Energy dissipation mechanism and ballistic characteristic optimization in foam sandwich panels against spherical projectile impact

This study systematically examines the energy dissipation mechanisms and ballistic characteristics of foam sandwich panels(FSP)under high-velocity impact using the explicit non-linear finite element method.Based on the geometric topology of the FSP system,three FSP configurations with the same areal density are derived,namely multi-layer,gradient core and asymmetric face sheet,and three key structural parameters are identified:core thickness(t_(c)),face sheet thickness(t_(f))and overlap face/core number(n_(o)).The ballistic performance of the FSP system is comprehensively evaluated in terms of the ballistic limit velocity(BLV),deformation modes,energy dissipation mechanism,and specific penetration energy(SPE).The results show that the FSP system exhibits a significant configuration dependence,whose ballistic performance ranking is:asymmetric face sheet>gradient core>multi-layer.The mass distribution of the top and bottom face sheets plays a critical role in the ballistic resistance of the FSP system.Both BLV and SPE increase with tf,while the raising tcor noleads to an increase in BLV but a decrease in SPE.Further,a face-core synchronous enhancement mechanism is discovered by the energy dissipation analysis,based on which the ballistic optimization procedure is also conducted and a design chart is established.This study shed light on the anti-penetration mechanism of the FSP system and might provide a theoretical basis for its engineering application.

Health diagnosis of ultrahigh arch dam performance using heterogeneous spatial panel vector model

Currently,more than ten ultrahigh arch dams have been constructed or are being constructed in China.Safety control is essential to long-term operation of these dams.This study employed the flexibility coefficient and plastic complementary energy norm to assess the structural safety of arch dams.A comprehensive analysis was conducted,focusing on differences among conventional methods in characterizing the structural behavior of the Xiaowan arch dam in China.Subsequently,the spatiotemporal characteristics of the measured performance of the Xiaowan dam were explored,including periodicity,convergence,and time-effect characteristics.These findings revealed the governing mechanism of main factors.Furthermore,a heterogeneous spatial panel vector model was developed,considering both common factors and specific factors affecting the safety and performance of arch dams.This model aims to comprehensively illustrate spatial heterogeneity between the entire structure and local regions,introducing a specific effect quantity to characterize local deformation differences.Ultimately,the proposed model was applied to the Xiaowan arch dam,accurately quantifying the spatiotemporal heterogeneity of dam performance.Additionally,the spatiotemporal distri-bution characteristics of environmental load effects on different parts of the dam were reasonably interpreted.Validation of the model prediction enhances its credibility,leading to the formulation of health diagnosis criteria for future long-term operation of the Xiaowan dam.The findings not only enhance the predictive ability and timely control of ultrahigh arch dams'performance but also provide a crucial basis for assessing the effectiveness of engineering treatment measures.

Mechanical Behavior of Panels Reinforced with Orthogonal Plant Fabrics: Experimental and Numerical Assessment

The construction sector is one of the main sources of pollution,due to high energy consumption and the toxic substances generated during the processing and use of traditional materials.The production of cement,steel,and other conventional materials impacts both ecosystems and human health,increasing the demand for ecological and biodegradable alternatives.In this paper,we analyze the properties of panels made from a combination of plant fibers and castor oil resin,analyzing the viability of their use as construction material.For the research,orthogonal fabrics made with waste plant fibers supplied by a company that deals with the manufacture of furniture and craft products were used.These fabrics were made with strips of plant fibers of the Calamus rotang,Bambusa vulgaris,Heteropsis flexuosa,and Salix viminalis species.To improve their compatibility with the castor oil resin,a cold argon plasma treatment was applied.The effect of the treatment on the properties of the fibers and the panels was analyzed.The density,water absorption capacity,and swelling percentage were evaluated.Tensile,compression,static bending,and linear buckling tests were carried out.The study found that panels made with treated fiber fabrics exhibited a reduction of approximately 10%in absorption capacity and up to 35%in swelling percentage values.Panels made with Bambusa vulgaris fabrics exhibited the highest strength and stiffness values.Numerical models were constructed using commercial finite element software.When comparing the numerical results with the experimental ones,differences of less than 15%were seen,demonstrating that the models allow adequately predicting the analyzed properties.On comparing the values obtained with the characteristic values of oriented strand board,the results suggest that panels made with unconventional materials could replace commercial panels traditionally made with wood-based fibers and particles and other composite materials in several applications in the construction industry.

How R&D investment promotes green technology innovation in the context of digitalization?-An empirical analysis based on provincial panel data

Green technology innovation is an important driving force and source to promote my country’s high-quality development,and it is the core path to achieve sustainable development.This paper uses my country’s provincial panel data from 2016 to 2019 to study the impact mechanism of R&D investment on green technology innovation,and introduces the level of digitization,using the panel threshold model to discuss its role in the impact mechanism of R&D investment on green technology innovation.The study found that when the level of digitalization in a region is low,increasing R&D investment does not necessarily improve the ability of green technology innovation;when the level of digitalization is relatively high,R&D investment has a positive role in promoting green technology innovation.Therefore,it is necessary to improve policies to encourage enterprises to increase investment in research and development;at the same time,it is necessary to promote the coordinated development of digital foundation,digital investment,digital literacy,digital economy and digital application,and promote the deep integration of digitalization and green technology innovation.

