Search Results (430)

Accurate wind speed forecasting is critical for reliable wind-power integration, yet it remains challenging due to the strongly non-stationary and inherently multi-scale nature of atmospheric processes. While deep learning models—such as LSTM, GRU, and Transformer architectures—achieve competitive short- and medium-term performance, they frequently suffer from spectral bias, hyperparameter sensitivity, and reduced generalization under heterogeneous operating regimes. To address these limitations, we propose a multi-scale spectral–recurrent framework, termed RFF-RNN, which integrates multi-band Random Fourier Feature (RFF) encodings with parameterizable recurrent backbones. A key innovation of our approach is the deliberate relaxation of strict shift-invariance constraints; by jointly optimizing spectral frequencies, phase biases, and bandwidth scales alongside the neural weights, the framework dynamically shapes a fully data-driven spectral embedding. To ensure robust adaptation, we employ a two-stage optimization strategy combining gradient-based inner-loop learning with outer-loop Bayesian hyperparameter tuning. Our extensive evaluations on a controlled synthetic benchmark and six geographically diverse real-world wind datasets (spanning the USA, China, and the Netherlands) demonstrate the superiority of the proposed framework. Statistical validation via the Friedman test confirms that RFF-enhanced models—particularly RFF-GRU and RFF-LSTM—systematically outperform standard recurrent networks and state-of-the-art Transformer architectures (Autoformer and FEDformer). The proposed approach yields significantly lower error metrics (MAE and RMSE) and higher explained variance ($R^2$), while exhibiting remarkable resilience against error accumulation at extended forecasting horizons. Full article

Background: Neurodegenerative diseases are a major health problem, and the neuropsychiatric symptoms seen in these diseases adversely impact the lives of patients, families, and caregivers. Inappropriate aggressive behavior is a highly disruptive symptom and a leading cause of institutionalization. There are no approved drugs specifically for the treatment of problematic aggression, and the off-label use of antipsychotics has limited benefit with significant side effects and safety risks. This review discusses dysregulated arginine vasopressin (AVP) signaling in fear–threat circuitry as a key driver of inappropriate aggression. Because the AVP 1a receptor (V1aR) is the dominant subtype in the CNS, the selective antagonism of this receptor represents a well-rationalized target for the treatment of aggression across neurodegenerative, psychiatric, and neurodevelopmental disorders. Objectives: Our goal was to summarize the basis for using V1aR antagonists as a treatment for irritability and aggressive behavior. We describe its discovery, biosynthesis, receptor pharmacology, and CNS distribution, emphasizing V1aR localization in central fear–threat circuits. Translational evidence from animal studies, pharmacological neuroimaging, and lesion network mapping is presented. These data support the suggestion that heightened vasopressinergic tone biases socioemotional information processing toward negative valence, increasing threat sensitivity and the likelihood of inappropriate aggressive responses. Emerging clinical data support this framework. Highly selective, CNS-penetrant V1aR antagonists reduced aggressive behavior and had an excellent safety profile in phase 2 studies in Huntington’s disease and intermittent explosive disorder, with efficacy signals across caregiver-reported, clinician-rated, and incident-based measures. Furthermore, pharmacological neuroimaging showed that V1aR antagonism normalizes AVP-induced alterations in activity within fear–threat circuitry. Conclusions and Future Directions: Preclinical, translational, and clinical findings to date support V1aR antagonism as a promising strategy for treating pathological aggression across disorders. Additional experimental medicine studies and clinical trials are needed to conclusively establish efficacy in various disease populations, and we note the need for improved trial designs and analytical methods as part of the development process. Full article

The traditional paradigm of pharmaceutical research is characterized by substantial inefficiency, requiring extensive timelines and billions of dollars while suffering from high clinical attrition rates. The integration of generative artificial intelligence (AI) is driving a paradigm shift from empirical experimentation toward predictive, data-driven innovation. This review evaluates state-of-the-art applications of these technologies across the drug discovery and development pipeline. By analyzing multi-omics data streams, AI models can elucidate complex disease mechanisms and identify novel therapeutic targets. Deep generative architectures facilitate the algorithmic creation of novel molecular entities, enabling the design of therapeutics with complex polypharmacological profiles. Furthermore, AI is enhancing the clinical testing phase through large language models (LLMs) that improve patient enrollment and through synthetic control arms (SCAs) that provide computational alternatives to traditional placebo groups. Despite these advances, the scientific community must address inherent algorithmic biases stemming from demographic underrepresentation and mitigate the risks of data hallucinations. Ultimately, realizing the full translational potential of generative AI in precision medicine may require the widespread adoption of explainable AI (XAI) frameworks and rigorous data standards. Full article

