Course evaluation is a critical component in higher education pedagogy. It not only serves to identify limitations in existing course designs and provide a basis for curricular innovation, but also to offer quantitative insights for university administrative decision-making. Traditional evaluation methods, primarily comprising student surveys, instructor self-assessments, and expert reviews, often encounter challenges, including inherent subjectivity, feedback delays, inefficiencies, and limitations in addressing innovative teaching approaches. Recent advancements in large language models (LLMs) within artificial intelligence (AI) present promising new avenues for enhancing course evaluation processes. This study explores the application of LLMs in automated course evaluation from multiple perspectives and conducts rigorous experiments across 100 courses at a major university in China. The findings indicate that: (1) LLMs can be an effective tool for course evaluation; (2) their effectiveness is contingent upon appropriate fine-tuning and prompt engineering; and (3) LLM-generated evaluation results demonstrate a notable level of rationality and interpretability.

As digital infrastructures continue to expand globally, organizations and individuals alike face an ever-growing threat from ransomware attacks, which disrupt critical systems and hold sensitive data hostage. The development of an adaptable and precise detection mechanism is crucial to counter the sophisticated and evolving tactics employed by ransomware actors. This research introduces the Dynamic Anomaly Matrix Embedding (DAME) framework, a highly novel approach designed to detect ransomware through a combination of matrix embedding techniques and dynamic behavioral profiling, achieving a substantial advancement over traditional detection models. DAME’s architecture allows for the real-time monitoring and analysis of system behaviors, effectively differentiating ransomware activity from legitimate processes with minimal false positives and negatives. Evaluation results demonstrate that DAME not only maintains a high detection accuracy and precision but also optimally balances processing speed and resource utilization, proving effective even in resource-constrained environments. Comparative analysis further reveals that DAME surpasses traditional detection models, such as Support Vector Machines and Random Forest, in both efficiency and adaptability to novel ransomware variants. The presented findings affirm DAME’s robustness as a cybersecurity solution and underscore its significant contributions to advancing autonomous ransomware detection methodologies.

Soft magnetic composites cast with recycled ferrites and construction binders like cement, epoxy, or asphalt are highly robust and possess the lowest core up to 100 kHz in a wide temperature range. This paper shows how by using precision-enhanced magnetic testing and sophisticated hysteresis modeling, the performance of these materials could be significantly improved with respect to the stress effect on losses. A composite material grade with minimal losses was developed by benefiting from the behavior of ferrite compositions yielding zero crystalline anisotropy at temperatures below 0°C under compressive stress.

Gastrointestinal (GI) tract cancers account for a substantial portion of the global cancer burden, where early diagnosis is critical for improved management and patient outcomes. The complex aetiologies and overlapping symptoms across GI cancers often delay diagnosis, leading to suboptimal treatment strategies. Cancer-related queries are crucial for timely diagnosis, treatment, and patient education, as access to accurate, comprehensive information can significantly influence outcomes. However, the complexity of cancer as a disease, combined with the vast amount of available data, makes it difficult for clinicians and patients to quickly find precise answers. To address these challenges, we leverage large language models (LLMs) such as GPT-3.5 Turbo to generate accurate, contextually relevant responses to cancer-related queries. Pre-trained with medical data, these models provide timely, actionable insights that support informed decision-making in cancer diagnosis and care, ultimately improving patient outcomes. We calculate two metrics: A1 (which represents the fraction of entities present in the model-generated answer compared to the gold standard) and A2 (which represents the linguistic correctness and meaningfulness of the model-generated answer with respect to the gold standard), achieving maximum values of 0.546 and 0.881, respectively.

In this letter, an error is identified in Proposition 3 presented in Björnson et al. (2020) which includes an erroneous expression to compute the optimal number of reflecting elements that minimizes the total power consumption and maximizes the energy efficiency in intelligent reflecting surface (IRS)-supported transmission. The correct version of this expression and a few comments are provided in this letter, and simulation results are also presented to support this claim.

Generative artificial intelligence (GenAI), particularly large language models (LLMs), has revolutionized various applications by producing coherent and contextually relevant text. However, despite their advancements, LLMs are prone to hallucinations-instances where the AI generates inaccurate or fabricated information. Retrieval-augmented generation (RAG) has emerged as a technique to enhance GenAI by integrating external knowledge sources beyond the model's training data. While RAG improves factual grounding, it alone cannot fully eliminate hallucinations. To address this limitation, agentic workflows that incorporate external tools such as APIs, search engines, and self-reflective mechanisms offer a promising solution. These workflows enable models to iteratively assess and refine their outputs, thereby reducing errors and enhancing factual accuracy. This paper presents a novel framework that combines agentic workflows with RAG within 6G networks to achieve more reliable generative AI by deploying autonomous agents that reflect on outputs and leverage real-time knowledge from external sources to improve response quality and accuracy. We explore the deployment of these workflows in 6G-enabled edge environments, facilitating scalable, real-time knowledge integration and model refinement. Our framework addresses current limitations in RAG-enhanced services by utilizing 6G edge intelligence for data fusion, dynamic knowledge base updates, and customizable AI service delivery. Through a multi-agent system comprising generator and critic agents, we effectively reduce hallucinations via iterative self-criticism, paving the way for more reliable and accurate generative AI services across diverse applications.

