An international relations quantitative evaluation model (IRQEM) based on an integrated method.

International relations (IR) have great uncertainty and instability. Bad IR or conflicts will bring about heavy economic losses and widespread social unrest domestically and internationally. The accurate prediction for bilateral relations can support decision making for timely responses, which will be used to find ways to maintain development in the complex international situation. An international relations quantitative evaluation model (IRQEM) is proposed by integrating a variety of research models and methods like the interpretative structural modeling method (ISM), Bayesian network (BN) model, the Bayesian search (BS), and the expectation-maximization (EM) algorithm, which is novel for IR research. Factors from several different fields are identified as BN nodes. Each node is assigned different state values. The hierarchical structure of these BN nodes is obtained by ISM. The data collection of 192 cases is used to construct the BN model by GeNIe 4.0. The IRQEM can be used to evaluate the influence of emergencies on IR. The critical factors of IR also can be explored through our proposed model. Results show that the prediction of bilateral relations under emergencies can be realized by updating the indicator set when emergencies occur. The capability to anticipate threats of IR changes is advanced by optimizing the reporting information of IR forecasting through a combination of qualitative and quantitative methods, charts, and texts. Relevant analysis results can provide support for national security decision making.

The petroleum ether extracts of Chloranthus fortunei(A. Gray) Solms-Laub.with bioactivities: A rising source in HCC drug treatment.

ETHNOPHARMACOLOGICAL RELEVANCE: Hepatocellular Carcinoma (HCC) is an aggressive killer worldwide with high incidence and mortality. The herb Chloranthus fortunei (A. Gray) Solms-Laub is known as "Si Ji Feng" and is classified as a Feng-type medicine in classic Yao medicines. According to Yao's medical beliefs, Chloranthus fortunei has the functions of dispelling Feng, regulating qi, detoxifying, promoting blood circulation, etc. Folk uses its decoctions to treat stagnant liver conditions, such as liver abscesses, cirrhosis, hepatitis, and liver cancer. However, the bioactivity and mechanisms of Chloranthus fortunei extract against HCC have not been reported. AIM OF THE STUDY: To investigate the anti-HCC bioactivity and potential mechanism of the extract of Chloranthus fortunei (CFS). MATERIALS AND METHODS: Using 70% ethanol for reflux extraction of CFS resulted in the CFS ethanol extract, followed by sequential extractions with petroleum ether, chloroform, ethyl acetate, and n-butanol, yielding four fractions. The CCK-8 assay was utilized to examine the cytotoxic effects of 4 fractions on MHCC97-H and HepG2 cells, exploring the most effective component, namely petroleum ether extracts of CFS (PECFS). The major active ingredients of PECFS were identified using LC/MS technology, and the impact on cell proliferation and apoptosis in HCC cells was studied. The key genes and proteins in the pathway were validated using RT-PCR and Western blotting. BALB/c nude mice were chosen for tumor xenotransplantation and PECFS therapy. hinders the proliferation of HCC cells and promotes apoptosis. RESULTS: Among the four fractions, it was found that PECFS have the highest antiproliferative activity against MHCC97-H and HepG2 cells (IC50 = 13.86, 10.55 µg/mL), with sesquiterpene compounds being the primary active constituents. The antiproliferative activity of PECFS on HCC cells was linked to the inhibition of cell cloning, invasion, and metastasis abilities, as well as the arrest of the cell cycle at the G2/M phase. Additionally, exerts pro-apoptotic effects on HCC cells by upregulating the pro-apoptotic protein Bax, downregulating the anti-apoptotic protein Bcl-2, and activating the expression of the Caspase family. Moreover, protein and m-RNA expression data showed that PECFS inhibits HCC cell proliferation and promotes apoptosis by regulating the PI3K/AKT/mTOR pathway. Besides, after PECFS treatment, tumor growth in nude mice was suppressed. CONCLUSION: PECFS can inhibit the viability of HCC cells by acting on the PI3K/AKT/mTOR pathway, demonstrating anti-tumor potential. This study's findings suggest that PECFS may represent a promising source of novel agents for liver cancer treatment, providing scientific evidence for the traditional application of CFS in treating HCC.

