A software system undergoes many code changes, which may affect code quality and maintainability. Refactoring techniques are a common strategy for restructuring source code and improving internal architecture. However, refactoring activities are limited to a catalog of well-known modifications, and we intend to investigate modifications to the source code, looking for patterns that affect its internal quality. In this work, we present a process for identifying code improvements and then learning from the modifications. This process extracts syntactic patterns of code changes and classifies them based on their impact on software maintainability. We executed the classification process on 132,819 code changes from seven GitHub repositories, obtaining 2405 significant code change patterns. Most code change patterns involve simple modifications to structural elements that have only a slight impact on maintainability. These modifications include renaming or moving code elements, simple refactoring, and adding a new implementation. We analyzed both simple modification patterns and many modifications to identify their impact on internal source code quality. The results show how significant a subset of identified patterns is to internal source code quality, as it improves or reduces maintainability variation.

A spectrum analyzer is an essential instrument in telecommunications for observing and analyzing the power distribution of a signal across different frequencies. Traditionally, these devices are expensive and complex, limiting their accessibility. This paper presents an affordable spectrum analyzer prototype using a software-defined radio (SDR) module and a Raspberry Pi, coupled with a 10.1-inch touchscreen. Based on the HackRF One and Raspberry Pi 4B+, the system uses GNU Radio to capture, analyze, and display electromagnetic-signal spectra from 1 MHz to 6 GHz. The user-friendly interface and artificial intelligence-based voice module enable easy, accessible real-time selection of frequencies, bandwidth adjustment, and signal visualization, applicable to 5G and 6G~networks.

In conventional question answering systems, general-purpose large language models (LLMs), despite their strong capabilities in language understanding and generation, exhibit notable limitations in scenarios with stringent factuality requirements. Their outputs often lack explicit evidential grounding, making them prone to hallucinations and inconsistent responses. Moreover, LLMs do not inherently guarantee determinism when performing operations over structured data—such as aggregation, conditional filtering, and cross-field constraints—thereby undermining result consistency and reliability. To address these issues, we propose an internal-data-first framework for controllable question answering in high-risk scenarios. The framework categorizes knowledge sources into unstructured documents and structured data, enabling evidence-constrained generation via retrieval-augmented generation (RAG) and database-backed, verifiable query execution via restricted, read-only structured queries. In addition, an UNK branch is introduced as a safe degradation mechanism that triggers refusal when inputs lack sufficient evidence, exceed system capability boundaries, or fail to meet confidence requirements, thereby suppressing hallucinations and unauthorized generation. To enable controlled selection among the two execution pathways (RAG/SQL) and the safety degradation branch (UNK) at the system level, we design a learned router based on XGBoost with confidence-thresholded selective prediction, which preferentially activates UNK refusals for low-confidence or out-of-distribution inputs. We validate the proposed framework using a graduate admissions consultation system as an exemplar application, constructing both a document knowledge base and structured score tables, and conducting controlled comparisons across multiple system variants with multi-metric evaluations. Experimental results indicate that, under the current controlled evaluation setting, the proposed framework exhibits relatively stable behavior under complex query formulations and demonstrates practical engineering potential in high-risk vertical-domain question answering scenarios.

Uplink/downlink (UL/DL) decoupled access has emerged as a promising paradigm for heterogeneous cellular vehicle-to-everything (C-V2X) networks in beyond 5G (B5G) and 6G systems. In multi-operator scenarios, wireless service provider (WSP) selection becomes critical for vehicles to ensure communication quality while minimizing costs. This paper investigates the performance analysis and WSP selection problem in UL/DL decoupled access C-V2X networks. We derive tractable expressions for spectral efficiency of both UL and DL using stochastic geometry, considering three decoupled access cases where UL and DL independently associate with macro base stations (MBSs) or small base stations (SBSs). We formulate a hierarchical game framework combining evolutionary game for vehicle WSP selection and non-cooperative game for WSP bandwidth allocation. An evolutionary game algorithm is proposed to reach the equilibrium, and the uniqueness of Nash equilibrium in bandwidth allocation is proved. Extensive simulations validate the analytical results and demonstrate the convergence and stability of the proposed game framework.

Conventional fixed phase-shift strategies for parallel PV converters fail to minimize output ripple under heterogeneous input conditions, while communication-based synchronous methods incur high costs and reliability risks. Furthermore, standard global optimization algorithms like conventional Particle Swarm Optimization (PSO) suffer from slow convergence, hindering real-time application. To address these limitations, this paper proposes a communication-free distributed ripple suppression method based on an enhanced PSO with randomized phase shifting. Unlike traditional approaches, our method enables autonomous convergence without inter-unit communication. Crucially, a randomized pre-scanning mechanism narrows the search space, accelerating convergence significantly. Simulation results demonstrate that the proposed method reaches a steady state in merely 5 ms, which is 50% faster than conventional PSO (~10 ms) and eliminates communication latency. Under severe heterogeneous conditions, the technique reduces output voltage ripple to 0.66 V (a 53% reduction) compared to the unoptimized 1.21 V, vastly outperforming fixed interleaving strategies that show negligible improvement. The approach also ensures robust stability during load steps and plug-and-play operations, offering a superior low-cost and high-speed solution for distributed PV systems.

