This paper develops a power-based microscopic energy consumption model for battery electric vehicles (BEVs) entitled Virginia Tech Comprehensive Power-based Energy Consumption Model 2.0 (VT-CPEM 2.0). This model avoids bang-bang control, is calibrated using US Environmental Protection Agency (EPA) city and highway energy consumption ratings; along with publicly available vehicle and roadway pavement parameters; and captures regenerative braking. The model validation considers three BEVs and three drive cycles. The test results show that the estimated energy consumption rates by the proposed model are consistent with in-laboratory data collected by the Argonne National Laboratory producing an average error of 3.9% and a maximum error of less than 7.5% for three vehicles across three drive cycles. Additionally, the model demonstrates that the optimal speed of BEVs increases with an increase in the auxiliary power.

In this paper, we present "Comprehensify," a Chrome extension designed to enhance learning by providing personalized content summarization using large language models (LLMs). By analyzing user knowledge levels across different topics and reading web page content, Comprehensify generates summaries tailored to the user's comprehension level. We integrate various models, including GPT-4o, GPT-4o-mini, LLaMA 3, and Claude 3, and implement a client-side cache as well as a server-side cache to optimize performance. Additionally, user authentication is employed to ensure personalized experiences. Our evaluation shows promising results in providing effective learning aids, with potential future enhancements to further improve the system's functionality.

Color normalization is one of the most important preprocessing steps to enhance the accuracy of image classification, especially when there is high variability in lighting and color contrast. This paper discusses the performance of using advanced color normalization techniques such as perceptual color spaces, LAB, and HSV in CNNs for image classification tasks. The contribution will be at the application level: the application of different color normalization techniques to datasets with heterogeneous lighting and color conditions, training, and testing the performance of CNN models. Experimental results highlight that normalizations in perceptual color spaces boost the general accuracy significantly, especially under poor and diverse lighting conditions, when compared with classical preprocessing in RGB. In general, LAB-based normalization performed better than the others in achieving better robustness across datasets. Choosing among these methods involves varying computational costs where there is a trade-off between efficient preprocessing and classification gains. The proper color normalization technique can definitely improve robustness; thus, the insights into optimizing a preprocessing pipeline for image classification tasks were presented in this study.

Rainfall forecasting has recently attained the highest level of academic relevance, due to its intricacies and enduring uses, including flood forecasting and pollutant concentration level monitoring. It is difficult to predict rainfall due to its complicated nature, which is impacted by a number of meteorological, oceanic, and geographical elements. In order to get over these limitations, methods that combine time-series data with machine learning techniques are being investigated. The goal of this project is to apply machine learning algorithms to reliably forecast rainfall. Current models rely on intricate statistical models that are frequently excessively expensive, both financially and computationally, or aren't used in downstream applications. Using simple rainfall estimating models based on traditional machine learning methods and Deep Learning architectures that are effective for various downstream applications, this work offers a comparative examination. In order to capture both short-term and long-term rainfall trends, we test a variety of machine learning models, such as Random Forest, Support Vector Machines, and Long Short-Term Memory (LSTM) networks. Additionally, the study investigates how data preprocessing methods and hyperparameter adjustment can improve the model's performance. Real-time meteorological data from several geographical locations was used to validate the model, and the findings showed that it could make predictions more accurately than conventional statistical techniques.

Silicon photonics is the most viable and cost-effective platform for next generation optical interconnects. One of the key optical devices in these interconnects is an optical modulator which, along with photodetectors, effectively defines performance of the whole optical link. However, due to its centrosymmetric structure, silicon does not offer linear electro-optic effects, leaving plasma dispersion effect as the only feasible modulation mechanism for high speed applications. The performance of plasma dispersion-based modulators is affected by several design variables, affecting various modulator figures of merit (FOMs) where performance trade-off exists. This necessitates design optimization, requiring reasonably accurate mathematical predictive models for all the FOMs. This paper presents a rigorous full 3D model for the junction capacitance of silicon-on-insulator interleaved junction optical phase modulators with submicron dimensions. Junction capacitance is instrumental in calculating power consumption per bit, modulation speed and efficiency of optical modulators. Solution of Drift-Diffusion and Poisson's equations were carried out on 3D finite-element-mesh and Maxwell's equations were solved using Finite-Difference-Frequency-Domain (FDFD) method on non-uniform rectangular grid to calculate confinement factor. Whole of the modeling process has been detailed and all the coefficients required in the model are presented. Model validation suggests < 10 % RMS error.

