Search Results (31,596)

Salt stress is a major abiotic stress that threatens citrus yield and quality. To elucidate the molecular mechanisms underlying differential salt tolerance in citrus rootstocks, we performed an integrative transcriptomic and metabolomic analysis of salt-sensitive trifoliate orange (Poncirus trifoliata) and salt-tolerant Goutoucheng (Citrus aurantium) under 60 mM NaCl treatment for 12 h and 24 h. Physiological observations confirmed that Goutoucheng exhibited less growth inhibition and leaf damage than trifoliate orange. Transcriptome sequencing identified 2081 and 1588 differentially expressed genes (DEGs) in trifoliate orange at 12 h and 24 h, respectively, compared with 1166 and 997 DEGs in Goutoucheng. Metabolome profiling revealed 217 and 173 differentially accumulated metabolites (DAMs) in trifoliate orange versus 162 and 239 DAMs in Goutoucheng at the two time points. KEGG pathway analysis showed that DEGs were mainly enriched in the Mitogen-activated protein kinase (MAPK) signaling pathway—plant, plant hormone signal transduction, and flavonoid biosynthesis—and DAMs were mainly enriched in flavonoid biosynthesis, starch and sucrose metabolism, and glutathione metabolism. Integrative nine-quadrant and two-way orthogonal partial least squares analyses further pinpointed flavonoid biosynthesis as a central hub in salt response. Notably, quercetin derivatives accumulated preferentially in the salt-tolerant rootstock Goutoucheng. Several transcription factor families—including HSF, MYB, NAC, HB-HD-ZIP, C2H2, bHLH, AP2/ERF, and Trihelix—may enhance antioxidant capacity under salt stress by regulating flavonoid accumulation. Collectively, these results indicated that coordinated regulation of flavonoids contributed critically to salt stress adaptation in citrus rootstocks. The identified DEGs, DAMs, and transcription factors provide candidate targets for genetic improvement of salt tolerance in citrus. Full article

The widespread dissemination and societal impact of fake news underscore the critical need for effective detection. Existing methods remain limited, as they often fail to learn joint representations from multi-modal data and rely heavily on complete social interaction signals. Such information is frequently unavailable in practice, especially during the early propagation stages. To address early fake news detection in social media, this paper proposes a hierarchical cross-modal fusion framework with structured LLM-simulated social interaction (HCF-LSIM). The framework employs a progressive cross-modal attention mechanism to systematically align semantic representations across multiple levels, integrating textual, thematic, and visual features. Additionally, HCF-LSIM designs an LLM-powered social interaction simulator that generates structured triplets from adapted user profiles, effectively compensating for missing real-time interaction data. Experiments on public benchmarks demonstrate strong performance, with accuracies of 93.5% on Weibo and 87.2% on X (formerly Twitter), ranking first on Weibo and second on Twitter. Full article

Real-time precise point positioning (PPP) is increasingly supported by open satellite-broadcast state-space representation (SSR) services, yet standalone operation with a single service remains vulnerable to limited constellation support, correction outages, latency variations, and service-dependent modeling inconsistencies. In the Asia-Pacific region, MADOCA-PPP and PPP-B2b provide two publicly accessible and complementary SSR sources, but their consistent fusion before user-level PPP estimation remains insufficiently investigated. This paper proposes a correction-domain fusion framework that combines MADOCA-PPP and PPP-B2b orbit and clock corrections before PPP estimation, rather than merging final positioning solutions. Inter-service discrepancies and unknown cross-correlations are handled by a bias-state-aware structured covariance intersection strategy, in which the relative weighting is derived from the respective correction information (inverse variance), preserving statistical consistency and avoiding overconfident fusion. A unified multi-GNSS PPP scheme further supports signal-priority harmonization, broadcast-ephemeris adaptation, correction-age control, and GLONASS inter-frequency and differential code bias handling. Static-station per-epoch (pseudo-kinematic) and offshore kinematic experiments validate the framework. In the static-station test, fusion raised the mean number of valid satellites from 21.98 and 14.98 to 26.56 and improved the horizontal RMS to 0.033 m—better than either standalone service (0.037 m, 0.079 m)—confirming a genuine combination rather than source selection, while the 3D RMS (0.068 m) matched the best standalone service (0.066 m). In the offshore test, fusion achieved the best overall accuracy (0.232 m horizontal, 0.290 m 3D, versus 0.332 m and 0.313 m for the standalone services) and the most satellites (25.4). It also degraded most slowly with increasing elevation cut-off, outperforming both services about threefold at 40°. A normalized-innovation-squared check confirmed the fused covariance is consistent and not overconfident (median ≈ 1.1; within the 99% bound in 100% of epochs). Under single-service outages from 30 s to 600 s, fusion maintained 100.0% availability, confirming its advantage in redundancy, continuity, and resilience. Full article