The Galileo High Accuracy Service (HAS) provides free-of-charge corrections for PPP through both the E6b signal and the internet. Currently, HAS targets a horizontal and vertical accuracy of 15 cm and 20 cm, respectively (68% confidence level) for static users. Although the service is not yet fully operational, it already delivers orbit and clock corrections, as well as satellite code biases. This paper evaluates the current performance of HAS, showing positioning errors below 5 cm in both horizontal and vertical components. However, the convergence time required to reach these accuracies remains relatively long. To address this limitation, ionospheric corrections were estimated from a European network of 34 stations and added to the processing. The results show a clear improvement in both accuracy and convergence time: horizontal and vertical errors were reduced by half, as well as the horizontal convergence time. To complete the HAS correction set, only satellite phase biases were missing. These were also generated using the same European network. Although no improvement was observed when including them, no degradation was found either. This suggests that, with further refinement, HAS could significantly benefit from phase biases and achieve even better positioning performance. Full article

Chronic non-healing wounds, prevalent in diabetic and vascular diseases, arise from dysregulated chemokine signaling that disrupts angiogenesis, immune coordination, and tissue remodeling. This review synthesizes current knowledge on chemokine biology in wound repair, with a focus on their spatiotemporal regulation across the hemostasis, inflammation, proliferation, and remodeling phases. We detail chemokine classification (CC, CXC, CX3C, and C families), receptor interactions, and downstream pathways, including G protein-dependent and β-arrestin-biased mechanisms. Furthermore, we evaluate emerging therapeutic strategies, including neutralizing antibodies, receptor antagonists, engineered chemokines, and biomaterial-based delivery systems designed to restore chemokine gradient integrity and promote healing. Recent advances in structural biology and protein engineering are highlighted as enabling the design of biased ligands and multi-target inhibitors to overcome chemokine redundancy. The review concludes that precision modulation of chemokine networks offers a promising translational framework to redirect chronic inflammation toward regenerative healing, thereby addressing a significant unmet clinical need in chronic wound management. Full article

Weak measurement enables the amplification of weak physical effects via post-selection and has become an important tool in precision optical metrology; however, conventional schemes based on mean-pointer shifts suffer from response saturation, limited linear range, and stringent stability requirements. Here, we propose and experimentally demonstrate a weak-measurement scheme based on spectral-interference trough shifts, where the zero-intensity points of the post-selected spectrum act as the measurement pointer, establishing an analytical mapping between the trough displacement and the target phase or time delay. Theoretical analysis shows that, under detector resolution limits, the measurement resolution depends solely on the frequency of extinction point and is independent of weak-value singular amplification or bias-phase modulation, thereby maintaining high sensitivity while avoiding pointer saturation. Experiments demonstrate that the trough-shift scheme achieves significantly better agreement between measured and theoretical sensitivities than biased weak measurement and provides a stable linear response without additional bias-compensation structures, reaching a minimum resolvable phase variation at the ${10}^{-7}$ level. Moreover, the approach intrinsically supports multi-period traceable measurements and exhibits strong robustness against intensity fluctuations and spectral distortions, offering a promising route toward high-sensitivity, large-dynamic-range, and stable weak measurement-based optical sensing. Full article

