The escalating frequency and sophistication of cyber threats means the need of the development of more effective detection mechanisms. The Dynamic Entropy Analysis (DEA) framework introduces a novel approach to ransomware detection through real-time monitoring of entropy variations within system files and processes, enabling the identification of malicious activities with high accuracy. Comprehensive evaluation of the DEA framework demonstrated a detection accuracy of 94.5%, surpassing traditional signature-based and behavior-based methods. The framework's adaptability to emerging ransomware variants was evidenced by a consistent detection rate exceeding 90% over a six-month period. Scalability assessments revealed effective operation across networks ranging from 50 to 500 devices, with minimal impact on system performance, as indicated by consistent detection accuracy and acceptable resource utilization metrics. Detection latency analysis indicated prompt identification of ransomware activities, with detection occurring within 0.5 to 2.5 seconds across various samples. The modular design of the DEA framework facilitates seamless integration with existing security infrastructures, enhancing its feasibility for real-world deployment. These findings underscore the potential of the DEA framework to serve as a proactive and efficient solution in mitigating the impact of ransomware attacks across diverse computing environments.

Tibetan's low-resource status has hindered its digital preservation, particularly in Handwritten Character Recognition (HCR). The field lacks foundational models aligned with modern technological advances as well as effective data augmentation methods. For online HCR, the primary challenge is appropriately leveraging temporal information. To address these challenges, we propose TSTN, a spatiotemporal network model tailored for Tibetan online HCR. TSTN excels at capturing both temporal and spatial features from online samples. Using the MRG-OHTC dataset with 583 Tibetan characters, experimental results demonstrate TSTN's superiority, achieving a highest accuracy of 89.26% and a stable average accuracy of 85.13%. To further mitigate data scarcity, we implement geometric data augmentation to enhance accuracy and validate TSTN's robustness in trajectory sequence feature extraction. Furthermore, we generate new samples by segmenting and recombining Tibetan character components, demonstrating the reliability and effectiveness of this approach in boosting recognition performance. Our structureaware data augmentation SplitMix methods further improve performance by a maximum of 1.75%. Our code will be made publicly available soon.

This paper presents a lightweight modified rod antenna with tapered feeding, designed for high-power microwave (HPM) applications. The proposed antenna is capable of transmitting narrowband high-power pulses centered at 2.45 GHz. Numerical simulations were conducted to assess the antenna's performance, followed by prototype fabrication and experimental validation. The manufactured antenna achieved a bandwidth of 3.07 GHz (3.52 GHz-6.59 GHz) and a total gain of 5 dBi. The absence of dielectric materials and the designed 50ohm input impedance facilitate direct connection to an HPM generator, making the proposed design highly suitable for defense and radar applications.

The intricate challenge of maintaining semantic coherence and contextual alignment in text generation has driven the development of innovative embedding recalibration methodologies. Contextual Embedding Recalibration introduces a dynamic mechanism that iteratively adjusts token representations to reflect evolving semantic requirements within extended sequences. The recalibration framework leverages hierarchical token encoding and adaptive weight computation to balance localized fidelity and global coherence, addressing limitations of traditional static embedding strategies. Empirical evaluations across diverse tasks demonstrate substantial improvements in perplexity reduction, contextual consistency, and robustness under varied conditions, including noisy inputs and cross-domain generalization. A key feature of the recalibration layer is its seamless integration with existing transformer architectures, achieved through non-linear transformations and entropy-based regularization mechanisms. Quantitative analyses reveal that the recalibrated model excels in maintaining long-range dependencies, reducing semantic drift, and aligning tokens effectively in multi-turn dialogues and narrative completions. The ability to process out-of-vocabulary tokens with improved accuracy further highlights its adaptability to real-world linguistic variability. By incorporating computationally efficient techniques, the recalibration process achieves scalability while preserving performance gains across large datasets. The presented results demonstrate the efficacy of embedding recalibration in enhancing the representational depth and contextual reasoning capabilities of large-scale transformer models. Cross-domain evaluations confirm the robustness of the recalibrated model, indicating its versatility for applications requiring context-sensitive natural language generation. The findings not only address fundamental challenges in token representation but also offer a foundational framework for advancing machine learning methodologies in broader linguistic contexts. The proposed recalibration approach establishes a benchmark for future explorations into adaptive embedding mechanisms within artificial intelligence frameworks.

