This paper presents a detailed blueprint for implementing a production-grade adaptive learning system for large-scale government training and education. Building on previous research, this work outlines a potential standard for government training. While we envision a robust, evolving framework, advancements in XR, AI, and reasoning techniques such as chain-of-thought prompting and forest-of-thought (FoT) prompting will drive ongoing refinement (Bi et al., 2024; Wei et al., 2022). Our approach focuses on engineering underpinnings, CI/CD pipelines, compliance measures, cost optimization, and monitoring techniques critical for secure deployment. Chain-of-thought prompting, a reasoning strategy that enhances AI adaptability by breaking down complex problems, strengthens our system’s accuracy and effectiveness (Brown et al., 2020; Kojima et al., 2022). Building upon this, the FoT framework further enhances reasoning capabilities by integrating multiple reasoning trees and employing sparse activation strategies, leading to improved accuracy and computational efficiency (Bi et al., 2024). Drawing from prior data and research, we propose methods to validate curriculum effectiveness and guide dynamic instructional design (Ma et al., 2014; Pane et al., 2015). We address government training needs across military readiness, diplomatic language instruction, homeland security, and workforce skill enhancement. The MLOps pipeline, from data ingestion to model training and deployment, ensures scalability and efficiency (N. Chen & Lin, 2021; Kreutz & Villamizar, 2020). Open-source tooling, cloud-native architectures, and adherence to federal frameworks (e.g., FedRAMP, FIPS 140-2, NIST) form a cohesive, modular system (FedRAMP, 2023; National Institute of Standards and Technology, 2002, 2020). By integrating automation, security, cost-effective resource management, and advanced reasoning methods, including FoT, this blueprint delivers adaptive learning at scale, meeting rigorous standards while enhancing training outcomes and operational efficiency.  

Large language models (LLMs) have seen rapid advancements in their ability to integrate and generate knowledge from various sources. However, the challenge of efficiently combining information across different modalities-such as text, images, and audio-persists. In this work, we introduce the Iterative Retrieval Augmentation for Multi-Modal Knowledge Integration and Generation (IRAMIG) framework, designed to enhance the integration of multi-modal knowledge. By employing an iterative retrieval mechanism, IRAMIG allows for the dynamic fetching of relevant data informed by context from previous queries. The architecture comprises modality-specific retrieval modules and a fusion component that synthesizes knowledge, ultimately enriching the model's understanding and output generation. Experiments conducted across image captioning and multi-modal question answering tasks reveal significant performance improvements when compared to existing methods, showcasing IRAMIG's potential to refine knowledge integration and enhance the quality of generation in complex multi-modal scenarios.

We develop a novel multi-objective reinforcement learning (MORL) framework to jointly optimize wireless network selection and autonomous driving policies in a multi-band vehicular network operating on conventional sub-6GHz spectrum and Terahertz frequencies. The proposed framework is designed to (i) maximize the traffic flow and minimize collisions by controlling the vehicle's motion dynamics (i.e., speed and acceleration), and (ii) enhance the ultra-reliable low-latency communication (URLLC) while minimizing handoffs (HOs). We cast this problem as a multi-objective Markov Decision Process (MOMDP) and develop solutions for both predefined and unknown preferences of the conflicting objectives. Specifically, deep-Q-network and double deep-Q-network-based solutions are developed first that consider scalarizing the transportation and telecommunication rewards using predefined preferences. We then develop a novel envelope MORL solution which develop policies that address multiple objectives with unknown preferences to the agent. While this approach reduces reliance on scalar rewards, policy effectiveness varying with different preferences is a challenge. To address this, we apply a generalized version of the Bellman equation and optimize the convex envelope of multi-objective Q values to learn a unified parametric representation capable of generating optimal policies across all possible preference configurations. Following an initial learning phase, our agent can execute optimal policies under any specified preference or infer preferences from minimal data samples. Numerical results validate the efficacy of the envelope-based MORL solution and demonstrate interesting insights related to the inter-dependency of vehicle motion dynamics, HOs, and the communication data rate. The proposed policies enable autonomous vehicles to adopt safe driving behaviors with improved connectivity.