Accurate GNSS timing is fundamental to Power Time Synchronization Systems (PTSS). However, conventional substation infrastructures remain vulnerable to sophisticated spoofing attacks. In this research, a sensing-assisted array antenna-based spoofing mitigation method is proposed. The proposed architecture operates at the signal front-end and incorporates a dedicated spoofing sensing path to estimate the Direction-of-Arrival (DoA) of malicious signals, enabling adaptive null steering while preserving authentic satellite reception. To provide reliable spatial reference for DoA estimation, a unified high-precision attitude determination method is developed for compact 10 cm-scale array antennas under single-frequency and environmental error conditions. The method integrates the Constrained Least-squares AMBiguity Decorrelation Adjustment (C-LAMBDA)-based constrained ambiguity resolution, redundant antenna element-based vertical accuracy enhancement, and iterative refinement to mitigate centimeter-level environmental biases. Semi-simulated experiments demonstrate that the proposed method achieves baseline vector Root Mean Square Errors (RMSE) below 5 mm in horizontal components and approximately 10 mm in vertical components. The resulting attitude accuracies reach 2° in heading, 6° in pitch, and 4° in roll, while eliminating over 80% of systematic environmental phase errors with an average convergence within 6 iterations. These results satisfy the spatial accuracy requirements for effective spoofing suppression and front-end signal purification. Consequently, a robust technical approach is established for enhancing the anti-spoofing capabilities of PTSS without modifying existing infrastructure. Full article

Multi-objective evolutionary optimization faces significant challenges due to guidance mismatch under complex Pareto-front geometries. This paper proposes a dynamic self-organizing map-assisted evolutionary algorithm (D-SOMA), a manifold-aware framework that harmonizes knowledge-informed priors with unsupervised objective-space characterization. Specifically, a knowledge-informed guided resampling strategy is formulated to bridge stochastic initialization and targeted exploitation. By distilling spatial distribution priors from the decision-variable boundaries of early-stage elite solutions, it establishes a high-quality starting population biased towards promising regions. To capture the intrinsic geometry of the evolving population, a self-organizing map (SOM)-based adaptive subregion characterization strategy leverages the topological preservation of self-organizing maps to extract latent modeling parameters. This strategy adaptively determines subregion centers and influence radii, enabling a data-driven partitioning that respects the underlying manifold structure. Furthermore, a density-driven phase-responsive scale adjustment strategy is introduced. By synthesizing spatial density feedback and temporal evolutionary trajectories, it dynamically modulates the characterization granularity K, thereby maintaining a rigorous balance between geometric modeling fidelity and computational overhead. Extensive experiments on 50 benchmark problems from the DTLZ, WFG, MaF and RWMOP suites demonstrate that D-SOMA is statistically superior to seven state-of-the-art algorithms, exhibiting robust convergence and superior diversity across diverse problem landscapes. Full article

Background: Hypoxic and oxidative stress states tightly regulate bone and periodontal remodeling, yet the field lacks an integrated conceptual framework explaining how fluctuating oxygen availability and redox signaling determine anabolic versus catabolic outcomes. Although hypoxia-inducible factor-1α (HIF-1α), reactive oxygen species (ROS), and reperfusion injury are individually well-studied, their coordinated role in defining tissue remodeling thresholds remains unclear. Methods: This Perspective synthesizes mechanistic evidence from cellular, molecular, and tissue-level studies on hypoxia, redox biology, perfusion dynamics, osteoimmunology, and bone remodeling. Published data were evaluated to characterize how oxygen tension, ROS generation, and inflammatory signaling interact under mechanical or metabolic stress. A conceptual model (“Hypoxostat Model”) was constructed to describe the regulatory balance between hypoxia-driven catabolism and oxygenation-driven anabolism. Hypothesis: The Hypoxostat Model proposes that tissues operate within a dynamic oxygen-dependent regulatory window. Moderate hypoxia transiently activates HIF-1α, angiogenesis, and osteogenic compensation, whereas deeper or sustained hypoxia collapses perfusion, increases ROS, amplifies IL-1β/TNF-α/IL-17A signaling, and promotes RANKL-mediated osteoclastogenesis. Reoxygenation phases trigger additional oxidative bursts, further biasing tissues toward destructive remodeling. Thin periodontal phenotypes exhibit reduced perfusion reserve and increased sensitivity to hypoxia–ROS transitions, lowering their threshold for entry into catabolic remodeling domains. Conclusions: Hypoxia and redox signaling function as a bistable regulatory system controlling bone and periodontal remodeling. The Hypoxostat Model provides a unifying framework linking oxygen tension, ROS dynamics, inflammatory cytokines, and remodeling outcomes. Recognizing hypoxia–reoxygenation behavior as a mechanistic switch may improve prediction of tissue vulnerability and guide therapeutic strategies aimed at modulating redox balance or enhancing local perfusion. Full article

