Search Results (25,949)

Enhancing the flame retardancy of polymeric materials by adding only eco-friendly ammonium polyphosphate (APP) while simultaneously maintaining high-temperature resistance has become a challenge. Talcum has been introduced as a cooperative agent into the silicone rubber/APP system to investigate the effect of talcum on flame retardancy, thermal stability, and high-temperature resistance. The machining process induces the orientation of talcum in the system. The ceramifiable silicone rubber blends containing oriented talcum (e.g., sample SA₆T₄) exhibited superb flame retardancy, including an LOI of 29.4%, a UL-94 rating of V-0, and a peak heat release rate (PHRR) of 250.2 kW·m⁻². More importantly, the blends present excellent thermal stability and high-temperature resistance, characterized by outstanding self-supporting properties and dimensional stability. Based on the structural analysis of the blends and their residues, the made of action for the improved flame retardancy may be attributed to the formation of a compact barrier layer. This layer is formed by oriented talcum platelets combined with phosphoric acid, from the thermal decomposition of APP, promoting crosslinking, thereby achieving a good inhibition barrier to inhibit heat feedback from the condensation zone. The excellent thermal stability and high-temperature resistance of the ceramifiable silicone rubber blends may be ascribed to a cooperative effect between APP and talcum at high temperatures, which facilitates the formation of ceramic structures. The novel ceramifiable silicone rubber composite has potential applications as flame-retardant sealing components for rail transit equipment and encapsulation materials for new energy battery modules. Full article

Although various phosphorescent organic light-emitting diodes (PhOLEDs) have been developed, their lifetimes remain shorter than those of fluorescent OLEDs. In this study, a novel Pt(II) complex featuring a tetradentate ligand composed of bipyridine and phenoxypyridine, referred to as LL-O, was synthesized and fully characterized to evaluate its potential as a dopant for PhOLEDs. Geometry-optimized calculations indicate that LL-O adopts a distorted square–planar structure around the Pt(II) center. The complex displays bluish-green emission with maxima at 490 and 518 nm. However, it exhibits a low photoluminescence quantum yield (4%), primarily due to a dominant non-radiative decay rate that surpasses the radiative decay rate. Natural transition orbital analysis reveals that the emission of LL-O originates from a combination of triplet ligand-centered (³LC), triplet ligand-to-ligand charge-transfer (³LL′CT), and triplet metal-to-ligand charge-transfer (³MLCT) transitions. This compound also demonstrates high thermal stability (decomposition temperature > 340 °C) and an appropriate HOMO energy level (−5.58 eV), making it suitable for use as a dopant in versatile PhOLEDs. Full article

Electroencephalography (EEG) has attracted significant attention as an effective modality for interaction between the physical and virtual worlds, with EEG-based person identification serving as a key gateway to such applications. Despite substantial progress in EEG-based person identification, several challenges remain: (1) how to design an end-to-end EEG-based identification pipeline; (2) how to perform automatic electrode selection for each user to reduce redundancy and improve discriminative capacity; (3) how to enhance the backbone network’s feature extraction capability by suppressing irrelevant information and better leveraging informative patterns; and (4) how to leverage higher-level information in EEG signals to achieve intent recognition (i.e., EEG-based task/activity recognition under controlled paradigms) on top of person identification. To address these issues, this article proposes, for the first time, a unified deep learning framework that integrates automatic electrode selection, person identification, and intent recognition. We introduce a novel backbone network, AES-MBE, which integrates automatic electrode selection (AES) and intent recognition. The network combines a channel-attention mechanism with a multi-scale bidirectional encoder (MBE), enabling adaptive capture of fine-grained local features while modeling global temporal dependencies in both forward and backward directions. We validate our approach using the PhysioNet EEG Motor Movement/Imagery Dataset (EEGMMIDB), which contains EEG recordings from 109 subjects performing 4 tasks. Compared with state-of-the-art methods, our framework achieves superior performance. Specifically, our method attains a person identification accuracy of 98.82% using only 4 electrodes and an average intent recognition accuracy of 91.58%. In addition, our approach demonstrates strong stability and robustness as the number of users varies, offering insights for future research and practical applications. Full article

