Optimization Of Devices Research Articles

Decoding roughness perception in distributed haptic devices

Abstract The ability to render realistic texture perception using haptic devices has been consistently challenging. A key component of texture perception is roughness. When we touch surfaces, mechanoreceptors present under the skin are activated and the information is processed by the nervous system, enabling perception of roughness/smoothness. Several distributed haptic devices capable of producing localized skin stretch have been developed with the aim of rendering realistic roughness perception; however, current state-of-the-art devices rely on device fabrication and psychophysical experimentation to determine whether a device configuration will perform as desired. Predictive models can elucidate physical mechanisms, providing insight and a more effective design iteration process. Since existing models (1, 2) are derived from responses to normal stimuli only, they cannot predict the performance of laterally actuated devices which rely on frictional shear forces to produce localized skin stretch. They are also unable to predict the augmentation of roughness perception when the actuators are spatially dispersed across the contact patch or actuated with a relative phase difference (3). In this study, we have developed a model that can predict the perceived roughness for arbitrary external stimuli and validated it against psychophysical experimental results from different haptic devices reported in the literature. The model elucidates two key mechanisms: (i) the variation in the change of strain across the contact patch can predict roughness perception with strong correlation and (ii) the inclusion of lateral shear forces is essential to correctly predict roughness perception. Using the model can accelerate device optimization by obviating the reliance on trial-and-error approaches.

Machine learning-assisted SCAPS device simulation for photovoltaic parameters prediction of [formula omitted] perovskite solar cells

Perovskite solar cells (PSCs) have garnered significant research attention because of their remarkable surge in power conversion efficiency (PCE) surpassing 26% within a short timeframe. However, the widespread adoption of these highly efficient PSCs is hampered by the inherent toxicity associated with lead (Pb)-based perovskites, which currently dominate the field. Researchers have explored alternative cations like Ge2+ and Sn2+ Owing to their identical oxidation states to Pb2+, paving the way for lead-free PSC development. By examining the critical factors influencing CsSnI3 thin film solar cells’ performance as well as their interdependencies, this work seeks to improve the efficiency of these devices. To do so, machine learning algorithms (ML) are employed to figure out the critical factors influencing the completion of CsSnI3 solar cells. A new dataset of 7870 data points is generated through simulations with SCAPS-1D to effectively depict the perovskite solar cell. Five ML regression algorithms are thereafter utilized to predict the photovoltaic parameters of solar cells, such as the fill factor (FF), open circuit voltage (VOC), short circuit current (JSC), and PCE. The study shows that random forest (RF) and decision tree (DT) are the best performing algorithms, among which DT produces high accuracy and low error for predicting the PCE with a coefficient of determination R2 of 0.99, Pearson’s coefficient (Rvalue) of 0.99, Spearman’s correlation coefficient (ρ) of 0.99 and root mean square error of 0.08 on the testing set. Finally, this study offers valuable guidance for further experiment optimization by shedding light on the optimal device dimensions and essential components.

Benchmarking the Performance of Lithium and Sodium‐Ion Batteries With Different Electrode and Electrolyte Materials