The rapid proliferation of cloud data centers has intensified concerns over carbon emissions, energy efficiency, and sustainability. Virtual machine (VM) placement is a pivotal control lever, yet existing methods rarely couple carbon intensity signals with computationally tractable multi-objective optimization. In this paper, we propose CASO (Carbon-Aware Surrogate-Guided Optimization), a novel framework that integrates an online adaptive Radial Basis Function (RBF) surrogate model with a self-adaptive hybrid PSO-DE swarm optimizer for real-time VM placement in geo-distributed edge cloud environments. CASO simultaneously minimizes carbon emissions, energy consumption, SLA violation rate, and network latency under strict host capacity and Quality-of-Service (QoS) constraints. Three key innovations differentiate CASO: (i) an online surrogate update mechanism that refines fitness approximations incrementally as workload patterns evolve; (ii) a carbon intensity weighting scheme anchored to real-time Grid Emission Factor (GEF) signals; and (iii) an adaptive parameter controller that autonomously tunes swarm exploration–exploitation trade-offs without hand-crafting. Experiments on the publicly available Alibaba Cluster Trace (cluster-trace-v2026-GenAI) dataset within a CloudSim-Plus environment show that CASO reduces carbon emissions by up to 31.4%, energy consumption by 27.9%, and SLA violations by 18.8% compared to the strongest baseline while converging 3.8× faster than the strongest baseline (ADEDL). Full article

Multiple three-phase machine drives are inherently fault-tolerant due to their multiphase structure; however, they remain susceptible to inverter-related faults. A common fault is the loss of gate signals in one inverter leg, resulting in an open-phase condition. Under such conditions, a reverse conduction path is established through the freewheeling diodes of the faulted leg, leading to uncontrolled freewheeling diode current generation. The resulting freewheeling diode current becomes particularly critical in the field-weakening region, when the back-EMF may exceed the DC-link voltage and a large reverse current can occur. This paper derives an analytical expression for real-time prediction of the freewheeling diode current in a triple three-phase surface-mounted permanent magnet synchronous machine drive. The method is applicable in both the constant-torque and field-weakening regions. The analytical prediction is validated through comparison with both experimentally measured and numerically simulated freewheeling diode current waveforms over a wide range of operating points, including no-load and loaded conditions. The results show that the proposed model accurately reproduces the envelope and conduction boundaries, while maintaining good agreement with simulations and measurements. The predicted current can be utilized in post-fault control, fault detection, and sensorless position estimation. Full article

Underwater acoustic (UWA) channels are inherently complex, with pronounced variability arising from multipath propagation, time variability, Doppler effects, and nonstationary ocean conditions. Such variability often leads to unstable communication reliability when conventional single-carrier signaling and fixed reception strategies are employed. In practical UWA environments, performance degradation may occur when channel characteristics deviate from the assumed regime, thereby limiting system robustness. To address this reliability challenge, this study develops an unequal-rate frequency–phase keying (URFPK) signaling strategy that combines a low-rate frequency component with a high-rate phase component. A corresponding receiver structure is designed, employing parallel coherent and noncoherent processing to enhance robustness under dynamic channel conditions. In addition, a reduced-complexity noncoherent procedure is introduced to improve computational efficiency. Simulation results demonstrate substantially improved robustness under severe UWA distortions. Full-scale sea trials further validate the engineering effectiveness of the proposed approach, achieving communication success rate improvements of $18.62$% and $9.39$% over baseline schemes within short intervals and maintaining an overall success rate exceeding 91% over extended transmissions. These results indicate that the URFPK signaling strategy provides a practical and robust mechanism for improving UWA link reliability in dynamic UWA channels. Full article

