bluuebunny/crossref_metadata_2025 huggingface dataset:
A simple liquid extraction for simultaneous determination of 12 opioid ligands in plasma by LC-MS/MS
Authors: Yang Dan , Zheng Qiuda , Ahmed Fahad , Parat Marie-Odile , Tscharke Benjamin J.
Published: March 2022

A simple LC-MS/MS and extraction method for 12 opioids/metabolites was developed and validated. Extraction recoveries were satisfactory with values between 81% and 116%. The LLOQ of 12 opioids/metabolites was between 0.01 and 0.38 μg L−1
Reaction dynamics within a cluster environment
Authors: Briant Marc , Mestdagh Jean-Michel , Gaveau Marc-André , Poisson Lionel
Published: March 2022

This perspective article reviews experimental and theoretical works where rare gas clusters and helium nanodroplets are used as a nanoreactor to investigate chemical dynamics in a solvent environment.
Microwave-assisted synthesis of a Z-scheme heterojunction Ag/AgBr@BiOBr/Bi2O3 photocatalyst for efficient organic pollutant degradation under visible light
Authors: Nguyen Van Quang , Mahadadalkar Manjiri A. , Rabie Abdelrahman M. , Shim Jae-Jin
Published: March 2022

A novel Ag/AgBr@BiOBr/Bi2O3 Z-scheme plasmonic photocatalyst decomposed organic pollutants efficiently with high stability under visible light because of singlet oxygen formation.
Engineering neurovascular organoids with 3D printed microfluidic chips
Authors: Salmon Idris , Grebenyuk Sergei , Abdel Fattah Abdel Rahman , Rustandi Gregorius , Pilkington Thomas , Verfaillie Catherine , Ranga Adrian
Published: March 2022

Organoid vascularization using an open-well microfluidic chip allows for interaction of organoids with vasculature in a temporally and spatially controllable manner. Chip are printed using a consumer-grade 3D printer, making the approach inexpensive and widely accessible.
Fluorimetric and ratiometric colorimetric dual-mode detection of organophosphorus pesticides based on carbon dots/DTNB
Authors: Feng Yaoyao , Qu Yujiao , Sun Xiaobo , Pan Wei , Wang Jinping
Published: March 2022

Carbon dots/DTNB as fluorimetric and ratiometric colorimetric dual-mode probes for the detection of chlorpyrifos.
Synthesis of a 2D Cu@TiO2 composite via the design of a 1D Cu-based coordination polymer precursor for efficient and selective photodegradation of dyes
Authors: Zhang Ya-Qian , Xu Ning , Liu Yu , Zhang Xiao-Sa , Li Wen-Ze , Zhao Hong-Tian , Luan Jian
Published: March 2022

A 2D Cu@TiO2 composite was synthesized via low-temperature calcination of a 1D Cu-CP. Moreover, the Cu@TiO2 membrane was fabricated by a simple filtration of the Cu@TiO2 composite which exhibited selective photodegradation activity for GV.
Exploring the feasibility and conduction mechanisms of P-type nitrogen-doped β-Ga2O3 with high hole mobility
Authors: Ma Congcong , Wu Zhengyuan , Jiang Zhuoxun , Chen Ying , Ruan Wei , Zhang Hao , Zhu Heyuan , Zhang Guoqi , Kang Junyong , Zhang Tong-Yi , Chu Junhao , Fang Zhilai
Published: March 2022

The feasibility and conductivity mechanisms of p-type N-doped β-Ga2O3 are demonstrated and clarified using first-principles hybrid functional calculations and the phase transition technique.
Dearomative Michael addition involving enals and 2-nitrobenzofurans realized under NHC-catalysis
Authors: Dyguda Mateusz , Skrzyńska Anna , Sieroń Lesław , Albrecht Łukasz
Published: March 2022

This manuscript describes enantioselective dearomative Michael addition realized under N-heterocyclic carbene activation via reaction of homoenolates with 2-nitrobenzofurans leading to the formation of biologically important 2,3-disubstituted-heterocyclic scaffolds.
Revealing the role of HBr in propane dehydrogenation on CeO2(111) via DFT-based microkinetic simulation
Authors: Jan Faheem , Lian Zan , Zhi Shuaike , Yang Min , Si Chaowei , Li Bo
Published: March 2022

