Corporate Philanthropic Giving: Active Responsibility Or Passive Ingratiation? Evidence From Chinese Family-Controlled Listed Companies

This paper examines the impact of political connection on family-controlled listed firms’ philanthropic giving activities toward the 2008 Wenchuan Earthquake in China, and stock price reactions to such activities. Using the 542 Chinese listed companies controlled by private owners as the sample, it was found that firms with political connection are more likely to donate. Besides, focusing on the 244 donating firms, it was found that there is a positive impact of the donation amount on stock price response. What’s more, the positive stock price reactions toward the donation announcement made by firms with political connection are not as strong as that of firms without such connection.  Regression results indicate that although family-controlled firms with political connection are more likely to donate, their activities can not generate as much positive stock price effect as their no-political connection counterparts. These results reveal that both political interferences and market mechanisms have critical impact on corporate philanthropic behavior in China

LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization

With the demand for standardized large-scale livestock farming and the development of artificial intelligence technology, a lot of research in area of animal face recognition were carried on pigs, cattle, sheep and other livestock. Face recognition consists of three sub-task: face detection, face normalizing and face identification. Most of animal face recognition study focuses on face detection and face identification. Animals are often uncooperative when taking photos, so the collected animal face images are often in arbitrary directions. The use of non-standard images may significantly reduce the performance of face recognition system. However, there is no study on normalizing of the animal face image with arbitrary directions. In this study, we developed a light-weight angle detection and region-based convolutional network (LAD-RCNN) containing a new rotation angle coding method that can detect the rotation angle and the location of animal face in one-stage. LAD-RCNN has a frame rate of 72.74 FPS (including all steps) on a single GeForce RTX 2080 Ti GPU. LAD-RCNN has been evaluated on multiple dataset including goat dataset and gaot infrared image. Evaluation result show that the AP of face detection was more than 95% and the deviation between the detected rotation angle and the ground-truth rotation angle were less than 0.036 (i.e. 6.48{\deg}) on all the test dataset. This shows that LAD-RCNN has excellent performance on livestock face and its direction detection, and therefore it is very suitable for livestock face detection and Normalizing. Code is available at https://github.com/SheepBreedingLab-HZAU/LAD-RCNN/Comment: 8 figures, 5 table

Design of slurries for 3D printing of sodium-ion battery electrodes

Additive manufacturing of battery electrodes, using syringe deposition 3D printing or direct ink writing methods, enables intricate microstructural design. This process differs from traditional blade or slot-die coating methods, necessitating tailored physical properties of composite slurries to ensure successful deposition. Inadequately optimised slurries result in non-uniform extrusion, and challenges such as nozzle swelling or slumping, result in compromised structural integrity of the print, limiting the resolution. This study focuses on developing slurry design principles by thoroughly characterising the rheology of several water-based hard carbon anode slurry, both in shear and extension. Hard carbon is chosen as a material of significant importance for future sodium-ion batteries, and an example for this optimisation. The slurry composition is tailored to introduce yield stress by incorporating network-forming binder (carrageenan) and additive (carbon nanotubes), effectively reducing spreading, and preserving the printed coating's structure. Validation is performed through printing a large width line and evaluating spread. The same slurry is deposited on a smaller 150 μm nozzle, which introduces die swell and spreading effects. This offers insights for further optimization strategies. The strategies developed in this research for characterizing and optimizing the rheology through formulation lay the groundwork for the advancement of detailed 3D printed electrodes, contributing to the progress of additive manufacturing technologies in the field of battery manufacturing.</p

Structural basis for the photoconversion of a phytochrome to the activated far-red light-absorbing form

Phytochromes are a collection of bilin-containing photoreceptors that regulate numerous photoresponses in plants and microorganisms through their ability to photointerconvert between a red light-absorbing, ground state Pr and a far-red light-absorbing, photoactivated state Pfr1,2. While the structures of several phytochromes as Pr have been determined3-7, little is known about the structure of Pfr and how it initiates signaling. Here, we describe the three-dimensional solution structure of the bilin-binding domain as Pfr using the cyanobacterial phytochrome from Synechococcus OSB’. Contrary to predictions, light-induced rotation of the A but not the D pyrrole ring is the primary motion of the chromophore during photoconversion. Subsequent rearrangements within the protein then affect intra- and interdomain contact sites within the phytochrome dimer. From our models, we propose that phytochromes act by propagating reversible light-driven conformational changes in the bilin to altered contacts between the adjacent output domains, which in most phytochromes direct differential phosphotransfer

Skeletal loading regulates breast cancer-associated osteolysis in a loading intensity-dependent fashion

Osteocytes are mechanosensitive bone cells, but little is known about their effects on tumor cells in response to mechanical stimulation. We treated breast cancer cells with osteocyte-derived conditioned medium (CM) and fluid flow-treated conditioned medium (FFCM) with 0.25 Pa and 1 Pa shear stress. Notably, CM and FFCM at 0.25 Pa induced the mesenchymal-to-epithelial transition (MET), but FFCM at 1 Pa induced the epithelial-to-mesenchymal transition (EMT). This suggested that the effects of fluid flow on conditioned media depend on flow intensity. Fluorescence resonance energy transfer (FRET)-based evaluation of Src activity and vinculin molecular force showed that osteopontin was involved in EMT and MET switching. A mouse model of tumor-induced osteolysis was tested using dynamic tibia loadings of 1, 2, and 5 N. The low 1 N loading suppressed tumor-induced osteolysis, but this beneficial effect was lost and reversed with loads at 2 and 5 N, respectively. Changing the loading intensities in vivo also led to changes in serum TGFβ levels and the composition of tumor-associated volatile organic compounds in the urine. Collectively, this study demonstrated the critical role of intensity-dependent mechanotransduction and osteopontin in tumor-osteocyte communication, indicating that a biophysical factor can tangibly alter the behaviors of tumor cells in the bone microenvironment

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

Analysis of Large Phenotypic Variability of EEC and SHFM4 Syndromes Caused by K193E Mutation of the TP63 Gene

EEC (ectrodactyly, ectodermal dysplasia, clefting; OMIM 604292) is an autosomal dominant developmental disorder resulting mainly from pathogenic mutations of the DNA-binding domain (DBD) of the TP63 gene. In this study, we showed that K193E mutation in nine affected individuals of a four-generation kindred with a large degree of phenotypic variability causes four different syndromes or TP63-related disorders: EEC, Ectrodactyly-ectodermal dysplasia (EE), isolated ectodermal dysplasia, and isolated Split Hand/Foot Malformation type 4 (SHFM4). Genotype-phenotype and DBD structural modeling analysis showed that the K193-located loop L2-A is associated with R280 through hydrogen bonding interactions, while R280 mutations also often cause large phenotypic variability of EEC and SHFM4. Thus, we speculate that K193 and several other DBD mutation-associated syndromes may share similar pathogenic mechanisms, particularly in the case of the same mutation with different phenotypes. Our study and others also suggest that the phenotypic variability of EEC is attributed, at least partially, to genetic and/or epigenetic modifiers

A novel interestingness measure based on fusion model for association rules mining

Aiming at the shortcomings of the traditional "support-confidence" association rules mining framework and the problems of mining negative association rules, the concept of interestingness measure is introduced. Analyzed the advantages and disadvantages of some commonly used interestingness measures at present, and combined the cosine measure on the basis of the interestingness measure model based on the difference idea, and proposed a new interestingness measure model. The interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule. According to this model, an association rules mining algorithm based on the interestingness measure fusion model is proposed to improve the accuracy of mining. Experiments show that the algorithm has better performance and can effectively help mining positive and negative association rules