bluuebunny/crossref_metadata_2025_split huggingface dataset:
Restoration of antibacterial activity of inactive antibiotics via combined treatment with a cyanographene/Ag nanohybrid
Authors: Hochvaldová Lucie , Panáček David , Válková Lucie , Prucek Robert , Kohlová Věra , Večeřová Renata , Kolář Milan , Kvítek Libor , Panáček Aleš 
Published: March 2022

AbstractThe number of antibiotic-resistant bacterial strains is increasing due to the excessive and inappropriate use of antibiotics, which are therefore becoming ineffective. Here, we report an effective way of enhancing and restoring the antibacterial activity of inactive antibiotics by applying them together with a cyanographene/Ag nanohybrid, a nanomaterial that is applied for the first time for restoring the antibacterial activity of antibiotics. The cyanographene/Ag nanohybrid was synthesized by chemical reduction of a precursor material in which silver cations are coordinated on a cyanographene sheet. The antibacterial efficiency of the combined treatment was evaluated by determining fractional inhibitory concentrations (FIC) for antibiotics with different modes of action (gentamicin, ceftazidime, ciprofloxacin, and colistin) against the strains Escherichia coli, Pseudomonas aeruginosa, and Enterobacter kobei with different resistance mechanisms. Synergistic and partial synergistic effects against multiresistant strains were demonstrated for all of these antibiotics except ciprofloxacin, which exhibited an additive effect. The lowest average FICs equal to 0.29 and 0.39 were obtained for colistin against E. kobei and for gentamicin against E. coli, respectively. More importantly, we have experimentally confirmed for the first time, that interaction between the antibiotic's mode of action and the mechanism of bacterial resistance strongly influenced the combined treatment’s efficacy.
Case Study: Implementation and evaluation of a team-based authentic research project module for large cohorts
Authors: Lund Torben , Clyne Rosemary K. 
Published: March 2022

Abstract
Acquiring skills needed to plan and conduct research and communicate research outcomes are key learning aims in biochemical and biomedical disciplines. Final-year projects/dissertations are high-impact educational activities that commonly feature in undergraduate curricula. When cohort sizes exceed infrastructure and staff capacity, traditional models of supervised projects may not be feasible. This case study aims to share one model of practice with colleagues similarly engaged in design and delivery of final-year projects and research. Here, we outline the implementation and evaluation of a team-based, final-year research module on a transnational joint programme. Investigative Skills module was piloted in 2016–2017 and continues to run annually for &gt;100 students. The research component is conducted over a timetabled, two-week block. In student questionnaires, the majority of respondents agreed that the projects were authentic, interesting and appropriate. The favourite aspect for most of the respondents was performing experimental work/doing research. Over 80% agreed that working in teams was conducive to accomplishing their goals, and their ideal team size is three to five students per team. The majority agreed that there was sufficient experimental work to do, but that more than two weeks practical time would be beneficial. The feedback has given insight into the whole of the student research experience of Investigative Skills, which is a sustainable model for authentic dissertation research for large cohorts.
Online Training to Improve Service Provider Implementation of Letter–Sound Correspondence Instruction for Individuals Who Use Augmentative and Alternative Communication
Authors: Caron Jessica G. , O'Brien Meghan , Weintraub Rachel 
Published: March 2022

Purpose:
Service providers have limited training in adapting and implementing literacy instruction for individuals who use augmentative and alternative communication (AAC), and therefore, opportunities to participate in phonological-based literacy activities are minimal. An online training was developed to teach service providers how to implement a systematic lesson in letter–sound correspondence (LSC) instruction with high fidelity for individuals who use AAC.

Method:

The Active Implementation Framework was used to guide the investigation of implementation fidelity and intervention effectiveness after service provider completion of an online training in LSC instruction. Study 1 trained five speech-language pathologists, one paraeducator, and six special educators in schools (
N
= 12), and Study 2 trained four speech-language pathology master's students. Single case across participant methodology was used for both studies with an implementation fidelity checklist to measure changes in instructional behaviors. Additionally, social validity data from service providers were collected. Preliminary data on intervention effectiveness for individuals who use AAC were collected for three LSCs after service providers were trained.

