Virginia Tech

The self‐assembly of amphiphilic bottlebrush block copolymers (BCPs), featuring backbones densely grafted with two types of side chains, is less well understood compared to linear BCPs. In particular, the solution self‐assembly of tapered bottlebrush BCPs—cone‐shaped BCPs with hydrophilic or hydrophobic tips—remains unexplored. This study investigates eight tapered and four cylindrical bottlebrush BCPs with varied ratios of hydrophobic polystyrene (PS) and hydrophilic poly(acrylic acid) (PAA) side chains, synthesized via sequential addition of macromonomers using ring‐opening metathesis polymerization (SAM‐ROMP). Self‐assembled nanostructures formed in water were analyzed using cryogenic transmission electron microscopy, small‐angle neutron scattering, and dynamic light scattering. Most BCPs generated multiple nanostructures with surface protrusions, including spherical micelles, cylindrical micelles, and vesicles, alongside transitional forms like ellipsoids and semi‐vesicles. Coarse‐grained molecular dynamics simulations supported the experimental findings, which revealed two distinct self‐assembly pathways. The first involved micelle fusion, producing elliptical and cylindrical aggregates, sometimes forming Y‐junctions. The second pathway featured micelle maturation into semivesicles, which developed into vesicles or large compound vesicles. This work provides the first experimental evidence of vesicle formation via semivesicles in bottlebrush BCPs and demonstrates the significant influence of cone directionality on self‐assembly behavior in these cone‐shaped polymeric amphiphiles.

Disturbances in ionospheric Total Electron Content (dTEC) with frequencies of ∼ ${\sim}$1–100 mHz can be driven from above by processes in the magnetosphere and below by processes on the Earth's surface and lower atmosphere. Past studies showed the potential of dTEC as a diagnostic of magnetospheric Ultra Low Frequency (ULF) wave activity and demonstrated that ULF dTEC can impact space weather by, for example, changing ionospheric conductance. However, most past work has focused on single event studies, lacked magnetospheric context, or used sampling rates too low to capture most ULF waves. Here, we perform a statistical study using Time History of Events and Macrsoscale Interactions during Substorms (THEMIS) satellite conjunctions with a ground‐based magnetometer and Global Navigation Satellite System (GNSS) receiver at ∼ ${\sim}$65° magnetic latitude. We find that magnetospheric ULF waves generate dTEC variations across the broad range of frequencies examined in this study (∼ ${\sim}$2–50 mHz), and that ULF dTEC wave power is correlated with Kp, AE, solar wind speed, and magnetic field wave power observed in the magnetosphere and on the ground. We further find that magnetospheric ULF waves generate dTEC amplitudes up to <∼4 ${< } \sim 4$ TECU (∼30% ${\sim} 30\%$ background), with the largest amplitudes occurring during geomagnetically active conditions, at frequencies below 7 mHz, and at local times near midnight. We finally discuss the implications of our results for magnetosphere‐ionosphere coupling and remote sensing techniques related to ULF waves.

A large proportion of adults in the developing world remain without access to formal banking. We assess the effectiveness of a network‐based information delivery strategy in fostering interest to learn about and subscribe to mobile money services in rural and peri‐urban communities in Peru. We posit that lack of information about mobile money technology is a barrier to financial inclusion, which can be mitigated through social proximity. We designed a randomized controlled trial where workshops were led by individuals personally known to participants (local ambassadors–treatment) or by external agents (control). We find that attendance and BiM subscription rates were twice as high in the local ambassadors' group, especially among low‐trust individuals.

Background: Alzheimer's disease and related dementias (ADRD) are neurodegenerative disorders that afflict 1 in 9 older adults. As pharmacological interventions for ADRD are often ineffective and cause rampant side effects, interest has increased in finding adjunctive, non-pharmacological approaches. Music therapy may be especially beneficial for individuals with ADRD and their caregivers as music is a form of non-verbal communication. Objective: In this case series, we describe a 12-week group music therapy program for individuals with ADRD and their caregivers. Methods: Brain activity was recorded with hyperscanning electroencephalography (EEG) during each music therapy session from the individual with ADRD (n = 3), caregiver (n = 3), and music therapist (n = 1). Video recordings allowed for assessment of movement behavior and affective state responses. Results: This 12-week case series of group music therapy for individuals and their caregivers had a 66% retention and 95.8% adherence rate. We had success collecting behavioral and neural data using 360-degree video capture in combination with EEG. Video recordings allowed us to analyze affective state and nonverbal communication metrics. After pre-processing, neural recordings were clean and able to be analyzed for various neural metrics of interest. Conclusions: A human-centered design approach can be helpful for implementing longitudinal, non-pharmacological interventions in this vulnerable population. A team-science approach with a collective of creative arts therapists, neuroscientists, dementia care experts, creative technologists, and gerontology experts contributed to the conduction of this work. Future studies should examine the effects of music therapy on behavioral and neural outcomes, especially as it relates to interpersonal behavioral and neural synchrony.

