Search Results (836)

Cannabinoids (CBs) derived from Cannabis sativa have attracted growing interest for dermatological applications due to their anti-inflammatory, antiproliferative, antimicrobial, antifibrotic, and antipruritic properties. However, their clinical translation is significantly limited by physicochemical and pharmacokinetic challenges, including poor aqueous solubility, lipophilicity, instability, variable skin penetration, and inconsistent bioavailability. At the molecular level, CBs modulate keratinocyte proliferation, sebocyte activity, fibroblast function, melanocyte balance, and immune signalling through CB1/CB2 receptors, TRP channels, and PPARγ pathways. Evidence supports their potential in the treatment of psoriasis, atopic dermatitis, acne, allergic contact dermatitis, pruritus, scleroderma, and skin cancers. Clinical evidence remains preliminary: topical and oral formulations have demonstrated anti-inflammatory, antiproliferative, antibacterial, and antifibrotic effects, with improvements in pruritus, lesion severity, and quality of life in early-phase studies. However, most trials are small, uncontrolled, and lack placebo comparators, limiting generalisability. To overcome formulation barriers and enhance dermal delivery, advanced pharmaceutical strategies such as liposomes, nanoemulsions, polymeric nanoparticles, micelles, and transdermal systems have been investigated to improve stability, controlled release, and targeted skin deposition while minimising systemic exposure. This review integrates mechanistic insights, clinical evidence, and emerging nanotechnology-enabled delivery approaches, emphasising rational formulation design and translational considerations necessary for advancing CBs toward standardised and clinically reliable dermatological therapeutics. Full article

Point cloud semantic segmentation is a core component in indoor scene understanding and autonomous driving. Under weak point-level supervision, only a small subset of points is annotated, making effective use of unlabeled points critical yet non-trivial. Many existing approaches rely on prediction confidence to filter pseudo labels or enforce consistency, which can bias training toward easy points and amplify early mistakes. Consequently, confidently wrong predictions may be reinforced, while uncertain points around class boundaries or in geometrically complex regions are less utilized, limiting further gains. An evidential uncertainty decomposition framework is introduced for weakly supervised point cloud semantic segmentation. Network outputs are interpreted as evidential distributions, and uncertainty is decomposed to separate lack-of-knowledge uncertainty from boundary-related ambiguity, providing a more informative reliability signal for unlabeled points. Based on this signal, different constraints are applied to different subsets: reliable points are trained with pseudo labels together with prototype-based regularization to encourage intra-class compactness; boundary-ambiguous points are guided by evidential consistency to improve boundary learning; and points with high epistemic uncertainty are excluded from pseudo-label-based supervision to mitigate error reinforcement. In addition, an uncertainty calibration term on sparsely labeled points helps stabilize training. Experiments on S3DIS, ScanNet-V2, and SemanticKITTI yield 67.7%, 59.7%, and 53.3% mIoU, respectively, with only 0.1% labeled points, comparing favorably with prior weakly supervised point cloud segmentation methods. Full article

Toll-like receptor 7 (TLR7) agonism offers a promising avenue for antiviral intervention. This study characterizes AXC-715, a novel small-molecule agonist that selectively targets TLR7 to elicit broad-spectrum antiviral effects. Structural analysis of the AXC-715–hTLR7 complex (PDB ID: 5GMH) elucidates the molecular basis of receptor activation. AXC-715 occupies the interface of TLR7 monomers, establishing critical hydrogen bonds with D555 and T586, alongside π-π and π-alkyl interactions with F408, V381, and L557. These interactions effectively promote and stabilize the active TLR7 dimeric conformation. Functionally, AXC-715 activates NF-κB signaling in a P65-dependent manner without inducing cytotoxicity in PK-15 or THP-1 cells. In vitro assays demonstrated that AXC-715 potently inhibits the replication of both pseudorabies virus (PRV) and vesicular stomatitis virus (VSV) by specifically impairing viral replication, distinct from adsorption, entry, assembly, or release processes. The antiviral effect was abolished in TLR7-knockout PK-15 cells, confirming the strict dependence of AXC-715 on on-target TLR7 signaling. These findings highlight AXC-715 as a potent TLR7 agonist that stabilizes receptor dimerization to inhibit viral replication, providing a valuable framework for developing TLR7-based antiviral therapeutics. Full article

