This paper proposes a three-layer unmanned combat aerial vehicle (UCAV) dogfight frame where Deep reinforcement learning (DRL) is responsible for high-level maneuver decision. A four-channel low-level control law is firstly constructed, followed by a library containing eight basic flight maneuvers (BFMs). Double deep Q network (DDQN) is applied for BFM selection in UCAV dogfight, where the opponent strategy during the training process is constructed with DT. Our simulation result shows that, the agent can achieve a win rate of 85.75% against the DT strategy, and positive results when facing various unseen opponents. Based on the proposed frame, interpretability of the DRL-based dogfight is significantly improved. The agent performs yo-yo to adjust its turn rate and gain higher maneuverability. Emergence of "Dive and Chase" behavior also indicates the agent can generate a novel tactic that utilizes the drawback of its opponent.

Video Frame Interpolation (VFI) involves generating intermediate frames by leveraging both preceding and succeeding frames to enhance video quality. Traditional methods, particularly those based on U-Net architectures, often suffer from high computational complexity and memory demands due to their large parameter counts. In this paper, we introduce the Square Funnel Network (SF-Net), a novel architecture that significantly reduces the number of parameters while maintaining competitive performance. SF-Net employs a unique design that increases the third dimension of the input frame in deeper layers instead of increasing the number of filters, resulting in a simpler and more efficient model. Our architecture utilizes no more than 64 filters in nearly all layers, with only the final two layers using 128 filters each. Through both objective and subjective evaluations, SF-Net demonstrates high visual quality and efficiency, making it suitable for applications with limited computational resources. This work presents a promising direction for VFI, emphasizing parameter reduction without sacrificing performance.

This paper presents a new method to characterize the linearity of on-chip filters, considering the interaction between cascaded blocks. Unlike conventional methods, this approach uses only one buffer, simplifying the design and improving accuracy. This method accurately extracts the filter's transfer function in both its bandpass and stopband. The filter and buffer are designed using GlobalFoundries 22nm FDX technology, incorporating a back-gate biasing tuning mechanism in the buffer design. Results show a variation of ≈ 0.17 V and ≈ 0.19 V in the threshold voltage of PMOS and NMOS devices, respectively, for |1.2 V | shift in their back-gate voltage, aiming to maintain the performance of the buffer under process variation. The post-layout simulations demonstrate that the new method achieves a filter linearity of IIP 3 = 10.46 dBm, with an accuracy of 99.4% compared to the standalone filter's linearity. Similar consistency is observed across various process corners.

A crucial security feature called multifactor authentication (MFA) is intended to improve the security of sensitive data and digital identities. By employing many levels of authentication to confirm a user's identity, it greatly increases the difficulty for unauthorized users to access systems and data. Recent advancements in machine learning (ML) and artificial intelligence (AI) have strengthened MFA systems and improved their usability and security.

Determining the electric field distribution using electromagnetic solvers requires significant computational effort, a high time consumption, and operator training. In this paper, a deep learning (DL)-based method is specifically developed to reconstruct the electric field distribution generated by antennas, based on the dielectric properties of the breast. ResNet and UNet were employed to predict the electric field within complex breast structures. Nine distinct breast dielectric models were utilized to create the initial dataset. A novel data augmentation technique was applied and the size was increased by three to address the challenge of limited dataset size. The electric field distribution data were generated using commercially available finite element method-based electromagnetic simulations. DL architectures were customized to fit the study and trained over 4374 breast slices. ResNet demonstrated an average Signal-to-Noise Ratio (SNR) of 23.7±6.7 dB and an average Normalized Mean Squared Error (NMSE) of 0.0029±0.0039 across 504 test breast slices, while UNet averaged 24.55±6.64 dB and 0.0024±0.0031 respectively. Additionally, a masked loss calculation was incorporated to enhance the learning accuracy of the implemented models. This approach yielded an increase of 0.9 dB in average SNR for ResNet and a 0.8 dB SNR increase for the UNet architecture on average. These results indicate that the proposed DL models effectively reconstruct electric field distributions with substantial accuracy, highlighting its potential for practical applications in real-time microwave medical imaging and treatment planning. Further refinement and validation with a more diverse set of breast models could enhance the model's applicability and performance.

Efficient heat management and control in optical devices, facilitated by advanced thermo-optic materials, is critical for many applications such as photovoltaics, thermal emitters, mode-locked lasers, and optical switches. Here, we investigate the thermooptic properties of 2D graphene oxide (GO) films by precisely integrating them onto microring resonators (MRRs) with control over the film thicknesses and lengths. We characterize the refractive index, extinction coefficient, thermo-optic coefficient, and thermal conductivity of for the GO films with different layer numbers and degrees of reduction, including reversible reduction and enhanced optical bistability induced by photo-thermal effects. Experimental results show that the thermo-optic properties of 2D GO films vary widely with the degree of reduction, with significant polarization anisotropy, enabling efficient polarization sensitive devices. The versatile thermo-optic response of 2D GO substantially expands the scope of functionalities and devices that can be engineered, making it promising for a diverse range of thermo-optic applications.

