Oil film mixed lubrication of heavy-load friction pairs: Theoretical modeling, solution methods, and applications

ABSTRACT The 2026 FIFA Men’s World Cup will be the first to be co-hosted by three countries, an expanded tournament featuring 48 national teams. Its organization is likely to incur diverse logistical, technological, political, and diplomatic challenges. This paper examines key issues, including coordinating security, managing crowds, and facilitating social control at football mega-events. It outlines contexts of World Cup hosting specific to the event and the host nations of Canada, Mexico and USA, before reviewing literature on mega-event securitization. The theoretical underpinning and methods are subsequently articulated, drawing on international relations, crowd management, and sports marketing scholarship. This longitudinal research also examines interview data collected across previous World Cups. The article details key political and security dimensions. It explores how social media and marketing principles can shape public perceptions and outlines significant organizational challenges facing the 2026 World Cup as evident at the time of writing.

In this work, we combine theoretical and experimental methods to study the P+(3P) + D2 → PD+ + D reaction. As a result, the absolute cross-section as a function of collision energy is obtained. Experimentally, the cross-section is measured using the guided ion beam technique (GIB), where P+ is produced by VUV photons at the SOLEIL synchrotron using PCl3 as a precursor. Theoretically, the cross-section is calculated from first principles. The potential energy surfaces of the three electronic states correlating with the P+(3P) are constructed by fitting MRCI points, and reaction dynamics are performed on each of them independently, hence neglecting couplings. The total cross-section is then obtained from the weighted contribution of each considered electronic state. Our findings show good agreement between the measured and calculated cross-sections, with a small discrepancy indicating that spin-orbit and non-adiabatic coupling, not considered in this work, may play a role in this reaction. The results hereafter presented demonstrate that the chemistry of the third-row atomic cations with molecular hydrogen is generally unfavoured, unlike their second-row homologues, thus manifesting the existence of boundaries to the applicability of the so-called chemical analogy (i.e., assuming the same chemical behaviour for elements belonging to the same group).

ABSTRACT In this review, we present current developments on modeling ultrafast transitions in solids triggered by intense, atto‐ to femtosecond pulses from X‐ray free electron lasers. This specific irradiation regime requires dedicated simulation tools based on theoretical and computational methods, involving the elements of classical and quantum physics. Our in‐house codes, XCASCADE‐3D, XTANT, and XSPIN, can follow various X‐ray‐induced transitions in irradiated solids, ranging from purely electronic transitions, changing solid's optical and magnetic properties to rapid structural changes, bringing the irradiated material into the warm‐dense‐matter, or plasma state.

Aiming at the challenge of simultaneously controlling ride comfort and wheel grounding performance for mining dump trucks, this paper proposes a multi-dimensional synergistic optimization control (MDSOC) strategy based on model predictive control (MPC) for active hydro-pneumatic suspension. First, an accurate hydro-pneumatic suspension and hinged mining truck full-vehicle-dynamics model is established, and the model accuracy is validated through actual vehicle testing. Subsequently, an MDSOC-MPC for active hydro-pneumatic suspension is constructed to minimize the mean square root of the three-axis acceleration of the body, pitch angle, roll angle, and wheel dynamic tire load. Comparative analysis is performed with traditional single-MPC longitudinal, lateral, and vertical control, and the simulation results showed: under emergency braking conditions, the root mean square (RMS) value of the pitch angle is reduced by 18.2%; under single and double-shift conditions, the RMS values of the roll angle are reduced by 40.4% and 30%, respectively; under D-class random road, the RMS values of the longitudinal, lateral, and vertical body acceleration are significantly reduced by 22%, 21.5%, and 21.2%, respectively, while the RMS values of pitch angle and roll angle are reduced by 22.5%, and 20.2%, respectively, systematically improving riding comfort, vehicle wheel contact, and driving safety. This study provides a theoretical basis and feasible engineering methods for the active control of hydro-pneumatic suspension systems in heavy engineering vehicles.

