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We analyze changes in the business network of Interwar Yugoslavia using computational network analysis. We extract data from Compass, a yearly directory of directors in Central European companies. We combine qualitative and quantitative methods to give insight into the evolution of the Yugoslavian business in a significant historical context.

We demonstrate the use of a convolutional neural network to perform neutron-gamma pulse shape discrimination, where the only inputs to the network are the raw digitised SiPM signals from a dual scintillator detector element made of 6LiF:ZnS(Ag) scintillator and PVT plastic. A realistic labelled dataset was created to train the network by exposing the detector to an AmBe source, and a data-driven method utilsing a separate PMT was used to assign labels to the recorded signals. This approach is compared to the charge integration and continuous wavelet transform methods and is found to provide superior levels of discrimination, achieving an AUC of 0.995 +/- 0.003. We find that the neural network is capable of extracting interpretable features directly from the raw data. In addition, by visualising the high-dimensional representations of the network with the t-SNE algorithm, we discover that not only is this method robust to minor mislabeling of the training dataset but that it is possible to identify an underlying substructure within the signals that goes beyond the original labelling. This technique could be utilised to explore and cluster complex, raw detector data in a novel way that may reveal more insights than standard analysis methods., 13 pages, 8 figures, submitted to Nucl. Instr. Meth. A

Over the last few years, the microfluidics phenomenon coupled with the Internet of Things (IoT) using innovative nano-functional materials has been recognized as a sustainable and economical tool for point-of-care testing (POCT) of various pathogens influencing human health. The sensors based on these phenomena aim to be designed for cost-effectiveness, make it handy, environment-friendly, and get an accurate, easy, and rapid response. Considering the burgeoning importance of analytical devices in the healthcare domain, this review paper is based on the gist of sensing aspects of the microfabricated paper-based analytical devices (μPADs). The article discusses the various used design methodologies and fabrication approaches and elucidates the recently reported surface modification strategies, detection mechanisms viz., colorimetric, electrochemical, fluorescence, electrochemiluminescence, etc. In a nutshell, this article summarizes the state-of-the-art research work carried out over the nano functionalized paper-based analytical devices and associated challenges/solutions in the point of care testing domain.

In the present study, iodide-mediated 3,3′,5,5′-tetramethylbenzidine (TMB)-H2O2 reaction system was applied to a microfluidic paper-based analytical device (μPAD) for non-enzymatic colorimetric determination of H2O2. The proposed system is portable and incorporates a μPAD with a machine learning-based smartphone app. A smartphone app called “Hi-perox Sens” capable of image capture, cropping and processing was developed to make the system simple and user-friendly. Briefly, circular μPADs were designed and tested with varying concentrations of H2O2. Following the color change, the images of the μPADs were taken with four different smartphones under seven different illumination conditions. In order to make the system more robust and adaptive against illumination variation and camera optics, the images were first processed for feature extraction and then used to train machine learning classifiers. According to the results, TMB + KI showed the highest classification accuracy (97.8%) with inter-phone repeatability at t = 30 s under versatile illumination and maintained its accuracy for 10 minutes. In addition, the performance of the system was also comparable to two different commercially available H2O2 kits in real samples.

Significance: High-speed three-dimensional (3D) super-resolution microscopy is a unique tool to investigate various biological phenomena; yet the technology is not broadly adopted due to its high cost and complex system design. Aim: We present a compact, low-cost, and high-speed 3D structured illumination microscopy (SIM) based on a digital micromirror device and binary holography to visualize fast biological events with super-resolution. Approach: The 3D SIM uses a digital micromirror device to generate three laser foci with individually controllable positions, phases, and amplitudes via binary holography at the back aperture of objective lens to form optimal 3D structured patterns. Fifteen raw images are sequentially recorded and processed by the 3D SIM algorithm to reconstruct a super-resolved image. Results: Super-resolution 3D imaging at a speed of 26.7 frames per second is achieved with a lateral and axial resolution of 155 and 487 nm, which corresponds to a 1.65- and 1.63-times resolution enhancement, respectively, comparing with standard deconvolution microscopy. Conclusions: The 3D SIM realizes fast super-resolution imaging with optimal 3D structured illumination, which may find important applications in biophotonics.

Significance: Intraplaque hemorrhage (IPH) is an important indicator of plaque vulnerability. Early detection could aid the prevention of stroke. Aim: We aim to detect IPH with single wavelength PA imaging in vivo and to improve image quality. Approach: We developed a singular value decomposition (SVD)-based filter to detect the nonstationary and stationary components in ultrasound data. A PA mask was created to detect stationary (IPH) sources. The method was tested ex vivo using phantoms and in vivo in patients. Results: The flow and IPH channels were successfully separated in the phantom data. We can also detect the PA signals from IPH and reject signals from the carotid lumen in vivo. Generalized contrast-to-noise ratio improved in both ex vivo and in vivo in US imaging. Conclusions: SVD-based filtering can successfully detect IPH using a single laser wavelength, opening up opportunities for more economical and cost-effective laser sources.