Complex Query Answering (CQA) on knowledge graphs is a fundamental yet challenging task, which can be formalized as answering a subset of first-order logic queries containing logical conjunction, disjunction, negation, and existential quantifiers. Recent research reveals that Link Predictors (LPs) trained on 1-hop queries can generalize to various types of complex queries. However, existing methods neglect crucial characteristics of LPs' outputs, including the effects of highly relevant entities and uncertainty. What's worse, as they model logical operations by fuzzy set operations, these methods suffer from problems like inflexibility, sensitivity to noise, and inconsistency with priority in human cognition, which limits their performance, especially on queries with negation. To address these challenges, we propose an efficient fuzzy system for CQA that requires no extra training overheads and is plug-and-play with existing LP-based methods. Firstly, we expand the output of LPs by two complementary membership functions of weak and strong relevance, which help to distinguish the target entities from highly relevant and irrelevant entities. Subsequently, we model logical operations through fuzzy rule bases and infer the final predictions via defuzzification, providing a flexible and tractable scheme for modeling logical operations. Finally, the effectiveness of the proposed fuzzy system is validated by its outstanding performance on benchmark datasets when compared to state-of-theart methods. The source code of our proposed method is available at https://anonymous.4open.science/r/FuzzSys-CQA-E285.

Underwater acoustic backscatter communication, which allows an underwater sensor node (USN) to communicate with the surface sink node (SN) by reflecting the acoustic signal from the SN, is a promising solution for enabling the Internet of Underwater Things. However, underwater backscattering is vulnerable to jamming attacks, especially from a gliding autonomous underwater vehicle (AUV) that can inject jamming signal to the underwater acoustic channel accordingly. This paper considers the attack-defense interactions between the AUV jammer and the SN defender in their transmit power allocations of jamming signals and acoustic signals over multiple USNs. We formulate the strategic power allocation problem for the SN and AUV as an asymmetric anti-jamming Colonel Blotto (CB) game model with a finite budget of total transmit powers. In particular, the competitive interactions between the SN and the AUV is modeled as the competition of two players in the game for limited resource budgets over the battlefield set. We analyze the mixedstrategy Nash equilibrium to the game, and obtain the closedform performance bound of the defense power allocation strategy. Numerical results show the superiority of the proposed CB game based multi-USN secure transmission scheme as compared to the benchmarks in terms of the expected sum-utility.

The increasing sophistication of ransomware attacks poses significant challenges for organizations attempting to secure their digital infrastructures. Simulating such attacks requires innovative solutions that can adapt to rapidly evolving threats and provide comprehensive testing of security measures. A novel approach utilizing autonomous AI systems for ransomware simulations offers unprecedented scalability and adaptability, significantly enhancing the traditional red teaming process. This paper explores how AI-driven attack simulations generate novel, previously unseen ransomware vectors, evaluating their success rates, stealth capabilities, and effectiveness against various defensive countermeasures. Through a modular architecture, the framework described integrates reinforcement learning to continuously refine attack strategies, ensuring that it remains effective in both simple and complex network environments. The results demonstrate that AI-generated simulations not only match but often surpass the capabilities of traditional red teaming methods, especially in terms of novelty and detection evasion. Moreover, the scalability of this approach allows for continuous, large-scale testing without requiring significant human oversight, making it an ideal solution for organizations aiming to stay ahead of emerging ransomware tactics. Future work could focus on refining the AI models further and expanding their defensive applications to create a fully integrated cybersecurity solution.

Treap is a balanced search tree that relies on a random distribution of keys to keep the tree balanced. This paper introduces a novel data structure that expands the concept of binary treap to N-ary trees. We outline the structural characteristics, including the parent-child relationship, and discuss the relative arrangement of elements with different priorities within this tree data structure. Furthermore, we analyze algorithms for performing operations such as searching, inserting, and deleting. Additionally, to evaluate the runtime of the operations mentioned above, we implemented the proposed data structure in C programming language and conducted several experiments. The proposed data structure achieves insertion and deletion operations in expected logarithmic time in average case. Furthermore, the suggested data structure is much simpler to implement than other n-ary trees like B-Trees [2]. This paper makes a valuable contribution to the field by offering a new approach to N-ary trees, which may be beneficial in the development of storage engines.

The classical definition of a charge is limited to assigning opposite polarities to it, which is used to design electric current circuits whose charges are sustained by an energy from a potential (voltage) difference for example. However, recent research and reviews of physical plasma chaotic behaviour revealed that the energy acquired by the charges can be studied closely in space and time domains using novel attributions such as charge (dimness) or (brightness). This is both qualitatively and computationally significant especially when building display screens. After the decline of physical plasma screens technology more than10 years ago, it has been recognized again as it provides unique displays for a variety of media channels. The research presented here concerns working on the so - called mixed modes pixels screens technology, where each screen pixel is allowed to house light emitting diodes elements in addition to making room for a physical plasma compartment to house an electric discharge. Switching between, or mixing, the output from these elements is what defines the overall viewer experience. Moreover, it will be shown that recent research on tracking charge transport across a display screen, allows for a selective capture of images from the sender and receiver at the screen surface, thereby enabling a possible two way televised transmission.

