In search of strategic assets through cross-border merger and acquisitions: evidence from Chinese multinational enterprises in developed economies

Drawing on multiple cases of cross-border merger and acquisitions (CBMAs) by Chinese multinational enterprises (CMNEs), we investigate their search of strategic assets in developed economies (DEs). It is a received view that CMNEs use CBMAs to access strategic assets in DEs so as to address their latecomer disadvantages and competitive weakness. This paper aims to identify the nature of strategic assets that sought after by CMNEs and the post-CBMA integration approach, a partnering approach, adopted in enabling access to these assets. The findings reveal that CMNEs possess firm-specific assets that give them competitive advantages at home and seek for complementary strategic assets in the similar domain, but at a more advanced level. The partnering approach helps securing these strategic assets through no or limited integration, giving autonomy to target firm management team, retaining talents and creating synergy

Signatures of a gapless quantum spin liquid in the Kitaev material Na3_3Co2−x_{2-x}Znx_xSbO6_6

The honeycomb-lattice cobaltate Na$_3$Co$_2$SbO$_6$ has recently been proposed to be a proximate Kitaev quantum spin liquid~(QSL) candidate. However, non-Kitaev terms in the Hamiltonian lead to a zigzag-type antiferromagnetic~(AFM) order at low temperatures. Here, we partially substitute magnetic Co$^{2+}$ with nonmagnetic Zn$^{2+}$ and investigate the chemical doping effect in tuning the magnetic ground states of Na$_3$Co$_{2-x}$Zn$_x$SbO$_6$. X-ray diffraction characterizations reveal no structural transition but quite tiny changes on the lattice parameters over our substitution range $0\leq x\leq0.4$. Magnetic susceptibility and specific heat results both show that AFM transition temperature is continuously suppressed with increasing Zn content $x$ and neither long-range magnetic order nor spin freezing is observed when $x\geq0.2$. More importantly, a linear term of the specific heat representing fermionic excitations is captured below 5~K in the magnetically disordered regime, as opposed to the $C_{\rm m}\propto T^3$ behavior expected for bosonic excitations in the AFM state. Based on the data above, we establish a magnetic phase diagram of Na$_3$Co$_{2-x}$Zn$_x$SbO$_6$. Our results indicate the presence of gapless fractional excitations in the samples with no magnetic order, evidencing a potential QSL state induced by doping in a Kitaev system.Comment: 10 pages, 5 figure

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Feasibility Analysis and Implementation of Adaptive Dynamic Reconfiguration of CNN Accelerators

In multi-tasking scenarios with dynamically changing loads, the parallel computing of convolutional neural networks (CNNs) causes high energy and resource consumption in the system. Another critical problem is that previous neural network hardware accelerators are often limited to fixed scenarios and lack the function of adaptive adjustment. To solve these problems, a reconfiguration adaptive system based on the prediction of algorithm workload is proposed in this paper. Deep Learning Processor Unit (DPU) from Xilinx has excellent performance in accelerating network computing. After summarizing the characteristics of hardware accelerators and gaining an in-depth understanding of the DPU structure, we propose a regression model for CNNs runtime prediction and a guidance scheme for adaptive reconfiguration combined with the characteristics of Deep Learning Processor Unit. For different DPU sizes, the accuracy of the proposed prediction model achieves 90.7%. With the dynamic reconfiguration technology, the proposed strategy can enable accurate and fast reconfiguration. In the load change scenario, the proposed system can significantly reduce power consumption

Designing supramolecular self-assembly nanomaterials as stimuli-responsive drug delivery platforms for cancer therapy

Summary: Stimuli-responsive nanomaterials have attracted substantial interest in cancer therapy, as they hold promise to deliver anticancer agents to tumor sites in a precise and on-demand manner. Interestingly, supramolecular chemistry is a burgeoning discipline that entails the reversible bonding between components at the molecular and nanoscale levels, and the recent advances in this area offer the possibility to design nanotherapeutics with improved controllability and functionality for cancer therapy. Herein, we provide a comprehensive summary of typical non-covalent interaction modes, which primarily include hydrophobic interaction, hydrogel bonding, host-guest interaction, π-π stacking, and electrostatic interaction. Special emphasis is placed on the implications of these interaction modes to design novel stimuli-responsive drug delivery principles and concepts, aiming to enhance the spatial, temporal, and dosage precision of drug delivery to cancer cells. Finally, future perspectives are discussed to highlight current challenges and future opportunities in self-assembly-based stimuli-responsive drug delivery nanotechnologies for cancer therapy

Design of a Generic Dynamically Reconfigurable Convolutional Neural Network Accelerator with Optimal Balance

In many scenarios, edge devices perform computations for applications such as target detection and tracking, multimodal sensor fusion, low-light image enhancement, and image segmentation. There is an increasing trend of deploying and running multiple different network models on one hardware platform, but there is a lack of generic acceleration architectures that support standard convolution (CONV), depthwise separable CONV, and deconvolution (DeCONV) layers in such complex scenarios. In response, this paper proposes a more versatile dynamically reconfigurable CNN accelerator with a highly unified computing scheme. The proposed design, which is compatible with standard CNNs, lightweight CNNs, and CNNs with DeCONV layers, further improves the resource utilization and reduces the gap of efficiency when deploying different models. Thus, the hardware balance during the alternating execution of multiple models is enhanced. Compared to a state-of-the-art CNN accelerator, Xilinx DPU B4096, our optimized architecture achieves resource utilization improvements of 1.08× for VGG16 and 1.77× for MobileNetV1 in inference tasks on the Xilinx ZCU102 platform. The resource utilization and efficiency degradation between these two models are reduced to 59.6% and 63.7%, respectively. Furthermore, the proposed architecture can properly run DeCONV layers and demonstrates good performance

A Mixed-Precision Implementation of the Density Matrix Renormalization Group

Using the mixed precision strategy to optimize quantum chemistry codes has been proved promising in saving computational cost and maintaining chemical accuracy. Here, an efficient mixed-precision density matrix renormalization group (DMRG) scheme, containing a two-level mixed-precision hierarchy, is developed and demonstrated. At the coarse-grained level, based on the discovery that the single-precision orthogonalization may cause the DMRG generate a totally wrong answer, a feasible single-precision-sweep DMRG method with double-precision orthogonalization process is implemented. At the fine-grained level, a mixed-precision diagonalization algorithm is developed. This algorithm runs specific operations in the single-precision while preserving double-precision accuracy. Combining these two method, a hybrid mixed-precision scheme is presented. By applying this scheme, the DMRG single-point energy calculations are accelerated up to 131%. Mixed-precision DMRG yielded energies are accurate and deviate less than 0.01 kcal/mol compared with standard DMRG calculations