Treatment for large critical-sized bone defects and impaired fracture healing remain challenging. Clinically used protein-based osteoinductive factors, such as recombinant bone morphogenetic proteins (BMPs), can be effective; however, they are costly and limited by stability, dose-delivery issues, and safety concerns. Preclinical small molecules offer an alternative because they are chemically stable, scalable to manufacture, and readily integrated for systemic administration or localized release from scaffolds, hydrogels, cements, and implant coatings. With an emphasis on delivery formats and mechanistic themes, this review examines small molecules that have been shown to improve bone regeneration in preclinical models, contrasting those of biological origin with synthetic and repurposed compounds. Across studies, these selected compounds promote osteoblast commitment, differentiation, and matrix mineralization via BMP/Smad signaling and Wnt/beta-catenin (β-catenin) activation, often through glycogen synthase kinase-3 beta (GSK-3β) inhibition or relief of pathway antagonism or Hedgehog (Hh) pathway stimulation. Beyond osteoinduction, several candidates address issues that commonly limit repair, including angiogenesis, oxidative stress, inflammatory tone, osteoimmune regulation, and suppression of osteoclast-mediated resorption. Direct head-to-head comparisons are rare across both classes and reporting heterogeneity complicates interpretation. Key translational gaps include limited cytotoxicity and immunologic profiling, dose and release optimization, durability of benefit, and insufficient evaluation of rational combinations. More rigorous in vivo studies, including larger animal models and standardized outcome metrics, are needed to prioritize promising candidates and guide clinical development. Full article

Ultra-precision thinning of hard and brittle materials like monocrystalline silicon demands high dynamic stability in thinning spindle. To address the challenge of accurately detecting subtle spindle chatter anomalies in industrial environments characterized by high noise and limited data, this paper proposes a physics-guided dual-stream attention fusion transfer network (PG-AFNet). First, a physics-guided signal preprocessing method was developed. Using variational mode decomposition (VMD) and continuous wavelet transform (CWT) masking, one-dimensional dynamic features and high-frequency regions of interest (ROIs) rich in transient impact features were extracted. Second, the PG-AFNet architecture was designed. By introducing an attention mechanism, it achieves deep integration of one-dimensional purely dynamic sequences with two-dimensional spatiotemporal visual textures to capture surface damage features caused by subtle vibrations. Finally, systematic validations were conducted using a real silicon wafer thinning dataset with 197 real samples. By overcoming small-sample limitations via physical augmentation, PG-AFNet achieved an 82.45% (86.64% after data augmentation) diagnostic accuracy, significantly outperforming traditional baselines. Furthermore, a large-scale cross-load validation on the diverse CWRU dataset yielded an exceptional 99.68% accuracy under mixed-load conditions, conclusively verifying the model’s robust domain generalization. Lastly, a rigorous ablation study explicitly quantified the indispensable contributions of the physics-guided dual-stream architecture and attention fusion. This research provides a feasible theoretical foundation for intelligent surface quality monitoring in semiconductor hard-brittle material processing. Full article

Distributed beamforming in UAV swarms requires fast and accurate carrier-phase alignment under sparse connectivity and propagation-induced phase bias. This paper proposes a physics-aware decentralized synchronization framework for quasi-static UAV swarm beamforming by integrating momentum-accelerated Metropolis–Hastings consensus with position-aided phase pre-compensation. To preserve phase evolution on the circular manifold, a sinusoidal coupling law is adopted, while the momentum term improves convergence in sparse random geometric graphs. A propagation model is further established to characterize how geometric separation and ranging uncertainty translate into residual phase error and coherent power loss. Under small-signal conditions, local stability is analyzed, and Monte Carlo simulations are conducted to evaluate convergence, synchronization accuracy, robustness, and beam-focusing performance. Results show that, at 2.4 GHz with low-centimeter ranging uncertainty, the proposed method achieves sub-wavelength synchronization accuracy while providing an effective balance among convergence speed, accuracy, and complexity. Compared with standard Metropolis–Hastings, fixed-weight, and other accelerated consensus methods, the proposed scheme converges faster over most sparse topologies. Although its steady-state accuracy is slightly lower than that of filter-based predictive methods such as KF-DFPC in some cases, those schemes incur higher implementation and computational overhead. Therefore, from the perspectives of decentralized realization and practical deployment, the proposed method is more suitable for lightweight phase synchronization in distributed UAV swarms. Full article

