In this work we demonstrate the proof-of-concept of real-time discrimination between patches of serine or isoleucine monomers in the primary structure of custom-engineered, macro-dipole-like peptides, at uni-molecular level. We employed single-molecule recordings to examine the ionic current through the α-hemolysin (α-HL) nanopore, when hydrophilic serine or hydrophobic isoleucine residues, flanked by segments of oppositely charged arginine and glutamic amino acids functioning as a voltage-dependent ‘molecular brake’ on the peptide, were driven at controllable rates across the nanopore. The observed differences in the ionic currents blockades through the nanopore, visible at time resolutions corresponding to peptide threading through the α-HL’s constriction region, was explained by a simple model of the volumes of electrolyte excluded by either amino acid species, as groups of three serine or isoleucine monomers transiently occupy the α-HL. To provide insights into the conditions ensuring optimal throughput of peptide readout through the nanopore, we probed the sidedness-dependence of peptide association to and dissociation from the electrically and geometrically asymmetric α-HL.

The present study investigates the failure mechanisms of industrial fiber materials, using a custom designed fiber cutting performance test bench. The fracture morphologies of single PA6 fibers are examined by scanning electron microscopy. The analysis reveals that fiber cutting can be distinguished according to four distinct stages of fiber failure represented by shearing, cutting, brittle fracture, and tensile failure, which are the result of different mechanisms active during the processes of crack initiation, extension and fracture. The results of fractographic analysis are further verified by an analysis of the blade assembly speed with respect to time over the entire fracture failure process based on high-speed camera data. The results of fractographic analysis and blade assembly speed are fully consistent.

In recent years, data providers are generating and streaming a large number of images. More particularly, processing images that contain faces have received great attention due to its numerous applications, such as entertainment and social media apps. The enormous amount of images shared on these applications presents serious challenges and requires massive computing resources to ensure efficient data processing. However, images are subject to a wide range of distortions in real application scenarios during the processing, transmission, sharing, or combination of many factors. So, there is a need to guarantee acceptable delivery content, even though some distorted images do not have access to their original version. In this paper, we present a framework developed to estimate the images' quality while processing a large number of images in real-time. Our quality evaluation is measured using an integration of a deep network with random forests. In addition, a face alignment metric is used to assess the facial features. Experimental results have been conducted on two artificially distorted benchmark datasets, LIVE and TID2013. We show that our proposed approach outperforms the state-of-art methods, having a Pearson Correlation Coefficient (PCC) and Spearman Rank Order Correlation Correlation Coefficient (SROCC) with subjective human scores of almost 0.942 and 0.931 while minimizing the processing time from 4.8ms to 1.8ms.

This research paper presents a deep learning approach to early detection of skin cancer using image augmentation techniques. The authors propose a two-stage image augmentation technique that involves the use of geometric augmentation and generative adversarial network (GAN) to classify skin lesions as either benign or malignant. This research utilized the public HAM10000 dataset to test the proposed model. Several pre-trained models of CNN were employed, namely Xception, Inceptionv3, Resnet152v2, EfficientnetB7, InceptionresnetV2, and VGG19. Our approach achieved accuracy, precision, recall, and F1-score of 96.90%, 97.07%, 96.87%, 96.97%, respectively, which is higher than the performance achieved by other state-of-the-art methods. The paper also discusses the use of SHapley Additive exPlanations (SHAP), an interpretable technique for skin cancer diagnosis, which can help clinicians understand the reasoning behind the diagnosis and improve trust in the system. Overall, the proposed method presents a promising approach to automated skin cancer detection that could improve patient outcomes and reduce healthcare costs.

Self-supervised learning is a method that learns general representation from unlabeled data. Masked image modeling (MIM), one of the generative self-supervised learning methods, has drawn attention showing state-of-the-art performance on various downstream tasks, though showing poor linear separability resulting from the token-level approach. In this paper, we propose a contrastive learning-based multi-view masked autoencoder for MIM, exploiting an image-level approach by learning common features from two different augmented views. We strengthen MIM by learning long-range global patterns from contrastive loss. Our framework adopts simple encoder-decoder architecture, learning rich and general representation by following a simple process: 1) two different views are generated from an input image with random masking and by contrastive loss, we can learn semantic distance of the representations generated by an encoder. By applying a high mask ratio, 80%, it works as strong augmentation and alleviates the representation collapse problem. 2) With reconstruction loss, decoder learns to reconstruct an original image from the masked image. We assess our framework by several experiments on benchmark datasets of image classification, object detection, and semantic segmentation. We achieve 84.3% fine-tuning accuracy on ImageNet-1K classification and 76.7% in linear probing, exceeding previous studies and show promising results on other downstream tasks. Experimental results demonstrate that our work can learn rich and general image representation by applying contrastive loss to masked image modeling.

Medical image analysis is an important branch in the field of medicine, which mainly uses image processing and analysis techniques to interpret and diagnose medical image data. Medical image data helps doctors to effectively observe and diagnose patients' body structures, tissues and lesions. Medical image analysis has been an important research area in the medical field, and it is important for disease diagnosis, treatment planning, and condition monitoring. In recent years, the rapid development of deep learning and computer vision technologies has contributed greatly to the automation, multimodal data fusion, real-time application, and accuracy improvement of medical image analysis. In addition, the development of deep learning has given rise to some new research areas in medical image analysis, such as Generative Adversarial Networks (GANs) for synthetic medical images, self-supervised learning for unsupervised feature learning, and neural network interpretability. In this paper, we will introduce some optimisation methods for medical images which are effective in improving the accuracy, efficiency and reliability of medical image analysis.

Multimodal medical image fusion is a fundamental but challenging problem in the fields of brain science research and brain disease diagnosis, and it is challenging for sparse representation (SR)-based fusion to characterize activity level with single measurement and no loss of effective information. In this paper, the Kronecker-criterion-based SR framework is applied for medical image fusion with a patch-based activity level integrating salient features of multiple domains. Inspired by the formation process of vision system, the spatial saliency is characterized by textural contrast (TC), which is composed of luminance and orientation contrasts to promote more highlighted texture information to participate in the fusion process. As substitution of the conventional l1-norm-based sparse saliency, a metric of sum of sparse salient features (SSSF) is used for promoting more significant coefficients to participate in the composition of activity level measure. The designed activity level measure is verified to be more conducive to maintain the integrity and sharpness of detailed information. Various experiments on multiple groups of clinical medical images verify the effectiveness of the proposed fusion method on both visual quality and objective assessment. Furthermore, the research work of this paper is helpful for further detection and segmentation of medical images.

Industrial nameplates serve as a means of conveying critical information and parameters. In this work, we propose a novel approach for rectifying industrial nameplate pictures utilizing a probabilistic Hough transform. Our method effectively corrects for distortions and clipping, and features a collection of challenging nameplate pictures for analysis. To determine the corners of the nameplate, we employ a progressive probability Hough transform, which not only enhances detection accuracy but also possesses the ability to handle complex industrial scenarios. The results of our approach are clear and readable nameplate text, as demonstrated through experiments that show improved accuracy in model identification compared to other methods.

Many dedicated designs for real-time operations provide functionality on fixed-sized operators, but where speed, scalability, and flexibility are required, extensive research is demanded. Dedicated designs can provide real-time processing for many applications. This paper presents an FPGA-based design of a general image processor. The proposed design is based on a fixed-point representation of binary numbers. The proposed design provides a mechanism to manage matrices on-chip along with matrix arithmetic. The matrices are represented with simple identifiers and microinstruction that assist in the computation of many operations which are useful for solving complex problems. The design was successfully implemented and tested using VHDL language. The proposed design is an efficient architecture as a standalone processor with all embedding computational resources necessary for an embedded image processing application.

A 'little brother' of pain, itch is an unpleasant sensation that creates a specific urge to scratch. To date, various machine-learning based image classifiers (MBICs) have been proposed for quantitative analysis of itch-induced scratch behaviour of laboratory animals in an automated, non-invasive, inexpensive and real-time manner. In spite of MBICs' advantages, the overall performances (accuracy, sensitivity and specificity) of current MBIC approaches remains inconsistent, with their values varying from ~50% to ~99%, for which the reasons underlying have yet to be investigated further, both computationally and experimentally. To look into the variation of the performance of MBICs in automated detection of itch-induced scratch, this article focuses on the experimental data recording step, and reports here for the first time that MBICs' overall performance is inextricably linked to the sharpness of experimentally recorded video of laboratory animal scratch behaviour. This article furthermore demonstrates for the first time that a linearly correlated relationship exists between video sharpness and overall performance (accuracy and specificity, but not sensitivity) of MBICs, and highlight the primary role of experimental data recording in rapid, accurate and consistent quantitative assessment of laboratory animal itch.

To address three important issues related to extraction of water features from Landsat imagery, i.e., selection of water indexes and classification algorithms for image classification, collection of ground truth data for accuracy assessment, this study applied four sets (ultra-blue, blue, green, and red light based) of water indexes (NWDI, MNDWI, MNDWI2, AWEI_ns, and AWEI_s) combined with three types of image classification methods (zero-water index threshold, Otsu, and kNN) to 24 selected lakes across the globe to extract water features from Landsat-8 OLI imagery. 1440 (4x5x3x24) image classification results were compared with the extracted water features from high resolution Google Earth images with the same (or ±1 day) acquisition dates through computing the Kappa coefficients. Results show the kNN method is better than the Otsu method, and the Otsu method is better than the zero-water index threshold method. If the computational cost is not an issue, the kNN method combined with the ultra-blue light based AWEI_ns is the best method for extracting water features from Landsat imagery because it produced the highest Kappa coefficients. If the computational cost is taken into account, the Otsu method is a good choice. AWEI_ns and AWEI_s are better than NDWI, MNDWI and MNDWI2. AWEI_ns works better than AWEI_s under the Otsu method, and the average rank of the image classification accuracy from high to low is the ultra-blue, blue, green, and red light-based AWEI_ns.

It is well known that fiber sizing is one of the most important component in the manufacturing of composites, and it does affect the mechanical properties including strength and stiffness. In this work, the influence of fiber sizing levels on the mechanical properties of carbon fibers (CF) is reported at room temperature by using single fiber tensile testing (Favimat+), single fiber pullout testing (SFPO), and interfacial elemental analysis using X-ray photoelectron spectroscopy (XPS). Standard modulus of CF (7±0.2 μm in diameter) were sized using two Michelman commercially available sizing formulations. The average solid content for each sizing formulation is 26.3 ± 0.2% and 34.1 ± 0.2%, respectively. HEXION RIMR 135 with curing agent RIMH 137 was used as a model thermoset epoxy matrix during the SFPO measurements. A predictive engineering fiber sizing methodology is also developed. Sizing amounts of 0.5, 1, and 2 wt. % on the surface of the fiber is achieved for both sizing formulations. For each fiber sizing level, 50 single fiber tensile testing experiments and 20 single fiber pull-out tests were conducted. The ultimate tensile strength (σult) of the carbon fibers and the interfacial shear strength (τ_app) of the single fiber composite were analyzed. it is also investigated the effect of the sizing levels on the interfacial shear stress and the O/C (Oxygen/Carbon) surface composition ratio. As a result, generalized fiber sizing and characterization methods were established. The developed methods can be used to characterize the strength and interfacial shear strength of any man-made fibers with different sizing formulations and solid contents, irrespective of the matrix i.e. thermoset or thermoplastic.

