Power electronic converter requires a low-power auxiliary supply to operate its control circuits. This paper presents the design and implementation of an auxiliary power supply for a three-phase inverter utilizing an active power factor correction(PFC)-based flyback converter. The auxiliary power supply provides an isolated and stable DC voltage to IGBT gate drivers, DSP controller, sensing circuit, power relay circuits and heat sink fans in three-phase inverters. The active PFC flyback converter rated for 48 W, converts universal input of 90-265 V, 50 Hz AC to 24 V, 2 A DC output. The LT3798 controller IC provides a powerful and efficient solution for designing a flyback converter with active PFC. It ensures that the input current closely follows the input voltage, achieving a power factor equal to unity. This improves power quality, minimizes harmonic distortion and ensures EMI compliance with international standards such as CISPR 32. This IC regulates the output voltage and current at varying load conditions for a wide range of input voltage. The design method involves selecting the appropriate transformer, output capacitor, snubber circuit for the MOSFET and diode, an EMI filter, and protection circuits for the controller. In addition, the paper discusses practical hardware aspects related to thermal management using PCB layout. The experimental results validate the performance of the proposed design, affirming its suitability and effectiveness for the required application.

Objective To investigate the effects of bilateral paravertebral block (PVB) at the T12 on pain and adverse effects after uterine artery embolization (UAE). Design Prospective, double-blind, and randomized-controlled study. Setting Second Affiliated Hospital of Soochow University. Population Forty patients undergoing UAE for the treatment of symptomatic fibroids. Methods Patients scheduled for UAE were randomly assigned to receive either a bilateral PVB at T12 with 20 ml 0.375% ropivacaine or a simulated block. The multimodal analgesia protocol for all patients included systematic administration of flurbiprofen axetil, phloroglucinol, and a sufentanil intravenous patient-controlled analgesia. Main outcome measures The primary outcome was the intravenous cumulative sufentanil consumption within the first 24 h postoperatively. Secondary outcomes included numerical rating scale pain scores at 0, 1, 2, 4, 6 and 24 h postoperatively and so on. Results Thirty-six patients were analyzed per the established scheme (four patients were dropped because of the discontinuous use of patient-controlled analgesia). The 24-h total sufentanil consumption was statistically lower in the PVB group compared with the control group (30 [25 to 34] versus 40 [34 to 44] μg; median difference, -10; 95%CI, -16 to -6; P < 0.001). The PVB group had a lower maximal numerical rating scale within 24 h postoperatively (3.4 [±1.2] versus 6.1 [±1.9]; mean difference, 2.7; 95% CI, 1.7 to 3.8; P < 0.001). Conclusions T12 PVB in conjunction with multimodal analgesia provides effective perioperative pain alleviation and reduces the consumption of pain-related opioids after UAE. Funding No funding was obtained for this study.

Transesterification is an essential process in bioproduct production, with catalyst choice being critical for reaction efficiency and selectivity. Lipases are effective biocatalysts due to their specificity and mild reaction conditions. This study employed computational chemistry and laboratory experiments to compare the enzymatic transesterification of ariri coconut oil with α-bisabolol and ethanol. Computational analyses using molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) evaluated reaction stability and efficiency. The transesterification reactions carried out in the laboratory were carried out under different conditions using Burkholderia cepacia lipase as a biocatalyst. The products were analyzed by thin-layer chromatography (TLC). Computational results indicated that the activation energy (ΔG‡) for the reaction with α-bisabolol exceeded 20 kcal/mol, while for ethanol, it was 19.2 kcal/mol, suggesting a lower energy barrier for ethanol. Gibbs free energy (ΔG) was over 20 kcal/mol for α-bisabolol and 13 kcal/mol for ethanol, confirming ethanol as more energetically favorable. Laboratory tests showed product formation only in reactions with ethanol, aligning with computational findings. These results demonstrate ethanol’s efficiency for enzymatic transesterification of ariri oil. This work contributes to bioproduct innovation by fostering sustainable processes, leveraging local natural resources, and integrating advanced biocatalysis and computational chemistry techniques.

Antibiotic resistance (AR) is a serious worldwide health concern that is made worse in Nigeria by uncontrolled antibiotic use, poor literacy rates, and a lack of proper healthcare infrastructure. This study explores the relationship between AR literacy and public health in Nigeria, emphasizing the main causes of this issue, including socioeconomic constraints, inadequate healthcare systems, and disinformation. In addition to raising the incidence of incurable illnesses, AR also raises healthcare expenses, exacerbates inequality, and endangers the security of the global health system. Improving community awareness, including AR information into school curricula, educating healthcare professionals, and implementing stronger laws on the sale and use of antibiotics are some strategies to solve this problem. Innovative approaches are also explored to fight AR, including collaborations with local leaders and mobile health initiatives. Enhancing AR literacy through a multifaceted approach is vital for reducing antibiotic abuse, improving public health outcomes, and supporting global efforts to tackle antibiotic resistance. 