Force fields based on the Mie (λ, 6) potential, combined with theoretical methods and molecular simulations, offer a promising framework for predicting the thermophysical properties of fluids. Despite this potential, the availability of reliable combining rules for unlike interaction parameters in mixtures remains limited, thereby constraining the broader application of Mie (λ, 6)-based force fields. In this study, a new set of combining rules for the Mie (λ, 6) potential is proposed, derived by using a distortion model for the repulsive interaction and a geometric mean approximation for the attractive interaction, combined with first-order mathematical approximations. The capability of the new combining rules was first evaluated for noble gas pairs modeled with the Lennard-Jones potential, a specific case of Mie (λ, 6) potential with λ = 12, for which experimentally derived data on unlike interaction parameters are available. The results showed noticeably better agreement with experimentally derived values than those obtained using the two commonly used combining rules. Further assessment was carried out through the evaluation of Henry's law constants, phase diagrams, and excess molar volumes, which are highly sensitive to cross-interactions, for various binary mixtures modeled using the Mie chain coarse-grained force field, obtained from NVT-GEMC, NPT-GEMC, and NPT-MC simulations, respectively. For mixtures with similar Mie (λ, 6) potential parameters for the components, all of the combining rules, including the new ones, yielded comparable predictions. In contrast, for asymmetric systems with significant force field parameter disparities, the new rules yielded substantially improved accuracy relative to experimental data for all considered thermodynamic properties, whereas the commonly used combining rules exhibited poor performance with markedly larger deviations. These findings highlight the improved robustness and broader applicability of the proposed combining rules for extending Mie (λ, 6)-based force fields to complex fluid mixtures.

Abstract Reactions between atomic nuclei are measured in great detail in terrestrial laboratory experiments; transferring and extrapolating this knowledge to how the same reactions act within cosmic environments presents major challenges. Cross-disciplinary efforts are needed in view of the many nuclear reactions that govern the chemical evolution of the universe, and occur in a broad range of stellar plasma conditions that require astrophysical exploration. The variety of quiescent and explosive astrophysical environments for nuclear processes reaches from Big Bang conditions through stellar interiors to a multitude of explosive processes of and near compact stars. Since the early identification of ’processes’ associated with the buildup of elements or nucleosynthesis, new insights have been obtained on the complexity of nuclear reaction mechanisms. This article will provide an overview in nuclear processing shaped by reactions during near equilibrium conditions, cooling and freeze out times. The emergence of molecular-like nucleon configurations within nuclei incurs important features at the low energies given in stellar interiors. Multiple capture and fusion reactions are key in the overall nucleosynthesis patterns. Here we use $$^{12}$$ 12 C induced capture and fusion processes to illustrate the challenge of low-energy measurements and the challenges of using theoretical methods to extrapolate measurements towards energy regimes within cosmic sources. Slow and rapid neutron captures processes facilitate the gradual buildup of heavy elements. Particle beam experiments at accelerator facilities above and deep underground simulate stellar reactions, and new experimental facilities and methods complement these by providing short-lived isotope-separated beams and high-flux photon and neutron sources in a new generation of laboratories, with laser driven plasma facilities and particle storage rings as the latest tools for the experimenters. This is complemented by improved theoretical tools to calculate the quantum effects of nuclear reactions at the various cosmic conditions. Astronomical signatures of nuclear reactions from within cosmic sources are deduced through a growing range of observational tools. This ranges from the determination of rapidly changing light curves characterizing cosmic explosions from supernova, novae, and kilonovae, through gamma-ray lines and presolar grains to the detection of rare neutrino particles from our Sun to distant cosmic events. High resolution spectroscopy of distant stars has been expanded to objects and transient events measured in the X-ray and the gamma energy range of the electromagnetic spectrum. The analysis of vibrational behavior of stars in astro-seismology provides new tools in probing stellar interiors. The isotopic analysis of meteoritic inclusions provides detailed information about various nucleosynthesis sources, which are important tools for the understanding of complex dynamic convection and mixing processes in the interior of stars. While requiring care in interpreting observational data, to account for various biases and systematics, this variety of tools provides new opportunities and synergies. Chemical-evolution models provide a bridge through stellar-abundance archeology and have recently developed to also describe the complex dynamics during the evolution of galaxies. This article seeks to summarize the efforts to determine experimental and theoretical efforts for a better understanding of the complex mechanisms that lead to the compositional evolution of our universe, supplemented by an overview of the broad range of observational tools that have been developed to test the experimental data and the theoretical interpretation of nuclear processes in the cosmos.

Secondary organic aerosols (SOAs) are key components of aerosols, but their formation pathways remain incompletely elucidated. This study reveals a new pathway for SOA formation in water microdroplets by employing both experimental and theoretical methods. It is found that inert CO2 can react rapidly with atmospheric organic acids in water microdroplets, producing low-volatility compounds that contribute to SOA formation. Radical quenching experiments and direct observation of carbocations indicate that the carbocations generated from organic acids are the key active intermediates. Such carbocations are proposed to subsequently react with the counterion HCO3-, derived from CO2, to yield the observed products. Density functional theory calculations confirm that the carbocation mechanism is most favorable, with a reaction energy barrier of 5.24 kcal/mol. Further studies find that various organic acids can undergo similar reactions, and the reaction efficiency is positively correlated with the number and radius of halogen atoms but negatively correlated with carbon chain length. Overall, this study unveils a novel pathway for SOA formation involving ubiquitous CO2 and organic acids, offering mechanistic insights into the chemistry of atmospheric CO2 and SOAs.

