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Energy storage high-entropy ceramics are famous for their ultrahigh power density and ultrafast discharge rate. However, achieving a synchronous combination of high energy density and efficiency along with intelligent temperature-monitorable function remains a significant challenge. Here, based on high-entropy strategy and phase field simulation, the polarization response of domains in Bi 0.5 Na 0.5 TiO 3 -based ceramics is optimized by constructing a concomitant nanostructure of defect dipole polarization and a polymorphic relaxor phase. The optimal ceramic possesses a high recyclable energy storage density (11.23 J cm −3 ) and a high energy storage efficiency (90.87%) at 670 kV cm − 1 . Furthermore, real-time temperature sensing is explored based on abnormal fluorescent negative thermal expansion, highlighting the application of intelligent cardiac defibrillation pulse capacitors. This study develops an effective strategy for enhancing the overall energy storage performance of ferroelectric ceramics to overcome the problems of insufficient energy supply and thermal runaway in traditional counterparts. The authors construct a nanostructure consisting of defect dipole polarization and polymorphic relaxor phases. The high-entropy ceramic achieves an energy density of 11.23 J cm −3 , an efficiency of 90.87%, along with temperature sensing feature.

Background Pilot and feasibility studies provide information to be used when planning a full trial. A sufficient sample size within the pilot/feasibility study is required so this information can be extracted with suitable precision. This work builds upon previous reviews of pilot and feasibility studies to evaluate whether the target sample size aligns with recent recommendations and whether these targets are being reached. Methods A review of the ISRCTN registry was completed using the keywords “pilot” and “feasibility”. The inclusion criteria were UK-based randomised interventional trials that started between 2013 (end of the previous review) and 2020. Target sample size, actual sample size and key design characteristics were extracted. Descriptive statistics were used to present sample sizes overall and by key characteristics. Results In total, 761 studies were included in the review of which 448 (59%) were labelled feasibility studies, 244 (32%) pilot studies and 69 (9%) described as both pilot and feasibility studies. Over all included pilot and feasibility studies ( n  = 761), the median target sample size was 30 (IQR 20–50). This was consistent when split by those labelled as a pilot or feasibility study. Slightly larger sample sizes (median = 33, IQR 20–50) were shown for those labelled both pilot and feasibility ( n  = 69). Studies with a continuous outcome ( n  = 592) had a median target sample size of 30 (IQR 20–43) whereas, in line with recommendations, this was larger for those with binary outcomes (median = 50, IQR 25–81, n  = 97). There was no descriptive difference in the target sample size based on funder type. In studies where the achieved sample size was available ( n  = 301), 173 (57%) did not reach their sample size target; however, the median difference between the target and actual sample sizes was small at just minus four participants (IQR −25–0). Conclusions Target sample sizes for pilot and feasibility studies have remained constant since the last review in 2013. Most studies in the review satisfy the earlier and more lenient recommendations however do not satisfy the most recent largest recommendation. Additionally, most studies did not reach their target sample size meaning the information collected may not be sufficient to estimate the required parameters for future definitive randomised controlled trials.

Objective: The objective was to establish a machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS. Methods: 80% of sepsis patients selected randomly from the MIMIC-IV database, without prior pulmonary conditions and with nonpulmonary infection sites, were used to construct prediction models through machine learning techniques (including K-nearest neighbour, extreme gradient boosting, support vector machine, deep neural network, and decision tree methods). The remaining 20% of patients were utilized to validate the model’s accuracy. Additionally, local data were employed for further model validation. Results: A total of 11,409 patients were included, with the most common type of infection being bloodstream infection. A total of 7,632 (66.9%) patients developed nonpulmonary sepsis-associated ARDS (NPS-ARDS). Patients with NPS-ARDS had significantly longer ICU stays (6.2 ± 5.2 days vs. 4.4 ± 3.7 days, p  < 0.01) and higher 28-day mortality rates (19.5% vs. 14.9%, p  < 0.01). Both internal and external validation demonstrated that the model constructed with the extreme gradient boosting method had high accuracy. In the internal validation, the model predicted NPS-ARDS and mortality in such patients with accuracies of 77.5% and 71.8%, respectively. In the external validation, the model predicted NPS-ARDS and mortality in these patients with accuracies of 78.0% and 81.4%, respectively. Conclusion: The model established via the extreme gradient boosting method can predict the occurrence and mortality of nonpulmonary sepsis-associated ARDS to a certain extent.

