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Background When transitioning from high school, autistic job seekers often navigate three different pathways to employment: University, Job Coaching, and Self-Directed (defined as those job seekers who independently complete the job search process, without formal support). Assistive technology may aid job seekers throughout the job seeking process. The aim of this study is to learn more about the challenges and assistive technology that autistic job seekers encounter while navigating these three different employment pathways. Methods Qualitative semi-structured interviews were conducted with fifteen stakeholders in the United States, autistic job seekers and support personnel, within each pathway of the hiring process to gather information regarding the challenges autistic job seekers encounter, and the assistive technology they use to address those challenges. Results From a thematic analysis of these interviews, we found that autistic job seekers along each pathway commonly move through the following, phases of the hiring process or “checkpoints”: resume building, networking, job search, job application, and interviews. Autistic job seekers also face challenges within each checkpoint, such as knowing when and what to disclose; self-efficacy, anxiety, and communication challenges; and a lack of communication from potential employers. We also learned that some self-directed autistic job seekers, when compared to those in the University and Job Coaching pathways, may not be using assistive technologies available in the job search process. From our interviews, we also learned the types of assistive technology that autistic job seekers and assistants use in the job seeking process which can be classified as organizational tools, connectivity tools, and visual media tools. Conclusion and implications Our findings revealed a necessity to connect self-directed autistic job seekers to assistive technology available. Based on these results, we present suggestions for future research and design suggestions for developing assistive technology for autistic job seekers. What this paper adds? We define three career pathways for autistic job seekers: University, Job Coaching and Self Directed. To learn more about the hiring process for autistic job seekers and the assistive technology used within each pathway, we conducted a need-finding study. As a contribution of this study, we discovered challenges along each checkpoint in the hiring process, as well as various forms of assistive technology used to support autistic job seekers when encountering those challenges. For our second contribution, we use the information from these interviews to provide suggestions for the design of future assistive technology within the hiring process, potentially supporting the self-efficacy of autistic job seekers, during this process. 

Abstract PurposeAn increasing number of autistic students in the United States are seeking post-secondary education. In response, some post-secondary institutions have established Autism Support Programs (ASP) to address the comprehensive needs of this population. There is little up-to-date, comprehensive information about which institutions host these programs, what types of services they offer, and what is required to access them. MethodsExpanding on previous research, we introduce a new method, which utilizes established data science techniques, to identify ASPs at post-secondary institutions in the U.S. Our technique also allows us to identify the characteristics of the ASPs, including admissions requirements, cost, structure, and supports offered. ResultsResults highlight our method is more efficient and more robust than previous methods from the literature. For example, we identify 49 schools hosting ASPs that were not identified in past literature searches. We report on the characteristics of identified ASPs such as application process, most common supports and program cost. ConclusionThe bi-directional change in the number of ASPs shows that this is an evolving field, requiring automated tools to enable regular updates to data. Although it is promising that a relative handful of U.S. schools have established these programs, a large majority of post-secondary institutions have not, and for those that host them, barriers to access exist, including the necessity of an ASD diagnosis, coupled with up-front and ongoing costs. 

Abstract Using 155 distributed seismic stations spanning Alaska and western Canada, we document how environmental factors like storms and sea ice influence microseismic noise. We examine power spectral densities of continuous seismic data and focus on secondary microseisms (5–10 s) and short period secondary microseisms (1–2 s) from 2018 to 2021. We cross‐correlate the height of ocean waves across the region with the power spectral density time series. We find that the Gulf of Alaska is the dominant source of secondary microseisms in Alaska. The eastern Gulf, in particular, produces more energetic secondary microseisms despite, at times, lower overall wave amplitudes. We find that the short period secondary microseismic noise is produced in the coastal waters and attenuates quickly moving inland. We show that this band is heavily modulated by the influence of sea ice in the coastal ocean by comparing it with sea ice concentrations. We also document how these two microseismic bands vary seasonally and spatially as they respond to different environmental phenomena. We find that this seismic energy closely tracks the seasonal arrival and departure of sea ice in the coastal waters. We also compare the inter‐annual variability of short period secondary microseisms in the northern Arctic from 2009 to 2023 with shorefast ice data. The findings of this study are crucial for monitoring global climate change through seismology. 

