Gut hormones, and short bowel syndrome: The enigmatic role of glucagon-like peptide-2 in the regulation of intestinal adaptation

Short bowel syndrome （SBS） refers to the malabsorption of nutrients, water, and essential vitamins as a result of disease or surgical removal of parts of the small intestine. The most common reasons for removing part of the small intestine are due to surgical intervention for the treatment of either Crohn＇s disease or necrotizing enterocolitis. Intestinal adaptation following resection may take weeks to months to be achieved, thus nutritional support requires a variety of therapeutic measures, which include parenteral nutrition. Improper nutrition management can leave the SBS patient malnourished and/or dehydrated, which can be life threatening. The development of therapeutic strategies that reduce both the complications and medical costs associated with SBS/long-term parenteral nutrition while enhancing the intestinal adaptive response would be valuable. Currently, therapeutic options available for the treatment of SBS are limited. There are many potential stimulators of intestinal adaptation including peptide hormones, growth factors, and neuronally-derived components. Glucagon-like peptide-2 （GLP-2） is one potential treatment for gastrointestinal disorders associated with insufficient mucosal function. A significant body of evidence demonstrates that GLP-2 is atrophic hormone that plays an important role in controlling intestinal adaptation. Recent data from clinical trials demonstrate that GLP-2 is safe, well-tolerated, and promotes intestinal growth in SBS patients. However, the mechanism of action and the localization of the glucagon-like peptide-2 receptor （GLP-2R） remains an enigma. This review summarizes the role of a number of mucosal-derived factors that might be involved with intestinal adaptation processes; however, this discussion primarily examines the physiology, mechanism of action, and utility of GLP-2 in the regulation of intestinal mucosal growth.

Covid-19 CT Lung Image Segmentation Using Adaptive Donkey and Smuggler Optimization Algorithm

COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increasethe existing healthcare schemes in preventing the deadly virus. Nevertheless,separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes inthe characteristics of the infection. To resolve these issues, a new inf-Net (LungInfection Segmentation Deep Network) is designed for detecting the affectedareas from the CT images automatically. For the worst segmentation results,the Edge-Attention Representation (EAR) is optimized using AdaptiveDonkey and Smuggler Optimization (ADSO). The edges which are identifiedby the ADSO approach is utilized for calculating dissimilarities. An IFCM(Intuitionistic Fuzzy C-Means) clustering approach is applied for computingthe similarity of the EA component among the generated edge maps andGround-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation(SSS) structure is designed using the Randomly Selected Propagation (RP)technique and Inf-Net, which needs only less number of images and unlabelleddata. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed usinga Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all theadvantages of the disease segmentation done using Semi Inf-Net and enhancesthe execution of multi-class disease labelling. The newly designed SSMCSapproach is compared with existing U-Net++, MCS, and Semi-Inf-Net.factors such as MAE (Mean Absolute Error), Structure measure, Specificity(Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-AlignmentMeasure are considered for evaluation purpose.

Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

The design of an antenna requires a careful selection of its parameters to retain the desired performance.However,this task is time-consuming when the traditional approaches are employed,which represents a significant challenge.On the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended performance.In this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial antenna.The proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep network.This optimized network is used to retrieve the metamaterial bandwidth given a set of features.In addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

Combined Adaptive Multiple Subtraction Based on Optimized Event Tracing and Extended Wiener Filtering

The surface-related multiple elimination(SRME) method is based on feedback formulation and has become one of the most preferred multiple suppression methods used. However, some differences are apparent between the predicted multiples and those in the source seismic records, which may result in conventional adaptive multiple subtraction methods being barely able to effectively suppress multiples in actual production. This paper introduces a combined adaptive multiple attenuation method based on the optimized event tracing technique and extended Wiener filtering. The method firstly uses multiple records predicted by SRME to generate a multiple velocity spectrum, then separates the original record to an approximate primary record and an approximate multiple record by applying the optimized event tracing method and short-time window FK filtering method. After applying the extended Wiener filtering method, residual multiples in the approximate primary record can then be eliminated and the damaged primary can be restored from the approximate multiple record. This method combines the advantages of multiple elimination based on the optimized event tracing method and the extended Wiener filtering technique. It is an ideal method for suppressing typical hyperbolic and other types of multiples, with the advantage of minimizing damage of the primary. Synthetic and field data tests show that this method produces better multiple elimination results than the traditional multi-channel Wiener filter method and is more suitable for multiple elimination in complicated geological areas.

