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This paper presents the first-order supersymmetric rational extension of the quantum anisotropic harmonic oscillator (QAHO) in multiple dimensions, including full-line, half-line, and their combinations. The exact solutions are in terms of the exceptional orthogonal polynomials. The rationally extended potentials are isospectral to the conventional QAHOs., Comment: 19 pages, 2 figures and 2 tables

The non-linear collision induced fragmentation plays a crucial role in modeling several engineering and physical problems. In contrast to linear breakage, it has not been thoroughly investigated in the existing literature. This study introduces an innovative method that leverages the Elzaki integral transform as a preparatory step to enhance the accuracy and convergence of domain decomposition, used alongside the projected differential transform method to obtain closed-form or series approximations of solutions for the collisional breakage equation (CBE). A significant advantages of this technique is its capability to directly address both linear and nonlinear differential equations without the need for discretization or linearization. The mathematical framework is reinforced by a thorough convergence analysis, applying fixed point theory within an adequately defined Banach space. Additionally, error estimates for the approximated solutions are derived, offering more profound insights into the accuracy and dependability of the proposed method. The validity of this approach is demonstrated by comparing the obtained results with exact or finite volume approximated solutions considering several physical examples. Interestingly, the proposed algorithm yields accurate approximations for the number density functions as well as moments with fewer terms and maintains higher precision over extended time periods.

In this paper we explore independently for the first time three chemical potentials (baryon $\mu_B$, charged $\mu_Q$, and strange $\mu_S$) in the Chiral mean-field (CMF) model. We designed and implemented \texttt{CMF++}, a new version of the CMF model rewritten in \texttt{C++} that is optimized, modular, and well-documented. \texttt{CMF++} has been integrated into the MUSES Calculation Engine as a free and open source software module. The runtime improved in more than 4 orders of magnitude across all 3 chemical potentials, when compared to the legacy code. Here we focus on the zero temperature case and study stable, as well as metastable and unstable, vacuum, hadronic, and quark phases, showing how phase boundaries vary with the different chemical potentials. Due to the significant numerical improvements in \texttt{CMF++}, we can calculate for the first time high-order susceptibilities within the CMF framework to study the properties of the quark deconfinement phase transition. We found phases of matter that include a light hadronic phase, strangeness-dominated hadronic phase, and quark deconfinement within our $\mu_B$, $\mu_S$, $\mu_Q$ phase space. The phase transitions are of first, second (quantum critical point), and third order between these phases and we even identified a tricritical point., Comment: 63 pages, 22 figures

The way the topological structure transforms from a decoupled to a coupled state in multiplex networks has been extensively studied through both analytical and numerical approaches, often utilizing models of artificial networks. These studies typically assume uniform interconnections between layers to simplify the analytical treatment of structural properties in multiplex networks. However, this assumption is not applicable for real networks, where the heterogeneity of link weights is an intrinsic characteristic. Therefore, in this paper, link weights are calculated considering the node's reputation and the impact of the inter-layer link weights are assessed on the overall network's structural characteristics. These characteristics include synchronization time, stability of synchronization, and the second-smallest eigenvalue of the Laplacian matrix (algebraic connectivity). Our findings reveal that the perturbation in link weights (intra-layer) causes a transition in the algebraic connectivity whereas variation in inter-layer link weights has a significant impact on the synchronization stability and synchronization time in the multiplex networks. This analysis is different from the predictions made under the assumption of equal inter-layer link weights., Comment: 22 pages, 14 figures, 3 tables

The transition to online examinations and assignments raises significant concerns about academic integrity. Traditional plagiarism detection systems often struggle to identify instances of intelligent cheating, particularly when students utilize advanced generative AI tools to craft their responses. This study proposes a keystroke dynamics-based method to differentiate between bona fide and assisted writing within academic contexts. To facilitate this, a dataset was developed to capture the keystroke patterns of individuals engaged in writing tasks, both with and without the assistance of generative AI. The detector, trained using a modified TypeNet architecture, achieved accuracies ranging from 74.98% to 85.72% in condition-specific scenarios and from 52.24% to 80.54% in condition-agnostic scenarios. The findings highlight significant differences in keystroke dynamics between genuine and assisted writing. The outcomes of this study enhance our understanding of how users interact with generative AI and have implications for improving the reliability of digital educational platforms., Comment: Accepted for publication at The IEEE International Joint Conference on Biometrics (IJCB2024), contains 9 pages, 3 figures, 3 tables