SUMMARY This study examines the feature space of seismic waveforms often used in machine learning applications for seismic event detection and classification problems. Our investigation centres on the southern Alaska region, where the seismic record captures diverse seismic activity, notably from the calving of marine-terminating glaciers and tectonic earthquakes along active plate boundaries. While the automated discrimination of earthquakes and glacier quakes is our nominal goal, this data set provides an outstanding opportunity to explore the general feature space of regional seismic phases. That objective has applicability beyond ice quakes and our geographic region of study. We make a noteworthy discovery that features rooted in the spectral content of seismic waveforms consistently outperform statistical and temporal features. Spectral features demonstrate robust performance, exhibiting resilience to class imbalance while being minimally impacted by factors such as epicentral distance and signal-to-noise ratio. We also conduct experiments on the transferability of the model and find that transferability primarily depends on the appearance of the waveforms. Finally, we analyse misclassified events and find examples that are identified incorrectly in the original regional catalogue. 

ABSTRACT Seismic data contains a continuous record of wind influenced by different factors across the frequency spectrum. To assess the influences of wind on ground motion, we use colocated wind and seismic data from 110 stations in the Alaska component of the EarthScope Transportable Array. We compare seismic probability power spectral densities and wind speed and direction during 2018 to develop a quantitative measure of the seismic sensitivity to wind. We observe a pronounced increase in seismic energy as a function of wind speed for almost all stations. At frequencies below the microseism band, our observations agree with previous authors in finding that sensor emplacement and ground materials are important, and that much of the wind influence likely comes from associated changes in barometric pressure. Wind has the least influence in the microseism band, but that is only because its contribution to noise is much smaller than the ubiquitous microseism background. At frequencies above the microseism band, we find that wind sensitivity is correlated with land cover type, increasing with vegetation height. This sensitivity varies seasonally, which we attribute to snow insulation, the burial of vegetation and objects around the station, and potentially the role of frozen ground. Wind direction also manifests in seismic data, which we attribute to turbulent air on the lee side of station huts coupling with the ground and the seismometer borehole cap. We find some dependence on bedrock type, with a greater seismic response in unconsolidated sediment. These results provide guidance on site selection and construction, and make it possible to forecast seismic network performance under different wind conditions. When we examine the factors at work in a warming climate, we find reason to anticipate increasing seismic noise from wind in the Arctic over the decades to come. 

The SD-FIRST program helps fill in the gaps of first-generation students’ home-to-college transition, provides a robust support system by connecting existing campus resources, and provides guidance for staff and faculty on interactions and unique challenges with this student population. Programmatic elements specific for first-generation students, driven by evidence-based resiliency research, were developed to provide academic, social, and economic support. The expected outcome of the SD-FIRST program is to achieve a sustainable increased retention and graduation rate, and an increase in emotional intelligence for students participating in the program. The initial cohort of SD-FIRST scholars began in the fall 2021 semester, and the details of the program as well as initial implementation are included in this paper. 

Abstract The addition of 108 infrasound sensors—a legacy of the temporary USArray Transportable Array (TA) deployment—to the Alaska regional network provides an unprecedented opportunity to quantify the effects of a diverse set of site conditions on ambient infrasound noise levels. TA station locations were not chosen to optimize infrasound performance, and consequently span a dramatic range of land cover types, from temperate rain forest to exposed tundra. In this study, we compute power spectral densities for 2020 data and compile new ambient infrasound low- and high-noise models for the region. In addition, we compare time series of root-mean-squared (rms) amplitudes with wind data and high-resolution land cover data to derive noise–wind speed relationships for several land cover categories. We observe that noise levels for the network are dominated by wind, and that network noise is generally higher in the winter months when storms are more frequent and the microbarom is more pronounced. Wind direction also exerts control on noise levels, likely as a result of infrasound ports being systematically located on the east side of the station huts. We find that rms amplitudes correlate with site land cover type, and that knowledge of both land cover type and wind speed can help predict infrasound noise levels. Our results show that land cover data can be used to inform infrasound station site selection, and that wind–noise models that incorporate station land cover type are useful tools for understanding general station noise performance.