In this research, we examine a special property of some prime numbers that goes beyond separating primes from odd composites. Rather, this property serves to divide the set of primes into two groups: those having it and those lacking it. Incredibly, these two groups portray an almost equal distribution of primes amongst them. This phenomenon is explored in order to reveal some aspects concerning the basic characteristics and structure of prime numbers. We also provide further insight on what this trait implies within prime number theory through rigorous analysis as well as mathematical inspection which shines a new light on the enigmatic properties of these fundamental mathematical entities.

Objective: Clinical adoption of innovative EEG technology is contingent on the non-inferiority of the new devices relative to conventional ones. We present the four key results from testing the signal quality of Zeto’s WR19 EEG system against a conventional EEG system conducted on patients in a clinical setting. Methods: We performed 30-minute simultaneous recordings using the Zeto WR19 (zEEG) and a conventional clinical EEG system (cEEG) in a cohort of 15 patients. We compared the signal quality between the two EEG systems by computing time domain statistics, spectral density, and signal-to-noise ratio. Results: All the statistical comparisons resulted in signal quality non-inferior relative to cEEG. (i) Time domain statistics, including the Hjorth parameters, showed equivalence between the two systems, except for a significant reduction of sensitivity to electric noise in zEEG relative to cEEG. (ii) The point-by-point waveform correlation between the two systems was acceptable (r>0.6; P<0.001). (iii) Each of the 15 datasets showed a high spectral correlation (r>0.99; P<0.001) and overlapping spectral density across all electrode positions, indicating no systematic signal distortion. (iv) The mean signal-to-noise ratio (SNR) of the zEEG system exceeded that of the cEEG by 4.82 dB, equivalent to 16% improvement. Conclusion: In terms of signal quality, the zEEG system is non-inferior to conventional clinical EEG systems with respect to all relevant technical parameters that determine EEG readability and interpretability. Significance: Zeto’s WR19 wireless dry electrode system has signal quality in the clinical EEG space equivalent to traditional cEEG recordings.

Unlike terrestrial wireless stations, using drones as aerial wireless stations allows for improved wireless services due to their 3D mobility. Therefore, model interactions play a pivotal role in determining the locations and trajectories of drones and their performance. In this paper, we discuss the interactions between models in the Internet of drones under different real-life scenarios. These models include: the channel path loss models, intelligent reflecting surface (IRS) placement model, solar energy model, wireless power transfer (WPT) model, and power consumption model. Moreover, we discuss learned lessons and highlight future research directions relevant to these models.

Radio Frequency (RF) superconductivity has been a mainstay of accelerator science for decades. However, its benefits have yet to be applied to proton synchrotrons with demanding tuning requirements. For example, the Main Injector (MI), Fermilab's high-energy proton synchrotron, currently utilizes 20+ ferrite-loaded cavities for a targeted 1.2 s acceleration cycle. Harnessing the extremely high gradients associated with superconductivity, the required number of cavities could be reduced by an order of magnitude, dramatically lowering operational power requirements even with cryogenic considerations. Additionally, the current plans for the Fermilab Accelerator Complex Evolution (ACE) initiative involve almost doubling the number of cavities in MI if the same designs are to be used, further highlighting the potential benefits of superconductivity. These advantages are attractive, but to date, no tunable superconducting cavity suitable for MI has been proposed due to the incompatibility of conventional broadband tuning methods with superconductivity. Here, we present a tunable superconducting cavity concept capable of record-breaking performance. Tuning will be accomplished by using high-speed linear actuators to vary the insertion depth of metallic plungers into the cavity volume. This tuning concept is theoretically viable with currently available technology and will be fully compatible with a superconducting cavity.

Commodity-of-the-shelf (COTS) data streaming platforms, e.g., Apache Kafka, RabbitMQ, have been powering companies from several sectors to move and extract further insights from their data. Yet, little is known about the suitability of these platforms for mission critical applications such as disaster recovery, first-aid response and military coordination. This paper provides a provocative discussion on how prepared current data streaming platforms are for mission critical scenarios. We argue that, despite their intrinsic reliability and security features, these platforms are still hard to test/operate which ultimately hinders trust on their safety and security conditions. Moreover, they commonly assume a powerful underlying infrastructure, which makes it tough to keep a high performance in resource-constrained environments (e.g., satellite networks). We conclude by identifying relevant research directions to ameliorate the performance and suitability of data streaming platforms for mission-critical applications.

