Miracle-dz/UniFAR-FTUs huggingface dataset:
Inﬂuence of Corn Processing and Frequency of Feeding on Cow Performance 1,2

Twenty cows, including ﬁve ﬁtted with rumen cannulae, were used to study the inﬂuence of corn processing and frequency of feeding on milk yield and ruminal fermentation characteristics. Cows were assigned to ﬁve treatments in a 5 × 5 Latin square experiment. Each period was 3 wk. Cows were fed 45% forage and 55% grain in a total mixed ration. Diets contained 35% corn either coarsely ground and fed once a day (1 × ), ﬁnely ground (FGC) fed 1 × , steam-ﬂaked (SFC) fed 1 × , FGC fed four times a day (4 × ), or SFC fed 4 × . Processing of corn and frequency of feeding had no inﬂuence on dry matter intake. Digestibility of starch was increased 6 and 3 percentage units by feeding SFC corn compared with coarsely and ﬁnely ground corn, respectively. Cows fed SFC or FGC produced 4% more milk with lower fat content compared with coarsely ground corn. Increasing the feeding frequency did not improve milk fat content. The fat-corrected milk yield was not different among treatments. Feeding SFC resulted in a low ace-tate-to-propionate ratio in the rumen ﬂuid than FGC. Cows fed SFC produced 45 and 115 g more milk protein per cow/d than cows fed FGC or coarse, respectively. With the value of increased milk protein observed in this study, it would be more economical to feed SFC or ﬁnely ground corn to dairy cows compared with coarse ground. The breakeven price of ﬂaking corn in this study was $32 and $12/metric tonne compared with coarse and FGC, respectively. Based on a survey conducted by the authors, the price of ﬂaking corn in the United
Seasonal climate summary southern hemisphere (summer 2013-14): warmer than average summer; neutral ENSO conditions

Southern hemisphere circulation patterns and associated anomalies for the austral summer 2013–14 are reviewed, with emphasis given to the Pacific Basic climate indicators and Australian rainfall and temperature patterns. Near neutral Pacific indicators persisted during summer. Sea surface temperatures were slightly warmer than average in the equatorial western Pacific while there were cooler than average temperatures over the eastern Pacific. Above average sea surface temperatures were recorded in the eastern Indian Ocean off Western Australia and along the southern coast of Australia. The Indian Ocean Dipole remained at a neutral phase following the neutral spring values. The SAM was mildly negative but well within neutral range for summer. Summer rainfall was above average for Australia as a whole, though there was a distinct difference between rainfall received in the west and east. Rainfall was above average across most of far northern Australia, the western half of Australia except for southwest Western Australia, and southern South Australia. Rainfall was below average for most of Tasmania, Victoria and the eastern halves of New South Wales and Queensland. Areas in northeastern New South Wales and southeast Queensland had their lowest summer rainfall on record. Maximum and minimum temperatures were both above average, with several periods of exceptionally hot weather recorded during summer.
Adaptation of the Multi-Concept Multivariate Elo Rating System to Medical Students' Training Data

Accurate estimation of question difficulty and prediction of student performance play key roles in optimizing educational instruction and enhancing learning outcomes within digital learning platforms. The Elo rating system is widely recognized for its proficiency in predicting student performance by estimating both question difficulty and student ability while providing computational efficiency and real-time adaptivity. This paper presents an adaptation of a multi concept variant of the Elo rating system to the data collected by a medical training platform, a platform characterized by a vast knowledge corpus, substantial inter-concept overlap, a huge question bank with significant sparsity in user question interactions, and a highly diverse user population, presenting unique challenges. Our study is driven by two primary objectives: firstly, to comprehensively evaluate the Elo rating systems capabilities on this real-life data, and secondly, to tackle the issue of imprecise early stage estimations when implementing the Elo rating system for online assessments. Our findings suggest that the Elo rating system exhibits comparable accuracy to the well-established logistic regression model in predicting final exam outcomes for users within our digital platform. Furthermore, results underscore that initializing Elo rating estimates with historical data remarkably reduces errors and enhances prediction accuracy, especially during the initial phases of student interactions.
Supplemental Material for “Quantum Coherent Control of a Single-Molecular Polariton Rotation”

To help readers understand how we obtain the key equations in the main text, we provide further details in this Supplemental Material, organized as follows. In Sec. I, we derive the control condition for generating the maximum degree of orientation for a two-state molecule in the absence (presence) of a weak static field. In Sec. II, we derive the maximum post-pulse orientation for a single molecular polariton. An analytical solution is derived in Sec. III for describing the pulsed-driven molecular polariton. Finally, we show the optimal amplitude and phase conditions in Sec. IV for analytically designing molecule-driving pulses.
Mapping the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19

