In B5G and 6G wireless networks, large-scale antenna arrays introduce spherical wavefronts in the near-field region, presenting both opportunities and challenges for communication and positioning. To address this, we propose a novel near-field positioning method using a bio-inspired dendritic liquid neural network (DLNN). The DLNN features a multi-branch dendritic structure, with each neuron processing signals through multiple parallel dendrites and a dynamic gating mechanism to adapt the weighting and fusion of outputs. This enhances its ability to capture complex spatiotemporal features. Additionally, the liquid layer with leaky integration processes time-series data, enabling strong adaptability and robust temporal memory. Simulation results show that DLNN outperforms other deep learning models in localization accuracy and robustness, with RMSE values of 0.1706 meters at 40 dBm and 0.2642 meters at 10 dBm, compared to 0.2836 meters and 0.2986 meters from other models. Its inference latency of 0.0580 seconds demonstrates excellent accuracy, speed, and energy efficiency, making DLNN highly suitable for real-time positioning in resource-constrained environments.

Abstract This paper addresses the issue of outdated channel state information (CSI) caused by the coupling between Doppler frequency shift and CSI feedback delay in high-mobility internet of vehicles (IoV) scenarios. It establishes a multi-reconfigurable intelligent surface (RIS)-assisted decode-and-forward (DF) relaying communication system model for IoV and introduces a first-order auto-regressive (AR1) channel model to characterise the correlation between the estimated CSI and the actual channel. It also proposes a dynamic RIS selection mechanism. Under generalised- k fading channels, approximate closed-form expressions are derived for the system outage probability, bit error rate (BER) and ergodic capacity based on the statistical properties of the end-to-end signal-to-noise ratio. Furthermore, the problem of maximising system energy efficiency is formulated subject to constraints on total transmit power, minimum transmission rate and BER. Block coordinate descent and a phase matching technique are then employed to obtain a closed-form solution for the RIS phase shifts. The original problem is subsequently decoupled into a one-dimensional power allocation problem. Based on this, analytical solutions are derived for the piecewise stationary points and the feasible region pruning criterion via the Lambert W function. Simulation results demonstrate that, under outdated CSI conditions, the proposed multi-RIS-assisted DF relaying cooperative scheme significantly outperforms both the DF-only and multi-RIS-only schemes. Additionally, system performance improves as the number of RIS reflecting elements increases, in line with the law of diminishing marginal returns.

Langbeinite-type Cs2UⅣLn(PO4)3 (Ln = Nd, Gd, Tm) single crystals are synthesized via the flux method. Following chemical analysis and phase characterization, their acid-base stability and chemical durability are systematically evaluated. The acid-base stability (pH = 2–12) of the powdered samples reveal that these compounds possess stability across a broad pH range. Additionally, the Product Consistency Test (PCT) determine that the normalized leaching rates of Cs to be approximately 10–3–10–4 g·m−2·d−1, while those of U and Ln are about 10–7–10–9 g·m−2·d−1. These results demonstrate that Cs2UⅣLn(PO4)3 exhibits excellent chemical durability, with the normalized leaching rates of all three constituent nuclides being several orders of magnitude lower than the standard requirement for high-level radioactive waste immobilization (10−1 g·m−2·d−1). This study indicates that the phosphate langbeinite-structured Cs2UⅣLn(PO4)3 is a highly promising matrix material for the immobilization of multi-component radionuclides.

