Miracle-dz/UniFAR-FTUs huggingface dataset:
Too Much of a Good Thing: Rethinking Feed Formulation and Feeding Practices for Zinc in Swine Diets to Achieve One Health and Environmental Sustainability

Simple Summary Zinc (Zn) is an essential nutrient required by all organisms, but excess use may result in environmental pollution and contribute to the development of antimicrobial resistant bacteria. Although Zn is involved in numerous physiological functions in pigs, accurate determination of dietary requirements has been difficult because homeostasis is tightly regulated, and biomarkers indicative of Zn adequacy are lacking. Dietary Zn concentrations are determined by Zn in common feed ingredients, amount of supplemental Zn added from premixes, use of pharmacological doses of Zn to control post-weaning diarrhea and promote growth, and use of elevated dietary Zn in late gestation to reduce pre-weaning piglet mortality. As a result, Zn concentrations of swine diets are highly variable within countries and across the globe. Dietary Zn regulations in the E.U. were implemented to reduce the negative environmental impacts associated with Zn accumulation in agricultural soils resulting from long-term swine manure application as a consequence of feeding pharmacological Zn concentrations to weaned pigs. However, several alternative feeding strategies could allow the strategic use of elevated Zn concentrations to achieve productivity advantages, while also substantially reducing Zn excretion in manure, which are both important contributors toward improving environmental sustainability of global pork production systems. Abstract The objectives of this review were to summarize current knowledge of Zn in swine nutrition, environmental concerns, potential contribution to antimicrobial resistance, and explore the use of alternative feeding strategies to reduce Zn excretion in manure while capturing improvements in productivity. Zinc is a required nutrient for pigs but is commonly supplemented at concentrations that greatly exceed estimated requirements. Feeding pharmacological concentrations of Zn from ZnO to pigs for 1 to 2 weeks post-weaning reduces post-weaning diarrhea and improves growth performance. Feeding elevated dietary levels of Zn to sows during the last 30 days of gestation can reduce the incidence of low-birth-weight pigs and pre-weaning mortality. Most of the dietary Zn consumed by pigs is not retained in the body and is subsequently excreted in manure, which led several countries to impose regulations restricting dietary Zn concentrations to reduce environmental impacts. Although restricting Zn supplementation in swine diets is a reasonable approach for reducing environmental pollution, it does not allow capturing health and productivity benefits from strategic use of elevated dietary Zn concentrations. Therefore, we propose feeding strategies that allow strategic use of high dietary concentrations of Zn while also reducing Zn excretion in manure compared with current feeding practices.
Connecting continua and curves of equilibria of the Cahn-Hilliard equation on the square

We state an alternative for paths of equilibria of the Cahn-Hilliard equation on the square, bifurcating from the trivial solution at eigenfunctions of the form $w_{ij}=\cos(\pi ix)\cos(\pi j y)$, for $i,j \in \N$. We show that the paths either only connect the bifurcation point $m_{ij}$ with $-m_{ij}$ and are separated from all other paths with even more symmetry, or they contain a loop of nontrivial solutions connecting the bifurcation point $m_{ij}$ with itself. In any case the continua emerging at $m_{ij}$ and $-m_{ij}$ are equal. For fixed mass $m_0=0$ we furthermore prove that the continua bifurcating from the trivial solution at eigenfunctions of the form $w_{i0}+w_{0i}$ or $w_{ij}$, for $i,j \in \N$ are smooth curves parameterized over the interaction length related parameter $\lambda$.
Line-driven winds from accretion disks. I - Effects of the ionization structure

The basic characteristics of line-driven winds from UV accretion disks were investigated analytically and numerically. Stellar winds have been compared with the outflow from a disk surrounding a supermassive black hole. It is found that in the approximation of vertically streaming radiation close to the disk photosphere, the existence of steady disk winds depends crucially on their ionization structure, a basic difference when compared to the stellar winds. The necessary criteria for the line radiation force to support the disk wind are presented and discussed.
Elasticity of a percolation system: silica smoke

Cab-O-Sil powder is a cluster aggregate of silica spheres formed from silica smoke. The fractal dimension of clusters of 14 nm diameter spheres is determined to be , in good agreement with that calculated for the cluster-aggregate model, . Measurements of Young's modulus (Y) of packed 7 nm Cab-O-Sil powder, with an occupied volume fraction in the range 0.035 3.55. The measured result is attributed to bond bending within a complex percolation structure.
Guided-Crop Image Augmentation for Small Defect Classification

