High-resolution manometry has been the gold standard for examining human colon motility, but this method has significant drawbacks, including high costs, lengthy procedures, and patient discomfort. This has led us to explore ultrasonography as a potential alternative. We discovered that conventional machine-learning techniques for segmenting gut walls have been hindered by tracking errors. The present study introduces an innovative approach for monitoring and segmenting colonic walls in ultrasound videos by combining advanced computer vision techniques with U-Net models. The approach involved training a deep-learning U-Net model specifically for intestinal wall segmentation by manually preparing the dataset. Once trained, the model segments and analyzes colonic walls in real-time ultrasound videos, generating spatiotemporal contraction maps and content flow analyses. The method was validated using patient and volunteer data, achieving high Intersection over Union (IoU) scores. The U-Net model demonstrated superior performance to traditional machine learning techniques in tracking colonic walls, even in low-resolution videos. This work showcases how deep learning models can enhance the accuracy and reliability of colonic motility studies using ultrasound, paving the way for more efficient and patientfriendly diagnostic methods.

This paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed Apriori methods. The study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. Security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. The proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems.

This paper proposes a controller design methodology based on passivity to mitigate control interactions between the transmission grid and current-controlled (CC) grid-forming (GFM) converters. To this end, the paper derives an analytical dq-frame model valid above 20 Hz for a GFM modular multilevel converter (MMC) with cascaded virtual impedance and current controller. The paper identifies how the different CC-GFM control parameters impact the MMC's AC side admittance passivity. Furthermore, the study shows that straightforwardly enforcing passivity requirements on the AC admittance conflicts with ensuring essential grid-forming characteristics, such as the 'voltage-source-behind-impedance' behavior, and that a trade-off is needed. To address this trade-off, the proposed framework employs dedicated loop-shaping techniques, which ensure passivity within predefined frequency ranges while preserving essential grid-forming characteristics. Moreover, the methodology extends beyond MMCs and is relevant to other voltage source converters. Experimental and numerical results in time and frequency domains, respectively, show that the design effectively mitigates potential adverse interactions while maintaining the desired 'voltage-source-behind-impedance' behavior.

The increasing complexity of mobile networks as they evolve towards 6 th Generation (6G), coupled with the importance of providing fast responses to performance degradation issues, requires the adoption of cost-effective Trouble-Ticket (TT) management systems by Mobile Network Operators (MNOs). These systems are pivotal in ensuring compliance with Service Level Agreements (SLAs) and maintaining Quality of Service (QoS) standards. In this context, the introduction of a new Smart Operations (SO) framework referred to as Smart Trouble Ticket Management (STTM), promotes the integration of Artificial Intelligence (AI)/Machine Learning (ML) to leverage performance data, aiming at early performance degradation detection and an efficient handling of TTs. This paper presents a novel methodology for STTM that relies on a ML clustering algorithm to identify performance degradation instances when cells transition to lower-ranked clusters. The benefits of this dynamic approach are twofold. Firstly, it offers an automated and adaptable process capable of accommodating diverse Key Performance Indicators (KPIs). Secondly, it enhances the reliability of TT opening and closure compared to conventional approaches, particularly those reliant on fixed threshold values. The proposed methodology, tested on live network data, achieved an improvement of 5.53% in TT Opening Accuracy Rate (OAR), and reduced the TT Repeat Call Rate (RCR) by 17.57%.

This letter proposes a new control scheme for the provision of adaptive inertial response by virtual synchronous generators. In contrast to state-of-the-art methods, the proposed strategy proactively allocates higher inertia values at the onset of frequency disturbances while respecting the energy limits of the associated energy storage system. The performance of the developed technique is assessed by means of rms simulations on the IEEE 39-bus system. The results demonstrate that the proposed scheme enhances the dynamic performance of the power system by reducing the rate of change of frequency and delaying the occurrence of the frequency extrema (nadir/zenith).

Optimization problems in energy planning are often tackled using Mixed Integer Linear Programming (MILP) models. While MILP formulations are renowned for their efficiency and reliability, the computational burden increases significantly with the number of integer variables, making detailed optimization impractical for large power systems. To address this challenge, energy planners frequently employ clustering techniques to select representative periods, reducing the problem's dimensionality. However, despite extensive research on clustering algorithms, there is not yet consensus on their application in energy planning; their accuracy, potential limitations, and alternative solutions. This paper aims to provide new relevant insights on the subject. Building on existing literature, we examined several multivariate time series clustering methods, including hierarchical, fuzzy, and k-means clustering, using both centroid and medoid representations for cluster centers. The analysis incorporates the most common technologies used in modern power systems, such as solar, onshore and offshore wind, and storage systems (i.e., Battery Energy Storage System-BESSand hydrogen). The paper also includes a numerical example of a capacity expansion plan, utilizing the PyPSA tool.

