Cobalt Nanoparticles Encapsulated in Nitrogen-Doped Carbon Nanotube as Bifunctional-Catalyst for Rechargeable Zn-Air Batteries

Developing economic and efficient non-noble-metal electrocatalysts toward oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) is vitally important to improve the performance and economic outlook of alkaline-based rechargeable Zn-air battery technologies. In this work, a nitrogen-doped carbon nanotube encapsulated with metallic cobalt nanoparticles (Co@NC) was synthesized through a facile method and subsequent pyrolysis treatment. The field emission scanning electron microscope (FESEM), high resolution transmission electron microscopy (HRTEM), Raman spectra investigations demonstrate that the presence of Co induces the formation of carbon nanotube during the pyrolysis process and increase degree of graphitization of carbon nanotubes. The electrode activity is assessed by comparing OER with ORR indicators (ΔE). The ΔE value of Co@NC is 0.91 V, which is lower than the commercialized Pt/C (1.1 V) and nitrogen-doped carbon (NC) (1.17 V). The Co@NC-based primary Zn-air battery display an open circuit potential of 1.4 V, a high power density of 137 mW·cm−2, and outstanding energy density (708.3 mAh·kgZn-1 and 868.9 Wh kgZn-1 at 10 mA·cm−2), which batter than the commercialized Pt/C

Defending Against Backdoor Attacks by Quarantine Training

Deep neural networks (DNNs) are powerful yet vulnerable to backdoor attacks simply by adding backdoor samples to the training set without controlling the training process. To filter out the backdoor samples in the training set, this paper proposes a novel and effective backdoor defense method called Quarantine Training (QT). Specifically, QT creates a quarantine class for each class in the training set and relabels all sample labels to associate with their corresponding quarantine classes during training. In this process, the backdoor samples are gradually categorized into the quarantine classes, thus effectively filtering out the backdoor samples. Experiments on multiple benchmark datasets with a variety of backdoor attacks demonstrate that QT has state-of-the-art backdoor defense performance without reducing the prediction accuracy of benign samples - and even improving it. Our codes are available at https://github.com/Chengx-Yu/Quarantine-Training

Permanent Magnet Flux Linkage Analysis and Maximum Torque per Ampere (MTPA) Control of High Saturation IPMSM

The maximum torque per ampere (MTPA) control is significant for improving the efficiency of the interior permanent magnet synchronous motor (IPMSM). However, for the high saturation IPMSM, the change of the permanent magnet (PM) flux linkage is more complicated, which can cause the MTPA control to deviate from the optimal solution. Therefore, an improved MTPA control method for the high saturation IPMSM is proposed in this paper. Compared with other methods, the proposed method improves the conventional models of flux linkage and torque by analyzing the nonlinear variation of the PM flux linkage with the dq-axis currents. Subsequently, an expression suitable for the MTPA control of high saturated IPMSM is derived based on the improved models. The proposed parameter fitting models are then fitted using data from 11 operating points and incorporated into the MTPA optimization algorithm to obtain the MTPA curve. Finally, the effectiveness of the proposed method in enhancing the control accuracy of the MTPA angle is verified through simulations and experiments

Spatial Distribution Pattern and Sampling Plans for Two Sympatric <i>Tomicus</i> Species Infesting <i>Pinus yunnanensis</i> during the Shoot-Feeding Phase

Tomicus minor (Hartig) and Tomicus yunnanensis Kirkendall and Faccoli are two sympatric species that infest Pinus yunnanensis (Franchet) in southwest China, contributing to growth losses. Accurate sampling plans are needed to make informed control decisions for these species. We investigated three pine forests within experimental sites in Yuxi, Yunnan province, China from 2016 to 2018. The spatial distribution patterns of two pine shoot beetles during the shoot-feeding phase were determined using Taylor’s power law. The optimum sample sizes and stop lines for precision levels of 0.25 and 0.10 were calculated. The model was validated using an additional 15 and 17 independent field datasets ranging in density from 0.06 to 1.90 beetles per tree. T. minor and T. yunnanensis adults showed aggregated spatial distributions. For T. minor, sample sizes of 41 and 259 trees were adequate for a D of 0.25 and 0.10, respectively, while for T. yunnanensis, a mean density of one individual per tree required sample sizes of 33 plants (D = 0.25) and 208 plants (D = 0.10). The software simulations of this sampling plan showed precision levels close to the desired levels. At a fixed-precision level of 0.25, sampling is easily achievable. This sampling program is useful for the integrated pest management (IPM) of two sympatric Tomicus species

Virtual-Wall Model for Molecular Dynamics Simulation

A large number of molecules are usually required to model atomic walls in molecular dynamics simulations. A virtual-wall model is proposed in this study to describe fluid-wall molecular interactions, for reducing the computational time. The infinite repetition of unit cell structures within the atomic wall causes the periodicity of the force acting on a fluid molecule from the wall molecules. This force is first calculated and then stored in the memory. A fluid molecule appearing in the wall force field is subjected to the force from the wall molecules. The force can then be determined by the position of the molecule relative to the wall. This model avoids excessive calculations of fluid-wall interactions and reduces the computational time drastically. The time reduction is significant for small fluid density and channel height. The virtual-wall model is applied to Poiseuille and Couette flows, and to a flow in a channel with a rough surface. Results of the virtual and atomic wall simulations agree well with each other, thereby indicating the usefulness of the virtual-wall model. The appropriate bin size and cut-off radius in the virtual-wall model are also discussed

