应用Minigene剪接变异体分析技术诊断PMM2基因非经典剪接位点新变异的致病性

目的:研究Minigene剪接变异体分析技术在诊断磷酸甘露糖变位酶2(PMM2)相关先天性糖基化障碍(PMM2-CDG)中的价值,探讨磷酸甘露糖变位酶2(PMM2)基因剪接位点新变异对其转录产物的影响。方法:通过对1例PMM2-CDG患儿进行高通量测序查找可能的遗传学病因,利用Minigene剪接变异体分析技术,研究PMM2基因新剪接位点变异的致病性。根据美国医学遗传学与基因组学学会(ACMG)指南,判断新变异的致病性。结果:遗传学分析发现患儿系PMM2基因母源性c.691G>A(p.Val231Met)变异和父源性c.447+5G>A变异复合杂合子。Minigene剪接变异体分析发现:变异c.447+5G>A导致PMM2基因转录产物形成r.348_447del转录本,为致病性PMM2基因变异。患儿的临床特征为皮肤巩膜黄染,血清总胆红素、非结合胆红素和总胆汁酸明显升高,白蛋白明显降低,甲胎蛋白、铁蛋白和促甲状腺素等升高,对症支持治疗效果欠佳。结论:Minigene剪接变异体分析可为PMM2-CDG确诊和家系遗传咨询提供新的分子标记物,扩展了PMM2基因变异谱,为该病的临床诊治提供新的参考依据。

A Novel Multiple DBC-staked units Package to Parallel More Chips for SiC Power Module

Silicon carbide(SiC) power modules play an essential role in the electric vehicle drive system. To improve their performance, reduce their size, and increase production efficiency, this paper proposes a multiple staked direct bonded copper(DBC) unit based power module packaging method to parallel more chips. This method utilizes mutual inductance cancellation effect to reduce parasitic inductance. Because the conduction area in the new package is doubled, the overall area of power module can be reduced. Entire power module is divided into smaller units to enhance manufacture yield, and improve design freedom. This paper provides a detailed design, analysis and fabrication procedure for the proposed package structure. Additionally, this paper offers several feasible solutions for the connection between power terminals and DBC untis. With the structure, 18dies were paralleled for each phase-leg in a econodual size power module. Both simulation and double pulse test results demonstrate that, compared to conventional layouts, the proposed package method has 74.8% smaller parasitic inductance and 34.9% lower footprint.

Analysis of the Effect of Temperature and Relative Humidity on the Reliability of a Photovoltaic Module

Photovoltaic energy occupies a significant place in the renewable energy market, with photovoltaic (PV) modules playing a vital role in converting solar energy into electricity. However, their effectiveness is likely to be affected by variations in environmental conditions, including temperature and relative humidity. The study examines the impact of these major climatic factors on the reliability of PV modules, aiming to provide crucial information for optimizing and managing these systems under varying conditions. Inspired by Weibull’s law to model the lifespan of components, we proposed a mathematical model integrating a correction factor linked to temperature and relative humidity. Using this approach, simulations in Matlab Simulink reveal that increasing temperature and relative humidity have an adverse impact on the reliability and lifespan of PV modules, with a more pronounced impact on temperature. The results highlight the importance of considering these environmental parameters in the management and optimization of photovoltaic systems to ensure their long-term efficiency.

A High Resolution Convolutional Neural Network with Squeeze and Excitation Module for Automatic Modulation Classification

Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.

A review on the cooling of energy conversion and storage systems using thermoelectric modules

Exploitation of sustainable energy sources requires the use of unique conversion and storage systems,such as solar panels,batteries,fuel cells,and electronic equipment.Thermal load management of these energy conversion and storage systems is one of their challenges and concerns.In this article,the thermal management of these systems using thermoelectric modules is reviewed.The results show that by choosing the right option to remove heat from the hot side of the thermoelectric modules,it will be a suitable local cooling,and the thermoelectric modules increase the power and lifespan of the system by reducing the spot temperature.Thermoelectric modules were effective in reducing panel temperature.They increase the time to reach a temperature above 50℃ in batteries by 3 to 4 times.Also,in their integration with fuel cells,they increase the power density of the fuel cell.