Urban low-altitude unmanned aerial vehicle (UAV) logistics networks face critical operational bottlenecks due to complex three-dimensional spatial blockages, continuous communication diffraction, and severe vulnerability to information-layer threats such as Automatic Dependent Surveillance—Broadcast (ADS-B) signal anomalies. To address these interconnected challenges, this paper proposes an event-driven, cloud–edge collaborative digital twin framework to guarantee continuous multi-link communication and flight safety. The architecture operates through a dual-tier “Teacher–Student” paradigm. Under secure conditions, a cloud digital twin acts as a high-capacity “Teacher,” employing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to partition heterogeneous user topologies. It then utilizes an energy-guided stochastic diffusion sampling (EGSDS) method to refine initial macroscopic routing, generating precise, outage-free global trajectories by systematically minimizing non-line-of-sight (NLoS) observation penalties and kinematic regularization costs. To counteract signal anomalies, a distributed Time Difference of Arrival (TDOA) anchor network continuously validates UAV coordinate integrity. If a threshold is breached, control authority is instantly transferred to the UAV’s edge digital twin. This resource-constrained edge tier relies on a localized “Student” network trained via progressive distillation. By compressing the computationally heavy iterative diffusion process into a rapid one-step inference model, the UAV autonomously generates a secure, short-range emergency path that strictly adheres to minimum communication thresholds. Once interference clears, the cloud seamlessly regains control to complete the logistics mission. Experimental results demonstrate that the proposed scheme significantly outperforms conventional heuristic routing methods in cloud-based scenarios. Furthermore, the edge-based distillation mechanism substantially improves the overall trajectory survival rate under signal anomalies, ensuring resilient and continuous logistics operations. Full article

A novel non-contact piezoelectric motor modulated by electromagnetic force is proposed in this work. The motor consists of a driving system and a transmission system. The transmission system includes a driving torque modulation mechanism and a keeping torque modulation mechanism. The calculation model of the magnetic forces of the motor is deduced, based on which the calculated equations of the magnetic driving torque, the magnetic keeping torque, the total torque, and the torque fluctuation applied to the rotor are presented. The transfer functions of the motor torque and its proportional-integral (PI) control are also given. Compensation control is used to remove the torque fluctuation. Via the derived equations, the effects of the system parameters on the system gain and time constant are investigated. Moreover, the step responses of the motor torque and the effects of the system parameters on them are analyzed, as are the step responses of the closed-loop control system with a PI controller. Furthermore, the torque fluctuation of the rotor is investigated, and its compensation signals are determined. Finally, the compensation control of the torque fluctuation is realized by adding feedback compensation signals. Full article

Magnetic anomaly detection is vulnerable to environmental noise and insufficient prior target information, making non-periodic anomaly signals difficult to detect at low-signal-to-noise-ratio (SNR) conditions. This paper proposes a detection method based on a multi-parameter-constrained mirror dual-branch biased monostable stochastic resonance (SR) system. Nonlinear odd-order bias terms are introduced into the conventional biased monostable potential function to build a multi-parameter-controllable SR model. This improves regulation of potential-well width, depth, and wall morphology, enhancing noise-energy utilization and responses to non-periodic features. Considering peak-type, valley-type, and bipolar anomaly morphologies, a mirror dual-branch SR structure is developed to cooperatively detect features with different polarities. To preserve temporal waveforms and time–frequency structures during parameter optimization, a composite metric combining the correlation coefficient and wavelet-domain image structural similarity index is constructed. Multi-fidelity robust Bayesian optimization is used to obtain a unified robust parameter set for the magnetic anomaly signal family. Experiments with simulated colored noise and measured geomagnetic noise show that the proposed method effectively recovers magnetic anomaly features under strong noise. At −19 dB SNR, its detection probability remains above 80%. Compared with orthogonal basis function decomposition, empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise, the method achieves better noise suppression, feature preservation, and detection performance under low-SNR conditions. Full article

In impact testing, evaluating multiple-impact signals is critical for verifying whether a test setup can reproduce penetration response signals and ensure reliable results. To overcome the limitations of traditional methods, including incomplete indicator coverage, subjective weighting, and poor consistency, this study proposes a fuzzy comprehensive evaluation (FCE) framework based on time–frequency features and combined weighting. Using multi-layer penetration response signals as the matching target, a multidimensional indicator system covering time-domain features, frequency-domain features, and signal quality and stability is established. A combined weighting method integrating AHP, EWM, and CRITIC is then developed, and subjective and objective weights are fused using the geometric mean method. A fuzzy comprehensive evaluation model is used to quantify the matching degrees of multiple sets of multiple-impact signals, and robustness is verified through weight consistency tests and sensitivity analysis. The results show that the evaluated signal sets are rated “Excellent”. Under reasonable weight combinations, the probability of obtaining an “Excellent” result reaches 99.94%, and the maximum variation caused by a ±10% perturbation in a single indicator weight is only 0.0087. The proposed framework provides a practical tool for evaluating multi-layer penetration response simulations and can be extended to other complex dynamic signal-matching problems. Full article