Two‐Dimensional Metal–Organic Frameworks‐Based Electrocatalysts for Oxygen Evolution and Oxygen Reduction Reactions

Due to unique intrinsic properties and flexible structure modulations, 2D metal–organic frameworks (MOFs)‐based materials have become the most promising candidates in the electrocatalysts of energy‐conversion systems such as oxygen evolution reaction (OER) and oxygen reduction reaction (ORR). Herein, the background of fundamental 2D MOFs is first introduced and 2D MOF‐based electrocatalysts are classified based on OER and ORR. Meanwhile, the recent advances in the 2D MOF‐based OER and ORR electrocatalysts are emphasized and discussed including the subtle synthesis strategy, novel structures, unique morphologies, superior electrocatalytic performances, and the underlying reaction mechanism. Finally, the challenges and future perspectives for the developments and research of 2D MOFs‐based OER and ORR electrocatalysts are proposed

Initial Location Preference Together with Aggregation Pheromones Regulate the Attack Pattern of <i>Tomicus brevipilosus</i> (Coleoptera: Curculionidae) on <i>Pinus kesiya</i>

Research Highlights: We found that the initial attack location together with the aggregation pheromones played an important role in mediating the aggressive behavior of T. brevipilosus on P. kesiya. Background and Objectives: T. brevipilosus was identified as an aggressive species, which possesses the ability to kill live, healthy P. kesiya. In this scenario, we study the top-down attack pattern of T. brevipilosus on P. kesiya during the entirety of the reproductive period. Materials and Methods: We investigated the phenology of trunk attack on P. kesiya over a period of three years in Pu&#8217;er City, China. The hindguts extracts of the females and males T. brevipilosus were analyzed by coupled gas chromatography-mass spectrometry (GC-MS). The candidate aggregation pheromone compounds of T. brevipilosus were determined through electrophysiology experiments (electroantennographic detection, EAD and electroantennography, EAG), laboratory olfactometer bioassays, and field trapping. Results: we found that the pioneer beetles preferentially infested the crown of P. kesiya at the early stage of attack following spring flight with the later arriving beetles selectively attacking the lower area of the trunk to avoid intraspecific competition and better utilize limited resources, which exhibits a top-down attack pattern. During gallery initiation, the beetles release aggregation pheromones to attract conspecifics to conduct a mass attack. The chemical analyses indicated that the hindgut extracts of gallery-initiating beetles contained a larger amount of myrtenol, cis-verbenol, trans-verbenol, and verbenone. Myrtenol and trans-verbenol were identified as candidate aggregation pheromone compounds. In addition, a blend of these two components with S-(&#8722;)-&#945;-pinene and S-(&#8722;)-&#946;-pinene attracted more T. brevipilosus individuals in a field bioassay. Conclusions: We concluded that the preference for the initial attack location together with the aggregation pheromones played an important role in mediating the top-down attack pattern of T. brevipilosus on P. kesiya

Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter

An integrated navigation algorithm based on a multiple fading factors Kalman filter (MFKF) is proposed to solve the problems that the Kalman filtering (KF) algorithm easily brings about diffusion when the model becomes a mismatched or noisy, and the MFKF accuracy is reduced when the fading factor is overused. Based on the innovation covariance theory, the algorithm designs an improved basis for judging filtering anomalies and makes the timing of the introduction of the fading factor more reasonable by switching the filtering state. Different from the traditional basis of filter abnormality judgment, the improved judgment basis adopts a recursive way to continuously update the estimated value of the innovation covariance to improve the estimation accuracy of the innovation covariance, and an empirical reserve factor for the judgment basis is introduced to adapt to practical engineering applications. By establishing an inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation model, the results show that the average positioning accuracy of the proposed algorithm is improved by 26.52% and 7.48%, respectively, compared with the KF and MFKF, and shows better robustness and self-adaptability

First assistant experience in total laparoscopic pancreaticoduodenectomy: accelerating the learning curve for an operator

Abstract Objective Compare and analyze clinical data of total laparoscopic pancreaticoduodenectomy (TLPD) cases for surgeons with / without first assistant experience (FAE) in TLPD. Probe influence of FAE in TLPD on the learning curve for an operator. Methods The clinical data of 239 patients, that underwent TLPD performed by two surgeons between January 2017 and January 2022) in our department, were consecutively collected and divided into two groups (A and B). Group A cases were operated by Surgeon A, with FAE of 57 TLPDs in our department prior to initial TLPD as an operator. Group B cases were operated by Surgeon B with no FAE of TLPD. Cumulative sum (CUSUM) method developed learning curves. Clinical data and both surgeons’ learning curves were statistically compared between both groups. Results Between both groups, no statistically significant variations were observed for pre-operative health conditions. Reduced surgical duration, blood loss and transfusion volume during surgery, together with reductions in major post–operative complication rates and reduced hospital/ICU stays were identified within Group A, having statistically significant variations. The technical plateau phases of the learning curves were approximately 25–41 cases and 35–51 cases, for Surgeon A and Surgeon B, respectively. Conclusion FAE in TLPD can accelerate the learning curve of TLPD for an operator, with safer surgical procedures and enhanced post–operative recovery