Integration of Augmented Reality (AR) and Virtual Reality (VR) in medical education is one of the incredible innovations that have taken place in the recent past, revolutionizing the manner in which health practitioners acquire their skills and refine them. Many traditional methods require practical engagement in clinical settings, which sometimes are limited and involve a degree of risk for patients. On the other hand, AR and VR propose immersive and interactive environments that simulate realistic medical scenarios in which trainees can practice procedures without ethical concerns regarding live patients. This new approach increases not only the level of engagement of learners but also the understanding of complex anatomy and clinical conditions. In a health sector that moves forward with the tide of technological changes, there is a need to look into the efficacy and impacts of these tools so that a platform can be set for debate on their potential revolutionizing of medical education and improving patient outcomes.

Mathematical modelling of visual perception in two dimensions is described. Although perception is ubiquitously mentioned in literature and it is used in various contexts, its explicit measurable presentation has not yet been given beyond verbal descriptions. In this seminal work the visual perception is explicitly defined for clarity. Based on this definition, it is treated as a mathematical entity in the form of probability in a particular circular region. Theoretically, human vision is expressed in advanced probabilistic terms and the enigmatic relation between vision and perception is mathematically formulated. Here, the perception concerns a special range of interest, coined as vision circle, where percepts describe the vision act of human as the retinal ophthalmic sensing. Perceptual Intelligence Quotient (PIQ) is introduced, that it is subject to measurement. The results of the theoretical considerations are shown to be closely matching the common visual perception experiences as well as the reported visual perception studies, including the variation of peripheral acuity. Two eye-tracking experiments demonstrate that saccadic movement behavior of human is reasonably matching the prediction based on the novel perception theory. The work demystifies the visual perception and understanding its role in sciences. As to the cybernetics, perception plays an important role in the control of complex, integrated processing systems, which are interfaced by indispensable man-machine control systems. In these systems, the role of perception is critical, referring to the hazard situations. The implications of the study are investigated and explained in two such cybernetics applications, too.

Generic elbow prostheses often fail to meet the specific anatomical needs of patients, leading to high misalignment rates in Total Elbow Arthroplasty (TEA). This misalignment negatively impacts surgical efficiency, patient satisfaction, and critical functional outcomes, including range of motion and load-bearing capacity. Although personalized prostheses could address these challenges, current practices rely on ad-hoc intraoperative adjustments that frequently fall short of meeting individual anatomical requirements. A key obstacle to achieving truly personalized designs is the absence of a quantitative method for evaluating alignment. To tackle this issue, we developed a 3D computational model to simulate TEA and introduced the Alignment Improvement Metric (AIM), a novel quantitative tool for assessing prosthesis alignment. Using AIM, we designed three personalized prosthesis approaches optimized for individual, group, and cohort scenarios. Computational validation on 120 clinical participants demonstrated an average alignment improvement of up to 30% with just 3 prostheses, highlighting the potential of this AI-driven solution to enhance TEA outcomes and patient satisfaction.

WordPress, originally developed as a blogging platform, has grown into a powerful content management system (CMS) that underpins over 40% of websites worldwide. Its versatility, accessibility, and extensibility have made it an indispensable tool for software developers. This paper examines the critical role WordPress plays in software development, highlighting its contributions to rapid prototyping, scalability, integration with modern technologies, and democratizing software creation. By analyzing real-world case studies and exploring the extensive ecosystem of plugins, themes, and APIs, this paper underscores how WordPress fosters innovation and accelerates development timelines.

This paper presents the analysis and effects of frequency diversity on finite uniform circular array for generation of range dependent Orbital Angular Momentum (OAM) modes. The analysis reveals that the array factor expression in terms of Bessel's function for a circular array with finite no. of elements possess a phase error in the azimuthal domain at any transverse plane due to which the range dependency of OAM modes is affected. In this report a detailed analysis of the phase distribution on concentric circles for an exact array factor expression without considering any assumption or approximation has been carried out. Effect of changing parameters such as total number of elements and frequency increment value is analyzed and compared with the approximated array factor expression. Fourier analysis in the azimuthal domain reflects the presence of desired modes at a specific range for finite array cases. Generated phase and amplitude profile validate the occurrence of range dependent OAM modes at specified distance. Analysis of phase error for different frequency increment for finite no. of elements is also done. A prior knowledge of the available modes at a specified distance away from the antenna is crucial for improving the spectral efficiency of a communication system or radar.

This Technical paper presents an innovative approach to enhancing cybersecurity by leveraging advanced Network Address Allocation (NAA) techniques. Using dynamic address assignment and automated management systems, the paper demonstrates how organizations can proactively reduce vulnerabilities, enhance network visibility, and improve overall security. The inclusion of automated solutions for identifying available IP addresses in real-time further optimizes the NAA process. This approach has been shown to significantly reduce attack surfaces, improve monitoring, and ensure more resilient industrial networks. By adopting these innovations, industries can stay ahead of evolving cyber threats, ensuring secure and uninterrupted operations in the digital age.

