A Deep Learning Framework for Anesthesia Depth Prediction from Drug Infusion History.

In the target-controlled infusion (TCI) of propofol and remifentanil intravenous anesthesia, accurate prediction of the depth of anesthesia (DOA) is very challenging. Patients with different physiological characteristics have inconsistent pharmacodynamic responses during different stages of anesthesia. For example, in TCI, older adults transition smoothly from the induction period to the maintenance period, while younger adults are more prone to anesthetic awareness, resulting in different DOA data distributions among patients. To address these problems, a deep learning framework that incorporates domain adaptation and knowledge distillation and uses propofol and remifentanil doses at historical moments to continuously predict the bispectral index (BIS) is proposed in this paper. Specifically, a modified adaptive recurrent neural network (AdaRNN) is adopted to address data distribution differences among patients. Moreover, a knowledge distillation pipeline is developed to train the prediction network by enabling it to learn intermediate feature representations of the teacher network. The experimental results show that our method exhibits better performance than existing approaches during all anesthetic phases in the TCI of propofol and remifentanil intravenous anesthesia. In particular, our method outperforms some state-of-the-art methods in terms of root mean square error and mean absolute error by 1 and 0.8, respectively, in the internal dataset as well as in the publicly available dataset.

Impact of preoperative therapy on surgical outcomes of laparoscopic total gastrectomy for gastric/gastroesophageal junction cancer.

Objective: As laparoscopic surgery is widely applied for primarily treated gastric cancer (GC)/gastroesophageal junction cancer (GEJC) and gains many advantages, the feasibility of laparoscopic total gastrectomy (LTG) for GC/GEJC patients who have received preoperative therapy (PT) has come to the fore. This study aims to analyze the safety and feasibility of LTG after PT for GC/GEJC patients. Methods: We retrospectively analyzed the data of 511 patients with GC/GEJC undergoing LTG, of which 405 received LTG (LTG group) and 106 received PT+LTG (PT-LTG group) at Nanfang Hospital between June 2018 and September 2022. The surgical outcomes were compared between the two groups. Results: The surgical duration was significantly longer in the PT-LTG group (P<0.001), while the incidence of intraoperative complications (P=1.000), postoperative complications (LTG group vs. PT-LTG group: 26.2% vs. 23.6%, P=0.587), the classification of complication severity (P=0.271), and postoperative recovery was similar between two groups. Notably, the incidence of anastomotic complications of esophagojejunostomy was also comparable between the two groups (LTG group vs. PT-LTG group: 5.9% vs. 5.7%, P=0.918). The univariate and multivariate analysis confirmed that positive proximal margin [positive vs. negative: odds ratio (OR)=14.094, 95% confidence interval (95% CI): 2.639-75.260, P=0.002], rather than PT, has an impact on anastomotic complications after LTG (OR=0.945, 95% CI: 0.371-2.408, P=0.905). Conclusions: PT did not increase the surgical risk of LTG for GC/GEJC. Therefore, considering the positive effect of PT on long-term survival, the broader application of PT and LTG for GC/GEJC is supported by our findings.

Effects of rice-prawn (Macrobrachium nipponense) co-culture on the microbial community of soil.

In the Lixiahe region of China, co-culture has been rapidly promoted in flooded paddy fields owing to its ecological and economic benefits. Rice-prawn co-culture can reduce the damage of crab and shrimp to rice growth and paddy field and substantially change the soil microbial community and soil fertility. In this study, we compared changes in the soil microbial community and soil fertility in waterlogged paddies under conventional rice culture (CR), rice-prawn (Macrobrachium nipponense) co-culture (RP), and pond culture (PC). The microbial abundance in RP was significantly higher than that in CR. RP soil microbial diversity was significantly higher than PC soil microbial diversity. The dominant bacteria in RP soil were Proteobacteria, Chloroflexi, and Bacteroidetes. Compared with those in CR, total organic matter (TOM) and total nitrogen in RP were relatively stable, available potassium and available phosphorus (AP) decreased, and other indicators increased significantly. Soil fertility significantly benefited from co-culture, with total organic carbon (TOC) increasing. Interactive relationship analysis showed that TOM, TOC, AP, and NH4+-N were the main factors affecting the microbial community. Co-occurrence network analyses showed that network modularity increased with co-culture, indicating that a unique soil microbial community formed under co-culture, improving the adaptability and tolerance to co-culture. Thus, RP is a suitable culture method for this commercially important species. The results of this study can inform the practical operation of fertilizer use and sustainable development of rice-prawn aquaculture systems. KEY POINTS: • Microbial abundance and diversity increased under rice-prawn co-culture. • Co-culture significantly improved soil fertility, with an increase in TOC. • Rice-prawn co-culture is an ecologically suitable culture method for prawns.

