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This paper explores social acceptance of different wood-based energy applications in the Great Lakes region in the Upper Midwest, USA. Specifically, the paper examines attitudes and perceptions towards residential wood burning, commercial-scale wood boilers for heat, combined heat and power (CHP) facilities, and industrial power generation in Michigan. In 2021, we conducted a state-wide survey (n = 207) and 30 semi-structured interviews with individuals representing different stakeholder perspectives, including regulatory agencies, biomass industry leaders, non-governmental organizations and scientists who have shaped biomass debates, forest products workers, and Michigan residents. Results suggest that the depth of an individual's knowledge about different types of wood energy applications generally led to more positive attitudes, particularly for smaller-scale uses of wood for heat. Despite perceived environmental, economic, and human health risks of using wood for different energy applications, across all stakeholder groups, 70% of respondents perceived environmental benefits, 61% perceived economic benefits, and 57% perceived health benefits. Changing economic and environmental conditions in different parts of the state contributed to people's attitudes toward different forms of wood energy and their perceptions of associated risks and benefits. A key recommendation of this study is that policy-makers and energy developers seek to understand the complex factors that influence social acceptance when planning for the adoption of new types of wood-based bioenergy technologies. 

This paper explores social acceptance of different wood-based energy applications in the Great Lakes region in the Upper Midwest, USA. Specifically, the paper examines attitudes and perceptions towards residential wood burning, commercial-scale wood boilers for heat, combined heat and power (CHP) facilities, and industrial power generation in Michigan. In 2021, we conducted a state-wide survey (n = 207) and 30 semi-structured interviews with individuals representing different stakeholder perspectives, including regulatory agencies, biomass industry leaders, non-governmental organizations and scientists who have shaped biomass debates, forest products workers, and Michigan residents. Results suggest that the depth of an individual's knowledge about different types of wood energy applications generally led to more positive attitudes, particularly for smaller-scale uses of wood for heat. Despite perceived environmental, economic, and human health risks of using wood for different energy applications, across all stakeholder groups, 70% of respondents perceived environmental benefits, 61% perceived economic benefits, and 57% perceived health benefits. Changing economic and environmental conditions in different parts of the state contributed to people's attitudes toward different forms of wood energy and their perceptions of associated risks and benefits. A key recommendation of this study is that policy-makers and energy developers seek to understand the complex factors that influence social acceptance when planning for the adoption of new types of wood-based bioenergy technologies. 

Autonomous vehicles (AV), one of the transportation industry’s biggest innovations of the past few decades, bring the promise of safer roads and significantly lower vehicle-related fatalities. While many studies have found largely positive consumer opinions regarding operating and owning such a vehicle, older adults (55+) tend to express concerns about the safety and operational risks of a vehicle with unknown capabilities. To investigate how older adults and AVs may interact, we conducted an improv- style enactment-based participatory design pilot study. We found that initial concerns about trust and safety can be diminished through training and repetitive successful vehicle operation. Additionally, our participants provided insights into the AV design considerations, needs, and interactions for older adults. These findings add to the collective body of autonomous vehicle research by demonstrating that the needs of this growing population, who may benefit significantly from access to AVs, should be considered by manufacturers. 

Fully autonomous or “self-driving” vehicles represent a potentially transformative shift in personal mobility. Given the emerging nature of self-driving vehicle technologies, however, guidance for accessible implementation is limited. It has been suggested that the result is that much of this emerging technology is being designed in a manner that will render it largely inaccessible for persons with disabilities. Borrowing from object-oriented programming we identify common barriers to accessibility which we argue are de facto antipatterns in the design of accessible self-driving vehicle technology. Drawing from the literature and our own studies we describe design commonalities (anti-patterns) which we argue may pose problems for persons with disabilities. We believe that this work may provide direction for designers regarding how to better support the needs of persons with a range of disabilities in the self-driving vehicle context. 

Self-driving vehicles have been described as one of the most significant advances in personal mobility of the past century. By minimizing the role of arguably error-prone human drivers, self-driving vehicles are heralded for improving traffic safety. Primarily driven by the technology’s potential impact, there is a rapidly evolving body of literature focused on consumer preferences. Missing, we argue, are studies that explore the needs and design preferences of older adults (60+). This is a significant knowledge gap, given the disproportionate impact that self-driving vehicles may have concerning personal mobility for older adults who are unable or unwilling to drive. Within this paper, we explore the design and interaction preferences of older adults through a series of enactment-based design sessions. This work contributes insights into the needs of older adults, which may prove critical if equal access to emerging self-driving technologies are to be realized. 

