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Deep learning is popularly used for analyzing pathology images, but variations in image properties can limit the effectiveness of the models. The study aims to develop a method that transfers the variability present in the training set to unseen images, improving the model's ability to make accurate inferences. YOLOv5 was trained on peripheral blood and bone marrow sample images and Neural Color Transfer techniques were used to incorporate invariance. The results showed significant improvement in detecting WBCs from untrained samples after normalization, highlighting the potential of deep learning-based normalization techniques for inference robustness.

Recently, outstanding identification rates in image classification tasks were achieved by convolutional neural networks (CNNs). to use such skills, selective CNNs trained on a dataset of well-known images of metal surface defects captured with an RGB camera. Defects must be detected early to take timely corrective action due to production concerns. For image classification up till now, a model-based method has been utilized, which indicated the predicted reflection characteristics of surface defects in comparison to flaw-free surfaces. The problem of detecting steel surface defects has grown in importance as a result of the vast range of steel applications in end-product sectors such as automobiles, households, construction, etc. The manual processes for detections are time-consuming, labor-intensive, and expensive. Different strategies have been used to automate manual processes, but CNN models have proven to be the most effective rather than image processing and machine learning techniques. By using different CNN models with fine-tuning, easily compare their performance and select the best-performing model for the same kinds of tasks. However, it is important that using different CNN models either from fine tuning can be computationally expensive and time-consuming. Therefore, our study helps the upcoming researchers to choose the CNN without considering the issues of model complexity, performance, and computational resources. In this article, the performance of various CNN models with transfer learning techniques are evaluated. These models were chosen based on their popularity and impact in the field of computer vision research, as well as their performance on benchmark datasets. According to the outcomes, DenseNet201 outperformed the other CNN models and had the greatest detection rate on the NEU dataset, falling in at 98.37 percent.

The available software to study the spectroscopic characteristics of atoms, ions, and molecules runs on a server, e.g., the general-purpose atomic structure package (GRASP) and R-matrix method. A Python program has been developed to compute Transition Probabilities, oscillator strengths, Line strengths, matrix elements, and radii of the orbit for lithium and its iso-electronic sequence. The program is straightforward, easily applicable without installation, and uses built-in Python libraries. It can be run on personal computers core I3 and above. The effective charge, effective quantum numbers, and energies of upper and lower levels serve as input parameters for computing the spectral quantities mentioned above. As a case study, we implemented our program on Li I, F VII, Na IX, Al XI, Mg X, and Fe XXIV to calculate transition probabilities, oscillator strengths, and line strengths. The results are compared and found to align with the corresponding values in the NIST data., Comment: The Program can be obtained from the authors

A novel material consisting of a monolayer of C$_{60}$ buckyballs with hexagonal symmetry has recently been observed experimentally, named graphullerene. In this study, we present a comprehensive \textit{ab-initio} theoretical analysis of the electronic and optical properties of both pristine and impurity-engineered monolayer graphullerene using spin-dependent density functional theory (spin-DFT). Our findings reveal that graphullerene is a direct band gap semiconductor with a band gap of approximately 1.5 eV at the $\Gamma$ point, agreeing well with experimental data. Notably, we demonstrate that by adding impurities, in particular substitutional nitrogen, substitutional boron, or adsorbent hydrogen, to graphullerene results in the formation of spin-dependent deep donor and deep acceptor levels, thereby giving rise to a variety of half-semiconductors. All the impurities exhibit a magnetic moment of approximately $\mu_B$ per impurity. This impurity engineering enables the tuning of spin-polarized exciton properties in graphullerene, with spin-dependent band gap energies ranging from 0.43 eV ($\lambda \sim$ 2.9 $\mu$m) to 1.5 eV ($\lambda \sim$ 820 nm), covering the near-infrared (NIR) and short-wavelength infrared (SWIR) regimes. Our results suggest that both pristine and impurity-engineered graphullerene have significant potential for the development of carbon-based 2D semiconductor spintronic and opto-spintronic devices., Comment: 11 pages, 7 figures

