Guidelines and consensus:Jejunoileostomy for diabetes mellitussurgical norms and expert consensus(2023 version)

Diabetes mellitus(DM)is a group of diseases characterized by high blood glucose caused by insufficient absolute or relative secretion of insulin.Once diagnosed,patients need long-term treatment with hypoglycemic drugs.Currently,the existing first-line hypoglycemic drugs do not provide effective treatment for DM and its complications.In the past,the first generation and the second generation of weight loss surgery,such as gastric bypass and sleeve gastric surgery,had strict body mass index requirements.Moreover,post-surgery,patients are prone to fluctuating hypoglycemia,gastroesophageal reflux,and dumping syndrome.Hence,the curative effect of this type of surgery was compromised to a certain extent.Jejunoileostomy is a third-generation surgery for patients with DM,which has been shown to improve glucose and lipid metabolism,without changing the original gastrointestinal tract structure.Different from previous weight loss surgeries,jejunoileostomy has been clinically observed to delay the development of DM-related complications.Additionally,the postoperative complications are mild and do not affect the patient’s quality of life.Based on our clinical observations from multi-center large samples,our team developed a consensus on the operative period and perioperative management of jejunoileostomy as a reference for clinical researchers.

The Roadmap of 2D Materials and Devices Toward Chips

Due to the constraints imposed by physical effects and performance degra certain limitations in sustaining the advancement of Moore’s law.Two-dimensional(2D)materials have emerged as highly promising candidates for the post-Moore era,offering significant potential in domains such as integrated circuits and next-generation computing.Here,in this review,the progress of 2D semiconductors in process engineering and various electronic applications are summarized.A careful introduction of material synthesis,transistor engineering focused on device configuration,dielectric engineering,contact engineering,and material integration are given first.Then 2D transistors for certain electronic applications including digital and analog circuits,heterogeneous integration chips,and sensing circuits are discussed.Moreover,several promising applications(artificial intelligence chips and quantum chips)based on specific mechanism devices are introduced.Finally,the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed,and potential development pathways or roadmaps are further speculated and outlooked.

NiMo基非晶合金电催化析氢反应的进展

电催化水分解产生绿色氢气是发展可持续能源体系的重要途径之一。由过渡金属Ni、Mo组成的具有高活性、低成本的非晶态合金被广泛认为是一种有效的制氢催化剂。重点关注以镍钼(NiMo)基非晶合金为代表的催化剂,总结非晶态合金的制备方法及其在催化领域的应用,分别叙述NiMo合金、NiMo基中熵合金和NiMo基高熵合金在电催化析氢反应(HER)中的应用。结果表明,在合理的功能导向设计下,通过对关键科学及技术问题的解决,NiMo基非晶态合金催化剂能够表现出良好的析氢性能,最后提出在制备技术、组分调控、纳米化、电子结构以及活化机理5个方面还有待解决的问题。这对于催化剂的设计、制备、构效关系及催化机理的研究具有一定的参考价值。

Design of a high sensitivity and wide range angular rate sensor based on exceptional surface

It is found that when the parity–time symmetry phenomenon is introduced into the resonant optical gyro system and it works near the exceptional point,the sensitivity can in theory be significantly amplified at low angular rate.However,in fact,the exceptional point is easily disturbed by external environmental variables,which means that it depends on harsh experimental environment and strong control ability,so it is difficult to move towards practical application.Here,we propose a new angular rate sensor structure based on exceptional surface,which has the advantages of high sensitivity and high robustness.The system consists of two fiber-optic ring resonators and two optical loop mirrors,and one of the resonators contains a variable ratio coupler and a variable optical attenuator.We theoretically analyze the system response,and the effects of phase and coupling ratio on the system response.Finally,compared with the conventional resonant gyro,the sensitivity of this exceptional surface angular rate sensor can be improved by about 300 times at low speed.In addition,by changing the loss coefficient in the ring resonator,we can achieve a wide range of 600 rad/s.This scheme provides a new approach for the development of ultra-high sensitivity and wide range angular rate sensors in the future.