According to the World Health Organization (WHO), COVID-19 has caused more than 6.5 million deaths, while over 600 million people are infected. With regard to the tools and techniques of disease analysis, spatial analysis is increasingly being used to analyze the impact of COVID-19. The present review offers an assessment of research that used regional data systems to study the COVID-19 epidemic published between 2020 and 2022. The research focuses on: categories of the area, authors, methods, and procedures used by the authors and the results of their findings. This input will enable the contrast of different spatial models used for regional data systems with COVID-19. Our outcomes showed increased use of geographically weighted regression and Moran I spatial statistical tools applied to better spatial and time-based gauges. We have also found an increase in the use of local models compared to other spatial statistics models/methods.
Investigation on How Internet Users’ read Online Cookie Consent and Subsequent Privacy Decision-making

As the use of online platforms continues to grow, individuals are faced with an increasing number of privacy-related decisions. Bal-ancing the benefits of sharing personal information with the potential risks of data misuse is a challenge for internet users. Informed privacy decision-making is crucial, yet these decisions are influenced by various factors and complex privacy settings. This study explores the concept of privacy nudges, which are designed to assist users in making privacy-conscious choices while respecting their autonomy. Presentation nudges, a specific type of privacy nudge, manipulate the presentation of information to guide user behavior. However, unethical use of presentation nudges can lead to unintended data disclosure. This research investigates the impact of presentation nudges on users’ privacy decisions using eye-tracking technology. The current experiment seeks to understand how different presentations of online cookie consent influence user’s privacy-related decisions and their level of engagement in reading privacy policy.
Enhancing Desirable Food Behaviors by Increasing or Decreasing Disgust: Designing and Testing Infographics

Paul Ekman Group, 2022). Disgust can be triggered by sensory properties of an object, ideational concerns because of what the object is, where it has been and what it has touched, and an anticipation of harm following consumption (e.g., social or bodily harm) (Haidt et al., 1997; Rozin & Fallon, 1987). The effect of encountering a disgusting stimulus is commonly followed by rejection and is explained as a disease avoidance system (Curtis et al., 2011). However, there are also stimuli that evoke a level of disgust that attracts attention rather than resulting in rejection. This includes the domains of art and literature as well as food (Korsmeyer, 2011; Lemke & de Boer, 2022). For example, consumers can enjoy eating cheese with a strong and pungent smell as part of cultivating their taste (Korsmeyer, 2011).
Securing Social Networks: Insights from Simulation-Based Analysis on Information Leakage

—With the rise of social networking sites (SNS), a growing concern has emerged regarding the unintentional disclosure of sensitive information, also known as information leakage. This study investigates the causes and consequences of information leakage on SNS through a simulation-based approach. A virtual environment replicating user interactions and data sharing patterns on popular platforms was developed, simulating various scenarios involving unintentional data disclosure, malicious actor activity, and data breaches. The results revealed that oversharing and weak privacy settings were the most common causes of information leakage, while social engineering attacks and data breaches also posed significant risks. The study highlights the importance of user education, improved platform security, and robust privacy controls to mitigate the risks associated with information leakage on SNS.
resist-Effect of Carburization on the Microstructure and Laser Weldability of 316L Stainless Steel

This study aims to investigate the influence of the carburizing layer on the microstructure of laser-welded joints in 316L stainless steel, aiming to enhance the properties of stainless steel components through the incorporation of carburization. Through a comprehensive experimental approach, including nanoindentation and Vickers hardness measurements, the microstructural analysis of the laser-welded specimens was conducted. The nanoindentation re-sults reveal a significant increase in hardness, with the carburizing layer exhibiting a remarkable 215% improvement compared to the base material. In order to effectively harness the wear resistance for various applications, the examination of macroscopic characteristics in laser-welded joints reveals the necessity of regulating heat input and minimizing defects to attain welds of superior quality. Additionally, the phase transformation behavior of stainless steel during welding was found to be altered due to dilution in the fusion zone and the presence of high carbon content in the heat-affected zone of the carburizing layer. Overall, this research provides valuable insights into the micro-structural changes induced by carburization and its implications for the welding performance and mechanical properties of stainless steel.
Can Transportation Networks Facilitate Adaptation to Environmental Extremes?

