复合型灾害中公众多重应对行为特征与影响因素研究

伴随着快速的工业化、城市化、全球化,现代灾害风险正日益呈现鲜明的复合型特征。复合型灾害充斥着不确定性、紧急性与威胁性,使灾害来临时的个体应对行为实施面临着信息冲击更为混乱、决策时间更短、风险程度更高等严峻挑战。灾害情境的变化给传统灾害风险与应急管理模式带来了冲击,也对公众的多重应对能力提出了新要求。研究从2022年9月四川省部分地区居民经受突发公共卫生事件、地震自然灾害的复合型灾害场景出发,通过案例分析和结构方程模型的方法,对复合型灾害中公众多重应对行为的特征及影响因素展开探索。研究结果表明,科学的个体权衡和信息环境等因素显著影响公众的多重应对行为效能,公众在复合型灾害应对中能否做出有效的多重应对行为,是增强社会防范和应对处置灾害事故能力、提高自救、互救能力的关键。据此,应将风险情境变动特征纳入灾害宣教体系,加强灾变情境下信息整合与资源配备能力建设,同时积极制定复合型灾害应急预案并展开应急演练。研究为提高全社会抵御复合型灾害能力、筑牢防灾减灾救灾人民防线提供了有益启示。

Highly Sensitive Ammonia Gas Sensors at Room Temperature Based on the Catalytic Mechanism of N,C Coordinated Ni Single-Atom Active Center

Significant challenges are posed by the limitations of gas sensing mechanisms for trace-level detection of ammonia(NH3).In this study,we propose to exploit single-atom catalytic activation and targeted adsorption properties to achieve highly sensitive and selective NH3 gas detection.Specifically,Ni singleatom active sites based on N,C coordination(Ni-N-C)were interfacially confined on the surface of two-dimensional(2D)MXene nanosheets(Ni-N-C/Ti_(3)C_(2)Tx),and a fully flexible gas sensor(MNPE-Ni-N-C/Ti_(3)C_(2)Tx)was integrated.The sensor demonstrates a remarkable response value to 5 ppm NH3(27.3%),excellent selectivity for NH3,and a low theoretical detection limit of 12.1 ppb.Simulation analysis by density functional calculation reveals that the Ni single-atom center with N,C coordination exhibits specific targeted adsorption properties for NH3.Additionally,its catalytic activation effect effectively reduces the Gibbs free energy of the sensing elemental reaction,while its electronic structure promotes the spill-over effect of reactive oxygen species at the gas-solid interface.The sensor has a dual-channel sensing mechanism of both chemical and electronic sensitization,which facilitates efficient electron transfer to the 2D MXene conductive network,resulting in the formation of the NH3 gas molecule sensing signal.Furthermore,the passivation of MXene edge defects by a conjugated hydrogen bond network enhances the long-term stability of MXene-based electrodes under high humidity conditions.This work achieves highly sensitive room-temperature NH3 gas detection based on the catalytic mechanism of Ni single-atom active center with N,C coordination,which provides a novel gas sensing mechanism for room-temperature trace gas detection research.

电针治疗围绝经期轻中度抑郁障碍患者的疗效分析

目的观察电针治疗围绝经期轻中度抑郁障碍患者的临床疗效。方法将60例符合纳入标准的围绝经期轻中度抑郁障碍患者随机分为药物对照组(口服草酸艾司西酞普兰)和电针组,各30例,采用汉密尔顿抑郁量表(HAMD-17)进行疗效评估。结果经过12周的治疗,2种治疗方法对围绝经期轻中度抑郁障碍患者均有显著疗效,电针组总有效率高于药物对照组(P<0.05);电针组HAMD-17总分低于药物对照组(P<0.05);电针组抑郁症状、入睡困难症状评分较药物对照组低(P<0.05)。结论电针可有效干预围绝经期轻中度抑郁障碍,改善患者临床症状,提高生活质量。

