Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined QoS Analysis of Smart Distribution Grids with Constraint Satisfaction Problem and Network Calculus

The expansion of renewable energies requires implementing Smart Grid Services (SGSs) in the distribution grid. Analytical verification of the SGSs’ Quality of Service (QoS) requirements is crucial for reliable operation. This paper proposes a two-step approach: a constraint satisfaction problem (CSP) considers network infrastructure and resource allocations to find candidates’ solutions. Then, the solutions are evaluated using Network Calculus (NC) to calculate delay bounds at the worst-case and compare them to the requirements. If necessary, alternative solutions are generated. The NC framework is adjustable and considers different service priorities by implementing scheduling policies such as strict priority, weighted fair queuing, weighted round robin, and deficit round robin. In the end, the best-performing parameters are derived for a sample network and the results manifest that the NC framework can be used to analyze random communication networks.
Project-Based Learning: Differentiating Instruction for the 21st Century

Preface 1. Engaging Students Through Project Based Learning 2: Project Based Learning in the Classroom 3. Designing Projects for Project Based Learning 4. Instructional Technologies in Project Based Learning 5. Instructional Strategies in Project Based Learning 6. Assessment Options For Project Based Learning Appendix A: Relationship Between PBL and State Curricular Standards
International Journal of Heat and Fluid Flow

A review of recent advances in the study of high Reynolds number turbulent boundary layers is given. The emergent regime of very large-scale structures in the logarithmic region and their subsequent influence on the near-wall cycle challenges many of the previously held assumptions regarding scaling of turbulent boundary layers at high Reynolds numbers. Experimental results are presented to illustrate the superimposition of large-scale energy onto the near-wall cycle, together with an interaction well described by an amplitude modulation effect. Both phenomena are shown to increase in magnitude (as compared to viscous-scaled events) as Reynolds number increases. These observations lead to a possible model for a statistically representative near-wall velocity signal (giving accurate energy spectra) based on a given filtered velocity signal from the log region of a high Reynolds number turbulent flow. 2010 Elsevier Inc. All rights reserved.
For Better or for Worse: Default Effects and 401(K) Savings Behavior

In the last several years, many employers have decided to automatically enroll their new employees in the company 401(k) plan. Using several years of administrative data from three large firms, we analyze the impact of automatic enrollment on 401(k) participation rates, savings behavior, and asset accumulation. We find that although employees can opt out of the 401(k) plan, few choose to do so. As a result, automatic enrollment has a dramatic impact on retirement savings behavior: 401(k) participation rates at all three firms exceed 85%, but participants tend to anchor at a low default savings rate and in a conservative default investment vehicle. We find that initially, about 80% of participants accept both the default savings rate (2% or 3% for our three companies) and the default investment fund (a stable value or money market fund). Even after three years, half of the plan participants subject to automatic enrollment continue to contribute at the default rate and invest their contributions exclusively in the default fund. The effects of automatic enrollment on asset accumulation are not straightforward. While higher participation rates promote wealth accumulation, the low default savings rate and the conservative default investment fund undercut accumulation. In our sample, these two effects are roughly offsetting on average. However, automatic enrollment does increase saving in the lower tail of the savings distribution by dramatically reducing the fraction of employees who do not participate in the 401(k) plan.
X-ray Classification and Prediction of Lung Disease Using Convolutional Neural Network (CNN)