Contemporary academic research covers a wide range of topics, illustrating the complex and dynamic challenges and opportunities faced by societies worldwide. This paper examines the influence of literature in forming cultural identities and the significant effects of technology on conventional frameworks. Present academic works provide indepth perspectives on the integration of humanistic considerations and technological progress, highlighting their joint importance in addressing modern societal problems.

This research is in the domain of Natural Language Processing and delves into statistical methods that are intrinsic to the input text, that is not needing any external corpus or training. The methods are used to calculate similarity of sentences in Hindi. The study is performed on sentence pairs in English and Hindi and the differences are analysed with the structural and grammar attributes of the languages. The methods chosen mathematically operate at different levels from character to phrase. The purpose is to identify methods at which a language performs differently than the other, and how different they are with the actual truth. The conclusions can be elemental when considering building larger applications that may need to use one of these methods or hybrid of these methods. An analysis on various methods applied on a sentence pair dataset with both the languages Hindi and English is conducted using visualisation of the produced output of these methods against their actual scores.

A pure weight-based test case generator for context-free grammars is introduced, where weights are set for each rule and used as unique driver to identify which one to apply during the derivation process; we demonstrate why static flat weights generally cannot work whereas, introducing a simple penalty mechanism, a dynamic weight-based approach is always able to produce random but finite sentences; then, looking at the source grammar from an original perspective, we proceed towards the definition of constraints through which, while still guaranteeing the convergence of the derivation, static but balanced weights can be identified: balanced weights own the interesting capability to drive the generation of an inexhaustible number of extremely random, structurally complex but finite sentences by an automatic test case generator having a dramatically simple derivation logic. Thereafter, we illustrate how to design a weight-based system which, by exploiting the characteristics of balanced weights, can be easily configured to produce sentences that address any specific coverage metric, including those not having a definite generative strategy yet. A complete example of the application of the balanced concept and the weight-based system is then included and experimental results are eventually shown.

This research investigates the Waveon, a Dark Matter Candidate, and the second released by Infinite 8 Industries; a fundamental quantum entity that revolutionizes our understanding of light, energy, and matter. Identified through advanced wave interference techniques, the Waveon represents the irreducible building block of light waves, distinct from the photon, and embodies pure wave properties. In-depth analysis reveals characteristics such as quantized frequency ranges, amplitude variability, and multidimensional energy capacity. The Waveon's stability and dynamic behavior are supported by the following research.

Cloud-Edge Computing Continuum (CEC) system, where edge and cloud nodes are seamlessly connected, is dedicated to handle substantial computational loads offloaded by end-users. These tasks can suffer from delays or be dropped entirely when deadlines are missed, particularly under fluctuating network conditions and resource limitations. The CEC is coupled with the need for hybrid task offloading, where the task placement decisions concern whether the tasks are processed locally, offloaded vertically to the cloud, or horizontally to interconnected edge servers. In this paper, we present a distributed hybrid task offloading scheme (HOODIE) designed to jointly optimize the tasks latency and drop rate, under dynamic CEC traffic. HOODIE employs a model-free deep reinforcement learning (DRL) framework, where distributed DRL agents at each edge server autonomously determine offloading decisions without global task distribution awareness. To further enhance the system pro-activity and learning stability, we incorporate techniques such as Long Short-term Memory (LSTM), Dueling deep Q-networks (DQN), and double-DQN. Extensive simulation results demonstrate that HOODIE effectively reduces task drop rates and average task processing delays, outperforming several baseline methods under changing CEC settings and dynamic conditions.