With the widespread application of artificial intelligence (AI) in fields like online shopping, digital education, communication, and entertainment, it has greatly facilitated users' decision-making. However, it also brings social and ethical challenges, among which consumer trust in AI is a prominent issueconsumers are concerned about privacy protection, algorithm normal operation, and the fairness and transparency of AI outputs. This study adopts a combination of theoretical and empirical methods to explore the factors influencing consumers' trust in AI. An anonymous survey was conducted with 80 predominantly young, educated, and AI-familiar respondents. The results show that consumers' trust in AI is multi-factorial, with privacy, personalization, and reliability being the core influencing factors. Additionally, consumer spending habits (e.g., higher trust among frequent digital service users than offline-dependent groups) and AI technical attributes (e.g., authoritative professionalism and recommendation authenticity) also impact trust. Younger and more technology-educated users tend to have higher trust in AI, while the lack of privacy, personalization, or reliability will lead to low consumer trust, restricting AI's acceptance and market success. This study aims to contribute to discussions on the ethical application of AI, provide references for enterprises to develop trusted AI products, and support the formulation of user rights protection policies.

Perovskite solar cells (PSCs), which represent a new generation of photovoltaic technologies, have demonstrated great commercialization potential due to their high PCE, good stability, and low‐cost processing capability. However, defects, energy level mismatch, ion migration, and environmental instability at the interfaces of functional layers severely limit device performance and stability. This article systematically reviews recent advances in interface engineering for high‐performance PSCs, which focus on key regulation strategies at the hole transport layer (HTL)/perovskite, electron transport layer (ETL)/perovskite, and electrode interfaces. These strategies include energy level alignment optimization, defect passivation, suppression of ion migration, improvement of interfacial wettability, and construction of multifunctional composite interfaces. In addition, the roles of advanced characterization techniques, such as GIWAXS, time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS), and TRPL, and theoretical simulation methods are summarized. Finally, future development directions of interface engineering for promoting high efficiency, high stability, and commercial PSCs are discussed.

Abstract Enhancing the thermal efficiency of heat exchangers is a critical objective in modern thermal management systems, aimed at achieving energy savings, material optimization, and improved performance. The turbulent heat transfer and flow properties of graphene oxide-water nanofluid in a horizontal tube under constant heat flux circumstances are investigated in this work using both theoretical and experimental methods. To address the limited understanding of graphene oxide nanofluids under turbulent regimes, both numerical simulations using the Finite Volume Method (FVM) and experimental validation were conducted for nanoparticle concentrations of 0.025-0.1 wt% and Reynolds numbers from 5000 to 18000. Results revealed that the Nusselt number increased with both nanoparticle concentration and Reynolds number, achieving a maximum enhancement of 36.36%, while the friction factor increased by 128.57% at a nanoparticle concentration of 0.1 wt%. Despite the moderate increase in pressure drop, the overall thermal performance improvement demonstrates the potential of graphene oxide-water nanofluids as efficient coolants for advanced heat exchangers and energy systems.

Theoretical and practical methods for retrofitting industrial heat exchanger networks using genetic algorithm coupled with mathematical optimization methods

A new green corrosion inhibitor for mild steel in 1 M HCl using Erodium cicutarium (L.) leaf extract: Insights from both theoretical and experimental methods

A comprehensive review on droplet impact dynamics on liquid surfaces: Experimental and numerical investigations of momentum, mass, and heat transfer.

This article presents a study that combined theoretical and empirical methods in a longitudinal approach to develop and validate the Mechatronic Reference Model for Innovation (MRM4i), a detailed framework for designing and developing mechatronic products. The text aims to present the model in terms of cycles and revisions and to compare it with the V- and W-models for mechatronic design, as well as with previous reference models in new product development (NPD). The primary characteristic of the MRM4i is to connect traditional concepts of new product development reference models—such as phases, decisions, documents, and prototypes—with the core principles of mechatronic design, as outlined in the V-Model and W-Model. The concepts and their implementation were exemplified through a longitudinal case study at a company, in which technical artifacts for four mechatronic products were presented and discussed, and compared to V/W-Models. Validation issues are outlined, and future research directions are presented.

