Miracle-dz/UniFAR-FTUs huggingface dataset:
University of Groningen Compositional Features of HDL Particles Interact with Albuminuria to Modulate Cardiovascular Disease

Lipoproteins containing apolipoprotein B modify associations of elevated urinary albumin excretion (UAE) with cardiovascular disease (CVD). Additionally, it is known that elevated UAE alters high-density lipoprotein functionality. Accordingly, we examined whether HDL features might also modify UAE-associated CVD. Multivariable Cox proportional-hazards modeling was performed on participants of the PREVEND (Prevention of Renal and Vascular Endstage Disease) study at the baseline screening with standard lipid/lipoprotein analyses and, three-to-four years later (second screen), with nuclear magnetic resonance lipoprotein analyses focusing on HDL parameters including HDL particle (HDL-P) and apolipoprotein A-I concentrations. These were used with UAE and derived measures of HDL apoA-I content (apoA-I/HDL-C and apoA-I/HDL-P) in risk models adjusted for gender, age, apoB, diabetes, past CVD history, CRP and GFR. Interaction analysis was also performed. Baseline screening revealed significant associations inverse for HDL-C and apoA-I and direct for apoA-I/HDL-C. The second screening demonstrated associations inverse for HDL-P, large HDL-P, medium HDL-P, HDL size, and apoA-I/HDL-P. Significant interactions with UAE included apoA-I/HDL-C at the baseline screening, and apoA-I/HDL-P and medium HDL-P but not apoA-I/HDL-C at the second screening. We conclude that features of HDL particles including apoA-I/HDL-P, indicative of HDL apoA-I content, and medium HDL-P modify associations of elevated UAE with CVD risk.
Prediction of Chronic Kidney Disease using Data Mining Feature Selection and Ensemble Method

The failure of the kidney is affected the whole human body and it can be a cause of the seriously ill and cause of deaths. Machine learning and data mining techniques are the most significant role in disease prediction with high-performance rate and used to help decision makers to assemble and understand information. The performance of classification techniques depends on the feature of the data set. To improve the accuracy of classification used feature selection method by reducing the dimensions of the feature and used ensemble or combine a model of the algorithm. In this research K-Nearest Neighbor, J48, Artificial Neural Network, Naive Bayes and Support Vector Machine classification techniques were used to diagnose Chronic Kidney Disease. To predict chronic kidney disease, build two important models. Namely, feature selection method and ensemble model. To build chronic kidney disease prediction, used Info gain attributes evaluator with ranker search engine and wrapper subset evaluator with the best first engine was used. The result showed that the K-nearest neighbor classifier by using Wrapper Sub set Evaluator with Best first search engine feature selection method has 99% accuracy, J48 with Info Gain Attribute Evaluator with ranker search engine has 98.75, Artificial Neural Network with Wrapper Sub set Evaluator with Best first search engine has 99.5% accuracy, Naive Bayes with Wrapper Sub set Evaluator with Best first search engine has 99% accuracy, Support Vector Machine with Info Gain Attribute Evaluator with ranker has 98.25% accuracy in prediction of chronic kidney disease compared to other with and without feature section method. The second model building method ensemble model by combing the five heterogeneous classifiers based on a voting algorithm. The effectiveness of the proposed ensemble model was examined by comparison of the base classifier. The experimental result showed that the proposed ensemble model achieved 99% accuracy.
Embracing Technological Similarity for the Measurement of Complexity and Patent Thickets

Clear and well-defined patent rights can incentivize innovation by granting monopoly rights to the inventor for a limited period of time in exchange for public disclosure of the invention. However, when a product draws from intellectual property held across multiple firms (including fragmented intellectual property or patent thickets), contracting failures may lead to suboptimal economic outcomes (Shapiro 2000). Researchers have developed several measures to gauge the extent and impact of patent thickets. This paper contributes to that literature by proposing a new measure of patent thickets that incorporates patent claim similarity to more precisely identify technological similarity, which is shown to increase the information contained in the measurement of patent thickets. Further, the measure is universally computable for all patent systems. These advantages will enable more accurate measurement and allow for novel economic research on technological complexity, fragmentation in intellectual property, and patent thickets within and across all patent jurisdictions.
Developing Traﬃc Congestion Detection Model Using Deep Learning Approach: A Case Study of Addis Ababa City Road

