Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of advanced imaging biomarkers at kidney failure in patients with ADPKD: a pilot study

ABSTRACT Background Autosomal dominant polycystic kidney disease (ADPKD) presents with variable disease severity and progression. Advanced imaging biomarkers may provide insights into cystic and non-cystic processes leading to kidney failure in different age groups. Methods This pilot study included 39 ADPKD patients with kidney failure, stratified into three age groups (<46, 46–56, >56 years old). Advanced imaging biomarkers were assessed using an automated instance cyst segmentation tool. The biomarkers were compared with an age- and sex-matched ADPKD cohort in early chronic kidney disease (CKD). Results Ht-total parenchymal volume correlated negatively with age at kidney failure. The median Ht-total parenchymal volume was significantly lower in patients older than 56 years. Cystic burden was significantly higher at time of kidney failure, especially in patients who reached it before age 46 years. The cyst index at kidney failure was comparable across age groups and Mayo Imaging Classes. Advanced imaging biomarkers showed higher correlation with Ht-total kidney volume in early CKD than at kidney failure. Cyst index and parenchymal index were relatively stable over 5 years prior to kidney failure, whereas Ht-total cyst volume and cyst parenchymal surface area increased significantly. Conclusion Age-related differences in advanced imaging biomarkers suggest variable pathophysiological mechanisms in ADPKD patients with kidney failure. Further studies are needed to validate the utility of these biomarkers in predicting disease progression and guiding treatment strategies.
The Intrinsic Similarity of Topological Structure in Biological Neural Networks

Most previous studies mainly have focused on the analysis of structural properties of individual neuronal networks from C. elegans. In recent years, an increasing number of synapse-level neural maps, also known as biological neural networks, have been reconstructed. However, it is not clear whether there are intrinsic similarities of structural properties of biological neural networks from different brain compartments or species. To explore this issue, we collected nine connectomes at synaptic resolution including C. elegans, and analyzed their structural properties. We found that these biological neural networks possess small-world properties and modules. Excluding the Drosophila larval visual system, these networks have rich clubs. The distributions of synaptic connection strength for these networks can be fitted by the truncated pow-law distributions. Additionally, compared with the power-law model, a log-normal distribution is a better model to fit the complementary cumulative distribution function (CCDF) of degree for these neuronal networks. Moreover, we also observed that these neural networks belong to the same superfamily based on the significance profile (SP) of small subgraphs in the network. Taken together, these findings suggest that biological neural networks share intrinsic similarities in their topological structure, revealing some principles underlying the formation of biological neural networks within and across species.
Multi-Criteria, Graph-Based Road Centerline Vectorization Using Ordered Weighted Averaging Operators

Abstract In this paper a novel road vectorization methodology based on image space clustering technique and weighted graph theory is presented. The proposed methodology describes a road as a set of optimized points on the centerline which should be connected by defining a number of appropriate criteria. The main contribution of this paper is to design a weighting scheme for combining a small number of road identities using Ordered Weighted Averaging ( owa ) operators by defining appropriate decision strategy. In this regard, a novel geometric criterion is introduced. Result of the OWA aggregation specifies weight of each edge in the road network graph. Comparing the proposed approach with two state-of-the-art image space clustering-based road vectorization methods proves its efficiency to deal with roads with different widths, parallel roads with different distances, different types of intersections, and also noise clusters. Obtaining improved quality measures for several high-resolution images, demonstrates the successfulness of the vectorization approach.
Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be

Soil liquefaction remains an important and interesting problem that has attracted the development of enumerable prediction models. Increasingly, these models are utilizing algorithmic learning or “artificial intelligence” (AI). The rapid growth of AI in the liquefaction literature is unsurprising, given its ease of implementation and potential advantages over traditional statistical methods. However, AI liquefaction models have been widely ignored by practitioners and researchers alike; the objective of this article is to investigate “why? ” Through a sample review of 75 publications, we identify several good reasons. Namely, these models frequently (1) are not compared to state-of-practice models, making it unclear why they should be adopted; (2) depart from best practices in model development; (3) use AI in ways that may not be useful; (4) are presented in ways that overstate their complexity and make them unapproachable; and (5) are discussed but not actually provided, meaning that no one can use the models even if they wanted to. These prevailing problems must be understood, identified, and remedied, but this does not mean that AI itself is problematic or that all prior efforts have been without merit or utility. Instead, understanding these recurrent shortcomings can help improve the direction and perceptions of this growing body of work. Toward this end, we highlight papers that are generally free from these shortcomings, and which demonstrate applications where AI is more likely to provide value in the near-term: permitting new modeling approaches and potentially improving predictions of liquefaction phenomena.
3D/4D printed bio-piezoelectric smart scaffolds for next-generation bone tissue engineering

Piezoelectricity in native bones has been well recognized as the key factor in bone regeneration. Thus, bio-piezoelectric materials have gained substantial attention in repairing damaged bone by mimicking the tissue’s electrical microenvironment (EM). However, traditional manufacturing strategies still encounter limitations in creating personalized bio-piezoelectric scaffolds, hindering their clinical applications. Three-dimensional (3D)/four-dimensional (4D) printing technology based on the principle of layer-by-layer forming and stacking of discrete materials has demonstrated outstanding advantages in fabricating bio-piezoelectric scaffolds in a more complex-shaped structure. Notably, 4D printing functionality-shifting bio-piezoelectric scaffolds can provide a time-dependent programmable tissue EM in response to external stimuli for bone regeneration. In this review, we first summarize the physicochemical properties of commonly used bio-piezoelectric materials (including polymers, ceramics, and their composites) and representative biological findings for bone regeneration. Then, we discuss the latest research advances in the 3D printing of bio-piezoelectric scaffolds in terms of feedstock selection, printing process, induction strategies, and potential applications. Besides, some related challenges such as feedstock scalability, printing resolution, stress-to-polarization conversion efficiency, and non-invasive induction ability after implantation have been put forward. Finally, we highlight the potential of shape/property/functionality-shifting smart 4D bio-piezoelectric scaffolds in bone tissue engineering (BTE). Taken together, this review emphasizes the appealing utility of 3D/4D printed biological piezoelectric scaffolds as next-generation BTE implants.
A hybrid genetic algorithm for stochastic job-shop scheduling problems

Job-shop scheduling problems are among most studied problems in last years because of their importance for industries and manufacturing processes. They are classified as NP-hard problems in the strong sense. In order to tackle these problems several models and methods have been used. In this paper, we propose a hybrid metaheuristic composed of a genetic algorithm and a tabu search algorithm to solve the stochastic job-shop scheduling problem. Our contribution is based on a study of the perturbations that affect the processing times of the jobs. These perturbations, due to machine failures, occur according to a Poisson process; the results of our approach are validated on a set of instances originating from the OR-Library [14]. On the basis of these instances, the hybrid metaheuristic is used to solve the stochastic jobshop scheduling problem with the objective of minimizing the makespan as first objective and the number of critical operations as second objective during the robustness analysis. Indeed, the results show that a high value of the number of critical operations is linked to high variations of the makespan of the perturbed schedules, or in other words to a weak robustness of the relating GA’s best schedule. Consequently, critical operations are not only good targets for optimizing a schedule, but also a clue of its goodness when considering stochastic and robustness aspects: the less critical operations it contains, the better it is.
Motion Planning of Mobile Manipulator for Navigation Including Door Traversal

Door traversal is an essential task for a mobile manipulator to achieve autonomous navigation in indoor environment. However, it remains challenging since it requires the coupled motion of the robot and door and is composed of several sub-problems such as approaching, opening, passing through, and closing the door. This letter proposes a planning framework that formulates these sub-problems as a single problem and computes a path for the mobile manipulator to navigate from the initial position, traverse through the door, and arrive at the target position. The proposed framework obtains the path of the whole-body configuration in two steps. First, the path for the pose of the mobile robot and the path for the door angle are computed by using the graph search algorithm. In graph search, an integer variable called area indicator is defined as an element of state, which indicates where the robot is located relative to the door. Especially, the area indicator expresses a process of door traversal. In the second step, the configuration of the manipulator is computed by the Inverse Kinematics (IK) solver from the path of the mobile robot and door angle. The proposed framework has a distinct advantage over the existing methods that manually determine several parameters such as which direction to approach the door and the angle of the door required for passage. The effectiveness of the proposed framework was validated through experiments with a mobile manipulator.
Assessment of Knowledge, Physical Activity and Calcium Intake among Female University Students

Insufficient calcium intake and being physically inactive lead to osteoporosis. This study aimed to assess the level of knowledge about osteoporosis, physical activity levels and their correlation with calcium intake. 276 female undergraduate students participated in this study. A set of questionnaires was distributed. Majority students had low knowledge (76.1%), moderate physical activity level (47.1%) and did not meet the recommended calcium intake (79.0%). There was a moderate to strong positive correlation but not significant between knowledge (r= 0.051, p=0.121) and physical activity (r = 0.081, p=0.181) to dietary calcium intake. In conclusion, students had poor knowledge about osteoporosis. Keywords: calcium; dietary; female; osteoporosis eISSN: 2398-4287 © 2023. The Authors. Published for AMER & cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), and cE-Bs (Centre for Environment-Behaviour Studies), College of Built Environment, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v8i24. 4632
Komlos Limits and Fatou's Lemma in Several Dimensions

Abstract Using Komlos' Theorem, a sequence decomposition result due to Gaposhkin, and two results due to Artstein, we prove a result concerning the properties of Komlos limits. We then show that a stronger version of Fatou's Lemma in several dimensions can be deduced from Artstein's version of the Lemma. The version of Fatou's Lemma proved here subsumes the most recent version of the Lemma in several dimensions given by Balder.
Admissibility of a variance estimator in finite population sampling under Warner's randomized response plan with multiple responses

ABSTRACT Let P be the proportion of individuals in a finite population possessing a sensitive attribute. We consider the problem of unbiased estimation of (i) the variance of a linear unbiased estimator of P and (ii) the population variance P (1—P) for a given probability sampling design under Warner's (1965) randomized response (RR) plan when independent responses are obtained from each sampled individual as many times as he/she is selected in the sample and prove the admissibility of a quadratic unbiased estimator for each.
Combined scattering loss of radiation belt relativistic electrons by simultaneous three‐band EMIC waves: A case study

Multiband electromagnetic ion cyclotron (EMIC) waves can drive efficient scattering loss of radiation belt relativistic electrons. However, it is statistically uncommon to capture the three bands of EMIC waves concurrently. Utilizing data from the Electric and Magnetic Field Instrument Suite and Integrated Science magnetometer onboard Van Allen Probe A, we report the simultaneous presence of three (H+, He+, and O+) emission bands in an EMIC wave event, which provides an opportunity to look into the combined scattering effect of all EMIC emissions and the relative roles of each band in diffusing radiation belt relativistic electrons under realistic circumstances. Our quantitative results, obtained by quasi‐linear diffusion rate computations and 1‐D pure pitch angle diffusion simulations, demonstrate that the combined resonant scattering by the simultaneous three‐band EMIC waves is overall dominated by He+ band wave diffusion, mainly due to its dominance over the wave power (the mean wave amplitudes are approximately 0.4 nT, 1.6 nT, and 0.15 nT for H+, He+, and O+ bands, respectively). Near the loss cone, while 2–3 MeV electrons undergo pitch angle scattering at a rate of the order of 10−6–10−5 s−1, 5–10 MeV electrons can be diffused more efficiently at a rate of the order of 10−3–10−2 s−1, which approaches the strong diffusion level and results in a moderately or heavily filled loss cone for the atmospheric loss. The corresponding electron loss timescales (i.e., lifetimes) vary from several days at the energies of ~2 MeV to less than 1 h at ~10 MeV. This case study indicates the leading contribution of He+ band waves to radiation belt relativistic electron losses during the coexistence of three EMIC wave bands and suggests that the roles of different EMIC wave bands in the relativistic electron dynamics should be carefully incorporated in future modeling efforts.
Langmuir Turbulence Controls on Observed Diurnal Warm Layer Depths

The turbulent ocean surface boundary layer (OSBL) shoals during daytime solar surface heating, developing a diurnal warm layer (DWL). The DWL significantly influences OSBL dynamics by trapping momentum and heat in a shallow near‐surface layer. Therefore, DWL depth is critical for understanding OSBL transport and ocean‐atmosphere coupling. A great challenge for determining DWL depth is considering wave‐driven Langmuir turbulence (LT), which increases vertical transport. This study investigates observations with moderate wind speeds (4–7 m/s at 10 m height) and swell waves for which breaking wave effects are less pronounced. By employing turbulence‐resolving large eddy simulation experiments that cover observed wind, wave, and heating conditions based on the wave‐averaged Craik‐Lebovich equation, we develop a DWL depth scaling unifying previous approaches. This scaling closely agrees with observed DWL depths from a year‐long mooring deployment in the subtropical North Atlantic, demonstrating the critical role of LT in determining DWL depth and OSBL dynamics.
The Difference in Subpopulation Structures in Sex-Sorted and Non-Sorted Semen by Discriminant Analysis of Bull Sperm Motility Data Collected by a Computer-Assisted Sperm Analysis System

We examined the motility characteristics of sperm from sex-sorted bull semen as a model for low-quality (expected low conception rate) sperm compared to that of sperm from non-sorted semen (expected average conception rate) using cluster analysis followed by discriminant analysis. The results indicated that sex-sorted semen contained sperm with hyperactivation-like motility as the main subpopulation immediately after thawing, and this subpopulation decreased after 2 h of incubation. The main subpopulation in the non-sorted semen had progressive motility that was maintained during incubation. A conventional comparison of the mean values of kinematic parameters could not distinguish the samples. In conclusion, discriminant analysis using data from a cluster analysis of motile sperm may accurately describe differences in the structures of sperm motility subpopulations between low and normal fertility semen.
QUANTITATIVE ASSAY OF BINARY AND TERNARY CARDIOVASCULAR MIXTURES USING ABSORPTION SUBTRACTION SPECTROPHOTOMETRIC METHOD

Abstract Objective:Objective: Development and validation of specific spectrophotometric analytical method for the simultaneous determination of co-administered cardiovascular mixtures consisting of Aspirine (ASP) and Prasugrel (PRA) binary mixture and Ticagrelor (TICA), Irbesartan (IRB) and Hydrochlorothazide (HCT) ternary mixture. Methods:Methods: The proposed spectrophotometric method was based on Absorbance Subtraction, where, when a mixture of two drugs X and Y having overlapped spectra, intersecting at isoabsorptive point and Y spectrum is extended more than X, while X doesn’t show any absorbance (A 2 ) at another wavelength ( λ 2 ), X and Y could be determined in the mixture by simple mathematical manipulation Results:Results: This method obeyed Beer’s law in the concentration range of 5-50 μ g/ml for ASP, 5-25 μ g/ml for PRAS, 10-30 μ g/ml for TICA, 0.5-2 μ g/ml for IRB, 4-10 μ g/ml for HCT. The suggested method was applied on laboratory prepared mixtures and was successfully applied in quality control analysis of combined pharmaceutical dosage forms. The developed spectrophotometric method was validated statistically and recovery studies were carried out, where excellent percentage recoveries were obtained. The developed spectrophotometric method was also compared to reference HPLC methods. Conclusion:Conclusion: The developed spectrophotometric method based on absorption subtraction is an easy, simple and time saving method, that was able to determine the active ingredients in the binary and ternary pharmaceutical formulation without any interference from then excipients.
Physical activity and arterial hypertension

Arterial hypertension is the most important modifiable cardiovascular risk factor for all-cause morbidity and mortality worldwide. It is associated with an increased risk of cardiovascular diseases (CVD) and cognitive impairment. In this regard, accumulating evidence points to beneficial effects of intensified blood pressure lowering by implementation of protective lifestyle patterns (e.g. physical activity, diet) or pharmacotherapy. The aim of this clinical review is to summarize the role of physical activity and/or exercise in prevention and treatment of arterial hypertension. Epidemiology, prospective observational studies, and randomized controlled trials have shown positive effects of physical activity on blood pressure levels in patients with hypertension and in patients with high-normal and normal blood pressure. Thus, physical activity can prevent or slow down micro- and macrovascular end-organ damage. Physical activity should be recommended for prevention and treatment of all hypertension grades, but most patients with hypertension will concomitantly require drug treatment (depending on hypertension grade and individual cardiovascular risk profile). Currently, increasing attention is directed to the role of personalized exercise prescriptions for prevention and treatment of arterial hypertension and for addressing cardiometabolic risk.
MetaProbformer for Charging Load Probabilistic Forecasting of Electric Vehicle Charging Stations

The penetration of electric vehicles (EV) has been increasing rapidly in recent years. Electric vehicle charging load poses a huge demand on the power grids. The forecasting for electric vehicle charging load, especially for the charging load of EV charging stations, is of significant importance for the safe operation of power grids. However, most of the existing forecasting methods fail to capture the long-term dependencies efficiently and assume the availability of a large amount of training data. Hence, they cannot address newly built charging stations with scarce historical charging load data. Meanwhile, most of the methods focus on point forecasting, which lacks risk consideration. In this work, we aim to leverage the benefits of Transformer-based models for EV charging forecasting. Specifically, we propos Probformer, a Transformer-based forecasting model for charging load forecasting. To enable Probformer to adapt fast to unseen environments, we further extend it to MetaProbformer, a meta-learning-based forecasting framework. Extensive experiments have been done on real-world datasets for both point forecasting and probabilistic forecasting. Experimental results show that our methods can consistently outperform baseline methods by a large margin.
Joint User Clustering and Graph Coloring Based Pilot Assignment for Cell-Free Massive MIMO Systems

Pilot contamination due to pilot reuse seriously mitigates the performance of the cell-free massive multiple-input multiple-output (MIMO) systems. In this paper, we propose a joint pilot assignment scheme that employs user clustering and graph coloring (UC-GC) to reduce pilot contamination. The proposed method consists of two steps: firstly, we utilize AP selection to classify all users; secondly, we assign pilots to users with more severe pilot contamination using the graph coloring algorithm and then assign pilots to the remaining users. The numerical simulation results show that the proposed scheme outperforms existing pilot assignment schemes and significantly improves throughout with low complexity.
Non-fragile recursive sliding mode dynamic surface control with adaptive neural network

A non-fragile recursive sliding mode dynamic surface adaptive control method is proposed for a class of uncertain,mismatched nonlinear system.By employing the neural network(NN) to approximate the system uncertainty and designing the recursive sliding mode dynamic surface to synthesize the interaction of the tracking error in each step of backstepping scheme,we make the method to get rid of the ‘explosion of complexity' associated with the backstepping control and to avoid being fragile to the perturbation in both the filter time constant and adaptive parameters of neural network in the traditional dynamic surface control.Stability analysis verifies the semi-global,uniform,and ultimate boundedness(SUUB) for all the states of the closed-loop system,and guarantees the tracking error to converge to an arbitrarily small neighborhood of the origin.