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As social networks have developed to be a ubiquitous platform of public opinion spreading, it becomes more and more crucial for maintaining social security and stability by accurately predicting various trends of public opinion dissemination in social networks. Considering the fact that the dissemination of online public opinion is a dynamic process full of uncertainty and complexity, this study establishes a novel conformable fractional discrete grey model with linear time-varying parameters, namely the CFTDGM(1,1) model, for more accurate prediction of online public opinion trends.

First, the conformable fractional accumulation and difference operators are employed to build the CFTDGM(1,1) model for enhancing the traditional integer-order discrete grey model with linear time-varying parameters. Then, to improve forecasting accuracy, a base value correction term is introduced to optimize the iterative base value of the CFTDGM(1,1) model. Next, the differential evolution algorithm is selected to determine the optimal order of the proposed model through a comparison with the whale optimization algorithm and the particle swarm optimization algorithm. The least squares method is utilized to estimate the parameter values of the CFTDGM(1,1) model. In addition, the effectiveness of the CFTDGM(1,1) model is tested through a public opinion event about “IG team winning the championship”. Finally, we conduct empirical analysis on two hot online public opinion events regarding “Chengdu toddler mauled by Rottweiler” and “Mayday band suspected of lip-syncing,” to further assess the prediction ability and applicability of the CFTDGM(1,1) model by comparison with seven other existing grey models.

The test case and empirical analysis on two recent hot events reveal that the CFTDGM(1,1) model outperforms most of the existing grey models in terms of prediction performance. Therefore, the CFTDGM(1,1) model is chosen to forecast the development trends of these two hot events. The prediction results indicate that public attention to both events will decline slowly over the next three days.

A conformable fractional discrete grey model is proposed with the help of conformable fractional operators and a base value correction term to improve the traditional discrete grey model. The test case and empirical analysis on two recent hot events indicate that this novel model has higher accuracy and feasibility in online public opinion trend prediction.

This paper explores ontological assumptions of disasters and introduces some concepts from Chinese disaster scholarship. The authors suggest an approach to explore and engage with different ontologies of disaster without direct comparison, that can further interdisciplinary and cross-cultural collaboration.

By reviewing the academic literature and focussing on two recent key translational texts by Chinese scholars, the authors show what can be revealed about ontology and the potential influence on thinking about human-environment interactions and disaster risk reduction (DRR) policy.

In Chinese disaster studies, the goal of a “harmonious human-environment relationship” is a foundational concept. There is a clear hierarchical and ontological distinction between humans and the natural ecological system viewed as an integrated whole, with underlying rules that can be discovered by scientific research to enable management of a harmonious relationship.

The authors suggest a practical way to begin with the following questions: What is the societal goal/aim? What is nature? What is society? How do these interact to create disasters? And what are the implications for DRR research and practice? The authors also demonstrate the importance of probing and understanding the underlying ontologies that are the foundation for theory, which in turn is the foundation for policy and action.

Identification of ontological differences in interdisciplinary and cross-cultural research collaborations and working across these boundaries is challenging and rarely questioned. Yet, as demonstrated here, considering ontological assumptions of the causes of disaster, within and across cultures and disciplines, is essential for collaboration and further research.

The purpose of this study is to advance the computational modeling of liquid splashing dynamics, while balancing simulation accuracy and computational efficiency, a duality often compromised in high-fidelity fluid dynamics simulations.

This study introduces the fluid efficient graph neural network simulator (FEGNS), an innovative framework that integrates an adaptive filtering layer and aggregator fusion strategy within a graph neural network architecture. FEGNS is designed to directly learn from extensive liquid splash data sets, capturing the intricate dynamics and intrinsically complex interactions.

FEGNS achieves a remarkable 30.3% improvement in simulation accuracy over traditional methods, coupled with a 51.6% enhancement in computational speed. It exhibits robust generalization capabilities across diverse materials, enabling realistic simulations of droplet effects. Comparative analyses and empirical validations demonstrate FEGNS’s superior performance against existing benchmark models.

The originality of FEGNS lies in its adaptive filtering layer, which independently adjusts filtering weights per node, and a novel aggregator fusion strategy that enriches the network’s expressive power by combining multiple aggregation functions. To facilitate further research and practical deployment, the FEGNS model has been made accessible on GitHub (https://github.com/nanjinyao/FEGNS/tree/main).

The main objective of this work is to develop a boundary treatment in lattice Boltzmann method (LBM) for curved and moving boundaries and using this treatment to study numerically the flow around a rotating isothermal circular cylinder with/without heat transfer.

A multi‐distribution function thermal LBM model is used to simulate the flow and heat transfer around a rotating circular cylinder. To deal with the calculations on the surface of cylinder, a novel boundary treatment is developed.

The results of simulation for flow and heat transfer around a rotating cylinder including the evolution with time of velocity field, and the lift and drag coefficients are compared with those of previous theoretical, experimental and numerical studies. Excellent agreements show that present LBM including boundary treatment can achieve accurate results of flow and heat transfer. In addition, the effects of the peripheral‐to‐translating‐speed ratio, Reynolds number and Prandtl number on evolution of velocity and temperature fields around the cylinder are tested.

There is a large class of industrial processes which involve the motion of fluid passing rotating isothermal circular cylinders with/without heat transfer. Operations ranging from paper and textile making machines to glass and plastics processes are a few examples.

A strategy for LBM to treat curved and moving boundary with the second‐order accuracy for both velocity and temperature fields is presented. This kind of boundary treatment is very easy to implement and costs less in computational time.

The purpose of this paper is to improve the performance and quality of Ti-6Al-4V fabricated by laser powder bed fusion.

