Determining the appropriate number of clusters in unsupervised learning is a central problem in statistics and data science. Traditional validity indices such as Calinski-Harabasz, Silhouette, and Davies-Bouldin-depend on centroid-based distances and therefore degrade in high-dimensional or contaminated data. This paper proposes a new robust, nonparametric clustering validation framework, the High-Dimensional Between-Within Distance Median (HD-BWDM), which extends the recently introduced BWDM criterion to high-dimensional spaces. HD-BWDM integrates random projection and principal component analysis to mitigate the curse of dimensionality and applies trimmed clustering and medoid-based distances to ensure robustness against outliers. We derive theoretical results showing consistency and convergence under Johnson-Lindenstrauss embeddings. Extensive simulations demonstrate that HD-BWDM remains stable and interpretable under high-dimensional projections and contamination, providing a robust alternative to traditional centroid-based validation criteria. The proposed method provides a theoretically grounded, computationally efficient stopping rule for nonparametric clustering in modern high-dimensional applications.

It is critical to design efficient beamforming in reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems for enhancing spectrum utilization. However, conventional methods often have limitations, either incurring high computational complexity due to iterative algorithms or sacrificing performance when using heuristic methods. To achieve both low complexity and high spectrum efficiency, an unsupervised learning-based beamforming design is proposed in this work. We tailor image-shaped channel samples and develop an ISAC beamforming neural network (IBF-Net) model for beamforming. By leveraging unsupervised learning, the loss function incorporates key performance metrics like sensing and communication channel correlation and sensing channel gain, eliminating the need of labeling. Simulations show that the proposed method achieves competitive performance compared to benchmarks while significantly reduces computational complexity.

Accurate segmentation of brain tumors from 3D multimodal MRI is vital for diagnosis and treatment planning across diverse brain tumors. This paper addresses the challenges posed by the BraTS 2023, presenting a unified transfer learning approach that applies to a broader spectrum of brain tumors. We introduce HT-CNNs, an ensemble of Hybrid Transformers and Convolutional Neural Networks optimized through transfer learning for varied brain tumor segmentation. This method captures spatial and contextual details from MRI data, fine-tuned on diverse datasets representing common tumor types. Through transfer learning, HT-CNNs utilize the learned representations from one task to improve generalization in another, harnessing the power of pre-trained models on large datasets and fine-tuning them on specific tumor types. We preprocess diverse datasets from multiple international distributions, ensuring representativeness for the most common brain tumors. Our rigorous evaluation employs standardized quantitative metrics across all tumor types, ensuring robustness and generalizability. The proposed ensemble model achieves superior segmentation results across the BraTS validation datasets over the previous winning methods. Comprehensive quantitative evaluations using the DSC and HD95 demonstrate the effectiveness of our approach. Qualitative segmentation predictions further validate the high-quality outputs produced by our model. Our findings underscore the potential of transfer learning and ensemble approaches in medical image segmentation, indicating a substantial enhancement in clinical decision-making and patient care. Despite facing challenges related to post-processing and domain gaps, our study sets a new precedent for future research for brain tumor segmentation. The docker image for the code and models has been made publicly available, this https URL.

Addressing the challenge of removing atmospheric fog or haze from digital images, known as image dehazing, has recently gained significant traction in the computer vision community. Although contemporary dehazing models have demonstrated promising performance, few have thoroughly investigated high-resolution imagery. In such scenarios, practitioners often resort to downsampling the input image or processing it in smaller patches, which leads to a notable performance degradation. This drop is primarily linked to the difficulty of effectively combining global contextual information with localized, fine-grained details as the spatial resolution grows. In this chapter, we propose a novel framework, termed the Streamlined Global and Local Features Combinator (SGLC), to bridge this gap and enable robust dehazing for high-resolution inputs. Our approach is composed of two principal components: the Global Features Generator (GFG) and the Local Features Enhancer (LFE). The GFG produces an initial dehazed output by focusing on broad contextual understanding of the scene. Subsequently, the LFE refines this preliminary output by enhancing localized details and pixel-level features, thereby capturing the interplay between global appearance and local structure. To evaluate the effectiveness of SGLC, we integrated it with the Uformer architecture, a state-of-the-art dehazing model. Experimental results on high-resolution datasets reveal a considerable improvement in peak signal-to-noise ratio (PSNR) when employing SGLC, indicating its potency in addressing haze in large-scale imagery. Moreover, the SGLC design is model-agnostic, allowing any dehazing network to be augmented with the proposed global-and-local feature fusion mechanism. Through this strategy, practitioners can harness both scene-level cues and granular details, significantly improving visual fidelity in high-resolution environments.

