Miracle-dz/UniFAR-FTUs huggingface dataset:
Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System

Simple Summary Swine nursery mortality is highly impacted by the pre-weaning performance of the piglets. Even though the importance of the pre-weaning phase on the downstream post-weaning performance is acknowledged, predictive modeling has yet to be described in the swine industry to predict the downstream nursery performance of groups of pigs based on their previous pre-weaning phase. One obstacle to building such predictive models is that pieces of information concerning the factors impacting swine mortality are collected with separate record-keeping programs and stored in unconnected databases, creating multiple unutilized data stream clusters. Thus, in this study, we described the process of building a data-wrangling pipeline that automatically integrates diverse and dispersed data streams collected from one swine production company, creating then a master table that was utilized to predict the mortality of groups of pigs during the nursery phase. Abstract The performance of five forecasting models was investigated for predicting nursery mortality using the master table built for 3242 groups of pigs (~13 million animals) and 42 variables, which concerned the pre-weaning phase of production and conditions at placement in growing sites. After training and testing each model’s performance through cross-validation, the model with the best overall prediction results was the Support Vector Machine model in terms of Root Mean Squared Error (RMSE = 0.406), Mean Absolute Error (MAE = 0.284), and Coefficient of Determination (R2 = 0.731). Subsequently, the forecasting performance of the SVM model was tested on a new dataset containing 72 new groups, simulating ongoing and near real-time forecasting analysis. Despite a decrease in R2 values on the new dataset (R2 = 0.554), the model demonstrated high accuracy (77.78%) for predicting groups with high (>5%) or low (<5%) nursery mortality. This study demonstrated the capability of forecasting models to predict the nursery mortality of commercial groups of pigs using pre-weaning information and stocking condition variables collected post-placement in nursery sites.
Forward production of prompt neutrinos in the atmosphere and at high-energy colliders

The atmospheric neutrino flux at very high energies is dominated by prompt neutrinos, mostly contributed by the decays of charmed hadrons produced in the forward direction by cosmic ray interactions with air nuclei. Theoretical predictions of the prompt atmospheric neutrino flux have large uncertainties mainly related to charm hadron production. Prompt neutrinos can also be studied through high-energy colliders. In particular, two ongoing forward experiments and the proposed Forward Physics Facility at the LHC can detect forward prompt neutrinos. We will present the kinematic regions relevant to the prompt atmospheric neutrino flux in terms of collider kinematic variables, the collision energy $\sqrt{s}$ and the center-of-mass rapidity of charm hadrons $y$, and discuss implications of the forward experiments at the LHC on the theoretical predictions of the prompt atmospheric neutrino flux.
A stochastic SIRI epidemic model with relapse and media coverage

This work is devoted to investigate the existence and uniqueness of a global positive solution for a stochastic epidemic model with relapse and media coverage. We also study the dynamical properties of the solution around both disease-free and endemic equilibria points of the deterministic model. Furthermore, we show the existence of a stationary distribution. Numerical simulations are presented to confirm the theoretical results.
Implications from neutrino limits at IceCube from the brightest gamma-ray burst GRB 221009A

The non-detection of neutrinos from the brightest gamma-ray burst (GRB), GRB 221009A at the IceCube neutrino observatory allows us to put constraints on GRB model parameters like the dissipation radius, the Lorentz factor, and the cosmic-ray loading factor. We model the neutrino spectra and discuss the resulting limits on allowed parameter spaces. We find that our results are strong enough to constrain proton acceleration near the photosphere. Our new limit from a single source, is comparable and hence complimentary to the IceCube stacking limit. We also discuss the prospects for quasi-thermal neutrinos from the subphotosphere region in the context of neutron decoupling and colliding neutron-loaded flows, where we find that the latter might lead to a few events at IceCube with dedicated searches. Such observations at next generation neutrino detectors like IceCube-Gen2 and KM3NeT are crucial and would provide important constraints and insights into understanding subphotospheric emissions from GRBs.
Lasing characteristics of single-crystalline CsPbCl3 perovskite microcavities under multiphoton excitation

Nonlinear optical characteristics of single-crystalline CsPbCl3 perovskite microcavities under multiphoton excitation are studied. The high 3-photon absorption coefficient of the clusters of CsPbCl3 microplates is found to be 2.1 × 10−5 cm3 GW−2 for the support of low-temperature amplified spontaneous emission. Furthermore, stable lasing emission is observed from single CsPbCl3 microplates under 3-photon excitation at 83 K. Hence, we have shown that single-crystalline CsPbCl3 perovskite semiconductor microstructures have excellent nonlinear optical properties for the realization of novel upconversion lasing devices.
In-Situ Detection of Small Biomolecule Interactions Using a Plasmonic Tilted Fiber Grating Sensor

