mlsa-iai-msu-lab/ru_sci_bench_zho_cite_reranking huggingface dataset:
解毒通络调肝方对2型糖尿病ZDF大鼠糖脂代谢的影响

目的观察2型糖尿病ZDF大鼠造模情况及解毒通络调肝方对其糖脂代谢的影响。方法雄性9周龄ZDF大鼠(88只)和ZL大鼠(10只),ZDF大鼠以Purina#5008高脂饲料喂养,ZL大鼠以普通颗粒饲料喂养。注意观察大鼠的一般状态、体重及随机血糖变化;成模后ZDF大鼠随机分为模型组、盐酸二甲双胍组、解毒通络调肝方高中低剂量组(TCMH、 TCMM、TCML) 5组,对照组为ZL大鼠,二甲双胍和解毒通络调肝方分别灌胃二甲双胍组和中药组大鼠,等体积蒸馏水灌胃模型组与对照组大鼠,每日1次,持续4周。4周后尾静脉取血,测定TG、HDL - C、LDL - C、TC、FINS、FBG、HbA1c,计算ISI。结果ZDF大鼠造模至第11周进食量明显增多,体重增加迅速,随机血糖均值为(18.18 ± 5.47) mmol /L,第12、13周随机血糖仍有上升,但与11周对比无统计学意义(P > 0.05),大鼠造模成功;药物干预后,与模型组相比,二甲双胍组、 TCMH、TCMM、TCML组大鼠体重有下降趋向,但无统计学意义(P > 0.05),二甲双胍组与TCMH组FBG下降,ISI上升,具有统计学差异(P < 0.05),二甲双胍组、TCMH、TCMM、TCML组TC、HDL - C及TCMH组TG含量显著下降(P < 0.01, P < 0.05);与二甲双胍组相比,TCMH组TG有所下降(P < 0.05),TC、HDL - C明显下降(P < 0.01) 。结论以高脂饲料喂养9周龄雄性ZDF大鼠至11周,可以制备稳定的自发性2型糖尿病ZDF大鼠动物模型;解毒通络调肝方能降低2型糖尿病ZDF大鼠血糖及血脂水平,增强胰岛素敏感性,改善胰岛素抵抗,TCMH组在以上诸方面的作用尤为突出。
益气温阳护卫汤对哮喘大鼠Thl7 /Treg细胞及细胞因子的影响

目的研究益气温阳护卫汤对哮喘大鼠Thl7 /Treg细胞及其细胞因子的影响。方法将144只SPF级雌性SD大鼠随机分为正常对照组(A组),模型组(B组),益气温阳护卫汤低、中、高剂量组(C、D、E组),地塞米松组(F组) 。除A组外其他组卵蛋白(OVA)氢氧化铝溶液腹腔致敏,并每天雾化吸入OVA激发,致敏后第16天开始,每天雾化吸入OVA前1h,按照1mg /kg剂量的地塞米松灌服F组,1g /ml、2g /ml、4g /ml剂量的益气温阳护卫汤分别灌服C、D、E组,A组雾化予0.9%氯化钠溶液代替,各组分别持续1、2、4周3个时间段,各时间段结束时,每组分别处死大鼠8只,检测BALF中Thl7、Treg细胞比例、Thl7 /Treg比值及肺组织IL - 17、IL - 17A和IL - 10表达情况。结果与正常对照组比较,各时间点模型组Thl7细胞比例、IL - 17蛋白表达、IL - 17A mRNA表达水平均上调,Treg细胞比例、IL - 10蛋白表达、IL - 10 mRNA表达水平均下调,Th17 /Treg比值明显升高(P < 0.05);与模型组比较,各时间点地塞米松组、中高剂量组Thl7细胞比例、IL - 17蛋白表达、IL - 17A mRNA表达水平明显下降(P < 0.05),Treg细胞比例、IL - 10蛋白表达、IL - 10mRNA表达水平明显升高(P < 0.05),Th17 /Treg比值明显下降(P < 0.05),高剂量组比中剂量组更明显(P < 0.05) 。Thl7细胞比例与IL - 17A mRNA相对表达水平呈正相关,Treg细胞比例与IL - 10 mRNA相对表达水平呈正相关。结论益气温阳护卫汤可能通过干预Thl7 /Treg细胞失衡及细胞因子IL - 17、IL - 17A和IL - 10表达,使Thl7 /Treg细胞趋于平衡而达到控制哮喘气道炎症的目的。
白族居民吸烟行为和尼古丁依赖现状及其影响因素分析