Numerous dynamic and complicated processes characterize development from the oocyte to the embryo. However, given the importance of functional transcriptome profiles, long non-coding RNAs, single-nucleotide polymorphisms, and alternative splicing during embryonic development, the effect that these features have on the blastomeres of 2-, 4-, 8-, 16-cell, and morula stages of development have not been studied. Here, we conducted a scRNA-seq survey of cells from sheep from the oocyte to the blastocyst developmental stages. We then carried out experiments to identify and functionally analyze the transcriptome profiles, long non-coding RNAs, single-nucleotide polymorphisms (SNPs), and alternative splicing (AS). We founded that between the oocyte and zygote groups significantly down-regulated genes and the second-largest change in gene expression occurred between the 8- and 16-cell stages. We used various methods to construct a profile to characterize cellular and molecular features and systematically analyze the related GO and KEGG profile of cells of all stages from the oocyte to the blastocyst. This large-scale, single-cell atlas provides key cellular information and will likely assist clinical studies in improving preimplantation genetic diagnosis.

Guided image filter implicitly balances noise and details based on local variance, achieving noise suppression while preserving prominent edges. However, we have observed limitations in using variance to distinguish between noise and image details, resulting in the suppression of fine structures alongside noise. Furthermore, the goal of enhancing image details, which is crucial in image filtering, has not been adequately addressed in edge-preserving filter design. In this paper, we approach image filtering as a problem of spectral energy reconfiguration, where the gain of each spectral band is adaptively regulated to achieve simultaneous noise suppression and detail enhancement. Instead of relying on variance, we employ a linear noise estimator to obtain the Signal-to-Noise Ratio (SNR) measurement necessary for dynamic gain regulation. Similar to the guided image filter, we determine the filter coefficients by solving the closed-form solution of the optimizer. We demonstrate that the proposed extended guided filter effectively integrates information from multiple spectral bands locally, allowing for the joint enhancement of meaningful details while simultaneously smoothing noise.

Video lectures are becoming more popular and in demand as online classroom teaching is becoming more prevalent. Massive Open Online Courses (MOOCs), such as NPTEL, have been creating high-quality educational content that is freely accessible to students online. A large number of colleges across the country are now using NPTEL videos in their classrooms. So more video lectures are being recorded, maintained, and uploaded. These videos generally contain information about that video before the lecture begins. We generally observe that these educational videos have metadata containing five to six attributes: Institute Name, Publisher Name, Department Name, Professor Name, Subject Name, and Topic Name. It would be easy to maintain these videos if we could organize them according to their categories. The indexing of these videos based on this information is beneficial for students all around the world to efficiently utilise these videos. In this project, we are trying to get the metadata information mentioned above from the video lectures.

Pediatric interventional cardiology procedures are essential in diagnosing and treating congenital heart disease in children; however, they raise concerns about potential radiation exposure. Managing radiation doses and assessing image quality in angiographs becomes imperative for safe and effective interventions. This systematic review aims to comprehensively analyze the current understanding of physical image quality metrics relevant for characterizing X-ray systems used in fluoroscopy-guided pediatric cardiac interventional procedures, considering the main factors reported in the literature that influence this outcome. A search in Scopus and Web of Science, using relevant keywords and inclusion/exclusion criteria, yielded fourteen relevant articles published between 2000 and 2022. The physical image quality metrics reported were noise, signal-to-noise ratio, contrast, contrast-to-noise ratio, and high contrast spatial resolution. Various factors influencing image quality were investigated, such as polymethyl methacrylate thickness (often used to simulate water equivalent tissue thickness), operation mode, anti-scatter grid presence and tube voltage. Objective evaluations using these metrics ensure impartial assessments for main factors affecting image quality, improving in the characterization fluoroscopic X-ray systems, and aiding informed decisions to safeguard pediatric patients during procedures.

Neural networks were treated as black boxes for a long time. Previous works have unearthed what aspects of an image were important for convolutional layers at different positions in the network. This was done using deconvolutional networks. In this paper, we examine how well a convolutional neural network performs when those convolutional layers which are relatively unimportant for a particular image (i.e., the image does not produce one of the strongest activations) are skipped in the training, validating, and testing process.

This paper presented a feature vector using a different statistical texture analysis of brain tumor from MRI image. The statistical feature texture is computed using GLCM (Gray Level Co-occurrence Matrices) of Brain Nodule structure. For this paper, the brain nodule segmented using strips method to implemented marker watershed image segmentation based on PSO (Particle Swarm Optimization) and Fuzzy C-means clustering (FCM). Furthermore, the four angles 0^o, 45^o, 90^o and 135^o are calculated the segmented brain image in GLCM. The four angular directions are calculated using texture features are correlation, energy, contrast and homogeneity. The texture analysis is performed a different types of images using past years. So the algorithm proposed statistical texture features are calculated for iterative image segmentation. These results show that MRI image can be implemented in a system of brain cancer detection.

Introduction: The association of gene variants for interleukin 6(IL-6) (rs1800796), interleukin 10 (IL-10) (rs1800896) and tumor necrosis factorα (TNFα (rs1800629) with the occurrence of spontaneous preterm birth (PTB) was investigated to determine whether these genetic variants are a risk factor. Materials and methods: 199 blood samples from pregnant women who had given birth prematurely and 200 control blood samples were analyzed to determine single nucleotide polymor-phisms (SNPs) of genes for IL-6 (rs1800796), IL-10 (rs1800896) and TNFα (rs1800629). The control samples were samples from pregnant women with term delivery. Isolation of DNA was per-formed on mini-spin columns according to the manufacturer's protocol. The quality and purity of the isolated DNA were tested using a Qubit 3 fluorometer. Genotyping was performed with an ABI PRISM 7500 SDS using TaqMan SNP genotyping assays. The genotypes obtained were an-alyzed using the 7500 Software v2.3 package. Results: Carriers of the A/A genotype for the rs1800629 of the TNFα gene have 4.81 times greater chance of late-onset PTB compared to carriers of the G/G and A/G genotypes in the recessive inheritance model. The presence of the G/G genotype in the recessive inheritance model compared with the G/A and A/A genotypes for the rs1800896 of the IL-10 gene represents a potentially protective factor, with mothers in the term-birth group having an almost 2-fold lower odds of PTB in general and an almost 10-fold lower odds of early PTB. On the other hand, carriers of the A/G genotype of rs1800896 have a 1.54-fold higher chance of preterm birth in general and a 1.6-fold higher chance of late preterm birth in the super dominant inheritance model compared to the A/A and G/G genotypes in the group of mothers with PTB. In this study, no association was found between PTB and rs1800796 of the IL-6 gene. Conclusion: rs1800629 in mothers was associated with PTB. rs1800896 shows a potentially protective effect for the occurrence of PTB in this study. No association was found between PTB and rs1800796.

Effectively addressing juvenile delinquency among adolescents in Nigeria necessitates a keen focus on parenting styles and family structures. Given that parents serve as primary custodians and paramount influencers in socializing their children, their pivotal roles cannot be understated. When parents conscientiously strive to exemplify positive role models for their children, the likelihood of yielding optimal and desired outcomes is significantly enhanced. Regrettably, a considerable number of parents have embraced parenting styles that inadvertently expose their children to various forms of juvenile delinquency. The article explores the complex relationship between parenting styles and juvenile delinquency, shedding light on the connections between family structure and delinquent behavior. Also, the connection between marital discord and juvenile delinquency as well as the impacts of single parenting, were explored. The theoretical framework employed in this article is the Social Learning Theory, formulated by Albert Bandura in 1977. The article underlines that a substantial portion of delinquent acts among adolescents can be attributed to familial discord and the specific parenting styles employed. Consequently, this study advocates for a paradigm shift towards more effective and constructive parenting styles. Additionally, it emphasizes the imperative for the government and relevant institutions to accord due importance to the institution of the family in their policies and interventions.

Although hundreds of keV in energy gain have already been demonstrated in dielectric laser accelerators (DLAs), there remains challenge in creating structures that can confine electrons for multiple millimeters. We focus here on dual gratings with single sided drive, which have experimentally demonstrated energy modulation numerous times. Using a FTDT simulation to find the fields within various DLA structures and correlating these results with particle tracking simulation, we look at the impact of teeth height and width, as well as gap and offset, on the performance of these structures. We find a tradeoff between electron throughput and acceleration, but that for any given grating geometry there is a gap and offset that will allow some charge acceleration. For our 780 nm laser wavelength, this results in a 1200 nm optimal gap size for most gratings.

A multispectral superconducting nanowire single photon detector sensitive to different incident photon wavelength bands, is proposed. The SNSPD consists of a Distributed Bragg Reflector, a gold mirror, and two regions employing four NbN nanowires. Using the DBR both as a filter and a reflector, two distinct detection bands are created. The first detection band has a peak absorbance of > 0.75 at a wavelength of 1143 nm, while the second band has an absorbance of > 0.70 in the wavelength range 1440 to 2000 nm. The design of the device can be tuned to provide sensitivity in different wavelength bands. While SNSPDs do not typically provide photon wavelength sensitivity, the band-selection design proposed in this work opens up potential applications for future quantum communication technology.

GDF5 gene is involved in the development of skeletal elements, synovial joint formation, tendons, ligaments, and cartilage. Several polymorphisms are present within the gene and two of them, rs143384 and 143383, have been reported as correlated with osteoarticular disease or muscle flexibility. The aim of this research is to verify if the worldwide distribution among human populations of rs143384 polymorphism has been shaped by selective pressure, or if it’s the result of random genetic drift events. Ninety-four individuals of both sexes, 18-28 years old, from Sardinia were analyzed. We observed the following genotype frequencies: 28.72% of homozygotes AA, 13.83% of homozygotes GG, and 57.45% of heterozygotes AG. Allele frequencies were 0.574 for allele A and 0.426 for alle G. Relationships between populations were verified by Multidimensional Scaling (MDS). Our data show: i) a clear heterogeneity within the African populations; ii) a strong differentiation between Africans and the other populations: iii) the Sardinian population is placed withing the European cluster. To reveal possible traces of selective pressure, Population Branch Statistic (PBS) was calculated; both SNPs rs143384 and 143383 have low PBS values, suggesting that there are no signals of selective pressure in those areas of the gene.

The use of fiber optics as a tool for different kinds of geotechnical monitoring can become highly attractive and cost effective when compared to conventional instruments such as piezometers, inclinometers, among others. A single fiber optic cable may cover a larger monitoring area compared to conventional instrumentation, and the possibility of monitoring more than one physical quantity with the same fiber optic cable. Consulting the literature, it is possible to find several different examples where distributed fiber optic systems are being used. For the use of any sensor, a calibration curve is required. In the case of strain sensors, calibration is required to derive strain values from the frequency measurement quantity. However, fiber optic sensor cable manufacturers often do not provide their own calibration parameters and consult the values in specialized literature, which can result in monitoring errors. In this context, this article presents a bench adjusted for tests on single mode fiber optic cables, as well as results of tensile tests aimed at defining the function of strain variations of two different optical fiber cables, manufactured by different companies, using two different distributed interrogators. This paper also proposes a methodology for calibrating of the fiber optic cables deformation since the calibration parameters are provided by the cable manufacturers, which can result in errors depending on the application. It should be noted that there are few manufacturers of fiber optic cables aimed at application in civil engineering. Therefore, with the calibration methodology proposed in this paper, it is intended to transmit to the academic community the possibility of obtaining calibration parameters of any fiber optic cable, even those manufactured for telecommunications purposes and not only for cables manufactured with the intended use in civil engineering. Because of this fact, researchers will not be restricted to the acquisition of special cables for their applications. Based on the results, it was possible to conclude that the application of calibrated fiber optic sensors in experimental piles foundations is viable, in order to evaluate the load-displacement behavior of these elements, under different loading conditions.