Bi-tunable terahertz absorber based on strontium titanate and Dirac semimetal.

We proposed a polarization-insensitive absorber based on strontium titanate (STO) and bulk Dirac semimetal (BDS) in the terahertz (THz) region. The center frequency of the absorption peak can be independently regulated by temperature or Fermi energy level of STO or BDS, respectively. The numerical simulation result reveals that the peak absorptivity reaches to 99.98% at 2.16 THz when the temperature and Fermi energy were set at 300 K and 20 meV, respectively. Interestingly, by adjusting the temperature of STO from 250 to 400 K, the simulation results indicate that the center frequency can be tuned from 1.94 to 2.53 THz, and peak absorptivity can be maintained above 99% at normal incident. As the Fermi energy EF of Dirac semimetal increases from 10 to 60 meV, the center frequency can be changed from 2.14 to 2.44 THz and the amplitude of absorption peaks can be tuned from 99.9% to 82.8%. Impedance matching theory was used to understand the tunable performance. Furthermore, interference theory was employed to further explain the absorption mechanism of the proposed absorber. The absorber achieves bi-controlled absorptance via two independently controllable methods, which may provide guidance to research tunable, smart and multifunctional terahertz devices.

A randomized controlled trial of a modified wheelchair arm-support to reduce shoulder pain in stroke patients.

OBJECTIVE: To evaluate the effect of modified wheelchair arm-support to mitigate hemiplegic shoulder pain and reduce pain frequency in stroke patients. DESIGN: A single-blind randomized controlled trial using computer-generated simple randomization. SETTING: Participants recruited from inpatients at the Guangdong Provincial Hospital of Chinese Medicine. SUBJECTS: A total of 120 patients with stroke were divided into two groups. INTERVENTIONS: All subjects underwent basic rehabilitation training and wheelchair assistance with eight weeks follow-up period. Patients in the treatment group additionally received modified wheelchair arm-support for at least 60 minutes a day, six days a week, for four weeks. OUTCOME MEASURES: Primary outcome was measured by the Visual Analogue Pain Scale or Numeric Pain Rating Scale. Secondary outcome was measured using the Upper Extremity Fugl-Meyer Assessment scale, Modified Barthel Index and Quality of Life Index. Measurements were made at 4 weeks and 12 weeks, following the intervention. RESULTS: Patients age from 21 to 83 years (mean ± SD = 62.41 ± 12.26). The average duration of disease was 1.9 ± 1.3 months. At four weeks, the median of pain intensity was higher in the control group (median, interquartile range = 3, 5.75 vs. 2, 3.75; P = 0.059). At 12 weeks, the median of pain intensity was higher in the control group (median, interquartile range = 3, 5.00 vs. 0, 1.00; P < 0.001). At 12 weeks, patients with shoulder pain were higher in the control group (6 vs. 1; P < 0.05). CONCLUSION: Using the modified wheelchair arm-support could lead to the mitigation of hemiplegic shoulder pain and reduction in pain incidence in stroke patients. It may also improve the patients' quality of life.

Linear vs. Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Images.

As a new machine learning approach, the extreme learning machine (ELM) has received much attention due to its good performance. However, when directly applied to hyperspectral image (HSI) classification, the recognition rate is low. This is because ELM does not use spatial information, which is very important for HSI classification. In view of this, this paper proposes a new framework for the spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are an improvement of linear ELM (LELM). However, based on lots of experiments and much analysis, it is found that the LELM is a better choice than nonlinear ELM for the spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learns such a distribution using the LBP. The proposed method not only maintains the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines, and Pavia University, demonstrate the good performance of the proposed method.