Non-small cell lung cancer (NSCLC) represents over 80% of all lung cancer cases and still has a huge mortality worldwide. Targeting epidermal growth-factor receptor (EGFR) alterations with overall response rates of more than 80% has provided a paradigm shift in the treatment of NSCLC; however, NSCLC patients harbouring uncommon mutations and exon 20 insertions still have a dismal prognosis underscoring the urgent need to develop novel EGFR tyrosine kinase inhibitors (TKIs) with proven activity against these EGFR alterations. Zipalertinib is a newly developed oral, irreversible compound which is characterized by its unique pyrrolopyrimidine structure which discriminates this novel TKI from others. It is active against the classical mutations (i.e., del19, L858R) and some of the uncommon mutations (e.g., T790M, G719X, S768I, L861Q, but not C797S) and is predominantly active in NSCLC cells harbouring exon20ins. Zipalertinib is currently being extensively evaluated in several clinical NSCLC trials (REZILIENT 1–4) and has shown significant clinical activity in NSCLC patients with uncommon mutations, exon20ins, and in brain metastases (REZILIENT 3 trial). Moreover, zipalertinib in combination with platinum-based chemotherapy followed by zipalertinib monotherapy as first-line therapy is currently being evaluated in the pivotal, ongoing REZILIENT 3 randomized trial. In addition, the efficacy of zipalertinib is also studied in the adjuvant setting (REZILIENT 4 trial, stage IB-IIIA NSCLCs with exon20ins and uncommon mutations). The role and the integration of therapies targeting exon20ins or uncommon mutations into the first- and second-line treatment armamentarium for NSCLC patients is not yet fully established, and the therapeutic impact of monotherapies (e.g., sunvozertinib, firmonertinib) versus combinations with standard platinum-based chemotherapy (e.g., zipalertinib, amivantamab) currently still lacks robust evidence to further change the therapeutic landscape for these patients. Therefore, results from the ongoing trials are eagerly awaited and are expected to shed some light on these open questions. Full article

Proton exchange membrane fuel cells (PEMFC) are recognized as promising next-generation energy technology. Yet, their performance is critically limited by inefficient gas transport and water management in conventional flow channels. Current rectangular gas channels (GC) restrict reactive gas penetration into the gas diffusion layer (GDL) due to insufficient longitudinal convection. At the same time, the complex multiphase interactions at the mesoscale pose challenges for numerical modeling. To address these limitations, this study proposes a novel cathode channel design featuring laterally contracted fin-shaped barrier blocks and develops a mesoscopic multiphase coupled transport model using the lattice Boltzmann method combined with the volume-of-fluid approach (LBM-VOF). Through systematic investigation of multiphase flow interactions across channel geometries and GDL surface wettability effects, we demonstrate that the optimized barrier structure induces bidirectional forced convection, enhancing oxygen transport compared to linear channels. Compared with the traditional straight channel, the optimized composite channel achieves a 60.9% increase in average droplet transport velocity and a 56.9% longer droplet displacement distance, while reducing the GDL surface water saturation by 24.8% under the same inlet conditions. These findings provide critical insights into channel structure optimization for high-efficiency PEMFC, offering a validated numerical framework for multiphysics-coupled fuel cell simulations. Full article

This paper introduces a novel multidimensional half-discrete Hardy–Hilbert-type inequality that simultaneously addresses several key extensions in the literature. The inequality incorporates a general parameterized kernel involving a scalar term and the $\beta$-norm of a vector, and replaces the traditional discrete coefficient with a partial sum. Under suitable parameter conditions, the resulting inequality is sharper and preserves the optimal constant factor. The proof employs a systematic combination of weight-function techniques, parameter introduction, real-analysis methods, and the Euler–Maclaurin summation formula. Equivalent characterizations of the best possible constant are provided, and several meaningful corollaries are deduced, thereby unifying and generalizing a series of earlier inequalities. Full article

Patients with Relapsed/Refractory Diffuse Large B-cell Lymphoma (R/R DLBCL) harbor a poor prognosis. Novel therapies, such as bispecific antibodies (BsAbs), provide an effective therapeutic option for such patients. BsAbs are studied both as monotherapy and combination therapy for patients with R/R DLBCL with promising results. Unlike cellular therapies, such as autologous stem cell transplant (ASCT) or chimeric antigen receptor therapy (CAR-T), BsAbs are more amenable to administration in a community setting, given the lower incidence and severity of key toxicities, such as cytokine release syndrome (CRS) and immune effector cell-associated neurologic syndrome (ICANS). Deployment of BsAbs in the community setting requires operational considerations and a multidisciplinary team approach. This review will discuss the currently approved BsAb treatment regimens and our community institution’s experience in implementing BsAbs. Full article

