Large language models (LLMs) are increasingly being used in network operations (NetOps) and artificial intelli gence for IT operations (AIOps) for tasks ranging from telemetry retrieval and incident diagnosis to configuration planning and bounded remediation. As these systems acquire greater access to operational tools, a central ques tion is whether operational assurance develops commensurately with the authority granted to them. This survey examines that question through a structured, evidence-stratified review of agentic NetOps and AIOps, span ning directly evaluated LLM systems, operational mechanisms, transferred evidence, and normative guidance. We organise the field around autonomy, tool scope, evidence traces, assurance controls, evaluation, security, and governance, and introduce an operational assurance contract that links each autonomy level to permitted tools, required evidence, independent gates, execution budgets, rollout and rollback duties, and audit require ments. The synthesis indicates a capability-assurance gap: evidence is comparatively strong for read-oriented assistance and tool-grounded diagnosis, but becomes substantially less complete as systems approach configura tion change, bounded execution, and closed-loop operation. We therefore develop a workflow-level evaluation framework covering evidence quality, tool use, policy and invariant compliance, staged execution, recovery, cal ibration, cost, and human intervention, together with a threat model for prompt-borne attacks, compromised operational evidence, excessive agency, privilege boundaries, and auditability. The resulting view treats agentic NetOps and AIOps as constrained operational control, in which increasing autonomy requires correspondingly stronger evidence, independently enforced safeguards, and recovery mechanisms.
The microphysical properties of stratus cloud systems vary significantly across different regions and stages of development. This study investigates the microphysical characteristics of a postfrontal stratus cloud system with snowfall based on aircraft observations conducted on 1-2 March 2025 over Xingtai, Hebei Province, China. Distinct variability is observed, with thinner supercooled liquid clouds at the periphery and thicker clouds near the surface front, characterized by mixed-phase cloud below and ice cloud above. Clear differences are observed between the mixed-phase and ice-phase layers, as the mixed-phase layer shows higher RH and lower ice crystal number concentrations (Ni) and ice water content (IWC). Distinct vertical variations in supercooled cloud droplet properties, such as cloud droplet number concentrations (Nc), effective radius (re), and liquid water content (LWC), are also observed across different regions. Additionally, the ice crystal habit varies with altitude, with small dendritic and plate-like crystals aloft and large irregular aggregates dominating the mid- and lower layers, where small crystals possibly produced by second ice production are also observed. During the dissipating stage, decreasing RH and rising temperature within cloud correspond to reductions in Ni and IWC, along with broader particle spectra in both ice crystals and supercooled cloud droplets. These findings highlight the pronounced spatiotemporal variability of stratus cloud microphysics within the observed postfrontal system and suggest potential mechanisms in cloud evolution.
Precise joint prediction of wind and solar energy is significant for the running of renewable power grid. Nevertheless, either wind or solar energy is affected by various factors, exhibiting distinct characteristics, which pose great challenges for prediction. Therefore, this paper proposes a Temporal Decoupling and Distribution Learning Joint Prediction Model (TDDL-JP), which analyzes complex time series data by time series decomposition method and extracts spatial distribution features by combining distribution learning mechanism, aiming to achieve precise regional joint prediction of wind and photovoltaic power. A multi-level full-band decomposition module is used for high and low frequency iterative decomposition to extract timing features at different frequencies. In view of the mapping relationship between the dynamic mode of subsequences and meteorological features, a feature mode decoupling module is used to learn the meteorological feature extraction and fusion scheme of each subsequence. A cross-scale, dual-branch feature extraction module is proposed to sense the local and global spatial feature weights, enhancing predictive performance. To verify the effectiveness of the model, ablation experiments, comparative experiments, and seasonal sensitivity experiments were conducted on the research area dataset. Compared with the current optimal benchmark model, the comprehensive MAPE decreased by 12.4%, effectively improving the joint prediction accuracy of wind and solar power.
The rotating triboelectric nanogenerator (R-TENG) has emerged as a core technology in micro-energy harvesting and micro-signal detection. However, the high impedance, high-voltage and low-current output characteristics of R-TENG structures, lead to bottlenecks such as impedance mismatch, difficult topological adaptation and weak anti-interference capabilities. Under controlled bench-test conditions with 1-3 Hz mechanical excitation, customized rectification and energy storage circuits improve R-TENG energy harvesting efficiency from 60% to 70%-95%, a 10%-35% gain primarily by mitigating impedance mismatch. Sensing accuracy for rotational and wind speed is +/- 0.5%-+/- 2% of full scale, varying with methods and environmental interference. This paper systematically reviews the research progresses of the micro-energy conversion and micro-signal processing technologies for R-TENG. Regarding micro-energy harvesting, the key technologies such as rectification, energy storage optimization, and DC-DC buck conversion are analyzed systematically. For micro-signal processing, indepth analysis is conducted on signal processing methods such as filtering, noise reduction, and feature extraction in typical application scenarios such as motor speed estimation and wind speed measurement. The core contributions include: established a comprehensive analysis framework that bridges theoretical foundations and engineering applications; highlighted the performance metrics and practicality through structured comparisons; identified the research gaps in circuit stability, model generalizability and adaptation to extreme environments. Finally, this article analyzes the development directions of R-TENG such as intelligent adaptability, anti-interference ability, and universal modularization, which will help promote the innovative application of RTENG in micro-energy harvesting and micro-signal processing.
Using high-resolution satellite observations and numerical simulations, this study investigates how orographically induced disturbances stimulate boundary-layer horizontal convective rolls (HCRs) and precondition the convective environment prior to afternoon convection over Hainan Island in southern China. As indicated by organized cloud streets, the island's HCR characteristics undergo a pronounced transition as the prevailing lowlevel flow shifts during the summer monsoon onset. When low-level monsoonal southwesterlies dominate, elongated ridge-dependent HCRs extending over 100 km become prevalent and are closely aligned with the major mountain ridges. This pattern contrasts sharply with the densely packed but randomly distributed typical HCRs observed when low-level southeasterlies prevail before monsoon onset. Numerical simulations show that elongated stripe-like convergence zones develop extending from the ridges as the lower troposphere destabilizes in response to late-morning surface heating. The associated updrafts drive the formation of broader and deeper roll circulations than the typical densely distributed HCRs. The upward branches of these ridge-dependent HCRs promote horizontal moisture pooling and vertical moistening, whereas the downward branches produce the opposite effect, extending from the mountain ridges toward the northeastern part of the island where afternoon convection frequently occurs. In addition to downstream surface heating, warm air advected from the ridge regions further strengthens low-level ascent and horizontal convergence, and they also help form a locally neutral layer, thereby reducing convective inhibition beneath the elongated cloud streets. These results suggest that, when the background low-level flow is favorably aligned with local orographic features, orographicallyinduced mechanical disturbances can organize ridge-dependent HCRs and substantially reshape the convective environment.