Molecular characterization of an interleukin-4/13B homolog in grass carp (Ctenopharyngodon idella) and its role in fish against Aeromonas hydrophila infection.

Mammalian interleukin 4 (IL-4) and interleukin 13 (IL-13) molecules are anti-inflammatory cytokines mediating the alternative activation of macrophages. However, the role of fish IL-4/13 homologs in monocytes/macrophages (MO/MФ) polarization remains unclear. In this study, we have functionally identified an IL-4/13B homolog in grass carp (Ctenopharyngodon idella), which is termed as CiIL-4/13B. Multiple alignment showed that CiIL-4/13B shared the typical characteristics and structure with other known fish IL-4/13. Phylogenetic analysis showed that CiIL-4/13B is evolutionarily closely related to zebrafish (Danio rerio) and common carp (Cyprinus carpio) IL-4/13B. CiIL-4/13B mRNA was constitutively expressed in tissues and peripheral blood lymphocytes (PBLs) examined, with its highest expression seen in PBLs. Following Aeromonas hydrophila infection, CiIL-4/13B mRNA expression was upregulated. Recombinant CiIL-4/13B (rCiIL-4/13B) was overexpressed in Escherichia coli and purified for a functional study. Using prepared anti-rCiIL-4/13B antiserum, Western blot analysis showed that native CiIL-4/13B in grass carp plasma is N-glycosylated. Intraperitoneal injection of bioactive rCiIL-4/13B significantly increased the survival rate of grass carp against A. hydrophila, and decreased the tissue bacterial load, with a higher dose having better effects. Bioactive rCiIL-4/13B treatment decreased nitrite production and mRNA expression of proinflammatory cytokines (IL-1ß and TNF-α), while it increased arginase activity and mRNA expression of anti-inflammatory cytokines (TGF-ß and IL-10). The phagocytosis by grass carp MO/MФ had no significant changes by the 8 h treatment of bioactive rCiIL-4/13B compared to that of the negative control, while it was significantly inhibited by the 24 h treatment of bioactive rCiIL-4/13B. The inhibitory effect of rCiIL-4/13B on MO/MФ phagocytosis may be a consequence of MO/MФ proliferation. In summary, our results suggest that CiIL-4/13B plays a protective effect in grass carp against A. hydrophila by inducing alternatively activated MO/MФ.

[Effect of Comprehensive Protocol of Integrative Medicine on Motor Function, Activity of Daily Living and Quality of Life in Hemiplegia Patients after Stroke].

OBJECTIVE: To explore the effects of integrative medicine (IM) rehabilitation protocolon motor function, activity of daily living, and quality of life (QOL) in hemiplegia patients after stroke. METHODS: Totally 120 patients with post-stroke hemiplegia were allocated to four groups using sealed envalope drawing, i.e., the rehabilitation group, the Chinese medical treatment group, the acupuncture group, and the comprehensive rehabilitation group, 30 cases in each group. Based on routine rehabilitative training, patients in the Chinese medical treatment group, the acupuncture group, and the compre-hensive rehabilitation group received standardized treatment based on syndrome typing, Shi's Consciousness-Restoring Resuscitation acupuncture, Chinese herbs + acupuncture comprehensive rehabilitatino protocol, respectively. The treatmet cycle consisted of 4 weeks with 24-week follow-ups. Fugl-Meyer motor assessment (FMA), Modified Barthel Index (MBI), and Stroke-Specific Quality of Life Scale(SS-QQL), and safety assessment were taken as main effect indices before treatment, at week 4 of treatment, at week 12 and 24 of follow-ups, respectively. RESULTS: There was no statistical difference in FMA score, MBI score, SS-QOL score among the four groups before treatment (P > 0.05). These scores were significantly improved in the four groups at week 4 of treatment, week 12 and 24 of follow-ups, respectively (P < 0.05). Besides, FMA score and SS-QOL score were significantly improved in the comprehensive rehabilitation group at each corresponding time point, as compared with other treatment groups (P < 0.05). CONCLUSIONS: The comprehensive protocol could significantly improve motor function, activity of daily living in hemiplegia patients after stroke, and further improve their QOL. Its effect was better than other single treatment.