We demonstrate monolithically grown germanium-tin (GeSn) on silicon avalanche photodiodes (APDs) for infrared light detection. A relatively thinner Ge buffer design was adopted to allow effective photo carriers to transport from the GeSn absorber to the Si multiplication layer such that clear punch-through behavior and a saturated primary responsivity of 0.3 A/W at 1550 nm were observed before avalanche breakdown in GeSn/Si APDs for the first time. The spectral response covers 1500 to 1700 nm. The measured punch-through and breakdown voltages are 15 and 17 V, respectively. Undisputed multiplication gain was obtained with the maximum value of 4.5 at 77 K, and 1.4 at 250 K, directly in reference to the saturated primary responsivity from the same device rather than a different GeSn p-i-n photodiode in previous reports. A peak responsivity was measured as 1.12 A/W at 1550 nm and 77 K., Comment: 8 pages, 5 figures, invited paper

X-ray is one of the prevalent image modalities for the detection and diagnosis of the human body. X-ray provides an actual anatomical structure of an organ present with disease or absence of disease. Segmentation of disease in chest X-ray images is essential for the diagnosis and treatment. In this paper, a framework for the segmentation of X-ray images using artificial intelligence techniques has been discussed. Here data has been pre-processed and cleaned followed by segmentation using SegNet and Residual Net approaches to X-ray images. Finally, segmentation has been evaluated using well known metrics like Loss, Dice Coefficient, Jaccard Coefficient, Precision, Recall, Binary Accuracy, and Validation Accuracy. The experimental results reveal that the proposed approach performs better in all respect of well-known parameters with 16 batch size and 50 epochs. The value of validation accuracy, precision, and recall of SegNet and Residual Unet models are 0.9815, 0.9699, 0.9574, and 0.9901, 0.9864, 0.9750 respectively., Comment: 10 pages, 3 figures

Owing to its true direct bandgap and tunable bandgap energies,GeSn alloys are increasingly attractive as gain media for mid-IR lasers that can be monolithically integrated on Si. Demonstrations of optically pumped GeSn laser at room under pulsed condition and at cryogenic temperature under continuous-wave excitation show great promise of GeSn lasers to be efficient electrically injected light sources on Si. Here we report electrically injected GeSn lasers using Fabry-Perot cavity with 20, 40, and 80 micron ridge widths. A maximum operating temperature of 140 K with lasing threshold of 0.756 kA/cm2 at 77 K and emitting wavelength of 2722 nm at 140 K was obtained. The lower threshold current density compared to previous works was achieved by reducing optical loss and improving the optical confinement. The peak power was measured as 2.2 mW/facet at 77 K.

The presence of muscles throughout the active parts of the body such as the upper and lower limbs, makes electromyography-based human-machine interaction prevalent. However, muscle signals are stochastic and noisy. These noises can be regular and irregular. Irregular noises due to movements or electrical switching require dynamic filtering. Conventionally, filters are stacked, which trims and delays the signal unnecessarily. This study introduces a decontamination technique involving a supervised rewarding strategy to drive a deep Q-network-based agent (supDQN). It applies one of three filters to decontaminate a 1sec long surface electromyography signal, which is dynamically contaminated. A machine learning agent identifies whether the signal after filtering is clean or noisy. Accordingly, a reward is generated. The identification accuracy is enhanced by using a local interpretable model-agnostic explanation. The deep Q-network is guided by this reward to select filter optimally while decontaminating a signal. The proposed filtering strategy is tested on four noise levels (-5 dB, -1 dB, +1 dB, +5 dB). supDQN filters the signal desirably when the signal-to-noise ratio (SNR) is between -5 dB to +1 dB. It filters less desirably at high SNR (+5 dB). A normalized root mean square (nRMSE) is formulated to depict the difference of filtered signal from ground truth. This is used to compare supDQN and conventional methods including wavelet denoising with debauchies and symlet wavelet, high order low pass filter, notch filter, and high pass filter. The proposed filtering strategy gives an average value nRMSE of 1.1974, which is lower than the conventional filters.

