Forward Step Research Articles

Effect duration of a self-applied talocrural joint mobilization on restricted dorsiflexion: a repeated measures design

ABSTRACT Objectives We aimed to determine the effect duration of a talocrural mobilization on individuals with restricted dorsiflexion during a static weight bearing lunge test (WBLT) and dynamic 3D motion capture-based peak ankle dorsiflexion during a forward step down (FSD) task. Secondarily, we aimed to correlate any immediate changes in ankle mobility with concurrent changes in proximal joint kinematics during the FSD post-mobilization. Methods Seventy-six individuals were screened for dorsiflexion restriction, of which 26 (15 females, 22.3 ± 2.2 years old, body mass index 25.2 ± 2.9 kg/m2) qualified with a WBLT of ≤ 35° on at least one limb. A baseline WBLT measure and 3D motion capture of 5 consecutive FSD repetitions on a 6-inch box were obtained. Participants then viewed an instructional video of a talocrural joint self-mobilization using a resistance band. WBLT and FSD were collected again immediately post-mobilization and at 5-min intervals for 60 min or until the WBLT returned to baseline for 2 consecutive measures. Results WBLT dorsiflexion showed a mean increase of 6.5 degrees (p < 0.001) post-mobilization. The effect faded over time and no longer differed from baseline 25 min post-mobilization (p = 0.964). Dynamic peak ankle dorsiflexion did not change post-mobilization at any time point (p ≥ 0.546). No 3D kinematic time-course changes were observed at the hip or knee. However, immediate raw alterations in dorsiflexion correlated with alterations for hip and knee flexion. Discussion/Conclusion A talocrural joint mobilization increased static dorsiflexion per the WBLT for a 20–25-min period with regression to baseline. However, increased dynamic ankle dorsiflexion was not observed during the FSD task. Improved mobility alone does not appear to change movement patterns. Clinicians should be aware of both effect duration and the potential need for task-specific training to better facilitate dynamic utilization of increased mobility.

Designing a Brushed DC Motor Driver with a Novel Adaptive Learning Algorithm for the Automotive Industry

In this study, a stepper driver, which is suitable for use in the automotive industry, designed for general use, capable of adaptive learning in the systems in which it is used, and with CAN and universal asynchronous receiver–transmitter (UART) communication options, was designed. This design was made with a PIC18F25K22 microcontroller unit (MCU). Rotor speed, armature current, and terminal voltage can be measured on the proposed brushed DC motor drive system. It gives the desired response by evaluating obstacles and other situations in which high currents are drawn from the source. The speed measurement of the vehicle and the open and closed status of an automatic door can be monitored. The main contribution of the designed PCB is an adaptive learning algorithm (ALA) that uses the pulses of the encoder, estimates the position of the step, and manages the operation process to prevent mechanical damage at the final point of the motor. Furthermore, unexpected hitting and pinching which are defined as obstacles to the driver can be controlled by monitoring the current value drawn from the driver. The benefit of this method is that the life of the mechanical step is increased due to the management of the forward and backward step operation, preventing potential accidents.

Development and Application of a Tornado Database for the Chinese Mainland

This study develops a preliminary tornado database for the Chinese mainland using information provided by the Yearbook of Meteorological Disasters in China (YMDC) from 2003 to 2019 and other public media report data. This database includes tornado occurrence time, geographical location, intensity, and damage descriptions. A modified Enhanced Fujita (EF) scale criterion adapted to the description of the damage indicator (DI) and degree of damage (DOD) in the YMDC and media reports is presented to better estimate the tornado intensity. The spatial and temporal distribution characteristics of tornadoes in Chinese mainland are examined. A stochastic simulation algorithm is proposed to perform a risk assessment of tornado hazards. The occurrence of tornadoes is randomly sampled using negative binomial distribution. The kernel density estimation (KDE) method based on the Gaussian kernel is applied to estimate the probability density of geographically dependent tornado occurrence before randomly generating tornado occurrence locations. The probability and conditional probability of tornado occurrence with different intensity levels are calculated for different counties in Jiangsu and Guangdong Provinces using Bayes’ theorem. The database provides a forward step toward rational assessment of tornado-induced disasters in Chinese mainland. The database can be accessed at https://doi.org/10.4121/19801633.v1 (Zhang et al., 2022).

