Experiential learning, defined as the result, source, or context of knowledge obtained from direct experience, is increasingly bolstered by technology and human-computer interaction (HCI) principles and protocols. This article will describe how technology-based human-computer interaction (HCI) has been used in experiments to enhance direct knowledge experience. In this paper, immersive technologies (such as Virtual Reality, Augmented Reality, and haptics) will be described based on cognitive learning theory, empirical research and practical experience gained from implementing cutting-edge technology and maximizing HCI principles in constructive learning. Additionally, this article will describe how AI can be used to create individualized learning environments according to a student's needs in the future. While cost, technical issues and specialised trainings are the obstacle to deliver personalized learning experiences especially to economically disadvantaged students, technological change opportunities are projected, and these technologies will take educational outcomes to the next level.

Many computing services need to be accessed through the Internet, which makes them inherently exposed to a large number of cybersecurity threats. Several attacks are thwarted by intrusion prevention mechanisms, but with the size and complexity of current systems, an attacker can break in sooner or later. Attackers can exploit a system vulnerability or steal user access credentials. Once the intrusion occurs, it is very likely that the state of the system is corrupted by the attacker. The classical solution against these corruptions is to keep regular backups to allow the reversal of the effects of intrusions. However, changes to the state made since the last backup are permanently lost. This situation has led to the development of work on intrusion recovery systems that revert the effect of attacks on the state of the system without losing legitimate changes. We present a survey of the literature in the area, explaining the different approaches and suggesting open directions for future research.

In design and optimization of intensive care unit (ICU) networks, one common practice is to prioritize the treatment for patients of higher emergency levels, while ensuring fairness to other patients by guaranteeing a certain Quality of Service (QoS) level. One common approach to realize such priority arrangement is bed reservation policy, which designates a certain number of last occupied beds in each hospital to be exclusively used by certain patient classes. In this paper, we propose an approach that can significantly improve the computational efficiency in obtaining the optimal reservation thresholds for each patient class given their respective requirements, in a non-hierarchical ICU model (where the external emergency patients can possibly be allocated to any ICU hospital) which has been shown to be computationally challenging in performance evaluation and optimization. Specifically, we apply the Information Exchange Surrogate Approximation (IESA) to analytically approximate the key QoS metrics under given reservation thresholds, and the integer Particle Swarm Optimization (PSO) algorithm to search for the optimal threshold based on the approximation results by IESA. We demonstrate numerically, with the real data from ICUs in Hong Kong, that IESA approximation can obtain reasonably accurate results for QoS metrics, and thus lead to accurate optimal reservation thresholds. In addition, our proposed approach combining IESA and PSO can significantly reduce the computation time by more than four orders of magnitude, compared to the state-of-the-art evaluation and optimization methods in existing research for similar problems, especially for ICU networks with practical sizes.

Learning a discriminative model to distinguish a target from its surrounding distractors is essential to generic visual object tracking. Dynamic target representation adaptation against distractors is challenging due to the limited discriminative capabilities of prevailing trackers. We present a new visual Prompting mechanism for generic Visual Object Tracking (PiVOT) to address this issue. PiVOT proposes a prompt generation network with the pre-trained foundation model CLIP to automatically generate and refine visual prompts, enabling the transfer of foundation model knowledge for tracking. While CLIP offers broad category-level knowledge, the tracker, trained on instance-specific data, excels at recognizing unique object instances. Thus, PiVOT first compiles a visual prompt highlighting potential target locations. To transfer the knowledge of CLIP to the tracker, PiVOT leverages CLIP to refine the visual prompt based on the similarities between candidate objects and the reference templates across potential targets. Once the visual prompt is refined, it can better highlight potential target locations, thereby reducing irrelevant prompt information. With the proposed prompting mechanism, the tracker can generate improved instance-aware feature maps through the guidance of the visual prompt, thus effectively reducing distractors. The proposed method does not involve CLIP during training, thereby keeping the same training complexity and preserving the generalization capability of the pretrained foundation model. Extensive experiments across multiple benchmarks indicate that PiVOT, using the proposed prompting method can suppress distracting objects and enhance the tracker. The code is publicly available. 

Counterfeit devices are a growing threat to trust in electronic devices, involving the production and distribution of fake components or reselling out-of-spec ones. These pose risks like compromised functionality, security vulnerabilities, and economic losses when purchasing Commercial Off-The-Shelf (COTS) devices like FPGA and microcontrollers. To address this, we introduce VARI-CHECK, a low-overhead method for authenticating COTS devices. VARI-CHECK leverages inherent fabrication process variations to create digital signatures for device authentication. We validate this approach through experiments on FPGA devices, training a machine learning model to identify non-original devices. We test it on 100 oscillating/bistable circuit elements across 40 FPGA devices, achieving 80.4% success with Isolation Forest and 81.25% with One Class SVM.