Lung disease diagnosis is crucial for timely detection and effective treatment. Deep learning techniques, such as Convolutional Neural Networks (CNNs), have shown remarkable success in image classification tasks. This study aims to develop a lung disease prediction model using CNNs applied to X-ray images. The model utilizes a labelled dataset of lung X-ray images containing both healthy cases and various lung abnormalities. Pre-processing techniques are applied to enhance image quality and extract relevant features. CNN architecture is employed to learn and classify the lung images, leveraging its ability to capture spatial features. The dataset is divided into training and validation sets to optimize model parameters by minimizing prediction errors during training. Hyper parameter tuning is performed to identify the optimal combination of learning rate, batch size, and network architecture. Evaluation of the trained model is conducted on the validation set using standard metrics like accuracy, precision, recall, and F1-score. The model's performance is assessed across different lung disease classes to identify biases or weaknesses. To assess generalizati on and real-world performance, a separate testing set is used, distinct from the training and validation sets. Continuous monitoring and updates are essential to ensure the model remains accurate and up-to-date, especially with the availability of new data or the need for improvements. The proposed lung disease prediction model holds promise in assisting healthcare professionals with early detection and diagnosis of lung abnormalities. It has the potential to enhance patient outcomes and alleviate the burden on healthcare systems.
Prevalence and Antibiotic Susceptibility Patterns of Bacteria associated with Urinary Tract Infections among Pregnant Women attending Antenatal Clinic in Ahmadu Bello University Teaching Hospital, Shika-Zaria, Nigeria

Bacterial urinary tract infection is one of the common health problems during pregnancy which can lead to complications. The misuse of antibiotics against the infection has led to emergence of antibiotic resistance bacteria. The aim of the study was to determine the prevalence and antibiotic susceptibility patterns of bacteria associated with urinary tract infections among pregnant women, attending antenatal clinic in Ahmadu Bello University Teaching Hospital, Shika-Zaria, Nigeria. A total of 110clean catch midstream urine (MSU) samples of pregnant women were collected and analyzed from August to November, 2019. The samples were subjected to bacteriological count using spread plate method. Isolation and identification of bacteria were carried out using cultural and biochemical tests. The isolates were subjected to antibiotic susceptibility test using Agar disc diffusion method. The results obtained showed that out of 110 samples collected, 70 (64%) were positive for UTI. Prevalence of UTI was higher among age group <18years (72.7%). Prevalence of UTI based on gestational age showed higher rate in second trimester (71.7%). Prevalence of UTI based on symptoms showed higher rate among women with pain during urination (75.0%) while those with lower back pain had the lowest rate of UTI (50.0%). Prevalence of UTI based on predisposing factors showed higher rate (73.3%) among those that used toilet roll to clean up after defecation than those that used water (62.1%). Those that practice the method of cleaning up from anus to vagina after defecation had higher rate of UTI (68.8%) than those that clean up from vagina to anus (59.7%). However, the rate was not statistically significant (p>0.05). ­ Non-diabetic pregnant women had 63.7% prevalence of UTI while diabetic pregnant women had (62.5%). Staphylococcus aureus account for 16.4% of UTI cases in this study area. This was followed by Escherichia coli (10.9%), while the least implicated bacteria was Klebsiella spp (10%). Klebsiella species was 63.6% susceptible and resistant to gentamicin and amoxicillin respectively. Staphylococcus aureus was 94.4% susceptible and 44.4% resistance to streptomycin and trimethoprim-sulfamethoxazole respectively. Escherichia coli had the highest Multi-Drug Resistant in this study. Due to prevalence of UTI in this study area, it is essential to screen pregnant women for UTI in order to avoid complications during child delivery. Streptomycin was the drug of choice in treating the infection.
Partitioning of Sr, Ba, Rb, Y, and LREE between alkali feldspar and peraluminous silicic magma

Abstract Samples from well-known rhyolitic systems in the western USA were analyzed to determine trace-element partition coefficients between sanidine and coexisting melt. Both DSr and DEu correlate positively with An concentration in sanidine. Barium, not surprisingly, shows more correlation with Or in sanidine. All three show negative correlations with whole-rock Al2O3. Partition coefficients for REE show a decreasing trend with strong positive DEu. DLREE correlate negatively with the whole-rock Al2O3. In peraluminous rocks, Al2O3 in magma seems to exert a major influence on partition coefficients of trace elements. With increasing Al2O3 concentration, the melt composition becomes increasingly similar to that of feldspar. Incompatible trace elements (Rb, Y, Nb, and Zr) in sanidine show positive trends whereas compatible trace elements (Sr, Eu, and Ba) show negative correlations when plotted against whole-rock Al2O3. LREEs, which are expected to substitute for Ca in the sanidine structure, behave like Sr and Eu. Because trace-element partitioning is influenced by both crystal and magma compositions, multiple regression analysis has been used to predict trace-element partition coefficients. The equations will assist workers in the selection of partition coefficients used in petrogenetic models of silicic systems.
An experimental approach for determining unsaturated hydraulic properties of rock fractures