The social costs of environmental extremes hinge critically on society’s ability to adapt. Based on transaction records from China’s card payment network, we compile daily travel ﬂows and estimate how the country’s high-speed railway (HSR) system facilitates the use of intercity travel as a means of adaptation to extreme air pollution. We ﬁnd that HSR expansion reduced realized pollution exposure—that is, exposure based on travelers’ actual locations when pollution occurs—by an amount that implies a mortality ben-eﬁt of 8.5 million life-years saved. This occurs primarily due to travelers’ changing towards destinations with predictably cleaner air. JEL: O18, Q53, Q54, R41
The power of identifiability analysis for dynamic modeling in animal science: A practitioner approach

Constructing dynamic mathematical models of biological systems requires estimating unknown parameters from available experimental data, usually using a statistical fitting procedure. This procedure is usually called parameter identification, parameter estimation, model fitting, or model calibration. In animal science, parameter identification is often performed without analytic considerations on the possibility of determining unique values of the model parameters. These analytic studies are related to the notion of structural identifiability. The structural identifiability analysis is a powerful tool for model construction because it informs whether the parameter identification problem is well-posed. In case of lack of identifiability, structural identifiability analysis is helpful to determine which actions (e.g., model reparameterization, choice of new data measurements) are needed to render the model parameters identifiable (when possible). The mathematical technicalities associated with structural identifiability analysis are very sophisticated. However, the development of dedicated, freely available software tools enables the application of identifiability analysis without needing to be an expert in mathematics and computer programming. We refer to such a non-expert user as a practitioner for hands-on purposes. In this paper, we propose to adopt a practitioner approach that takes advantage of available software tools to integrate identifiability analysis in the modeling practice in the animal science field. The application of structural identifiability implies switching our regard of the parameter identification problem as a downstream process (after data collection) to an upstream process (before data collection). This upstream approach will substantially improve the workflow of model construction toward robust and valuable models in animal science. Illustrative examples with different levels of complexity support our work. The source codes of the examples are provided for learning purposes and to promote open science practices.
Gear-Ratio-Aware Standard Cell Layout Framework for DTCO Exploration

With advances in lithography technology, the minimum metal pitch (MP) becomes smaller than the contacted poly pitch (CPP). This difference has long prompted the need to seek an optimal ratio between CPP and MP. Automated cell synthesis with conditional design rules offers a valuable lever to speed up the technology exploration process and to identify the best “gear ratio” (GR) for Design-Technology Co-optimization (DTCO) exploration. Existing approaches for cell layout generation frameworks have primarily supported uniform grids with limited gear ratio options. In this work, we present SMTCell, a new exploratory framework for cell layout generation that allows flexible gear ratio options using a graph-based data structure. We employ distance-based objective functions and conditional design rule parameters to adapt to varying pitch values. This approach enables us to investigate and discover optimal layouts under diverse technology node settings. An acceleration feature drastically trims the solution space, resulting in a speed increase of up to 19 × without sacrificing the quality of the original solutions. With SMTCell, cell synthesis automation can be configured to accommodate a wide range of design choices. We conduct an empirical study to assess the impact of gear ratio at block-level synthesis, place and route (SP&R) outcomes, with the ultimate goal of identifying the most effective technology and standard cell configurations in terms of design power, performance and area (PPA) metrics.
MODELING THE INFLUENCE OF LIGHT QUALITY ON GROWTH AND SYNTHESIS OF NATURAL PRODUCTS BY THE DIAZOTROPIC BLUE-GREEN ALGA CYLINDROSPERMUM MUSCICOLA MTC-30602

: The present communication deals with light quality of different colours (Blue, Green, Red and White) on the growth and synthesis of photosynthetic pigments along with other value added products by the diazotropic blue-green alga Cylindrospermum muscicola MTC-30602. C. muscicola batch cultured for 10 days under controlled culture conditions. All four colours of light were observed to have an impact on the yields of dry biomass, chlorophyll, total carotenoids, phycobilin (c-phycocyanin, c-allophycocyanin, and c-phycoerythrin), total carbohydrate, total protein, and total fat. The results obtained indicated that increased production of biomass (dry weight), chlorophyll-a, total carotenoids, total protein was obtained when illuminated with white light. However, maximum synthesis of total carbohydrate, c-phycocyanin and c-phycoerythrin were observed under the influence of green light while c-allophycocyanin and lipid under the influence of red light. The findings suggested that exposure to various light colours may be in charge of the synthesis of additional naturally occurring compounds with added value, such as photosynthetic pigments.
How do the Asian Economies Compete with Japan How do the Asian Economies Compete with Japan in the US Market, China Exceptional? A Triangular in the US Market, China Exceptional? A Triangular Trade Approach Trade Approach