The traﬃc system is one of the core requirements of a civilized world and the development of the country depends on it in many aspects. In Ethiopia, the number of vehicles and pedestrians is increasing at a high rate from time to time. Excessive numbers of traﬃc on roads and improper control of traﬃc create traﬃc congestion. Uncontrolled traﬃc congestion hinders the transportation of goods and commuters from place to place and increases the volume of carbon emitted into the air. It can also either hampers or stagnates schedule, business, and commerce. Many images and video processing approaches have been researched in the literature on how to detect traﬃc congestion. One such approach is that of using background and foreground subtraction, convolutional neural network, and Average frame diﬀerence and deep learning method used to detect traﬃc congestion from diﬀerent video sources. From the review one-stage object detector identiﬁed as the best methods to detect traﬃc congestion with acceptable accuracy and speed. In this study one-stage object detectors are used to detect traﬃc congestion from recorded video. Data is collected from diﬀerent video footage and frames extracted from videos to prepare a dataset for the thesis. The extracted frames were labeled manually as congested and uncongested. To train, the models pre-trained weights were used. YOLOV3 and YOLOV5 model used for experimentation. Accuracy and speed metrics used to evaluate the performance of the models. A YOLOV3 model achieved 41.6 FPS and 68.6 % mAP on a testing dataset.
Super-charging supply chains - through 'relational integration' for 'overall value'

‘Relational integration’ means more than mere ‘structural integration’; and is needed for generating ‘genuine’ and sustainable collaboration in construction. However, higher levels of integration can evidently not be reached without a specially formulated and consolidated set of focal points. Relational integration and convergent agendas can therefore be targeted through directing a common focus on the ‘overall value’ of the ‘supply network’. To this end, ‘Relationally Integrated Value Networks’ (RIVANS) are conceptualised to engage and empower their members towards both short-term and long-term overall ‘network value elements’ that must be suitably structured and made explicit. These common network value elements and corresponding goals should then loom larger in project landscapes, but should also be designed to co-exist with each set of network member-specific value objectives. Whilst aligning the latter as much as possible towards the former, it is recognised that each organisation e.g. a sub-contractor or specialist supplier, will have some other (‘extra-network’ or ‘beyond network’) needs; and may indeed be part of other value networks. However, each network can benefit from healthy inputs from, and benchmarking against other networks. Secondly, the strengths of each network will be enhanced by the steady development of each of its members. The paper will explore the potential and pitfalls in developing such RIVANS, incorporating relevant outputs from two case studies of enlightened teamworking, and two subsequent Workshops deliberating RIVANS possibilities. The needs for, and potential impacts of the RIVANS initiative are heightened in the present major economic downturn, and indeed during other periodic troughs in industry and market cycles. Relationally integrated networks should be more resilient in withstanding such pressures, while achieving critical efficiencies for reaching necessarily higher performance levels in general.
Structure formation in homogeneous freely decaying rotating turbulence

One of the most striking features of rotating turbulence is the inevitable appearance of large-scale columnar structures. Whilst these structures are frequently observed, the processes by which they are created are still poorly understood. In this paper we consider the emergence of these structures from freely decaying, rotating turbulence with Ro ∼ 1. Our study follows the conjecture by Davidson, Staplehurst & Dalziel (J. Fluid Mech., vol. 557, 2006, p. 135) that the structure formation may be due to linear inertial wave propagation, which was shown to be consistent with the growth of columnar eddies in inhomogeneous turbulence. Here we extend that work and consider the case of homogeneous turbulence. We describe laboratory experiments where homogeneous turbulence is created in a rotating tank. The turbulence is generated with Ro ∼ 1, and as the energy decays, the formation of columnar vortices is observed. The axial growth of these columnar structures is then measured using two-point correlations and in all cases the results are consistent with structure formation via linear inertial wave propagation. In particular, we obtain a self-similar collapse of the two-point correlations when the axial coordinate is normalized by Ωtb, where b is a measure of the integral scale in the horizontal plane and Ω is the rotation rate. Although our results do not exclude the possibility of significant nonlinear dynamics, they are consistent with the conjecture of Davidson et al. (2006) that linear dynamics play a strong guiding hand in structure formation.
Complex Basal Conditions and Their Influence on Ice Flow at the Onset of the Northeast Greenland Ice Stream