Review of Thermal Design of SiC Power Module for Motor Drive in Electrical Vehicle Application

In the current vehicle electric propulsion systems,the thermal design of power modules heavily relies on empirical knowledge,making it challenging to effectively optimize irregularly arranged Pinfin structures,thereby limiting their performance.This paper aims to review the underlying mechanisms of how irregularly arranged Pinfins influence the thermal characteristics of power modules and introduce collaborative thermal design with DC bus capacitor and motor.Literature considers chip size,placement,coolant flow direction with the goal of reducing thermal resistance of power modules,minimizing chip junction temperature differentials,and optimizing Pinfin layouts.In the first step,algorithms should efficiently generating numerous unique irregular Pinfin layouts to enhance optimization quality.The second step is to efficiently evaluate Pinfin layouts.Simulation accuracy and speed should be ensured to improve computational efficiency.Finally,to improve overall heat dissipation effectiveness,papers establish models for capacitors,motors,to aid collaborative Pinfin optimization.These research outcomes will provide essential support for future developments in high power density motor drive for vehicles.

Engineering vascularized organotypic tissues via module assembly

Adequate vascularization is a critical determinant for the successful construction and clinical implementation of complex organotypic tissue models. Currently, low cell and vessel density and insufficient vascular maturation make vascularized organotypic tissue construction difficult,greatly limiting its use in tissue engineering and regenerative medicine. To address these limitations, recent studies have adopted pre-vascularized microtissue assembly for the rapid generation of functional tissue analogs with dense vascular networks and high cell density. In this article, we summarize the development of module assembly-based vascularized organotypic tissue construction and its application in tissue repair and regeneration, organ-scale tissue biomanufacturing, as well as advanced tissue modeling.

A Hybrid Integrated and Low-Cost Multi-Chip Broadband Doherty Power Amplifier Module for 5G Massive MIMO Application

In this paper,a hybrid integrated broadband Doherty power amplifier(DPA)based on a multi-chip module(MCM),whose active devices are fabricated using the gallium nitride(GaN)process and whose passive circuits are fabricated using the gallium arsenide(GaAs)integrated passive device(IPD)process,is proposed for 5G massive multiple-input multiple-output(MIMO)application.An inverted DPA structure with a low-Q output network is proposed to achieve better bandwidth performance,and a single-driver architecture is adopted for a chip with high gain and small area.The proposed DPA has a bandwidth of 4.4-5.0 GHz that can achieve a saturation of more than 45.0 dBm.The gain compression from 37 dBm to saturation power is less than 4 dB,and the average power-added efficiency(PAE)is 36.3%with an 8.5 dB peak-to-average power ratio(PAPR)in 4.5-5.0 GHz.The measured adjacent channel power ratio(ACPR)is better than50 dBc after digital predistortion(DPD),exhibiting satisfactory linearity.

Research on the LA-UMamba Model for Asymmetric Modules with Added Auxiliary Information

Deep learning techniques are revolutionizing the developmentof medical image segmentation.With the advancement of Transformer models,especially ViT and Swin-Transformer,which enhances the remote-dependent modeling capability of the model through the self-attention mechanism,better segmentation performance can be achieve.Moreover,the high computational cost of Transformer has motivated researchers to explore more efficient models,such as the Mamba model based on state-space modeling(SSM),and for the field of medical segmentation,reducing the number of model parameters is also necessary.In this study,a novel asymmetric model called LA-UMamba was proposed,which integrates visual Mamba module to efficiently capture complex visual features and remote dependencies.The classical design of U-Net was adopted in the upsampling phase to help reduce the number of references and recover more details.To mitigate the information loss problem,an auxiliary U-Net downsampling layer was designed to focus on sizing without extracting features,thus enhancing the protection of input information while maintaining the efficiency of the model.The experiments were conducted on the ACDC MRI cardiac segmentation dataset,and the results showed that the proposed LA-UMamba achieves proved performance compared to the baseline model in several evaluation metrics,such as IoU,Accuracy,Precision,HD and ASD,which improved that the model is successful in optimizing the detail processing and reducing the complexity of the model,providing a new perspective for further optimization of medical image segmentation techniques.