[Abstract] Political conflicts among trading partners have changed their forms with ever-increasing flows of foreign direct investment. A decrease in the exports of Japan might merely be a reflection of a global production shift by Japanese multinational corporations. We investigate the effect of Japanese trade on the exports of other countries to the United States in the 1990s. In our sample we include eight Asian countries besides the US and Japan. With the trade data disaggregated at the HS 4-digit level, we regress the exports of an Asian country to the US on the Japanese exports to the US and the third-country, and the Japanese FDI to a third-country in a panel data specification. Among eight countries investigated, we find the evidence that Chinese and Japanese exports are substitutes in the US market while the exports of China to the US are partly promoted by Japanese FDI to China. The estimation result confirms a view that China competes vigorously with Japan in the US market while Japanese multinationals are adjusting their production bases to China in a process of reforming a new global production network.
Ambient PM2.5 Temporal Variation and Source Apportionment in Mbarara, Uganda

Air pollution is the leading environmental cause of death globally, and most mortality occurs in resource-limited settings such as sub-Saharan Africa. The African continent experiences some of the worst ambient air pollution in the world, yet there are relatively little African data characterizing ambient pollutant levels and source admixtures. In Uganda, ambient PM 2.5 levels exceed international health standards. However, most studies focus only on urban environments and do not characterize pollutant sources. We measured daily ambient PM 2.5 concentrations and sources in Mbarara, Uganda from May 2018 through February 2019 using Harvard impactors fitted with size-selective inlets. We compared our estimates to publicly available levels in Kampala, and to World Health Organization (WHO) air quality guidelines. We characterized the leading PM 2.5 sources in Mbarara using x-ray fluorescence and positive matrix factorization. Daily PM 2.5 concentrations were 26.7 µ g m –3 and 59.4 µ g m –3 in Mbarara and Kampala, respectively ( p < 0.001). PM 2.5 concentrations exceeded WHO guidelines on 58% of days in Mbarara and 99% of days in Kampala. In Mbarara, PM 2.5 was higher in the dry as compared to the rainy season (30.8 vs. 21.3, p < 0.001), while seasonal variation was not observed in Kampala. PM 2.5 concentrations did not vary on weekdays versus weekends in either city. In Mbarara, the six main ambient PM 2.5 sources identified included (in order of abundance): traffic-related, biomass and secondary aerosols, industry and metallurgy, heavy oil and fuel combustion, fine soil, and salt aerosol. Our findings confirm that air quality in southwestern Uganda is unsafe and that mitigation efforts are urgently needed. Ongoing work focused on improving air quality in the region may have the greatest impact if focused on traffic and biomass-related sources.
Foam-Embedded Soft Robotic Joint With Inverse Kinematic Modeling by Iterative Self-Improving Learning

Soft robotic arms have gained significant attention owing to their flexibility and adaptability. Nonetheless, the instability due to their high-elasticity structure further leads to the difficulty of precise kinematic modeling and control. This letter introduces a novel solution employing foam-embedded joint design (Fe-Joint), effectively mitigating oscillations and enhancing motion stability. This innovation is integrated into the new continuum soft robotic arm (Fe-Arm). Through iterative design optimization, the Fe-Arm attains superior mechanical performance and control capabilities, enabling a settling state in 0.4 seconds post external force. Enabled by the quasi-static behavior of Fe-Arm, we propose a long short-term memory network (LSTM) based iterative self-improving learning strategy (ISL) for end-to-end inverse kinematics modeling, tailored to Fe-Arm's mechanical traits, enhancing modeling performance with limited data. Investigating key control parameters, we achieve target trajectory modeling errors within 9% of the workspace radius. The generalization potential of the ISL method is demonstrated using the pentagonal trajectory and on a different Fe-Arm configuration.
A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content

Accurate digital soil mapping (DSM) of soil organic carbon (SOC) is still a challenging subject because of its spatial variability and dependency. This study is aimed at comparing six typical methods in three types of DSM techniques for SOC mapping in an area surrounding Changchun in Northeast China. The methods include ordinary kriging (OK) and geographically weighted regression (GWR) from geostatistics, support vector machines for regression (SVR) and artificial neural networks (ANN) from machine learning, and geographically weighted regression kriging (GWRK) and artificial neural networks kriging (ANNK) from hybrid approaches. The hybrid approaches, in particular, integrated the GWR from geostatistics and ANN from machine learning with the estimation of residuals by ordinary kriging, respectively. Environmental variables, including soil properties, climatic, topographic, and remote sensing data, were used for modeling. The mapping results of SOC content from different models were validated by independent testing data based on values of the mean error, root mean squared error and coefficient of determination. The prediction maps depicted spatial variation and patterns of SOC content of the study area. The results showed the accuracy ranking of the compared methods in decreasing order was ANNK, SVR, ANN, GWRK, OK, and GWR. Two-step hybrid approaches performed better than the corresponding individual models, and non-linear models performed better than the linear models. When considering the uncertainty and efficiency, ML and two-step approach are more suitable than geostatistics in regional landscapes with the high heterogeneity. The study concludes that ANNK is a promising approach for mapping SOC content at a local scale.