Parity-time symmetry has great potential for mode selection in multimode resonators. However, in a PT-symmetry system with saturable absorption mechanisms, the random background noise can initiate single-mode oscillation at any of the maxima within the gain spectrum, i.e., potential PT frequencies. Such randomness impedes the acquisition of high-quality signals at desired frequencies. Here, we propose a method to obtain deterministic PT-symmetry single-mode oscillation in a filterless multimode resonator through one-shot injection. With this technique, we change the system's gain spectrum and enhance the gain discrepancy. Utilizing the frequency domain saturable absorption of the resonator, oscillation at desired mode can maintain its frequency after the withdrawal of the injection signal. To validate the concept, we established a polarization-division multiplexed dual-loop optoelectronic oscillator (OEO) with a 1-km long cavity operated under PT-symmetry conditions. By one-shot injecting the PT-OEO, we effectively eliminate the randomness arising from the relatively flat gain spectrum, facilitating oscillation at any desired potential PT frequencies from 1.8 to 9 GHz without requiring elaborate frequency tuning structures. Moreover, the one-shot injection technique produces ultra-low phase noise performance, achieving a remarkable -158.6 dBc/Hz@10 kHz. This performance level stands in close comparison with the best phase noise values recorded for OEOs.

Extended reality (XR) and 6G networks are set to transform mobile immersive experiences, with privacy and security being paramount in XR communications. Achieving secure and reliable XR experiences while meeting high-resolution and low-latency needs are challenging for wireless networks. A novel security-aware cross-layer communication management framework is proposed, employing zero-trust spatiotemporal physical layer level manipulations for moving-target defense (MTD). Driven by deep reinforcement learning (DRL) and realtime monitoring, the proposed framework adaptively reprograms the network configuration to maximize the user's quality of experience (QoE), reduce the overall latency, and minimize the attacker's intercept probability. The framework was evaluated in a simulated scenario featuring an indirect multi-hop communication setup. The results show that the proposed framework effectively and efficiently secures XR user communications while maintaining QoE outperforming conventional Q-learning algorithms, making it suitable for mission-critical XR applications.

In cellular network technologies like 5G and 6G, achieving high data rates, exceptional reliability, and minimal latency is paramount. The continuous expansion of smart wireless technologies, sensors, and communication systems has led to an exponential increase in data traffic, which increases the demand for enhanced privacy and security solutions that are efficient and reliable as attackers get more aggressive. While numerous authentication and encryption methods have been proposed in the literature to safeguard communication, the landscape of threats and attacks continues to evolve, jeopardizing the security of network entities. In response to this evolving threat landscape, my research introduces an innovative solution to enhance security measures. My approach hinges on dynamic and unpredictable key generation and utilization, setting a new strategy for safeguarding against network breaches. Notably, this work marks the first instance of introducing this mechanism and concept (Key Hopping), promising a more resilient security paradigm. Simulation results demonstrate a high-security performance in most security aspects.

Geostationary orbit (GEO) microwave sounding technology, which can continuously monitor Earth and intensively observe weather conditions such as strong convection, has unique advantages. An observing system simulation experiment (OSSE) is an important means to evaluate new meteorological instruments and explore assimilation techniques for new observations. In this paper, an OSSE based on the China Fengyun-4M (FY-4M) GEO microwave satellite is introduced in detail. The nature run (NR) of the OSSE is generated by the Mesoscale Weather Numerical Forecast System of the China Meteorological Administration (CMA-MESO), while the Global Forecast System of the China Meteorological Administration (CMA-GFS) is used to achieve the four-dimensional variational (4D-Var) assimilation and prediction experiment. The atmospheric parameters from the NR are used as inputs to the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV) model to simulate the FY-4M GEO microwave brightness temperature. The OSSE results show that assimilating geostationary orbit microwave data has a positive effect on typhoon forecasting. Specifically, the shorter the observation time interval is, the smaller the observation noise, and the better the assimilation effect. There are differences in the role of different frequency band channels in improving forecasts; the best observation effect can only be achieved by setting the observation error appropriately, and the data preprocessing strategy is crucial for improving the assimilation quality. The GEO microwave OSSE further reveals the influence of future FY-4M geostationary microwave observations on typhoon forecasts and the issues that need to be addressed when assimilating these new data.