ABSTRACTSodium‐ion (Na‐ion) batteries are considered a promising alternative to lithium‐ion (Li‐ion) batteries due to the abundant availability of sodium, which helps mitigate supply chain risks associated with Li‐ion batteries. Many studies have focused on the design of Li‐ion batteries, exploring their energy, power, and cost aspects. However, there is still a lack of similar research conducted on Na‐ion batteries. A comparison of the cell voltage characteristics and rate capability of sodium and lithium‐ion batteries using different types of electrodes and electrolytes. For sodium‐ion batteries electrolytes used are NaPF6 and NaClO4 and electrodes used are NaCoO2, NaNiO2, NaFePO4, (Na3V2(PO4)3), graphite, hard carbon, sodium metal, and sodium titanate. For lithium‐ion batteries with LiPF6 and KOH electrolytes and electrodes as LiCoO2, NMC, LVP, Li2MnSiO4, graphite, silicon, lithium titanate (LTO), lithium metal. A thorough analysis of six important performance metrics is part of the investigation: Ragone plots, Electrolyte salt concentration versus spatial coordinate, electrolyte potential versus spatial coordinate, Cell voltage versus battery cell state of charge, Cell voltage versus time, and state variable versus time. Comparing operating voltage and rated capacity NMC and graphite is selected for lithium‐ion batteries as this combination provides operating voltage up to 4.2 V and a rated capacity of 275 Wh/kg, for sodium‐ion for NaCoO2 and hard carbon which has an operating voltage of 2.5–3.8 V and rated capacity around 200 Wh/kg and another combination of electrode as NaFePO4 and sodium metal with NaClO4 electrolyte has a maximum operating voltage of 2.8–3.8 V and rated capacity around 200 Wh/kg. This paper shows significant influence of electrolyte selection on battery performance. The Ragone plots demonstrate that LiPF6 electrolytes in lithium‐ion batteries and NaPF6 electrolytes in sodium‐ion batteries both exhibit superior specific energy densities compared to their KOH and NaClO4 counterparts, respectively. The work presented in this paper encourages researchers to select alternate electrolytes and electrodes for lithium‐ion and sodium‐ion batteries in order to obtain optimal device performance.

Modelling and Numerical Evaluation of Photovoltaic Parameters of a Highly Efficient Perovskite Solar Cell Based on Methylammonium Tin Iodide.

Designing a high-performance solar cell structure requires the understanding of material innovation, device engineering, charge behavior, operation characteristics and efficient photoconversion of light to generate electricity. This study offers a detailed numerical evaluation of the device physics in a highly efficient methylammonium-based perovskite solar cell (PSC) of the configuration, FTO/WO3/CH₃NH₃SnI₃/GO/Fe. Utilizing the SCAPS-1D device simulator, an impressive open-circuit voltage (Voc) of 1.3184 V, short-circuit current density (Jsc) of 35.10 mA/cm2, Fill factor (FF) of 78.38 %, and power conversion efficiency (PCE) of 36.24 % were achieved. The model cell exhibits a robust photon capture of 100 % quantum efficiency between 360 and 750 nm. The study also presents a temperature-dependent band alignment diagram which posted a built-in potential (Vbi) of 0.62 eV. The Vbi at 400 K was found to be 0.58 eV indicating that the model cell exhibits a decent temperature tolerance, and can retain approximately 93 % of its power at 400 K. Through Mott-Schottky capacitance analysis, deeper insights into the space-charge region are inferred, while recombination-generation investigations emphasize the significance of electronic properties in optimizing device performance. This paper, therefore, lays the foundation for future studies, offering clear pathways for device optimization and identifying key areas that require further investigation.

High efficiency Sb and Ag double doped Cu2ZnSn(S, Se)4 thin film solar cells realized by post-heat treatment process of heterojunction

Cu2ZnSn (S, Se)4 (CZTSSe) semiconductors have become a recent focus of research owing to their cost-effectiveness, environmental friendliness, and high efficiency. However, insufficient band alignment between the CZTSSe absorption layer and the CdS buffer layer significantly hampers the high conversion efficiency of CZTSSe devices. In this study, problems related to energy-band matching were solved by the post-heat treatment (PHT) of the absorber and buffer layers. Based on obtaining a high-quality absorption layer through double-doped (Cu, Ag)2Zn(Sn, Sb)(S, Se)4 (CAZTSSSe), the impact of the PHT process of CAZTSSSe/CdS on the device performance and heterojunction quality was investigated. The PHT process promoted the reciprocating motion of elements in the heterojunction region, directly inducing the movement of Cd towards the absorption layer and Cu towards the buffer layer. Furthermore, the PHT method enhances the recrystallization of CdS, resulting in the formation of a CdS layer with excellent crystalline quality. After PHT processing, a more favorable conduction band arrangement was obtained, which helps to reduce non-radiative recombination. Ultimately, when the PHT temperature reaches 200 °C, we get an optimal device with an efficiency of up to 9.44 %, as open circuit voltage (VOC) escalates to 431 mV, and lower fill factor (FF) attains 55.63 %. It is believed that this PHT process can be better applied in thin film cells to obtain high-efficiency solar cells.

