Miracle-dz/UniFAR-FTUs huggingface dataset:
MOLECULAR DYNAMICS APPROACH TO SONOLUMINESCENT BUBBLES

Bubbles in a standing sound field can be trapped at a pressure antinode and driven to strongly nonlinear oscillations with fast collapse, whereby shock waves and also faint, short light pulses are emitted. The physical and chemical processes in the interior of the bubbles associated with these phenomena are still not completely understood and also defy spatially resolved experimental investigation. Numerical modeling primarily relies on continuum methods by solving partial differential equations. Here an alternative approach is presented. The processes within collapsing sonoluminescing bubbles are investigated by molecular dynamics (MD) simulations with several million particles.
Convergence analysis of an alternating direction method of multipliers for the identification of nonsmooth diffusion parameters with total variation

The paper presents a numerical method for identifying discontinuous conductivities in elliptic equations from boundary observations. The solutions to this inverse problem are obtained through a constrained optimization problem, where the cost functional is a combination of the Kohn–Vogelius and Total Variation functionals. Instead of regularizing the Total Variation stabilization functional, which is commonly used in the literature, we introduce an Alternating Direction Method of Multipliers to preserve the favorable properties of non-smoothness and convexity. The discretization is carried out using a mixed finite element/volume method, while the numerical solutions are iteratively computed using a variant of the Uzawa algorithm. We show the surjectivity of the derivatives of the constraints related to the discrete optimization problem and derive a source condition for the discrete inverse problem. We then investigate the convergence analysis and establish the convergence rate. Finally, we conclude with some numerical experiments to illustrate the efficiency of the proposed method.
Comparison of Laplace and Double Pareto Penalty: LASSO and Elastic Net

Lasso (Tibshirani, 1996) and Elastic Net (Zou and Hastie, 2005) have been widely used in various fields for simultaneous variable selection and coefficient estimation. Bayesian methods using a conditional Laplace and a double Pareto prior specification have been discussed in the form of hierarchical specification. Full conditional posterior distributions with each priors have been derived. We compare the performance of Bayesian lassos with Laplace prior and the performance with double Pareto prior using simulations. We also apply the proposed Bayesian hierarchical models to real data sets to predict the collapse of governments in Asia.
Energy- and time-optimal reconfiguration of spacecraft clusters with collision avoidance

Spacecraft cluster reconfiguration is one of the enabling technologies to ensure non-traditional attributes of distributed space systems. This work treats energy- and time-optimal reconfiguration problems for both circular and eccentric reference orbits. Furthermore, typical local and coupling constraints have been taken into account. Particularly, focus is given to two coupling constraints: final configuration constraints and collision avoidance constraints. For final configuration constraints, a parameterization method is applied to ensure that the reconfiguration problem can be solved as only one optimization problem, rather than a large number of optimization problems resulting from the traditional discretization method. A generalized formulation is proposed for non-convex collision avoidance constraints, which are then convexified via linearization and convex restriction technology. This method provides the affine approximation as a special case. After incorporating above constraints, the reconfiguration problem is formulated as an open-loop optimal control problem, which is solved via the Gauss pseudospectral method (GPM). By virtue of elegant features of GPM, those solutions can serve as a counterpart and stepping stone for a distributed implementation of reconfiguration algorithms. Various simulations demonstrate that minimum-energy/time cluster reconfiguration problems with collision avoidance for circular and eccentric reference orbits can be solved effectively and efficiently using GPM.
A Randomized Proper Orthogonal Decomposition Method for Reducing Large Linear Systems

The proper orthogonal decomposition (POD) method is a powerful tool for reducing large data systems which can quickly overwhelm modern computing tools. In this thesis we provide a link between randomized projections and statistical methods by introducing the randomized POD method. We also apply the POD method to a heat transfer finite element model and image compression. In doing so we demonstrate the practical use and quantify the error introduced by the POD method.
Classification of Hyperspectral Data by

