The normal debonding of the ice-rock interface subjected to dynamic loads with certain frequencies threatens the safety of defense equipment deployed in the polar region. Capturing the frequency of the dynamic load that can excite the debonding behavior on the ice-rock interface cannot be achieved successfully by solving the dynamic model in time domain. The frequency domain generalized multi-symplectic method (FD-GMSM) is developed to reveal the debonding mechanism of the ice-rock interface in this paper. Based on the Hamiltonian variational principle, the ice-rock interaction dynamic model considering the viscoplasticity of the ice, the linear elasticity of the rock, as well as the spring-damping bond property of the interface is established. Then, a FD-GMSM is proposed to perform the high-fidelity simulation of the ice-rock interfacial debonding, which performs the structure-preserving iteration for the dynamic response of the interfacial debonding. Compared to the Fourier transform method (FTM) for time domain integral results, the low simulation cost and the stable multi-symplectic residual of FD-GMSM are presented in the results of the frequency domain simulation. In the simulation, the resonant frequency distribution of the ice-rock system is captured, the significance of which is that, the resonation in the system amplifies the interfacial normal stress to exceed the critical bond strength. The aforementioned results reveal the quadratic nonlinear relationship between the resonant frequency value and its order. Furthermore, the number of the interfacial debonding regions is positively correlated with the order of the resonant frequency, which implies that more interfacial normal stress peaks can be obtained when the excitation frequency equals to a higher order resonant frequency of the system. The verified results reported in this paper imply that, the proposed FD-GMSM can be used to predict the debonding behaviors of the ice-rock interface, which will further be used to guide the site selection of the important defense equipment in the polar region.
This study examines the consolidation phase of artificial intelligence (AI) research in sales between 2017 and early 2026. Using a bibliometric analysis of Scopus data, with robustness checks across Web of Science, we identify the field’s intellectual and thematic structure during its rapid expansion.Contrary to the assumption that AI in sales primarily reflects a technological efficiency paradigm, we find three structural shifts. First, predictive analytics remains foundational but is increasingly embedded in human-AI collaboration. Second, interactional and trust-related themes move from peripheral to central, indicating a shift from performance optimization toward socio-technical integration. Third, legitimacy pressures such as transparency and corporate digital responsibility emerge as organizing dimensions rather than auxiliary concerns.Applying the TOE framework to explain these shifts across technological, organizational, and environmental dimensions, this study clarifies how AI in sales evolves through layering rather than replacement. The findings provide a research agenda for AI-enabled selling’s next phase.
This article explores how to use the Experimental Organization Development (ExOD) approach to transform higher education institutions (HEIs) and applies it to ecological and social sustainability. The study investigates learning processes within universities and experimental pathways toward transformation. This undertaking matters because top-down directives at HEIs often fail to achieve lasting changes. Practically, this research explores alternatives for initiating change. Theoretically, we examine organizational learning processes for sustainability in public administration systems through a case study approach. The article compares and synthesizes findings from two cases that applied the ExOD approach to sustainability challenges at two German universities. The study collected qualitative and quantitative data to identify transformational processes and the potential of the chosen approach. The results from the two cases show broad consensus among participants that changes toward sustainability are necessary. In both cases, issues of status and hierarchies play an important role in preventing change. The ExOD approach can mitigate these issues to some degree because its participatory principle fosters acceptance and voluntary commitment to institutional transformation. Nevertheless, ExOD has limited scope and impact, and it primarily produces pilot projects and proofs of concept. Overall, this study implies that low-risk experimentation can facilitate organizational learning for institutional sustainability transitions.
In light of escalating global crises, a transformation towards more resilient and sustainable food systems is needed. Despite advantages for biodiversity, mitigation of climate change, and the resilience of food systems, the number of small-scale farms declined drastically over recent decades. Community Supported Agriculture (CSA) has emerged as a promising social innovation to ensure the viability of small-scale farms, yet it is still a niche phenomenon, and approaches are needed to support its diffusion. In four case studies in structurally weak regions of Germany, we established regional innovation ecosystems of promotors with power, knowledge, and network competencies to support the conversion of farms to CSA. In regional workshops using participatory research methods, 32-36 promotors evaluated barriers to CSA conversion and identified their capacities to reduce them. Barriers related to a lack of potential members and knowledge gaps were rated as most important, yet several capacities to address them were identified (e.g. involving civil society groups, advising on funding, lobbying to the ministry). Hardly any strategies were developed in relation to unsuitable farm structures, lock-in-effects, regulatory barriers and lack of suitable infrastructure. By acting in networks, promotors can pool competencies and reduce regional structural barriers that appear insurmountable to farmers. However, to build fruitful innovation ecosystems in rural areas, actors at different governance levels have to be involved to improve policy frameworks and political commitment.
Self-assembled monolayers (SAMs) on metal oxides play a pivotal role in tailoring surfaces for advanced hybrid and nanoscale architectures; however, their adsorption behavior is strongly governed by the interfacial chemistry of the underlying oxide. In this study, we systematically investigate the adsorption characteristics of two model acidic and basic SAM molecules with identical 12-carbon alkyl chains, tridecanoic acid (TDA) and dodecylamine (DDA), on WO3, TiO2, and NiO surfaces. These organics exhibit distinct dissociation mechanisms and surface affinities, enabling a detailed comparison across oxides with markedly different isoelectric points (IEPs). WO3 and TiO2 possess low IEPs (similar to 3 and similar to 4), whereas NiO shows a significantly higher IEP (similar to 10), providing a well-defined platform for probing the influence of surface charge on SAM adsorption. Leveraging these insights, we further fabricated TiO2-NiO composite substrates to assess the potential for area-selective SAM functionalization driven by IEP contrast. Comprehensive characterization combining contact angle measurements, XPS and ToF-SIMS was employed to quantify surface coverage and elucidate chemical and elemental compositions. The findings show that each SAM exhibits a clear preference for particular oxide surfaces based on surface charge interactions, enabling selective molecular modification of composite-oxide substrates and providing new opportunities for constructing advanced three-dimensional hybrid organic-inorganic architectures.