Developing channel models typically requires aggregating channel measurements and the corresponding extracted propagation parameters from different research institutions to form a sufficiently large data basis. However, uncertainties arising from limitations of the sounding hardware and algorithms may greatly impact the comparability between sounding results. Especially, (sub-) THz channel sounding is challenged by a potentially low SNR. At the same time, high Doppler shifts may occur due to the high carrier frequencies, limiting the time spans for coherent or incoherent data processing. Hence, the channel dynamics additionally restrict the processing gain for the SNR. In this paper, we address these challenges metrologically from several perspectives. First, we discuss methods of baseband waveform precoding to adapt it to the sounder hardware or the employed high-resolution estimation algorithms. This allows using available transmit power in an optimal sense. Second, the assessment of the sounder performance requires a traceable reference allowing tracing back measurements (or estimated propagation parameters) to a physical ground truth. Therefore, we propose and discuss an over-the-air artifact allowing a joint verification of delay and Doppler parameters in a multipath scenario. The evaluations of exemplary sub-THz measurements with a multicarrier-based sounder highlight the strong interplay between sounder hardware and estimation algorithms, especially when coping with the mutual interference of parameters from multiple propagation paths. Hence, a metrological assessment always requires considering the full processing pipeline from the unprocessed measurements up to the extracted propagation parameters.

Effective detection of sophisticated cyber threats requires innovative frameworks capable of analyzing the complex and evolving behaviors of malicious software. The Temporal Behavior Chain Analysis (TBCA) framework introduces a novel methodology for identifying ransomware activity through the examination of temporally dependent system interactions. By constructing directed acyclic graphs to represent behavioral sequences, the framework captures subtle anomalies that traditional detection methods fail to identify. Machine learning techniques, including Hidden Markov Models and neural networks, enhance the framework's ability to discern malicious patterns from benign behaviors with high accuracy and efficiency. Evaluations demonstrated superior detection rates across multiple ransomware families, including LockBit, BlackCat, and Hive, even when advanced obfuscation techniques were employed. The modular design of TBCA ensures seamless integration into existing infrastructures while maintaining scalability for deployment in diverse operational environments. Resource efficiency was validated through extensive scalability testing, which confirmed the framework's capability to process high volumes of data with minimal computational overhead. Comparative analyses revealed significant improvements over traditional detection approaches, particularly in handling emerging and previously unseen ransomware variants. Temporal modeling of sequential dependencies enables early detection, reducing potential system damage and facilitating real-time mitigation strategies. The integration of adaptive decision-making mechanisms minimizes false positives, enhancing reliability in high-stakes environments. Cross-platform evaluations further highlighted the robustness of the framework, demonstrating its effectiveness on Windows, Linux, and macOS systems. The TBCA framework establishes a comprehensive foundation for advancing proactive cybersecurity defenses against increasingly complex and dynamic threats.

This paper presents channel measurements and modeling at 28 and 160 GHz in an industrial-style open office environment with angular information at the receiver. Channel impulse response snapshots are captured at 145 measurement positions for both frequencies and are evaluated in terms of power metrics, delay, and angular characteristics. Frequencydependent models for path loss, delay, and angular spread of arrival and K-factor are estimated, and large-scale parameter cross-correlations are evaluated. The results show that delay and angle spread are not frequency dependent in this environment although twice as many multi-path components were identified at 28 GHz compared to 160 GHz.

The IPv6 Routing Protocol for LLNs (RPL) is fundamental to the Internet of Things (IoT), providing essential routing capabilities for devices operating in constrained environments. At the heart of RPL's efficiency is the Trickle Algorithm (TA), designed to optimize the dissemination of control messages across networks, balancing timely updates with the imperative to conserve bandwidth and energy. This paper focuses on validating the implementation of the TA within the Latest version of Contiki-OS (Contiki-NG), a leading experimental platform for IoT networks. By comparing the results of theoretical models with practical simulations of Contiki-NG, we aim to determine the reliability of the Contiki-NG representation of the TA behaviour. Our analysis reveals a high degree of agreement between theoretical expectations and simulated results, underscoring Contiki-NG's robustness as a simulation tool for IoT research. The findings affirm the precision of Contiki-NG implementation and highlight the platform's value in facilitating the development and testing of IoT protocols. By ensuring the reliability of these simulations, our work strengthens the confidence of the IoT research community in using Contiki-NG as a foundation for exploring innovative solutions to the unique challenges of IoT networks.

