much the visual element is embedded in the Italian historical and cultural context, as the choice (or omission) of particular images impacted the interpretation of the book. By examining the Italian editions of Little Women published in this time frame, we will also be able to retrace the links with the American illustrated history of the book and bear testimony to the powerful impact of the 1934 film version on printed editions. [ABSTRACT FROM AUTHOR]