The photogating effect, induced by a light‐driven gate voltage, modulates the potential energy of the active channel in field‐effect transistors, leading to a high photoconductive gain of these devices. The effect is particularly pronounced in low‐dimensional structures, especially in graphene field‐effect transistors. Along with unusual optical and electrical properties, graphene with ultra‐high carrier mobility and a highly sensitive surface generates a strong photogating effect in the structure, making it an excellent element for detecting light‐sensitive biomolecules. In this work, graphene field‐effect transistor biosensors is demonstrated for the rapid detection of photoactive yellow protein in an aqueous solution under optical illumination. The devices exhibit millisecond‐scale response times and achieve a detection limit below 5.8 fM under blue‐light excitation, consistent with the absorption characteristics of the protein. The photogating effect in graphene field‐effect transistors provides a promising approach for developing high‐performance, light‐sensitive biosensors for biomolecular detection applications.

As the innovation of smart devices and internet-of-things (IoT), smart homes have become prevalent. People tend to transform residences into smart homes by customizing off-the-shelf smart home platforms, instead of creating IoT systems from scratch. Among the alternatives, Home Assistant (HA) is one of the most popular platforms. It allows programmers (i.e., home residents or smart-home creators) to smartify homes by (S1) integrating selected devices into the system, and (S2) programming YAML-based software to control those devices. Unfortunately, due to the diversity of devices and complexity of automatic configurations, many programmers have difficulty correctly creating YAML files. Consequently, their smart homes may not work as expected, causing frustration and concern in people. This paper presents a novel study on issues of YAML-based automation configuration in smart homes (issues related to S2). We mined the online forum Home Assistant Community for discussion threads related to programming of automation configuration. By manually inspecting 190 threads, we revealed 3 categories of concerns: implementation, optimization, and debugging. Under each category, we classified discussions based on the issue locations and technical concepts involved. Among debugging discussions, we further classified discussions based on users’ resolution strategies; we also applied existing analysis tools to buggy YAML files, to assess the tool effectiveness. Our study reveals the common challenges faced by programmers and frequently applied resolution strategies. There are 129 (68%) examined issues concerning debugging, but existing tools can detect at most 14 of the issues and fix none. It implies that existing tools provide limited assistance in automation configuration. Our research sheds light on future directions in smart home programming.

This study aims to analyze the dimensions that determine an enotourist’s experience when (s)he visits wineries. In addition, in a novel approach, this work examines the influence of wine routes on this experience. On the basis of a set of reviews posted by wine tourists on TripAdvisor, Latent Dirichlet Allocation analysis is conducted to identify the dimensions that determine the wine tourism experience. Subsequently, ordinal logistic regression analysis is performed to identify the most determinant dimensions of visitors’ assessments of their experiences and the influence of wine routes. Results indicate that the “staff” dimension, associated with the treatment provided by tour guides, is the most determinant dimension. In addition, significant differences are observed in the assessment of attributes across the different wine routes.

The United States Water Withdrawals Database (USWWD) provides a standardized compilation of user-level water withdrawal data across 42 US states. USWWD provides time series of water withdrawals at unprecedented spatial and temporal resolutions, encompassing 188,597 unique water users, 353,082 points of diversion and use, and 57,559,412 withdrawal volumes across various sectors. USWWD integrates diverse state-level data sources, standardizing information on water users, withdrawal locations, volumes, source types, and primary water use categories. The withdrawal data combines both direct measurements and various estimation techniques, reflecting the diverse methods utilized by different state agencies in reporting water usage. USWWD addresses significant gaps in national water use data, enabling researchers to conduct detailed analyses of water withdrawal patterns, trends, and drivers across space, time, and sectors. This granular dataset supports a wide range of applications, including water resource management, planning, and policy development. By providing the most detailed national water use data to date, USWWD facilitates new understanding of how society uses water.

Snakes are a useful model for gaining insights into the relationships between predator and prey sizes and resource utilization because their anatomy limits the size of prey that can be swallowed whole. However, data are sparse regarding how commonly gape‐limited predators eat or attempt to eat prey with sizes up to or exceeding their maximal gape. Thus, for an invasive predator, the brown treesnake (Boiga irregularis), we fed captive snakes dead birds with an extremely large range (17%–447%) of relative prey area (RPA = prey cross‐sectional area/snake gape area) to test the predictive value of RPA for snakes attempting to ingest or successfully ingesting prey. As expected, RPA significantly predicted (logistic regression p < 0.0001) the probability of birds being eaten, with an upper size limit similar to the maximal gape of the snakes. Although RPA also significantly predicted (p = 0.003) the probability of attempting to eat a bird, it was less accurate in predicting attempts than successes, and many snakes attempted to eat birds too large to swallow. Twenty‐five snakes attempted to eat birds with RPA ranging from 130% to 447%. The longest durations of unsuccessful feeding attempts were often for values of RPA near 100% rather than the extremely large values. For six large birds with mean measured RPA = 93%, the prey diameter soon after ingestion averaged 14% less than that measured prior to ingestion, which can allow snakes to consume 30% more mass than would otherwise be possible. Our findings complement a recent field study that concluded brown treesnakes regularly attempt to eat live birds too large to swallow. Our results also greatly expanded the known range of avian prey sizes that these snakes attempt to eat. Consequently, brown treesnakes pose a risk to birds with sizes well beyond the limit on prey size imposed by gape.