This paper presents Resilient Edge-IVA (Intelligent Video Analytics), an integrated framework designed to ensure real-time inference stability and high-speed embedding-based similarity search in resource-constrained edge computing environments. Conventional systems often face Quality of Experience (QoE) degradation caused by computational overhead and hardware-level bottlenecks. To address these challenges, this study proposes a “Whole-cycle” methodology employing a perception-driven, three-tier adaptive control algorithm. This algorithm dynamically modulates encoding parameters, such as resolution and bitrate, by utilizing real-time inference latency and CPU utilization as feedback signals. Furthermore, the framework incorporates an event-density-based Data Diet mechanism. This mechanism selectively adjusts video quality based on object detection results, preserving high-fidelity imagery for critical events while significantly reducing data volume during static intervals. The backend implements a hybrid storage architecture combining the Milvus vector database for CLIP-based high-dimensional visual embeddings with a PostgreSQL relational database for structured metadata. These systems are linked via a deterministic hash key to ensure data atomicity and facilitate high-speed, multi-dimensional embedding-based retrieval. Experimental evaluations conducted on a Raspberry Pi 5 and Hailo-8 NPU demonstrate that the proposed framework maintains a frame drop rate below 0.3% even under extreme workloads, providing a 13-fold improvement in operational stability over static configurations. The results also confirm a 54.2% reduction in total storage occupancy and a Hash Mapping Consistency (HMC) score of 0.89. These findings validate the framework’s effectiveness in reconciling real-time processing stability with storage efficiency. Building upon this baseline, future research will extend the framework to multi-class environments, targeting applications such as Intelligent Transport Systems (ITS). Full article

Sarcoidosis is a multisystem granulomatous disorder of uncertain origin which still presents major therapeutic dilemmas. Longstanding dependence on corticosteroids, while effective for acute inflammation, carries considerable adverse effects over time. Advances in deciphering sarcoidosis pathobiology—including aberrant Janus kinase (JAK)- signal transducer and activator of transcription (STAT) signaling, mechanistic target of rapamycin (mTOR)-driven metabolic shifts, Th1/Th17.1 immune skewing, effector T-cell exhaustion, and granuloma-centered cytokine circuits—have revealed several targets for intervention. The treatment options are rapidly changing: the SARCORT trial showed that low-dose prednisolone is non-inferior to higher prednisolone doses; the pivotal PREDMETH trial validated methotrexate as a feasible first-line steroid-sparing option; efzofitimod, a novel immunomodulator targeting neuropilin-2, produced steroid-reducing effects in Phase IIbut not in Phase III trials; and JAK inhibitors are accumulating evidence across cutaneous and systemic presentations. The 2025 World Association for Sarcoidosis and Other Granulomatoses (WASOG) statement supports a move toward earlier steroid-sparing approaches. This review methodically connects sarcoidosis molecular and pathophysiological mechanisms to new targeted treatments, examines clinical trial evidence, and proposes future directions toward biomarker-driven individualized care. Full article

Grouting and sealing in gas drainage boreholes are two of the critical measures to ensure efficient coal seam gas extraction. However, traditional cement grouting often leads to debonding and cracking of the slurry–coal cemented body under external load, resulting in poor sealing performance. To suppress crack propagation and achieve borehole reinforcement and efficient sealing, this study compares the mechanical properties and crack evolution characteristics of slurry–coal cemented samples grouted with different modified materials. Five types of cement-based sealing materials, including ordinary Portland cement, were used for grouting coal rock in boreholes. By employing an acoustic emission signal acquisition system and a non-contact full-field strain measurement system, the tensile mechanical properties of coal before and after grouting were compared. The influence of material properties on the reinforcement capacity of borehole coal was analyzed, along with the failure process characteristics and final failure morphology of the slurry–coal cemented body under Brazilian splitting load. Finally, the effects of material toughness and bond strength on the brittleness index and failure mode of the slurry–coal cemented samples under Brazilian splitting conditions were discussed. The results show that the tensile strength improvement rates of the samples were 26.9%, 55.3%, 48.4%, 8.6%, and 45.6%, respectively. Distinct from previous studies focusing on fractured grouting or intact coal rock, this work for the first time systematically reveals the non-monotonic influence of the combination of material toughness and bond strength on the reinforcement effect of borehole coal samples and proposes an evaluation framework based on quantitative acoustic emission crack type analysis and the concept of effectiveness threshold. The varying degrees of tensile strength enhancement indicate differences in the reinforcement capabilities of grouting materials with different properties. The acoustic emission signals during the failure process of the slurry–coal cemented body exhibited typical stage-specific characteristics, though material properties altered the failure modes. By quantifying the intrinsic properties and crack characteristics of the slurry–coal cemented body using the brittleness index and grayscale histograms, this study provides a theoretical basis for guiding efficient sealing of gas drainage boreholes through an in-depth understanding of the mechanical behavior and crack evolution of borehole coal samples before and after grouting under Brazilian splitting conditions. Full article