Efficient and Stable Inverted Perovskite Solar Modules Enabled by Solid-Liquid Two-Step Film Formation

A considerable efficiency gap exists between large-area perovskite solar modules and small-area perovskite solar cells.The control of forming uniform and large-area film and perovskite crystallization is still the main obstacle restricting the efficiency of PSMs.In this work,we adopted a solid-liquid two-step film formation technique,which involved the evaporation of a lead iodide film and blade coating of an organic ammonium halide solution to prepare perovskite films.This method possesses the advantages of integrating vapor deposition and solution methods,which could apply to substrates with different roughness and avoid using toxic solvents to achieve a more uniform,large-area perovskite film.Furthermore,modification of the NiO_(x)/perovskite buried interface and introduction of Urea additives were utilized to reduce interface recombination and regulate perovskite crystallization.As a result,a large-area perovskite film possessing larger grains,fewer pinholes,and reduced defects could be achieved.The inverted PSM with an active area of 61.56 cm^(2)(10×10 cm^(2)substrate)achieved a champion power conversion efficiency of 20.56%and significantly improved stability.This method suggests an innovative approach to resolving the uniformity issue associated with large-area film fabrication.

Analysis of thermal management and anti-mechanical abuse of multi-functional battery modules based on magneto-sensitive shear thickening fluid

Electric vehicles(EVs)have garnered significant attention as a vital driver of economic growth and environmental sustainability.Nevertheless,ensuring the safety of high-energy batteries is now a top priority that cannot be overlooked during large-scale applications.This paper proposes an innovative active protection and cooling integrated battery module using smart materials,magneto-sensitive shear thickening fluid(MSTF),which is specifically designed to address safety threats posed by lithium-ion batteries(LIBs)exposed to harsh mechanical and environmental conditions.The theoretical framework introduces a novel approach for harnessing the smoothed-particle hydrodynamics(SPH)methodology that incorporates the intricate interplay of non-Newtonian fluid behavior,capturing the fluid-structure coupling inherent to the MSTF.This approach is further advanced by adopting an enhanced Herschel-Bulkley(H-B)model to encapsulate the intricate rheology of the MSTF under the influence of the magnetorheological effect(MRE)and shear thickening(ST)behavior.Numerical simulation results show that in the case of cooling,the MSTF is an effective cooling medium for rapidly reducing the temperature.In terms of mechanical abuse,the MSTF solidifies through actively applying the magnetic field during mechanical compression and impact within the battery module,resulting in 66%and 61.7%reductions in the maximum stress within the battery jellyroll,and 31.1%and 23%reductions in the reaction force,respectively.This mechanism effectively lowers the risk of short-circuit failure.The groundbreaking concepts unveiled in this paper for active protection battery modules are anticipated to be a valuable technological breakthrough in the areas of EV safety and lightweight/integrated design.

Analysis for Effects of Temperature Rise of PV Modules upon Driving Distance of Vehicle Integrated Photovoltaic Electric Vehicles

The development of vehicle integrated photovoltaics-powered electric vehicles (VIPV-EV) significantly reduces CO2 emissions from the transport sector to realize a decarbonized society. Although long-distance driving of VIPV-EV without electricity charging is expected in sunny regions, driving distance of VIPV-EV is affected by climate conditions such as solar irradiation and temperature rise of PV modules. In this paper, detailed analytical results for effects of climate conditions such as solar irradiation and temperature rise of PV modules upon driving distance of the VIPV-EV were presented by using test data for Toyota Prius and Nissan Van demonstration cars installed with high-efficiency InGaP/GaAs/InGaAs 3-junction solar cell modules with a module efficiency of more than 30%. The temperature rise of some PV modules studied in this study was shown to be expressed by some coefficients related to solar irradiation, wind speed and radiative cooling. The potential of VIPV-EV to be deployed in 10 major cities was also analyzed. Although sunshine cities such as Phoenix show the high reduction ratio of driving range with 17% due to temperature rise of VIPV modules, populous cities such as Tokyo show low reduction ratio of 9%. It was also shown in this paper that the difference between the driving distance of VIPV-EV driving in the morning and the afternoon is due to PV modules’ radiative cooling. In addition, the importance of heat dissipation of PV modules and the development of high-efficiency PV modules with better temperature coefficients was suggested in order to expand driving range of VIPV-EV. The effects of air-conditioner usage and partial shading in addition to the effects of temperature rise of VIPV modules were suggested as the other power losses of VIPV-EV.