Results:
Prior to the online training, service providers implemented LSC instruction with low fidelity. After the training, 11 service providers in Study 1 and all four speech-pathology master's students in Study 2 demonstrated large gains in implementation fidelity. Eight of the nine individuals who use AAC demonstrated improvement in LSC.

Conclusion:
Access to the online training has the potential to change the implementation behavior of service providers and take steps toward closing the research-to-practice gap surrounding early phonological skill acquisition for individuals who use AAC.

Supplemental Material:

https://doi.org/10.23641/asha.19400741
Multi‐gradient‐direction based deep learning model for arecanut disease identification
Authors: Mallikarjuna S. B. , Shivakumara Palaiahnakote , Khare Vijeta , Basavanna M. , Pal Umapada , Poornima B. 
Published: March 2022

AbstractArecanut disease identification is a challenging problem in the field of image processing. In this work, we present a new combination of multi‐gradient‐direction and deep convolutional neural networks for arecanut disease identification, namely, rot, split and rot‐split. Due to the effect of the disease, there are chances of losing vital details in the images. To enhance the fine details in the images affected by diseases, we explore multi‐Sobel directional masks for convolving with the input image, which results in enhanced images. The proposed method extracts arecanut as foreground from the enhanced images using Otsu thresholding. Further, the features are extracted for foreground information for disease identification by exploring the ResNet architecture. The advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut images. Experimental results on the dataset of four classes (healthy, rot, split and rot‐split) show that the proposed model is superior in terms of classification rate.
Secure LQG control for a Quadrotor under false data injection attacks
Authors: Lin Hong , Sun Pei , Cai Chenxiao , Lu Shan , Liu He 
Published: March 2022

AbstractThe linear quadratic Gaussian (LQG) control for a quadrotor unmanned aerial vehicle (UAV) under false data injection attacks is studied. The LQG control depends on optimal state estimation, while this type of attacks makes the optimal state estimation unimplementable in practice. To address this problem, the authors prose a framework to detect attacks and then augment the information for controller design. Based on this framework, the LQG control is designed and a sufficient condition for the security of the closed‐loop control system is given. Finally, the LQG controller is applied to a quadrotor UAV, and some experiments are carried out to illustrate its effectiveness.
A fast robust numerical continuation solver to a two‐dimensional spectral estimation problem
Authors: Zhu Bin , Liu Jiahao 
Published: March 2022

AbstractThis paper presents a fast algorithm to solve a spectral estimation problem for two‐dimensional random fields. The latter is formulated as a convex optimization problem with the Itakura‐Saito pseudodistance as the objective function subject to the constraints of moment equations. The structure of the Hessian of the dual objective function is exploited in order to make possible a fast Newton solver. Then the Newton solver is incorporated to a predictor‐corrector numerical continuation method which is able to produce a parametrized family of solutions to the moment equations. Two sets of numerical simulations are performed to test the algorithm and spectral estimator. The simulations on the frequency estimation problem show that our spectral estimator outperforms the classical windowed periodograms in the case of two hidden frequencies and has a higher resolution. The other set of simulations on system identification indicates that the numerical continuation method is more robust than Newton's method alone in ill‐conditioned instances.
Analysis of temperature field of medium frequency transformer based on improved thermal network method
Authors: Xu Wanlun , Huang Pei , Liu Zhenxing , Zhao Min , Dian Renjun , Wang Peng , Wang Qi , Wang Dan , Shen Tong 
Published: March 2022

