With the evolution from 5G to 6G and the popularization of carrier aggregation technology, mobile terminal devices face challenges in miniaturizing multiplexers and handling concurrent signals across a wide-bandgap. This study proposes a numerical de-embedding design method. By precisely calculating and compensating for the parasitic loads, it achieves deterministic synthesis of matching networks. Based on this method, we designed and fabricated a monolithic integrated surface acoustic wave triplexer on a lithium tantalite on insulator (LTOI) substrate for dual-frequency GPS L1/L5 positioning and Bluetooth (BT) communication. To suppress a specific spurious mode on the LTOI substrate and improve the isolation, a notch inductor structure was embedded into the design. The monolithic integrated triplexer measures 2.5 mm × 2.0 mm × 0.6 mm after packaging. The insertion loss is less than 1.6 dB in both the L1 and L5 bands. In the BT band, the average loss within any 18 MHz range of the passband is below 2.5 dB. The return loss for all three channels is better than −10 dB, while the isolation between channels is maintained at ≈40 dB. The successful fabrication of this triplexer not only validates the design method but also demonstrates its potential for application in miniaturized wearable devices.

Background: Resilient healthcare systems prevent, absorb, and learn from stressors. Identifying resilient performance in healthcare systems can be challenging due to complex underlying processes and associated outcomes. This study aimed to identify resilient performance in US outpatient hemodialysis (HD) facilities during the COVID-19 pandemic by characterizing associated operational factors and classifying longitudinal patterns in bloodstream infections (BSI) rates using machine learning. Methods: This study used longitudinal BSI data reported to National Healthcare Safety Network (NHSN) by outpatient HD facilities during pre-pandemic (April 1, 2018–April 30, 2019) and pandemic (April 1, 2021–April 30, 2023) periods. For each period, facilities were classified into distinct patterns based on facility-level BSI rates (cases per 100 patient months) using k-means clustering for longitudinal data (KmL), an unsupervised machine-learning method. CMS Dialysis Facility data provided key facility characteristics and NHSN Annual Dialysis Facility Surveys provided use of Core Interventions for Dialysis BSI Prevention. Facility resilience was operationalized as classification in a lower BSI rate cluster during the pandemic period. Associations between KmL classification during the pandemic and facility operational factors were assessed using multivariable logistic regression. Result: Of 7084 outpatient HD facilities, 4907 (69%) reported BSIs to NHSN during both periods and linked to CMS data. KmL grouped facilities into two clusters for each period. During the pandemic, a lower-rate cluster (n=4132, 84%) and a higher rate cluster (n=775, 16%) had mean BSI rates of 0.19 and 0.76 per 100 patient-months, respectively. Both clusters reported strong implementation of Core Interventions; however, the higher-rate cluster had increased odds of implementing ≤64% of interventions. In the multivariable model, facilities in the higher-rate pandemic cluster were associated with being in the higher-rate pre-pandemic cluster, structural factors (non-profit ownership, non-chain facility status, having <20 dialysis stations), processes (increased central vascular catheter use, lower use of antiseptic-impregnated catheter end caps, rarely administering antibiotics before obtaining blood cultures for suspected BSIs), and geography (Northeast or Midwest US location, rurality). Conclusion: Using machine learning, two distinct BSI rate trajectories emerged before and during the COVID-19 pandemic, with key operational differences between facility groups. Facilities demonstrating lower and stable BSI trajectories during pandemic could be interpreted as exhibiting more resilient performance under system stressors. This approach can inform resilient performance across other patient safety processes, healthcare settings, and system stressors. Understanding structures and processes associated with patient safety outcomes during disruption can support public health prioritization and targeted interventions to strengthen healthcare system resilience.

We investigate the oscillation of the large-scale circulation (LSC) in turbulent Rayleigh–Bénard convection by combining laboratory experiments and numerical simulations. The experiments are conducted mainly in a laterally confined rectangular cell, and the flow fields are measured by particle image velocimetry in the vertical mid-plane. It is found that the velocity field exhibits not only the commonly observed oscillation frequencies $(f\tau _o=1,2)$ but also higher-order harmonic frequencies $(f\tau _o\gt 2)$, where $\tau _o$ is the turnover time of the LSC. Spectral proper orthogonal decomposition (SPOD) reveals that the coherent structures underlying these frequencies display an approximate azimuthal periodicity when viewed in polar coordinates. These structures can be interpreted as travelling waves, consistent with the advected-oscillation picture proposed by Brown & Ahlers (2009 J. Fluid Mech., vol. 638, pp. 383–400). A data-driven resolvent analysis, used here as an effective input–output model inferred from the experimental data, yields response modes and gain peaks that are consistent with the SPOD results, and support the interpretation that the dominant oscillations are selected through linear amplification about the mean circulation. This perspective is further supported by direct numerical simulation in a rectangular cell and by velocity measurements in a cylindrical cell. These findings provide an additional viewpoint for understanding the oscillation mechanism of the LSC in turbulent Rayleigh–Bénard convection.