An experimental approach for determining the unsaturated hydraulic properties (the relations between capillary pressure, saturation and unsaturated hydraulic conductivity) of rock fractures is developed and tested. Applying this approach to a single fracture, and with only water flowing, the capillary pressure–saturation and unsaturated hydraulic conductivity–capillary pressure relationships of the fracture during drainage and imbibition can be determined simultaneously. To facilitate the test of the validity of the experimental approach and to elucidate the characteristics of water flow in unsaturated fractures, an analogous fracture with parallel, connected channels of different apertures was fabricated. Experiments of unsaturated water flow in the analogous fracture were carried out. Some characteristics of water flow in unsaturated fractures (hysteresis between drainage and imbibition, etc.) were elucidated. Comparison of measured saturation values and theoretical saturation values corresponding to different apertures at the beginning of drainage and imbibition shows that the experimental approach presented in this paper is valid.
Seismic velocities within the sedimentary succession of the Canada Basin and southern Alpha-Mendeleev Ridge, Arctic Ocean: evidence for accelerated porosity reduction?

Author Posting. © Crown Copyright, 2015. This article is posted here by permission of Oxford University Press for personal use, not for redistribution. The definitive version was published in Geophysical Journal International 204 (2016): 1-20, doi:10.1093/gji/ggv416.
Weakly Supervised Semantic Segmentation via Box-Driven Masking and Filling Rate Shifting

Semantic segmentation has achieved huge progress via adopting deep Fully Convolutional Networks (FCN). However, the performance of FCN-based models severely rely on the amounts of pixel-level annotations which are expensive and time-consuming. Considering that bounding boxes also contain abundant semantic and objective information, an intuitive solution is to learn the segmentation with weak supervisions from the bounding boxes. How to make full use of the class-level and region-level supervisions from bounding boxes to estimate the uncertain regions is the critical challenge for the weakly supervised learning task. In this paper, we propose a mixture model to address this problem. First, we introduce a box-driven class-wise masking model (BCM) to remove irrelevant regions of each class. Moreover, based on the pixel-level segment proposal generated from the bounding box supervision, we calculate the mean filling rates of each class to serve as an important prior cue to guide the model ignoring the wrongly labeled pixels in proposals. To realize the more fine-grained supervision at instance-level, we further propose the anchor-based filling rate shifting module. Unlike previous methods that directly train models with the generated noisy proposals, our method can adjust the model learning dynamically with the adaptive segmentation loss. Thus it can help reduce the negative impacts from wrongly labeled proposals. Besides, based on the learned high-quality proposals with above pipeline, we explore to further boost the performance through two-stage learning. The proposed method is evaluated on the challenging PASCAL VOC 2012 benchmark and achieves 74.9<inline-formula><tex-math notation="LaTeX">$\%$</tex-math><alternatives><mml:math><mml:mo>%</mml:mo></mml:math><inline-graphic xlink:href="song-ieq1-3301302.gif"/></alternatives></inline-formula> and 76.4<inline-formula><tex-math notation="LaTeX">$\%$</tex-math><alternatives><mml:math><mml:mo>%</mml:mo></mml:math><inline-graphic xlink:href="song-ieq2-3301302.gif"/></alternatives></inline-formula> mean IoU accuracy under weakly and semi-supervised modes, respectively. Extensive experimental results show that the proposed method is effective and is on par with, or even better than current state-of-the-art methods.
Weakly Supervised Semantic Segmentation by Knowledge Graph Inference

