This paper investigates network slicing in radio access networks (RANs) that support multiple virtual operators, focusing on maximizing operator utility and ensuring service isolation on a shared physical platform. Network slicing enables efficient resource management and allows for flexible scaling of services based on diverse operator requirements. We evaluate RAN resource slicing for virtual operators by addressing two key challenges: resource reservation and allocation. Operators predict future resource demands using artificial neural networks and reserve a portion of physical resources accordingly. Following reservation, the allocation process is modeled as a Markov decision process aimed at maximizing revenue, which is solved using reinforcement learning algorithms. This dual optimization approach ensures utility maximization and efficient service delivery for virtual operators while balancing network load and enhancing system robustness in dynamic environments. Additionally, our approach accounts for fluctuating user traffic patterns and varying operator priorities, demonstrating significant potential for improving resource utilization, maintaining service-level agreements, and fostering scalable, resilient network operations in highly virtualized environments. Through comprehensive simulation, we establish that the proposed method leads to a significant improvement in resource utilization and operator utility across various traffic loads and operator priority scenarios, clearly demonstrating its competitive advantage in the context of resource reservation and allocation for network slicing in virtualized environments.

We present a new algorithm to automatically convert 3-dimensional left atrium surface meshes into a standard 2-dimensional space: a Left Atrial Positioning System (LAPS). Methods: Forty-five contrast-enhanced 4-dimensional computed tomography datasets were collected from 30 subjects. The left atrium volume was segmented using a trained neural network and converted into a surface mesh. LAPS coordinates were calculated on each mesh by computing lines of longitude and latitude on the surface of the mesh with reference to the center of the posterior wall and the mitral valve. LAPS accuracy was evaluated with one-way transfer of coordinates from a template mesh to a synthetic ground truth, which was created by registering the template mesh and pre-calculated LAPS coordinates to a target mesh. The Euclidian distance error was measured between each test node and its ground truth location. Results: The median point transfer error was 2.13 mm between follow-up scans of the same subject (n=15) and 3.99 mm between different subjects (n=30). The left atrium was divided into 24 anatomic regions and represented on a 2D square diagram. Conclusion: The Left Atrial Positioning System is fully automatic, accurate, robust to anatomic variation, and has flexible visualization for mapping data in the left atrium. Significance: This provides a framework for comparing regional LA surface data values in both follow-up and cohort studies.

Congestion due to morning and evening traffic peaks has caused economic losses amounting to billions of dollars annually. Thus, accurately predicting the departure time of commuters is of interest to transportation planners, engineers, and elected officials alike. We develop a statistically-informed deep learning approach to improve commuter departure time prediction models. Specifically, we leverage elements of the proportional hazards model, a class of time-to-event prediction approaches, to augment vanilla deep neural network (DNN) architectures. The proposed approach also employs collaborative filtering to segment the commuter population into distinct behavioral classes, allowing tailored predictions for specific commuter profiles. We find that our class of survival analysis-enhanced DNNs outperforms conventional neural networks in predicting trip departure times, while also offering more interpretability through the hazard coefficients.

AGI-powered human-machine systems for social computing offer significant benefits, including enhanced functionality, improved performance, increased time efficiency, and reduced computational costs in the digital society's social computing sectors. This study provides a systematic review of AGIpowered human-machine systems, examining the employed technologies and infrastructure, while highlighting their applications in social computing. The findings reveal that many of these systems rely heavily on natural language processing technologies, which are applied across various fields, demonstrating their versatility. However, the limitations of existing AGI-powered humanmachine systems present valuable opportunities for innovation. Designing, developing, and implementing secure, robust, and ethical systems can address these challenges, promoting growth and ensuring sustainability in the digital society.

The ever growing demand for services with high requirements is a significant force behind the evolution of cellular networks. End-to-End (E2E) analysis is a key methodology for the assesment of service performance, often paired with Machine Learning (ML) algorithms. However, ML comes with its own security challenges. This work will study attaks on the data, some of the major security challenges in ML systems. Namely, it will study poisoning and evasion in ML regressors, offering different perspectives on their impact in the outcome of the system within a mobile network and the effects on user perception. Moreover, new metrics for computing the statistical variation of model predictions are proposed to evaluate the attack effects. A realistic testbed is used to evaluate the proposed mechanisms.