Systematic reviews (SRs) are critical for evidence-based research but are time-consuming and labor-intensive. The rapid expansion of academic publications further challenges the performance and applicability of existing screening and classification methods. While large language models (LLMs) present new opportunities for automation, limited research has examined whether they can achieve classification performance comparable to human reviewers in large-scale, multi-class settings. With the goal of improving classification performance, we proposed an LLM-based framework that leverages full-text key-insight extraction to enhance literature classification. We constructed a manually curated dataset of 900 articles from 17 published SRs to quantitatively evaluate the classification capabilities of LLMs. The results provided empirical evidence of LLMs’ potential in supporting large-scale SRs and introduced a practical pathway for improving efficiency and reliability in evidence synthesis. Empirical results showed that key-insight-based classification (KBC) significantly outperforms abstract-based classification (ABC). We implemented a confidence-weighted voting (CWV) mechanism using multiple LLMs to improve robustness. The CWV method achieved the highest macro F1-score of 0.796, substantially exceeding KBC (0.732), ABC (0.676), and unsupervised K-means clustering (0.446). By employing zero-shot LLMs, our approach demonstrated the potential for enhanced adaptability across diverse domains and classification tasks without requiring fine-tuning, demonstrating that a carefully designed pipeline can enable LLMs to achieve classification performance comparable to human reviewers.

The introduction of advanced automation and human-artificial intelligence (AI) teaming is expected to permit more efficient use of airspace in the face of increasing air transport demand. Additionally, the development of next-generation aircraft to support net-zero has introduced more complexity into the future flight deck and informational requirements. This study evaluates a design for an ‘intelligent assistant’ system that could share tasks with the pilot during engine failure and pilot incapacitation events, promoting greater reliance on system interaction as workload increases. Four professional pilots were split into two groups to perform six and eight scenarios, respectively. The aim was to identify the task-related information for the designed system to promote transparency to the pilots. Three modalities varied across each scenario (visual, auditory and physical) to evaluate the combination of modality to increase pilot monitoring and interaction with the system. Analysis of participant feedback indicated key limitations to existing human-machine-interaction design, with current operational procedures creating disparity between the system and pilots’ authority to handle the scenario. Additionally, the use of audio narration was negatively received by participants, primarily due to the potential overlap between other audio stimuli, masking the perception of task-critical audio prompts and delaying critical flight tasks from being performed. Design considerations were generated for future ‘intelligent assistant’ systems, with further research required to understand the effect of each modality on pilot reliance on these ‘intelligent assistant’ systems.

Dietary plant-derived bioactive compounds for enhancing physiological health are becoming a prevalent strategy for antibiotic alternatives. Our study revealed the effects and underlying mechanism of osthole (OST) and OST-tetramethylpyrazine (TMP) compound in Litopenaeus vannamei based on network pharmacology, molecular docking and a 42-d feeding trial verification. The results illustrated that OST and OST-TMP compound significantly improved the survival rate, weight gain rate, specific growth rate and feed conversion ratio, strengthening the growth performance of L. vannamei. Meanwhile, combining the predictive results from network pharmacology and molecular docking, we propose that OST and TMP synergistically enhance the antioxidant defence capacity of shrimp through the synergistic Nrf2 signalling pathway, thereby enhancing the expression of total antioxidant capacity, superoxide dismutase, catalase and glutathione peroxidase. Furthermore, OST-TMP exhibited a significant increase of the immune response in haemocyte and intestine of shrimp, increasing the expression of antimicrobial peptide and lysozyme and suppressing the inflammatory factors, via the synergistic (NF-κB) and complementary targets predicted by network pharmacology. Additionally, gut microbiota composition of L. vannamei was improved, and the dominant genera were correlated with intestinal immune in the compound groups. For the first time, we elucidated the mechanism of plant-derived bioactive compounds mediating physiological health in aquatic animals via a new strategy of network pharmacology-molecular docking-experimental verification and identified the optimal addition amount of OST-TMP in shrimp (150 mg/kg TMP + 20 mg/kg OST), providing a technical safeguard for the animal health and the safety of aquatic products.