Background: This study aimed to explore the effects of neuromuscular electrical stimulation (NMES) combined with water-based resistance training on muscle activation and coordination during freestyle kicking. Methods: Thirty National Level male freestyle swimmers were randomly assigned to an experimental group (NMES + water-based training) or a control group (water-based training only) for a 12-week intervention. The experimental group received NMES pretreatment before each session. Underwater surface electromyography (sEMG) synchronized with high-speed video was used to collect muscle activation data and corresponding kinematic information during the freestyle kick. The sEMG signals were then processed using time-domain analysis, including integrated electromyography (iEMG), which reflects the cumulative electrical activity of muscles, and root mean square amplitude (RMS), which indicates the intensity of muscle activation. Non-negative matrix factorization (NMF) was further applied to extract and characterize muscle synergy patterns. Results: The experimental group showed significantly higher iEMG and RMS values in key muscles during both kicking phases. Within the core propulsion synergy, muscle weighting of vastus medialis and biceps femoris increased significantly, while activation duration of the postural adjustment synergy was shortened. The number of synergies showed no significant difference. Conclusions: NMES combined with water-based resistance training enhances muscle activation and optimizes neuromuscular coordination strategies, offering a novel approach to improving sport-specific performance. Full article

The aim of this study was to develop porous curdlan (Cur)–whey protein isolate (WPI) biomaterials and evaluate their properties as potential cartilage scaffolds. A novel combined fabrication method involving ion-exchange dialysis, porogen leaching, freezing, and freeze-drying was employed to obtain a porous structure. Two types of scaffolds differing in protein content (5 wt.% and 7.5 wt.%) were fabricated and designated as Cur_WPI_5% and Cur_WPI_7.5%, respectively. The microstructure of the biomaterials was analyzed using stereomicroscopy and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). Physicochemical properties, including wettability and absorption capacity, were also evaluated. In addition, the viability and proliferation of osteoblasts (hFOB 1.19 cell line) in direct contact with scaffolds were assessed. The results demonstrated that both biomaterials exhibited a porous, rough, and hydrophilic structure, as well as a high liquid absorption capacity. Cell culture studies revealed that the Cur_WPI_7.5% scaffold showed greater cytocompatibility, promoting not only osteoblast viability and but also proliferation in vitro. Overall, these findings demonstrate that the developed curdlan/WPI scaffolds, particularly Cur_WPI_7.5%, possess structural and physicochemical properties favorable for cartilage tissue regeneration, highlighting their potential as promising scaffold for future applications. Full article

Background: Neuromuscular electrical stimulation (NMES) is an effective exogenous neuromuscular activation method widely used in sports training and rehabilitation. However, existing research primarily focuses on land-based sports or single-joint movements, with limited in-depth exploration of its intervention effects and underlying neuromuscular control mechanisms for complex, multi-joint coordinated aquatic activities like breaststroke swimming. This study aimed to investigate the effects of NMES combined with traditional resistance training on neuromuscular function during sport-specific technical movements in breaststroke athletes. Methods: A randomized controlled trial was conducted with 30 national-level or above breaststroke athletes assigned to either an experimental group (NMES combined with traditional squat resistance training) or a control group (traditional squat resistance training only) for an 8-week intervention. A specialized fully waterproof wireless electromyography (EMG) sensor system (Mini Wave Infinity Waterproof) was used to synchronously collect surface EMG signals from 10 lower limb and trunk muscles during actual swimming, combined with high-speed video for movement phase segmentation. Changes in lower limb explosive power were assessed using a force plate. Non-negative matrix factorization (NMF) muscle synergy analysis was employed to compare changes in muscle activation levels (iEMG, RMS) and synergy patterns (spatial structure, temporal activation coefficients) across different phases of the breaststroke kick before and after the intervention. Results: Compared to the control group, the experimental group demonstrated significantly greater improvements in single-leg jump height (Δ = 0.06 m vs. 0.03 m) and double-leg jump height (Δ = 0.07 m vs. 0.03 m). Time-domain EMG analysis revealed that the experimental group showed more significant increases in iEMG values for the adductor longus, adductor magnus, and gastrocnemius lateralis during the leg-retraction and leg-flipping phases (p < 0.05). During the pedal-clamp phase, the experimental group exhibited significantly reduced activation of the tibialis anterior alongside enhanced activation of the gastrocnemius. Muscle synergy analysis indicated that post-intervention, the experimental group showed a significant increase in the weighting of the vastus medialis and biceps femoris within synergy module 4 (SYN4, related to propulsion and posture) (p < 0.05), a significant increase in rectus abdominis weighting within synergy module 3 (SYN3, p = 0.033), and a significant shortening of the activation duration of synergy module 2 (SYN2, p = 0.007). Conclusions: NMES combined with traditional resistance training significantly enhances land-based explosive power in breaststroke athletes and specifically optimizes neuromuscular control strategies during the underwater breaststroke kick. This optimization is characterized by improved activation efficiency of key muscle groups, more economical coordination of antagonist muscles, and adaptive remodeling of inter-muscle synergy patterns in specific movement phases. This study provides novel evidence supporting the application of NMES in swimming-specific strength training, spanning from macroscopic performance to microscopic neural control. Full article