面向认知物联网的隐蔽通信智能功率控制

针对认知物联网的安全问题,提出了一种基于生成对抗网络的认知物联网隐蔽通信智能功率控制算法。首先将认知物联网隐蔽通信问题转化为认知物联网用户和窃听者之间的动态博弈问题,然后利用生成器模仿认知物联网用户,利用鉴别器模仿窃听者,两者分别采用3层神经网络构建,并通过二人零和博弈实现学习优化过程,最终达到纳什均衡,获得隐蔽功率控制方案。仿真结果表明,所提出的算法收敛速度快,不仅可以获得近似最优的隐蔽功率控制方案,而且在未来认知物联网中更具有实用性。

管子管板角焊缝射线检测应用

通过采用微焦点棒阳极X射线检测管子-管板角焊缝,制作仿真灵敏度鉴定试块,设计制造补偿块并进行灵敏度鉴定试验,可以进一步控制相应产品质量,减少热交换器泄露率,保障生产安全。

不锈钢小径管超声导波周向检测应用

针对电厂不锈钢小径管进行在役超声波导波周向检测,对发现的超声导波显示信号进行确认,重点检测钢管表面直道缺陷。使用超声导波检测可以在一定程度上解决在役检测时现场空间不可达而导致不能全面检测的问题,同时缩短检测时间,现场操作便捷性好,提高检测效率,在常规超声波方法无法有效检测时是一种有效的补充。

一种低成本带吸收电容的防浪涌电路

传统的浪涌保护电路对元器件的功率等级要求较高,导致整个电路结构的性能降低,硬件成本提高.设计了一种低成本带吸收电容防浪涌的电路.通过理论分析、软件仿真以及实验测试证明了该电路结构采用更低功率等级的金属-氧化物半场效晶体管(metal-oxidesemiconductor field-effect transistor,MOSFET),能在降低浪涌保护电路成本的同时,提升浪涌电路电压等级以及电路的稳定性.

行星大样本演化的研究进展

随着系外行星观测样本的大量积累,大样本演化方法作为天体物理的重要方法,继在恒星物理中广泛应用后,在行星统计特征的研究中开始变得更加重要。行星大样本演化方法就是通过简化的行星形成演化模型,对大量初始条件(原行星盘参数)和边界条件(星周环境)同时进行模拟,给出行星的各种统计性质,并直接与观测上的行星样本统计特征做比较。该方法对于限制行星形成和演化的理论模型具有重要意义。对行星大样本演化模型进行概述,简要介绍了行星大样本演化的主要统计结果和最新进展,并对其未来发展作出了展望。

超声导波检测技术在小径管检测中的应用

无缝钢管中的原料缺陷在使用中被发现时,常规的超声、涡流检测方法就因结构、工件位置等原因显得不再适用。超声导波检测以其检测速度快,检测范围大的特点,快速解决了这一问题。本文设计实验将超声导波与常规检测方法进行对比,结果表明,检验灵敏度完全满足常规检验的验收要求,不失为一种检验的好方法。

一种用于Boost PFC变换器的改进关断时间控制策略

针对Boost PFC变换器的关断时间控制策略在工作模式切换时可能出现的交流输入电流失真的问题,提出了一种改进的关断时间控制策略,该策略在多种工作模式下,不需要任何模式识别和控制算法,即可实现单位功率因数校正,与传统的关断时间控制策略相比,能够减小输入电流失真,提高THD性能。

Highly stretchable,durable,and breathable thermoelectric fabrics for human body energy harvesting and sensing