We present an experimental study on electron and X-ray generation from the interaction of a hundreds of TW femtosecond laser with microchannels. Leveraging the guiding effect of the channel structure on both the laser and electrons, a well-collimated electron beam is achieved, with a beam charge of 1.5 nC (>10 MeV), a slope temperature of 9.1 MeV and a nearly constant divergence angle (~14°) over a broad energy range (10–50 MeV). Meanwhile, we demonstrate a ring-shaped X-ray source generated through bremsstrahlung radiation mechanism from electrons collision with channel walls, exhibiting a characteristic energy of 90 keV and emittance of 0.8 mm mrad. Three-dimensional simulations elucidate the underlying acceleration dynamics. It is found that elongated channels facilitate the formation of well-collimated electron beams. These results establish the foundation for applications of channel guided electrons and secondary radiation sources and represent a key step toward the controlled manipulation of particle sources in laser-driven plasmas.

Laser-driven plasma wakefield acceleration (LWFA) offers exceptionally high acceleration gradients and can produce high-brightness electron beams. However, the laser-to-electron energy conversion efficiency typically remains limited to a few percent. Theoretically, the self-mode transition from LWFA to beam-driven plasma wakefield acceleration (PWFA) provides a pathway for fully utilizing the laser energy. Here, we demonstrate the single-stage LPWFA (hybrid LWFA–PWFA) scheme, validated through comparative experiments using a 300 TW tightly focused laser interacting with sub-critical density nitrogen gas targets. The experiments produce an electron beam with charge of approximately 31 nC above 6 MeV and approximately 116 nC above 2 MeV. The laser-to-electron energy conversion efficiency is approximately 6.1% (>6 MeV) and 16.4% (>2 MeV), respectively. Particle-in-cell simulations confirm that the single-stage LPWFA mechanism depletes the laser energy and enables continual electron injection. This high-charge, multi-MeV electron beam has great value in the generation of high-brightness $\unicode{x3b3}$-rays and high-flux neutron sources.

Meta-analysis synthesizes evidence from multiple randomized clinical trials and informs evidence-based practices across various medical domains. Recently, causally interpretable meta-analysis has been proposed and applied to treatment evaluations for target populations, requiring individual participant data (IPD). Standard meta-analysis assumes transportability or exchangeability of a (conditional) relative effect (such as relative risk or odds ratio), which may be violated when the relative effects are correlated with the baseline risks across clinical trials. In addition, the weighted average of some study-specific effect measures such as the (log) odds ratios or the (log) hazard ratios is non-collapsible and does not correspond to any target population. Furthermore, when the randomization ratios between treated versus untreated arms vary across trials, confounding bias may occur. To address these challenges, we propose a causal meta-analysis (CMA) framework using only aggregated data, enabling causally interpretable and accurate estimation for different target populations. The CMA adjusts its weights for treatment effect across various target populations, including the average treatment effect (ATE), the ATE on the treated (ATT) population, the ATE on the control (ATC) population, and the ATE in the overlap (ATO) population. Mathematically, we discover the connection between traditional meta-analysis estimators and CMAs. For example, the Mantel–Haenszel weighted meta-analysis is equivalent to the CMA with ATO.

Radio recombination line (RRL) maser is a useful tool to study massive star formation regions with ionised gas close to new born massive stars. Masers often show sharp line profiles and/or extreme narrow widths, and high brightness temperatures. However, RRL masers were rarely detected only in several sources. Here we report the detection of sharp line profiles of the RRL H29$\alpha$, which can be interpreted as maser candidates, in two sources within W49A, a mini-starburst region in our Galaxy. These observations, conducted with high resolution ($\sim0.03''$) using the Atacama Large Millimeter/sub-millimeter Array (ALMA), reveal high brightness temperatures up to $\sim$9 000 K for H29$\alpha$ emission in another two sources, which might also be regarded as maser candidates. Additionally, suggestions for efficiently identifying RRL maser candidates are also provided.