The world's leading laboratories continue intensive research into the properties of nanocomposites-materials that have unique, both physical and chemical properties that are useful in practice. Along with the discovery of new materials, new technologies are being developed and attempts are being made to create mathematical models capable of describing phenomena in hollow quantum resonators-quantum dots, lines, and other cumulative-dissipative 3D structures of nanometer dimensions. Mathematical models make it possible to develop new materials, classify their properties and discover new patterns. As a result of the study of nanocomposites, the author has discovered and classified more than 31 polarization quantum-size effects. In this paper, it is proved that only by applying the foundations of cumulative quantum mechanics (CQM) can one explain and classify all quantum-size effects discovered by the author. These quantum size effects led to the discovery of the principles of physical doping and the classification of doping into physical and chemical doping. During physical doping, the modification of the properties of the nanocomposite is carried out with the help of nano-and microstructures of foreign material, which have a high affinity for free electrons. In this case, the fractions of foreign (alloying, other) material do not penetrate into the crystal lattice. They only decorate the nano-or microcrystals of the support material in the nanocomposite. A dopant with a high affinity for free electrons is charged with a negative charge, while a doped nanocrystal is charged with a positive charge. Therefore, physical doping of nanocomposites leads to the generation of electric fields that act as catalysts for various reactions, contributes to the strengthening of nanocomposites by Coulomb compression, an increase in the luminescent properties of phosphors, an increase in conductivity up to 10 10 times, and other properties, due to quantum size effects due to local violation of electrical neutrality. The models we have developed, which are effective for explaining nano-effects, have been used by us to explain angstrom-and femto-phenomena in cumulative-dissipative structures.

6G wireless networks will integrate communication, computing, localization, and sensing capabilities while meeting the needs of high reliability and trustworthiness. In this paper, we develop similar techniques as those used by communication modules of previous generations for the sensing functionality of 6G networks. Specifically, this paper introduces the concept of extended automatic repeat request (e-ARQ) for integrated sensing and communications (ISAC) networks. We focus on multi-static sensing schemes, in which the nodes receiving the reflected sensing signals provide the transmitting nodes with configurable levels of feedback about the sensing result. This technique improves the sensing quality via retransmissions using adaptive parameters. We show that our proposed e-ARQ boosts the sensing quality in terms of detection accuracy and provides a sense of adaptability for applications supported by ISAC networks.

The traditional approaches in material research and hardware design are insufficient to address the evolving Operation and Maintenance (O&M) demands in contemporary power electronics. Overengineering and data acquisition practices lead to unsustainable costs and reduced profit margins. Digital Twins (DTs), defined as real-time simulation models of physical systems, emerge as promising solutions to meet stringent O&M requirements. In power electronics, DTs offer significant potential in thermal management, crucial for control performance, safety, and system lifespan. This paper aims to analyze the development of computationally efficient and high-fidelity DTs tailored for power electronics applications, emphasizing their predictive reliability. To achieve this goal, the proposed physics-based approach is enhanced by integrating Data-Driven Artificial Intelligence (AI)based techniques. The predictive reliability of the DTs produced through this workflow is then experimentally validated against a power electronic converter designed for induction heating applications. Additionally, the feasibility of real-time execution is demonstrated, affirming the practical applicability of the developed DTs.

Surface electromyography (sEMG) signals are electrical signals released by muscles during movement, which can directly reflect the muscle conditions during various actions. When a series of continuous static actions are connected along the temporal axis, a sequential action is formed, which is more aligned with people's intuitive understanding of real-life movements. The signals acquired during sequential actions are known as sequential sEMG signals, including an additional dimension of sequence, embodying richer features compared to static sEMG signals. However, existing methods show inadequate utilization of the signals' sequential characteristics. Addressing these gaps, this paper introduces the Spatio-Temporal Feature Extraction Network (STFEN), which includes a Sequential Feature Analysis Module based on static-sequential knowledge transfer, and a Spatial Feature Analysis Module based on dynamic graph networks to analyze the internal relationships between the leads. The effectiveness of STFEN is tested on both modified publicly available datasets and on our acquired Arabic Digit Sequential Electromyography (ADSE) dataset. The results show that STFEN outperforms existing models in recognizing sequential sEMG signals. Experiments have confirmed the reliability and wide applicability of STFEN in analyzing complex muscle activities. Furthermore, this work also suggests STFEN's potential benefits in rehabilitation medicine, particularly for stroke recovery, and shows promising future applications.