This study investigates the role of centrosomal protein CEP55 in immune evasion by liver cancer cells and evaluates the effects of its knockout using CRISPR-Cas9 technology. CEP55-knockout models were established in human hepatocellular carcinoma cell lines Huh7 and HepG2, and alterations in immune-related molecules, tumor cell behavior, and antitumor immune responses were systematically assessed. CEP55 knockout significantly reduced PD-L1 expression while upregulating MHC class I levels, thereby enhancing tumor immunogenicity. Mechanistically, CEP55 deletion attenuated STAT1 activation, particularly under interferon-γ (IFN-γ) stimulation, suggesting involvement of the IFN-γ-STAT1 signaling axis in CEP55-mediated immune regulation. In parallel, CEP55 knockout markedly decreased intracellular reactive oxygen species (ROS) levels and suppressed the secretion of immunosuppressive cytokines IL-10 and TGF-β, indicating remodeling of the immunosuppressive tumor microenvironment. Functional assays demonstrated that CEP55 deficiency inhibited tumor cell migration and invasion and promoted apoptosis. Importantly, co-culture experiments revealed that CEP55 knockout enhanced T cell effector function, as evidenced by increased secretion of IFN-γ and Granzyme B and restored T cell-mediated cytotoxicity, even in the presence of IFN-γ stimulation. Collectively, these findings indicate that CEP55 promotes liver cancer immune escape and malignant progression through modulation of STAT1-dependent PD-L1/MHC-I expression, oxidative stress, and immunosuppressive signaling. Targeting CEP55 may therefore represent a potential strategy to improve antitumor immune recognition in liver cancer.

A series of Na3Gd2(BO3)3: xSm3+ (x = 0.01–0.11) phosphors were synthesized via a high-temperature solid-state reaction method. X-ray diffraction (XRD) confirms that all prepared samples are pure phase. Scanning electron microscopy (SEM) and energy dispersive X-ray spectrometry (EDS) reveal a uniform elemental distribution. Under 406 nm excitation, the phosphor exhibits orange-red emission centered at 597 nm, with the maximum intensity achieved at x = 0.05. The critical energy transfer distance is approximately 24.59 Å, suggesting that the concentration quenching mechanism is dominated by dipole–dipole interactions. Meanwhile, the fluorescence lifetime decreases from 1.88 ms to 0.77 ms with increasing Sm3+ concentration, owing to enhanced non-radiative interactions. The Commission Internationale de l’Éclairage (CIE) chromaticity coordinates lie in the orange-red region, and the sample exhibits high color purity exceeding 99%. Furthermore, the material demonstrates exceptional thermal stability, retaining 85.9% of its initial emission intensity at 423 K with a thermal activation energy of 0.25067 eV. A white light-emitting diode (WLED) fabricated using a near-ultraviolet chip emits stable white light with CIE coordinates of (0.3018, 0.3476) and a correlated color temperature of 5963 K. These findings indicate that the Na3Gd2(BO3)3: Sm3+ is a promising orange-red phosphor for WLED applications.

This paper studies a joint active and passive beamforming design method of IRS-assisted MISO downlink vehicular-SWIPT communication system. To maximize the received energy of the vehicle terminal, the optimization is subject to constraints on total transmit power, signal to interference plus noise ratio (SINR), and IRS phase shift. The resulting non-convex optimization problem with coupled variables, is addressed through an alternate iterative approach that employs semidefinite relaxation and first-order Taylor expansion. The results demonstrate significant performance improvement with the proposed algorithm over the benchmark methods.

In this work, we propose a general neural network structure based on KAN-Transformer for the electromagnetic response prediction of Micro/nano-optical devices, which can be used for inverse design of multiple kinds of devices. Validated on multi-domain datasets, our method demonstrates high accuracy (0.5%-5% error in key parameter, including coupling efficiency, amplitude, and phase) and scalability for accelerated photonic device development. The methodology can be easily extensible to various micro/nano-optical devices, offering a scalable framework for accelerated photonic device design.

An ion-exchanged Cu-SSZ-13 catalyst exhibited excellent performance in the deep oxidation of dichloromethane, achieving a high reaction rate of 2.41 mmol g-1 h-1 at 200 °C without producing chlorine-containing byproducts. Detailed characterizations indicated abundant isolated Cu species, specifically, [ZCu2+(OH)]+ and Z2Cu2+, in the catalyst. Quantitative analysis of the activity results demonstrated that the Z2Cu2+ in a 6-membered ring had the highest activity (with a turnover frequency of 1.8 × 10-3 s-1 at 200 °C), which was 5-fold higher than that of the [ZCu2+(OH)]+ species in an 8-membered ring and 9-fold higher than the acid sites in the SSZ-13 support. Importantly, the isolated Cu species remained intact during the reaction, ensuring good catalyst stability. In contrast, CuO particles, present in a higher amount in an impregnated Cu/SSZ-13 catalyst, were readily chlorinated, leading to notable deactivation. Moreover, theoretical calculations unraveled the reaction routes, clarifying the crucial role of the frustrated Lewis pair (Cu6mr···O4mr) species in accelerating the reaction. Therefore, this study not only broadens the application scope of SSZ-13-based catalysts but also provides valuable insights for designing efficient catalysts for the catalytic removal of chlorinated volatile organic compounds.