This study explores the challenges of frequency instability in isolated island power systems predominantly powered by fluctuating renewable sources like solar panels and wind turbines. These non-interconnected islands with a high reliance on renewables are prone to significant frequency fluctuations due to the variable nature of renewable energy sources and their inherently low inertia. The academic community has identified fast-reacting energy storage systems, such as batteries and pump storage, as potential solutions to offset these frequency variations through rapid frequency response services during power imbalances. However, simulations conducted in this paper, suggest that storage systems might struggle to effectively counter severe frequency excursions. This limitation stems from inherent delays in measurement, computation, and communication within their control systems. To improve the stability of these renewable-heavy, non-interconnected islands, we propose a viable approach: integrating renewables behind the energy storage systems. This method totally decouples renewables from the grid, ensuring that the network frequency and the rotational speed of thermal generators remain unaffected by abrupt changes in renewable power output. Adopting this strategy could facilitate the integration of additional renewable energy sources in non-interconnected islands without compromising the stability of their power systems.

Predicting the occurrence of solar activities have been a major concern over time. Solar flare which is a predominant solar activity affects communication and power systems here on earth. In this research paper, various machine learning and visualization techniques were deployed to discover hidden pattern within dataset and develop machine learning models for predicting the class of solar flares. The used datasets were provided by NGDC, SILSO and SWPC space bodies. These datasets contain Solar flare datasets, sunspot datasets and Active region magnetic properties data. These datasets were associated using python programming language with Active region number and occurrence data been the important features for association of flares, sunspots and Active region magnetic properties. A robust single dataset was generated. Visualization was then performed on the dataset using various visualization techniques such as bar plots, scatter plots, box plots and bubble plots. For model development, 4 machine learning models were deployed which are Random forest, Decision Tree, Bagging Classifier and Support Vector Machine (SVM). Random forest outperformed other models on predicting the class of an associated flare by producing an accuracy of 79% while SVM performed poorly on the used dataset.

Unmanned Aerial Vehicles (UAVs), due to their low cost and high flexibility, have been widely used in various scenarios to enhance network performance. However, the optimization of UAV trajectories in unknown areas or areas without sufficient prior information, still faces challenges related to poor planning performance and low distributed execution. These challenges arise when UAVs rely solely on their own observation information and the information from other UAVs within their communicable range, without access to global information. To address these challenges, this paper proposes the Qedgix framework, which combines graph neural networks (GNNs) and the QMIX algorithm to achieve distributed optimization of the Age of Information (AoI) for users in unknown scenarios. The framework utilizes GNNs to extract information from UAVs, users within the observable range, and other UAVs within the communicable range, thereby enabling effective UAV trajectory planning. Due to the discretization and temporal features of AoI indicators, the Qedgix framework employs QMIX to optimize distributed partially observable Markov decision processes (Dec-POMDP) based on centralized training and distributed execution (CTDE) with respect to mean AoI values of users. By modeling the UAV network optimization problem in terms of AoI and applying the Kolmogorov-Arnold representation theorem, the Qedgix framework achieves efficient neural network training through parameter sharing based on permutation invariance. Simulation results demonstrate that the proposed algorithm significantly improves convergence speed while reducing the mean AoI values of users. The code is available at https://github.com/UNIC-Lab/Qedgix.

The establishment of lunar bases introduces unprecedented challenges in energy management, where precise DC power forecasting is pivotal for ensuring uninterrupted power supply and efficient resource utilization in microgrids. With primary reliance on solar energy, supplemented by battery systems, forecasting energy availability becomes critical due to the Moon's unique conditions, such as prolonged night cycles, temperature extremes, and unpredictable dust storms. Conventional forecasting methods fall short in addressing these challenges, paving the way for advanced artificial intelligence (AI) techniques. This paper reviews state-of-the-art AI-driven methods, including machine learning (ML), deep learning (DL), hybrid models, and ensemble approaches. By synthesizing insights from existing literature and highlighting innovative applications, this review provides a comprehensive analysis of AI's transformative role in lunar power systems. Emphasis is placed on hybrid and optimization-based methods, supported by detailed flowcharts illustrating workflows. The paper identifies key research gaps and proposes future directions, including lightweight AI models for computational efficiency, digital twin integration for real-time optimization, and quantum computing for advanced forecasting. Additionally, it explores the integration of AI with other lunar base systems such as thermal regulation and life support, underscoring the interdisciplinary nature of sustainable extraterrestrial operations. The findings underscore AI's potential to revolutionize energy management in lunar microgrids, ensuring sustainable extraterrestrial operations.