Tylorrhynchus heterochaetus is an aquatic food with both edible and medicinal value in China. With a protein-rich body wall, it has strong potential for producing bioactive peptides. To explore its potential as a source of immunomodulatory peptides, in this study, flavor enzymes were selected as the optimal hydrolases, and the hydrolyzed products were subjected to ultrafiltration fractionation. The <3000 Da portion exhibited the most effective immune-stimulating activity in RAW 264.7 macrophages, enhancing phagocytosis and promoting the secretion of tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and nitric oxide (NO) in a concentration dependent manner. Peptide omics analysis, combined with the activity and safety screened by bioinformatics, identified 43 candidate peptides. Molecular docking predicts that three novel peptides, LPWDPL, DDFVFLR and LPVGPLFN, exhibit strong binding affinity with toll-like receptor 4/myeloid differentiation factor-2 (TLR4/MD-2) receptors through hydrogen bonding and hydrophobic/π stacking interactions. Synthetic verification confirmed that these peptides were not only non-toxic to cells at concentrations ranging from 62.5 to 1000 µg/mL, but also effective in activating macrophages and stimulating the release of immune mediators. This study successfully identified the specific immunomodulatory peptides of the Tylorrhynchus heterochaetus, supporting its high-value utilization as a natural source of raw materials for immunomodulatory functional foods. Full article

This study developed a design framework for porous mixtures using a 100% sustainable non-bituminous epoxy–polyurethane binder system. Conventional design protocols for porous asphalt mixtures exhibit limitations in accurately controlling void content and mixture composition. This study proposed a novel design framework for porous mixtures containing 100% sustainable binder based on statistical analysis and theoretical calculations. The relationships among target air voids, binder content, and aggregate gradation were systematically analyzed, and calculation formulas for coarse aggregate, fine aggregate, and mineral filler contents were derived. A mix design framework was further established by applying the void-filling theory, where the combined volume of binder, fine aggregate, and filler equals the void volume of the coarse aggregate skeleton, thereby ensuring precise control of the target void ratio. Additionally, mixing procedures were investigated with emphasis on feeding sequence, compaction method, and mixing temperature. Results indicated that the optimized feeding sequence significantly improved binder distribution; specimens compacted using the Marshall double-sided compaction method achieved a density of 89.60%. Rheological analysis revealed that at 30 °C, the viscosities of sustainable binder and polyurethane filler were 1280 mPa·s and 6825 mPa·s, respectively, suggesting optimal mixture uniformity. The proposed methodology and process parameters provide essential technical guidance for engineering applications of porous mixtures containing 100% sustainable binder. Full article