目的分析白族居民吸烟行为和尼古丁依赖现状并探讨其影响因素,为预防和控制当地烟草暴露提供科学依据。方法于2018年4-5月采用多阶段分层随机抽样方法在云南省大理州剑川县抽取2 501名≥35岁白族居民进行面对面问卷调查,内容包括个人基本情况、吸烟情况和尼古丁依赖情况等。采用SPSS 22.0软件进行χ~2检验、趋势χ~2检验,采用多因素logistic回归分析尼古丁依赖的影响因素。结果调查人群现在吸烟率为30.5%,其中男性的现在吸烟率(65.3%)明显高于女性(1.0%),差异有统计学意义(χ~2=1 211.378,P<0.01)。在现在吸烟者中,尼古丁依赖率为34.91%,其中男性为35.03%,女性为28.57%。吸烟类型以吸过滤嘴香烟为主(84.5%),初次吸烟年龄和规律吸烟年龄都以≥18岁人群为主(分别为65.6%和78.1%),52.0%的现在吸烟者在公共场所吸烟,68.4%的现在吸烟者从未尝试过戒烟,88.1%的吸烟人群每日吸烟量≥10支。多因素logistic回归分析结果显示,随着年龄的增加,尼古丁依赖的可能性下降(OR=0.984,P<0.05),规律吸烟年龄≥18岁者较规律吸烟年龄<18岁者不容易发生尼古丁依赖(OR=0.565,P<0.05);而家庭年收入>5 700元者较家庭年收入≤5 700元者易发生尼古丁依赖(OR=1.386,P<0.05)。结论白族居民现在吸烟率较高,应加强白族居民烟草危害的健康教育,采取有效措施改变当地居民的吸烟行为,从而进一步降低吸烟率和尼古丁依赖率。
心脏瓣膜置换术后假性室壁瘤破裂并新瘤体形成1例

讨论左心室假性室壁瘤(left ventricular pseudoaneurysm, LVPA)破裂发生率达30%~45%。超声主要表现:①左心室游离壁连续性中断,形成与左心室相通的囊性无回声区,瘤颈直径小于瘤腔;②瘤壁不与心内膜及心肌相连续;③瘤颈处见双期双向分流血流信号;④合并血栓时,瘤内可见实性等回声;⑤瘤体破裂时,可合并新的假性室壁瘤形成。本例为二尖瓣置换术后左心室破裂形成后壁假性室壁瘤,瘤体破裂周围心包及纤维结缔组织包裹形成新瘤体,伴瘤体内血栓形成,极为罕见;其超声表现符合LVPA;发病原因可能为风湿性病变损害心肌及腱索、切除乳头肌导致心肌压力应力增加。超声心动图能明确左心室假性室壁瘤的位置、大小、有无破裂、瘤体内血流、附壁血栓及并发症等,具有重要临床价值。
关注软组织肿瘤临床病理及分子检测新进展

软组织肿瘤亚型众多,且肉瘤性病变恶性程度高、预后差,给临床诊治带来巨大挑战。随着以二代测序为代表的高通量分子检测手段的开展和应用,基于分子异常的软组织肿瘤新病种也在不断涌现,以往一些分化不明或未分化的肿瘤经过二代测序也得到了重新认识,软组织肿瘤的分类基础正在从形态学分类转向分子分类,在协助病理诊断、临床治疗策略制定和生物学行为预测等方面具有重要价值。
阴阳调理灸联合耳穴揿针治疗围绝经期失眠47例

