bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of melatonin in the maturation medium on developmental competence of bovine oocytes exposed to heat shock and on embryo quality
Authors: Lima P. H. , Souza J. C. , Borges A. M. , Lima R. R. , Jasmin J. , Leite A. C. , Silva E. B. M. , Faria L. R. , Alves N. G. 
Published: July 2022

Context Heat stress has been related to reduced fertility in cattle, which may reflect damage to developing oocytes. The deleterious effects of heat stress on oocytes may involve an increased production of reactive oxygen species (ROS). The addition of antioxidants, such as melatonin, to the in vitro maturation medium of heat-stressed bovine oocytes may be advantageous for reducing ROS concentrations, thereby enhancing the subsequent fertilisation and development to the blastocyst stage. Aims The present study evaluated the in vitro production and quality of bovine embryos derived from oocytes matured under heat-shock conditions with increasing melatonin concentrations. Methods Cumulus–oocyte complexes from Bos taurus × Bos indicus cows were matured under heat shock (12 h at 41°C, followed by 12 h at 38.5°C) in medium without melatonin (control) or with 10−12, 10−9, 10−6 and 10−3 mol/L melatonin. Logistic regression equations were adjusted when the main effect of melatonin was observed. Key results The cleavage rate on Day 3 of culture, the production of blastocysts on Days 7 and 8, the number and proportion of apoptotic cells in expanded blastocysts, and the proportion of apoptotic cells in the inner cell mass (ICM) were not affected by melatonin. The proportion of 8- to 16-cell-stage embryos on Day 3 and the proportion of ICM cells in expanded blastocysts were influenced by the melatonin concentration. Through logistic regression, it was estimated that the proportion of 8- to 16-cell-stage embryos increased linearly as the melatonin increased until 10−3 mol/L, and that the proportion of ICM cells in blastocysts was maximised between 10−6 and 10−4 mol/L melatonin, and decreased thereafter. Conclusions Melatonin at 10−3 mol/L in the in vitro maturation (IVM) of heat-shocked oocytes stimulated the first embryo cleavages but reduced the ratio of ICM cells in blastocysts. The embryo quality may be improved by melatonin between 10−6 and 10−4 mol/L. Implications The addition of melatonin to IVM medium has the potential to improve the quality of bovine embryos from heat-stressed oocytes.
Finding relevance in smartphone advertising
Authors: Yus Francisco 
Published: July 2022

Abstract
Smartphone ads compete for the user’s attention, which is initially intended to focus on other areas of the small
screen of the device. Despite this competition, smartphone advertisements aim to produce as much cognitive reward as possible in
exchange for the mental effort expended in their processing, that is, they aim at the audience’s relevance, as claimed by
relevance theory (Sperber and Wilson 1995), a theory in which cyberpragmatics (Yus 2011) is rooted. This paper addresses several key qualities of effective smartphone
advertising from a cyberpragmatics perspective that focuses on possible sources of relevance of online communication, and now
applied to smartphone ads. Furthermore, it is claimed that today’s smartphone-based advertising cannot be accounted for
pragmatically without the incorporation of key terms such as contextual constraint and non-propositional
effect, which add to more traditional pragmatic accounts of online communication (Yus 2017a, 2021a).
Mechanical loading prediction through accelerometry data during walking and running
Authors: Veras Lucas , Diniz‐Sousa Florêncio , Boppre Giorjines , Resende‐Coelho Ana , Moutinho‐Ribeiro Edgar , Devezas Vítor , Santos‐Sousa Hugo , Preto John , Vilas‐Boas João Paulo , Machado Leandro , Oliveira José , Fonseca Hélder 
Published: July 2022