Single-track experiments were conducted during the fabrication process to obtain the single tracks with excellent wettability to narrow the process parameter window. The effects of process parameters on the build surface, cross-section, relative density, defects, surface roughness, microstructure and mechanical properties of the parts were analyzed through multilayer fabrication experiments and surface optimization experiments.

The point distance has the greatest influence on the build surface of the fabricated parts, and the unmelted defects can be eliminated when the point distance is 35 µm. The relative density of the fabricated parts decreased with the increase of the point distance, and the hatch spacing has different characteristics with respect to the relative density of the fabricated parts under different laser powers. It was observed that the most of experimental groups with higher relative densities than 99%, and the highest density could reach 99.99%. The surface roughness can be reduced to less than 10 µm through remelting optimization.

The research results can provide theoretical support for scientific researchers and data support for engineers.

Within a gated community, management of common property presents great challenges. Therefore, the diagnostic social ecological system (SES) framework proposed by Elinor Ostrom providing a holistic understanding of complex collective action problems in terms of management of commons is used to investigate key institutional-social-ecological factors influencing collective action in the context of gated communities.

In this paper, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was used to systematically screen and review the relevant literature from 2000 to 2022, where 28 papers were selected for further analysis.

The study systematically identifies and categorises a series of variables related to self-organizing management in the gated community, and consequently a SES-based gated community management framework is developed. Based on the conceptual framework, the paper discusses logical interrelationships of institutional-social-ecological factors and their impacts on collective action performance of gated communities.

Apart from requiring empirical validation, the conceptual SES-based gated community management framework is certainly subject to continuous improvement in terms of refinement and addition of other potential determinants of gated community collective action.

Not only the review paper provides updates on the latest gated-community collective action research, it also contributes theoretically by conceptualizing the SES framework and its institutional–social–ecological design principles in gated community management. Studying these factors should also be of practical significance because the findings ultimately offer policy insights and management strategies that help policy-makers, property developers and local communities to govern such neighbourhood common resources efficiently and sustainably.

dding dopants to a powder bed could be a cost-effective method for spatially varying the material properties in laser powder bed fusion (LPBF) or for evaluating new materials and processing relationships. However, these additions may impact the selection of processing parameters. Furthermore, these impacts may be different when depositing nanoparticles into the powder bed than when the same composition is incorporated into the powder particles as by ball milling of powders or mixing similarly sized powders. This study aims to measure the changes in the single bead characteristics with laser power, laser scan speed, laser spot size and quantity of zirconia nanoparticle dopant added to SS 316 L powder.

A zirconia slurry was inkjet-printed into a single layer of 316 SS powder and dried. Single bead experiments were conducted on the composite powder. The line type (continuous vs balling) and the melt pool geometry were compared at various levels of zirconia doping.

The balling regime expands dramatically with the zirconia dopant to both higher and lower energy density values indicating the presence of multiple physical mechanisms that influence the resulting melt track morphology. However, the energy density required for continuous tracks was not impacted as significantly by zirconia addition. These results suggest that the addition of dopants may alter the process parameter ranges suitable for the fabrication of high-quality parts.

This work provides new insight into the potential impact of material doping on the ranges of energy density values that form continuous lines in single bead tests. It also illustrates a potential method for spatially varying material composition for process development or even part optimization in powder bed fusion without producing a mixed powder that cannot be recycled.

The main purpose of this paper is to develop a novel thermal lattice Boltzmann method (LBM) based on finite volume (FV) formulation. Validation of the suggested formulation is performed by simulating plane Poiseuille, backward-facing step and flow over circular cylinder.

For this purpose, a cell-centered scheme is used to discretize the convection operator and the double distribution function model is applied to describe the temperature field. To enhance stability, weighting factors are defined as flux correctors on a D2Q9 lattice.

The introduction of pressure-temperature-dependent flux-control coefficients in the streaming operator, in conjunction with suitable boundary conditions, is shown to result in enhanced numerical stability of the scheme. In all cases, excellent agreement with the existing literature is found and shows that the presented method is a promising scheme in simulating thermo-hydrodynamic phenomena.

A stable and accurate FV formulation of the thermal DDF-LBM is presented.

This review provides an overview of recent advances in electrochemical sensors for analyte detection in saliva, highlighting their potential applications in diagnostics and health care. The purpose of this paper is to summarize the current state of the field, identify challenges and limitations and discuss future prospects for the development of saliva-based electrochemical sensors.

The paper reviews relevant literature and research articles to examine the latest developments in electrochemical sensing technologies for saliva analysis. It explores the use of various electrode materials, including carbon nanomaterial, metal nanoparticles and conducting polymers, as well as the integration of microfluidics, lab-on-a-chip (LOC) devices and wearable/implantable technologies. The design and fabrication methodologies used in these sensors are discussed, along with sample preparation techniques and biorecognition elements for enhancing sensor performance.

Electrochemical sensors for salivary analyte detection have demonstrated excellent potential for noninvasive, rapid and cost-effective diagnostics. Recent advancements have resulted in improved sensor selectivity, stability, sensitivity and compatibility with complex saliva samples. Integration with microfluidics and LOC technologies has shown promise in enhancing sensor efficiency and accuracy. In addition, wearable and implantable sensors enable continuous, real-time monitoring of salivary analytes, opening new avenues for personalized health care and disease management.

This review presents an up-to-date overview of electrochemical sensors for analyte detection in saliva, offering insights into their design, fabrication and performance. It highlights the originality and value of integrating electrochemical sensing with microfluidics, wearable/implantable technologies and point-of-care testing platforms. The review also identifies challenges and limitations, such as interference from other saliva components and the need for improved stability and reproducibility. Future prospects include the development of novel microfluidic devices, advanced materials and user-friendly diagnostic devices to unlock the full potential of saliva-based electrochemical sensing in clinical practice.