A robust and cost-effective fiber-optic ultrasound sensor based on a slope-symmetric Fabry–Perot interferometer (FPI) is presented, employing dual-channel quadrature-biased heterodyne interrogation with an acousto-optic modulator (AOM). By introducing a 200 MHz frequency shift that yields an effective π/2 phase offset between the direct (unshifted) and frequency-shifted optical paths, the system ensures complementary sensitivity: when one channel operates at zero slope on the FPI transfer function (minimum sensitivity), the other resides at maximum slope, providing inherent immunity to laser wavelength drift and environmental perturbations. Experimental validation demonstrates reliable ultrasound detection across varying operating points. At quadrature extremes, one channel achieves peak amplitudes of ±2 V while the other is quiescent, whereas intermediate points enable simultaneous detection with amplitudes of ±1.5 V (AOM channel) and ±0.05–0.1 V (direct channel), accompanied by corresponding DC levels ranging from ~0.4 V to 1.6 V. The AOM channel utilizes simple envelope detection after 9.5–11.5 MHz bandpass filtering, maintaining low cost, though coherent mixing is suggested for enhanced weak-signal performance. The angle-symmetric FPI design, combined with gold-disk reflector adaptations and potential femtosecond laser micromachining, further reduces fabrication costs without sacrificing finesse or sensitivity. This quadrature-biased approach offers superior stability compared to single-channel systems, making it highly suitable for practical applications in photoacoustic imaging, nondestructive testing, and structural health monitoring. Full article

Invariant natural killer T (iNKT) cells are a unique lymphocyte subset that bridge innate and adaptive immunity through recognition of glycolipid antigens presented by CD1d. Upon activation by ligands such as α-galactosylceramide (α-GalCer), iNKT cells rapidly secrete cytokines, including IFN-γ and TNF-α, thereby activating dendritic cells, natural killer (NK) cells, and cytotoxic T lymphocytes (CTLs) to promote antitumor immunity. Despite their therapeutic promise, clinical translation has been limited by rapid α-GalCer clearance, induction of iNKT cell anergy following repeated stimulation, and the immunosuppressive tumor microenvironment (TME). Recent advances in lipid-engineered nanoparticle systems offer solutions to these challenges by improving ligand stability, enhancing antigen-presenting cell targeting, and enabling controlled release that sustains Th1-biased activation while reducing anergy. Liposomal and polymer-based nano-formulations enhance bioavailability and promote more durable IFN-γ-mediated responses. In parallel, chimeric antigen receptor (CAR)-engineered iNKT cells provide antigen-specific tumor targeting while preserving intrinsic CD1d-restricted immunomodulatory functions, demonstrating encouraging safety and efficacy in early-phase studies. Combination strategies further strengthen iNKT-based immunotherapy. Integration with chemotherapy, immune checkpoint inhibitors such as anti-PD-1 and anti-CTLA-4, and cytokine support enhances effector activation, counteracts TME-induced suppression, and improves therapeutic outcomes. However, challenges remain, including optimization of dosing, control of off-target immune activation, scalable manufacturing, and long-term safety evaluation. Collectively, the convergence of nanotechnology, CAR engineering, and rational combination approaches establishes iNKT cell-based therapy as a promising next-generation immunotherapeutic strategy. Continued refinement of delivery systems, genetic engineering platforms, and translational protocols may enable durable immune reprogramming and improved clinical outcomes in resistant and immunosuppressive cancers. Full article