The ice stream geometry and large ice surface velocities at the onset region of the Northeast Greenland Ice Stream (NEGIS) are not yet well reproduced by ice sheet models. The quantification of basal sliding and a parametrization of basal conditions remains a major gap. In this study, we assess the basal conditions of the onset region of the NEGIS in a systematic analysis of airborne ultra‐wideband radar data. We evaluate basal roughness and basal return echoes in the context of the current ice stream geometry and ice surface velocity. We observe a change from a smooth to a rougher bed where the ice stream widens, and a distinct roughness anisotropy, indicating a preferred orientation of subglacial structures. In the upstream region, the excess ice mass flux through the shear margins is evacuated by ice flow acceleration and along‐flow stretching of the ice. At the downstream part, the generally rougher bed topography correlates with a decrease in flow acceleration and lateral variations in ice surface velocity. Together with basal water routing pathways, this hints to two different zones in this part of the NEGIS: the upstream region collecting water, with a reduced basal traction, and downstream, where the ice stream is slowing down and is widening on a rougher bed, with a distribution of basal water toward the shear margins. Our findings support the hypothesis that the NEGIS is strongly interconnected to the subglacial water system in its onset region, but also to the subglacial substrate and morphology.
Modeling the OEC with Two New Biomimetic Models: Preparations, Structural Characterization, and Water Photolysis Studies of a Ba–Mn Box Type Complex and a Mn4N6 Planar-Diamond Cluster

The oxygen-evolving complex (OEC) is the native enzyme that catalyzes the oxidation of water in natural photosynthesis. Two new classes of manganese cluster complexes of formula Ba2Mn2L12(H3L1)2(CH3OH)4 1 and Mn4L26Cl2 2 were prepared (H4L1 = N,N′-(ethane-1,2-diyl)bis(2-hydroxybenzamide); L2 = methyl picolinimidate) and characterized by standard techniques including microanalysis, IR spectroscopy, ESI spectrometry, and magnetic susceptibility measurements. X-ray diffraction studies of these complexes revealed (i) a box-type structure for 1 formed by two redox-active manganese(III) ions and two barium(II) ions connected by two bridging bisamido-bisphenoxy ligand molecules; and (ii) a planar-diamond array for Mn4N6 cluster 2 where the picolinimidates act as chelating ligands through the two nitrogen atoms. The ability of 1 and 2 to split water has been studied by means of water photolysis experiments. In these experiments, the oxygen evolution was measured in aqueous media in the presence of p-benzoquinone (acting as the hydrogen acceptor), the reduction of which was followed by UV-spectroscopy. The relevant photolytic activity found for 1 is in contrast to the inactivity of 2 in the photolytic experiments. This different behavior is discussed on the basis of the structure of the biomimetic models and the proposed reaction mechanism for this process supported by DFT calculations.
The three-dimensional structure of momentum transfer in turbulent channels

Abstract The quadrant analysis of the intense tangential Reynolds stress in plane turbulent channels is generalized to three-dimensional structures (Qs), with special emphasis on the logarithmic and outer layers. Wall-detached Qs are background stress fluctuations. They are small and isotropically oriented, and their contributions to the mean stress cancel. Wall-attached Qs are larger, and carry most of the mean Reynolds stresses. They form a family of roughly self-similar objects that become increasingly complex away from the wall, resembling the vortex clusters in del Álamo et al. (J. Fluid Mech., vol. 561, 2006, pp. 329–358). Individual Qs have fractal dimensions of the order of $D= 2$, slightly fuller than the clusters. They can be described as ‘sponges of flakes’, while vortex clusters are ‘sponges of strings’. The number of attached Qs decays away from the wall, but the fraction of the stress that they carry is independent of their sizes. A substantial fraction of the stress resides in a few large objects extending beyond the centreline, reminiscent of the very large structures of several authors. The predominant logarithmic-layer structure is a side-by-side pair of a sweep (Q4) and an ejection (Q2), with an associated cluster, and shares dimensions and stresses with the conjectured attached eddies of Townsend (J. Fluid Mech., vol. 11, 1961, pp. 97–120). Those attached eddies tend to be aligned streamwise from each other, located near the side walls between the low- and high-velocity large-scale streaks, but that organization does not extend far enough to explain the very long structures in the centre of the channel.
The Short-Term Effects of Progressive vs. Conventional Core Stability Exercise in Rehabilitation of Nonspecific Chronic Low Back Pain