Currently, existing efforts in Weakly Supervised Semantic Segmentation (WSSS) based on Convolutional Neural Networks (CNNs) have predominantly focused on enhancing the multi-label classification network stage, with limited attention given to the equally important downstream segmentation network. Furthermore, CNN-based local convolutions lack the ability to model the extensive inter-category dependencies. Therefore, this paper introduces a graph reasoning-based approach to enhance WSSS. The aim is to improve WSSS holistically by simultaneously enhancing both the multi-label classification and segmentation network stages. In the multi-label classification network segment, external knowledge is integrated, coupled with GCNs, to globally reason about inter-class dependencies. This encourages the network to uncover features in non-salient regions of images, thereby refining the completeness of generated pseudo-labels. In the segmentation network segment, the proposed Graph Reasoning Mapping (GRM) module is employed to leverage knowledge obtained from textual databases, facilitating contextual reasoning for class representation within image regions. This GRM module enhances feature representation in high-level semantics of the segmentation network's local convolutions, while dynamically learning semantic coherence for individual samples. Using solely image-level supervision, we have achieved state-of-the-art performance in WSSS on the PASCAL VOC 2012 and MS-COCO datasets. Extensive experimentation on both the multi-label classification and segmentation network stages underscores the effectiveness of the proposed graph reasoning approach for advancing WSSS.
Built-in Foreground / Background Prior for Weakly-Supervised Semantic Segmentation : Supplementary Material

Here, we provide a validation and evaluation of our foreground/background masks. To this end, we made use of 10% of randomly chosen training images from the Pascal VOC dataset. We then generated foreground/background masks for these images using our approach, which relies on the activations of the fourth and fifth layers of the segmentation network pre-trained on ImageNet (i.e., before fine-tuning it for semantic segmentation). These masks can then be compared to ground-truth foreground/background masks obtained directly from the pixel level annotations. We compare our masks with the objectness criterion of [1] and [2], which was employed by [3] and [4, 5] for the purpose of weakly-supervised semantic segmentation. Note that, some approaches such as [6, 2] which have been used for weakly-supervised semantic segmentation [4, 7, 5] require training data with pixel-level/bounding box annotations, and thus are not really comparable to our approach. Note also that a complete evaluation of objectness methods goes beyond the scope of this paper, which focuses on weakly-supervised semantic segmentation. The objectness methods of [1] and [2] produce a per-pixel foreground probability map. For our comparison to be fair, we further refined these maps using the same dense CRF as in our approach. In Table 1, we provide the results of these experiments in terms of mean Intersection Over Union (mIOU) with respect to the ground-truth masks. Note that our masks are more accurate than those of [1, 2]. In Fig. 1, we show some qualitative results of these three approaches. Note that this further evidences the benefits of our foreground/background masks. In particular, our masks yield a much better object localization accuracy.
Weakly Supervised Semantic Segmentation via Alternative Self-Dual Teaching

Current weakly supervised semantic segmentation (WSSS) frameworks usually contain the separated mask-reﬁnement model and the main semantic region mining model. These approaches would contain redundant feature extraction back-bones and biased learning objectives, making them computational complex yet sub-optimal to addressing the WSSS task. To solve this problem, this paper establishes a compact learning framework that embeds the classiﬁcation and mask-reﬁnement components into a uniﬁed deep model. With the shared feature extraction backbone, our model is able to facilitate knowledge sharing between the two components while preserving a low computational complexity. To encourage high-quality knowledge interaction, we propose a novel alternative self-dual teaching (ASDT) mechanism. Unlike the conventional distillation strategy, the knowledge of the two teacher branches in our model is alternatively distilled to the student branch by a Pulse Width Modulation (PWM), which generates PW wave-like selection signal to guide the knowledge distillation process. In this way, the student branch can help prevent the model from falling into local minimum solutions caused by the imperfect knowledge provided of either teacher branch. Comprehensive experiments on the PASCAL VOC 2012 and COCO-Stuff 10K demonstrate the effectiveness of the proposed alternative self-dual teaching mechanism as well as the new state-of-the-art performance of our approach.
A Model of Ganymede's Magnetic and Plasma Environment During the Juno PJ34 Flyby