In recent years, deep learning-based joint source-channel coding (DJSCC) has gained significant attention for its impressive performance in image transmission. Unlike traditional separate source-channel coding (SSCC) methods, DJSCC performs particularly well in low signal-to-noise ratio (SNR) and limited bandwidth environments. However, ensuring the security of private information during transmission remains a critical concern. A notable limitation of DJSCC is its incompatibility with traditional encryption methods used for secure communications, making it vulnerable to eavesdropping attacks. To address this issue, we propose integrating a chaotic map encryption method into the DJSCC framework for secure wireless image transmission. This approach leverages chaotic sequence to shuffle the position of the elements in latent space without altering the values of the latent tensor. This allows the encryption process to be designed independently of DJSCC, eliminating the need for retraining the end-to-end model. Our proposed method preserves DJSCC's superior transmission characteristics, ensuring high-quality image reconstruction at the receiver, while effectively ensuring the security against deep learning-based known plaintext attacks (Deep KPA).

Constrained environments, such as indoor and urban settings, present a significant challenge for accurate moving object positioning due to the diminished line-of-sight (LoS) communication with the wireless anchor used for positioning. The 5th generation new radio (5G NR) millimeter wave (mmWave) spectrum promises high multipath resolvability in the time and angle domains, enabling the utilization of multipath signals for such problems rather than mitigating their effects. This paper investigates the benefits of integrating multipath signals into 5G LoS-based positioning systems with onboard motion sensors (OBMS). We provide a comprehensive analysis of the positioning system’s performance in various conditions of erroneous 5G measurements and outage scenarios, which offers insights into the system’s behavior in challenging environments. To validate our approach, we conducted a road test in downtown Toronto, utilizing actual OBMS measurements gathered from sensors installed in the test vehicle. The results indicate that utilization of multipath signals for wireless positioning operating in multipath-rich environments (e.g. urban and indoor) can bridge 5G LoS signal outages, thus enhancing the reliability and accuracy of the positioning solution. The redundant measurements obtained from the multipath signals can enhance the system’s robustness, particularly when low-cost 5G receivers with a limited angle or range measurements are present. This holds true even when only considering the utilization of single-bounce reflections (SBRs).

Observations made using antenna arrays are often limited by the noise coming from the antenna system itself and from the environment. In demanding applications such as radioastronomy, the thermal noise emitted by the multilayered substrate below the antenna array may become significant and needs to be properly characterized. Both the intensity of the noise at each port and the correlation of the noise at pairs of ports need to be quantified. In this paper, we provide a spectral formulation to calculate the noise correlation matrix of antenna arrays. The method is based on the propagative and evanescent plane wave spectrum emitted by each pair of active antenna ports into the lossy layered medium, and can thus be implemented as a post-processing step combined with any full-wave numerical solver. This formulation allows us to account for layered media with different physical temperatures at each layer. Numerical examples are provided using Square Kilometer Array (SKA) stations lying on a finite ground plane, itself lying on a layered semi-infinite soil. The behaviours of noise power and noise correlation coefficients are studied for different positions of the antennas on the ground plane. This is done considering a multilayered soil with different moisture levels. A very good agreement with the commercial software FEKO is observed regarding the evaluation of the noise correlation coefficients. Moreover, the efficiency of the algorithm in terms of computational time is shown by comparison with FEKO. The direction-dependent system noise temperature picked up by a full SKA station in presence of the soil is also given.

In the face of current modular robots that rely on manual coding to generate deformation instructions and are limited by binary instructions, this research proposes a Cognitive Modular Control (CMC) architecture inspired by ACT-R theory. Build simulation tool ReoForm to conduct experiments through simulation-based experimental design. The data results show that each module of the architecture demonstrates feasibility and universality in terms of time-to-completion, energy consumption, manipulability index and robustness indicators.

Many internet services are personalized to the interests of users. The personalization is usually based on the user profiles extracted from the interactions of users with internet applications. The profile, however, may naturally reveal private information about the user. Hence, the users urge the applications to protect their privacy, although they are interested in getting the personalization services as far as possible. To formulate such requirements, we propose differential privacy with semantic neighboring, which relies on a novel notion of neighboring datasets. Two datasets are basically defined to be the neighbors of one another if one is obtained by eliminating the records from the other proportional to the extent to which the records are semantically related to what the user deems private. We also develop a Laplace mechanism for enforcing the privacy policies specified in this way. The main challenge in devising this mechanism is deciding on an effective definition of the sensitivity of a given query on log datasets so that the mechanism can arrive at higher levels of utility. The results of our experimental study demonstrate that the proposed mechanism can achieve acceptable privacy parameters while retaining the usefulness of the underlying personalization service.