AbstractMedium frequency transformer (MFT) plays a key role in future power systems and new energy systems. It is very important for the safe operation of MFT to correctly calculate the temperature rise and hot spot position. According to the heat dissipation of transformer, the equivalent thermal network model of the core‐type transformer is established in this paper. The traditional thermal network model ignores the heat dissipation of the magnetic core column and the inner surface of the yoke to the air. In this paper, the magnetic core column is divided into the air surrounding part and winding surrounding part. The heat dissipation on the inner surface of the yoke is further considered. The improved thermal network model is verified by finite element simulation. The results show that compared with the traditional thermal network model, the improved thermal network model proposed in this paper can more comprehensively analyse the temperature field distribution and calculate the node temperature more accurately.
End‐to‐end Tangut character database building and recognition method
Authors: Ma Jinlin , Cao Yunrui , Ma Ziping , Wei Lin , Hao Chaohua 
Published: March 2022

AbstractCharacter recognition is an important research topic nowadays, and a large amount of excellent work has appeared. In contrast, research related to the recognition of Tangut characters is still in the initial stage. Creating databases and effective recognition methods that can support the recognition of Tangut characters remain a great challenge. In this paper, a labeling method based on Multi‐Model and Multi‐Prediction (MMMP) is proposed, which built a Tangut character database (TCD) and an enhanced database (called “TCD‐E”) covering 6077 classes, and five test sets were also built for specific tasks. To recognize Tangut characters effectively and quickly, a 5‐layer end‐to‐end Tangut Characters Recognition Network (TCRNet) based on CNN using shallow neural networks is designed. Its recognition accuracy on TCD‐E reaches 97.96. Based on TCRNet, an end‐to‐end Similar Tangut Characters Recognition Network (STCRNet) is further proposed by improving the loss function by combining the softmax loss function with the central loss function, and its test accuracy on similar Tangut characters test set (called “TCD‐E‐S”) is 0.70 higher than TCRNet. Experiments show that TCD and TCD‐E can provide data support for Tangut character recognition. The recognition accuracy of TCRNet and STCRNet surpasses the previous best results.
Dual‐attention guided network for facial action unit detection
Authors: Song Wenyu , Shi Shuze , Wu Yuxuan , An Gaoyun 
Published: March 2022

AbstractAttention mechanism has recently aroused increasing concerns in the field of computer vision like Action Unit (AU) detection. Because facial AU exists in a fixed local area of a human face, it is advantageous to apply the attention mechanism to AU detection. A Dual‐Attention Guided Network (DAGNet) is proposed for automatically AU detection, which introduces dual attention to selectively extract deep features. Dual attention refers to predefined explicitly models feature dependencies from spatial and channel attention, respectively, based on the semantics of AU label. In addition, since the global and local features show different facial attributes and supplement mutually, the proposed DAGNet learns feature representations from global and local perspectives, respectively. Learning global and local features simultaneously during training can lead to better generalization performance; a fusion module is designed for aggregating all the learned information to construct a unified architecture for end‐to‐end AU detection. Extensive experiments on two challenging datasets, BP4D and DISFA, result in F1‐scores of 64.0% and 62.6%, respectively, which shows that the proposed DAGNet achieves the performance of the state‐of‐the‐art in the field of image‐based AU detection.
Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer
Authors: Yu Sudan , Chen Zhiqing , Heidari Ali Asghar , Zhou Wei , Chen Huiling , Xiao Lei 
Published: March 2022

AbstractIn this paper, an efficient sine cosine differential gradient‐based optimization method is proposed for identifying unknown parameters of photovoltaic models. In the simulation, parameter identification is formulated as an objective function to be minimized based on the error between the estimated and experimental data. Based on the original gradient‐based optimization method, the proposed method combines the sine cosine method and the mutation crossover of the differential evolution algorithm. Specifically, the crossover operator enables the algorithm to avoid local optima; and meanwhile, the sine cosine strategy encourages the new individual to calculate the worst position. The simulation results demonstrate that the new optimization method can achieve the minimal root mean square error and obtain better optima relative to other algorithms in different photovoltaic cells. Therefore, the proposed optimization method has great potential to be used for estimating photovoltaic model parameters.
Target depth estimation by deep neural network based on acoustic interference structure in deep water
Authors: Guo Yue , Duan Rui , Yang Kunde 
Published: March 2022