Significance Most shape-morphing materials rely on nonrenewable fossil resources or finely extracted biomaterials, which need strict reaction control, elaborate processing equipment, or prefabricated templates to achieve controllable transformation. To circumvent these challenges, we developed an eco-friendly and scalable strategy for programmable shape evolution that integrates easy-to-process pollen biomass with cost-effective digital printing. Using this approach, specific geometrical features and architectures were customized to build complex materials with user-defined, shape-morphing abilities. These fabrication efforts were complemented by computational simulations to build quantitative and mechanistic insights into the biomaterial’s characteristics for creating complex shapes with methods that are suitable for scalable manufacturing., We demonstrate how programmable shape evolution and deformation can be induced in plant-based natural materials through standard digital printing technologies. With nonallergenic pollen paper as the substrate material, we show how specific geometrical features and architectures can be custom designed through digital printing of patterns to modulate hygrophobicity, geometry, and complex shapes. These autonomously hygromorphing configurations can be “frozen” by postprocessing coatings to meet the needs of a wide spectrum of uses and applications. Through computational simulations involving the finite element method and accompanying experiments, we develop quantitative insights and a general framework for creating complex shapes in eco-friendly natural materials with potential sustainable applications for scalable manufacturing.

Significance: Early detection of oral cancer is vital for high-risk patients, and machine learning-based automatic classification is ideal for disease screening. However, current datasets collected from high-risk populations are unbalanced and often have detrimental effects on the performance of classification. Aim: To reduce the class bias caused by data imbalance. Approach: We collected 3851 polarized white light cheek mucosa images using our customized oral cancer screening device. We use weight balancing, data augmentation, undersampling, focal loss, and ensemble methods to improve the neural network performance of oral cancer image classification with the imbalanced multi-class datasets captured from high-risk populations during oral cancer screening in low-resource settings. Results: By applying both data-level and algorithm-level approaches to the deep learning training process, the performance of the minority classes, which were difficult to distinguish at the beginning, has been improved. The accuracy of “premalignancy” class is also increased, which is ideal for screening applications. Conclusions: Experimental results show that the class bias induced by imbalanced oral cancer image datasets could be reduced using both data- and algorithm-level methods. Our study may provide an important basis for helping understand the influence of unbalanced datasets on oral cancer deep learning classifiers and how to mitigate.

Significance: Sub-diffuse optical properties may serve as useful cancer biomarkers, and wide-field heatmaps of these properties could aid physicians in identifying cancerous tissue. Sub-diffuse spatial frequency domain imaging (sd-SFDI) can reveal such wide-field maps, but the current time cost of experimentally validated methods for rendering these heatmaps precludes this technology from potential real-time applications. Aim: Our study renders heatmaps of sub-diffuse optical properties from experimental sd-SFDI images in real time and reports these properties for cancerous and normal skin tissue subtypes. Approach: A phase function sampling method was used to simulate sd-SFDI spectra over a wide range of optical properties. A machine learning model trained on these simulations and tested on tissue phantoms was used to render sub-diffuse optical property heatmaps from sd-SFDI images of cancerous and normal skin tissue. Results: The model accurately rendered heatmaps from experimental sd-SFDI images in real time. In addition, heatmaps of a small number of tissue samples are presented to inform hypotheses on sub-diffuse optical property differences across skin tissue subtypes. Conclusion: These results bring the overall process of sd-SFDI a fundamental step closer to real-time speeds and set a foundation for future real-time medical applications of sd-SFDI such as image guided surgery.

Significance: Effective vein visualization is critically important for several clinical procedures, such as venous blood sampling and intravenous injection. Existing technologies using infrared device or ultrasound rely on professional equipment and are not suitable for daily medical care. A regression-based vein visualization method is proposed. Aim: We visualize veins from conventional RGB images to provide assistance in venipuncture procedures as well as clinical diagnosis of some venous insufficiency. Approach: The RGB images taken by digital cameras are first transformed to spectral reflectance images using Wiener estimation. Multiple regression analysis is then applied to derive the relationship between spectral reflectance and the concentrations of pigments. Monte Carlo simulation is adopted to get prior information. Finally, vein patterns are visualized from the spatial distribution of pigments. To minimize the effect of illumination on skin color, light correction and shading removal operations are performed in advance. Results: Experimental results from inner forearms of 60 subjects show the effectiveness of the regression-based method. Subjective and objective evaluations demonstrate that the clarity and completeness of vein patterns can be improved by light correction and shading removal. Conclusions: Vein patterns can be successfully visualized from RGB images without any professional equipment. The proposed method can assist in venipuncture procedures. It also shows promising potential to be used in clinical diagnosis and treatment of some venous insufficiency.