This paper presents the application of Infrared Thermography to measure distribution transformer efficiency. The method is very intuitive, practical, and easy to use. The results obtained with the proposed method are compared to those obtained by the standard procedure, showing excellent agreement with them. The paper presents the nature of the losses in energy transformation, the theoretical basis of the proposed method, and the comparison of its results with those obtained from the standard procedure.

The definition of the rated voltage of a hydropower generator is a difficult task that depends on several electrical, mechanical, and construction features. Naturally, the trade-off between influence variables must be solved to reach a minimum cost. This work contributes to analyzing the most critical constraints, exposing the authors' experience in solving this trade-off, and proposing an expedited formulation for defining the rated voltage of a generator based on successfully constructed generators deployed worldwide.

Functional near-infrared spectroscopy (fNIRS) is well-suited for hyperscanning in naturalistic situations, offering significant potential for assessing social brain function in everyday life. Previous studies have reported inter-brain synchrony during social interactions and sought to explore its mechanisms by correlating behavioral events with brain signals. However, commonly used regression analyses, such as General Linear Models (GLM), rely on target events hypothesized as explanatory variables. This reliance introduces a dependency on the researcher's assumptions, which can compromise replicability in social neuroscience. While such dependency may be less problematic in strictly controlled experimental paradigms focused on specific hypotheses, it poses significant challenges in naturalistic experiments like social interactions, where numerous events and signals may serve as potential explanatory variables. To address this limitation, we introduced a new approach: signed-normalized mutual information (sNMI). This method enables a two-way analysis to evaluate relationships between event sequences and brain synchrony. Through simulations and real-data applications, we evaluated the performance of the proposed method. The results showed that sNMI analysis performed comparably to regression analysis in detecting both inter-brain synchrony and within-brain synchrony (i.e., brain connectivity). Moreover, applying sNMI to a tapping dataset revealed the expected patterns of synchrony between the lateral sides of the motor area. These findings validate the effectiveness of sNMI as a robust two-way analytical tool for studying brain synchrony.

Considering the importance of the effects of VFTO on high-voltage equipment, this article evaluates the influence of VFTO on capacitive bushings. Simulated results of VFTO application indicated changes in the voltage distribution along the bushing, leading to an unequalized electric field across the layers. Voltages at the grounding connection point reached high levels. These findings confirm an adverse operating condition to which capacitive bushings are subjected during VFTO occurrences, pointing to a potential cause of premature failures observed in recent years.

The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios-including different task loads, data sizes, and MEC capacities-show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. These enhancements highlight SARMTO's potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications.

As big data applications become increasingly complex, their architectures-comprising various middleware and processing components-demand continuous refinement to maintain optimal performance. This paper presents OSTIA (On-the-fly Static Topology Inference Analysis), a tool designed to support the continuous architecting of dataintensive applications (DIAs) by addressing three key areas: detecting common anti-patterns, performing algorithmic manipulations, and facilitating formal verification of architectural models. OSTIA operates by reverse-engineering the topologies of big data applications, such as those built on Apache Storm and Apache Hadoop, to ensure compliance with framework-specific constraints and identify potential design flaws. Through case studies involving both industrial and open-source applications, I demonstrate OSTIA's effectiveness in improving the reliability and performance of streaming and batch-processing systems. This paper also highlights OSTIA's recent enhancements, including formal verification capabilities and additional heuristics, and discusses its impact on the iterative process of continuous architecting.

Roadside mowing is a necessary, high-risk occupation to ensure visibility for on-road traffic. In Indiana, contractors are hired by the Indiana Department of Transportation (INDOT) to mow over 11,000 miles of roadsides using tractors with flex-wing mowers and hand-held string trimmers. Operations have significant risks to all crew members. Autonomous mowers have gained interest as a potential solution to reduce the risk. INDOT has expressed interest in how to evaluate the technological readiness of autonomous mowers. This work developed ROWMow Sim, a digital-twin autonomous mowing simulator that can test and evaluate roadside mowing operations. ROWMow Sim generates virtual environments of real-world roadways, providing an informative testbed with zero risk. Virtual models of sensors and vehicles were developed to be tested in virtual roadways prior to real-world deployment. ROWMow Sim provides rapid creation of various environment conditions, repeatable tests, ground truth data analysis, mower configuration studies, and direct simulation-to-real-world transfers. Autonomous navigation strategies developed on a virtual string trimming vehicle were transferred directly to the physical vehicle. ROWMow Sim contributes to the exploration of safer and more efficient vegetation management operations for INDOT and the on-road traffic.