阴阳调理灸以平衡阴阳为枢机,结合经络、腧穴作用,通过调整灸序、灸量、施灸时间来调和人体阴阳平衡。任脉为阴脉之海,可调理全身阴经之气血。且任脉汇集上焦之宗气、中焦水谷之气及下焦原气三焦之气,集诸多募穴于一经,与脏腑联系密切,其中脘、气海、关元等穴联用,可同时培补先天与后天。对于围绝经期失眠患者先行固本调任灸,灸量大,施灸时间长,灸毕即用掌按法循按施灸部位使真气聚集,以此发挥补阴纳阳、固本补虚、恢复阴经之气血的作用。督脉乃阳脉之海,入络于脑,温通督脉可以调节脑之功能,激发人体阳气,调整脏腑功能。固本调任灸后行温阳通督灸,灸1壮即止,以此畅通气血,沟通阴阳,调节元神之府,达到镇静安神的功效。本病治疗应重在阴阳双补,在着重补阴的同时应适当补阳,在任脉上重灸3壮,在督脉上灸1壮,在鼓动阳气的同时又可以促进阴液的生成,补阴以纳阳,潜阳以入阴,调节一身阴阳平衡。耳乃宗脉之所聚,五脏六腑、十二经脉皆有络于耳,耳穴可调节相应躯体部位和脏腑的功能。神门、交感具有镇静安神的功效,能双向调节大脑神经中枢的抑制与兴奋活动;内分泌有助于平衡人体的激素水平,增强机体对激素水平波动的适应性;肾、心、肝可滋养肾阴、养心安神、疏肝养阴,通过调理相应脏腑功能从根本上改善围绝经期患者的睡眠质量。揿针治疗疼痛感小,且患者可通过每日多次自行按压延长针刺效应,缓解了阴阳调理灸治疗间隔时间长的缺陷。47例患者治疗后PSQI各维度评分及总分均较治疗前有明显改善,说明阴阳调理灸配合耳穴揿针能有效改善围绝经期失眠患者的睡眠障碍。
手部聚氨酯发泡剂高压注射伤的临床治疗

目的报告聚氨酯发泡剂高压注射伤的临床治疗方式及效果。方法回顾性研究自2018年2月至2020年11月,我科共收治手部聚氨酯发泡剂高压注射伤患者9例。统计手术治疗次数及方式,分析治疗效果。彻底清创后I期直接闭合创面4例,Ⅱ期缝合创面4例,游离皮瓣修复创面1例。行一次手术治疗4例,两次4例,三次1例。结果本组9例患者伤口均愈合良好,均未发生感染。术后所有病例随访1.5~6.0个月,采用TAM评定标准进行评定:优6例,良2例,可1例。结论聚氨酯发泡剂高压注射伤为特殊类型的手部高压注射伤,因聚氨酯发泡剂化学性质稳定,损伤机制主要在于高压产生的物理力及注射物膨胀力对组织造成的损伤。应在I期尽早对组织进行彻底清创和减压,如无合并骨筋膜室综合征或皮肤缺损的患者可I期直接闭合创面,无需多次手术。
探索科学前沿,服务区域发展--记第16届青藏高原地球科学学术年会

由中国科学院青藏高原研究所主办,中国青藏高原研究会、中国地质学会协办的"第16届青藏高原地球科学学术年会"于2021年6月2~3日在北京召开.本次年会主题为:青藏高原地球系统科学.学术交流包括31个大会特邀报告、34个分组研讨报告及39个展板交流报告,涵盖了地质学、地球物理学、地理学、大气科学、生态学、考古学等多个研究领域.来自近50家高校、科研单位的院士、专家、学者、管理人员等300余人参加本次年会,与会者就此展开了多方位的学术交流,进一步明确了青藏高原重大科学问题的研究方向,并为今后地球系统科学的发展献计献策.
Vector analysis of electric-field-induced antiparallel magnetic domain evolution in ferromagnetic/ferroelectric heterostructures

