Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Overstocking at the Feed Bunk on Indicators of Cow Temperament

Our objective was to investigate the effect of overstocking the feed bunk on dairy cow behavioral responses to human approach and reactivity to blood sampling. One hundred and twenty dry Holstein cows were allocated to 1 of 2 treatment groups with different stocking densities at the feedbunk (Overstocked (OS): 0.88 headlocks/cow; Understocked (US): 1.17 headlocks/cow). Over 2 testing periods (7 d apart), flight response was assessed using a humanapproach test, with a 5-point ordinal scale defining the distance at which the cow stepped away from the approaching experimenter (0 = not approachable from 3 m to 4 = cow moves away when experimenter is 0 m from the cow). A qualitative assessment was also made of the cow’s response to the experimenter using a visual analogue scale (VAS) that included the terms: relaxed, nervous, alert, shy, aggressive, social, and curious. Reactivity to blood sampling via the coccygeal vein was assessed in the pen using a 4-point scale (0 = least reactive to 3 = most reactive). Data were analyzed through a mixed model analysis, using treatment, time, and their interaction. The relationship between qualitative measures was assessed using a Pearson correlation. There was a treatment by time interaction whereby flight response scores decreased with time in OS cows and increased with time in US cows (OS: 1.65 to 1.47, US: 1.33 to 1.68; P = 0.02). Reactivity to blood sampling did not differ by treatment (OS: 1.11, US: 0.98; P = 0.47), and there was also no treatment by time interaction (OS: 1.17 to 1.11, US: 1.01 to 0.98; P = 0.88). The overall correlation between qualitative terms was low. However, the terms ‘relaxed’ and ‘nervous’ had a significant negative correlation (OS: r = -0.71; P < 0.0001; US: r = -0.61; P 2 < 0.0001). In conclusion, overstocking the feed bunk affected the animal’s response to an approaching human. Cows in the OS treatment became less approachable over time, which may indicate fear, stress, or an increase in arousal. Future research should investigate the effect that overstocking may have on cow temperament for a longer duration, as this may further decrease approachability.
Solution of the inverse problem in solidification of binary alloy by applying the ACO algorithm

The paper presents a solution of the inverse problem consisting in reconstruction of the heat flux and the distribution of temperature in the process of binary alloy solidification when the temperature measurements in the selected points of the alloy are known. The considered task is mathematically modelled by means of the heat conduction equation with the substitute thermal capacity and with the liquidus and solidus temperatures varying in dependence on the concentration of the alloy component, whereas for describing the concentration the lever arm model is applied. An important part of the procedure consists in minimization of some functional executed with the aid of ACO algorithm.
Incremental Approximation Feature Selection With Accelerator for Rough Fuzzy Sets by Knowledge Distance

Feature selection method with rough sets based on incremental learning has the major advantage of the higher efficiency in a dynamic information system, which has attracted extensive research. However, the incremental approximation feature selection with an accelerator (IAFSA) remains ambiguous for a dynamic information system with fuzzy decisions (ISFD). Driven by this concern, the nonincremental approximation feature selection is first presented by fuzzy knowledge distance (FKD). Second, the incremental theory of FKD is constructed with a batch of objects appended to or removed from the dynamic ISFD. Subsequently, an acceleration mechanism to eliminate redundant information granules is developed to reduce the sample space. Eventually, two categories of IAFSA based on FKD are presented. The experiments reflect the efficiency and effectiveness of the developed IAFSA algorithms.
HIGH REDSHIFT GALAXY CLUSTERS IN ELIAS-N1/N2 FIELDS WITH A NEW COLOR SELECTION TECHNIQUE

Galaxy clusters, the largest gravitationally bound systems, are an important subject of study to place constraints on cosmological models. Moreover, they are excellent places to test galaxy evolution models in connection to their environments. To date, massive clusters have been found unexpectedly (Kang & Im 2009; Gonzales et al. 2012) and the evolution of galaxies in clusters is still controversial (Elbaz et al. 2007; Faloon et al. 2013). Finding galaxy cluster candidates at z > 1 in a wide, deep imaging survey data will enable us to solve such issues of modern extragalactic astronomy. We report new candidate galaxy clusters in one of the wide and deep survey elds, the European Large Area ISO Survey North1 (ELAIS-N1) and North2 (ELAIS-N2) elds, covering a sky area of 8.75 deg 2 and 4.85 deg 2 each. We also suggest a new useful color selection technique to separate z > 1 galaxies from low - z galaxies by combining multi-wavelength data.
Synchronous Games in the Situation Calculus (Extended Abstract)

