DSCom: A Data-Driven Self-Adaptive Community-Based Framework for Influence Maximization in Social Networks

Influence maximization aims to find a subset of seeds that maximize the influence spread under a given budget. In this paper, we mainly address the data-driven version of this problem, where the diffusion model is not given but needs to be inferred from the history cascades. Several previous works have addressed this topic in a statistical way and provided efficient algorithms with theoretical guarantee. However, in their settings, though the diffusion parameters are inferred, they still need users to preset the diffusion model, which can be an intractable problem in real-world practices. In this paper, we reformulate the problem on the attributed network and leverage the node attributes to estimate the closeness between the connected nodes. Specifically, we propose a machine learning-based framework, named DSCom, to address this problem in a heuristic way. Under this framework, we first infer the users' relationship from the diffusion dataset through attention mechanism and then leverage spectral clustering to overcome the influence overlap problem in the lack of exact diffusion formula. Compared to the previous theoretical works, we carefully designed empirical experiments with parameterized diffusion models based on real-world social networks, which prove the efficiency and effectiveness of our algorithm

Effect of Extrusion Parameters on Soybean Protein Conformation

The study investigated the effect of different extrusion parameters: temperature, material moisture of raw material, and screw speed on the conformation of soybean protein. After being extruded, soy protein isolate (SPI) was measured for protein solubility, zeta potential, particle size, protein subunits and protein secondary structure. Additionally, the formation mechanism of fibrous soybean protein was uncovered. The results showed that with an increase in extrusion temperature, the natural structure of SPI was destroyed, the internal groups were exposed, and the content of disulfide bonds increased, promoting the formation of protein aggregates. Too high or too low temperature was not conducive to the formation and stability of protein aggregates. At too low or too high raw material moisture content, a relatively complete extrudate was difficult to form at the extrusion mouth, while at raw material moisture contents of 20%–22%, the extrudate was in the best state due to the protective effect of water, and a uniform SPI aggregate was formed. Low screw speed (below 130 r/min) could lead to insufficient mechanical energy input, so that the protein was not completely denatured and depolymerized. While at high screw speed (above 140 r/min), the effect of high shear force promotes the destruction of the original natural structure of proteins, resulting in the formation of large protein aggregates. In summary, under appropriate extrusion conditions, the optimal extrudate quality can be obtained, while extreme extrusion conditions make extrusion molding difficult or result in nonuniform texture. This study can provide a theoretical basis for optimizing the quality of fibrous soybean protein

Gradual Verification for Smart Contracts

Blockchains facilitate secure resource transactions through smart contracts, yet these digital agreements are prone to vulnerabilities, particularly when interacting with external contracts, leading to substantial monetary losses. Traditional verification techniques fall short in providing comprehensive security assurances, especially against re-entrancy attacks, due to the unavailable implementations of external contracts. This paper introduces an incremental approach: gradual verification. We combine static and dynamic verification techniques to enhance security, guarantee soundness and flexibility, and optimize resource usage in smart contract interactions. By implementing a prototype for gradually verifying Algorand smart contracts via the pyTEAL language, we demonstrate the effectiveness of our approach, contributing to the safe and efficient execution of smart contracts

Experimental study on evolution behaviors of triaxial-shearing parameters for hydrate-bearing intermediate fine sediment

Evolution behaviors of triaxial shearing parameters are very important for geo-technical re- sponse analysis during the process of extracting natural gas from hydrate-bearing reservoirs. In order to explore the effects of hydrate formation/decomposition on triaxial shearing behaviors of intermediate fine sediment, natural beach sand in Qingdao, China, which was sieved from 0.1 to 0.85 mm, was used and a series of triaxial shear tests were carried out in this paper. The principle of critical state was firstly used to explain the mechanism of strain softening and/or hardening failure mode. Moreover, an empirical model was provided for axial-lateral strain and corresponding model parameters calculation. Evolution rules of critical strength parameters were analyzed prominently. The results show that failure mode of sediment is controlled by several parameters, such as effective confining pressure, hydrate saturation, etc. Different axial-lateral strain model coefficients’ effect on strain relationships are different, probing into the physical meaning of each coefficient is essential for further understanding of strain relationships. Complex geo-technical response should be faced with the progress of producing natural gas from hydrate-bearing reservoir, because of sudden change of failure pattern and formation modulus. Further compressive study on critical condition of failure pattern is needed for proposed promising hydrate-bearing reservoirs.Cited as: Li, Y., Liu, C., Liu, L., Sun, J., Liu, H., Meng, Q. Experimental study on evolution behaviors of triaxial-shearing parameters for hydrate-bearing intermediate fine sediment. Advances in Geo-Energy Research, 2018, 2(1): 43-52, doi: 10.26804/ager.2018.01.0

Impact of Industrial Structure Upgrading on Green Total Factor Productivity in the Yangtze River Economic Belt

The Yangtze River economic belt is an inland river economic belt with international influence composed of 11 provinces and municipalities in the Yangtze River Basin. This paper uses the super-efficiency model to calculate the green total factor productivity of 11 provinces and municipalities in the Yangtze River economic belt (YREB). Then we establish a model to study the impact of industrial structure upgrading, industrial structure rationalization, and environmental regulation on green total factor productivity (GTFP). Empirical analysis shows that the industrial structure upgrading and environmental regulation have a significant impact on GTFP and show regional characteristics. The more developed the economy and the higher the industrial structure, the greater the impact of upgrading and environmental regulation on GTFP. Compared with other control variables, the urbanization rate impacts GTFP, followed by regional economic development

