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AbstractUrban expansion models (UEMs) help unfold the mechanism, future pathways, and relevant repercussions of urban landscape dynamics. Although a plethora of UEMs have been developed, the field of open-access modeling platform for urban expansion simulation still presents gaps in terms of representing urban development events, regulating multilevel urban morphologies, and reflecting underlying regularities of physical urban growth. This study develops an open-access Multiengine Urban Expansion Simulation (MUSE) platform together with software that addresses some of these limitations. MUSE employs patch-based concepts and different patch generation engines to represent urban development events of distinctive types, enabling control over spatial morphology at landscape, class and patch levels. Moreover, MUSE incorporates two general regularities on urban development waves and urban diffusion and coalescence to govern physical urban expansion processes. Experiments in two cities examine MUSE’s ability in simulating realistic urban dynamics, and a series of synthetic experiments verify the operational behavior of the patch generation engines. These experiments show that MUSE can simulate a range of urban expansion types, including infill, edge growth and leapfrog expansion, and various morphologies, such as aggregated, sprawl, fragmented and compact. MUSE can empower practitioners to better project, analyze and understand complex urbanization dynamics. [ABSTRACT FROM AUTHOR]

Electrocorticography (ECoG) is a critical tool in preclinical neuroscience research for studying global network activity. However, integrating ECoG with functional magnetic resonance imaging (fMRI) has posed challenges, due to metal electrode interference with imaging quality and heating around the metallic electrodes. Here, we introduce recent advancements in ECoG grid development that utilize a polymer-thick film on an organic substrate (PTFOS). PTFOS offers notable advantages over traditional ECoG grids. Firstly, it significantly reduces imaging artifacts, ensuring minimal interference with MR image quality when overlaying brain tissue with PTFOS grids. Secondly, during a 30-min fMRI acquisition, the temperature increase associated with PTFOS grids is remarkably low, measuring only 0.4 °C. These findings suggest that utilizing ECoG with PTFOS grids has the potential to enhance the safety and efficacy of neurosurgical procedures. By providing clearer imaging results and mitigating risk factors such as excessive heating during MRI scans, PTFOS-based ECoG grids represent a promising advancement in neurosurgical technology. Furthermore, we describe a cutting-edge open-source system designed for simultaneous electrophysiology and fMRI. This system stands out due to its exceptionally low input noise levels (<0.6 V peak-to-peak), robust electromagnetic compatibility (it is suitable for use in MRI environments up to 9.4 teslas), and the inclusion of user-programmable real-time signal-processing capabilities. The open-platform software is a key feature, enabling researchers to swiftly implement and customize real-time signal-processing algorithms to meet specific experimental needs. This innovative system has been successfully utilized in several rodent EEG/fMRI studies, particularly at magnetic field strengths of 4.7 and 9.4 teslas, focusing on the somatosensory system. These studies have allowed for detailed observation of neural activity and responses within this sensory system, providing insights that are critical for advancing our understanding of neurophysiological processes. The versatility and high performance of our system make it an invaluable tool for researchers aiming to integrate and analyze complex datasets from advanced imaging and electrophysiological recordings, ultimately enhancing the depth and scope of neuroscience research. [ABSTRACT FROM AUTHOR]

This research aims to examine the effects of developments in Artificial Intelligence (AI) and open-access software on the visions of academicians. The research was conducted using the phenomenology pattern, one of the qualitative research methods, and semi-structured in-depth interviews were preferred during the data collection process. Descriptive and coding-based qualitative content analysis methods were used in the research process. Emerging themes include the use of artificial intelligence in higher education; situation analysis of the use of AI in academics (SWOT); and strategic dimension of using AI in higher education. These themes explain in detail the potential effects of AI and open-access software on the strategy and competitiveness dimensions of academics' visions. It is expected that the study will make significant contributions to understanding the changes that AI and open-access software will create in the academic world and how these technologies can be integrated into the way academics work. [ABSTRACT FROM AUTHOR]

Highlights: • The combination of biomechanical analysis and open-access Kinovea software enables the study of musculoskeletal and articular wear of experimentally reproduced tasks. • The repeated use of the right arm during indirect percussion and abrasion in lithic production could increase the changes in bone robusticity of specific muscle attachments observed in the osteoarchaeological record. • Motion ranges and postural angles analysis can generate more accurate representations of "virtual humans" in their archaeological context. Abstract: Understanding the physical impact of ancient labours has become an important experimental bioarchaeology area. Complex motion capture systems and digital tools have been used in biomechanical analysis during the reproduction of manual tasks. However, these systems are costly, so the researchers have explored alternative digital solutions. Therefore, the open-access Kinovea software was checked to confirm its reliability in characterizing the physical loads associated with particular works of ancient times. In this case study, the authors have analyzed the central postural angles and muscle chains involved in the indigenous manufacturing process of rotary stone mills, in the high mountains of Tenerife. The study included a virtual motion capture analysis carried out during the different phases of the experimental reproduction of this process; it was defined from the archaeological record of the quarries-workshops of Las Cañadas del Teide National Park (Canary Islands, Spain) volcanic millstones. The results of this study have demonstrated the software's effectiveness to virtually analyze the significant differences in posture between work techniques, observing a predominance of the use of m. biceps brachii, the m. brachioradialis, and the elbow joint during the manufacture of stone mills. On the other hand, Kinovea also has excellent potential in virtual archaeology, giving users tools to generate the average postural angles. As a result, building "virtual skeletons" in more precise work postures has been possible. This may serve as the base element to create complete body representations in virtual environments.

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Life cycle assessment (LCA) models and databases have increased in size, resolution, and complexity, requiring analysts to rely on an ever‐increasing number of uncertain model inputs. Such increased complexity calls for systematic approaches to assessing the uncertainty of the output results of LCA models and the sensitivity of LCA model outputs to the model's uncertain inputs. In this contribution, we provide a theoretical basis and present a practical software implementation that combines uncertainty analysis and moment‐independent global sensitivity analysis, which can be readily applied to full‐scale LCA models. We implemented our approach in the Activity‐Browser open source LCA software and it is made available for use in LCA studies. We demonstrate the approach and software implementation with a case study of crystalline silicon photovoltaics. [ABSTRACT FROM AUTHOR]