bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impacts of Individual and Pair Housing on Calves
Authors: Mahendran Sophie 
Published: March 2024

Abstract
Calf rearing is a large financial cost to farms, with particular importance for dairy heifer replacements where the forward investment does not receive any payback for 3–4 years. Ensuring the critical pre-weaning phase is carried out optimally and understanding the impact that housing has on this time is important. Historically, individual calf housing has been used to reduce disease transmission among calves, but there is limited evidence that pair housing has any detrimental impact on this. Instead, research over the past decade has served to highlight the range of benefits that pair housing can provide. Calves housed with others have increased play behaviours, which is a positive indicator of well-being. Pair-housed calves start to eat concentrates at an earlier age and in greater amounts, which can have positive impacts on growth rates, and longer-term improvements in reducing age at first calving and improving first lactation milk yields. Pair housing allows calves to start forming social relationships, which can have a calming effect in stressful situations such as group changes and can improve behaviours when heifers enter the milking herd. Pair-raised calves also have reduced food neophobia which can help adult cattle cope with diet changes that can occur frequently in the dairy herd. This growing body of evidence suggests that pair housing of calves results in overall better health and welfare compared to individual housing, therefore practitioners should help encourage farmers to move towards this housing type.

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Can Clinical Audit Influence the Actions of Farmers and Vets to Benefit Sheep Health and Welfare?
Authors: Charles David 
Published: March 2024

Abstract
Clinical audits have been used in human healthcare for a long time, however, their use in the livestock sector is low, with very few examples in the literature. By using clinical audits to assess treatment procedures and outcomes, evidence-based veterinary medicine (EBVM) guidance can be drawn up to influence medication selection and usage. One of the key benefits of this is to help reduce antibiotic over-prescription and, influence the category of an antibiotic selected (using the European Medicines Agency categorisation of antibiotics).This case study will use an ovine caesarean section and assisted vaginal delivery (AVD) clinical audit (Charles and Stockton, 2022) as a case example. This case will also reference unpublished data assessing the impact of the 2022 audit recommendations which was discussed at the Sheep Veterinary Society Conference, 2023 (Charles, 2023b).

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© The Author 2023
Dairy Calf Feeding in the UK
Authors: Button Ellie 
Published: March 2024

Abstract
When a dairy calf is reared, it is separated from its mother, to allow the mother’s milk to be used for human consumption. Calves reared manually should be in a healthy and safe environment and with efficient use of resources. The choice of milk replacer or whole milk, and the equipment, amount and frequency with which it is fed must all be given meticulous thought and, importantly, kept as consistent as possible from day-to-day so that calves know what to expect. The details of how best to do this are elaborated in this article, as are three short case studies that highlight three incorrect practices in calf feeding and how they were corrected.

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MSCS-ICP: point cloud registration method using multi-view spatial coordinate system–ICP
Authors: Song Limei , Zhang Jipeng , Luo Jing , Zhao Yuxiaolong , Qiao Zhi 
Published: March 2024

Abstract
The effectiveness of point cloud registration critically determines three-dimensional (3D) reconstruction accuracy involving multi-view sensors. We introduce a multi-view point cloud registration method based on multi-view spatial coordinate system–ICP to solve the problem of 3D point cloud registration from different viewpoints. By integrating a spatial rotation axis line, our method successfully establishes the spatial coordinate system tailored for multi-view sensors, ensuring that 3D point clouds derived from various perspectives are optimally positioned initially. We employ the ICP technique for point cloud merging, facilitating a seamless transition from coarse to refined registration of these multi-view 3D point clouds. During the process of spatial rotation axis line fitting, we present a Ransac-based algorithm tailored for axis line fitting that effectively removes outliers, thus significantly improving the fitting precision. Experimental results from a standard sphere reconstruction reveal that within a measurement scope of 1.3–1.9 m, our proposed method boasts a maximum error of just 0.069 mm, an average absolute error of 0.039 mm, and a root mean square error of 0.043 mm. The speed of our point cloud registration outpaces that of alternative methods. Our method notably elevates the precision and velocity of 3D point cloud registration across diverse views, demonstrating commendable adaptability and resilience.
Synergistic reduction of nitrophenols by Au-CDs nanoconjugates with NaBH4
Authors: Priyadarshini Eepsita , Minzar Mohd , Pandey Saurabh , Rawat Kamla 
Published: March 2024