Suppression of low-frequency ultrasound broadband vibration using star-shaped single-phase metamaterials

In order to suppress the low-frequency ultrasound vibration in the broadband range of 20 k Hz—100 k Hz,this paper proposes and discusses an acoustic metamaterial with low-frequency ultrasound vibration attenuation properties,which is configured by hybrid arc and sharp-angle convergent star-shaped lattices.The effect of the dispersion relation and the bandgap characteristic for the scatterers in star-shaped are simulated and analyzed.The target bandgap width is extended by optimizing the geometry parameters of arc and sharp-angle convergent lattices.The proposed metamaterial configured by optimized hybrid lattices exhibits remarkable broad bandgap characteristics by bandgap complementarity,and the simulation results verify a 99%vibration attenuation amplitude can be obtained in the frequency of20 k Hz—100 k Hz.After the fabrication of the proposed hybrid configurational star-shaped metamaterial by 3D printing technique,the transmission loss experiments are performed,and the experimental results indicate that the fabricated metamaterial has the characteristics of broadband vibration attenuation and an amplitude greater than 85%attenuation for the target frequency.These results demonstrate that the hybrid configurational star-shaped metamaterials can effectively widen the bandgap and realize high efficiency attenuation,which has capability for the vibration attenuation in the application of highprecise equipment.

Diagenetic evolution and reservoir quality of the Oligocene sandstones in the Baiyun Sag, Pearl River Mouth Basin, South China Sea

The Oligocene Zhuhai sandstones are significant reservoirs for hydrocarbons in the Baiyun Sag, South China Sea.For effective appraisal, exploration and exploitation of such a deep-water hydrocarbon sandstone, samples of five wells from depths of 850 m to 3 000 m were studied. A series of comprehensive petrographic and geochemical analyses were performed to unravel the diagenetic features and their impact on the reservoir quality.Petrographically, the sandstones are dominated by feldspathic litharenites and lithic arenites with fine to medium grain sizes and moderate to good sorting. The reservoir quality varies greatly with a range of porosity from 0.2% to 36.1% and permeability from 0.016 ×10~(–3) μm~2 to 4 301 ×10~(–3) μm~2, which is attributed to complex diagenetic evolution related to sedimentary facies;these include compaction, cementation of calcite, dolomite, siderite and framboidal pyrite in eogenetic stage;further compaction, feldspar dissolution, precipitation of ferrocalcite and ankerite, quartz cements, formation of kaolinite and its illitization, precipitation of albite and nodular pyrite, as well as hydrocarbon charge in mesogenetic stage. The dissolution of feldspar and illitization of kaolinite provide internal sources for the precipitation of quartz cement, while carbonate cements are derived from external sources related to interbedded mudstones and deep fluid. Compaction is the predominant factor in reducing the total porosity, followed by carbonate cementation that leads to strong heterogeneity. Feldspar dissolution and concomitant quartz and clay cementation barely changes the porosity but significantly reduces the permeability.The high-quality reservoirs can be concluded as medium-grained sandstones lying in the central parts of thick underwater distributary channel sandbodies(>2 m) with a high content of detrital quartz but low cement.

Extensive numerical simulations on competitive growth between the Edwards–Wilkinson and Kardar–Parisi–Zhang universality classes

Extensive numerical simulations and scaling analysis are performed to investigate competitive growth between the linear and nonlinear stochastic dynamic growth systems, which belong to the Edwards–Wilkinson(EW) and Kardar–Parisi–Zhang(KPZ) universality classes, respectively. The linear growth systems include the EW equation and the model of random deposition with surface relaxation(RDSR), the nonlinear growth systems involve the KPZ equation and typical discrete models including ballistic deposition(BD), etching, and restricted solid on solid(RSOS). The scaling exponents are obtained in both the(1 + 1)-and(2 + 1)-dimensional competitive growth with the nonlinear growth probability p and the linear proportion 1-p. Our results show that, when p changes from 0 to 1, there exist non-trivial crossover effects from EW to KPZ universality classes based on different competitive growth rules. Furthermore, the growth rate and the porosity are also estimated within various linear and nonlinear growths of cooperation and competition.