Previous studies have shown an association between frailty and multimorbidity, but the underlying mechanisms are still unclear. Therefore, this study aimed to investigate the potential chain mediating roles of sleep quality and anxiety in the relationship between frailty and multimorbidity. This cross-sectional study used data from the first follow-up of the Liyang cohort study on chronic diseases and risk factors monitoring in China (Liyang Study), which comprised 2874 participants aged ≥ 60 years from 17 health centres in Liyang City. Multimorbidity was defined based on 13 self-reported chronic conditions. A modified version of the frailty phenotype was used to assess frailty. Sleep quality and anxiety were assessed using the Pittsburgh Sleep Quality Index and the 7-item Generalized Anxiety Disorder Scale, respectively. Spearman correlation analysis was employed to examine the correlations between frailty, sleep quality, anxiety, and multimorbidity. A chain mediation analysis of sleep quality and anxiety on the relationship between frailty and multimorbidity was conducted using the SPSS PROCESS macro (Model 6). In this study, significant correlations were observed between frailty, sleep quality, anxiety, and multimorbidity (P < 0.01). Frailty had a direct impact on multimorbidity (unstandardised coefficient [B] = 0.196, bootstrap 95

Recovery of graphite from spent lithium-ion batteries (LIBs) is of great importance to sustainable development of battery industry and environmental protection. Herein, we propose a novel and simple strategy for the reconstruction and recovery of waste graphite. By roasting mixture of waste graphite and melamine, N-doping and interlayer broadening for graphite are achieved simultaneously. The formation of C-N bond induces defect generation, and the gases produced from melamine decomposition enlarge the interlayer spacing, both of which are conducive to the transport and storage of lithium ion. Density functional theory analysis shows that the regenerated N-doped graphite (NG) has a lower adsorption energy than commercial graphite. NG electrode exhibits excellent cycling stability. It delivers a high specific capacity of 406 mAh g- 1 at 1C after 1000 cycles, which exceeds that of commercial graphite. In situ XRD analysis further demonstrates that NG electrode has good reversibility and superior lithium-ion storage ability. This research provides a facile method to accomplish the closed-loop utilization of graphite in LIBs.

Organic photothermal agents (PTAs) with high-performance near-infrared properties hold great promise for precision phototherapy and bioimaging. The development of efficient PTAs depends mainly on advancements in molecular synthesis. However, synthetic approaches for organic PTAs typically involve tedious processes and the consumption of noble metal catalysts, which could leave residues affecting the products' biosafety. In the past few years, a handful of charge transfer complex (CTC) PTAs have been reported. Unfortunately, typical CTCs disintegrate into their donor and acceptor components in water because of their stronger hydrogen bonds with water. To address this issue, facile and synthesis-free super-stable interfacial charge-transfer nanocrystals (H-CTC NPs) have been reported for increasing immunogenic cell death and efficient photoimmunotherapy against tumor recurrence. Water-dispersible H-CTC NPs between pyrene-4,5,9,10-tetrone (PT, acceptor) and indolo[2,3-alpha]carbazole (IC, donor) were prepared with strong intermolecular hydrogen bonds. With this approach, H-CTC NPs are the first examples of stable CTC NPs in water, achieving record-high stability with preferable photothermal conversion efficiency. H-CTC NPs typically cause immunogenic cell death (ICD) and photothermal tumor ablation in vivo. Moreover, distal recurrent tumors are inhibited through the immune synergism between ICD and immune checkpoint therapy. This work developed superstable CTC nanocrystals and explored new pathways for high-performance photoimmunotherapy against recurrent tumors.