In this study, we investigate the role of bulk viscosity in $f(Q,T)$ gravity in explaining late-time cosmic acceleration. This model, an extension of symmetric teleparallel gravity, introduces viscosity into cosmic matter dynamics for a more realistic representation. Specifically, we consider the linear form of $f (Q, T) =\alpha Q + \beta T$, where $\alpha$ and $\beta$ are free model parameters. To assess the model, we derive its exact solution and use Hubble parameter $H(z)$ data and Pantheon + SNe Ia data for parameter estimation. We employ the $\chi^2$ minimization technique alongside the MCMC random sampling method to determine the best-fit parameters. Then, we analyze the behavior of key cosmological parameters, including the deceleration parameter, bulk viscous matter-dominated universe density, effective pressure, and the effective EoS parameter, accounting for the viscous type fluid. We observe a transition in the deceleration parameter from a positive (decelerating) to a negative (accelerating) phase at transition redshift $z_t$. The matter density shows the expected positive behavior, while the pressure, influenced by viscosity, exhibits negative behavior, indicative of accelerating expansion. Furthermore, we investigate the energy conditions and find that while the NEC and DEC meet positivity criteria, the SEC is violated in the present and future epochs. The $Om(z)$ diagnostic suggests that our model aligns with quintessence behavior. Finally, our $f(Q,T)$ cosmological model, incorporating bulk viscosity effects, provides a compelling explanation for late-time cosmic behavior, consistent with observational data., Comment: Physics of the Dark Universe accepted version

The exponential growth in the number of Internet of Things (IoT) devices has seen the introduction of several Lightweight Encryption Algorithms (LEA). While LEAs are designed to enhance the integrity, privacy and security of data collected and transmitted by IoT devices, it is hazardous to assume that all LEAs are secure and exhibit similar levels of protection. To improve encryption strength, cryptanalysts and algorithm designers routinely probe LEAs using various cryptanalysis techniques to identify vulnerabilities and limitations of LEAs. Despite recent improvements in the efficiency of cryptanalysis utilising heuristic methods and a Partial Difference Distribution Table (PDDT), the process remains inefficient, with the random nature of the heuristic inhibiting reproducible results. However, the use of a PDDT presents opportunities to identify relationships between differentials utilising knowledge graphs, leading to the identification of efficient paths throughout the PDDT. This paper introduces the novel use of knowledge graphs to identify intricate relationships between differentials in the SIMON LEA, allowing for the identification of optimal paths throughout the differentials, and increasing the effectiveness of the differential security analyses of SIMON., Comment: 10 pages, 10 figures, 2 algorithms, accepted by the 4th International Conference on Electrical, Computer and Energy Technologies (ICECET) to be presented in July 2024

Rapid growth in the number of small sensor devices known as the Internet of Things (IoT) has seen the development of lightweight encryption algorithms. Two well-known lightweight algorithms are SIMON and SIMECK which have been specifically designed for use on resource-constrained IoT devices. These lightweight encryption algorithms are based on the efficient Feistel block structure which is known to exhibit vulnerabilities to differential cryptanalysis. Consequently, it is necessary to test these algorithms for resilience against such attacks. While existing state-of-the-art research has demonstrated novel heuristic methods of differential cryptanalysis that improve time efficiency on previous techniques, the large state sizes of these encryption algorithms inhibit cryptanalysis time efficiency. In this paper, we introduce Versatile Investigative Sampling Technique for Advanced Cryptanalysis (VISTA-CRYPT) - a time-efficient enhancement of differential cryptanalysis of lightweight encryption algorithms. The proposed technique introduces a simple framework of quota sampling that produces state-of-the-art results with time reductions of up to $76\%$ over existing techniques. Further, we present a preliminary graph-based analysis of the output differentials for the identification of relationships within the data and future research opportunities to further enhance the performance of differential cryptanalysis. The code designed for this work and associated datasets will be available at https://github.com/johncook1979/simon-cryptanalysis., Comment: 24 pages, 21 figures, 7 tables