Video game addiction in psychiatric adolescent population: A hospital-based study on the role of individualism from South China.

BACKGROUND: For decades, video game-related behaviors have been investigated in different psychologic research, much of whose attention has been paid to video game addiction (VGA), while the differences between VGA and social media addiction (SMA) should have deserved more attention. In addition to detecting common risk factors of VGA, one core question is whether social inclination (individualism or collectivism) matters. OBJECT: The objectives of this study were to clarify the prevalence of VGA and SMA,, identify the influencing factors of VGA, and clarify the relationships between VGA and adolescents' individualism-collectivism inclination. METHOD: The survey was conducted among 110 adolescent psychiatric patients. For each interviewee, psychological scales were filled face to face. Path analysis was used to examine the causation structure of the childhood trauma-related symptoms. RESULT: The prevalence of VGA was 40.9% (45 out of 110), and it was 41.8% for SMA (46 out of 110); childhood trauma, social media addiction, the individualistic inclination, and the rate of homosexuality were observed to be independent indicators for video game addiction (r2  = 0.46). CONCLUSION: Psychological counseling on patients' internet-related behaviors may focus on the individualistic personality and possible childhood trauma, which are two important risk factors of video game addiction. It is recommended to distinguish between video game addiction and social addiction in clinical practice.

A national risk analysis model (NRAM) for the assessment of COVID-19 epidemic.

COVID-19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision-making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision-making in the context of COVID-19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID-19 at the national level.

An open time-series simulated dataset covering various accidents for nuclear power plants.

Nuclear energy plays an important role in global energy supply, especially as a key low-carbon source of power. However, safe operation is very critical in nuclear power plants (NPPs). Given the significant impact of human-caused errors on three serious nuclear accidents in history, artificial intelligence (AI) has increasingly been used in assisting operators with regard to making various decisions. In particular, data-driven AI algorithms have been used to identify the presence of accidents and their root causes. However, there is a lack of an open NPP accident dataset for measuring the performance of various algorithms, which is very challenging. This paper presents a first-of-its-kind open dataset created using PCTRAN, a pre-developed and widely used simulator for NPPs. The dataset, namely nuclear power plant accident data (NPPAD), basically covers the common types of accidents in typical pressurised water reactor NPPs, and it contains time-series data on the status or actions of various subsystems, accident types, and severity information. Moreover, the dataset incorporates other simulation data (e.g., radionuclide data) for conducting research beyond accident diagnosis.

A Universal Lab-on-Salt-Particle Approach to 2D Single-Layer Ordered Mesoporous Materials.

The advantages of existing ordered mesoporous materials have not yet been fully realized, due to their limited accessibility of in-pore surface and long mass-diffusion length. A general, controllable, and scalable synthesis of a family of two-dimensional (2D) single-layer ordered mesoporous materials (SOMMs) with completely exposed mesopore channels, significantly improved mass diffusion, and diverse framework composition is reported here. The SOMMs are synthesized via a surface-limited cooperative assembly (SLCA) on water-removable substrates of inorganic salts (e.g., NaCl), combined with vacuum filtration. As a proof of concept, the obtained CeO2 -based SOMMs show superior catalytic performance in CO oxidation with high conversion efficiency, ≈33 times higher than that of conventional bulk mesoporous CeO2 . This SLCA is a promising approach for developing next-generation porous materials for various applications.

A General and Straightforward Route to Noble Metal-Decorated Mesoporous Transition-Metal Oxides with Enhanced Gas Sensing Performance.