HBr triggered the activities of catalytic propane dehydrogenation with balanced stability and selectivity.
The effect of alkyl substitution position of thienyl outer side chains on photovoltaic performance of A–DA′D–A type acceptors
Authors: Kong Xiaolei , Zhu Can , Zhang Jinyuan , Meng Lei , Qin Shucheng , Zhang Jianqi , Li Jing , Wei Zhixiang , Li Yongfang
Published: March 2022

Two isomeric A–DA′D–A type SMAs o-TEH and m-TEH were designed and synthesized, and the PCE of the OSC based on PBQ6:m-TEH reached 18.51%.
Spiers Memorial Lecture: Assembly-based pathways of crystallization
Authors: De Yoreo James J. , Nakouzi Elias , Jin Biao , Chun Jaehun , Mundy Christopher J.
Published: March 2022

We review recent investigations into crystal growth by particle attachment, with an emphasis on oriented attachment.
A new method for shellac binder detection in ancient building mortars
Authors: Zhang Di , Zhang Bingjian , Zhu Longguan
Published: March 2022

Shellac, also known as lac, is composed mainly of aleuritic acid.
Acceleration of oxidation promoted by laccase irradiation with red light
Authors: Giraldi Valentina , Marchini Marianna , Di Giosia Matteo , Gualandi Andrea , Cirillo Martina , Calvaresi Matteo , Ceroni Paola , Giacomini Daria , Cozzi Pier Giorgio
Published: March 2022

Irradiation with red light is able to improve yields and shorten the reaction time in enzymatic reactions.
Dehydration of methanol and ethanol over ferrierite originated layered zeolites – the role of acidity and porous structure
Authors: Święs Aneta , Kowalczyk Andrzej , Gil Barbara , Chmielarz Lucjan
Published: March 2022

Opened porous structures of ITQ-6 and ITQ-36, are more effective in catalytic dehydration of ethanol to diethyl ether than microporous ferrierite. Surface acidity determines catalytic performance of the zeolite catalysts in alcohol dehydration.
A minireview on the synthesis of single atom catalysts
Authors: Guo Jiawen , Liu Huimin , Li Dezheng , Wang Jian , Djitcheu Xavier , He Dehua , Zhang Qijian
Published: March 2022

The two routes for the synthesis of SACs are reviewed and the advantages and disadvantages of each method are compared.
Pure silica-supported transition metal catalysts for the non-oxidative dehydrogenation of ethane: confinement effects on the stability
Authors: De Sudipta , Aguilar-Tapia Antonio , Ould-Chikh Samy , Zitolo Andrea , Hazemann Jean-Louis , Shterk Genrikh , Ramirez Adrian , Gascon Jorge
Published: March 2022

All-silica MFI zeolite was used as a support for the synthesis of promoter-free robust transition metal catalysts. Effects of different physical parameters and catalyst deactivation mechanism were studied for the ethane dehydrogenation reaction.
Design of 3d transition metal anchored B5N3 catalysts for electrochemical CO2 reduction to methane
Authors: Zhang Yuefeng , Zeng Zhiyuan , Li Hao
Published: March 2022

3d transition metal anchored B5N3 materials were analyzed as potential catalysts for electrochemical CO2 reduction to methane.
Simultaneous enhancement of thermally activated delayed fluorescence and photoluminescence quantum yield via homoconjugation
Authors: Montanaro Stephanie , Pander Piotr , Mistry Jai-Ram , Elsegood Mark R. J. , Teat Simon J. , Bond Andrew D. , Wright Iain A. , Congrave Daniel G. , Etherington Marc K.
Published: March 2022

A new iptycene unequivocally validates homoconjugation as a viable strategy to simultaneously enhance TADF reverse intersystem crossing and radiative decay.
2D tungsten nanosheets: ascendant nonlinear absorption properties in the ultraviolet band
Authors: Yuan Junjie , Xin Yi , Wang Xinyu , Liu Yiping , Zhao Zhenyu , Du Binjian , Xing Fei , Zhang Fang
Published: March 2022