As processing power becomes more affordable, computer vision tends to push towards controlling complex processes in the industry. Surface inspection, with changing environmental conditions and the usual lack of homogeneity of the inspected parts, makes it a real challenge to overcome even for a skilled specialist. In addition, the scarcity of positive samples and the extremely small size of the defects, makes it even harder to cluster them in different classes. In this work, we propose a novel training strategy tailored to handle these challenges for the problem of image defect segmentation and classification. First, we propose a Context Aggregation Network with different dilation factors, in order to keep as much information as possible from every feature map, especially for the smallest defects. By splitting the loss in classification and segmentation and positively weighing both terms, we accomplish an optimal learning process counteracting possible imbalances in the dataset. Additionally, we introduce a novel guided-crop image augmentation method, which generates new images by cropping real defects from existing images, pasting them in real non-defective ones and finally tweaking their configuration. This augmentation strategically performed, guided by the evolution of each class loss, allows the model to identify better the least common and complicated to identify defects. We validate our solution with the Magnetic Tile and the Severstal Steel Defect Detection dataset, demonstrating that our approach consistently outperforms models such as ResNet-50, DenseNet-121, HRNet or UPerNet.
Electrical and optical properties of Black Phosphorus under Strain effects: A First-principles Study

In this work, the impact of the pressure on the electronic and optical characteristics of bulk Black Phosphorus (BP) is investigated using first principles calculations. In this context, the density functional theory (DFT), including Perdew-Burke-Ernzerhof Generalized Gradient Approximation (PBE-GGA) and the screened hybrid (YS-PBE0) functionals with van der Waals correction is used to study the material properties under pressure effects. The band structures of bulk BP with and without pressure effects are extracted. It is revealed from DFT results that the band gap decreases with pressure increasing, where low a band gap value of 0.09 eV is achieved by introducing the critical pressure of 1.5 GPa. Further increasing the pressure leads to semi-metallic properties in bulk BP. In addition, the optical properties of the material under pressure are investigated by extracting the dielectric function and the optical constants under pressure effects. It is revealed that the strained bulk BP can enable enhanced optical performances over the middle infrared (Mid-IR) spectral band, making it highly suitable for the design of high performance photosensors appropriate for optical communication applications.
Effects of prescattering attenuation of spectropolarimetric line profiles

We present the wavelength dependent linear polarization and position angle variations arising from single Thomson scattering of continuum stellar radiation in an axisymmetric, hot, planar disk, which is in bulk motion. The wavelength dependence arises through the combination of Doppler shifted line attenuation, prior to scattering, and the Doppler redistribution of the scattered radiation due to the bulk and thermal motions of the disk electrons. We find that when the scattering electrons are cold, there are large polarization and position angle variations across the resulting spectrapolarimetric absorption line profile-agreeing with previous investigations. However, when the thermal Doppler velocities of the scattering electrons are comparable to or greater than the electron bulk velocity (as is of ten the case), then the scattered line profile is broadened and the amplitude of the polarimetric variations are considerably reduced. The properties of the electron thermal smearing function are such that the equivalent width of any scattered spectrapolarimetric feature is independent on the electron temperature-a consequence of the conservation of areas under convolution. An important result of this is that for an axisymmetric circumstellar envelope the position angle variations will average to zero across any line formed through the above process. We expect that the effect of multiple line blending will yield a depolarization equivalent width is given by thesum of the individual equivalent widths and the position angle variations will average to zero. The implications of this are that the large depolarizations observed in the UV spectra of pi Aquarii, zeta Tauri and PP Carinae by the Wisconsin Ultraviolet Photo Polarimeter Experiment (WUPPE) can only be explained with prescattering attenuation of stellar flux if there are a sufficient number of multiple overlapping lines in that part of the spectrum.
On the decay rate of isotropic turbulence laden with microparticles

This paper is concerned with the two-way coupling effects on the decay rate of isotropic turbulence laden with solid spherical microparticles whose response time, τp, is much smaller than the Kolmogorov time scale (τp≪τk). The particles volumetric concentration, C0, is small enough (C0∼10−4) to neglect particle–particle interactions, and the material density of the particle is much larger than the fluid density (δ=ρp/ρf≫1). We obtain asymptotic analytical solutions for the instantaneous particle velocity and kinetic energy spectrum, in the limit τp≪τk, which indicate that the two-way coupling increases the fluid inertia term in the fluid momentum equation by the factor (1+C0δ). Consequently, the high-wave number components of the spectra of turbulence energy and dissipation develop in time as ∼exp[−2νk2t/(1+C0δ)]. The net result is a reduction of the decay rate of turbulence energy compared to that of particle-free turbulence (i.e., the one-way coupling case where C0δ≡0). We also perform direct numerical .
Channel-Spatial Mutual Attention Network for 360° Salient Object Detection