Recent progress on fabrication, spectroscopy properties, and device applications in Sn-doped CdS micro-nano structures

One-dimensional semiconductor materials possess excellent photoelectric properties and potential for the construction of integrated nanodevices. Among them, Sn-doped CdS has different micro-nano structures, including nanoribbons,nanowires, comb-like structures, and superlattices, with rich optical microcavity modes, excellent optical properties, and a wide range of application fields. This article reviews the research progress of various micrometer structures of Sn-doped CdS, systematically elaborates the effects of different growth conditions on the preparation of Sn-doped CdS micro-nano structures, as well as the spectral characteristics of these structures and their potential applications in certain fields. With the continuous progress of nanotechnology, it is expected that Sn-doped CdS micro-nano structures will achieve more breakthroughs in the field of optoelectronics and form cross-integration with other fields, jointly promoting scientific, technological, and social development.

A novel dual-channel thermo-optic locking method for the whispering gallery mode microresonator

Mode locking can be effectively achieved by using the thermo-optic effects in the whispering gallery mode(WGM)optical microcavity,without the help of external equipment.Therefore,it has the advantages of small size,low integration costs,and self-locking,which shows great potential for application.However,the conventional single-channel microcavity thermal-locking method that relies solely on internal thermal balance will inevitably be disturbed by the external environment.This limitation affects the locking time and stability.Therefore,in this paper,we propose a new method for closed-loop thermal locking of a dual-channel microcavity.The thermal locking of the signal laser and the thermal regulation of the control laser are carried out respectively by synchronously drawing a dual-path tapered fiber.The theoretical model of the thermal dynamics of the dual-channel microcavity system is established,and the influence of the control-laser power on the thermal locking of the signal laser is confirmed.The deviation between the locking voltage of the signal laser and the set point value is used as a closed-loop feedback parameter to achieve long-term and highly stable mode locking of the signal laser.The results show that in the 2.63 h thermal-locking test,the locking stability is an order of magnitude higher than that of the single tapered fiber.This solution addresses the issue of thermal locking being disrupted by the external environment,and offers new possibilities for important applications such as spectroscopy and micro-optical sensor devices.

A homogeneous and mechanically stable artificial diffusion layer using rigid-flexible hybrid polymer for high-performance lithium metal batteries

Artificial solid electrolyte interphase(SEI) is promising to inhibit uncontrollable lithium dendrites and enable long cycling stability for lithium metal batteries. However, the essential mechanical stability is limited since organic layers generally have low modulus whereas intrinsic brittleness for inorganic ones remains a great concern. Polymer-based SEIs with rigid and flexible chains in adequate mechanical properties are supposed to address this issue. Herein, a homogeneous and mechanically stable diffusion layer is achieved by blending rigid chains of polyphenylene sulfone(PPSU) with flexible chains of poly(vinylidene fluoride)(PVDF) in a hybrid membrane, enabling uniform diffusion and stabilizing the lithium metal anode. The Li||Cu cell with the protected electrode exhibits a long lifetime more than 450 cycles(0.5 m A cm^(-2), 1.0 m A h cm^(-2))(fourfold longer than the control group) with higher average Coulombic efficiency of 98.7%. Enhanced performances are also observed at Li||Li and full cell configurations. The improved performances are attributed to the controlled morphology and stable interphase, according to scanning electron microscopy(SEM) and electrochemical impedance. This research advances the idea of uniform lithium plating and provides a new insight on how to create a homogeneous and mechanically stable diffusion layer using rigid-flexible polymers.

Mechanism of immune attack in the progression of obesity-related type 2 diabetes

Obesity and overweight are widespread issues in adults,children,and adolescents globally,and have caused a noticeable rise in obesity-related complications such as type 2 diabetes mellitus(T2DM).Chronic low-grade inflammation is an important promotor of the pathogenesis of obesity-related T2DM.This proinflammatory activation occurs in multiple organs and tissues.Immune cellmediated systemic attack is considered to contribute strongly to impaired insulin secretion,insulin resistance,and other metabolic disorders.This review focused on highlighting recent advances and underlying mechanisms of immune cell infiltration and inflammatory responses in the gut,islet,and insulin-targeting organs(adipose tissue,liver,skeletal muscle)in obesity-related T2DM.There is current evidence that both the innate and adaptive immune systems contribute to the development of obesity and T2DM.

