N′-(Diphenyl­methyl­ene)-2-hydroxy­benzo­hydrazide

In the title compound, C20H16N2O2, intra­molecular N—H⋯O and inter­molecular O—H⋯O hydrogen bonds are found. The inter­molecular hydrogen bonds link the mol­ecules into an infinite chain along the c axis. The dihedral angles between the aromatic rings are 16.9 (3), 80.8 (3) and 64.6 (3)

Aqua­bis(2-chloro­acetato-κO)(1,10-phenanthroline-κ2 N,N′)copper(II)

In the title complex, [Cu(C2H2ClO2)2(C12H8N2)(H2O)], the CuII ion is five-coordinated by two N atoms [Cu—N = 2.005 (2) and 2.029 (2) Å] from the 1,10-phenanthroline ligand, two O atoms [Cu—O = 1.943 (2)–1.966 (2) Å] from two 2-chloro­acetate ligands and one water mol­ecule [Cu—O = 2.253 (2) Å] in a distorted square-pyramidal geometry. The crystal structure exhibits inter­molecular O—H⋯O hydrogen bonds, short Cl⋯Cl contacts [3.334 (1) Å] and π–π inter­actions [centroid–centroid distance 3.621 (11) Å]

Closed-loop transcutaneous auricular vagus nerve stimulation for the improvement of upper extremity motor function in stroke patients: a study protocol

BackgroundTranscutaneous auricular vagus nerve stimulation (taVNS) has garnered attention for stroke rehabilitation, with studies demonstrating its benefits when combined with motor rehabilitative training or delivered before motor training. The necessity of concurrently applying taVNS with motor training for post-stroke motor rehabilitation remains unclear. We aimed to investigate the necessity and advantages of applying the taVNS concurrently with motor training by an electromyography (EMG)-triggered closed-loop system for post-stroke rehabilitation.MethodsWe propose a double-blinded, randomized clinical trial involving 150 stroke patients assigned to one of three groups: concurrent taVNS, sequential taVNS, or sham control condition. In the concurrent group, taVNS bursts will synchronize with upper extremity motor movements with EMG-triggered closed-loop system during the rehabilitative training, while in the sequential group, a taVNS session will precede the motor rehabilitative training. TaVNS intensity will be set below the pain threshold for both concurrent and sequential conditions and at zero for the control condition. The primary outcome measure is the Fugl-Meyer Assessment of Upper Extremity (FMA-UE). Secondary measures include standard upper limb function assessments, as well as EMG and electrocardiogram (ECG) features.Ethics and disseminationEthical approval has been granted by the Medical Ethics Committee, affiliated with Zhujiang Hospital of Southern Medical University for Clinical Studies (2023-QX-012-01). This study has been registered on ClinicalTrials (NCT05943431). Signed informed consent will be obtained from all included participants. The findings will be published in peer-reviewed journals and presented at relevant stakeholder conferences and meetings.DiscussionThis study represents a pioneering effort in directly comparing the impact of concurrent taVNS with motor training to that of sequential taVNS with motor training on stroke rehabilitation. Secondly, the incorporation of an EMG-triggered closed-loop taVNS system has enabled the automation and individualization of both taVNS and diverse motor training tasks—a novel approach not explored in previous research. This technological advancement holds promise for delivering more precise and tailored training interventions for stroke patients. However, it is essential to acknowledge a limitation of this study, as it does not delve into examining the neural mechanisms underlying taVNS in the context of post-stroke rehabilitation

Machine learning-assisted prediction and optimization of solid oxide electrolysis cell for green hydrogen production

The solid oxide electrolysis cell (SOEC) holds great promise to efficiently convert renewable energy into hydrogen. However, traditional modeling methods are limited to a specific or reported SOEC system. Therefore, four machine learning models are developed to predict the performance of SOEC processes of various types, operating parameters, and feed conditions. The impact of these features on the SOEC's outputs is explained by the Shapley additive explanations and partial dependency plot analyses. The preferred model is integrated with a genetic algorithm to determine the optimal values of each input feature. Results show the improved extreme gradient enhanced regression (XGBoost) algorithm is the core of the machine learning model of the process since it has the highest R2 (> 0.95) in the three outputs. The electrolytic cell descriptors have a greater impact on the system performance, contributing up to 54.5%. The effective area, voltage, and temperature are the three most influential factors in the SOEC system, contributing 21.6%, 16.6%, and 13.0% to its performance. High temperature, high pressure, and low effective area are the most favorable conditions for H2 production rate. After conducting multi-objective optimization, the optimal current intensity and hydrogen production rate were determined to be 1.61 A/cm2 and 1.174 L/(h·cm2)

Towards Consistent Object Detection via LiDAR-Camera Synergy

As human-machine interaction continues to evolve, the capacity for environmental perception is becoming increasingly crucial. Integrating the two most common types of sensory data, images, and point clouds, can enhance detection accuracy. Currently, there is no existing model capable of detecting an object's position in both point clouds and images while also determining their corresponding relationship. This information is invaluable for human-machine interactions, offering new possibilities for their enhancement. In light of this, this paper introduces an end-to-end Consistency Object Detection (COD) algorithm framework that requires only a single forward inference to simultaneously obtain an object's position in both point clouds and images and establish their correlation. Furthermore, to assess the accuracy of the object correlation between point clouds and images, this paper proposes a new evaluation metric, Consistency Precision (CP). To verify the effectiveness of the proposed framework, an extensive set of experiments has been conducted on the KITTI and DAIR-V2X datasets. The study also explored how the proposed consistency detection method performs on images when the calibration parameters between images and point clouds are disturbed, compared to existing post-processing methods. The experimental results demonstrate that the proposed method exhibits excellent detection performance and robustness, achieving end-to-end consistency detection. The source code will be made publicly available at https://github.com/xifen523/COD.Comment: Accepted to IEEE SMC 2024. The source code will be made publicly available at https://github.com/xifen523/CO

Research on the Damage Evolution Law of Branch Wellbore Based on Damage Mechanics

Multilateral wells can effectively develop complex reservoirs at a lower cost, which, in turn, enhances the overall efficiency of oilfield exploitation. However, drilling branch wells from the main wellbore can disrupt the surrounding formation stresses, leading to secondary stress concentration at the junctions, which, in turn, causes wellbore instability. This study established a coupled analysis model for wellbore stability in branch wells by integrating seepage, stress, and damage. The model explained the instability mechanisms of branch wellbores under multi-physics coupling conditions. The results showed that during drilling, the thin, interwall section of branch wells had weak resistance to external loads, with significant stress concentration and a maximum damage factor of 0.267, making it prone to instability. As drilling time progressed, fractures in the surrounding rock mass of the wellbore continuously formed, propagated, and interconnected, causing a sharp increase in the permeability of the damaged area. The seepage direction of drilling fluid in the wellbore tended towards the severely damaged interwall section, leading to a rapid increase in pore pressure there. With increasing distance from the interwall tip, the resistance to external loads strengthened, and the formation damage factor, permeability, pore pressure, and equivalent plastic strain all gradually decreased. When the drilling fluid density increased from 1.0 g/cm3 to 1.5 g/cm3, the maximum equivalent plastic strain around the wellbore decreased from 0.041 to 0.014, a reduction of 65.8%, indicating that appropriately increasing the drilling fluid density can effectively reduce the risk of wellbore instability