The pursuit for industrial energy efficiency and the mitigation of greenhouse gas emissions motivate researchers to develop or restructure energy materials that can lower the global energy carbon footprint. This study hybridizes three basic Triply periodic minimal surface topologies using into one shape. A novel heat exchanger is proposed by hybridization of 3 basic topologies; Schoen Gyroid, Schwarz Diamond and Fischer Koch S to leverage and combine their individual thermo hydraulic properties. The aluminium alloy used is a thin walled AlSi10Mg. Hybridization was achieved by level set trigonometric functions and modeled by MSLattice. The new shape was simulated as an evaporator using Simulink, MATLAB 2020a running the Tetrafluoroethane (R134a) on the material with a geothermal (sustainable energy) heat source. The highest surface area density is 29.59 l/mm with a wall thickness of 0.12 mm. The best obtained steady state values of specific enthalpy and heat flow rates are 439.4 kJ/kg and 0.34 kW/s respectively. This novelty sheds some light into the 2 phase 3 zone evaporator’s zone length and mass fraction of phase transformation and zonal fluid mass along the volume length, promising better thermo hydraulic results as more Triply periodic minimal surfaces are hybridized.

The work describes the creation of the original model of the RPD Module, a component of the Remote PHY Node. This model incorporates GCP/R-DEPI/R-UEPI integration, which has been implemented using cutting-edge packet processing technologies for the enhanced data transmission between CCAP Core and the cable modem via DOCSIS 4.0. The model is open-source and, therefore, does not impose limitations on various applications and improvements for hybrid fiber-coaxial networks. The developed model enhances the performance of processing data packets and improves the reliability of next-generation broadband cable telecommunication networks.

Modular-Multilevel-Cascade Converters are the preferable choice for high-voltage and high-power applications. The price of simple scalability, modularity, and flexibility is paid by a large number of components, especially semiconductors and capacitors. Such components are prone to failure, so fault detection and diagnosis, fault-tolerant control concepts, and reliability analysis are research fields within this topology. This paper presents several fault-tolerant approaches for the class of Modular-Multilevel Converters under the severe failure of a whole cluster and compares them in detail. Furthermore, the different fault modes are emphasized, and operation recommendations are given. All approaches are extensively derived and verified by simulation and experimental results in a steady-state. Additionally, experimental results of transients, i.e., the transition from normal-to fault-operation and power steps, are provided for the most suitable proposed approach. The results show the proper functionality of the control strategies. The simple implementation enables easy integration into (existing) systems and thus provides better fault-tolerance.

Life-threatening situations in and around lifts, escalators, and travellators are known to cause severe injury and, in some cases, death. We research the real-time risk and analyse the recent spate of injuries by "people movers". We analyse these critical systems that follow a consistent pattern of catastrophic failure. Our research reviews the technology, its application, and the risks to humans from using these systems with low awareness of the dangers of careless use. The study identifies parameters that warn against impending failures by increasing awareness of the risk, such as the shear force experienced by human joints whilst stepping on or off systems in operation, especially with distracted, unrestricted children. We investigate and discuss the dangers of carrying forbidden items onto these utilities. Our investigation delves into electro-mechanical people mover technologies and suggests behavioural and ML-based edge intelligence to preempt accidents.

Long-term stability of output characteristics of piezoresistive pressure sensor chips in the form of microelectromechanical system (MEMS) is one of the most important parameters. The research analyzes the variation of useful signal of high sensitive pressure sensor chips and its errors in mechanical and temperature characteristics, which are also time-dependent. The main feature of this study is the application of the previously developed construction of mechanical part, which allows to achieve a balance between extremely high sensitivity and low error rates for ultra-low pressure range. The research demonstrates the changes after 5 years of aging for 24 pressure sensor chip samples (chip dimension 6.15x6.15 mm 2) for pressure range of 0.5 kPa with sensitivity S0,5 after = (34.5 ± 6.0) mV/V/kPa, nonlinearity 2KNL 0.5 after = (0.71 ± 0.47) %/FS and temperature hysteresis up to 0.5% in the temperature range from-30 ⁰C to +60 ⁰C. The results of long-term stability of useful signal shows that the errors for 10 out of 24 samples does not exceed the values of 1.5%, which mostly connects with influence of residual mechanical stresses (RMS) from the assembly design of pressure sensor.