This paper proposes a method for enhancing the security of wireless communication systems aided by multiple reconfigurable intelligent surfaces (RISs). The method considers not only the interactions between multiple RISs and the resulting multi-channel cascades, but also the potential eavesdropping threats to legitimate user communications. To solve the problem of maximizing secrecy rate, this paper proposes an efficient alternating optimization (AO) algorithm. The algorithm achieves joint optimization of base station beamforming and RIS phase shifts by iteratively applying sequential convex approximation (SCA) and semidefinite relaxation (SDR) techniques. The algorithm can effectively transform the complex non-convex optimization problem into a series of subproblems, and then iteratively optimize these subproblems alternately to ultimately obtain a high-quality solution to the original problem. The simulation results show that the multi-RIS aided communication system greatly improves the security performance compared to the single RIS and dual RIS system. Additionally, a comparative evaluation with three other algorithms from the literatures reveals that the proposed method outperforms existing solutions under identical parameter settings, highlighting the superiority of multi-RIS co-optimization in mitigating eavesdropping threats and enhancing information confidentiality. At the same time, we also compare the impacts of real-world situations, such as imperfect channel state information and RIS discrete phase shifts, on the security performance of multi-RIS systems. This reserach provides valuable theoretical guidance and research directions for the future implementation of multi-RIS systems in real-world scenarios.

AIM:Several kinds of harmful yeasts may cause spoilage of foods and even endanger health. As a natural active ingredient, cordycepin has been found to be effective against bacteria. However, the antifungal mechanism of the cordycepin is uncertain. In this work, the antifungal activity and mechanism of cordycepin against yeasts has been studied. METHODS AND RESULTS:The effects of cordycepin on cell structure, biomacromolecule substances, respiratory metabolism, and nucleic acid were analyzed through scanning electron microscopy (SEM), ultraviolet-visible (UV-vis) spectrophotometer, fluorescence titration, etc. The results indicated that cordycepin was more effective in antifungal activity against Saccharomyces cerevisiae, Candida albicans and Zygosaccharomyces rouxii, compared to potassium sorbate. SEM observations revealed irreversible damage to the cell wall and membrane, leading to intracellular biological macromolecules leakage, which was proved by the quantification of biological macromolecular substances. Moreover, it was found that cordycepin inhibited respiratory metabolic pathways of S. cerevisiae, C. albicans and Z. rouxii by affecting the HMP and TCA pathways. In addition, cordycepin had intercalated binding and electrostatic interaction with DNA. CONCLUSIONS:Cordycepin exhibited high antifungal activity with a minimum inhibitory concentration of 0.125-0.05 mg/ml against Saccharomyces cerevisiae, Candida albicans and Zygosaccharomyces rouxii. Cordycepin exerted antibacterial effects through damaging cell membranes, inhibiting respiratory metabolism and binding DNA to affect fungal physiological functions.

To tackle the challenge of the precise construction of catenane‐based mechanically interlocked macromolecules with well‐defined topological arrangements, starting from a novel [2]catenane building block with dendrimer growth sites, catenane‐branched dendrimers have been precisely constructed via an efficient and controllable divergent approach. Notably, to the best of our knowledge, the third‐generation dendrimer which consists of twenty‐one [2]catenane branches is the most complicated discrete [2]catenane oligomer synthesized to date. Interestingly, the switchable coconformation transformations of [2]catenane branches triggered by sodium ions and cryptands lead to an integrated and amplified expansion–contraction motion of the resultant dendrimers, achieving reversible size regulation at the nanoscale. The unique recognition sites of the resultant catenane‐branched dendrimers also facilitate the reversible binding of drug and dye guests. This work not only addresses the synthetic challenge of highly branched catenane dendrimers but also provides a new platform for dynamic supramolecular functional materials.