Significance: Photoacoustic imaging (PAI) is a powerful emerging technology with broad clinical applications, but consensus test methods are needed to standardize performance evaluation and accelerate translation. Aim: To review consensus image quality test methods for mature imaging modalities [ultrasound, magnetic resonance imaging (MRI), x-ray CT, and x-ray mammography], identify best practices in phantom design and testing procedures, and compare against current practices in PAI phantom testing. Approach: We reviewed scientific papers, international standards, clinical accreditation guidelines, and professional society recommendations describing medical image quality test methods. Observations are organized by image quality characteristics (IQCs), including spatial resolution, geometric accuracy, imaging depth, uniformity, sensitivity, low-contrast detectability, and artifacts. Results: Consensus documents typically prescribed phantom geometry and material property requirements, as well as specific data acquisition and analysis protocols to optimize test consistency and reproducibility. While these documents considered a wide array of IQCs, reported PAI phantom testing focused heavily on in-plane resolution, depth of visualization, and sensitivity. Understudied IQCs that merit further consideration include out-of-plane resolution, geometric accuracy, uniformity, low-contrast detectability, and co-registration accuracy. Conclusions: Available medical image quality standards provide a blueprint for establishing consensus best practices for photoacoustic image quality assessment and thus hastening PAI technology advancement, translation, and clinical adoption.

With the outbreak and pandemic of COVID-19, point-of-care testing (POCT) systems have been attracted much attention due to their significant advantages of small batches of samples, user-friendliness, easy-to-use and simple detection. Among them, flexible biosensors show practical significance as their outstanding properties in terms of flexibility, portability, and high efficiency, which provide great convenience for users. To construct highly functional flexible biosensors, abundant kinds of polymers substrates have been modified with sufficient properties to address certain needs. Paper-based biosensors gain considerable attention as well, owing to their foldability, lightweight and adaptability. The other important flexible biosensor employs textiles as substrate materials, which has a promising prospect in the area of intelligent wearable devices. In this feature article, we performed a comprehensive review about the applications of flexible biosensors based on the classification of substrate materials (polymers, paper and textiles), and illustrated the strategies to design effective and artificial sensing platforms, including colorimetry, fluorescence, and electrochemistry. It is demonstrated that flexible biosensors play a prominent role in medical diagnosis, prognosis, and healthcare.

Significance: Polyp size is important for selecting the surveillance interval or treatment policy. Nevertheless, it is challenging to accurately estimate the polyp size during endoscopy. An easy and cost-effective function to assist in polyp size estimation is required. Aim: To propose a virtual scale function for endoscopy and evaluate its performance and expected accuracy. Approach: An adaptive virtual scale behavior was demonstrated. The measurement error of the virtual scale along the distance between the tip of the endoscope and the object plane was evaluated using graph paper. The accuracy of polyp size estimation by an expert endoscopist was compared with the accuracy of the biopsy forceps method using phantom images. Results: The measurement errors of the virtual scale were ≤ 0.7 mm when the distance to the graph paper, which faced the tip of the endoscope, varied from 4 to 30 mm. The accuracy with the virtual scale was significantly higher than that obtained with biopsy forceps (5.3 ± 5.5 % versus 11.9 ± 9.4 % , P

Intelligent packaging is an emerging technology, aiming to improve the standard communication function of packaging. Radio frequency identification (RFID) assisted smart packaging is of high interest, but the uptake is limited as the market needs cost-efficient and sustainable applications. The integration of screen printed antennas and RFID chips as smart labels in reusable cardboard packaging could offer a solution. Although paper is an interesting and recyclable material, printing on this substrate is challenging as the ink conductivity is highly influenced by the paper properties. In this study, the best paper/functional silver ink combinations were first selected out of 76 paper substrates based on the paper surface roughness, air permeance, sheet resistance and SEM characterization. Next, a flexible high frequency RFID chip (13.56 MHz) was connected on top of screen printed antennas with a conductive adhesive. Functional RFID labels were integrated in cardboard packaging and its potential application as reusable smart box for third party logistics was tested. In parallel, a web-based software application mimicking its functional abilities in the logistic cycle was developed. This multidisciplinary approach to developing an easy-scalable screen printed antenna and RFID-assisted smart packaging application is a good example for future implementation of hybrid electronics in sustainable smart packaging. ispartof: MATERIALS vol:14 issue:19 ispartof: location:Switzerland status: published

Quasi-static ultrasound elastography (USE) is an imaging modality that measures deformation (i.e. strain) of tissue in response to an applied mechanical force. In USE, the strain modulus is traditionally obtained by deriving the displacement field estimated between a pair of radio-frequency data. In this work we propose a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. The network is trained in an unsupervised way, by optimising a similarity metric between the reference and compressed image. Our training loss is also composed of a regularisation term that preserves displacement continuity by directly optimising the strain smoothness, and a temporal continuity term that enforces consistency between successive strain predictions. In addition, we propose an open-access in vivo database for quasi-static USE, which consists of radio-frequency data sequences captured on the arm of a human volunteer. Our results from numerical simulation and in vivo data suggest that our recurrent neural network can account for larger deformations, as compared with two other feed-forward neural networks. In all experiments, our recurrent network outperformed the state-of-the-art for both learning-based and optimisation-based methods, in terms of elastographic signal-to-noise ratio, strain consistency, and image similarity. Finally, our open-source code provides a 3D-slicer visualisation module that can be used to process ultrasound RF frames in real-time, at a rate of up to 20 frames per second, using a standard GPU.