Language can disclose a lot about a person's emotional condition, social standing, and even personality. In the proposed research, the authors looked at the words that are most helpful in determining if a person is depressed or not. Additionally, the authors tried to include a variety of dataset features to see if they could help us form a more accurate judgement. The authors obtained the data from the social networking site Reddit. It includes erratic messages and comments made by a group of depressed people. Given that it performed the best, the authors used logistic regression using the Term Frequency-Inverse Document Frequency (TFIDF) for the proposed model. One of the factors that contributed to a marginal speed improvement was the average time between two consecutive postings or comments. In F1-score, the proposed model fared better than other models utilising the same dataset. The diagnosis of mental disease is turning into a significant issue as people's understanding of the value of mental health grows. Many psychiatrists found it challenging to make a diagnosis of mental disease in a patient due to the complexity of each mental disorder, making it impossible to start the patient on the right course of treatment before it was too late. However, the significance of integrating into people's daily life has produced a setting in which further knowledge about a patient's mental illness may be acquired.

Much has been written about the representation of noisy linear 2-ports. Here we present a theory of noisy Nports. We show how in the general case there are (2N)!/(N !) 2 equivalent representations and give the transformations relating them. We also discuss singular cases in which some of the transformations are not possible as well as how to measure the noise properties of an N-port.

In the past 20 years, improvements in night-time light (NTL) remote sensing have spurred a resurgence of interest in the mapping of human economic activity. Nevertheless, the full potential of NTL data for urban research is constrained by a relatively coarse spatial resolution and the blooming effect. Downscaling NTL data is a potential solution, aiming to obtain fine-resolution nocturnal data with high accuracy. Most existing remotely sensed image fusion techniques were developed for optical remote sensing images taken during the day. When NTL images are compared to optical images, they exhibit a greater quantity of dark (low value) pixels, higher levels of background noise, and a more obvious blooming effect. In this paper, we proposed a spatially non-stationary, geostatistical-based downscaling technique (random forest area-to-point Kriging) to downscale NTL data while accounting explicitly for the point spread function, thus, dealing with the blooming effect specific to NTL data. We compared several image fusion algorithms for downscaling while reducing the blooming effect. Numerical experiments on two megacities showed that downscaling was improved both numerically and visually by taking the PSF into consideration. During the RF regression, the R 2 increased and the RMSE decreased for both study regions, when accounting for the PSF. For the ATPK-based residual part, considering the PSF led to increased accuracy of prediction. The suggested methodology has the potential to increase the detail and accuracy of the NTL data available for modeling socioeconomic phenomena at the city scale, with wide potential for application in future socioeconomic research.

In the wake of the COVID-19 pandemic, efficiently allocating ICU resources for critical patients has become crucial, especially for those with chronic conditions. This study harnesses machine learning (ML) to forecast ICU admissions among COVID-19 patients in Kuwait, analyzing a dataset of 4399 patients to identify pivotal predictors for ICU needs. Employing cross-validation and Synthetic Minority Over-sampling Technique (SMOTE) to tackle data imbalance, the predictive variables were refined using backward feature selection with logistic regression and evaluated model interpretability with Shapley additive explanations (SHAP). The Support Vector Machine (SVM) model outperformed other models with an area under the curve (AUC) of 0.91, and the Extra Tree (ET) model showed better performance with an accuracy of 96.42%. Critical predictors included demographics, clinical outcomes like shortness of breath, elevated d-dimer levels, and abnormal chest X-rays. This research not only underscores the potential of ML in critical healthcare decision-making during pandemics but also highlights its role in discovery science, suggesting broader applications in healthcare and other scientific domains. The study advances medical informatics by integrating ML with healthcare, offering insights into disease dynamics and improving resource allocation strategies.

This work proposes a WebXR-based cross-platform conceptual architecture, leveraging the A-Frame and Networked-Aframe frameworks, in order to facilitate the development of an open, accessible, and interoperable metaverse. By introducing the concept of spatial web app, this research contributes to the discourse on the metaverse, offering an architecture that democratizes access to virtual environments and extended reality through the web, and aligns with Tim Berners-Lee's original vision of the World Wide Web as an open platform in the digital realm.