In this work, we propose MEDICO, a multiview deep generative model for molecule generation, structural optimization, and the SARS-CoV-2 inhibitor discovery. To the best of our knowledge, MEDICO is the first-of-this-kind graph generative model that can generate molecular graphs similar to the structure of targeted molecules, with a multiview representation learning framework to sufficiently and adaptively learn comprehensive structural semantics from targeted molecular topology and geometry. We show that our MEDICO significantly outperforms the state-of-the-art methods in generating valid, novel, and unique molecules under benchmarking comparisons, particularly achieving $\tilde {8}5 \%$ improvement compared with the state-of-the-art methods in terms of validity. Importantly, we showcase that the multiview deep learning model enables us to generate not only the molecules structurally similar to the targeted molecules but also the molecules with desired chemical properties. Moreover, case study results on targeted molecule generation for the SARS-CoV-2 main protease (Mpro) show that we successfully generate new small molecules with desired drug-like properties for the Mpro by integrating molecular docking into our model as a chemical priori, potentially accelerating the de novo design of COVID-19 drugs. Furthermore, we apply MEDICO to the structural optimization of three well-known Mpro inhibitors (N3, 11a, and GC376) and achieve $\tilde {8}8 \%$ improvement compared with the origin inhibitors in their binding affinity to Mpro, demonstrating the application value of our model for the development of therapeutics for SARS-CoV-2 infection.

Accurate prediction of pathogenic variants in human disease-associated genes would have a profound effect on clinical decision-making; however, it remains a significant challenge due to the overwhelming number of these variants. We propose graph neural network for multimodal annotation-based pathogenicity prediction (GNN-MAP), a novel deep learning framework that effectively integrates multimodal annotations and similarity relationships among variants to predict the pathogenicity of multi-type variants. Trained on the ClinVar dataset, GNN-MAP exhibits superior predictive performance in internal validation and orthogonal test datasets, accurately predicting variant pathogenicity. Notably, GNN-MAP enables accurate prediction of the pathogenicity of rare variants and highly imbalanced datasets. Furthermore, it achieves high performance in the pathogenicity prediction of inherited retinal disease-specific variants, highlighting its effectiveness in disease-specific variant prediction. These findings suggest that the robust capability of GNN-MAP to predict pathogenicity across multiple variant types and datasets holds significant potential for applications in research and clinical settings.

A small peptide encoded by a non-coding RNA (ncRNA), known as a non-coding peptide (ncPEP), is emerging as a critical regulator and biomarker in cancer, holding immense promise for immunotherapy. However, the systematic identification of ncPEPs remains a challenge because existing computational methods typically analyze peptides based on sequence alone. Sequence-only analysis overlooks the fundamental biological principle that multiple distinct peptides can be translated from a single non-coding RNA transcript, thus sharing a common transcriptional origin. Here, we address this limitation by developing HGCPep, a deep learning framework that leverages hypergraphs to model these intrinsic relationships. In our model, each ncRNA is represented as a hyperedge connecting the set of peptides it encodes, thereby enriching peptide feature representations with transcriptional context. We demonstrate that HGCPep, which integrates a hypergraph neural network with a convolutional neural network, outperforms state-of-the-art methods in identifying cancer-associated ncPEPs. Furthermore, dimensionality reduction of the learned embeddings reveals distinct clustering of ncPEPs by cancer type, illustrating how the model effectively deciphers complex biological associations. Our work introduces a new method for ncPEP analysis and provides a powerful tool for discovering novel therapeutic targets in oncology. The dataset and source code of our proposed method can be found via https://github.com/Longwt123/HGCPep_Github.