The rapid advancement of Artificial Intelligence (AI) in healthcare presents a unique opportunity for advancements in obstetric care, particularly through the analysis of cardiotocography (CTG) for fetal monitoring. However, the effectiveness of such technologies depends upon the availability of large, high-quality datasets that are suitable for machine learning. This paper introduces the Oxford Maternity (OxMat) dataset, the world's largest curated dataset of CTGs, featuring raw time series CTG data and extensive clinical data for both mothers and babies, which is ideally placed for machine learning. The OxMat dataset addresses the critical gap in women's health data by providing over 177,211 unique CTG recordings from 51,036 pregnancies, carefully curated and reviewed since 1991. The dataset also comprises over 200 antepartum, intrapartum and postpartum clinical variables, ensuring near-complete data for crucial outcomes such as stillbirth and acidaemia. While this dataset also covers the intrapartum stage, around 94% of the constituent CTGS are antepartum. This allows for a unique focus on the underserved antepartum period, in which early detection of at-risk fetuses can significantly improve health outcomes. Our comprehensive review of existing datasets reveals the limitations of current datasets: primarily, their lack of sufficient volume, detailed clinical data and antepartum data. The OxMat dataset lays a foundation for future AI-driven prenatal care, offering a robust resource for developing and testing algorithms aimed at improving maternal and fetal health outcomes.

In the expanding field of the Internet of Things (IoT), wireless channel estimation is a significant challenge. This is specifically true for low-power IoT (LP-IoT) communication, where efficiency and accuracy are extremely important. This research establishes two distinct LP-IoT wireless channel estimation models using Artificial Neural Networks (ANN): a Feature-based ANN model and a Sequence-based ANN model. Both models have been constructed to enhance LP-IoT communication by lowering the estimation error in the LP-IoT wireless channel. The Feature-based model aims to capture complex patterns of measured Received Signal Strength Indicator (RSSI) data using environmental characteristics. The Sequence-based approach utilises predetermined categorisation techniques to estimate the RSSI sequence of specifically selected environment characteristics. The findings demonstrate that our suggested approaches attain remarkable precision in channel estimation, with an improvement in MSE of $88.29\%$ of the Feature-based model and $97.46\%$ of the Sequence-based model over existing research. Additionally, the comparative analysis of these techniques with traditional and other Deep Learning (DL)-based techniques also highlights the superior performance of our developed models and their potential in real-world IoT applications.

Purpose: Facial expression production and perception in autism spectrum disorder (ASD) suggest potential presence of behavioral biomarkers that may stratify individuals on the spectrum into prognostic or treatment subgroups. Construct validity and group discriminability have been recommended as criteria for identification of candidate stratification biomarkers. Methods: In an online pilot study of 11 children and young adults diagnosed with ASD and 11 age- and gender-matched neurotypical (NT) individuals, participants recognize and mimic static and dynamic facial expressions of 3D avatars. Webcam-based eye-tracking (ET) and facial video tracking (VT), including activation and asymmetry of action units (AUs) from the Facial Action Coding System (FACS) are collected. We assess validity of constructs for each dependent variable (DV) based on the expected response in the NT group. Then, the Boruta statistical method identifies DVs that are significant to group discriminability (ASD or NT). Results: We identify one candidate ET biomarker (percentage gaze duration to the face while mimicking static 'disgust' expression) and 14 additional DVs of interest for future study, including 4 ET DVs, 5 DVs related to VT AU activation, and 4 DVs related to AU asymmetry in VT. Based on a power analysis, we provide sample size recommendations for future studies. Conclusion: This pilot study provides a framework for ASD stratification biomarker discovery based on perception and production of facial expressions., Comment: 18 pages, 3 figures, 5 tables

Bacteria are prolific at colonizing diverse surfaces under a widerange of environmental conditions, and exhibit fascinating examples of self-organization across scales. Though it has recently attracted considerable interest, the role of mechanical forces in the collective behavior of bacterial colonies is not yet fully understood. Here, we construct a model of growing rod-like bacteria, such as Escherichia coli based purely on mechanical forces. We perform overdamped molecular dynamics simulations of the colony starting from a few cells in contact with a surface. As the colony grows, microdomains of strongly aligned cells grow and proliferate. Our model captures both the initial growth of a bacterial colony and also shows characteristic signs of capturing the experimentally observed transition to multilayered colonies over longer timescales. We compare our results with experiments on E. coli cells and analyze the statistics of microdomains., Comment: 8 pages, 5 figures

