Abstract
Many complex systems exist in the physical world and therefore can be modeled by networks in which their nodes and edges are embedded in space. However, classical network motifs only use purely topological information and disregard other features. In this paper we introduce a novel and general subgraph abstraction that incorporates spatial information, therefore enriching its characterization power. Moreover, we describe and implement a method to compute and count our spatial subgraphs in any given network. We also provide initial experimental results by using our methodology to produce spatial fingerprints of real road networks, showcasing its discrimination power and how it captures more than just simple topology.

Abstract
This paper proposes a tool that extracts data from computational tomography (CT) scans of long bones, applies filters to allow a distinction between cortical and cancellous tissue, and converts the tissues into a three-dimensional (3D) model that can be used to generate finite element meshes. In order to identify the best segmentation technique for the problem under study, cortical, cancellous and medulla tissue segmentation was tested based on image histogram information, simple Hounsfield scale (HU) information, HU scale information with morphological operator filters, and active contour methods (active contour, random walker segmentation and findContours). These segmentations were evaluated qualitatively through a visual comparison and quantitatively through the calculation of the Dice Coefficient (DICE) and Mean-Squared Error (MSE) parameters. The developed algorithm presents a Dice higher than 0.95 and a MSE lower than 0.01 for cortical tissue segmentation, which allows it to be used as a bone characterization method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
In this paper we explain how to compute gradients of functions of the form G = 1/2 Sigma(m)(i=1) (Ey(i) - C-i )(2), which often appear in the calibration of stochastic models, using automatic adjoint differentiation and parallelization. We expand on the work of Goloubentsev and Lakshtanov and give approaches that are faster and easier to implement. We also provide an implementation of our methods and apply the technique to calibrate European options.

Abstract
Accurate and practical load modeling plays a critical role in the power system studies including stability, control, and protection. Recently, wide-area measurement systems (WAMSs) are utilized to model the static and dynamic behavior of the load consumption pattern in real-time, simultaneously. In this article, a WAMS-based load modeling method is established based on a multi-residual deep learning structure. To do so, a comprehensive and efficient load model founded on combination of impedance-current-power and induction motor (IM) is constructed at the first step. Then, a deep learning-based framework is developed to understand the time-varying and complex behavior of the composite load model (CLM). To do so, a residual convolutional neural network (ResCNN) is developed to capture the spatial features of the load at different location of the large-scale power system. Then, gated recurrent unit (GRU) is used to fully understand the temporal features from highly variant time-domain signals. It is essential to provide a balance between fast and slow variant parameters. Thus, the designed structure is implemented in a parallel manner to fulfill the balance and moreover, weighted fusion method is used to estimate the parameters, as well. Consequently, an error-based loss function is reformulated to improve the training process as well as robustness in the noisy conditions. The numerical experiments on IEEE 68-bus and Iranian 95-bus systems verify the effectiveness and robustness of the proposed load modeling approach. Furthermore, a comparative study with some relevant methods demonstrates the superiority of the proposed structure. The obtained results in the worst-case scenario show error lower than 0.055% considering noisy condition and at least 50% improvement comparing the several state-of-art methods.

Abstract
The use of Virtual Reality (VR) technology to train professionals has increased over the years due to its advantages over traditional training. This paper presents a study comparing the effectiveness of a Virtual Environment (VE) and a Real Environment (RE) designed to train firefighters. To measure the effectiveness of the environments, a new method based on participants' Heart Rate Variability (HRV) was used. This method was complemented with self-reports, in the form of questionnaires, of fatigue, stress, sense of presence, and cybersickness. An additional questionnaire was used to measure and compare knowledge transfer enabled by the environments. The results from HRV analysis indicated that participants were under physiological stress in both environments, albeit with less intensity on the VE. Regarding reported fatigue and stress, the results showed that none of the environments increased such variables. The results of knowledge transfer showed that the VE obtained a significant increase while the RE obtained a positive but non-significant increase (median values, VE: before - 4 after - 7, p = .003; RE: before - 4 after - 5, p = .375). Lastly, the results of presence and cybersickness suggested that participants experienced high overall presence and no cybersickness. Considering all results, the authors conclude that the VE provided effective training but that its effectiveness was lower than that of the RE.

Abstract
Leadership styles and human capital are important drivers of innovation processes. The way the leader interacts with the organization members can pre-empt or leverage innovation processes as leaders influence, empower and motivate other individuals in the achievement of their goals. Human capital is an important driver of innovation and competitiveness, as it will shape the uniqueness of the company as well as the process to obtain skills, capabilities, knowledge and expertise. As such, the main objectives of the paper are to analyze the impact of leadership styles on the innovation process and also to address the moderation effect of the human capital on the previous relation. Four leadership styles-autocratic, transactional, democratic, and transformational-were considered to measure their impacts on the innovation process, considering the alternative types of innovations. The 2018 Community Innovation Survey (CIS) database was used, encompassing Portuguese data, covering the 2016-2018 period, with a sample of 13702 firms. In regard to the empirical part, first, an exploratory analysis was run to better understand the connection between the leadership styles and the innovative strategies followed by an econometric estimation encompassing 28 logit models to disentangle the specific impacts of each leader on each innovation type. Evidence proves that autocratic and transactional leadership styles have a negative impact on innovation and transformational and democratic leadership impact innovation positively. Furthermore, human capital was found to moderate the relationship between leadership styles and the innovation process; i.e., under the same leadership style, the presence of additional skills leverages innovative propensity. The paper brings relevant insights for both managers and policymakers, highlighting that innovation will be accelerated if firms implement more participatory (democratic and transformational) leadership styles and also if they invest in competences to promote knowledge internalization and share. All in all, participatory leadership combined with the internal skills is proved to be an efficient combination for innovation to take place; as such, policy instruments must promote the coexistence of these two factors.