J-TEXT achievements in turbulence and transport in support of future device/reactor

Following the reconstruction of the TEXT tokamak at Huazhong University of Science and Technology in China, renamed as J-TEXT, a plethora of experimental and theoretical investigations has been conducted to elucidate the intricacies of turbulent transport within the tokamak configuration. These endeavors encompass not only the J-TEXT device’s experimental advancements but also delve into critical issues pertinent to the optimization of future fusion devices and reactors. The research includes topics on the suppression of turbulence, flow drive and damping, density limit, non-local transport, intrinsic toroidal flow, turbulence and flow with magnetic islands, turbulent transport in the stochastic layer, and turbulence and zonal flow with energetic particles or helium ash. Several important achievements have been made in the last few years, which will be further elaborated upon in this comprehensive review.

Advanced Numerical Modeling of BaZrS3 Chalcogenide Perovskite Cells: Titanium Alloying and Back Surface Field Effects

Chalcogenide perovskites are emerging as superior alternatives to hybrid halide perovskites for photovoltaic applications due to their non-carcinogenic composition and enhanced environmental resilience, including superior resistance to moisture. This study focuses on the computational modeling of BaZrS3 (Barium Zirconium Sulfide) based solar cells, featuring a native bandgap of ∼1.71 eV. Through precise bandgap engineering via Ti alloying at the Zr site, the study aims to optimize the bandgap to the ideal range for photovoltaic efficiency. The device architecture is further refined by adjusting parameters such as layer thickness, doping densities, trap densities and metal contacts. The optimum device efficiencies at this stage were found to be 22.45 % for BaZrS3; 23.91 % for Ba(Zr0.99Ti0.01)S3; 26.64 % for Ba(Zr0.98Ti0.02)S3; and 27.74 % for Ba(Zr0.97Ti0.03)S3. Additionally, the incorporation of various back surface fields (BSFs) (CdTe, CIGS, CZTSe, Cu2Te, FeSi2, GeSe, PbS, µc-Si, SnTe, SnS, Sn2S3, and WSe2) is investigated to enhance photo-absorption. The findings indicate that a Ti alloying concentration of 3 % at the Zr site, coupled with a Cu2Te BSF, achieves the highest efficiency, approximately 30 %. These optimized structures present a robust framework for developing efficient, stable, and non-toxic photovoltaic devices utilizing chalcogenide perovskites.

Device Specifications for Neural Network Training with Analog Resistive Cross‐Point Arrays Using Tiki‐Taka Algorithms

Recently, specialized training algorithms for analog cross‐point array‐based neural network accelerators have been introduced to counteract device non‐idealities such as update asymmetry and cycle‐to‐cycle variation, achieving software‐level performance in neural network training. However, a quantitative analysis of how these algorithms affect the relaxation of device specifications is yet to be conducted. This study provides a detailed analysis by elucidating the device prerequisites for training with the Tiki‐Taka algorithm versions 1 (TTv1) and 2 (TTv2), which leverage the dynamics between multiple arrays to compensate for device non‐idealities. A multiparameter simulation is conducted to assess the impact of device non‐idealities, including asymmetry, retention, number of pulses, and cycle‐to‐cycle variation, on neural network training. Using pattern‐recognition accuracy as a performance metric, the required device specifications for each algorithm are revealed. The results demonstrate that the standard stochastic gradient descent algorithm requires stringent device specifications. Conversely, TTv2 permits more lenient device specifications than the TTv1 across all examined non‐idealities. The analysis provides guidelines for the development, optimization, and utilization of devices for high‐performance neural network training using Tiki‐Taka algorithms.

Modeling and Optimization of Modified TiO2 with Aluminum and Magnesium as ETL in MAPbI3 Perovskite Solar Cells: SCAPS 1D Frameworks.