Study on Image Recognition Algorithm for Residual Snow and Ice on Photovoltaic Modules

The accumulation of snow and ice on PV modules can have a detrimental impact on power generation,leading to reduced efficiency for prolonged periods.Thus,it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV modules.To address this issue,the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images.Furthermore,the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules,allowing for the establishment of a residual ice and snow recognition process.This process utilizes both the external ellipse method and the pixel statistical method to refine the identification process.The effectiveness of the proposed algorithm is validated through extensive testing with isolated and continuous snow area pictures.The results demonstrate the algorithm’s accuracy and reliability in identifying and quantifying residual snow and ice on PV modules.In conclusion,this research presents a valuable method for accurately detecting and quantifying snow and ice coverage on PV modules.This breakthrough is of utmost significance for PV power plants,as it enables predictions of power generation efficiency and facilitates efficient PV maintenance during the challenging winter conditions characterized by snow and ice.By proactively managing snow and ice coverage,PV power plants can optimize energy production and minimize downtime,ensuring a sustainable and reliable renewable energy supply.

Heat dissipation enhancement method for finned heat sink for AGV motor driver's IGBT module

With the widespread use of high-power and highly integrated insulated gate bipolar transistor(IGBT),their cooling methods have become challenging.This paper proposes a liquid cooling scheme for heavy-duty automated guided vehicle(AGV)motor driver in port environment,and improves heat dissipation by analyzing and optimizing the core component of finned heat sink.Firstly,the temperature distribution of the initial scheme is studied by using Fluent software,and the heat transfer characteristics of the finned heat sink are obtained through numerical analysis.Secondly,an orthogonal test is designed and combined with the response surface methodology to optimize the structural parameters of the finned heat sink,resulting in a 14.57%increase in the heat dissipation effect.Finally,the effectiveness of heat dissipation enhancement is verified.This work provides valuable insights into improving the heat dissipation of IGBT modules and heat sinks,and provides guidance for their future applications.

Unsupervised multi-modal image translation based on the squeeze-and-excitation mechanism and feature attention module

The unsupervised multi-modal image translation is an emerging domain of computer vision whose goal is to transform an image from the source domain into many diverse styles in the target domain.However,the multi-generator mechanism is employed among the advanced approaches available to model different domain mappings,which results in inefficient training of neural networks and pattern collapse,leading to inefficient generation of image diversity.To address this issue,this paper introduces a multi-modal unsupervised image translation framework that uses a generator to perform multi-modal image translation.Specifically,firstly,the domain code is introduced in this paper to explicitly control the different generation tasks.Secondly,this paper brings in the squeeze-and-excitation(SE)mechanism and feature attention(FA)module.Finally,the model integrates multiple optimization objectives to ensure efficient multi-modal translation.This paper performs qualitative and quantitative experiments on multiple non-paired benchmark image translation datasets while demonstrating the benefits of the proposed method over existing technologies.Overall,experimental results have shown that the proposed method is versatile and scalable.

银表面防变色膜PMM性能研究

采用硫华气相腐蚀实验、紫外光曝露加速腐蚀实验和电化学极化曲线法对PMTA(1-巯基 - 5 -苯基四氮唑 )、MBO(2 -巯基苯并恶唑 )、MBI(2 -巯基苯并咪唑 )、复配缓蚀剂PMM的防银变色作用进行了比较和研究 ,结果表明 ,PMM防银变色性能最好 ,显示出协同效应 .防变色膜的AES和XPS研究结果表明 ,PMM在银表面上所形成的防变色膜比单组分缓蚀剂防变色膜致密且稳定 ,从而能够更有效地防银变色 .

