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Asthma affects 25 million people in the United States, and its prevalence is increasing. Access to care and adherence to prescribed asthma-treatment programs remain the principal formidable challenges for asthma management. Telemedicine offers substantial opportunities for improved asthma care of patients across the full range of socioeconomic strata. Ever-improving digital tools for asthma assessment and treatment are key components of telemedicine platforms for asthma management. These include a variety of remote patient-monitoring devices, digital inhaler systems, and mobile-health applications that facilitate ongoing assessment and adherence to treatment protocols. Digital tools for monitoring treatment focus on tracking medication use, inhalation technique, and physiological markers such as peak-flow rate and pulse-oximetry. Telemedicine visits allow for elements of assessment via video, approximating or duplicating many aspects of in-person visits, such as evaluating a patient's general appearance, breathing effort, and cough. Challenges remain in ensuring equitable access to these technologies, especially in rural and low-income areas, and in maintaining patient privacy and data security in digital platforms.

Abstract The catastrophic December 2022-January 2023 nine atmospheric rivers in California underscore the urgent need to better understand such high-risk weather extremes. Here we applied a machine learning clustering tool to understand the activity of atmospheric river clusters. Reanalysis results show that clusters with high density, that is the time fraction under atmospheric river conditions within a cluster, exhibit more frequent high-category atmospheric rivers, alongside an increased likelihood for extreme precipitation and severe land surface responses. The key circulation patterns of atmospheric river clusters are primarily attributed to subseasonal variability. Furthermore, the occurrence and density of atmospheric river clusters are modulated by the daily variability of the geopotential height field. Climate model projections suggest that atmospheric river clusters with higher density and higher categories will be more frequent as warming level increases. Our findings emphasize the important role of atmospheric river clusters in the development of climate adaptation and resilience strategies.

A significant percentage of bridges in the United States are serving beyond their 50-year design life, and many of them are in poor condition, making them vulnerable to fatigue cracks that can result in catastrophic failure. However, current fatigue crack inspection practice based on human vision is time-consuming, labor intensive, and prone to error. We present a novel human-centered bridge inspection methodology to enhance the efficiency and accuracy of fatigue crack detection by employing advanced technologies including computer vision and augmented reality (AR). In particular, a computer vision-based algorithm is developed to enable near-real-time fatigue crack detection by analyzing structural surface motion in a short video recorded by a moving camera of the AR headset. The approach monitors structural surfaces by tracking feature points and measuring variations in distances between feature point pairs to recognize the motion pattern associated with the crack opening and closing. Measuring distance changes between feature points, as opposed to their displacement changes before this improvement, eliminates the need of camera motion compensation and enables reliable and computationally efficient fatigue crack detection using the nonstationary AR headset. In addition, an AR environment is created and integrated with the computer vision algorithm. The crack detection results are transmitted to the AR headset worn by the bridge inspector, where they are converted into holograms and anchored on the bridge surface in the 3D real-world environment. The AR environment also provides virtual menus to support human-in-the-loop decision-making to determine optimal crack detection parameters. This human-centered approach with improved visualization and human–machine collaboration aids the inspector in making well-informed decisions in the field in a near-real-time fashion. The proposed crack detection method is comprehensively assessed using two laboratory test setups for both in-plane and out-of-plane fatigue cracks. Finally, using the integrated AR environment, a human-centered bridge inspection is conducted to demonstrate the efficacy and potential of the proposed methodology.

This paper describes the use of wireless smart sensors for examining the underlying mechanism for the wind-induced vibration of high-mast illumination pole (HMIP) structures. HMIPs are tall, slender structures with low inherent damping. Video recordings of multiple HMIPs showed considerable vibrations of these HMIPs under wind loading in the state of Kansas. The HMIPs experienced cyclic large-amplitude displacements at the top, which can produce high-stress demand and lead to fatigue cracking at the bottom of the pole. In this study, the natural frequencies of the HMIP were assessed using pluck tests and finite element modeling, and the recorded vibration frequencies were obtained through computer vision-based video analysis. Meanwhile, a 30.48 m tall HMIP with three LED luminaires made of galvanized steel located in Wakeeney, Kansas, was selected for long-term vibration monitoring using wireless smart sensors to investigate the underlying mechanism for the excessive wind-induced vibrations. Data analysis with the long-term monitoring data indicates that while vortex-induced vibration occurs frequently at relatively low amplitude, buffeting-induced vibration was the leading cause of the excessive vibrations of the monitored HMIP. The findings provide crucial information to guide the design of vibration mitigation strategies for these HMIP structures.

Transitioning to a hydrogen economy has the potential to mitigate carbon dioxide emissions. The hydrogen leakage rate and the production pathways appear, based on simulations with a global model, as key leverages to reach a clear climate benefit from a large-scale transition to a hydrogen economy.

The atmospheric concentration of methane has more than doubled since the start of the Industrial Revolution. Methane is the second-most-abundant greenhouse gas created by human activities and a major driver of climate change. This APS-Optica report provides a technical assessment of the current state of monitoring U.S. methane emissions from oil and gas operations, which accounts for roughly 30% of U.S. anthropogenic methane emissions. The report identifies current technological and policy gaps and makes recommendations for the federal government in three key areas: methane emissions detection, reliable and systematized data and models to support mitigation measures, and effective regulation.