ABSTRACTCurrently, there is no way to assess mechanical loading variables such as peak ground reaction forces (pGRF) and peak loading rate (pLR) in clinical settings. The purpose of this study was to develop accelerometry‐based equations to predict both pGRF and pLR during walking and running. One hundred and thirty one subjects (79 females; 76.9 ± 19.6 kg) walked and ran at different speeds (2–14 km·h−1) on a force plate–instrumented treadmill while wearing accelerometers at their ankle, lower back and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave‐one‐out cross‐validation was used to calculate prediction accuracy and Bland–Altman plots. Our pGRF prediction equation was compared with a reference equation previously published. Body mass and peak acceleration were included for pGRF prediction and body mass and peak acceleration rate for pLR prediction. All pGRF equation coefficients of determination were above 0.96, and a good agreement between actual and predicted pGRF was observed, with a mean absolute percent error (MAPE) below 7.3%. Accuracy indices from our equations were better than previously developed equations. All pLR prediction equations presented a lower accuracy compared to those developed to predict pGRF. Walking and running pGRF can be predicted with high accuracy by accelerometry‐based equations, representing an easy way to determine mechanical loading in free‐living conditions. The pLR prediction equations yielded a somewhat lower prediction accuracy compared with the pGRF equations.
Atomistic simulations of mechanical response of a heterogeneous fcc/bcc nanolayered composite
Authors: Xu Kezhong , Zhai Hua , He Linghui , Ni Yong , Lu Pin , Wang Gangfeng , Liu Xuepeng 
Published: July 2022

Abstract
Molecular dynamics simulations are performed to study the mechanical properties and deformation mechanisms of a heterogeneous face-centered cubic/ body-centered cubic Cu/Ta nanolayered composite under uniaxial tension and compression. The results show that the stress–strain curves exhibit two main yield points in tension while only one yield point during compression, and the deformation primarily experiences three stages. The first stage is linearly elastic at small strains, followed by the nucleation and propagation of dislocations and stacking faults in the Cu layers, and eventually the Ta layers yield to plastic deformation. The yield of the specimen is mainly determined by the dislocation evolution in the hard phase (i.e. Ta layers), which leads to a sharp drop in the stress–strain curve. We show that the heterogeneous nanolayered composite exhibits a good deformation compatibility during compression but an obvious deformation incompatibility between Cu and Ta layers in tension. The temperature effect is also systematically investigated. It is revealed that the yield of the specimen at higher temperature depends only on the dislocation evolution in the thick Ta layers, and the yield strengths in tension and compression both decrease with the increasing temperature. In particular, our computations show that high temperature can significantly suppress the dislocation activities in the Cu layers during deformation, which results in a lower dislocation density of the Cu layers compared with that of the Ta layers and thus causing an incompatible fashion among the constituent layers.
Rigorous justification for the space–split sensitivity algorithm to compute linear response in Anosov systems
Authors: Chandramoorthy Nisha , Jézéquel Malo 
Published: July 2022

Abstract
Ruelle (1997 Commun. Math. Phys.
187 227–41; 2003 Commun. Math. Phys.
234 185–90) (see also Jiang 2012 Ergod. Theor. Dynam. Syst.
32 1350–69) gave a formula for linear response of transitive Anosov diffeomorphisms. Recently, practically computable realizations of Ruelle’s formula have emerged that potentially enable sensitivity analysis of certain high-dimensional chaotic numerical simulations encountered in the applied sciences. In this paper, we provide full mathematical justification for the convergence of one such efficient computation, the space–split sensitivity, or S3, algorithm (Chandramoorthy and Wang 2022 SIAM J. Appl. Dyn. Syst.
21 735–81). In S3, Ruelle’s formula is computed as a sum of two terms obtained by decomposing the perturbation vector field into a coboundary and a remainder that is parallel to the unstable direction. Such a decomposition results in a splitting of Ruelle’s formula that is amenable to efficient computation. We prove the existence of the S3 decomposition and the convergence of the computations of both resulting components of Ruelle’s formula.
New analytical solutions by the application of the modified double sub-equation method to the (1 + 1)-Schamel-KdV equation, the Gardner equation and the Burgers equation
Authors: Yépez-Martínez H , Inc Mustafa , Rezazadeh Hadi 
Published: July 2022