FM interference suppression for PRC-CW radar based on adaptive STFT and time-varying filtering

The influence of frequency modulation （FM） interfer- ence on correlation detection performance of the pseudo random code continuous wave （PRC-CW） radar is analyzed. It is found that the correlation output deteriorates greatly when the FM inter- ference power exceeds the anti-jamming limit of the radar. Accord- ing to the fact that the PRC-CW radar echo is a wideband pseudo random signal occupying the whole TF plane, while the FM in- terference only concentrates in a small portion, a new method is proposed based on adaptive short-time Fourier transform （STFT） and time-varying filtering for FM interference suppression. This method filters the received signal by using a binary mask to excise only the portion of the TF plane corrupted by the interference. Two types of interference, linear FM （LFM） and sinusoidal FM （SFM）, under different signal-to-jamming ratio （S JR） are studied. It is shown that the proposed method can effectively suppress the FM interference and improve the performance of target detection.

Optimized Adaptive Fuzzy Forecasting System with Application

In this paper, a fuzzy forecasting system is designed and implemented by which an original forecasting model can be obtained by data learning. The model parameters can then be adaptively optimized through gradient information of real-time data. Thus, the system is of extinguished adaptive feature and self-learning capability. Afterwards, experimental research efforts are put forward to carry out electric power load forecasting. Experimental results demonstrate the satisfactory performances of the intelligent forecasting system.

Intestinal mucosal atrophy and adaptation

Mucosal adaptation is an essential process in gut ho- meostasis. The intestinal mucosa adapts to a range of pathological conditions including starvation, short-gut syndrome, obesity, and bariatric surgery. Broadly, these adaptive functions can be grouped into proliferation and differentiation. These are influenced by diverse interactions with hormonal, immune, dietary, nervous, and mechanical stimuli. It seems likely that clinical out- comes can be improved by manipulating the physiol- ogy of adaptation. This review will summarize current understanding of the basic science surrounding adapta- tion, delineate the wide range of potential targets for therapeutic intervention, and discuss how these might be incorporated into an overall treatment plan. Deeper insight into the physiologic basis of adaptation will identify further targets for intervention to improve clini- cal outcomes.

Effect of bowel rehabilitative therapy on structural adaptation of remnant small intestine: animal experiment

AIM: To investigate the individual and the combined effects of glutamine, dietary fiber, and growth hormone on the structural adaptation of the remnant small bowel. METHODS: Forty-two adult male Sprague-Dawley rats underwent 85% mid-small bowel resection and received total parenteral nutrition (TPN) support during the first three postoperational days.From the 4th postoperational day, animals were randomly assigned to receive 7 different treatments for 8 days: TPNcon group, receiving TPN and enteral 20 g x L(-1) glycine perfusion; TPN+Gln group, receiving TPN and enteral 20 g x L(-1) glutamine perfusion; ENcon group, receiving enteral nutrition (EN) fortified with 20 g x L(-1) glycine; EN+Gln group, enteral nutrition fortified with 20 g x L(-1) glutamine; EN+Fib group, enteral nutrition and 2 g x d(-1) oral soybean fiber; EN+GH group, enteral nutrition and subcutaneous growth hormone (GH) (0.3 IU) injection twice daily; and ENint group, glutamine-enriched EN, oral soybean fiber, and subcutaneous GH injection. RESULTS: Enteral glutamine perfusion during TPN increased the small intestinal villus height (jejunal villus height 250 microm +/- 29 microm in TPNcon vs 330 microm +/- 54 microm in TPN+Gln, ileal villus height 260 microm +/- 28 microm in TPNcon vs 330 microm +/- 22 microm in TPN+Gln, P【0.05) and mucosa thickness (jejunal mucosa thickness 360 microm +/- 32 microm in TPNcon vs 460 microm +/- 65 microm in TPN+Gln, ileal mucosa thickness 400 microm +/- 25 microm in TPNcon vs 490 microm +/- 11 microm in TPN+Gln,P【 0.05) in comparison with the TPNcon group. Either fiber supplementation or GH administration improved body mass gain (end body weight 270 g +/- 3.6g in EN+Fib, 265.7 g +/- 3.3 g in EN+GH, vs 257 g +/- 3.3 g in ENcon, P【 0.05), elevated plasma insulin-like growth factor (IGF-I) level (880 microg x L(-1). 52 microg x L-(-1) in EN+Fib,1200 microg x L(-1). 96 microg x L-(-1) in EN +/- GH, vs 620 microg x L(-1).43 microg x L-(-1) in ENcon, P【 0.05), and increased the villus height (jejunum 560 microm +/- 44 microm in EN +/- Fib, 530 microm +/- 30 microm in EN +/- GH, vs 450 microm +/- 44 microm in ENcon, ileum 400 microm +/- 30 microm in EN+Fib, P【0.05) and the mucosa thickness (jejunum 740 microm +/- 66 microm in EN +/- Fib, 705 microm +/- 27 microm in EN +/- GH, vs 608 microm +/- 58 microm in ENcon, ileum 570 microm +/- 27 microm in EN +/- Fib, 560 microm +/- 56 microm in remnant jejunum and ileum. Glutamine-enriched EN produced little effect in body mass, plasma IGF-I level, and remnant small bowel mucosal structure. The ENint group had greater body mass (280 g +/- 2.2g), plasma IGF-I level (1450 microg x L(-1). 137 microg x L-(-1)), and villus height (jejunum 620 microm +/- 56 microm, ileum 450 microm +/- 31 microm) and mucosal thickness (jejunum 800 microm +/- 52 microm, ileum 633 microm +/- 33 microm) than those in ENcon, EN+Gln (jejunum villus height and mucosa thickness 450 microm +/- 47 microm and 610 +/- 63 microm, ileum villus height and mucosa thickness 330 microm +/- 39 microm and 500 microm +/- 52 microm), EN+GH groups (P【0.05), and than those in EN+Fib group although no statistical significance was attained. CONCLUSION: Both dietary fiber and GH when used separately can enhance the postresectional small bowel structural adaptation. Simultaneous use of these two gut-trophic factors can produce synergistic effects on small bowel structural adaptation. Enteral glutamine perfusion is beneficial in preserving small bowel mucosal structure during TPN, but has little beneficial effect during EN.