We develop a situation calculus-based account of multi-player synchronous games. These are represented as action theories called situation calculus synchronous game structures (SCSGSs) that involve a single action tick whose eects depend on the combination of moves chosen by the players. Properties of games, e.g., winning conditions, playability, weak and strong winnability, etc. can be expressed in a rst-order variant of alternating-time mu-calculus. Computationally eective verication can be performed. SCSGSs can be viewed as a variant of the Game Description Language (GDL) where states are represented by rst-order theories.
A Method to Compute Frequency Domain Models Based on Direction-Dependent Derivative of the Variation of Voltage and Current Phasors

Harmonic pollution and other potential problems are increasing due to the rising demand for Power Electronic devices in low-voltage distribution systems. The adequate modeling of the harmonic characteristics of these devices is essential for assessing their impact. Frequency Domain Models (FDMs) are based on either matrix or tensor definitions, and different methods have been proposed to compute those models' parameters. This article proposes a method to compute matrix or tensor-based FDMs by exploiting the nonholomorphic properties of complex functions. Thus, the direction-dependent derivative of the variation of voltage-current phasors is optimized over measurements set. By proving that tensor and matrix-based models are equivalent, it is guaranteed that the method can be applied to compute both models. The Proposed and Tensorial Parametrization methods are compared using the power of the error signal as a percentage of the power of the current signal achieving a one-digit reduction in a proportion of 4:1.
Adaptive adjustment with semantic embedding for zero-shot object detection

Abstract. Traditional zero-shot object-detection algorithms detect images of untrained classes in the model with the help of semantic embedding. However, these approaches may perform poorly due to the limitations of fixed semantic embedding. Given that fixed semantic attributes lead to a lack of generalization capabilities in the model, a semantic enhancement mechanism is proposed to update the semantic embedding, which is used to serve the needs of the visual space. Specifically, considering that the original semantic space is not enough to construct a visual-semantic mapping relationship, an augmented semantic embedding (ASE) approach is designed to supplement semantic attribute information. Then, a semantic channel attention mechanism is used to adjust the ASE. The adjustment strategy retains adequate attribute information, which is highly relevant to visual features. Finally, to alleviate the domain shift problem, a clustering association strategy is introduced to establish an inferred relationship, which ensures that the predictor is generalized to the unseen domain during training. The superiority of the proposed method is demonstrated by the MS-COCO and PASCAL VOC datasets.
Unleashing Spinal Cord Repair: The Role of cAMP-Specific PDE Inhibition in Attenuating Neuroinflammation and Boosting Regeneration after Traumatic Spinal Cord Injury

Traumatic spinal cord injury (SCI) is characterized by severe neuroinflammation and hampered neuroregeneration, which often leads to permanent neurological deficits. Current therapies include decompression surgery, rehabilitation, and in some instances, the use of corticosteroids. However, the golden standard of corticosteroids still achieves minimal improvements in functional outcomes. Therefore, new strategies tackling the initial inflammatory reactions and stimulating endogenous repair in later stages are crucial to achieving functional repair in SCI patients. Cyclic adenosine monophosphate (cAMP) is an important second messenger in the central nervous system (CNS) that modulates these processes. A sustained drop in cAMP levels is observed during SCI, and elevating cAMP is associated with improved functional outcomes in experimental models. cAMP is regulated in a spatiotemporal manner by its hydrolyzing enzyme phosphodiesterase (PDE). Growing evidence suggests that inhibition of cAMP-specific PDEs (PDE4, PDE7, and PDE8) is an important strategy to orchestrate neuroinflammation and regeneration in the CNS. Therefore, this review focuses on the current evidence related to the immunomodulatory and neuroregenerative role of cAMP-specific PDE inhibition in the SCI pathophysiology.
Examining Generalizability of Nutrient-Based Food Patterns and Their Cross-Sectional Associations with Cardiometabolic Health for Hispanic/Latino Adults in the US: Results from the National Health and Nutrition Examination Survey (NHANES) and the Hispanic Community Health Study/Study of Latinos (HCH