Robust and Reversible Thermal/Electro‐Responsive Supramolecular Polymeric Adhesives via Synergistic Hydrogen‐Bonds and Ionic Junctions

Adhesive conducting elastomers are rising materials towards cutting‐edge applications in wearable and implantable soft electronics. Yet, engineering the conductive adhesives with robust and tunable interfacial bonding strength is still in its infancy stage. We herein identify a structurally novel supramolecular polymer scaffold, characterized by synergistic coexistence of hydrogen‐bonding (H‐bonding) interactions and electrostatic ionic junctions, endowing the robust and tunable elastic conducting adhesives with remarkable thermal/electro‐responsive performance. H‐bonding association and electrostatic interaction play orthogonal yet synergistic roles in the strong supramolecular adhesive formation, serving as the leveraging forces for opposing both cohesion and adhesion energy. To do so, six‐arm star‐shaped random copolymers P(DAP‐co‐Thy‐co‐MBT)6 are strategically designed, bearing H‐bonding PDAP (poly(diaminopyridine acrylamide)) and PThy (poly(thymine)) segments, which can form hetero‐complementary DAP/Thy H‐bonding association, along with conductive poly(ionic liquid)s segment: PMBT, (poly(1‐[2‐methacryloylethyl]‐3‐methylimidazolium bis(trifluoromethane)‐sulfonamide)). DAP/Thy H‐bonding association, along with electrostatic ionic interaction, can yield dual supramolecular forces crosslinked polymeric networks with robust cohesion energy. Moreover, coexistence of poly(ionic liquid)s can impact and interfere the configuration of H‐bonding association, liberate more free DAP and Thy motifs to form H‐bonds towards substrate, affording strong surface adhesion in a synergistic manner. This work demonstrates a significant forward step towards potential adhesives devoted to hybrid electronic devices.

Robust and Reversible Thermal/Electro-Responsive Supramolecular Polymeric Adhesives via Synergistic Hydrogen-Bonds and Ionic Junctions.

Adhesive conducting elastomers are rising materials towards cutting-edge applications in wearable and implantable soft electronics. Yet, engineering the conductive adhesives with robust and tunable interfacial bonding strength is still in its infancy stage. We herein identify a structurally novel supramolecular polymer scaffold, characterized by synergistic coexistence of hydrogen-bonding (H-bonding) interactions and electrostatic ionic junctions, endowing the robust and tunable elastic conducting adhesives with remarkable thermal/electro-responsive performance. H-bonding association and electrostatic interaction play orthogonal yet synergistic roles in the strong supramolecular adhesive formation, serving as the leveraging forces for opposing both cohesion and adhesion energy. To do so, six-arm star-shaped random copolymers P(DAP-co-Thy-co-MBT)6 are strategically designed, bearing H-bonding PDAP (poly(diaminopyridine acrylamide)) and PThy (poly(thymine)) segments, which can form hetero-complementary DAP/Thy H-bonding association, along with conductive poly(ionic liquid)s segment: PMBT, (poly(1-[2-methacryloylethyl]-3-methylimidazolium bis(trifluoromethane)-sulfonamide)). DAP/Thy H-bonding association, along with electrostatic ionic interaction, can yield dual supramolecular forces crosslinked polymeric networks with robust cohesion energy. Moreover, coexistence of poly(ionic liquid)s can impact and interfere the configuration of H-bonding association, liberate more free DAP and Thy motifs to form H-bonds towards substrate, affording strong surface adhesion in a synergistic manner. This work demonstrates a significant forward step towards potential adhesives devoted to hybrid electronic devices.