Western honey bees (Apis mellifera), a cornerstone to crop pollination in the U.S., are faced with an onslaught of challenges from diseases caused by parasites, pathogens, and pests that affect this economically valuable pollinator. Natural products (NPs), produced by living organisms, including plants and microorganisms, can support health and combat disease in animals. NPs include both native extracts and individual compounds that can reduce disease impacts by supporting immunity or directly inhibiting pathogens, pests, and parasites. Herein, we describe the screening of NPs in laboratory cage studies for their effects on honey bee disease prevention and control. Depending on the expected activity of compounds, we measured varied responses, including viral levels, honey bee immune responses, and symbiotic bacteria loads. Of the NPs screened, several compounds demonstrated beneficial activities in honey bees by reducing levels of the critical honey bee virus deformed wing virus (DWV-A and-B), positively impacting the gut microbiome or stimulating honey bee immune responses. Investigations of the medicinal properties of NPs in honey bees will contribute to a better understanding of their potential to support honey bee immunity to fight off pests and pathogens and promote increased overall honey bee health. These investigations will also shed light on the ecological interactions between pollinators and specific floral food sources.

This article interrogates the curious dismissal of madness from the critical landscape surrounding J. M. Coetzee’s In the Heart of the Country, and makes suggestions concerning how madness works in the novel and why—given certain critical and historical pressures—it has been persistently sidelined. An analysis of the novel in light of Coetzee’s scholarship on Samuel Beckett suggests that Magda’s discourse, like those of many Beckettian narrators, follows patterns of affirmation and auto-negation, constituting a fiction of what Coetzee calls “net zero.” In particular, Magda extends this pattern to the taking on and casting off of identities, perhaps in the style of the hermit crab she puts forward as an image of herself. An intertextual examination of the semantic and rhetorical range of madness as it appears in Coetzee’s other fiction and scholarship reveals that madness, for Coetzee, consistently denotes: on the one hand, a contagious force moving throughout a social body, and on the other hand, the labor of writing under the threat of illegibility—a threat conditioned in large part by the madness of the social body. By infecting the writer who might record its workings in history and thereby inhibiting or distorting that record, madness likewise appears in historical record as “net zero.” Thus, rather than simply being mad, Magda’s relationship with madness is emblematic of the (dis)appearance of madness in and from history.

American Foulbrood (AFB) is a deadly bacterial disease affecting pupal and larval honey bees. AFB is caused by the endospore-forming bacterium Paenibacillus larvae (PL). Propolis, which contains a variety of organic compounds, is a product of bee foraging and is a resinous substance derived from botanical substances found primarily in trees. Several compounds from the class of caffeic acid esters, which are commonly found in propolis, have been shown to have antibacterial activity against PL. In this study, six different caffeic acid esters were synthesized, purified, spectroscopically analyzed, and tested for their activity against PL to determine the minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs). Caffeic acid isopropenyl ester (CAIE), caffeic acid benzyl ester (CABE), and caffeic acid phenethyl ester (CAPE) were the most effective in inhibiting PL growth and killing PL cell with MICs and MBCs of 125 µg/mL when used individually, and a MIC and MBC of 31.25 µg/mL for each compound alone when CAIE, CABE, and CAPE are used in combination against PL. These compounds inhibited bacterial growth through a bactericidal effect, which revealed cell killing but no lysis of PL cells after 18 h. Incubation with CAIE, CABE, and CAPE at their MICs significantly increased reactive oxygen species levels and significantly changed glutathione levels within PL cells. Caffeic acid esters are potent bactericidal compounds against PL and eliminate bacterial growth through an oxidative stress mechanism.

Traditional ultrasonic testing (UT) techniques have been widely used to detect surface and sub-surface defects of welds. UT inspection is a contact method which burdens the manufacturer by storing hot specimens for inspection when the material is cool. Additionally, UT is only valid for 5 mm specimens or thicker and requires a highly skilled operator to perform the inspections and interpret the signals. Infrared thermography (IRT) has the potential to be implemented for weld inspections due to its non-contact nature. In this study, the feasibility of using IRT to overcome the limitations of UT inspection is investigated to detect inclusion, porosity, cracking, and lack of fusion in 38 weld specimens with thicknesses of 3, 8 and 13 mm. UT inspection was also performed to locate regions containing defects in the 8 mm and 13 mm specimens. Results showed that regions diagnosed with defects by the UT inspection lost heat faster than the sound weld. The IRT method was applied to six 3 mm specimens to detect their defects and successfully detected lack of fusion in one of them. All specimens were cut at the locations indicated by UT and IRT methods which proved the presence of a defect in 86% of the specimens. Despite the agreement with the UT inspection, the proposed IRT method had limited success in locating the defects in the 8 mm specimens. To fully implement in-line IRT-based weld inspections more investigations are required.

The Model Minority Myth for Asians as a group disguises the challenges facing Hmong and other underrepresented Southeast Asian groups. Using the theoretical framework of Academic Capital Formation (ACF), this qualitative study examines the implications of the Model Minority Myth by focusing on the experiences and educational challenges of Hmong American high school students in an urban setting in the Midwest. Results from this study alludes to a bimodal pattern of achievement amongst the Asian American Pacific Islander community, emphasizing the importance of increasing visibility for underrepresented Southeast Asians, such as Hmong Americans, with regards to access to higher education.

This book review examines "German-Jewish Life Writing in the Aftermath of the Holocaust: Beyond Testimony" by Helen Finch. The review delves into the experiences of four postwar German-Jewish writers and their struggles to publish their works. It explores the themes of the Holocaust, sexual abuse, and racialized violence present in their writings. The review also questions the argument made by Finch regarding the rejection of survivors' testimonies and raises concerns about the validity of her conclusions. [Extracted from the article]