A weighted prior tensor train decomposition method for community detection in multi-layer networks.

Community detection in multi-layer networks stands as a prominent subject within network analysis research. However, the majority of existing techniques for identifying communities encounter two primary constraints: they lack suitability for high-dimensional data within multi-layer networks and fail to fully leverage additional auxiliary information among communities to enhance detection accuracy. To address these limitations, a novel approach named weighted prior tensor training decomposition (WPTTD) is proposed for multi-layer network community detection. Specifically, the WPTTD method harnesses the tensor feature optimization techniques to effectively manage high-dimensional data in multi-layer networks. Additionally, it employs a weighted flattened network to construct prior information for each dimension of the multi-layer network, thereby continuously exploring inter-community connections. To preserve the cohesive structure of communities and to harness comprehensive information within the multi-layer network for more effective community detection, the common community manifold learning (CCML) is integrated into the WPTTD framework for enhancing the performance. Experimental evaluations conducted on both artificial and real-world networks have verified that this algorithm outperforms several mainstream multi-layer network community detection algorithms.

Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration.

Modern visual recognition models often display overconfidence due to their reliance on complex deep neural networks and one-hot target supervision, resulting in unreliable confidence scores that necessitate calibration. While current confidence calibration techniques primarily address single-label scenarios, there is a lack of focus on more practical and generalizable multi-label contexts. This paper introduces the Multi-Label Confidence Calibration (MLCC) task, aiming to provide well-calibrated confidence scores in multi-label scenarios. Unlike single-label images, multi-label images contain multiple objects, leading to semantic confusion and further unreliability in confidence scores. Existing single-label calibration methods, based on label smoothing, fail to account for category correlations, which are crucial for addressing semantic confusion, thereby yielding sub-optimal performance. To overcome these limitations, we propose the Dynamic Correlation Learning and Regularization (DCLR) algorithm, which leverages multi-grained semantic correlations to better model semantic confusion for adaptive regularization. DCLR learns dynamic instance-level and prototype-level similarities specific to each category, using these to measure semantic correlations across different categories. With this understanding, we construct adaptive label vectors that assign higher values to categories with strong correlations, thereby facilitating more effective regularization. We establish an evaluation benchmark, re-implementing several advanced confidence calibration algorithms and applying them to leading multi-label recognition (MLR) models for fair comparison. Through extensive experiments, we demonstrate the superior performance of DCLR over existing methods in providing reliable confidence scores in multi-label scenarios.

Prevalence of and risk factors for low back pain among professional drivers: a systematic review and meta-analysis.