Background. Ethnicity, cultural background, and geographic location differ significantly amongst the US Hispanic/Latino population. These characteristic differences can greatly define measured diet and its relationship with cardiometabolic disease, thus influencing generalizability of results. Objective. We aimed to examine dietary patterns of Hispanic/Latino adults and their association with cardiometabolic risk factors (high cholesterol, hypertension, obesity, diabetes) across two representative studies with differing sampling strategies. Methods. Data were collected from Mexican or Other Hispanic adult participants from 2007-2012 National Health and Nutrition Examination Survey (NHANES, n=3,209) and 2007-2011 Hispanic Community Health Survey/Study of Latinos (HCHS/SOL, n=13,059). Nutrient-based food patterns (NBFPs) were derived using factor analysis on nutrient intake data estimated from 24-hour dietary recalls and interpreted using common foods prominent in these nutrients. Cross-sectional association between NBFPs (quintiles) and cardiometabolic risk factors, defined by clinical measures and self-report, were estimated using survey-weighted logistic regression. Results. Five NBFPs were identified in both studies: (1) meats, (2) grains/legumes, (3) fruits/vegetables, (4) dairy, and (5) fats/oils. Association to cardiometabolic risk factors differed by NBFP and study. In HCHS/SOL, persons in the highest quintile of meats NBFP had higher odds of diabetes (OR=1.43, 95%CI: 1.10, 1.86) and obesity (OR=1.36, 95%CI: 1.14, 1.63). Those in the lowest quintile of grains/legumes NBFP (OR=1.22, 95%CI: 1.02, 1.47) and the highest quintile of fats/oils (OR=1.26, 95%CI: 1.03, 1.53) also had higher odds of obesity. In NHANES, NBFPs associated with higher odds of diabetes included those in the lowest quintile of dairy (OR=1.66, 95%CI: 1.01, 2.72) and highest quintile of grains/legumes (OR=2.10, 95%CI: 1.26, 3.50). Persons in the fourth quintile of meats (OR=0.68, 95%CI: 0.47, 0.99) had lower odds of cholesterol. Conclusion. Diet-disease relationships among Hispanic/Latino adults vary according to two representative studies. These differences have research and practical implications when generalizing inferences on heterogeneous underrepresented populations.
Ultrasonic Torsion Mode Guided Wave Probe Design for Local Detection of Vehicle-mounted Winding Hydrogen Storage Cylinders

At present, the application of the traditional nondestructive testing technologies for hydrogen storage tanks is limited because of their requests to make the hydrogen storage tank to be out of service. The damage to the cylinder surface and some microstructures will change the echo-guided wave signal. However, there is no special cylinder detection probe now. In this paper, a probe of ultrasonic guided wave excitation generator is designed for vehicle-mounted winding hydrogen storage cylinders. The research results of this paper are of great significance for the safety of hydrogen storage tanks in new energy vehicles.
Research on the inverse kinematic algorithm of the robot arm of the minecart

A reverse kinematics solution algorithm for the robot arm of the minecart is proposed based on the structure and motion characteristics of the robot arm, the geometric method is combined with the algebra method, and the trajectory of each joint point on the mechanical arm is calculated inversely by using the motion trajectory of the roller on the mining wheel to reverse-calculate the trajectory of each joint point on the mechanical arm, and the joint angle of the mechanical arm is obtained according to the trajectory of each joint point. The results are compared and analyzed through the Robots Toolbox toolbox in Matlab to verify the correctness and validity of the inverse calculation results. It lays a foundation for future research on key issues such as path planning and trajectory tracking control of the robot arm of the minecart.
A new rapid positioning strategy for the impact-point using dynamic mode decomposition and time difference of arrival