The exact solution of N- dimensional radial Schr\"odinger equation with the generalized Cornell potential has been obtained using the Laplace transformation (LT) method. The energy eigenvalues and the corresponding wave functions for any state have been determined. The eigenvalues for some special cases of the generalized Cornell potential are obtained. The present results are applied to calculate the mass spectra of heavy quarkonium systems such as charmonium and bottomonium and the bc meson. A comparison is discussed with the experimental data and recent works. The present results are improved in comparison with other recent studies and are in a good agreement with the experimental data. The effect of the dimensional number (N) on the meson masses has been studied. We note that the meson masses increase in higher dimensions.

Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Challenge has provided a large number of multi-institutional MRI scans as a benchmark for glioma segmentation algorithms. This paper describes our contribution to the BraTS 2022 Continuous Evaluation challenge. We propose a new ensemble of multiple deep learning frameworks namely, DeepSeg, nnU-Net, and DeepSCAN for automatic glioma boundaries detection in pre-operative MRI. It is worth noting that our ensemble models took first place in the final evaluation on the BraTS testing dataset with Dice scores of 0.9294, 0.8788, and 0.8803, and Hausdorf distance of 5.23, 13.54, and 12.05, for the whole tumor, tumor core, and enhancing tumor, respectively. Furthermore, the proposed ensemble method ranked first in the final ranking on another unseen test dataset, namely Sub-Saharan Africa dataset, achieving mean Dice scores of 0.9737, 0.9593, and 0.9022, and HD95 of 2.66, 1.72, 3.32 for the whole tumor, tumor core, and enhancing tumor, respectively. The docker image for the winning submission is publicly available at (this https URL).

We proposed a novel code called cyclic shift (CS) code to overcome the drawbacks exists in traditional Spectral Amplitude Coding- Optical Code Division Multiple Access (SAC-OCDMA) codes that have been presented in the past few years. The proposed cyclic shift code has simple construction and large cardinality in selecting the code weight and the number of users. It also has zero cross correlation which allows it to suppress both Multiple Access Interference (MAI) and Phase Induced Intensity Noise (PIIN). Moreover, the frequency bins of the proposed code exist beside each other which reduce the number of filters needed to encode and decode the data. Therefore, the receiver design becomes simple and cost efficient. We compared the performance of our proposed code to the traditional codes and show that our proposed code gives better performance than the traditional SAC-OCDMA codes. A mathematical analysis of CS code has been derived. Simulation analysis for CS code has been carried out using optisystem ver.13.

Selecting the right journal for your research paper is a pivotal decision in the academic publishing journey. This paper aims to guide researchers through the process of choosing a suitable journal for their work by discussing key criteria and offering practical tips.

The increasing reliance on AI-driven 5G/6G network infrastructures for mission-critical services highlights the need for reliability and resilience against sophisticated cyber-physical threats. These networks are highly exposed to novel attack surfaces due to their distributed intelligence, virtualized resources, and cross-domain integration. This paper proposes a fault-tolerant and resilience-aware framework that integrates AI-driven anomaly detection, adaptive routing, and redundancy mechanisms to mitigate cascading failures under cyber-physical attack conditions. A comprehensive validation is carried out using NS-3 simulations, where key performance indicators such as reliability, latency, resilience index, and packet loss rate are analyzed under various attack scenarios. The deduced results demonstrate that the proposed framework significantly improves fault recovery, stabilizes packet delivery, and reduces service disruption compared to baseline approaches.

In this work, we introduce a nonparametric clustering stopping rule algorithm based on the spatial median. Our proposed method aims to achieve the balance between the homogeneity within the clusters and the heterogeneity between clusters. The proposed algorithm maximises the ratio of the variation between clusters and the variation within clusters while adjusting for the number of clusters and number of observations. The proposed algorithm is robust against distributional assumptions and the presence of outliers. Simulations have been used to validate the algorithm. We further evaluated the stability and the efficacy of the proposed algorithm using three real-world datasets. Moreover, we compared the performance of our model with 13 other traditional algorithms for determining the number of clusters. We found that the proposed algorithm outperformed 11 of the algorithms considered for comparison in terms of clustering number determination. The finding demonstrates that the proposed method provides a reliable alternative to determine the number of clusters for multivariate data.