A thin-film surface plasmon resonance (SPR) sensor is presented using a prism-coupled Kretschmann configuration and an optimized multilayer architecture incorporating black phosphorus (BP) as an ultrathin overlayer. The response is modeled at 633 nm under TM polarization using the transfer-matrix method. Low-concentration sensing conditions in the 1–5 ng/mL range are represented through small effective-refractive-index perturbations of the aqueous sensing medium, providing a preliminary optical framework for evaluating refractive-index response in biosensing-related scenarios. The coupling prism, Au film thickness, and Si₃N₄ spacer thickness are optimized to control resonance depth, linewidth, and angular shift. The optimized CaF₂/Au/Si₃N₄/BP configuration exhibits systematic condition-dependent displacement of the SPR minimum and an evanescent-field distribution that remains strongly localized at the sensing interface while extending into the sensing medium, enabling refractive-index interrogation. High angular sensitivity is obtained at low levels, reaching 517.62°/RIU at 2 ng/mL and 482.82°/RIU at 1 ng/mL, with quality factors above 120 RIU⁻¹ in the same regime. Composite indicators (figure of merit and contrast signal factor) peak at intermediate levels, whereas resonance broadening at higher levels reduces the quality factor and increases the inferred limit of detection, evidencing a sensitivity–resolution trade-off. Benchmarking against reported SPR platforms indicates that BP-assisted interface engineering provides a competitive low-level operating window within a preliminary refractive-index-sensing framework that is relevant to future biosensor design. These results motivate further experimental validation, including BP stabilization, surface biofunctionalization, and practical implementation under liquid-phase sensing conditions. Full article

Measuring soil moisture is crucial for optimizing agricultural irrigation, but also, from an energy efficiency standpoint, for the proper design of very-low-enthalpy geothermal energy (VLEGE) facilities. VLEGE represents a renewable energy resource with great potential for residential and industrial applications, as it can provide heating and cooling with high energy efficiency and minimal environmental impact. Soil moisture plays a decisive role in the thermal performance of VLEGE facilities, where small variations in water content can significantly alter the thermal conductivity of the soil and, consequently, the efficiency of their horizontal heat exchangers. This paper presents a low-cost capacitive soil moisture sensor featuring optimized interdigitated electrodes and a controlled dielectric coating that ensures mechanical and electrical stability in subsurface environments. The novelty of this work lies in the validated integration of optimized IDE design, dielectric protection, embedded capacitance acquisition, and gravimetric calibration into a low-cost soil water content measurement device for environmental, agricultural, and VLEGE applications. The developed system converts capacitance variations into direct estimates of soil water content through an integrated microcontroller-based signal-conditioning stage. The developed device is robust, reliable, and readily reproducible. Furthermore, given its low cost (around €50 if manufactured manually; mass-produced it would be much cheaper) and its excellent sensitivity and precision, it is ideal for setting up continuous monitoring networks, even for domestic applications, both in VLEGE installations and in other application domains, such as agriculture and environmental monitoring, where soil moisture measurement is a crucial parameter. This work contributes to the development of more efficient and accessible solutions for harnessing geothermal energy, particularly in installations where dynamic tracking of soil moisture is essential to ensure stable long-term performance. Full article

The hybrid parallel operation of the grid-following (GFL) converter and the grid-forming (GFM) converter has become a typical scenario in distribution networks. The vastly different control philosophies and dynamics between the two give rise to complex small-signal stability issues, especially under weak grids. Traditional methods primarily rely on equivalent models or impedance-based approaches at fixed operating points, which struggle to reveal the system instability mechanisms when the capacity ratio between the two types of converters changes. This paper establishes a three-port dynamic average model for a grid-connected system with heterogeneous GFL-GFM converters. Using the participation factor analysis method, the system’s dominant modes are identified, and the key parameters influencing oscillations at different frequencies, as well as their formation processes, are revealed. Furthermore, a stability analysis method for variable capacity ratios is proposed. This method re-performs modal analysis based on the varying capacities of the GFM and GFL converters, revealing the dominant factors and influencing mechanisms of system instability during capacity transitions. Finally, a simulation model is built in PSCAD/EMTDC to verify the correctness of the proposed three-port model and the theoretical analysis results. Full article