Sustainability-Conscious Stakeholders and CSR: Evidence from IJVs of Ghana

Corporate social responsibility (CSR) activities of international joint ventures (IJVs) are considered a way for multinational corporations (MNCs) to be embedded in local communities. Existing literature generally assumes that MNC research applies to IJV, however, the research of IJV’s CSR practices is often ignored. In particular, it is unclear which stakeholders become important factors in influencing the CSR practices of IJVs in developing countries. This paper aims to examine the structural characteristics of IJVs and propose a framework for the CSR practice of IJVs established in Ghana. The theoretical standpoint of this research is built upon the stakeholder and institutional theories. Using stepwise regression, a framework is developed to better understand and identify the forces within the local market that stimulate CSR. Consumers, competitors, and local communities are considered to be the key stakeholders driving IJV CSR actions. In addition, this paper has identified significant differences in CSR practice related to the IJV’s ownership structure. This study contributes to the literature on furthering knowledge of CSR and IJVs. Furthermore, it also provides practical implications for MNCs to better integrate into the local market and the host country in order to promote the development of stakeholders related to IJVs

Multinational companies’ coordination mechanism for extending corporate social responsibility to Chinese suppliers

From the global supply chain perspective, this research explores how multinational companies (MNCs) can extend corporate social responsibility (CSR) practices to emerging countries such as China. A two-stage supply chain game model consisting of a Chinese supplier and a MNC is constructed. The study finds that an increase in the level of the Chinese supplier's CSR increases the product demand and the stakeholders' economic profits, but reduces the supplier's economic return; the product demand and the stakeholders' benefits increase along with the product green degree improvement, but the changes in the Chinese supplier's economic profits are jointly affected by the level of CSR and green production efficiency. The supply chain coordination can be achieved based on a revenue sharing contract. Finally, the effects of revenue sharing fraction and supplier's CRS level on product green degree, supplier's revenue and MNC's earnings are discussed by numerical simulation

Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model

Landslides have been frequently occurring in the high mountainous areas in China and poses serious threats to peoples’ lives and property, economic development, and national security. Detecting and monitoring quiescent or active landslides is important for predicting risks and mitigating losses. However, traditional ground survey methods, such as field investigation, GNSS, and total stations, are only suitable for field investigation at a specific site rather than identifying landslides over a large area, as they are expensive, time-consuming, and laborious. In this study, the feasibility of using SBAS-InSAR to detect landslides in the high mountainous areas along the Yunnan Myanmar border was tested first, with fifty-four IW mode Sentinel-1A ascending scenes from 12 January 2019 to 8 December 2020. Next, the Yolo deep-learning model with Gaofen-2 images captured on 5 December 2020 was tested. Finally, the two techniques were combined to achieve better performance, given each of them has intrinsic limitations on landslide detection. The experiment indicated that the combination could improve the match rate between detection results and references, which implied that the performance of landslide detection can be improved with the fusion of time series SAR images and optical images

Construction of BiOIO₃/AgIO₃ Z‑Scheme Photocatalysts for the Efficient Removal of Persistent Organic Pollutants under Natural Sunlight Illumination

The efficient removal of persistent organic pollutants (POPs) in natural waters is vital for human survival and sustainable development. Photocatalytic degradation is a feasible and cost-effective strategy to completely disintegrate POPs at room temperature. Herein, we develop a series of direct Z-scheme BiOIO3/AgIO3 hybrid photocatalysts via a facile deposition–precipitation method. Under natural sunlight irradiation, the light intensity of which is ∼40 mW/cm2, a considerable rate constant of 0.185 min–1 for photodecomposing 40 mg/L MO is obtained over 0.5 g/L Bi@Ag-5 composite photocatalyst powder, about 92.5 and 5.3 times higher than those of pristine AgIO3 and BiOIO3. The photoactivity of Bi@Ag-5 for photodecomposing MO under natural sunlight illumination surpasses most of the reported photocatalysts under Xe lamp illumination. After natural sunlight irradiation for 20 min, 95% of MO, 82% of phenol, 78% of 2,4-DCP, 54% of ofloxacin, and 88% of tetracycline hydrochloride can be photodecomposed over Bi@Ag-5. Relative to the commercial photocatalyst TiO2 (P25), Bi@Ag-5 exhibits greatly higher photoactivity for the treatment of MO–phenol–tetracycline hydrochloride mixture pollutants in the scale-up experiment of 500 mL of solution, decreasing COD, TOC, and chromaticity value by 52, 19, and 76%, respectively, after natural sunlight irradiation for 40 min. The photodegradation process and mechanism of MO have been systematically investigated and proposed. This work provides an archetype for designing efficient photocatalysts to remove POPs

Effect of ferulic acid on duodenal intestinal antioxidant capacity of Jilin White Geese.

Effect of ferulic acid on duodenal intestinal antioxidant capacity of Jilin White Geese.</p