Abstract
Developing sustainable and innovative approaches for the efficient reduction of nitrophenols is crucial for environmental remediation, for managing health concerns posed by their widespread presence as hazardous pollutants in industrial effluents and contaminated water. We report the use of 12.9 ± 1 nm (TEM data) sized gold carbon dot nanoconjugates (Au@CDs) for catalytic conversion of o, m, p-nitrophenols to aminophenols by sodium borohydride. A simple approach was followed to synthesize ultra-small and highly stable Au@CDs, using citric acid and PEG as reducing and stabilizing agents. X-ray diffraction analysis verified the formation of nano-crystalline nanoconjugates. These nanoconjugates showed a remarkable catalytic activity in the range of 0.22–0.33 s−1 (varying with nanoconjugate concentration) which was much higher compared to conventional chemical methods of reduction. All the catalytic reaction experiments were performed at room temperature (27 ± 2 °C). Furthermore, an increase in rate constant was observed with increasing concentration of nanoconjugates. The catalytic activity of Au@CDs nanoconjugates was observed to be in order of m-nitrophenol > o-nitrophenol > p-nitrophenol with apparent rate constant (k
aap) values of 0.068, 0.043 and 0.031, respectively. Comparative analysis with GNPs, CDs and Au@CDs nanoconjugates stated that the nanoconjugates had superior catalytic activity. The research can have significant implications in the development of new strategies for environmental remediation and biomedical applications.
Measuring velocity and acceleration of a moving object in linear motion using proximity sensor of smartphone
Authors: Khongthiamsri Narongsak , Puttharugsa Chokchai 
Published: March 2024

Abstract
This paper describes an alternative method for measuring the time or time interval of a moving object in linear motion using a smartphone’s proximity sensor. Two smartphones are stationary, placed on an inclined plane, and measure the time or time interval of a moving object (with attached a cardboard) passing through the sensor using the Phyphox app. The obtained data are used to calculate both velocity and acceleration for the moving object. This experiment is easy to set up with readily available objects and prevents damage to the smartphones.
Magnetic behaviour of a mixed-spin ising model on a two-dimensional square-octagon lattice in a dynamic magnetic field
Authors: Wang Xue-Jiao , Jiang Wei 
Published: March 2024

Abstract
The magnetic behaviours of a two-dimensional square-octagon lattice with mixed spin 5/2 and spin 1 magnetic atoms are investigated in a dynamic magnetic field using effective field theory with correlations. We studied the effects of exchange coupling, anisotropy, oscillating magnetic fields, and temperature on the dynamic magnetic and thermodynamic properties of the square-octagon lattice. The dynamic magnetisation, order parameters, susceptibility, internal energy, and phase diagram of the system were obtained. These results significantly contribute to our understanding of the dynamic magnetic properties of two-dimensional materials. The ability to manipulate and control magnetic behaviour in two dimensions holds promise for the creation of more efficient and versatile devices.
Cosmological gravity on all scales. Part III. Non-linear matter power spectrum in phenomenological modified gravity
Authors: Srinivasan Sankarshana , Thomas Daniel B. , Battye Richard 
Published: March 2024

Abstract
Model-independent tests of gravity with cosmology are important when testing extensions to the standard cosmological model. To maximise the impact of these tests one requires predictions for the matter power spectrum on non-linear scales. In this work we validate the ReACT approach to the non-linear matter power spectrum against a suite of phenomenological modified gravity N-body simulations with a time-varying gravitational constant, covering a wider range of parameter space than previously examined. This vanilla application of ReACT has limited range and precision due to the different concentration-mass relation c(M) that occurs when gravity is modified. We extend this approach with a fitting function for a modified concentration-mass relation, allowing for accurate (1%) computation of the matter power spectrum up k = 2 h Mpc-1 across a substantial range of parameter space. This fitting function allows precision model-independent tests of modified gravity to be carried out using the data from upcoming large scale structure surveys.
On probing turbulence in core-collapse supernovae in upcoming neutrino detectors
Authors: Mukhopadhyay Mainak , Sen Manibrata 
Published: March 2024