We have proposed and experimentally demonstrated a plasmonic tilted fiber Bragg grating based biosensor for in-situ detection of the small biomolecule S-adenosyl-L-homocysteine (AdoHcy). The sensor can also monitor the molecule's interaction with the protein Set7 in real time. The biosensor is made by coating a 50-nm-thick gold film over an 18° tilted fiber Bragg grating. A spectrally dense comb of backward-propagating cladding resonance modes with Q-factor as high as 104 is excited due to the tilted grating. The spectral overlap between the excited cladding modes and the broader absorption feature of the surface plasmon makes this device a unique tool for high-accuracy measurement of small shifts of plasmon resonance wavelength. Surface biomolecular functionalization of the gold film enables us to not only detect the corresponding biomolecule but also study its interaction with other biomolecules. Our experimental results demonstrate that our biosensor can detect AdoHcy at concentration as low as 1 nM. Furthermore, we have successfully real-time monitored interaction between the protein Set7 and AdoHcy at different concentrations. Our measurements are consistent with the biological theory that the association rate of a pair of biomolecules depends on their concentrations.
Quantitative Estimates of Generalized Boolean Sum Operators of Blending Type

ABSTRACT Sharma (Appl. Math. Comput. 259:741–752) introduced the mixed summation integral-type two-dimensional q–Lupaş–Phillips–Bernstein operators , wherein he established the rate of approximation by applying Korovkin theorem and studied the weighted approximation properties. The goal of this paper is to establish a Voronovskaja-type theorem and introduce the associated generalized Boolean Sum (GBS) case of these operators and study the degree of approximation by the Lipschitz class of Bögel continuous functions and the mixed modulus of smoothness. Furthermore, we show the rate of convergence of the bivariate operators and the corresponding GBS operators by illustrative graphics and numerical examples using Maple algorithms.
A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model’s rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model’s dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC’s recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain’s mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.
Effect on osseointegration of dental implants treated with carboxyethylphosphonic acid and functionalized with BMP-2: preliminary study on a minipig model

Introduction: Rough titanium surfaces biofunctionalised by osteogenic proteins, such as BMP-2, have been shown to accelerate the osseointegration process and reduce waiting times for prosthetic loading. The preclinical study presented here compared the bone in contact with the implant and bone neoformation and density between titanium (Ti) implants with a conventional etched surface (SLA type) and others treated with carboxyethylphosphonic acid (CEPA) and bone morphogenetic protein 2 (BMP-2), after 4 weeks of implantation in the tibia of a minipig model. Methods: Sixteen implants (eight experimental and eight control) of Ti-Al16-V4 with a tapered screw design and internal hexagonal connection were randomly inserted into the tibiae of four minipigs, four in each tibia. The experimental implants were treated with CEPA and BMP-2 and sterilised with gamma radiation (25 KG). The insertion torque was 40 N and primary stability was measured with the Osstell® device (ISQ 64 ± 2.6). Five bone parameters were evaluated: bone in contact with the implant (BIC), bone in contact with the corrected implant (BICc), new bone formation (BV/TV), bone density between threads (BAI/TA) and peri-implant bone density (BAP/TA). A histomorphometric study was performed and the samples were digitised with Adobe Photoshop Cs6. Statistical analysis of the variables was performed using SAS 9.4. Results: After a period of 4 weeks, no significant clinical signs were observed and all implants were integrated. Light microscopy of the experimental group revealed an ICB with no signs of fiber tissue, but with areas of ectopic new bone in the medullary space. Statistical analysis showed significant results for BIC and BICc (p = 0.0001 and p = 0.001, respectively). No statistical signification was found for the other parameters evaluated. Conclusion: Despite the limitations of this study, our results demonstrated that dental implant surfaces treated with CEPA and BMP-2 improve their biological response to osseointegration.
Template for estimating uncertainties of measured neutron-induced fission cross-sections

A template for estimating uncertainties (unc.) of measured neutron-induced fission, (n,f), cross-sections (cs) is presented. This preliminary template not only lists all expected unc. sources but also supplies ranges of unc. , estimates for correlations between unc. of the same and different experiments which can be used if the information is nonexistent. If this template is applied systematically when estimating experimental covariances for an evaluation, it may help in pinpointing missing unc. for individual datasets, identifying unreasonably low unc. , and estimating correlations between different experimental datasets. Thus, a detailed unc. estimate – usually, a time-intensive procedure – can be undertaken more consistently and efficiently. As an example, it is shown that unc. and correlations of 239 Pu(n,f) by Merla et al. [Proceedings of the Conference on Nuclear Data for Science and Technology 1991 Julich (Springer-Verlag, Berlin, 1992) pp. 510–513], which are questionably low in the GMA database underlying the neutron cs standards evaluations, are distinctly larger at 14.7 MeV and more strongly correlated if this template is used for reestimating the associated covariances.
Leading transformative professional learning for inclusion across the teacher education continuum: lessons from online and on-site learning communities

