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A fully coupled level set-based topology optimization of flexible components in multibody systems is considered. Thereby, using the floating frame of reference approach, the flexible components are efficiently modeled and incorporated in multibody systems. An adjoint sensitivity analysis is utilized to obtain the gradient of the objective function with respect to a set of density-like design variables assigned to elements included in the underlying finite element model. The utilized adjoint sensitivity analysis provides a gradient, which is within numerical limits exact. In this process, the parametrization of material properties of finite elements has a significant influence on the calculated gradient, in particular for poorly filled elements. These influences are studied in detail. As an application example, the compliance minimization problem of a flexible piston rod in a transient slider-crank mechanism is considered. For this model, the influence of different parametrization methods on the obtained gradient is discussed, and a gradient strategy is proposed to overcome numerical issues included in different parametrization laws. Using this gradient strategy within a level set-based algorithm, a topology optimization of the flexible piston rod is performed. The corresponding results are then compared with optimization results provided by the method of moving asymptotes (MMA). Moreover, the computational effort of the sensitivity analysis is high and scales with the number of design variables. In this work, a gradient approximation is introduced using radial basis functions (RBFs). This helps to develop an appropriate gradient for a level set-based topology optimization of the flexible components in multibody systems, where the RBF-based design space reduction decreases the computational effort of the utilized sensitivity analysis. Finally, the efficiency gain obtained by the introduced design space reduction is demonstrated by optimization examples.

In structural optimization, the level set method is known as a well-established approach for shape and topology optimization. However, special care must be taken, if the design domains are sparsely-filled and slender. Using steepest descent-type level set methods, slender structure topology optimizations tend to instabilities and loss of structural cohesion. A sole step size control or a selection of more complex initial designs only help occasionally to overcome these issues and do not describe a universal solution. In this paper, instead of updating the level set function by solving a Hamilton–Jacobi partial differential equation, an adapted algorithm for the update of the level set function is utilized, which allows an efficient and stable topology optimization of slender structures. Including different adaptations, this algorithm replaces unacceptable designs by modifying both the pseudo-time step size and the Lagrange multiplier. Besides, adjustments are incorporated in the normal velocity formulation to avoid instabilities and achieve a smoother optimization convergence. Furthermore, adding filtering-like adaptation terms to the update scheme, even in case of very slender structures, the algorithm is able to perform topology optimization with an appropriate convergence speed. This procedure is applied for compliance minimization problems of slender structures. The stability of the optimization process is shown by 2D numerical examples. The solid isotropic material with penalization (SIMP) method is used as an alternative approach to validate the result quality of the presented method. Finally, the simple extension to 3D optimization problems is addressed, and a 3D optimization example is briefly discussed.

External exposure to skin from beta-emitter radionuclides following severe reactor accidents or nuclear testing can result in beta burning and other health complications. The skin absorbed dose coefficient (SADC) measures the energy deposition into the skin during such accidents. The U.S. Environmental Protection Agency has published several reports to measure the possible energy deposition into the skin in such accidents. However, the most recent SADC published by Federal Guidance Report (FGR) 12 was computed only at one meter above the contaminated surface. Therefore, it was necessary to develop a model to estimate the absorbed dose coefficients for skin at different heights. In this manuscript, Geant4, a Monte Carlo simulator toolkit, was used to estimate the absorbed dose coefficients from electron sources located on the soil surface with energies ranging from 0.1 to 4 MeV. The energy deposited from primary electrons, secondary electrons, and photons in a 50 µm thick layer of epidermis tissue (Basal Cells Layer) located at a depth of 50 µm from the skin surface was estimated at several discrete heights of human leg phantom. More than 40% of the total energy deposited comes from secondary electrons and photons in energy sources of 0.1 and 0.2 MeV on average, but for higher energies, this percentage is less than 1%, which indicates primary electrons are the main source of the deposited energy in the skin. Furthermore, the results showed the energy deposited into skin closer to the ground was 50–100% higher than the previously estimated doses for 1 m above the ground. The results from Geant4 showed a great correlation (R2 = 0.972) with the FGR 12 data at one meter height, and they were aligned with the published values from FGR 12, which validated the simulation results. Therefore, the calculated dose coefficients for different energy sources and different heights could be used in radiation protection measurements.