Ground vortex formation beneath aeroengine intakes during near-ground operations represents an energy-related aerodynamic issue, as it degrades inlet flow quality, induces pressure distortion, and reduces the effective utilization of incoming kinetic energy. This study investigates the unsteady characteristics of ground vortex flow under headwind conditions and its influence on foreign object ingestion (FOI) in an aeroengine intake. Three-dimensional unsteady Reynolds-averaged Navier–Stokes (URANS) simulations coupled with a Lagrangian Discrete Phase Model (DPM) are employed to resolve the interaction between intake-induced vortices and dispersed particles near the ground. The results indicate that the ground vortex rapidly develops into a quasi-periodic state, generating significant unsteady total pressure distortion at the intake face, with peak fluctuations reaching approximately 10% of the mean value. This flow non-uniformity reflects a deterioration of inlet energy distribution and is detrimental to downstream compression efficiency. Particle ingestion behavior is strongly dependent on particle density and diameter. Low-density and small particles are more readily entrained into the vortex core and ingested, whereas particles with higher density or larger size exhibit increased inertia and reduced sensitivity to vortex-induced energy transport. The ingestion region is biased toward the lower portion of the intake, consistent with the vortex core location. These findings provide insight into vortex-induced energy distortion and FOI mechanisms, offering guidance for improving aeroengine intake design and energy-efficient operation during near-ground conditions. Full article

Accurate identification of Alzheimer’s disease (AD)-related cellular characteristics from microscopy images is essential for understanding neurodegenerative mechanisms at the cellular level. While most computational approaches focus on macroscopic neuroimaging modalities, cell type classification from microscopy remains relatively underexplored. In this study, we propose a hybrid vision transformer–convolutional neural network (ViT–CNN) framework that integrates DeiT-Small and EfficientNet-B7 to classify three AD-related cell types—astrocytes, cortical neurons, and SH-SY5Y neuroblastoma cells—from phase-contrast microscopy images. We perform a comparative evaluation against conventional CNN architectures (DenseNet, ResNet, InceptionNet, and MobileNet) and prompt-based multimodal vision–language models (GPT-5, GPT-4o, and Gemini 2.5-Flash) using zero-shot, few-shot, and chain-of-thought prompting. Experiments conducted with stratified fivefold cross-validation show that the proposed hybrid model achieves a test accuracy of 61.03% and a macro F1 score of 61.85, outperforming standalone CNN baselines and prompt-only LLM approaches under data-limited conditions. These results suggest that combining convolutional inductive biases with transformer-based global context modeling can improve generalization for cellular microscopy classification. While constrained by dataset size and scope, this work serves as a proof of concept and highlights promising directions for future research in domain-specific pretraining, multimodal data integration, and explainable AI for AD-related cellular analysis. Full article

This paper proposes an advanced three-dimensional optical encryption technique based on double random phase encryption for the simultaneous encryption of two primary datasets. While conventional double random phase encryption offers high-speed encryption, it suffers from low data efficiency. To address this issue, the proposed method assigns the first primary dataset to the amplitude and the second to the phase. However, this approach faces a critical limitation: the phase information becomes undefined or lost when the amplitude is zero. Therefore, we introduce a biased amplitude encoding scheme for double random phase encryption to ensure the mathematical recoverability of the phase component. In the proposed method, a biased value $ϵ$ is added to the amplitude part during the double random phase encryption encryption process and subsequently subtracted from the decrypted data to recover the two primary datasets. To verify the effectiveness of our approach, we employ synthetic aperture integral imaging and volumetric computational reconstruction. The experimental results show that while the first dataset remains lossless, the lossy characteristics of the second dataset are significantly mitigated. Full article

A hybrid graphene-VO₂ reconfigurable terahertz metamaterial absorber is proposed for broadband radar cross-section (RCS) reduction and high-performance sensing. The designed structure leverages the phase transition property of VO₂ and the electrostatic tunability of graphene to achieve dynamic switching between ultra-broadband and narrowband absorption states. When VO₂ is in the metallic state and graphene is unbiased, the absorber exhibits over 90% absorption across 0.82~3.50 THz, corresponding to a relative bandwidth of 124%. In the narrowband mode, with VO₂ in the insulating state and graphene biased (E_f = 1 eV), a sharp absorption peak exceeding 60% is achieved at 1.48 THz. The symmetrical design ensures polarization insensitivity and wide-angle stability. Applications in broadband RCS reduction higher than 10 dB and refractive index sensing with a sensitivity of 24.86 GHz/RIU are demonstrated, surpassing conventional terahertz sensors. This work provides a promising platform for adaptive terahertz stealth and sensing systems. Full article