Explore the vast range of titles available.

Traditional concert-hall evaluations primarily rely on ISO 3382-1 scalar parameters (e.g., C50 and C80), which summarize temporal energy behavior but provide limited insight into the directional composition of early reflections, particularly in geometrically shadowed seating zones. This paper presents a first-order Ambisonics (FOA)-based 3D acoustic sensing framework to diagnose under-balcony directional imbalance, with emphasis on early vertical-reflection deficiency. Scene-based FOA impulse responses (WXYZ) were measured at 11 audience positions (P1–P11) in the National Concert Hall (Taipei) and analyzed using intensity-based direction-of-arrival (DoA) proxies, axis-resolved directional energy build-up, and a distributional descriptor based on directional spatial entropy. Results are presented at three representative frequencies (125 Hz, 1 kHz, and 4 kHz) and analyzed within full (0–200 ms), early (0–80 ms), and late (80–200 ms) windows. While the magnitude proxy ∣pmeas(f)∣ exhibits strong seat-to-seat variability and does not support a uniform attenuation assumption under the balcony, direction-resolved metrics reveal a consistent under-balcony signature. Specifically, the early–late vertical energy discrepancy ΔRz=Rzearly−Rzlate is persistently negative at under-balcony positions (P7–P11) across all three frequencies, indicating a selective reduction in early vertical contribution relative to the late field. Directional entropy analysis further shows predominantly negative ΔHn=Hnearly−Hnlate, with more negative values in the under-balcony group, consistent with stronger early directional constraint in shadowed seats. Spatial trend maps are provided via Gaussian RBF interpolation within the audience domain for visualization only. The proposed FOA-based diagnostic framework provides a practical and physically interpretable approach to identify direction-specific early-reflection deficits that remain masked in conventional scalar evaluations, supporting mechanism-oriented assessment and targeted intervention in geometrically constrained listening areas.

Hydroalkylation, the direct addition of a C(sp 3 )–H bond across an olefin, is a desirable strategy to produce valuable, complex structural motifs in functional materials, pharmaceuticals, and natural products. Herein, we report a reliable method for accessing α-branched amines via nickel-catalyzed hydroalkylation reactions. Specifically, by using bis(cyclooctadiene)nickel (Ni(cod) 2 ) together with a phosphine ligand, we achieved a formal C(sp 3 )–H bond insertion reaction between olefins and N-sulfonyl amines without the need for an external hydride source. The amine not only provides the alkyl motif but also delivers hydride to the olefin by means of a nickel-engaged β–hydride elimination/reductive elimination process. This method provides a platform for constructing chiral α-branched amines by using a P-chiral ligand, demonstrating its potential utility in organic synthesis. Notably, a sulfonamidyl boronate complex formed in situ under basic conditions promotes ring-opening of the azanickellacycle reaction intermediate, leading to a significant improvement of the catalytic efficiency. Catalytic addition of a carbon chain and a hydrogen across a double bond has often required an added hydride source. Here the authors show a method to add alkanes with an amino functionality to olefins, wherein a nickel catalyst uses the amine itself as the hydride source, obviating an external hydride reagent.

Determining the quality of Ti-6Al-4V parts fabricated by selective laser melting (SLM) remains a challenge due to the high cost of SLM and the need for expertise in processes and materials. In order to understand the correspondence of the relative density of SLMed Ti-6Al-4V parts with process parameters, an optimized extreme gradient boosting (XGBoost) decision tree model was developed in the present paper using hyperparameter optimization with the GridsearchCV method. In particular, the effect of the size of the dataset for model training and testing on model prediction accuracy was examined. The results show that with the reduction in dataset size, the prediction accuracy of the proposed model decreases, but the overall accuracy can be maintained within a relatively high accuracy range, showing good agreement with the experimental results. Based on a small dataset, the prediction accuracy of the optimized XGBoost model was also compared with that of artificial neural network (ANN) and support vector regression (SVR) models, and it was found that the optimized XGBoost model has better evaluation indicators such as mean absolute error, root mean square error, and the coefficient of determination. In addition, the optimized XGBoost model can be easily extended to the prediction of mechanical properties of more metal materials manufactured by SLM processes.