Addressing the challenge of removing atmospheric fog or haze from digital images, known as image dehazing, has recently gained significant traction in the computer vision community. Although contemporary dehazing models have demonstrated promising performance, few have thoroughly investigated high-resolution imagery. In such scenarios, practitioners often resort to downsampling the input image or processing it in smaller patches, which leads to a notable performance degradation. This drop is primarily linked to the difficulty of effectively combining global contextual information with localized, fine-grained details as the spatial resolution grows. In this chapter, we propose a novel framework, termed the Streamlined Global and Local Features Combinator (SGLC), to bridge this gap and enable robust dehazing for high-resolution inputs. Our approach is composed of two principal components: the Global Features Generator (GFG) and the Local Features Enhancer (LFE). The GFG produces an initial dehazed output by focusing on broad contextual understanding of the scene. Subsequently, the LFE refines this preliminary output by enhancing localized details and pixel-level features, thereby capturing the interplay between global appearance and local structure. To evaluate the effectiveness of SGLC, we integrated it with the Uformer architecture, a state-of-the-art dehazing model. Experimental results on high-resolution datasets reveal a considerable improvement in peak signal-to-noise ratio (PSNR) when employing SGLC, indicating its potency in addressing haze in large-scale imagery. Moreover, the SGLC design is model-agnostic, allowing any dehazing network to be augmented with the proposed global-and-local feature fusion mechanism. Through this strategy, practitioners can harness both scene-level cues and granular details, significantly improving visual fidelity in high-resolution environments.

. In this paper, an effective computer-aided diagnosis (CAD) system is presented to detect MI signals using the convolution neural network (CNN) for urban healthcare in smart cities. Two types of transfer learning techniques are employed to retrain the pre-trained VGG-Net (Fine-tuning and VGG-Net as fixed feature extractor) and obtained two new networks VGG-MI1 and VGG-MI2. In the VGG-MI1 model, the last layer of the VGG-Net model is replaced with a specific layer according to our requirements and various functions are optimized to reduce overfitting. In the VGG-MI2 model, one layer of the VGG-Net model is selected as a feature descriptor of the ECG images to describe it with informative features. Considering the limited availability of dataset, ECG data is augmented which has increased the classification performance. Physikalisch-technische bundesanstalt (PTB) Diagnostic ECG database is used for experimentation, which has been widely employed in MI detection studies. In case of using VGG-MI1, we achieved an accuracy, sensitivity, and specificity of 99.02%, 98.76%, and 99.17%, respectively and we achieved an accuracy of 99.22%, a sensitivity of 99.15%, and a specificity of 99.49% with VGG-MI2 model. Experimental results validate the efficiency of the proposed system in terms of accuracy sensitivity, and specificity.

Quantifying the impact of a scholarly paper is of great significance, yet the effect of geographical distance of cited papers has not been explored. In this paper, we examine 30,596 papers published in Physical Review C, and identify the relationship between citations and geographical distances between author affiliations. Subsequently, a relative citation weight is applied to assess the impact of a scholarly paper. A higher-order weighted quantum PageRank algorithm is also developed to address the behavior of multiple step citation flow. Capturing the citation dynamics with higher-order dependencies reveals the actual impact of papers, including necessary self-citations that are sometimes excluded in prior studies. Quantum PageRank is utilized in this paper to help differentiating nodes whose PageRank values are identical.

The present research explores the effects of Percentage Fe incorporation on the structure and antibacterial activity of Fe ZSM 5. Silica extracted from local rice husk straw (white particles) by applying NaOH and HCl solutions for consecutive chemical treatment was used for hydrothermal synthesis of FeZSM 5 with constant Si(Fe plus Al) ratios. The chemical and physical changes of ZSM 5 and Fe ZSM 5 surfaces were investigated by X ray diffraction analysis (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), UV Vis spectroscopy, differential scanning calorimetry (DSC), and pore structure analysis by N2 adsorption at 196 . XRD analysis revealed the typical ZSM 5 structure with new diffraction lines attributed to the iron silicate phase. FTIR spectral analysis of ZSM 5 samples containing iron display a new band at 656 cm that is ascribed to the SiOFe group. The antibacterial activity of such coatings towards different kinds of bacteria, such as S. pneumonia, B. subtilis, E. coli and P. aeruginosa, and fungi, such as A. fumigatus and C. albicans, for investigated ZSM 5 and Fe (20 and 100 percentages) samples showed selective antibacterial actions.

Understanding how intrinsic decoherence affects the interplay between geometry, dynamics, and entanglement in quantum systems is a central challenge in quantum information science. In this work, we develop a unified framework that explores this interplay for a pair of interacting spins governed by an XXZ-type Heisenberg model under an external magnetic field and intrinsic decoherence. We quantify entanglement using the concurrence measure and examine its evolution under decoherence, revealing that intrinsic noise rapidly suppresses entanglement as it increases. We then analyze the Hilbert-Schmidt and Bures distances between quantum states and show how both the degree of entanglement and the noise rate modulate these distances and their associated quantum speeds. Importantly, we demonstrate that the Hilbert Schmidt speed is more responsive to entanglement and coherence loss than the Bures speed, making it a powerful tool for probing the geometry of quantum dynamics. Moreover, we solve the quantum brachistochrone problem in the presence of intrinsic decoherence, identifying the minimal evolution time and the corresponding optimal entangled states. Finally, we explore the geometric phase accumulated during the system's evolution. Our results show that decoherence hinders geometric phase accumulation, while entanglement counteracts this effect, enhancing phase stability.

