Electronics

Value investing remains grounded in intrinsic value estimation, margin-of-safety reasoning, and disciplined fundamental analysis, but its practical execution is increasingly constrained by the scale, heterogeneity, and velocity of modern financial information. Recent advances in artificial intelligence (AI), particularly large language models and automated information-extraction systems, create new opportunities to accelerate financial analysis; however, their outputs remain probabilistic, context-dependent, and potentially error-prone, making governance and verification essential. This article proposes an AI-assisted value investing framework that integrates automated extraction, valuation modeling, explainability, and human-in-the-loop (HITL) supervision into a unified decision-support architecture. The framework is organized into three layers: (i) a data layer for traceable extraction and normalization of structured and unstructured financial information; (ii) a modeling layer for automated key performance indicator (KPI) computation, forecasting support, and discounted cash flow (DCF) valuation; and (iii) an explainability and governance layer for traceability, verification, model-risk control, and analyst oversight. A central contribution of the paper is the operational characterization of prompt literacy as a determinant of analytical reliability, showing that structured, context-aware prompts materially affect extraction correctness, usability, and verification effort. The framework is evaluated through a case study using Rivanna AI on three large U.S. beverage firms—namely, The Coca-Cola Company, PepsiCo, and Keurig Dr Pepper—selected as a controllead, information-rich setting for comparative analysis. The results indicate that the proposed workflow can reduce end-to-end analysis time from approximately 25–40 h in a traditional manual process to approximately 8–12 h in an AI-assisted setting, including citation/source verification, unit and period reconciliation, and review of key valuation assumptions. Rather than eliminating analyst effort, AI shifts it from manual information processing toward verification, adjudication, and interpretation. Overall, the findings position AI not as an autonomous decision-maker, but as a governed reasoning accelerator whose effectiveness depends on structured human guidance, traceability, and disciplined validation. In value investing, a discipline traditionally grounded in labor-intensive fundamental analysis and disciplined intrinsic value estimation, AI introduces the potential to scale analytical coverage and accelerate evidence synthesis. However, AI systems in financial contexts are probabilistic, context-sensitive, and inherently dependent on human interaction, raising critical questions about reliability, governance, and operational integration. This article proposes a structured framework for AI-driven value investing that preserves the foundational principles of intrinsic value, margin of safety, and economic reasoning, while redesigning the analytical workflow through automation, explainability, and human-in-the-loop (HITL) supervision. The proposed architecture integrates three layers: (i) an AI-enabled data layer for traceable extraction and normalization of structured and unstructured financial information; (ii) a modeling and valuation layer combining automated KPI computation, machine learning forecasting, and discounted cash flow (DCF) valuation; and (iii) an explainability and governance layer ensuring traceability, verification, and model risk control. A central contribution of this work is the operational characterization of prompt literacy, namely the ability to formulate structured, context-aware requests to AI systems, as a critical determinant of system reliability and analytical correctness. Through a focused case study using an AI-assisted analysis platform (Rivanna AI) on three U.S. beverage firms, we provide evidence that structured prompt formulation can improve extraction consistency, reduce verification overhead, and increase workflow efficiency in a human-supervised setting. In this setting, analysis time decreased from a manual range of approximately 25–40 h to 8–12 h with AI assistance and HITL validation, while preserving traceability and decision accountability. The reported hour savings should be interpreted as conservative estimates from the initial deployment phase; additional efficiency gains are expected as operational maturity increases, driven by learning-economy effects. The findings position AI not as an autonomous decision-maker but as a probabilistic reasoning accelerator whose effectiveness depends on structured human guidance, verification discipline, and prompt-driven interaction. These results redefine the role of the financial analyst from manual data processor to reasoning architect, responsible for designing, guiding, and validating AI-assisted analytical workflows. Full article

Existing model predictive control (MPC) schemes based on virtual voltage vectors (VVVs) for dual three-phase permanent magnet synchronous motors (DT-PMSMs) typically employ a limited set of voltage vectors, which restricts further improvement in steady-state performance. Moreover, the design of switching sequences lacks systematic consideration, focusing mainly on harmonic current suppression while neglecting practical engineering challenges associated with software-layer implementation. This paper proposes an optimized model predictive torque control (MPTC) method for DT-PMSMs using an expanded voltage vector set. First, to enhance steady-state performance, an extended control set of voltage vectors is designed, which introduces not only new directions but also two distinct voltage amplitude levels, resulting in a total of 48 voltage vectors. Second, to alleviate the significant computational burden caused by traversing the extended set for prediction, a candidate voltage vector selection table is constructed based on the sector position of the stator flux linkage and the requirements for torque and flux adjustment. This approach reduces the computational load to only 10 predictive calculations per control cycle, avoiding exhaustive traversal of the extended set. Furthermore, for all VVVs in the control set, a switching sequence combining active voltage vectors with zero vectors is designed to facilitate straightforward digital implementation. Finally, experimental results are provided to validate the effectiveness of the proposed method. Full article

In modern power grids, the detection of electricity theft is crucial for ensuring the safety and stability of the power system and reducing revenue losses. Current electricity theft detection methods do not take into account the spectral space features contained in the original time series data sequences, and thus are unable to adapt to the complex and ever-changing scenarios of electricity theft. This paper proposes an electricity theft detection model TSFPTD that integrates time series signals and their synchronous spectral features. The multi-modal model constructs the synchronous spectral modal space corresponding to the time series data through a deep wavelet network. It is found that this newly generated synchronous modal space contains implicit features that cannot be revealed by the original time series data. The explicit features of the time series data space and the implicit features of the synchronous spectral modal space are fused and aligned for the detection of power theft behavior. The performance verification experiment of the model was completed on the real dataset released by State Grid Corporation of China. The electricity theft detection accuracy of the TSFPTD model reached over 96.83%, and its performance is superior to the existing electricity theft detection methods. Full article

This study investigates the impact of non-ideal wordline sidewall angles caused by photoresist profile variation during the wordline etching process in DRAMs employing a buried-channel array transistor (BCAT) structure. Using Technology Computer-Aided Design (TCAD), a two-dimensional (2D) BCAT-based DRAM cell was modeled to analyze the resulting variations in device characteristics as well as write and hold operations. The simulation results show that increased etch slope angles lead to degradation in device performance, including failure to meet the read pass/fail criterion and data retention during the 300 ms hold interval. To mitigate these issues, we inserted a buried oxide (BOX) layer beneath the active wordline (AWL). The incorporation of the BOX layer effectively improved overall device robustness and reduced the degradation induced by non-ideal etch slopes. Full article

In the field of intelligent inspection, high-definition video data collected by quadruped robot dogs face severe transmission and storage constraints. Although existing advanced lossy video coding standards can significantly improve compression efficiency, they inevitably introduce severe compression artifacts in low-bit-rate scenarios. To address this issue, this paper proposes a video decoding quality enhancement network named Video Quality Restoration Network (VQRNet), based on a dual-stream architecture. Specifically, the Local Feature Extraction component incorporates a Progressive Feature Fusion Module (PFFM) with a four-stage progressive structure. By integrating reparameterized convolution and attention mechanisms, PFFM focuses on capturing high-frequency texture details to repair small-scale distortions. Simultaneously, the Multi-Scale Lightweight Spatial Attention Module (MLSA) performs spatial feature recalibration, leveraging multi-scale convolution to adaptively identify and enhance key spatial regions, specifically addressing multi-scale distortion. In the Global Feature Extraction component, the State-Space Attention Module (SSAM) combines State-Space Models (SSMs) with attention mechanisms to capture long-range dependencies and contextual information, for large-scale distortions caused by high-intensity compression. To verify the performance of the proposed algorithm, a dedicated dataset comprising 20 real-world video sequences captured by quadruped robot dogs (partitioned into 15 training and 5 testing sequences) was constructed, and the VTM 23.4 reference software was employed to simulate compression degradation using four quantization parameters (QP 30, 35, 40, and 45). Experimental results demonstrate that VQRNet outperforms state-of-the-art quality enhancement methods in terms of core metrics, including PSNR and SSIM, specifically including MIRNet, NAFNet, TRRHA, and CTNet. In the QP = 30 scenario, VQRNet achieves an average PSNR of 40.33 dB, a significant improvement of 3.32 dB over the VTM 23.4 baseline (37.01 dB), while demonstrating significant advantages in computational complexity and parameter efficiency—requiring only 5.27 G FLOPs and 1.40 M parameters, with an average inference latency of only 11.82 ms per 128 × 128 patch. This work provides robust technical support for the efficient video perception of quadruped robot dogs. Full article

This work presents the design of a compact, omnidirectional, and circular polarized antenna for first-person-view (FPV) drones. An FPV drone is an aircraft used in sporting activities, where the pilot, equipped with a visor, controls the drone’s activities. Due to the high velocity of FPV and the required low reaction time, the radio connection must be safe and accurate. An omnidirectional antenna with circular polarization, high gain, low weight, and good mechanical robustness is mandatory for FPV design. The proposed antenna is designed and fabricated on a ceramic substrate operating at 5.8 GHz. The prototypes tested ensure that the proposed design is a potential candidate for FPV applications. A set of antenna prototypes has been designed, fabricated, and assessed numerically and experimentally, demonstrating the capabilities and potential of the proposed antenna design. Full article

The applications of machine learning (ML) in power electronics are expanding with time, providing effective tools that reduce design complexity and enhance predictive accuracy. In high-power semiconductor devices, such as thyristors and high-power diodes, electrical parameters may directly influence electro-thermal behavior, reliability, and overall device performance. Consequently, accurate prediction and classification of average current are critical to ensure optimal device selection, optimize design, and assess performance. In this article, a comprehensive dataset based on data from industrial thyristors capturing electrical and structural parameters relevant to current handling capability is utilized to classify and predict the average current of devices. Additionally, Shapley additive explanation (SHAP) analysis has been performed, highlighting the importance of crucial parameters and identifying the impact of each parameter on model output. Moreover, several ML models, including artificial neural networks (ANNs), support vector machines (SVMs), ensembles, and Gaussian process regression (GPR) are implemented and then compared to assess their performance. The proposed methodology provides manufacturers and designers with data-driven design tools that enhance reliability assessments and facilitate optimized device selection for high-power applications. Full article

Whether a counterexample is genuine or spurious fundamentally influences the effectiveness and completeness of loop invariant inference, which is a core component of automated program verification. However, reliably determining the validity of a counterexample remains a challenging task. In this paper, we present a systematic evaluation of large language models (LLMs) on this problem. We construct a benchmark of program states that serve as counterexamples, categorized into three representative types: (i) pre-states of inductive counterexamples derived from LLM-proposed invariants and (ii–iii) boundary states derived from correct inductive invariants, where the states themselves either violate (ii) or satisfy (iii) the program’s precondition. Ground-truth labels are established using a state-of-the-art program verifier. We evaluate multiple LLMs under diverse prompting strategies. Our results show that LLMs perform well on the first two types of counterexamples in the benchmark but poorly on the third. Moreover, LLMs are substantially more accurate in classifying spurious counterexamples than genuine ones. These findings offer valuable guidance for future research on LLM-assisted loop invariant inference. Full article

In the context of the cross-disciplinary integration of data science and archival management, archival openness auditing stands as a critical process for public information access but faces challenges in processing long texts with sparse core information. To address this, this paper proposes an Assisted Archival Auditing Model (ALC-MCFN) based on deep semantic understanding and decision transparency. The model aims to leverage intelligent analytics to optimize the decision-making process of archival openness. Regarding deep semantic understanding, a semantic-aware dynamic truncation mechanism is first employed to effectively remove redundancy while preserving key logical structures. Subsequently, by fusing global, local, and logical semantic features extracted by BERT, TextCNN, and TextGCN, the model overcomes the limitations of single-view feature representation. Furthermore, to address the “black box” issue of deep learning in compliance auditing, the SHAP method is introduced to provide post hoc interpretability. By visualizing the contribution of key textual features to the auditing results, the model enhances the transparency and trustworthiness of decision-making. Experimental results demonstrate that ALC-MCFN outperforms mainstream baseline models, with a 77.21% F1-score on the self-built archival domain OParchives dataset (1.15 percentage points higher than the BERT baseline), providing robust data science support for risk control and efficiency improvement in intelligent archival management. Full article

Personal protective equipment (PPE) detection requires architectures balancing accuracy and computational efficiency for real-time safety monitoring. This study presents the first comprehensive benchmarking and systematic comparative evaluation of YOLO26 (released January 2026) against YOLOv11 across diverse PPE detection scenarios, with the primary goal of providing evidence-based deployment guidelines rather than proposing a new architecture. A total of 30 model configurations were evaluated across 5 model scales, 2 architectures, and 3 datasets under rigorously controlled conditions using identical hardware (NVIDIA A100-80GB), hyperparameters, and COCO-pretrained initialization across CHV (133 images, 6 classes), SHEL5K (1000 images, 3 classes), and SH17 (1620 images, 17 classes) datasets. Results reveal consistent scale-dependent patterns: YOLOv11 excels at nano and small scales across all datasets, while YOLO26 achieves superiority at large and X-Large scales with advantages ranging from 1.3 to 3.1 percent mAP50–95. An exploratory negative correlation ($r=-0.98$, $n=3$) between dataset size and YOLO26 performance advantage was observed; given the small number of data points, this should be interpreted as a preliminary finding warranting further investigation rather than a statistically robust relationship. YOLOv11 provides 15 to 20 percent faster training and 9 to 18 percent faster inference, while YOLO26 demonstrates superior parameter efficiency (0.0237 vs. 0.0233 mAP per million parameters). Findings provide evidence-based, conditional deployment guidance for industrial safety applications: YOLOv11 is recommended for latency-constrained edge scenarios at nano/small scales, while YOLO26 is preferred for accuracy-critical applications at large/X-Large scales with limited training data. These recommendations address key challenges in few-shot learning, small object detection, and data-scarce deployment regimes, and are intended as practical guidelines rather than claims of general architectural superiority. Full article

To address the stringent cost and performance requirements of commercial Satellite-on-the-Move (SOTM) terminals, we propose a Genetic Algorithm (GA)-based design for a millimeter-wave Phased-Array-Fed Lens (PAFL). This antenna is specifically intended to be the electronic scanning module within a hybrid mechanical–electronic steering architecture. In this hybrid configuration, wide-angle coverage is handled by mechanical positioning, while the PAFL is responsible for high-precision fine tracking and jitter compensation within a critical ±15° field of view. By utilizing a small-scale active array to illuminate a large passive planar lens, this design significantly reduces hardware costs compared to full phased arrays. To mitigate phase aberrations and gain loss inherent in such compact focal-to-diameter (F/D) systems, a two-stage co-optimization strategy is introduced. It globally optimizes the lens phase distribution and subsequently synthesizes feed excitation codebooks to dynamically correct residual errors. A Ka-band prototype comprising an 8 × 8 active feed and a 28 × 28 transmitarray lens was fabricated. Measurements demonstrated stable scanning within the required ±15° range with a gain variation of less than 1.5 dB, achieving a peak directivity of 28.9 dBi and sidelobe levels below −12 dB. Full article

High-quality text corpora are essential for knowledge graph construction and domain-specific large model pre-training in technology-intensive industries, with the steel metallurgy sector serving as a representative case. However, many industrial documents remain in scanned or PDF formats, where general-purpose Optical Character Recognition (OCR) systems exhibit systematic errors when recognizing Chinese metallurgical documents. In particular, visually similar Chinese characters that differ by only minor strokes are frequently confused, leading to severe degradation of text reliability and cascading errors in downstream knowledge extraction. This paper proposes FeOCR, a general-purpose domain-adaptive framework for machine-printed Chinese characters, which is specifically evaluated within the context of the steel metallurgy industry. The framework integrates visual character disambiguation with context-aware semantic correction. We first construct a metallurgy-specific OCR dataset emphasizing high-frequency confusable Chinese word pairs and enhance data diversity through font perturbation and noise synthesis. Parameter-efficient fine-tuning (LoRA) is then applied to adapt a general OCR model to domain-specific visual patterns. Furthermore, a Large Language Model-based correction module performs semantic refinement of residual errors under domain lexical constraints. Experiments demonstrate significant reductions in character and word error rates, especially for confusable technical terms, providing a reliable foundation for industrial Chinese document digitization. Full article

Full-duplex (FD) technology holds great promise for enhancing the spectral efficiency of Mobile Ad Hoc Networks (MANETs) and Wireless Sensor Networks (WSNs). However, the practical performance gain of FD over Half-Duplex (HD) is highly sensitive to the dynamic nature of traffic loads and residual self-interference. Existing Optimal Dynamic Selection Strategies (ODSS) often rely on static workload assumptions within a single time window, failing to capture long-term traffic fluctuations. Consequently, applying instantaneous switching strategies in highly bursty environments necessitates excessively frequent mode switching (e.g., the switching frequency can approach the total number of time windows), incurring prohibitive signaling overhead and unignorable MAC-layer adaptation delays. To overcome these concrete bottlenecks, this paper proposes a comprehensive traffic-aware adaptive duplex mode selection framework. First, we model the multi-scale dynamic workload using Dynamic Activated Probability in Short-term (DAPS) and Long-term (DAPL), effectively characterizing both bursty traffic (via Beta distribution) and Markov-modulated stable traffic. Second, by integrating physical layer performance analysis, we define the Break-even Workload Point (BWP) to partition traffic into Oversaturated (OZ) and Unsaturated (UZ) Workload Zones (WZs). Furthermore, to handle unknown future traffic with low complexity, we propose the Pre-scheduling Duplex selection based on the Workload zone Estimation (PDWE) algorithm. PDWE leverages a Hidden Markov Model (HMM) combined with a Rollout algorithm to estimate hidden traffic states and adaptively pre-schedule duplex modes. Simulation results demonstrate that the proposed strategy achieves near-optimal throughput (approximately 91% of the ideal ODSS) while reducing the duplex switching frequency by two orders of magnitude compared to instantaneous switching strategies. This approach offers a robust cross-layer solution for next-generation self-organizing networks. Full article

The digital transformation of the tourism industry faces a dual challenge: the fragmentation of data across platforms and the lack of immersive “try-before-you-buy” experiences. While Large Language Models (LLMs) have revolutionized information synthesis, they typically lack real-time visual verification capabilities. This paper proposes a novel, multimodal AI Agent architecture that integrates advanced natural language planning with photorealistic 3D visualization. We present a system where a conversational agent, powered by Gemini 2.5 Flash, orchestrates a suite of dynamic tools to build structured travel itineraries (flights, hotels, activities) while simultaneously deploying a neural rendering engine. This engine utilizes a modular Structure-from-Motion (SfM) pipeline feeding into 3D Gaussian Splatting (3DGS) to render navigable, high-fidelity digital twins of hotel facilities directly within the chat interface. Positioned as a Technology Readiness Level 4 (TRL 4) proof of concept (PoC), this work demonstrates the technical feasibility of the multimodal integration between conversational logic and automated visual synthesis. The results demonstrate the technical feasibility of a pipeline that dynamically binds LLM inference to 3D spatial data, providing a foundation for high-fidelity, interactive travel consultancy. Full article

The emergence of chiplet-based architectures represents a paradigm shift in post-Moore’s Law computing systems, offering substantial cost and yield advantages through functional disaggregation. However, the heterogeneity of inter-chiplet communication introduces unique performance challenges that conventional partitioning strategies fail to address. In this work, the ways in which poor workload partitioning degrades communication performance in chiplet-based systems are comprehensively characterized. We demonstrate, through a detailed experimental analysis, that suboptimal workload partitioning can increase inter-chiplet communication latency by up to a factor of 10 and inflate network congestion beyond sustainable levels as systems scale. Our findings show that optimized partitioning strategies can achieve an 87.4% reduction in inter-chiplet traffic, improve system throughput by a factor of 8.75, and enhance energy efficiency by a factor of 10.3 compared to naive partitioning approaches. We further characterize how these effects scale with system size, revealing that the communication overhead can consume 85% of the execution time in poorly partitioned 16-chiplet systems, compared to only 35% in well-partitioned configurations. This work provides essential insights into the communication-aware design space of chiplet systems and validates the critical importance of sophisticated workload partitioning algorithms. Full article

Insulated-gate bipolar transistor (IGBT) power modules suffer efficiency degradation at elevated operating junction temperatures. The thermal sensitivity of the collector–emitter saturation voltage (V_CEsat) induces thermal stress imbalance, constraining system efficiency and reliability. A multi-resistor cascade network model for carrier-storage trench-gate IGBTs (CS-IGBTs) is established. The simulation results agree with the measurements within 10% error. The model decomposes the temperature coefficient contributions of individual structural regions. Analysis reveals that the drift region resistance dominates the V_CEsat temperature coefficient. Based on this finding, a co-doping strategy is proposed through simultaneously increasing the doping concentration in the carrier-storage layer and P⁺ collector. This approach reduces the temperature sensitivity of carrier mobility in the drift region, thereby optimizing V_CEsat’s temperature sensitivity. For the fabricated 1200 V/40 A CS-IGBT, the V_CEsat temperature coefficient decreases from 2.38 mV/K to 1.76 mV/K over 300 K to 450 K, which represents a 25.4% reduction. The total switching loss at 450 K decreases from 9.32 mJ to 8.70 mJ, achieving a 6.7% improvement. This device-level optimization suppresses V_CEsat’s temperature sensitivity and switching losses, enhancing efficiency in high-temperature power module applications. Full article

Indoor scene reconstruction remains challenging due to the prevalence of low-texture regions such as walls, floors, and ceilings, where weak photometric signals hinder accurate geometric recovery. While 3D Gaussian Splatting (3DGS) achieves impressive novel view synthesis, existing methods struggle with geometric accuracy in textureless areas due to uniform treatment of scene regions. We propose a texture-complexity-based 3D Gaussian Splatting strategy that leverages geometric priors for high-fidelity indoor reconstruction. Our method extracts planar priors through Manhattan frame alignment and refines them with Segment Anything Model (SAM) masks, enabling texture-aware initialization: planar priors guide Gaussian placement in low-texture regions, while dense feature matching ensures accurate initialization in high-detail areas. During optimization, geometric regularization through depth-plane loss, normal-surface loss, and normal-consistency loss maintains structural integrity. Evaluations on ScanNet++, MuSHRoom, and Replica datasets demonstrate state-of-the-art performance, with training completed in under 1 h. Our approach balances geometric accuracy with photometric fidelity, providing a practical solution for high-fidelity indoor mesh extraction from Gaussian representations. Full article