Miracle-dz/UniFAR-FTUs huggingface dataset:
The efficacy of using olive cake as a by-product and probiotic supplementation on growth performance and blood characteristics of broiler chickens

Olive cake has high nutritional content, which can be used as a protein and fat source in poultry diets. The main aim of the work was to determine the impact of using different levels of Olive cake as food processing by-products and probiotics in broiler diets and their efficacy on growth performance and blood characteristics. In the feeding experiment, 336-day-old male broiler chicks (Ross 308) were raised for 42 days. They were randomly divided into seven designed groups experimentally; each included four replicates and 12 chicks. The dietary treatments were as follows; Basal diet without Olive cake (T1), Basal diet with (4%, 8% and 12%) olive cake for (T2, T3 and T4), respectively, and Basal diet with (4%, 8% and 12%) Olive cake + 0.4 g/kg Iraqi probiotic for (T5, T6 and T7), respectively. The results indicated that 4% OC with 0.4 g/kg probiotic showed the highest live body weight, body weight gain, European Production Efficacy Factor and improved Feed conversion ratio at the end of the experiment. Also, the supplementation of Olive cake with or without probiotics substantially increased lymphocyte numbers and lowered Heterophil/Lymphocyte (H/L) ratio. It decreased cholesterol levels compared to the control group at the end of the bird's life. Also, haematological and biochemical parameters significantly (P<0.05) were improved when 4% Olive cake 0.4 g/kg probiotic was added to the diet of broilers. In conclusion, Olive cake and probiotic supplementation positively impacted chicken performance and broilers' blood characteristics. https://creativecommons.org/licenses/by-nc/4.0/
Prevalence of Post-COVID-19 Syndrome and Related Factors among University Employees: A Prospective Cohort Study

Objective: The term post-COVID (coronavirus disease) is used to refer to the presence of prolonged symptoms 12 weeks or more after the disease treatment. This study aimed to evaluate the presence of symptoms and anxiety in patients with COVID-19 who did not require inpatient care at the third and sixth months following symptom onset. Materials and Methods: The study is a prospective cohort study involving 471 university employees who contracted COVID-19 between October 2020 and October 2021. Data were obtained through the disease contact follow-up program implemented at the university and phone interviews conducted at the third and sixth months from symptom onset. Results: The study group comprised 361 individuals at three months and 109 at six months from symptom onset. The study found that symptoms persisted in 116 (32.1%) people in the third month and in 47 (13.0%) people in the sixth month. The most common symptoms at three months were shortness of breath, fatigue, and fatigue, while fatigue, fatigue, and shortness of breath were the most common symptoms at six months. Conclusion: Understanding the long-term effects of coronavirus will enhance the management of the disease. As a result, the follow-up of symptomatic COVID-19 and post-COVID-19 patients will become more systematic and effective.
ClaimDistiller: Scientific Claim Extraction with Supervised Contrastive Learning

The growth of scientific papers in the past decades calls for effective claim extraction tools to automatically and accurately locate key claims from unstructured text. Such claims will benefit content-wise aggregated exploration of scientific knowledge beyond the metadata level. One challenge of building such a model is how to effectively use limited labeled training data. In this paper, we compared transfer learning and contrastive learning frameworks in terms of performance, time and training data size. We found contrastive learning has better performance at a lower cost of data across all models. Our contrastive-learning-based model ClaimDistiller has the highest performance, boosting the F1 score of the base models by 3–4%, and achieved an F1=87.45%, improving the state-of-the-art by more than 7% on the same benchmark data previously used for this task. The same phenomenon is observed on another benchmark dataset, and ClaimDistiller consistently has the best performance. Qualitative assessment on a small sample of out-of-domain data indicates that the model generalizes well. Our source codes and datasets can be found here: https://github.com/lamps-lab/sci-claim-distiller.
Substrate‐independent analysis of microcrystalline silicon thin films using UV Raman spectroscopy

Surface‐sensitive UV Raman spectroscopy is used to analyze the crystallinity of silicon films less than 20 nm thick directly on silicon wafers. The 325‐nm excitation has a Raman detection thickness of only 13 nm within the silicon film, thus eliminating signal from the substrate. We demonstrate measured crystallinities of microcrystalline silicon thin films that are consistent with the microstructure observed in transmission electron microscopy. Comparison is also made to ellipsometry, which is less able to accurately determine crystallinity than UV Raman spectroscopy. The UV Raman approach is particularly useful for layers grown on substrates of the same material but with different microstructure, and can be extended to non‐silicon materials.
Research on the Inﬂuence Mechanism of Urban Elderly People’s Willingness to Care for Leisure in Rural Areas