Background/Objectives: Bursera bipinnata (DC.) Engl. resin (locally known as “copal blanco”) is traditionally used in Mexican ethnomedicine to treat infected wounds and skin inflammation, but the bioactive constituents underlying these effects remain largely uncharacterized. This study aimed to identify the compounds responsible for the wound-healing properties of the resin through bioactivity-guided fractionation and to evaluate their anti-inflammatory and antibacterial activities as complementary mechanisms supporting tissue repair. Methods: Crude resin (1.2–5.0 mg/mL) was assayed for anti-inflammatory activity in the TPA-induced ear-edema model in BALB/c mice, for antibacterial activity (MIC) against six clinically relevant strains, and for wound-healing activity in a murine excisional model with pirfenidone (PFD) as the reference drug (n = 5 per group). Bioactivity-guided fractionation followed by spectroscopic elucidation (¹H- and ¹³C-NMR, IR, EI-MS) led to the isolation of five constituents. Stigmasterol, the most active compound, was subsequently evaluated in an LPS-induced systemic inflammation model (oral administration, 20 mg/kg/day × 3 days) to characterize its immunomodulatory profile (TNF-α, IL-1β, IL-6, IFN-γ, IL-10) and in the wound-healing model to quantify local IL-6, IL-10 and TGF-β1 in skin homogenates. Results: The crude resin (5.0 mg/mL) achieved 99.63% wound closure at day 12 and a 49.08% reduction in TPA-induced ear edema, comparable to indomethacin (55.76%). The resin displayed selective antibacterial activity against Streptococcus pyogenes (MIC 125 µg/mL) and Salmonella typhimurium (MIC 250 µg/mL). Bioactivity-guided fractionation yielded the phytosterol stigmasterol (1), three lupane-type triterpenoids (lupeol acetate (2), lupenone (3), 3-epilupeol (5)), and the sesquiterpenoid caryophyllene oxide (4). At an equimolar 1 µM concentration, stigmasterol (1) shortened the mean wound-healing time to 10.3 ± 0.4 days, comparable to pirfenidone, and was associated with attenuation of systemic TNF-α, IL-1β and IL-6 peaks and with sustained local IL-10 and TGF-β1 expression. Histological assessment confirmed accelerated re-epithelialization and improved collagen organization. The resin was non-irritant in the OECD 404 acute dermal test (Primary Irritation Index = 0.00). Conclusions: These findings provide pharmacological evidence supporting the traditional use of B. bipinnata resin for wound healing. Stigmasterol (1), together with the lupane-type triterpenoids lupenone (3) and 3-epilupeol (5), were identified as key bioactive constituents. The data are consistent with a coordinated immunomodulation, in which stigmasterol is associated with reduced systemic pro-inflammatory signalling and increased local IL-10/TGF-β1 expression, an interpretation that should be confirmed in chronic and impaired wound-healing models. Full article

The extraction of speech emotion features—particularly at the utterance level—constitutes a critical yet challenging aspect of Speech Emotion Recognition (SER). Emotion represents high-level paralinguistic information, and not all segments within a speech signal carry emotionally salient cues, especially in longer utterances. Conventional methods that process entire utterances may thus waste computational resources and introduce irrelevant acoustic interference. To address this, we propose AdaPre-Selection, an adaptive pre-selection mechanism designed to identify and extract emotion-rich segments from speech signals. Acting as a flexible front-end compression module, AdaPre-Selection consists of two complementary components: an Active Emotion Positioning (AEP) module and a Passive Emotion Constraint (PEC) module. Within AEP, Temporal Length Selection (TLS) and Start Time Point Selection (STPS) operate jointly to adaptively locate the optimal emotional segment in each utterance. Evaluated on two benchmark datasets (IEMOCAP and MSP-IMPROV) using four state-of-the-art SER models, AdaPre-Selection consistently outperforms common preprocessing baselines and delivers the most substantial improvement in recognition performance. Full article