Does Co-Location Accelerate Knowledge Outflows from FDI? The Role of MMC Subsidiaries' Technology Sourcing Strategies

Despite the strategic importance of the knowledge outflows from FDI for local firms’ competitiveness, no study has focused on the speed at which this phenomenon takes place. However, this issue is crucial since the speed at which firms absorb external knowledge influences the time they need to carry out subsequent innovations, their ability to adapt to external changes and enter new markets, thus ultimately affecting their chances to achieve a competitive advantage. This paper tries to fill this gap, by investigating the temporal patterns of knowledge outflows between foreign subsidiaries and firms located in host-regions. Combining International Business literature with insights on Innovation Strategy, we provide evidence on the timing of this phenomenon, and discuss the role played by multinational firms’ technology sourcing strategies

Decreasing weight particle swarm optimization combined with unscented particle filter for the non-linear model for lithium battery state of charge estimation.

Accurate estimation of State of Charge (SOC) of wireless sensor network nodes is of great significance for wireless sensor network layout. A combination strategy method based on unscented particle filter using weight particle swarm optimization (PSO UPF) algorithm is proposed to improve estimation accuracy. The particle filter (PF) algorithm is usually used to deal with nonlinear problems, easily falling into particle degeneration and particle shortage. The unscented particle filter (UPF) algorithm can overcome the shortcomings by using the unscented Kalman filter (UKF) to generate the importance density function. Meanwhile, the particle swarm optimization (PSO) algorithm could improve the resampling process to solve particle shortage. Thus, the combination strategy improves the importance density function and the resampling method simultaneously. With the simulation comparison of PF, UPF and PSO UPF algorithms, the results show that the proposed algorithm has higher estimation accuracy with the root mean square error less than 1%. Furthermore, the proposed algorithm could achieve good accuracy with few particles, which could save running time and improve the estimate efficiency

Enhancing thermoelectric performance of Bi2Te3-based nanostructures through rational structure design

Nanostructuring has been successfully employed to enhance the thermoelectric performance of Bi2Te3 due to its obtained low thermal conductivity. In order to further reduce the thermal conductivity, we designed a hierarchical nanostructure assembled with well-aligned Bi2Te3 nanoplates using Te nanotubes as templates by a facile microwave-assisted solvothermal synthesis. From the comparisons of their thermoelectric performance and theoretical calculations with simple Bi2Te3 nanostructures, we found that Te/Bi2Te3 hierarchical nanostructures exhibit a higher figure-of-merit due to the optimized reduced Fermi level and enhanced phonon scattering, as well as the suppressed bipolar conduction. This study provides an effective approach to enhance the thermoelectric performance of Bi2Te3-based nanostructures by rationally designing the nanostructures

Automatic Data Transformation Using Large Language Model: An Experimental Study on Building Energy Data

Existing approaches to automatic data transformation are insufficient to meet the requirements in many real-world scenarios, such as the building sector. First, there is no convenient interface for domain experts to provide domain knowledge easily. Second, they require significant training data collection overheads. Third, the accuracy suffers from complicated schema changes. To bridge this gap, we present a novel approach that leverages the unique capabilities of large language models (LLMs) in coding, complex reasoning, and zero-shot learning to generate SQL code that transforms the source datasets into the target datasets. We demonstrate the viability of this approach by designing an LLM-based framework, termed SQLMorpher, which comprises a prompt generator that integrates the initial prompt with optional domain knowledge and historical patterns in external databases. It also implements an iterative prompt optimization mechanism that automatically improves the prompt based on flaw detection. The key contributions of this work include (1) pioneering an end-to-end LLM-based solution for data transformation, (2) developing a benchmark dataset of 105 real-world building energy data transformation problems, and (3) conducting an extensive empirical evaluation where our approach achieved 96% accuracy in all 105 problems. SQLMorpher demonstrates the effectiveness of utilizing LLMs in complex, domain-specific challenges, highlighting the potential of their potential to drive sustainable solutions.Comment: 10 pages, 7 figure

Acceptor-acceptor type isoindigo-based copolymers for high-performance n-channel field-effect transistors

Two acceptor-acceptor (A-A) type copolymers (PIIG-BT and PIIGTPD) with backbones composed exclusively of electron-deficient units are designed and synthesized. Both copolymers show unipolar n-type operations. In particular, PIIG-BT shows electron mobility of up to 0.22 cm2 V1 s1. This is a record value for n-type copolymers based on lactam cores.close3

Au impact on GaAs epitaxial growth on GaAs (111)B substrates in molecular beam epitaxy

GaAs growth behaviour under the presence of Au nanoparticles on GaAs {111}(B) substrate is investigated using electron microscopy. It has been found that, during annealing, enhanced Ga surface diffusion towards Au nanoparticles leads to the GaAs epitaxial growth into {113}(B) faceted triangular pyramids under Au nanoparticles, governed by the thermodynamic growth, while during conventional GaAs growth, growth kinetics dominates, resulting in the flatted triangular pyramids at high temperature and the epitaxial nanowires growth at relatively low temperature. This study provides an insight of Au nanoparticle impact on GaAs growth, which is critical for understanding the formation mechanisms of semiconductor nanowires. (C) 2013 American Institute of Physics. [http://dx.doi.org/10.1063/1.4792053

Delivery Efficiency of miR-21i-CPP-SWCNT and Its Inhibitory Effect on Fibrosis of the Renal Mesangial Cells

MicroRNA 21 (miR-21) was proved to cause renal fibrosis and the inhibition of miR-21 would improve the poor prognosis in renal cell carcinoma diseases. The complementary oligonucleotide of mature miR-21 was considered to be an effective intracellular miR-21 inhibitor (miR-21i). The directly effective delivery of miR-21i into fibrotic cell is a facile method for treatment of renal fibrosis. Herein, the miR-21i-CPP-SWCNT delivery system, synthesized via single-walled carbon nanotube (SWCNT) and cell-penetrating peptide (CPP), was taken as a novel fibrosis-targeting therapeutic carrier. The miR-21i and CPP firstly bind together via electrostatic forces, and subsequently miR-21i-CPP binds to the surface of SWCNTs via hydrophobic forces. CPP could endow the delivery system with targeting property, while SWCNT would enhance its penetrating ability. The exogenous miR-21i released from the designed miR-21i-CPP-SWCNTs had successfully inhibited the expression of fibrosis-related proteins in renal mesangial cells (RMCs). We found that the expression of TGF-β1 proteins was more sensitive to miR-21i-CPP-SWCNT than the expression of α-SMA proteins