This article introduces a new approach to address the spectrum scarcity challenge in 6G networks by implementing the enhanced licensed shared access (ELSA) framework. Our proposed auction mechanism aims to ensure fairness in spectrum allocation to mobile network operators (MNOs) through a novel weighted auction called the fair Vickery-Clarke-Groves (FVCG) mechanism. Through comparison with traditional methods, the study demonstrates that the proposed auction method improves fairness significantly. We suggest using spectrum sensing and integrating UAV-based networks to enhance efficiency of the LSA system. This research employs two methods to solve the problem. We first propose a novel greedy algorithm, named market share-based weighted greedy algorithm (MSWGA) to achieve better fairness compared to the traditional auction methods and as the second approach, we exploit deep reinforcement learning (DRL) algorithms, to optimize the auction policy and demonstrate its superiority over other methods. Simulation results show that the deep deterministic policy gradient (DDPG) method performs superior to soft actor critic (SAC), MSWGA, and greedy methods. Moreover, a significant improvement is observed in fairness index compared to the traditional greedy auction methods. This improvement is as high as about 27% and 35% when deploying the MSWGA and DDPG methods, respectively.

In this paper, we propose an address-dependent divided-bit-line (BL) NAND Flash memory for reduction in read latency. Energy in bit-line path can be also reduced with this design. A single switching MOSFET is added per BL. When a memory cell located closer to the sense amplifier is selected for read, the switch gets turned off. The sense amplifier sees shorter BL with smaller parasitic capacitance and resistance, resulting in lower latency and energy in BL path. When a memory cell located farther from the sense amplifier is selected for read, the switch gets turned on. The sense amplifier sees the original bit-line when the on-resistance of the switch is sufficiently low in comparison with the BL resistance. The averaged BL access time and energy can be minimized analytically when the switch is placed at xSW of 0.58 and 0.50, respectively, where xSW indicates the switch location (xSW = 0 at the closest to the sense amplifier and xSW = 1 at the farthest from the sense amplifier), assuming that the memory cells are accessed in random. With the proposed design, the average read latency and energy can be reduced to 62 % and 75 %, respectively. The effectiveness of the proposed design was validated with SPICE. This paper also investigates 1) the case where hot memory cells are placed in the closer side whereas cold cells are in the farther side and 2) the case where more switches are built in every BL. In this paper, NAND Flash memory is considered to quantifying the effectiveness of the proposed design on read latency and energy.

In order to achieve the ubiquitous connectivity expected by 6G wireless networks, in this paper, an artificial intelligence (AI) framework is proposed for integrating intelligent omni-surface (IOS) into the cell-free (CF) network while leveraging millimeter wave frequency bands. The proposed AI framework allows the wireless network to allocate the necessary power for beamforming from the required base stations (BSs) to serve the users positioned on both sides of the IOS, thereby extending the wireless system's coverage area. An optimization problem is formulated to maximize the achievable rates of the users, thereby maximizing the signal-to-interference-plus-noise ratio (SINR) while saving power to support the maximum number of users in the proposed CF network. To solve the formulated NP-hard problem, a novel AI-based framework is proposed that allocates power to users on both sides of the IOS, enabling fulldimensional coverage. In particular, a deep deterministic policy gradient (DDPG)-based deep reinforcement learning approach is employed to allocate the required power for beamforming from the necessary BSs in the CF network, effectively serving users on both sides of the IOS. Simulation results show that the proposed DDPG-based framework outperforms state-of-the-art baselines such as advantage actor-critic and deep Q-network methods, achieving cumulative SINR improvements ranging from 5.21 dB to 5.23 dB and 6.43 dB to 6.96 dB, and cumulative achievable rate enhancements ranging from 5.45 bps/Hz to 5.47 bps/Hz and 6.67 bps/Hz to 7.20 bps/Hz, correspondingly, considering users only on one side and on both sides of the IOS.