In this work, we conduct 360° panoramic salient object detection by taking advantage of both the global and local visual cues of 360° images, with a novel channel-spatial mutual attention network (CSMA-Net). The key component of the CSMA-Net is the proposed CSMA module, which cascades channel-/spatial-weighting-based mutual attentions. The objective of our CSMA module is to refine and fuse the bottleneck features from two separate encoders with different planar representations of 360° panorama as inputs, i.e., equirectangular image and cube map. Our CSMA-Net outperforms 10 state-of-the-art segmentation methods based on the proposed 360° SOD benchmark where multiple fine-tuning and testing strategies are applied to the widely-used 360° datasets. Extensive experimental results illustrate the effectiveness and robustness of the proposed CSMA-Net1.
DPAM: A New Deep Parallel Attention Model for Multiple License Plate Number Recognition

License plate number recognition is challenging for complex scenes containing multiple vehicles of different types, shapes, distances etc. To recognize multiple license plate numbers in an image, we propose a new model, called Deep Parallel Attention Model (DPAM), which simultaneously extracts unique features at character levels. The proposed model exploits the observation that the combination of alphanumeric characters does not have correlation at semantic level for extracting the features. This led to the introduction of parallelism for feature extraction at character levels to make it efficient in terms of time to fit in a real time environment. To test the proposed model, we consider our own dataset consisting of Indian license plate numbers and other standard datasets to show the superiority of the proposed model over the existing methods in terms of recognition rate. Furthermore, the proposed method is tested on scene text dataset to show its ability to detect text in natural scene images.
Comparing Engineering Students’ and Professionals’ Conceptions of Ambiguity

Engineers are frequently confronted with complex, unique, and challenging problems. Many of our most pressing engineering problems contain ambiguous elements, and a core activity of engineering is being able to solve these complex problems effectively. While engineering problems are often described as ambiguous, ambiguity has not been clearly defined in the literature in the context of engineering problem solving. This work-in-progress paper describes our initial results to understand how ambiguity is experienced during engineering problem solving. We interviewed both engineering students and engineering professionals about ambiguous problems they have encountered. We found that both groups identified technical ambiguity as the core element of engineering problem solving. They also described differences between classroom and workplace problems, with students describing classroom problems as "purposefully" ambiguous. Students had strong negative emotional reactions to ambiguity, in contrast to professionals who seemed to accept ambiguity as a common element in engineering problems. Our initial findings suggest that changes to engineering education practice that allow students to become comfortable with ambiguity would better prepare them for the ambiguous problems they will face in the workplace.
Structured and open Challenge-Based Learning in Engineering Ethics Education

Challenge-based learning (CBL) is an educational method in which an external stakeholder (company, community, NGO, …) brings in a real-life complex problem. CBL becomes increasingly popular in engineering education in general. The application of CBL in engineering ethics education, however, is lacking behind, as it requires specific design specifications. An ethics CBL course has commonalities with CBL courses without specific ethics learning goals, but they also need specific considerations in the design. A structural approach how and why to apply CBL in engineering ethics education is needed, both from an educational research and educational support perspective. This contribution will explore this gap. With the research questions: "What are particularities of CBL pedagogies in engineering ethics education? " These pedagogies will be described by using van den Akker’s spider-web curriculum model and Ethics Goal Model. We describe specificities for CBL engineering ethics courses for the different course components. We conclude that the tension between structured and open CBL approaches is crucial and that more research can be done in the different curriculum aspects described in the model of van den Akker.
Enhancing student engagement with personalized gamification and adaptive learning strategies

This Research to Practice Work In Progress paper presents our experience and preliminary results of introducing adaptive and gamified digital learning lessons to lower primary school students. To improve motivation and engagement, besides the adaptivity of the learning content itself, the game elements like points, and rewards were added to the learning lessons. Additionally, the activities supported two different game modes, adventure and competitive. In adventure mode, students were solving the assigned tasks to discover interesting new facts about the world around them, and in the competitive mode, they were collecting points and trophies to beat their classmates.The preliminary study was conducted with two small groups of third-grade students (age 8-9, N=18) who used tablet computers to access and complete the prepared mathematics learning activities. A questionnaire was organized to collect additional information and student attitudes toward each of the approaches.The preliminary results show that personalized gamification has the potential to improve student engagement, which could, eventually, lead to more successful learning.
Investigating the Transferability of the Productive Beginnings of Engineering Judgment Framework from Interview Data to Group Discourse Data

Our research team has been working to understand how students develop and use mathematical models—the practice of engineering judgment—while completing open-ended modeling problems (OEMPs) during their undergraduate engineering science courses. We have used retrospective interviews, in which students review their work and discuss what they were thinking when they originally completed the problem, to develop a Productive Beginnings of Engineering Judgment (PBJ) Framework. This paper presents the transferability of our PBJ framework to a new data set—students’ discourse as they complete an OEMP in a small group. Examining this discourse data confirms that the PBJ Framework applies to students’ engineering judgment in-the-moment while working on an open-ended assignment. We also discuss three potential new codes for our PBJ Framework that need further exploring, and how students’ written assignments do not fully capture the engineering judgment they used when completing the OEMP with their peers.
Explainable AI for Credit Assessment in Banks