This paper presents a semi-supervised technique for the solution of ill-posed classification problems in remote sensing applications. The proposed technique is based on semi­ supervised support vector machines (S3VMs) implemented in the primal formulation of the learning problem. In particular, a global optimization algorithm, based on the continuation method, is adopted in the learning phase of the classifier according to an iterative learning procedure. The use of this algorithm can result in a better approximation to the global minimum of the associated cost function. Experimental results, obtained on hyperspectral remote sensing images, point out the advantages and the limitation of the proposed continuation S3VM (cS 3VMs) with respect to other implementations of S3VMs. I. INTRODUCTION The recent development of hyperspectral remote sensing systems makes it possible to discriminate among land-cover classes that are spectrally very similar. Nonetheless, the high­ dimensional feature space of acquired hyperspectral data is critical for the definition of reliable training sets capable of a complete representation of land-cover classes and made up of a sufficient number of representative training samples. Supervised classification of remote sensing data is strongly affected by the properties of the available training set and the number of training samples. In the remote sensing literature, different approaches to the solution of ill-posed problems have been proposed. The two most promising approaches are based on the use of kernel methods (e.g., support vector machines (SYMs)) (1) and of semi-supervised techniques (which perform the learning of the classifier by using both labeled and unlabeled samples) (2). Recently, to exploit the advantages of both the approaches, the authors proposed to solve ill-posed classification problems in remote sensing according to semi-supervised support vector machine (S3YM) classifiers (3),(4). In the literature, SYMs are usually implemented according to the optimization in a dual formulation (obtained by applying the Lagrange theory to the constrained minimization problem associated with the primal formulation) (5). The same strategy is followed for the implementation of the semi-supervised SYMs, like for example in the combinatorial optimization technique proposed by Joachims (6) (which is denoted by S3YM Li g ht in the paper). It consists of an iterative self-labeling algorithm with the increasing penalty value C* for unlabeled
Dynamics of Ginzburg-Landau vortices: The pinning effect

Nous demontrons rigoureusement la loi dynamique pour le mouvement des tourbillons de Ginzburg-Landau avec un mecanisme de « pinning » en dimension > 2. Comme corollaire de ce resultat, nous obtenons la loi de mouvement des filaments de tourbillons et de la sous-variete engendree par ces tourbillons.
Predictors of a 30-day mortality following the first episode of stroke among patients admitted at referral hospitals in Dodoma, central Tanzania: A prospective longitudinal Observational Study

Background: Stroke is the leading cause of disability and the second most common cause of death after ischemic heart disease worldwide. A better understanding of the predictors of early post-stroke mortality provides opportunity for interventions that promote favourable post-stroke outcomes. Objective: This study aimed to determine incidence and risk factors associated with 30-day mortality among adult patients admitted with first episode of stroke at referral hospitals in Dodoma. Methods: The study employed a prospective longitudinal observational design. Adult patients with confirmed acute stroke by Computed Tomography scan or Magnetic Resonance Imaging, admitted to Dodoma Referral Hospitals were enrolled in the study. The National Institute of Health Stroke Scale was used to assess stroke severity at baseline. A comparison of risk factors, clinical profiles, and mortality was done using the Chi-square test. A binomial logistic regression model was used to determine the predictors of 30 days mortality in patients with stroke while 30-days probability of survival was estimated using Kaplan-Meier analysis. Results: Out of 226 patients with first-ever stroke, 121(54%) were males and the population mean age was 63(15) years. 140(62%) had Ischaemic stroke subtype, and 154(68%) survived at 30 days of stroke after admission. Patient with history of smoking 2.4 [95% CI (1.0 - 5.6), p = 0.048], loss of consciousness 2.7 [95% CI (1.2 - 6.4; p = 0.019] and unequal pupil size 13.7 [95% CI (4.1 - 58.1, p < 0.001 were significantly more associated with mortality within 30-days. The median survival was 7 (3-9) days, whereas alcohol drinkers and those aged above 60 years had a shorter time to mortality compared to non-alcohol drinkers and those aged < 60 years. Conclusion: The study reveals high incidence of mortality within 30 days after the first episode of stroke, with the highest proportion die within seven days of being hospitalized. Advanced age of 60 years and above, smokers, alcoholic users, and severe stroke at admission warrant special attention.
Study of the effects of solvent and temperatures on the self-assemble surface patterns in azopolymer films