A multiple scattering model for passive radiative transfer (RT) in vegetation that accounts for the vertical profile of the plant structure is developed, offering advancements over the commonly-used single-layer uniform scattering models prevalent in the vegetated land surface microwave remote sensing. The proposed model takes into account the complexities of the canopy morphology with vertical heterogeneity, enabling the representation of overlapping vegetation species applicable to diverse plant types and growth stages. Additionally, it serves as a valuable tool for understanding the influence of the vegetation vertical structure on the microwave brightness temperatures. The model is constructed based on high-order solutions to the RT equations, obtained through a numerical iterative approach with an efficient interpolation scheme for algorithm acceleration. This methodology facilitates the accurate distinction of the contributions to the brightness temperature from each scattering order and scattering mechanism, ensuring a comprehensive consideration of multiple scattering effects within various vegetated scenarios. The model is validated using the SMAPVEX12 L-band forest data set, encompassing a wide range of soil moisture variations. Comparisons are made between the brightness temperatures simulated by the newly developed multiple-scattering model with a continuous profile or layered profile and those obtained from a uniform single-layer model. Results demonstrate significant improvements in the multi-layered or the continuously profiled model, showing improved agreement with the measured brightness temperatures. Furthermore, the proposed model is parameterized by matching the high-order solutions to the RT equation to the widely adopted reduced order albedo-tau formalism. The resulting equivalent parameters are linked to the geometries and the electromagnetic properties of the vegetation layer, while also incorporating the effects of multiple scattering. Comparative analysis of the equivalent parameters derived from the layered model and those derived from the single-layer model reveals that the vertical heterogeneity of the vegetation structure has a notable influence on the effective scattering albedo and it yields a value more consistent with the albedo as chosen in the SMAP/SMOS inversion algorithms. Meanwhile, the impact of the vegetation vertical profile on the effective optical thickness and the effective transmissivity of the vegetation layer is weak.These insights are essential for the retrieval of soil moisture and vegetation characteristics including the plant vertical structures in microwave remote sensing.

Emotions arise from a complex interplay of various factors, including conscious experience, physiological processes, and contextual elements. Although emotions are inherently dynamic processes, this aspect is oftentimes neglected in experimental protocols. In this study, we employed dynamical systems theory to investigate the time-varying self-assessed emotion ratings. We used the continuous ratings of the publicly available CASE dataset, in which thirty individuals rated their level of arousal and valence while watching videos designed to evoke four different emotions. Firstly, we analyzed the univariate dynamics by reconstructing the phase space from the arousal and valence series separately and quantified their regularity and spatial complexity by using three metrics: Fuzzy, Sample, and Distribution Entropy. Then, we combined the arousal and valence series by proposing a novel index, the Multichannel Distribution Entropy (MDistEn), to estimate the complexity of the bivariate phase space. By coupling the two dimensions, we found that MDistEn resulted as an effective marker of fear, showing patterns statistically different from all of the other stimuli (p-value≤0.001). These findings support the investigation of the time-varying dynamics of annotated emotion ratings, as a promising pathway to discriminate the onset of fear-related pathological states.

Large Language Models (LLMs) have immense potential to transform the telecommunications industry. They could help professionals understand complex standards, generate code, and accelerate development. However, traditional LLMs struggle with the precision and source verification essential for telecom work. To address this, specialized LLM-based solutions tailored to telecommunication standards are needed. Retrieval-augmented generation (RAG) offers a way to create precise, fact-based answers. This paper proposes TelecomRAG, a framework for a Telecommunication Standards Assistant that provides accurate, detailed, and verifiable responses. Our implementation, using a knowledge base built from 3GPP Release 18 specification documents, demonstrates how this assistant surpasses generic LLMs, offering superior accuracy, technical depth, and verifiability, and thus significant value to the telecommunications field.