A Reversible Perspective on Petri Nets and Event Structures

Event structures have emerged as a foundational model for concurrent computation, explaining computational processes by outlining the events and the relationships that dictate their execution. They play a pivotal role in the study of key aspects of concurrent computation models, such as causality and independence, and have found applications across a broad range of languages and models, spanning realms like persistence, probabilities, and quantum computing. Recently, event structures have been extended to address reversibility, where computational processes can undo previous computations. In this context, reversible event structures provide abstract representations of processes capable of both forward and backward steps in a computation. Since their introduction, event structures have played a crucial role in bridging operational models, traditionally exemplified by Petri nets and process calculi, with denotational ones, i.e., algebraic domains. In this context, we revisit the standard connection between Petri nets and event structures under the lenses of reversibility. Specifically, we introduce a subset of contextual Petri nets, dubbed reversible causal nets , that precisely correspond to reversible prime event structures. The distinctive feature of reversible causal nets lies in deriving causality from inhibitor arcs, departing from the conventional dependence on the overlap between the postset and preset of transitions. In this way, we are able to operationally explain the full model of reversible prime event structures.

Multi-stage planning of clean resources and energy storage assets with hybrid uncertainty modeling for low-carbon resilient distribution systems

Climate change drives the urgent need for low-carbon and resilient energy system transitions. However, current planning methods ignore the inherent conflicts between carbon emission reduction and resilience enhancement, failing to optimally balance asset allocation for both aspects. They also neglect the long-term dynamic and stochastic nature of transitions, exposing systems to hybrid uncertainties. This paper presents a multi-stage dynamic planning method for clean resources and energy storage assets in power distribution networks. First, to facilitate low-carbon and resilient transitions, adaptive, stage-wise planning decisions are optimally determined under various planning strategies to mitigate risks stemming from hybrid uncertainties. Second, to precisely quantify the impact of hybrid uncertainties on decision-making, a systematic hybrid-uncertainty modeling approach is designed to capture short- and long-term uncertainties arising from both exogenous and endogenous factors. Third, to alleviate computational complexity, a decomposition-based stochastic dynamic dual integer programming method with augmented cut generation is introduced, which decomposes the original problem into stage-wise subproblems coordinated through forward and backward steps. Numerical results show that the proposed method generates cost-effective planning schemes, yielding $60 K in savings and reducing carbon emissions by 3853 tons. The method also demonstrates strong scalability, with a 52% reduction in solution time for large-scale planning problems.

The impact of anterior knee displacement on knee joint load during the forward bow step in Tai Chi.

While the forward bow step is a crucial component of Tai Chi (TC) practice, little research has been conducted on its impact on knee joint load and muscle coordination. This study aims to investigate the effects of three different knee forward positions during the TC forward bow step on knee joint loading. Twenty TC practitioners were recruited, and motion capture systems, force platforms, and surface electromyography were utilized to synchronously collect biomechanical parameters of three types of forward bow steps: knee joint not exceeding the tip of the foot (NETT), knee joint forward movement level with the tip of the foot (LTT), and knee joint forward movement exceeding the tip of the foot (ETT). Ligament and muscle forces were calculated using OpenSim software for musculoskeletal modeling and simulation. One-way ANOVA was used to analyze the variations of the indicators during the peak anterior displacement of the knee joint in three movements. Additionally, spm1d one-way ANOVA was employed to examine the variations in the one-dimensional curve of the indicators throughout the entire movement process. Compared with LTT and ETT, the NETT posture was associated with significantly decreased knee flexion angle (F = 27.445, p = 0.001), knee anterior-posterior translation (F = 36.07, p < 0.001), flexion-extension torque (F = 22.232, p = 0.001), ligament force (F = 9.055, p = 0.011). Additionally, there was also a significant reduction in muscle strength, including quadriceps (F = 62.9, p < 0.001), long biceps femoris (F = 18.631, p = 0.002), lateral gastrocnemius (F = 24.933, p = 0.001) and soleus (F = 7.637, p = 0.017). This study further confirms that in the forward lunge movement of Tai Chi, the knee joint load is mainly concentrated during the forward movement phase. Compared to the knee joint load at the NETT position, the load is greater at the LTT position; and compared to the LTT position, the load is even greater at the ETT position.

