We report herein an electrochemical method for decarboxylative halogenation of aryl propynoic acids under transition‐metal‐free conditions. In undivided cell, diverse and KX were employed to synthesize a series haloalkynes. Mechanistic studies demonstrated the possible involvement free‐radical intermediate in transformation.

As a promising data analysis technique, sparse modeling has gained widespread traction in the field of image processing, particularly for recovery. The matrix rank, served as measure sparsity, quantifies sparsity within Kronecker basis representation given piece format. Nevertheless, practical scenarios, much are intrinsically multi-dimensional, and thus, using format will inevitably yield sub-optimal outcomes. Tensor decomposition (TD), high-order generalization decomposition, been widely...

Purpose The purpose of this paper is to develop and present a hybrid design fabrication method based on rapid prototyping (RP) electrochemical deposition (ED) techniques fabricate pressure wind‐tunnel model with complex internal structure sufficient mechanical strength. Design/methodology/approach After offsetting inward by applied coating thickness, the airfoil was modified three pairs deflecting control surfaces 24 surface taps passages. stereolithography (SL) prototype components were...

In this paper, the rapid fabrication method based on stereolithography (SL) and electrochemical deposition is described in detail mechanical test results of composite nickel-coated SL parts are presented. Coatings electrodeposited nickel prototypes result increases Young's modulus, UTS, flexural strength. Electrodeposited coating has dramatically improved overall strength stiffness parts. The adhesive roughened resin-nickel interface higher than original. particular, influence surface...

Recently, fusing low-resolution hyperspectral images (LR-HSI) with high-resolution multispectral (HR-MSI) to obtain HSI (HR-HSI) has become an emerging study. In this letter, Bayesian nonlocal Canonical Polyadic (CP) factorization (BNCPF) is proposed for LR-HSI HR-MSI, which applies CP on the tensors of HR-HSI. Compared vanilla scheme applying HR-HSI, reveal balanced low-rank properties along different modes, and thus can better capture their intrinsic low-rankness. To avoid immense CP-ranks...

3D printing, also known as additive manufacturing, has been considered the next big thing that can potentially spread cell phone industry. Nowadays, product resolution restricted by material and fabrication process is ranging from 20 to 300 pm still trends toward higher precision. However, tradeoff between time costs always a dilemma in Fused Deposition Modeling (FDM) technique. In contrast, idea of Laminated Object Manufacturing (LOM) applied for rapid prototyping recently. this work, based...

Abstract A metal-free electrochemical method for vicinal difunctionalization of various alkenes with dibromomethane in alcohol as solvent has been well established to synthesize the corresponding β-bromo-α-alkyloxyalkanes good functional group tolerance under ambient conditions. Preliminary mechanistic studies indicate oxidation bromine source occurs prior that alkene substrate involvement radical during electrolysis.

Tensor network (TN) is a powerful framework in machine learning, but selecting good TN model, known as structure search (TN-SS), challenging and computationally intensive task. The recent approach TNLS~\cite{li2022permutation} showed promising results for this task, however, its computational efficiency still unaffordable, requiring too many evaluations of the objective function. We propose TnALE, new algorithm that updates each structure-related variable alternately by local enumeration,...

Tensor network structure search (TN-SS), aiming at searching for suitable tensor (TN) structures in representing high-dimensional problems, largely promotes the efficacy of TN various machine learning applications. Nonetheless, finding a satisfactory using existing algorithms remains challenging. To develop more effective and avoid human labor-intensive development process, we explore knowledge embedded large language models (LLMs) automatic design TN-SS algorithms. Our approach, dubbed...

Abstract After systematic realization of decarboxylative functionalization carboxylic acids under heating conditions in our group, we herein reported an electrochemical method for Ni‐catalyzed oxygenation arylacetic open air conditions. The protocol provided corresponding carbonyls including aldehydes and ketones moderate to satisfactory yields with good functional group tolerance, furthermore, the practicability advantage was highlighted through oxidative decarboxylation acid‐containing...

Recent works put much effort into tensor network structure search (TN-SS), aiming to select suitable (TN) structures, involving the TN-ranks, formats, and so on, for decomposition or learning tasks. In this paper, we consider a practical variant of TN-SS, dubbed TN permutation (TN-PS), in which good mappings from modes onto vertices (core tensors) compact representations. We conduct theoretical investigation TN-PS propose practically-efficient algorithm resolve problem. Theoretically, prove...

Tensor network (TN) representation is a powerful technique for computer vision and machine learning. TN structure search (TN-SS) aims to customized achieve compact representation, which challenging NP-hard problem. Recent "sampling-evaluation"-based methods require sampling an extensive collection of structures evaluating them one by one, resulting in prohibitively high computational costs. To address this issue, we propose novel paradigm, named SVD-inspired decomposition (SVDinsTN), allows...

The model of network security situational awareness allows for accurate prediction forthcoming incidents by analyzing historical data, thereby helping to detect and respond potential threats in advance protect security. In this paper, a novel approach is introduced, which can predict future events traffic data. utilizes the LSTM neural structure, capture long-term dependencies time series data better occurrence cybersecurity incidents. model, firstly preprocessed, including standardization...

Due to the rapid global population growth, addressing issue of insufficient food production has become crucial. In this research, a new maize yield grade prediction method is proposed predict future grades by analyzing historical agricultural indicators. The model combines Convolutional Network (CNN) and Long Short-Term Memory (LSTM) recurrent neural network build hybrid CNN-LSTM-PSO based on Particle Swarm Optimization algorithm (PSO) for better grade. model, data indicators were firstly...