While many boards adopt technology committees to support firm innovation, the impact of such committees is largely unexplored. We draw on agency and resource dependence theories to suggest that technology committees can improve firm innovation (patenting and new product introductions). We further hypothesize that relevant committee expertise (technology and executive expertise) enhances the effectiveness of the committee, and that the benefit of committee expertise is strengthened when coupled with financial resource provision. Our results support our theorizing about the impact of technology committees—they positively impact new product introductions, although they had no impact on patenting. We also found that committee expertise enhances committee effectiveness, but only when accompanied by greater financial resources. We discuss the implications of optional board structures, such as technology committees and their composition, on firm innovation.

The study of river dynamics has long relied on the analysis of traditional in situ hydrographs. This graphical representation of temporal variability at a given location is so ubiquitous that the mere term “hydrograph” is widely recognized as a time series. While such a “temporal hydrograph” is well suited for in situ data analysis, it fails to represent hydrologic variability across space at a given time; a perspective that characterizes satellite‐based hydrologic observations. Here we argue that the concept of “spatial hydrograph” should be the focus of its own dedicated scrutiny. We build “space series” of river discharge and present their analysis in the context of peak flow event detection. We propose the use of peak event spatial coverage, referred to as “length”, as an analog to event duration. Our analysis is performed in the Mississippi basin using a dense in situ network. We reveal that peak flow events range in length from around 75 to 1,800 km with a median (mean) value of 330 (520) km along the basin's largest rivers. Our analysis also suggests that spatial sampling needs to be a factor of 4 (2) finer in resolution than peak flow lengths to detect 81% ± 13% (70% ± 20%) of events and to estimate their length within 84% ± 3% (67% ± 12%) median accuracy. We evaluate the connection between temporal and spatial scales of peak flows and show that events with longer durations also affect larger extents. We finally discuss the implications for the design of satellite missions concerned with capturing floods across space.

The changes in phase viscosity at the oil-brine interface due to surfactant addition are critical under practical reservoir conditions. This study develops a computational, data-driven model to accurately estimate and predict peak phase viscosity in microemulsion systems at dynamic environments. Using equilibrium molecular dynamics (MD) simulations, we investigate a decane-sodium dodecyl sulfate (SDS)-brine system, generating viscosity data as of temperatures, pressures, surfactant concentrations, and salinities. The data, computed via the Einstein relation and Green-Kubo formula, provides robust training and test datasets for model development. Various machine learning (ML) based regression algorithms are employed on our dataset to train and fit the model. This study aims to compare the accuracy and correlation coefficients of these models, selecting the most precise model for predicting microemulsion phase viscosity under diverse reservoir conditions. Support Vector Regression (SVR) outperformed other models with an R² of 0.978 and 0.963 and mean absolute errors of 0.059 and 0.072 for training and test datasets, respectively. Unlike traditional empirical viscosity correlations, this model incorporates physics-based relationships, enhancing its adaptability to varying reservoir conditions. The proposed model accurately predicts microemulsion phase viscosity, including peak viscosity locations, across pressures, temperatures, salinities, and surfactant concentration. This work facilitates precise viscosity estimation, improving recovery efficiency under reservoir conditions.

Purpose This study evaluated head impact response between different helmet impact test systems by comparing the performance of ten polo helmets. Methods Helmets were evaluated using three test systems: a twin-wire guided drop tower, an oblique drop tower, and an impact pendulum. Impact tests were conducted at matched locations (front boss, side, rear boss) and speeds (3.46, 5.46 m/s). We employed a linear mixed model with helmet model as a random effect and calculated the least square mean differences between systems for peak linear acceleration (PLA), peak rotational acceleration (PRA), peak rotational velocity (PRV), and concussion risk. Correlations between systems by impact speed were explored, using linear models of each system as a function of the others, and calculated Spearman rank correlation coefficients between test systems for each dependent variable. Results Our results found distinct differences in PRA and concussion risk between the oblique and the pendulum impact systems due to the driving force. The acceleration range across helmet models was substantial, and responses differed between test systems at matched impact conditions. However, there were similarities between test systems in the rank order of helmet models. Head acceleration differences between helmets translated to larger differences in concussion risk between helmet models. Conclusion These trends provide a framework for comparing the headform’s response across varying loading conditions. When selecting a test system to evaluate helmets for a specific sport, it is essential to consider the relevant impact conditions and loading patterns to ensure that laboratory tests accurately represent real-world scenarios.