This study proposes an innovative framework called Evolutionary Generative Design (EGD), which integrates evolutionary computation with machine learning. In conventional evolutionary computation, the mapping between the phenotype space and the genotype space (encoding and decoding) has relied heavily on the designer's experience and expertise, posing significant challenges to automation. EGD utilizes autoencoders to automate the mapping process using existing data, enabling the dynamic generation of design spaces. EGD enables efficient exploration and evolutionary design generation by employing these generated design spaces as genotype spaces within evolutionary computation. The automatic creation of genotype spaces allows for dynamic searches of novel and optimized designs, significantly advancing the design process. To demonstrate the effectiveness of the proposed framework, this study constructs an interactive evolutionary computation (iEC) system and applies it to multiple design scenarios, showcasing improvements in customization. Variational Autoencoders (VAE) are employed as the autoencoder model in this framework. These findings highlight EGD's potential to revolutionize design exploration, paving the way for creative and datadriven design innovation.

Physically unclonable functions (PUFs) are used as low-cost cryptographic primitives in device authentication and secret key creation. SRAM-PUFs are well-known as entropy sources; nevertheless, due of non-deterministic noise environment during the power-up process, they are subject to low challenge-response repeatability. The dependability of SRAM-PUFs is usually accomplished by combining complex error correcting codes (ECCs) with fuzzy extractor structures resulting in an increase in power consumption, area, cost, and design complexity. In this study, we established effective metrics on the basis of the separatrix concept and cell mismatch to estimate the percentage of cells that, due to the effect of variability, will tend to the same initial state during power-up. The effects of noise and temperature in cell start-up processes were used to validate the proposed metrics. The presented metrics may be applied at the SRAM-PUF design phases to investigate the impact of different design parameters on the percentage of reliable cells for PUF applications.

The exponential rise in cyber threats has escalated the urgency for robust, automated defenses capable of identifying and mitigating sophisticated ransomware attacks. Current detection systems, predominantly reliant on static or rule-based methodologies, often fall short in recognizing rapidly evolving ransomware variants, necessitating adaptive solutions that address both accuracy and computational efficiency. In response, an innovative framework combining Adaptive Anomaly Clustering (AAC) with Threat Signature Prediction (TSP) is proposed to deliver a dynamically adaptive ransomware detection model. The AAC component isolates malicious activities through behavior-specific clustering, achieving high specificity in identifying ransomware, while TSP predicts potential ransomware signatures in real-time, allowing proactive defense mechanisms even for previously unseen ransomware. Together, AAC and TSP offer a comprehensive, layered detection approach that not only improves detection accuracy but also enhances response speed and scalability across diverse network conditions. Through rigorous evaluation, the framework demonstrates significant improvements over traditional methods in detection precision, false-positive reduction, and adaptability to novel ransomware threats, thus showing its potential for integration into advanced cybersecurity infrastructures.

Dynamic behaviors of the classical Kuramoto models have been widely studied. The dynamics of the all-to-all connected oscillator Ising machines (OIMs) is similar to that of the classical Kuramoto models, with the main difference being that there is an additional term in OIMs, called the second harmonic term. However, the dynamic behavior of an all-toall connected OIM is significantly different and its intricate properties are largely unexplored. In this paper, we study in detail the properties of the the all-to-all connected OIMs and explore their application as associative memory. The number of patterns such an OIM can store increases exponentially with respect to the number of oscillators. To improve the performance of the OIMs for associative memory, we propose new harmonic term so that the resulting OIM achieves pattern retrieval with high accuracy in the presence of a high level of noise.

Remote photoplethysmography (rPPG) is an active area of research that has extended beyond heart rate (HR) estimation to estimate more complex cardiac signals such as HR variability and blood oxygen saturation (SpO 2). As these applications extend to more challenging domains, the SNR of the physiological signal is increasingly important. This research is particularly useful for enhancing the accessibility and impact of rPPG technologies in consumer electronic devices, such as RGB cameras. Almost all videos recorded with consumer devices use prevailing codec settings, and such video compression is a source of signal degradation. While prior works have assessed the performance of rPPG algorithms under various video compression rates, they have not explored how specific compression configurations can be strategically optimized to better preserve the integrity of rPPG signals. This paper aims to address this gap by identifying a close connection between the projection plane from the principled plane-orthogonal-to-skin (POS) algorithm to the YCbCr color space commonly used in compression. Namely, these projection planes are equivalent, which is a crucial connection that motivates the consideration of alternative color spaces for video compression in physiological monitoring applications. Improved rPPG signal preservation at low bitrates can be achieved using the RGB24 color space for compression compared to YUV444 and YUV420. These results suggest opportunities to reduce error in rPPG applications by treating the video compression color space as a tunable setting in a system's design.