Artificial intelligence (AI) has made significant advances in recent years and opened up new possibilities in exploring applications in various fields such as biomedical, robotics, education, industry, etc. Among these fields, human hand gesture recognition is a subject of study that has recently emerged as a research interest in robotic hand control using electromyography (EMG). Surface electromyography (sEMG) is a primary technique used in EMG, which is popular due to its non-invasive nature and is used to capture gesture movements using signal acquisition devices placed on the surface of the forearm. Moreover, these signals are pre-processed to extract significant handcrafted features through time and frequency domain analysis. These are helpful and act as input to machine learning (ML) models to identify hand gestures. However, handling multiple classes and biases are major limitations that can affect the performance of an ML model. Therefore, to address this issue, a new mixture of experts extra tree (MEET) model is proposed to identify more accurate and effective hand gesture movements. This model combines individual ML models referred to as experts, each focusing on a minimal class of two. Moreover, a fully trained model known as the gate is employed to weigh the output of individual expert models. This amalgamation of the expert models with the gate model is known as a mixture of experts extra tree (MEET) model. In this study, four subjects with six hand gesture movements have been considered and their identification is evaluated among eleven models, including the MEET classifier. Results elucidate that the MEET classifier performed best among other algorithms and identified hand gesture movement accurately.

Spiking Neural Networks (SNNs), recognized for their biological plausibility and energy efficiency, employ sparse and asynchronous spikes for communication. However, the training of SNNs encounters difficulties coming from non-differentiable activation functions and the movement of spike-based inter-layer data. Spike-Timing Dependent Plasticity (STDP), inspired by neurobiology, plays a crucial role in SNN's learning, but its still lacks the conscious part of the brain used for learning. Considering the issue, this research work proposes a Consciousness Driven STDP (CD-STDP), an improved solution addressing inherent limitations observed in conventional STDP models. CD-STDP, designed to infuse the conscious part as coefficients of long-term potentiation (LTP) and long-term depression (LTD), exhibit a dynamic nature. The model connects LTP and LTD coefficients to current and past state of synaptic activities, respectively, enhancing consciousness and adaptability. This consciousness empowers the model to effectively learn while understanding the input patterns. The conscious coefficient adjustment in response to current and past synaptic activity extends the model's conscious and other cognitive capabilities, offering a refined and efficient approach for real-world applications. Evaluations on MNIST, FashionMNIST and CALTECH datasets showcase $CD$-STDP's remarkable accuracy of 98.6%, 85.61% and 99.0%, respectively, in a single hidden layer SNN. In addition, analysis of conscious elements and consciousness of the proposed model on SNN is performed., Comment: 10 pages, 5 figures, Journal

The worm gearbox is a high-speed transmission system that plays a vital role in various industries. Therefore it becomes necessary to develop a robust fault diagnosis scheme for worm gearbox. Due to advancements in sensor technology, researchers from academia and industries prefer deep learning models for fault diagnosis purposes. The optimal selection of hyperparameters (HPs) of deep learning models plays a significant role in stable performance. Existing methods mainly focused on manual tunning of these parameters, which is a troublesome process and sometimes leads to inaccurate results. Thus, exploring more sophisticated methods to optimize the HPs automatically is important. In this work, a novel optimization, i.e. amended gorilla troop optimization (AGTO), has been proposed to make the convolutional neural network (CNN) adaptive for extracting the features to identify the worm gearbox defects. Initially, the vibration and acoustic signals are converted into 2D images by the Morlet wavelet function. Then, the initial model of CNN is developed by setting hyperparameters. Further, the search space of each Hp is identified and optimized by the developed AGTO algorithm. The classification accuracy has been evaluated by AGTO-CNN, which is further validated by the confusion matrix. The performance of the developed model has also been compared with other models. The AGTO algorithm is examined on twenty-three classical benchmark functions and the Wilcoxon test which demonstrates the effectiveness and dominance of the developed optimization algorithm. The results obtained suggested that the AGTO-CNN has the highest diagnostic accuracy and is stable and robust while diagnosing the worm gearbox.

The vibration analysis of the bearing is very crucial because of its non-stationary nature and low signal-to-noise ratio. Therefore, a novel scheme for detecting bearing defects is put forward based on the extraction of single-valued neutrosophic cross-entropy (SVNCE) to address this issue. Initially, the artificial hummingbird algorithm (AHA) is used to make the feature mode decomposition (FMD) adaptive by optimizing its parameter based on a newly developed health indicator (HI) i.e. sparsity impact measure index (SIMI). This HI ensures full sparsity and impact properties simultaneously. The raw signals are disintegrated into different modes by adaptive FMD at optimal values of its parameters. The energy of these modes is calculated for different health conditions. The energy interval range has been decided based on energy eigen which are then transformed into single-valued neutrosophic sets (SVNSs) for unknown defect conditions. The minimum argument principle employs the least SVNCE values between SVNSs of testing samples (obtained from unknown bearing conditions) and SVNSs of training samples (obtained from known bearing conditions) to recognize the different defects in the bearing. It has been discovered that the suggested methodology is more adept at identifying the various bearing defects.