AbstractAutomatic and robust target depth estimation is an important issue of an active detection system working in the reliable acoustic path (RAP) environment. In this paper, the target depth‐sensitive acoustic interference structure is used as input, and a deep neural network (DNN) method is proposed to realize automatic depth estimation. Furthermore, to improve the robustness of the network, both the sound speed profile (SSP) uncertainty and the target trajectory uncertainty are considered in the training dataset. The former is modelled as a random weighted sum of the empirical orthogonal function (EOF), and the latter is modelled by the partial‐data‐lacking of the network input. The method was evaluated through simulated data and compared with the conventional method of matching the measured interference structure with the replicas. The presented method exhibits a higher accuracy of depth estimate in uncertain environments when the signal‐to‐noise ratio (SNR) is higher than −6 dB, but its performance deteriorates rapidly as the SNR decreases further. The method is also verified by semi‐simulation with experimental data in deep water.
Oleic acid is an endogenous ligand of TLX/NR2E1 that triggers hippocampal neurogenesis
Authors: Kandel Prasanna , Semerci Fatih , Mishra Rachana , Choi William , Bajic Aleksandar , Baluya Dodge , Ma LiHua , Chen Kevin , Cao Austin C. , Phongmekhin Tipwarin , Matinyan Nick , Jiménez-Panizo Alba , Chamakuri Srinivas , Raji Idris O. , Chang Lyra , Fuentes-Prior Pablo , MacKenzie Kevin R. , Benn Caroline L. , Estébanez-Perpiñá Eva , Venken Koen , Moore David D. , Young Damian W. , Maletic-Savatic Mirjana 
Published: March 2022

Significance
Adult hippocampal neurogenesis underpins learning, memory, and mood but diminishes with age and certain illnesses. The orphan nuclear receptor TLX/NR2E1 regulates neural stem and progenitor cell self-renewal and proliferation, but its orphan status has hindered its utilization as a therapeutic target to modulate adult neurogenesis. Here, we deorphanize TLX and report that oleic acid is an endogenous, metabolic ligand of TLX. These findings open avenues for future therapeutic modulation of TLX to counteract cognitive and mental decline in aging and diseases associated with decreased neurogenesis.
Phylogenetic inference of where species spread or split across barriers
Authors: Landis Michael J. , Quintero Ignacio , Muñoz Martha M. , Zapata Felipe , Donoghue Michael J. 
Published: March 2022

SignificanceGeography molds how species evolve in space. Strong geographical barriers to movement, for instance, both inhibit dispersal between regions and allow isolated populations to diverge as new species. Weak barriers, by contrast, permit species range expansion and persistence. These factors present a conundrum: How strong must a barrier be before between-region speciation outpaces dispersal? We designed a phylogenetic model of dispersal, extinction, and speciation that allows regional features to influence rates of biogeographic change and applied it to the neotropical radiation ofAnolislizards. Separation by water induces a threefold steeper barrier to movement than equivalent distances over land. Our model will help biologists detect relationships between evolutionary processes and the spatial contexts in which they operate.
Preverbala-marking in Palenquero Creole
Authors: Smith Hiram L. 
Published: March 2022

AbstractFormally similar grammatical features in a creole and its genetic or areal relatives may indicate substrate transfer, lexifier influence, or grammaticalization. Against this backdrop, the present study investigates the origin(s) of the preverbal past markerain Palenquero Creole (Colombia). Results from distributional analysis and tests for significance indicate that several diachronically-related meanings area-marked at rates approaching obligatory, suggesting advanced grammaticalization. Comparative results for Peninsularhaber+ PP suggest that past marking has grammaticalized much further in half the time in Palenquero Creole than in its lexifier, Spanish. Why? I argue, against traditional accounts about the origins ofa, that, given the contact history of Palenquero speakers, most likely a pre-existing Kikongo prefixal form merged with an already grammaticalizinghaber, thus propelling grammaticalization in the creole. The synchronic patterning shows adherence to typological patterns observed for perfectives in line with well-known constraints on competition and selection in contact languages, such as their grammatical congruence or particular social ecologies.
Occupational physical activity trends from 1987 to 2017: A nationally representative sample of 160,509 Spanish adults
Authors: Calatayud Joaquín , Morera Álvaro , Casaña José , del Pozo Cruz Borja , Andersen Lars L. , López‐Bueno Rubén 
Published: March 2022