Abstract
In this research we present the application of the modified double sub-equation guess solution together with the analytical solutions of the Riccati equation to obtain new analytical exact solutions to the (1 + 1)-Schamel-KdV equation, the (1 + 1)-dimensional Gardner equation (or combined KdV-mKdV) and the nonlinear evolution (1 + 1)-dimensional Burgers equation. Results show some conditions between the allowed values of the interaction coefficients and the parameters of the allowed analytical solutions of the double sub-equation guess solution. The resulting new soliton solutions have by first time the proper real phase value behavior, not seen in previous applications of the double sub-equation method. Additionally, it has been revealed that handle the complexiton process with two different traveling variables have as an important consequence that the mixing between trigonometric and hyperbolic solutions is only observed for the analytical solutions of the nonlinear evolution (1 + 1)-dimensional Burgers equation, while no mixing between trigonometric and hyperbolic solutions is observed for the solutions of the (1 + 1)-Schamel-KdV ((1 + 1)-S-KdV) equation or the (1 + 1)-dimensional Gardner equation. Numerical simulations are provided in 3-D graphs and 2-D plots representing the general characteristics of the resulting analytical solutions.
Structure of stellar remnants with coupling to a light scalar
Authors: Gao Christina , Stebbins Albert 
Published: July 2022

Abstract
In this paper, we study how a Yukawa coupling of the Standard Model fermions to a light scalar field affects the stellar structure of cold stellar remnants such as neutron stars. We elucidate the stellar structure phenomenology using a simple model of a massive scalar coupled to a single dominant fermion with no other interactions. For a broad scalar mass range (10-10 eV ≪ mϕ
≪ 103 eV for neutron stars) we show that the equation-of-state and stellar structure depends on the effective coupling ℊ = gf

mf
/mϕ
, where gf
is the Yukawa coupling, mf
is the fermion mass, and mϕ
is the scalar kinematic mass at nuclear densities. If ℊ > 𝒪(1) the Yukawa coupled matter exhibits various anomalous behaviors including hydrodynamic instability, negative pressure, distinct phases (soft and hard) of matter with sharp phase boundaries between them and with the vacuum. These anomalies can lead to stars consisting of only soft, only hard, or a hybrid of soft and hard matter. These stars can have either sign for the slope of the mass-radius relation, anomalously large and small masses, gaps in allowed radii, multiple radii for the same mass, thin crusts and radiate anomalously large amounts of energy when they form (in the form of neutrinos for neutron stars). To the extent that these anomalies have not and/or will not be observed limits the effective coupling to ℊ < 𝒪(1). We argue this phenomenology is generic to stars with Yukawa coupled matter.
Spike prediction on primary motor cortex from medial prefrontal cortex during task learning
Authors: Wu Shenghui , Qian Cunle , Shen Xiang , Zhang Xiang , Huang Yifan , Chen Shuhang , Wang Yiwen 
Published: July 2022

Abstract

Objectives. Brain–machine interfaces (BMIs) aim to help people with motor disabilities by interpreting brain signals into motor intentions using advanced signal processing methods. Currently, BMI users require intensive training to perform a pre-defined task, not to mention learning a new task. Thus, it is essential to understand neural information pathways among the cortical areas in task learning to provide principles for designing BMIs with learning abilities. We propose to investigate the relationship between the medial prefrontal cortex (mPFC) and primary motor cortex (M1), which are actively involved in motor control and task learning, and show how information is conveyed in spikes between the two regions on a single-trial basis by computational models. Approach. We are interested in modeling the functional relationship between mPFC and M1 activities during task learning. Six Sprague Dawley rats were trained to learn a new behavioral task. Neural spike data was recorded from mPFC and M1 during learning. We then implement the generalized linear model, the second-order generalized Laguerre–Volterra model, and the staged point-process model to predict M1 spikes from mPFC spikes across multiple days during task learning. The prediction performance is compared across different models or learning stages to reveal the relationship between mPFC and M1 spike activities. Main results. We find that M1 neural spikes can be well predicted from mPFC spikes on the single-trial level, which indicates a highly correlated relationship between mPFC and M1 activities during task learning. By comparing the performance across models, we find that models with higher nonlinear capacity perform significantly better than linear models. This indicates that predicting M1 activity from mPFC activity requires the model to consider higher-order nonlinear interactions beyond pairwise interactions. We also find that the correlation coefficient between the mPFC and M1 spikes increases during task learning. The spike prediction models perform the best when the subjects become well trained on the new task compared with the early and middle stages. The results suggest that the co-activation between mPFC and M1 activities evolves during task learning, and becomes stronger as subjects become well trained. Significance. This study demonstrates that the dynamic patterns of M1 spikes can be predicted from mPFC spikes during task learning, and this will further help in the design of adaptive BMI decoders for task learning.
Entanglement diagnostics for efficient VQA optimization
Authors: Kim Joonho , Oz Yaron 
Published: July 2022