A single fracture is the basic unit of fracture medium, and the roughness of fracture wall surface is an important factor influencing hydraulic characteristics of the flow in bedrock fracture. However, effects of the shape and density of roughness elements (various bulges/pits on rough fracture wall surfaces) on water flow in a single rough fracture have not been thoroughly discovered. Thus the water flow in single fracture with different shapes and densities of roughness elements was simulated by using Fluent software in this study. The results show that in wider fractures the flow rate mainly depends on fracture aperture, while in narrow and close fracture medium the surface roughness of fracture wall is the main factor of head loss of seepage; there is a negative power exponential relation between the hydraulic gradient index and the average fracture aperture, i.e. with the increase of fracture aperture, the relative roughness of fracture and the influence weight of hydraulic gradient both decrease; and in symmetrical-uncoupled fractures there is a super-cubic relation between the discharge per unit width and average aperture. Above results would help to deepen the understanding of rough fracture seepage.

Microfluidic devices have been extensively investigated in recent years in fields including ligand-binding analysis, chromatographic separation, molecular dynamics, and DNA sequencing. To prolong the observation of a single molecule in aqueous buffer, the solution in a sub-micron scale channel is driven by the electric field and reversed after a fixed delay following each passage, so that the molecule passes back and forth through the laser focus and the time before irreversible photobleaching is extended. However, this practice requires complex chemical treatment to the inner surface of the channel to prevent unexpected sticking to the surface and the confined space renders features, such as a higher viscosity and lower dielectric constant, which slow the Brownian motion of the molecule compared to the bulk liquid. In this paper, we have fixed a capillary microchannel with an inner diameter of 2 microns on top of a piezo stage to recycle the molecule and collected the fluorescence by a confocal microscope. The passing times of the molecule through the laser focus are calculated by a real-time control system based on an FPGA and the commands of translation are given to the piezo stage through a feedback algorithm. We have achieved a maximum number of recycles of more than 200 and developed a maximum-likelihood estimation of the diffusivity of the molecule, which attains results of the same magnitude as previous reports. This technique simplifies the overall procedure of the single-molecule recycling and could be useful for the ligand-binding studies of biomolecules.

Reverse water gas shift reaction (RWGS) is an important process which plays a vital role in many CO2 utilization related reactions. Noble metals are the most active catalysts in RWGS, but the high price and low reserve strangled their applications. In the present work, we reported a non-transition-metal MgAl2O4 catalyst which showed outstanding activity and stability at high temperatures in the RWGS reaction and improved performance after doping of single atomic Nin+. The catalyst can obtain 46% of CO2 conversion in durability test of 75 h at 800 °C under high weight hourly space velocities (225 000 ml g-1 h-1). The adsorption sites, possible reaction route, and effects of Nin+ single atoms on the (111) surface of MgAl2O4 for RWGS were investigated by in situ DRIFTS and DFT calculations. The results indicated that the rate determining reaction step of RWGS on MgAl2O4 and Ni (SA)/MgAl2O4 were both the reaction of OH* + H* → H2O* + *, but the energy barrier was significantly reduced after introducing single atomic Nin+. Nin+ atoms can increase the hydroxyl coverage on the surface of catalyst and Al3+ sites near the Nin+ ion are considered as the predominant active sites for RWGS reactions.

Light absorption in single nanowires (NWs) is one of the most crucial factors for photovoltaic applications. In this paper, we carried out a detailed investigation of light absorption in single rectangular NWs (RNWs). We show that the RNWs exhibit improved light absorption compared with the square NWs (SNWs), which can be attributed to the symmetry-breaking structure that can increase the light path length by increasing the vertical side and the enhanced leaky mode resonances (LMRs) by decreasing the horizontal side. We found that the light absorption in silicon RNWs can be enhanced by engineering the horizontal and vertical sides, the photocurrent is significantly increased by 276.5% or 82.9% compared with that of the SNWs with the same side length as the horizontal side of 100 nm or the vertical side of 1000 nm, respectively. This work provides an effective way for designing high-efficiency single NW photovoltaic devices based on the symmetry breaking from the SNWs to RNWs.

Light trapping in single nanowires (NWs) are of vital importance for photovoltaic applications. However, circular NWs (CNWs) can limit its light-trapping ability due to high geometrical symmetry. In this work, we present a detailed study of light trapping in single NWs with an elliptical cross-section (ENWs). We demonstrate that the ENWs exhibit significantly enhanced light trapping compared with the CNWs, which can be ascribed to the symmetry-broken structure that can orthogonalize the direction of light illumination and the leaky mode resonances (LMRs). That is, the elliptical cross-section can simultaneously increase the light path length by increasing the vertical axis and reshape the LMR modes by decreasing the horizontal axis. We found that the light absorption can be engineered via tuning the horizontal and vertical axes, the photocurrent is significantly enhanced by 374.0% (150.3%, 74.1%) or 146.1% (61.0%, 35.3%) in comparison with that of the CNWs with the same diameter as the horizontal axis of 100 (200, 400) nm or the vertical axis of 1000 nm, respectively. This work advances our understanding of how to improve light trapping based on the symmetry breaking from the CNWs to ENWs and provides a rational way for designing high-efficiency single or self-assembled NW photovoltaic devices.

HIV-1 assembly is a complex mechanism taking place at the plasma membrane of the host cell. It requires nice spatial and temporal coordination to end up with a full immature virus. Researchers have extensively studied HIV-1 assembly molecular mechanism during the past decades, in order to dissect the respective roles of viral proteins, viral genome and host cell factors. Nevertheless, the time course of the process has been observed in living cells only a decade ago. The very recent revolution of optical microscopy, combining high speed and high spatial resolution now permit to study assemblies and their consequences at the single molecule level within (living) cells. In this review, after a short description of these new approaches, we will show how HIV-1 assembly in cells has been revisited using these advanced super resolution microscopy techniques and how much it could make a bridge in studying assembly from the single molecule to the host cell.

Optical tweezers offer a non-contact method for selecting single cells and translocating them from one microenvironment to another. We have characterized the optical tweezing of yeast S. cerevisiae and can manipulate single cells at velocities up to 0.77 mm/s using laser powers of 40 mW from a 785 nm diode laser. We have fabricated and tested three cell isolation devices; a micropipette, a PDMS chip and laser machined fused silica chip and we have isolated single bacteria, yeast and cyanobacteria cells. The most effective isolation was achieved in PDMS chip, where single yeast cells were grown and observed for 18 hours without contamination. The duration of budding in S. cerevisiae was not affected by the laser parameters used, but the time from tweezing until the first budding event began increased with increase laser energy (laser power x time). Cells tweezed using 25 mW for 1 minute were viable after isolation. We have constructed a micro-consortium of yeast cells, and a co-culture of yeast and bacteria, using optical tweezers in combination with the PDMS network of channels and isolation chambers, which may impact on both industrial biotechnology and understanding pathogen dynamics.

We conducted an in-situ crystal structure analysis of ferroselite at non-ambient conditions. The aim is to provide a solid ground to further the understanding of the properties of this material in a broad range of conditions. Ferroselite, marcasite-type FeSe2, was studied under high pressures up to 46 GPa and low temperatures, down to 50 K using single-crystal microdiffraction techniques. High pressure and low temperatures were generated using a diamond anvil cell and a cryostat. We found no evidences of structural instability in the explored P-T space. The deformation of the orthorhombic lattice is slightly anisotropic. As expected, the compressibility of the Se-Se dumbbell, the longer bond in the structure, is larger than that of the Fe-Se bonds. Less obvious is the behavior of the octahedral bonds, the shorter bond is the most compressible determining a small increase in the octahedron distortion with pressure. We also achieved a robust structural analysis of ferroselite at low temperature in the diamond anvil cell. Structural changes upon temperature decrease are small but qualitatively similar to those produced by pressure.

Layered superconductors are attractive because some of them show high critical temperatures. While their crystal structures are similar, those compounds are composed of many elements. Compounds with many elements tend to be incongruent melting compounds, thus, their single crystals cannot be grown via the melt-solidification process. Hence, these single crystals have to be grown below the decomposition temperature, and then the flux method, a very powerful tool for the growth of these single crystals with incongruent melting compounds, is used. This review shows the flux method for single-crystal growth technique by self-flux, chloride-based flux, and HPHT (high-pressure and high-temperature) flux method for many-layered superconductors: high-T_c cuprate, Fe-based and BiS₂-based compounds.

During the high-speed flight of a vehicle in the atmosphere, surface friction with the air generates aerodynamic heating. The aerodynamic heating phenomenon can create extremely high temperatures near the surface. These high temperatures impact material properties and the structure of the aircraft, so thermal deformation measurement is essential in aerospace engineering. This paper revisits high-temperature deformation measurement using the digital image correlation (DIC) technique under high-intensity blackbody radiation with a precise speckle pattern fabrication and a heat haze reduction method. The effect of speckle pattern on the DIC measurement has been thoroughly studied at room temperature, but high-temperature measurement studies have not reported such effects so far. We found that the commonly used methods to reduce the heat haze effect could produce incorrect results. Hence, we propose a new method to mitigate heat haze effects. An infrared radiation heater was employed to make an experimental setup that could heat a specimen up to 950 ℃. First, we mitigated image saturation using a short-wavelength bandpass filter with blue light illumination, a standard procedure for high-temperature DIC deformation measurement. Second, we studied how to determine the proper size of the speckle pattern under a high-temperature environment. Third, we devised a reduction method for the heat haze effect. As proof of the effectiveness of our developed experimental method, we successfully measured the deformation of stainless steel 304 specimens from 25 ℃ to 800 ℃. The results confirmed that this method can be applied to the research and development of thermal protection systems in the aerospace field.

The Double Random Phase Encoding (DRPE) image encryption method has garnered significant attention in color image processing and optical encryption, thanks to its parallel encryption of R, G, B. However, DRPE-based color image encryption faces two challenges. Firstly, it disregards the correlation of R, G, and B, compromising the encrypted image's robustness. Secondly, DRPE schemes relying on Discrete Fourier Transform (DFT) and Discrete Fractional Fourier Transform (DFRFT) are vulnerable to linear attacks, such as Known Plaintext Attack (KPA) and Chosen Plaintext Attack (CPA). Quantum walk stands out as a remarkable tool for designing modern cryptographic mechanisms, offering resistance to potential attacks from both classical and quantum computers.Therefore, this study presents an optical color image encryption algorithm that combines two-dimensional quantum walk with 24-bit plane permutation, dubbed OCT. This approach employs pseudo-random numbers generated by Two-Dimensional Quantum Walking (TDQW) for phase modulation in DRPE and scrambles the encrypted image's real and imaginary parts using the Generalized Arnold Transform. The 24-bit plane permutation helps reduce correlation of the R, G, B, while the Generalized Arnold Transform bolsters DRPE's resistance to linear attacks. By incorporating TDQW, the key space is significantly expanded. Experimental results validate the effectiveness and security of proposed method.