A Fault Feature Extraction Model in Synchronous Generator under Stator Inter-Turn Short Circuit Based on ACMD and DEO3S

This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.

The Detection of Inter-Turn Short Circuits in the Stator Windings of Sensorless Operating Induction Motors

This work proposes an alternative strategy to the use of a speed sensor in the implementation of active and reactive power based model reference adaptive system (PQ-MRAS) estimator in order to calculate the rotor and stator resistances of an induction motor (IM) and the use of these parameters for the detection of inter-turn short circuits (ITSC) faults in the stator of this motor. The rotor and stator resistance estimation part of the IM is performed by the PQ-MRAS method in which the rotor angular velocity is reconstructed from the interconnected high gain observer (IHGO). The ITSC fault detection part is done by the derivation of stator resistance estimated by the PQ-MRAS estimator. In addition to the speed sensorless detection of ITSC faults of the IM, an approach to determine the number of shorted turns based on the difference between the phase current of the healthy and faulty machine is proposed. Simulation results obtained from the MATLAB/Simulink platform have shown that the PQ-MRAS estimator using an interconnected high-gain observer gives very similar results to those using the speed sensor. The estimation errors in the cases of speed variation and load torque are almost identical. Variations in stator and rotor resistances influence the performance of the observer and lead to poor estimation of the rotor resistance. The results of ITSC fault detection using IHGO are very similar to the results in the literature using the same diagnostic approach with a speed sensor.

助行康复机器人自适应步态控制方法研究

为了提高助行康复机器人在不同坡度上的稳定性,合理的步态控制方法显得尤为重要。这里以助行康复机器人为研究对象,提出了一种将长短期记忆网络和动态运动基元结合的步行康复机器人自适应步态控制方法。将传感器采集信息与动态运动基元中步态控制项相结合,对参考步态进行调整。同时通过更改动态运动基元的时间项和空间项对步态进行二次调整以适应不同的坡度。通过试验对该控制方法的可用性进行验证。结果表明,该方法可以在(0~20)°斜坡进行自适应步态变换,具有一定的实用价值。

改进黑猩猩算法的光伏发电功率短期预测

针对晴空、非晴空条件下光伏出力预测精度不高等问题,提出一种改进K均值(K-means++)算法和黑猩猩优化算法CHOA(chimpanzee optimization algorithm)相结合,优化最小二乘支持向量机LSSVM(least squares support vector machine)的模型,进行光伏功率预测。首先,利用密度聚类和混合评价函数改进K-means++对原始数据进行自适应类别划分。其次,通过相关性分析和随机森林特征提取构建模型的输入特征集。最后,根据特征集建立基于DK-PCHOA-LSSVM的短期光伏发电预测模型。结合实际算例,结果表明:该模型在恶劣天气下预测精度明显优于其他模型,验证了其有效性和优越性。