Core stability exercise programs are considered as a fundamental physical therapy treatment for chronic low back pain (LBP). However, it is still unclear which core stability program (progressive vs. conventional) is the most effective. A randomized controlled trial (RCT) was conducted to compare two core stability strategy strategies; progressive dynamic muscular stabilization technique (DMST) and conventional McGill Big 3 (MB3) in the rehabilitation of nonspecific chronic LBP. Thirty males diagnosed with nonspecific chronic LBP patients aged 33.57 ± 5.28 years were recruited and randomly assigned to; DMST and MGB3. All patients received additional conventional pain management treatment. The outcome measures were pain severity (motion, standing, sitting), functional disability, trunk endurance, lumbopelvic control, and body balance. All patients were assessed at baseline, 3rd week, and 6th week. A mixed design ANOVA showed both DMST and MGB3 improves all outcomes (p ≤ 0.05) specifically the pain severity and functional disability (p < 0.001, ηp2 = 0.81), trunk endurance (p < 0.001, ηp2 = 0.30), lumbopelvic control (p < 0.001, ηp2 = 0.242), and body balance (p = 0.003, ηp2 = 0.46) compared to baseline. However, no significant difference was found in all of the outcomes when comparing DMST and MBG3 (p > 0.05). In conclusion, both progressive DMST and conventional MGB3 core stability exercise programs are effective for nonspecific chronic LBP rehabilitation.
FLOW INJECTION SPECTROPHOTOMETRIC DETERMINATION OF NARINGENIN IN SUPPLEMENTS USING SOLID-PHASE REACTOR CONTAINING IMMOBILIZED MANGANESE DIOXIDE

Objective: Naringenin (NAR) is a part of the human daily diet, and it plays an important role in human health for its biological functions. This study describes a new, sensitive, simple, and accurate method for determining NAR in supplements.Methods: The method is based on oxidative coupling reaction between NAR and N, N-dimethyl-p-phenylenediamine in an alkaline medium using manganese dioxide immobilized in cellulose acetate as online oxidant agent to form a colored product which can be monitored at λ max598 nm. Results: Several operating parameters such as reactor column length, particles size, chemicals, and physicals reaction conditions were studied. The proposed method was sensitive and good repeatable, the linear range of NAR concentration was from 1 to 70 μg/ml with a limit of detection of 0.292 μg/ml, and recovery range of analysis was 99.55–100.48%. Conclusion: The proposed method was successfully applied for determining NAR in supplements.
Do Customers Perceive Reviews as Manipulated? A Warranting Theory Perspective

Online customer reviews proved to have an influence on customer’s purchase. However, most online reviews don’t always prove effective in guiding the purchase process, because of fake reviews. While e-commerce platforms do tend to incorporate ways to counter review manipulation, customer perception on review quality is more important. In this study we aim to understand the impression mechanism of online reviews. Using warranting theory, as theoretical lens we found that textual and review characteristics play a crucial role in forming an impression amongst the customers. Further, research suggest that higher contamination of reviews influence customers to perceive reviews less authentic.
Immobilization of trypsin enzyme on silver nanoparticles