Advancements in Photovoltaic Panel Fault Detection Techniques

This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.

Composite Panels from the Combination of Rice Husk and Wood Chips with a Natural Resin Based on Tannins Reinforced with Sugar Cane Molasses Intended for Building Insulation: Physico-Mechanical and Thermal Properties

The objective of this work is to develop new biosourced insulating composites from rice husks and wood chips that can be used in the building sector. It appears from the properties of the precursors that rice chips and husks are materials which can have good thermal conductivity and therefore the combination of these precursors could make it possible to obtain panels with good insulating properties. With regard to environmental and climatic constraints, the composite panels formulated at various rates were tested and the physico-mechanical and thermal properties showed that it was essential to add a crosslinker in order to increase certain solicitation. an incorporation rate of 12% to 30% made it possible to obtain panels with low thermal conductivity, a low surface water absorption capacity and which gives the composite good thermal insulation and will find many applications in the construction and real estate sector. Finally, new solutions to improve the fire reaction of the insulation panels are tested which allows to identify suitable solutions for the developed composites. In view of the flame tests, the panels obtained are good and can effectively combat fire safety in public buildings.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

Comprehensive Examination of Solar Panel Design: A Focus on Thermal Dynamics

In the 21st century, the deployment of ground-based Solar Photovoltaic (PV) Modules has seen exponential growth, driven by increasing demands for green, clean, and renewable energy sources. However, their usage is constrained by certain limitations. Notably, the efficiency of solar PV modules on the ground peaks at a maximum of 25%, and there are concerns regarding their long-term reliability, with an expected lifespan of approximately 25 years without failures. This study focuses on analyzing the thermal efficiency of PV Modules. We have investigated the temperature profile of PV Modules under varying environmental conditions, such as air velocity and ambient temperature, utilizing Computational Fluid Dynamics (CFD). This analysis is crucial as the efficiency of PV Modules is significantly impacted by changes in the temperature differential relative to the environment. Furthermore, the study highlights the effect of airflow over solar panels on their temperature. It is found that a decrease in the temperature of the PV Module increases Open Circuit Voltage, underlining the importance of thermal management in optimizing solar panel performance.

基于Panel-data的区际产业转移粘性分析

利用面板数据模型,以江苏省为例,对28个制造业的科技进步、产业集群、区域人力资本积累、沉没成本和资产专用性、劳动力跨区域流动、制度创新、政府阻力等因素对产业转移粘性的关系进行了实证研究,并依据各个因素的贡献率得出了跨区域产业转移的初步结论。

开放式基金赎回问题研究——基于Panel-Data的Granger因果检验

本文应用新近发展的Panel-Data Granger因果检验方法,检验开放式基金的赎回率和基金单位净值(NAPS)增长率以及基金累计净值(ACCNAV)增长率之间的因果关系。通过对2002年12月31日以前成立的17支基金的14个季度数据的研究发现:(1)基金单位净值增长率和基金累计净值增长率并不是基金赎回率的Granger原因,一直困扰着开放式基金的"赎回困惑"不过是一个"假象";(2)基金赎回率是基金单位净值增长率和基金累计净值增长率的Granger原因,高的基金赎回率大大的降低了基金单位净值增长率和基金累计净值增长率。

Panel-Data下Granger因果检验的理论和应用发展综述

Panel-Data下Granger因果检验的相关理论是最近几年才发展起来的,现有的研究提出了关于Panel-Data下Granger因果检验的四个基本假设:同质无因果关系假设(HNCH)、同质因果关系假设(HCH)、异质因果关系假设(HECH)以及异质无因果关系假设(HENCH),根据检验参数的特点给出三种类型的检验模型:固定系数模型、随机系数模型和混合固定随机系数模型。目前,还只有固定系数模型的相关理论较为完善,另外两种模型的检验还都存在一定的难度。因此,只有从理论研究和实际应用两个方面对该理论进行阐述,并对现有的理论进行简要的评述,才可指出其存在的不足及可能的改进方向。

基于Panel-Data模型的地方财政科技投入与经济增长关系分析

运用平行面板数据的基本理论,对1996～2005年全国30个省市的地方财政科技投入与地方经济增长的关系进行实证分析,结果表明地方财政科技投入对地方经济发展的影响在各个地区及年份存在着很大的差异。

基于Panel-Data模型的江苏城市居民文化消费的实证研究

本文运用面板数据和聚类分析对江苏省13个地区城市居民文化消费问题进行了实证分析,研究发现前期文化消费比当期居民可支配收入对城市居民文化消费的正向影响更大,而且各地区文化消费倾向和前期文化消费的影响程度不同,因此,为了提高江苏省各个地区的文化消费水平,政府应因地制宜制定差异化政策。