A topic of growing interest in urban remote sensing is the automated extraction of geometrical building information for 3D city modeling. Roof geometry information is useful for applications such as urban planning, solar potential estimation and telecommunication installation planning, and wind flow simulations for pollutant diffusion analysis. Recent research has proven that the advance in remote sensing technologies and deep learning methods offer the prospects of deriving the roof structure information accurately and efficiently. In this study, we propose a Vectorized Roof Extractor- method based on Fully Convolutional Networks (FCNs) and advanced polygonization method to extract roof structure from aerial imagery and a normalized Digital Surface Models (nDSM) in a regularized vector format. The roof structure consists of building outlines, external edges of the building roof, inner rooflines, internal intersections of the main roof planes. The methodology is comprised of segmentation, vectorization and post-processing for outer rooflines, external edges of the building roof, and inner rooflines, and internal intersections of the main roof planes. For the comparison, we adapt the Frame field Learning (FFL) method originally designed to extract building polygons [1]. Our experiments are conducted on a custom data set derived for the city of Enschede, The Netherlands, using aerial imagery, nDSM and manually digitized training polygons. The results show that the proposed Vectorized Roof Extractor outperformed adapted FFL on PoLiS distance with values of 3.5 m and 1.2 m for outlines and inner rooflines, respectively. Furthermore, the model surpassed the adapted FFL on PoLiS-thresholded F-score for outlines and inner rooflines, with 0.31 and 0.57, respectively. The Vectorized Roof Extractor produced adequate visual results, with straighter walls and fewer missed inner roofline detections. It can predict buildings with common walls thanks to skeleton graph computation. To summarize, the proposed method is suitable for urban applications and has the potential to be improved further.

This paper presents the latest developments in microCT, both globally and locally, for supporting the additive manufacturing industry. There are a number of recently developed capabilities which are especially relevant to the non-destructive quality inspection of additive manufactured parts; and also for advanced process optimization. These new capabilities are all locally available but not yet utilized to their full potential, most likely due to a lack of knowledge of these capabilities. The aim of this paper is therefore to fill this gap and provide an overview of these latest capabilities, showcasing numerous local examples.

With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a background image from cluttered scenes. Traditionally, statistical measures and existing image quality techniques have been applied for evaluating the quality of the reconstructed background images. Though these quality assessment methods have been widely used in the past, their performance in evaluating the perceived quality of the reconstructed background image has not been verified. In this work, we discuss the shortcomings in existing metrics and propose a full reference Reconstructed Background image Quality Index (RBQI) that combines color and structural information at multiple scales using a probability summation model to predict the perceived quality in the reconstructed background image given a reference image. To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores. The quality assessment measures are evaluated by correlating their objective scores with human subjective ratings. The correlation results show that the proposed RBQI outperforms all the existing approaches. Additionally, the constructed datasets and the corresponding subjective scores provide a benchmark to evaluate the performance of future metrics that are developed to evaluate the perceived quality of reconstructed background images.

This paper describes the first results of an Image Recognition Based Location (IRBL) for mobile application focusing on the procedure to generate a Database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surrounding is needed. In order to achieve this objective a complete 3D survey of two different environment (Bangbae metro station of Seoul and E.T.R.I. building in Daejeon – Republic of Korea) was performed using LiDAR (Light Detection And Ranging) instrument and the obtained scans were processed in order to obtain a spatial model of the environments. From this, two databases of reference images were generated using a specific software realized by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allow to generate synthetically different RGB-D images) centered in the each scan position in the environment. Later, the external parameters (X, Y, Z, ω, φ, κ) and the range information extracted from the DB images retrieved, are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper the survey operations, the approach for generating the RGB-D images and the IRB strategy are reported. Finally the analysis of the results and the validation test are described.

In the dynamic landscape of sustainable computing, use of edge devices is paramount for reducing the need for large-scale centralized data centers. By processing data locally, edge devices minimize the energy-intensive computing in data centers, improving the overall performance, cost-effectiveness whereas reducing the environmental impact. Edge devices may constitute edge clusters composed of resource frugal Single Board Computers (SBC) such as Raspberry Pi etc. The small form-factor and energy efficiency of these computers makes them ideal for processing large data on the edge. Despite their potential, traditional Hadoop configurations struggle to optimize performance in heterogeneous SBC clusters due to disparities in computing resources. Consequently, we propose modifications to the Yet Another Resource Negotiator (YARN) scheduling mechanism to address these challenges. Our proposed changes include the introduction of a Frugality Index and an adaptiveConfig policy. The Frugality Index categorizes SBC nodes based on their capabilities, enabling intelligent resource allocation. The adaptiveConfig policy dynamically adjusts resource allocation in response to workload and cluster conditions, enhancing system efficiency. Additionally, we introduce a fetch_threshold for reduce tasks to improve task prioritization based on locality and data processing efficiency. We evaluate our approach using a 13-node SBC cluster and conduct experiments with CPU-intensive and IO-intensive Hadoop benchmarks. The results demonstrate significant performance improvements compared to native YARN settings, with execution times 4.7 times faster than the worst_native and 1.9 times faster than the best_native scenarios. Furthermore, the proposed adaptiveConfig policy implementing the frugality index and a fetch_threshold outperforms the native YARN by 5.86 times and 1.79 times in Terasort and wordcount executions respectively. Our findings underscore the effectiveness of our approach in managing the heterogeneous nature of SBC clusters and optimizing performance across various hardware configurations. The adaptive policies prove well-suited to the frugal SBC-cluster context, yielding enhanced outcomes and paving the way for sustainable big data processing initiatives.

The article proposes a mathematical model for creating playlists based on the emotional utility of the consumer, aiming to maintain high user interest in music streaming platforms (Lugos Abarca, 2024). The proposed method follows these steps: first, it calculates the MEEPE of Songs on the digital platform; subsequently, it quantitatively defines the Consumer's MEEPI before exposure to music; then, it operates the MEEPE of the Songs with the consumer's initial MEEPI using the known resolution process (Lugos Abarca, 2023); finally, it employs an emotional utility function to determine possible levels of emotional satisfaction and establish the playback order. This results in the creation of an efficient theoretical model. It is recommended to expand the computational study of the model in future research by using a broader set of songs and considering various initial MEEPI of the consumer. The article's originality lies in its ability to predict, quantitatively and theoretically, the level of satisfaction a music consumer could experience when listening to a playlist.

The objective of this research is to propose a utility function that calculates the possible level of satisfaction that a music consumer would achieve after listening to a song according to the emotional response obtained from a song. Assuming that the consumer's musical preferences are satisfied if the predominant emotions of his current emotional state increase; the probabilistic emotional-musical model (Lugos Abarca, 2023) is used to construct such a utility function. By posing a hypothetical scenario, realistic results were obtained, which guarantee, in principle, its use in theoretical practice. The originality of this research lies in proposing a mathematical tool to study, quantitatively, the decision making of a musical consumer influenced by his emotions. The main conclusion being its importance in the music industry, since the function could predict, theoretically, whether a song will be accepted or rejected by the consumer in comparison with the competition.

During the last few years, advancement in the area of biochemistry, science of the material world, engineering and computer-aided testing has directed towards advancement of high-throughput tools for the sake of profiling information encoded in a gene. Single-cell RNA-sequencing (scRNA-seq) tools capable to examine the sequence data from each individual cells and it shows within population variety and permit exploring of cell conditions and transformation by extreme resolution, possibly give out cell sub-types or gene expression fluctuations that are shaded in mass sequencing processes, which shows population-averaged evaluations. Yet, the major disadvantage for this tool is the lack of success to pick out location related details of the RNA transcriptome, as this needs tissue detachment and cell isolation. Location based transcript determination is among the advancements in the area of medical biotechnology as this can find out the molecules like RNA dataset in their intact physical placement in tissue segment with spatial context at the scale of a single-cell, which is very advantageous as compare to single-cell sequencing techniques. These approaches give key observation in the areas of biomedical field sub-disciplines like neurology, embryology, carcinoma study, immune cell investigation and histological activities. Generally, this review mainly focused towards single-cell sequencing methods, technology development, its challenges observed, different expression data analysis mechanisms and their applications in different areas, such as in cancer research, microbes, central nervous system, sex organs and immune-biology, intensifying the essentiality of sequencing tools at single-cell level for the characterization of extremely dynamic individual cells.

A microfinance scheme has emerged as a crucial tool in reducing poverty gaps in many developing countries. A notable player in the arena is Amanah Ikhtiar Malaysia (AIM), a pioneer in microfinancing in Malaysia since 1987. AIM’s microfinance program injects small-scale businesses, particularly benefiting single mother entrepreneurs, contributing to capacity-building and empowerment. However, the advent of the COVID-19 pandemic, coupled with Movement Control Order (MCO), has adversely affected various business sectors and vulnerable groups, including single mothers engaged in microfinancing enterprises. This study employs primary data to assess the impact of AIM’s microfinancing programs on single-mother entrepreneurs during the crisis. Utilizing a quantitative approach with a two-stage sampling process involving 422 respondents in urban areas, the research evaluates the relationship between empowerment indicators (economic, social, digital, and psychological) and management and governance aspects of program effectiveness. The findings reveal that the microfinancing program’s overall effectiveness, economic, digital and psychological empowerment indicators, and program management are at a moderate level, while social empowerment is high. Economic and digital empowerment, along with effective management, positively and significantly influence program effectiveness, whereas social empowerment has a negative and significant impact. The study concludes with recommendations for enhancing AIM’s microfinance program, equipping single mother entrepreneurs to better navigate future challenges, and informing policy formulation related to their empowerment.

Electrocatalytic and photocatalytic hydrogen evolution reaction (HER) provides a promising approach to clean energy generation. Bimetallic single-atom catalysts raised and have been explored to be advanced catalysts for HER. It is urgent to review and summarize the recent advances in developing bimetallic single-atom HER catalysts. Firstly, the fundamentals of bimetallic single-atom catalysts are presented, highlighting their unique configuration of two isolated metal atoms on supports and resultant synergistic effects. Secondly, recent advances in bimetallic single-atom catalysts for electrocatalytic HER under acidic/alkaline conditions are then reviewed, including W-Mo, Ru-Bi, Ni-Fe, Co-Ag, and other dual-atom systems on graphene and transition metal dichalcogenides (TMDs) with enhanced HER activity versus monometallic analogs due to geometric and electronic synergies. Then, Photocatalytic bimetallic single-atom catalysts on semiconducting carbon nitrides for solar H2 production are also discussed. Finally, an outlook is provided on opportunities and challenges in precisely controlling bimetallic single-atom catalyst synthesis and gaining in-depth mechanistic insights into bimetallic interactions.

Purpose: Therapeutic targeting of RAF1 is promising, but it requires further investigation for the relation between clinical features and RAF1 aberrations regarding the MAPK signaling pathway in various solid tumors to realize the precision medicine. Methods: Between October 2019 and June 2023, Samsung Medical Center, 3,895 patients with metastatic cancer patients received a next-generation sequencing (NGS) using TruSight Oncology 500 (TSO500) assay as a routine clinical practice. We surveyed the incidence of RAF1 aberration including mutation (single-nucleotide variant [SNV]), amplification (copy-number variation), and fusion. Results: In 3,895 metastatic cancer patients, 77 (2.0%) had RAF1 aberrations in their tumor specimen. Of 77 patients, 44 (1.1%) had RAF1 mutations (SNV), 25 (0.6%) had RAF1 amplification and 10 (0.3%) had RAF1 fusions. Among 10 patients with RAF1 fusion, concurrent RAF1 amplification and RAF1 mutation were detected in one patient each. The most common tumor types were bladder cancer (11.5%), followed by ampulla of Vater (AoV) cancer (5.3%), melanoma (3.0%), gallbladder (GB) cancer (2.6%), and gastric cancer (2.3%). Microsatellite instability high (MSI-H) tumors were found in 5 out of 76 patients (6.6%) with RAF1 aberration, while MSI-H tumors were found only in 2.1% of patients with wild-type RAF1 cancer (p < 0.0001). Conclusion: We showed that when patients with metastatic solid cancer receive NGS test, approximately 2.0% have RAF1 aberrations in their tumor specimen.