The perovskite device, incorporating a modified nanostructure of TiO2 as the electron transport layer, has been investigated to enhance its performance compared to the pure TiO2 device. Various materials undergo electrochemical doping or treatment on TiO2 to improve their photocatalytic application, thereby enhancing the current density, minimizing recombination, and improving device stability. In this study, a numerical SCAPS simulation was employed to validate experimental findings from the literature. According to the literature, this marks the first instance of doping Al3+ and Mg2+ on TiO2 due to their ionic radius comparable to that of Ti4+, at different doping concentrations. The device was modeled and simulated with the experimental parameters of bandgap, series, and shunt resistances for pure TiO2, aluminum-doped TiO2 (Al-TiO2), and magnesium-doped TiO2 (Mg-TiO2). From the validated results, the Al-TiO2 and Mg-TiO2-based devices' configurations with minimum percentage errors of 0.427 and 2.771%, respectively, were selected and simulated across nearly 90 (90) configurations to determine the optimum device model. Optimizing absorber thickness, bandgap, doping concentration, metal electrode, as well as series and shunt resistance resulted in enhanced device performance. According to the proposed model, Al-TiO2 and Mg-TiO2 configurations achieved higher power conversion efficiency values of 19.260 and 19.860%, respectively. This improvement is attributed to the reduction in recombination rates through the injection of a higher photocurrent density.

Electromechanical properties and piezoelectric potentials of one-dimensional GaN nanostructures: A bond relaxation investigation

From the viewpoint of atomic bond relaxation, an analytical approach was put forward to elucidate the physical origins of crystal size and cross-sectional shape dependency of piezoelectric potentials in GaN nanowires and nanotubes. It is demonstrated that (i) size-induced increase in piezoelectric potential is attributed to the coupling effect of the rising piezoelectric coefficient and both the reducing dielectric constant and elastic constant caused by the surface atomic coordination number loss, bond energy perturbation, and surface-to-volume ratio rising; (ii) as the number of sides for polygonal nanowires or nanotubes with the same equivalent radius decreases, the surface-to-volume ratio rises, and the piezoelectric potential increases; and (iii) the nanotubes can generate a piezoelectric potential higher than their nanowire counterparts due to their larger surface-to-volume ratios. The proposed formulation offers a scientific basis for the fabrication, optimization, and modulation of one-dimensional GaN-based piezoelectric nanometer devices.

Buckling of planar curved beams with finite prebuckling deformation

The serpentine structure with a sufficiently thick cross section has recently been proposed as an important design concept in stretchable electronics, which features mechanically stable in-plane deformation mechanism and very low electrical resistance, bringing unique advantages for devices compared with the traditional thin ribbon layout. However, unduly increasing the thickness is well known to sacrifice the overall flexibility and functionality of devices. Such a contradiction leads to challenges in structural stability, as a relatively thick but insufficient serpentine structure may eventually undergo the out-of-plane buckling after significant in-plane prebuckling deformation and appreciable alterations in initial configuration, which is ignored by most conventional buckling theories (CBTs) and linear buckling analysis in commercial finite element analysis software, producing intolerable errors when predicting the critical loads. In this paper, a systematic and straightforward theory considering the finite prebuckling deformation (FPD buckling theory) is established to investigate the underlying mechanism. Two sets of governing equations related to the prebuckling and FPD buckling behavior are obtained. Four representative examples, including two classical problems of planar curved beams and two typical loading conditions of serpentine structures, have been carefully studied. Comparisons with the accurate geometrically-nonlinear-analysis-based (GNAB) buckling analysis have amply demonstrated the validity of our theory in predicting the reinforcement effect of prebuckling deformation on the buckling resistance of structures. Key dimensionless geometric parameters that govern this effect have also been identified, providing direct and effective guidance for the design and optimization of stretchable electronic devices.

Achieving beyond 30% efficiency for hole-transport-layer-free CsSnI3 perovskite solar cell: a comprehensive simulation study