The present article introduces a MATLAB-based simulation framework for the implementation and assessment of a lane-change system for autonomous vehicles. The framework comprises algorithms for lane detection, decision-making about lane change execution, and display of the vehicle's actions. A synthetic dataset is created to replicate lane-marked roadways, offering a controlled setting for system testing and validation. The framework is additionally assessed utilizing performance criteria including accuracy, precision, and area under the curve (AUC). The validation includes parameter estimation, goodness-of-fit metrics, and graphical comparisons. The results indicate that the Rayleigh model outperforms the other models in capturing the characteristics of lane change behavior.

An improved understanding of pest population dynamics is critical for more informed and effective management of agricultural pests. A Digital Twin (DT) that spatially visualises pest population dynamic modelling and monitoring, has great potential for facilitating effective and collaborative pest management. We have developed a proofof-concept DT of agricultural pest monitoring that provides dynamic and real-time estimates of pest population throughout an agricultural landscape, while accounting for weather, landuse and pest control methods being applied. This proof-ofconcept DT demonstrates both the viability and suitability of the DT framework for agricultural pest monitoring and management; it also showcases the potential of DTs as valuable decision-support tools in the agricultural landscape.

Parametric Semantic Lattices (PSL) offer a new approach to enhancing semantic representation and contextual processing in advanced language modeling architectures. By embedding hierarchical structures that capture both local and global linguistic dependencies, PSL addresses key limitations observed in traditional transformer-based frameworks. The adaptive nature of the lattice configurations enables the dynamic interpretation of syntactic and semantic relationships across diverse linguistic inputs. Quantitative analysis reveals significant reductions in perplexity and marked improvements in semantic coherence when compared to baseline models. Through leveraging multi-dimensional encoding, the PSL framework enhances interpretability while maintaining computational efficiency across long text sequences. Experiments demonstrate its scalability across extensive datasets and various hardware configurations, validating its application to resource-intensive natural language processing tasks. Evaluation under conditions of noisy input highlights its robustness in maintaining contextual fidelity and accuracy. Practical applications span areas such as automated translation, sentiment analysis, and advanced query resolution, showcasing its versatility. The integration of PSL into transformers introduces minimal computational overhead while yielding substantial performance gains. Results further highlight its ability to retain long-range dependencies, a critical factor for tasks requiring deeper contextual understanding. By redefining structural modeling within transformers, PSL opens new avenues for the development of contextually aware artificial intelligence systems. The findings collectively underscore its transformative potential in advancing the state of language modeling and representation.

Non-terrestrial networks (NTNs) are crucial for 6G communications but face challenges like classical communication bottlenecks. Semantic communication (SemCom) offers a solution, but efficient resource allocation remains unresolved. This paper proposes a joint approach for resource allocation and semantic information selection in low-earth orbit (LEO) satellite-based image transmission. A novel metric, Quality of Semantic (QoSem), is introduced to measure the SemCom performance. An optimization problem is formulated to maximize QoSem by allocating computing, power, and bandwidth resources and selecting optimal semantic information. Furthermore, we propose a novel deep reinforcement learning (DRL) algorithm, namely Mixed-Action Decision Intelligent Semantic-aware Soft-Actor Critic (MADIS-SAC), to handle the problem's nonconvexity and NP-hard complexity. Results show 82.5% of QoSem improvement compared to baselines under resource constraints.

Potential for subsynchronous interactions (SSI) involving inverter-based resources is often screened for using small-signal impedance scans. However, these impedance scans are often slow to compute and it is often desired to study a wide range of operating conditions. This paper presents modeling practices and a new scanning approach for performing impedance-based SSI screening studies that can reduce the required computation time by orders of magnitude with negligible effect on accuracy. Relevant principles are illustrated using a type3 wind turbine model, using two different reactive power control strategies that have significantly different SSI behavior. The discussion of modeling practices includes per-unitization, mechanical dynamics, timestep selection, PLL design, and negative-sequence behavior and covers both time-saving simplifications and pitfalls or unsuitable simplifications to avoid. The new scanning approach involves identifying sets of input frequencies that do not interfere with one another’s outputs and using crest factor minimization to develop multisine test signals.

