The rapid rise of Artificial Intelligence (AI) technologies is reshaping Software Engineering (SE) practice, unlocking new opportunities while introducing human-centered challenges. Although prior research acknowledges behavioral and other non-technical factors affecting AI integration, most studies still emphasize technical concerns and offer limited insight into how teams adapt to and trust AI systems. This work proposes a Behavioral Software Engineering (BSE)-informed, human-centric framework to support SE organizations during early AI adoption. We employed a mixed-methods methodology to construct and refine the framework. A literature review of organizational change models established its theoretical foundation, and thematic analysis of interview data produced concrete, actionable steps. The resulting framework comprises nine dimensions: AI Strategy Design, AI Strategy Evaluation, Collaboration, Communication, Governance and Ethics, Leadership, Organizational Culture, Organizational Dynamics, and Up-skilling, each supported by design principles and actionable steps. To collect preliminary practitioner feedback, we conducted a survey (N=105) and two expert workshops (N=4). Survey responses show that Up-skilling (15.2%) and AI Strategy Design (15.1%) received the highest $100-method allocations, highlighting their perceived centrality in early AI initiatives. Findings suggest that organizations currently prioritize procedural aspects such as strategy design, while human-centered guardrails remain comparatively underdeveloped. Early feasibility checks of the workshops reinforced these patterns and highlighted the importance of grounding the framework in real-world practice. By identifying critical behavioral dimensions and offering actionable guidance, this contribution provides practitioners with a pragmatic roadmap for navigating the socio-technical complexity of early AI adoption and outlines future research directions for human-centric AI in SE.
Context. We investigate the binding energies of atoms to interstellar dust particles, which play a key role in their growth and evolution as well as the chemical reactions on their surfaces. Aims. We aim to compute the binding energies of abundant elements in the interstellar medium (C, N, O, Mg, Al, Si, S, Ca, Fe, and Ni) to silicate dust. Methods. We used the Geometries, Frequencies, and Non-covalent Interactions Tight Binding (GFN1-xTB) method to compute the binding energies. An FeMgSiO4 periodic surface model was amorphized using a molecular dynamics simulation. We then calculated the binding energies of each element to 81 local minima on the resulting surface. Results. A range of binding energies was found for each element. The mean of the binding energies follows the order Si (15.3 eV) > Ca (13.5 eV) > Al (12.8 eV) > C (9.2 eV) > O (8.1 eV) > N (6.4 eV) > Fe (5.9 eV) > S (5.2 eV) > Mg (2.6 eV). The probability distribution of binding energies for each element except Ca is statistically consistent with a log-normal distribution. Conclusions. In general, Si, Ca, and Al atoms have large binding energies, while the binding energies of the other atoms (C, N, O, Mg, S, Fe and Ni) are weaker. However, even the weakest computed binding energies for these elements are still far stronger than the energies associated with dust temperatures typical of the ambient interstellar medium, suggesting that silicate grains are generally stable against sublimation. We estimate sublimation temperatures for silicate grains to range from 1600 K to 3000 K depending on assumed grain size and lifetime. These binding energies on silicate dust grains, estimated from first principles for the first time, provide invaluable input to models of dust evolution and dust-catalyzed chemical reactions in the interstellar medium and grain dynamics in circumstellar environments such as asymptotic giant branch stars and protoplanetary disks.
Constraining the timing, provenance and paleogeographic relationships of Cretaceous karst bauxites in the Austroalpine realm remains challenging due to their highly weathered, polygenetic nature and the general lack of datable fossils. X-ray diffraction (XRD) data show consistent boehmite-hematite assemblages in the Alpine deposits, whereas the Transdanubian bauxites (Als & oacute;pere, Ihark & uacute;t) additionally contain gibbsite. Heavy mineral spectra are dominated by the ultrastable zircon-rutile-tourmaline assemblage, with subordinate kyanite, sillimanite and Cr-spinel pointing to contributions from medium- to high-grade metamorphic and ultramafic sources. Detrital zircon U-Pb spectra record mostly Proterozoic, Cadomian, Caledonian, Variscan, and Permian age components with regional contrasts. The Northern Calcareous Alps are dominated by Variscan ages, while Permian signatures are more prominent in the Transdanubian Range. Santonian (similar to 85 Ma) zircons at Kufstein reflect distal aeolian input from the Banatite magmatism. Zircon (U-Th)/He data reveal distinct low-temperature histories of the sources: Russbach contains a 90-75 Ma cooling signal reflecting Eoalpine metamorphic core complexes, whereas Jurassic-Early Cretaceous ages from Ihark & uacute;t indicate sourcing from Upper Austroalpine units. Statistical comparisons confirm clustering among Santonian deposits but reveal heterogeneity in Albian and Turonian sites. The data indicate that Northern Calcareous Alps and Transdanubian Range bauxites formed from geographically distinct yet lithologically similar sources, with the rising central Austroalpine nappes acting as a topographic divide. The results refine the timing of bauxitization in the Alps, showing that some deposits, such as Russbach, are younger than previously thought, and that bauxitization was diachronous and largely controlled by tectonics during the Cretaceous.
Machine learning algorithms for myoelectric pattern recognition require substantial user-specific training data, limiting broader applications of electromyography (EMG) in human-computer interfacing. Here, we present a framework for EMG-mediated motor intent decoding designed to function for new users without collecting user-specific training data. We introduce a Transformer-based architecture, termed the Spatially Aware Feature-learning Transformer (SAFT), which processes EMG time windows with variable numbers of channels from arbitrary spatial electrode configurations by combining channel-wise temporal feature extraction with learned spatial encoding of electrode positions and attention across channels. This enables training of a single model across heterogeneous EMG datasets. In the present study, large-scale supervised pretraining refers to pretraining on a pooled corpus of 29 public EMG databases comprising 506 subjects, 108 movement classes, and approximate to 9.9 million nonrest EMG windows after preprocessing. A pretrained SAFT model was fine-tuned on a held-out database and evaluated for cross-user performance. On the 3DC benchmark, the pretrained-only model achieved 28.7% balanced accuracy (vs. 10% chance), while pretrained and fine-tuned cross-user SAFT models achieved 81.8% balanced accuracy, comparable to conventional user-specific linear discriminant analysis (LDA) models (82.9%). These findings indicate the feasibility of EMG intent decoding models that work "out of the box" without end-user calibration.
The Bell Beaker phenomenon, noted for its distinctive pottery and profound social transformations, emerged in Central and Western Europe during the late Neolithic to early Bronze Age. This study investigated a Bell Beaker cemetery in Otzing, Germany, utilizing a multi-isotopic approach that incorporated strontium (87Sr/ 86Sr), sulfur (δ34S), carbon (δ13C, 14C), and nitrogen (δ15N) isotopes analyzing 16 samples. The primary objective was to assess potential differences in δ34S and 87Sr/ 86Sr isotope values to explore mobility patterns and diet among the individuals interred at the site. By analyzing these isotopic signatures, we aimed to elucidate the origins and movement of these individuals, as well as their dietary practices, which can provide a clearer picture of social connections and exchanges within the Bell Beaker population in southern Germany. This analysis contributes to our understanding of how mobility, resource use, and community interaction influenced cultural developments during this period. The results indicated a relatively stable diet and limited mobility, with notable deviations suggesting early interactions or migration, further illuminating the dynamics of Bell Beaker communities during this transformative era of cultural change.