The development of high-performance lead-free K 0.5 Na 0.5 NbO 3 -based piezoceramics for replacing commercial lead-containing counterparts is crucial for achieving environmentally sustainable society. Although the proposed new phase boundaries (NPB) can effectively improve the piezoelectricity of KNN-based ceramics, the difficulty of achieving saturated poling and the underlying multiscale structures resolution of their complex microstructures are urgent issues. Here, we employ a medium entropy strategy to design NPB and utilize texture engineering to induce crystal orientation. The developed K 0.5 Na 0.5 NbO 3 -based ceramics enjoys both prominent piezoelectric performance and satisfactory Curie temperature, thus exhibiting an ultrahigh energy harvesting performance as well as excellent transducer performance, which is highly competitive in both lead-free and lead-based piezoceramics. Comprehensive structural analysis have ascertained that the field-induced efficient multiscale polarization configurations irreversible transitions greatly encourages high saturated poling. This study demonstrates a strategy for designing high-performance piezoceramics and establishes a close correlation between the piezoelectricty and the underlying multiscale structures. There are difficulty of achieving saturated poling and understanding of multi-scale structures in (K, Na)NbO 3 ceramics. Here, the authors find atomic-scale polymorphic distortion and micrometer-scale high-density thin striped domains, which is key for high saturated poling.

The contradiction between high piezoelectricity and uniquely poor temperature stability generated by polymorphic phase boundary is a huge obstacle to high-performance (K, Na)NbO 3 -based ceramics entering the application market as Pb-based substitutes. We possess the phase boundary by mimicking Pb(Zr, Ti)O 3 ’s morphotropic phase boundary structure via the synergistic optimization of diffusion phase boundary and crystal orientation in 0.94(Na 0.56 K 0.44 )NbO 3 −0.03Bi 0.5 Na 0.5 ZrO 3 −0.03(Bi 0.5 K 0.5 )HfO 3 textured ceramics. As a result, a prominent comprehensive performance is obtained, including giant d 33 of 550 ± 30 pC/N and ultrahigh temperature stability ( d 33 change rate less than 1.2% within 25-150 °C), representing a significant breakthrough in lead-free piezoceramics, even surpassing the Pb-based piezoelectric ceramics. Within the same temperature range, the d 33 change rate of the commercial Pb(Zr, Ti)O 3 −5 ceramics is only about 10%, and more importantly, its d 33 (~ 350 pC/N) is much lower than that of the (K, Na)NbO 3 -based ceramics in this work. This study demonstrates a strategy for constructing the phase boundary with MPB feature, settling the problem of temperature instability in (K, Na)NbO 3 -based ceramics. The authors induce a phase boundary similar to Pb(Zr, Ti)O 3 ’s morphotropic phase boundary structure in (K, Na)NbO 3 -based ceramics, which exhibit high residual polarization as well as a significantly improved piezoelectric constant d 33 .

Non-pulmonary sepsis often induces Acute Respiratory Distress Syndrome (ARDS). Dysregulated inflammation and coagulation disorders play important roles in the development of non-pulmonary sepsis-associated ARDS (NPS-ARDS). Heparin, with its potential anticoagulant and anti-inflammatory properties, may be used in the treatment of NPS-ARDS. This is a retrospective observational study that uses Structured Query Language (SQL) to extract clinical data of NPS-ARDS patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Based on the dosage of heparin, patients were divided into three groups: low-dose heparin treatment group (0-5000u/d), medium-dose heparin treatment group (5000u-10000u/d), and high-dose heparin treatment group (greater than 10000u/d). Propensity score matching (1:1) was used to match similar patients from the NPS-ARDS patients who did not use heparin to each heparin treatment group. The study compares the effects of heparin at different dosages on short-term mortality (7-day, 28-day, and 60-day mortality) and one-year cumulative survival rate in NPS-ARDS patients. PSM reduced the impact of confounding factors on the results to some extent. Low and medium doses of heparin did not improve patient mortality. However, high-dose heparin improved the short-term mortality of NPS-ARDS patients (7-day mortality: 4.1% vs. 14.3%, P < 0.001; 28-day mortality: 9.4% vs. 22.6%, P < 0.001; 60-day mortality: 13.2% vs. 24.8%, P = 0.001) and one-year cumulative survival rate (Log Rank = 8.349, P = 0.004), but it also prolonged ICU stay (6.7 ± 6.2 days vs. 5.7 ± 4.8 days, P = 0.041) and invasive mechanical ventilation (11.7 ± 6.9 hours/day vs. 5.7 ± 4.8 hours/day, P < 0.001). In patients with NPS-ARDS, high-dose heparin was associated with significantly improved short- and long-term survival, albeit at the cost of prolonged ICU stay and mechanical ventilation.