Harmonics mitigation in distribution networks comprising smart online electric vehicles chargers based on equal sharing algorithm

This study focuses on analyzing the distribution network's performance due to the scheduled integration of electric vehicles (EVs). In this context, an online equal sharing scheduled algorithm is utilized to monitor and manage the charging process of EVs inside the charging stations (CSs). By this way, the planned charging demand may aid in minimizing the power stresses at the point of common coupling (PCC). Afterwards, the penetration of CSs into practical distribution networks is investigated and resolved from power quality perspectives. Meanwhile, voltage and current harmonic distortions of 3.3 % and 8.3 %, respectively resulted at the PCC due to installing these harmonic injection sources. It is worth mentioning that diverse topologies of passive filters are interrogated in this research for harmonics mitigation purposes. Single tuned passive filters demonstrate their superiority over other architectures in harmonics suppression with the simplest and most cost-effective solution. Additionally, the overloading stresses over the installed package substation of about 7 % are also alleviated permitting the CS to charge with full capacity. Finally, this work presents a forward step in the fields of EVs smart charging and harmonics mitigation in practical distribution gids.

Surface Decorated SnOx modifiers Boost Oxygen Reduction and Hydrogen Evolution Reaction Performance of Pt Nanorods

The development of efficient, durable and commercially competitive catalysts for oxygen reduction reaction (ORR) and hydrogen evolution reaction (HER) is undoubtedly a challenging task for fuel cell technology. In this context, for the first time, we developed a bi-metallic heterogeneous nanocatalyst (NC) comprising high-density atomic SnOx-clusters anchored Pt-nanorods (denoted as SnOx@Pt) via formic acid reduction method (FAM) as a bifunctional catalyst for ORR and HER (Fig.1). The as-prepared SnOx@Pt nanorods exhibit unprecedented high mass activity (MA) of 160 mAmg-1 (Pt) at 0.85 V vs RHE in acidic ORR (0.5M HClO4), which outperformed the commercial J.M.-Pt/C (20 wt.% Pt) catalyst by 1.4 folds. Of special relevance, SnOx@Pt nanorods exhibit remarkable durability when operated up to 5000 cycles in accelerated durability test (ADT) and retain their 87% MA as that of the initial condition. Additionally, as-prepared SnOx@Pt nanorods demonstrated a notable lower overpotential (η) of 48 mV at the cathodic current density of 10 mAcm-2 and the Tafel slope of 33 mVdec -1 in acidic HER (0.5M H2SO4). These overpotential and Tafel slope values are significantly lower compared to commercial J.M.-Pt/C catalyst (η = 60 mV and Tafel slope = 38 mVdec-1). Moreover, such material retained its 100% performance in the chronoamperometric (CA) stability test up to 6h, which shows its capability in potential commercialization. The cross-referencing results of physical inspections and electrochemical analysis reveal that such a high-performance of SnOx@Pt nanorods is attributed to the local synergetic collaboration between SnOx modifiers and neighboring Pt active sites. More specifically, the SnOx modifiers promote the H-OH bond cleavage during HER, while simultaneously promotes the desorption of oxygen species on Pt surface in ORR, which later triggers the reduction reaction performances. Meanwhile, SnOx provides a shielding effect to Pt during harsh reduction conditions and thus high stability of SnOx@Pt nanorods is achieved. Of utmost importance, this study not only unveils a novel geometric design but also provides the mechanistic understanding behind the superior electrochemical properties of the unique SnOx modifiers in ORR and HER. Hence, we envision that the proposed rationale will be a forwarding step for the development of high-performance low Pt content catalysts for fuel cells with superior electrochemical properties far beyond the physical nature of transition elements. Keywords: Oxygen reduction reaction, hydrogen evolution reaction, fuel cells, nanocatalysts, mass activity

The loss mechanisms and vortex dynamics of rotor platform step geometries in a low-speed research compressor stage

The construction and assembly of an axial compressor blade row introduces real geometric features, which leads to the deviation of flow characteristics from the intended design. According to the realistic assembly errors observed in the four-stage low-speed research compressor developed by Shanghai Jiao Tong University, two representative step geometries at the rotor platform were abstracted. Unsteady simulations were carried out in the stage environment to study the influence of hub discontinuities on compressor performance and loss mechanisms. The comparisons of smooth hub, elevated hub (EH), and a wedged hub (WH) demonstrated that all the step geometries resulted in increased loss inside both rotor and stator rows. The loss induced by WH was more sensitive to step heights than EH. Under the influence of forward facing step, rotor incidence was reduced and horseshoe vortex (HV) was exacerbated, leading to the accumulation and separation of low momentum fluids at the pressure side corner. This led to a redistribution of losses across the span. In the lower half span, the relative total pressure loss increased almost linearly with step heights, while in the tip region, it reduced slightly. The backward facing step generated the step corner vortices (SCV) that shed and propagated downstream. The SCV interacted with the HV and cavity leakage vortex in the stator passage. Coupled with the augmented stator inflow angle, the corner separation at the suction side was dramatically intensified and led to a significant loss increase.

