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The drilling-hammer method is adopted for the breaking over submerged reefs in waterway regulation engineering. However, rocks with pre-drilled holes are highly susceptible to the repetitive impacts from drop hammers or drilling equipment. Exploring the dynamic responses and failure behaviors of drilled-hole rocks under repetitive impacting is important for promoting implementation of this technique. In this study, the repetitive impacting tests were conducted on the drilled rocks by the split Hopkinson pressure bar to investigate the rock bearing capacity, deformation property, failure behavior, and energy evolution. The results indicate that the presence of pre-drilled holes and the decreasing loading rates attenuate the bearing capacity of rocks subjected to repetitive impacting. In contrast, the rock deformation capacity is impacted subtly by the impact loading rate and increases with the number of impacts and borehole size. The drilled rock failure occurred with the prevalence of intergranular fractures; subsequently, the proportion of transgranular fractures increased, contributing to the loading rate-dependent bearing capacity. The inverted S-shaped damage evolution curve was assessed using the inverted logistic growth model. The specimen absorbed few incident energies for fracture generation and propagation, and the rock with a larger drill size displays higher energy utilization and larger fragments. • Illustrated the influences of repetitive impacting on the bearing capacity and deformability of pre-cracked hole rocks. • Revealed the failure behavior and fracture mechanism of pre-cracked hole rocks under repetitive impacting. • Depicted the damage evolution of pre-cracked hole rocks under repetitive impacting by the inverted logistic growth model. • Investigated the dynamic energy dissipation of pre-cracked hole rocks under repetitive impacting. [ABSTRACT FROM AUTHOR]

To implement sustainable water resource management, the industries that produce a huge amount of wastewater are aiming to recycle wastewater. Reverse osmosis (RO) is an advanced membrane process that can produce potable water from wastewater. However, the presence of diverse pollutants in the wastewater necessitates effective pretreatment to ensure successful RO implementation. This study evaluated the efficiency of microfiltration (MF) and ultrafiltration (UF) as two pretreatment methods prior to RO, i.e., MF-RO and UF-RO, for recycling poultry slaughterhouse wastewater (PSWW). The direct treatment of PSWW with RO (direct RO) was also considered for comparison. In this study, membrane technology serves as a post treatment for PSWW, which was conventionally treated at Sanderson Farm. The results demonstrated that all of the processes, including MF-RO, UF-RO, and direct RO treatment of PSWW, rejected 100% of total phosphorus (TP), over 91.2% of chemical oxygen demand (COD), and 87% of total solids (TSs). Total nitrogen (TN) levels were reduced to 5 mg/L for MF-RO, 4 mg/L for UF-RO, and 9 mg/L for direct RO. In addition, the pretreatment of PSWW with MF and UF increased RO flux from 46.8 L/m2 h to 51 L/m2 h, an increase of approximately 9%. The product water obtained after MF-RO, UF-RO, and direct RO meets the required potable water quality standards for recycling PSWW in the poultry industry. A cost analysis demonstrated that MF-RO was the most economical option among membrane processes, primarily due to MF operating at a lower pressure and having a high water recovery ratio. In contrast, the cost of using RO without MF and UF pretreatments was approximately 2.6 times higher because of cleaning and maintenance expenses related to fouling. This study concluded that MF-RO is a preferable option for recycling PSWW. This pretreatment method would significantly contribute to environmental sustainability by reusing well-treated PSWW for industrial poultry purposes while maintaining cost efficiency. [ABSTRACT FROM AUTHOR]

In recent years, deep convolutional neural networks (DCNNs) have shown promising performance in medical image analysis, including breast lesion classification in 2D ultrasound (US) images. Despite the outstanding performance of DCNN solutions, explaining their decisions remains an open investigation. Yet, the explainability of DCNN models has become essential for healthcare systems to accept and trust the models. This paper presents a novel framework for explaining DCNN classification decisions of lesions in ultrasound images using the saliency maps linking the DCNN decisions to known cancer characteristics in the medical domain. The proposed framework consists of three main phases. First, DCNN models for classification in ultrasound images are built. Next, selected methods for visualization are applied to obtain saliency maps on the input images of the DCNN models. In the final phase, the visualization outputs and domain-known cancer characteristics are mapped. The paper then demonstrates the use of the framework for breast lesion classification from ultrasound images. We first follow the transfer learning approach and build two DCNN models. We then analyze the visualization outputs of the trained DCNN models using the EGrad-CAM and Ablation-CAM methods. We map the DCNN model decisions of benign and malignant lesions through the visualization outputs to the characteristics such as echogenicity, calcification, shape, and margin. A retrospective dataset of 1298 US images collected from different hospitals is used to evaluate the effectiveness of the framework. The test results show that these characteristics contribute differently to the benign and malignant lesions' decisions. Our study provides the foundation for other researchers to explain the DCNN classification decisions of other cancer types. [ABSTRACT FROM AUTHOR]