The OpenFlexure Microscope is an accessible, 3Dprinted robotic microscope, with sufficient image quality to resolve diagnostic features including parasites and cancerous cells. As access to lab-grade microscopes is a major challenge in global healthcare, the OpenFlexure Microscope has been developed to be manufactured, maintained and used in remote environments, supporting point of care diagnosis. The steps taken in transforming the hardware and software from an academic prototype towards an accepted medical device include addressing technical and social challenges, and are key for any innovation targeting impact in low-resource healthcare.

In this study, we predict inventory for an IoTenabled vending machine warehouse servicing approximately 1,500 vending machines with the goal of timely replenishing, achieving cost effectiveness, reducing stock waste, optimising the available resources and ensuring fulfilment of consumer demand. The study deploys four different ML algorithms, namely, Extreme gradient boosting, Autoregressive integrated moving average with/without exogenous variables (ARIMA/ARIMAX), Facebook Prophet (Fb Prophet), and Support Vector Regression (SVR). The study unfolds in two phases. First, we utilise conventional historical sales data variables to make the prediction whereas in the second phase, we systematically introduced external variables including weekday, sales deviation flag, and holiday flags into our ML algorithms. The results indicate a significant performance boost using external variables with extreme gradient boosting achieving the lowest (Mean Absolute Error) MAE of 22, followed by ARIMAX, FB Prophet, and SVR with MAE values of 27, 37, and 38, respectively.

Artificial Intelligence (AI) is rapidly transforming educational management, presenting innovative opportunities to foster inclusive leadership within multicultural and globalized contexts. This qualitative study explores the potential of AI to enhance inclusive leadership by enabling real-time responsiveness, data-driven decision-making, and improved cultural competency. Utilizing content and Hybrid Thematic Content Analysis (HTCA) based on a comprehensive literature review, this research compares traditional leadership approaches with AI-integrated models, focusing on their impact on practice, theory, and policy within educational institutions. Key findings indicate that AI promotes inclusivity through proactive, culturally aware interventions and continuous tracking of inclusion metrics, which advance accountability and strategic planning. This study provides critical insights into the ethical considerations and operational benefits of AI in educational leadership, underscoring the need for balanced, human-centered AI applications that align with institutional values and support sustainable educational transformation.

Unmanned Aerial Vehicles (UAV) play a critical role in the low-altitude economy; however, they face significant challenges in network coverage during transit. This paper investigates the characteristics and challenges of communication networks for UAV. First, we delineate typical scenarios, traffic characteristics, and the dual-layer heterogeneous network topology necessary for three-dimensional continuous coverage and network coexistence. Additionally, the paper addresses the channel characteristics and specific challenges faced by UAV Integrated Sensing and Communication (ISAC) networks, including the limitations of traditional APAA-based networks in terms of cost, complexity, and site selection. Subsequently, we present Reconfigurable Intelligent Surface (RIS)-assisted networks as a viable solution for enhancing UAV signal coverage. Key technical characteristics of RIS are discussed, along with design principles, antenna tilt design, and the application of new beam types. We also explore the implications of high-frequency bands and their absorption peaks on signal attenuation, as well as network architecture designs aimed at improving UAV signal coverage and facilitating network coexistence. Furthermore, the importance of standardization in RIS-based UAV networks is emphasized. We also present field trial results evaluating the effectiveness of RIS in improving UAV coverage. Finally, we outline future technology trends, highlighting potential advancements that could further optimize UAV communication systems.

This study investigates the impact of exam stress on the sleep patterns of adolescents, who naturally experience disrupted sleep due to physiological changes. The research aims to identify how exam-related stress influences sleep quality by analyzing brain waves. To achieve this, the brain waves of two adolescents were recorded using a research-grade EEG device called Muse 2. The study mainly focused on the relative power of delta waves (in Bels) and slow-wave sleep (SWS), both of which are necessary for waking up refreshed and restored. The findings revealed that the power of delta waves were affected by exam stress, but other brain waves such as alpha, theta, and beta remained unchanged. This suggests that exam stress primarily impacts deep or SWS in adolescents, leading to a decline in overall sleep quality. To conclude, exam stress exacerbates sleep fragmentation in adolescents by disrupting SWS, which is essential for physical and mental restoration.

The increasing reliance on machine-generated text across various applications has highlighted the importance of ensuring high-quality outputs that are both coherent and contextually accurate. Conventional approaches to prompt engineering often involve manual tuning, which introduces limitations in scalability and consistency, particularly for tasks that demand real-time responsiveness. A novel token-level-guided automatic prompt optimization (TAPO) framework has been developed to address these challenges, offering an adaptive mechanism that refines prompts through real-time feedback at the token level. Through its integration with the Mistral model, the framework significantly improves fluency, coherence, and factual accuracy across a range of text generation tasks. The results demonstrate that the TAPO framework outperforms human-designed prompts by dynamically adjusting token probabilities, providing a more efficient and scalable solution for high-quality text generation. This contribution opens new possibilities for fully automated prompt optimization in LLMs, reducing human intervention while maintaining the adaptability required for complex language tasks.

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.