Object detection is a popular image processing technique, widely used in numerous applications for detecting and locating objects in images or videos. While being one of the fastest algorithms for object detection, Single-Shot Multibox Detection (SSD) networks are also computationally very demanding, which limits their usage in real-time edge applications. Even though the SSD post-processing algorithm is not the most complex segment of the overall SSD object detection network, it is still computationally demanding and can become a bottleneck with respect to processing latency and power consumption, especially in edge applications with limited resources. When using hardware accelerators to accelerate backbone CNN processing, the SSD post-processing step implemented in software can become critical for high-end applications where high frame rates are required, as this paper shows. To overcome this problem, we propose Puppis, an architecture for hardware acceleration of SSD post-processing algorithm. As experiments will show, our solution will lead to an average SSD post-processing speedup of 34.42 when compared with a software implementation. Furthermore, execution of a complete SSD network will be on average 45.39 times faster than software implementation when the proposed Puppis SSD hardware accelerator is used together with some existing CNN accelerators.

To avoid the endless accumulation of QTL effects during pyramiding breeding, QTL epistasis plays a role of homeostasis. Four single-segment substitution lines with heading date QTLs were utilized to construct pyramiding materials with dual QTLs and triple QTLs, allowing for the estimation of QTL epistatic effects on heading date in rice. The epistatic components of dual QTL and triple QTL interactions were analyzed using data from three seasons of two years. All four QTLs can be considered as dominant loci, demonstrating superdominance. 75% of dual QTL interactions and 81.25% of triple QTL interactions obtained statistically significant results, reaffirming the prevalence of epistasis. Interactions of four QTLs with three positive effects and one negative effect generated 62.5% negative dual QTL epistatic effects and 57.7% positive triple QTL epistatic effects, forming the balance relationship “positive-negative-positive”. In fact, QTL effects were consistently partially neutralized by mixed QTL epistatic effects so as to control the infinite accumulation of QTL effects. QTL epistasis plays a role of homeostasis on heading date in rice. However, there were two exceptions. The directions of OsMADS50 and Hd3a-2 effects were consistently aligned with those of their mixed epistases, suggesting that the QTL or gene could be employed in pyramiding breeding with different objectives. This study elucidated the mechanism of epistatic interactions among four QTLs and provided valuable genetic resources for improving heading date in rice.

Understanding the catalytic performance of different materials is of crucial importance for further technological advancements. This especially relates to the behavior of different classes of catalysts under operating conditions. Here we analyze the effects of local coordination of metal centers (Mn, Fe, Co) in graphene-embedded Single-Atom Catalysts (SACs). We have started from well-known M@N4-graphene catalysts and systematically replaced nitrogen atoms with oxygen or sulfur atom to obtain M@OxNy-graphene and M@SxNy-graphene SACs (x+y=4). We show that local coordination strongly affects the electronic structure and the reactivity towards hydrogen and oxygen species. However, the stability is even more affected. Using the concept of Pourbaix plots, we show that the replacement of nitrogen atoms coordinating metal center with O or S destabilizes SACs towards the dissolution, while the metal centers get easily covered by O and OH acting as additional ligands at high anodic potentials and high pH values. Thus, not only should local coordination be considered in terms of the activity of SACs, but it is also necessary to consider its effects on the speciation of SACs' active centers under different potential and pH conditions.

There has been considerable research on reconstructing 3D shapes from single-view images; however, preserving the detailed information of the input image remains difficult. In this paper, we propose the application of a gradient map to train a network, aimed at improving the visual quality of fine-grained details such as the thin and tiny components of generated shapes. Each gradient map was created from the original voxel data, and each value represented the amount of information per volume. Here, the gradient map was defined by several methods that mathematically quantify and represent the detailed structure of an object. By applying this map to the loss function in training, we could induce the network to intensively train partial details, such as thin and narrow parts. We demonstrated that the detailed information was well-recovered when a weight that is proportional to the gradient value was applied to the loss. Furthermore, it is expected that our method will contribute to the development of 3D technologies related to the construction of virtual space for simulation and new customer experience.

The quest for accurate and more efficient methods for solving periodic/oscillatory problems is gaining more attention in recent time. This paper presents the construction and implementation of a family of exponentially–fitted Obrechkoff methods. A single–step Obrechkoff method involving terms up to the fourth derivatives was used as the base method. We also present the stability and convergence properties of the constructed family of methods. Two numerical examples were use to illustrate the performance of the constructed methods.

A Weyl semimetal is a novel crystal with low-energy electronic excitations that behave as Weyl fermions. It has received worldwide interest and was believed to have opened the next era of condensed matter physics after graphene and three-dimensional topological insulators. Howev-er, it is not easy to obtain a single large-size crystal because there are many nucleations in the preparation process. Here, a bottom-seed CVT growth method is proposed in this paper, and we acquired the large-size, high-quality NbAs single crystals up to 5x4x4 mm3 finally. X-ray diffrac-tion and STEM confirmed that they are tetragonal NbAs, which the key is to use seed crystal in vertical growth furnace. Notably, the photoelectric properties of the crystal are obtained under the existing conditions, which paves the way for the follow-up work.

The rise of data science in biology stimulates interdisciplinary collaborations to address fundamental questions. Here, we report the outcome of the first SINFONIA symposium focused on revealing the mechanisms governing plant reproductive development across biological scales. The intricate and dynamic target networks of known regulators of flower development remain poorly understood. To analyze development from the genome to the final floral organ morphology, high-resolution data that capture spatiotemporal regulatory activities are necessary and require advanced computational methods for analysis and modeling. Moreover, frameworks to share data, practices and approaches that facilitate the combination of varied expertise to advance the field are called for. Training young researchers in interdisciplinary approaches and science communication offers the opportunity to establish a collaborative mindset to shape future research.

Micro-blade design is an important factor in the cutting of single cells and other biological structures. This paper describes the fabrication process of three dimensional (3D) micro-blades for the cutting of single cells in a microfluidic “guillotine” intended for fundamental wound repair and regeneration studies. Our microfluidic guillotine consists of a fixed 3D micro-blade centered in a microchannel to bisect cells flowing through. We show that the Nanoscribe two-photon polymerization direct laser writing system is capable of fabricating complex 3D micro-blade geometries. However, structures made of the Nanoscribe IP-S resin have low adhesion to silicon, and they tend to peel off from the substrate after at most two times of replica molding in poly(dimethylsiloxane) (PDMS). Our work demonstrates that the use of a secondary mold replicates Nanoscribe printed features faithfully for at least 10 iterations. Finally, we show that complex micro-blade features can generate different degrees of cell wounding and cell survival rates compared with simple blades possessing a vertical cutting edge fabricated with conventional 2.5D photolithography. Our work lays the foundation for future applications in single cell analyses, wound repair and regeneration studies, as well as investigations of the physics of cutting and the interaction between the micro-blade and biological structures.

Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to-date we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.

In this research paper, a high gain transformer less inverter modeling which is used for the smart grid technology or as the stand alone for home appliances is proposed. The proposed transformer less topology provides a higher voltage gain, low cost, small size and simple control stand alone transformer less inverter for home appliances. The above inverter is modeled by using software which is known as MATLAB/ Simulimk.

Two types of trenches cross-section in conventional vertical and brand new reverse-V-shape have fabricated on SCD substrate by micro-jet water-assist laser, the epitaxial lateral overgrowth technique has applied by microwave plasma chemical vapor deposition system in forming multiple micrometer-size channels. Raman and SEM techniques have applied in analyze both types growth layer characterization. Optical microscope has used to test microchannels hollowness. As a result, with the brand new reverse-V-shape trench, epitaxial lateral overgrowth layer reaches higher SCD surface morphology and crystal quality.

Quantum leaps advances in the single-molecule investigative science have been made possible over the past decades through the implication of nanopores, as versatile components on dedicated biosensors. Here, we employed the nanopore-tweezing technique to capture amino acid-functionalized, peptide-nucleic acids (PNA) with -hemolysin-based nanopores, and correlate the ensuing stochastic fluctuations of the ionic current through the nanopore with the composition and order of bases in the PNAs primary structure. We demonstrate that while the system enables detection of distinct bases on homopolymeric PNA or triplet bases on heteropolymeric strands, it also reveals rich insights into the conformational dynamics of the entrapped PNA within the nanopore, relevant for perfecting the recognition capability single-molecule sequencing.

Single cell RNA-seq (scRNA-seq) profiles conceal temporal and spatial tissue developmental information. De novo reconstruction of single cell temporal trajectory has been fairly addressed, but reverse engineering single cell 3D spatial tissue localization is hitherto landmark based, and de novo spatial reconstruction is a compelling computational open problem. Here we show that a new algorithm - named D-CE - for coalescent embedding of single cell transcriptomic networks can address this open problem. We rely merely on the spatial information encoded in the expression patterns of developmental signal transcription factor (DST) genes, and we find that D-CE of cell-cell association DST-transcriptomic networks reliably reconstructs the Geo-seq or single cell samples’ 3D spatial tissue distribution. Comparison to the novoSpaRC and CSOmap (recent and only available de novo 3D spatial reconstruction methods) on 16 datasets and 681 reconstructions, reveals a significantly distinctive superior performance of D-CE.

Single nanowires (NWs) are of great importance for optoelectronic applications, especially solar cells serving as powering nanoscale devices. However, weak off-resonant absorption can limit its light-harvesting capability. Here, we propose a single NW coated with the graded-index dual shells (DSNW). We demonstrate that with appropriate thickness and refractive index of the inner shell, the DSNW exhibits significantly enhanced light trapping compared with the bare NW (BNW), and the NW only coated with the outer shell (OSNW) and the inner shell (ISNW), which can be attributed to the optimal off-resonant absorption mode profiles due to the improved coupling between the reemitted light of the transition modes of the leak mode resonances of the Si core and the nanofocusing light from the dual shells with the graded refractive index. We found that the light absorption can be engineered via tuning the thickness and the refractive index of the inner shell, the photocurrent density is significantly enhanced by 134% (56%, 12%) in comparison with that of the BNW (OSNW, ISNW). This work advances our understanding of how to improve off-resonant absorption by applying graded dual-shell design and provides a new choice for designing high-efficiency single NW photovoltaic devices.

This paper investigated building data from multispectral and single-photon Lidar systems. The multispectral datasets from the individual channels and fused channels were explored. The multispectral and single-photon Lidar data were compared across multiple aspects: the data acquisition geometry, number of echoes, intensity, density, resolution, data defects, noise level, and the absolute and relative accuracy. In addition, we explored the performance of the multispectral and single-photon data for roof plane detection for eight complex/stylish buildings to investigate the suitability of these data for 3D building reconstruction. The building data from the single-photon and multispectral Lidar systems were evaluated with respect to the reference building vector data with an accuracy of better than 5 cm. The advantages and disadvantages of both technologies and their applications in the urban building environment are discussed.