Place attachment has gained considerable attention in disaster studies, though there is little consensus on how to conceptualize or measure this construct in post-disaster environments. Many of the place attachment scales used in disaster studies come from studies of recreational or high-amenity areas, and we do not know whether or to what extent these measures translate to disaster contexts. This paper addresses gaps in our understanding of place attachment in disaster contexts by reviewing the measurement of place attachment in the literature and by presenting findings from an empirical study of place attachment in a post-disaster environment, namely a survey study of survivors ( n  = 675) of the 2013 Moore, Oklahoma, USA, tornado. Through this study, we identified four dimensions of place attachment: place identity, place dependence, neighborhood quality, and detachment. We also identified several factors that were related to dimensions of place attachment after the disaster, including social participation, exposure, and risk perception. We close by suggesting avenues for future research.

The Navajo Nation faces critical challenges in developing housing that is resilient to climate change while honoring cultural heritage. Socio‐economic disparities, limited infrastructure, and extreme environmental conditions demand innovative solutions that integrate sustainable practices with traditional Navajo values. This study critically examines the potential of smart design‐build technologies to create resilient, culturally appropriate housing tailored to the Navajo Nation’s unique needs, while interrogating the normative assumptions that often accompany Western frameworks of sustainability and innovation. This research combines a multidisciplinary literature review with a graduate‐level design studio’s explorative and applied insight. The literature review synthesizes advancements in sustainable technologies—such as off‐grid power systems, alternative materials, and participatory design methods—through a decolonial lens that challenges dominant planning paradigms. A conceptual framework was constructed to evaluate the intersection of cultural coherence, technological viability, material sustainability, socio‐environmental adaptability, and governance. Off‐grid solutions, including solar panels and wind turbines, offer clean energy alternatives, while locally sourced materials, like earth‐based and carbon‐environmentally informed additive manufacturing solutions, provide cost‐effective, low‐carbon options suitable for the arid climate. The study emphasizes participatory design, engaging local communities in developing housing solutions that align with cultural values and modern needs. By combining traditional Navajo architectural principles—such as circular forms and earthen materials—with smart technologies, the resulting designs are resilient, sustainable, and socially relevant. The design studio component enabled graduate students to explore speculative housing prototypes grounded in this framework, evaluated in dialogue with Navajo cultural liaisons and contextual constraints, thereby centering Indigenous perspectives in both process and output. The findings contribute to the broader discourse on smart, resilient infrastructure, offering insights for policymakers, designers, and funders to support localized, culturally and environmentally informed housing solutions in Indigenous communities.

The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated moving average (ARIMA), Kalman filter and support vector machines (SVR), artificial neural network (ANN), and hybrid models have been used for accurate prediction of wind speed with various forecast horizons. This study intends to incorporate all these methods to achieve a higher WSP accuracy as, thus far, hybrid wind speed predictions are mainly made by using multivariate time series data. To do so, an error correction algorithm for the probability-density-based wind speed prediction model is introduced. Moreover, a comparative analysis of the performance of each method for accurately predicting wind speed for each time step of short-term forecast horizons is performed. All the models studied are used to form the prediction model by optimizing the weight function for each time step of a forecast horizon for each model that contributed to forming the proposed hybrid prediction model. The National Oceanic and Atmospheric Administration (NOAA) and System Advisory Module (SAM) databases were used to demonstrate the accuracy of the proposed models and conduct a comparative analysis. The results of the study show the significant improvement on the performance of wind speed prediction models through the development of a proposed hybrid prediction model.

Statistical mechanics of walks defined on the spatial graphs of the city of Lubbock (10,421 nodes) and the Texas Tech University (TTU) campus pedestrian network (1466 nodes) are used for evaluating structural isolation and the integration of graph nodes, assessing their accessibility and navigability in the graph, and predicting possible graph structural modifications driving the campus evolution. We present the betweenness and closeness maps of the campus, the first passage times to the different campus areas by isotropic and anisotropic random walks, as well as the first passage times under the conditions of traffic noise. We further show the isolation and integration indices of all areas on the campus, as well as their navigability and strive scores, and energy and fugacity scores. The TTU university campus, a large pedestrian zone located close to the historical city center of Lubbock, mediates between the historical city going downhill and its runaway sprawling body.