Experimental backward integration for state-dependent differential Riccati equation (SDDRE): A case study on flapping-wing flying robot

Backward integration (BI) is a classical approach for solving optimal control arising from linear quadratic regulator (LQR) design in differential form. It proposes a two-round solution, the first one starting from a final boundary condition to the initial one to generate the optimal gain, and the next one, solving the control system in a forward loop. Implementation of the BI on the nonlinear optimal control, the state-dependent differential Riccati equation (SDDRE), is a challenge since in the backward loop, the state information is missing and is not straightforward like the LQR. Hence, a control for backward motion is required to regulate the system from the terminal to the initial desired states. While there have been some valuable works on the theoretical implementation of the BI in simulations for the SDDRE, this approach has not been reported in experiments for the best knowledge of the authors. Here in this work, the SDDRE is solved using the BI and two backward and forward solution steps, and it is experimentally applied for a flapping-wing flying robot (FWFR). The execution of the control system is done onboard using Raspberry Pi 4B as the processor which has limited computational capacity. The trajectory tracking of a line in a closed limited space is proposed for the FWFR flight. The objective is to position the robot bird at the corner of the rectangular limited space with minimum error in translation. The results have been presented for 21 flights to show the repeatability and presented the best-case minimum error of 10 (cm) at the end of the trajectory, in the YZ-plane. Considering approximately 12 (m) flight path, the error is found less than 1 percent of the travel distance. The results were compared with forward integration to confirm the correctness of the computation. The experimental flight dataset, MATLAB simulation codes, and experimental Python codes are available as supplementary material for this work in the online version of the paper.

Research into Prediction Method for Pressure Pulsations in a Centrifugal Pump Based on Variational Mode Decomposition-Particle Swarm Optimization and Hybrid Deep Learning Models.

Centrifugal pump pressure pulsation contains various signals in different frequency domains, which interact and superimpose on each other, resulting in characteristics such as intermittency, non-stationarity, and complexity. Computational Fluid Dynamics (CFD) and traditional time series models are unable to handle nonlinear and non-smooth problems, resulting in low accuracy in the prediction of pressure fluctuations. Therefore, this study proposes a new method for predicting pressure fluctuations. The pressure pulsation signals at the inlet of the centrifugal pump are processed using Variational Mode Decomposition-Particle Swarm Optimization (VMD-PSO), and the signal is predicted by Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM) model. The results indicate that the proposed prediction model combining VMD-PSO with four neural networks outperforms the single neural network prediction model in terms of prediction accuracy. Relatively high accuracy is achieved by the VMD-PSO-CNN-LSTM model for multiple forward prediction steps, particularly for a forward prediction step of 1 (Pre = 1), with a root mean square error of 0.03145 and an average absolute percentage error of 1.007%. This study provides a scientific basis for the intelligent operation of centrifugal pumps.

Suppressing the HIFU interference in ultrasound guiding images with a diffusion-based deep learning model