ABSTRACTThe aim of the study was to estimate temporal and updated trends of occupational physical activity (OPA) among Spanish adult population during the period between 1987 and 2017. We used all available rounds of the Spanish National Health Survey (160,509 adults, mean age 44.6 years [SD 16.6], 50.3% women) and multivariate regressions to determine temporal trends of high OPA from 1987 to 2017 in the general working population of Spain. The highest OPA prevalence was observed for participants aged 31−49 years in the 2003 survey. The prevalence of high OPA was generally low, but increased slightly over time in the 18‐30, 50‐64, and 65‐75 years old (p < 0.001 for trend). In contrast, a general reduction in OPA was observed in the 16‐17 years old (p < 0.001 for trend). Men consistently showed higher OPA prevalence than women. This study reports an increase in OPA prevalence in Spain from 1987 to 2017, which is low and significant in most socio‐demographic groups, but small in absolute terms. The youngest group had a marked decreased in OPA prevalence over the course of the study. Men consistently showed higher OPA prevalence than women, but differences have become smaller in recent years. Our results may assist in the establishment of interventional strategies and future policies among specific subgroups.Highlights
There is an increase in OPA prevalence in Spain from 1987 to 2017.
The youngest group had a marked decreased in OPA prevalence over the course of the study.
Men consistently showed higher OPA prevalence than women, but differences have become smaller in recent years.
Frequent national surveillance of OPA to monitor long‐term development of the working environment should be mandatory.
Nonintrusive temperature measurement of a combustion field by femtosecond laser-induced third harmonic generation
Authors: Li Ziting , Chen Jinming , Liu Zhaoxiang , Yao Jinping , Yu Jianping , Li Yi , Rong Fengyi , Wang Pengfei , Xie Hongqiang , Cheng Ya 
Published: March 2022

Abstract
We report a comparative experimental investigation of femtosecond laser-induced third harmonic generation (THG) in an ethanol flame and in air. It was found that the third harmonic (TH) signal produced in the presence of a combustion field can be greatly enhanced, in comparison to that generated in air, and that the enhancement factor depends strongly on the experimental parameters, such as the focal length, relative position of the flame and filament, and laser repetition frequency. Moreover, by replacing the flame with a point temperature controller, a similar signal enhancement of THG was observed, and the TH signal exhibited a nonlinear growth with the heating temperature. Further analysis indicated that the observed enhancement of THG originates from the suppression of Gouy-phase-induced destructive interference due to the disturbed gas density under high-temperature heating. The high sensitivity of the TH signal to the combustion temperature helps formulate an effective and straightforward approach to achieve nonintrusive temperature measurements in the combustion field.
A pre-assembly analysis technology of aircraft components based on measured data
Authors: Peng Yun , Hao Long , Huang Xiang , Li ShuangGao 
Published: March 2022

Abstract
Compared with the traditional assembly simulation based on theoretical models, this paper proposes a new pre-assembly analysis method of aircraft components based on measured data. Specifically, before the actual assembly of the product, digital measurement methods are used to obtain the measured data of the target features of the manufactured parts. Subsequently, the measured data is processed and reconstructed to obtain the actual geometric shape of the part, based on which the product is pre-assembled and analyzed to evaluate the assembly quality in advance. Finally, according to the analysis results, the assembly process is adjusted in time to reduce assembly trial and error and improve assembly quality and efficiency. This article systematically introduces the implementation process of the method, which is illustrated through two cases study on aircraft wing box assembly process. Experimental results demonstrate the feasibility and effectiveness of this proposed method for assembly of large aircraft components.