Bunyakovsky conjecture is proved to be true.

The trend towards sensor miniaturization has heightened interest in optimizing sensor array configurations across various scientific and industrial applications that imply multichannel measurements. In this article, we present an extended version of our previous approach, adapting it to the realworld conditions in magnetoencephalography (MEG). At each step the method selects the sensor location that maximizes the ROI-related signal-to-noise ratio (SNR), followed by the projection recursively applied to the gain matrix to orthogonalize the subsequent stereotypic steps with respect to the source subspace served by the already selected sensors. This algorithm offers high computational efficiency and flexibility with respect to physical constraints. We dub our approach as Optimal Layout Design via Recursively Applied Leadfield Elimination (OLD RALFE). Our analysis of the optimized MEG sensor layouts obtained with OLD RALFE shows that the proposed algorithm performs similarly to several other significantly more computationally demanding sensor layout optimization approaches in terms of both SNR and the Cramér-Rao lower bound (CRLB) on the spatial resolution of the inverse problem solution. OLD RALFE demonstrated in application to MEG, is broadly applicable to many other multichannel measurement challenges.

Reconfigurable Intelligent Surfaces (RIS) represent transformative technologies for next-generation wireless communications, offering advanced control over electromagnetic wave propagation. While RIS have been extensively studied, Stacked Intelligent Metasurfaces (SIM), which extend the RIS concept to multilayered systems, present significant modeling and optimization challenges. This work addresses these challenges by introducing an optimization framework for SIM that, unlike previous approaches, is based on a comprehensive model without relying on specific assumptions, allowing for broader applicability of the results. We first present a model based on multi-port network theory for characterizing a general electromagnetic collaborative object (ECO) and derive a general framework for ECO optimization. We then introduce the SIM as an ECO with a specific architecture, providing insights into SIM optimization for various architectures and discussing the complexity in each case. Finally, we analyze the impact of commonly used assumptions, demonstrating that employing a complete model-one that does not rely on the unilateral hypothesis and accounts for mutual coupling among SIM elements-can yield better performance, using the realization of a 2D DFT as a case study.

This paper introduces Convergent Hierarchical Reasoning (CHR), a unified artificial intelligence (AI) framework that integrates four advanced reasoning paradigms-Chain-of-Thought (CoT), Forest-of-Thought (FoT), Trilevel Reasoning Graph (TRG), and Logarithmic Memory Networks (LMNs)-into a single, plug-and-play module. CHR offers end-to-end interpretability, multi-tree exploration, multi-tiered decision-making, and efficient long-sequence handling, making it especially suitable for high-stakes government applications. Agencies often struggle with strict compliance requirements, data sensitivity, and resource constraints when implementing AI for large-scale training, simulation, and decision support. By merging CoT, FoT, TRG, and LMNs into a seamless system, CHR alleviates the complexity and security risks associated with manually integrating multiple modules. Its architecture facilitates one-stop deployment within FedRAMP-approved cloud environments, incorporating real-time monitoring and automated checks aligned with NIST and FIPS standards. CHR A Preprint Early evaluations indicate significant improvements in both interpretability and computational efficiency, vital for applications ranging from military readiness to cybersecurity threat analysis. With parallel reasoning trees (FoT) and stepwise clarity (CoT) operating under a tiered decision-making structure (TRG), CHR ensures that high-level goals inform operational tactics. Meanwhile, LMNs efficiently manage extended historical data, minimizing computational overhead for long-sequence tasks. Although CHR is primarily designed for government and defense settings, its modular and transparent nature makes it equally valuable in industry and academia. By unifying advanced reasoning with compliance-focused MLOps, CHR demonstrates a scalable path for adopting next-generation AI, offering robust oversight and clear accountability in mission-critical scenarios.