Electric field (E-field) control of magnetism based on magnetoelectric coupling is one of the promising approaches for manipulating the magnetization with low power consumption.The evolution of magnetic domains under in-situ E-fields is significant for the practical applications in integrated micro/nano devices.Here,we report the vector analysis of the E-field-driven antiparallel magnetic domain evolution in FeCoSiB/PMN-PT(011) multiferroic heterostructures via in-situ quantitative magneto-optical Kerr microscope.It is demonstrated that the magnetic domains can be switched to both the 0° and 180° easy directions at the same time by E-fields,resulting in antiparallel magnetization distribution in ferromagnetic/ferroelectric heterostructures.This antiparallel magnetic domain evolution is attributed to energy minimization with the uniaxial strains by E-fields which can induce the rotation of domains no more than 90°.Moreover,domains can be driven along only one or both easy axis directions by reasonably selecting the initial magnetic domain distribution.The vector analysis of magnetic domain evolution can provide visual insights into the strain-mediated magnetoelectric effect,and promote the fundamental understanding of electrical regulation of magnetism.
Perspectives and challenges for lead-free energy-storage multilayer ceramic capacitors

The growing demand for high-power-density electric and electronic systems has encouraged the development of energy-storage capacitors with attributes such as high energy density,high capacitance density,high voltage and frequency,low weight,high-temperature operability,and environmental friendliness.Compared with their electrolytic and film counterparts,energy-storage multilayer ceramic capacitors (MLCCs) stand out for their extremely low equivalent series resistance and equivalent series inductance,high current handling capability,and high-temperature stability.These characteristics are important for applications including fast-switching third-generation wide-bandgap semiconductors in electric vehicles,5G base stations,clean energy generation,and smart grids.There have been numerous reports on state-of-the-art MLCC energy-storage solutions.However,lead-free capacitors generally have a low-energy density,and high-energy density capacitors frequently contain lead,which is a key issue that hinders their broad application.In this review,we present perspectives and challenges for lead-free energy-storage MLCCs.Initially,the energy-storage mechanism and device characterization are introduced;then,dielectric ceramics for energy-storage applications with aspects of composition and structural optimization are summarized.Progress on state-of-the-art energy-storage MLCCs is discussed after elaboration of the fabrication process and structural design of the electrode.Emerging applications of energy-storage MLCCs are then discussed in terms of advanced pulsed power sources and high-density power converters from a theoretical and technological point of view.Finally,the challenges and future prospects for industrialization of lab-scale lead-free energy-storage MLCCs are discussed.
Effects of particle size of dielectric fillers on the output performance of piezoelectric and triboelectric nanogenerators

Recently, piezoelectric/triboelectric nanogenerators based on piezoelectric composite materials have been intensively studied to achieve high electrical output performance.In this work,flexible BaTiO_3(BT)/PDMS nanocomposite films with various sizes and concentrations were fabricated and used as the nanogenerators.The influence of dielectric properties on the electrical output of nanogenerators was studied as well as the structure of the composites.The dielectric constant increased from 6.5 to 8 with the concentration of BT nanoparticles and decreased with the frequency from 10~2 to 10~6 Hz.Furthermore, the dielectric constant showed 11% decrease with the temperature range from 30 to 180 ℃.It was found that the concentration of BT nanoparticles has promoted the electrical output of nanogenerators.The output voltage and current are all enhanced with the BT nanoparticles, which reached 200 V and 0.24 μA in TENG with 40 wt% BT nanoparticles,respectively.The selected device exhibited the power of 0.16 mW and employed to demonstrate its ability to power wearable/portable electronics by lighting the LEDs.
Controllable synthesis of different morphologies of CuO nanostructures for tribological evaluation as water-based lubricant additives