ABSTRACT Professional learning (PL) for inclusion is a key policy focus internationally, arising from a growing commitment to the goal of a rights-based approach to education for all. Transformative teacher PL for inclusion is paramount to this goal but it is a complex endeavour, as evidenced in the persistent knowledge-practice gap relating to inclusive practice. Models of collaborative inquiry hold promise for affecting teacher change, yet there is limited research on how such models can support quality teacher PL for inclusion. This qualitative cross-case analysis focuses on two models of collaborative inquiry in different contexts in the Republic of Ireland (RoI): a professional learning community (PLC) for inclusive practice in a primary school and an online learning community (OLC) that supported preservice teacher learning during school placement. Parallel findings across the two case studies demonstrated changes in participants’ beliefs, efficacy and practice, arising from collaborative inquiry that was characterised by critical dialogue and public sharing of work. External facilitation of the learning communities supported the creation of a ‘safe space’ which was paramount to transforming PL contexts. We proffer design principles for sustainable collaborative PL approaches across teacher education contexts that support teachers to navigate the complexity of enacting inclusive practice.
Exploring Factors Affecting Intention to Use Chatgpt for Searching Finance-Related Information

The emergence of ChatGPT has revolutionized information seeking behavior. This study aims to explore the potential factors that influence the adoption of ChatGPT as a tool for searching finance related information. This study also distributed the online questionnaire to gather the responses from existing users. The findings indicate that ease of use, usefulness, herding behavior, and social influence significantly affect users’ intention to use ChatGPT for searching finance related information. However, this study also revealed that there is no significant relationship between individuals who used ChatGPT for searching finance related information and their intention for information checking. These results provide valuable insights for future studies and underscore the potential of ChatGPT as a tool for searching finance related information.
Bayesian inference for the skewness parameter of the scalar skew-normal distribution

The skew-normal distribution includes the normal distribution as a special case. This family of distributions has a shape parameter that denes the direction of the asymmetry of the distribution, also called skewness parameter. The main focus of this paper will be showing that the Bayesian approach using MCMC methods is a good alternative to make inference under the skewness parameter. We present a brief discuss about prior choice and propose an approximation for the Jereys prior developed by Liseo and Loperdo (2006). We also provide an approximation for the bias correction factor proposed by Sartori (2006). A simulation study is presented to compare the bias, mean square error and interval estimates using the maximum likelihood and dierent Bayes estimators. Finally, hypotheses
Hysteresis Compensation Using Extended High-Gain Observer and Dynamic Inversion

Hysteresis is a nonlinearity exhibited by a wide class of smart materials, such as piezoelectrics and shape memory alloys, and it presents challenges in the control of smart material-actuated systems (for example, piezo-based nanopositioning systems). Existing methods for hysteresis compensation typically require an explicit model of the hysteresis, which tends to be high-dimensional operators and entails significant complexity in model identification and inversion. In this paper a novel hysteresis compensation method based on extended-high-gain observers and dynamic inversion is presented, which does not assume any specific hysteresis model. An extended high-gain observer is used to estimate the hysteresis output as well as other unknown dynamics of the system model, and then dynamic inversion is implemented to cancel the effect of hysteresis. With a mild assumption on the system and the input nonlinearity, the analysis of the closed-loop system under output feedback shows fast performance recovery to the trajectories of a target system, and that the tracking error converges exponentially to zero. Simulation results are presented to support the efficacy of the proposed approach.
Optimization of the parameters in complex-Padé Fourier finite-difference migration = Otimização dos parâmetros na migração por diferenças finitas e Fourier utilizando a expansão de Padé complexa

Complex-Padé Fourier finite difference (CPFFD) migration is a one-way wave-equation technique that allows for a better treatment of evanescent modes than i s real counterpart. As for real FFD migration, its parameters can be optimized in order to improve the imaging steeply dipping reflectors. The dip limitation of the FFD operator depends on the variati on of the velocity field. We provide a wide-angle approximation for the one-way continuation op erator by means of optimization of the most important velocity-dependent parameter. We discuss t he achieved quality of the approximate dispersion relation for different functions under conside ration of the number of terms in the Padé approximation and the branch-cut rotation angle. The optim ized parameters are chosen based on the migration results and the computational cost. The implemen tations are validated on the Marmousi data set and SEG/EAGE salt model data. Key Word: Depth migration, Fourier finite-difference, complex Padé a pproximation, parameter optimization, steep dips.
Effects of Irradiation Dose of Sheet-Like Electron Beam and its Cathode Voltage on Impact Strength of Carbon Fiber Reinforced Thermoplastic Polyamide just before Shipping