Renal cell carcinoma (RCC) is one of the most lethal genitourinary malignancies with poor prognoses, since it is largely resistant to chemotherapy, radiotherapy, and targeted therapy. The persistence of cancer stem cells (CSCs) is the major cause of treatment failure with RCC. Recent evidence showed that dopamine receptor D2 (DRD2)-targeting antipsychotic drugs such as penfluridol exert oncostatic effects on several cancer types, but the effect of penfluridol on RCC remains unknown. Here, we uncovered penfluridol suppressed in vitro cell growth and in vivo tumorigenicity of various RCC cell lines (Caki-1, 786-O, A498, and ACHN) and enhanced the Sutent (sunitinib)-triggered growth inhibition on clear cell (cc)RCC cell lines. Mechanistically, upregulation of endoplasmic reticulum (ER) stress-induced unfolded protein response (UPR) was critical for autophagy-mediated apoptosis induced by penfluridol. Transcriptional inhibition of OCT4 and Nanog via inhibiting GLI1 was important for penfluridol-induced stemness and proliferation inhibition. The anticancer activities of penfluridol on ccRCC partially occurred through DRD2. In clinical ccRCC specimens, positive correlations of DRD2 with GLI1, OCT4, and Nanog were observed and their expressions were correlated with worse prognoses. Summarizing, DRD2 antagonists such as penfluridol induce UPR signaling and suppress the GLI1/OCT4/Nanog axis in ccRCC cells to reduce their growth through inducing autophagy-mediated apoptosis and stemness inhibition. These drugs can be repurposed as potential agents to treat ccRCC patients.

Oral mucositis is a common adverse effect of cancer therapy. Probiotics have been shown to exert anti-inflammatory and immunomodulatory effects. We performed a meta-analysis of randomized controlled trials (RCTs) to investigate whether probiotics can prevent cancer therapy–induced oral mucositis. We searched PubMed, Embase, Cochrane Library, and ClinicalTrials.gov databases for trials related to probiotics and oral mucositis published before September 2022; no language restrictions were applied. The primary outcome was the incidence of oral mucositis and severe oral mucositis. Secondary outcomes were the requirement for enteral nutrition during treatment, body weight loss, and decreased quality of life. The study has been registered in PROSPERO (number: CRD 42022302339). Eight RCTs, including 708 patients, were reviewed; however, a meta-analysis of only seven trials could be performed. Three trials using Lactobacilli-based probiotics reported that the incidence of oral mucositis in the probiotic group was significantly low (risk ratio [RR] = 0.84, 95% confidence interval [CI] = 0.77–0.93, p = 0.0004). Seven trials reported a significantly low incidence of severe oral mucositis in the probiotic group (RR = 0.65, 95% CI = 0.53–0.81, p < 0.0001). The requirement of enteral nutrition was significantly low in the probiotic group (odds ratio = 0.34, 95% CI: 0.13–0.92, p < 0.05). This study demonstrated the effectiveness of probiotics in the prevention and mitigation of cancer therapy–induced oral mucositis. We recommend the use of probiotics to prevent and treat oral mucositis during cancer therapy.

Automated optical inspection (AOI) of wood surfaces is critical for ensuring product quality in the furniture and manufacturing industries; however, existing defect detection systems often struggle to generalize across complex grain patterns and diverse defect types. This study proposes a wood defect recognition model employing a Gabor Convolutional Network (GCN) that integrates convolutional neural networks (CNNs) with Gabor filters. To systematically optimize the network’s architecture and improve both detection accuracy and computational efficiency, the Taguchi method is employed to tune key hyperparameters, including convolutional kernel size, filter number, and Gabor parameters (frequency, orientation, and phase offset). Additionally, image tiling and augmentation techniques are employed to effectively increase the training dataset, thereby enhancing the model’s stability and accuracy. Experiments conducted on the MVTec Anomaly Detection dataset (wood category) demonstrate that the Taguchi-optimized GCN achieves an accuracy of 98.92%, outperforming a baseline Taguchi-optimized CNN by 2.73%. Results confirm that Taguchi-optimized GCNs enhance defect detection performance and computational efficiency, making them valuable for smart manufacturing.