Dans cette these nous presentons differents motifs formes par auto-organisation de materiaux azopolymeres soumis a un rayonnement lumineux. Nous avons montre que de nombreux reseaux de surface photoinduits, jusqu'a 10, peuvent etreenregistres sur la meme zone d’un film azopolymere pourvu que la polarisation du faisceau d'ecriture soit bien choisie. Cet effet a permis la creation d'une grande variete de motifs complexes. Dans cette these, nous montrons de telles structures complexes formees a la surface de couches minces d'azopolymere via un ensemble de reseaux superposes (jusqu'a 16 sur la meme zone). Nous creons ainsi des structures dites quasi-cristal a 2 dimensions. La surface d'azopolymere est alors utilisee comme un moule afin d'etre repliquee avec un elastomere. La couche d’elastomere ainsi produite possede la meme structure que le moule enazopolymere. Cette couche est flexible, transparente et etirable et nous l'avons utilise pour le depot de nano-objets uniques. Les caracteristiques de deux surfaces photoinduites de deux azopolymeres chiraux ont ete comparees: une surface aleatoire de type gaussien et une surface avec des reseaux quasi-aleatoires. Ces motifs ont ete obtenus avec une technique simple de type ‘bottom-up’ en eclairant la couche mince d'azopolymere avec un seul faisceau laser. La surface avec des reseaux quasi-aleatoires peut etre utilisee pour le couplage de la lumiere dans plusieurs directions dans un film ultra-mince. Ces deux surfaces ont ete utilisees comme moule et repliquees sur un elastomere transparent. Elles montrent un tres bon piegeage de la lumiere. Plus precisement, le piegeage de la lumiere est 20% meilleur avec les reseaux quasialeatoires qu'avec la surface aleatoire de type gaussien et il est de l'ordre de 40%.
Investigating the impact of working arrangements and lifestyle factor importance on environmental consciousness

This study investigates the impact of people’s demographics, preferred working arrangements and utilization of their free time on environmental consciousness. Such an investigation is timely because of the changing expectations around working, and the rising popularity of work-from-home arrangements leading to increased flexibility. Although the link between income and environmental impact is well understood, the mediating factors between working arrangements and environmental concerns remain unclear. Through the analysis of a nationally representative survey conducted in Japan, we clarify people’s desire to work shorter hours, subject to the maintenance of income levels, along with a desire to consider revised working arrangements if environmental improvements can be anticipated as a result. One important finding of this research is that those who prioritize their free time for family and friends have a much higher probability of valuing the environment and thus reducing their carbon footprint. These findings have implications for policy design which encourages low impact working arrangements, the sharing economy and a family friendly society.
PD-L1 is Fascinating but IDO Needs Attention in Non-HCV and Non-HBV-Associated Hepatocellular Carcinoma Patients