Stretchable thermoelectrics have recently attracted widespread attention in the field of self-powered wearable electronics due to their unique capability of harvesting body heat.However,it remains challenging to develop thermoelectric materials with excellent stretchability,durable thermoelectric properties,wearable comfort,and multifunctional sensing properties simultaneously.Herein,an advanced preparation strategy combining electrospinning and spraying technology is proposed to prepare carbon nanotube(CNT)/polyvinyl pyrrolidone(PVP)/polyurethane(PU)composite thermoelectric fabrics that have high air permeability and stretchability(~250%)close to those of pure PU nanofiber fabrics.Furthermore,PVP can not only improve the dispersion of CNTs but also act as interfacial binders between the CNT and the elastic PU skeleton.Consequently,both the electrical conductivity and the Seebeck coefficient remain unchanged even after bending 1000 times.In addition,self-powered sensors for the mutual conversion of finger temperature and language and detection of the movement of joints to optimize an athlete's movement state were successfully fabricated.This study paves the way for stretchable thermoelectric fabrics with fascinating applications in smart wearable fields such as power generation,health monitoring,and human–computer interaction.

A Novel Artificial Neuron-Like Gas Sensor Constructed from CuS Quantum Dots/Bi2S3 Nanosheets

Real-time rapid detection of toxic gases at room temperature is particularly important for public health and environmental monitoring.Gas sensors based on conventional bulk materials often suffer from their poor surface-sensitive sites,leading to a very low gas adsorption ability.Moreover,the charge transportation efficiency is usually inhibited by the low defect density of surface-sensitive area than that in the interior.In this work,a gas sensing structure model based on CuS quantum dots/Bi_(2)S_(3) nanosheets(CuS QDs/Bi_(2)S_(3) NSs)inspired by artificial neuron network is constructed.Simulation analysis by density functional calculation revealed that CuS QDs and Bi_(2)S_(3) NSs can be used as the main adsorption sites and charge transport pathways,respectively.Thus,the high-sensitivity sensing of NO_(2) can be realized by designing the artificial neuron-like sensor.The experimental results showed that the CuS QDs with a size of about 8 nm are highly adsorbable,which can enhance the NO_(2) sensitivity due to the rich sensitive sites and quantum size effect.The Bi_(2)S_(3) NSs can be used as a charge transfer network channel to achieve efficient charge collection and transmission.The neuron-like sensor that simulates biological smell shows a significantly enhanced response value(3.4),excellent responsiveness(18 s)and recovery rate(338 s),low theoretical detection limit of 78 ppb,and excellent selectivity for NO_(2).Furthermore,the developed wearable device can also realize the visual detection of NO2 through real-time signal changes.

Multi-Objective Optimization Design through Machine Learning for Drop-on-Demand Bioprinting

Drop-on-demand (DOD) bioprinting has been widely used in tissue engineering due to its highthroughput efficiency and cost effectiveness. However, this type of bioprinting involves challenges such as satellite generation, too-large droplet generation, and too-low droplet speed. These challenges reduce the stability and precision of DOD printing, disorder cell arrays, and hence generate further structural errors. In this paper, a multi-objective optimization (MOO) design method for DOD printing parameters through fully connected neural networks (FCNNs) is proposed in order to solve these challenges. The MOO problem comprises two objective functions: to develop the satellite formation model with FCNNs;and to decrease droplet diameter and increase droplet speed. A hybrid multi-subgradient descent bundle method with an adaptive learning rate algorithm (HMSGDBA), which combines the multisubgradient descent bundle (MSGDB) method with Adam algorithm, is introduced in order to search for the Pareto-optimal set for the MOO problem. The superiority of HMSGDBA is demonstrated through comparative studies with the MSGDB method. The experimental results show that a single droplet can be printed stably and the droplet speed can be increased from 0.88 to 2.08 m·s^-1 after optimization with the proposed method. The proposed method can improve both printing precision and stability, and is useful in realizing precise cell arrays and complex biological functions. Furthermore, it can be used to obtain guidelines for the setup of cell-printing experimental platforms.