In outdoor shooting range experiments, locating the projectile impact point is typically done through manual search, which can be time-consuming and costly. To improve experimental efficiency and reduce costs, this paper proposes a new approach for localizing the impact point using the dynamic mode decomposition (DMD) extension method and time difference of arrival (TDOA) based on seismic waves. The DMD extension method is applied to one-dimensional seismic signals. Additionally, diagonal averaging is applied to recover the reconstructed matrix, which could achieve better noise reduction than conventional singular value decomposition and empirical mode decomposition methods. Numerical simulations are then conducted to analyze the impact point positioning model, resulting in an absolute location error within 20 m in a 1000 m square area. The work demonstrates that the joint positioning algorithm combining on DMD and TDOA achieves high positioning accuracy, making it a promising tool for future experiments on outdoor shooting ranges.
Effect of pre-strain on microstructure and stamping ability of TA2 titanium alloy and H260 low-alloy steel

Applying pre-strain can not only change the microstructure and the mechanical property of metal material but also affect sheet metal stamping ability. In this paper, taking H260 low-alloy steel and TA2 titanium alloy as experimental materials, pre-strain ranging from 0 to 0.16 was applied using the one-step and the multi-step method respectively. Microstructure observation, tensile test, bending test, and cup bulging test were conducted on the pre-strained samples. Results show that in the pre-strain loading stage, the slipping and twinning deformation occurs in the TA2 alloy sample while dislocation sliding mainly happens in H260 low-alloy. With pre-strain increasing, the product of ultimate tensile strength and elongation rate gradually decreases, and the true ultimate tensile stress and accumulating elongation change rarely. The “product of yielding strength and elongation rate” can be enhanced by applying a pre-strain of 0.04 on the condition that plasticity is still at a high level. In the pre-strain range of 0 - 0.16, a bending test with a 30.8 °-opening angle can be completed on H260 steel. The springback angle changes from negative to positive with an increase in pre-strain. The bending ability of TA2 titanium alloy is very poor, for a crack can easily occur even in the original sample. The cupping value decreases gradually with an increase in pre-strain. The cupping value of TA2 titanium alloy is higher than that of H260 low alloy steel for the constant pre-strain. A quite large difference exists between the two experimental materials. Pre-strain loading pass has no significant effect on the microstructure evolution, mechanical properties, and stamping properties.
Design of high quantum efficiency SPAD single-photon detector based on metasurface

This work discusses several metasurface structure types, their manufacturing processes, and the design and simulation of nanopyramid arrays, square slot arrays, and funnel arrays to improve absorption. In particular, a silicon-based continuous thin gold film-covered deep grooved thin metal structure was constructed and examined. It surpasses the silicon materials’ absorption thresholds, considerably expanding the NIR’s absorption range to 2000 nanometers. More information is provided regarding the impact of trench depth on cavity pattern and near-infrared absorption. Its preparation process is detailed, and its compatibility with SPAD single-photon detectors is confirmed. This information is likely to encourage the use of SPAD in domains such as near-infrared optical communication and other low-light detecting technologies.
Serendipitous detection of Anopheles stephensi in Kisumu, Kenya in June 2022

In June 2022, a pool of five mosquitoes that were morphologically classified as Anopheles gambiae and caught in Kisumu (Kenya) were tested for blood-meal analysis. Of the 19.6% (11/56) amplicon sequence variants assigned to mosquito species using basic local alignment search tool (BLAST), one had 15 hits matching Anopheles stephensi.
Freezing of gait assessment with inertial measurement units and deep learning: effect of tasks, medication states, and stops

