Learn to survive crises: The role of firm resilience,innovation capabilities and environmental dynamism

Exogenous crises, while disruptive, may also present learning opportunities that could affect a firm's viability and performance. In this study, we examine how exogenous crises can constitute learning opportunities and assess their impact on firm survival. In particular, we investigate the role of learning in response to exogenous crises and how firm resilience, innovation capabilities, and environmental dynamism influence this relationship. Drawing from crisis management and organisational learning literature, we propose that these factors can bolster the connection between learning from crises and firm survival. To test our hypotheses, we conduct a nuanced analysis using both regression analysis and Fuzzy Set Qualitative Comparative Analysis (fsQCA) on data from 249 Italian manufacturing Small and Medium-sized Enterprises (SMEs). This approach allows us to simultaneously examine the impact of firm resilience, innovation capabilities, and environmental dynamism on the relationship between learning from crises and firm survival. Our findings offer theoretical and practical insights into the role of learning from crises in a firm's survival. They also highlight the importance of embracing learning opportunities in crisis situations and suggest that how firms deal with crises could be an opportunity to fine-tune their internal processes and thrive in the long run.     

Composition-Controllable Syntheses and Property Modulations from 2D Ferromagnetic Fe5Se8 to Metallic Fe3Se4 Nanosheets

Exploring new-type 2D magnetic materials with high magnetic transition temperature and robust air stability has attracted wide attention for developing innovative spintronic devices. Recently, intercalation of native metal atoms into the van der Waals gaps of 2D layered transition metal dichalcogenides (TMDs) has been developed to form 2D non-layered magnetic TMDs, while only succeeded in limited systems (e.g., Cr₂S₃, Cr₅Te₈). Herein, composition-controllable syntheses of 2D non-layered iron selenide nanosheets (25% Fe-intercalated triclinic Fe₅Se₈ and 50% Fe-intercalated monoclinic Fe₃Se₄) are firstly reported, via a robust chemical vapor deposition strategy. Specifically, the 2D Fe₅Se₈ exhibits intrinsic room-temperature ferromagnetic property, which is explained by the change of electron spin states from layered 1T'-FeSe₂ to non-layered Fe-intercalated Fe₅Se₈ based on density functional theory calculations. In contrast, the ultrathin Fe₃Se₄ presents novel metallic features comparable with that of metallic TMDs. This work hereby sheds light on the composition-controllable synthesis and fundamental property exploration of 2D self-intercalation induced novel TMDs compounds, by propelling their application explorations in nanoelectronics and spintronics-related fields.     

c-axis phonons and the enhancement of pairing correlations in high-temperature superconductors

We consider the two-dimensional Hubbard model including electron-phonon interaction. Strong local correlations (U limit) are taken into account within the mean-field approximation for auxiliary boson fields. Phonon-assisted transitions between intraand interlayer states are introduced as the source of coupling between two-dimensional CuO₂ layers. This type of processes effectively leads to the nonlinear (quadratic) interaction of intralayer electrons withc-axis phonons. We construct the Eliashberg equations for the resulting Hamiltonian and evaluate the superconducting transition temperatureT _c. Our model calculation demonstrates that a pronounced enhancement ofT _c in thed-wave channel is possible. The largest enhancement ofT _c tends to take place for small hole concentrations. This means that the coupling toc-axis phonons could compete with two-dimensional correlations responsible for the onset of antiferromagnetic order. It is remarkable that the two-dimensional features in the normal state are hardly affected by this specific interlayer interaction. Therefore,c-axis two-phonon-mediated interlayer coupling can cooperate with interlayer pair tunneling and substantially contribute to an increased pairing.     