The emergence of unexpected incidents in electrical power systems (EPSs) has led to partial disruptions of the national electricity grid, necessitating the deployment of emergency diesel generators (EDGs) to restore power supply to affected areas. This study aims to evaluate the operational effectiveness of three distinct EDG power plants associated with an oil facility and a nearby gas platform during power outages, specifically focusing on scenarios that involve starting power transformers (PTs) and induction motors (IMs). Through the application of technical specifications provided by Stamford Company and the utilization of the EMTP-RV software tool, the EDGs, featuring identical excitation systems and governor parameters, were systematically modeled and analyzed for comparative purposes. The results of this comparative investigation are anticipated to yield significant cost reductions for power plant operations while enhancing overall system safety.

Inspired by human workers who perform complicated sewing tasks by repeating relatively simple operations, this paper proposes a fixture-free automated sewing system using a dual-arm manipulator and an ordinary sewing machine to sew two aligned fabrics along the edges, a common task in garment production. The proposed sewing system has a five-layer architecture: perception, dual-arm sewing Petri net, fundamental operations, control primitives, and hardware layers. This architecture decomposes various complex sewing tasks into sequences of fundamental operations. To meet the real-time requirement of automated sewing, a High-speed Fabric Edge Detection System (Hi-FEDS) is further proposed for the perception layer, which formulates the fabric edge detection problem for sewing as a classification problem of predefined distributed anchors. The anchor distribution is modeled by the Gaussian Uniform Mixture Model (GUMM). The proposed method achieves high-speed fabric edge detection at an average of 120 FPS, with an average error of about one pixel. Finally, an experimental robotic sewing platform is developed and the sewing results show that the proposed system achieves high-quality sewing across fabrics of various shapes and materials.

Terahertz (THz) communication enables ultra-highspeed wireless data transfer, especially for near-field communication (NFC). This study focuses on joint angle-of-arrival (AoA) and range estimation, crucial for near-field scenarios, using short dipole antennas model instead of point source one. Point source antennas, which can not be fabricated, are assumed in existing works for NFC, while short dipoles are practically fabricated and they behave as point sources in the far field. Therefore, assumption of point source antenna structure deviates significantly from the actual behavior of the antennas in the near field, which limits the existing solutions for NFC so far. Exploiting the complex electromagnetic theory of short dipole antenna, we propose a framework for AoA and range estimation that incorporates this short dipole antenna characteristic. Maximum likelihood (ML) based estimation is proposed, which combines an iterative method for high precision and computational efficiency along with the search space segmentation approach recursively. Numerical simulations demonstrate improved mean square error (MSE) and symbol error rate (SER) performance compared to the existing solutions based on the inaccurate point source assumption, proving its viability for near-field THz systems.

Traffic congestion affects the country's conveyance system, especially when emergencies occur at traffic lights. To solve this problem, we developed a light control system that responds to emergency vehicle signals via radio frequency (RF) transmission. This system uses the PIC 16F877A microcontroller to update the transmission light in emergency situations and return to normal when the situation is resolved. The system aims to reduce accidents at intersections by providing a clear path for emergency vehicles. The project enables wireless communication via radio frequency transmission at a frequency of 434 MHz, allowing lights to be changed in response to emergency vehicles. Future improvements may include integration with in-flight flight controls to enhance the current lighting system.

The purpose of the review is to provide a comprehensive analysis of Qwen 2.5, highlighting its advancements in AI models. Key findings indicate that Qwen 2.5 features significant improvements in dataset size (expanding from 7 trillion to 18 trillion tokens), enhanced multilingual support (29+ languages focused on Asian languages), and innovative architectural mechanisms that facilitate efficient context processing and multimodal integration. Qwen 2.5's significance in the field of AI lies in its opensource approach, which encourages collaboration and adoption in various applications, making it a formidable competitor against existing AI models from other major players like OpenAI and Meta. Its advanced capabilities make it particularly relevant for industries looking to leverage AI for diverse tasks, as it addresses challenges in processing long texts and integrating multiple data modalities. The model's design and performance strengthen Alibaba's position in the AI landscape, positioning Qwen 2.5 as a crucial development to examine for future AI advancements.