基于GA-LSTM自适应卡尔曼滤波的路面不平度识别

准确、快速地识别出车辆当前行驶的路面激励信息,是实现智能底盘控制进而保证车辆平顺性的关键。针对传统路面不平度识别算法准确率低、自适应性差等问题,提出了基于遗传算法(genetic algorithm,GA)优化长短期记忆神经网络(long short-term memory networks,LSTM)自适应卡尔曼滤波的路面不平度识别算法。基于2自由度车辆悬架模型,通过灰色关联法选择LSTM神经网络的特征输入变量,并采用GA优化LSTM神经网络的模型参数以准确识别路面等级,并据此实时更新卡尔曼滤波器算法中的噪声矩阵,实现了在复杂路况下对路面不平度的自适应识别。仿真和试验研究表明,所提出的基于GA-LSTM自适应卡尔曼滤波算法能够快速准确的识别路面不平度与路面等级,与传统卡尔曼滤波算法相比,相关系数、均方根误差和最大绝对误差分别提高3.11%、37.5%和51.2%,表明所提算法对复杂工况具有很好的自适应能力。

CE认证之ETSI 301893-Adaptivity

自适应性测试使用的干扰信号可以由一个矢量信号发生器生成。如果测试信号需要覆盖多个20MHz的信道,则需要采用相应宽带宽的干扰信号进行干扰测试,或者采用多次测试来覆盖这相邻的多个20MHz的信道。

适用于SiC MOSFET的漏源电压积分自适应快速短路保护电路研究

SiC MOSFET因其高击穿电压、高开关速度、低导通损耗等性能优势而被广泛应用于各类电力电子变换器中。然而,由于其短路耐受时间仅为2~7μs,且随母线电压升高而缩短,快速可靠的短路保护电路已成为其推广应用的关键技术之一。为应对不同母线电压下的Si C MOSFET短路故障,文中提出一种基于漏源电压积分的自适应快速短路保护方法(drain-sourcevoltageintegration-basedadaptivefast short-circuit protection method,DSVI-AFSCPM),研究所提出的DSVI-AFSCPM在硬开关短路(hardswitchingfault,HSF)和负载短路(fault under load,FUL)条件下的保护性能,进而研究不同母线电压对DSVI-AFSCPM的作用机理。同时,探究Si CMOSFET工作温度对其响应速度的影响。最后,搭建实验平台,对所提出的DSVI-AFSCPM在发生硬开关短路和负载短路时不同母线电压、不同工作温度下的保护性能进行实验测试。实验结果表明,所提出的DSVI-AFSCPM在不同母线电压下具有良好的保护速度自适应性,即母线电压越高,短路保护速度越快,并且其响应速度受Si CMOSFET工作温度影响较小,两种短路工况下工作温度从25℃变化到125℃,短路保护时间变化不超过90 ns。因此,该文为Si CMOSFET在不同母线电压下的可靠使用提供一定技术支撑。

基于自适应短时傅里叶变换的品质因子Q值估算方法

品质因子Q是描述地下介质对地震波吸收衰减强弱程度的参数,同时也是地层含油气性的重要标志。在地震资料Q估算中,常用的方法是短时傅里叶变换方法,当窗函数被选定以后,其时频分辨率就固定了。针对该问题,提出一种自适应窗短时傅里叶变换的方法,以获得更准确的瞬时中心频率,并利用峰值频移法来估算品质因子Q。首先,利用固定窗长的短时傅里叶变换来提取信号的瞬时中心频率作为初始频率;然后,根据初始频率自适应计算不同频率的窗长,并利用自适应窗长短时傅里叶变换来求取瞬时中心频率;最后,结合峰值频移法得到高分辨率的品质因子Q值。利用合成数据和实际数据进行了测试,结果表明,相比于固定时窗短时傅里叶变换方法,自适应短时傅里叶变换方法具有更好的时间和频率分辨率,可以获得更高分辨率的品质因子Q值。该结果可以为地下介质的研究提供更准确、可靠的工具,有助于更好地了解地下结构和油气资源分布情况。

基于多窗宽核密度估计的风电功率超短期自适应概率预测

精准的风电功率预测是保证新型电力系统安稳运行、促进风电消纳的重要手段。针对核密度估计所求分位数在不同置信度下鲁棒性差的问题,提出多窗宽核密度估计方法,根据不同置信度生成不同窗宽的核密度估计值,实现了风电功率的超短期自适应概率预测。首先,结合风电功率曲线和数据驱动模型,建立基于改进双向长短期记忆网络的风电功率超短期确定性预测模型。其次,推导了最优窗宽核密度估计方法,并基于此构建多窗宽核密度估计误差拟合模型,在不同置信度下自适应生成最优窗宽并构建预测区间。最后,基于实际运行数据验证模型的可行性与有效性。结果表明,所提模型可有效提高确定性预测的精度和概率预测的鲁棒性。