The NLA properties of 2D tungsten nanosheets were explored by the open aperture Z-scan technique. The results demonstrated their excellent NLA values in the ultraviolet band, which could draw people’s attention to transition metal elements.
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

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 >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

Arecanut 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

The 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

This 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

Medium 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

Character 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

Attention 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

In 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

Automatic 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.
Redshift evolution of the H I detection rate in radio-loud active galactic nuclei
Authors: Murthy Suma , Morganti Raffaella , Kanekar Nissim , Oosterloo Tom
Published: March 2022

We present a search for associated H I 21 cm absorption in a sample of 29 radio-loud active galactic nuclei (AGNs) at 0.7 < z < 1, carried out with the upgraded Giant Metrewave Radio Telescope. We detect H I 21 cm absorption against none of our target AGNs, obtaining 3σ upper limits to the optical depth of ≲1% per 50 km s−1 channel. The radio luminosity of our sources is lower than that of most AGNs searched for H I 21 cm absorption at similar redshifts in the literature, and, for all targets except two, the UV luminosity is below the threshold 1023 W Hz−1, above which the H I in the AGN environment has been suggested to be completely ionised. We stacked the H I 21 cm spectra to obtain a more stringent limit of ≈0.17% per 50 km s−1 channel on the average H I 21 cm optical depth of the sample. The sample is dominated by extended radio sources, 24 of which are extended on scales of tens of kiloparsecs. Including similar extended sources at 0.7 < z < 1.0 from the literature, and comparing with a low-z sample of extended radio sources, we find statistically significant (≈3σ) evidence that the strength of H I 21 cm absorption towards extended radio sources is weaker at 0.7 < z < 1.0 than at z < 0.25, with a lower detection rate of H I 21 cm absorption at 0.7 < z < 1.0. Redshift evolution in the physical conditions of H I is the likely cause of the weaker associated H I 21 cm absorption at high redshifts, due to either a low H I column density or a high spin temperature in high-z AGN environments.
Optimization of Crystallization Process Condition of Nutmeg Seed Oleoresin
Authors: Ananingsih Victoria K. , Soedarini Bernadeta , Konstantia Bernardine A. A. , Wibowo Andreas
Published: March 2022

Nutmeg (Myristica fragransHoutt) is a native spice from Indonesia. It contains oleoresin which has a unique aroma. Extraction of nutmeg seed oleoresin is carried out by ultrasound assisted extraction (UAE). The shelf life of nutmeg seed oleoresin can be extended by microencapsulation which is conducted by crystallization using sucrose as coating material. This study was aimed to optimize the crystallization condition process of nutmeg seed oleoresin. The formulation used were nutmeg seed oleoresin (5 gram, 10 gram, 15 gram), sucrose (30 gram, 35 gram, 40 gram), and water (15 gram, 20 gram, 25 gram). Physicochemical characteristics measured were moisture content, water activity (aw), color, total oil, surface oil, trapped oil, and antioxidant activity. Optimization of process conditions were analyzed by Response Surface Method. The results showed that nutmeg seed oleoresin (13.266 gram), sucrose (35.047 gram) and water (20.019 gram) produced the optimum antioxidant activity of microencapsulated oleoresin. Furthermore, the application of nutmeg seed oleoresin (11.088 gram), sucrose (36.381 gram) and water (19.331 gram) resulted in the optimum trapped oil percentage of microencapsulated oleoresin. These methods can be applied to produce nutmeg seed oleoresin powder which then could be used as spices and beverage ingredient.
Characterization of Toothpaste Made With Unfermented Cocoa Powder (Theobroma cacao L) Againts Bacteria Streptococcus mutans
Authors: Yumas Medan , Loppies Justus Elisa , Khaerunnisa  , Ramlah Sitti , Rosniati  , Lullung Alfrida
Published: March 2022