Number connection test A (NCT-A) and digit symbol test (DST), the preferential neuropsychological tests to detect minimal hepatic encephalopathy (MHE) in China, haven't been standardized in Chinese population. We aimed to establish the norms based on a multi-center cross-sectional study and to detect MHE in cirrhotic patients. NCT-A and DST were administered to 648 healthy controls and 1665 cirrhotic patients. The regression-based procedure was applied to develop demographically adjusted norms for NCT-A and DST based on healthy controls. Age, gender, education, and age by education interaction were all predictors of DST, while age, gender, and education by gender interaction were predictors of log10 NCT-A. The predictive equations for expected scores of NCT-A and DST were established, and Z-scores were calculated. The norm for NCT-A was set as Z ≤ 1.64, while the norm for DST was set as Z ≥ − 1.64. Cirrhotic patients with concurrent abnormal NCT-A and DST results were diagnosed with MHE. The prevalence of MHE was 8.89% in cirrhotic patients, and only worse Child–Pugh classification (P = 0.002, OR = 2.389) was demonstrated to be the risk factor for MHE. The regression-based normative data of NCT-A and DST have been developed to detect MHE in China. A significant proportion of Chinese cirrhotic patients suffered from MHE, especially those with worse Child–Pugh classification. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have demonstrated promising performance in classifying breast lesions in 2D ultrasound (US) images. Exiting approaches typically use pre-trained models based on architectures designed for natural images with transfer learning. Fewer attempts have been made to design customized architectures specifically for this purpose. This paper presents a comprehensive evaluation on transfer learning based solutions and automatically designed networks, analyzing the accuracy and robustness of different recognition models in three folds. First, we develop six different DCNN models (BNet, GNet, SqNet, DsNet, RsNet, IncReNet) based on transfer learning. Second, we adapt the Bayesian optimization method to optimize a CNN network (BONet) for classifying breast lesions. A retrospective dataset of 3034 US images collected from various hospitals is then used for evaluation. Extensive tests show that the BONet outperforms other models, exhibiting higher accuracy (83.33%), lower generalization gap (1.85%), shorter training time (66 min), and less model complexity (approximately 0.5 million weight parameters). We also compare the diagnostic performance of all models against that by three experienced radiologists. Finally, we explore the use of saliency maps to explain the classification decisions made by different models. Our investigation shows that saliency maps can assist in comprehending the classification decisions. [ABSTRACT FROM AUTHOR]

Achieving rapid response and recovery rates at low operating temperatures for NO 2 is desirable for the application of sensors in industrial safety. In this study, we integrate a thickness tunable 2D graphdiyne (GDY) film into ZnO nanowalls (GDY/ZnO) via thermal evaporation of GDY, where ZnO nanowalls are used as the template to confine the growth space of the GDY film. In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) is used to reveal the transformation process of NO 2. Ultraviolet photoemission spectroscopy (UPS) is utilized to analyze changes about the energy band of the materials. Density functional theory (DFT) calculations explain the reason for the selectivity of the samples to NO 2. The scanning electron microscopy (SEM) results demonstrate that the thickness of the GDY film with a specific mesoporous structure (25‐53 nm) increases as the thermal evaporation temperature rises from 400 to 800°C.The representative ZG-600, synthesized with a thickness of 46 nm via thermal evaporation at 600°C, exhibits superior NO 2 sensing performance. This is evidenced by its ultrahigh response (13.86, at 100 ppm and 150°C) and rapid response/recovery speeds (150 s/198 s), outperforming the ZnO sensor (198 s and 326 s). Moreover, it offers remarkable stability. • GDY nanoparticles were decorated on the surface of ZnO nanowall. • GDY/ZnO heterojunction with adsorbed oxygen and oxygen vacancies has been synthesized. • GDY/ZnO composite nanowall exhibited highly better NO 2 sensing performance. • The electrons transfer and redox reaction between GDY and ZnO has been discussed. [ABSTRACT FROM AUTHOR]

The current two-dimensional (2D) material based NH 3 sensors generally suffer from low response values and sluggish recovery dynamics, which is resulted from their inferior conductivity. Herein, high conductivity (0.045 S/m) of 2D hydrogen substituted graphyne (HsGY) nanosheets are successfully synthesized employing aqueous/organic interface method and using them for the first time for the detection of NH 3 at room temperature. The resultant gas sensing performance shows that the 2D HsGY nanosheets exhibit a high gas response value (4.9) to 100 ppm NH 3 and fast recovery time (108 s) at the room temperature. The NH 3 gas sensing mechanism is discussed by utilizing density functional theory (DFT) calculations. The present study provides novel gas sensing material for the development of high-performance room temperature NH 3 sensors. This study successfully synthesized two-dimensional hydrogen substituted graphyne (HsGY) nanosheets using an aqueous/organic interface method and applied them for the detection of NH 3 at room temperature for the first time. HsGY nanosheets exhibit excellent sensing performance towards NH 3 , due to their high conductivity and continuous thin gauze like morphology, which facilitates gas transport. [Display omitted] [ABSTRACT FROM AUTHOR]

In recent years, the potential use and development of acetone gas sensors in health monitoring and self-diagnostic applications has attracted increasing attention. Thus, accurate sensing of acetone is of crucial importance in the non-invasive diagnosis of diabetes. However, challenges remain in the detection of acetone, such as high concentrations detection limit, high operating temperature and slow response dynamics. Herein, vertically aligned Ce-doped mesoporous NiO nanowalls are prepared on Ag-Pd interdigitated electrode substrate using an in-situ growth method. The gas sensing performances show that the as-made 4 mol% Ce-doped NiO nanowalls has excellent gas response value (3.2) to 10 ppm acetone at a lower operating temperature (175 ℃) with fast adsorption and desorption equilibrium (13 s/7 s). Vertically aligned mesoporous NiO nanowalls doped by Ce element could pave the way for ultra-sensitive and low power consumption acetone gas sensors for breath gas detection. • Simple method: in situ growth method. • Excellent detection conditions: Low lower detection limits at lower temperatures. • The fast equilibrium of adsorption and desorption. [ABSTRACT FROM AUTHOR]