A novel, compact multiple-input-multiple-output (MIMO) antenna design, occupying a mere 25 × 39 mm², is proposed for ultra-wideband (UWB) applications. This innovative antenna comprises dual square-shaped radiating elements and a modified T-shaped stub, incorporating a vertical slot to mitigate mutual coupling. Key performance metrics, including impedance matching, inter-port mutual coupling, voltage standing wave ratio (VSWR) and correlation coefficient, are scrutinized. Simulation outcomes reveal an impressive impedance bandwidth spanning 2.5 to 11.8 GHz (S₁₁ ≤-10 dB), accompanied by an exceptionally low envelope correlation coefficient (<0.002) across the entire frequency spectrum. Notably, inter-element isolation exceeds-23 dB throughout the operational band. The proposed UWB MIMO antenna efficiently covers prominent frequency bands, encompassing WCDMA, WLAN, WiMax and UWB, making it an attractive solution for multi-standard wireless communication systems.

Substations and transmission lines are inspected by unmanned aerial vehicle (UAV) on a large scale, and the captured images are usually delivered to the cloud to be detected by manual or convolutional neural network (CNN) intelligent algorithms for detecting foreign objects or defects, as these cannot be detected locally online by UAVs at this stage. In this paper, we propose a lightweight model based on improved wavelet scattering deep network, which can realize local online recognition of foreign objects on edge devices. The lightweight model contains an improved wavelet scattering network and a 3- layer small deep network. Among them, the wavelet scattering network is constructed considering the extraction of foreign object features and the computational complexity, using biorthogonal (bior) wavelets basis including bior1.1, bior2.2, and bior1.3 wavelets to construct a 3-layer scattering network and compute the modulus and scattering values of the nodes in each layer, replacing the functions of convolutional and pooling layers in the CNN. The small deep network contains 3 fully connected layers and 2 ReLU activation functions to recognize the extracted features. The experimental results show that the accuracy of this lightweight model is higher than 90% for all kinds of foreign object recognition; the accuracy of recognizing 1280*720 foreign object images on the Orange Pi 5B edge device is 94.9%, which is better than other models, and the processing speed is 149.3 frames per second (FPS), which enables local frame-by-frame online detection when the frame rate of the UAV captured video equal to or less than 60 FPS. The algorithm can also efficiently detect high-resolution images up to 1920*1080 with recognition speed of 67 FPS.

In this study, we investigate the optical parameters of plasmonic Titanium Nitride (TiN) thin films using the RefFIT software, employing the Drude-Lorentz model for data fitting. TiN is an emerging material for plasmonic applications due to its high melting point, wear resistance, and stability at various temperatures, positioning it as a viable alternative to traditional noble metals. Our research focuses on fabricating, characterizing and extracting optical and electrical properties of TiN thin films deposited on silicon wafers using ellipsometry and Fourier Transform Infrared (FTIR) spectroscopy.We analyze six different samples, varying in thickness and composition, to determine key parameters such as plasma frequency and damping constants. The simultaneous fitting of reflectance and ellipsometry data in RefFIT provided results consistent with existing literature, establishing the software’s reliability for such analysis. Additionally, we validated the RefFIT results by comparing them with alternative models and experimental data, demonstrating the necessity of incorporating Lorentz terms alongside Drude components for accurate optical characterization.Our findings reveal that TiN films exhibit desirable plasmonic properties, with carrier concentration and relaxation times aligning with known trends in nitrides. The robustness of RefFIT in modeling these properties confirms its utility for future research in plasmonic materials. This study underscores the potential of TiN in advanced plasmonic devices and encourages further exploration using comprehensive modeling techniques.