Urban elderly to rural leisure pension is not only a useful supplement to the traditional pension model, but also can promote the transfer of urban pension industry to rural areas, drive the ﬂow of urban factor of production resources to rural areas, and help rural revitalization strategy. Based on the theory of planned behavior and from the micro-perspective of social psychology, this paper establishes a model of the impact mechanism of urban elderly’s rural leisure pension behavior willingness. Using the questionnaire data of urban elderly obtained from a social survey, a structural equation model is chosen to verify the impact path and coefﬁcient between the behavior attitude, subjective norms, self-efﬁcacy, external control, and rural leisure behavior intention. The results show that: the attitude of urban elderly to rural leisure pension has a signiﬁcant positive impact on behavior intention; subjective norms and self-efﬁcacy have a signiﬁcant positive impact on the attitude of urban elderly to rural leisure pension, which has an indirect impact onthebehaviorintentionofruralleisurepensions;theimpactofexternalcontrolon theurbanelderlytoruralleisurepensionbehaviorintentionhasnotreachedasta-tisticallysigniﬁcantlevel. Atthesametime,usingdemographicvariablestoverify andanalyzetheimpactofbehaviorattitudeandbehaviorintention,thestudyfound thatthegender,age,educationlevel,retirement,healthstatus,averagemonthly income, and children’s frequent home visits of urban elderly are signiﬁcantly correlated with behavior attitude measurement variables, but they are not correlated with behavior intention measurement variables. Based on the research conclusion, thispaper putsforwardtargeted countermeasures and suggestionsfromthe aspects of strengthening publicity and guidance, improving the retirement treatment and health level of urban elderly, and improving rural infrastructure and public service facilities.
Generalizing Shallow Water Simulations with Dispersive Surface Waves

This paper introduces a novel method for simulating large bodies of water as a height field. At the start of each time step, we partition the waves into a bulk flow (which approximately satisfies the assumptions of the shallow water equations) and surface waves (which approximately satisfy the assumptions of Airy wave theory). We then solve the two wave regimes separately using appropriate state-of-the-art techniques, and re-combine the resulting wave velocities at the end of each step. This strategy leads to the first heightfield wave model capable of simulating complex interactions between both deep and shallow water effects, like the waves from a boat wake sloshing up onto a beach, or a dam break producing wave interference patterns and eddies. We also analyze the numerical dispersion created by our method and derive an exact correction factor for waves at a constant water depth, giving us a numerically perfect re-creation of theoretical water wave dispersion patterns.
Investigating How Liverpool City Council Use Big Data to Control Covid-19 Transmission

The emergence of the Covid-19 pandemic brought the world irre-versibledamagebothfromtheperspectivesofhumanlivesandthesocialeconomy. Conversely,bigdatatechnologyfacilitatedthedisease’smonitoringandmeasure-ment.Therefore,thispaperinvestigatedtheapproachesutilizedfrombigdatafor pandemicmonitoringandcontrol,particularlywithintheLiverpoolcitycouncil, fromthreestages:dataprocessingandstorage,datamodellingandevaluation,and datavisualizationfordecisionmaking.
Investigating Methods of Modelling Financial Time Series with Empirical Analysis

The volatility of a time series is the extent to which it varies in value over time. It has important applications in finance, as it indicates the likelihood of a stock or financial indicator changing drastically from its current value. Early papers such as Mandelbrot (1963) noted the tendency for periods of high and low volatility to be each grouped together in clusters. This idea led to the important findings presented by Engle (1982), which showed that a model allowing for a changing conditional variance was able to capture this clustering feature commonly found in financial data. This project aims to outline some of the key characteristics of financial time series and to explore how effectively they can be captured by ARCH and GARCH models. The methods of how to apply the concepts in practice are investigated, demonstrating their advantages over more basic models. Initially, basic time series concepts are revised and the procedure for creating and evaluating linear time series models is outlined, focusing on the “ARMA” model. It is found that after selecting and fitting the best ARMA model for a set of FTSE 100 closing prices data, there remains substantial shortcomings, namely the inability to fully explain the existing correlations in squared returns data. The classes of ARCH and GARCH models are introduced, and the important properties of possessing a heavy-tailed distribution as well as the ability to produce volatility clusters are demonstrated. The ARCH model is found to be able to greatly improve on the ARMA model’s weaknesses, but has disadvantages such as requiring large numbers of parameters. When applied to the same data set, the GARCH model improves on these disadvantages. Some modifications of these models are shown, and possible methods for creating and evaluating forecasts of the conditional variance are investigated. The closing prices from the Standard and Poor 500 stock index are analysed using summary statistics to further demonstrate the traits which financial time series possess, and the data is tested for the presence of weekly effects. Models for the conditional mean and conditional variance are analysed to find the best model for the data. Forecasts for the conditional variance are compared, with the exponential smoothing method and Student-t GARCH models being found to produce the smallest error and hence the best forecast accuracy. This project confirms through theoretical and practical analysis the strengths of conditional variance models and it is hoped that it can be used as a guide for applying and evaluating these models for other data sets in the future.
A Cross-Cultural Study on Group Performance, Leadership Styles, Power Distance, and Group Organizational Citizenship Behavior