自旋-轨道耦合作用下极化激元凝聚中的调制不稳定性

利用线性稳定性分析方法,对存在自旋-轨道耦合(SOS)作用的二维极化激元玻色-爱因斯坦凝聚(BEC)系统中的调制不稳定性(MI)进行了研究.分析了组分内部,组分之间以及SOC相互作用对系统调制不稳定性的影响.结果显示,当系统内部不存在SOC作用,组分之间的相互作用为0,组分内部存在排斥作用时,不会出现调制不稳定性,组分内部存在吸引作用时,会出现调制不稳定性,并且调制不稳定性区间长度随吸引作用的增强而增加;组分之间相互作用不为0时,组分之间的相互作用以平方形式出现,其正负不会对调制不稳定性产生实质性影响.存在SOC相互作用时,SOC相互作用会引起增益谱曲线的不规则振荡,破坏原来的调制不稳定性区间.

白及PMM基因cDNA序列克隆及甘露糖合成相关基因的表达特性分析

【目的】克隆白及磷酸甘露糖变位酶(PMM)基因(BsPMM)cDNA序列,并检测甘露糖合成相关基因的表达特性,为研究甘露糖合成相关基因的调控功能及白及多糖合成机制提供理论依据。【方法】采用RT-PCR克隆BsPMM基因cDNA序列,对其进行生物信息学分析,并采用实时荧光定量PCR(qRT-PCR)检测BsPMM和GDP-甘露糖焦磷酸化酶基因(BsGMP)在不同品种(桂及1号和桂及2号)、生育期(苗期、生长旺期和成熟期)和组织(叶片、茎、假鳞茎和根)中的表达特性,同时测定不同生育期假鳞茎的多糖和甘露糖含量。【结果】克隆获得的BsPMM基因cDNA全长1062bp,包含一个759bp的开放阅读框(ORF),编码252个氨基酸,该基因编码的蛋白BsPMM定位于细胞质,含一个从细胞内部到外部的跨膜螺旋区;不稳定系数为41.67,为不稳定蛋白;总平均疏水指数为-0.304,为亲水性蛋白,含植物PMM蛋白特有的4个保守结构域,属于HAD超家族成员。BsPMM蛋白与单子叶植物尤其是兰科植物铁皮石斛和蝴蝶兰PMM蛋白的亲缘关系较近,与罂粟、葡萄和芦笋等双子叶植物PMM的亲缘关系较远。BsPMM和BsGMP基因在不同品种、组织和生育期均有表达,且二者在假鳞茎中表达量显著高于其他组织(P<0.05),区别在于桂及1号在生长旺期的表达量最高,而桂及2号在苗期的表达量最高。桂及1号和桂及2号假鳞茎中甘露糖含量和多糖含量均随生育期推移呈逐渐升高的变化趋势。【结论】BsPMM和BsGMP基因表达具有时空、组织和品种特异性,可能是调控多糖合成代谢途径中的关键基因,参与白及甘露糖和多糖的合成。

复配缓蚀剂(PMM)对银缓蚀效果的电化学研究

采用极化曲线测量和交流阻抗法比较研究了 1 苯基 5 巯基四氮唑 (PMTA)、2 巯基苯并恶唑 (MBO)和2 巯基苯并咪唑 (MBI)以及复配缓蚀剂 (PMM)在Na2 S和HSCH2 COOH溶液中对银的缓蚀性能。结果表明 ,PMM对银具有很好的缓蚀效果 ,表现出协同效应。

潜艇PMM实验的CFD仿真技术研究

操纵性是现代潜艇最重要的总体性能之一。为预报潜艇的操纵性能,需求得所有的水动力导数。借助CFD技术和动网格技术数值模拟小振幅平面运动机构试验,求解了SUBOFF主艇体的水动力导数,并与混合分布法计算的附加质量项作了比较,二者符合良好,从而验证了方法的有效性。流场的求解基于RANS方程和RNGk-ε湍流模型,动网格的处理采用Hrvoje Jasak和Zeljko Tukovic的方法,压力和速度的耦合采用PIMPLE方法解耦。流场的控制方程采用有限体积法离散且分离式求解,网格运动的控制方程采用有限元方法离散,网格的分裂采用体和面同时分裂法。