We propose a universal approach for modeling complex integrated photonic resonators based on the scattering matrix method. By dividing devices into basic elements including directional couplers and connecting waveguides, our approach can be used to model integrated photonic resonators with both unidirectional and bidirectional light propagation, with the simulated spectral response showing good agreement with experimental results. A simplified form of our approach, which divides devices into several independent submodules such as microring resonators and Sagnac interferometers, is also introduced to streamline the calculation of spectral transfer functions. Finally, we discuss the deviations introduced by approximations in our modeling, along with strategies for improving modeling accuracy. Our approach is universal across different integrated platforms, providing a useful tool for designing and optimizing integrated photonic devices with complex configurations.

Deep supervised learning has revolutionized medical image segmentation although its success heavily relies on the availability of large volumes of training data with high-quality annotations. Self-supervised learning (SSL) methods have been recently introduced to address the annotation starving problem. In this paper, we report a general, scalable framework that combines contrastive and generative SSL methods to effectively learn robust and task-specific representations from large unannotated medical image datasets. Specifically, the SSL strategy is based on restoring transformed sub-volume images and contrasting the representations of paired transformed sub-volume images. By fine-tuning the pre-trained encoder-decoder network on small annotated medical image datasets, improved segmentation performance is efficiently achieved. The performance of our method was evaluated on two X-ray computed tomography (CT) image segmentation tasks and achieved better than state-of-theart results when 3D-segmenting a) psoas muscles in 60 torso CT images, and b) 20 thoracic organs at risk in a public SegTHOR dataset, demonstrating the effectiveness and broad applicability of our novel framework.

Ransomware attacks have increasingly threatened the integrity of digital infrastructures, compromising sensitive data and disrupting business operations worldwide. The complexity and rapid evolution of ransomware strains render traditional detection methods insufficient, prompting the need for more proactive and adaptive solutions. A novel machine learning-based method for automated file trap selection is introduced, offering early detection of ransomware through strategically deployed decoy files. This approach leverages machine learning models, such as Random Forest, SVM, and Neural Networks, to identify and respond to ransomware behaviors with high accuracy and low false positive rates. Additionally, reinforcement learning is employed to dynamically optimize the placement of file traps, ensuring that the system adapts to emerging threats in real-time. Comprehensive experiments across various ransomware families reveal that the system consistently achieves robust detection, minimal latency, and scalability across different environments. The findings emphasize the effectiveness of combining advanced machine learning algorithms with real-time feature extraction to significantly enhance ransomware detection capabilities.

The ability of modern machine learning systems to generate contextually relevant and coherent text is often challenged when external knowledge must be integrated into the generative process. Addressing this challenge, a novel approach was developed to dynamically reduce token-level uncertainty during retrieval-augmented text generation. By modifying the Mistral model to incorporate a token-level uncertainty estimation layer, the generation process was significantly enhanced in both precision and fluency. Experiments conducted on multiple retrieval-based tasks demonstrated substantial improvements in retrieval precision, perplexity, and uncertainty reduction, showing the modified model's ability to generate more coherent outputs, particularly in scenarios involving ambiguous or incomplete retrieved information. The recalibration of token predictions during the generation process directly contributed to the model's robustness and reliability, making it more adaptable to the complexities of retrieval-augmented contexts. Results indicated that the proposed method offers a significant advancement in improving the alignment between retrieved documents and the generated responses, leading to more accurate and contextually appropriate outputs across diverse datasets.

1. IntroductionTime series analysis is a critical tool used in various industries for forecasting future trends based on historical data. From finance to agriculture, healthcare to energy, the ability to predict future outcomes plays a vital role in strategic decision-making. However, time series forecasting is fraught with challenges due to its dependency on past data trends, seasonality, and potential anomalies. Errors in time series analysis can lead to significant project failures, affecting not only financial outcomes but also operational efficiencies and strategic directions.