Abstract
We consider information spreading measures in randomly initialized variational quantum circuits and introduce entanglement diagnostics for efficient variational quantum/classical computations. We establish a robust connection between entanglement measures and optimization accuracy by solving two eigensolver problems for Ising Hamiltonians with nearest-neighbor and long-range spin interactions. As the circuit depth affects the average entanglement of random circuit states, the entanglement diagnostics can identify a high-performing depth range for optimization tasks encoded in local Hamiltonians. We argue, based on an eigensolver problem for the Sachdev–Ye–Kitaev model, that entanglement alone is insufficient as a diagnostic to the approximation of volume-law entangled target states and that a large number of circuit parameters is needed for such an optimization task.
Efficiency forecasting of electric bundle assembly with use of ANN model
Authors: Burduk A , Dybała B , Machado J 
Published: July 2022

Abstract
Management of production systems requires making immediate decisions based on the data generated in bulk by IT systems. In this case, it can be helpful to use models of artificial neural networks (ANN) that, on the grounds of accessible data, will determine results of the made decision. One of the key problems in production companies is determination of execution time and cost of a production order. The problem is especially important in a company manufacturing a variable product line with a big part of manual operations. In the article, the way of building an ANN model for efficiency forecasting of the assembly process of electric bundles is presented. With regard to the very wide and variable product line, the products with different complexity degree are manufactured on three types of assembly lines. The assembly processes are performed on the assembly lines manually by groups of workers, so efficiency of the process is influenced mostly by skills and experience of these workers. Therefore, numbers of new assembly workers assigned to individual assembly lines and quantities of new products in the production schedule are selected as explanatory variables in the ANN model. The explained variable in the ANN model is production volume of the manufactured electric bundles.
Intelligent support system for monitoring machining processes
Authors: Rojek I , Dostatni E , Diakun J , Kotlarz P 
Published: July 2022

Abstract
The intelligent support system for monitoring machining processes was objective of our research. The paper presents process of development of this system which can used in real enterprises. Methodology: to create of the system we use methods of artificial intelligence (neural networks, decision trees and rules, fuzzy logic), using AI methods we created procedures support monitoring machining processes: diagnosis of machining process disturbances using decision rules, control of stability of the machining process based on control card analysis using neural networks, control of surface roughness parameter using neural networks and decision trees and monitoring machine tool through control and compensation of thermal deformations of ball screws of CNC machine tools using neural networks. To optimize of AI models we compared many the models with different structures, chose the best models and used them in intelligent system.
Intelligent Fault Diagnosis Tool For Power Line Systems
Authors: Piechoczek E , Przystalka P , Kopacz G 
Published: July 2022