Heart disease has been the leading cause of mortality globally. The necessity for quick access to trustworthy, dependable, and practical processes for early diagnosis and disease management pertains to numerous risk factors for heart disease. In the current global environment, detecting heart disease through early-onset manifestations is challenging. This has the potential to be fatal if not stopped in time. In isolated, semiurban, or rural locations without access to heart specialists, accurate risk prediction and analysis may be essential for the early detection of cardiac issues. Artificial Intelligence (AI) and robotics are currently used in medical research. This addresses the urgent need for better ways to find, diagnose, and treat heart disease. To close the gap between theory and reality, we offer a dataset on cardiovascular disease that has been carefully put together. The variables in the dataset are age, gender, subtypes, symptoms, risk factors, and result variables that can be either 1 or 0. The

In the past decades when capacitive loads were less common. As a result, electrical networks only experienced electrical wave distortions on inductive loads, such as industrial pumps, refrigeration units, injection compressors, and electric motors; phase angle issues in the power triangle accounted for the majority of these issues. The classic, adding power factor capacitors were seem to be capable of solving majority of such issues. [1-3]. However, harmonic distortions on capacitive loads were later produced by excessive use of computers, energy-saving light bulbs, mobile chargers, and routers; These distortions had a direct influence on transformer stations and significantly affected electrical network performance. At present times, the public and private electric power generation and distribution companies around the world have recently become aware of these problems and have imposed heavy fines on this type of due to the fact that it reduces the efficiency of state-level distribution stations while simultaneously undermining the principle of equitable power distribution.

This paper proposes a method for modeling the dynamic motion of the fabric using the oriented bounding box (OBB) and applies it to the collision avoidance between the fabric and the obstacle. The dynamic motion of the fabric cannot be modeled accurately because the fabric motion and deformation are affected by many hard-to-estimate factors. In the proposed method, the fabric state is represented by the OBB instead of a detailed fabric model. The fabric motion is represented by a time series of OBBs and modeled by the proposed OBB-LSTM. Using the OBB-LSTM, a simple dynamic collision avoidance method is designed to plan a robot endpoint trajectory that connects the start point and the goal point without collision between the fabric attached to the robot end-effector and the obstacle. The effectiveness of the proposed method is demonstrated in collision avoidance experiments under different obstacle settings using a real robot system.

Radio map (RM) is a promising technology that can obtain pathloss based on only location, which is significant for 6G network applications to reduce the communication costs for pathloss estimation. However, the construction of RM in traditional is either computationally intensive or depends on costly sampling-based pathloss measurements. Although the neural network (NN)-based method can efficiently construct the RM without sampling, its performance is still suboptimal. This is primarily due to the misalignment between the generative characteristics of the RM construction problem and the discrimination modeling exploited by existing NN-based methods. Thus, to enhance RM construction performance, in this paper, the sampling-free RM construction is modeled as a conditional generative problem, where a denoised diffusion-based method, named RadioDiff, is proposed to achieve high-quality RM construction. In addition, to enhance the diffusion model's capability of extracting features from dynamic environments, an attention U-Net with an adaptive fast Fourier transform module is employed as the backbone network to improve the dynamic environmental features extracting capability. Meanwhile, the decoupled diffusion model is utilized to further enhance the construction performance of RMs. Moreover, a comprehensive theoretical analysis of why the RM construction is a generative problem is provided for the first time, from both perspectives of data features and NN training methods. Experimental results show that the proposed RadioDiff achieves state-of-the-art performance in all three metrics of accuracy, structural similarity, and peak signal-to-noise ratio. The code is available at https://github.com/UNIC-Lab/RadioDiff.