This paper addresses the problem of achieving any desired group consensus in multi-agent systems (MAS) operating in arbitrary signed networks. By focusing on arbitrary network topologies, we develop a novel control strategy that drives the agents' states toward any desired state, independent of initial conditions or network topology. A key contribution is the introduction of a switching control mechanism that expands the range of reachable states, ensuring that any targeted outcome is attained for a given initial condition without any constraints on the network structure. Our approach not only guarantees the formation of desired clusters but also ensures that each agent converges to the specified consensus value within its cluster. Mathematical analysis establishes conditions for achieving system stability and reachability to any desired state, based on which the control input is designed. Finally, the theoretical results are validated through extensive simulation results.

In recent years, deep learning has significantly advanced the field of medical image recognition, enabling accurate diagnosis and prognosis prediction from complex image data. This paper proposes a novel convolutional neural network (CNN) architecture tailored for medical image recognition tasks, such as disease detection and tissue classification. Our framework incorporates residual connections and attention mechanisms, enhancing feature extraction and representation. The model is evaluated on two standard datasets: chest X-rays for pneumonia detection and histopathological images for tumor classification. The results demonstrate state-of-the-art performance, with accuracy and sensitivity outperforming baseline methods. The proposed method offers a robust solution for automated medical image analysis, with potential applications across various domains in healthcare.

We propose an artificial intelligence (AI) modeling method that utilizes an arbitrary voxelized mesh of a small PCB via structure, without relying on parametric geometry. Our AI model, based on a multilayer convolutional neural network (CNN), significantly reduces simulation time for small PCB via structures. By employing advanced AI techniques, we enhance the accuracy and quality of predictions while substantially reducing the size of the neural network model compared to our previous work using a fully connected neural network (FCN). The CNN-based model predicts 2-port scattering parameters with a median Huber error of 0.16%, achieving computational speeds over 13,000 times faster than traditional full-wave field solvers.

The fabrication and assembly of High Temperature Superconducting (HTS) magnets can be significantly streamlined by (1) direct deposition of HTS onto modular components and (2) laser-ablated grooves to bound and guide the electric currents over the superconducting surfaces. “Coils” bounded by consecutive grooves can be individually powered, or connected in series, or in parallel. Applications include plasma-confinement devices such as stellarators and magnets for particle accelerators. On the way to HTS magnets generating more complicated three-dimensional fields, this paper validates the technique for two cylindrically symmetric magnets made of standard conductors. The optimized grooving pattern is considered as an inverse problem that is resolved using a least squares approach with Tikhonov regularization. This approach was first applied to design a magnet that replicates the magnetic field configuration of a microwave source of the gyrotron type. Gyrotrons require a particular profile of magnetic field, which our aluminium prototype successfully reproduced with 10−1 precision. The second one, in copper, is a small-size, reduced-field magnet for Magnetic Resonance Imaging (MRI). This application requires highly uniform longitudinal fields. The achieved precision (about 50 ppm) is exceptional for an MRI magnet just getting out of the factory, and can easily meet hospital standards after minimal shimming or other error field correction. Future work and other potential applications of wide, patterned conductors or superconductors are also discussed.

Global warming is a prominent problem in the world, and a lot of research has been done on how to stop or slow it down. The main cause of global warming is pollution, especially the four main pollutants–carbon monoxide, ozone, nitrogen dioxide, and sulfur dioxide.1 With the use of AI, models can be built that provide insight into pollutant levels across regions of the US. In this project, linear regression models were built that predicted these four pollutant levels across four regions of the US. Pollution levels were studied and used to determine the impact of each pollutant in each region. Results showed that ozone in the west was predicted to have the worst pollutant levels, suggesting resources can be allocated to this region over others to lessen the harmful effects of pollution.