In this work, a comparison between three broadband methods used to estimate the propagation constant of planar transmission lines is presented. The goal of this comparison is to study how possible random measurement errors can affect the use of the aforementioned methods commonly used, since in ideal conditions the same solution is obtained from all of them. For this purpose, a sensitivity analysis is carried out in order to study the similarities and differences and how errors in measured Sparameters and in line lengths affect the attenuation and the phase constant obtained from each method. Subsequently, a minimization approach that consists of a least-square estimation using a criteria to choose the optimal line lengths is proposed to minimize measurement errors. Finally, an experiment has been designed, manufactured using microstrip transmission lines, and measured to validate the developed theory. Results corroborate the proposed theory and show an excellent agreement with electromagnetic simulations in the 0.1-to 50-GHz frequency band, therefore assessing the suitability of the proposed error analysis.

Given the wide adoption of multimodal sensors (e.g., camera, lidar, radar) by autonomous vehicles (AVs), deep analytics to fuse their outputs for a robust perception become imperative. However, existing fusion methods often make two assumptions rarely holding in practice: i) similar data distributions for all inputs and ii) constant availability for all sensors. Because, for example, lidars have various resolutions and failures of radars may occur, such variability often results in significant performance degradation in fusion. To this end, we present t-READi, an adaptive inference system that accommodates the variability of multimodal sensory data and thus enables robust and efficient perception. t-READi identifies variation-sensitive yet structure-specific model parameters; it then adapts only these parameters while keeping the rest intact. t-READi also leverages a cross-modality contrastive learning method to compensate for the loss from missing modalities. Both functions are implemented to maintain compatibility with existing multimodal deep fusion methods. The extensive experiments evidently demonstrate that compared with the status quo approaches, t-READi not only improves the average inference accuracy by more than 6% but also reduces the inference latency by almost 15× with the cost of only 5% extra memory overhead in the worst case under realistic data and modal variations.

Hydrogen fuel cell vehicles (HFCVs) are a promising technology for reducing vehicle emissions and improving energy efficiency. Due to the ongoing evolution of this technology, there is limited comprehensive research and documentation regarding the performance of HFCVs. To address this void, the paper develops a simple HFCV energy consumption model using new fuel cell efficiency estimation methods. The suggested energy efficiency model employs real-time vehicle speed, acceleration, and roadway grade as input parameters for calculating instantaneous hydrogen energy consumption rates, battery energy consumption, and total energy consumption. The results suggest that the model's forecasts align well with real-world data, demonstrating average error rates of 0.0% and -0.1% for fuel cell energy and total energy consumption across all four cycles. However, it was observed that the error rate for a particular cycle, such as the UDDS, can be as high as 13.1%. Moreover, the study confirmed the reliability of the suggested model through validation with independent data. The findings indicated that the proposed model precisely predicted energy consumption, with an error rate of 6.7% for fuel cell estimation and 0.2% for total energy estimation compared to empirical data. Additionally, instantaneous SOC predictions from the model closely match observed instantaneous SOC measurements, highlighting the model's effectiveness in estimating real-time changes in the battery SOC. The study investigated the energy impact of various intersection controls to assess the applicability of the proposed energy model. The simplicity of the proposed model enables easy estimation of energy consumption using traffic simulation models.

Gastrointestinal (GI) bleeding is a major global health issue contributing to mortality rates. Wireless Capsule Endoscopy (WCE) has transformed non-invasive GI tract examination by offering rich video data for mucosal inspection and detecting problems like bleeding lesions. However, manually evaluating large WCE video files is time-consuming and prone to errors. In this work, we employ a suite of AI models, each meticulously designed for specific tasks in GI bleeding diagnosis. ResNet50, a deep convolutional neural network, excels in classification tasks, ensuring accurate categorisation. YOLOv8, a powerful object detection algorithm, effectively identifies objects, even in faint and subtle instances. To achieve precise segmentation, we developed a novel model incorporating Primary Feature Construction (PFC), Compact Split-Attention (CSA), and Semantic Feature Enhancement Module (SFEM) blocks, outperforming other state-of-the-art models regarding segmentation accuracy. Given the critical impact of AI models on patient well-being, explainability is paramount in the medical field. We have used GradCAM to visualize the feature maps extracted by the model, providing information on the decisionmaking process of the model.