Hydrogen evolution reaction (HER) is one of the most important reaction in electrochemistry. This is not only because it is the simplest way to produce high purity hydrogen and the fact that it is the side reaction in many other technologies. HER actually shaped current electrochemistry because it was in focus of active research for so many years (and it still is). The number of catalysts investigated for HER is immense, and it is impossible to overview them all. In fact, it seems that the complexity of the field overcomes the complexity of HER. The aim of this review is to point out some of the latest developments in HER catalysis, current directions and some of the missing links between a single crystal, nanosized supported catalysts and, recently emerging, single atom catalysts for HER

In this paper, the author obtains an analytic exact form of Heaviside function, which is also known as Unit Step function and constitutes a fundamental concept of the Operational Calculus.In particulat, this function is explicitly expressed in a very simple manner by the aid of purely algebraic representations. The novelty of this work is that the proposed explicit formula is not performed in terms of non – elementary special functions, e.g. Dirac delta function or Error function and also is neither the limit of a function, nor the limit of a sequence of functions with point wise or uniform convergence. Hence, it may be much more appropriate and useful in the computational procedures which are inserted into Operational Calculus techniques and other engineering practices.

Recent advances in single-atom catalysis resulted in readily accessible materials whose application in most catalytic reactions mediated by conventional nanoparticle-based catalysts often results in higher activity and selectivity. Can we expect catalysis by atomically dispersed atoms to find practical applications? Which are the hurdles to be overcome prior to widespread uptake of atomically dispersed metals in industrial synthetic processes and in hydrogen fuel cells?

The use of motion capture has increased from last decade in a varied spectrum of applications like film special effects, controlling games and robots, rehabilitation system, animations etc. The current human motion capture techniques use markers, structured environment, and high resolution cameras in a dedicated environment. Because of rapid movement, elbow angle estimation is observed as the most difficult problem in human motion capture system. In this paper, we take elbow angle estimation as our research subject and propose a novel, markerless and cost-effective solution that uses RGB camera for estimating elbow angle in real time using part affinity field. We have recruited five (5) participants of (height, 168 ± 8 cm; mass, 61 ± 17 kg) to perform cup to mouth movement and at the same time measured the angle by both RGB camera and Microsoft Kinect. The experimental results illustrate that markerless and cost-effective RGB camera has a median RMS errors of 3.06° and 0.95° in sagittal and coronal plane respectively as compared to Microsoft Kinect.

Although good field emission from graphene has been demonstrated from a wide variety of different microfabricated structures, very few of them can be used to improve the design of cold field emitters for electron microscopy applications. Most of them consist of densely packed nano-emitters, which produce a large array of defocused overlapping electron beams, and therefore cannot be subsequently focused down to a single nanometer electron probe. This paper reviews the kind of single-tip cathode structures suitable in cold field emission guns for instruments such as scanning electron microscopy, transmission electron microscope or scanning transmission electron microscopy, and reviews progress in fabricating them from graphene-based materials.

Solvent-free, single-ion conducting electrolytes are sought after for use in electrochemical energy storage devices. Here, we investigate the ionic conductivity and how this property is influenced by segmental mobility and conducting ion number in crosslinked single-ion conducting polyether-based electrolytes with varying tethered anion and counter-cation types. Crosslinked electrolytes are prepared by the polymerization of poly(ethylene glycol) diacrylate (PEGDA), poly(ethylene glycol) methyl ether acrylate, and ionic monomers. The ionic conductivity of the electrolytes is measured and interpreted in the context of differential scanning calorimetry and Raman spectroscopy measurements. A lithiated crosslinked electrolyte prepared with PEG₃₁DA and STFSI monomers is found to have a lithium ion conductivity of 3.2 × 10^-6 and 1.8 × 10⁻⁵ S/cm at 55 and 100 °C, respectively. The percentage of unpaired anions for this electrolyte was estimated at about 23% via Raman spectroscopy. Despite the large variances in metal cation – STFSI binding energies as predicted via DFT and large variations in ionic conductivity, STFSI-based crosslinked electrolytes with the same charge density and varying cations (Li, Na, K, Mg, and Ca) were estimated to all have unpaired anion populations in the range of 19 to 29%.

The past two decades has seen a growing demand for high-power, high-voltage utility scale inverters mostly fueled by the integration of large solar PV and wind farms. Multilevel inverters have emerged as the industry choice for these megawatt range inverters because their reduced voltage stress, capable of generating an almost sinusoidal voltage, in-built redundancy, among others. This paper present a new Switched-Source Multilevel Inverter (SS MLI) architecture. The new inverter show superior over existing topologies. It has reduced voltage stress on the semiconductor, uses less number of switches –reduced size/weight/cost and increased efficiency. The new SSMLI is comprised of two voltage sources (V₁, V₂) and 6 switches. It is capable of generating 5-level output voltage in symmetric modes (i.e., V₁ = V₂), and 7-level output voltage in asymmetric modes (i.e., V₁ ≠ V₂). To demonstrate the validity of the proposed inverter, simulations results using MATLAB^® /Simulink^® for 5- and 7-level output voltages are presented . The simulations are also verified experimentally using a laboratory prototype.

The rationalization of single molecule magnets’ (SMMs) magnetic properties by quantum mechanical approaches represents a major task in the field of the Molecular Magnetism. The fundamental interpretative key of molecular magnetism is the phenomenological Spin Hamiltonian and the understanding of the role of its different terms by electronic structure calculations is expected to steer the rational design of new and more performing SMMs. This paper deals with the ab initio calculation of isotropic and anisotropic exchange contributions in the Fe(III) dimer [Fe₂(OCH₃)₂(dbm)₄]. This system represents the building block of one of the most studied Single Molecule Magnets ([Fe₄RC(CH₂O)₃)₂(dpm)₆] where R can be an aliphatic chain or a phenyl group just to name the most common functionalization groups) and its relatively reduced size allows the use of a high computational level of theory. Calculations were performed using CASSCF and NEVPT2 approaches on the X-ray geometry as assessment of the computational protocol, which has then be used to evinced the importance of the outer coordination shell nature through organic ligand modelization. Magneto-structural correlations as function of internal degrees of freedom for isotropic and anisotropic exchange contributions are also presented, outlining for the first time the extremely rapidly changing nature of the anisotropic exchange coupling.

X-ray tomography is an effective non-destructive testing method for industrial quality control. Limited angle tomography can be used to reduce the amount of data that needs to be acquired and thus speed up the process. In some industrial applications, however, objects are flat and layered, and laminography is preferred. It can deliver 2D images of the structure of a layered object at a particular depth from very limited data. An image at a particular depth is obtained by summing those parts of the data that contribute to that slice. This produces a sharp image of that slice while superimposing a blurred version of structures present at other depths. In this paper, we investigate an optimal experimental design (OED) problem for laminography that aims to determine the optimal source positions. Not only can this be used to mitigate imaging artifacts, it can also speed up the acquisition process in case where moving the source and detector is time consuming (e.g., in robotic arm imaging systems). We investigate the imaging artifacts in detail through a modified Fourier Slice Theorem. We address the experimental design problem within the Bayesian risk framework using empirical Bayes risk minimization. Finally, we present numerical experiments on simulated data.

Hyperspectral (HS) imaging plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. Hyperspectral image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper investigates the effectiveness of three dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward’s Linkage Strategy using Mutual Information (WaLuMI) for vein detection using HS images. A HS image dataset, encompassing left and right-hand images captured from 100 subjects with varying skin tones was created and annotated using anatomical data to represent vein and non-vein areas within the images. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with The Support Vector Machine binary classification were determined using the Training dataset. The performance of the three dimensionality reduction based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection.

Fatigue fractures in materials are the main cause of approximately 80% of all material failures, and it is believed that such failures can be predicted and mathematically calculated in a reliable manner. It is possible to establish prediction modalities in cases of fatigue fracture, according to three fundamental variables in fatigue, such as volume, number of fracture cycles, as well as applied stress, with the integration of Weibull constants (length characteristic). This investigation was carried out mechanical fatigue tests on specimens smaller than 4 mm2 in section of different industrial materials for their subsequent analysis through precision computed tomography in search of microfractures. The measurement of these microfractures, along with their metrics and classifications, was recorded. A convolutional neural network trained with deep learning was used to achieve the detection of microfractures in image processing. The detection of microfractures in images with 480x854 or 960x960 pixels is the primary objective of this network, and its accuracy is above 95%. Images that have microfractures and those that do not are classified by the network. Subsequently, by means of image processing, the microfracture is isolated. Finally, the images that do contain this feature are interpreted by image processing to obtain their area, perimeter, characteristic length, circularity, orientation, and type microfracture metrics. All values will be obtained in pixels and converted to metric units (μm) through a conversion factor based on image resolution.

There is growing recognition of the adverse effects of body image dissatisfaction (BID) and eating disorder (ED) symptoms on adolescent health. The aim of this study was to estimate the prevalence of ED symptoms, BID, and their relationship in adolescents from public schools in Southern Brazil. A total of 782 schoolchildren (male: n=420, female: n=362); age: 15 ± 0,4 years) answered a self-administrated questionnaire to identify sociodemographic data. Children´s Figure Rating Scale was adopted to identify body image and Eating Attitudes Test (EAT-26) was applied to investigate ED symptoms. Inferential statistics and hierarchical model-controlled logistic regression were used for association between variables. Most of the schoolchildren reported being satisfied with their bodies. However, we observed a higher prevalence of dissatisfaction among girls for being overweight and thinness among boys. Female students and students from schools located in the central area of the city showed higher chances of developing ED symptoms, and the absence of symptoms of ED appeared to act as a protective factor against BID in schoolchildren. Results of this study show the need to reflect on these factors that influence the development of ED and non-acceptance of their own body in a population concerned with their physical appearance.

Autonomous image recognition has numerous potential applications in the field of planetary science and geology. For instance, having the ability to classify images of rocks would allow geologists to have immediate feedback without having to bring back samples to the laboratory. Also, planetary rovers could classify rocks in remote places and even in other planets without needing human intervention. Shu et al. classified 9 different types of rock images using a Support Vector Machine (SVM) with the image features extracted autonomously. Through this method, the authors achieved a test accuracy of 96.71%. In this research, Convolutional Neural Networks(CNN) have been used to classify the same set of rock images. Results show that a 3-layer network obtains an average accuracy of 99.60% across 10 trials on the test set. A version of Self-taught Learning was also implemented to prove the generalizability of the features extracted by the CNN. Finally, one model has been chosen to be deployed on a mobile device to demonstrate practicality and portability. The deployed model achieves a perfect classification accuracy on the test set, while taking only 0.068 seconds to make a prediction, equivalent to about 14 frames per second.

The development of unmanned satellite space technology is increasingly willing, the emergence of medium resolution satellites with sensitivity and spectral variants such as Landsat is very effective in observing environmental changes, while the purpose of this study is to monitor the development of built-in land using image transformation techniques, estimating built-in land changes. The research method uses the NDVI image transformation technique, NDBI and Built Up Index, with Landsat satellite image data obtained from USGS. Accuracy sampling is done by purposive sampling with confusion matrix accuracy test technique. The research results were found. developed land for the period 2004 - 2010 with a percentage of 19.25%, for stages 2010 - 2018 with a percentage of 30.25%. The land development was built based on the area of ​​the highest sub-district in the Kubung area in the early period with a percentage of 7.20% then in the second period with a percentage of 32.23%. The quality of the accuracy of the results of image analysis using confusion matrix technique with an image accuracy level in a field sample of 185 with an image accuracy of 86.04%.