Background/Aim Hepatocellular carcinoma (HCC) is one of the most common forms of liver cancer that is modulated by the immune system. Programmed cell death ligand-1 (PD-L1) has emerged as a novel therapeutic target in various cancers. Indoleamine 2,3-dioxygenase (IDO) is an immunosuppressive enzyme that is associated with poor prognoses in various cancer types. The aim of this study was to investigate the PD-L1 expression, and clinicopathological features of non-HCV and non-HBV-associated HCC patients, including IDO expression. Patients and Methods In this study, immunohistochemical analysis was performed to analyze the expression of PD-L1 and IDO. Formalin-fixed paraffin-embedded HCC tumor tissues (n=50) were obtained from the pathology department, at Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH&RC) in Lahore, Pakistan between 2005 and 2022. All the patients were HBV and HCV negative. Furthermore, it was a rare group of patients with no previous history of any viral hepatitis. In addition, for categorical and continuous variables chi-square or Fisher exact test and Mann–Whitney U-test was performed. Results Of 50 tissue specimens, PD-L1+ was observed in 21 [high: 12 (24%), low: 9 (18%)] and PD-L1- was observed in 29 HCC patients. IDO+ was observed in all 50 specimens [high: 42 (84%), low: 8 (16%)]. Additionally, both PD-L1 and IDO had high expression in 11 (22%) patients. While both PD-L1 and IDO had low expression in 2 (4%) patients. Furthermore, in IDO+/PD-L1- group, 20 (69%) out of 29 patients died while in the IDO+/PD-L1+ group, 9 (43%) out of 21 patients died. Conclusion Evaluation of IDO and PD-L1 expression may add therapeutic advantage in non-HCV and non-HBV-associated HCC patients that overexpress IDO. Further validation in a larger cohort is warranted.
3D Path Planning for Multiple UAVs Using Pythagorean Hodograph Curves

This paper presents path planning of multiple UAVs for simultaneous arrival on target. The path planning is divided into phases: firstly design of flyable paths and secondly producing flyable and safe paths achieve the mission. Spatial PH curves are used to produce the paths. The flyable paths are produced by satisfying the maximum curvature and torsion bounds of the UAVs. Latter the flyable paths are tuned to meet the safety conditions and in turn achieve the mission by producing the paths equal in lengths. The curvature bounds and safety conditions and producing paths of equal lengths are all achieved by increasing the curvatures of the paths.
Multivariate Analysis of Multiple Response Data

Multiple response questions, also known as a pick any/J format, are frequently encountered in the analysis of survey data. The relationship among the responses is difficult to explore when the number of response options, J, is large. The authors propose a multivariate binomial probit model for analyzing multiple response data and use standard multivariate analysis techniques to conduct exploratory analysis on the latent multivariate normal distribution. A challenge of estimating the probit model is addressing identifying restrictions that lead to the covariance matrix specified with unit-diagonal elements (i.e., a correlation matrix). The authors propose a general approach to handling identifying restrictions and develop specific algorithms for the multivariate binomial probit model. The estimation algorithm is efficient and can easily accommodate many response options that are frequently encountered in the analysis of marketing data. The authors illustrate multivariate analysis of multiple response data in three applications.
Efficiency of sediment transport by flood and its control in the Lower Yellow River

This paper presents the characteristics of sediment transport by flood in the Lower Yellow River with the reach from Huayuankou to Gaocun, which is regarded as a typical braided pattern. The Artificial Neural Network Model on Water Use for Sediment Transport (WUST) by flood was established based on the measured data from 1980 to 1998. Consequently, simulations of controlling process of sediment transport by flood were made in terms of the control theory under different scenarios. According to the situation of sediment transport by flood in the Lower Yellow River, Open-Loop control system and feedback control system were adopted in system design. In the Open-Loop control system, numerical simulations were made to reveal the relationship between average discharge of flood and the WUST with varying sediment concentrations. The results demonstrate that sediment concentration has significant influence on the controlling process of flood flow to WUST. It is practical and efficient to control WUST if sediment concentration is less than 20 kg/m3. In the feedback control system, controlling processes of sediment concentration and flood discharge for sediment transport were simulated respectively under given conditions, and it was found that sediment transport process could be controlled completely by sediment concentration and discharge at the inlet of the reach from Huayuankou to Gaocun. Using the same method, controlling processes of sediment transport by flood in other reaches in the Lower Yellow River were also simulated. For the case of sediment concentration being 20 kg/m3, the optimized controlling discharge ranges from 2390 to 2900 m3/s in the lower reach of Huayuankou. This study is also of significance to flood control and flushing sediment in the Lower Yellow River with proper operation modes of Xiaolangdi Reservoir.
Engineering polyphenol-based carriers for nucleic acid delivery