This short communication develops an existence result for a general non-linear parabolic equation. The method of proof comprises an application of the Newton's method combined with a proximal approach and a Banach fixed point theorem.

Among the various connection forms of tubular joints, tube-gusset joints are widely used in practical engineering due to their simple structure and convenient manufacturing process. When the bearing capacity of the tubular joint is insufficient, filling the chord with concrete has proven to be an effective method of strengthening. However, fewer studies have been conducted on the failure mechanism, force transmission mechanism, and bearing capacity calculation model of the concrete-filled circular steel tubular (CFCST)-gusset joints. In this study, four CFCST-gusset K-joints and one circular hollow section (CHS)-gusset K-joint were experimentally tested, considering parameters such as the chordal diameter-thickness ratio ( γ), the web to chord diameter ratio ( β) and wall thickness ratio ( τ), the gusset plate to web thickness ratio ( t g/ t i). According to experimental investigation, the finite element method (FEM) was employed to analyse the influence of these parameters on the mechanical behaviour of the CFCST-gusset K-joints. The results show that the CFCST-gusset K-joints exhibited two distinct failure modes, namely, web member failure and gusset plate failure. However, the CHS-gusset K-joint failed due to the excessive plastic deformation of the chord tube. The gusset plate to web thickness ratio t g/ t i is the most critical factor affecting the failure patterns and bearing capacity of the CFCST-gusset K-joints. The value of t g/ t i should be larger than 2 in practical engineering to prevent joint failure. A least-square method was used to fit the bearing capacity computational model of the CFCST-gusset K-joints. Regression validation confirmed the high accuracy of this computational model.

A Physics-Informed Neural Network (PINN) model base on deep learning has been proposed for predicting low-cycle fatigue (LCF) and thermo-mechanical fatigue (TMF) life. By analyzing the LCF and TMF data of compacted graphite iron (CGI), characteristic parameters were identified that can simultaneously represent both types of fatigue, achieving a unification of the parameters for the two fatigue life models. The incorporation of fatigue life physical information as a constraint in the loss function of the deep neural network enabled accurate predictions of LCF and TMF for CGI under small-sample conditions. Comparative analysis results indicated that the deep learning-based PINN model outperformed traditional machine learning models in terms of prediction accuracy. Additionally, comparisons with traditional LCF and TMF life prediction models showed that the deep learning-based PINN model achieves high prediction accuracy while possessing generalization and extrapolation capabilities unattainable by traditional models. These results demonstrate that the PINN model exhibits high accuracy and versatility.

Urban rivers often experience significant water quality degradation due to the pollution from wet-weather discharges. Accurate pollution source identification (PSI) is essential for effective river management. However, traditional PSI methods face challenges, including high computational demands and difficulties in addressing equifinality. To overcome these issues, this study introduces an innovative approach that integrates deep learning (DL) with data assimilation (DA). Three DL models-simple Convolutional Neural Networks, ResNet, and UNet-were evaluated as surrogate models for the computationally expensive river water quality model (RWQM). Additionally, three advanced DA methods - DREAM(ZS), ESMDA, and ILUES - were applied to estimate high-dimensional RWQM parameters. In a numerical case study of a river segment involving 50 unknown parameters across five pollution sources, we assessed the performance of eight approaches for PSI and examined the impacts of monitoring schemes and observation errors. Results showed that UNet provided the highest accuracy in surrogate modeling, while ILUES delivered the best DA performance. The combined UNet-ILUES approach demonstrated a remarkable improvement in computational efficiency, achieving a 406-fold gain compared to the RWQM-ILUES approach. Validated through a real-world water quality degradation event in the Outer Qinhuai River, the UNet-ILUES approach demonstrates strong potential as an efficient solution for characterizing the dynamics of pollution from WWDs in urban rivers, leveraging the combined strengths of DL and DA.  

Acute Kidney Injury (AKI), when left untreated, transitions into chronic kidney disease (CKD). The role of CD4 + T lymphocytes has long been associated with determining the disease fate due to their ability to modulate the immune response by releasing cytokines and assisting the actions of CD8 + T lymphocytes, macrophages and aiding in the secretion of immunoglobulins by B cells. In this review, we focus on the roles of Th1, Th2, Th9, Th17, Th22, and TFH cells and the signaling cascades driving AKI progression, resolution, and cell death. Murine models have been pivotal in elucidating pro- and anti-inflammatory cytokine modulation. Diagnosing AKI requires potent biomarkers to assess disease severity and outcomes, which, coupled with immunological insights, could improve AKI treatment and reduce mortality. This review provides a roadmap of immune dynamics from AKI onset through resolution and related cell death events.