新疆各区域产业结构对经济增长的贡献研究——基于panel-data的实证分析

新疆地域广大,各区域经济发展很不平衡,区域产业结构对经济增长的贡献有很大不同。本文通过panel-data计量模型,采用1978年-2007年(1978=100)30年的统计数据,对新疆各区域产业结构对经济增长的贡献作用进行了实证分析,得出一些启示和建议,为政府优化产业结构、促进区域经济协调发展提供理论依据。

我国旅游经济增长对星级饭店规模的弹性系数分析——基于Panel-Data模型

文章选取2001～2010年全国31个省域的数据,基于panel-data模型,采用空间面板回归和聚类分析方法,研究中国省域星级饭店规模与旅游经济增长的关系。按照旅游经济增长对星级饭店规模的弹性系数,将全国分为四类地区。研究发现:各类区域星级饭店对旅游经济的影响程度有很大差异,且存在一定的空间相关性。对于部分星级饭店规模已趋于饱和的省域,应注重星级酒店的市场营销,发展经济型酒店和家庭旅馆;而对于弹性系数较大的区域,则应加强发展星级酒店,扩大星级酒店的规模,提高接待水平。

运用Panel-data模型对河南省城镇居民消费行为的分析

本文运用Panel-data模型对2000-2004年河南省城镇居民消费行为进行研究,分析收入等级因素和时间因素对居民消费的影响,得到河南省收入等级差异对居民消费行为有较大的影响,同时随着时间的变化河南省城镇居民消费行为也发生较大的变化。

基于Panel-data的江苏省区域经济增长要素分析

利用江苏省2002—2010年的面板数据,研究资本要素、制度因素、消费结构对江苏省区域经济增长的影响。研究表明:资本和外贸依存度对经济增长的贡献显著,对3大区域的经济增长都起到积极促进的作用;农村恩格尔系数对于苏中地区的贡献是显著的,说明提高苏中地区农村的消费水平有助于促进苏中经济的增长。

血清外泌体microRNAs单一及组合panel对结直肠癌的诊断价值

目的 检测血清外泌体miR-15b、miR-16、miR-21和miR-31在结直肠癌患者中的表达水平,并评估其对结直肠癌的诊断价值。方法 选取2018年3月至2022年5月本院收治的123例直肠癌患者(结直肠癌组)及117例大肠腺瘤患者作为研究对象(结直肠癌组),并纳入150例健康对照者作为对照(健康对照组)。提取血清外泌体中miR-15b、miR-16、miR-21和miR-31,qRT-PCR比较4种miRNA在各组中表达水平差异。受试者工作特征(ROC)曲线评价4种miRNA单一及组合panel对结直肠癌的诊断价值。结果 结直肠癌组血清miR-15b、miR-16、miR-21和miR-31水平高于健康对照组(P <0.05),且miR-15b、miR-21和miR-31水平高于结直肠腺瘤组(P <0.05)。Ⅲ-Ⅳ期、伴淋巴结浸润、低分化程度结直肠癌组患者血清miR-15b、miR-21和miR-31水平分别高于Ⅰ-Ⅱ期、不伴淋巴结浸润、中-高分化程度患者,miR-15b、miR-21和miR-31水平与临床TNM分期、伴淋巴结浸润呈正相关,与分化程度呈负相关。4种miRNA中诊断价值最高的指标是miR-15b,其敏感度和特异度分别为81.33%和91.80%(P <0.05);且miR-15b、miR-21和miR-31的组合panel具有更好的诊断价值,其敏感度和特异度分别为95.06%和94.44%(P <0.05)。结论 miR-15b、miR-16、miR-21和miR-31在结直肠癌患者外周血血清中异常表达,miR-15b、miR-21和miR-31水平与结直肠癌的发病及进展密切相关,且miR-15b、miR-21和miR-31组合panel具有作为结直肠癌的新型诊断模型的潜力。

基于Panel-Data模型的山东省区域旅游问题研究

旅游业已被公认为"朝阳产业"、"无烟工业",是集吃、住、行、游、购、娱为一体的综合性很强的产业,其光明的发展前景已为世人所公认。旅游业对其相关产业的牵动比例为1:4,可以充分带动地方经济的发展,发展潜力很大。本文运用了山东省17地市的国内旅游情况的合成数据,通过建立完整的计量模型,进行计量分析,进而发现各地市每增加一万人次的旅游者,对该地区的旅游经济的影响存在一定的差异,并且对于差异进行聚类分析,得出全省各地市的不同属类,为山东省日后旅游市场的更好发展提出了一些相应的建议。

基于panel-data模型的居民收入差距对消费结构影响的计量分析——以湖北省城镇居民为例

数据显示改革开放30年来,湖北省城镇居民的收入差距在不断扩大,这在一定程度上会抑制居民消费结构的升级,影响消费总需求,进而影响经济的快速增长.利用panel-data模型,对湖北省城镇居民收入差距和消费结构的关系进行实证分析,结论表明,不同收入等级的居民对各种消费品具有不同的自发性需求,在消费结构上存在显著性差异,从而为调整和实施相应的消费政策提供依据.