The focus of this study is to identify whether resilience and sustainable development can be used as an image for strategic planning of the city marketing. Resilience is about building and planning for future proof the cities. How urban challenges and crisis have the lowest impact and the maximum of bounce back and evolution. Resilience is part of the sustainable development. Thus, it is important for the decision-makers to define the mission on their strategic planning in a holistically way taking into consideration the basic assets of a city, the environment, the economy and the society and how can all of them can be combined to marketing the city and take into consideration the internal and external environment. As the past few years’ city marketing has become an important tool for the urban development. The main goal is to show how city marketing can be applied on a city that tries to be more resilient and more sustainable by using strategic urban planning to set the vision, to identify the challenges and the problematic areas and to set new goals and objectives in order to plan and build to future proof the complexity of an urban system. For answering the questions of this article we use two case studies Rotterdam (Netherlands) and Thessaloniki (Greece), using a literature review and researches conducted alongside with a benchmarking of their resilient strategies as both of the cities are members of the Resilient Cities Network. From a different perspective of resilient thinking, both of the cities have managed to use resilience as a marketing image for further sustainable development.

In this paper, a technique of image watermarking using multiple images as watermarks is presented. The technique is based on transform domain functions including discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) with an image as the host signal i.e. the watermarks will be used as proofs of the authenticity of the host image. The technique is executed by performing multilevel DWT followed by applying DCT and SVD to both the host and watermark. Multiple watermarks are used for the insurance of better security level. The scheme is immune to common image processing operations & some attacks and exhibits PSNR of 108.3781dB, normalized cross correlation (NCC) over 0.99 and normalized correlation (NC) over 0.99.

The aim of the research was getting to know the risk of dependency on physical exercising in individual sportspeople and the relationship with body dissatisfaction and motivation. 225 triathletes, swimmers, cyclists and athletes- with ages going from 18 to 63 years old took part in the research, of which 145 were men (M = 35.57 ±10.46 years) and 80 women (M = 32.83 ±10.31 years). The EDS-R was used to study the dependency on exercising, BSQ to study body dissatisfaction, BREQ-3 to know the motivation of participants and BIAQ to analyse conducts of avoidance to body image. The obtained results show that 8.5% of the subjects had risk of dependency on exercising and that 18.2% tend to have corporal dissatisfaction, without meaningful differences in the kind of sport they practiced. However, there were important differences concerning the dependency on physical exercise (15% vs 4.8%) and body dissatisfaction (31.1% vs 11%) in relation to sex, being the higher percentage referring to women. The introjected regulation and the conduct of food restriction were the predictor variables of the dependency on exercising and corporal dissatisfaction.

Much has been learned about the interaction between myosin and actin through biochemistry, in vitro motility assays and cryo-electron microscopy of F-actin decorated with myosin heads. Comparatively less is known about actin-myosin interactions within the filament lattice of muscle, where myosin heads function as independent force generators and thus most measurements report an average signal from multiple biochemical and mechanical states. All of the 3-D imaging by electron microscopy that has revealed the interplay of the regular array of actin subunits and myosin heads within the filament lattice has been accomplished using the flight muscle of the large waterbug Lethocerus sp. Lethocerus flight muscle possesses a particularly favorable filament arrangement that enables all the myosin cross-bridges contacting the actin filament to be visualized in a thin section. This review covers the history of this effort and the progress toward visualizing the complex set of conformational changes that myosin heads make when binding to actin in several static states as well as fast frozen actively contracting muscle. The efforts have revealed a consistent pattern of changes to the myosin head structures determined by X-ray crystallography needed to explain the structure of the different acto-myosin interactions observed in situ.

At present, business strategies in SMEs (Small and medium enterprises) are crucial for consolidation in highly competitive markets, in achieving a better image and in business profitability. One of the strategies that have the most success and business success are sustainable practices and social responsibility such as: ISO 14001 and ISO 26001. The literature related to sustainable business is based mainly on the theory of resources and capabilities, and in theory based on Stakeholders. These currents state that companies should focus on profitable strategies to ensure significant and long-term results, in order to achieve organizational and financial results for stakeholders. In this work, the sample consists of 215 companies from the commerce, services and industry sectors, located in the southern region of the State of Sonora in Mexico. The objective of the work is to analyze the influence of ISO 14001 and 26001 standards on the image and profitability of SMEs. The statistical analysis of the data has been carried out through the linear regression technique by OLS (Ordinary Least Squares). The findings prove that the ISO 14001 standard is the one that most influences the improvement of the business image and the level of profitability of the SME. In addition, we discovered that ISO 26001 has a partial influence on the image and profitability of the SME.

As the demand for a more sustainable society increases, adopting a sustainable banking approach serves as a competitive advantage for banks that are focused on attaining bank loyalty. This study revolves around understanding the role of sustainable banking practices on bank loyalty, while exploring the mediating effect of corporate image in the relationship between sustainable banking practices and bank loyalty. 511 data derived from customers of the banking sector was adopted for this study. Result from the structural equation modeling shows that sustainable banking practices positively and directly affects bank loyalty and corporate image, corporate image directly and positively affect bank loyalty, and also mediates in the relationship between sustainable banking practices and bank loyalty.

An effective on-board cloud detection method in small satellites would greatly improve the downlink data transmission efficiency and reduce the memory cost. In this paper, an ensemble method combining a lightweight U-Net with wavelet image compression is proposed and evaluated. The red, green, blue and infrared waveband images from Landsat-8 dataset are trained and tested to estimate the performance of proposed method. The LeGall-5/3 wavelet transform is applied on the dataset to accelerate the neural network and improve the feasibility of on-board implement. The experiment results illustrate that the overall accuracy of the proposed model achieves 97.45% by utilizing only four bands. Tests on low coefficients of compressed dataset have shown that the overall accuracy of the proposed method is still higher than 95%, while its inference speed is accelerated to 0.055 second per million pixels and maximum memory cost reduces to 2Mb. By taking advantage of mature image compression system in small satellites, the proposed method provides a good possibility of on-board cloud detection based on deep learning.

Detector-based spectral CT offers the possibility of obtaining spectral information from which discrete acquisitions at different energy levels can be derived, yielding so-called virtual monoenergetic images (VMI). In this study, we aimed to develop a jointly optimized deep learning framework based on dual-energy CT pulmonary angiography (DE-CTPA) data to generate synthetic monoenergetic images (SMI) for improving automatic pulmonary embolism (PE) detection in single-energy CTPA scans. For this purpose, we used two data sets: our institutional DE-CTPA data set D1 comprising polyenergetic arterial series and the corresponding VMI at low-energy levels (40 keV) with 7,892 image pairs, and a 10% subset of the 2020 RSNA Pulmonary Embolism Detection Challenge data set D2, which consisted of 161,253 polyenergetic images with dichotomous slice-wise annotations (PE/no PE). We trained a fully convolutional encoder-decoder on D1 to generate SMI from single-energy CTPA scans of D2, which were then fed into a ResNet50 network for training of the downstream PE classification task. The quantitative results on the reconstruction ability of our framework revealed high-quality visual SMI predictions with reconstruction results of 0.984 ± 0.002 (structural similarity) and 41.706 ± 0.547 dB (peak-signal-to-noise ratio). PE classification resulted in an AUC of 0.84 for our model, which achieved improved performance compared to other naive approaches with AUCs up to 0.81. Our study stresses the role of using joint optimization strategies for deep learning algorithms to improve automatic PE detection. The proposed pipeline may prove to be beneficial for computer-aided detection systems and could help rescue CTPA studies with suboptimal opacification of the pulmonary arteries from single-energy CT scanners.

Advancements in deep learning and availability of medical imaging data have led to use of CNN based architectures in disease diagnostic assisted systems. In spite of the abundant use of reverse transcription-polymerase chain reaction (RT-PCR) based tests in COVID-19 diagnosis, CT images offer an applicable supplement with its high sensitivity rates. Here, we study classification of COVID-19 pneumonia (CP) and non-COVID-19 pneumonia (NCP) in chest CT scans using efficient deep learning methods to be readily implemented by any hospital. We report our deep network framework design that encompasses Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory (biLSTM) architectures. Our study achieved high specificity (CP: 98.3%, NCP: 96.2% Healthy: 89.3%) and high sensitivity (CP: 84.0%, NCP: 93.9% Healthy: 94.9%) in classifying COVID-19 pneumonia, non-COVID-19 pneumonia and healthy patients. Next, we provide visual explanations for the CNN predictions with gradient-weighted class activation mapping (Grad-CAM). The results provided a model explainability by showing that Ground Glass Opacities (GGO), indicators of COVID-19 pneumonia disease, were captured by our CNN network. Finally, we have implemented our approach in three hospitals proving its compatibility and efficiency.

Advanced image processing will be crucial for emerging technologies such as autonomous driving, where the requirement to quickly recognize and classify objects under rapidly changing, poor visibility environments in real time will be needed. Photonic technologies will be key for next-generation signal and information processing, due to their wide bandwidths of 10’s of Terahertz and versatility. Here, we demonstrate broadband real time analog image and video processing with an ultrahigh bandwidth photonic processor that is highly versatile and reconfigurable. It is capable of massively parallel processing over 10,000 video signals simultaneously in real time, performing key functions needed for object recognition, such as edge enhancement and detection. Our system, based on a soliton crystal Kerr optical micro-comb with a 49GHz spacing with >90 wavelengths in the C-band, is highly versatile, performing different functions without changing the physical hardware. These results highlight the potential for photonic processing based on Kerr microcombs for chip-scale fully programmable high-speed real time video processing for next generation technologies.

This paper presents novel feature descriptors and classification algorithms for automated scoring of HER2 in Whole Slide Images (WSI) of breast cancer histology slides. Since a large amount of processing is involved in analyzing WSI images, the primary design goal has been to keep the computational complexity to the minimum possible level and to use simple, yet robust feature descriptors that can provide accurate classification of the slides. We propose two types of feature descriptors that encode important information about staining patterns and the percentage of staining present in ImmunoHistoChemistry (IHC) stained slides. The first descriptor is called a characteristic curve which is a smooth non-increasing curve that represents the variation of percentage of staining with saturation levels. The second new descriptor introduced in this paper is an LBP feature curve which is also a non-increasing smooth curve that represents the local texture of the staining patterns. Both descriptors show excellent interclass variance and intraclass correlation, and are suitable for the design of automatic HER2 classification algorithms. This paper gives the detailed theoretical aspects of the feature descriptors and also provides experimental results and comparative analysis.

Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modelling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images is the requirement for extensive quality assessment for ensuring that they have the texture characteristics and gray-tone features of real images. This paper presents texture feature analysis of synthetic ultrasound images using local binary patterns (LBP) and demonstrates the usefulness of a set of LBP features for image quality assessment. Experimental results presented in the paper clearly show how these features could provide an accurate quality metric that correlates very well with subjective evaluations performed by clinical experts.

Radiotherapy commonly utilizes CBCT for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into HU values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow FOV systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent GAN processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm’s ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.

Skin image analysis using artificial intelligence (AI) has recently attracted significant research interest, particularly for analyzing skin images captured by mobile devices. Acne is one of the most common skin conditions with profound effects in severe cases. In this study, we developed an AI system called AcneDet for automatic acne object detection and acne severity grading using facial images captured by smartphones. AcneDet includes two models for conducting two tasks: (1) a Faster R-CNN-based deep learning model for the detection of acne lesion objects of four types including blackheads/whiteheads, papules/pustules, nodules/cysts, and acne scars; and (2) a LightGBM machine learning model for grading acne severity using the Investigator’s Global Assessment (IGA) scale. The output of the Faster R-CNN model, i.e., the counts of each acne type, were used as input for the LightGBM model for acne severity grading. A dataset consisting of 1,572 labeled facial images captured by both iOS and Android smartphones was used for training. The results show that the Faster R-CNN model achieves a mAP of 0.54 for acne object detection. The mean accuracy of acne severity grading by the LightGBM model is 0.85. With this study, we hope to contribute to the development of artificial intelligent systems that are able to help acne patients understand more about their conditions and support doctors in acne diagnosis.