In recent years, the growing significance of lead-free CsSnI3 perovskite can be credited to its outstanding optoelectronic properties and environmentally friendly nature. Nevertheless, the photovoltaic potential of CsSnI3 is limited due to challenges in achieving defect-free device structures. The current study thoroughly analyzed the performance of CsSnI3-based perovskite solar cells (PSCs) using the SCAPS-1D software. An in-depth investigation was performed on multiple physical parameters, including the thickness of perovskites layer, acceptor density (NA), operating temperature, defect densities, shunt resistance (RSh) and series resistance (RS). This comprehensive study aimed to identify the optimal device configuration that yields the highest power conversion efficiency (PCE) for the hole-transport-layer (HTL)-free CsSnI3-based PSCs. The obtained results confirmed that it is crucial to decrease the number of defects (Nt) at the perovskites/electron transport layer (ETL) interface to improve the efficiency of CsSnI3-based PSCs. The optimized device demonstrated exceptional performance, achieving an open-circuit voltage (VOC) of 1.12 V, a fill factor (FF) of 85.08%, a short-circuit current density (JSC) of 33.29 mA cm−2 and an efficiency of 31.87%. This high efficiency simulated result provide valuable insights into the design of high-performance CsSnI3-based PSCs, paving the way for potential breakthroughs in cost-effective and eco-friendly solar energy technologies.

Evaporated Copper‐Based Perovskite Dynamic Memristors for Reservoir Computing Systems

AbstractDynamic memristors are considered as the optimal hardware devices for reservoir computing (RC) enabled by their nonlinear conductance variations. This significantly reduces the extensive training workload typically required by traditional neural networks. Lead halide perovskites, with their tunable band structure and active ion migration properties, have emerged as highly promising materials for developing dynamic memristors. However, large‐scale and consistently stable production remains a challenge for perovskite functional films, while lead elements' toxicity and environmental impact also partly restrict their practical device utilization. In this work, lead‐free copper‐based perovskite (i.e., CsCu2I3) films are prepared by thermal evaporation for constructing dynamic memristors. The effective conductivity modulation of CsCu2I3‐based memristor can be utilized in artificial neural networks, achieving a high handwritten digit recognition accuracy of 91.2%. In addition, the RC system is also constructed based on the dynamic behavior of the devices, by which a letter recognition accuracy of 98.2% with simple training is achieved. This technology provides a feasible pathway to construct copper‐based perovskite dynamic memristors for future neural network information processing.

Buried interface modification of condensation reflux-processed SnO2 electron transport layers for CsPbBr3 perovskite solar cells

Low temperature solution-processed SnO2 is favorable for electron transport layer (ETL) of flexible perovskite solar cells (PSCs) owing to their process compatibility. Here, we propose a route of condensation reflux-processed SnO2 ETLs for PSCs using SnCl2 source. However, the low temperature SnCl2-derived SnO2 ETLs are inherently associated with a high defect state density as well as a large amount of H+ and Cl- residual on the surface, which will cause open-circuit voltage (VOC) loss of the devices. To tackle this issue, aqueous solutions of KOH, CsOH, and KCl have been employed to regulate the buried interfaces. The effects of different cations (K+ and Cs+) and anions (OH- and Cl-) on the photovoltaic performance of the devices have been comparatively explored. The buried interface with KOH modification showed the best effect, which not only improved the quality and light absorption of CsPbBr3 perovskite layer but also markedly reduced the defect state density at the interface. The optimal device with an architecture of FTO/SnO2/KOH/CsPbBr3/carbon exhibited a champion power conversion efficiency (PCE) of 7.80 %, VOC of 1.46 V, short-circuit current density (JSC) of 7.50 mA/cm2, and fill factor (FF) of 0.71, compared with the pristine counterpart with a PCE of 5.86 %, VOC of 1.40 V, JSC of 6.37 mA/cm2, and FF of 0.66, respectively. The strategy of low temperature-processed SnO2 ETLs by KOH interface modification may also provide an avenue for other-typed flexible PSCs manufacture.

Atomic‐Scale Structural Dynamics at a‐Si:H/c‐Si Heterointerface During Low‐Temperature Thermal Annealing