Customizable 3D avatar-based facial expression stimuli may improve user engagement in behavioral biomarker discovery and therapeutic intervention for autism, Alzheimer's disease, facial palsy, and more. However, there is a lack of customizable avatar-based stimuli with Facial Action Coding System (FACS) action unit (AU) labels. Therefore, this study focuses on (1) FACS-labeled, customizable avatar-based expression stimuli for maintaining subjects' engagement, (2) learning-based measurements that quantify subjects' facial responses to such stimuli, and (3) validation of constructs represented by stimulus-measurement pairs. We propose Customizable Avatars with Dynamic Facial Action Coded Expressions (CADyFACE) labeled with AUs by a certified FACS expert. To measure subjects' AUs in response to CADyFACE, we propose a novel Beta-guided Correlation and Multi-task Expression learning neural network (BeCoME-Net) for multi-label AU detection. The beta-guided correlation loss encourages feature correlation with AUs while discouraging correlation with subject identities for improved generalization. We train BeCoME-Net for unilateral and bilateral AU detection and compare with state-of-the-art approaches. To assess construct validity of CADyFACE and BeCoME-Net, twenty healthy adult volunteers complete expression recognition and mimicry tasks in an online feasibility study while webcam-based eye-tracking and video are collected. We test validity of multiple constructs, including face preference during recognition and AUs during mimicry., Comment: 12 pages, 8 figures

The observation that every two-person adversarial game is an affine transformation of a zero-sum game is traceable to Luce & Raiffa (1957) and made explicit in Aumann (1987). Recent work of (ADP) Adler et al. (2009), and of Raimondo (2023) in increasing generality, proves what has so far remained a conjecture. We present two proofs of an even more general formulation: the first draws on multilinear utility theory developed by Fishburn & Roberts (1978); the second is a consequence of the ADP proof itself for a special case of a two-player game with a set of three actions.

Here, we describe a simple and unique architecture of a microfluidic mixer that can mix two streams (Water and Fluorescein Isothiocyanate (FITC) buffer) with 30% efficiency. The overall mixing in this design is dominated by 1D diffusion, and to enhance mixing, we used three different types of geometric obstacles inside the channel. Comsol multiphysics simulation software was used to validate the theoretical mixing efficiency (at Reynolds Number, Re ~ 0.1, 1, and 10) of this device. We utilized soft lithography and replica molding techniques to fabricate the device out of Polydimethylsiloxane (PDMS, a commonly used polymer) on a glass substrate. The effective length of our microfluidic mixer is 5mm, and the channel width is about 200 microns with 50 micron height. It is composed of three different shapes of obstacles (e.g., 6 cones, 3 arrays of rectangular bars, and 5 circular posts), and all of these are placed inside the main channel. FITC buffer (Diffusion co-efficient, D = 0.5 x 10-9 m2/s) and DI water were used to investigate the mixer performance at Re~1. Simulated and experimental results are based on approximately 1.1 mm2 flow area and suggest that 30% mixing is achievable with the current design.

In situ measurement of analytes for in vivo or in vitro systems has been challenging due to the bulky size of traditional analytical instruments. Also, frequent in vitro concentration measurements rely on fluorescence-based methods or direct slicing of the matrix for analyses. These traditional approaches become unreliable if localized and in situ analyses are needed. In contrast, for in situ and real-time analysis of target analytes, surface-engineered optical fibers can be leveraged as a powerful miniaturized tool, which has shown promise from bio to environmental studies. Herein, we demonstrate an optical fiber functionalized with gold nanoparticles using a dip-coating process to investigate the interaction of light with molecules at or near the surface of the optical fiber. Localized surface plasmon resonance from the light-matter interaction enables the detection of minute changes in the refractive index of the surrounding medium. We used this principle to assess the in situ molecular distribution of a synthetic drug (methylene blue) in an in vitro matrix (agarose gel) having varying concentrations. Leveraging the probed Z-height in diffused analytes, combined with its in silico data, our platform shows the feasibility of a simple optofluidic tool. Such straightforward in situ measurements of analytes with optical fiber hold potential for real-time molecular diffusion and molecular perturbation analyses relevant to biomedical and clinical studies.

We use renormalization group summed perturbation theory (RGSPT) to improve perturbation series in quantum chromodynamics in the determination of some of the standard model parameters., Comment: PhD Thesis