PURPOSE: A growing body of research indicates a correlation between occupational exposure, particularly among individuals in driving-related occupations, and the incidence of low back pain (LBP). METHODS: Databases were systematically searched, including PubMed, Embase, Web of Science, Cochrane Library, and SinoMed, from their inception through December 2023 for relevant studies of the prevalence and risk factors of LBP among professional drivers. Subsequent meta-analyses were performed utilizing Stata 17.0 and RevMan 5.4 software, while risk factor indicators were assessed using the Grading of Recommendations, Assessment, Development and Evaluation evidence quality grading system. RESULTS: A systematic review and meta-analysis comprising 19 studies involving 7,723 patients indicated that the incidence of LBP among drivers was 39% (95% confidence interval [CI] 0.20-0.57) in the past 7 days and 53% (95% CI 0.43-0.63) in the past 12 months. A subgroup analysis revealed a prevalence of 48% (95% CI 0.33-0.64) in 2005-2015 and 56% (95% CI 0.42-0.70) in 2016-2023. Among the identified factors, robust evidence highlighted age ≥ 41 years (odds ratio [OR] = 2.10; 95% CI 1.36-3.24; P = 0.0008), alcohol consumption (OR = 1.75; 95% CI 1.31-2.34; P = 0.0001), sleeping < 6 h/night (OR = 1.60; 95% CI 1.13-2.24; P = 0.007), uncomfortable seating (OR = 1.71; 95% CI 1.23-2.36; P = 0.001), improper driving posture (OR = 2.37; 95% CI 1.91-2.94; P < 0.00001), and manual handling (OR = 2.23; 95% CI 1.72-2.88; P < 0.00001) as significant risk factors for LBP. There was moderate evidence of a lack of exercise (OR = 1.78; 95% CI 1.37-2.31; P < 0.0001), working > 10 h/day (OR = 2.49; 95% CI 1.89-3.28; P < 0.00001), > 5 years' driving experience (OR = 2.12; 95% CI 1.66-2.69; P < 0.00001), a lack of back support (OR = 1.81; 95% CI 1.25-2.62; P = 0.002), high work-related pressure (OR = 2.04; 95% CI 1.59-2.61; P < 0.00001), and job dissatisfaction (OR = 1.57; 95% CI 1.23-2.01; P = 0.0003) as moderate risk factors. There was no evidence of body mass index or smoking as risk factors for LBP among professional drivers. CONCLUSION: The current evidence indicates an increasing annual trend in the prevalence of LBP among professional drivers. Factors including age ≥ 41 years, alcohol consumption, and sleeping < 6 h/night were among the 12 influential factors contributing to LBP in professional drivers. Enhancing awareness of these factors and formulating targeted preventive strategies may be beneficial.

POLQ inhibition attenuates the stemness and ferroptosis resistance in gastric cancer cells via downregulation of dihydroorotate dehydrogenase.

Gastric cancer (GC) contains subpopulations of cancer stem cells (CSCs), which are described as the main contributors in tumor initiation and metastasis. It is necessary to clarify the molecular mechanism underlying CSCs phenotype and develop novel biomarkers and therapeutic targets for gastric cancer. Here, we show that POLQ positively regulates stem cell-like characteristics of gastric cancer cells, knockdown of POLQ suppressed the stemness of GC cells in vitro and in vivo. Further mechanistic studies revealed that POLQ knockdown could downregulate the expression of dihydroorotate dehydrogenase (DHODH). DHODH overexpression rescued the reduced stemness resulted by POLQ knockdown. Furthermore, we found that POLQ expression correlated with resistance to ferroptosis, and POLQ inhibition renders gastric cancer cells more vulnerable to ferroptosis. Further investigation revealed that POLQ regulated DHODH expression via the transcription factors E2F4, thereby regulating ferroptosis resistance and stemness of gastric cancer cells. Given the importance of POLQ in stemness and ferroptosis resistance of GC, we further evaluated the therapeutic potential of POLQ inhibitor novobiocin, the results show that novobiocin attenuates the stemness of GC cells and increased ferroptosis sensitivity. Moreover, the combination of POLQ inhibitor and ferroptosis inducer synergistically suppressed MGC-803 xenograft tumor growth and diminished metastasis. Our results identify a POLQ-mediated stemness and ferroptosis defense mechanism and provide a new therapeutic strategy for gastric cancer.

A Transformer-Based Prediction Method for Depth of Anesthesia During Target-Controlled Infusion of Propofol and Remifentanil.

Accurately predicting anesthetic effects is essential for target-controlled infusion systems. The traditional (PK-PD) models for Bispectral index (BIS) prediction require manual selection of model parameters, which can be challenging in clinical settings. Recently proposed deep learning methods can only capture general trends and may not predict abrupt changes in BIS. To address these issues, we propose a transformer-based method for predicting the depth of anesthesia (DOA) using drug infusions of propofol and remifentanil. Our method employs long short-term memory (LSTM) and gate residual network (GRN) networks to improve the efficiency of feature fusion and applies an attention mechanism to discover the interactions between the drugs. We also use label distribution smoothing and reweighting losses to address data imbalance. Experimental results show that our proposed method outperforms traditional PK-PD models and previous deep learning methods, effectively predicting anesthetic depth under sudden and deep anesthesia conditions.

Effects of morphological traits on body weight and analysis of growth-related genes of Parabramis pekinensis at different ages.