This cross-sectional study examined the health impacts of occupational dust exposure on workers in Fujian Province, China, using data collected from 2020 to 2021. The primary objective was to assess the associations between occupational dust exposure and several adverse health outcomes, including abnormal chest X-ray (Abn-CXR), abnormal pulmonary function tests (Abn-PFTs), pneumoconiosis (PC), abnormal electrocardiograms (Abn-ECGs), abnormal liver function tests (Abn-LFTs), hypertension (HTN), and hearing loss (HL). logistic regression models were employed to identify significant risk factors. Stratified analyses by age and gender were performed to evaluate demographic differences in health risks. The results showed that workers currently employed, those with over 10 years of dust exposure, and workers exposed to silica, cement, or coal dust had a higher risk of Abn-CXR, Abn-PFTs, PC, Abn-ECGs, Abn-LFTs, HTN, and HL. Stratified analyses further revealed that male workers and individuals over 40 years old experienced a higher risk of abnormal health outcomes. These findings underscore the urgent need for targeted interventions, improved protective measures, and stricter occupational safety regulations to reduce the health burden associated with dust exposure in the workplace.

High-performance lead-free K 0.5 Na 0.5 NbO 3 piezoelectric ceramics present a practical alternative to lead-containing counterparts by effectively reducing potential environmental hazards. This advancement is particularly relevant to the development of ferroelectric heterojunction devices for biomedical applications. Here, we design and fabricate a frequency-adjustable ferroelectric heterojunction based on the developed K 0.5 Na 0.5 NbO 3 piezoelectric ceramics with a high piezoelectric coefficient ( d 33  = 680 pC/N). By leveraging flexible encapsulation, the heterojunction achieves miniaturization ( φ  = 13.3 mm, h  = 2.28 mm) and suitability for implantation. After penetrating the rat skull, the ultrasound generated by the heterojunction at a frequency of 3 MHz reaches a focal depth of about 7.9 mm, a focal width of approximately 480 μm at −6 dB, and millimeter-scale continuous focal tuning (1.5 mm) within a narrow frequency range (2.7–3.3 MHz). Additionally, the implanted heterojunction enables long-term and high-precision transcranial neuromodulation, and consequently yields therapeutic effects in a myocardial infarction animal model. Collectively, this study highlights a viable strategy for developing and applying lead-free ferroelectric heterojunctions, expanding their potential in brain modulation, and providing new insights into clinical treatments of myocardial infarction. The authors present a frequency-adjustable ferroelectric heterojunction based on K 0.5 Na 0.5 NbO 3 piezoelectric ceramic, which enabling therapeutic effects in a myocardial infarction animal model by long-term and high-precision transcranial neuromodulation.

Software traceability links are established between diverse artifacts of the software development process in order to support tasks such as compliance analysis, safety assurance, and requirements validation. However, practice has shown that it is difficult and costly to create and maintain trace links in non-trivially sized projects. For this reason, many researchers have proposed and evaluated automated approaches based on information retrieval and deep-learning. Generating trace links automatically can also be challenging – especially in multi-national projects which include artifacts written in multiple languages. The intermingled language use can reduce the efficiency of automated tracing solutions. In this work, we analyze patterns of intermingled language that we observed in several different projects, and then comparatively evaluate different tracing algorithms. These include Information Retrieval techniques, such as the Vector Space Model (VSM), Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), and various models that combine mono- and cross-lingual word embeddings with the Generative Vector Space Model (GVSM), and a deep-learning approach based on a BERT language model. Our experimental analysis of trace links generated for 14 Chinese-English projects indicates that our MultiLingual Trace-BERT approach performed best in large projects with close to 2-times the accuracy of the best IR approach, while the IR-based GVSM with neural machine translation and a monolingual word embedding performed best on small projects.

Two-dimensional phase unwrapping (2-D PU) is vital for reconstructing Earth’s surface topography and displacement from interferometric synthetic aperture radar (InSAR) data. Conventional algorithms rely on the postulate, but this assumption is often insufficient due to abrupt topographic changes and severe noise. To address this challenge, our research proposes a novel approach utilizing deep convolutional neural networks inspired by the U-Net architecture to estimate phase gradient information. Our approach involves downsampling the input data to extract crucial features, followed by upsampling to restore spatial resolution. We incorporate two attention mechanisms—feature pyramid attention (FPA) and global attention upsample (GAU)—and a residual structure in the network’s structure. Thus, we construct ResDANet (residual and dual attention net). We rigorously train ResDANet utilizing simulated datasets and employ an L1-norm objective function to minimize the disparity between unwrapped phase gradients and those calculated by ResDANet, yielding the final 2-D PU results. The network is rigorously trained using two distinct training strategies and encompassing three types of simulated datasets. ResDANet exhibits excellent robust performance and efficiency on simulated data and real data, such as China’s Three Gorges and an Italian volcano.