This article presents an eddy current tomography framework for imaging defects in metal structures. The tomography problem is formulated as a linear inverse problem with a binary solution vector. A Bayesian approach is utilized, incorporating a binary-inducing prior and determining the posterior probability conditioned on the measurements. Since recovering binary vectors from underdetermined linear measurements is NP-hard, an approximation to the true posterior is obtained by minimizing a Kullback-Leibler (KL) divergence. Alternatively, a convex optimization approach relaxes the binary constraint and applies alternating direction method of multipliers (ADMM) to compute a solution. The convergence of both algorithms is proven. To improve computational efficiency, the two algorithms are cascaded and augmented with a decomposition technique to form a hybrid algorithm. The proposed framework is validated experimentally with a prototype eddy current sensing probe, demonstrating the ability to image defects as small as 1 mm at various depths using a sensor array with 4 mm spacing.

Young's double slit experiments have been descripted by wave theories, i.e., before and after passing through a double slit, the light is waves. To test the wave description, we extend the double slit experiments to the comprehensive double slit experiments by utilizing Shield, metal tube,

This survey provides a comprehensive overview of the integration of Artificial Intelligence (AI) with Augmented Reality (AR) and Virtual Reality (VR) technologies. We explore the current state-of-the-art, key applications, challenges, and future directions in this rapidly evolving field. The survey covers AI techniques used to enhance AR and VR experiences, including computer vision, natural language processing, and machine learning. We also discuss the impact of this integration on various domains such as healthcare, education, entertainment, and industry. Finally, we identify open research questions and potential future developments in the convergence of AI, AR, and VR.

Fault Location (FL) is the key to fast clearance of faults and minimizing the duration and costs of these faults. Power networks manifest a complex behavior due to their intricate topological connections, phase asymmetry and time-varying loads. Traveling Wave (TW) analysis has been proven to provide accurate estimates of fault locations on power system transmission lines. In this project, a novel selfadaptive approach for the deployment of TWs for robust fault-location in power systems has been proposed. An ensemble approach is suggested for robust detection of the Time Of Arrival (TOA) of traveling waves using a large group of wavelet types. The implemented approach was tried on real-world measurement data from a transmissionlevel substation. The robustness of the method was tested in different noise scenarios. The results show a significantly accurate and robust performance of the scheme for determining fault locations.

The paper considers monostatic ISAC transceivers relying on in-band full-duplex (IBFD) capability to achieve simultaneous sensing and communication. These systems transmit a waveform for both communication and sensing and receive target echoes and incoming communication signals from other nodes. The major challenge is self-interference cancellation (SIC), suppressing interference from the leaked transmitted signal for both sensing and communications and the mutual interference between the echo for sensing and incoming signals for communication. This paper proposes an advanced joint analog and twostage digital SIC structure to address this challenge, enabling simultaneous communication and sensing in IBFD ISAC systems. The analog SIC structure leverages an analog least mean square (ALMS) loop with specific design constraints to preserve the integrity of sensing signals. A sample-and-hold mechanism is employed to avoid ALMS weighting coefficient variation caused by the strong reflected sensing signal and uplink communication signal. The novel two-stage digital SIC first cancels residual self-interference for sensing and then mitigates echo sensing signals for communication. Doppler effects in the echo signals are compensated during the second stage to ensure effective suppression of sensing signals and accurate retrieval of communication signals. Simulation results validate the proposed approach, showcasing its strong communication and sensing performance and robust SIC capabilities.

This study investigates single pixel nadir viewing detection limit of atmospheric ammonia for a range of area flux mapping satellite infrared sensor spectral resolutions (0.05 cm-1 to 2.0 cm-1) and measurement noise levels. The detection level of ammonia is computed directly from simulated satellite ammonia spectral signatures, which has the advantage of being independent of retrieval methodologies. Information on the frequency of a given detection limit, and the cumulative probability of detection, are provided as a function of instrument spectral resolution and noise. For example, a Cross-track Infrared Sounder-like instrument with a modest spectral resolution of 0.625 cm-1 and excellent signal-to-noise ratio of ~1600 would be able to detect ammonia on average ~70% of the time; these same instrument specifications will have a detection limit of 0.2 ppbv (surface) or 1.6x1015 molec cm-2 (total column) that can be achieved at a detection rate of 10% as it requires favourable infrared remote sensing conditions (large thermal contrast). Under more typical atmospheric states a detection limit of 0.5 ppbv (3.5x1015 molec cm-2) is achieved at a 50% detection rate. This detection limit information is valuable for applications that incorporate remote sensing data in conditions when the atmospheric  ammonia amounts are below the detection limit of the satellite sensor (e.g. non-growing season in crop fertilizer source regions). As the simulations use real-world atmospheric state observations as inputs the results can be used to provide general guidance on the detection limits of past, current, and potential new environmental flux mapping instruments used for ammonia monitoring covering large geographical regions.