The cleaning unit is a key functional component of grain combine harvesters, yet its operating parameters are still predominantly adjusted according to operator experience, resulting in limited adaptability to fluctuating working conditions. To enhance the intelligence and stability of the cleaning process, this study develops a fuzzy control approach supported by data-driven performance modeling. Based on multi-condition bench experiments, feeding rate, fan speed, cleaning sieve vibration frequency, and sieve opening were selected as input variables. Gaussian Process Regression (GPR) models were established to describe the nonlinear relationships between operating parameters and cleaning loss rate and impurity rate, and impurity rate was inferred online to compensate for the absence of a reliable sensor. Taking feeding rate variation as the primary disturbance, a dual-input fuzzy control strategy was designed using loss rate monitoring and model-predicted impurity rate as feedback signals. Simulation and bench test results show that, under small and moderate load disturbances (±20% and ±35%), the proposed method reduces either impurity rate or cleaning loss rate through coordinated parameter adjustment. Under large disturbances (±50%), performance deterioration cannot be fully eliminated, but its extent is alleviated compared with open-loop conditions. Full article

Diabetic foot ulcers (DFUs) are among the most serious and costly complications of diabetes, characterised by delayed healing, frequent infections, and a high risk of recurrence. Despite advances in wound care, many current therapies fail to address the multifactorial pathophysiology of diabetic wounds, including vascular dysfunction, immune dysregulation, chronic inflammation, and microbial imbalance. In this context, topical probiotics have emerged as a promising microbiome-based strategy aimed at restoring microbial balance while promoting tissue repair. This review summarises current evidence on the use of topical probiotics in diabetic wound healing, with a particular focus on DFUs, outlining key pathophysiological barriers to healing and examining how probiotic therapies may counteract these processes through antimicrobial, antibiofilm, immunomodulatory, and pro-angiogenic mechanisms. Preclinical studies suggest that topical probiotics may promote accelerated wound closure, reduce bacterial burden, modulate inflammatory responses, and enhance collagen deposition and angiogenesis following topical probiotic application. Early clinical studies investigations remain limited to small pilot studies and case series but have reported preliminary signals of enhanced healing and acceptable short-term tolerability in small exploratory cohorts. In addition, recent advances in probiotic delivery, such as bioengineered dressings, postbiotic formulations, and nano-enabled systems designed to improve stability and therapeutic performance, are also discussed. While existing data indicate biological plausibility and early clinical feasibility, larger, well-designed randomized controlled trials and deeper mechanistic investigations are still required to confirm efficacy, clarify safety in high-risk populations, and enable responsible clinical translation. Full article

With the growing significance of renewable energy resources and energy storage systems, the number of grid-connected inverters has been rising at an increasingly rapid pace. Generally, these inverters are directly integrated with the distribution network by synchronizing with the grid voltage at the point of common coupling. However, the low grid strength and varying R/X ratios, as the common characteristics of most distribution networks or weak grids, can lead to dynamic interactions that comprise stability and limit the power transfer capacity of grid-connected inverters. To ensure stable operation of the inverters, researchers must determine the stability boundary, described as the maximum power transfer capacity of grid-connected inverters under the premise of maintaining system small-signal stability. For this purpose, we propose to formulate a state-space model of the system in the synchronously rotating dq-frame of reference and perform eigenvalue analysis to determine the stability boundary. With a detailed model of the control structure and parameters of the grid-connected inverters, the stability boundary is identified as a surface with respect to different grid strengths and R/X ratios. Case study results of proposed eigenvalue analysis are compared with those of admittance model-based stability analysis as well as time-domain simulation using a switching model in Matlab/Simulink, validating the effectiveness and accuracy of the proposed eigenvalue analysis for stability boundary identification. Full article