AbstractThe integrity of the hydrogenated amorphous silicon/crystalline silicon (a‐Si:H/c‐Si) interface is essential for the enhanced performance of a‐Si:H/c‐Si heterojunction‐based devices. However, during annealing processes aimed at passivating silicon dangling bonds, unexpected Si epitaxy and nanotwin formation tend to emerge, even under low‐temperature conditions. Therefore, understanding the influence of such annealing on the a‐Si:H/c‐Si interfacial structure is therefore pivotal for device optimization. In this study, the atomic‐scale structural transformation of the a‐Si:H/c‐Si heterointerface subjected to low‐temperature annealing is delved into. The dynamic evolution of this interface is captured by employing in situ spherical aberration (CS)‐corrected transmission electron microscopy (TEM), molecular dynamics (MD) simulations, and density functional theory (DFT) calculations. The TEM observations indicated that Si epitaxy initiated before Si nanotwin formation, and these nanotwins are inclined to revert to epitaxial structures upon sustained annealing. Through MD and DFT insights, the thermodynamic and kinetic intricacies driving the concerted tri‐layer atomic shift characterizing the Si nanotwin‐to‐epitaxy transition are decoded. The findings shed light on the thermal behavior of a‐Si:H/c‐Si interfaces, offering new perspectives on the thermal management in silicon heterojunction devices.

Impact of Atomic Defects on Water Contact Angle of 2D Molybdenum Disulfide Surfaces.

Interfacial dynamics within nanofluidic systems are crucial for applications like water desalination and osmotic energy harvesting. Understanding these dynamics can inform the rational optimization of two-dimensional (2D) materials and devices for such applications. This study explores the wetting behavior of realistic 2D MoS2 surfaces incorporating vacancies and atomic steps, known as atomic defects. We employ a combined density functional theory (DFT) and molecular dynamics (MD) computational approach to elucidate the influence of atomic defects on the MoS2-water interface. DFT calculations are utilized to determine the charge distribution within MoS2. Subsequently, free energy calculations are obtained through MD simulations of the MoS2-water interface. Our findings underscore the importance of incorporating atomic defects into MoS2 surfaces for accurate water contact angle (WCA) predictions in nanofluidic simulations, particularly when using Abal et al. force field parameters. However, the force field developed by Liu et al. yielded more accurate results for pristine MoS2 surfaces. While these parameters provide reliable outcomes for pristine MoS2 surfaces, their application to surfaces with defects may lead to underestimation of WCA. This highlights the critical need for realistic surface representations in nanofluidic modeling to accurately capture the complex interactions between water and MoS2 materials.

On analysis of structural and optical absorption characterization of Bi35Sb5Se60 nanostructured thin films for photosensing application

In this extensive study, the primary focus is directed towards the intricate exploration of the optical absorption characteristics inherent in nanostructured Bi35Sb5Se60 thin films, prepared by thermal evaporation under a vacuum of 10−5 Torr, ensuring high purity and uniformity for optoelectronic applications. With a particular emphasis on their prospective application in photosensing devices. The research employs a comprehensive approach, integrating various spectrophotometric measurements that encompass both transmission and reflection analyses. The meticulous examination of the absorption edge characteristics reveals a direct energy gap of 1.82 eV, providing a foundational understanding of the material's electronic structure. The determination of the dispersion energy at 6.35 eV and the oscillator energy at 2.96 eV further enriches the understanding of the material's optical behavior, shedding light on its capacity to interact with incident light. A noteworthy highlight emerges with the identification of high photosensitivity exhibited by the heterojunction, particularly under lower reverse bias conditions, underscoring its potential utility in photosensing applications. The PL spectra reveal a highly broadened defect emission band with peaks at 475 nm, 490 nm, and 540 nm, providing insights into the recombination processes within the material. The identification of emission peaks related to the conduction band edge and acceptor levels further enhances the comprehension of the material's optical and electronic characteristics. underscores the potential of these nanostructured thin films for efficient photosensing devices. The findings presented in this research not only advance the understanding of the material's properties but also lay a robust foundation for further research and development in the design and optimization of photoresponsive devices within the realm of optoelectronic technologies.