In response to the evolving demands of microelectromechanical system (MEMS) integration, aiming to achieve high-performance electronic products, innovative interconnect solutions are becoming essential. The interconnects must possess key features, including the capability for miniaturization, low processing temperatures, and the ability to maintain a stable microstructure with optimized electrical, mechanical, and thermomechanical properties. To meet these demands, this study designed a novel Cu-Sn-based solid-liquid interdiffusion (SLID) interconnect. The study involved the implementation of a metallization stack, incorporating Co as a layer to interact with low-temperature Cu-Sn-In SLID and form intermetallic compounds (IMCs). Since Cu6(Sn,In)5 forms at a lower bonding temperature than other phases commonly observed in the Cu-Sn-In system, the study aimed to develop interconnects comprising a stable single-phase (Cu,Co)6(Sn,In)5. Bonding conditions were established for the Cu-Sn-In/Co system and the Cu-Sn/Co system as a reference. Thorough assessments of their thermomechanical reliability were conducted through hightemperature storage (HTS), thermal shock (TS), and tensile tests. The Cu-Sn-In/Co system emerged as a reliable low-temperature solution with the following key attributes: 1) a reduced bonding temperature compared to the Cu-Sn SLID interconnects, 2) the absence of the Cu3Sn phase and resulting void-free interconnects, and 3) high thermomechanical reliability, particularly following thermal annealing after bonding.

This study introduces the Subspace Rotation Algorithm (SRA), an innovative gradientfree method designed to discover the global optimal weight matrix. The SRA consists of two fundamental algorithms: the Left Subspace Rotation Algorithm (LSRA) and the Right Subspace Rotation Algorithm (RSRA). The combination of LSRA and RSRA, in two formats, LSRA-RSRA and RSRA-LSRA, can harness the advantages of both individual algorithms, resulting in enhanced performance. Our observations reveal that shallow and wide Multilayer Perceptrons (MLP), trained using RSRA-LSRA, achieve higher training accuracy compared to the Backpropagation (BP) algorithm. Moreover, when combining SRA with the BP algorithm, a remarkable impact on training MLP models is observed. Our experiments demonstrate that the BP algorithm may become trapped in local optima, while RSRA-LSRA, though capable of escaping local optima, may not fully realize its potential when the number of hidden nodes is limited. The synergy of RSRA-LSRA and the BP algorithm allows for the utilization of the advantages from both approaches, achieving optimal MLP performance with fewer hidden nodes.

While grid-forming (GFM) technology has gained increased attention for voltage-source converters (VSC) connected to low-inertial AC systems, there exists a gap in addressing their fault ride-through (FRT) capability under large grid disturbances. Specifically, the challenge lies in resolving the resynchronization issue of GFM-VSCs under current-limitation mode without relying on a phase-locked loop (PLL). To bridge this gap, this article proposes a novel approach called direct current-synchronization control (DCSC), which directly regulates the VSC current for synchronization. The validity of DCSC is substantiated by establishing equivalent relationships between current and power, as well as power and phase angle. Thus, achieving synchronization by controlling the VSC phase angle is made possible through the direct control of the VSC current. The stability boundary of DCSC is theoretically analyzed, concluding that DCSC has the same stability boundary as power synchronization control (PSC) with reactive power regulation in continued normal operations but a 90-degree stability boundary under large grid disturbances, irrespective of voltage magnitude. Additionally, boundary conditions for system stability assessment during large grid disturbances are established, and a control gain self-adaptability (CGSA) scheme is introduced to accelerate resynchronization after faults. Therefore, the DCSC scheme exhibits identical control dynamics to PSC with reactive power regulation in continued normal operations, while offering enhanced FRT performance under large grid disturbances. Experimental results validate the theoretical findings, affirming the effectiveness of the proposed control method.

This paper presents an algorithm for a team of heterogeneous mobile robots to estimate and adaptively sample a stationary, isotropic, Gaussian process. An estimation framework is proposed to assimilate measurements from robots with differing sensing capabilities (i.e., measurement noise variance). To improve computational efficiency of the Gaussian process regression, the survey area is divided into regions that each use a common semivariogram matrix constructed with a truncated measurement set determined by an adaptive selector. As the mission proceeds, a Voronoi-based algorithm periodically partitions a density function-representing time-varying sampling priority-to identify high-value sampling locations. The path for each robot is modeled as an mechanical system: a sequence of masses (waypoints) are interconnected by springs and dampers and pulled towards Voronoi cell centroids. At each path planning cycle the robots are iteratively simulated with heterogeneous dynamics (e.g., speed, turn radius) following their respective waypoint paths as stiffness/damping parameters are adjusted to satisfy mission time constraints. Numerical simulations show that the proposed approach reduces mapping error when compared to non-adaptive lawnmower coverage of a survey area. The algorithm is demonstrated experimentally using two cooperating autonomous surface vessels to map the bathymetry in a section of Lake Norman near Charlotte, NC.