As extreme weather events become more frequent and intense, energy demand during peak hours rises and the use of variable energy sources such as wind and solar expands. Scarcity pricing events have likewise surged in frequency and severity. Scarcity events often occur during periods of extreme weather and generation constraints. For instance, wind resources tend to underperform during heatwaves and cold snaps, while electricity prices spike as the sun sets, prompting dispatchable generators to respond. Gas plants can face supply limitations or be outpriced due to high gas heating demand.Hydroelectric resources, with their long-duration energy storage, flexibility, and low-carbon generation, will become increasingly valuable in balancing the growing share of variable energy resources as these forces drive increasing constraints in the future grid. This is especially true as policy shifts reduce reliance on fossil-fueled flexible generation and as extreme weather events and electricity market scarcity events intensify. This paper reports results from a generalized framework for historical operations and statistical analysis of critical generation assets during scarcity events, in this case during the Martin Luther King Jr. 2024 weekend polar vortex in the Pacific Northwest.

Rehabilitation is a critical component of healthcare services that often requires prolonged monitoring to achieve optimal outcomes. One of the major challenges in rehabilitation tracking is the necessity for patients to visit medical centers, which can be costly and time-consuming, especially for individuals in remote areas. Although telehealth solutions such as video conferencing have been introduced, they are predominantly qualitative and do not provide precise rehabilitation tracking. Consequently, there is a need for a robust, accessible, and accurate method for rehabilitation monitoring. To address this need, we introduce a quantified, easily accessible framework for extracting and analyzing human finger joint torques to enable virtual hand interaction. Hand landmarks from MediaPipe are integrated with the MANO hand model in PyBullet. The hand positions, rotations, and joint angles extracted from the landmarks are used as controller inputs to mirror human hand movements in a virtual environment. A PID controller was designed to accurately track joint angle trajectories, with its parameters optimized using genetic algorithms. The torques extracted from the metacarpophalangeal and proximal interphalangeal joints were validated, achieving mean squared errors of 4.346 × 10 −4 (N.m) 2 and 1.506 × 10 −5 (N.m) 2 , respectively. This quantification and accessibility of human joint dynamics information is highly desirable for tracking rehabilitation progress using exoskeleton robotics.

This paper presents a comprehensive cardiovascular modeling framework that integrates partial differential equations (PDEs), computational fluid dynamics (CFD), and advanced optimization techniques to simulate blood circulation with high accuracy. Unlike traditional models, which rely on simplified assumptions, this approach incorporates pulsatile flow dynamics, non-Newtonian blood viscosity effects, and patient-specific data to enhance predictive capabilities. The governing equations account for arterial compliance, shear stress variations, and complex boundary conditions, providing a more physiologically relevant representation of blood flow. Convergence analysis demonstrates that the numerical solutions approach the exact solution with an error function given by E h = ||u h − u|| L 2 + ||p h − p|| L 2 , confirming a significantly improved convergence rate over classical methods. Stability analysis ensures perturbation decay over time, satisfying d dt ||u ′ || 2 ≤ 0, which guarantees the dissipation of disturbances and validates numerical robustness. A major advancement of this work is the integration of patient-specific parameters, enabling tailored simulations for cardiovascular interventions. The model has clinical applications in optimizing ventricular assist devices (VADs), refining surgical techniques, and improving drug delivery strategies. Future work will focus on real-time validation using clinical data and further refinement of computational efficiency to enhance applicability in medical practice.

"Solar circuits" is a nomenclature for a photovoltaic panel, power manager, and microprocessor integrated on a System on a Chip that meet a predefined thermal design power envelope and volumetric design envelope for energy and space-constrained mobile devices such as cell phones, tablets, and laptops. The emerging market of Internet of Things, for over two decades, has prioritized interfaceless devices to either operate within extreme energy budgets or to rely on remote clients/terminals for offloading compute-intensive tasks. An emphasis on designing with energy generation and integration with interfaces in solar circuits expands the accessibility of IoT and mobile computers to developing markets as well as solutions requiring a "local first" design ethos. Solar circuit design will be contextualized within the precedent of Software Defined Hardware to specify an autonomous or batteryless capability requirement that utilizes energy harvesting or energy scavenging to meet "just enough" application needs. Furthermore, setting limits on system power consumption allows a solar circuit to serve as a proxy or "litmus test" for measuring (and subsequently demonstrating) the progress of Moore's Law within solar constraints. Performance of general purpose microprocessors that can be solar powered in mobile form factors is estimated to be consistently 25-30 years behind (in MIPS/W/mm^2) leading edge chips such as AMD Instinct and Apple A17. A number of heuristic methods are described in this paper to estimate the solar circuit requirements (in transistors, memory, and OPS) to run general purpose operating systems such as Microsoft Windows 95 and Linux 5.1 in modern, commercially viable process nodes, such as Global Foundries 22nm FD-SOI, TSMC's 22 and 12nm ULL, or Intel's 16nm FF - processes that all utilize low leakage.