Research on Narrowband Line Spectrum Noise Control Method Based on Nearest Neighbor Filter and BP Neural Network Feedback Mechanism

Thefilter-x least mean square(FxLMS)algorithm is widely used in active noise control(ANC)systems.However,because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to update thefilter coefficients,it has a certain delay,usually has a slow convergence speed,and the system response time is long and easily affected by the learning rate leading to the lack of system stability,which often fails to achieve the desired control effect in practice.In this paper,we propose an active control algorithm with near-est-neighbor trap structure and neural network feedback mechanism to reduce the coefficient update time of the FxLMS algorithm and use the neural network feedback mechanism to realize the parameter update,which is called NNR-BPFxLMS algorithm.In the paper,the schematic diagram of the feedback control is given,and the performance of the algorithm is analyzed.Under various noise conditions,it is shown by simulation and experiment that the NNR-BPFxLMS algorithm has the following three advantages:in terms of performance,it has higher noise reduction under the same number of sampling points,i.e.,it has faster convergence speed,and by computer simulation and sound pipe experiment,for simple ideal line spectrum noise,compared with the convergence speed of NNR-BPFxLMS is improved by more than 95%compared with FxLMS algorithm,and the convergence speed of real noise is also improved by more than 70%.In terms of stability,NNR-BPFxLMS is insensitive to step size changes.In terms of tracking performance,its algorithm responds quickly to sudden changes in the noise spectrum and can cope with the complex control requirements of sudden changes in the noise spectrum.

Current sensor based on diamond nitrogen-vacancy color center

High precision current measurement is very important for the calibration of various high-precision equipment and the measurement of other precision detection fields.A new current sensor based on diamond nitrogen-vacancy(NV)color center magnetic measurement method is proposed to realize the accurate measurement of current.This new current method can greatly improve the accuracy of current measurement.Experiments show that the linearity of the current sensor based on diamond NV color center can reach up to 33 ppm,which is superior to other current sensors and solves the problem of low linearity.When the range of input current is 5-40 A,the absolute error of the calculated current is less than 51μA,and the relative error is 2.42×10^(-6) at 40 A.Combined with the research content and results of the experiment,the application of the current sensor in the field of current precision measurement is prospected.

Ensemble of Population-Based Metaheuristic Algorithms

No optimization algorithm can obtain satisfactory results in all optimization tasks.Thus,it is an effective way to deal with the problem by an ensemble ofmultiple algorithms.This paper proposes an ensemble of population-based metaheuristics(EPM)to solve single-objective optimization problems.The design of the EPM framework includes three stages:the initial stage,the update stage,and the final stage.The framework applies the transformation of the real and virtual population to balance the problem of exploration and exploitation at the population level and uses an elite strategy to communicate among virtual populations.The experiment tested two benchmark function sets with fivemetaheuristic algorithms and four ensemble algorithms.The ensemble algorithms are generally superior to the original algorithms by Friedman’s average ranking andWilcoxon signed ranking test results,demonstrating the ensemble framework’s effect.By solving the iterative curves of different test functions,we can see that the ensemble algorithms have faster iterative optimization speed and better optimization results.The ensemble algorithms cannot fall into local optimumby virtual populations distribution map of several stages.The ensemble framework performs well from the effects of solving two practical engineering problems.Some results of ensemble algorithms are superior to those of metaheuristic algorithms not included in the ensemble framework,further demonstrating the ensemble method’s potential and superiority.

Effects of Ethanol Condensation Reflux Extraction and Ultrasound-assisted Extraction on the Content of Total Flavonoids of Pueraria edulis

With Pueraria edulis from different habitats in Yunnan Province,Guangdong Province and Guangxi Zhuang autonomous region as materials,the medicinal materials of P.edulis were extracted by ethanol condensation reflux extraction and ultrasound-assisted extraction,and the effects of different extraction methods the contents of total flavonoids in P.edulis from different areas were compared.The results showed that the best extraction time for P.edulis in Yunnan Province was 20 min,and that for Guangdong and Guangxi Zhuang Autonomous Region was 30 min.The yield of total flavonoids from P.edulis extracted by the condensation reflux extraction method was relatively better.The content of total flavonoids in P.edulis in Yunnan was the highest with an average content of 0.2557%,while the average content of total flavonoids in P.edulis in Guangdong was the lowest with an average content of 0.2108%.