Cocoa beans contain polyphenolic compounds/phenolic include catechin, epicatechin, anthocyanin, proanthocyanidin, phenolic acids, condensed tannins, alkaloids, teroid/terpenoids, flavonoids-other flavonoids, and some minor components. Polyphenol group compounds have antibacterial and anti-inflammatory effects. This research aims to determine the physico-chemical properties and effects of toothpaste containing active component of unfermented cocoa powder to the growth of Streptococcus mutans bacteria. This study uses a completely randomized design by varying concentration of unfermented cocoa powder used as active ingredient in toothpaste. Toothpaste was prepared with active ingredients of unfermented cocoa powder with concentration of 1.0; 2.5; 4, 0; 5.5% (w/w) and each repeated three times. The results indicated that the four concentrations of unfermented cocoa powder contained in toothpaste show an inhibitory effect against 20.936; and 21.039 mm. The fourth toothpastes have a viscosity above 400 dps, pH value above 7, therefore meet SNI 01-3524-1994 standard. Panelist scored this toothpaste with 3.
Review: Bioavailability and Activity Prediction of Bioactive Compounds of Red Fruit (Pandanus conoideus Lam.) and Pandan Grape (Sararanga sinousa Hemsley) by in silico Method
Authors: Suprijono Maria Matoetina , Purnamasari Vita , Tandoro Yohanes
Published: March 2022

Red Fruit (RF) and Pandan grape (PG) contained bioactive compounds that good for health. The structure influenced the availability and activity, including the interaction with another in the body. It needs time and financial consumption, whereas bioinformation technology offers the solution. The research aims to predict the availability and activity of RF and PG compounds by in silico method based on the SAR. Fourteen RF and PF phenolics were evaluated for availability, antioxidant, and antiglycation activity during binding to HSA. The flavonoids showed Pa>0,7 for antioxidant in general, free radical scavenging, and lipid peroxidase inhibitor activity. The availability was shown by the ligand’s capability to bind to HSA. The order of affinity energy from the largest to the smallest was RF flavonoid > PG anthocyanin > GP phenolic acids. Taxifolin and Quercetin still had antioxidant activity during binding because there are free hydroxyl groups. PG chlorogenic acid and RF flavonoids play as an anti-diabetic through antioxidant mechanism and HSA glycation disturbance. Based on this review, in silico method is effective as an analysis tool for the activity and mechanism prediction of food bioactive compounds.
Optimization of Physicochemical and Organoleptic Properties of Cookies made of Modified Cassava Flour and Mung Bean Flour
Authors: Leticia Celine Gabrielle , Trisnawati Chatarina Yayuk , Widyastuti Theresia Endang Widoeri , Srianta Ignatius , Tewfik Ihab
Published: March 2022

Wheat flour is usually the main ingredients of majority of cookies. Despite its high consumption, however it is not widely available. It will be efficacious to substitute wheat flour with modified cassava flour (MOCAF) combined with mung bean flour to enhance protein content of cookies. The aims of this study were (1) to assess the impact of different proportions of MOCAF to mung bean flour on the physicochemical and organoleptic characteristic of cookies, (2) to ascertain the optimal proportion of MOCAF to mung bean flour that generates ideal cookies based on the organoleptic characteristic of cookies. The research design used was Randomized Block Design with one factor. The proportion of MOCAF to mung bean flour consisted of 100:0, 85:15, 70:30, 55:45, and 40:60. The results showed that the proportion of MOCAF to mung bean flour affected moisture content, spread ratio, hardness, lightness, colour, and preferences for colour, hardness, and taste. The proportion 70:30 of MOCAF to mung bean flour showed the finest treatment and scored significant preference level for colour, hardness, taste, and ease of swallowing. The optimization of physicochemical and organoleptic properties of cookies with modified cassava flour and mung bean flour was successfully achieved and qualify the cookies nutrition.
Room temperature magnetic properties of Mn-Ga-B melt-spun ribbons
Authors: Saito Tetsuji , Nishio-Hamane Daisuke
Published: March 2022

In this study, we synthesized Mn65Ga35-δBδ (δ = 0–10) alloys using the melt-spinning technique. In the as-quenched state, the alloys consisted of the D019 and Mn8Ga5 phases in the as-quenched state. After heat treatments, the Mn65Ga35 alloy consisted of the D022 phase, whereas the Mn65Ga30B5 and Mn65Ga25B10 alloys were mainly composed of the D019 phase. The magnetization of the Mn65Ga30B5 and Mn65Ga25B10 alloys was smaller than that of the Mn65Ga35 alloy, but the Mn65Ga30B5 and Mn65Ga25B10 alloys exhibited higher coercivity than the Mn65Ga35 alloy. The highest coercivity of 8.7 kOe was measured at room temperature in the Mn65Ga30B5 alloy with the fined grains of the D019 phase.
Predicting spatial and seasonal patterns of wildlife–vehicle collisions in high-risk areas
Authors: Nguyen Hanh K. D. , Fielding Matthew W. , Buettel Jessie C. , Brook Barry W.
Published: March 2022