Abstract
Neutrino propagation through a turbulent medium can be highly non-adiabatic leading to distinct signatures in the survival probabilities. A core-collapse supernova can be host to a number of hydrodynamic instabilities which occur behind the shockfront. Such instabilities between the forward shock and a possible reverse shock can lead to cascades introducing turbulence in the associated matter profile, which can imprint itself in the neutrino signal. In this work, we consider realistic matter profiles and seed in the turbulence using a randomization scheme to study its effects on neutrino propagation in an effective two-flavor framework. We focus on the potential of upcoming neutrino detectors — DUNE and Hyper-Kamiokande to constrain the parameters characterizing turbulence in a supernova. We find that these experiments can effectively constrain the parameter space for the amplitude of the spectra, they will only have mild sensitivity to the spectral index, and cannot inform on deviations from the usual Kolmogorov 5/3 inverse power law. Furthermore, we also confirm that the double-dip feature, originally predicted in the neutrino spectra associated with forward and reverse shocks, can be completely washed away in the presence of turbulence, leading to total flavor depolarization.
Asymmetric two-dimensional diffraction grating via a vortex field in an inverted-Y-type atomic system
Authors: Zhang Duo , Wang Ke , Zhao Kunpeng , Wang Mei , Sun Zhaoyu 
Published: March 2024

Abstract
We propose a theoretical scheme to realize a two-dimensional (2D) diffraction grating in a four-level inverted-Y-type atomic system coupled by a standing-wave (SW) field and a Laguerre–Gaussian (LG) vortex field. Owing to asymmetric spatial modulation of the LG vortex field, the incident probe field can be lopsidedly diffracted into four domains and an asymmetric 2D electromagnetically induced grating is created. By adjusting the detunings of the probe field and the LG vortex field, the intensities of the LG vortex field and the coherent SW field, as well as the interaction length, the diffraction properties and efficiency, can be effectively manipulated. In addition, the effect of the azimuthal parameter on the Fraunhofer diffraction of the probe field is also discussed. This asymmetric 2D diffraction grating scheme may provide a versatile platform for designing quantum devices that require asymmetric light transmission.
Validation of SOLPS-ITER simulations against the TCV-X21 reference case
Authors: Wang Y. , Colandrea C. , Oliveira D.S. , Theiler C. , Reimerdes H. , Body T. , Galassi D. , Martinelli L. , Lee K. ,   the TCV Team
Published: March 2024

Abstract
This paper presents a quantitative validation of Scrape-Off Layer Plasma Simulation-ITER (SOLPS-ITER) simulations against the TCV-X21 reference case and provides insights into the neutral dynamics and ionization source distribution in this scenario. TCV-X21 is a well-diagnosed diverted L-mode sheath-limited plasma scenario in both toroidal field directions, designed specifically for the validation of turbulence codes (Oliveira, Body et al 2022 Nucl. Fusion
62 096001). Five new, neutrals-related observables are added here to the extensive, publicly available TCV-X21 dataset. These are three deuterium Balmer lines in the divertor and neutral pressure measurements in the common and private flux regions. The quantitative agreement metric used in the validation is combined with the conjugate gradient method to approach the SOLPS-ITER input parameters that return the best overall agreement with the experiment. A proof-of-principle test of this method results in a modest improvement in the level-of-agreement; the shortcomings impacting the result and how to improve the methodology are discussed. Alternatively, a scan of the particle and heat diffusion coefficients shows an improvement of 10.4% in the level-of-agreement, approximately twice as high as that achieved by the gradient method. This result is found for an increased transport coefficient compared to what is usually used for TCV L-mode plasmas. The simulations further indicate that ∼65% of the total ionization occurs in the SOL, from which ∼70% in the divertor regions, despite being a sheath-limited regime, motivating the inclusion of self-consistent neutral models in future turbulence simulations on the path towards improved agreement with the experiment.
Study on combined optimization technology of wind-solar-storage hybrid power generation based on electricity hydrogen complementarity
Authors: Liu Kaicheng , Jia Xiaoqiang , He Guixiong , Li Jiaxin , Wang Jin , Xu Hong 
Published: March 2024