Background: Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson's Disease (PD). Traditionally, FOG assessment relies on time-consuming visual inspection of camera footage. Therefore, previous studies have proposed portable and automated solutions to annotate FOG. However, automated FOG assessment is challenging due to gait variability caused by medication effects and varying FOG-provoking tasks. Moreover, whether automated approaches can differentiate FOG from typical everyday movements, such as volitional stops, remains to be determined. To address these questions, we evaluated an automated FOG assessment model with deep learning (DL) based on inertial measurement units (IMUs). We assessed its performance trained on all standardized FOG-provoking tasks and medication states, as well as on specific tasks and medication states. Furthermore, we examined the effect of adding stopping periods on FOG detection performance. Methods: Twelve PD patients with self-reported FOG (mean age 69.33 +/- 6.28 years) completed a FOG-provoking protocol, including timed-up-and-go and 360-degree turning-in-place tasks in On/Off dopaminergic medication states with/without volitional stopping. IMUs were attached to the pelvis and both sides of the tibia and talus. A multi-stage temporal convolutional network was developed to detect FOG episodes. FOG severity was quantified by the percentage of time frozen (%TF) and the number of freezing episodes (#FOG). The agreement between the model-generated outcomes and the gold standard experts' video annotation was assessed by the intra-class correlation coefficient (ICC). Results: For FOG assessment in trials without stopping, the agreement of our model was strong (ICC(%TF) = 0.92 [0.68, 0.98]; ICC(#FOG) = 0.95 [0.72, 0.99]). Models trained on a specific FOG-provoking task could not generalize to unseen tasks, while models trained on a specific medication state could generalize to unseen states. For assessment in trials with stopping, the model trained on stopping trials made fewer false positives than the model trained without stopping (ICC(%TF) = 0.95 [0.73, 0.99]; ICC(#FOG) = 0.79 [0.46, 0.94]). Conclusion: A DL model trained on IMU signals allows valid FOG assessment in trials with/without stops containing different medication states and FOG-provoking tasks. These results are encouraging and enable future work investigating automated FOG assessment during everyday life.
COVID-19 Infection Is Associated with Poor Outcomes in Patients with Intracerebral Hemorrhage

Background: Patients with ischemic stroke and concomitant coronavirus 2019 (COVID-19) infection have worse outcomes than those without this infection. However, research on the impact of COVID-19 infection on outcomes following hemorrhagic stroke remains limited. We aim to study whether concomitant COVID-19 infection leads to worse outcomes in spontaneous intracerebral hemorrhage (ICH). Design: We conducted an observational study using data from Get With The Guidelines Stroke, an ongoing, multi-center, nationwide quality assurance registry. Methods: We implemented a two-stage design: first, we compared outcomes of ICH patients with and without COVID-19 infection admitted during the pandemic (from March 2020 to February 2021). Second, we compared the same outcomes between ICH patients admitted before (March 2019 to February 2020) and during (March 2020 and February 2021) the pandemic. Main outcomes were poor functional outcome (defined as a modified Rankin Scale of 4 to 6 [mRS] at discharge), mortality and discharge to skilled nursing facility (SNF) or hospice. Results: The first stage included 60,091 COVID-19-negative and 1,326 COVID-19-positive ICH patients. In multivariable analyses, ICH patients with versus without COVID-19 infection had 68% higher odds of poor outcome (OR 1.68, 95%CI 1.41-2.01), 51% higher odds of mortality (OR 1.51, CI 1.33-1.71) and 66% higher odds of being discharged to a SNF/hospice (OR 1.66, 95%CI 1.43-1.93). The second stage included 62,743 pre-pandemic and 64,681 intra-pandemic ICH cases. In multivariable analyses, ICH patients admitted during versus before the COVID-19 pandemic had a 10% higher odds of poor outcome (OR 1.10, 95%CI 1.07-1.14), 5% higher mortality (OR 1.05, 95%CI 1.02-1.08) and no significant difference in the risk of being discharged to SNF/hospice (OR 0.93, 95%CI 0.90-0.95). Conclusions: The pathophysiology of the COVID-19 infection and changes in healthcare delivery during the pandemic played a role in worsening outcomes in this patient population. Further research is needed to identify these factors and understand their effect on the long-term outcome.
Mode Connections For Clinical Incremental Learning: Lessons From The COVID-19 Pandemic

Dynamic distribution shifts caused by evolving diseases and demographic changes require domain-incremental adaptation of clinical deep learning models. However, this process of adaptation is often accompanied by catastrophic forgetting, and even the most sophisticated methods are not good enough for clinical applications. This paper studies incremental learning from the perspective of mode connections, that is, the low-loss paths connecting the minimisers of neural architectures (modes or trained weights) in the parameter space. The paper argues for learning the low-loss paths originating from an existing mode and exploring the learned paths to find an acceptable mode for the new domain. The learned paths, and hence the new domain mode, are a function of the existing mode. As a result, unlike traditional incremental learning, the proposed approach is able to exploit information from a deployed model without changing its weights. Pre-COVID and COVID-19 data collected in Oxford University hospitals are used as a case study to demonstrate the need for domain-incremental learning and the advantages of the proposed approach.