Context Vehicle collisions with wildlife can injure or kill animals, threaten human safety, and threaten the viability of rare species. This has led to a focus in road-ecology research on identifying the key predictors of ‘road-kill’ risk, with the goal of guiding management to mitigate its impact. However, because of the complex and context-dependent nature of the causes of risk exposure, modelling road-kill data in ways that yield consistent recommendations has proven challenging. Aim Here we used a multi-model machine-learning approach to identify the spatio-temporal predictors, such as traffic volume, road shape, surrounding vegetation and distance to human settlements, associated with road-kill risk. Methods We collected data on the location, identity and wildlife body size of each road mortality across four seasons along eight roads in southern Tasmania, a ‘road-kill hotspot’ of management concern. We focused on three large-bodied and frequently affected crepuscular Australian marsupial herbivore species, the rufous-bellied pademelon (Thylogale billardierii), Bennett’s wallaby (Macropus rufogriseus) and the bare-nosed wombat (Vombatus ursinus). We fit the point-location data using ‘lasso-regularisation’ of a logistic generalised linear model (LL-GLM) and out-of-bag optimisation of a decision-tree-based ‘random forests’ (RF) algorithm for optimised predictions. Results The RF model, with high-level feature interactions, yielded superior out-of-sample prediction results to the linear additive model, with a RF classification accuracy of 84.8% for the 871 road-kill observations and a true skill statistic of 0.708, compared with 61.2% and 0.205 for the LL-GLM. The lasso rejected road visibility and human density, ranking roadside vegetation type and presence of barrier fencing as the most influential predictors of road-kill locality. Conclusions Forested areas with no roadside barrier fence along curved sections of road posed the highest risk to animals. Seasonally, the frequency of wildlife–vehicle collisions increased notably for females during oestrus, when they were more dispersive and so had a higher encounter rate with roads. Implications These findings illustrate the value of using a combination of attributive and predictive modelling using machine learning to rank and interpret a complexity of possible predictors of road-kill risk, as well as offering a guide to practical management interventions that can mitigate road-related hazards.
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

Significance

Geography 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

Formally 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

The 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.
Defects in Statically Unstable Solids: The Case for Cubic Perovskite α-CsPbI3
Authors: Wu Xiaowei , Ming Chen , Shi Jing , Wang Han , West Damien , Zhang Shengbai , Sun Yi-Yang
Published: March 2022

High-temperature phases of solids are often dynamically stable only. First-principles study of point defects in such solids at 0 K is prohibited by their static instability, which results in random structures of the defect-containing supercell so that the total energy of the supercell is randomly affected by structural distortions far away from the defect. Taking cubic perovskite α-CsPbI3 as an example, we first present the problem incurred by the static instability and then propose an approach based on molecular dynamics to carry out ensemble average for tackling the problem. Within affordable simulation time, we obtain converged defect ionization energies, which are unattainable by a standard approach and allow us to evaluate its defect tolerance property. Our work paves the way for studying defects in statically unstable solids.
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

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

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.
The stability of oncologic MRI radiomic features and the potential role of deep learning: a review
Authors: Scalco Elisa , Rizzo Giovanna , Mastropietro Alfonso
Published: March 2022

The use of MRI radiomic models for the diagnosis, prognosis and treatment response prediction of tumors has been increasingly reported in literature. However, its widespread adoption in clinics is hampered by issues related to features stability. In the MRI radiomic workflow, the main factors that affect radiomic features computation can be found in the image acquisition and reconstruction phase, in the image pre-processing steps, and in the segmentation of the region of interest on which radiomic indices are extracted. Deep Neural Networks (DNNs), having shown their potentiality in the medical image processing and analysis field, can be seen as an attractive strategy to partially overcome the issues related to radiomic stability and mitigate their impact. In fact, DNN approaches can be prospectively integrated in the MRI radiomic workflow to improve image quality, obtain accurate and reproducible segmentations and generate standardized images. In this review, DNN methods that can be included in the image processing steps of the radiomic workflow are described and discussed, in the light of a detailed analysis of the literature in the context of MRI radiomic reliability.
A new tool for modelling lattices of organic polaritons
Authors: Sturges Thomas J , Stobińska Magdalena
Published: March 2022