Accurate glomerular segmentation is crucial for clinical diagnosis and pathological analysis in renal histology research. To address the challenge of blurred boundaries between glomeruli and surrounding tissues in nephropathology analysis, this paper proposes EdgeAttenNet, an innovatively improved model based on the BGNet architecture that enhances the recognition accuracy of glomerular boundaries. The model primarily consists of three enhanced core modules improved from BGNet: Attention-Guided Boundary Module (AGBM), Edge-guided Feature Enhancement Module (EFEM), and Adaptive Fusion Context Module (AFCM). The AGBM first captures glomerular boundary information through multi-scale attention mechanisms. The EFEM enhances boundary feature representation at different scales by incorporating the extracted edge semantic information. Subsequently, the AFCM integrates these multi-scale features through attentional feature fusion to strengthen the model's global feature characterization capabilities. Experimental results demonstrate that EdgeAttenNet achieves superior glomerular segmentation performance over the baseline BGNet with a 1.76% improvement in mIoU, reaching 95.14% and 94.32% mIoU on the KPIs2024 and HuBMAP datasets, respectively. These results validate our model's excellent performance and robustness under complex pathological image conditions. The findings demonstrate EdgeAttenNet's great potential in clinical applications and provide new insights into addressing blurred boundaries in medical image segmentation.

Grid codes require High Voltage Direct Current (HVDC) converter stations to have AC Fault Ride-Through (AC-FRT) capability, which prevents disconnection during AC grid faults and requires them to support the faulty grid. A major challenge during AC-FRT operation is the faulty converter's reduced power capacity, which can lead to over-energizing of the Multi-Terminal Direct Current (MTDC) grid if said converter was exporting power from the MTDC grid prior to the fault. This paper presents an innovative strategy for dealing with DC grid over-energization during AC-FRT operation by utilizing the existing Metal Oxide Varistor (MOV) banks within DC Circuit Breakers (DCCBs) through a slight and economical modification to the DCCB architecture. By adopting this strategy, it is possible to reduce the size or number of Dynamic Break System (DBS) used in the MTDC grid. The proposed strategy is able to prevent overvoltage by clamping the DC voltage throughout the MTDC grid. Furthermore, it is able to attain a uniform distribution of energy dissipation in DCCBs throughout the MTDC grid without the need for communication and coordination and achieves a smooth transition between normal and AC-FRT operation and vice versa.

In this paper, a dynamic wireless power transfer (WPT) system is realized using a metamaterial-loaded Clapp free-running oscillator (FRO) at 50 MHz. The WPT transmitter (TX) acts as the feedback network of this Clapp FRO. Also, the WPT receiver (RX) acts as the load of this Clapp FRO through mutual inductive coupling. The metamaterial unit cell is a multiring-resonator (MRR) with low magnetic loss, placed between the TX and RX. When the mutual coupling between the TX/RX pair changes due to the variation of the TX/RX distance, the Clapp FRO dynamically adapts its operating frequency to match the frequency of peak efficiency. The MRR metamaterial loading allows for several advantages including frequency and efficiency stability and lower specific absorption-rate (SAR) value when compared to the FRO-WPT system without MRR metamaterial. The theory is verified by employing simulations and measurements. The fabricated TX and RX are compact and have 20×20 and 10×10 mm2 areas. The metamaterial has two stacked layers, each with an area of 20×20 mm2. The measured dc-RF efficiency is 45% at 10 mm TX/metamaterial separation and it peaks at 51.5% in the air with an output power of 17 dBm while consuming 13 mA from a 7.5 V supply. When the receiver is embedded in a phantom medium at 10 mm depth, the metamaterial-loaded FRO-WPT system has an output power and efficiency of 15.2 dBm and 42%, respectively when consuming a 10.5 mA from a 7.5 V supply.

We show that changes in transmittance, in TFBG hydrogen sensors coated with nanometric metal/metal-hydride coatings with highly lossy cladding modes, are primarily a function of the mode losses, which we demonstrate, can be well estimated from the coating reflectivity. Using this result, and a simplified set of coupled mode equations that depend only on the mode losses, the sensor transmittance is recovered. We use this computationally fast model to show how the change in resonance amplitudes with hydrogen loading, and the envelope of resonant peaks, is tuned via an multi-layer metal/metal-hydride coating.

Combining machine learning with physical models can significantly impact retrieval algorithms designed to invert geophysical parameters from remote sensing data. Such hybrid models integrate physical knowledge with domain expertise through a joint architecture, potentially enhancing performance by increasing the efficiency and flexibility of the physical model as well as the generalization and interpretability of the machine learning predictions. This work introduces a hybrid model for estimating forest height using single-baseline, single-polarization TanDEM-X interferometric coherence measurements. In this model, the vertical reflectivity profile is derived as a function of input features, including topographic and acquisition geometry descriptors, using a multilayer perceptron network. This profile is then used to invert forest height by leveraging the established physical relationship connecting the vertical reflectivity profile to forest height. The developed model is applied and validated on several TanDEM-X acquisitions over tropical sites with different acquisition geometries, and its performance is assessed against reference data derived from airborne LiDAR measurements.