Abstract
The wind-solar-storage hybrid power generation system is mainly composed of wind turbines, solar cell arrays, electrolytic cells, fuel cells, battery packs, intelligent controllers, multi-functional inverters, cables, supports and auxiliary parts to supply power to the load. The system utilizes wind and solar energy for power generation without an external power supply, and an energy storage module composed of an electrolytic cell, fuel cell, and battery is used to assist the power supply. In this paper, a fuel cell model, a wind power generation model and a solar power generation model are respectively constructed, and a small experimental platform was built to validate the model. The pulse width modulation (PWM) control method, combined with the auxiliary regulation of the system by the battery, reduces the impact of the volatility of wind power generation on the electrolytic cell and fuel cell, and makes the load current more stable under different working conditions. The simulation results show that the system has good compression resistance, fast response and good stability.
Hourly photovoltaic power prediction based on signal decomposition and deep learning
Authors: Feng Dongyang , Zhang Hanjin , Wang Zhijin 
Published: March 2024

Abstract
Accurate photovoltaic (PV) power prediction is important for the utilization of solar energy resources. However, PV power is non-stationary due to the variable influence of meteorological factors, which poses a challenge for accurate forecasting. In this paper, a hybrid method based on signal decomposition and a deep learning model is proposed. The hybrid model integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the Informer model. The CEEMDAN algorithm is used to separate different modes from the photovoltaic power sequence, enhancing its predictability. The deep learning model, the Informer, is employed to capture the complex relationship between photovoltaic power data and its historical data as well as external meteorological factors, ultimately enabling multi-step forecasting of photovoltaic power data. In hourly PV power forecasting experiments using a public dataset, the model exhibits significant performance improvements when compared to benchmark models such as LSTM, GRU, and Transformer. Specifically, the RMSE reduces by 6.07%-34.74% and the MAE reduces by 7.07%-37.5%. The results demonstrate that the hybrid model exhibits accurate predictive performance in the task of hourly photovoltaic power forecasting.
Research on optimal dispatching strategy of solar thermal-photovoltaic-wind combined power generation system
Authors: Li Zhengxi , Liu Tingxiang , Yang Libin , Zhou Wanpeng , Qi Yanming , Sun Mingfei , Ma Shangang 
Published: March 2024

Abstract
New energy photovoltaic generation of electricity and wind power have developed rapidly, and the installed capacity has been increasing, but their volatility and randomness have also caused a certain impact on the power grid. Considering that solar thermal power generation and photovoltaic power generation have natural complementary advantages, and solar thermal power generation and power output of wind power can complement each other in time., this paper proposes an optimal scheduling strategy for solar thermal-photovoltaic-wind alliance power generation system, which uses thermal storage device of the heat power station of solar energy to cope with the problem of wind and light abandonment of new energy power generation and promote the consumption level of renewable energy. The economic and stability deviations of the system before and in the launch the solar thermal power station are compared in depth and the simulation is verified in the IEEE30 node system. The results show that after the introduction of solar thermal power (CSP) station in the combined power generation system, the economy of the scientific is remarkable improvement, the wind turbine is also reduced, which realizes the full absorption of new energy.
Effect of flow channel cross-section shape on the performance of proton exchange membrane electrolysis cell
Authors: Hao Hongwei , Chen Mengdong , Kang Wei , Deng Zhanfeng , Mao Hangyin 
Published: March 2024