Using a hybrid model (kinetic ions, fluid electrons), we provide context for plasma and magnetic field observations from Juno's PJ34 flyby of Ganymede on 07 June 2021. We consider five model configurations that successively increase the complexity of Ganymede's atmosphere and ionosphere by including additional particle species and ionization mechanisms. We examine the density and flow patterns of pick‐up ions with small H2+ $\left({\mathrm{H}}_{2}^{+}\right)$ , intermediate (H2O+), and large O2+ $\left({\mathrm{O}}_{2}^{+}\right)$ masses in Ganymede's interaction region. The results are validated by comparing the modeled magnetic field and ion densities against time series from Juno's magnetometer and plasma instruments. Our major findings are: (a) Ganymede's internal dipole dominated the magnetic field signature observed inside the moon's magnetosphere, while plasma currents shaped the field perturbations within the “wake” region detected along the Jupiter‐averted magnetopause. (b) Ganymede's pick‐up tail leaves a subtle, but clearly discernible imprint in the magnetic field downstream of the moon. (c) Heavy pick‐up ions dominate ionospheric outflow and form a tail with steep outer boundaries. (d) During the flyby, the position of Ganymede's Jupiter‐facing magnetopause varied in time due to Kelvin‐Helmholtz waves traveling along the boundary layer. As such, the location of the Jupiter‐facing magnetopause observed by Juno represents only a single snapshot of this time‐dependent process. (e) Ionospheric hydrogen ions are partially generated outside of Ganymede's magnetopause, forming a dilute H2+ ${\mathrm{H}}_{2}^{+}$ corona that surrounds the moon's magnetosphere. (f) Most H2O+ ions are produced at low latitudes where field lines are closed, resulting in a very dilute pick‐up tail for this species.
Medial patellofemoral ligament reconstruction appears to be a better treatment than repair, proximal realignment, or conservative management for primary patellar dislocation: A network meta-analysis

Background: The purpose of this study was to compare the functional outcomes and re-dislocation rates of medial patellofemoral ligament (MPFL) reconstruction, MPFL repair, combined proximal realignment (CPR), and conservative management for primary patellar dislocation by conducting a systematic literature search of the available studies. The hypothesis was that MPFL repair and MPFL reconstruction would be better options for treating primary patellar dislocation. Methods: Randomized controlled trials or prospective studies of primary patellar dislocation treated with MPFL reconstruction, MPFL repair, CPR, or conservative management were identified from the MEDLINE, EMBASE, and the Cochrane Library databases through December 31, 2021. A total of 626 patients met the prespecified inclusion criteria. The methodological quality of each study was assessed using a risk of bias table, Detsky quality index, and Newcastle-Ottawa Scale. The end-point data collected included comparisons of the mean in functional scores on knee outcomes scales and the number of patients who experienced re-dislocation. A network meta-analysis of the relevant literature was performed to investigate which treatment showed better outcomes. Results: In total, 10 trials were included in this study. There was no statistically significant difference in the subgroup analysis in terms of the functional outcomes among MPFL reconstruction, MPFL repair, CPR, and conservative management. However, MPFL reconstruction showed statistically significantly better outcomes than MPFL repair, CPR, or conservative management in terms of the re-dislocation rate. Additionally, surface under the cumulative ranking curve percentage showed that MPFL reconstruction had a lower probability of re-dislocation than MPFL repair even though there was no significant difference (0.24, 95% confidence interval: 0.02–2.91). Conclusion: Using a network meta-analysis, this meta-analysis showed that there was no significant difference in functional outcomes in a subgroup analysis. In re-dislocation subgroup analysis, MPFL repair and MPFL reconstruction produced significantly better results than other treatments. Also, surface under the cumulative ranking curve percentage showed that MPFL reconstruction had a lower probability of re-dislocation than MPFL repair.