Reading comprehension and arithmetic computation are core components of academic ability in children. How they are learned and their neuro-cognitive foundations have long been key topics in pedagogy, psychology, educational neuroscience, and learning science. Previous research has predominantly focused on the cognitive mechanisms underlying reading comprehension or arithmetic computation from a disciplinary domain-specific perspective. The visual form-perception hypothesis, grounded in a disciplinary domain-general perspective, posits that visual form perception serves as a shared cognitive foundation for both reading and arithmetic abilities in children, with the lateral occipital complex potentially being a key brain region involved in language ability, mathematical ability and visual form perception This hypothesis not only advances theories regarding the cognitive neural basis underlying the development of reading comprehension and arithmetic computation, but also offers novel insights for teaching--learning theories and educational practices. Future research should explore the visual form perception hypothesis through longitudinal studies, investigations into cognitive mechanisms, and causality assessments

Aiming at the problem of access delay caused by the contention of resource requests from a large-scale distributed Unmanned aerial vehicle (UAV) network leading to unavoidable conflicts, we propose a grant-free access scheme based on the prediction channel. Specifically, we spread the transmit signals over multiple subcarriers by spreading the frequency, and summaries the signal detection problem for grant -free access as a multi-measurement vector (MMV) compression sensing problem. The channel is estimated based on beacon, and the orthogonal approximation message passing (OAMP)-MMV algorithm is used. Sparsity ratio and noise variance are learnt using variational inference (VI). Finally, the likelihood ratio (LLR) is used to identify the active nodes. Simulation results verify that the proposed scheme outperforms various baseline schemes and achieves low-latency and highly reliable random access.

Theoretical frameworks and measurements for interpersonal emotion regulation in adults are well-established. However, the understanding of interpersonal emotion regulation in young children remains unclear, and no measurement tools exist for their specific strategies. This study aimed to develop a questionnaire for assessing interpersonal emotion regulation in children aged 3-6. Through a series of four studies, we developed the Children’s Interpersonal Emotion Regulation Scale (CIERS). The final scale comprises 21 items across five factors: Extrinsic Emotion Improvement, Receiving Comfort from Others, Positive Emotion Sharing, Extrinsic Emotion Worsening, and Self-Soothing. The scale demonstrates excellent psychometric properties. Implications for future research are discussed. Moreover, no gender and age differences were found in each IER dimension. This research contributes to the broader emotion regulation literature by elucidating IER strategies in young children.

Storytelling is one of the beloved teaching methods for language teachers to enhance cognitive abilities and promote second language acquisition for young learners. Thus, teachers need to learn how to evaluate the storybooks as the quality of material will affect their teaching success. However, past studies have yet to look into how teachers can analyse storybooks through gamified perspectives in a field of Teach English to Speakers of Other Languages (TESOL), although many textbook evaluative methods exist. By utilising Chou’s (2015) Octalysis framework, the eight motivational core drives help teachers approach storytelling innovatively (e.g., Chris Haughton’s ”Do not Worry, Little Crab”) through proactive sociocultural perspectives to make proper adjustments in teaching vocabulary and the moral of a story to young students, based on my teaching experiences with wealthy grade 1 students (e.g., Han and Tibetan students) from private educational institutions in [] district, [] city, China. Consequently, incorporating gamification elements in storytelling can help teachers understand its strengths and weaknesses, advancing the current knowledge about the opportunities of using gamification in educational fields. Future teachers and educators should consider ways to motivate students following this approach in their teaching environments and thus make proper pedagogical adaptations.