Gene therapy, an effective medical intervention strategy, is increasingly employed in basic research and clinical practice for promising and unique therapeutic effects for diseases treatment, such as cardiovascular disorders, cancer, neurological pathologies, infectious diseases, and wound healing. However, naked DNA/RNA is readily hydrolyzed by nucleic acid degrading enzymes in the extracellular environment and degraded by lysosomes during intracellular physiological conditions, thus gene transfer must cross complex cellular and tissue barriers to deliver genetic materials into targeted cells and drive efficient activation or inhibition of the proteins. At present, the lack of safe, highly efficient, and non-immunogenic drug carriers is the main drawback of gene therapy. Considering the dense hydroxyl groups on the benzene rings in natural polyphenols that exert a strong affinity to various nucleic acids via hydrogen bonding and hydrophobic interactions, polyphenol-based carriers are promising anchors for gene delivery in which polyphenols serve as the primary building blocks. In this review, the recent progress in polyphenol-assisted gene delivery was summarized, which provided an easily accessible reference for the design of future polyphenol-based gene delivery vectors. Nucleic acids discussed in this review include DNA, short interfering RNAs (siRNA), microRNA (miRNA), double-strand RNA (dsRNA), and messenger RNA (mRNA).
H/ACA snRNP–dependent ribosome biogenesis regulates translation of polyglutamine proteins

Stem cells in many systems, including Drosophila germline stem cells (GSCs), increase ribosome biogenesis and translation during terminal differentiation. Here, we show that the H/ACA small nuclear ribonucleoprotein (snRNP) complex that promotes pseudouridylation of ribosomal RNA (rRNA) and ribosome biogenesis is required for oocyte specification. Reducing ribosome levels during differentiation decreased the translation of a subset of messenger RNAs that are enriched for CAG trinucleotide repeats and encode polyglutamine-containing proteins, including differentiation factors such as RNA-binding Fox protein 1. Moreover, ribosomes were enriched at CAG repeats within transcripts during oogenesis. Increasing target of rapamycin (TOR) activity to elevate ribosome levels in H/ACA snRNP complex–depleted germlines suppressed the GSC differentiation defects, whereas germlines treated with the TOR inhibitor rapamycin had reduced levels of polyglutamine-containing proteins. Thus, ribosome biogenesis and ribosome levels can control stem cell differentiation via selective translation of CAG repeat–containing transcripts.
Integration of metabolomics and peptidomics reveals distinct molecular landscape of human diabetic kidney disease

Diabetic kidney disease (DKD) is the most common microvascular complication of diabetes, and there is an urgent need to discover reliable biomarkers for early diagnosis. Here, we established an effective urine multi-omics platform and integrated metabolomics and peptidomics to investigate the biological changes during DKD pathogenesis. Methods: Totally 766 volunteers (221 HC, 198 T2DM, 175 early DKD, 125 overt DKD, and 47 grey-zone T2DM patients with abnormal urinary mALB concentration) were included in this study. Non-targeted metabolic fingerprints of urine samples were acquired on matrix-free LDI-MS platform by the tip-contact extraction method using fluorinated ethylene propylene coated silicon nanowires chips (FEP@SiNWs), while peptide profiles hidden in urine samples were uncovered by MALDI-TOF MS after capturing urine peptides by porous silicon microparticles. Results: After multivariate analysis, ten metabolites and six peptides were verified to be stepwise regulated in different DKD stages. The altered metabolic pathways and biological processes associated with the DKD pathogenesis were concentrated in amino acid metabolism and cellular protein metabolic process, which were supported by renal transcriptomics. Interestingly, multi-omics significantly increased the diagnostic accuracy for both early DKD diagnosis and DKD status discrimination. Combined with machine learning, a stepwise prediction model was constructed and 89.9% of HC, 75.5% of T2DM, 69.6% of early DKD and 75.7% of overt DKD subjects in the external validation cohort were correctly classified. In addition, 87.5% of grey-zone patients were successfully distinguished from T2DM patients. Conclusion: This multi-omics platform displayed a satisfactory ability to explore molecular information and provided a new insight for establishing effective DKD management.