Abstract
The paper focuses on the intelligent fault diagnosis system for automatic analysis of a huge amount of images with 4K resolution collected during flight inspection of the overhead power lines with the use of aerial platforms. The developed system can be used to detect and locate anomalies of power line structures e.g. anomalies of towers, conductors, dampers, insulators, etc. The purpose of the paper is to present the general architecture of the system and the results obtained during inspection of thousands of kilometer flights of power lines. The proposed system is based on the predefined deep neural network called Faster R-CNN which is dedicated for solving real-time object detection problems. The faster R-CNN-based neural model created on COCO dataset was additionally retrained by the authors applying images gathered as a result of flight inspections in order to obtain the high performance of the whole system. The comprehensive verification tests were carried out to prove the merits of the proposed solution.
Intelligent tools for improving quality and efficiency in production environment
Authors: Niemiec Krzysztof , Niemiec Katarzyna , Krenczyk Damian 
Published: July 2022

Abstract
The digital transformation in the world of retail consumers is developing very dynamically. Solutions that have recently been in research and development centers and laboratories of companies creating modern technologies, very quickly pass into everyday life and are taken for granted and necessary in private life. The world around us is changing and consumers are changing with it. Although it is estimated that Artificial Intelligence market is expected to grow at a compound annual growth rate (CAGR) of 52% from 2017 to 2025 [1], it is not that simple to introduce those up-to-day technologies in the production environment. The realities in the industrial and manufacturing environment are much different due to the fact, that not all new solutions may be imported into the production right after their appearance on the market. This article will present the basis of the quality management tools in comparison with new intelligent tools. Also, the reasons, why the intelligent tools are not that easy to be incorporated into production plants together with presentation how they might improve the quality of the product and efficiency of the production processes in the continuous production’s plants will be analyzed. Different tools will be presented with their advantages and disadvantages for the operators, management and production environment.
Estimating State of Charge of Lithium-ion Batteries by Using Ultrasonic Guided Waves Detection Technology
Authors: Gao Jie , Lyu Yan , He Cunfu 
Published: July 2022

Abstract
As a light weight and high power density energy, Lithium-ion batteries have become widely used in electric vehicles, energy storage systems, etc. Thus, accurately capturing the internal battery dynamics and properly estimating the state of charge of a lithium-ion battery attract academic research interest. A reliable battery detection method is particularly important. The mechanical properties (elastic modulus and density) can be affected by the level of lithiation of the electrodes and the volume expansion during charge and discharge cycling. In this work, a theoretical model of ultrasonic guided wave detection for cylindrical lithium-ion battery is established to purchase the guidance of the in-situ monitoring of the battery status. Several numerical cases about cylindrical Lithium cobalt oxide battery are studied, and the effect of the circumferential wave number and state of charge (SOC) on the dispersion characteristics are illustrated. Based on the extracted dispersion curves at different SOC, the relationship between the wave propagation characteristics of the ultrasonic guided waves and the SOC of the lithiumion battery is analyzed. The effective capture of the mapping relationship between SOC and acoustic behaviors can provide new ideas and solutions for the effective evaluation of the reliability and safety of power batteries.
Water Temperature Measurement Method Based on Square-Wave Signal Generator
Authors: Li Changjin , Cao Jian , Zhang Xing 
Published: July 2022

Abstract
Water temperature measurement is widely used and plays an important role in fishery breeding, agricultural irrigation, temperature control of household water heater and other aspects. People have a huge demand for water temperature measurement devices. The performance of the existing device is greatly affected by the environment and often requires manual intervention. The square-wave signal generator has simple structure, stable output, low energy consumption, easy operation and low price. The water temperature measurement method based on the square-wave signal generator can expand the application range of the square-wave signal generator, promote the convenience of measurement work and then improve the measurement sensitivity. Based on operational amplifier, Schmitt-Trigger Inverter and 555 Timer, the output frequency of the circuit changes according to the change of the overall resistance value of the circuit. A waterproof thermistor is added to the circuit and placed in water. The water temperature would change the resistance value of the overall circuit by changing the resistance value of the thermistor. Therefore, different output frequencies of the measuring circuit reflect different water temperatures. The working principles of the above three measuring circuits are specifically described, and their measuring characteristics are compared. It can be seen that the circuit based on 555 Timer has stable measuring performance and high sensitivity, which can be widely applied to the needs of water temperature measurement in production and life. What’s more, it provides a new idea for water temperature measurement.