Feasibility of terahertz imaging for discrimination of human hepatocellular carcinoma

BACKGROUND Hepatocellular carcinoma(HCC) is one of the most common malignant tumors worldwide, and novel methods for early/rapid diagnosis of HCC are needed.Terahertz(THz) spectroscopy is considered to have the potential to distinguish between normal liver tissue and HCC tissue; however, there are few reports on it.We conduct this observational study to explore the feasibility of THz imaging for the diagnosis of HCC.AIM To evaluate the feasibility of THz for discriminating between HCC and normal liver tissues using fresh tissue specimens obtained from HCC patients who had undergone surgery.METHODS Normal liver tissue and HCC tissue were cryosectioned into 50 μm-thick slicesand placed on cover glass. Two adjacent tissue sections were separated subjected to histopathological examination by hematoxylin and eosin staining or THz transmission examination, and THz images were compared with pathologically mapped images. We determined the typical tumor and normal liver tissue regions by pathological examination; the corresponding areas of adjacent sections were examined by THz transmission.RESULTS The transmission rate of HCC tissue was 0.15-0.25, and the transmission rate of typical HCC tissue was about 0.2. THz transmittance in normal liver tissue is slightly higher than 0.4, but there were many influencing factors, including the degree of liver cirrhosis, fat components, ice crystals in frozen sections, and apoptosis.CONCLUSION In conclusion, this study shows that THz imaging can detect HCC tissue. Further research will yield more detailed data of the THz transmission rates of HCC tissue with different degrees of differentiation.

Identification of predictive factors for post-transarterial chemoembolization liver failure in hepatocellular carcinoma patients: A retrospective study

BACKGROUND Post-transarterial chemoembolization(TACE)liver failure occurs frequently in hepatocellular carcinoma(HCC)patients.The identification of predictors for post-TACE liver failure is of great importance for clinical decision-making in this population.AIM To investigate the occurrence rate and predictive factors of post-TACE liver failure in this retrospective study to provide clues for decision-making regarding TACE procedures in HCC patients.METHODS The clinical records of HCC patients treated with TACE therapy were reviewed.Baseline clinical characteristics and laboratory parameters of these patients were extracted.Logistic models were used to identify candidates to predict post-TACE liver failure.RESULTS A total of 199 HCC patients were enrolled in this study,and 70 patients(35.2%)developed post-TACE liver failure.Univariate and multivariate logistic models indicated that microspheres plus gelatin embolization and main tumor size>5 cm were risk predictors for post-TACE liver failure[odds ratio(OR):4.4,95%confidence interval(CI):1.2-16.3,P=0.027;OR:2.3,95%CI:1.05-5.3,P=0.039,respectively].Conversely,HCC patients who underwent tumor resection surgery before the TACE procedure had a lower risk for post-TACE liver failure(OR:0.4,95%CI:0.2-0.95,P=0.039).CONCLUSION Microspheres plus gelatin embolization and main tumor size might be risk factors for post-TACE liver failure in HCC patients,while prior tumor resection could be a favorable factor reducing the risk of post-TACE liver failure.

Effect of CO_2 on the structural variation of Na_2WO_4/Mn/SiO_2 catalyst for oxidative coupling of methane to ethylene

In this work,the influence of CO2 on the structural variation and catalytic performance of Na2WO4/Mn/Si O2 for oxidative coupling of methane to ethylene was investigated. The catalyst was prepared by impregnation method and characterized by XRD,Raman and XPS techniques. Appropriate amount of CO2 in the reactant gases enhanced the formation of surface tetrahedral Na2WO4 species and promoted the migration of O in MOx,Na,W from the catalyst bulk to surface,which were favorable for oxidative coupling of methane. When the molar ratio of CH4/O2/CO2 was 3/1/2,enriched surface tetrahedral Na2WO4 species and high surface concentration of O in MOx,Na,W were detected,and then high CH4 conversion of 33.1% and high C2H4 selectivity of 56.2% were obtained. With further increase of CO2 in the reagent gases,the content of active surface tetrahedral Na2WO4 species and surface concentration of O in MOx,Na,W decreased,while that of inactive species（Mn WO4 and Mn2O3） increased dramatically,leading to low CH4 conversion and low C2H4 selectivity. It could be speculated that Na2WO4 crystal was transformed into Mn WO4 crystal with excessive CO2 added under the reaction conditions. Pretreatment of Na2WO4/Mn/Si O2 catalyst by moderate amount of CO2 before OCM also promoted the formation of Na2WO4 species.