Microcavity-polaritons in lattice geometries have been used to study a wide range of interesting physics. Meanwhile, organic materials have shown great promise on the road towards polaritonic devices, as the strong binding energy of their Frenkel excitons permits room temperature condensation and lasing. Whilst there are theoretical treatments of the condensation processes in planar organic microcavities, models of lattice geometries are lacking. Here, we introduce a model for describing the dynamics of lattices of zero-dimensional organic microcavities, where the dominant condensation mechanism involves the emission of a vibrational phonon. We also provide an open source software module that can be easily modified for any lattice geometry or dimension. The vibronic transition provides a tool for targeted condensation in a particular eigenmode of the system, which we highlight by examining a dimer and a dimerised chain. For the dimer, we observe a double resonance in the condensation efficiency that arises from tuning the condensate-reservoir detuning into resonance with either the symmetric or antisymmetric mode. This mechanism was also exploited in the dimerised chain, to selectively condense the system in to either the bulk states or the topological edge states, under homogeneous pumping of all cavities. We also showed an interesting signature of chiral transport when pumping a single cavity in the chain, where the direction of propagation depends on the sublattice being pumped.
Less is more: a new machine-learning methodology for spatiotemporal systems
Authors: Feng Sihan , Wang Kang , Wang Fuming , Zhang Yong , Zhao Hong
Published: March 2022

Machine learning provides a way to use only portions of the variables of a spatiotemporal system to predict its subsequent evolution and consequently avoids the curse of dimensionality. The learning machines employed for this purpose, in essence, are time-delayed recurrent neural networks with multiple input neurons and multiple output neurons. We show in this paper that such kinds of learning machines have a poor generalization ability to variables that have not been trained with. We then present a one-dimensional time-delayed recurrent neural network for the same aim of model-free prediction. It can be trained on different spatial variables in the training stage but initiated by the time series of only one spatial variable, and consequently possess an excellent generalization ability to new variables that have not been trained on. This network presents a new methodology to achieve fine-grained predictions from a learning machine trained on coarse-grained data, and thus provides a new strategy for certain applications such as weather forecasting. Numerical verifications are performed on the Kuramoto coupled oscillators and the Barrio−Varea−Aragon−Maini model.
Improving the performance of a gaze independent P300-BCI by using the expectancy wave
Authors: Xu Wei , Gao Pin , He Feng , Qi Hongzhi
Published: March 2022

Objective. A P300-brain computer interface (P300-BCI) conveys a subject’s intention through recognition of their event-related potentials (ERPs). However, in the case of visual stimuli, its performance depends strongly on eye gaze. When eye movement is impaired, it becomes difficult to focus attention on a target stimulus, and the quality of the ERP declines greatly, thereby affecting recognition efficiency. Approach. In this paper, the expectancy wave (E-wave) is proposed to improve signal quality and thereby improve identification of visual targets under the covert attention. The stimuli of the P300-BCI described here are presented in a fixed sequence, so the subjects can predict the next target stimulus and establish a stable expectancy effect of the target stimulus through training. Features from the E-wave that occurred 0 ∼ 300 ms before a stimulus were added to the post-stimulus ERP components for intention recognition. Main results. Comparisons of ten healthy subjects before and after training demonstrated that the expectancy wave generated before target stimulus could be used with the P300 component to improve character recognition accuracy (CRA) from 85% to 92.4%. In addition, CRA using only the expectancy component can reach 68.2%, which is significantly greater than random probability (16.7%). The results of this study indicate that the expectancy wave can be used to improve recognition efficiency for a gaze-independent P300-BCI, and that training contributes to induction and recognition of the potential. Significance. This study proposes an effective approach to an efficient gaze-independent P300-BCI system.