Achieving a strong bond between carbon fiber (CF) and recyclable thermoplastic polymer (TP) has always been highly sought after. So far, applying electron beam (EB) irradiation with optimal dose and cathode potential (Vc) has shown success in increasing mechanical properties of interlayered CFRTPs. However, with concern for durability and safety, higher strength is desired. Therefore, EB setting applying electron beam (EB) irradiation with cathode potential (Vc) to 170, 210, 225 or 250 kV was applied to CFRTPA (carbon fiber reinforced thermoplastic polyamide) articles just before shipping. Specimens were 9 CF plies alternating between 10 PA (polyamide) sheets, designated [TPA]10[CF]9. When optimal EB dose of 43.2 kGy is applied to both finished specimen surfaces after fabrication, experimental results show higher Vc setting of 250 kV can increase impact strength of the [TPA]10[CF]9 over that at 170 kV. In summary, the 250 kV-EB (250 kV) strengthens [TPA]10[CF]9 significantly, about 25 to 27% larger than that of 170 kV and zero (untreated). Based on Christenhusz and Reimer equation to calculate penetration depth, Dth of EBI into polymers, increasing Vc to 250 kV increased Dth to more than 2 times that at 170 kV.
Active components of Bupleuri Radix in the treatment of schizophrenia analyzed by network pharmacology and clinical verification

Background: Bupleuri Radix is a common Chinese medicinal material in traditional Chinese medicine. Currently, the therapeutic effect of treating schizophrenia is relatively well understood. However, there are fewer studies examining the underlying mechanisms of its treatment. The objective of the study was to investigate the primary mechanisms of Bupleuri Radix in treating schizophrenia through network pharmacology and clinical validation. Method: Network pharmacology revealed possible molecular mechanisms, followed by clinical verification. Sixty-seven schizophrenia patients undergoing treatment at the Hunan Brain Hospital between October and November 2022 were recruited and randomly divided into the olanzapine group and the olanzapine + Bupleuri Radix group. Additionally, 32 healthy people undergoing physical examinations during the same period were included as the control group. The patient’s positive and negative symptom scale scores were compared. qPCR was used to detect the mRNA expression levels of ESR1, mTOR, EIF4E, and SMAD4 in peripheral blood. Results: Through network pharmacological analysis, it was concluded in this study that Bupleuri Radix might regulate the mTOR, PI3K-Akt, and HIF-1 signaling pathways. Clinical experiments indicated that compared with before treatment, the positive and negative symptom scale scores and total scores of the two treatment groups were significantly decreased after treatment ( P < 0.01). In addition, the positive and negative symptom scale scores and total scores in the olanzapine + Bupleuri Radix group were significantly decreased ( P < 0.01) compared to the olanzapine group after treatment. Before treatment, ESR1 mRNA expression levels in peripheral blood were significantly higher in the two treatment groups than in the control group, whereas the mRNA expression levels of mTOR, EIF4E, and SMAD4 in peripheral blood were significantly lower ( P < 0.01). The mRNA expression levels of mTOR, EIF4E, and SMAD4 in peripheral blood were significantly higher after therapy than before treatment, whereas the mRNA expression levels of ESR1 in peripheral blood were significantly lower ( P < 0.01). After therapy, the olanzapine + Bupleuri Radix group’s mRNA expression levels of mTOR, EIF4E, and SMAD4 were significantly higher than those of the olanzapine group, whereas the mRNA expression levels of ESR1 were significantly lower ( P < 0.01). Conclusion: The mechanism of Bupleuri Radix ’s therapeutic efficacy in schizophrenia may involve the up-regulation of mTOR, EIF4E, and SMAD4 mRNA expression and the down-regulation of ESR1 mRNA expression in peripheral blood.
SEGMENTATION OF LOAD GROUPS ON A SINGLE PHASE KWH-METER USING THE PAYLOAD DATA HANDLING SYSTEM.

A minimum system that controlled by microcontroller has been established for load groups segmentation on a single-phase kWH-meter using the payload data handling system. Minimum system that controlled by microcontroller is integration of all entities through handshake mechanism for hardware and software. Handshaking the hardware in the form of a board for microcontroller ATMega32 with the availability of five ports each for 5 volt dc power connector, sensor, LCD 4x16, downloader, and output. The handshaking the software is the presence of programming to the ATmega32 microcontroller for system operation based-on BasCom language programming through seven stages: (i) pins configuration, (ii) variable declaration, (iii) declaration of constants, (iv) initialization, (v) main program, (vi) retrieve and send data, and (vii) output. System performance is shown, volt 4x16, downloader, (input) (output) ATMega32. volt of is no an value of while an value of volts ATmega32