Parabramis pekinensis was treated as research object in order to investigate the correlation between morphological traits and body weight. We measured 9 morphological indexes including total length (X1), body length (X2), body height (X3), head length (X4), snout length (X5), eye diameter (X6), eye distance (X7), caudal stalk length (X8) and caudal stalk height (X9). The principal morphological traits affecting body weight were screened out and the regression equation was established. The regression equation of Y1 (age 1 group) shape character (X) and weight (Y) was Y = - 169.183 + 32.544 × 3 + 10.263 × 4 + 15.655 × 7. The regression equation of Y2 (age 2 group) shape character (X) and weight (Y) was Y = - 694.082 + 7.725 × 1 + 72.822 × 3 + 77.023 × 6, the regression equation of Y3 (age 3 group) shape character (X) and weight (Y) was Y = - 1161.512 + 26.062 × 1 + 22.319 × 2- 107.218 × 5 + 83.901 × 7. Gene expression was consistent with these conclusions. TOR signaling pathway expression raised in Y1 then width increased. And GH-IGF-1 signaling pathway expression raised in Y2 then the length increased. In conclusion, the paper could prove that P. pekinensis showed a growth trend, which was increasing width first and length later. In some sense, the study not only enriched the basic biological data of P. pekinensis, but also provided waiting morphological traits for selective breeding of P. pekinensis artificial breeding in future.

Roles of m6A modification in oral cancer (Review).

Oral cancer is one of the highly malignant tumors with poor prognosis. The pathogenic mechanisms of oral cancer have remained to be fully elucidated and this brings significant challenges to the treatment. RNA modification is a common intracellular chemical modification that has been related to various pathological processes, such as blood diseases, immune system diseases and cancer. As the most common and abundant RNA modification in eukaryotic mRNA, N6­methyladenosine (m6A) modification has a crucial role in several cancers, including oral cancer. m6A modification directly affects gene expression levels and regulates various physiological and pathological processes. It has been demonstrated that m6A modification may affect the proliferation, migration and invasion of oral cancer cells by regulating the level of m6A modification. In the present review, the effects of m6A modification on the proliferation and death of oral cancer cells, as well as the occurrence and development of oral cancer, were analyzed in order to provide a new target for treatment. Furthermore, the roles of m6A modification in chemotherapy resistance and potential immunotherapy were analyzed and new treatment ideas were provided.

Decreased acetylation of HDGF improves oviduct production in Rana dybowskii, Rana amurensis, and Rana huanrenensis.

The oviduct of female Rana dybowskii is a functional food and can be used as a component of Traditional Chinese medicine. The differentially expressed genes enriched was screened in cell growth of three Rana species. We quantitatively analyzed 4549 proteins using proteomic techniques, enriching the differentially expressed proteins of Rana for growth and signal transduction. The results showed that log2 expression of hepatoma-derived growth factor (HDGF) was increased. We further verified 5 specific differential genes (EIF4a, EIF4g, HDGF1, HDGF2 and SF1) and found that HDGF expression was increased in Rana dybowskii. Through acetylation modification analysis, we identified 1534 acetylation modification sites in 603 proteins, including HDGF, and found that HDGF acetylation expression was significantly reduced in Rana dybowskii. Our results suggest that HDGF is involved in the development of oviductus ranae, which is regulated by acetylation modification.

Correntropy based semi-supervised concept factorization with adaptive neighbors for clustering.

Concept factorization (CF) has shown the effectiveness in the field of data clustering. In this paper, a novel and robust semi-supervised CF method, called correntropy based semi-supervised concept factorization with adaptive neighbors (CSCF), is proposed with improved performance in clustering applications. Specifically, on the one hand, the CSCF method adopts correntropy as the cost function to increase the robustness for non-Gaussian noise and outliers, and combines two different types of supervised information simultaneously for obtaining a compact low-dimensional representation of the original data. On the other hand, CSCF assigns the adaptive neighbors for each data point to construct a good data similarity matrix for reducing the sensitiveness of data. Moreover, a generalized version of CSCF is derived for enlarging the clustering application ranges. Analysis is also presented for the relationship of CSCF with several typical CF methods. Experimental results have shown that CSCF has better clustering performance than several state-of-the-art CF methods.