In this study, water soluble CuO nanostructures having nanobelt, nanorod, or spindle morphologies were synthesized using aqueous solutions of Cu(NO_3)_2·3H_2O and NaOH by adjusting the type of surface modifier and reaction temperature. The effect of morphologies of these various CuO nanostructures as water-based lubricant additives on tribological properties was evaluated on a UMT-2 micro-friction tester, and the mechanisms underlying these properties are discussed. The three different morphologies of CuO nanostructures exhibited excellent friction-reducing and anti-wear properties. Tribological mechanisms differed in the initial stage of frictional interactions, but in the stable stage, a tribochemical reaction film and adsorbed lubricious film on the rubbing surfaces played important roles in hindering direct contact between friction pairs, leading to improved tribological properties.
Intrinsically stretchable conjugated polymers for flexible optoelectronic devices

Conjugated Polymers (CPs) have been extensively explored in academia and industry since the discovery of conducting polymers in 1977, owing to their solution processability, structural tunability, and potential applications in a wide variety of optoelectronic devices, such as organic light-emitting diodes (OLED), organic photovoltatics (OPV) and organic field-effect transistors (OFET). As the key component of flexible and stretchable devices, the CPs with excellent tensile properties and optoelectronic properties have advantages in improving the devices' strain tolerance and deformation ability, but their development is still challenging. Bao and co-workers demonstrated that CPs with flexible spacers containing hydrogen bonds were endowed with inherent stretchability and even self-healing properties, further boosting the research of intrinsic stretchable CPs (Fig. 1b). The introduction of non-covalent bonds with dynamic reversibility is necessary for dissipating the inner stress, which is generated during the repeated deformation process of flexible electronics, such as stretching, folding, and bending.
Enhancing China's Three Red Lines strategy with water consumption limitations

Many countries face problems of scarcity and unsustainability in the water sector. In China, overexploitation of water has resulted in rapidly declining aquifers in important grainproducing areas, greatly reduced river flows, and often severe river pollution. Industrial water withdrawals have stagnated in recent years, but the share of water consumption has continuously increased in the manufacturing sector due to recirculation. Revegetation and ecological restoration are approaching sustainable water resource limits in many regions of China. Therefore, China has implemented several water policies reducing the impact of water shortages and pollution on the economy and domestic living standards and enforced a series of water laws since 2000. However, these efforts have not significantly reversed the trend of increasing water demand and quality degradation. In response, the Chinese government has established a ‘‘Stringent Water Resources Management System”, or the ‘‘Three Red Lines” (TRLs, Table 1, http://www.gov.cn/zhengce/content/2012-02/ 15/content_2311.htm), as a long-term framework for addressing key water challenges.
Wide-bandgap perovskites for indoor photovoltaics

With the rapid development of the Internet of Things (IoT), selfsustainable power sources are highly demanded for the wireless devices (e.g., sensors, actuators, and communication devices) that are connected and automated by IoT technology. Considering that a significant portion of these wireless devices are located in indoor environments, indoor photovoltaics (IPVs) that convert indoor light into usable electrical power have emerged as the most promising energy supplier for low power consumption devices. The narrow emission spectra (400-700 nm) of the commonly used artificial light sources such as fluorescent lamps (FLs) and light-emitting diodes (LEDs) therefore determine the optimal bandgap of indoor light harvesters (~1.9 eV).
Effects of a rooftop wind turbine on the dispersion of air pollutant behind a cube-shaped building

The concentration distribution of urban air pollutants is closely related to people's health.As an important utilization form of urban wind power,rooftop wind turbines have been widely used in cities.The wake effect of the rooftop wind turbines will change the flow behind buildings and then affect the pollutant dispersion.To this end,the pollutant dispersion behind the building is studied via the computational fluid dynamics method.The actuator disk model and idealized cube are adopted to model the wind turbine and the building,respectively.The study shows that the rooftop wind turbine can reduce the pollutant mass fraction near the ground and the pedestrian level.Due to the wake effect of the rooftop wind turbine,the turbulent fluctuation behind the building is weakened,and the spanwise pollutant dispersion is suppressed.Besides,the rooftop wind turbine weakens the downwash movement of the building,which enhances the vertical pollutant dispersion.
A quasi-coupled wind wave experimental framework for testing offshore wind turbine floating systems