Self-driving vehicles are the latest innovation in improving personal mobility and road safety by removing arguably error-prone humans from driving-related tasks. Such advances can prove especially beneficial for people who are blind or have low vision who cannot legally operate conventional motor vehicles. Missing from the related literature, we argue, are studies that describe strategies for vehicle design for these persons. We present a case study of the participatory design of a prototype for a self-driving vehicle human-machine interface (HMI) for a graduate-level course on inclusive design and accessible technology. We reflect on the process of working alongside a co-designer, a person with a visual disability, to identify user needs, define design ideas, and produce a low-fidelity prototype for the HMI. This paper may benefit researchers interested in using a similar approach for designing accessible autonomous vehicle technology. INTRODUCTION The rise of autonomous vehicles (AVs) may prove to be one of the most significant innovations in personal mobility of the past century. Advances in automated vehicle technology and advanced driver assistance systems (ADAS) specifically, may have a significant impact on road safety and a reduction in vehicle accidents (Brinkley et al., 2017; Dearen, 2018). According to the Department of Transportation (DoT), automated vehicles could help reduce road accidents caused by human error by as much as 94% (SAE International, n.d.). In addition to reducing traffic accidents and saving lives and property, autonomous vehicles may also prove to be of significant value to persons who cannot otherwise operate conventional motor vehicles. AVs may provide the necessary mobility, for instance, to help create new employment opportunities for nearly 40 million Americans with disabilities (Claypool et al., 2017; Guiding Eyes for the Blind, 2019), for instance. Advocates for the visually impaired specifically have expressed how “transformative” this technology can be for those who are blind or have significant low vision (Winter, 2015); persons who cannot otherwise legally operate a motor vehicle. While autonomous vehicles have the potential to break down transportation 

Autonomous vehicles (AVs) are closer to becoming a reality in changing the landscape of commercial and personal transportation. The launch of these vehicles come with the promise of improved road safety, reduced traffic fatalities, and enhanced mobility. However, there are questions as to whether the design of AVs will meet the needs of everyone, including people with disabilities and older adults. We argue that there exists no conceptual model that guide sthe inclusive design of autonomous vehicles to benefit all intended users. This paper proposes such a model, called the User Transportation-Activity Technology (UTT) model, which supports the inclusive design of AVs. We present a review of current models of assistive technology design and their drawbacks followed by an introduction of the UTT model and its application in AV design. This paper may benefit researchers, designers, and developers of autonomous vehicles interested in addressing accessible design issues in such vehicles. 

Poor computing identity contributes to a deficit in performance of Black American computing students compared to their other ethnic counterparts. As the demand for computing careers continues to increase, it is imperative to discover novel ways to improve the deficit of minority college and career performance in computing. Black American social media influencers that produce video log (vlog) commentary content were investigated on the YouTube platform to determine the influence they have on improving computing identity of minority students through computing and academic advisement. This exploratory study consists of two studies: (1) a synthesis of vlog commentary college and career advisement videos, coding for the quality of advisement, usability, and user experience, and (2) an advisor effectiveness and user experience survey using a selected minority social media influencer who provides computing college and career advisement. Findings suggest YouTube influencers were effective (over a series of measures), particularly for beginners in the computing field. As minorities heavily participate in and enjoy the use of YouTube and social media, computing advisement influencers on social media allow for supplemental advisement at a wide scale. Future studies intend to further experiment minority computing advisement through social media over a long term and at varying levels of interaction. Findings help validate existing academic advisement influencers and prompts new approaches for organizations and industries to engage with prospective Black American students and members for preparation and training purposes. 

Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the Universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT) and of the auto- and cross-correlation of galaxy position and weak lensing measurements ( $3\times 2\mathrm{pt}$) in the Dark Energy Survey (DES). We consider the cosmological correlation between the different tracers and we account for the systematic uncertainties that are shared between the large-scale lensing correlation functions and the small-scale lensing-based cluster mass calibration. Marginalized over the remaining $\mathrm{\Lambda }$cold dark matter ( $\mathrm{\Lambda }\mathrm{CDM}$) parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure ${\mathrm{\Omega }}_{\mathrm{m}}=0.300\pm 0.017$and ${\sigma }_8=0.797\pm 0.026$. Compared to constraints from primary cosmic microwave background (CMB) anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 ( $1.2\sigma$) for the two-parameter difference. We further obtain $S_8\equiv {\sigma }_8({\mathrm{\Omega }}_{\mathrm{m}}/0.3{)}^{0.5}=0.796\pm 0.013$which is lower than the measurement at the $1.6\sigma$level. The combined SPT cluster, DES $3\times 2\mathrm{pt}$, and datasets mildly prefer a nonzero positive neutrino mass, with a 95% upper limit $\sum m_{\nu }<0.25\mathrm{eV}$on the sum of neutrino masses. Assuming a $w\mathrm{CDM}$model, we constrain the dark energy equation of state parameter $w=-1.15_{-0.17}^{+0.23}$and when combining with primary CMB anisotropies, we recover $w=-1.20_{-0.09}^{+0.15}$, a $1.7\sigma$difference with a cosmological constant. The precision of our results highlights the benefits of multiwavelength multiprobe cosmology and our analysis paves the way for upcoming joint analyses of next-generation datasets. Published by the American Physical Society2025