Research on methods for detecting microwave-based noncontact vibration has garnered significant attention in recent years. To simplify system complexity and reduce costs, which would enable broader application of radar technology in daily life, we propose a low-complexity, high-precision continuous-wave (CW) radar system for noncontact vibration detection. This system employs a hardware-based approach for phase comparison to extract vibration information, enabling simultaneous detection of both vibration amplitude and frequency under a CW radar architecture. In this study, we establish a phase discrimination error model to characterize the inconsistent detection sensitivity of the hardware phase comparator in different phase intervals, and we further propose a phase compensation scheme to mitigate the nonlinearity of phase discrimination and the “null-point” problem in continuous phase comparison, consequently improving the sensitivity and precision of the proposed radar system. Through loudspeaker vibration and experiments on human vital signs, the system maintains a vibration amplitude detection accuracy above 90.3% within 1.8 m while achieving respiratory rate and heartbeat rate detection accuracies of 96.34% and 98.02%, respectively.

Given the expanding scale of offshore wind power development, strict spatial constraints on offshore platforms and multi-source power coupling present operational challenges during the collaborative transmission of near-shore and far-offshore wind power through a shared corridor. To address these issues, this paper proposes a collaborative transmission scheme based on the Self-Adaption Statcom and Line-Commutation Converter (SLCC). The technical and economic characteristics of three typical topologies—Modular Multilevel Converter (MMC) onshore grid connection, MMC direct transmission, and SLCC direct transmission—are compared and analyzed. The results demonstrate the advantages of the SLCC scheme in reducing the offshore platform footprint and lowering engineering costs. Furthermore, a hierarchical collaborative control strategy is designed to mitigate the power coupling between near-shore AC wind generation and far-offshore DC wind generation at the converter bus. The bottom layer utilizes a valve-side parallel Static Var Generator (SVG) to achieve reactive power self-balance and quasi-resonant suppression of specific harmonics. In the top layer, an LCC active power-following control strategy based on instantaneous power feedback is implemented. This achieves the logical decoupling of near-shore and far-offshore wind power transmission. The effectiveness of the proposed scheme in managing wind power fluctuations, riding through AC faults, and maintaining stable operation under weak grid conditions is verified using the PSCAD/EMTDC software.

In spite of the importance of a detailed description of the filamentary current constriction of the IGBT during the turn-off operation that could lead to the device’s failure, there are to date no quantitative 3D simulation results of the filament growth and dynamic that can be compared with experimental results. In this paper we present 3D numerical simulations on the failure mode in the Unclamped Inductance Switching (UIS) test operation, extended to the full device area, which will be usefully compared with detailed experimental results on a large number of trench IGBT test samples. For the first time extended 3D dynamic electrothermal simulations of the whole die are made, to take into account both the electric and thermal effects of the filamentary conduction in avalanche mode. The onset of a filament growth condition for a current level just above the turnover voltage evaluated by the 3D simulations, and the area of the filament, obtained for the first time, are well in agreement with the quantitative data extracted by the experimental evaluations. Moreover, the thermal heating due to the filament formation is found to be quite independent from the current level, because it depends on the current density in the filament, rather than on the injected current. The delay time between the filament formation and the final failure time seen in the experimental results is verified to be due to the movement of the filament all around the chip surface in search of a cooler spot. The movement of the filament along the whole die area is verified for the first time by full area 3D electrothermal dynamic simulations, with times in agreement with the experimental delay between filament formation and final failure seen in all the failure reports.

Spiking neural networks (SNNs) support energy-efficient machine intelligence because event-driven computation and sparse activity map naturally to low-power digital hardware. In practical implementations, however, membrane states, synaptic weights, and thresholds are represented with finite-precision integer arithmetic. Quantization, clipping, and overflow can therefore alter network dynamics rather than merely approximate a higher-precision model. This paper adopts an integer-state dynamical perspective, modeling a quantized SNN with a hardware-relevant update rule as a deterministic map on a bounded integer lattice. Rather than claiming recurrence itself as a new property, we focus on how finite-precision representation and implementation semantics shape observed recurrent regimes and activity patterns. We introduce a shift-based update rule with integer-valued states and investigate its behaviour through simulation-based analysis with network sizes N=30–130, connection densities 0.1–0.9, and bit widths 1 to 16 over T = 1000 steps. The results show bounded and recurrent temporal structure with strong quantization sensitivity. The observed regimes depend heavily on the semantics of representation and the scaling choices. These findings suggest that numerical precision can act as a dynamical design variable and provide useful implications for hardware-oriented SNN design, while motivating future work on attractor analysis and FPGA/ASIC validation.

The extensive application of unmanned aerial vehicles (UAVs) in power inspection, military operations and environmental monitoring demands stronger robustness and adaptability for autonomous decision-making systems. Existing methods suffer from heavy map dependence, high computational complexity and insufficient exploration and generalization. Traditional approaches based on expert rules and planning algorithms only suit fixed scenarios and degrade severely in complex dynamic environments. To address these problems, this paper proposes a complex-scene-oriented autonomous decision-making method for UAVs (CADU). It builds a closed-loop decision chain by integrating perception, strategy and execution modules, and adopts curiosity mechanism and contrastive learning to enhance exploration and adaptability. Experimental results show that the proposed CADU achieves an average reward of 0.85, a trajectory smoothness of 0.87, a flight stability of 0.85, and a cumulative collision count of 8±1.2, which significantly outperforms DDPG, PPO and SAC baselines. It provides a reliable and efficient scheme for UAV autonomous decision-making in complex scenarios.