Background and objectivesIn ultrasound guided high-intensity focused ultrasound (HIFU) surgery, it is necessary to transmit sound waves at different frequencies simultaneously using two transducers: one for the HIFU therapy and another for the ultrasound imaging guidance. In this specific setting, real-time monitoring of non-invasive surgery is challenging due to severe contamination of the ultrasound guiding images by strong acoustic interference from the HIFU sonication. MethodsThis paper proposed the use of a deep learning (DL) solution, specifically a diffusion implicit model, to suppress the HIFU interference. We considered the images contaminated with HIFU interference as low-resolution images, and those free from interference as high-resolution. While suppressing HIFU interference using the diffusion implicit (HIFU-Diff) model, the task was transformed into generating a high-resolution image through a series of forward diffusion steps and reverse sampling. A series of ex-vivo and in-vivo experiments, conducted under various parameters, were designed to validate the performance of the proposed network. ResultsQuantitative evaluation and statistical analysis demonstrated that the HIFU-Diff network achieved superior performance in reconstructing interference-free images under a variety of ex-vivo and in-vivo conditions, compared to the most commonly used notch filtering and the recent 1D FUS-Net deep learning network. The HIFU-Diff maintains high performance with ‘unseen’ datasets from separate experiments, and its superiority is more pronounced under strong HIFU interferences and in complex in-vivo situations. Furthermore, the reconstructed interference-free images can also be used for quantitative attenuation imaging, indicating that the network preserves acoustic characteristics of the ultrasound images. ConclusionsWith the proposed technique, HIFU therapy and the ultrasound imaging can be conducted simultaneously, allowing for real-time monitoring of the treatment process. This capability could significantly enhance the safety and efficacy of the non-invasive treatment across various clinical applications. To the best of our knowledge, this is the first diffusion-based model developed for HIFU interference suppression.

Colistin: Lights and Shadows of an Older Antibiotic.

The emergence of antimicrobial resistance represents a serious threat to public health and for infections due to multidrug-resistant (MDR) microorganisms, representing one of the most important causes of death worldwide. The renewal of old antimicrobials, such as colistin, has been proposed as a valuable therapeutic alternative to the emergence of the MDR microorganisms. Although colistin is well known to present several adverse toxic effects, its usage in clinical practice has been reconsidered due to its broad spectrum of activity against Gram-negative (GN) bacteria and its important role of "last resort" agent against MDR-GN. Despite the revolutionary perspective of treatment with this old antimicrobial molecule, many questions remain open regarding the emergence of novel phenotypic traits of resistance and the optimal usage of the colistin in clinical practice. In last years, several forward steps have been made in the understanding of the resistance determinants, clinical usage, and pharmacological dosage of this molecule; however, different points regarding the role of colistin in clinical practice and the optimal pharmacokinetic/pharmacodynamic targets are not yet well defined. In this review, we summarize the mode of action, the emerging resistance determinants, and its optimal administration in the treatment of infections that are difficult to treat due to MDR Gram-negative bacteria.

Adaptive Graph Spatial-Temporal Attention Networks for long lead ENSO prediction

El Niño-Southern Oscillation (ENSO) is a crucial factor in global climate change, which can lead to disasters such as floods, droughts, and heavy rainfall worldwide. Although accurate long-lead prediction of ENSO is essential, the intricate time-varying spatial correlations make it challenging. In this paper, we propose a novel deep learning architecture, called Adaptive Graph Spatial-Temporal Attention Network (AGSTAN), to model the extensive spatial–temporal interactions in ENSO. We first develop a dynamic spatial graph module with self-attention mechanism, wherein the structure is adapted to the characteristics of the evolving states. Furthermore, we introduce the long-term temporal sequence module to capture long-range dependency between time-series outputs and inputs, and generate accurate long lead predictions with only one forward step. To validate its efficacy and robustness, we conducted experiments on the simulated and historical climate datasets. Results showcase that AGSTAN possesses superior performance in ENSO prediction, outperforming contemporary deep learning models and boasting a 23-month forecasting horizon with heightened correlation and minimized bias.

A backward cycling programme for people with Parkinson's disease: a feasibility and preliminary results study.

To assess the feasibility of backward cycling for people with Parkinson's disease. Secondary objectives were to assess changes in gait and balance following a 6-week program. A single-group prospective pre-test, post-test study with 1-month follow-up. Twenty-six people with Parkinson's disease (mean age: 69 (7.74) years, gender: 83% males, time since diagnosis: 6 (4.44) years). Participants pedaled backward on a stationary bicycle for 30 minutes at moderate intensity twice a week for 6 weeks. Feasibility was assessed by acceptability, suitability, and burden. Data collected at pre- and post-intervention with 1-month follow-up included backward stepping response variables, forward/backward gait variables, Mini-Balance Evaluation Systems Test (MBT), and 6 Minute Walk Test. There was a high retention rate (95.8%) and adherence rate (100%) with one adverse event and minimal burden. Significant improvements were seen in step count and excursion distance during backward stepping responses, forward and backward gait velocity, forward step length, and the Mini-BESTest. Backward cycling was a feasible intervention for people with Parkinson's disease, demonstrating low burden with high retention and adherence rates, and it is a safe exercise with the potential for benefits in gait and balance variables.