Two sets of experiments in the St.Anthony Falls Laboratory(SAFL)wave tank facility and atmospheric wind tunnel are integrated to provide a scaled representation of a floating wind turbine under heave and pitch motions due to ocean waves.The quasi-coupling is established by controlling the turbine rotor speed to generate a thrust force mimicking steady or fluctuating wind gusts in the wave tank,and by using two actuators to oscillate a miniature turbine in the wind tunnel.Measured pitch and heave motions under varying waves are scaled down using rotor geometry and the wake meandering frequency to study the effect of the floating platform kinematics on the evolution and characteristics of the oscillating turbine wake.For a limited case of experimental conditions results provide a phenomenological and quantitative description of the floating-turbine system under variable waves and simulated wind gusts.Specifically,we demonstrate that wind gusts contribute to increase the platform pitch range,and that periodic large scale flow patches of high and low momentum flow are generated by the oscillating rotor in the turbulent boundary layer and are coherently convected through the wake.Both mechanisms could amplify the pitch response of downwind floating turbine units within the offshore power plant,in particular if the wave and/or wind forcing frequencies happen to approach the pitch natural frequency of the floating system.
Platform motion minimization using model predictive control of a floating offshore wind turbine

Wind turbines are installed offshore with the assistance of a floating platform to help meet the world's increasing energy needs.However,the incident wind and extra incident wave disturbances have an impact on the performance and operation of the floating offshore wind turbine(FOWT)in comparison to bottom-fixed wind turbines.In this paper,model predictive control(MPC)is utilized to overcome the limitation caused by platform motion.Due to the ease of control synthesis,the MPC is developed using a simplified model instead of high fidelity simulation model.The performance of the controller is verified in the presence of realistic wind and wave disturbances.The study demonstrates the effectiveness of MPC in reducing platform motions and rotor/generator speed regulation of FOWTs.
Temporal variations in coda attenuation associated with the 2008 Wenchuan (MW7.9) Earthquake in SW, China

In this study, waveform data obtained fromWestern Sichuan Seismic Array (WSSA) in China was utilized to reveal the temporal variations in coda attenuation around the eastern Tibet Plateau and Western Sichuan. Based on the single-scattering model, coda attenuation factor Q_c~(-1) is calculated in narrower overlapping frequency bands at 1.0e24 Hz by measuring the coda decay rates for local earthquakes before and after the Wenchuan mainshock. The temporal variations in coda attenuation are investigated within three periods. The period I lasted from January 2007 to the end of 2007. The period II lasted from January 2008 tillWenchuan earthquake. The period III lasted from the mainshock to the end of 2008. The resulted temporal variations demonstrate an increase in average Q_c~(-1) by approximately 35%e45% in the vicinity of Longmenshan and 30%e35% in Sichuan Basin after the Wenchuan earthquake in lower frequency bands within 1.25e8 Hz. On the contrary, the average Q_c~(-1) is shown to decrease by approximately 10%e18% in southern segment of Longmenshan, 15%e38% in the Chuan-Dian block, and 10e12% in the South China block. These results are confirmed by a statistical t-test at 99.9% confidence level. No statistically significant change in Q_c~(-1) (<10%) is found in the Songpan-Ganzi block after the mainshock. Temporal variations of coda attenuation differ significantly in individual blocks after the mainshock. The coda attenuation is proposed to be a beacon to tectonic static stress changes associated with the Wenchuan earthquake.
Interpretable deep learning for roof fall hazard detection in underground mines