Background Borderline personality disorder is linked to challenges such as low career success, relationship difficulties, increased conflicts, and lower life satisfaction, often due to poor distress tolerance and high aggression. Psychotherapy is the main treatment, with medication as a supplementary option. This study investigates the effectiveness of imagery rescripting in reducing aggression and improving distress tolerance compared to medication-as-usual. Method The study used an experimental design with pre-tests and post-tests and included a control group. Thirty-two individuals with borderline personality disorder were matched and assigned to experimental and control groups. Over 5 weeks, the experimental group received one weekly session of mental imagery reconstruction along with medication-as-usual, while the control group only received medication-as-usual. Findings The results indicated that imagery rescripting along with medication can be effective in reducing aggression from 103.53 to 86.93 (mean = 16.60, SD = 6.23, p = 0.000) and increasing distress tolerance from 26.86 to 38.59 (mean = 11.73, SD = 5.37, p = 0.000) in people with borderline personality disorder, and medication alone is also effective in reducing aggression from 104.64 to 85.78 (mean = 18.85, SD = 9.13, p = 0.000). However, there was no significant difference in the aggression scale between the two groups (mean = 1.14, SD = 3.13, p = 0.717). Conclusion The imagery rescripting technique may help individuals with BPD reduce self-directed anger and improve emotional tolerance by addressing the roots of unpleasant emotions. This technique shows promise in enhancing distress tolerance and potentially decreasing self-harming behaviors.

While advancements in the prediction of wind turbine loads and wakes within the actuator line method large eddy simulation (ALM-LES) framework have been an active research topic in recent years, the modelling of the tower and nacelle has received comparatively less attention. This paper investigates different numerical modelling methods for the representation of the support structure and discusses the implications and trade offs associated with each method. Three support structure modelling approaches are investigated: explicitly meshing the support structure; a direct forcing meshless representation; and a combination of actuator disc/line method for the representation of the nacelle/tower. All methods contribute to a more realistic representation of the near wake mean flow when compared to experimental measurements. In the measured far wake, the interaction between the tower and rotor wakes creates asymmetries in the mean flow profiles. The meshed and cell-blocking numerical representations are capable of replicating these far wake asymmetries. In contrast, the actuator method has a less pronounced effect on the far wake mean flow.

Title PageManuscript type: Case Image

Digital pre-distortion (DPD) technology is an efficient method to linearize power amplifiers (PAs). Current DPD techniques usually demand a sampling rate 5-7 times the source signal bandwidth in order to produce an effective pre-distorted signal. Nevertheless, since the signal bandwidth in 5G systems typically exceeds 100MHz, the needed sampling rate will significantly raise the power consumption. Thus, a low sampling rate pre-distortion structure is proposed in this letter. The structure only conducts linear operations in the digital domain and then utilizes the nonlinearity of the compensatory PA to directly generate a pre-distorted signal in the analog domain. The simulation results show that the suggested technique can achieve a similar suppression performance as conventional schemes when the sampling rate reduced to the same as the bandwidth of the source signal.

In this work, the CCS-mass trends of ion mobility-derived collision cross section (IM-derived CCS) of negatively and singly charged iron metal center (Fe(II) and Fe(III)) coordinated with 3 or 4 ligands being halides or carboxylate generated by electrospray operating in negative ionization mode have been reported. The CCS-mass trends were fitted using the equation CCS = A ×mass pow (where A is an apparent density parameter and pow is an apparent rotationally averaged shape parameters). The value of the pow parameter is generally between 0.5 to 1, where 2/3 corresponds to the pow value describing a spherical shape as sensed by ion mobility. Iron-halide complexes led to pow parameters well below the typical limit of 0.5, which could only be explained by refining the fitting equation using a linear combination of these A and pow parameters. The latter find their physical meaning in terms of inhomogeneous mass distribution within the rotationally averaged volume of the iron-ligands complex ions. By acquiring the CCS-mass trend of iron-halide and iron-carboxylate complexes, it was possible to predict the IM-derived CCS and the CCS-mass trends of any combination of iron-halides/carboxylates complexes. The results show no differences in trend between planar trigonal and tetrahedral geometries according to valence shell electron pair repulsion (VSEPR) theory (Gillespie-Nyholm models)

Conduction system pacing (CSP), encompassing His bundle pacing (HBP) and left bundle branch area pacing (LBBAP), has emerged as a physiological pacing technique designed to activate the heart’s intrinsic conduction system. CSP is a promising alternative to traditional right ventricular pacing (RVP) for bradycardia and to biventricular pacing (BVP) for cardiac resynchronization therapy (CRT). This paper outlines key considerations for achieving successful CSP implantation, including procedural techniques, available tools, and programming strategies.