Banks’ credit scoring models are required by financial authorities to be explainable. This paper proposes an explainable artificial intelligence (XAI) model for predicting credit default on a unique dataset of unsecured consumer loans provided by a Norwegian bank. We combined a LightGBM model with SHAP, which enables the interpretation of explanatory variables affecting the predictions. The LightGBM model clearly outperforms the bank’s actual credit scoring model (Logistic Regression). We found that the most important explanatory variables for predicting default in the LightGBM model are the volatility of utilized credit balance, remaining credit in percentage of total credit and the duration of the customer relationship. Our main contribution is the implementation of XAI methods in banking, exploring how these methods can be applied to improve the interpretability and reliability of state-of-the-art AI models. We also suggest a method for analyzing the potential economic value of an improved credit scoring model.
Neutron Irradiation Testing and Analysis of a Fault-Tolerant RISC-V System-on-Chip

The radiation in harsh environments affects electronic systems, inducing permanent and temporary errors. These effects lead to unpredictable behaviors detrimental to critical applications and fail-safe systems. This work evaluates the reliability of a fault-tolerant RISC-V System-on-Chip (SoC) under atmospheric neutron irradiation in a particle accelerator. Prior work has analyzed the effectiveness of the hardening techniques of this SoC in simulation and provided a preliminary characterization in an irradiation facility. The applied hardening techniques showed a significant reliability improvement compared to the unhardened implementation of the SoC. The system executed a performance benchmark as workload, which finished correctly in most runs despite suffering from Single Event Effects (SEEs). This work presents a detailed analysis of the experimental results, reporting error rates and classification, extending the analysis given in previous works. Finally, a comprehensive discussion of implementation limitations and the proposition of further improvements are provided.
Narrowing Attention in Capsule Networks

Despite their recent success capsule networks (CapsNets) are still very computationally intensive and fail to achieve state-of-the-art performances on advanced datasets. As a consequence CapsNets are usually combined with additional conventional feature extraction layers to solve complex tasks. Based on the hypothesis that more efficient and distinct routing can alleviate these drawbacks, we propose a novel CapsNet algorithm, which utilises narrowed attention to determine the coupling coefficients between lower and higher level capsules. In particular, we employ tiny subnetworks with sigmoid activation functions to enforce concise routing decisions, thus reducing the tendency of CapsNets to explain the entire image rather than focusing on the essential information for a given task. This non-iterative routing strategy is computationally fast and memory efficient, results in interpretable coupling decisions and can be easily integrated into existing models due to its strong alignment with capsule theory. In addition, these solely capsule-based models are robust to a wide range of image transformations, have stable convergence characteristics and can be further improved by capsule-specific yet straightforward applications of dropout and batch normalisation. In a series of experiments, we demonstrate that narrowed attention routing enables the training of deep capsule networks without the need for additional feature extraction layers, while outperforming existing CapsNet architectures on a variety of well-known benchmark datasets.
Contribution of Non-Coding RNAs to Anticancer Effects of Dietary Polyphenols: Chlorogenic Acid, Curcumin, Epigallocatechin-3-Gallate, Genistein, Quercetin and Resveratrol

Growing evidence has been accumulated to show the anticancer effects of daily consumption of polyphenols. These dietary polyphenols include chlorogenic acid, curcumin, epigallocatechin-3-O-gallate, genistein, quercetin, and resveratrol. These polyphenols have similar chemical and biological properties in that they can act as antioxidants and exert the anticancer effects via cell signaling pathways involving their reactive oxygen species (ROS)-scavenging activity. These polyphenols may also act as pro-oxidants under certain conditions, especially at high concentrations. Epigenetic modifications, including dysregulation of noncoding RNAs (ncRNAs) such as microRNAs, long noncoding RNAs, and circular RNAs are now known to be involved in the anticancer effects of polyphenols. These polyphenols can modulate the expression/activity of the component molecules in ROS-scavenger-triggered anticancer pathways (RSTAPs) by increasing the expression of tumor-suppressive ncRNAs and decreasing the expression of oncogenic ncRNAs in general. Multiple ncRNAs are similarly modulated by multiple polyphenols. Many of the targets of ncRNAs affected by these polyphenols are components of RSTAPs. Therefore, ncRNA modulation may enhance the anticancer effects of polyphenols via RSTAPs in an additive or synergistic manner, although other mechanisms may be operating as well.