This study considers a scenario in which an Unmanned Aerial Vehicle (UAV) equipped with a Reconfigurable Intelligent Surface (RIS) cooperates with a fixed RIS to enhance communication with a mobile User Equipment (UE) vehicle. A joint optimization problem is formulated to maximize the UE's communication rate by controlling the UAV's flight trajectory and the phase shifts of both RISs. Given the system complexity and environmental dynamics, a solution is proposed that integrates a Deep Deterministic Policy Gradient (DDPG) algorithm with phase-shift alignment to optimize continuous UAV trajectories and RIS configurations. Simulation results confirm that the proposed method achieves stable reward convergence within 1,000 training episodes. Compared with benchmark approaches, the algorithm improves communication rates by at least 3 dB over random trajectory and phase-shift strategies in dual-RIS deployments. The study further presents optimal UAV trajectories under varying base station and RIS placements and evaluates algorithm performance across different vehicle speeds. Objective This study investigates a vehicular communication scenario in which a UAV-mounted RIS cooperates with a fixed RIS to assist a mobile UE device. A joint optimization framework is established to maximize UE communication rates during movement by simultaneously optimizing the UAV trajectory and the phase shifts of both RISs. To address system complexity and environmental dynamics, a DDPG algorithm is employed for continuous trajectory control, while a low-complexity phase-shift alignment method configures the RISs. Simulation results show that the proposed algorithm achieves stable reward convergence within 1,000 training episodes and improves communication rates by at least 3 dB compared with randomized trajectory and phase-shift baselines. It also outperforms alternative reinforcement learning approaches, including Twin Delayed Deep Deterministic policy gradient (TD3) and Soft Actor-Critic (SAC). Optimal UAV trajectories are derived for various base station and RIS deployment scenarios, with additional simulations confirming robustness across a range of vehicle speeds. Methods This study establishes a Multiple-Input Single-Output (MISO) system in which a UAV-mounted RIS cooperates with a fixed RIS to support mobile vehicular communication, with the objective of maximizing user information rates. To address the complexity of continuous trajectory control under dynamic environmental conditions, a DDPG-based algorithm is developed. The phase shifts of RIS elements are optimized using a low-complexity alignment method. A reward function based on the achievable information rates of vehicular users is designed to guide the agent's actions and facilitate policy learning. The proposed framework enhances adaptability by dynamically optimizing UAV trajectories and RIS configurations under time-varying channel conditions. Results and Discussions (1) The convergence behavior of the DDPG algorithm is verified in Fig. 3, where the reward values progressively converge as the number of training episodes increases. (2) Fig. 4 shows the effect of varying the number of RIS elements on system performance, indicating that additional elements lead to a steady increase in reward values, confirming the channel gain enhancement provided by RIS deployment. (3) As shown in Fig. 5, the DDPG algorithm outperforms baseline methods and demonstrates greater adaptability to target scenarios; concurrently, optimized RIS phase shifts yield significantly higher rewards than random configurations, validating the proposed phase-alignment strategy. (4) Figs. 6'7 highlight notable variations in UAV trajectories and system performance across different base station and RIS deployments, demonstrating the adaptability of the trajectory optimization strategy. Fig. 8 further compares performance across scenarios with optimized UAV trajectories, highlighting the algorithm's versatility. (5) System performance under different UE mobility speeds is evaluated in Fig. 9, showing a performance decline at higher speeds, indicating strong efficacy in low-speed environments but reduced effectiveness under high-speed conditions. These results collectively illustrate the operational strengths and limitations of the proposed framework in dynamic vehicular communication systems. Conclusions This paper investigates a vehicular communication scenario assisted by both fixed and UAVmounted mobile RISs, aiming to maximize UE information rates under dynamic mobility conditions. A joint optimization framework is developed, combining dual-RIS phase shift alignment based on channel state information with UAV trajectory planning using a DDPG algorithm. The proposed method features a low-complexity design that addresses both network architecture and RIS configuration challenges. Extensive simulations under varying vehicular speeds, RIS element counts, and base station deployments demonstrate the algorithm's superiority over SAC, TD3, and randomized phase shift strategies. Results further highlight the framework's adaptability to heterogeneous base station-RIS topologies and reveal performance degradation at higher vehicle speeds, indicating the need for future research into real-time adaptive mechanisms.

This paper studies the jointly robust beamforming design for reconfigurable intelligent surface (RIS) assisted internet of vehicle (IoV) where the transceiver antennas have impairments. In order to obtain the optimal robust beamforming, the optimization problem is formulated to maximize the total spectral efficiency (or sum rate per Hertz) of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) users with transceiver hardware impairments subject to the signal to interference plus noise ratio (SINR) requirements, the interference threshold between vehicle users, the limited total transmit power, and RIS phase shift constraints. To solve this complex non-convex problem, an alternating optimization method is proposed to obtain the optimal transmission precoding vectors and RIS reflection matrix. The simulation results have verified that it is necessary to consider hardware impairments for jointly designing the active and reactive beamforming in multi-antenna transmission scenario and there are 0.1 bit/s/Hz uniform growth and 0.2 bit/s/Hz linear growth in terms of spectral efficiency compared with non-robust algorithm and without RIS algorithm, respectively.