A physics-constrained and data-driven method for modeling supersonic flow

A fast solution of supersonic flow is one of the crucial challenges in engineering applications of supersonic flight. This article introduces a deep learning framework, the supersonic physics-constrained network (SPC), for the rapid solution of unsteady supersonic flow problems. SPC integrates deep convolutional neural networks with physics-constrained methods based on the Euler equation to derive a new loss function that can accurately calculate the flow fields by considering the spatial and temporal characteristics of the flow fields at the previous moment. Compared to purely data-driven methods, SPC significantly reduces the dependency on training data volume by incorporating physical constraints. Additionally, the training process of SPC is more stable than that of data-driven methods. Taking the classic supersonic forward step flow as an example, SPC can accurately calculate strong discontinuities in the flow fields, while reducing the data volume by approximately 60%. In the generalization test experiment for forward step flow and compression ramp flow, SPC also demonstrates good predictive accuracy and generalization capability under different geometric configurations and inflow conditions.

A voltage-driven dual-mode MoSe2 photodetector with graphene as van der Waals contact

Two-dimensional (2D) molybdenum selenide (MoSe2) is promising for use in the development of photodetectors for the harvesting of light from the ultraviolet to the near-infrared band, while high responsivity and fast response speed are difficult to simultaneously realize. Herein, we present a dual-mode MoSe2 photodetector with asymmetric electrodes, in which graphene and Cr metal are utilized as ohmic and Schottky contacts, respectively. The photodiode possesses fabulous Schottky characteristics, with a rectification ratio of ∼250 and a low dark current of ∼40 pA at −1 V. Under forward bias voltage of 1 V, the photodetector works in photoconductive mode with a slow response speed (decay time: ∼5 min) but high responsivity (632 mA W−1). However, at reverse bias voltage, the photodetector acts as a photovoltaic-type device due to the Schottky barrier between Cr and MoSe2. Because of the reinforced built-in electric field, the photodetector driven at −5 V shows much faster response speeds (rise time: 1.96 ms; decay time: 755 µs). This study provides a deep understanding of asymmetric structure MoSe2 photodetectors operated in two modes, which promotes a forward step toward 2D material optoelectronics.

Population Pharmacokinetics of Dasatinib in Healthy Subjects.

Dasatinib is one of the tyrosine kinase inhibitors. The main use of these agents is inhibition of cancerous cell proliferation. The therapeutic importance of tyrosine kinase inhibitors raises the necessity of many types of investigations, especially the pharmacokinetic analysis of these drugs in humans. This analysis, along with other investigations and clinical research, will contribute to the overall knowledge of the drug. This study focused on the population pharmacokinetics of dasatinib. The objective of the study was to investigate the sources of the variability of dasatinib in a population pharmacokinetics study in healthy participants. We utilized 4180 plasma observations from 110 subjects who were administered SPRYCEL® on two separate occasions under fasting conditions; data from 20% of the subjects (22 subjects) were extracted for the purpose of internal model evaluation and data from 88 subjects were used in modeling. The model was evaluated by visual predictive check of three different datasets. A two-compartmental model with first order absorption and transit compartment was considered the simplest base model to describe the data based on the corrected Bayesian information criterion evaluation. Covariates were tested through conditional sampling for the stepwise approach-screening procedure in Monolix 2020R1 version. Conditional sampling for the stepwise approach was used to include the correlated covariates within the base model in the forward inclusion step and then to eliminate them backwardly to ensure that the key covariates were kept in the model at the final stage. The effect of body mass index on the absorption rate constant was considered as significant covariate in the final established model. Visual predictive check for simulations, 20% of the original dataset (internal dataset) and an external dataset demonstrated the appropriateness of the final model. Population pharmacokinetic modeling was performed to describe dasatinib pharmacokinetics in healthy subjects. Body mass index was considered as a factor that might be used in the future along with studies on patients to adjust the dosing regimens. Dasatinib is classified as a highly variable drug; this variability was demonstrated in the study by the effect of body mass index on the absorption rate constant.