Understanding of the recovery phase of a disaster cycle is still in its infancy. Recent major disasters such as Hurricane Sandy have revealed the inability of existing policies and planning to promptly restore infrastructure, residential properties, and commercial activities in affected communities. In this setting, a thorough grasp of housing recovery decisions can lead to effective post-disaster planning by policyholders and public officials. The objective of this research is to integrate vignette and survey design to study how family bonds affected rebuilding/relocating decisions after Hurricane Sandy. Multinomial logistic regression was used to investigate respondents’ family structures before Sandy and explore whether their relationships with family members changed after Sandy. The study also explores the effect of the aforementioned relationship and its changes on households’ plans to either rebuild/repair their homes or relocate. These results were compared to another multinomial logistic regression which was applied to examine the impact of familial bonds on respondents’ suggestions to a vignette family concerning rebuilding and relocating after a hurricane similar to Sandy. Results indicate that respondents who lived with family members before Sandy were less likely to plan for relocating than those who lived alone. A more detailed examination shows that this effect was driven by those who improved their relationships with family members; those who did not improve their family relationships were not significantly different from those who lived alone, when it came to rebuilding/relocation planning. Those who improved their relationships with family members were also less likely to suggest that the vignette family relocate. This study supports the general hypothesis that family bonds reduce the desire to relocate, and provides empirical evidence that family mechanisms are important for the rebuilding/relocating decision-making process.

Background. Role of interleukin 17A (IL-17A) in carcinogenesis and cancer growth is controversial. Although some researches support its antitumor activity, some others suggest that it promotes the growth and development of different types of cancer including skin cancer by activation of STAT3. Although the function of the cytokines such as IL-17A has been extensively studied in various types of cancer, nonmelanoma skin cancer (NMSC) has not received much attention. Therefore, the present study was aimed to investigate the serum levels of IL-17A in NMSC patients. Methods. This cross-sectional study was performed on 60 patients with basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) as well as 57 age-sex matched healthy individuals as control group. Measurement of IL-17A serum levels in both case and control groups was performed by a commercially reliable sandwich enzyme-linked immunosorbent assay (ELISA) kit. Results. In this study, we observed that IL-17A serum levels in NMSC patients were significantly higher than the control group P<0.001. Also, both BCC and SCC patients had higher levels of IL-17A in their sera in comparison to the controls (P=0.001 and P<0.001, respectively). However, there was no significant difference between SCC and BCC patients regarding serum levels of IL-17A. Conclusion. According to our results, it can be concluded that IL-17A may play a role in inducing the growth and progression of NMSC and it can be used as a therapeutic target in these patients in future.

PurposeBuilding information modeling (BIM) is a process of creating an intelligent virtual model integrating project data from design to construction and operation. BIM models enhance the process of communicating the progress of construction to stakeholders and facilitate integrated project delivery, coordination and clash detection. However, barriers within the construction industry in Ethiopia has led to slow BIM adoption in the country. The aim of this paper is to identify perceived BIM barriers, provide a platform to quantify their importance and develop a regression model to link individual's personal/professional attributes to their perception of BIM barrier.Design/methodology/approachTo address the objectives of this research, an online survey was developed to collect feedback from construction professionals in Ethiopia on 20 major adoption barriers extracted from a thorough review of literature. Relative importance index and strength of consensus metric were employed to identify the significance of barriers. This was then succeeded by performing exploratory factor analysis to determine the major constructs of BIM barriers which was then used to develop a multivariate regression model linking respondents' personal attributes to their perception of BIM barrier.FindingsResults revealed the importance of project complexity and BIM maturity level in prioritizing barriers that are more relevant under various contexts. More specifically, results indicated the following study highlights: Project complexity led to higher perceived weights for lack of appropriate physical/cloud infrastructures, and a BIM standard. Higher levels of BIM maturity signified the importance of BIM internal issues such as liability, licensing and maintenance issues among other adoption barriers. Female participants tended not to consider intangibility of BIM benefits as a major barrier towards BIM adoption compared to male participants. Age of the participants turned out to be the least important factor in their prioritization of BIM perceived adoption barriers.Originality/valueWhile many research studies have explored BIM adoption barriers in various countries around the world, none to the best of the authors' knowledge have attempted to develop a model to highlight the impact of individuals' personal/professional attributes on their perception of adoption barriers within their community which can help with prioritizing the barriers that are deemed to be more important given the characteristics of the community under study. Our result indicated the importance of BIM maturity level and project complexity in prioritizing barriers associated with BIM adoption within Ethiopia's construction industry.