Aiming to address the safety and stability issues caused by typical faults of Unmanned Aerial Vehicle (UAV) navigation sensors, a novel fault-tolerant method is proposed, which can capture the temporal dependencies of fault feature evolution, and complete the classification, prediction, and data reconstruction of fault data. In this fault-tolerant method, the feature extraction module adopts the FNKPCA method—integrating the Frobenius Norm (F-norm) with Kernel Principal Component Analysis (KPCA)—to optimize the kernel function’s ability to capture signal features, and enhance the system reliability. By combining FNKPCA with Support Vector Machine (SVM) and Bidirectional Long Short-Term Memory (BiLSTM), an active fault-tolerant processing method, namely FNKPCA–SVM–BiLSTM, is obtained. This study conducts comparative experiments on public datasets, and verifies the effectiveness of the proposed method under different fault states. The proposed approach has the following advantages: (1) It achieves a detection accuracy of 98.64% for sensor faults, with an average false alarm rate of only 0.15% and an average missed detection rate of 1.16%, demonstrating excellent detection performance. (2) Compared with the Long Short-Term Memory (LSTM)-based method, the proposed fault-tolerant method can reduce the RMSE metrics of Global Positioning System (GPS), Inertial Measurement Unit (IMU), and Ultra-Wide-Band (UWB) sensors by 77.80%, 14.30%, and 75.00%, respectively, exhibiting a significant fault-tolerant effect. Full article

Polycystic Ovary Syndrome (PCOS) is characterized by a self-perpetuating vicious cycle between insulin resistance (IR) and hyperandrogenism (HA). While lifestyle management remains the internationally recommended first-line treatment, current clinical management, primarily relying on combined oral contraceptives and metformin, offers symptomatic relief or “masking” of the phenotype but fails to adequately disrupt this core pathophysiological loop, while also carrying potential intergenerational safety concerns. This review systematically evaluates the paradigm shift toward mechanism-based precision medicine. First, we analyze emerging precision-targeted therapies that intervene in specific pathological nodes: (1) metabolic regulators (e.g., GLP-1RAs, SGLT2i, and brown adipose tissue (BAT) activators) that target systemic glucotoxicity and the novel “BAT-Ovarian axis”; (2) neuroendocrine modulators (e.g., NK3R antagonists) that act as negative modulators of the hyperactive GnRH pulse generator; and (3) innovative androgen synthesis inhibitors (e.g., Artemisinins) that utilize a degradation-at-source mechanism. Complementing these, we explore the strategic value of Natural Products through the lens of “Network Pharmacology”, highlighting their ability to restore systemic homeostasis via multi-target modulation. Finally, we address critical translational challenges, specifically the need to establish long-term reproductive and offspring safety, providing a roadmap for developing true disease-modifying treatments for PCOS. Distinct from reviews limited to isolated therapeutic modalities, this article uniquely bridges current clinical management with emerging organ-specific precision targets and natural product networks. Full article

Accurate Short-Term Load Forecasting (STLF) is paramount for the stable and economical operation of power systems, particularly in the context of high renewable energy penetration, which exacerbates load volatility and non-stationarity. The prevailing advanced “decomposition–ensemble” paradigm, however, faces two significant challenges when processing non-stationary signals: (1) The performance of Variational Mode Decomposition (VMD) is highly dependent on its hyperparameters (K, α), and traditional meta-heuristic algorithms (e.g., GA, GWO, PSO) are prone to converging to local optima during the optimization process; (2) Deep learning predictors struggle to dynamically weigh the importance of multi-dimensional, heterogeneous features (such as the decomposed Intrinsic Mode Functions (IMFs) and external climatic factors). To address these issues, this paper proposes a novel, adaptive hybrid forecasting framework, namely IRIME-VMD-CDA-LSTNet. Firstly, an Improved Rime Optimization Algorithm (IRIME) integrated with a Gaussian Mutation strategy is proposed. This algorithm adaptively optimizes the VMD hyperparameters by targeting the minimization of average sample entropy, enabling it to effectively escape local optima. Secondly, the optimally decomposed IMFs are combined with climatic features to construct a multi-dimensional information matrix. Finally, this matrix is fed into an innovative Cross-Dimensional Attention (CDA) LSTNet model, which dynamically allocates weights to each feature dimension. Ablation experiments conducted on a real-world dataset from a distribution substation demonstrate that, compared to GA-VMD, GWO-VMD, and PSO-VMD, the proposed IRIME-VMD method achieves a reduction in Root Mean Square Error (RMSE) of up to 18.9%. More importantly, the proposed model effectively mitigates the “prediction lag” phenomenon commonly observed in baseline models, especially during peak load periods. This framework provides a robust and high-accuracy solution for non-stationary load forecasting, holding significant practical value for the operation of modern power systems. Full article