Controllable and efficient synthesis of noble metal/transition-metal oxide (TMO) composites with tailored nanostructures and precise components is essential for their application. Herein, a general mercaptosilane-assisted one-pot coassembly approach is developed to synthesize ordered mesoporous TMOs with agglomerated-free noble metal nanoparticles, including Au/WO3 , Au/TiO2 , Au/NbOx , and Pt/WO3 . 3-mercaptopropyl trimethoxysilane is applied as a bridge agent to cohydrolyze with metal oxide precursors by alkoxysilane moieties and interact with the noble metal source (e.g., HAuCl4 and H2 PtCl4 ) by mercapto (SH) groups, resulting in coassembly with poly(ethylene oxide)-b-polystyrene. The noble metal decorated TMO materials exhibit highly ordered mesoporous structure, large pore size (≈14-20 nm), high specific surface area (61-138 m2 g-1 ), and highly dispersed noble metal (e.g., Au and Pt) nanoparticles. In the system of Au/WO3 , in situ generated SiO2 incorporation not only enhances their thermal stability but also induces the formation of ε-phase WO3 promoting gas sensing performance. Owning to its specific compositions and structure, the gas sensor based on Au/WO3 materials possess enhanced ethanol sensing performance with a good response (Rair /Rgas = 36-50 ppm of ethanol), high selectivity, and excellent low-concentration detection capability (down to 50 ppb) at low working temperature (200 °C).

Rational Synthesis and Gas Sensing Performance of Ordered Mesoporous Semiconducting WO3/NiO Composites.

Semiconducting metal oxides have attracted increasing attention in various fields due to their intrinsic properties. In this study, a facile solvent evaporation-induced multicomponent co-assembly approach coupled with a carbon-supported crystallization strategy is employed to controllably synthesize crystalline mesoporous nickel oxide-doped tungsten oxides in an acidic THF/H2O solution with poly(ethylene oxide)-b-polystyrene diblock copolymers (PEO-b-PS) as the structure-directing agent, tungsten(VI) chlorides as WO3 precursors, and Ni(AcAc)2 as the NiO precursor. The obtained materials possess a face-centered cubic mesoporous structure, large pore size (∼30 nm), high surface area (30-50 m2 g-1), large pore volume (0.15-0.19 cm3 g-1), and ultralarge pore windows (12-16 nm) connecting adjacent mesopores, and the mesoporous WO3 framework was decorated by ultrafine NiO nanocrystals. Due to their well-connected porous structure and high surface areas with rich WO3-NiO interfaces, the composite materials exhibit superior gas sensing performance with an ultrafast response (∼4 s), high sensitivity (Ra/Rg = 58 ± 5.1), and selectivity to 50 ppm H2S at a relatively low working temperature (250 °C). The chemical mechanism study reveals complicated surface reactions of WO3/NiO-based gas sensors, and SO2, WS2, and NiS intermediates were found to be generated during the gas sensing process.

Ordered Mesoporous Tin Oxide Semiconductors with Large Pores and Crystallized Walls for High-Performance Gas Sensing.

Owing to their distinct chemical and physical properties, mesoporous metal oxide semiconductors have shown great application potential in catalysis, electrochemistry, energy conversion, and energy storage. In this study, mesoporous crystalline SnO2 materials have been synthesized through an evaporation-induced co-assembly (EICA) method using poly(ethylene oxide)-b-polystyrene diblock copolymers as the template, tin chlorides as the tin sources, and tetrahydrofuran as the solvent. By controlling conditions of the co-assembly process and employing a carbon-supported thermal treatment strategy, highly ordered mesoporous SnO2 materials with a hexagonal mesostructure (space group P63/mmc) and crystalline pore walls can be obtained. The mesoporous SnO2 is employed for fabricating gas sensor nanodevices which exhibit an excellent sensing performance toward H2S with high sensitivity (170, 50 ppm) and superior stability, owing to its high surface area (98 m2/g), well-connected mesopores of ca. 18.0 nm, and high density of active sites in the crystalline pore walls. The chemical mechanism study reveals that both SO2 and SnS2 are generated during the gas sensing process on the SnO2-based sensors.