Intrauterine growth restriction (IUGR) is a leading cause of perinatal mortality and morbidity, and IUGR survivors are at increased risk of neurodevelopmental deficits. No effective interventions are currently available to improve the structure and function of the IUGR brain before birth. This study investigated the protective effects of low‐intensity pulsed ultrasound (LIPUS) on postnatal neurodevelopmental outcomes and brain injury using a rat model of IUGR induced by maternal exposure to dexamethasone (DEX). Pregnant rats were treated with DEX (200 μg/kg, s.c.) and LIPUS daily from gestational day (GD) 14 to 19. Behavioral assessments were performed on the IUGR offspring to examine neurological function. Neuropathology, levels of neurotrophic factors, and CaMKII‐Akt‐related molecules were assessed in the IUGR brain, and expression of glucose and amino acid transporters and neurotrophic factors were examined in the placenta. Maternal LIPUS treatment increased fetal weight, fetal liver weight, and placental weight following IUGR. LIPUS treatment also increased neuronal number and myelin protein expression in the IUGR brain, and attenuated neurodevelopmental deficits at postnatal day (PND) 18. However, the number of oligodendrocytes or microglia was not affected. These changes were associated with the upregulation of brain‐derived neurotrophic factor (BDNF) and placental growth factor (PlGF) protein expression, and enhancement of neuronal CaMKII and Akt activation in the IUGR brain at PND 1. Additionally, LIPUS treatment promoted glucose transporter (GLUT) 1 production and BDNF expression in the placenta, but had no effects on GLUT3 or amino acid transporter expression. Our findings suggest that antenatal LIPUS treatment may reduce IUGR‐induced brain injury via enhancing cerebral BDNF/CaMKII/Akt signaling. These data provide new evidence that LIPUS stimulation could be considered for antenatal neuroprotective therapy in IUGR. In a rat model of IUGR, growth restriction and loss of neurons and myelin in the brain are linked to behavioral deficits.LIPUS induces cerebral BDNF/CaMKII/Akt cascade and increases placental GLUT1 and BDNF in IUGR.LIPUS could be a treatment to reverse offspring growth restriction in IUGR.

Aims: The precise mechanisms underlying the pathogenesis of opioid-induced thermal hyperalgesia and tolerance are not yet fully understood. Methods: In adult CD-1 mice, repeated morphine treatment was used to examine the expression of the non-canonical pathway of sonic hedgehog signaling, behavioral changes, and neurochemical alterations induced by morphine in the spinal cord and DRG. Additionally, to delve into the underlying mechanisms of the non-canonical pathway of Shh signaling in morphine-induced thermal hyperalgesia (MITH) and tolerance, we utilize the brain-derived neurotrophic factor (BDNF) inhibitor. Results: Morphine administration repeatedly resulted in apparent thermal hyperalgesia and tolerance. The initiation and maintenance of MITH and tolerance, as well as related neurochemical alterations, were greatly inhibited by pharmacological and genetic suppression of the mTOR. By blocking the mTOR/p70 ribosomal S6 protein kinase 1 (S6K1)/Gli1 signaling, the morphine-induced increase in BDNF was considerably inhibited. Moreover, mTOR activator injection in naive mice resulted in significant heat hyperalgesia and BDNF upregulation. Suppression of BDNF effectively mitigated the development of thermal hyperalgesia induced by the mTOR activator. Conclusion: These findings indicate that the non-canonical pathway of Shh signaling might serve as a crucial mediator in the development of MITH and tolerance through the regulation of BDNF expression.

The acidic extracellular microenvironment has become an effective target for diagnosing and treating tumors. A pH (low) insertion peptide (pHLIP) is a kind of peptide that can spontaneously fold into a transmembrane helix in an acidic microenvironment, and then insert into and cross the cell membrane for material transfer. The characteristics of the acidic tumor microenvironment provide a new method for pH-targeted molecular imaging and tumor-targeted therapy. As research has increased, the role of pHLIP as an imaging agent carrier in the field of tumor theranostics has become increasingly prominent. In this paper, we describe the current applications of pHLIP-anchored imaging agents for tumor diagnosis and treatment in terms of different molecular imaging methods, including magnetic resonance T1 imaging, magnetic resonance T2 imaging, SPECT/PET, fluorescence imaging, and photoacoustic imaging. Additionally, we discuss relevant challenges and future development prospects.

This study aims to conduct an assessment of the dynamic characteristics of a proposed 6.6 kW bidirectional bridgeless three-leg interleaved totem-pole power factor correction (PFC) boost converter developed for the front-end stage of electric vehicle onboard charger applications during load cycles. This proposed PFC boost converter integrates the self-developed silicon carbide (SiC) power MOSFET modules for achieving high efficiency and high power density. To assess the switching transient behavior, power loss, and efficiency of the SiC MOSFET power modules, a fully integrated electromagnetic-circuit coupled simulation (ECCS) model that incorporates an electromagnetic model, an equivalent circuit model, and an SiC MOSFET characterization model are used. In this simulation model, the impact of parasitic effects on the system’s performance is considered. The accuracy of the ECCS model is confirmed through comparing the calculated results with the experimental data obtained through the double pulse test and the closed-loop converter operation. Furthermore, a comparative study between the interleaved and non-interleaved topologies is also performed in terms of power loss and efficiency. Additionally, the performance of the SiC MOSFET-based PFC boost converter is further compared with that of the silicon (Si) insulated gate bipolar transistor (IGBT)-based one. Finally, a parametric analysis is carried out to explore the impact of several operating conditions on the power loss of the proposed totem-pole PFC boost converter.