基于多模式分解和多分支输入的光伏功率超短期预测

针对光伏发电功率随机性强、波动性大导致其预测精度不高的问题,提出一种基于自适应近邻传播聚类(adaptive affinity propagation clustering,adAP)、多模式分解、多分支输入组合的光伏功率预测方法。首先,基于相关性分析找到与光伏发电功率高度相关的气象因素,并利用快速傅里叶变换(fast Fourier transform,FFT)将光伏输出功率从时域转换到频域,与相关度高的气象因素一起作为adAP算法的聚类特征,对具有相似气象特征的日场景进行分类;其次,对聚类相似日较少且输出功率波动剧烈天气类型中的气象相关因素和光伏输出功率添加高斯白噪声,并将其与原始数据合并,达到倍增样本的效果,以提升模型的泛化能力和鲁棒性;然后,使用变分模态分解(variational mode decomposition,VMD)、奇异谱分解(singular spectrum decomposition,SSD)和群分解(swarm decomposition,SWD)对光伏功率、辐照度和温度进行分解,削弱原始序列的波动性,丰富模型的输入特征;最后,搭建多分支的残差网络(residual network,ResNet)和长短期记忆网络(long short term memory network,LSTM)模型,提取数据的时间特征和波动特征,合并后输入到门控循环单元网络(gated recurrent unit network,GRU)中,建立历史特征和未来光伏输出功率的联系,得到预测结果。实验结果表明,所提出的多模型组合预测方法在光伏功率波动较缓天气情况下,能够保持较高的预测精度;在波动剧烈天气情况下,能够较大地提升预测精度。

GNSS拒止时基于并行CNN-BiLSTM回归和残差补偿的UAV导航误差校正方法

全球导航卫星系统(GNSS)拒止时,GNSS/惯性导航系统(INS)组合导航系统的性能严重下降,导致无人机集群导航误差快速发散.目前,利用神经网络预测位置与速度代替GNSS导航信息可校正无人机INS误差,但该方法仍存在定位误差较高且在轨迹突变时预测精度急剧下降的问题.因此,提出了一种基于卷积-双向长短时记忆网络联合残差补偿的位置与速度预测方法,用于提高位置与速度预测精度.首先,针对GNSS拒止后GNSS/INS组合导航系统定位误差较高的问题,提出卷积神经网络(CNN)与双向长短时记忆网络(BiLSTM)的融合模型,该模型可建立惯性测量单元(IMU)动力学测量数据与GNSS导航信息之间的关系,实现较准确的位置和速度预测.其次,针对轨迹突变时预测效果急剧下降的问题,提出并行CNNBiLSTM回归架构,在预测位置与速度的同时,挖掘IMU动力学测量数据、预测值与预测残差之间的关系,预测并补偿预测残差,增强模型在轨迹突变时的预测精度.仿真结果表明,所提模型在预测准确性、有效性和稳定性方面都优于CNN-LSTM、LSTM网络模型.

CEEMDAN-WPE-CLSA超短期风电功率预测方法研究

提出了一种结合自适应噪声完全集合经验模态分解、加权排列熵、卷积神经网络、长短期记忆网络和自注意力机制的超短期风电功率预测方法。首先,利用自适应噪声完全集合经验模态分解将原始风电功率时间序列自适应分解为一系列的模态分量,降低原始序列的非线性和波动性;其次,根据加权排列熵计算各模态分量间的相似性并对相似的分量进行重组,以修正自适应噪声完全集合经验模态分解的过度分解问题,使得修正后的模态分量更具规律性;最后,将重组后的分量输入卷积长短期记忆网络进行时序建模,并利用自注意力机制对卷积长短期记忆网络的神经元权重进行重新分配,提高了卷积长短期记忆网络对输入特征不确定性的适应能力。在此基础上,明确了自注意力机制和自适应噪声完全集合经验模态分解、加权排列熵在风电功率预测中的作用机制,以及风电功率信号包含的重要物理信息,证明了自适应噪声完全集合经验模态分解、加权排列熵以及自注意力机制在风电功率信号模态分解和长短期记忆网络隐层输出权重分配中的有效性。