We present the reconfiguration of a circular feed horn operating in bandwidth from 1.4 GHz to 1.8 GHz for radio astronomical applications, adapting it to the 21 cm band (1.42 GHz), and performing observations of the hydrogen line. The proposed model was implemented using cylindrical structures. The design, simulation, measured performance, and reconfiguration are detailed. This feed horn is used at the prime focus of a parabolic reflector antenna. A prototype was designed and characterized, and the simulated and measured results were in good agreement. The reflection coefficients (S 11) of the feed horn was below-15 dB, and the voltage standing wave ratio (VSWR) remained below 1.43. The gains obtained from simulation and measurement were 12 dBi and 12.3 dBi, respectively. To determine the suitability of the proposed design in a practical environment, spectral measurements were performed using an FPGA-based spectrometer. The results showed that the intensity of the signal reached 3.5 dB more than that of the original feed horn. The overall performance of the proposed feed horn suggests that it can be applied to the optics of the 4.9 m reflector antenna.

Big Data analytics has become an essential tool for industries to make informed decisions, enhance operational efficiency, and gain deep insights from diversified vast amounts of data. This paper explores the transformative power of Big Data analysis, focusing on four core types: Exploratory, Descriptive, Sentiment, and Predictive analysis. These methods are critical in sectors such as healthcare, retail, finance, and transportation, enabling businesses to identify trends, improve customer satisfaction, and forecast future outcomes. The paper discusses the methodology, required resources, and enabling technologies for effective Big Data utilization, offering a comprehensive framework for implementation. A discussion on challenges and strategies for overcoming them is included, alongside real-world applications that demonstrate Big Data's tangible benefits. Finally, a forward-looking conclusion emphasizes the importance of continuous adaptation and innovation to leverage Big Data's full potential.

A fundamental challenge in contemporary computational linguistics lies in the ability of language models to adaptively capture and maintain complex contextual relationships within diverse and dynamically shifting textual inputs. Transformer-Based Cascade Synthesis offers a pioneering solution to this problem through a cascading, multi-layered framework that progressively refines contextual embeddings at each stage, creating a more flexible, responsive model architecture capable of complex context handling across extended text sequences. Employing a structured series of transformations, the synthesis approach recalibrates embedding layers to achieve a balance between context sensitivity and computational efficiency, facilitating coherent, syntactically accurate language generation even in complex linguistic scenarios. Experimental results demonstrate substantial improvements in contextual coherence, lexical diversity, and syntactic precision, suggesting that Transformer-Based Cascade Synthesis achieves a marked enhancement in model adaptability and response fidelity compared to traditional static embedding methods. This methodology's effectiveness across diverse language tasks highlights its potential to set a new standard for adaptability in large language model architectures, contributing meaningfully to the broader pursuit of human-like language comprehension in artificial intelligence.

With the ability to create seamless connectivity that can be automated, the Internet of Things (IoT) has transformed the way industries work, as well as how we live everyday. Yet the proliferation of IoTs has exposed glaring security flaws in their ecosystems, which implicate the use, the enterprise, and the policymaker comprehensively. Then this review will look at the landscape of IoT security in terms of emerging threats, vulnerabilities within the system and existing defense mechanisms. The study, examining key topics such as cybersecurity challenges, data breach prevention, and improvement of resilience for IoT, is a result of a thorough analysis of high impact research. It further points out the contributions of emerging technologies, such as blockchain, machine learning and edge computing in solving those problems. Moreover, since current research lacks metrics for securing resource constrained devices, ensuring interoperability and providing scalable defenses for heterogeneous environments, there are gaps identified in this review. This study proposes a strategic framework for future research and innovation to improve security of the IoT ecosystem and to foster trust and resilience in a world of increasing connectivity.

This paper introduces DynamicRenderNet, a novel neural network architecture designed to optimize graphics rendering in real-time Augmented Reality (AR) and Virtual Reality (VR) games. By adaptively adjusting rendering parameters based on scene complexity and user interaction, DynamicRenderNet significantly improves frame rates and reduces latency. Our model utilizes a deep learning approach to analyze real-time input data, such as camera feed, user head movements, and scene geometry, to predict optimal rendering settings. This enables dynamic resource allocation and prioritization, leading to a more efficient and responsive rendering pipeline. Experimental results demonstrate substantial improvements in frame rates and reduced latency, resulting in a more immersive and visually appealing AR/VR experience.