二维直方图θ-划分最大平均离差阈值分割算法

鉴于常用二维直方图区域直分法存在错分, 最近提出的斜分法不具普遍性, 而基于L1范数的最小一乘准则比最小二乘准则更为合理且简捷, 提出了适用面更广的基于二维直方图θ-划分和最大类间平均离差的图像阈值分割算法. 首先给出了二维直方图θ-划分方法, 采用4条平行斜线及1条其法线与灰度级轴成 θ 角的直线划分二维直方图区域, 按灰度级和邻域平均灰度级的加权和进行阈值分割, 斜分法可视为该方法中θ=45° 的特例; 然后导出了二维直方图θ-划分最大类间平均离差阈值选取公式及其快速递推算法; 最后给出了θ 取不同值时的分割结果及运行时间. θ 取较小值时, 边界形状准确性较高, θ 取较大值时, 抗噪性较强, 应用时可根据实际图像特点及需求合理选取 θ 的值. 与常规二维直方图直分最大类间方差法及最大类间平均离差法相比, 所需运行时间相近, 但本文提出的方法所得分割结果更为准确, 抵抗噪声更为稳健, 且存储空间也大为减少.     

MF-TDMA信道分配研究

MF-TDMA因其灵活的分配策略和信道利用率在通信系统中广泛使用。由于信道资源有限性以及用户需求的多变性,使得如何将有限的信道最大的利用以便为更多的用户服务成为MF-TDMA的关键问题。针对这一问题,文章首先对MF-TDMA信道结构进行处理,将MF-TDMA的信道分配问题转为二维装箱问题。然后,针对该问题的常规算法FFA进行改进,提出了基于最小资源浪费率的FFA贪心算法（IFFA）。最后,对给出的结果进行了简单的验证。     

随机采样的2DPCA人脸识别方法

在2DPCA的基础上提出一种随机采样的2DPCA人脸识别方法--RRS-2DPCA.同传统通过对特征或投影向量进行采样的方法不同的是,RRS-2DPCA(Row Random Sampling 2DPCA)将随机采样建立于图像的行向量集中,然后在行向量子集中执行2DPCA.在ORL、Yale和AR人脸数据集上进行实验,结果表明RRS-2DPCA不仅具很好的识别性能和运算效率,而且对参数具有很大的稳定性.另外针对2DPCA和RRS-2DPCA对光线、遮挡等不鲁棒问题,进一步提出了局部区域随机采样的2DPCA方法LRRS-2DPCA(Local Row Random Sampling 2DPCA),将RRS-2DPCA执行在人脸图像的局部区域中.实验结果表明LRRS-2DPCA不仅具有较好的鲁棒性更大大的提高了RRS-2DPCA的识别性能.     

基于2D-DCT变换的MWD遥传信号泵冲干扰消除方法

遥传技术是随钻测量(MWD,measurement while drilling)系统中的一项关键技术,目前大多数MWD系统均采用泥浆脉冲方式进行数据传输.钻井现场噪声环境复杂,特别是泥浆泵的泵冲干扰,其强度较大,如果其频率落入脉冲信号的带内,则会对地面接收信号造成严重干扰.因此若不能对脉冲信号带内的泵冲干扰进行很好的抑制,则会限制遥传系统对各种工况的适应能力.提出了一种基于二维离散余弦变换(2D-DCT)的泵冲干扰消除方法,该方法将地面接收到的一维泥浆压力信号转换为二维信号,然后进行2D-DCT变换,从而能在变换域进行泵冲干扰的消除处理.     

基于旋转谐振结构的单芯片二维电场传感器

提出并研制了一种二维电场检测传感芯片,将四个电场测量微型单元和旋转式驱动微结构集成在3. 5 mm ×3. 5 mm的敏感结构上,实现了单芯片的电场二维测量.介绍了传感器的工作原理、敏感结构的设计,以及基于绝缘体上硅( SOI)工艺的单芯片微型二维电场传感器制备工艺技术.成功研制出传感器原理样机,研究了微型二维电场传感器的标定方法,开发了用于电场二维标定的测试装置,并在室温常压下对传感器进行了二维标定.实验结果表明:该传感器能够有效减小电场的轴间耦合干扰,测量误差优于7. 04%,线性度可达到1. 25%.     

网络弹性研究进展

网络弹性(Network Resilience)必将作为下一代异构网络必要的设计与操作属性,被定义为在面临不同的失效或挑战时,能够提供和保持可接受服务水平的能力。从系统的角度对网络弹性进行了深入的研究,首先对网络弹性从定义、模型、实现与评估等方面进行深入的分析,接着讨论基于态势感知的网络弹性研究模型,并详细阐述了模型的每一层功能及实现方法,最后给出了网络弹性研究的发展方向。