CNN-LSTM network-based damage detection approach for copper pipeline using laser ultrasonic scanning.

Copper pipeline is a commonly used industrial transmission pipeline. Nondestructive testing of copper pipeline early damage is very important. Laser scanning has attracted extensive attention because it can realize the visualization of guided wave propagation and non-contact on-line detection. However, the damage points detection in laser scanning imaging method rely on the difference between the damage points signals and surrounding normal points signals. This limits the applicability of laser scanning and may lead to inaccurate in large-area detection. Facing with such challenges, a damage detection method based on CNN-LSTM network is proposed for laser ultrasonic guided wave scanning detection in this paper, which can detect each scanning point signal without relying on the surrounding detection points signals. Firstly, the proposed data conversion algorithm is used to preprocess the laser scanning signals. Next, CNN-LSTM network is used to train the damage detection model. Four 1D Conv channels with different convolution kernel sizes and depths are designed in Convolutional Neural Network (CNN) module. The module can extract the signal time domain features. Then the features are input into the Long Short-Term Memory Network (LSTM) for feature extraction and classification. Finally, the CNN-LSTM is trained using the laser scanning detection data collected on the copper pipeline with crack and corrosion damages, and applied to detect the copper pipeline damage signal. At the same time, the state-of-the-art methods is compared with proposed method. The experimental results show that the detection accuracy of the method is 99.9%, 99.9%, 99.8% and 99.8% for copper pipeline 0.5 mm deep crack damage, penetrating crack damage, corrosion damage and inside crack damage, respectively. The damage location and size can be accurately detected by the proposed method.

Research on cost accounting of enterprise carbon emission (in China).

Enterprises in China face two major challenges about their existence and energy supply at present. One is the difficulty in providing enough energy at an acceptable and reasonable price; the other is a severe environmental issue caused by over-consumption of energy. The government and relevant enterprises, therefore, mainly focus on carbon emission reduction, and the cost accounting of carbon emission, an essential prerequisite, and object of carbon emission reduction, should be further attention. The carbon emission cost is divided into internal cost and external cost, combined with the extended accounting model and cost calculation. This can comprehensively measure and reflect the two costs of the life cycle of the product, provide more relevant data and information support for the deepening and development of the circular economy, and provide an effective cost information basis and guide enterprise managers for scientific decision-making and governance.

Current practice of postoperative fasting: results from a multicentre survey in China.

OBJECTIVE: A gap between clinical practice and evidence is common. The present multicentre study was designed to explore the actual postoperative fasting practice, including the instructed fasting time from the ward staff and the actual postoperative fasting time. DESIGN: Multicentre survey. SETTING: Four tertiary hospitals in Shenzhen City, China. PARTICIPANTS: A total of 988 patients completed a survey on instructed and actual postoperative fasting. OUTCOMES: All patients received postoperative instructed fasting time from the ward staff. The median instructed fasting time for fluids from ward staff was 6 hours (IQR, 4-6 hours), and the median instructed fasting time for solid food was also 6 hours (IQR 5-6 hours) after surgery. The actual postoperative fasting time, including fluid and solid food intake, was significantly longer than the time recommended by the ward staff (both p<0.001). RESULTS: The median time to postoperative first flatus (FFL) was 16.5 hours (IQR 8-25.5 hours), and the median time to postoperative first faeces (FFE) was 41 hours (IQR 25-57 hours). The fasting time was significantly shorter than the time to FFL and the time to FFE, regardless of surgery type or anaesthesia type (all p<0.001). Postoperative nausea and vomiting (PONV) occurred in 23.6% of patients. After surgery, 58.70% of patients reported thirst, and 47.47% reported hunger. No ileus occurred. CONCLUSION: Approximately half of the patients reported thirst and hunger postoperatively. Patients initiated oral intake earlier than the time to FFL or FFE without increasing serious complications. This study may support the rationale for interventions targeting postoperative oral intake time in future studies.