This multinational study focuses on leadership style (i.e., directive leadership, supportive leadership ) and power distance affecting group organizational citizenship behavior (GOCB). Further, it explores the impact of GOCB on group performance . This empirical inquiry collected data from public and private workers from diverse sectors in Indonesia and Mexico. The questionnaire was carefully translated into Indonesian and Spanish gaining more than two hundred responses from each country. The study uses structural equation modeling (SEM) with Partial Least Square (PLS) type. The results show that power distance has a positive and signiﬁcant effect on directive leadership in Mexico and Indonesia, yet the effect in Indonesia is not signiﬁcant. Further, Power distance has a positive and signiﬁcant impact on supportive leadership in Indonesia, yet has a positive but not signiﬁcant effect in Mexico. Moreover, Power distance has a negative and insigniﬁcant effect on GOCB for respondents in Indonesia and Mexico. Also, both directive leadership and supportive leadership have a positive and signiﬁcant effect on GOCB in both countries, and ultimately GOCB has a positive and signiﬁcant impact on group performance in Indonesia and Mexico .
Learning to Reason and Memorize with Self-Questioning

Large language models have been shown to struggle with limited context memory and multi-step reasoning [1]. We propose a simple method for solving both of these problems by allowing the model to ask questions and answer them. Unlike recent scratchpad approaches, the model can deviate from the input context at any time for self-questioning. This allows the model to recall information and perform reasoning on the fly as it reads the context, thus extending its memory and enabling multi-step reasoning. Our experiments on two synthetic tasks demonstrate that our method can successfully generalize to more complicated instances from their training setup by performing self-questioning at inference time
The Effect of Different Implementations of the Weak Temperature Gradient Approximation in Cloud Resolving Models

The Weak Temperature Gradient (WTG) approximation is a popular method used to couple convection in limited‐area domain simulations with large‐scale dynamics. However, several different schemes have been created to implement this approximation, and these different WTG schemes show a wide range of different results in an idealized framework. Our investigation shows that different model behavior is caused by the treatment of the different baroclinic modes by the different WTG schemes. More specifically, we hypothesize that the relative strengths of the baroclinic modes plays a large role in these differences, and show that modifying these schemes such that they treat the baroclinic modes in a similar manner accounts for many of the significant differences observed.
New Principles For Stabilization Policy

In a broad class of discrete-time rational-expectations models, I consider stabilization-policy rules making the policy instrument react with coe cient φ ∈ R to a (past, current, or expected future) generic variable at time horizon h ∈ Z, possibly among other variables. Using two complex-analysis theorems, I establish analytically some simple, easily interpretable, necessary or su cient conditions on φ and h for these rules to ensure local-equilibrium determinacy. These conditions lead to new, general principles for stabilization policy in terms of whether, and how strongly or weakly, to react to any variable, at any horizon, in any model. Building on these conditions, I characterize circumstances under which the long-run Taylor principle is (not) necessary, (not) su cient, or irrelevant for determinacy. I also provide the rst hard guidelines for nding rules with robust determinacy properties across alternative models.
Light Emission of Streamers in Dielectric Liquids

Liquid insulation is widely used in various high voltage equipments crucial to a modern electrical grid. It is thus important to understand the behaviour and limits of performance for such liquids. In these experiments, the emitted light from streamers in pure cyclohexane and pyrene dissolved in cyclohexane (0.06m) were studied using photomultipliers, optical filters, an impulse generator and a point to plane electrode geometry with gap distance of ∼ 4.0 mm. Needles with point radii of ∼ 3 μm and ∼ 15 μm were used as anodes, and were placed either in the bulk liquid or along a glass surface. The emitted ultraviolet light was studied qualitatively using a bandpass filter with peak transmittance at 200 nm, and the experimental results indicate that the fraction of emitted ultraviolet light increases with applied voltage before reaching a constant level in all experiments except when the needle electrode was placed along a glass surface, where the fraction increased without reaching a constant level. The streamers observed in these experiments had an average propagation speed that corresponds to second mode streamers, but extrapolation of the experimental results support the hypothesis of photoionization being a major propagation mechanism of higher mode streamers. Furthermore, a strong light impulse, hereby named ”onset flares” in this thesis, at the onset of streamer formation has been observed in preliminary studies. These high energy photons are believed to be an important feed forward mechanism for fast streamers with propagation velocity ∼100 mm/μs. This phenomenon has otherwise not been reported. The relation between the amplitude and delay time defined as the time from the applied negative impulse voltage reaching 90% of its minimum to the occurrence of an onset flare was studied. In some of the experiments, it was found that the amplitude decreased with
Query-Centric Trajectory Prediction

