Multifactorial engineering of biomimetic membranes for batteries with multiple high-performance parameters

Lithium–sulfur (Li–S) batteries have a high specific capacity, but lithium polysulfide (LPS) diffusion and lithium dendrite growth drastically reduce their cycle life. High discharge rates also necessitate their resilience to high temperature. Here we show that biomimetic self-assembled membranes from aramid nanofibers (ANFs) address these challenges. Replicating the fibrous structure of cartilage, multifactorial engineering of ion-selective mechanical, and thermal properties becomes possible. LPS adsorption on ANF surface creates a layer of negative charge on nanoscale pores blocking LPS transport. The batteries using cartilage-like bioinspired ANF membranes exhibited a close-to-theoretical-maximum capacity of 1268 mAh g−1, up to 3500+ cycle life, and up to 3C discharge rates. Essential for safety, the high thermal resilience of ANFs enables operation at temperatures up to 80 °C. The simplicity of synthesis and recyclability of ANFs open the door for engineering high-performance materials for numerous energy technologies.

Lava Volume from Remote Sensing Data: Comparisons with Reverse Petrological Approaches for Two Types of Effusive Eruption

Five effusive eruptions of Piton de la Fournaise (La Réunion) are analyzed to investigate temporal trends of erupted mass and sulfur dioxide (SO2) emissions. Daily SO2 emissions are acquired from three ultraviolet (UV) satellite instruments (the Ozone Monitoring Instrument (OMI), the Ozone Mapping and Profiler Suite (OMPS), and the Tropospheric Monitoring Instrument (TROPOMI)) and an array of ground-based UV spectrometers (Network for Observation of Volcanic and Atmospheric Change (NOVAC)). Time-averaged lava discharge rates (TADRs) are obtained from two automatic satellite-based hot spot detection systems: MIROVA and MODVOLC. Assuming that the lava volumes measured in the field are accurate, the MIROVA system gave the best estimation of erupted volume among the methods investigated. We use a reverse petrological method to constrain pre-eruptive magmatic sulfur contents based on observed SO2 emissions and lava volumes. We also show that a direct petrological approach using SO2 data might be a viable alternative for TADR estimation during cloudy weather that compromises hot spot detection. In several eruptions we observed a terminal increase in TADR and SO2 emissions after initial emission of evolved degassed magma. We ascribe this to input of deeper, volatile-rich magma into the plumbing system towards the end of these eruptions. Furthermore, we find no evidence of volatile excess in the five eruptions studied, which were thus mostly fed by shallow degassed magma.

Design and Structural Simulations of a Custom Li-Po Accumulator for Low Range, Lightweight, Single-Seater, Open Cockpit, and Open-Wheeled Racecar

Electric, hybrid, and fuel cell vehicles are the future of the automobile industry, and power source design is one of the most crucial steps in designing these vehicles. This paper aims to design and structurally simulate a custom accumulator—which powers an electric vehicle, for a lightweight, single-seater formula-style racecar. The work is dependent on the model-based design and CAD model approach. Mathematical modeling on SCILAB is used to model equations to get the characteristics of the accumulator, such as the energy, capacity, current, voltage, state of charge, and discharge rates. The output of this model gives the configuration of the battery pack as several cells in series and parallel to adequately power the tractive system. An accumulator container is designed to safeguard the cells from external impacts and vibrational loads, which otherwise can lead to safety hazards. Following this, the Finite Element Analysis (FEA) performed on the accumulator resulted in maximum peak deformation of 0.56 mm, ensuring the safety check against various external loads. Further, the finer stability of the battery pack was virtually validated after performing the vibrational analysis, resulting in a deformation of 3.5493 mm at a 1760.8 Hz frequency.

INFLUENCE OF RAT CENTRAL THALAMIC NEURONS ON FORAGING BEHAVIOR IN A HAZARDOUS ENVIRONMENT

The basolateral amygdala (BL) is a major regulator of foraging behavior. Following BL inactivation, rats become indifferent to predators. However, at odds with the view that the amygdala detects threats and generate defensive behaviors, most BL neurons have reduced firing rates during foraging and at proximity of the predator. In search of the signals determining this unexpected activity pattern, this study considered the contribution of the central medial thalamic nucleus (CMT), which sends a strong projection to BL, mostly targeting its principal neurons. Inactivation of CMT or BL with muscimol abolished the rats’ normally cautious behavior in the foraging task. Moreover, unit recordings revealed that CMT neurons showed large but heterogeneous activity changes during the foraging task, with many neurons decreasing or increasing their discharge rates, with a modest bias for the latter. A generalized linear model revealed that CMT neurons encode many of the same task variables as principal BL cells. However, the nature (inhibitory vs. excitatory) and relative magnitude of the activity modulations seen in CMT neurons differed markedly from those of principal BL cells but were very similar to those of fast-spiking BL interneurons. Together, these findings suggest that, during the foraging task, CMT inputs fire some principal BL neurons, recruiting feedback interneurons in BL, resulting in the widespread inhibition of principal BL cells.

A computational approach for generating continuous estimates of motor unit discharge rates and visualizing population discharge characteristics

Objective: Successive improvements in high density surface electromyography and decomposition techniques have facilitated an increasing yield in decomposed motor unit (MU) spike times. Though these advancements enhance the generalizability of findings and promote the application of MU discharge characteristics to inform the neural control of motor output, limitations remain. Specifically, 1) common approaches for generating smooth estimates of MU discharge rates introduce artifacts in quantification, which may bias findings, and 2) discharge characteristics of large MU populations are often difficult to visualize. Approach: In the present study, we propose support vector regression (SVR) as an improved approach for generating smooth continuous estimates of discharge rate and compare the fit characteristics of SVR to traditionally used methods, including Hanning window filtering and polynomial regression. Furthermore, we introduce ensembles as a method to visualize the discharge characteristics of large MU populations. We define ensembles as the average discharge profile of a subpopulation of MUs, composed of a time normalized ensemble average of all units within this subpopulation. Analysis was conducted with MUs decomposed from the tibialis anterior (N = 2128), medial gastrocnemius (N = 2673), and soleus (N = 1190) during isometric plantarflexion and dorsiflexion contractions. Main Result: Compared to traditional approaches, we found SVR to alleviate commonly observed inaccuracies and produce significantly less absolute fit error in the initial phase of MU discharge and throughout the entire duration of discharge. Additionally, we found the visualization of MU populations as ensembles to intuitively represent population discharge characteristics with appropriate accuracy for visualization. Significance: The results and methods outlined here provide an improved method for generating estimates of MU discharge rate with SVR and present a unique approach to visualizing MU populations with ensembles. In combination, the use of SVR and generation of ensembles represent an efficient method for rendering population discharge characteristics.

Compulsory and voluntary admissions in comparison: A 9-year long observational study

Background: Few studies, so far, have been specifically designed to highlight the features related to Compulsory Admissions (CA) and Voluntary Admissions (VA) in Italian psychiatric emergency wards. Aims: The main purpose of this observational study was to compare the sociodemographic and clinical characteristics of VA and CA and to explore possible predictors of re-admissions. Methods: During a 6-month Index Period (February, the 1st–July, the 31st 2008) all psychiatric admissions were documented and then followed-up through all available informatic systems for the next 9 years. Results: Out of 390 hospitalizations, 101 (25.9%) were compulsory (CA rate was 2.79 per 10,000 inhabitants per year, mean duration of hospitalizations of 7.33 ± 7.84 days). Diagnoses were recorded for the 325 patients who had been hospitalized during index period: schizophrenic psychoses ([ p = .042], in particular schizophrenia [ p = .027]), manic episode ( p = .044), and delusional disorders ( p = .009) were associated with CA; conversely, the diagnosis of unipolar major depression ( p = .005) and personality disorders ( p = .048) were significantly more frequent in VA. The 325 admitted patients were followed up for 1,801 person-years. No significant differences were found in terms of drop-outs, transferring, and discharge rates, and mortality rates due to both natural causes and suicides. Factors associated with at least one compulsory readmission were younger age and having had a previous CA ( p = .011); conversely having been engaged with psychiatric services for over 1 year prior to index hospitalization was protective for a subsequent CA ( p = .013). Conclusions: After a 40-year old political reform, the current study shows that, in a context of integrated outpatient and inpatient services, engagement with outpatient care may be protective for compulsory rehospitalization.

COVID-19 as a Catalyst for Same-Day Discharge Total Shoulder Arthroplasty

The COVID-19 pandemic caused major disruptions to the healthcare system, but its impact on the transition to same-day discharge shoulder arthroplasty remains unexplored. This study assessed the effect of COVID-19 on length of stay (LOS), same-day discharge rates, and other markers of resource use after elective total shoulder arthroplasty. A total of 508 consecutive patients undergoing elective primary total shoulder arthroplasty between 2019 and 2021 were identified and divided into 2 cohorts: “pre-COVID” (March 2019–March 2020; n = 263) and “post-COVID” (May 2020–March 2021; n = 245). No elective shoulder arthroplasties were performed at our practice between 18 March and 11 May 2020. Outcome measures included LOS, same-day discharge, discharge location, and 90-day emergency department (ED) visits, readmissions and reoperations. There were no significant differences in baseline preoperative patient characteristics. Shoulder arthroplasty performed post-COVID was associated with a shorter LOS (12 vs. 16 h, p = 0.017) and a higher rate of same-day discharge (87.3 vs. 79.1%, p = 0.013). The rate of discharge to skilled nursing facilities was similarly low between the groups (1.9 vs. 2.0%, p = 0.915). There was a significant reduction in the rate of 90-day ED visits post-COVID (7.4 vs. 13.3%, p = 0.029), while there were no differences in 90-day reoperation (2.0 vs. 1.5%, p = 0.745) or readmission rates (1.2 vs. 1.9%, p = 0.724). The COVID-19 pandemic seems to have accelerated the shift towards shorter stays and more same-day discharge shoulder arthroplasties, while reducing unexpected acute health needs (e.g., ED visits) without adversely affecting readmission and reoperation rates.

Identification of Operational Design Domain for Autonomous Truck Mounted Attenuator System on Multilane Highways

The Autonomous Truck Mounted Attenuator (ATMA) vehicle system is a technology that leverages connected and automated vehicle (CAV) capabilities for maintenance of transportation infrastructure. Promoted by FHWA and state departments of transportation (DOTs), it is a niche CAV application in leader–follower style, intended to remove DOT workers from the following maintenance truck, to reduce fatalities in work zones. Because practicable guidance for deployment of this technology is largely missing in MUTCD, state DOTs have been making their own deployment criteria. In this manuscript, we focus on the operational design domain (ODD) problem—under what traffic conditions should ATMA be deployed. Modeling efforts are first focused on the derivation of an effective discharge rate that can be associated with a moving bottleneck caused by slow-moving ATMA vehicles on a multilane highway. Then, based on the demand input and discharge rates, microscopic traffic flow models calculate vehicle delay and density, which the Highway Capacity Manual (HCM) suggests are key indicators of a multilane highway’s level of service (LOS). In this way, the linkage between AADT and LOS is analytically established. NGSIM data is used for the model validation and shows that the developed model correctly captures the effective discharge rate discount caused by moving bottlenecks. The modeling results demonstrate that roadway performance is sensitive to the K factor and D factor, as well as the operating speed of ATMA and, if LOS = C is a desirable design objective, a good AADT threshold to use would be around 40,000 vehicles per day.

Comparison of Bayesian quantile and frequency-oriented regressions in studying the trend of discharge changes in several hydrometric stations of Gorganroud basin in Iran.

Climate change causes fluctuations in temperature and precipitation. As a result, it affects the discharge of rivers, the most important consequence of which is the tendency toward extreme events such as torrential rains and widespread droughts. River discharge is one of the most important climatic and hydrological parameters. Investigating the changes in this parameter is one of the main prerequisites in the management and proper use of water resources and rivers. Most trend detection studies are based on analyzing changes in the mean or middle of the data. They do not provide information on how changes occur in different data ranges. Therefore, to investigate parameter changes in a different range of the data series, various regression models were proposed. Frequentist quantile regression and Bayesian quantile regression models were used to estimate their trend and trend slope in different quantiles of discharge in different seasons of the year for Arazkouseh, Tamar, and Galikesh stations of Gorganroud basin in northern Iran with the statistical period of 1346–1396 (1966–2016). The results show that in most seasons of the year, high discharge rates for all 3 stations have decreased with a steep slope, and only in summer, Tamar and Galikesh stations have had an increasing trend, but low discharge rates have not changed significantly. Spatially, the discharge values at Arazkouseh station have a decreasing trend with a higher slope rate, and in terms of time, the most decreasing trend has been in spring. Comparing the models also shows that the Bayesian quantile regression model provides more accurate and reliable results than the frequency-oriented quantile regression model. In general, quantile regression models are useful for predicting and estimating extreme high and low discharge changes for better management to reduce flood and drought damage.

Acute pain and self-directed discharge among hospitalized patients with opioid-related diagnoses: a cohort study

Background Patients with substance use disorders are more likely than those without to have a self-directed hospital discharge, putting them at risk for poor health outcomes including progressing illness, readmissions, and death. Inadequate pain management has been identified as a potential motivator of self-directed discharge in this patient population. The objective of this study was to describe the association between acute pain and self-directed discharges among persons with opioid-related conditions; the presence of chronic pain in self-directed discharges was likewise considered. Methods We employed a large database of all hospitalizations at acute care hospitals during 2017 in the city of Philadelphia to identify adults with opioid-related conditions and compare the characteristics of admissions ending with routine discharge versus those ending in self-directed discharge. We examined all adult discharges with an ICD-10 diagnoses related to opioid use or poisoning and inspected the diagnostic data to systematically identify acute pain for the listed primary diagnosis and explore patterning in chronic pain diagnoses with respect to discharge outcomes. Results Sixteen percent of the 7972 admissions involving opioid-related conditions culminated in self-directed discharge, which was more than five times higher than in the general population. Self-directed discharge rates were positively associated with polysubstance use, nicotine dependence, depression, and homelessness. Among the 955 patients with at least one self-directed discharge, 15.4% had up to 16 additional self-directed discharges during the 12-month observation period. Those admitted with an acutely painful diagnosis were almost twice as likely to complete a self-directed discharge, and for patients with multiple admissions, rates of acutely painful diagnoses increased with each admission coinciding with a cascading pattern of worsening infectious morbidity over time. Chronic pain diagnoses were inconsistent for those patients with multiple admissions, appearing, for the same patient, in one admission but not others; those with inconsistent documentation of chronic pain were substantially more likely to self-discharge. Conclusions These findings underscore the importance of pain care in disrupting a process of self-directed discharge, intensifying harm, and preventable financial cost and suffering. Each admission represents a potential opportunity to provide harm reduction and treatment interventions addressing both substance use and pain.