Introduction and Aim:Immobilization of enzyme on silver nanoparticles (AgNPs) is one way to improve their stability and activity and can be reused for large-scaleapplications. The present study was aimed to evaluateand characterizethetrypsin enzyme immobilized on biogenicallysynthesized silver nanoparticles.Materials and Methods: Immobilization of trypsin enzyme was optimized with time and varying concentration of silver nanoparticles, which were greensynthesized using avocado seedextracts. The residual activity of trypsin enzyme after immobilization was estimated. The reusabilityand temperature stability of the immobilized enzyme were studied. Results:The immobilized enzyme showed maximum activity of 1006.0 U during the time duration of 18 hr of incubation with AgNPs. Reusability of immobilized enzyme in avocado AgNPs was assessed under optimal conditions, the activity of immobilized enzyme was loss after 5 repeated cycles. The enzyme captured on AgNPswas released in the presence of anionic detergent. Temperaturestability was assessed for immobilized enzyme on AgNPs and the immobilized enzyme was stable up to55ºC and later it loss their activity. Conclusion: The study reports a feasible method of enzyme immobilization on biogenically synthesized silver nanoparticles. The enzyme immobilized on silver nanoparticles is mainly through non-covalent forces. Further studies are required to improve the reusability of enzyme. Keywords: Silver nanoparticles;enzyme immobilization;trypsin;temperature stability;reusability;detergent.
Submitted to the Annals of Statistics CONVERGENCE OF EIGENVECTOR EMPIRICAL SPECTRAL DISTRIBUTION OF SAMPLE COVARIANCE MATRICES By

The eigenvector empirical spectral distribution (VESD) is a useful tool in studying the limiting behavior of eigenvalues and eigenvectors of covariance matrices. In this paper, we study the convergence rate of the VESD of sample covariance matrices to the deformed MarčenkoPastur (MP) distribution. Consider sample covariance matrices of the form Σ1/2XX∗Σ1/2, where X = (xij) is an M × N random matrix whose entries are independent random variables with mean zero and variance N−1, and Σ is a deterministic positive-definite matrix. We prove that the Kolmogorov distance between the expected VESD and the deformed MP distribution is bounded by N−1+ for any fixed > 0, provided that the entries √ Nxij have uniformly bounded 6th moments and |N/M − 1| ≥ τ for some constant τ > 0. This result improves the previous one obtained in [52], which gave the convergence rate O(N−1/2) assuming i.i. d. X entries, bounded 10th moment, Σ = I and M < N . Moreover, we also prove that under the finite 8th moment assumption, the convergence rate of the VESD is O(N−1/2+ ) almost surely for any fixed > 0, which improves the previous bound N−1/4+ in [52].
Analyzing the Influence of Various Factors for Vegetable Price using Data Mining

The price fluctuation of vegetables is one of the economic problems faced by every country, including Sri Lanka. Many factors such as environmental conditions as well as supply, demand, social, cultural, and political situations of the country cause the price of vegetables to fluctuate. In this study, the Waikato Environment for Knowledge Analysis (WEKA) tool and association based Apriori algorithms used to identify the most influential factors that affect price fluctuation. Results show that the low supply from cultivation areas is caused by an increased vegetable price and favourable supply from cultivation areas is caused by decrease vegetable price. Prices of vegetable varieties demonstrated mixed movements because of supply variability from respective areas. The findings of this study can be used by farmers to make their production plans, customers to plan their budget, and sellers to make their marketing plans. Keywords— Data mining, Association rule, Apriori algorithm, Vegetable price patterns
College Men's Perceptions of Their Leadership Practice: Unpacking Power and Influence

This qualitative exploratory study focuses on the leadership experiences of college men who held leadership roles in campus organizations. The researchers examined the students’ experiences of leading their organizations and group members and the students’ perceptions of gender roles influencing their leadership practice. Four male participants at a medium-sized, religiously-affiliated university engaged in in-depth interviews and a focus group with the researchers, who used a blend of qualitative approaches to conduct the study and analyze the data. Key themes emerged around a balance between task and relationship-building; the distinction between power and influence; a desire to do better; understanding leadership as generative; and resisting the masculine/feminine leadership dichotomies. Implications are presented for professional practice and research.
Bull Trout Recovery Planning : A review of the science associated with population structure and size

Science Team Members: Brostrom, Jody – Idaho Fishery Resource Office, Ahsahka, ID Cummings, Tim – Columbia River Fisheries Program Office, Vancouver, WA Delavergne, Judy – Ecological Services, Field Office, Wenatchee, WA Fredenberg, Wade – Creston Fish Center, Creston, MT Schaller, Howard – Columbia River Fisheries Program Office, Vancouver, WA Wilson, Paul – Columbia River Fisheries Program Office, Vancouver, WA Whitesel, Tim – Columbia River Fisheries Program Office, Vancouver, WA Zydlewski, Gayle – Abernathy Fish Technology Center, Longview, WA