Population balance models often integrate fundamental kernels, including sum, gelling and Brownian aggregation kernels. These kernels have demonstrated extensive utility across various disciplines such as aerosol physics, chemical engineering, astrophysics, pharmaceutical sciences and mathematical biology for the purpose of elucidating particle dynamics. The objective of this study is to refine the semi-analytical solutions derived from current methodologies in addressing the nonlinear aggregation and coupled aggregation-breakage population balance equation. This work presents a unique semi-analytical approach based on the homotopy analysis method (HAM) to solve pure aggregation and couple aggregation-fragmentation population balance equations, which is an integro-partial differentia equation. By decomposing the non-linear operator, we investigate how to utilize the convergence control parameter to expedite the convergence of the HAM solution towards its precise values in the proposed method.

The non-linear collision-induced breakage equation has significant applications in particulate processes. Two semi-analytical techniques, namely homotopy analysis method (HAM) and accelerated homotopy perturbation method (AHPM) are investigated along with the well-known finite volume method (FVM) to comprehend the dynamical behavior of the non-linear system, i.e., the concentration function, the total number and the total mass of the particles in the system. The theoretical convergence analyses of the series solutions of HAM and AHPM are discussed. In addition, the error estimations of the truncated solutions of both methods equip the maximum absolute error bound. To justify the applicability and accuracy of these methods, numerical simulations are compared with the findings of FVM and analytical solutions considering three physical problems.

This article aims to provide approximate solutions for the non-linear collision-induced breakage equation using two different semi-analytical schemes, i.e., variational iteration method (VIM) and optimized decomposition method (ODM). The study also includes the detailed convergence analysis and error estimation for ODM in the case of product collisional ($K(\epsilon,\rho)=\epsilon\rho$) and breakage ($b(\epsilon,\rho,\sigma)=\frac{2}{\rho}$) kernels with an exponential decay initial condition. By contrasting estimated concentration function and moments with exact solutions, the novelty of the suggested approaches is presented considering three numerical examples. Interestingly, in one case, VIM provides a closed-form solution, however, finite term series solutions obtained via both schemes supply a great approximation for the concentration function and moments.

Citrus diseases pose threats to citrus farming and result in economic losses worldwide. Nucleic acid and serology-based methods of detection and, immunochromatographic assays are commonly used but these laboratory tests are laborious, expensive and might be subjected to cross-reaction and contamination. Modern optical spectroscopic techniques offer a promising alternative as they are label-free, sensitive, rapid, non-destructive, and demonstrate the potential for incorporation into an autonomous system for disease detection in citrus orchards. Nevertheless, the majority of optical spectroscopic methods for citrus disease detection are still in the trial phases and, require additional efforts to be established as efficient and commercially viable methods. The review presents an overview of fundamental working principles, the state of the art, and explains the applications and limitations of the optical spectroscopy technique including the spectroscopic imaging approach (hyperspectral imaging) in the identification of diseases in citrus plants. The review highlights (1) the technical specifications of optical spectroscopic tools that can potentially be utilized in field measurements, (2) their applications in screening citrus diseases through leaf spectroscopy, and (3) discusses their benefits and limitations, including future insights into label-free identification of citrus diseases. Moreover, the role of artificial intelligence is reviewed as potential effective tools for spectral analysis, enabling more accurate detection of infected citrus leaves even before the appearance of visual symptoms by leveraging compositional, morphological, and chemometric characteristics of the plant leaves. The review aims to encourage stakeholders to enhance the development and commercialization of field-based, label-free optical tools for the rapid and early-stage screening of citrus diseases in plants.

The current study explores the novel potential of traversable wormhole solutions within the framework of Finsler geometry, incorporating conformal symmetry alongside traceless fluid dynamics. Using the Conformal Killing vector approach, we have discussed the wormholes based on traceless fluid within the intriguing framework of Finsler geometry. The field equations and the associated conformal factor are obtained specifically under the condition of conformal motion in Finsler geometry. Furthermore, we have successfully derived and examined the shape function, considering a range of values for the Finslerian parameter $\lambda$. Our investigation extends to fundamental physical characteristics such as proper radial distance, active mass function, and total gravitational energy, aiming to understand their influence on the traversability of the wormhole. The observation of energy condition violations provides evidence for the exotic matter's presence near the throat, reinforcing the assertion of the Finslerian wormhole's traversability., Comment: 17 pages, 8 figures