SAP systems form the backbone of critical enterprise operations, making their security paramount in the face of escalating cyber threats. This paper examines the unique challenges of cybersecurity within SAP environments, highlighting vulnerabilities inherent in complex enterprise resource planning (ERP) systems and the implications of distributed architectures. It explores methodologies to enhance risk management, secure configuration, and compliance with evolving regulatory frameworks. Drawing insights from real-world engagements and research, the paper offers a roadmap for future innovations in securing SAP systems, emphasizing automated threat detection, robust governance, and strategies to mitigate risks from insider threats and supply chain vulnerabilities.

Machine learning models in online environments are vulnerable to various adversarial attacks, such as those from generative adversarial networks (GANs) attacks and more powerful attacks based on recent advances in quantum computing. Federated Learning (FL), a modern privacy preserving distributed machine learning technique, also faces modern data management concerns related to quantum computing. Limiting adversarial learning through quantum mechanics lends itself as an effective security mechanism. This paper introduces a novel quantumencrypted FL framework that integrates randomly-generated quantum noise into an FL environment to improve security against adversarial learning algorithms. These algorithms can conduct reconstruction attacks, which increase privacy leakage through adversarially generated data. Specifically, GAN attacks are effective at creating data samples that resemble training data. Traditional computing techniques can be improved by appending random noise generated by quantum computers as a quantum verification method. The proposed hybrid-quantum encryption approach can fortify a network against generative reconstruction attacks by adding quantum noise to each participant's training data samples. To evaluate the effectiveness of the proposed quantum encryption techniques for protecting FL against GANs, we encrypt the MNIST dataset with quantum-generated keys and then distribute the data in a privacy-preserving manner.

Purpose: This study compares the performance of small, fine-tuned LLMs with the large original models on two radiation oncology clinical tasks: treatment modality selection and ICD-10 code prediction. Methods: We used data of 403 treatment modality selection cases and 377 ICD-10 code prediction cases. The models used were fine-tuned LLaMA 2 7B and Mistral 7B, as well as the larger LLaMA 3.1 70B and 405B models for comparison. We evaluated the accuracy of all models on these two tasks. Results: Both fine-tuned LLaMA 2 7B and Mistral 7B outperformed LLaMA 3.1 70B and 405B in treatment modality selection task. The fine-tuned LLaMA 2 7B and Mistral 7B models achieved accuracy comparable to the larger models in ICD-10 code prediction. Conclusions: Small models, fine-tuned with radiation oncology data, can achieve performance comparable to-or even surpass-that of larger models.

A modification is proposed to the conventional magnetic reluctance circuit (MRC) in which a dependent MMF source, which is a function of an output current, is replaced by a passive element, an inductor. This eases the analysis and simulation of the MRC by breaking the closed feedback loop between the output current and the resulting dependent MMF source. The proposed approach is verified by simulations which show an excellent match between the classical and modified MRCs.

This work develops real time model and associate control for a grid-tied battery energy storage system (BESS), based on the real industrial system specifications, 362kW/1499kWh lithium-ion BESS. An active and reactive power controller based on the dq reference frame is designed for the grid-tied inverter. To ensure system performance, an LCL filter with passive damping is also developed and analyzed. The system is tested using the Controller Hardwarein-the-Loop (CHIL) platform: Typhoon. The results demonstrate the controller's effective reference tracking across varying load conditions, with the filter design achieving a 3.3% output current THD, emphasizing the inverter's optimized performance.

The uncertain nature and variability in solar power generation requires the development of methods to further improve the solar forecasting, specifically in very short term to drive the system operational decisions. With regard to this issue, this paper provides an innovative approach for predicting solar power in very short term using a Suboptimal Multiple Fading Extended Kalman Filter (SMFEKF). In this method, the fading factors are adjusted dynamically resulting in the SMFEKF having reduced computational burden as compared to traditional Kalman Filters such as Uncented Kalman Filter (UKF) and Extended Kalman Filter (EKF). Improving estimation precision is achieved through dynamically updating the covariance of process noise and measurement noise. In the proposed method, the historical solar irradiance data, numerical weather predictions, and the real-time power measurements are used to optimize prediction precision. The proposed SMFEKF demonstrates slightly better accuracy but significantly reduced computational burden and time.