Switching figure-of-merit, optimal design, and power loss limit of (ultra-) wide bandgap power devices: A perspective

Power semiconductor devices are utilized as solid-state switches in power electronics systems, and their overarching design target is to minimize the conduction and switching losses. However, the unipolar figure-of-merit (FOM) commonly used for power device optimization does not directly capture the switching loss. In this Perspective paper, we explore three interdependent open questions for unipolar power devices based on a variety of wide bandgap (WBG) and ultra-wide bandgap (UWBG) materials: (1) What is the appropriate switching FOM for device benchmarking and optimization? (2) What is the optimal drift layer design for the total loss minimization? (3) How does the device power loss compare between WBG and UWBG materials? This paper starts from an overview of switching FOMs proposed in the literature. We then dive into the drift region optimization in 1D vertical devices based on a hard-switching FOM. The punch-through design is found to be optimal for minimizing the hard-switching FOM, with reduced doping concentration and thickness compared to the conventional designs optimized for static FOM. Moreover, we analyze the minimal power loss density for target voltage and frequency, which provides an essential reference for developing device- and package-level thermal management. Overall, this paper underscores the importance of considering switching performance early in power device optimization and emphasizes the inevitable higher density of power loss in WBG and UWBG devices despite their superior performance. Knowledge gaps and research opportunities in the relevant field are also discussed.

Impact of Intracranial Hemorrhage After Endovascular Treatment for Medium Vessel Occlusion.

Endovascular treatment (EVT) for medium vessel occlusion (MeVO) raises concern about hemorrhagic complications; however, its clinical impact has not been elucidated. Therefore, we investigated the association between intracranial hemorrhage (ICH) after EVT for MeVO and functional outcomes. We conducted a post hoc analysis of the Japan Registry of NeuroEndovascular Therapy 4, a nationwide registry in Japan from 2015 to 2019 including 13 479 patients who underwent EVT for acute ischemic stroke. This study included 2465 patients with MeVO from 166 participating centers in Japan. We compared patients who underwent EVT for MeVO according to their hemorrhagic complication after EVT (no ICH, asymptomatic ICH, and symptomatic ICH). Outcomes included a modified Rankin scale (mRS) score at 30 days and all-cause mortality within 30 days. We estimated the odds ratios (ORs) and their CIs using a multivariable logistic regression model. Among 2394 patients analyzed, 302 (12.6%) developed ICH, with 95 (31.5%) being symptomatic. Compared with the no-ICH group (n = 2092), the asymptomatic and symptomatic ICH groups had a lower proportion of patients with an mRS score of 0 to 2 at 30 days (41% vs 34%, vs 7.4%, P for trend <.001), with an adjusted ORs of 0.77 (95% CI, 0.53-1.12) and 0.12 (95% CI, 0.05-0.30) in the asymptomatic and symptomatic ICH groups, respectively. The adjusted common ORs of one-point shift of mRS score at 30 days in the asymptomatic ICH group was 0.76 (95% CI, 0.57-0.99) and that of the symptomatic ICH group was 0.13 (0.07-0.23), compared with the no-ICH group. ICH after EVT for MeVO was associated with worse outcomes, whether they were symptomatic or not. The optimal treatment devices or techniques to reduce ICH after EVT for MeVO are crucial.

Predicting Steady-State Metabolic Power in Cerebral Palsy, Stroke, and the Elderly During Walking With and Without Assistive Devices.

Individuals with walking impairment, such as those with cerebral palsy, often face challenges in leading physically active lives due to the high energy cost of movement. Assistive devices like powered exoskeletons aim to alleviate this burden and improve mobility. Traditionally, optimizing the effectiveness of such devices has relied on time-consuming laboratory-based measurements of energy expenditure, which may not be feasible for some patient populations. To address this, our study aimed to enhance the state-of-the-art predictive model for estimating steady-state metabolic rate from 2-min walking trials to include individuals with and without walking disabilities and for a variety of terrains and wearable device conditions. Using over 200 walking trials collected from eight prior exoskeleton-related studies, we trained a simple linear machine learning model to predict metabolic power at steady state based on condition-specific factors, such as whether the trial was conducted on a treadmill (level or incline) or outdoors, as well as demographic information, such as the participant's weight or presence of walking impairment, and 2 minutes of metabolic data. We demonstrated the ability to predict steady-state metabolic rate to within an accuracy of 4.71 ± 2.7% on averageacross allwalking conditionsand patient populations, including with assistive devices andondifferent terrains. This work seeks to unlock the use of in-the-loop optimization of wearable assistive devices in individuals with limited walking capacity. A freely available MATLAB application allows other researchers to easily apply our model.