合并MET基因变异的非小细胞肺癌治疗研究进展

间质-上皮细胞转化因子(mesenchymal-epithelial transition factor,MET)长期以来被认为是非小细胞肺癌(non-small cell lung cancer,NSCLC)发生发展过程中除表皮生长因子受体(epidermal growth factor receptor,EGFR)、间变性淋巴瘤激酶(anaplastic lymphoma kinase,ALK)、原癌基因酪氨酸蛋白激酶(c-ros oncogene 1 receptor tyrosine kinase,ROS1)之外最重要和最有前景的驱动基因。近年来,靶向MET的治疗药物不断被研发并应用于临床。首先是合并MET外显子14跳跃突变的NSCLC患者疗效得到进一步的提高。另外,晚期EGFR突变型NSCLC患者EGFR酪氨酸激酶受体抑制剂(EGFR tyrosine kinase inhibitors,EGFR-TKIs)耐药后继发MET扩增时,联合MET-TKIs和EGFR-TKIs的方案带来了明显的生存获益等诸多进展。本文综述了在不同情况下,NSCLC患者合并MET不同变异类型的治疗进展,为临床治疗策略的选择提供参考。

A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems:Applications and Trends

Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In the past decades,numerous swarm intelligence algorithms have been developed,including ant colony optimization(ACO),particle swarm optimization(PSO),artificial fish swarm(AFS),bacterial foraging optimization(BFO),and artificial bee colony(ABC).This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures.It provides an overview of the various swarm intelligence algorithms and their advanced developments,and briefly provides the description of their successful applications in optimization problems of engineering fields.Finally,opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

不同血液净化模式对终末期肾病患者的自身免疫、炎症应激及生活质量的影响

目的探讨在不同血液净化模式下,患者体内免疫细胞、炎症因子、生存质量变化情况及与其之间的关系。方法采用便利抽样法,选取2015年6月-2016年6月在该院肾内科血液净化中心的终末期肾病(ESRD)患者138例。其中82例为血液透析(HD)组,39例为高通量血液透析(HFHD)组,17例为血液透析滤过(HDF)组。血液净化6个月前后,采用流式细胞仪、细胞生物法、酶联免疫吸附法检测3组患者的免疫细胞(CD4^+,CD8^+,CD25^+,CD4^+/CD8^+)百分比、C-反应蛋白(CRP)、可溶性白细胞介素2受体(s IL-2R)、白细胞介素6(IL-6)、肿瘤坏死因子α(TNF-α)血清含量及生存质量(SF-36)评分,比较血液净化前后自身及各组间的变化。结果血液净化6个月后,3组患者体内免疫细胞、炎症因子除CD8^+、TNF-α外,其余免疫细胞(CD4^+,CD25^+,CD4^+/CD8^+)含量、炎症因子(CRP、s IL-2R、IL-6)水平及生存质量各维度评分及总评分比较,差异有统计学意义(P<0.05);与HD组相比,HDF组、HFHD组患者体内免疫细胞含量升高、炎症因子水平降低、生存质量各维度评分及总评分升高;其中HFHD组患者体内免疫细胞含量及生存质量各维度评分和总评分升高幅度最大,与HDF组比较,差异有统计学意义,HDF组患者体内炎症因子下降幅度最大,与HFHD组相比,差异无统计学意义。3组患者免疫细胞含量、炎症因子水平、生存质量各维度评分及总评分自身前后比较,除HD组自身前后差别无统计学意义外,其余两组HDF组、HFHD组患者自身前后比较均差异有统计学意义(P<0.05)。相关分析显示:免疫细胞、炎症因子与HD组患者生存质量评分无关;HFHD组和HDF组免疫细胞除CD8^+外,其余与其生存质量呈正相关,炎症因子除TNF-α外,其余与其生存质量呈负相关。结论 HDF、HFHD均可升高患者体内免疫细胞含量,降低炎症因子水平,提高患者生存质量,以HFHD效果较佳。

Back propagation artificial neural network for community Alzheimer's disease screening in China

AIzheimer＇s disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer＇s disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 （95% confidence interval： 0.868-0.968）, sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer＇s disease in patients selected from the community.

Prediction of landslide displacement with dynamic features using intelligent approaches

Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic displacement.These models may have bad performances,when the dynamic features of landslide triggers are incorporated.This paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposition with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic displacement.Implementation procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement prediction.This case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise manner.By contrast,the accuracy can be significantly improved using the dynamic predictive method.This paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement.