Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries

Lithium-sulfur (Li-S) batteries are described extensively in the literature, but existing computational models aimed at scientific understanding are too complex for use in applications such as battery management. Computationally simple models are vital for exploitation. This paper proposes a non-linear state-of-charge dependent Li-S equivalent circuit network (ECN) model for a Li-S cell under discharge. Li-S batteries are fundamentally different to Li-ion batteries, and require chemistry-specific models. A new Li-S model is obtained using a ‘behavioural’ interpretation of the ECN model; as Li-S exhibits a ‘steep’ open-circuit voltage (OCV) profile at high states-of-charge, identification methods are designed to take into account OCV changes during current pulses. The prediction-error minimization technique is used. The model is parameterized from laboratory experiments using a mixed-size current pulse profile at four temperatures from 10 °C to 50 °C, giving linearized ECN parameters for a range of states-of-charge, currents and temperatures. These are used to create a nonlinear polynomial-based battery model suitable for use in a battery management system. When the model is used to predict the behaviour of a validation data set representing an automotive NEDC driving cycle, the terminal voltage predictions are judged accurate with a root mean square error of 32 mV

Lithium-sulfur cell equivalent circuit network model parameterization and sensitivity analysis

Compared to lithium-ion batteries, lithium-sulfur (Li-S) batteries potentially offer greater specific energy density, a wider temperature range of operation, and safety benefits, making them a promising technology for energy storage systems especially in automotive and aerospace applications. Unlike lithium-ion batteries, there is not a mature discipline of equivalent circuit network (ECN) modelling for Li-S. In this study, ECN modelling is addressed using formal ‘system identification’ techniques. A Li-S cell’s performance is studied in the presence of different charge/discharge rates and temperature levels using precise experimental test equipment. Various ECN model structures are explored, considering the trade-offs between accuracy and speed. It was concluded that a ‘2RC’ model is generally a good compromise, giving good accuracy and speed. Model parameterization is repeated at various state-of-charge (SOC) and temperature levels, and the effects of these variables on Li-S cell’s ohmic resistance and total capacity are demonstrated. The results demonstrate that Li-S cell’s ohmic resistance has a highly nonlinear relationship with SOC with a break-point around 75% SOC that distinguishes it from other types of battery. Finally, an ECN model is proposed which uses SOC and temperature as inputs. A sensitivity analysis is performed to investigate the effect of SOC estimation error on the model’s accuracy. In this analysis, the battery model’s accuracy is evaluated at various SOC and temperature levels. The results demonstrate that the Li-S cell model has the most sensitivity to SOC estimation error around the break-point (around 75% SOC) whereas in the middle SOC range, from 20% to 70%, it has the least sensitivity

Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model

ABSTRACT: Numerical models for tides, storm surge, and wave runup have demonstrated ability to accurately define spatially varying flood surfaces. However these models are typically too computationally expensive to dynamically simulate the full parameter space of future oceanographic, atmospheric, and hydrologic conditions that will constructively compound in the nearshore to cause both extreme event and nuisance flooding during the 21st century. A surrogate modeling framework of waves, winds, and tides is developed in this study to efficiently predict spatially varying nearshore and estuarine water levels contingent on any combination of offshore forcing conditions. The surrogate models are coupled with a time-dependent stochastic climate emulator that provides efficient downscaling for hypothetical iterations of offshore conditions. Together, the hybrid statistical-dynamical framework can assess present day and future coastal flood risk, including the chronological characteristics of individual flood and wave-induced dune overtopping events and their changes into the future. The framework is demonstrated at Naval Base Coronado in San Diego, CA, utilizing the regional Coastal Storm Modeling System (CoSMoS; composed of Delft3D and XBeach) as the dynamic simulator and Gaussian process regression as the surrogate modeling tool. Validation of the framework uses both in-situ tide gauge observations within San Diego Bay, and a nearshore cross-shore array deployment of pressure sensors in the open beach surf zone. The framework reveals the relative influence of large-scale climate variability on future coastal flood resilience metrics relevant to the management of an open coast artificial berm, as well as the stochastic nature of future total water levels.This work was funded by the Strategic Environmental Research Development Program (DOD/SERDP RC-2644). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. F. J. Mendez, A. Rueda, and L. Cagigal acknowledge the partial funding from the Spanish Ministry of Science and Innovation, project Beach4cast PID2019-107053RB-I00. The authors thank the Scripps Center for Coastal Studies for their efforts to deploy, recover, and process surf zone pressure sensor data used as validation in this study. The authors thank Melisa Menendez for sharing GOW2 hindcast data for Southern California. The authors thank the sea-level rise projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool

CanStem111P trial: A Phase 3 Study of napabucasin (NAPA) plus nab-paclitaxel (nPTX) with gemcitabine (Gem) in adult patients with metastatic pancreatic adenocarcinoma (mPDAC) - Trial in progress

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Diagnostic utility of whole genome sequencing in adults with B-other acute lymphoblastic leukemia

Genomic profiling at diagnosis of B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) in adults is used to guide disease classification, risk stratification and treatment decisions. Patients for which diagnostic screening fails to identify disease defining or risk stratifying lesions are classified as B-other ALL. We screened a cohort of 652 BCP-ALL cases enrolled in UKALL14 to identify and perform whole genome sequencing (WGS) on paired tumor-normal samples. For 52 B-other patients we compared WGS findings to data from clinical and research cytogenetics. WGS identifies a cancer associated event in 51/52 cases, this includes an established subtype defining genetic alteration in 5/52 that were previously missed by standard-of-care genetics. Of the 47 true B-other ALL we identified a recurrent driver in 87% (41). Complex karyotype by cytogenetics emerges as a heterogeneous group, including distinct genetic alterations associated with either favorable (DUX4-r) or poor outcomes (MEF2D-r, IGK::BCL2). For a subset of 31 cases, we integrate findings from RNA-sequencing (RNA-seq) analysis to include fusion gene detection, and classification by gene expression. Compared to RNA-seq, WGS was sufficient to detect and resolve recurrent genetic subtypes, however RNA-seq can provide orthogonal validation of findings. In conclusion, we demonstrate that WGS can identify clinically relevant genetic abnormalities missed by standard-of-care testing and identify leukemia driver events in virtually all cases of B-other ALL

Enclosing a pen reduced time to response to questionnaire mailings

OBJECTIVES: To assess the effectiveness of including a pen in postal questionnaires on response rate, necessity of reminders, time to response, and completeness of response to the primary outcome question (POQ).   STUDY DESIGN AND SETTING: A two-arm randomized controlled trial (RCT) embedded within the screening of older women for prevention of fracture trial (SCOOP). Women, aged 70-75 years, were randomized to receive a pen with their questionnaire (n = 3,826) or to receive the questionnaire alone (n = 3,829). The results were combined with another embedded RCT in a meta-analysis.   RESULTS: A response rate of 92.4% was observed in the pen group compared with 91.3% in the control group (odds ratio [OR] = 1.16; 95% confidence interval [CI]: 0.98, 1.37; P = 0.08). There was a difference in reminders required (OR = 0.88; 95% CI: 0.79, 0.98; P = 0.02), time to response (hazard ratio = 1.06; 95% CI: 1.01, 1.11; P = 0.01) and some difference in the completeness of response to the POQ (OR = 1.18; 95% CI: 1.00, 1.39; P = 0.05). The pooled OR from the meta-analysis for response rate was 1.21 (95% CI: 1.05, 1.39; P = 0.01).   CONCLUSION: Inclusion of a pen with postal questionnaires potentially has a positive impact on response rates and the number of reminders required. There may be some reduction in time to response. Studies of different participant groups are needed to test the effectiveness over more diverse populations

The cellular and synaptic architecture of the mechanosensory dorsal horn

The deep dorsal horn is a poorly characterized spinal cord region implicated in processing low-threshold mechanoreceptor (LTMR) information. We report an array of mouse genetic tools for defining neuronal components and functions of the dorsal horn LTMR-recipient zone (LTMR-RZ), a role for LTMR-RZ processing in tactile perception, and the basic logic of LTMR-RZ organization. We found an unexpectedly high degree of neuronal diversity in the LTMR-RZ: seven excitatory and four inhibitory subtypes of interneurons exhibiting unique morphological, physiological, and synaptic properties. Remarkably, LTMRs form synapses on between four and 11 LTMR-RZ interneuron subtypes, while each LTMR-RZ interneuron subtype samples inputs from at least one to three LTMR classes, as well as spinal cord interneurons and corticospinal neurons. Thus, the LTMR-RZ is a somatosensory processing region endowed with a neuronal complexity that rivals the retina and functions to pattern the activity of ascending touch pathways that underlie tactile perception

Clinical and biomechanical factors associated with falls and rheumatoid arthritis: Baseline cohort with longitudinal nested case-control study

OBJECTIVE: To identify the clinical and biomechanical characteristics associated with falls in people with RA. METHODS: A total of 436 people ≥60 years of age with RA completed a 1 year prospective survey of falls in the UK. At baseline, questionnaires recorded data including personal and medical history, pain and fatigue scores, health-related quality of life (HRQoL), physical activity and medication history. The occurrence of falls wasmonitored prospectively over 12 months by monthly self-reporting. A nested sample of 30 fallers (defined as the report of one or more falls in 12 months) and 30 non-fallers was evaluated to assess joint range of motion (ROM), muscle strength and gait parameters. Multivariate regression analyses were undertaken to determine variables associated with falling. RESULTS: Compared with non-fallers (n = 236), fallers (n = 200) were older (P = 0.05), less likely to be married (P = 0.03), had higher pain scores (P < 0.01), experienced more frequent dizziness (P < 0.01), were frequently taking psychotropic medications (P = 0.02) and reported lower HRQoL (P = 0.02). Among those who underwent gait laboratory assessments, compared with non-fallers, fallers showed a greater anteroposterior (AP; P = 0.03) and medial-lateral (ML) sway range (P = 0.02) and reduced isokinetic peak torque and isometric strength at 60° knee flexion (P = 0.03). Fallers also showed shorter stride length (P = 0.04), shorter double support time (P = 0.04) and reduced percentage time in swing phase (P = 0.02) and in knee range of motion through the gait cycle (P < 0.01). CONCLUSION: People with RA have distinct clinical and biomechanical characteristics that place them at increased risk of falling. Assessment for these factors may be important to offer more targeted rehabilitation interventions