Objective: To evaluate the association between intermittent water supply, Family Health Unit (FHU) coverage, and COVID-19 spread among older adults (≥70 years) in Bauru, São Paulo, and to provide insights for targeted public policies. Methods: This retrospective cohort study was conducted from March 2020 to July 2021 using secondary data. Variables included age (60–69, 70–79, 80–104), gender (female, male), FHU coverage (present, absent), water supply (continuous, intermittent), and geographic region (South, Center, East, North, West). Poisson regression models were used to estimate relative risks (RR). Findings: A total of 7,540 COVID-19 cases were recorded. The West region accounted for 42.45% of cases, while the South region had the lowest proportion (14.20%). Individuals aged 60–69 years comprised 41.34% of cases, followed by 70–79 (32.11%) and 80–104 (26.58%). Lack of FHU coverage was associated with 83.51% of cases. Intermittent water supply increased infection risk by 17.5% (RR = 1.18; 95% CI: 1.08–1.28). After adjustment, the Center region showed higher transmissibility (RR = 1.85; 95% CI: 1.54–2.22), whereas the West region had lower transmissibility (RR = 0.53; 95% CI: 0.47–0.60). Conclusions: Insufficient FHU coverage and intermittent water supply were key determinants of COVID-19 spread, particularly among older adults. These findings underscore the importance of strengthening urban infrastructure and ensuring consistent water access to mitigate public health challenges in medium-sized cities.

The aim of the work is studying the aeroelastic response of the 15 MW NREL-IEA large-scale wind turbine using a high-fidelity fluid-structure interaction solver that combines large-eddy simulation with a modal computational structural dynamics solver through a two-way coupling. The fluid solver employs the actuator line model to simulate the interaction between the turbine blades and the fluid and the immersed boundary method to model the presence of the tower and nacelle. The results are compared with those obtained by the OpenFAST software, which is a well-known numerical tool for engineering predictions. A series of simulations have been performed with and without the presence of the tower and nacelle to better understand the effects of these components on flow structures and structural deformations. The largest discrepancies among the solvers have been observed in correspondence with the blade passage in front of the tower, which induces an abrupt alteration in the local incidence angle of the flow.

Objective: To investigate the diagnostic value of MAGLUMI chemiluminescent immunoassay (CLIA) for detecting Golgi protein 73 (GP73) in patients with chronic liver disease. Methods: A total of 212 patients with chronic liver disease were selected as the research subjects. METAVIR pathological staging was performed according to LSM values, and GP73 levels were detected by CLIA. Spearman analysis was used to analyze the correlation between GP73, LSM and METAVIR staging. The diagnostic efficacy of GP73 was analyzed using the ROC curve based on METAVIR staging. Results: The enrolled patients included 37 patients in F0/F1, 80 patients in F2, 61 patients in F3, and 34 patients in F4. There were significant differences in GP73 levels in each stage ( p<0.001 for all stages). Spearman correlation analysis showed that GP73 levels were positively correlated with LSM and METAVIR stages. The AUC of GP73 in diagnosing significant liver fibrosis (F≥2), advanced liver fibrosis (F≥3), and cirrhosis (F=4) were 0.78 (95% CI: 0.72~0.84, p<0.0001), 0.83 (95% CI: 0.75~0.89, p<0.0001), 0.90 (95% CI: 0.80~0.96, p<0.0001), respectively. Conclusion: GP73 detected by CLIA was positively correlated with liver fibrosis stage and LSM, and had important clinical value in differential diagnosis of liver fibrosis and cirrhosis in patients with chronic liver disease.

This study explores the complex pathogenesis of Alzheimer’s disease (AD), emphasizing the critical roles of advanced glycation end-products (AGEs), oxidative stress, and metabolic dysfunctions. The AGE-RAGE pathway is highlighted for its contribution to neurodegeneration, amyloid-beta (Aβ) accumulation, and tau protein pathology, proposing it as a possible treatment target. Natural compounds, such as antioxidants and anti-glycative agents, offer promising neuroprotective effects by inhibiting these processes and improving cognitive function. The metabolic link between AD and type 2 diabetes further complicates disease progression, suggesting that targeting glucose metabolism could provide novel therapeutic strategies. Despite challenges such as blood-brain barrier penetration and genetic variability, future research should focus on personalized, multi-target therapies. These therapies would aim to mitigate oxidative stress, mitochondrial dysfunction, and neuroinflammation, offering hope for more effective treatments and enhanced quality of life for individuals with AD.

We introduce a deep learning-based hybrid beamforming (HBF) strategy for millimeter-wave transmission systems, specifically addressing the challenges posed by phase noise of local oscillators. Our approach utilizes a deep neural network to optimize precoding and combining matrices based on channel state information. We incorporate the symbol index through an adaptive attention mechanism and employ a self-supervised learning approach with a phase-noise-aware loss function to mitigate the effects of phase noise. While primarily focused on phase noise, our method also accommodates other practical constraints, such as limited-resolution phase shifter and imperfect channel estimation. Simulation results demonstrate that our design outperforms traditional and deep-learning based HBF methods in terms of data rate both in scenarios impacted only by phase noise and compounded distortion scenarios including low-resolution phase shifters and channel estimation errors.