Predicting the future trajectories of surrounding agents is essential for autonomous vehicles to operate safely. This paper presents QCNet, a modeling framework toward pushing the boundaries of trajectory prediction. First, we identify that the agent-centric modeling scheme used by existing approaches requires re-normalizing and re-encoding the input whenever the observation window slides forward, leading to redundant computations during online prediction. To overcome this limitation and achieve faster inference, we introduce a query-centric paradigm for scene encoding, which enables the reuse of past computations by learning representations independent of the global spacetime coordinate system. Sharing the invariant scene features among all target agents further allows the parallelism of multi-agent trajectory decoding. Second, even given rich encodings of the scene, existing decoding strategies struggle to capture the multimodality inherent in agents' future behavior, especially when the prediction horizon is long. To tackle this challenge, we first employ anchor-free queries to generate trajectory proposals in a recurrent fashion, which allows the model to utilize different scene contexts when decoding waypoints at different horizons. A refinement module then takes the trajectory proposals as anchors and leverages anchor-based queries to refine the trajectories further. By supplying adaptive and high-quality anchors to the refinement module, our query-based decoder can better deal with the multimodality in the output of trajectory prediction. Our approach ranks 1st on Argoverse 1 and Argoverse 2 motion forecasting benchmarks, outperforming all methods on all main metrics by a large margin. Meanwhile, our model can achieve streaming scene encoding and parallel multi-agent decoding thanks to the query-centric design ethos.
Guidance of Medical Students After the Outbreak of COVID-19: A Meta-analysis

The outbreak of COVID-19 has had a signiﬁcant inﬂuence on medical students, which resulted in various psychological health problems. To provide the basis for mental health education, we conducted this meta-analysis to accurately assess the global prevalence of mental health problems among medical students during the COVID-19 pandemic. Based on the guidance of PRISMA, 3219 articles were searched in CNKI, CQVIP, PubMed, Web of Science, and EBSCO. 10 articles were ﬁnally included in the statistical analysis after ﬁltering by the article selection criteria. All data analysis were performed by Stata software, and a random effect model was used to combine correlation coefﬁcient. A sensitivity analysis, publication bias, meta-regression, and subgroup analysis were also conducted in our analysis. In conclusion, the pooled prevalence of anxiety, stress, and depression symptoms among these medical students was 31% (95%CI: 20%– 41%), 27% (95%CI: 1%–53%) and 36% (95%CI: 15%–58%), respectively. The results of our analysis suggested that the prevalence of depression symptom in medical students increased during the COVID-19 pandemic and was higher than that in students with different majors. And it is necessary and urgent to screen the depression symptom of medical students and take appropriate intervention measures.
Entanglement Entropy of Scalar Fields in Causal Set Theory

Entanglement entropy is now widely accepted as having deep connections with quantum gravity. It is therefore desirable to understand it in the context of causal sets, especially since they provide the UV cutoff needed to render entanglement entropy finite in a natural and covariant manner. Defining entropy in a causal set is not straightforward because the type of canonical hypersurface-data on which definitions of entanglement typically rely is not available in a causal set. Instead, we appeal to a more global expression given in [1] which, for a gaussian scalar field, expresses the entropy of a spacetime region in terms of the field’s correlation function within that region. We first consider this spacetime entropy for a 1 + 1-dimensional “causal diamond” in a flat continuous spacetime immersed in the vacuum within a larger causal diamond (our choice of vacuum being the Sorkin-Johnston vacuum described more fully in Chapter 2). The spacetime entropy of the smaller diamond in this case measures (when interpreted spatially) the entanglement between a line-segment and its complement within a larger line-segment. In this situation we carry out the computation numerically for a massless scalar field. The required ultraviolet cutoff is implemented as a truncation on spacetime mode sums, and we find excellent agreement with the expected form of the entropy (i.e. an area law) from conformal field theory. Carrying this formula over to a causal set, one obtains an entanglement entropy which is finite with a natural UV cutoff and Lorentz invariant. Herein we evaluate this entropy for causal sets sprinkled into a 1 + 1-dimensional causal diamond in flat spacetime, and specifically for a smaller order-interval (causal diamond) within a larger concentric one. We find in the first instance an entropy that obeys a (spacetime) volume law instead of the expected (spatial) area law. We find, however, that one can obtain the expected area law by following a prescription for truncating the eigenvalues of a certain “Pauli-Jordan” operator and the projections of their eigenfunctions on the Wightman function that enters into the entropy formula. We also study the “entropy of coarse-graining” generated by thinning out the causal set, and we compare it with what one obtains by similarly thinning out a chain of harmonic oscillators, finding the same “universal” behaviour in both cases.