With the increasing penetration of renewable energy sources (RESs) and diverse operation modes, frequency regulation of multi-area modern power systems is significantly affected by the communication constraints and network threats. This paper establishes a decentralized load frequency control (LFC) model to incorporate the electric vehicles (EVs) as flexible regulation resources and deal with frequency deviation from the adjacent area as the load disturbances. Then, under unforeseen network environments, a memory-driven fuzzy adaptive event-triggered mechanism (MDFA-ETM) is designed to adaptively select the effective historically triggered information. Through the reasonable usage of memory-driven dependent data, this strategy aims to improve data transmission efficiency of the control signal for frequency regulation. In addition, an acknowledgment (ACK) auxiliary protocol based adaptive communication method is introduced with a recovery control scheme to enhance transmission reliability and control continuity. Furthermore, qualitative analysis with control design is derived using piecewise asymmetric Lyapunov functional. Finally, an IEEE 9-bus test system is applied to assess the decentralized control design with quantitative analyze. Experiment tests on a hardware-in-the-loop (HIL) test platform are conducted to verify that the designed control scheme can be applied in practical systems.
Rapid urbanization and intensifying rainfall have increased pluvial flood risk in high-density cities; however, urban green space (UGS) planning rarely integrates stormwater-regulation deficits, population-oriented potential spatial availability, distributional inequality, and land constraints. In central Chengdu, China, this study developed a framework linking Functional–Climatic Hydrological Units (FHUs)-based diagnosis, a risk-adjusted two-step floating catchment area model (Risk-2SFCA), population-weighted distributional assessment, and two-stage UGS optimization. FHUs were delineated from urban function, landscape morphology, and major road boundaries, and an FHU-level supply–demand balance was evaluated under six rainfall scenarios. Risk-2SFCA measured the alignment between effectiveness-adjusted UGS capacity and flood-susceptibility-adjusted population-equivalent demand within standardized network-based planning neighborhoods. Candidate sites were selected using a maximal covering location problem, followed by optimization of mean availability, inequality, and priority-weighted residual deficits using the differential-evolution-enhanced Non-dominated Sorting Genetic Algorithm II. Results showed that the mean FHU-level balance declined from 14.24 mm under 56.39 mm rainfall to −24.00 mm under 110.14 mm rainfall, and larger deficits were associated with greater observed inundation depth. Availability classifications were more sensitive to neighborhood thresholds than to impedance speed, while alternative population representations preserved broad citywide patterns but changed local and group-specific estimates. The entropy-weighted compromise allocated 4,202.26 ha of UGS among 141 candidate sites, with 74.2% of the allocated area concentrated in service-deficit zones. Within these zones, mean availability increased from 25.37 to 32.32, and the population-weighted Gini coefficient decreased from 0.86 to 0.81. The framework supports fine-grained, risk-sensitive, and distributionally informed green-infrastructure planning under land constraints.
Alpha-fetoprotein (AFP) plays multiple roles in hepatocellular carcinoma (HCC), driving tumor progression and immune evasion. However, clinical measurement of serum AFP levels often fails to reflect intratumoral AFP expression, especially for early-stage or well-differentiated HCC. Current imaging modalities lack the sensitivity and specificity to visualize AFP spatially within tumors. Herein, an AND-gated afterglow probe is engineered to be sequentially activated by two features of HCC microenvironment: acidic pH and elevated AFP. The probe consists of afterglow nanoparticles (PZ-NPs) functionalized with BHQ3-modified dual-aptamer DNA. Initially quenched via afterglow resonance energy transfer (ARET), the probe is activated sequentially through pH-induced triplex folding and AFP binding, which cause conformational tightening and disrupt ARET to restore afterglow luminescence. Such an activation requires an AND logic of three inputs (i.e. light, H+ and AFP), which enables a limit of detection for AFP as low as 0.31nM and ensures high specificity with negligible response to interferents. In orthotopic HCC, the probe achieved an 8.3-fold tumor-to-liver ratio (vs. 1.4-fold for fluorescence) with signals that are colocalized with luciferase-defined tumors and able to detect curcumin-mediated AFP suppression in good consistency with immunohistochemistry results. Ex vivo imaging of resected livers further confirmed tumor-specific activation, supporting AFP-selective in vivo imaging and microenvironment-gated biomarker detection in HCC.
Soil nematodes are critical bioindicators of ecosystem responses to environmental change, yet their spatial patterns across geographic gradients in alpine grasslands of the Qinghai-Tibetan Plateau (QTP) are not well characterized. We examined the taxonomic composition, diversity, trophic structure, and ecological traits of soil nematodes along longitudinal, latitudinal, and altitudinal gradients on the eastern QTP. From 2019 to 2021, 90 sampling sites were investigated across these gradients. Taxonomic composition varied markedly, and total nematode abundance and all trophic groups showed unimodal longitudinal patterns, decreased with latitude and increased with altitude. Taxonomic richness and Shannon index also exhibited unimodal longitudinal patterns and significant latitudinal decreases, but no clear altitudinal trend. Trophic structure also varied, with bacterivore relative abundance increased longitudinally and latitudinally, whereas predator-omnivore decreased longitudinally. With increasing altitude, the relative abundances of bacterivores and fungivores decreased, while herbivores increased and dominated at 3500 m. Ecological indices displayed contrasting spatial patterns, with cp2 peaking at 101°E and cp3–5 showing opposite longitudinal trends, and their latitudinal patterns converging near 30°N. Mean annual precipitation, temperature, and aboveground biomass were the primary drivers of nematode distributions. These results reveal complex geographical patterns of soil nematode communities in eastern QTP alpine grasslands and indicate 101°E, 30°N and 3500 m as key ecological transition zones. This highlights the joint influence of longitude, latitude, and altitude on nematode community assembly. Environmental tolerance thresholds may further shape these spatial patterns, influencing climate responses and ecosystem functions such as carbon cycling and nutrient mineralization.
Underwater wireless sensor networks (UWSNs) have exhibited significant importance in various oceanic applications (e.g., oceanographic monitoring and submarine resource exploration), which imperatively requires sophisticated routing protocols (e.g., hybrid acoustic-optical routing algorithms) for efficient underwater data delivering. However, most existing hybrid acoustic-optical routing algorithms lack mechanisms for cluster head protection and void handling, limiting network survivability and efficiency. This article proposes an adaptive clustering routing algorithm with dynamic hierarchical head backup (ACHB) for hybrid UWSNs, which employs acoustic waves for omnidirectional intracluster data collection and optical waves for high-rate intercluster transmission. Unlike traditional approaches, the proposed ACHB algorithm introduces a hierarchical backup mechanism to enhance cluster head survivability, an adaptive clustering strategy based on node density and depth to balance energy consumption, and a backup-based void handling scheme to improve packet delivery rate. These design features allow ACHB to avoid frequent reclustering overhead while effectively optimizing energy efficiency. The key parameters for ACHB implementation include the dynamic clustering radius (adjusted via node density and depth for energy balancing), energy threshold E-th (for cluster head detection and backup activation), and minimum cluster head distance d(c) (for enhanced network coverage and separation). Simulation results indicate that the proposed ACHB algorithm can obtain up to about 63.6%, 42.0%, and 37.6% improvements in PDR, node survivability rate, and average residual energy, respectively, when compared to the classical routing algorithms adopted in the simulations.