This paper addresses the optimization of electric vehicle (EV) charging facility configuration on highways by proposing a collaborative planning method that integrates driver anxiety psychology, mixed traffic flow dynamics, and service area queuing characteristics. By abstracting the road travel and service area replenishment processes into an integrated queuing network, a system analysis framework is constructed to characterize the coupling relationship of “facility supply, traffic assignment, and state feedback.” On this basis, a bi-level optimization model is established with the objective of minimizing the generalized total social cost. The upper level makes decisions on the coordinated quantities of fixed charging piles and mobile charging vehicles, while the lower level describes the stochastic user equilibrium behavior of drivers under the influence of real-time congestion and anxiety. To tackle the high-dimensional nonlinear nature of the model, an efficient solution algorithm based on simultaneous perturbation stochastic approximation (SPSA) is designed. A case study of the Nei-Yi Expressway demonstrates that compared with the traditional peak demand proportional allocation method, the proposed approach can better balance construction costs, operation and dispatching costs, and user travel experience under limited investment, significantly reducing waiting times and psychological anxiety costs. It provides theoretical methods and decision support for planning a resilient energy replenishment network that achieves “fixed facilities ensuring base load and mobile resources responding to peak demands.”

In this article, we provide an overview of theoretical and computational methods for studying magnetic van der Waals materials with special emphasis on first-principles computations and their application. First, we discuss the issues, such as how to properly consider electronic interactions via weak interlayer couplings, the exchange-correlation functional used for density-functional theory calculations, Hund physics, and dynamical mean-field theory methods. We then switch gears to the optical properties of magnetic van der Waals materials with specific emphasis on intriguing excitons and the interplay between spin and orbital degrees of freedom. Finally, we discuss chiral phonons and magnon-phonon interactions.

Abstract Performative prediction refers to scenarios where model predictions influence the underlying data distribution they aim to predict. A desirable property in this context is performative stability , where model predictions are already optimal for the distribution they induce, indicating converged model parameters and no need for further retraining. Achieving performative stability requires characterizing the data distribution map $$\mathcal {D}(\theta )$$ , i.e., the relationship between predictions and the resulting distribution shifts. Current studies typically quantify distribution differences using metrics like $$\mathcal {W}_1$$ distance or $$\chi ^2$$ divergence, which may not provide isometric embeddings or maintain metric equivalence in practical scenarios, limiting their applicability across various data distribution maps. Moreover, the crucial smoothness parameter $$\beta $$ in existing work is often unobtainable in performative scenarios, constraining the real-world utility of current theoretical results and methods. To address these challenges, we develop an algorithm that learns a performatively stable model for arbitrary data distribution maps without requiring the joint smoothness parameter $$\beta $$ . Specifically, we introduce a new $$\hat{\varepsilon }$$ -sensitivity measure for $$\mathcal {D}(\theta )$$ , quantified by the gradient of the loss function, which naturally and directly characterizes how distribution shifts affect the optimization of the objective function. Based on this sensitivity, we formulate a $$\gamma $$ -strongly convex loss function and optimize the deployed model accordingly, where $$\gamma $$ is derived from the defined $$\hat{\varepsilon }$$ , eliminating the need for the $$\beta $$ -joint smoothness assumption. Our theoretical results guarantee the convergence of the deployed model to performative stability. Extensive experiments on synthetic and real-world datasets with diverse data distribution maps demonstrate the superiority of our method over state-of-the-art techniques in two key aspects: prediction accuracy and performative stability.

Concrete-Filled Steel Tube (CFST) arch bridges have developed rapidly in long-span bridge construction, with spans continuously increasing and structural systems becoming more diverse. As structures become more slender, stability issues have gradually become a key factor limiting further development. In recent years, researchers at home and abroad have conducted extensive studies on the static stability, dynamic stability, and nonlinear stability during the construction stage of CFST arch bridges, forming a relatively systematic theoretical and analytical framework. Existing studies have revealed the main factors affecting the ultimate load capacity of the structure from geometric nonlinearity, material nonlinearity, and initial imperfections, and have discussed key issues such as rise-to-span ratio, arch shape parameters, and system transition during construction. Overall, research on the in-service stability of CFST arch bridges has become mature, but systematic studies on the evolution of stability during the full construction process under ultra-long spans and the coupling mechanisms of multiple factors are still relatively limited. Further development of theoretical and analytical methods for construction-stage stability is of great significance for promoting the advancement of CFST arch bridges to larger spans.

Teacher education programs have taken the Truth and Reconciliation Commission’s Calls to Action seriously (TRC, 2015), with many adding a mandatory Indigenous education course to their programs as well as weaving Indigenous knowledges into other courses. Still, there appears to be a disconnect between learning about Indigenous Peoples and integrating this knowledge into the classroom. This study examines the self-reported efficacy of pre-service teachers’ understandings and application of Indigenous knowledges at various stages in their program. Drawing upon a decolonizing theoretical approach and mixed methods methodology, data from a survey and personal autoethnographies are utilized to shed light upon the disconnect. Findings indicate that while the mandatory Indigenous education course led to an increase in efficacy, the explicit weaving of Indigenous knowledges in other courses needs to be made visible. The lack of time to both decolonize pre-service teacher thinking and consider how this knowledge can be applied in the classroom, points to the necessity of an additional Indigenous education course as part of teacher education programming.