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.

The pre-Hadoop era of data processing represents a pivotal phase in the evolution of modern computational ecosystems, setting the stage for the Big Data revolution. For decades, organizations across industries-from aviation and manufacturing to departmental operationsinvested heavily in era-specific data processing solutions to derive actionable insights critical for business growth and success. Progressing from manual file systems to relational databases and eventually data warehouses, these systems addressed fundamental challenges in data storage, processing, and retrieval. However, they faced persistent barriers in scalability, heterogeneity, and real-time analytics, often leading to the phenomenon of information overload. This paper examines the genesis of Big Data concepts, highlighting the descriptive "Vs" of volume, velocity, variety, and veracity that defined both the pre-Hadoop systems and the modern era. It explores the limitations of traditional systems, their impact on strategic decision-making, and mitigation efforts through investments in OLAP technologies. Furthermore, the study emphasizes the enduring influence of legacy systems on emerging solutions like Hadoop, AI-integrated analytics, edge computing, and distributed architectures. By linking historical paradigms to contemporary advancements, this discussion provides insights into the dynamic trajectory of data engineering and trends shaping the future of information processing. .

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.

Maintaining contextual coherence in extended text generation remains a significant challenge in computational linguistics. The introduction of the Stochastic Hierarchical Embedding (SHE) mechanism offers a novel approach to this issue, incorporating stochastic variability within hierarchical embeddings to enhance adaptability and coherence in language models. This study details the integration of SHE into a contemporary open-source Large Language Model (LLM), providing a comprehensive mathematical framework and outlining the modifications required for effective implementation. Experimental evaluations demonstrate that SHE significantly improves contextual coherence and prompt convergence, as evidenced by higher Contextual Coherence Scores (CCS) and Prompt Convergence Rates (PCR). Additionally, the SHE-augmented LLM exhibits enhanced generalization capabilities across diverse linguistic tasks and increased robustness to input noise. These findings suggest that SHE presents a promising advancement in the development of more coherent and adaptable LLMs.

由于太赫兹无线通信拥有广阔的未开发频谱，它正在成为未来 6G 网络的关键技术。使用模拟相控天线阵列对于缓解太赫兹信号的显著衰减特别有效，从而确保高质量的通信。通过采用精心设计的移相器，这些阵列可以实现波束成形，将信号增益集中在空间中以补偿信号损失。然而，在超宽带情况下，相控阵系统的性能会受到影响，因为移相器通常是为中心频率设计的，但应用于整个频带。当子载波频率与中心频率不同时，这将导致标准波束成形方法中出现明显的波束偏差和散焦。在本文中，我们提出了模拟波束成形方法来增强视距太赫兹超宽带相控发射机阵列系统的性能。利用空间啁啾特性，我们开发了移相器设计方案，以在整个频带上实现更均匀的等效信道增益，以适应各种应用场景。我们通过模拟结果证明了所提出方法的有效性和优势，与最先进的模拟波束形成方案相比，结果显示出显着的改进，包括提高吞吐量和降低误码率。

This paper focuses on Ambimorphic prototype research in the field of information engineering. Firstly, it elaborates the research background, purpose and significance, and analyzes the concept of Ambimorphic prototype and related theoretical foundations. It then introduces in detail the application of public key-private key simulation in information security and communication systems, as well as the design and implementation of telephone semantic tools, including requirements analysis, architecture design and key technologies. Through experiments, performance tests and result analyses are conducted on both, including individual tests and comprehensive application tests. Finally, it summarizes the research results, limitations, and looks forward to future research directions, providing new ideas and methods for the development of information engineering.

The concept of resolution is fundamental to any sensing or measurement system, as it establishes a reference for the intrinsic detection limit of any acquisition process. Although this concept originally emerged in the context of DC measurements, it should be more clearly defined in a generic acquisition system where the signal is inherently stochastic. This paper revisits the sensing and measurement process through the lens of Information Theory, where resolution is considered in relation to the distributions of random variables. Additionally, it will introduce a new parameter, the "number of resolution levels", which is valuable because it encapsulates the amount of information conveyed by a measurement system. The results demonstrate how this framework can address key design questions, such as determining the optimal discretization for an A/D converter given input-referred noise from a transducer, and how to optimize the system from a resolution perspective. Theoretical results will be compared with numerical simulations of an analog noisy interface coupled with an A/D converter.