Roof falls due to geological conditions are major hazards in the mining industry, causing work time loss, injuries, and fatalities. There are roof fall problems caused by high horizontal stress in several largeopening limestone mines in the eastern and midwestern United States. The typical hazard management approach for this type of roof fall hazards relies heavily on visual inspections and expert knowledge. In this context, we proposed a deep learning system for detection of the roof fall hazards caused by high horizontal stress. We used images depicting hazardous and non-hazardous roof conditions to develop a convolutional neural network (CNN) for autonomous detection of hazardous roof conditions. To compensate for limited input data, we utilized a transfer learning approach. In the transfer learning approach, an already-trained network is used as a starting point for classification in a similar domain. Results show that this approach works well for classifying roof conditions as hazardous or safe, achieving a statistical accuracy of 86.4%. This result is also compared with a random forest classifier, and the deep learning approach is more successful at classification of roof conditions. However, accuracy alone is not enough to ensure a reliable hazard management system. System constraints and reliability are improved when the features used by the network are understood. Therefore, we used a deep learning interpretation technique called integrated gradients to identify the important geological features in each image for prediction. The analysis of integrated gradients shows that the system uses the same roof features as the experts do on roof fall hazards detection. The system developed in this paper demonstrates the potential of deep learning in geotechnical hazard management to complement human experts, and likely to become an essential part of autonomous operations in cases where hazard identification heavily depends on expert knowledge. Moreover, deep learning-based systems reduce expert exposure to hazardous conditions.
Classification of clustered microseismic events in a coal mine using machine learning

Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines. Although machine learning has been widely applied in seismic data processing, feasibility and reliability of applying this technique to classify spatially clustered seismic events in underground mines are yet to be investigated. In this research, two groups of seismic events with a minimum local magnitude (M_L) of -3 were observed in an underground coal mine. They were respectively located around a dyke and the longwall face. Additionally, two types of undesired signals were also recorded. Four machine learning methods, i.e. random forest (RF), support vector machine (SVM), deep convolutional neural network (DCNN), and residual neural network (ResNN), were used for classifying these signals. The results obtained based on a primary dataset showed that these seismic events could be classified with at least 91% accuracy. The DCNN using seismogram images as the inputs reached the best performance with more than 94% accuracy. As mining is a dynamic progress which could change the characteristics of seismic signals, the temporal variance in the prediction performance of DCNN was also investigated to assess the reliability of this classifier during mining. A cascaded workflow consisting of database update, model training, signal prediction, and results review was established. By progressively calibrating the DCNN model, it achieved up to 99% prediction accuracy. The results demonstrated that machine learning is a reliable tool for the automatic discrimination of spatially clustered seismicity in underground mining.
Quantum enhanced convolutional neural networks for NISQ computers

The interplay between quantum physics and machine learning may lead to unprecedented perspectives for both fields [1].On the one hand,ideas and techniques from machine learning,or more broadly artificial intelligence,can be exploited to tackle challenging problems in the quantum domain.On the other hand,judiciously designed quantum algorithms may also enhance,speed up,or innovate machine learning with quantum computers.Undoubtedly,the studies of the interplay between quantum physics and machine learning would benefit both fields,and the emergent research frontier of quantum machine learning has become one of today's most rapidly growing interdisciplinary fields [1].
Implementing fractional Fourier transform using SH_0 wave computational metamaterials in space domain

Through the design of artificial structures,metamaterials can control the incident wave in the sub-wavelength range to achieve functions that natural materials cannot achieve,such as high absorption,negative refraction,cloaking [1-3],asymmetric propagation [4,5],and holography [6-8].In recent years,the new concept of computational metamaterials [9-12] has opened up a new direction for analog computing,providing high-throughput,energy-free computing operations for special computing tasks,including spatial integration,derivative and convolution [11],time domain integration,derivative and Hilbert transform [13].In the spatial domain,some acoustic computing metamaterials [14] including integrators and differentiators have been implemented.However,the elastic wave computational metamaterial lacks a design method to simultaneously modulate the phase and amplitude to control the elastic wave [15-17].Qiu et al.[18] proposed a metasurface consisting of multiple parallel strips with varied widths which can steer and focus SH_0 wave in plates.They used the nature of the phase(group)velocity of the quasi-SH_0 decreasing(increasing)with increasing strip width [19].Unfortunately,the amplitude of SH_0 wave was in lack of consideration.