The stress dependence of the magnetic properties of non-oriented Fe-Si steel sheets has been investigated by measurement and analysis of hysteresis loop, magnetization curve, and energy losses taken at different peak polarization values Jp (0.5 T – 1.5 T) between DC and f = 400 Hz. The salient feature of the material response to the stress lies in the monotonic deterioration of the soft magnetic properties, across the whole (Jp - f) domain, on passing from the maximum tensile stress (σ = +30 MPa) to the maximum compression (σ = -30 MPa). This is understood in terms of stress-induced redistribution of the domains between easy axes, making magnetic hardening by compression directly related to unfavorably directed domains and 90° domain-wallmediated magnetization transitions. The loss decomposition is carried out across the whole investigated frequency range, taking into account the skin effect at the highest frequencies. Quasi-static and dynamic losses follow a same trend with σ, both monotonically increasing on passing from the tensile to the compressive stress limits, according to the theoretically expected relationship existing between the hysteresis and the excess loss components. The latter is shown to identify the correlation regions where the magnetization is reversed of size comparable with the average grain size and loosely following the dependence of the loss figure on the applied stress.

Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an established treatment for motor impairment due to Parkinson's disease (PD) progression. While treated subjects mostly experience significant amelioration of symptoms, some still report adverse effects. In particular, changes in gait patterns due to the electrical stimulation have shown mixed results across studies, with overall gait velocity improvement described as the core positive outcome. This retrospective study investigates changes in the gait parameters of 50 PD patients before and 6 months after STN-DBS, by exploiting a purely data-driven approach. First, unsupervised learning identifies clusters of subjects with similar variations in the gait parameters after STN-DBS. This analysis highlights two dominant clusters (Silhouette score: 0.45, Dunn index: 0.18), with one of them associated to a worsening in walking. Then, supervised machine learning models (i.e., Support Vector Machine and Ensemble Boosting models) are trained using pre-surgery gait parameters, clinical scores, and demographic information to predict the two gait change clusters. In a Leave-One-Subject-Out validation, the best model achieves balanced accuracy over 82%, denoting moderate predictability of both clusters. Moreover, feature importance analysis reveals the variability in the step width and in the step length asymmetry during the preoperative gait test as promising biomarkers to predict gait response to STN-DBS.

Dynamic Behavioral Profiling (DBP) presents a novel approach for enhancing ransomware detection through continuous, adaptive monitoring of system behaviors, effectively addressing limitations inherent in static and heuristic-based methods. DBP operates through a multi-stage system architecture, enabling real-time analysis of deviations from baseline behaviors, which are indicative of ransomware activities across various operational phases. Employing techniques such as adaptive thresholding, entropy-based analysis, and time-series modeling, DBP identifies anomalies associated with encryption processes, high-frequency file modifications, and system anomalies linked to ransomware, providing a robust mechanism for early threat detection. Extensive evaluations of DBP reveal significant improvements in detection accuracy, particularly against polymorphic variants, achieving a high level of resilience in identifying unknown threats that evade signature-based methods. Furthermore, DBP demonstrates low false-positive rates, achieving a balance between sensitivity to ransomware indicators and specificity for benign anomalies. By managing computational overhead and optimizing resource consumption across different network conditions, DBP maintains operational efficiency suitable for largescale implementation in diverse cybersecurity environments. Through an analysis of DBP's performance metrics-detection accuracy, false positives, latency, and resource utilization-the results substantiate DBP's applicability as a scalable, effective solution in mitigating ransomware threats within complex and dynamic digital infrastructures.

We present an automatic procedure to fully design a metasurface antenna, including the feeding structure. This feature is achieved by performing preliminary full-wave simulations of the feeding structure and by properly modelling PEC regions inside a current-based numerical design method. The synthesized metasurface antennas are implemented and simulated with a fullwave commercial solver, showing excellent agreement with the predicted performances.

This paper comments on a recently published TPAMI paper presenting an iris recognition algorithm. While the approach is intriguing, we can identify several inconsistencies and errors in the exposition. Additionally, their comparison with the state-of-the-art methods lacks fairness. I take this opportunity to clarify and underline these errors, aiming to assist fellow researchers like me who are interested in advancing biometrics research.