Energy Efficient Transmission in Underlay CR-NOMA Networks Enabled by Reinforcement Learning

In order to improve the energy efficiency(EE)in the underlay cognitive radio(CR)networks,a power allocation strategy based on an actor-critic reinforcement learning is proposed,where a cluster of cognitive users(CUs)can simultaneously access to the same primary spectrum band under the interference constraints of the primary user(PU),by employing the non-orthogonal multiple access(NOMA)technique.In the proposed scheme,the optimization of the power allocation is formulated as a non-convex optimization problem.Additionally,the power allocation for different CUs is based on the actor-critic reinforcement learning model,in which the weighted data rate is set as the reward function,and the generated action strategy(i.e.the power allocation)is iteratively criticized and updated.Both the CU’s spectral efficiency and the PU’s interference constrains are considered in the training of the actor-critic reinforcement learning.Furthermore,the first order Taylor approximation as well as other manipulations are adopted to solve the power allocation optimization problem for the sake of considering the conventional channel conditions.According to the simulation results,we find that our scheme could achieve a higher spectral efficiency for the CUs compared to a benchmark scheme without learning process as well as the existing Q-learning based method,while the resultant interference affecting the PU transmission can be maintained at a given tolerated limit.

Intelligent Covert Communication Design for Cooperative Cognitive Radio Network

Extensive research attentions have been devoted to studying cooperative cognitive radio networks(CCRNs),where secondary users(SU)providing cooperative transmissions can be permitted by primary users(PU)to use spectrum.In order to maximize SU’s utility,SU may transmit its own information during the period of cooperative transmission,which stimulates the use of covert transmission against PU’s monitoring.For this sake,this article reviews the motivations of studying covert communications in CCRN.In particular,three intelligent covert transmission approaches are developed for maximizing SU’s utility in CCRNs,namely,intelligent parasitic covert transmission(IPCT),intelligent jammer aided covert transmission(IJCT)and intelligent reflecting surface assisted covert transmission(IRSC).Further,some raw performance evaluations are discussed,and a range of potential research directions are also provided.

High Secure Sequence Design in Frequency Hopping Communications

Frequency-hopping(FH) technique is widely used in high-secure communications by exploiting its capabilities of mitigating interference and confidentiality. However, electronic attacks in wireless systems become more and more rigorous, which poses huge challenges to the use of the number theory based and chaos theory assisted sequences. The structure of the FH sequence directly affects the performance of FH communication systems. In this paper, the novel FH sequence generation scheme is proposed with the aid of the so-called Government Standard(GOST) algorithm, which achieves a promising balance between efficiency and security. Moreover, the security performance of the proposed algorithm is analyzed, which reveals that it is more resistant to impossible differential attacks than the widely-used Data Encryption Standard(DES) algorithm. The numerical results show that the FH sequences generated by the GOST algorithm significantly outperform the ones generated by the DES algorithm and chaotic theory in terms of the randomness and complexity.

Myocardial injury is associated with higher mortality in patients with coronavirus disease 2019:a meta-analysis

In December 2019,coronavirus disease 2019(COVID-19)caused by a novel coronavirus(SARS-CoV-2)broke out in Wuhan,China,and has spread widely all over the world,reaching the pandemic level.[1]According to the latest WHO report,693,224 cases of COVID-19 were confirmed globally as of March 30,2020,with more than 33,000 deaths.[2]Because COVID-19 is highly contagious and harmful,it is crucial to determine the predictors of severe infection and death for risk stratification and guiding clinical treatment and intervention.