Objective: This study aimed to develop and evaluate a 3-degree-of-freedom (DoF) robotic system for the safe delivery of cardiac sheaths through challenging anatomical structures, including the fossa ovalis and pathways with tight curves. Methods: The robot and its kinematic model were built on a previously proposed single-DoF actuation module and bending model. A sheath delivery strategy (SDS) was developed, combining two control methods: tip position control to approach an optimal entry point and point-constrained control to maintain consistent navigation through this point, minimizing tissue contact. Technical performance was evaluated through trajectoryfollowing and point-crossing tests, followed by feasibility experiments in a simulated scenario. Trials were conducted by three cardiologists using a validated phantom model under fluoroscopic guidance, comparing SDS with joint control (JC) and manual control (MC). Results: Average root mean square errors were 2.10 mm for tip position control and 1.86 mm for point-constrained control. SDS outperformed MC with significantly shorter trajectory lengths and lower root mean square jerk. Compared to JC, SDS reduced sheath-induced movements (an indirect measure of force) and increased retraction success rates at the fossa ovalis. Conclusion: The proposed robotic system reduced tissue wall contact compared to JC and provided smoother, more controlled operations than MC, ensuring safer and more effective delivery through confined pathways. Significance: This work contributes to advancing robotic-assisted cardiac sheath delivery, providing a reliable and safer method for navigating challenging anatomical structures.

Over the past few decades, elderly care has become an increasingly concerning problem across the world. As humans age, health can deteriorate severely, leaving the elderly population prone to suffering from various diseases. The lack of proper resources and effective services has further exacerbated this crisis. Existing solutions to this problem are not sustainable or practical. This research paper examines the growing need for effective elderly care solutions due to an aging global population. It analyzes the current market, highlighting the shortcomings of existing robotic assistive devices like Robear and HUG. The paper proposes improvements for future robotic designs, emphasizing autonomous operation, AI integration, enhanced mechanical design, and cost reduction to create more effective and widely accessible solutions. The substantial market potential for these improved robotic systems is also discussed, citing projected market growth and the increasing number of elderly individuals worldwide. Finally, the paper concludes by stressing the need for safer, more adaptable, and affordable robotic aids to enhance the quality of life for both elderly individuals and their caregivers.  

While ultimate strength analysis methods exist for steel marine structures, there remains a critical gap in understanding the probabilistic behavior of cracked aluminum stiffened plates, particularly in high-speed craft where operational uncertainties compound material and damage variabilities. This gap is addressed through the development of a novel probabilistic framework for analyzing the ultimate strength of cracked aluminum stiffened plates in high-speed craft by integrating nonlinear finite element analysis (NLFEA) with Non-Intrusive Chaotic Radial Basis Function (NICRBF). The framework systematically accounts for uncertainties in crack parameters (size, location, orientation) and material properties, while incorporating operational factors such as wave-induced loads and slamming effects. Through 10000 numerical simulations, it was demonstrated that crack length significantly influences ultimate strength variability (COV ranging from 0.268 to 0.353), while crack orientation affects mean ultimate stress between 253.92-295.73 MPa. For operational conditions, probability of failure increases markedly with vessel speed, reaching 0.994 at design velocity under 8m wave heights. The NICRBF approach provides an efficient computational framework while maintaining accuracy. These findings provide quantitative guidance for structural reliability assessment and safety-oriented design of high-speed aluminum vessels, particularly in determining operational limits based on wave height and speed combinations. The developed framework offers a practical tool for marine engineers to evaluate structural integrity under uncertain damage scenarios.

The recent EU regulation on Markets in Crypto Assets Regulation (MiCA), represents a significant progress in establishing a multi-jurisdiction framework for crypto-assets that will enable the greater participation of consumers in the digital assets industry. One type of token recognized by MiCA is the asset-referenced token, where the value-bearing asset and the provenance metadata supporting the token's claim are located external to the token. We discuss a number of design considerations for the on-chain and off-chain metadata for MiCA's asset-referenced tokens, with the goal of evolving towards a framework where technical standards can be developed for the assets metadata and related smart contracts technology. The EU Data Spaces provides an interesting data management paradigm that could be used for the decentralized registries infrastructure needed to manage the provenance metadata for various assets.