Abstract
To study the thermal and mass distribution in proton exchange membrane electrolytic cells (PEMEC), a two-phase numerical model of a single-channel electrolytic cell was established. The polarization performance and gas-heat distribution in a single-channel electrolytic cell under five different flow channel cross-sections (rectangle, trapezoid, inverted trapezoid, triangle, and circle) were studied. The findings indicate that the inverted trapezoidal flow channel’s gas buildup effect is rather severe in both the catalytic and diffusion layers. In the trapezoidal flow channel, the temperature of the proton exchange membrane region is lower, and the electrochemical performance of the rectangular flow channel electrolytic cell is the best, with an improvement rate of up to 11.47%. The electrochemical performance of the triangular and circular flow channels is the worst.
Ultra-short term wind power forecasting method based on improved VMD
Authors: Fu Yunzhi , Zhao Yan , Yu Yuan 
Published: March 2024

Abstract
A CNN-GRU ultra-short term wind power prediction method based on improved VMD is proposed. Firstly, in the data processing part, according to the irregularity and non-stationary peculiarities of wind power, the variational mode decomposition algorithm is adopted to decompose the original sequence of wind power to reduce the non-stationarity. At the same time, considering that the parameter setting and selection in the variational modal decomposition algorithm are of great significance to the decomposition effect, the improved whale improvement method is adopted to find the best parameters in VMD to avoid the problem of poor decomposition effect caused by artificial setting. Finally, the CNN-GRU and GRU models are simulated and compared on the Matlab platform through the field wind farm data.
Application of artificial intelligence technology in photovoltaic power generation prediction
Authors: Ge Junxiong , Gao Baiyu , Zhou Zhongxu , Pang Zhenjiang , Wang Xinping , Hong Haimin , Zhan Zhaowu 
Published: March 2024

Abstract
As a new type of clean energy, photovoltaic power generation has been widely used in production and life. Compared with traditional energy, because the energy of photovoltaic power generation comes from the sun, photovoltaic power generation is mainly affected by the weather, which leads to the intermittency and volatility of this energy and makes the supply and demand relationship of this kind of energy more complex and changeable. With the continuous development of artificial intelligence technology, the field of photovoltaic power generation forecasting is also gradually realizing intelligence. At present, the photovoltaic power generation prediction model based on machine learning and deep learning has gradually become mainstream, which can effectively improve prediction accuracy and efficiency. In addition, the use of artificial intelligence technology can also achieve real-time monitoring and diagnosis of the operating status of photovoltaic power generation systems, further improving the stability and reliability of the system. In the future, with the continuous development and application of artificial intelligence technology, the field of photovoltaic power generation prediction will usher in broader development prospects.
Evaluation and correction of wind energy forecast in Yangjiang
Authors: Liu Yang , Zhu Yan , Zhang Yanxia , Deng Hua , Cheng Chi , Xu Peihua , Gao Sheng , Meng Dan 
Published: March 2024

Abstract
Wind power generation is one of the most promising new energy sources. It is important to study effective short-term wind speed forecasting methods. In this paper, the CMA-GD model is applied to forecast the wind speed over South China in four typical months (January, April, August, and October) in 2022. Taking Longyue Wind Farm in Yangjiang as an example, the simulated wind speed and wind direction are evaluated based on the observation of this station at four heights (10 m, 30 m, 50 m, and 70 m). The results show that the CMA-GD model has a good effect on forecasting the next day’s 24 h wind speed of Longyue Wind Farm. Overall, the correlation coefficients (R) between forecast and observation are 0.77-0.81, and the root-mean-square errors (RMSE) are 1.80-2.08 m·s-1. With the increase in altitude, the simulation effect is a little better. For different months, the R is as follows: October>August>April>January, and the RMSE and mean absolute error (MAE) are as follows: October>January>August>April. For diurnal variation, the wind speed simulation effect in the night is better than that in the day. Due to the influence of the subtropical monsoon climate and local mountain microclimate in April and August, there are some deviations in the wind speed simulation during 4-8 p.m. The difference in wind direction between forecast and observation are as follows: August<January<October<April, the average wind direction differences are between 2-17°, with little difference in the vertical direction. After the forecast wind speed is corrected by the random forest (RF) algorithm, the R of wind speed at each height is 0.92, and RMSEs are 1.07-1.27 m·s-1. The revised wind speed forecast has the same diurnal variation characteristics as the observation, indicating that the random forest algorithm can effectively reduce the model forecast error.