Infrared small target detection remains challenging in applications such as long-range surveillance and early warning due to the fact that infrared images rely on thermal radiation, which results in limited texture cues and a low signal-to-noise ratio for the targets. Although recent deep networks have improved representation capability, they often exhibit two persistent limitations. Fine target details are gradually weakened through successive downsampling, and edge-related priors are not sufficiently exploited to stabilize target responses under background interference. To alleviate these issues, an Edge-Prior Guided Dual-Branch Enhancement Network (EGDENet) is proposed, a dual-branch framework that injects edge priors into feature learning for infrared small target detection. An auxiliary edge-aware branch is introduced to complement the main encoder–decoder stream. Specifically, a Multi-directional Sobel Edge Extraction (MSEE) module is designed to adaptively reweight multi-directional edge responses, thereby strengthening boundary-sensitive representations. Furthermore, a Difference-Aware Gated Fusion (DAGF) module leverages Gated Spatial Convolution to capture subtle variations in the features and employs depthwise separable convolution along with adaptive enhancement to effectively integrate the extracted edge information. In addition, an Edge Pixel Integration (EPI) Loss is present to couple edge sensitivity with pixel-wise supervision. This loss improves the edge sensitivity of infrared small targets. The proposed EGDENet is evaluated on three benchmark datasets: NUAA-SIRST, IRSTD-1K, and SIRST-Aug. The experimental results show that our method outperforms or matches the performance of state-of-the-art methods. Full article

Industrialization has caused extensive environmental change, including a global surge in plastic production and pollution. This has resulted in the accumulation of microplastics (MPs; <5 mm) and nanoplastics (NPs; <1 μm) in ecosystems worldwide. MPs originate from both primary sources, such as cosmetics and industrial applications, and secondary sources, through the degradation of larger plastic debris. As a result, MPs and NPs have become ubiquitous contaminants, posing significant toxicological risks to living organisms. These persistent pollutants are diverse polymers that vary in size, shape, and chemical composition, making their impacts on organism physiology complex and difficult to disentangle. Plastic pollution is particularly severe in aquatic environments, where particles accumulate from terrestrial sources such as urban dust, agricultural runoff, industrial discharges, and wastewater effluents. Although most research has centered on marine ecosystems, emerging evidence indicates that freshwater environments may contain comparable or even higher concentrations of MPs. Once inside the body, MPs can translocate into tissues and exert toxic effects on multiple organ systems. Collectively, plastic pollution poses not only physiological but also neurological and behavioral risks to aquatic life, with potential consequences for ecosystem stability and trophic interactions. Both MPs and NPs are sufficiently small to cross the blood–brain barrier, raising concerns about their potential impacts on the nervous system by interfering with neuronal function and brain development. Plastic particles can accumulate in neural tissues, inducing oxidative stress, neuroinflammation, and disruption of neurotransmitter signaling. Such neurotoxic effects are linked to altered locomotion, feeding, predator avoidance, and social behaviors across multiple species. This review examines current evidence on the neurotoxic effects of plastic pollution in aquatic organisms and underscores the urgent need for further research and action to mitigate its impact. In light of escalating plastic production and inadequate waste management, the growing evidence that MPs and NPs disrupt aquatic nervous systems, behavior, and ecosystem stability underscores an urgent need for intensified research, improved mitigation strategies, particularly for nanoplastics, and the accelerated development of truly safe and sustainable alternatives. Full article

Mitigating pollution in cities where transportation powered by fossil fuels has a significant impact on human health is a public health priority. Although electric vehicles are one solution to this problem, their high acquisition and maintenance costs have limited their rapid adoption; therefore, other solutions may be useful in supporting reduction efforts. Therefore, this paper proposes a power control system for an Anion Exchange Membrane Fuel Cell (AEMFC) generator powered by hydrogen with the capacity to supply a direct current (DC) motor of 0.75 kW. A mathematical model of the AEMFC was proposed, and the parameters were adjusted to obtain polarization and power curves defining safe operating ranges (12.45–17.9 V). A boost converter was designed to increase the voltage of the cell output to 48 V to meet the requirements of the DC motor. The performance of the power converter was studied by analyzing its small-signal ripple, operating modes, and efficiency. The models and simulations were implemented using MATLAB and PSIM. A cascaded control system with proportional–integral (PI) and proportional–integral–derivative (PID) controllers was implemented to maintain voltage stability in the presence of input and load variation. The results show that the AEMFC is reliable and that the boost converter presents an efficiency higher than 98% in continuous mode. The robustness of the model was validated through simulations and using a prototype. Full article