The screening of baggage using X-ray scanners is now routine in aviation security with automatic threat detection approaches, based on 3D X-ray computed tomography (CT) images, known as Automatic Threat Recognition (ATR) within the aviation security industry. These current strategies use pre-defined threat material signatures in contrast to adaptability towards new and emerging threat signatures. To address this issue, the concept of adaptive automatic threat recognition (AATR) was proposed in previous work. In this paper, we present a solution to AATR based on such X-ray CT baggage scan imagery. This aims to address the issues of rapidly evolving threat signatures within the screening requirements. Ideally, the detection algorithms deployed within the security scanners should be readily adaptable to different situations with varying requirements of threat characteristics (e.g., threat material, physical properties of objects). We tackle this issue using a novel adaptive machine learning methodology with our solution consisting of a multi-scale 3D CT image segmentation algorithm, a multi-class support vector machine (SVM) classifier for object material recognition and a strategy to enable the adaptability of our approach. Experiments are conducted on both open and sequestered 3D CT baggage image datasets specifically collected for the AATR study. Our proposed approach performs well on both recognition and adaptation. Overall our approach can achieve the probability of detection around 90% with a probability of false alarm below 20%. Our AATR shows the capabilities of adapting to varying types of materials, even the unknown materials which are not available in the training data, adapting to varying required probability of detection and adapting to varying scales of the threat object.

Active reconfigurable intelligent surfaces (RISs) are a novel and promising technology that allows controlling the radio propagation environment while compensating for the product path loss along the RIS-assisted path. In this letter, we consider the classical radar detection problem and propose to use an active RIS to get a second independent look at a prospective target illuminated by the radar transmitter. At the design stage, we select the power emitted by the radar, the number of RIS elements, and their amplification factor in order to maximize the detection probability for a fixed probability of false alarm and a common (among radar and RIS) power budget. An illustrative example is provided to assess the achievable detection performance, also in comparison with that of a radar operating alone or with the help of a passive RIS.

Advisor-advisee relationship is important in academic networks due to its universality and necessity. Despite the increasing desire to analyze the career of newcomers, however, the outcomes of different collaboration patterns between advisors and advisees remain unknown. The purpose of this paper is to find out the correlation between advisors' academic characteristics and advisees' academic performance in Computer Science. Employing both quantitative and qualitative analysis, we find that with the increase of advisors' academic age, advisees' performance experiences an initial growth, follows a sustaining stage, and finally ends up with a declining trend. We also discover the phenomenon that accomplished advisors can bring up skilled advisees. We explore the conclusion from two aspects: (1) Advisees mentored by advisors with high academic level have better academic performance than the rest; (2) Advisors with high academic level can raise their advisees' h-index ranking. This work provides new insights on promoting our understanding of the relationship between advisors' academic characteristics and advisees' performance, as well as on advisor choosing.

By Using the variational Monte Carlo (VMC) method, we calculate the 1s{\sigma}_g state energies, the dissociation energies and the binding energies of the hydrogen molecule and its molecular ion in the presence of an aligned magnetic field regime between 0 a.u. and 10 a.u. The present calculations are based on using two types of compact and accurate trial wave functions, which are put forward for consideration in calculating energies in the absence of magnetic field. The obtained results are compared with the most recent accurate values. We conclude that the applications of VMC method can be extended successfully to cover the case of molecules under the effect of the magnetic field.

The possibility of magnetization resonant control in a Josephson superconductor-ferromagnet-superconductor $\varphi_{0}$ junction shunted by an $LC$ circuit is demonstrated. As a result of the resonance of Josephson oscillations with oscillations in the circuit, a time-independent superconducting current arises in the junction. Due to the coupling of the Josephson phase and the magnetization of the ferromagnetic layer, the resulting superconducting current leads to a deviation of the easy axis from its initial position and to a precession of the magnetization around the tilted axis. We show that the tilt value increases with the increasing spin-orbit interaction and the Josephson to magnetic energy ratio. An analytical expression for the magnetization tilt is obtained, which agrees well with the results of numerical calculations. The emerging possibility of resonant control of magnetization in a shunted $\varphi_{0}$ junction can be used in the development of novel technologies in the field of superconducting electronics and spintronics.