Deep Learning is a recent and important addition to the computational toolbox available for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration, super-resolution, and light-field imaging, and discuss how the latest Deep Learning research can be applied to other image reconstruction tasks such as structured illumination, spectral deconvolution, and sample stabilisation. Despite its successes, Deep Learning also poses significant challenges, has often misunderstood capabilities, and overlooked limits. We will address key questions, such as: What are the challenges in obtaining training data? Can we discover structures not present in the training data? And, what is the danger of inferring unsubstantiated image details?

The key to simulating soil erosion is to calculate the vegetation cover (C) factor. Methods that apply remote sensing to calculate C factor at regional scale cannot directly use the C factor formula. That is because the C factor formula is obtained by experiment, and needs the coverage ratio data of croplands, woodlands and grasslands at standard plot scale. In this paper, we present a C factor conversion method from a standard plot to a km-sized grid based on large sample theory and multi-scale remote sensing. Results show that: 1) Compared with the existing C factor formula, our method is based on the coverage ratio of croplands, woodlands and grasslands on a km-sized grid, takes the C factor formula obtained from the standard plot experiment and applies it to regional scale. This method improves the applicability of the C factor formula, and can satisfy the need to simulate soil erosion in large areas. 2) The vegetation coverage obtained by remote sensing interpretation is significantly consistent (paired samples t-test, t = −0.03, df = 0.12, 2-tail significance p < 0.05) and significantly correlated with the measured vegetation coverage. 3) The C factor of the study area is smaller in the middle, southern and northern regions, and larger in the eastern and western regions. The main reason for that is the distribution of woodlands, the Hunshandake and Horqin sandy lands and the valleys affected by human activities. 4) The method presented in this paper is more meticulous than the C factor method based on the vegetation index, improves the applicability of the C factor formula, and can be used to simulate soil erosion on large scale and provide strong support for regional soil and water conservation planning.

Resistance against amitraz in varroa mite populations has become a subject of interest in recent years due to increasing reports of reduced field efficacy of amitraz treatments, especially from some beekeepers in France and the United States. The loss of amitraz as a reliable tool to effectively reduce varroa mite infestation in the field could severely worsen the position of beekeepers in the fight to keep varroa infestation rates in their colonies at low levels. In this publication, we present data from French apiaries, collected in the years 2020 and 2021. These data include field efficacy of an authorized amitraz-based varroa treatment (Apivar®) and results of laboratory sensitivity assays of varroa mites exposed towards the reference LC90 concentration of amitraz. In addition, a total of 240 varroa mites from Eastern, Central, and Southern regions in France that were previously classified as either “sensitive” or “resistant” towards amitraz in a laboratory sensitivity assay were genotyped. The genetic analyses of mite samples are focused on the β−adrenergic-like octopamine receptor, which is considered as the main target site for amitraz in varroa mites. Special attention was given to a single nucleotide polymorphism (SNP) at position 260 of the ORβ-2R-L gene that had previously been described by Hernández Rodríguez et al. (2021) as associated to the resistance to amitraz in French varroa mites. Our findings confirm that amitraz resistance occurs in patches or “islands of resistance” with a less severe reduction in treatment efficacy compared to pyrethroid resistance or coumaphos resistance in varroa mites. The results of our genetic analyses of varroa mites call into question the hypothesis of the SNP at position 260 of the ORβ-2R-L gene being directly responsible for amitraz resistance development.

With the rising cost of animal feed protein, finding affordable and effective substitutes is crucial. Walnut kernel cake, a polyphenols, fiber, protein and fat-rich byproduct of walnut oil extraction, has been underexplored as a potential protein replacement in pig feed. In this study, we found that feeding large Diqing Tibetan pigs walnut kernel cake promoted adipose deposition and improved pork quality during pig growth. Transcriptome analysis revealed the upregulation of genes ANGPTL8, CCNP, ETV4, and TRIB3, associated with adipose deposition. Pathway analysis highlighted enrichment in adipose-deposition-related pathways, including PPAR, Insulin, PI3K-Akt, Wnt, and MAPK signaling. Further analysis identified DEGs (differentially expressed genes) positively correlated with adipose-related traits, such as PER2 and PTGES. Single-cell transcriptome data pointed to the specific expression of CD248 and PTGES in adipocyte progenitor/stem cells (APSCs), pivotal for adipocyte differentiation and adipose deposition regulation. This study demonstrates walnut kernel cake's potential to substitute soybean cake in pig feed, providing high-quality protein and promoting adipose deposition. It offers insights into feed protein replacement, human functional food, fat metabolism, and related diseases, with marker genes and pathways supporting pig breeding and pork quality improvement.

We used a mixed-method single-case experimental research design to examine the effect of modelling (peer versus non-peer) on exercise self-efficacy in stroke survivors who participated in a community-based exercise program. Quantitative data were obtained using a ABCA design: (A1) no model/baseline 1 (3 weeks); (B) peer model (6 weeks); (C) non-peer model (6 weeks); and (A2) no model/baseline 2 (3 weeks). Four participants completed self-efficacy questionnaires after each weekly session. Qualitative data were obtained using researcher diaries and two semi-structured interviews: after B and A2. Based on quantitative and qualitative results, participants reported higher exercise self-efficacy in the model conditions, with ratings appearing highest for the non-peer model. This finding could be due to a lack of full integration of the peer model and low feelings of similarity. Modelling in general could help people recovering from a stroke increase their exercise self-efficacy, but non-peer models may not be most advantageous.

Recently, nanobodies have taken center stage in research on drug discovery and development. Several therapeutic possibilities using nanobodies are presently undergoing clinical trials and waiting for FDA approval. The goal of this study was to emphasize the potential of nanobodies as therapeutic agents by concentrating on the most recent published studies that examined their properties, manufacturing, and possible applications. This article demonstrates that the unique properties of nanobodies in comparison to conventional antibodies, which are based on their small size and offer a number of benefits, make them seem to have a promising future. These advantages include the ability to access complex or hidden target sites that may be elusive to their larger antibody counterparts, and increased resilience against extreme conditions like tempera-ture changes and pH variations. As a result, nanobodies seem to have a bright future as adaptable tools for imaging in cancer and non-malignant diseases, as well as for in vitro and in vivo diagnostic and therapeutic potential for a variety of conditions, including oncology, infectious, metabolic, neurological, and other conditions like ophthalmologic, immune-mediated, and genetic disorders. More research is required to determine their effectiveness and safety in clinical applications. The current analysis offers a thorough overview of the therapeutic uses for nanobody products that are either on the market or undergoing clinical trials.

Single parenting is a social phenomenon that has always been in existence and is as old as mankind, and today the number of single-parent families has been rampantly increasing. As a result, the researcher conducted a study in the Gambella region of Ethiopia to investigate the economic and educational implications of single parenting on female adolescent students. The study employed a qualitative research approach with a case study research design. The researchers used the purposive sampling technique to select the research area and research participants for in-depth interviews and focus group discussions, and eleven interviewees, seventeen participants in focus group discussions within two groups, and seven key informants participated. Interviews were audio-recorded, transcribed, translated, and thematically analyzed. Interviews with research participants revealed that adolescent girl students from single-parent homes experience financial difficulties. The study also explored that students with single parents are more likely to be absent from school, to come late to school, to score low marks, and to drop out of school. Therefore, having an understanding of the benefits of having intact parents for the well-being of adolescents and raising children together should be advocated.

In recent years, graphene has received so much attention because of its superlative properties and its potential to revolutionize electronics especially in VLSI. This study analyzes the effect of single event upset (SEU) in SRAM cell which employs metal-oxide semiconductor type graphene nano-ribbon field effect transistor (MOS-GNRFET) and compares the results respectively with another SRAM cell designed in PTM 10nm FinFET node. Our simulation show there is a change in data stored in the SRAM after heavy ion strike. However, it recovers from radiation effects after 0.46 ns for GNRFET and 0.51 ns for FinFET. Since the degradation observed in Q and Qb of GNRFET SRAM are 2.7X and 2.16X as compared to PTM nano-MOSFET, we can conclude that GNRFET is less robust to single effect upset. In addition, the stability of SRAM can be improved by increasing the supply voltage VDD.

Single-cell RNA sequencing data facilitates investigation of cell heterogeneity and subpopulations as well as differentially abundant states however modern single-cell RNA sequencing datasets are growing in size and complexity requiring advances in the bioinformatic methods that analyze them. Many methods exist for each step of analysis including read alignment, normalization, quality control, batch effect correction, imputation and dimensionality reduction. With so many options to choose from at each step of the analysis, benchmarking and a synthesis of the literature on the methods available is necessary to inform biological researchers on the most optimal workflow for their data. Here, recent key methods of analysis are highlighted with a focus on methods that facilitate identification of cell subpopulations and differentially abundant cell states. With a constantly expanding toolset for each step in single-cell RNA sequencing dataset analysis, biological researchers should stay informed to utilize the most applicable methods for their own analyses.

Two mononuclear Ru(III) complexes of formula trans-[RuCl4(Hgua)(dmso)]·2H2O (1) and trans-[RuCl4(Hgua)(gua)]·3H2O (2) [Hgua = protonated guanine (gua), dmso = dimethyl sulfoxide] have been synthesized and characterized magnetostructurally. Compounds 1 and 2 crystallize in the monoclinic system with space groups P21/n and Pc, respectively. Each Ru(III) ion in 1 and 2 is six-coordinate and bonded to four chloride ions and one (1) or two (2) nitrogen atoms from guanine molecules and one sulfur atom (1) of a dmso solvent molecule, generating quasi regular octahedral geometries in both cases. In their crystal packing, the Ru(III) complexes are self-assembled mainly through an extended network of N-H⋯Cl hydrogen bonds and π⋯Cl type intermolecular interactions, forming novel supramolecular structures based on this paramagnetic 4d metal ion. Variable-temperature dc magnetic susceptibility measurements performed on microcrystalline samples of 1 and 2 show a different magnetic behavior. While 1 is a ferromagnetic compound at low temperature, 2 exhibits a behavior typical of noninteracting mononuclear Ru(III) complexes with S = 1/2. Ac magnetic susceptibility measurements reveal slow relaxation of the magnetization in the presence of external dc fields only for 2, hence indicating the occurrence of field-induced single-ion magnet (SIM) phenomenon in this mononuclear guanine-based Ru(III) complex.

In this study, the influence of graphene oxide nanoparticles on the bond-slip behavior of fiber and fly ash based geopolymer paste was examined. Geopolymer paste incorporating graphene oxide nanoparticles solution was cast in half briquetted specimens and embedded with a fiber. Three types of fibers were used: steel, polypropylene, and basalt. The pullout test was performed at two distinct speeds: 1 mm/second and 3 mm/second. Results showed that the addition of graphene oxide increased the compressive strength of geopolymer by about 7%. The bond-slip responses of fibers embedded in geopolymer mixed with graphene oxide exhibited higher peak stress and toughness as compared to those embedded in normal geopolymer. Each fiber type also showed different mode of failure. Both steel and polypropylene fibers showed full bond-slip responses due to their high ductility. Basalt fiber, on the other hand, because of its brittleness, failed by fiber fracture mode which showed no-slip in pull out responses. Both bond strength and toughness were found to be rate sensitive. The sensitivity was higher in graphene oxide/geopolymer than in conventional geopolymer.